Reviewerly/exHarmony huggingface dataset:
An End-to-End Human Abnormal Behavior Recognition Framework for Crowds With Mentally Disordered Individuals

Abnormal or violent behavior by people with mental disorders is common. When individuals with mental disorders exhibit abnormal behavior in public places, they may cause physical and mental harm to others as well as to themselves. Thus, it is necessary to monitor their behavior using visual surveillance systems. However, it is challenging to automatically detect human abnormal behavior (especially for individuals with mental disorders) based on motion recognition technologies. To address these issues, in the current work, we propose an end-to-end abnormal behaviour detection framework from a new perspective in conjunction with the Graph Convolutional Network (GCN) and a 3D Convolutional Neural Network (3DCNN). Specifically, we first train a one-class classifier to extract features and estimate abnormality scores. To improve the performance of abnormal behavior detection, GCN is used to model the similarity between video clips for the correction of noisy labels. Then, based on this framework, GCN recognizes the normal behavior clips in the abnormal video and removes them, while the clips identified as abnormal behavior are retained. Finally, a 3D CNN is used to extract spatiotemporal features to classify different abnormal behaviors. In order to better detect the violent behavior of individuals with mental disorders, the paper focuses on the UCF-Crime dataset with various types of violent behaviors. By experimenting with this dataset, the classification accuracy reaches 37.9%, which is significantly better than that of the current state-of-the-art approaches.
Detecting Deepfakes Using Deep Learning

Images play an important role in defining human perception, and the power to manipulate such images gives immense power to malicious users. The new advancement in Artificial Intelligence, has altogether worked on the quality and productivity in creating counterfeit face pictures; for instance, the face manipulated by GANs is sensible to such an extent that it is hard to recognize the validness, either by the computer or by people. To improve the accuracy of recognizing facial pictures created by AI from genuine facial ones, an enhanced model has been proposed in this paper which is dependent on profound learnings like Deep Learning, Convolutional Neural Network (CNN), and Error Level Analysis (ELA). Our findings push the boundaries of understanding DeepFake detection and our solution to detect these images is based on the concepts of image error level and Deep learning. Our model uses the Convolutional Neural Network (CNN) architecture that utilizes error level analysis (ELA) to pre-process the images. We have utilized a dataset comprising on 24,000 images with equal split of real and deepfake images to traing and test our model. We were able to achieve an accuracy of 99%. The proposed model has a shorter training time and higher efficiency than most other methods for DeepFake detection.
Supervised vs Unsupervised Learning on Gaze Data to Classify Student Distraction Level in an Educational VR Environment

Educational VR may help students by being more engaging or improving retention compared to traditional learning methods. However, a student can get distracted in a VR environment due to stress, mind-wandering, unwanted noise, external alerts, etc. Student eye gaze can be useful for detecting these distraction. We explore deep-learning-based approaches to detect distractions from gaze data. We designed an educational VR environment and trained three deep learning models (CNN, LSTM, and CNN-LSTM) to gauge a student's distraction level from gaze data, using both supervised and unsupervised learning methods. Our results show that supervised learning provided better test accuracy compared to unsupervised learning methods.
Toward an omniopticon: the potential of blockchain technology toward influencing vulnerable populations in contested markets

Purpose The purpose of this paper is to explore the use of blockchain technology in contested markets. The authors specifically consider the development and utilization of this accounting system as a device that might democratize contested markets for vulnerable populations, supporting contested entrepreneurs while “cooling” the moral contestation to the market. Design/methodology/approach This study analyzes the relationship between vulnerable populations and contested market activities, the inclusive development and potential trust created by a blockchain accounting information system and how this interaction potentially creates support for economic and social systems. Findings This paper demonstrates that, in an era of decreased trust especially as it relates to a digital, globalized marketplace, blockchain has the potential to create democracies of access, trust and agency. This system overcomes many of the deficiencies associated with transparency and accountability and connects market participants with society, strengthening its potential to bridge two opposing vulnerable population viewpoints necessary for possible contested market development. Research limitations/implications The authors contribute to a deeper understanding of the role of emerging technologies in the interconnectedness between vulnerable populations in a contested market. Recognizing that blockchain is an imperfect version of its ideal intention, the authors also discuss the limitations of the system with respect to corruption, collusion and potential issues of adoption, and how this reduces the influence of blockchain as a “cooling” device within contested markets. Practical implications The authors provide an illustrative example whereby an entire industry might be persuaded from avoidance to promotion of new traceability devices and supported in the development of an accessible market. Social implications Global government's economic support for social systems continues to experience significant declines. With ever-degrading healthcare, infrastructure, public education, childcare, etc., new sources of economic influx are often desired. One potential source of additional funds is from the tax revenues derived from contested market transactions, those stigmatized industries often operating illegally. With substantial public distrust, blockchain potentially provides such industries with democratization and the trust necessary to transition the industry into a legal environment, with tax revenues benefiting various social systems. Originality/value This study goes beyond the preliminary discussions of the benefits and consequences of blockchain. Instead, the authors focus on the use of blockchain within contested markets and its ability to influence vulnerable populations. The authors also consider the use of blockchain-based accounting information systems to provide a holistic and more democratic platform from a regulatory, market participant and societal standpoint.
Fast Isomorphic State Channels

State channels are an attractive layer-two solution for improving the throughput and latency of blockchains. They offer optimistic offchain settlement of payments and expedient offchain evolution of smart contracts between multiple parties without any assumptions beyond those of the underlying blockchain. In the case of disputes, or if a party fails to respond, cryptographic evidence collected in the offchain channel is used to settle the last confirmed state onchain, such that in-progress contracts can be continued under mainchain consensus. In this paper, we introduce Hydra, an isomorphic multi-party state channel. Hydra simplifies offchain protocol and smart-contract development by directly adopting the layer-one smart contract system, allowing the same code to be used on- and off-chain. Taking advantage of the extended UTxO model, we develop a fast off-chain protocol for evolution of Hydra heads (our isomorphic state channels) that has smaller round complexity than all previous proposals and enables the state channel processing to advance on-demand, concurrently and asynchronously. We establish strong security properties for the protocol, and we present and evaluate extensive simulation results that demonstrate that Hydra approaches the physical limits of the network in terms of transaction confirmation time and throughput while keeping storage requirements at the lowest possible. Finally, our experimental methodology may be of independent interest in the general context of evaluating consensus protocols.
Refactoring embedded software: A study in healthcare domain

In embedded software industry, stakeholders usually promote run-time properties (e.g., performance, energy efficiency, etc.) as quality drivers, which in many cases leads to a compromise at the levels of design-time qualities (e.g., maintainability, reusability, etc.). Such a compromise does not come without a cost; since embedded systems need heavy maintenance cycles. To assure effective bug-fixing, shorten the time required for releasing updates, a refactoring of the software codebase needs to take place regularly. Objective: This study aims to investigate how refactorings are applied in ES industry; and propose a systematic approach that can guide refactoring through a 3-step process for refactoring: (a) planning; (b) design; and (c) evaluation. The aforementioned goals were achieved by conducting a single case study in a company that develops medical applications for bio-impedance devices; and follows a rather systematic refactoring process in periodic timestamps. Three data collection approaches have been used: surveys, interviews (10 practitioners), and artifact analysis (51 refactoring activities). The results of the study suggest that: (a) maintainability and reusability are the design-time quality attributes that motivate the refactoring of Embedded Software (ES), with 30% of the participants considering them as of “Very High” importance; (b) the refactorings that are most frequently performed are “Extract Method”, “Replace Magic Number with Constant” and “Remove Parameter”. We note that the “Extract Method” refactoring has an applicability of more than over 80%; and (c) to evaluate the refactoring process engineers use tools producing structural metrics, internal standards, and reviews. The outcomes of this study can be useful to both researchers and practitioners, in the sense that the former can focus their efforts on aspects that are meaningful to industry, whereas the latter are provided with a systematic refactoring process.
A nested parallel multiscale convolution for cerebrovascular segmentation

Cerebrovascular segmentation in magnetic resonance imaging (MRI) plays an important role in the diagnosis and treatment of cerebrovascular diseases. Many segmentation frameworks based on convolutional neural networks (CNNs) or U-Net-like structures have been proposed for cerebrovascular segmentation. Unfortunately, the segmentation results are still unsatisfactory, particularly in the small/thin cerebrovascular due to the following reasons: (1) the lack of attention to multiscale features in encoder caused by the convolutions with single kernel size; (2) insufficient extraction of shallow and deep-seated features caused by the depth limitation of transmission path between encoder and decoder; (3) insufficient utilization of the extracted features in decoder caused by less attention to multiscale features.Inspired by U-Net++, we propose a novel 3D U-Net-like framework termed Usception for small cerebrovascular. It includes three blocks: Reduction block, Gap block, and Deep block, aiming to: (1) improve feature extraction ability by grouping different convolution sizes; (2) increase the number of multiscale features in different layers by grouping paths of different depths between encoder and decoder; (3) maximize the ability of decoder in recovering multiscale features from Reduction and Gap block by using convolutions with different kernel sizes.The proposed framework is evaluated on three public and in-house clinical magnetic resonance angiography (MRA) data sets. The experimental results show that our framework reaches an average dice score of 69.29%, 87.40%, 77.77% on three data sets, which outperform existing state-of-the-art methods. We also validate the effectiveness of each block through ablation experiments.By means of the combination of Inception-ResNet and dimension-expanded U-Net++, the proposed framework has demonstrated its capability to maximize multiscale feature extraction, thus achieving competitive segmentation results for small cerebrovascular.
Nonconvex regularization for blurred images with Cauchy noise

&lt;p style='text-indent:20px;'&gt;In this paper, we propose a nonconvex regularization model for images damaged by Cauchy noise and blur. This model is based on the method of the total variational proposed by Federica, Dong and Zeng [SIAM J. Imaging Sci.(2015)], where a variational approach for restoring blurred images with Cauchy noise is used. Here we consider the nonconvex regularization, namely a weighted difference of &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ l_1 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-norm and &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ l_2 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-norm coupled with wavelet frame, the alternating direction method of multiplier is carried out for this minimization problem, we describe the details of the algorithm and prove its convergence. Numerical experiments are tested by adding different levels of noise and blur, results show that our method can denoise and deblur the image better.&lt;/p&gt;
Building 3D Morphable Models from a Single Scan

We propose a method for constructing generative models of 3D objects from a single 3D mesh. Our method produces a 3D morphable model that represents shape and albedo in terms of Gaussian processes. We define the shape deformations in physical (3D) space and the albedo deformations as a combination of physical-space and color-space deformations. Whereas previous approaches have typically built 3D morphable models from multiple high-quality 3D scans through principal component analysis, we build 3D morphable models from a single scan or template. As we demonstrate in the face domain, these models can be used to infer 3D reconstructions from 2D data (inverse graphics) or 3D data (registration). Specifically, we show that our approach can be used to perform face recognition using only a single 3D scan (one scan total, not one per person), and further demonstrate how multiple scans can be incorporated to improve performance without requiring dense correspondence. Our approach enables the synthesis of 3D morphable models for 3D object categories where dense correspondence between multiple scans is unavailable. We demonstrate this by constructing additional 3D morphable models for fish and birds and use them to perform simple inverse rendering tasks. We share the code used to generate these models and to perform our inverse rendering and registration experiments.
Software Product System Model: A Customer-Value Oriented, Adaptable, DevOps-Based Product Model

DevOps pipelines have brought notable advantages, such as fast and frequent software delivery to software production paradigms, but dynamically dealing with quality attributes desired by the customer employing a DevOps pipeline remains a challenge. This work aims to define the design of a systems thinking inspired model, called Software Product System Model (SPSM), applying a customer-value oriented, holistic approach for implementing quality requirements, and its application and evaluation in a large software house. The main features include dynamic control of quality gates, the parameters of which are driven by customer requirements and feedback from surveys. All of the inputs are collected in a product backlog and fed forward to the quality gates over the DevOps pipeline. SPSM was successfully deployed in a large software house extending a DevOps pipeline with an accompanying improvement of customer-value oriented key performance indicators for projects. In a 2-year-long case study, security and code quality were the main quality attributes, with the metrics on security vulnerabilities and unit test coverage. At the end of the 2020, the DevOps pipeline within SPSM provided a 69.50% decrease of security vulnerabilities of all software products, and a 29.43% increase in unit test coverage for the whole code base for increasing code quality. At the end of 2020, the project completion ratio was measured to be 99.50% and the Schedule Performance Index (SPI) was measured to be 99.78% as the average of 762 projects delivered. The flexibility of SPSM allowed the software house to adapt to changing customer expectations. A checklist is provided for the replicability of the model application.
Dual discriminator adversarial distillation for data-free model compression

Knowledge distillation has been widely used to produce portable and efficient neural networks which can be well applied on edge devices for computer vision tasks. However, almost all top-performing knowledge distillation methods need to access the original training data, which usually has a huge size and is often unavailable. To tackle this problem, we propose a novel data-free approach in this paper, named Dual Discriminator Adversarial Distillation (DDAD) to distill a neural network without the need of any training data or meta-data. To be specific, we use a generator to create samples through dual discriminator adversarial distillation, which mimics the original training data. The generator not only uses the pre-trained teacher’s intrinsic statistics in existing batch normalization layers but also obtains the maximum discrepancy from the student model. Then the generated samples are used to train the compact student network under the supervision of the teacher. The proposed method obtains an efficient student network which closely approximates its teacher network, without using the original training data. Extensive experiments are conducted to demonstrate the effectiveness of the proposed approach on CIFAR, Caltech101 and ImageNet datasets for classification tasks. Moreover, we extend our method to semantic segmentation tasks on several public datasets such as CamVid, NYUv2, Cityscapes and VOC 2012. To the best of our knowledge, this is the first work on generative model based data-free knowledge distillation on large-scale datasets such as ImageNet, Cityscapes and VOC 2012. Experiments show that our method outperforms all baselines for data-free knowledge distillation.
Exploring the dynamic relationship between Bitcoin and commodities: New insights through STECM model

This paper investigates the dynamic relationships between Bitcoin, Oil, Natural Gas, Gold and Coal in the short- and long-run over the period 2011–2018. For this end, we use a Smooth Transition Error Correction Model (STECM) to accommodate the presence of some stylized facts (the nonlinearity and asymmetry) in the adjustment process of Bitcoin towards their long-run equilibrium. We show significant long-run equilibrium links for Bitcoin, which seems to be asymmetric and nonlinear. Gold and Oil Brent Crude logarithmic prices potentially and significantly influence the Bitcoin logarithmic prices. Our empirical results also indicate that the current Bitcoin logarithmic prices have a significant and positive impact on their lagged values for both regimes, implying some symmetric memory effects over time. Such analysis of the interlinkages between different assets could be interesting from investment and academic perspectives.
Enhancing User Interest Modeling with Knowledge-Enriched Itemsets for Sequential Recommendation

Sequential recommendation which aims to predict a user's next interaction based on his/her previous behaviors, has attracted great attention. Recent studies mainly employ deep recurrent neural networks or self-attention networks to capture dynamic user preferences. However, existing methods merely focus on modeling users' clear interests in interacted items. We argue that for an interaction, the user may also have ambiguous interests in items that are semantically related to the interacted one. For comprehensively capturing user preferences, it is beneficial to discover potential interests from historical interactions at a broader itemset level. Therefore, in this paper, we propose a knowledge graph enhanced sequential recommendation model namely KGIE, which focuses on enhancing user interest modeling with knowledge-enriched itemsets by incorporating the knowledge graph. Specifically, in addition to item-level interest modeling with interacted items, we further construct knowledge-enriched itemsets that are extracted via high-order knowledge associations with the interacted items. For capturing personalized itemset-level interests, we design an attentive aggregation unit to combine item embeddings considering both inherent and contextual personalization signals. Furthermore, to balance the contributions of both two levels of interest modeling, we adaptively learn high-level preference representations with a gating fusion unit. Extensive experiments on three real-world datasets demonstrate the superior performance beyond state-of-the-art methods and recommendation interpretability of our model.
Multi-view Interaction Learning for Few-Shot Relation Classification

Conventional deep learning-based Relation Classification (RC) methods heavily rely on large-scale training dataset and fail to generalize to unseen classes when training data is scant. This work concentrates on RC tasks in few-shot scenarios in which models classify the unlabelled samples given only few labeled samples. Existing few-shot RC models consider the dataset as a series of individual instances and have not fully utilized interaction information among them. Interaction information is conducive to indicate the important areas and produce discriminating representations. So this paper proposes a novel interactive attention network (IAN) which uses inter-instance and intra-instance interactive information to classify the relations. Inter-instance interactive information is first introduced to solve the low-resource problem by capturing the semantic relevance between an instance pair. Intra-instance interactive information is then introduced to address the ambiguous relation classification issue by extracting the entity information inner an instance. Extensive numerical experimental results demonstrate the proposed method promotes the accuracy of down-stream task.
Measuring Healthcare Data Breaches

Over the past few years, healthcare data breaches have grown rapidly. Moreover, throughout the COVID-19 pandemic, the level of exposure to security threats increased as the frequency of patient visits to hospitals has also increased. During the COVID-19 crisis, circumstances and constraints such as curfew imposed on the public have resulted in a noticeable increase in Internet usage for healthcare services, employing intelligent devices such as smartphones. The Healthcare sector is being targeted by criminals internally and externally; healthcare data breaches impact hospitals and patients alike. To examine issues and discover insights, a comprehensive study of health data breaches is necessary. To this end, this paper investigates healthcare data breach incidents by conducting measurements and analysis recognizing different viewpoints, including temporal analysis, attack discovery, security attributes of the breached data, attack actors, and threat actions. Based on the analysis, we found the number of attacks is decreasing, although not precluding an increasing severity, the time of attack discovery is long across all targets, breached data does not employ basic security functions, threat actions are attributed to various vectors, e.g., malware, hacking, and misuse, and could be caused by internal actors. Our study provides a cautionary tale of medical security in light of confirmed incidents through measurements.