Academic literature on the topic 'Feature behavior analysis'

We describe an approach that uses causal and geometric reasoning to construct explanations for the purposes of the geometric features on the parts of a mechanical device. To identify the purpose of a feature, the device is simulated with and without the feature. The simulations are then translated into a “causal-process” representation, which allows qualitatively important differences to be identified. These differences reveal the behaviors caused and prevented by the feature and thus provide useful cues about the feature's purpose. A clear understanding of the feature's purpose, however, requires a detailed analysis of the causal connections between the caused and prevented behaviors. This presents a significant challenge because one has to understand how a behavior that normally takes place affects (or is affected by) another behavior that is normally absent. This article describes techniques for identifying such elusive relationships. These techniques employ a set of rules that can determine if one behavior enables or disables another that is spatially and temporally far away. They do so by geometrically examining the traces of the causal processes in the device's configuration space. Using the results of this analysis, our program can automatically generate text output describing how the feature performs its function.

Nowadays many trends are being in the area of medicine to predict the human behaviour and analysis of patient behaviour is being studied but the technical difficulty of cost efficient method to predict the behaviour of user is overcome in the proposed researched methodology .The mental health of the used can lead to good immunity system to be healthy in this pandemic of COVID-19. Hence After a detailed study on different human health disease classification techniques it is found that machine learning techniques are reliable for the feature extraction and analysis of the different human parameters. CNN is the most optimum choice of classification of diseases. Feature extraction and feature selection is automatically managed by the CNN layers, which reduces the training speed. Techniques like sensor-based feature extraction like EEG, ECG, etc. will be further explored using machine learning algorithms for detection of early detections of diseases from human behavior on different platforms in this research. Social behavior and eating habits play a vital role in disease detection. A system that combines such a wide variety of features with effective classification techniques at each stage is needed. The research in this paper contributes the review of the human behavior analysis through different body parameters, food habits and social media influences with social behavior of the person. The main objective of research is to analysis theses different area parameters to predict the early signs of the diseases.

This paper presents an empirical study for human behavior analysis based on three distinct feature extraction techniques: Histograms of Oriented Gradients (HOG), Local Binary Pattern (LBP) and Scale Invariant Local Ternary Pattern (SILTP). The utilised public videos representing spatio-temporal problem area of investigation include INRIA person detection and Weizmann pedestrian activity datasets. For INRIA dataset, both LBP and HOG were able to eliminate redundant video data and show human-intelligible feature visualisation of extracted features required for classification tasks. However, for Weizmann dataset only HOG feature extraction was found to work well with classifying five selected activities/exercises (walking, running, skipping, jumping and jacking).

In this paper, a deep confidence neural network algorithm is used to design and deeply analyze the risk warning model for stadium operation. Many factors, such as video shooting angle, background brightness, diversity of features, and the relationship between human behaviors, make feature attribute-based behavior detection a focus of researchers’ attention. To address these factors, researchers have proposed a method to extract human behavior skeleton and optical flow feature information from videos. The key of the deep confidence neural network-based recognition method is the extraction of the human skeleton, which extracts the skeleton sequence of human behavior from a surveillance video, where each frame of the skeleton contains 18 joints of the human skeleton and the confidence value estimated for each frame of the skeleton, and builds a deep confidence neural network model to classify the dangerous behavior based on the obtained skeleton feature information combined with the time vector in the skeleton sequence and determine the danger level of the behavior by setting the corresponding threshold value. The deep confidence neural network uses different feature information compared with the spatiotemporal graph convolutional network. The deep confidence neural network establishes the deep confidence neural network model based on the human optical flow information, combined with the temporal relational inference of video frames. The key of the temporal relationship network-based recognition method is to extract some frames from the video in an orderly or random way into the temporal relationship network. In this paper, we use several methods for comparison experiments, and the results show that the recognition method based on skeleton and optical flow features is significantly better than the algorithm of manual feature extraction.

This paper introduces a real-time video surveillance system which can track people and detect human abnormal behaviors. In the blob detection part, an optical flow algorithm for crowd environment is studied experimentally and a comparison study with respect to traditional subtraction approach is carried out. The different approaches in segmentation and tracking enable the system to track persons when they change movement unpredictably in occlusion. We developed two methods for the human abnormal behavior analysis. The first one employs Principal Component Analysis for feature selection and Support Vector Machine for classification of human behaviors. The proposed feature selection method is based on the border information of four consecutive blobs. The second approach computes optical flow to obtain the velocity of each pixel for determining whether a human behavior is normal or not. Both algorithms are successfully developed in crowded environments to detect the following human abnormal behaviors: (1) Running people in a crowded environment; (2) falling down movement while most are walking or standing; (3) a person carrying an abnormal bar in a square; (4) a person waving hand in the crowd. Experimental results demonstrate these two methods are robust in detecting human abnormal behaviors.

The current research on still image recognition has been very successful, but the study of action recognition for video classes is still a challenging topic. In this work, we propose a random projection-based human action recognition algorithm to address the lack of depth information in color information (RGB video frames) that is not easily affected by environmental factors such as illumination and the lack of ability to recognize actions along the direction of view. A network structure is designed to take the obvious advantage of long- and short-term memory networks for controlling and remembering long sequences of historical information. The network structure in this paper is constituted by multiple memory units. At the same time, this paper constructs the spatial features, temporal features, and depth features of the three recognition stream outputs into a feature matrix, whose feature matrix is divided into multiple temporal segments according to the temporal dimension, then inputs them into the network layer in order, and achieves the fusion of the feature matrix in this paper according to their correlation characteristics on the temporal axis. Here, we proposed the concept of random batch projection operators. This basically uses as much sublimitation information as possible to improve projection accuracy by randomly selecting several subdependencies as projections defined during projection. A compressed sensing design of human motion acceleration data for low-power body area networks is proposed, and the basic idea and implementation process of compressed sensing theory for human motion data compression and reconstruction in wireless body area networks are introduced in detail.

In recent years, massive open online courses (MOOCs) have received widespread attention owing to their flexibility and free access, which has attracted millions of online learners to participate in courses. With the wide application of MOOCs in educational institutions, a large amount of learners’ log data exist in the MOOCs platform, and this lays a solid data foundation for exploring learners’ online learning behaviors. Using data mining techniques to process these log data and then analyze the relationship between learner behavior and academic performance has become a hot topic of research. Firstly, this paper summarizes the commonly used predictive models in the relevant research fields. Based on the behavior log data of learners participating in 12 courses in MOOCs, an entropy-based indicator quantifying behavior change trends is proposed, which explores the relationships between behavior change trends and learners’ academic performance. Next, we build a set of behavioral features, which further analyze the relationships between behaviors and academic performance. The results demonstrate that entropy has a certain correlation with the corresponding behavior, which can effectively represent the change trends of behavior. Finally, to verify the effectiveness and importance of the predictive features, we choose four benchmark models to predict learners’ academic performance and compare them with the previous relevant research results. The results show that the proposed feature selection-based model can effectively identify the key features and obtain good prediction performance. Furthermore, our prediction results are better than the related studies in the performance prediction based on the same Xuetang MOOC platform, which demonstrates that the combination of the selected learner-related features (behavioral features + behavior entropy) can lead to a much better prediction performance.

In order to make full use of the effective information in the video, this paper proposes a multi-model interactive video behavior recognition method. In order to solve the problems of incomplete human target detection and redundant feature extraction, YOLO_V4 is used to detect the human body and remove the redundant background information. Then, it is proposed to introduce the channel attention model SE-NET into the Inception_V3 network, so as to strengthen the extraction of key features and make the network pay more attention to the details of key features. Finally, the feature information is sent to LSTM network with memory function for action recognition and classification. The multi-model mutual fusion algorithm proposed in this paper is tested and verified on an internationally published UT-Interaction data set. The experimental results show that the accuracy of interactive behavior recognition is improved, and the improved accuracy is 85.1%, which indicates that the multi-model fusion method has higher accuracy.

The variant, encryption, and confusion of WebShell results in problems in the detection method based on feature selection, such as poor detection effect and weak generalization ability. In order to solve this problem, a method of WebShell detection based on regularized neighborhood component analysis (RNCA) is proposed. The RNCA algorithm can effectively reduce the dimension of data while ensuring the accuracy of classification. In this paper, it is innovatively applied to a WebShell detection neighborhood, taking opcode behavior sequence features as the main research object, constructing vocabulary by using opcode sequence features with variable length, and effectively reducing the dimension of WebShell features from the perspective of feature selection. The opcode sequence selected by the algorithm is symmetrical with the source code file, which has great reference value for WebShell classification. On the issue of the single feature, this paper uses the fusion of behavior sequence features and text static features to construct a feature combination with stronger representation ability, which effectively improves the recognition rate of WebShell to a certain extent.

Around the world, the COVID-19 pandemic has created significant obstacles for education, driving people to discover workarounds to maintain education. Because of the excellent benefit of cheap-cost information distribution brought about by the advent of the Internet, some offline instructional activity started to go online in an effort to stop the spread of the disease. How to guarantee the quality of teaching and promote the steady progress of education has become more and more important. Currently, one of the ways to guarantee the quality of online learning is to use independent online learning behavior data to build learning performance predictors, which can provide real-time monitoring and feedback during the learning process. This method, however, ignores the internal correlation between e-learning behaviors. In contrast, the e-learning behavior classification model (EBC model) can reflect the internal correlation between learning behaviors. Therefore, this study proposes an online learning performance prediction model, SA-FEM, based on adaptive feature fusion and feature selection. The proposed method utilizes the relationship among features and fuses features according to the category that achieved better performance. Through the analysis of experimental results, the feature space mined by the fine-grained differential evolution algorithm and the adaptive fusion of features combined with the differential evolution algorithm can better support online learning performance prediction, and it is also verified that the adaptive feature fusion strategy based on the EBC model proposed in this paper outperforms the benchmark method.

The initiative of a sustainable material system needs to lower the environmental and economic impact of production processes and adopt new ways of synthesizing and re-using materials. Even though the current conventional manufacturing processes, such as powder metallurgy, casting, forging, and rolling, have already shown their excellent ability to manufacture a large variety of parts and efficiently yield high volume products. Nevertheless, there are still many obstacles in manufacturing metallic components, such as complicated process procedures, high-energy consumption, large material waste, and high machinery cost for reasons that the excess materials need to be removed and extra post-processing time needs to be taken to acquire desired shapes during the machining stage. Thus, finding innovative solutions for producing complex structures is becoming increasingly desirable in the industry. Innovative additive manufacturing (AM, also known as 3D printing) techniques have proved their capacity to manufacture metallic materials with designed complex shapes and tailored properties. The selective laser melting (SLM) is one of the most popular AM techniques, which has the ability to manufacture a wide range of metallic powders in a layer-wise manner and fabricate complex shapes without compromising dimensional accuracy. The toxicity, biocompatibility, corrosion resistance, and stress shielding effect are the key challenges for developing titanium biomaterials for orthopedic applications. Adding nontoxic alloying elements into titanium can solve the issues of toxicity and biocompatibility. One of the best solutions for minimizing the stress-shielding effect and prolonging implant lifetime is to tailor the modulus of implant materials closer to that of bones. Nb is a nontoxic alloying element and an excellent β phase stabilizer, which plays a significant role in reducing the elastic modulus and in improving the corrosion resistance of Ti-based alloys. Accordingly, obtaining a highperformance simple alloy by reducing the alloying elements and substituting toxic elements can facilitate the improvement of sustainability. Thus, the β-metastable Ti-Nb alloys with relatively low elastic modulus have been studied for orthopedic implants due to their high strength to weight ratio, excellent corrosion resistance, and high biocompatibility in the human body. In addition, the high reactivity of titanium with hydrogen and oxygen as well as the high melting points of titanium alloys make conventional manufacturing difficult and cost intensive. As such, the SLM provides an innovative solution to manufacture shape-complicated products in a building chamber under the flow of high purity argon gas to minimize oxidation. However, the availability, printability, and high cost of high-quality raw metallic alloy powder are the limits for the SLM process. The individual elemental powder is relatively cheap and easy to manufacture. Thus, the use of elemental powder mixture results in greater alloy choices as well as lower cost and wider commercial availability. The issues of resultant microstructural and chemical inhomogeneity of the produced parts using the powder mixture have been the major concerns and challenges in the field. Since the mechanical behaviors and chemical properties directly depend on the microstructural homogeneity and phase composition, an in-depth understanding of the effect of inhomogeneity is required. It is necessary to have further advances in manufacturing optimization to extend the benefit of low production costs. In particular, in-situ alloying prospects make SLM a potential route to use a powder mixture with near infinite chemical compositions to synthesize desired titanium alloys for broad applications. As such, synthesizing the proper titanium alloys using the SLM technique, minimizing defect formation, controlling phase composition, evaluating their properties, and investigating the performances of SLM-processed products could significantly advance the applications in various industries and academia. The aim is to apply the SLM technique to process titanium alloys for biomedical and industrial applications. The results help to improve the scientific understandings of the interrelation among alloy compositions, processes, microstructures, defects, properties, and deformation behaviors of 3D-printed parts. Chapter 1 introduces additive manufacturing (AM) has huge potential to realize new alloys with flexible design and easy manufacturing. Especially for the customized healthcare products and services, such as biomedical implants, prosthetics, and hip replacement. Titanium alloys have desirable properties for various applications. Combining additive manufacturing with affordable and biocompatible titanium alloys can further advance and benefit the healthcare industry. Accordingly, the objectives are to fabricate titanium alloys by SLM and to investigate the microstructure, mechanical performance, and corrosion properties. Chapter 2 overviews the type, utilization, and advantage of AM techniques, biomaterials, and titanium alloys. The SLM process can manufacture parts with high precision and superb asbuilt surface quality but relatively high residual stress due to the rapid cooling rate. The raw powder properties and processing parameters play important roles in the densification and mechanical property of built products. The physical factors in the melting process and simulation are shown to understand the melt pool characteristics and stability, which is the critical factor to a successful and desired part. The microstructure, mechanical properties, and corrosion performance of different titanium alloys are also reviewed in order to design the powder, understand the mechanism, and improve the properties. Chapter 3 shows insight into the manufacturing of a Ti-35Nb composite using SLM and post heat treatment. The results emphasize the capability of SLM to fabricate alloys from elemental powder mixtures, even suitable for those with a significant difference in melting point. It provides a significant advance in the understanding of the effect of microstructural inhomogeneity on the resultant mechanical and chemical properties. Heat treatment can further enhance the corrosion resistance of SLM-produced Ti-35Nb samples because the improved chemical homogeneity can facilitate the homogeneous formation of titanium oxides and niobium oxides. It presents a different method of synthesizing novel β-type composites at a relatively lower cost and in easy manufacture. Chapter 4 shows the microstructure, phase response, and mechanical properties of the SLM-fabricated Ti-35Nb using an elemental powder mixture with reduced Nb particle size and its heat-treated counterpart. The results provide significant advances in the understanding of the role of undissolved Nb particles, Nb-rich interfaces, and Ti-Nb-based β phases on the mechanical performance. The nanoindentation mappings provide direct evidence of the contribution of the different phase responses to overall mechanical properties. The Nb particle segregation zones have lower hardness and higher deformation compared to the Ti-Nb matrix. The as-SLMed Ti- 35Nb exhibits relatively high tensile yield strength (648 ± 13 MPa) due to the formation of dendritic β grains. However, the ductility is relatively low (3.9 ± 1.1%) as a result of the weak bonding of undissolved Nb particles within the matrix. The heat-treated counterpart shows a slightly lower yield strength (602 ± 14 MPa) but a nearly 43% increase in ductility (5.6 ± 1.9 %) due to the improved homogeneous Ti-Nb β phase. Chapter 5 shows the microstructure, phase composition, melt pool morphology, and mechanical properties of a prealloyed Ti-35Nb alloy manufactured using SLM and compares it to one produced using an elemental powder mixture. The SLM-processed Ti-35Nb from both feedstocks retained a high volume fraction of β phase due to adequate β stabilization by the Nb and the fast cooling of the SLM process; however, other phase compositions were quite different. The chemical heterogeneity and inhomogeneous microstructure of the SLM-produced sample from powder mixture are results of the fast cooling rate of the melt pool and the high difference of melting temperature and density between elemental powders. However, a uniform microstructure and chemical composition can be achieved in the SLMed prealloyed Ti-35Nb. The variances of powder morphology, density, and melting point between mixed powder and prealloyed powder induce different melt pool status, where the stability of the melt pool plays a critical role in the homogeneity and microstructure. The SLMed Ti-35Nb prealloyed powder samples present a slightly lower yield strength (485 ± 28 MPa) but higher plastic strain (23.5 ± 2.2 %). The excellent ductility has been attributed to the high homogeneity, strong interface bonding, and the existence of a large amount of β phase. Chapter 6 shows the understanding of the homogeneity effect on the coexistence of the acicular α″, β grains, and melt pool boundary for a homogeneous microstructure. It provides some new insight into the phase response and the effect of homogeneity on the SLMed Ti-35Nb alloy using prealloyed powder. The reduced elastic modulus of β phase (89.6 ± 2.1 GPa) is close to that of α″ phase (86.3 ± 2.0 GPa) from the indentation measurement, which is in favor of orthopedic implants application. It also reveals that the nanoindentation test can provide a fast mapping and considerable potential to evaluate the homogeneity, microstructural features, individual phase strength, and deformation behavior in a fine microstructure of SLM-fabricated metallic alloys. Chapter 7 shows the preliminary design in porous structures and compressive behavior of different prealloyed Ti-35Nb sandwich composite porous structures manufactured using SLM. The simulation results were in good agreement with the compression tests. The compression tests show that the sandwich composites with different layers have different deformation behavior and mechanical properties. The rhombic dodecahedron porous structure with added layers could achieve balanced compressive strength and ductility. The preliminary sandwich design with the verified finite element method (FEM) models can be employed in other metallic porous structures to improve the strength and ductility without affecting the porosity. Chapter 8 concludes the present findings in this thesis and suggests the future challenges and development using SLM to tailor titanium alloys for specific applications. As such, the SLM technique is a promising route to develop titanium alloys from powder mixture with wider alloy choices at a cheaper cost and in easier availability. Even though a uniform microstructure and chemical composition can be achieved in the SLM-produced Ti-35Nb using prealloyed powder, there are still challenges on how to achieve full melting of elemental powder particles and obtain a homogeneous β phase microstructure. With the investigation of β- type Ti-Nb alloys, this thesis aims to further understand the effect of the unmelted Nb particles in the synthesized Ti-Nb alloys and melt pool stability as well as improve the Nb melting, microstructure, and mechanical properties for industrial and biomedical applications. Understanding the effect of powder feedstock type and phase features of the SLM-produced Ti- 35Nb using prealloyed powder further provides insights into the homogeneity, microstructure, and resultant properties. The novel design in Ti-35Nb sandwich composite cellular structures can benefit biomedical and industrial applications. By taking advantage of the commercial availability and lower cost of elemental powder, finding solutions to achieve full melting and homogeneous microstructure for nontoxic and biocompatible β-type Ti-Nb alloys with promising mechanical and corrosion properties is significant in future research and development.

The mission of the research presented in this thesis is to give computers the power to sense and react to human activities. Without the ability to sense the surroundings and understand what humans are doing, computers will not be able to provide active, timely, appropriate, and considerate services to the humans. To accomplish this mission, the work stands on the shoulders of two giants: Machine learning and ubiquitous computing. Because of the ubiquity of sensor-enabled mobile and wearable devices, there has been an emerging opportunity to sense, learn, and infer human activities from the sensor data by leveraging state-of-the-art machine learning algorithms. While having shown promising results in human activity recognition, most existing approaches using supervised or semi-supervised learning have two fundamental problems. Firstly, most existing approaches require a large set of labeled sensor data for every target class, which requires a costly effort from human annotators. Secondly, an unseen new activity cannot be recognized if no training samples of that activity are available in the dataset. In light of these problems, a new approach in this area is proposed in our research. This thesis presents our novel approach to address the problem of human activity recognition when few or no training samples of the target activities are available. The main hypothesis is that the problem can be solved by the proposed NuActiv activity recognition framework, which consists of modeling the hierarchical and sequential structure of human activities, as well as bringing humans in the loop of model training. By injecting human knowledge about the hierarchical nature of human activities, a semantic attribute representation and a two-layer attribute-based learning approach are designed. To model the sequential structure, a probabilistic graphical model is further proposed to take into account the temporal dependency of activities and attributes. Finally, an active learning algorithm is developed to reinforce the recognition accuracy using minimal user feedback. The hypothesis and approaches presented in this thesis are validated by two case studies and real-world experiments on exercise activities and daily life activities. Experimental results show that the NuActiv framework can effectively recognize unseen new activities even without any training data, with up to 70-80% precision and recall rate. It also outperforms supervised learning with limited labeled data for the new classes. The results significantly advance the state of the art in human activity recognition, and represent a promising step towards bridging the gap between computers and humans.

Emotion adds an important element to the discussion of how information is conveyed and processed by humans; indeed, it plays an important role in the contextual understanding of messages. This research is centered on investigating relevant features for affect classification, along with modeling the multimodal and multitemporal nature of emotion. The use of formant-based features for affect classification is explored. Since linear predictive coding (LPC) based formant estimators often encounter problems with modeling speech elements, such as nasalized phonemes and give inconsistent results for bandwidth estimation, a robust formant-tracking algorithm was introduced to better model the formant and spectral properties of speech. The algorithm utilizes Gaussian mixtures to estimate spectral parameters and refines the estimates using maximum a posteriori (MAP) adaptation. When the method was used for features extraction applied to emotion classification, the results indicate that an improved formant-tracking method will also provide improved emotion classification accuracy. Spectral features contain rich information about expressivity and emotion. However, most of the recent work in affective computing has not progressed beyond analyzing the mel-frequency cepstral coefficients (MFCC’s) and their derivatives. A novel method for characterizing spectral peaks was introduced. The method uses a multi-resolution sinusoidal transform coding (MRSTC). Because of MRSTC’s high precision in representing spectral features, including preservation of high frequency content not present in the MFCC’s, additional resolving power was demonstrated. Facial expressions were analyzed using 53 motion capture (MoCap) markers. Statistical and regression measures of these markers were used for emotion classification along the voice features. Since different modalities use different sampling frequencies and analysis window lengths, a novel classifier fusion algorithm was introduced. This algorithm is intended to integrate classifiers trained at various analysis lengths, as well as those obtained from other modalities. Classification accuracy was statistically significantly improved using a multimodal-multitemporal approach with the introduced classifier fusion method. A practical application of the techniques for emotion classification was explored using social dyadic plays between a child and an adult. The Multimodal Dyadic Behavior (MMDB) dataset was used to automatically predict young children’s levels of engagement using linguistic and non-linguistic vocal cues along with visual cues, such as direction of a child’s gaze or a child’s gestures. Although this and similar research is limited by inconsistent subjective boundaries, and differing theoretical definitions of emotion, a significant step toward successful emotion classification has been demonstrated; key to the progress has been via novel voice and visual features and a newly developed multimodal-multitemporal approach.

Foreground object detection is a fundamental task in computer vision with many applications in areas such as object tracking, event identification, and behavior analysis. Most conventional foreground object detection methods work only in a stable illumination environments using fixed cameras. In real-world applications, however, it is often the case that the algorithm needs to operate under the following challenging conditions: drastic lighting changes, object shape complexity, moving cameras, low frame capture rates, and low resolution images. This thesis presents four novel approaches for foreground object detection on real-world datasets using cameras deployed on moving vehicles.The first problem addresses passenger detection and tracking tasks for public transport buses investigating the problem of changing illumination conditions and low frame capture rates. Our approach integrates a stable SIFT (Scale Invariant Feature Transform) background seat modelling method with a human shape model into a weighted Bayesian framework to detect passengers. To deal with the problem of tracking multiple targets, we employ the Reversible Jump Monte Carlo Markov Chain tracking algorithm. Using the SVM classifier, the appearance transformation models capture changes in the appearance of the foreground objects across two consecutives frames under low frame rate conditions. In the second problem, we present a system for pedestrian detection involving scenes captured by a mobile bus surveillance system. It integrates scene localization, foreground-background separation, and pedestrian detection modules into a unified detection framework. The scene localization module performs a two stage clustering of the video data.In the first stage, SIFT Homography is applied to cluster frames in terms of their structural similarity, and the second stage further clusters these aligned frames according to consistency in illumination. This produces clusters of images that are differential in viewpoint and lighting. A kernel density estimation (KDE) technique for colour and gradient is then used to construct background models for each image cluster, which is further used to detect candidate foreground pixels. Finally, using a hierarchical template matching approach, pedestrians can be detected.In addition to the second problem, we present three direct pedestrian detection methods that extend the HOG (Histogram of Oriented Gradient) techniques (Dalal and Triggs, 2005) and provide a comparative evaluation of these approaches. The three approaches include: a) a new histogram feature, that is formed by the weighted sum of both the gradient magnitude and the filter responses from a set of elongated Gaussian filters (Leung and Malik, 2001) corresponding to the quantised orientation, which we refer to as the Histogram of Oriented Gradient Banks (HOGB) approach; b) the codebook based HOG feature with branch-and-bound (efficient subwindow search) algorithm (Lampert et al., 2008) and; c) the codebook based HOGB approach.In the third problem, a unified framework that combines 3D and 2D background modelling is proposed to detect scene changes using a camera mounted on a moving vehicle. The 3D scene is first reconstructed from a set of videos taken at different times. The 3D background modelling identifies inconsistent scene structures as foreground objects. For the 2D approach, foreground objects are detected using the spatio-temporal MRF algorithm. Finally, the 3D and 2D results are combined using morphological operations.The significance of these research is that it provides basic frameworks for automatic large-scale mobile surveillance applications and facilitates many higher-level applications such as object tracking and behaviour analysis.

Android is the second most targeted operating system for malware authors and to counter the development of Android malware, more knowledge about their behavior is needed. There are mainly two approaches to analyze Android malware, namely static and dynamic analysis. Recently in 2017, a study and well labeled dataset, named AMD (Android Malware Dataset), consisting of over 24,000 malware samples was released. It is divided into 135 varieties based on similar malicious behavior, retrieved through static analysis of the file classes.dex in the APK of each malware, whereas the labeled features were determined by manual inspection of three samples in each variety. However, static analysis is known to be weak against obfuscation techniques, such as repackaging or dynamic loading, which can be exploited to avoid the analysis. In this study the second approach is utilized and all malware in the dataset are analyzed at run-time in order to monitor their dynamic behavior. However, analyzing malware at run-time has known weaknesses as well, as it can be avoided through, for instance, anti-emulator techniques. Therefore, the study aimed to explore the available sandbox environments for dynamic analysis, study the effectiveness of fingerprinting Android malware using one of the tools and investigate whether static features from AMD and the dynamic analysis correlate. For instance, by an attempt to classify the samples based on similar dynamic features and calculating the Pearson Correlation Coefficient (r) for all combinations of features from AMD and the dynamic analysis. The comparison of tools for dynamic analysis, showed a need of development, as most popular tools has been released for a long time and the common factor is a lack of continuous maintenance. As a result, the choice of sandbox environment for this study ended up as Droidbox, because of aspects like ease of use/install and easily adaptable for large scale analysis. Based on the dynamic features extracted with Droidbox, it could be shown that Android malware are more similar to the varieties which they belong to. The best metric for classifying samples to varieties, out of four investigated metrics, turned out to be Cosine Similarity, which received an accuracy of 83.6% for the entire dataset. The high accuracy indicated a correlation between the dynamic features and static features which the varieties are based on. Furthermore, the Pearson Correlation Coefficient confirmed that the manually extracted features, used to describe the varieties, and the dynamic features are correlated to some extent, which could be partially confirmed by a manual inspection in the end of the study.

This thesis examines mental health professionals' responses toward patients diagnosed with Borderline Personality Disorder (BPD) and presents a qualitative study of Dialectical Behaviour Therapists' (DBT) experiences in their work with young people with BPD features. A review of empirical literature regarding emotional, behavioural and attitudinal responses of professionals toward these patients identified a range of negative responses, distinguishable from responses toward patients with other mental health problems. The review highlights the consistency of responses in professionals working in a variety of roles with these patients in countries across the world, and points to the need for further research to understand the precipitants of these negative responses. Controversy surrounds the diagnosis of BPD during adolescence and hence the majority of research in this area focuses upon professionals working with adult patients. On the basis of evidence regarding the presence of BPD features during adolescence and the application of therapeutic approaches, such as DBT, to young people exhibiting these features, the empirical paper presents an Interpretative Phenomenological Analysis (IPA) of the lived experience of DBT therapists in this context. A super-ordinate theme of 'the impact of the therapy on the therapist' containing five sub-themes is presented.

High hydrodynamic pressure (HDP) treatment is a novel non-thermal technology that improves tenderness in foods by subjecting foods to underwater shock waves. In this study non-destructive and destructive testing methods, along with two mathematical models were explored to predict biomechanical behavior of beef loins subjected to HDP-treament. The first study involved utilizing ultrasound and imaging techniques to predict textural changes in beef loins subjected to HDP-treatment using Warner-Braztler shear force (WBS) scores and texture profile analysis (TPA) features for correlation. Ultrasound velocity correlated very poorly with the WBS scores and TPA features, whereas the imaging features correlated better with higher r-values. The effect of HDP-treatment variables on WBS and TPA features indicated that amount of charge had no significant effects when compared to location of sample and container size during treatment. Two mathematical models were used to simulate deformational behavior in beef loins. The first study used a rheological based modeling of protein gel as a preliminary study. Results from the first modeling study indicated no viscous interactions in the model and complete deformation failure at pressures exceeding 50 kPa, which was contrary to the real-life process conditions which use pressures in the order of MPa. The second modeling study used a finite element method approach to model elastic behavior. Shock wave was modeled as a non-linear and linear propagating wave. The non-linear model indicated no deformation response, whereas the linear model indicated realistic deformation response assuming transverse isotropy of the model beef loin. The last study correlated small- and large-strain measurements using stress relaxation and elastic coefficients of the stiffness matrix as small-strain measures and results of the study indicated very high correlation between elastic coefficients c11, c22, and c44 with TPA cohesiveness (r > 0.9), and springiness (r > 0.85). Overall results of this study indicated a need for further research in estimating mechanical properties of beef loins in order to understand the dynamics of HDP-treatment process better.
Ph. D.

Za posledné roky je zaznamenaný nárast prác zahrňujúcich komplexnú detekciu malvéru. Pre potreby zachytenia správania je často vhodné pouziť formát grafov. To je prípad antivírusového programu Avast, ktorého behaviorálny štít deteguje škodlivé správanie a ukladá ich vo forme grafov. Keďže sa jedná o proprietárne riešenie a Avast antivirus pracuje s vlastnou sadou charakterizovaného správania bolo nutné navrhnúť vlastnú metódu detekcie, ktorá bude postavená nad týmito grafmi správania. Táto práca analyzuje grafy správania škodlivého softvéru zachytené behavioralnym štítom antivírusového programu Avast pre proces hlbšej detekcie škodlivého softvéru. Detekcia škodlivého správania sa začína analýzou a abstrakciou vzorcov z grafu správania. Izolované vzory môžu efektívnejšie identifikovať dynamicky sa meniaci malware. Grafy správania sú uložené v databáze grafov Neo4j a každý deň sú zachytené tisíce z nich. Cieľom tejto práce bolo navrhnúť algoritmus na identifikáciu správania škodlivého softvéru s dôrazom na rýchlosť skenovania a jasnosť identifikovaných vzorcov správania. Identifikácia škodlivého správania spočíva v nájdení najdôležitejších vlastností natrénovaných klasifikátorov a následnej extrakcie podgrafu pozostávajúceho iba z týchto dôležitých vlastností uzlov a vzťahov medzi nimi. Následne je navrhnuté pravidlo pre hodnotenie extrahovaného podgrafu. Diplomová práca prebehla v spolupráci so spoločnosťou Avast Software s.r.o.

After a brief review of the scholarly discussion about the idea that context affects political behavior, this chapter proposes a model for the analysis of contextual effects on opinion formation and voting behavior. It highlights theoretical issues in the interplay of various contextual features and voter predispositions in bringing about contextual effects on voters. This model guides the analyses of contextual effects on voter behavior in Germany in the early twenty-first century. These analyses draw on rich data from multiple voter surveys and various sources of information about contextual features. The chapter also gives an overview of different methodological approaches and challenges in the analysis of contextual effects on voting behavior.

Shivers (1975) was David Cronenberg's first commercial feature and his first horror film. In a modern apartment block, a scientific project to unleash the id results in the equation of passion with contagion and predation. Because the writer-director's imaginative landscape arrived in the genre fully formed, the unique forms of this début have often been overlooked or mistaken for shortcomings. Cronenberg's most comedic film until Map to the Stars, Shivers is also his most spectacularly unnerving, throwing more images of extreme behavior at us than any of his subsequent films; it remains, with Crash, his most disquieting and transgressive film to date. This book's analysis addresses all channels of communication available to the 35mm sync-sound narrative feature, including shot composition, lighting, cinematographic texture, sound, the use of stock music, editing, costume, makeup, optical work, the screenplay, the casting, and the direction of the actors. This tour of Shivers as “cognitive territory” takes in architecture, cultural context, critical reception, and artistic legacy.

The primary argument of this chapter is that children’s and youth’s defiance may be misread and misinterpreted unless a greater understanding of the interplay of genders, races, and ethnicities is grasped. It analyzes various types of aggression to illustrate that the norms that determine harms from aggressive behavior need to be articulated and critiqued. The chapter sets out central characteristics of Oppositional Defiant Disorder (ODD) and Conduct Disorder (CD), then analyzes the larger context for understanding defiant behavior. Research on features of aggression in children’s play is included, and this leads to an analysis of how to understand the harms of aggression. The author also examines the matrix of raced, gendered, and classed intersections in the interpretation and reproduction of norms for behavior. This analysis of the construct of aggression makes it more difficult to interpret certain behavior as maladaptive defiant traits.The chapter ends with considerations as to why children (and adults) might have reasons for being defiant.

One of the most fertile areas of research has been the question of why the American political system has polarized so sharply over the past four decades. The academic debates about polarization have largely been carried out by mainstream scholars of political behavior and institutions. Scholars of American Political Development (APD) have a major opportunity to participate in a vital debate about the emergence of a central feature of the contemporary American system while mainstream scholars should come to appreciate that one cannot easily develop explanations for dynamic change with static models of institutions and behavior. This chapter reviews the literature on polarization to introduce scholars of APD to debates about the measurement of polarization and its causes Also areas in which our knowledge about polarization can be improved by historical–institutional analysis are identified.

In the process of data analysis, the investigator is often facing highly-volatile and random-appearing observed data. A vast body of literature shows that the assumption of underlying stochastic processes was not necessarily representing the nature of the processes under investigation and, when other tools were used, deterministic features emerged. Non Linear Time Series Analysis (NLTS) allows researchers to test whether observed volatility conceals systematic non linear behavior, and to rigorously characterize governing dynamics. Behavioral patterns detected by non linear time series analysis, along with scientific principles and other expert information, guide the specification of mechanistic models that serve to explain real-world behavior rather than merely reproducing it. Often there is a misconception regarding the complexity of the level of mathematics needed to understand and utilize the tools of NLTS (for instance Chaos theory). However, mathematics used in NLTS is much simpler than many other subjects of science, such as mathematical topology, relativity or particle physics. For this reason, the tools of NLTS have been confined and utilized mostly in the fields of mathematics and physics. However, many natural phenomena investigated I many fields have been revealing deterministic non linear structures. In this book we aim at presenting the theory and the empirical of NLTS to a broader audience, to make this very powerful area of science available to many scientific areas. This book targets students and professionals in physics, engineering, biology, agriculture, economy and social sciences as a textbook in Nonlinear Time Series Analysis (NLTS) using the R computer language.

Stochastic Actor Oriented Models for Network Dynamics are used for the statistical analysis of longitudinal network data collected as a panel. The probability model defines an unobserved stochastic process of tie changes, where social actors add new ties or drop existing ties in response to the current network structure; the panel observations are snapshots of the resulting changing network. The statistical analysis is based on computer simulations of this process, which provides a great deal of flexibility in representing data constraints and dependence structures. In this Chapter we begin by defining the basic model. We then explicate a new model for nondirected ties, including several options for the specification of how pairs of actors coordinate tie changes. Next, we describe coevolution models. These can be used to model the dynamics of several interdependent sets of variables, such as the analysis of panel data on a network and the behavior of the actors in the network, or panel data on two or more networks. We finish by discussing the differences between Stochastic Actor Oriented Models and some other longitudinal network models. A major distinguishing feature is the treatment of time, which allows straightforward application of the model to panel data with different time lags between waves. We provide a variety of applications in political science throughout.

The study of identifiable neurons, a common feature of invertebrate nervous systems, has made it possible to construct a detailed cell-to-cell connectivity map using electrophysiological methods that can inspire the design of biomimetic systems. This chapter describes how the analysis of the neural circuitry in the lobster stomatogastric ganglion (STG) has provided some general principles underlying oscillatory and rhythmic behavior in all animals. The rhythmic and oscillatory patterns produced by the two STG central pattern generating (CPG) circuits are a result of two cooperative mechanisms, intrinsically bursting pacemaker neurons and synaptic network properties. Also covered are the major neuromodulatory and neural control mechanisms. The chapter discusses how a deep knowledge of the stomatogastric circuitry has led to the development of electronic neurons for biomimetic devices that can be used for experimental and prosthetic applications The chapter concludes with a section on new techniques that may help with unraveling oscillatory circuits in the brain.

The chapter examines classes of grammatical markers that can serve more than one function, polyfunctional markers, spoiling the one-to-one form and function relation which is what morphology tends to do. There are areas of the grammar more prone to this behaviour suggesting that there may be at work principles of morphological organization that lie orthogonally to sign-based principles such as Transparency. The distributions attested in Tundra Nenets provide a fertile ground for exploration because they combine polyfunctionality with cumulative exponence, where a single paradigm indexes two sets of features. Recasting Blevins’ (2016) abstractive analysis as a default inheritance hierarchy the analysis is guided by insights from Paradigm Function Morphology and Sign Based Construction Grammar, and treats polyfunctionality as the realization of a unifying morphomic feature that abstracts away what is common between different morphosyntactic configurations.

AbstractPetri nets are one of the most prominent system-level formalisms for the specification of causality in concurrent, distributed, or multi-agent systems. This formalism is abstract enough to be analyzed using theoretical tools, and at the same time, concrete enough to eliminate ambiguities that would arise at implementation level. One interesting feature of Petri nets is that they can be studied from the point of view of true concurrency, where causal scenarios are specified using partial orders, instead of approaches based on interleaving.On the other hand, message sequence chart (MSC) languages, are a standard formalism for the specification of causality from a purely behavioral perspective. In other words, this formalism specifies a set of causal scenarios between actions of a system, without providing any implementation-level details about the system.In this work, we establish several new connections between MSC languages and Petri nets, and show that several computational problems involving these formalisms are decidable. Our results fill some gaps in the literature that had been open for several years. To obtain our results we develop new techniques in the realm of slice automata theory, a framework introduced one decade ago in the study of the partial order behavior of bounded Petri nets. These techniques can also be applied to establish connections between Petri nets and other well studied behavioral formalisms, such as the notion of Mazurkiewicz trace languages.

Abstract Human drivers have different driver behaviors when operating vehicles. These driving behaviors, including the driver’s preferred speed and rate of acceleration, impose a major impact on vehicle fuel consumption consequently. In this study, we proposed a feature-based driver behavior learning model from demonstrated driving data utilizing the Inverse Reinforcement Learning (IRL) approach to analyze various driver behaviors and their impacts on vehicle fuel consumption. The proposed approach models the individual driving style as cost function which is a linear combination of the features and their corresponding weights. The proposed IRL framework is used to find the model parameters that fit the observed driving style best. By using the learned driving behavior model, the most likely trajectories are computed and the optimized feature weights are used to analyze different driver behaviors. The different driver behaviors and their impacts on vehicle fuel consumption are then analyzed in real-world driving scenarios. Results show that the proposed IRL framework can successfully learn individual driver behaviors using vehicle trajectory data demonstrated by different real drivers. The learned driver behaviors promise a significant correlation between driving behavior and fuel consumption.

How much does money drive legislative outcomes in the United States? In this article, we use aggregated campaign finance data as well as a Transformer based text embedding model to predict roll call votes for legislation in the US Congress with more than 90% accuracy. In a series of model comparisons in which the input feature sets are varied, we investigate the extent to which campaign finance is predictive of voting behavior in comparison with variables like partisan affiliation. We find that the financial interests backing a legislator’s campaigns are independently predictive in both chambers of Congress, but also uncover a sizable asymmetry between the Senate and the House of Representatives. These findings are cross-referenced with a Representational Similarity Analysis (RSA) linking legislators’ financial and voting records, in which we show that “legislators who vote together get paid together”, again discovering an asymmetry between the House and the Senate in the additional predictive power of campaign finance once party is accounted for. We suggest an explanation of these facts in terms of Thomas Ferguson’s Investment Theory of Party Competition: due to a number of structural differences between the House and Senate, but chiefly the lower amortized cost of obtaining individuated influence with Senators, political investors prefer operating on the House using the party as a proxy.

The article deals with the actual issue of the specificity and algorithm of the introduction of innovative AR technologies in the process of psychological and pedagogical support of children with autism spectrum disorders (ASD). An innovative element of theoretical and methodological analysis of the problem and empirical research is the detection of vectors of a constructive combination of traditional psycho-correctional and psycho-diagnostic approaches with modern AR technologies. The analysis of publications on the role and possibilities of using AR technologies in the process of support children with ASD (autism spectrum disorder) and inclusive environment was generally conducted by surfing on the Internet platforms containing the theoretical bases for data publications of scientific journals and patents. The article also analyzes the priorities and potential outcomes of using AR technologies in psycho-correction and educational work with autistic children. According to the results of the analysis of scientific researches, Unified clinical protocol of primary, secondary (specialized), tertiary (highly specialized) medical care and medical rehabilitation “Autism spectrum disorders (disorders of general development)”, approaches for correction, development and education of children with ASD, AR technologies were selected for further implementation in a comprehensive program of psychological and pedagogical support for children with ASD. The purpose of the empirical study is the search, analysis and implementation of multifunctional AR technologies in the psycho-correctional construct of psychological and pedagogical support of children with ASD. According to the results of the pilot study, the priorities and effectiveness of using AR technologies in the development of communicative, cognitive, emotional-volitional, mnemonic abilities of children and actualization of adaptive potential and adaptive, socially accepted behaviors are made. The possibilities and perspectives of using AR technologies as an element of inclusive environment, with regard to nosology and phenomenology, need further investigation.

This report describes activity conducted in several lines of research associated with field-scale water and solute processes. A major effort was put forth developing a stochastic continuum analysis for an important class of problems involving flow of reactive and non reactive chemicals under steady unsaturated flow. The field-scale velocity covariance tensor has been derived from local soil properties and their variability, producing a large-scale description of the medium that embodies all of the local variability in a statistical sense. Special cases of anisotropic medium properties not aligned along the flow direction of spatially variable solute sorption were analysed in detail, revealing a dependence of solute spreading on subtle features of the variability of the medium, such as cross-correlations between sorption and conductivity. A novel method was developed and tested for measuring hydraulic conductivity at the scale of observation through the interpretation of a solute transport outflow curve as a stochastic-convective process. This undertaking provided a host of new K(q) relationships for existing solute experiments and also laid the foundation for future work developing a self-consistent description of flow and transport under these conditions. Numerical codes were developed for calculating K(q) functions for a variety of solute pulse outflow shapes, including lognormal, Fickian, Mobile-Immobile water, and bimodal. Testing of this new approach against conventional methodology was mixed, and agreed most closely when the assumptions of the new method were met. We conclude that this procedure offers a valuable alternative to conventional methods of measuring K(q), particularly when the application of the method is at a scale (e.g. and agricultural field) that is large compared to the common scale at which conventional K(q) devices operate. The same problem was approached from a numerical perspective, by studying the feasibility of inverting a solute outflow signal to yield the hydraulic parameters of the medium that housed the experiment. We found that the inverse problem was solvable under certain conditions, depending on the amount of noise in the signal and the degree of heterogeneity in the medium. A realistic three dimensional model of transient water and solute movement in a heterogeneous medium that contains plant roots was developed and tested. The approach taken was to generate a single realization of this complex flow event, and examine the results to see whether features were present that might be overlooked in less sophisticated model efforts. One such feature revealed is transverse dispersion, which is a critically important component in the development of macrodispersion in the longitudinal direction. The lateral mixing that was observed greatly exceeded that predicted from simpler approaches, suggesting that at least part of the important physics of the mixing process is embedded in the complexity of three dimensional flow. Another important finding was the observation that variability can produce a pseudo-kinetic behavior for solute adsorption, even when the local models used are equilibrium.

Target. Currently, the development of professional values and official behavior of civil servants are relevant for training personnel for the public authority system. One of the ways to form the personality of a civil servant who is a professional is to increase the real level of his social responsibility. The article is devoted to the study of the phenomenon of social responsibility of civil servants. Method or methodology of the work. The systematic, activity-based and axiological approaches were used as methodological principles in the work. The research methods were analysis and synthesis, movement from the general to the particular, comparison and analogy, movement from the abstract to the concrete, complex generalization and classification. Results. The main results of the study include the concretization of the concept of «social responsibility of civil servants», the identification of the essential properties of social responsibility, the determination of the features of its functioning, the formulation of urgent problems for further research in this aspect. Scope of the results. The scientific results of the article can be applied when conducting psychological and pedagogical research and organizing classes in educational institutions of higher education.

The scientific article deals with the analysis of peculiarities of the use of innovative AR/VR technologies in the process of developing future special- ists’ stress resistance. Based on the analysis of the introduction of AR/VR tech- nologies in the context of the implementation of a competency-based approach to higher education; modern studies on the impact of augmented reality on the emotional states and physiological features of a person in a stressful situation, the experience of cooperation of students and teachers at the Laboratory of Psy- chophysiological Research and STEAM-Laboratory has been described. Within the framework of the corresponding concept of cooperation, an integrative ap- proach to the process of personality’s stress resistance development has been designed and implemented. It is based on the complex combination of tradition- al psycho-diagnostic and training technologies with innovative AR/VR technol- ogies. According to the results it has been revealed that the implementation of a psycho-correction program with elements of AR technologies has promoted an increase of the level of personality’s emotional stability and stress resistance. The level of future specialists’ situational and personal anxiety has decreased; the level of insecurity, inferiority, anxiety about work, sensitivity to failures has also decreased; the level of flexibility of thinking and behavior, ability to switch from one type of activity to another one has increased; general level of person- ality’s adaptive abilities has also increased. The perspectives of further research include the analysis of the impact of AR/VR technologies on the future profes- sionals’ psychological characteristics in order to optimize the process of im- plementing a learner-centered approach into the system of higher education.

The article analyzes the main anti-propaganda accents in Mikhail Epstein’s essayistic argumentation about such messages of modern Russian propaganda as “Russia is threatened by an external enemy”, “Russia is a significant, powerful country”, “The collapse of the USSR was a tragedy”, “Russia is a special spiritual civilization”, “Our cause in Donbass is sacred”, “The enemy uses, or may use of illegal weapons”... A special emphasis is placed on the fact that the basis of these concepts is primarily ontological rather than ideological. Ideology is rather a cover for problematic Russian existence as a consequence of Russia’s problematic identity and for its inability to find itself in history. As a result, Russia is trying to resolve its historical issues geographically, through spatial expansion, trying to implement ideologemes such as “The Great Victory. We can repeat” or “Novorossia”. That is why M. Epstein clearly identifies the national and psychological basis of the Kremlin’s behavior in 2014-2021. М. Epstein easily refutes the main ideologemes of Russian propaganda. This gives grounds to claim that Russian political technologists use the classical principles of propaganda: ignore people who think; if the addressee is the masses, focus on a few simple points; reduce each problem to the lowest common denominator that the least educated person can repeat and remember; be guided by historical realities that appeal to well-known events and symbols and appeal to emotions, not to the mind. М. Epstein’s argumentation clearly points to another feature of modern Russian propaganda: if Soviet propaganda was concerned with the plausibility of its lies, then Kremlin propaganda does not care at all. It totally spreads lies, often ignoring even attempts to offer half-truth.

A short but productive cruise aboard RV William Kennedy tested various new field equipment near Halifax (port of departure and return) but also in areas that could also benefit science understanding. The GSC-A Gavia Autonomous Underwater Vehicle equipped with bathymetric, sidescan and sub-bottom profiler was successfully deployed for the first time on Scotian Shelf science targets. It surveyed three small areas: two across known benthic sponge, Vazella (Russian Hat) within a DFO-directed trawling closure area on the SE flank of Sambro Bank, bordering Emerald Basin, and one across known pockmarks, eroded cone-shaped depression in soft mud due to fluid efflux. The sponge study sites (~ 150 170 m water depth) were known to lie in an area of till (subglacial diamict) exposure at the seabed. The AUV data identified gravel and cobble-rich seabed, registering individual clasts at 35 cm gridded resolution. A subtle variation in seabed texture is recognized in sidescan images, from cobble-rich on ridge crests and flanks, to limited mud-rich sediment in intervening troughs. Correlation between seabed topography and texture with the (previously collected) Vazella distribution along two transects is not straightforward. However there may be a preference for the sponge in the depressions, some of which have a thin but possibly ephemeral sediment cover. Both sponge study sites depict a hereto unknown morphology, carved in glacial deposits, consisting of a series of discontinuous ridges interpreted to be generated by erosion in multiple, continuous, meandering and cross-cutting channels. The morphology is identical to glacial Nye, or mp;lt;"N-mp;lt;"channels, cut by sub-glacial meltwater. However their scale (10 to 100 times mp;lt;"typicalmp;gt;" N-channels) and the unique eroded medium, (till rather than bedrock), presents a rare or unknown size and medium and suggests a continuum in sub-glacial meltwater channels between much larger tunnel valleys, common to the eastward, and the bedrock forms. A comparison is made with coastal Nova Scotia forms in bedrock. The Emerald Basin AUV site, targeting pockmarks was in ~260 to 270 m water depth and imaged eight large and one small pockmark. The main aim was to investigate possible recent or continuous fluid flux activity in light of ocean acidification or greenhouse gas contribution; most accounts to date suggested inactivity. While a lack of common attributes marking activity is confirmed, creep or rotational flank failure is recognized, as is a depletion of buried diffuse methane immediately below the seabed features. Discovery of a second, buried, pockmark horizon, with smaller but more numerous erosive cones and no spatial correlation to the buried diffuse gas or the seabed pockmarks, indicates a paleo-event of fluid or gas efflux; general timing and possible mechanisms are suggested. The basinal survey also registered numerous otter board trawl marks cutting the surficial mud from past fishing activity. The AUV data present a unique dataset for follow-up quantification of the disturbance. Recent realization that this may play a significant role in ocean acidification on a global scale can benefit from such disturbance quantification. The new pole-mounted sub-bottom profiler collected high quality data, enabling correlation of recently recognized till ridges exposed at the seabed as they become buried across the flank and base of the basin. These, along with the Nye channels, will help reconstruct glacial behavior and flow patterns which to date are only vaguely documented. Several cores provide the potential for stratigraphic dating of key horizons and will augment Holocene environmental history investigations by a Dalhousie University student. In summary, several unique features have been identified, providing sufficient field data for further compilation, analysis and follow-up publications.

1.1 Macroeconomic summary Economic recovery has consistently outperformed the technical staff’s expectations following a steep decline in activity in the second quarter of 2020. At the same time, total and core inflation rates have fallen and remain at low levels, suggesting that a significant element of the reactivation of Colombia’s economy has been related to recovery in potential GDP. This would support the technical staff’s diagnosis of weak aggregate demand and ample excess capacity. The most recently available data on 2020 growth suggests a contraction in economic activity of 6.8%, lower than estimates from January’s Monetary Policy Report (-7.2%). High-frequency indicators suggest that economic performance was significantly more dynamic than expected in January, despite mobility restrictions and quarantine measures. This has also come amid declines in total and core inflation, the latter of which was below January projections if controlling for certain relative price changes. This suggests that the unexpected strength of recent growth contains elements of demand, and that excess capacity, while significant, could be lower than previously estimated. Nevertheless, uncertainty over the measurement of excess capacity continues to be unusually high and marked both by variations in the way different economic sectors and spending components have been affected by the pandemic, and by uneven price behavior. The size of excess capacity, and in particular the evolution of the pandemic in forthcoming quarters, constitute substantial risks to the macroeconomic forecast presented in this report. Despite the unexpected strength of the recovery, the technical staff continues to project ample excess capacity that is expected to remain on the forecast horizon, alongside core inflation that will likely remain below the target. Domestic demand remains below 2019 levels amid unusually significant uncertainty over the size of excess capacity in the economy. High national unemployment (14.6% for February 2021) reflects a loose labor market, while observed total and core inflation continue to be below 2%. Inflationary pressures from the exchange rate are expected to continue to be low, with relatively little pass-through on inflation. This would be compatible with a negative output gap. Excess productive capacity and the expectation of core inflation below the 3% target on the forecast horizon provide a basis for an expansive monetary policy posture. The technical staff’s assessment of certain shocks and their expected effects on the economy, as well as the presence of several sources of uncertainty and related assumptions about their potential macroeconomic impacts, remain a feature of this report. The coronavirus pandemic, in particular, continues to affect the public health environment, and the reopening of Colombia’s economy remains incomplete. The technical staff’s assessment is that the COVID-19 shock has affected both aggregate demand and supply, but that the impact on demand has been deeper and more persistent. Given this persistence, the central forecast accounts for a gradual tightening of the output gap in the absence of new waves of contagion, and as vaccination campaigns progress. The central forecast continues to include an expected increase of total and core inflation rates in the second quarter of 2021, alongside the lapse of the temporary price relief measures put in place in 2020. Additional COVID-19 outbreaks (of uncertain duration and intensity) represent a significant risk factor that could affect these projections. Additionally, the forecast continues to include an upward trend in sovereign risk premiums, reflected by higher levels of public debt that in the wake of the pandemic are likely to persist on the forecast horizon, even in the context of a fiscal adjustment. At the same time, the projection accounts for the shortterm effects on private domestic demand from a fiscal adjustment along the lines of the one currently being proposed by the national government. This would be compatible with a gradual recovery of private domestic demand in 2022. The size and characteristics of the fiscal adjustment that is ultimately implemented, as well as the corresponding market response, represent another source of forecast uncertainty. Newly available information offers evidence of the potential for significant changes to the macroeconomic scenario, though without altering the general diagnosis described above. The most recent data on inflation, growth, fiscal policy, and international financial conditions suggests a more dynamic economy than previously expected. However, a third wave of the pandemic has delayed the re-opening of Colombia’s economy and brought with it a deceleration in economic activity. Detailed descriptions of these considerations and subsequent changes to the macroeconomic forecast are presented below. The expected annual decline in GDP (-0.3%) in the first quarter of 2021 appears to have been less pronounced than projected in January (-4.8%). Partial closures in January to address a second wave of COVID-19 appear to have had a less significant negative impact on the economy than previously estimated. This is reflected in figures related to mobility, energy demand, industry and retail sales, foreign trade, commercial transactions from selected banks, and the national statistics agency’s (DANE) economic tracking indicator (ISE). Output is now expected to have declined annually in the first quarter by 0.3%. Private consumption likely continued to recover, registering levels somewhat above those from the previous year, while public consumption likely increased significantly. While a recovery in investment in both housing and in other buildings and structures is expected, overall investment levels in this case likely continued to be low, and gross fixed capital formation is expected to continue to show significant annual declines. Imports likely recovered to again outpace exports, though both are expected to register significant annual declines. Economic activity that outpaced projections, an increase in oil prices and other export products, and an expected increase in public spending this year account for the upward revision to the 2021 growth forecast (from 4.6% with a range between 2% and 6% in January, to 6.0% with a range between 3% and 7% in April). As a result, the output gap is expected to be smaller and to tighten more rapidly than projected in the previous report, though it is still expected to remain in negative territory on the forecast horizon. Wide forecast intervals reflect the fact that the future evolution of the COVID-19 pandemic remains a significant source of uncertainty on these projections. The delay in the recovery of economic activity as a result of the resurgence of COVID-19 in the first quarter appears to have been less significant than projected in the January report. The central forecast scenario expects this improved performance to continue in 2021 alongside increased consumer and business confidence. Low real interest rates and an active credit supply would also support this dynamic, and the overall conditions would be expected to spur a recovery in consumption and investment. Increased growth in public spending and public works based on the national government’s spending plan (Plan Financiero del Gobierno) are other factors to consider. Additionally, an expected recovery in global demand and higher projected prices for oil and coffee would further contribute to improved external revenues and would favor investment, in particular in the oil sector. Given the above, the technical staff’s 2021 growth forecast has been revised upward from 4.6% in January (range from 2% to 6%) to 6.0% in April (range from 3% to 7%). These projections account for the potential for the third wave of COVID-19 to have a larger and more persistent effect on the economy than the previous wave, while also supposing that there will not be any additional significant waves of the pandemic and that mobility restrictions will be relaxed as a result. Economic growth in 2022 is expected to be 3%, with a range between 1% and 5%. This figure would be lower than projected in the January report (3.6% with a range between 2% and 6%), due to a higher base of comparison given the upward revision to expected GDP in 2021. This forecast also takes into account the likely effects on private demand of a fiscal adjustment of the size currently being proposed by the national government, and which would come into effect in 2022. Excess in productive capacity is now expected to be lower than estimated in January but continues to be significant and affected by high levels of uncertainty, as reflected in the wide forecast intervals. The possibility of new waves of the virus (of uncertain intensity and duration) represents a significant downward risk to projected GDP growth, and is signaled by the lower limits of the ranges provided in this report. Inflation (1.51%) and inflation excluding food and regulated items (0.94%) declined in March compared to December, continuing below the 3% target. The decline in inflation in this period was below projections, explained in large part by unanticipated increases in the costs of certain foods (3.92%) and regulated items (1.52%). An increase in international food and shipping prices, increased foreign demand for beef, and specific upward pressures on perishable food supplies appear to explain a lower-than-expected deceleration in the consumer price index (CPI) for foods. An unexpected increase in regulated items prices came amid unanticipated increases in international fuel prices, on some utilities rates, and for regulated education prices. The decline in annual inflation excluding food and regulated items between December and March was in line with projections from January, though this included downward pressure from a significant reduction in telecommunications rates due to the imminent entry of a new operator. When controlling for the effects of this relative price change, inflation excluding food and regulated items exceeds levels forecast in the previous report. Within this indicator of core inflation, the CPI for goods (1.05%) accelerated due to a reversion of the effects of the VAT-free day in November, which was largely accounted for in February, and possibly by the transmission of a recent depreciation of the peso on domestic prices for certain items (electric and household appliances). For their part, services prices decelerated and showed the lowest rate of annual growth (0.89%) among the large consumer baskets in the CPI. Within the services basket, the annual change in rental prices continued to decline, while those services that continue to experience the most significant restrictions on returning to normal operations (tourism, cinemas, nightlife, etc.) continued to register significant price declines. As previously mentioned, telephone rates also fell significantly due to increased competition in the market. Total inflation is expected to continue to be affected by ample excesses in productive capacity for the remainder of 2021 and 2022, though less so than projected in January. As a result, convergence to the inflation target is now expected to be somewhat faster than estimated in the previous report, assuming the absence of significant additional outbreaks of COVID-19. The technical staff’s year-end inflation projections for 2021 and 2022 have increased, suggesting figures around 3% due largely to variation in food and regulated items prices. The projection for inflation excluding food and regulated items also increased, but remains below 3%. Price relief measures on indirect taxes implemented in 2020 are expected to lapse in the second quarter of 2021, generating a one-off effect on prices and temporarily affecting inflation excluding food and regulated items. However, indexation to low levels of past inflation, weak demand, and ample excess productive capacity are expected to keep core inflation below the target, near 2.3% at the end of 2021 (previously 2.1%). The reversion in 2021 of the effects of some price relief measures on utility rates from 2020 should lead to an increase in the CPI for regulated items in the second half of this year. Annual price changes are now expected to be higher than estimated in the January report due to an increased expected path for fuel prices and unanticipated increases in regulated education prices. The projection for the CPI for foods has increased compared to the previous report, taking into account certain factors that were not anticipated in January (a less favorable agricultural cycle, increased pressure from international prices, and transport costs). Given the above, year-end annual inflation for 2021 and 2022 is now expected to be 3% and 2.8%, respectively, which would be above projections from January (2.3% and 2,7%). For its part, expected inflation based on analyst surveys suggests year-end inflation in 2021 and 2022 of 2.8% and 3.1%, respectively. There remains significant uncertainty surrounding the inflation forecasts included in this report due to several factors: 1) the evolution of the pandemic; 2) the difficulty in evaluating the size and persistence of excess productive capacity; 3) the timing and manner in which price relief measures will lapse; and 4) the future behavior of food prices. Projected 2021 growth in foreign demand (4.4% to 5.2%) and the supposed average oil price (USD 53 to USD 61 per Brent benchmark barrel) were both revised upward. An increase in long-term international interest rates has been reflected in a depreciation of the peso and could result in relatively tighter external financial conditions for emerging market economies, including Colombia. Average growth among Colombia’s trade partners was greater than expected in the fourth quarter of 2020. This, together with a sizable fiscal stimulus approved in the United States and the onset of a massive global vaccination campaign, largely explains the projected increase in foreign demand growth in 2021. The resilience of the goods market in the face of global crisis and an expected normalization in international trade are additional factors. These considerations and the expected continuation of a gradual reduction of mobility restrictions abroad suggest that Colombia’s trade partners could grow on average by 5.2% in 2021 and around 3.4% in 2022. The improved prospects for global economic growth have led to an increase in current and expected oil prices. Production interruptions due to a heavy winter, reduced inventories, and increased supply restrictions instituted by producing countries have also contributed to the increase. Meanwhile, market forecasts and recent Federal Reserve pronouncements suggest that the benchmark interest rate in the U.S. will remain stable for the next two years. Nevertheless, a significant increase in public spending in the country has fostered expectations for greater growth and inflation, as well as increased uncertainty over the moment in which a normalization of monetary policy might begin. This has been reflected in an increase in long-term interest rates. In this context, emerging market economies in the region, including Colombia, have registered increases in sovereign risk premiums and long-term domestic interest rates, and a depreciation of local currencies against the dollar. Recent outbreaks of COVID-19 in several of these economies; limits on vaccine supply and the slow pace of immunization campaigns in some countries; a significant increase in public debt; and tensions between the United States and China, among other factors, all add to a high level of uncertainty surrounding interest rate spreads, external financing conditions, and the future performance of risk premiums. The impact that this environment could have on the exchange rate and on domestic financing conditions represent risks to the macroeconomic and monetary policy forecasts. Domestic financial conditions continue to favor recovery in economic activity. The transmission of reductions to the policy interest rate on credit rates has been significant. The banking portfolio continues to recover amid circumstances that have affected both the supply and demand for loans, and in which some credit risks have materialized. Preferential and ordinary commercial interest rates have fallen to a similar degree as the benchmark interest rate. As is generally the case, this transmission has come at a slower pace for consumer credit rates, and has been further delayed in the case of mortgage rates. Commercial credit levels stabilized above pre-pandemic levels in March, following an increase resulting from significant liquidity requirements for businesses in the second quarter of 2020. The consumer credit portfolio continued to recover and has now surpassed February 2020 levels, though overall growth in the portfolio remains low. At the same time, portfolio projections and default indicators have increased, and credit establishment earnings have come down. Despite this, credit disbursements continue to recover and solvency indicators remain well above regulatory minimums. 1.2 Monetary policy decision In its meetings in March and April the BDBR left the benchmark interest rate unchanged at 1.75%.

From this edition, the Financial Stability Report will have fewer pages with some changes in its structure. The purpose of this change is to present the most relevant facts of the financial system and their implications on the financial stability. This allows displaying the analysis more concisely and clearly, as it will focus on describing the evolution of the variables that have the greatest impact on the performance of the financial system, for estimating then the effect of a possible materialization of these risks on the financial health of the institutions. The changing dynamics of the risks faced by the financial system implies that the content of the Report adopts this new structure; therefore, some analyses and series that were regularly included will not necessarily be in each issue. However, the statistical annex that accompanies the publication of the Report will continue to present the series that were traditionally included, regardless of whether or not they are part of the content of the Report. In this way we expect to contribute in a more comprehensive way to the study and analysis of the stability of the Colombian financial system. Executive Summary During the first half of 2015, the main advanced economies showed a slow recovery on their growth, while emerging economies continued with their slowdown trend. Domestic demand in the United States allowed for stabilization on its average growth for the first half of the year, while other developed economies such as the United Kingdom, the euro zone, and Japan showed a more gradual recovery. On the other hand, the Chinese economy exhibited the lowest growth rate in five years, which has resulted in lower global dynamism. This has led to a fall in prices of the main export goods of some Latin American economies, especially oil, whose price has also responded to a larger global supply. The decrease in the terms of trade of the Latin American economies has had an impact on national income, domestic demand, and growth. This scenario has been reflected in increases in sovereign risk spreads, devaluations of stock indices, and depreciation of the exchange rates of most countries in the region. For Colombia, the fall in oil prices has also led to a decline in the terms of trade, resulting in pressure on the dynamics of national income. Additionally, the lower demand for exports helped to widen the current account deficit. This affected the prospects and economic growth of the country during the first half of 2015. This economic context could have an impact on the payment capacity of debtors and on the valuation of investments, affecting the soundness of the financial system. However, the results of the analysis featured in this edition of the Report show that, facing an adverse scenario, the vulnerability of the financial system in terms of solvency and liquidity is low. The analysis of the current situation of credit institutions (CI) shows that growth of the gross loan portfolio remained relatively stable, as well as the loan portfolio quality indicators, except for microcredit, which showed a decrease in these indicators. Regarding liabilities, traditional sources of funding have lost market share versus non-traditional ones (bonds, money market operations and in the interbank market), but still represent more than 70%. Moreover, the solvency indicator remained relatively stable. As for non-banking financial institutions (NBFI), the slowdown observed during the first six months of 2015 in the real annual growth of the assets total, both in the proprietary and third party position, stands out. The analysis of the main debtors of the financial system shows that indebtedness of the private corporate sector has increased in the last year, mostly driven by an increase in the debt balance with domestic and foreign financial institutions. However, the increase in this latter source of funding has been influenced by the depreciation of the Colombian peso vis-à-vis the US dollar since mid-2014. The financial indicators reflected a favorable behavior with respect to the historical average, except for the profitability indicators; although they were below the average, they have shown improvement in the last year. By economic sector, it is noted that the firms focused on farming, mining and transportation activities recorded the highest levels of risk perception by credit institutions, and the largest increases in default levels with respect to those observed in December 2014. Meanwhile, households have shown an increase in the financial burden, mainly due to growth in the consumer loan portfolio, in which the modalities of credit card, payroll deductible loan, revolving and vehicle loan are those that have reported greater increases in risk indicators. On the side of investments that could be affected by the devaluation in the portfolio of credit institutions and non-banking financial institutions (NBFI), the largest share of public debt securities, variable-yield securities and domestic private debt securities is highlighted. The value of these portfolios fell between February and August 2015, driven by the devaluation in the market of these investments throughout the year. Furthermore, the analysis of the liquidity risk indicator (LRI) shows that all intermediaries showed adequate levels and exhibit a stable behavior. Likewise, the fragility analysis of the financial system associated with the increase in the use of non-traditional funding sources does not evidence a greater exposure to liquidity risk. Stress tests assess the impact of the possible joint materialization of credit and market risks, and reveal that neither the aggregate solvency indicator, nor the liquidity risk indicator (LRI) of the system would be below the established legal limits. The entities that result more individually affected have a low share in the total assets of the credit institutions; therefore, a risk to the financial system as a whole is not observed. José Darío Uribe Governor