Reviewerly/exHarmony huggingface dataset:
A Novel MS-MeMBer Filter for Extended Targets Tracking

Conventional multi-sensor multi-target multi-Bernoulli (MS-MeMBer) filters are based on the assumption that each target produces at most one measurement per time step. However, this assumption is not always reasonable in practice as an extended target can generate multiple measurements per step due to the recent improvement in the sensor resolution. In this case, a potential estimation bias may occur in the current MS-MeMBer filters. Therefore, a novel extended target MS-MeMBer filter and its Gaussian inverse Wishart mixture implementation are given in this paper. Specifically, we modify the update process of the MS-MeMBer filter by assuming that the generation of extended target measurements follows an approximate Poisson-Body model. Simulation results validate that the proposed filter can effectively estimate the shape and position of the extended target.
International Context of Cybercrime and Cyber Law

It is clearly evident that as people become more dependent on technology, they become easier targets of cybercrime, as it also could evolve to bring about new problems. It is also important to realize to what extent it is understood by common people that either they are really a victim or can be the victim of a cybercrime. This research includes significance as well as international aspects of cyber laws, initiatives by the EU, USA, China, and the role of international forum for cybercrime. At the very least, it demonstrates the fact that cybercrime attacks are an almost routine form of criminality and most internet users are likely to face an attack on a daily basis.
Software Fault Prediction Based on Fault Probability and Impact

Nowadays, software tests prioritization is a crucial task. Indeed, testing exhaustively the whole software system can be very difficult, heavily time and resources consuming. Using machine learning algorithms to predict which parts of a software system are fault-prone can help testers to focus on high-risk parts of the code and improve resources allocation. This paper aims to investigate the potential of a risk-based model to predict fault-prone classes. The risk of classes is evaluated based on two factors: the probability that a class is fault-prone and its impact on the rest of the system. We used object-oriented metrics to capture the two risk factors. The risk of a class is modeled using the Euclidean distance. We built various variants of the risk-based model using a data-set from five versions of the ANT system. We used different machine learning algorithms (Naive Bayes, J48, Random Forest, Support Vector Machines, Multilayer Perceptron and Logistic Regression) to construct various models for fault and level of severity prediction. The objective was to distinguish between classes containing trivial and high severity faults. The considered model achieves good results for binary fault prediction. In addition, the overall multi-classification of severity levels is more than acceptable.
Image Representation based on quantum Computation and their Application: Milestone Ontogeny

Image processing plays a crucial role in our day to day life as it is helpful to solve important problems in real world. Recent progress in quantum information and computation led to the birth of a new field called Quantum Image Processing. This new field of image processing in quantum domain focuses on the development of the quantum image processing algorithms, which can run more efficiently on quantum computer. These quantum algorithms are better in terms of Image storage and retrieval with reduced computational complexity. In this article we present recent advancements in quantum image representation with different mathematical models and with their corresponding computational complexity.
CORMORANT

This paper presents the design and open source implementation of Cormorant, an Android authentication framework able to increase usability and security of mobile authentication. It uses transparent behavioral and physiological biometrics like gait, face, voice, and keystrokes dynamics to continuously evaluate the user's identity without explicit interaction. Using signals like location, time of day, and nearby devices to assess the risk of unauthorized access, the required level of confidence in the user's identity is dynamically adjusted. Authentication results are shared securely, end-to-end encrypted using the Signal messaging protocol, with trusted devices to facilitate cross-device authentication for co-located devices, detected using Bluetooth low energy beacons. Cormorant is able to reduce the authentication overhead by up to 97% compared to conventional knowledge-based authentication whilst increasing security at the same time. We share our perspective on some of the successes and shortcomings we encountered implementing and evaluating Cormorant to hope to inform others working on similar projects.
CloudCoT: A Blockchain-Based Cloud Service Dependency Attestation Framework

The security of cloud infrastructure is an important issue. Many solutions have been proposed to protect the integrity of cloud infrastructure through integrating Trusted Computing hardware. However, these existing solutions suffer from high complexity, repetition and latency. In this paper, we propose a blockchain based cloud service dependency attestation framework–CloudCoT (Cloud Chain-of-Trust). With CloudCoT which combines trusted computing and blockchain technology, cloud users are able to automatically extract the valid dependency of their applications deployed on cloud. And they can attest the valid dependency with low latency through its measurement mechanism and verification mechanism. In addition to the decentralized features, we can see that CloudCoT has higher efficiency while maintaining strong safety in experimental evaluation.
Syntax and the failure of analogical generalization: A commentary on Ambridge (2020)

The syntactic behaviour of human beings cannot be explained by analogical generalization on the basis of concrete exemplars: analogies in surface form are insufficient to account for human grammatical knowledge, because they fail to hold in situations where they should, and fail to extend in situations where they need to.
Unsupervised Conditional Adversarial Networks for Tax Evasion Detection

The identification of tax evasion plays an important role in ensuring tax order, promoting the level of tax collection and management, and reducing tax losses. With the advancements in data mining technology, many machine learning techniques have yielded results in identifying tax evasion. However, to realize satisfactory performance, these models require large amounts of human annotated data. In the tax field, unlabeled tax data are abundant, data annotation in a single region is expensive, and the distributions of characteristics differ among regions; these factors pose substantial difficulties in the development of an identification model. Existing tax evasion detection methods are either trained for single-region tasks, in which case they perform poorly on inter-region tax evasion identification due to the discrepancies in feature distributions, or utilize labeled data from both the target-task field and different but related auxiliary fields to reuse and transfer knowledge of the target domain data, in which case they cannot deal with scenarios in which there are no labeled data in target audit tasks. Although current unsupervised transfer learning techniques can train models in labeled regions for unlabeled regions, large intra-class distribution discrepancies cannot be perfectly minimized in tax evasion detection scenarios. To better address the above challenges, this paper proposes a general architecture, namely, the unsupervised conditional adversarial networks (UCAN) for tax evasion detection, which is the first approach to solve audit tasks in unlabeled target domains via inter-region transfer. Our architecture establishes an adversarial neural network adding label information in the distribution adapter, which can granularly adapt the joint probability distribution (JPD) of the data. We introduce a constraint that is based on the conditional maximum mean discrepancy (CMMD) of the extracted features to align the conditional probability distribution (CPD) of the deep representation. Our model is formed by combining the distribution adapter and the label predictor to realize end-to-end learning of unsupervised feature transfer. The experimental results demonstrate the outstanding performance of our model in all migration tasks compared with state-of-the-art approaches.
Blockchain Integration With Low-Power Internet of Things Devices

The exponential advancement in downsizing of electronic parts, wireless communication technologies, has provoked excellent progress in our overall population. In the Internet of Things (IoT) point of view, customary contraptions have turned out to be smart and autonomous. This vision has transformed into a reality because of the movement in development; anyway, there are still troubles in the security space, for instance, data resolute quality. The blockchain technology has transformed the advanced digital money space with the leading cryptographic money named Bitcoin. From a dynamic perspective, a blockchain is an appropriated record prepared for keeping up an unchanging log of exchanges happening in a system. As of now, this technology has pulled in enormous sensible eagerness for research regions past the finance section, one of them being the IoT. In this unique circumstance, the blockchain is seen as the missing association toward structure, a really decentralized, trustless, and secure condition for the IoT and, in this thesis, we discuss about the current IoT architecture, IoT security threats, brief about blockchain, and correlation of major blockchain innovations that can be coordinated with IoT gadgets and propose an Ethereum-based IoT architecture for low-power IoT devices.
Early Stage Detection of Psoriasis Using Artificial Intelligence and Image Processing

Kalbande, D. R. Khopkar, Uday Sharma, Avinash Daftary, Neil Kokate, Yash Dmello, RoystonA large part of the Indian population comes below the poverty line. There are thousands of people living in poor sanitation areas. Owing to this, a majority of ailments prevail in such areas. People do not have access to good medical facilities and at times may not even have the awareness to visit a doctor at the correct time. This has been observed especially in the case of skin diseases where failure to get the correct diagnosis and treatment in time often results in dire consequences. With the growing surge of Artificial Intelligence (AI), it has been realized that AI can be helpful in the medical arena as well. Thus, an AI-based approach is proposed for the classification of a skin disease Psoriasis by making use of a multi-class machine learning classification algorithm. Psoriasis is a noncontagious, chronic skin condition that results in the formation of patches of thickened, scaling skin. This algorithm uses uploaded images from the users’ smartphone to give a probability estimate for Psoriasis. Over 60 medically validated images of Psoriasis, acquired from Department of Skin and V.D of Seth G.S. Medical College and KEM Hospital, Parel, Mumbai, were used as training data. It uses a Neural Network algorithm for training the symptoms. Using this training data, an accuracy of above 85% was obtained.
Daily Routine Recognition with Visual Interactive Labeling by Fusing Acceleration and Audio Signals

This work focuses on a semi-supervised learning scheme for daily routine recognition. To annotate and process hours of sensor data, an efficient processing workflow is established for a two-step classification: first propagating the labels and second recognizing the daily routine. The first contribution is the extension of visual interactive labeling (VIL) to activity recognition challenges and time series data exploration. With the help of a coarse time diary and a graphical user interface, the data is interactively labeled, and a first classifier propagates these annotations. Afterwards, a second classifier is trained and evaluated on the cross-validated VIL label set. As an extension of previous work, the second contribution is, that not only acceleration sensors are utilized to analyze the routine, but also audio features enrich the environmental description power. The third contribution is the comparison to a topic model on a public data set, where our approach outperforms on average recall of 84.3% and precision of 88.7%. Moreover, the data is recorded by a hearing aid, which ultimately aims to personalize the device configuration to the daily routine of the wearer.
Extracting Cryptocurrency Price Movements from the Reddit Network Sentiment

Explosive growth in the value of cryptocurrencies like Bitcoin and Ethereum in recent years has attracted the attention of many speculators. Unlike traditional currencies, cryptocurrencies are not backed by any government agencies resulting in prices being strongly influenced by public opinion. Understanding the relationship between cryptocurrency prices and the public sentiment can lead to improved predictions of price movement. In this paper, we give an exploratory analysis of a network of 24 Reddit communities related to Bitcoin, Ethereum, or other cryptocurrencies to analyze Bitcoin and Ethereum price movements. We engineer a set of 112 time series features from submissions and comments made on the selected subreddits, run Granger causality tests on engineered time series against cryptocurrency price movements, and use these time series to forecast the cryptocurrency price movements using classification models. Results from these models support the Granger causality test results showing that with only lagged price values and lagged values from a single Reddit data derived feature, the direction of Bitcoin and Ethereum price movements can be predicted with 74.2% and 73.1% accuracy respectively.
U-statistics for estimating performance metrics in forensic handwriting analysis

A class of computationally efficient approximations to a set of natural U-statistics and related U-processes that arise in forensic and biometric comparisons have been developed. This paper details the asymptotic characterization of the natural U-statistics, the development of computationally efficient approximations and expected error bounds of said approximations. The developed statistical methods are presented in the context of forensic handwriting comparisons and are used to estimate the rate at which the random match probability (RMP) decreases as a function of the amount of writing available in the comparison samples. Although presented in a forensic handwriting comparison context, similar problems arise in machine learning or pattern recognition where the task is to classify a batch of exchangeable objects that arise from the same class. Similarly, the developed methods can be used to estimate the rate at which the error rate decreases as the number of objects to be classified increases.
Pedagogical aspects of aggression in primary school

The aggressive behavior of primary school age children is an important topic since such behavior is on the rise, and therefore has become the subject of interest and research of numerous experts.These studies have resulted in several theories, which present certain understandings and interpretations of the aggression phenomenon.The most frequently mentioned theories are: instinct theory;the theory that believes that aggression is the reaction to a particular situation; social learning theory; cognitive theory and, ultimately, biological theory.The aggressive behavior of students causes damage to others, but also to themselves, with thefar-reaching consequences to their development.Aggression is a phenomenon that manifests itself in a student's inclination to verbally or physically inflict harm on others, and appears as a reaction when a goal response suffers interference.It is a universal phenomenon, both by its presence everywhere and by its duration, because it has existedduring the entire human history. The important determinants of aggressive behaviorare: aggressive impulse, aggressive motive and aggressive feeling.Aggressive impulse is a natural reaction to being in danger and to being attacked by someone.Aggressive motive is an impetus to behavior where the aggressive act itself brings pleasure.Aggressive feeling arises from the fear of loss of security, it is not always manifested openly, and it is often not accompanied with aggressive actions.The causes of aggression are very complex, and the most frequently mentioned ones are family influence, school failure, age and gender differences, personality traits, and broader social circumstances. Measures to prevent aggressive behavior of students include intervention (reaction) and prevention measures.Intervention (reaction) measures imply that each school has to formulate an intervention (reaction) plan in the event of violence, while preventive measures imply a number of activities such as creating a safe environment for children, enclosing the school yard, rearranging the school premises to allow for better supervision of students, making the school nicer and cleaner, setting up video surveillance and employing professional security. Prevention measures also include certain programs that focus on reducing aggressive behavior of students (School without violence:towards a safe and enabling environment for children; Children and Police;School Officer; Firefighter in School; Cheer for fair play; Road and traffic safety for children, etc.). Peer mediation in primary schools is becoming more and more important in solving this problem.The goal of the Peer Mediation Project relates to the procedures that, with the least risk, encourage students to actively and creatively engage in creating a pleasant atmosphere at school. The goal of this paper is to understand the phenomenon of aggression in primary school, the theories that explain it more precisely, its causes and preventive measures, with special attention being paid to peer mediation.
Do CNN's features correlate with human fixations?

In recent years, CNNs are capturing the attention of a large community of researchers, attracted by the high performance of this approach and by the surprising results obtained in many recognition/classification activities. Unfortunately, the excellent performance of CNN-based systems is accompanied by a worryingly poor understanding of why they work so well. In this document, the basic mechanisms related to the extraction of points of interest (following the first convolution phases) are considered and compared to human fixations, with the aim of better understanding analogies and differences between computational models and human recognition. Alongside, the points of interest extracted from different layers are compared in order to evaluate similarity between different networks and the how interest points evolve within the same network. Human fixations and points of interest are initially used to construct density distribution maps; a novel similarity index is then proposed in order to compare these distribution maps. Experiments on the ETD database show that human fixations, on average, tend to be contained in the density of CNN points. In other words, human fixations seem to somehow optimize the active exploration of the image by properly using the information coming from the periphery of the visual field. These first interesting results could condition emerging models for visual recognition and visual indexing, pushing towards a more attentive exploitation of coarse structural information.
Citation-Based Author Contribution Measure for Byline-Independency

Quantifying the different contributions of co-authors is a challenging task, and existing methods often rely on the assumption that orders of author bylines reflect the contribution of co-authors. Byline-based approaches, however, render them incompatible to alphabetically-ordered bylines where authors are ordered by names, not by their contributions. The authors proposed a citation-based author contribution measure citeRatio independent of byline orders while capturing the importance of the first and last authors.
Application of an enhanced deep super-resolution network in retinal image analysis

Fundus imaging is widely used for the diagnosis of retinal diseases. Major ophthalmic diseases like glaucoma, diabetic retinopathy (DR), age-related macular degeneration (AMD) are diagnosed by examining retinal fundus images. Therefore, the efficient and reliable diagnosis largely depends upon the resolution of the images. In different diseased conditions, different pathologies and landmarks (haemorrhages, microaneurysms, exudates, blood vessels, optic disc and optic cup, fovea) of the retina get affected. In clinical situations it is often not possible to obtain good high-resolution images. Here, the techniques of super-resolution can be applied. The objective of super-resolution is to obtain a high-resolution image from a low-resolution input image. In this paper, we present results of the application of enhanced deep residual networks for single image super-resolution (EDSR) on retinal fundus images. This network is based on the SRResNet architecture involving skip connections. Using the public RIGA dataset, which consists of glaucoma and normal fundus images, we have trained the model using 2x, 4x and 8x scaling with three different optimizers each (namely ADAM, Stochastic Gradient Descent and RMSprop) to determine which optimizer is best for the different scales. We have also provided results obtained by varying the residual blocks in the network.
Explicit Alignment of Text and Speech Encodings for Attention-Based End-to-End Speech Recognition

In this work, we present a novel training procedure for attention-based end-to-end automatic speech recognition. Our goal is to push the encoder network to output only linguistic information, improving generalization performance particularly in low-resource scenarios. We accomplish this with the addition of a text encoder network, which the speech encoder is encouraged to mimic. Our main innovation is the comparison of the attention-weighted speech encoder outputs to the outputs of the text encoder - this guarantees two sequences of the same length that can be directly aligned. We show that our training procedure significantly decreases word error rates in all experiments and has the biggest absolute impact in the lowest resource scenarios.
Towards Real-Time Mispronunciation Detection in Kids' Speech

Modern mispronunciation detection and diagnosis systems have seen significant gains in accuracy due to the introduction of deep learning. However, these systems have not been evaluated for the ability to be run in real-time, an important factor in applications that provide rapid feedback. In particular, the state-of-the-art uses bi-directional recurrent networks, where a uni-directional network may be more appropriate. Teacher-student learning is a natural approach to use to improve a uni-directional model, but when using a CTC objective, this is limited by poor alignment of outputs to evidence. We address this limitation by trying two loss terms for improving the alignments of our models. One loss is an “alignment loss” term that encourages outputs only when features do not resemble silence. The other loss term uses a uni-directional model as teacher model to align the bi-directional model. Our proposed model uses these aligned bi-directional models as teacher models. Experiments on the CSLU kids' corpus show that these changes decrease the latency of the outputs, and improve the detection rates, with a trade-off between these goals.