Relevant bibliographies by topics / API activity recognition

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'API activity recognition'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "API activity recognition"

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Kim, Hyesuk, and Incheol Kim. "Human Activity Recognition as Time-Series Analysis." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/676090.

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We propose a system that can recognize daily human activities with a Kinect-style depth camera. Our system utilizes a set of view-invariant features and the hidden state conditional random field (HCRF) model to recognize human activities from the 3D body pose stream provided by MS Kinect API or OpenNI. Many high-level daily activities can be regarded as having a hierarchical structure where multiple subactivities are performed sequentially or iteratively. In order to model effectively these high-level daily activities, we utilized a multiclass HCRF model, which is a kind of probabilistic graphical models. In addition, in order to get view-invariant, but more informative features, we extract joint angles from the subject’s skeleton model and then perform the feature transformation to obtain three different types of features regarding motion, structure, and hand positions. Through various experiments using two different datasets, KAD-30 and CAD-60, the high performance of our system is verified.
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Sri Indrawanti, Annisaa, and Eka Prakarsa Mandyartha. "Mobile-based Activity Monitoring System for the Self-quarantine Patient." Applied Technology and Computing Science Journal 4, no. 1 (July 31, 2021): 56–62. http://dx.doi.org/10.33086/atcsj.v4i1.2085.

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Nowadays, not all the patient can be hospitalized because of the COVID-19 pandemics. So, the self-quarantine for the patient with the various diseases will be the given solution by the hospital. It would make the hospital needs a system that can monitor the activity and the position of the patient from a distance. Nowadays, mobile phone is equipped by the sensor that can detect the user movement. Not only the user’s position, but also the user’s activity. In this paper, it will be developed an activity and position monitoring system for the self-quarantine patient that can be used in their home. The mobile activity monitoring can be achieved by activity recognition using classification method. For the needs of performance testing, we evaluate some classification method for activity recognition to compare the among classification method for the activity recognition. Some tested classification methods are Naïve Bayes, KNN, KStar and TreeJ48. Furthermore, we tested the impact of sliding windows per N samples taken to the accuracy of the activity recognition. We choose the best N sample that could give the best accuracy for activity recognition. The system not only monitor the patient’s activity, but also the patient’s position. The position monitoring can be achieved using Google Maps API. The result is Naive bayes has the accuracy of 81.25%, KNN has the accuracy of 78.125%, KStar has the accuracy of 78.125% and TreeJ48 has the accuracy of 75%. The N sample that could give the best accuracy is 6 with the accuracy of 90.15%.
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Tuan Nguyen, Khai, Thanh Van Pham, Van Dung Nguyen, Long Thanh Do, An-Van Tran, and Duc-Tan Tran. "Development of a Smartphone Application for Safe Car Driving Using Google API and Built-in Sensor." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 02 (February 10, 2020): 178. http://dx.doi.org/10.3991/ijim.v14i02.11118.

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<span>In the world where streets brimmed with vehicles are no longer an foreign sight, vehicle accidents come along as one of unwanted consequences. Accidents are due to many reasons, though however the reason is, most of the times it leads to damaged vehicle(s), injuries or even fatality. In this research, we present Car Safe, a smartphone application which can make phone call reception decision, text readout to prevent distraction, driving time alert and send the location of the driver upon the occurrence of a detected crash. While the Google Activity Recognition API only detects driving state approximately 89% of the time, our proposed method yields more than 93% detection.</span>
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Gabryel, Marcin, Konrad Grzanek, and Yoichi Hayashi. "Browser Fingerprint Coding Methods Increasing the Effectiveness of User Identification in the Web Traffic." Journal of Artificial Intelligence and Soft Computing Research 10, no. 4 (October 1, 2020): 243–53. http://dx.doi.org/10.2478/jaiscr-2020-0016.

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AbstractWeb-based browser fingerprint (or device fingerprint) is a tool used to identify and track user activity in web traffic. It is also used to identify computers that are abusing online advertising and also to prevent credit card fraud. A device fingerprint is created by extracting multiple parameter values from a browser API (e.g. operating system type or browser version). The acquired parameter values are then used to create a hash using the hash function. The disadvantage of using this method is too high susceptibility to small, normally occurring changes (e.g. when changing the browser version number or screen resolution). Minor changes in the input values generate a completely different fingerprint hash, making it impossible to find similar ones in the database. On the other hand, omitting these unstable values when creating a hash, significantly limits the ability of the fingerprint to distinguish between devices. This weak point is commonly exploited by fraudsters who knowingly evade this form of protection by deliberately changing the value of device parameters. The paper presents methods that significantly limit this type of activity. New algorithms for coding and comparing fingerprints are presented, in which the values of parameters with low stability and low entropy are especially taken into account. The fingerprint generation methods are based on popular Minhash, the LSH, and autoencoder methods. The effectiveness of coding and comparing each of the presented methods was also examined in comparison with the currently used hash generation method. Authentic data of the devices and browsers of users visiting 186 different websites were collected for the research.
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Pranatawijaya, Viktor Handrianus. "PENERAPAN LOCATION BASED SERVICED (LBS) DALAM PROTOTIPE PENGENALAN RUANGAN DENGAN METODE EXTREME PROGRAMMING." Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika 15, no. 1 (January 10, 2021): 92–99. http://dx.doi.org/10.47111/jti.v15i1.1936.

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Location Based Service (LBS) is a service to provide information that has been created, compiled, selected or filtered by considering the location of users or other people or current mobile devices [1]. It is possible to implement LBS using GPS because mobile devices have GPS services [6]. Based on these services, making an application for room recognition can be done by utilizing the Google Maps API by implementing GIS, LBS, and GPS. Implementation of application with extreme programming methods is carried out in accordance with the stages. In the planning part, an analysis is carried out on the business needs and user stories, in the design, use case diagrams and activity diagrams are made, coding is done by implementing the design using the mobile programming language, and testing is done using black box testing. The application of LBS is carried out when calling the room information page which functions to find the location of the room and displays notifications and room information when the user enters the radius that has been determined in the room data. Determining the radius is very important to do so that the information about the room that is displayed does not overlap with other rooms. This is done to keep the room information displayed to the user only to display information about one room only. Therefore, when determining the radius of the room, it must be determined carefully so that the radius can create a circle that does not intersect with other radius circles. The room information displayed is obtained from data management by the admin through the building management page, rooms, and items. In managing, admins can add, edit, and delete data.
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Pranatawijaya, Viktor Handrianus. "PENERAPAN LOCATION BASED SERVICED (LBS) DALAM PROTOTIPE PENGENALAN RUANGAN DENGAN METODE EXTREME PROGRAMMING." Jurnal Teknologi Informasi: Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika 15, no. 1 (January 10, 2021): 92–99. http://dx.doi.org/10.47111/jti.v15i1.1936.

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Location Based Service (LBS) is a service to provide information that has been created, compiled, selected or filtered by considering the location of users or other people or current mobile devices [1]. It is possible to implement LBS using GPS because mobile devices have GPS services [6]. Based on these services, making an application for room recognition can be done by utilizing the Google Maps API by implementing GIS, LBS, and GPS. Implementation of application with extreme programming methods is carried out in accordance with the stages. In the planning part, an analysis is carried out on the business needs and user stories, in the design, use case diagrams and activity diagrams are made, coding is done by implementing the design using the mobile programming language, and testing is done using black box testing. The application of LBS is carried out when calling the room information page which functions to find the location of the room and displays notifications and room information when the user enters the radius that has been determined in the room data. Determining the radius is very important to do so that the information about the room that is displayed does not overlap with other rooms. This is done to keep the room information displayed to the user only to display information about one room only. Therefore, when determining the radius of the room, it must be determined carefully so that the radius can create a circle that does not intersect with other radius circles. The room information displayed is obtained from data management by the admin through the building management page, rooms, and items. In managing, admins can add, edit, and delete data.
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Aguileta, Antonio A., Ramon F. Brena, Oscar Mayora, Erik Molino-Minero-Re, and Luis A. Trejo. "Multi-Sensor Fusion for Activity Recognition—A Survey." Sensors 19, no. 17 (September 3, 2019): 3808. http://dx.doi.org/10.3390/s19173808.

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In Ambient Intelligence (AmI), the activity a user is engaged in is an essential part of the context, so its recognition is of paramount importance for applications in areas like sports, medicine, personal safety, and so forth. The concurrent use of multiple sensors for recognition of human activities in AmI is a good practice because the information missed by one sensor can sometimes be provided by the others and many works have shown an accuracy improvement compared to single sensors. However, there are many different ways of integrating the information of each sensor and almost every author reporting sensor fusion for activity recognition uses a different variant or combination of fusion methods, so the need for clear guidelines and generalizations in sensor data integration seems evident. In this survey we review, following a classification, the many fusion methods for information acquired from sensors that have been proposed in the literature for activity recognition; we examine their relative merits, either as they are reported and sometimes even replicated and a comparison of these methods is made, as well as an assessment of the trends in the area.
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Florea, George Albert, and Radu-Casian Mihailescu. "Multimodal Deep Learning for Group Activity Recognition in Smart Office Environments." Future Internet 12, no. 8 (August 9, 2020): 133. http://dx.doi.org/10.3390/fi12080133.

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Deep learning (DL) models have emerged in recent years as the state-of-the-art technique across numerous machine learning application domains. In particular, image processing-related tasks have seen a significant improvement in terms of performance due to increased availability of large datasets and extensive growth of computing power. In this paper we investigate the problem of group activity recognition in office environments using a multimodal deep learning approach, by fusing audio and visual data from video. Group activity recognition is a complex classification task, given that it extends beyond identifying the activities of individuals, by focusing on the combinations of activities and the interactions between them. The proposed fusion network was trained based on the audio–visual stream from the AMI Corpus dataset. The procedure consists of two steps. First, we extract a joint audio–visual feature representation for activity recognition, and second, we account for the temporal dependencies in the video in order to complete the classification task. We provide a comprehensive set of experimental results showing that our proposed multimodal deep network architecture outperforms previous approaches, which have been designed for unimodal analysis, on the aforementioned AMI dataset.
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Musch, Mark W., Donna L. Arvans, Margaret M. Walsh-Reitz, Kazuhiko Uchiyama, Mitsunori Fukuda, and Eugene B. Chang. "Synaptotagmin I binds intestinal epithelial NHE3 and mediates cAMP- and Ca2+-induced endocytosis by recruitment of AP2 and clathrin." American Journal of Physiology-Gastrointestinal and Liver Physiology 292, no. 6 (June 2007): G1549—G1558. http://dx.doi.org/10.1152/ajpgi.00388.2006.

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Apical membrane sodium hydrogen exchanger 3 (NHE3), a major pathway for non-nutrient-dependent intestinal Na+ absorption, is tightly regulated by second messenger systems that affect its functional activity and membrane trafficking. However, the events and components involved in NHE3 regulation are only partially understood. We report that the adaptor protein synaptotagmin I (Syt I) plays a pivotal role in cAMP- and Ca2+-induced cargo recognition of NHE3 and initiation of its endocytosis. Both mouse small intestine (jejunum) and Caco-2BBe Syt I coimmunoprecipitated with NHE3, particularly following increases in cellular cAMP or Ca2+. Following short interfering RNA (siRNA) suppression of Syt I expression, cAMP- and Ca2+-induced inhibition of NHE3 activity were still observed but NHE3 endocytosis was blocked, as assessed by 22Na influx and apical membrane biotin labeling, respectively. Similar effects on NHE3 inhibition and endocytosis were observed by siRNA suppression of either the μ-subunit of the adaptor protein 2 (AP2) complex or the heavy chain of clathrin. Coimmunoprecipitation analyses of NHE3 with these adaptor proteins revealed that cAMP- and Ca2+-induced NHE3-Syt I interaction preceded and was required for recruitment of AP2 and the clathrin complex. Confocal microscopy confirmed both the time sequence and protein associations of these events. We conclude that Syt I plays a pivotal role in mediating cAMP- and Ca2+-induced endocytosis of NHE3 (but not in inhibition of activity) through cargo recognition of NHE3 and subsequent recruitment of AP2-clathrin assembly required for membrane endocytosis.
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Silliman, Christopher C., Nathan J. D. McLaughlin, Marguerite R. Kelher, Samina Khan, Elisabeth K. Crawford, Kathryn Hassell, and Rachelle Nuss. "Lipids That Prime Neutrophils Are Present upon Recognition of the Acute Chest Syndrome." Blood 104, no. 11 (November 16, 2004): 3572. http://dx.doi.org/10.1182/blood.v104.11.3572.3572.

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Abstract The acute chest syndrome (ACS) is a form of acute lung injury (ALI) unique to sickle cell disease (SCD), and for children >10 years of age represents the major cause of morbidity and mortality. ALI is thought to be neutrophil (PMN)-mediated; however, the pathogenesis of ACS is not known but is linked to an increase of secretory phospholipase A2-IIA (sPLA2) activity within 24 hours of its development (Styles, Blood87:2573, 1996). We hypothesize that sPLA2 cleavage of membrane lipids generates biologically active lipophilic compounds that prime PMNs, which provokes ACS in patients with SCD, resulting in increased lipid priming activity during ACS. After obtaining informed consent, heparinized whole blood was drawn from children with SCD when well, at their annual comprehensive visit, and serially when admitted to the hospital, upon admission and every other day until discharge. Plasma was isolated, and the plasma lipids extracted. In selected patients the plasma was treated with buffer or sPLA2 for 1 hour at 37°C prior to lipid extraction. Lipids were assayed for their ability to prime fMLP-activation of the PMN oxidase as measured by the maximal rate of O2− production by the SOD-inhibitable reduction of cytochrome c at 550 nm. The data (mean ± SEM of fold increase over fMLP-treated controls of the lipid priming activity at baseline and over hospitalization) consists of three groups: patients with SCD, patients with SCD treated with daily hydroxyurea (HU), or patients with SCD treated with monthly, red cell exchanges (EX). At baseline, there was slightly more lipid priming activity in untreated SCD patients versus either HU- or EX-treated patients, which was not statistically different (Table). The lipid activity did not increase with vaso-occlusive crisis (VOC) but significantly increased in the one patient with ACS. Moreover, sPLA2 treatment of the plasma obtained 12 hours prior to developing ACS, while this patient was in VOC, demonstrated an increase in lipid priming activity versus the patient’s prior samples (0.96- to 1.73-fold control with sPLA2). The lipids from healthy African-American patients, did not exhibit increased activity vs controls (data not shown). We conclude that both HU and EX therapies may diminish the baseline activity of plasma lipids as compared to untreated children with SCD. In addition, sPLA2-treatment of the plasma 12 hours prior to the devlopment of ACS increased the amount of bioactive lipids released. Moreover, the amount of lipid priming activity was significantly increased during ACS as compared to baseline and during VOC. These preliminary results support the concept that the pathogenesis of ACS involves sPLA2 activity, which generates bioactive lipids that may provoke ACS, and need to be extended through the recruitment of more patients and completion of a multi-institutional trial. Lipid priming activity in children with SCD Group/Clinical Status Baseline VOC ACS Units are nmol O2−/min; † = p≤0.05 versus Baseline and VOC. SCD 1.8±0.8 1.3±0.1 2.3±0.2† SCD + HU 1.6±0.2 1.4±0.3 no data SCD + EX 1.5±0.2 1.8±0.2 no data
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Dissertations / Theses on the topic "API activity recognition"

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Sarzano, Nicolò. "Riconoscimento automatico di attività attraverso i sensori inerziali di uno smartphone: una valutazione sperimentale." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2016.

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In questa tesi vengono analizzati e confrontati vari metodi per la rilevazione delle attività motorie e di trasporto, attraverso uno smartphone, effettuate da un utente. L'analisi prende in considerazione sia elaborazioni dei sensori del dispositivo sia l'utilizzo di API dedicate all'activity recognition. L'obiettivo è quello di definire quale tecnica risulta essere la migliore per creare applicazioni Context-Aware.
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Albert, Florea George, and Filip Weilid. "Deep Learning Models for Human Activity Recognition." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20201.

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AMI Meeting Corpus (AMI) -databasen används för att undersöka igenkännande av gruppaktivitet. AMI Meeting Corpus (AMI) -databasen ger forskare fjärrstyrda möten och naturliga möten i en kontorsmiljö; mötescenario i ett fyra personers stort kontorsrum. För attuppnågruppaktivitetsigenkänninganvändesbildsekvenserfrånvideosoch2-dimensionella audiospektrogram från AMI-databasen. Bildsekvenserna är RGB-färgade bilder och ljudspektrogram har en färgkanal. Bildsekvenserna producerades i batcher så att temporala funktioner kunde utvärderas tillsammans med ljudspektrogrammen. Det har visats att inkludering av temporala funktioner både under modellträning och sedan förutsäga beteende hos en aktivitet ökar valideringsnoggrannheten jämfört med modeller som endast använder rumsfunktioner[1]. Deep learning arkitekturer har implementerats för att känna igen olika mänskliga aktiviteter i AMI-kontorsmiljön med hjälp av extraherade data från the AMI-databas.Neurala nätverks modellerna byggdes med hjälp av KerasAPI tillsammans med TensorFlow biblioteket. Det finns olika typer av neurala nätverksarkitekturer. Arkitekturerna som undersöktes i detta projektet var Residual Neural Network, Visual GeometryGroup 16, Inception V3 och RCNN (LSTM). ImageNet-vikter har använts för att initialisera vikterna för Neurala nätverk basmodeller. ImageNet-vikterna tillhandahålls av Keras API och är optimerade för varje basmodell [2]. Basmodellerna använder ImageNet-vikter när de extraherar funktioner från inmatningsdata. Funktionsextraktionen med hjälp av ImageNet-vikter eller slumpmässiga vikter tillsammans med basmodellerna visade lovande resultat. Både Deep Learning användningen av täta skikt och LSTM spatio-temporala sekvens predikering implementerades framgångsrikt.
The Augmented Multi-party Interaction(AMI) Meeting Corpus database is used to investigate group activity recognition in an office environment. The AMI Meeting Corpus database provides researchers with remote controlled meetings and natural meetings in an office environment; meeting scenario in a four person sized office room. To achieve the group activity recognition video frames and 2-dimensional audio spectrograms were extracted from the AMI database. The video frames were RGB colored images and audio spectrograms had one color channel. The video frames were produced in batches so that temporal features could be evaluated together with the audio spectrogrames. It has been shown that including temporal features both during model training and then predicting the behavior of an activity increases the validation accuracy compared to models that only use spatial features [1]. Deep learning architectures have been implemented to recognize different human activities in the AMI office environment using the extracted data from the AMI database.The Neural Network models were built using the Keras API together with TensorFlow library. There are different types of Neural Network architectures. The architecture types that were investigated in this project were Residual Neural Network, Visual Geometry Group 16, Inception V3 and RCNN(Recurrent Neural Network). ImageNet weights have been used to initialize the weights for the Neural Network base models. ImageNet weights were provided by Keras API and was optimized for each base model[2]. The base models uses ImageNet weights when extracting features from the input data.The feature extraction using ImageNet weights or random weights together with the base models showed promising results. Both the Deep Learning using dense layers and the LSTM spatio-temporal sequence prediction were implemented successfully.
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Book chapters on the topic "API activity recognition"

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Concone, Federico, Salvatore Gaglio, Giuseppe Lo Re, and Marco Morana. "Smartphone Data Analysis for Human Activity Recognition." In AI*IA 2017 Advances in Artificial Intelligence, 58–71. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70169-1_5.

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Htike, Zaw Zaw, Simon Egerton, and Ye Chow Kuang. "Model-Based Viewpoint Invariant Human Activity Recognition from Uncalibrated Monocular Video Sequence." In AI 2010: Advances in Artificial Intelligence, 142–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17432-2_15.

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Kareem, Syed Yusha, Luca Buoncompagni, and Fulvio Mastrogiovanni. "Arianna$$^{+}$$: Scalable Human Activity Recognition by Reasoning with a Network of Ontologies." In AI*IA 2018 – Advances in Artificial Intelligence, 83–95. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03840-3_7.

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Siddiqi, Faisal. "Paradoxes of Strategic Labour Rights Litigation: Insights from the Baldia Factory Fire Litigation." In Interdisciplinary Studies in Human Rights, 59–96. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73835-8_4.

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AbstractThis chapter focuses on the legal activism that followed the Ali Enterprises factory fire and its aftermath in Pakistan. This chapter has two purposes: firstly, it documents the legal proceedings that were initiated and pursued in the courts of Pakistan as well as its interconnected developments. Secondly, I aim to use this engagement with the legal proceedings of the Baldia factory fire aftermath as an opportunity for an in-depth reflection on the capacity and, finally, suitability of the judicial process to bring about justice in struggles over human and labour rights. Providing a rare and insider account of the legal proceedings in the Pakistani courts and its interconnected developments, I hope to lay the empirical foundation for the theoretical and strategic claims of this study. It is against the background and based on the experience with the litigation and legal advocacy following the Baldia fire that I examine the two what I perceive as “paradoxes” at the heart of the litigation. The first is the inseparability of the “limited justice” that may result from such litigation on one hand, and the “structural injustice” that informs and determines the conditions the litigation seeks to address—and transform—on the other hand. The second paradox concerns the inseparability of both law and lawlessness as regards the legal context of the litigation, advocacy and policy proposal elements that are here in play.My argument is that these apparently contradictory phenomena not only coexist alongside one another but that they guarantee each other’s existence. This analysis leads me to the conclusion that in order to understand and improve such forms of strategic litigation, it is necessary to measure its success and failure in terms of three distinct but interconnected criteria. These are the tactical, strategic and structural impacts of the litigation. Ultimately, I will argue for rejecting what is often perceived by involved stakeholders to be an unavoidable choice between nihilism, euphoria or incremental reform in this context. But, to the contrary, I will argue for a conception of legal struggles as a means of building sustainable and fruitful forms of resistance and of change based on the recognition and exploitation of these irreconcilable paradoxes rather than fruitless attempts to ignore or transcend these irreconcilable contradictions.
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Ahmed, Jessica. "Development of Specific Gamma Secretase Inhibitors." In Handbook of Research on Systems Biology Applications in Medicine, 423–37. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-076-9.ch025.

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Secretases are aspartic proteases, which specifically trim important, medically relevant targets such as the amyloid-precursor protein (APP) or the Notch-receptor. Therefore, changes in their activity can lead to dramatic diseases like M. Alzheimer caused by aggregation of peptidic fragments. On the other hand, the secretases are interesting targets for molecular therapy of the multiple myeloma, because the over-expressed Notch-receptor does not emerge into the native conformation until the cleavage by the presenilin, the active and catalytic subunit of the gamma secretase, occurs. Here, we focus on a novel methodology of structure-based drug development, feasible without prior knowledge of the target structure— analogy modeling. This combination of similarity screening, fold recognition, ligand-supported modeling, and docking is exemplarily illustrated for the structure of presenilin and specific inhibitors thereof.
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von Bernstorff, Jochen. "The Battle for the Recognition of Wars of National Liberation." In The Battle for International Law, 52–70. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198849636.003.0003.

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The chapter revisits the third world struggle for a full legal recognition of ‘wars of national liberation’ in the 1960s and 70s. Supported by famous United Nations (UN) resolutions, the growing number of ‘newly independent states’ had managed to confer increasing institutional legitimacy to the still-ongoing struggles for independence by incriminating colonialism and racism, as well as by actively promoting support for third-world self-determination. Armed revolts of independence movements against colonial or racist rule between 1945 and 1975, for example in Indonesia, Vietnam, Algeria, Kenya, Namibia, Angola, Guinea, and Western Sahara, figured as ‘wars of national liberation’ in various UN resolutions. Led from beginning to the victorious end by Georges Abi-Saab, the G77 battle for the full recognition of wars of national liberation framed these wars as ‘defensive’ military actions against continuing foreign ‘aggression’ through colonialism. During the 1960s and early 1970s, this move was strongly opposed by most Western authors, who argued that these conflicts were internal struggles and thus merely ‘civil wars’ or legitimate reactions to ‘terrorist’ activities. The chapter argues that even though the third world could ultimately secure a victory in this legal struggle, it could not prevent that Cold War interventionism of the superpowers and the former metropoles, as well as proxy-wars, nationalism and militarization further destabilized the societies in the ‘newly independent states’. decolonization, international legal transformations, Bandung, hegemony, boundary drawing, Sattelzeit, law of the sea, use of force, humanitarian law, human rights law
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Kushwah, Mahesh, and Rajneesh Rani. "Home Automation and Security System Using Internet of Things." In Advances in Wireless Technologies and Telecommunication, 235–58. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7335-7.ch012.

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The internet of things (IoT) is viewed as something that connects everyday objects like smart TV and smart phones. Automation is the future of human civilization. It's like operating various devices and machinery with minimum or no human intervention. In this chapter, home appliances and other electronic devices are proposed to be controlled over internet with the help of website or smartphone. Speech recognition technology has been implemented in this chapter which will help complete or partially visually impaired people or persons with physical disability. Smart CCTV concept has been developed in this chapter which will allow to operate the web camera whenever it detects motion and sleep at other times which will save energy as well as storage unlike current CCTV scenario. Also, smart CCTV captures images and video footage and provides the real-time status of the place via a registered email address, website, and mobile app. If you are outside, you can check the status of your home and be in line with it, and you will activate the home appliances from the web site and through speech.
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Conference papers on the topic "API activity recognition"

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Obuchi, Mikio, Wataru Sasaki, Tadashi Okoshi, Jin Nakazawa, and Hideyuki Tokuda. "Investigating interruptibility at activity breakpoints using smartphone activity recognition API." In UbiComp '16: The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2968219.2968556.

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Santana, Flavio Vinicius Vieira, Bruno Henrique Rasteiro, Larissa Cardoso Zimmermann, Luciana De Nardin, and Maria da Graça Campos Pimentel. "Applying Machine Learning Techniques in Older People Activity Recognition usingWearable and Mobile Devices." In XXV Simpósio Brasileiro de Sistemas Multimídia e Web. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/webmedia_estendido.2019.8140.

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We investigate the potential of the combined use of smartwatch accelerometer data and smartphone apps for online older adultsactivity recognition.We selected machine learning algorithms which resulted in a posteriori recognition accuracy of 98.92%. Our smartphone app, with the selected machine learning algorithms, carried out online recognition from data captured on the smartwatch. These results allow us, as future work, assess the accuracy of online recognition when the system is used by older adults.
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Sakai, Shinsuke, Toshikazu Shibasaki, Hiroaki Masatomo, Hiroshi Ishimaru, and Kazuyoshi Sekine. "Development of RBM Standard for Pressurized Equipments in Japan." In ASME 2012 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/pvp2012-78316.

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Many industries such as the petroleum, chemical, gas, and power industries that use power tools for maintenance are concerned with risk-based maintenance (RBM) in Japan. To promote RBM activities in Japan, a committee for developing RBM standards was established at the High Pressure Institute of Japan (HPI) in 2001. Development of the RBM standards (HPIS) was completed this year. This paper describes the activities of the RBM committee and presents an outline of the published standards. The committee is composed of 35 members representing universities, the National Research Institutes, and the petroleum, chemical, power electric, and heavy industries. Two subcommittees have also been organized, one for the development of RBM standards (WG1) and one for the development of an RBM handbook (WG2). The aim of WG1 is to develop the basic specifications for RBM standards. Thus far, the RBM standard named HPIS Z106 has been published. This standard was constructed with reference to API RP580 [1] and was developed so that it can be adapted to the Japanese specific situation. The aim of WG2 is to develop a handbook for actual application of RBM, and the RBM handbook HPIS Z107 Technical Report (TR) has been published. This handbook is composed of the following four parts: Part 1: General Information, Part 2: Wall Thinning Damage Factors, Part 3: Stress Corrosion Cracking (SCC) Damage Factors, and Part 4: Other Damage Factors. In this handbook, a concept for likelihood evaluation called the Failure Probability Index (FPI) is introduced. The new concept is described here, as well as a procedure for evaluating the FPI. In recognition of the fact that, in addition to developing standards to promote RBM activities in Japan, human resource development is also important, an RBM personnel certification system has been developed by HPI. An outline of this activity is also given here.
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Nurwulan, Nurul Retno, and Bernard C. Jiang. "Multiscale Entropy for Physical Activity Recognition." In APIT 2020: 2020 2nd Asia Pacific Information Technology Conference. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3379310.3379318.

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Di Blasi, Martin, and Zhan Li. "Pipeline Rupture Detection Based on Machine Learning and Pattern Recognition." In 2016 11th International Pipeline Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/ipc2016-64471.

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Pipeline ruptures have the potential to cause significant economic and environmental impact in a short period of time, therefore it is critical for pipeline operators to be able to promptly detect and respond to them. Public stakeholder expectations are high and an evolving expectation is that the response to such events be automated by initiating an automatic pipeline shutdown upon receipt of rupture alarm. These types of performance expectations are challenging to achieve with conventional, model-based, leak-detection systems (i.e. CPM–RTTMs) as the reliability measured in terms of the false alarm rate is typically too low. The company has actively participated on a pipeline-industry task force chaired by the API Cybernetics committee, focused on the development of best practices in the area of Rupture Recognition and Response. After API’s release of the first version of a Rupture Recognition and Response guidance document in 2014, the company has initiated development of its own internal Rupture Recognition Program (RRP). The RRP considers several rupture recognition approaches simultaneously, ranging from improvements to existing CPM leak detection to the development of new SCADA based rupture detection system (RDS). This paper will provide an overview of a specific approach to rupture detection based on the use of machine learning and pattern recognition techniques applied to SCADA data.
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Kamizono, Takuya, Hiromichi Abe, Kensuke Baba, Shigeru Takano, and Kazuaki Murakami. "Towards Activity Recognition of Learners by Kinect." In 2014 IIAI 3rd International Conference on Advanced Applied Informatics (IIAIAAI). IEEE, 2014. http://dx.doi.org/10.1109/iiai-aai.2014.45.

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Chen, Qin, Simon T. Perrault, Quentin Roy, and Lonce Wyse. "Effect of temporality, physical activity and cognitive load on spatiotemporal vibrotactile pattern recognition." In AVI '18: 2018 International Conference on Advanced Visual Interfaces. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3206505.3206511.

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Amroun, Hamdi, Mhamed Temkit, and Mehdi Ammi. "DNN-Based Approach for Recognition of Human Activity Raw Data in Non-Controlled Environment." In 2017 IEEE International Conference on AI & Mobile Services (AIMS). IEEE, 2017. http://dx.doi.org/10.1109/aims.2017.26.

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Fan, Xiaohu, Qubo Xie, Xuebin Li, Hao Huang, Jian Wang, Si Chen, Changsheng Xie, and Jiajing Chen. "Activity Recognition as a Service for Smart Home: Ambient Assisted Living Application via Sensing Home." In 2017 IEEE International Conference on AI & Mobile Services (AIMS). IEEE, 2017. http://dx.doi.org/10.1109/aims.2017.29.

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Imran, Hamza Ali, and Usama Latif. "HHARNet: Taking inspiration from Inception and Dense Networks for Human Activity Recognition using Inertial Sensors." In 2020 IEEE 17th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET). IEEE, 2020. http://dx.doi.org/10.1109/honet50430.2020.9322655.

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