Academic literature on the topic 'User activity detection'
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Journal articles on the topic "User activity detection"
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Zhu, Hao, and Georgios B. Giannakis. "Exploiting Sparse User Activity in Multiuser Detection." IEEE Transactions on Communications 59, no. 2 (2011): 454–65. http://dx.doi.org/10.1109/tcomm.2011.121410.090570.
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Mitra, U., and H. V. Poor. "Activity detection in a multi-user environment." Wireless Personal Communications 3, no. 1-2 (1996): 149–74. http://dx.doi.org/10.1007/bf00333928.
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Lee, Junho, and Seung-Hwan Lee. "Low dimensional multiuser detection exploiting low user activity." Journal of Communications and Networks 15, no. 3 (2013): 283–91. http://dx.doi.org/10.1109/jcn.2013.000051.
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Zou, Shihong, Huizhong Sun, Guosheng Xu, and Ruijie Quan. "Ensemble Strategy for Insider Threat Detection from User Activity Logs." Computers, Materials & Continua 65, no. 2 (2020): 1321–34. http://dx.doi.org/10.32604/cmc.2020.09649.
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Wang, Shuwen, Xingquan Zhu, Weiping Ding, and Amir Alipour Yengejeh. "Cyberbullying and Cyberviolence Detection: A Triangular User-Activity-Content View." IEEE/CAA Journal of Automatica Sinica 9, no. 8 (2022): 1384–405. http://dx.doi.org/10.1109/jas.2022.105740.
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Park, Hansol, Kookjin Kim, Dongil Shin, and Dongkyoo Shin. "BGP Dataset-Based Malicious User Activity Detection Using Machine Learning." Information 14, no. 9 (2023): 501. http://dx.doi.org/10.3390/info14090501.
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Recent advances in the Internet and digital technology have brought a wide variety of activities into cyberspace, but they have also brought a surge in cyberattacks, making it more important than ever to detect and prevent cyberattacks. In this study, a method is proposed to detect anomalies in cyberspace by consolidating BGP (Border Gateway Protocol) data into numerical data that can be trained by machine learning (ML) through a tokenizer. BGP data comprise a mix of numeric and textual data, making it challenging for ML models to learn. To convert the data into a numerical format, a tokenizer, a preprocessing technique from Natural Language Processing (NLP), was employed. This process goes beyond merely replacing letters with numbers; its objective is to preserve the patterns and characteristics of the data. The Synthetic Minority Over-sampling Technique (SMOTE) was subsequently applied to address the issue of imbalanced data. Anomaly detection experiments were conducted on the model using various ML algorithms such as One-Class Support Vector Machine (One-SVM), Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM), Random Forest (RF), and Autoencoder (AE), and excellent performance in detection was demonstrated. In experiments, it performed best with the AE model, with an F1-Score of 0.99. In terms of the Area Under the Receiver Operating Characteristic (AUROC) curve, good performance was achieved by all ML models, with an average of over 90%. Improved cybersecurity is expected to be contributed by this research, as it enables the detection and monitoring of cyber anomalies from malicious users through BGP data.
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Parwez, Md Salik, Danda B. Rawat, and Moses Garuba. "Big Data Analytics for User-Activity Analysis and User-Anomaly Detection in Mobile Wireless Network." IEEE Transactions on Industrial Informatics 13, no. 4 (2017): 2058–65. http://dx.doi.org/10.1109/tii.2017.2650206.
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Pathmaperuma, Madushi H., Yogachandran Rahulamathavan, Safak Dogan, and Ahmet Kondoz. "CNN for User Activity Detection Using Encrypted In-App Mobile Data." Future Internet 14, no. 2 (2022): 67. http://dx.doi.org/10.3390/fi14020067.
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In this study, a simple yet effective framework is proposed to characterize fine-grained in-app user activities performed on mobile applications using a convolutional neural network (CNN). The proposed framework uses a time window-based approach to split the activity’s encrypted traffic flow into segments, so that in-app activities can be identified just by observing only a part of the activity-related encrypted traffic. In this study, matrices were constructed for each encrypted traffic flow segment. These matrices acted as input into the CNN model, allowing it to learn to differentiate previously trained (known) and previously untrained (unknown) in-app activities as well as the known in-app activity type. The proposed method extracts and selects salient features for encrypted traffic classification. This is the first-known approach proposing to filter unknown traffic with an average accuracy of 88%. Once the unknown traffic is filtered, the classification accuracy of our model would be 92%.
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Bashir, Sulaimon Adebayo, Andrei Petrovski, and Daniel Doolan. "A framework for unsupervised change detection in activity recognition." International Journal of Pervasive Computing and Communications 13, no. 2 (2017): 157–75. http://dx.doi.org/10.1108/ijpcc-03-2017-0027.
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Purpose This purpose of this paper is to develop a change detection technique for activity recognition model. The approach aims to detect changes in the initial accuracy of the model after training and when the model is deployed for recognizing new unseen activities without access to the ground truth. The changes between the two sessions may occur because of differences in sensor placement, orientation and user characteristics such as age and gender. However, many of the existing approaches for model adaptation in activity recognition are blind methods because they continuously adapt the recognition model without explicit detection of changes in the model performance. Design/methodology/approach The approach determines the variation between reference activity data belonging to different classes and newly classified unseen data. If there is coherency between the data, it means the model is correctly classifying the instances; otherwise, a significant variation indicates wrong instances are being classified to different classes. Thus, the approach is formulated as a two-level architectural framework comprising of the off-line phase and the online phase. The off-line phase extracts of Shewart Chart change parameters from the training data set. The online phase performs classification of new samples and the detection of the changes in each class of activity present in the data set by using the change parameters computed earlier. Findings The approach is evaluated using a real activity-recognition data set. The results show that there are consistent detections that correlate with the error rate of the model. Originality/value The developed approach does not use ground truth to detect classifier performance degradation. Rather, it uses a data discrimination method and a base classifier to detect the changes by using the parameters computed from the reference data of each class to discriminate outliers in the new data being classified to the same class. The approach is the first, to the best of the authors’ knowledge, that addresses the problem of detecting within-user and cross-user variations that lead to concept drift in activity recognition. The approach is also the first to use statistical process control method for change detection in activity recognition, with a robust integrated framework that seamlessly detects variations in the underlying model performance.
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Kim, Park, Kim, Cho, and Kang. "Insider Threat Detection Based on User Behavior Modeling and Anomaly Detection Algorithms." Applied Sciences 9, no. 19 (2019): 4018. http://dx.doi.org/10.3390/app9194018.
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Insider threats are malicious activities by authorized users, such as theft of intellectual property or security information, fraud, and sabotage. Although the number of insider threats is much lower than external network attacks, insider threats can cause extensive damage. As insiders are very familiar with an organization’s system, it is very difficult to detect their malicious behavior. Traditional insider-threat detection methods focus on rule-based approaches built by domain experts, but they are neither flexible nor robust. In this paper, we propose insider-threat detection methods based on user behavior modeling and anomaly detection algorithms. Based on user log data, we constructed three types of datasets: user’s daily activity summary, e-mail contents topic distribution, and user’s weekly e-mail communication history. Then, we applied four anomaly detection algorithms and their combinations to detect malicious activities. Experimental results indicate that the proposed framework can work well for imbalanced datasets in which there are only a few insider threats and where no domain experts’ knowledge is provided.
Dissertations / Theses on the topic "User activity detection"
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Amanzi, Richard. "A natural user interface architecture using gestures to facilitate the detection of fundamental movement skills." Thesis, Nelson Mandela Metropolitan University, 2015. http://hdl.handle.net/10948/6204.
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Fundamental movement skills (FMSs) are considered to be one of the essential phases of motor skill development. The proper development of FMSs allows children to participate in more advanced forms of movements and sports. To be able to perform an FMS correctly, children need to learn the right way of performing it. By making use of technology, a system can be developed that can help facilitate the learning of FMSs. The objective of the research was to propose an effective natural user interface (NUI) architecture for detecting FMSs using the Kinect. In order to achieve the stated objective, an investigation into FMSs and the challenges faced when teaching them was presented. An investigation into NUIs was also presented including the merits of the Kinect as the most appropriate device to be used to facilitate the detection of an FMS. An NUI architecture was proposed that uses the Kinect to facilitate the detection of an FMS. A framework was implemented from the design of the architecture. The successful implementation of the framework provides evidence that the design of the proposed architecture is feasible. An instance of the framework incorporating the jump FMS was used as a case study in the development of a prototype that detects the correct and incorrect performance of a jump. The evaluation of the prototype proved the following: - The developed prototype was effective in detecting the correct and incorrect performance of the jump FMS; and - The implemented framework was robust for the incorporation of an FMS. The successful implementation of the prototype shows that an effective NUI architecture using the Kinect can be used to facilitate the detection of FMSs. The proposed architecture provides a structured way of developing a system using the Kinect to facilitate the detection of FMSs. This allows developers to add future FMSs to the system. This dissertation therefore makes the following contributions: - An experimental design to evaluate the effectiveness of a prototype that detects FMSs - A robust framework that incorporates FMSs; and - An effective NUI architecture to facilitate the detection of fundamental movement skills using the Kinect.
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Sagheer, Fakher. "Bayesian statistical methods for joint user activity detection, channel estimation, and data decoding in dynamic wireless networks." Electronic Thesis or Diss., Institut polytechnique de Paris, 2024. https://theses.hal.science/tel-04874844.
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L'accès multiple non-orthogonal grant-free (GF-NOMA) s'impose progressivement comme une partie intégrante de la couche physique des systèmes d'accès radio du futur. En permettant d'accéder à une station de base sans allocation explicite de ressources temps/fréquence/code, GF-NOMA permet non seulement d'améliorer l'efficacité spectrale, mais également de rendre possible des communications ultra fiables à faible latence (URLLC). De telles exigences permettront de répondre aux enjeux spécifiques d'applications sans fil telles que l'internet des objets, la réalité virtuelle, les jeux vidéo en ligne, les communications entre machines, véhicules, etc. Cependant, GF-NOMA introduit un nouveau défi inexistant dans les systèmes de communication classiques, à savoir la détection d'activité des utilisateurs : en plus de l'estimation du canal, de la détection et du décodage des utilisateurs interférant, la station de base réceptrice doit être en mesure de procéder à leur classification en deux catégories : ceux qui sont actifs et transmettent et ceux qui ne le sont pas. La massivité du système, l'absence de contrôle de puissance à l'émission et/ou d'orthogonalité des séquences pilotes des utilisateurs sont autant de caractéristiques qui compliquent les traitements en réception. Cette thèse a pour thème général l'étude de nouvelles méthodes statistiques basées sur des algorithmes à passage de messages sur des graphes factoriels (factor graphs) appropriés afin de traiter conjointement toutes ces tâches au niveau du récepteur. Sont étudiées plus précisément :- une méthode (1) d'inférence bayésienne hybride à base de l'algorithme de propagation de croyance (belief propagation algorithme, BP) et de l'algorithme de propagation de l'espérance (expectation propagation algorithme, EP) pour résoudre le problème conjoint de détection d'activité, estimation de canal, et détection multi-utilisateur dans un système GF-NOMA synchrone avec absence de contrôle de puissance à l'émission, séquences pilotes orthogonales et antennes réceptrices multiples. En introduisant un critère d'approximation pour exprimer le passage de messages sous forme de lois gaussiennes, l'estimation du canal et la détection multi-utilisateurs peuvent être traitées efficacement par l'algorithme EP. Ceci s'avérant impossible sous cette forme pour la détection d'activité des utilisateurs, un passage de messages sous forme BP est utilisé à cet effet. La méthode proposée inclut une étape d'estimation des hyperparamètres du modèle que sont l'énergie des signaux reçus et la corrélation spatiale entre les antennes réceptrices. Une variante à complexité réduite ignorant la corrélation spatiale entre antennes réceptrices est également proposée ;- une méthode (2) d'inférence bayésienne à base de l'algorithme EP exploitant des méthodes d'analyse complexe (dérivées de Wirtinger) permettant de traiter la détection d'activité des utilisateurs également sous la forme d'un algorithme à passage de messages gaussiens ;- une méthode (3) faisant précéder la méthode (2) d'une méthode d'acquisition comprimée bayésienne chargée de l'estimation initiale du canal et de l'activité des utilisateurs dans le contexte complexifié d'un accès massif avec séquences pilotes des utilisateurs non-orthogonales. L'évaluation par simulations de ces différentes méthodes est effectuée dans le cas particulier d'un système GF-NOMA synchrone par codage, entrelacement et modulation OFDM (GF-OFDM-IDMA). Les performances obtenues (mesurées en termes de taux d'erreur binaire résiduel pour la détection et le décodage, d'erreur quadratique moyenne pour l'estimation de canal, et de probabilités de fausse alarme et de non-détection pour la détection d'activité) se comparent favorablement par rapport à celles obtenues avec des méthodes classiques publiées dans la littérature. Mots clés : NOMA, grant-free, accès massif, OFDM, graphes factoriels, algorithmes à passage de messages, propagation de croyance, propagation de l'espérance<br>Grant-free non-orthogonal multiple access (GF-NOMA) is gradually becoming an integral part of the physical layer of future radio access systems. By allowing access to a base station without explicit allocation of time/frequency/code resources, GF-NOMA not only improves spectral efficiency, but also enables ultra-reliable low latency communications (URLLC) . Such requirements will make it possible to meet the specific challenges of wireless applications such as the Internet of Things, virtual reality, online video games, communications between machines, vehicles, etc.However, GF-NOMA introduces a new challenge that does not exist in conventional communication systems, namely user activity detection: in addition to channel estimation, detection and decoding of interfering users, the base station receiver must be able to classify them into two categories: those who are active and transmitting and those who are not. The massiveness of the system, the absence of power control on transmission and/or orthogonality of user pilot sequences are all characteristics which complicate processing at the receiver.The general subject of this thesis is the study of new statistical methods based on message passing algorithms on appropriate factor graphs in order to jointly handle all these tasks at the receiver level.Are studied more precisely:- a method (1) of hybrid Bayesian inference based on the belief propagation algorithm (BP) and the expectation propagation algorithm (EP) to solve the problem of joint activity detection, channel estimation, and multi-user detection in a synchronous GF-NOMA system with no transmit power control, orthogonal pilot sequences and multiple receiver antennas. By introducing an approximation criterion to express message passing as Gaussian laws, channel estimation and multi-user detection can be efficiently processed by the EP algorithm. This proving impossible in this form for detecting user activity, message passing in BP form is used for this purpose. The proposed method includes a step of estimating the hyperparameters of the model, which are the energy of the received signals and the spatial correlation between the receiving antennas. A reduced complexity variant ignoring the spatial correlation between receiving antennas is also proposed;- a method (2) of Bayesian inference based on the EP algorithm exploiting complex analysis methods (Wirtinger derivatives) making it possible to process user activity detection also in the form of a Gaussian message passing algorithm;- a method (3) preceding method (2) with a Bayesian compressed acquisition method responsible for the initial estimation of the channel and user activity in the more complex context of massive access with non-orthogonal pilot sequences for the users.The evaluation by simulations of these different methods is carried out in the particular case of a synchronous GF-NOMA system by coding, interleaving and OFDM modulation (GF-OFDM-IDMA). The performance obtained (measured in terms of residual bit error rate for detection and decoding, root mean square error for channel estimation, and false alarm and missed-detection probabilities for activity detection) compare favorably with those obtained with traditional methods published in the literature. Keywords: NOMA, grant-free, massive access, OFDM, factor graphs, message passing algorithms, belief propagation, expectation propagation
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Dias, Pereira dos Santos Augusto. "Using Motion Sensor and Machine Learning to Support the Assessment of Rhythmic Skills in Social Partner Dance: Bridging Teacher, Student and Machine Contexts." Thesis, The University of Sydney, 2019. https://hdl.handle.net/2123/21302.
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Social partner dance is a popular form of physical activity intended for enjoyment and pleasure. Rhythm is an essential aspect of dance, especially in relation to music; however, it can be a difficult psychomotor skill to learn. Rhythm learning is hindered because dance teachers have a short time during classes to assess and provide feedback to students. Current technical solutions enable automatic assessment and feedback for dance students; however, the available technology is either expensive or cumbersome. Recent improvement and decreased price of wearable technology offer new opportunities for creating tools to support dance teachers and students. The research questions of this thesis were 1) How do social partner dance teachers assess the development of rhythm skills in students? 2) How can we use motion sensors to extract rhythm-related information that enables the support of dance rhythm assessment and learning? 3) Is the extracted information valid, useful and relevant for teachers and students? This thesis proposed and investigated a technological solution that used motion sensors to extract rhythm-related information from students' movement performance, validated its accuracy and evaluated the benefits of such information for teachers and students. User-centred research was used in two studies to understand the context of social partner dance learning from the perspective of teachers and to evaluate the proposed solution with teachers and students. An iterative design approach was employed to guide the development of the algorithms to extract features from motion sensors. The accuracy of the technical solution was validated using machine learning methods with a ground truth reference of dance expert annotators using a video annotation tool to assess rhythmic skills in students performing dance steps. These thesis' findings are also relevant to other learning scenarios such as different dance styles and other psychomotor skills related to rhythm.
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Odehnal, Jiří. "Řízení a měření sportovních drilů hlasem/zvuky." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2019. http://www.nusl.cz/ntk/nusl-399705.
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This master's thesis deals with the design and development of mobile aplication for Android platform. The aim of the work is to implement a simple and user-friendly user interface that would support and assist the user in trainning and sport exercises. The thesis also include implementation of sound detection to support during exercises and voice instruction by application. In practice the application should help in making training exercises more comfortable without the user being forced to keep mobile device in hand.
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McEachern, Matthew. "Neural Voice Activity Detection and its practical use." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119733.
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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 87-90).<br>The task of producing a Voice Activity Detector (VAD) that is robust in the presence of non-stationary background noise has been an active area of research for several decades. Historically, many of the proposed VAD models have been highly heuristic in nature. More recently, however, statistical models, including Deep Neural Networks (DNNs) have been explored. In this thesis, I explore the use of a lightweight, deep, recurrent neural architecture for VAD. I also explore a variant that is fully end-to-end, learning features directly from raw waveform data. In obtaining data for these models, I introduce a data augmentation methodology that allows for the artificial generation of large amounts of noisy speech data from a clean speech source. I describe how these neural models, once trained, can be deployed in a live environment with a real-time audio stream. I find that while these models perform well in their closed-domain testing environment, the live deployment scenario presents challenges related to generalizability.<br>by Matthew McEachern.<br>M. Eng.
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Tholen, Andrea. "The use of animal activity data and milk components as indicators of clinical mastitis." Thesis, Virginia Tech, 2012. http://hdl.handle.net/10919/76826.
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A study was conducted to examine the correlation between a novel behavior monitoring system and a validated data logger. We concluded that the behavior monitoring system was valid for tracking daily rest time in dairy cows (R=0.96); however the correlation values for rest bouts and rest duration were relatively low, (R=0.64) (R=0.47), respectively. Daily monitoring of animal activity and milk components can be used to detect mastitis prior to clinical onset. Data from 268 cases with clinical mastitis and respective controls (n=268) from Virginia Tech and the University of Florida dairy herds were examined. Variables collected included daily milk yield, electrical conductivity, milk fat, protein, and lactose percent, as well as activity measurements including daily rest time, daily rest duration, daily rest bouts, and daily steps taken. Variables were collected for case and control cows in the 14 d prior to and after clinical diagnosis, for a total 29 d monitoring period. A milk sample was aseptically collected upon detection of clinical signs as observed by milker's at both farms. A statistical method (candisc discriminant analysis) was used to combine all measurements and sensitivity and specificity was calculated. Virginia Tech cows on d -1 (sensitivity=95%, specificity=95%), Virginia Tech and University of Florida cows on d -1 (sensitivity=88%, specificity=90). Overall, daily monitoring of animal activity and milk components can detect mastitis prior to onset of clinical signs of disease. This may allow producers to intervene and make proactive management decisions regarding herd health prior to clinical diagnosis.<br>Master of Science
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Azhar, Faisal. "Marker-less human body part detection, labelling and tracking for human activity recognition." Thesis, University of Warwick, 2015. http://wrap.warwick.ac.uk/69575/.
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This thesis focuses on the development of a real-time and cost effective marker-less computer vision method for significant body point or part detection (i.e., the head, arm, shoulder, knee, and feet), labelling and tracking, and its application to activity recognition. This work comprises of three parts: significantbody point detection and labelling, significant body point tracking, and activity recognition. Implicit body models are proposed based on human anthropometry, kinesiology, and human vision inspired criteria to detect and label significant body points. The key idea of the proposed method is to fit the knowledge from the implicit body models rather than fitting the predefined models in order to detect and label significant body points. The advantages of this method are that it does not require manual annotation, an explicit fitting procedure, and a training (learning) phase, and it is applicable to humans with different anthropometric proportions. The experimental results show that the proposed method robustly detects and labels significant body points in various activities of two different (low and high) resolution data sets. Furthermore, a Particle Filter with memory and feedback is proposed that combines temporal information of the previous observation and estimation with feedback to track significant body points in occlusion. In addition, in order to overcome the problem presented by the most occluded body part, i.e., the arm, a Motion Flow method is proposed. This method considers the human arm as a pendulum attached to the shoulder joint and defines conjectures to track the arm since it is the most occluded body part. The former method is invoked as default and the latter is used as per a user's choice. The experimental results show that the two proposed methods, i.e., Particle Filter and Motion Flow methods, robustly track significant body points in various activities of the above-mentioned two data sets and also enhance the performance of significant body point detection. A hierarchical relaxed partitioning system is then proposed that employs features extracted from the significant body points for activity recognition when multiple overlaps exist in the feature space. The working principle of the proposed method is based on the relaxed hierarchy (postpone uncertain decisions) and hierarchical strategy (group similar or confusing classes) while partitioning each class at different levels of the hierarchy. The advantages of the proposed method lie in its real-time speed, ease of implementation and extension, and non-intensive training. The experimental results show that it acquires valuable features and outperforms relevant state-of-the-art methods while comparable to other methods, i.e., the holistic and local feature approaches. In this context, the contribution of this thesis is three-fold: Pioneering a method for automated human body part detection and labelling. Developing methods for tracking human body parts in occlusion. Designing a method for robust and efficient human action recognition.
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Myles, Kimberly. "Activity-Based Target Acquisition Methods for Use in Urban Environments." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/28422.
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Many military conflicts are fought in urban environments that subject the U.S. soldier to a number of challenges not otherwise found in traditional battle. In the urban environment, the soldier is subject to threatening attacks not only from the organized army but also from civilians who harbor hostility. U.S. enemies use the civilian crowd as an unconventional tactic to blend in and look like civilians, and in response to this growing trend, soldiers must detect and identify civilians as a threat or non-threat. To identify a civilian as a threat, soldiers must familiarize themselves with behavioral cues that implicate threatening individuals. This study elicited expert strategies regarding how to use nonverbal cues to detect a threat and evaluated the best medium for distinguishing a threat from a non-threat to develop a training guide of heuristics for training novices (i.e., soldiers) in the threat detection domain. Forty experts from the threat detection domain were interviewed to obtain strategies regarding how to use nonverbal cues to detect a threat (Phase 1). The use of nonverbal cues in context and learning from intuitive individuals in the domain stood out as strategies that would promote the efficient use of nonverbal cues in detecting a threat. A new group of 14 experts judged scenarios presented in two media (visual, written) (Phase 2). Expert detection accuracy rates of 61% for the visual medium and 56% for the written medium were not significantly different, F (1, 13) = .44, p = .52. For Phase 3 of the study, a training development guide of heuristics was developed and eight different experts in the threat detection domain subjectively rated the heuristics for their importance and relevance in training novices. Nine heuristics were included in the training guide, and overall, experts gave all heuristics consistently high ratings for importance and relevance. The results of this study can be used to improve accuracy rates in the threat detection domain and other populations: 1) the soldier, 2) the average U.S. citizen, and 3) employees of the Transportation Security Administration.<br>Ph. D.
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Henriksson, Mikael. "Implementation of a Hardware Coordinate Wise Descend Algorithm with Maximum Likelihood Estimator for Use in mMTC Activity Detection." Thesis, Linköpings universitet, Datorteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-171071.
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In this work, a coordinate wise descent algorithm is implemented which serves the purpose of estimating active users in a base station/client wireless communication setup. The implemented algorithm utilizes the sporadic nature of users, which is believed to be the norm with 5G Massive MIMO and Internet of Things, meaning that only a subset of all users are active simultaneously at any given time. This work attempts to estimate the viability of a direct algorithm implementation to test if the performance requirements can be satisfied or if a more sophisticated implementation, such as a parallelized version, needs to be created.The result is an isomorphic ASIC implementation made in a 28 nm FD-SOI process, with proper internal word lengths extracted through simulation. Some techniques to lessen the burden on hardware without losing performance is presented which helps reduce area and increase speed of the implementation. Finally, a parallelized version of the algorithm is proposed, if one should desire to explore an implementation with higher system throughput, at almost no furtherexpense of user estimation error.
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Goodlich, Benjamin I. "Machine learning algorithms for the automatic detection and classification of physical activity in children with cerebral palsy who use mobility aids for ambulation." Thesis, Griffith University, 2019. http://hdl.handle.net/10072/389853.
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Background: Literature related to objective measurement of habitual physical activity (PA) disproportionately over represents children with Cerebral Palsy (CP) who are ambulant. Consequently, it is unknown if methods used to examine PA, such as machine learning models built on accelerometer data, are able to accurately detect PA in children with CP who use mobility aids for ambulation.
Objective: To develop and test machine learning models used for the automatic detection and classification of PA type in children with CP who use mobility aids for ambulation.
Methods: Eleven children and adolescents with CP, age 11±3yrs (range 6-16yrs); six females; Gross Motor Function Classification System (GMFCS) III: n=5 and IV: n=6 participated. Participants completed six PA trials of increasing intensity while wearing an ActiGraph GT3X+ accelerometer on the wrist, hip and thigh. PA trials included: supine rest, seated colouring, seated ball throwing, overground walking with a mobility aid, wheelchair propulsion and riding on a modified tricycle. Decision Tree (DT), Support Vector Machine (SVM) and Random Forest (RF) classifiers were trained on 40 features in the vector magnitude of raw acceleration signal using 5s non-overlapping windows. Performance was evaluated using leave-one-subject-out cross validation. Comparisons of performance were subsequently made between all single placement models, all combinations of two placement models, and models trained on data from all three placements.
Results: The best performing single-placement model was a RF classifier trained on wrist features, yielding an overall prediction accuracy of 79%. The best performing model built on a combination of two placements was a RF classifier trained on wrist and hip features, yielding an overall prediction accuracy of 92%. The combinations of multiple accelerometer placements were significantly more accurate than a single monitor alone. Models based on the combination of two placements were more accurate than those based on a combination of three placements; however, this difference was not significant.
Limitations: The PA protocol consisted of structured activity trials performed in a controlled, clinical environment. Thus, the performance of the models under free living conditions require further investigation. The sample size used may limit the generalisability and robustness of the findings given the variability in movement patterns of the population of interest.
Conclusions: Machine learning techniques afford robust and accurate classification of PA in children with CP who use mobility aids for ambulation (GMFCS III & IV) within a laboratory setting. This is significant, as it is the first study to develop methods for objectively measuring habitual PA in this population. Future research should investigate performance of the methods utilised in the current project in children engaged in free living conditions.<br>Thesis (Masters)<br>Master of Medical Research (MMedRes)<br>School of Medical Science<br>Griffith Health<br>Full Text
Books on the topic "User activity detection"
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Nazarov, Dmitriy, and Anton Kopnin. Information technologies in professional activity: data mining and business analytics. INFRA-M Academic Publishing LLC., 2024. http://dx.doi.org/10.12737/2110964.
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The textbook offers a comprehensive look covering a wide range of topics related to the role of information technology in the modern world. Starting with an overview of the history of information technology and its evolution, the book introduces the reader to professions in the field of information and digital technologies, emphasizing them in various professional fields. The content of the textbook includes a detailed analysis of key concepts such as Data Science, Data Mining and Machine Learning, and their role in healthcare, law, education, science and business. The textbook provides a detailed overview of Data Science methods and algorithms, including teaching methods with and without a teacher, as well as specific methods such as Dematel. In the section on business intelligence tools, special attention is paid to Yandex Cloud DataLens, its data analysis functionality, and practical recommendations on registration, the use of intelligent detection of patterns in data, visualization and the development of analytical panels are provided. The section of the textbook devoted to the R and Python programming languages contains recommendations on the use of the R and Python programming languages for statistical data analysis, a description of the main data types, operations, and specific algorithms used for analytical purposes. This section provides a quick guide on how to use the RStudio and PyCharm tools. The final section is devoted to the application of data analysis tools in real projects, providing the reader with the opportunity to immerse themselves in the world of data processing using advanced technologies and techniques. The presentation of the material of each chapter is accompanied by control questions and tests to consolidate the theoretical material, located at the end of the chapter. This textbook will be an indispensable resource for students, professionals and researchers who want to deeply understand and apply information and digital technologies in their professional activities.
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Meijer, Ewout H., and Bruno Verschuere. Detection Deception Using Psychophysiological and Neural Measures. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190612016.003.0010.
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The use of physiological signals to detect deception can be traced back almost a century. Historically, the polygraph has been used—and debated. This chapter discusses the merits of polygraph testing, and to what extent the introduction of measures of brain activity—most notably functional magnetic imaging (fMRI)—have solved the problems associated with polygraph testing. It discusses the different question formats used with polygraph and brain activity measures, and argues that these formats are the main factor contributing to the tests’ validity. Moreover, the authors argue that erroneous test outcomes are caused by errors in logical inferences, and that these errors will not be remedied by new technology. The biggest challenge for the field is to find a question format that isolates deception, and to corroborate laboratory data with methodologically sound field studies.
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Shaikh, Mohd Faraz. Machine Learning in Detecting Auditory Sequences in Magnetoencephalography Data : Research Project in Computational Modelling and Simulation. Technische Universität Dresden, 2021. http://dx.doi.org/10.25368/2022.411.
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Does your brain replay your recent life experiences while you are resting? An open question in neuroscience is which events does our brain replay and is there any correlation between the replay and duration of the event? In this study I tried to investigate this question by using Magnetoencephalography data from an active listening experiment. Magnetoencephalography (MEG) is a non-invasive neuroimaging technique used to study the brain activity and understand brain dynamics in perception and cognitive tasks particularly in the fields of speech and hearing. It records the magnetic field generated in our brains to detect the brain activity. I build a machine learning pipeline which uses part of the experiment data to learn the sound patterns and then predicts the presence of sound in the later part of the recordings in which the participants were made to sit idle and no sound was fed. The aim of the study of test replay of learned sound sequences in the post listening period. I have used classification scheme to identify patterns if MEG responses to different sound sequences in the post task period. The study concluded that the sound sequences can be identified and distinguished above theoretical chance level and hence proved the validity of our classifier. Further, the classifier could predict the sound sequences in the post-listening period with very high probability but in order to validate the model results on post listening period, more evidence is needed.
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Ourada, Jason D., and Kenneth L. Appelbaum. Intoxication and drugs in facilities. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199360574.003.0024.
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Active abuse of substances by inmates poses a challenge for correctional psychiatrists. Substance use disorders (SUD) are common among inmates, with higher prevalence usually found in those with general psychiatric conditions. Knowledge about substance use in correctional facilities fosters competent clinical intervention and enhances management at all levels. Psychiatrists working in jails and prisons have the challenging task of maintaining therapeutic alliances with patients who have co-occurring SUDs and also may be actively using substances. Patients might not spontaneously report use during incarceration because they fear retribution by correctional staff or not receiving needed treatment for medical and mental health problems. Psychiatrists need to remain aware of this and to screen for SUD and active substance use as part of comprehensive treatment planning. The clinical challenges in jails and prisons differ, and the substances found in facilities vary geographically. Active substance abuse by inmates presents clinical and systemic challenges for correctional psychiatrists. The interplay among mental health, medical, and custody staff regarding screening, detection, triage, management, and treatment lies at the heart of these challenges. Correctional psychiatrists make important contributions by providing direct assessment and treatment to inmates, and by offering educational, clinical, and policy consultations to other staff. These contributions help prevent potentially life-threatening complications of intoxication and withdrawal, ensure integrated and evidence-based care, and avoid misguided or ill-informed disciplinary or other institutional practices. This chapter highlights these differences, outlines clinical management, and describes an interdisciplinary approach to intervention.
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Walczak, Jean-Sébastien. Understanding the responsiveness of C-fibres. Edited by Paul Farquhar-Smith, Pierre Beaulieu, and Sian Jagger. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198834359.003.0006.
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In the paper discussed in this chapter, Ainsley Iggo used electrophysiology to characterize mechanosensory fibres from the saphenous nerve in cats. Using fine techniques of dissection he recorded from single units and therefore could discriminate between the various types of sensitivity of afferent fibres. This article describes properties of primary afferent neurons in response to precise calibrated mechanical stimuli and focused on mechanical sensitivity of C-fibres. In addition, the manuscript describes the properties of skin-receptor fields. The paper showed that not all C-fibres responded to high-intensity stimuli and that receptive fields were quite small. In addition, it provided a qualitative evaluation of stimuli necessary to activate those fibres. Hence, by isolating fibres that responded only to strong stimulation, this article showed that the peripheral nervous system is equipped with a specific apparatus for detecting nociceptive stimuli; this was a great step forward in understanding the physiology of pain.
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Chinoy, Hector, and Robert G. Cooper. Polymyositis and dermatomyositis. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199642489.003.0124.
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Polymyositis (PM), dermatomyositis (DM), and inclusion body myositis (IBM) form part of the idiopathic inflammatory myopathies (IIM), a heterogeneous group of rare autoimmune diseases characterized by an acquired proximal muscle weakness, raised muscle enzymes (including creatine kinase), inflammatory cell infiltrates in muscle biopsy tissue, electrophysiological abnormalities, and presence of circulating myositis-specific/myositis-associated autoantibodies. The underlying aetiology of IIM is poorly understood, but likely involves interactions between environmental and genetic risk factors. Myositis may also manifest in association with other connective tissue disorders. The predominant clinical presentation of IIM is skeletal muscle weakness, but many extramuscular features can also occur. Access to good neuropathological support is essential in securing an accurate IIM diagnosis and excluding non-inflammatory myopathies, although IBM is often difficult to distinguish from PM. Antibody testing can help define IIM clinical subtypes, including cancer-associated myositis, predict prognosis, and help in optimizing treatment decisions. MRI can be invaluable for differentiating disease activity from damage, and detecting treatment-induced interval changes. Therapeutic effectiveness of new and existing treatments (where the evidence base remains poor) depends on making a prompt diagnosis and initiating early and appropriately aggressive treatment to prevent establishment of muscle damage. This chapter attempts to summarize the salient features of IIM and update the reader about currently used diagnostics and treatment paradigms in this rare and understudied disease.
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Harper, Lorraine, and David Jayne. The patient with vasculitis. Edited by Giuseppe Remuzzi. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199592548.003.0160.
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The goals of treatment in renal vasculitis are to stop vasculitic activity and recover renal function. Subsequent strategies are required to prevent vasculitis returning and to address longer-term co-morbidities caused by tissue damage, drug toxicity, and increased cardiovascular and malignancy risk.Cyclophosphamide and high-dose glucocorticoids remain the standard induction therapy with alternative immunosuppressives, such as azathioprine, to prevent relapse. Plasma exchange improves renal recovery in severe presentations. Refractory disease resulting from a failure of induction or remission maintenance therapy requires alternative agents and rituximab has been particularly effective. Replacement of cyclophosphamide by rituximab for remission induction is supported by recent evidence. Methotrexate is effective in non-renal vasculitis but difficult to use in patients with renal impairment. Mycophenolate mofetil seems to be effective but there is less long-term evidence.Drug toxicity contributes to co-morbidity and mortality and has led to newer regimens with reduced cyclophosphamide exposure. Glucocorticoid toxicity remains a major problem with controversy over the rapidity with which glucocorticoids can be reduced or withdrawn.Disease relapse occurs in about 50% of patients. Early detection is less likely to lead to an adverse affect on outcomes. Rates of cardiovascular disease and malignancy are higher than in control populations but strategies to reduce their risk, apart from cyclophosphamide-sparing regimens, have not been developed. Thromboembolic events occur in 10% and may be linked to the recently identified autoantibodies to plasminogen and tissue plasminogen activator.Renal impairment at diagnosis is a strong predictor of patient survival and renal outcome. Other predictors include patient age, antineutrophil cytoplasmic antibody subtype, disease extent and response to therapy. Chronic kidney disease can stabilize for many years but the risks of end-stage renal disease are increased by acute kidney injury at presentation or renal relapse. Renal transplantation is successful with similar outcomes to other causes of end-stage renal disease.
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Ufimtseva, Nataliya V., Iosif A. Sternin, and Elena Yu Myagkova. Russian psycholinguistics: results and prospects (1966–2021): a research monograph. Institute of Linguistics, Russian Academy of Sciences, 2021. http://dx.doi.org/10.30982/978-5-6045633-7-3.
Full textAbstract:
The monograph reflects the problems of Russian psycholinguistics from the moment of its inception in Russia to the present day and presents its main directions that are currently developing. In addition, theoretical developments and practical results obtained in the framework of different directions and research centers are described in a concise form. The task of the book is to reflect, as far as it is possible in one edition, firstly, the history of the formation of Russian psycholinguistics; secondly, its methodology and developed methods; thirdly, the results obtained in different research centers and directions in different regions of Russia; fourthly, to outline the main directions of the further development of Russian psycholinguistics. There is no doubt that in the theoretical, methodological and applied aspects, the main problems and the results of their development by Russian psycholinguistics have no analogues in world linguistics and psycholinguistics, or are represented by completely original concepts and methods. We have tried to show this uniqueness of the problematics and the methodological equipment of Russian psycholinguistics in this book. The main role in the formation of Russian psycholinguistics was played by the Moscow psycholinguistic school of A.A. Leontyev. It still defines the main directions of Russian psycholinguistics. Russian psycholinguistics (the theory of speech activity - TSA) is based on the achievements of Russian psychology: a cultural-historical approach to the analysis of mental phenomena L.S. Vygotsky and the system-activity approach of A.N. Leontyev. Moscow is the most "psycholinguistic region" of Russia - INL RAS, Moscow State University, Moscow State Linguistic University, RUDN, Moscow State Pedagogical University, Moscow State Pedagogical University, Sechenov University, Moscow State University and other Moscow universities. Saint Petersburg psycholinguists have significant achievements, especially in the study of neurolinguistic problems, ontolinguistics. The most important feature of Russian psycholinguistics is the widespread development of psycholinguistics in the regions, the emergence of recognized psycholinguistic research centers - St. Petersburg, Tver, Saratov, Perm, Ufa, Omsk, Novosibirsk, Voronezh, Yekaterinburg, Kursk, Chelyabinsk; psycholinguistics is represented in Cherepovets, Ivanovo, Volgograd, Vyatka, Kaluga, Krasnoyarsk, Irkutsk, Vladivostok, Abakan, Maikop, Barnaul, Ulan-Ude, Yakutsk, Syktyvkar, Armavir and other cities; in Belarus - Minsk, in Ukraine - Lvov, Chernivtsi, Kharkov, in the DPR - Donetsk, in Kazakhstan - Alma-Ata, Chimkent. Our researchers work in Bulgaria, Hungary, Vietnam, China, France, Switzerland. There are Russian psycholinguists in Canada, USA, Israel, Austria and a number of other countries. All scientists from these regions and countries have contributed to the development of Russian psycholinguistics, to the development of psycholinguistic theory and methods of psycholinguistic research. Their participation has not been forgotten. We tried to present the main Russian psycholinguists in the Appendix - in the sections "Scientometrics", "Monographs and Manuals" and "Dissertations", even if there is no information about them in the Electronic Library and RSCI. The principles of including scientists in the scientometric list are presented in the Appendix. Our analysis of the content of the resulting monograph on psycholinguistic research in Russia allows us to draw preliminary conclusions about some of the distinctive features of Russian psycholinguistics: 1. cultural-historical approach to the analysis of mental phenomena of L.S.Vygotsky and the system-activity approach of A.N. Leontiev as methodological basis of Russian psycholinguistics; 2. theoretical nature of psycholinguistic research as a characteristic feature of Russian psycholinguistics. Our psycholinguistics has always built a general theory of the generation and perception of speech, mental vocabulary, linked specific research with the problems of ontogenesis, the relationship between language and thinking; 3. psycholinguistic studies of speech communication as an important subject of psycholinguistics; 4. attention to the psycholinguistic analysis of the text and the development of methods for such analysis; 5. active research into the ontogenesis of linguistic ability; 6. investigation of linguistic consciousness as one of the important subjects of psycholinguistics; 7. understanding the need to create associative dictionaries of different types as the most important practical task of psycholinguistics; 8. widespread use of psycholinguistic methods for applied purposes, active development of applied psycholinguistics. The review of the main directions of development of Russian psycholinguistics, carried out in this monograph, clearly shows that the direction associated with the study of linguistic consciousness is currently being most intensively developed in modern Russian psycholinguistics. As the practice of many years of psycholinguistic research in our country shows, the subject of study of psycholinguists is precisely linguistic consciousness - this is a part of human consciousness that is responsible for generating, understanding speech and keeping language in consciousness. Associative experiments are the core of most psycholinguistic techniques and are important both theoretically and practically. The following main areas of practical application of the results of associative experiments can be outlined. 1. Education. Associative experiments are the basis for constructing Mind Maps, one of the most promising tools for systematizing knowledge, assessing the quality, volume and nature of declarative knowledge (and using special techniques and skills). Methods based on smart maps are already widely used in teaching foreign languages, fast and deep immersion in various subject areas. 2. Information search, search optimization. The results of associative experiments can significantly improve the quality of information retrieval, its efficiency, as well as adaptability for a specific person (social group). When promoting sites (promoting them in search results), an associative experiment allows you to increase and improve the quality of the audience reached. 3. Translation studies, translation automation. An associative experiment can significantly improve the quality of translation, take into account intercultural and other social characteristics of native speakers. 4. Computational linguistics and automatic word processing. The results of associative experiments make it possible to reveal the features of a person's linguistic consciousness and contribute to the development of automatic text processing systems in a wide range of applications of natural language interfaces of computer programs and robotic solutions. 5. Advertising. The use of data on associations for specific words, slogans and texts allows you to predict and improve advertising texts. 6. Social relationships. The analysis of texts using the data of associative experiments makes it possible to assess the tonality of messages (negative / positive moods, aggression and other characteristics) based on user comments on the Internet and social networks, in the press in various projections (by individuals, events, organizations, etc.) from various social angles, to diagnose the formation of extremist ideas. 7. Content control and protection of personal data. Associative experiments improve the quality of content detection and filtering by identifying associative fields in areas subject to age restrictions, personal information, tobacco and alcohol advertising, incitement to ethnic hatred, etc. 8. Gender and individual differences. The data of associative experiments can be used to compare the reactions (and, in general, other features of thinking) between men and women, different social and age groups, representatives of different regions. The directions for the further development of Russian psycholinguistics from the standpoint of the current state of psycholinguistic science in the country are seen by us, first of all: in the development of research in various areas of linguistic consciousness, which will contribute to the development of an important concept of speech as a verbal model of non-linguistic consciousness, in which knowledge revealed by social practice and assigned by each member of society during its inculturation is consolidated for society and on its behalf; in the expansion of the problematics, which is formed under the influence of the growing intercultural communication in the world community, which inevitably involves the speech behavior of natural and artificial bilinguals in the new object area of psycholinguistics; in using the capabilities of national linguistic corpora in the interests of researchers studying the functioning of non-linguistic and linguistic consciousness in speech processes; in expanding research on the semantic perception of multimodal texts, the scope of which has greatly expanded in connection with the spread of the Internet as a means of communication in the life of modern society; in the inclusion of the problems of professional communication and professional activity in the object area of psycholinguistics in connection with the introduction of information technologies into public practice, entailing the emergence of new professions and new features of the professional ethos; in the further development of the theory of the mental lexicon (identifying the role of different types of knowledge in its formation and functioning, the role of the word as a unit of the mental lexicon in the formation of the image of the world, as well as the role of the natural / internal metalanguage and its specificity in speech activity); in the broad development of associative lexicography, which will meet the most diverse needs of society and cognitive sciences. The development of associative lexicography may lead to the emergence of such disciplines as associative typology, associative variantology, associative axiology; in expanding the spheres of applied use of psycholinguistics in social sciences, sociology, semasiology, lexicography, in the study of the brain, linguodidactics, medicine, etc. This book is a kind of summarizing result of the development of Russian psycholinguistics today. Each section provides a bibliography of studies on the relevant issue. The Appendix contains the scientometrics of leading Russian psycholinguists, basic monographs, psycholinguistic textbooks and dissertations defended in psycholinguistics. The content of the publications presented here is convincing evidence of the relevance of psycholinguistic topics and the effectiveness of the development of psycholinguistic problems in Russia.
Book chapters on the topic "User activity detection"
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Baek, Jonghun, Geehyuk Lee, Wonbae Park, and Byoung-Ju Yun. "Accelerometer Signal Processing for User Activity Detection." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30134-9_82.
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Jahn, Andreas, Marek Bachmann, Philipp Wenzel, and Klaus David. "Focus on the User: A User Relative Coordinate System for Activity Detection." In Modeling and Using Context. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57837-8_47.
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Scardino, Giuseppe, Ignazio Infantino, and Filippo Vella. "Recognition of Human Identity by Detection of User Activity." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39345-7_6.
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Miu, TungNgai, Chenxu Wang, Daniel Xiapu Luo, and Jinhe Wang. "Modeling User Browsing Activity for Application Layer DDoS Attack Detection." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59608-2_42.
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Grushka-Cohen, Hagit, Ofer Biller, Oded Sofer, Lior Rokach, and Bracha Shapira. "Simulating User Activity for Assessing Effect of Sampling on DB Activity Monitoring Anomaly Detection." In Policy-Based Autonomic Data Governance. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17277-0_5.
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Märker, Marcus, Sebastian Wolf, Oliver Scharf, Daniel Plorin, and Tobias Teich. "KNX-Based Sensor Monitoring for User Activity Detection in AAL-environments." In Ambient Assisted Living and Daily Activities. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13105-4_4.
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Tokhtabayev, Arnur, Anton Kopeikin, Nurlan Tashatov, and Dina Satybaldina. "Malware Analysis and Detection via Activity Trees in User-Dependent Environment." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65127-9_17.
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Lukashin, Aleksey, Mikhail Popov, Dmitrii Timofeev, and Igor Mikhalev. "Employee Performance Analytics Approach Based on Anomaly Detection in User Activity." In Proceedings of International Scientific Conference on Telecommunications, Computing and Control. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6632-9_28.
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Burghouwt, Pieter, Marcel Spruit, and Henk Sips. "Towards Detection of Botnet Communication through Social Media by Monitoring User Activity." In Information Systems Security. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25560-1_9.
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Ortega, Jose Luis Gomez, Liangxiu Han, and Nicholas Bowring. "Modelling and Detection of User Activity Patterns for Energy Saving in Buildings." In Emerging Trends and Advanced Technologies for Computational Intelligence. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33353-3_9.
Full textConference papers on the topic "User activity detection"
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Derya, Sertac, Shih-Chen Yu, Hsu-Wen Young, Eduard A. Jorswieck, Pin-Hsun Lin, and Shih-Chun Lin. "Joint Delay And User Activity Detection in Asynchronous Massive Access." In 2024 33rd Wireless and Optical Communications Conference (WOCC). IEEE, 2024. https://doi.org/10.1109/wocc61718.2024.10786075.
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Sun, Guangyue, Zhaoji Zhang, and Ying Li. "Hybrid Model-Data-Driven User-Activity Detection Network for Massive Random Access." In 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring). IEEE, 2024. http://dx.doi.org/10.1109/vtc2024-spring62846.2024.10683656.
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Zhang, Yuanyuan, Yongtian Luo, Jin Xu, Weihua Liu, Lixun Huang, and Zhe Zhang. "Structured Compressive Sensing Based Joint User Activity and Data Detection for Grant-Free mMTC." In 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP). IEEE, 2024. http://dx.doi.org/10.1109/icsp62122.2024.10743465.
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Khan, Muhammad Usman, Enrico Testi, Marco Chiani, and Enrico Paolini. "Blind User Activity Detection for Grant-Free Random Access in Cell-Free mMIMO Networks." In 2024 IEEE 8th Forum on Research and Technologies for Society and Industry Innovation (RTSI). IEEE, 2024. http://dx.doi.org/10.1109/rtsi61910.2024.10761492.
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Forsch, Christian, Alexander Karataev, and Laura Cottatellucci. "Distributed Joint User Activity Detection, Channel Estimation, and Data Detection via Expectation Propagation in Cell-Free Massive MIMO." In 2024 IEEE 25th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2024. http://dx.doi.org/10.1109/spawc60668.2024.10694527.
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Wang, Yixin, Yichen Wang, Tao Wang, and Julian Cheng. "Joint Channel Estimation and User Activity Detection for mmWave Grant-Free Massive MTC Networks Under Pilot Contamination Attack." In 2024 IEEE 99th Vehicular Technology Conference (VTC2024-Spring). IEEE, 2024. http://dx.doi.org/10.1109/vtc2024-spring62846.2024.10683251.
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Zhong, Sheng, Li Zhen, Shuchang Li, Ping Dong, and Hao Qin. "Joint User Activity Detection and Channel Estimation for Grant-Free Temporal-Correlated Random Access in LEO Satellite Based IoT." In 2024 IEEE 7th International Conference on Electronic Information and Communication Technology (ICEICT). IEEE, 2024. http://dx.doi.org/10.1109/iceict61637.2024.10671064.
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Hasara Pathmaperuma, Madushi, Yogachandran Rahulamathavan, Safak Dogan, and Ahmet M. Kondoz. "User Mobile App Encrypted Activity Detection." In ESCC '21: The 2nd European Symposium on Computer and Communications. ACM, 2021. http://dx.doi.org/10.1145/3478301.3478303.
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Swarnalaxmi, S., I. Elakkiya, M. Thilagavathi, Anil Thomas, and Gunasekaran Raja. "User Activity Analysis Driven Anomaly Detection in Cellular Network." In 2018 Tenth International Conference on Advanced Computing (ICoAC). IEEE, 2018. http://dx.doi.org/10.1109/icoac44903.2018.8939064.
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Hu, Qiaona, Baoming Tang, and Derek Lin. "Anomalous User Activity Detection in Enterprise Multi-source Logs." In 2017 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2017. http://dx.doi.org/10.1109/icdmw.2017.110.
Full textReports on the topic "User activity detection"
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Malik, Arun, Andrea López, and Paul E. Carrillo. Pollution or Crime: The Effect of Driving Restrictions on Criminal Activity. Inter-American Development Bank, 2016. http://dx.doi.org/10.18235/0011747.
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Driving restriction programs have been implemented in many cities around the world to alleviate pollution and congestion problems. Enforcement of such programs is costly and can potentially displace policing resources used for crime prevention and crime detection. Hence, driving restrictions may increase crime. To test this hypothesis, this paper exploits both temporal and spatial variation in the implementation of Quito, Ecuador's Pico y Placa program and evaluates its effect on crime. Both difference-in-difference and spatial regression discontinuity estimates provide credible evidence that driving restrictions can increase crime rates.
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Michaels, Trevor. Red-tailed boa (Boa constrictor) surveys at Salt River Bay National Park, St. Croix U.S. Virgin Islands: 2023 report of activities. National Park Service, 2024. http://dx.doi.org/10.36967/2303799.
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St. Croix is home to a variety of threatened and endangered (T&E) species that are at risk for predation by the invasive red-tailed boa (Boa constrictor), such as the St. Croix ground lizard (Amevia polyps), the ground-nesting least tern (Sterna antillarum), and the hawksbill sea turtle (Eretmochelys imbricata). Genetic analysis determined the original red-tailed boa population on St. Croix sourced from a single female released by a pet owner and its range expands every year. Presently, the main population of red-tailed boa is established on the west end of St. Croix and extends as far east as Salt River. One individual was found in Salt River Marina and additional sightings have occurred in Salt River Bay National Historical Park and Ecological Preserve (SARI) more recently. This inventory aims to search for red-tailed boas in two focal areas that park staff are actively restoring. The park will use information from this inventory to develop a boa removal program and protect sensitive native species like the ground-nesting least tern, the St. Croix ground lizard and the hawksbill sea turtle nests and increase the success of restoration. Snakes are cryptic species, often occurring in low density, and utilize complex habitat patterns. To increase the likelihood of detecting red-tailed boa, the Maryland/Delaware/D.C. Wildlife Services detector dog handling team partnered with the USDA-APHIS National Detector Dog Training Center to train and develop detector dogs to assist in determining the presence/absence of red-tailed boa for this project. Canines were trained to locate red-tailed boa and indicate its presence to the handler via barking three times near the identified target. Two dog detector teams traveled to Salt River Bay National Park (SARI) in St. Croix to conduct surveys for red-tailed boa in habitats likely to contain red-tailed boa in June 2023. Habitat varied throughout the surveys. Close to the bay, mangrove forests dominated and, as elevation increased, transects took place in almost exclusively dry tropical shrub forest. Each transect was surveyed by one dog team. The canine teams had no red-tailed boa detections within SARI. Canines showed proficiency at surveying for red-tailed boa populations in SARI. Given the proximity of confirmed detections to SARI, it is likely red-tailed boa will be in the park in the future, if they are not already. Additional surveys, whether by humans, canines, or both, are recommended in areas of the park that have not been previously surveyed.
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Barefoot, Susan F., Bonita A. Glatz, Nathan Gollop, and Thomas A. Hughes. Bacteriocin Markers for Propionibacteria Gene Transfer Systems. United States Department of Agriculture, 2000. http://dx.doi.org/10.32747/2000.7573993.bard.
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The antibotulinal baceriocins, propionicin PLG-1 and jenseniin G., were the first to be identified, purified and characterized for the dairy propionibaceria and are produced by Propionibacterium thoenii P127 and P. thoenii/jensenii P126, respectively. Objectives of this project were to (a) produce polyclonal antibodies for detection, comparison and monitoring of propionicin PLG-1; (b) identify, clone and characterize the propionicin PLG-1 (plg-1) and jenseniin G (jnG) genes; and (3) develop gene transfer systems for dairy propionibacteria using them as models. Polyclonal antibodies for detection, comparison and monitoring of propionicin PLG-1 were produced in rabbits. Anti-PLG-1 antiserum had high titers (256,000 to 512,000), neutralized PLG-1 activity, and detected purified PLG-1 at 0.10 mg/ml (indirect ELISA) and 0.033 mg/ml (competitive indirect ELISA). Thirty-nine of 158 strains (most P. thoenii or P. jensenii) yielded cross-reacting material; four strains of P. thoenii, including two previously unidentified bacteriocin producers, showed biological activity. Eight propionicin-negative P127 mutants produced neither ELISA response nor biological activity. Western blot analyses of supernates detected a PLG-1 band at 9.1 kDa and two additional protein bands with apparent molecular weights of 16.2 and 27.5 kDa. PLG-1 polyclonal antibodies were used for detection of jenseniin G. PLG-1 antibodies neutralized jenseniin G activity and detected a jenseniin G-sized, 3.5 kDa peptide. Preliminary immunoprecipitation of crude preparations with PLG-1 antibodies yielded three proteins including an active 3-4 kDa band. Propionicin PLG-1 antibodies were used to screen a P. jensenii/thoenii P126 genomic expression library. Complete sequencing of a cloned insert identified by PLG-1 antibodies revealed a putative response regulator, transport protein, transmembrane protein and an open reading frame (ORF) potentially encoding jenseniin G. PCR cloning of the putative plg-1 gene yielded a 1,100 bp fragment with a 355 bp ORF encoding 118 amino acids; the deduced N-terminus was similar to the known PLG-1 N-terminus. The 118 amino acid sequence deduced from the putative plg-1 gene was larger than PLG-1 possibly due to post-translational processing. The product of the putative plg-1 gene had a calculated molecular weight of 12.8 kDa, a pI of 11.7, 14 negatively charged residues (Asp+Glu) and 24 positively charged residues (Arg+Lys). The putative plg-1 gene was expressed as an inducible fusion protein with a six-histidine residue tag. Metal affinity chromatography of the fused protein yielded a homogeneous product. The fused purified protein sequence matched the deduced putative plg-1 gene sequence. The data preliminarily suggest that both the plg-1 and jnG genes have been identified and cloned. Demonstrating that antibodies can be produced for propionicin PLG-1 and that those antibodies can be used to detect, monitor and compare activity throughout growth and purification was an important step towards monitoring PLG-1 concentrations in food systems. The unexpected but fortunate cross-reactivity of PLG-1 antibodies with jenseniin G led to selective recovery of jenseniin G by immunoprecipitation. Further refinement of this separation technique could lead to powerful affinity methods for rapid, specific separation of the two bacteriocins and thus facilitate their availability for industrial or pharmaceutical uses. Preliminary identification of genes encoding the two dairy propionibacteria bacteriocins must be confirmed; further analysis will provide means for understanding how they work, for increasing their production and for manipulating the peptides to increase their target species. Further development of these systems would contribute to basic knowledge about dairy propionibacteria and has potential for improving other industrially significant characteristics.
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Cytryn, Eddie, Mark R. Liles, and Omer Frenkel. Mining multidrug-resistant desert soil bacteria for biocontrol activity and biologically-active compounds. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7598174.bard.
Full textAbstract:
Control of agro-associated pathogens is becoming increasingly difficult due to increased resistance and mounting restrictions on chemical pesticides and antibiotics. Likewise, in veterinary and human environments, there is increasing resistance of pathogens to currently available antibiotics requiring discovery of novel antibiotic compounds. These drawbacks necessitate discovery and application of microorganisms that can be used as biocontrol agents (BCAs) and the isolation of novel biologically-active compounds. This highly-synergistic one year project implemented an innovative pipeline aimed at detecting BCAs and associated biologically-active compounds, which included: (A) isolation of multidrug-resistant desert soil bacteria and root-associated bacteria from medicinal plants; (B) invitro screening of bacterial isolates against known plant, animal and human pathogens; (C) nextgeneration sequencing of isolates that displayed antagonistic activity against at least one of the model pathogens and (D) in-planta screening of promising BCAs in a model bean-Sclerotiumrolfsii system. The BCA genome data were examined for presence of: i) secondary metabolite encoding genes potentially linked to the anti-pathogenic activity of the isolates; and ii) rhizosphere competence-associated genes, associated with the capacity of microorganisms to successfully inhabit plant roots, and a prerequisite for the success of a soil amended BCA. Altogether, 56 phylogenetically-diverse isolates with bioactivity against bacterial, oomycete and fungal plant pathogens were identified. These strains were sent to Auburn University where bioassays against a panel of animal and human pathogens (including multi-drug resistant pathogenic strains such as A. baumannii 3806) were conducted. Nineteen isolates that showed substantial antagonistic activity against at least one of the screened pathogens were sequenced, assembled and subjected to bioinformatics analyses aimed at identifying secondary metabolite-encoding and rhizosphere competence-associated genes. The genome size of the bacteria ranged from 3.77 to 9.85 Mbp. All of the genomes were characterized by a plethora of secondary metabolite encoding genes including non-ribosomal peptide synthase, polyketidesynthases, lantipeptides, bacteriocins, terpenes and siderophores. While some of these genes were highly similar to documented genes, many were unique and therefore may encode for novel antagonistic compounds. Comparative genomic analysis of root-associated isolates with similar strains not isolated from root environments revealed genes encoding for several rhizospherecompetence- associated traits including urea utilization, chitin degradation, plant cell polymerdegradation, biofilm formation, mechanisms for iron, phosphorus and sulfur acquisition and antibiotic resistance. Our labs are currently writing a continuation of this feasibility study that proposes a unique pipeline for the detection of BCAs and biopesticides that can be used against phytopathogens. It will combine i) metabolomic screening of strains from our collection that contain unique secondary metabolite-encoding genes, in order to isolate novel antimicrobial compounds; ii) model plant-based experiments to assess the antagonistic capacities of selected BCAs toward selected phytopathogens; and iii) an innovative next-generation-sequencing based method to monitor the relative abundance and distribution of selected BCAs in field experiments in order to assess their persistence in natural agro-environments. We believe that this integrated approach will enable development of novel strains and compounds that can be used in large-scale operations.
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Hanna, Benjamin, Tom Bubenik, and Barbara Padgett. PR186-203813-R01 Literature Review Pipeline Mid-wall Defect Detection and FFS Assessment. Pipeline Research Council International, Inc. (PRCI), 2021. http://dx.doi.org/10.55274/r0012076.
Full textAbstract:
A literature review on the current industry understanding of mid-wall cracking and its detection was conducted. A summary of key factors influencing mid-wall cracking and modern integrity management approaches for the phenomenon are summarized. Based on the literature review and subject matter expert interviews, gaps in the published industry knowledge were identified. These gaps will be used to drive work in the direction of backfilling the valuable information that may be used to mitigate the onset of mid-wall cracking and to identify the presence of mid-wall cracking so that the threat can be actively and effectively managed.
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Wongpakdeea, Thinnapong, Karin Crenshaw, Hery Figueroa Wong, Duangjai Nacapricha, and Bruce McCord. Advancements in Analytical Techniques for Rapid Identification of Gunshot Residue and Low Explosives through Electrochemical Detection and Surface-Enhanced Raman Spectroscopy. Florida International University, 2024. https://doi.org/10.25148/gfjcsr.2024.7.
Full textAbstract:
This research focuses on developing two analytical methods for forensic investigations using electrochemical detection and surface-enhanced Raman spectroscopy. For electrochemical analysis, screen-printed carbon electrodes are used to detect metals and nitrate/nitrite compounds commonly found in gunshot residue. Gold electrodeposition and copper modification enhance sensitivity and catalytic activity, respectively. Additionally, a screen-printed gold electrode modified with gold nanoparticles enables surface-enhanced Raman spectroscopy, requiring only a single drop of sample solution. Testing includes various compounds relevant to forensic identification, with Origin software used for data analysis. These techniques provide rapid and precise onsite examination of gunshot residue and low explosives, eliminating the need for benchtop instruments. Overall, these advancements enhance forensic inquiries and contribute to the ongoing progress of forensic science, aiding law enforcement agencies worldwide in seeking justice.
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Chen, Yona, Jeffrey Buyer, and Yitzhak Hadar. Microbial Activity in the Rhizosphere in Relation to the Iron Nutrition of Plants. United States Department of Agriculture, 1993. http://dx.doi.org/10.32747/1993.7613020.bard.
Full textAbstract:
Iron is the fourth most abundant element in the soil, but since it forms insoluble hydroxides at neutral and basic pH, it often falls short of meeting the basic requirements of plants and microorganisms. Most aerobic and facultative aerobic microorganisms possess a high-affinity Fe transport system in which siderophores are excreted and the consequent Fe complex is taken up via a cognate specific receptor and a transport pathway. The role of the siderophore in Fe uptake by plants and microorganisms was the focus of this study. In this research Rhizopus arrhizus was found to produce a novel siderophore named Rhizoferrin when grown under Fe deficiency. This compound was purified and its chemical structure was elucidated. Fe-Rhizoferrin was found to alleviate Fe deficiency when applied to several plants grown in nutrient solutions. It was concluded that Fe-Rhizoferrin is the most efficient Fe source for plants when compared with other among microbial siderophores known to date and its activity equals that of the most efficient synthetic commercial iron fertilizer-Fe EDDHA. Siderophores produced by several rhizosphere organisms including Rhizopus Pseudomonas were purified. Monoclonal antibodies were produced and used to develop a method for detection of the siderophores produced by plant-growth-promoting microorganisms in barley rhizosphere. The presence of an Fe-ferrichrome uptake in fluorescent Pseudomonas spp. was demonstrated, and its structural requirements were mapped in P. putida with the help of biomimetic ferrichrome analogs. Using competition experiments, it was shown that FOB, Cop B and FC share at least one common determinant in their uptake pathway. Since FC analogs did not affect FOB or Cop-mediated 55Fe uptake, it could be concluded that these siderophores make use of a different receptor(s) than FC. Therefore, recognition of Cop, FOB and FC proceeds through different receptors having different structural requirements. On the other hand, the phytosiderophores mugineic acid (MA and DMA), were utilized indirectly via ligand exchange by P. putida. Receptors from different biological systems seem to differ in their structural requirements for siderophore recognition and uptake. The design of genus- or species-specific drugs, probes or chemicals, along with an understanding of plant-microbe and microbe-microbe relationships as well as developing methods to detect siderophores using monoclonal antibodies are useful for manipulating the composition of the rhizosphere microbial population for better plant growth, Fe-nutrition and protection from diseases.
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Burns, Malcom, and Gavin Nixon. Literature review on analytical methods for the detection of precision bred products. Food Standards Agency, 2023. http://dx.doi.org/10.46756/sci.fsa.ney927.
Full textAbstract:
The Genetic Technology (Precision Breeding) Act (England) aims to develop a science-based process for the regulation and authorisation of precision bred organisms (PBOs). PBOs are created by genetic technologies but exhibit changes which could have occurred through traditional processes. This current review, commissioned by the Food Standards Agency (FSA), aims to clarify existing terminologies, explore viable methods for the detection, identification, and quantification of products of precision breeding techniques, address and identify potential solutions to the analytical challenges presented, and provide recommendations for working towards an infrastructure to support detection of precision bred products in the future. The review includes a summary of the terminology in relation to analytical approaches for detection of precision bred products. A harmonised set of terminology contributes towards promoting further understanding of the common terms used in genome editing. A review of the current state of the art of potential methods for the detection, identification and quantification of precision bred products in the UK, has been provided. Parallels are drawn with the evolution of synergistic analytical approaches for the detection of Genetically Modified Organisms (GMOs), where molecular biology techniques are used to detect DNA sequence changes in an organism’s genome. The scope and limitations of targeted and untargeted methods are summarised. Current scientific opinion supports that modern molecular biology techniques (i.e., quantitative real-time Polymerase Chain Reaction (qPCR), digital PCR (dPCR) and Next Generation Sequencing (NGS)) have the technical capability to detect small alterations in an organism’s genome, given specific prerequisites of a priori information on the DNA sequence of interest and of the associated flanking regions. These techniques also provide the best infra-structure for developing potential approaches for detection of PBOs. Should sufficient information be known regarding a sequence alteration and confidence can be attributed to this being specific to a PBO line, then detection, identification and quantification can potentially be achieved. Genome editing and new mutagenesis techniques are umbrella terms, incorporating a plethora of approaches with diverse modes of action and resultant mutational changes. Generalisations regarding techniques and methods for detection for all PBO products are not appropriate, and each genome edited product may have to be assessed on a case-by-case basis. The application of modern molecular biology techniques, in isolation and by targeting just a single alteration, are unlikely to provide unequivocal evidence to the source of that variation, be that as a result of precision breeding or as a result of traditional processes. In specific instances, detection and identification may be technically possible, if enough additional information is available in order to prove that a DNA sequence or sequences are unique to a specific genome edited line (e.g., following certain types of Site-Directed Nucelase-3 (SDN-3) based approaches). The scope, gaps, and limitations associated with traceability of PBO products were examined, to identify current and future challenges. Alongside these, recommendations were made to provide the infrastructure for working towards a toolkit for the design, development and implementation of analytical methods for detection of PBO products. Recognition is given that fully effective methods for PBO detection have yet to be realised, so these recommendations have been made as a tool for progressing the current state-of-the-art for research into such methods. Recommendations for the following five main challenges were identified. Firstly, PBOs submitted for authorisation should be assessed on a case-by-case basis in terms of the extent, type and number of genetic changes, to make an informed decision on the likelihood of a molecular biology method being developed for unequivocal identification of that specific PBO. The second recommendation is that a specialist review be conducted, potentially informed by UK and EU governmental departments, to monitor those PBOs destined for the authorisation process, and actively assess the extent of the genetic variability and mutations, to make an informed decision on the type and complexity of detection methods that need to be developed. This could be further informed as part of the authorisation process and augmented via a publicly available register or database. Thirdly, further specialist research and development, allied with laboratory-based evidence, is required to evaluate the potential of using a weight of evidence approach for the design and development of detection methods for PBOs. This concept centres on using other indicators, aside from the single mutation of interest, to increase the likelihood of providing a unique signature or footprint. This includes consideration of the genetic background, flanking regions, off-target mutations, potential CRISPR/Cas activity, feasibility of heritable epigenetic and epitranscriptomic changes, as well as supplementary material from supplier, origin, pedigree and other documentation. Fourthly, additional work is recommended, evaluating the extent/type/nature of the genetic changes, and assessing the feasibility of applying threshold limits associated with these genetic changes to make any distinction on how they may have occurred. Such a probabilistic approach, supported with bioinformatics, to determine the likelihood of particular changes occurring through genome editing or traditional processes, could facilitate rapid classification and pragmatic labelling of products and organisms containing specific mutations more readily. Finally, several scientific publications on detection of genome edited products have been based on theoretical principles. It is recommended to further qualify these using evidenced based practical experimental work in the laboratory environment. Additional challenges and recommendations regarding the design, development and implementation of potential detection methods were also identified. Modern molecular biology-based techniques, inclusive of qPCR, dPCR, and NGS, in combination with appropriate bioinformatics pipelines, continue to offer the best analytical potential for developing methods for detecting PBOs. dPCR and NGS may offer the best technical potential, but qPCR remains the most practicable option as it is embedded in most analytical laboratories. Traditional screening approaches, similar to those for conventional transgenic GMOs, cannot easily be used for PBOs due to the deficit in common control elements incorporated into the host genome. However, some limited screening may be appropriate for PBOs as part of a triage system, should a priori information be known regarding the sequences of interest. The current deficit of suitable methods to detect and identify PBOs precludes accurate PBO quantification. Development of suitable reference materials to aid in the traceability of PBOs remains an issue, particularly for those PBOs which house on- and off-target mutations which can segregate. Off-target mutations may provide an additional tool to augment methods for detection, but unless these exhibit complete genetic linkage to the sequence of interest, these can also segregate out in resulting generations. Further research should be conducted regarding the likelihood of multiple mutations segregating out in a PBO, to help inform the development of appropriate PBO reference materials, as well as the potential of using off-target mutations as an additional tool for PBO traceability. Whilst recognising the technical challenges of developing and maintaining pan-genomic databases, this report recommends that the UK continues to consider development of such a resource, either as a UK centric version, or ideally through engagement in parallel EU and international activities to better achieve harmonisation and shared responsibilities. Such databases would be an invaluable resource in the design of reliable detection methods, as well as for confirming that a mutation is as a result of genome editing. PBOs and their products show great potential within the agri-food sector, necessitating a science-based analytical framework to support UK legislation, business and consumers. Differentiating between PBOs generated through genome editing compared to organisms which exhibit the same mutational change through traditional processes remains analytically challenging, but a broad set of diagnostic technologies (e.g., qPCR, NGS, dPCR) coupled with pan-genomic databases and bioinformatics approaches may help contribute to filling this analytical gap, and support the safety, transparency, proportionality, traceability and consumer confidence associated with the UK food chain.
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Graham, Timothy, and Katherine M. FitzGerald. Bots, Fake News and Election Conspiracies: Disinformation During the Republican Primary Debate and the Trump Interview. Queensland University of Technology, 2023. http://dx.doi.org/10.5204/rep.eprints.242533.
Full textAbstract:
We used Alexandria Digital, a world leading disinformation detection technology, to analyse almost a million posts on X (formerly known as Twitter) and Reddit comments during the first Republican primary debate and counterprogrammed Tucker Carlson and Donald Trump interview on the 23rd of August. What we did: • Collected 949,259 posts from the platform X, formerly known as Twitter. These posts were collected if they contained one of 11 relevant hashtags or keywords and were posted between 8:45pm and 11:15pm EST on 23rd August 2023. • Collected 20,549 comments from two separate Reddit threads. Both were discussion threads dedicated to the first Republican primary Debate and the Tucker Carlson and Donald Trump interview from r/Conservative and r/politics. • This methodology allowed us to capture narratives and conduct analysis of coordinated behaviour that occurred immediately before, during, and after the Republican primary debate and the airing of the Tucker Carlson interview of Donald Trump. What we found: • A coordinated network of over 1200 accounts promoting the conspiracy theory that Donald Trump won the 2020 United States presidential election that received over 3 million impressions on the platform X; • A sprawling bot network consisting of 1,305 unique accounts with a variety of clusters; • Some of the largest clusters were coordinated troll networks in support of Donald Trump; a coordinated network of misleading news outlets, and a clickbait Pro-Trump bot network. • No coordinated activity was found on Reddit during the Republican Primary Debate or in discussion of the Tucker Carlson and Donald Trump interview. What does this mean? • X is flooded with platform manipulation of various kinds, is not doing enough to moderate content, and has no clear strategy for dealing with political disinformation. • A haven for disinformation. While pre-Musk Twitter previously managed to moderate harmful conspiracy theories such as QAnon, X is now a safe space for conspiracy theorists and political disinformation. • That no evidence of coordinated influence activity was found on Reddit suggests the extensive rules and moderation either prevented or removed coordinated activity from the platform. • Worrying trends. Given the prevalence of mis- and disinformation during the debate and interview, the leadup to the US 2024 Presidential Election is likely to witness a surge of information disorder on the platform. • Trump is back. The reinstatement of Donald Trump’s X account has emboldened conspiracy theorists and the far right, who are interpreting this as a sign that the reason why Trump was suspended (incitement to violence) validates election fraud disinformation and activism. • Anything goes. The lack of a freely available Twitter Application Programming Interface (API) means that researchers, journalists, and regulators cannot monitor disinformation on X and hold the platform to account.
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Zárate-Solano, Héctor M., and Norberto Rodríguez-Niño. Consumer Prices Trends in Colombia: Detecting Breaks and Forecasting Inflation. Banco de la República, 2024. https://doi.org/10.32468/be.1289.
Full textAbstract:
Colombia’s annual inflation reached 13.3% in March 2023, the highest rate since the implementation of the inflation-targeting regime for monetary policy in 2000. However, some groups within the basket show signs of lower inflation, while others exhibit higher inflation. The persistence of this trend is actively debated, involving analysis of both year-to-year and month-to-month changes in price indices. In this paper, we use a time series methodology to identify shifts in inflation levels based on the 188 price indices that comprise the basket. We classify the trend breaks as positive or negative and aggregate them by tradable and non-tradable, core and regulated, and other CPI groups. Additionally, we employ these trend models, possibly with breaks, to forecast total (bottom-up and middle-up approaches) and group inflation for 2024. Our findings suggest that inflation will decline for most groups by the end of 2024 but will increase for some key groups. Forecast evaluation measures favor using some degree of aggregation, with breaks considerations, for forecasting both annual and monthly inflation.