Reviewerly/exHarmony huggingface dataset:
Digital evidence and its admissibility in the courtroom

The importance of digital forensics to the court cannot be underestimated. Digital forensics practitioners play a significant role in helping the courts establish credible evidence for a case, practising their duty to present evidence in neutral way. This chapter will explore this component of digital lawyering. It will assist students in developing an awareness of the sorts of evidence that can be retrieved from digital artefacts and devices and to be able to ask the right questions to determine its admissibility, relevance and authenticity. This knowledge helps digital lawyers to understand the contents of a forensics report; determine their examination and cross-examination strategies towards the expert witness, their own client and other witnesses; and be able to explain the evidence to the judge and jury.
A Matter of Life and Death

This chapter addresses the life of COVID-19 data, how it has been used to reshape our daily lives by directing intervention measures, and how new data-driven technologies have been deployed to try and help tackle the spread of the coronavirus. Specifically, it examines infection and death rates and the use of surveillance technologies designed to trace contacts, monitor movement, and regulate people's behaviour. The use of these technologies raised questions and active debate concerning the data life cycle and their effects on civil liberties and governmentality. Indeed, most of the critical analysis of contact tracing apps focused on its potential infringement of civil liberties, particularly privacy, since they require fine-grained knowledge about social networks and health status and, for some, location. The concern was that intimate details about a person's life would be shared with the state without sufficient data protection measures that would foreclose data re/misuse and ensure that data would be deleted after 14 days (at which point it becomes redundant) or stored indefinitely.
Opportunities For Digital Education In Higher Education

Currently, in the context of digitalization of society, the introduction of digital technologies has affected all spheres and systems of society, without exception: education, health care, etc. The education system is undergoing changes due to many factors that indicate the need to include digital technologies in the educational process. This is the request of the state in the field of digitalization of education, and the current COVID-19 pandemic, etc. Thus, the article is devoted to the problem of using digital educational technologies in the educational process of the university. The publication substantiates the expediency of using digital resources in the higher education system, highlights the advantages and disadvantages of introducing this technology in the organization of the educational process at a university. The result of the study is the conclusion about the importance of using digital technologies in the educational process of a university in the preparation of highly qualified specialists who meet the standards of modern times. At the same time, it is shown that digital educational technologies, although they are actively used in building the educational process at a university, are nevertheless not the only and optimal means for increasing the effectiveness of the educational process in higher education.
Smart Home Governance

This article focuses on the possibilities of using available procedures and knowledge from the corporate environment and their application to smart homes. Its aim is to propose a concept of smart home management style, which will be referred to in this work as "Smart Home Governance" (SHG) and the basic principles of a tool that could be used to implement this concept in the households.
Performance Analysis of Two-Stage Iterative Ensemble Method over Random Oversampling Methods on Multiclass Imbalanced Datasets

Data imbalance occurring among multiclass datasets is very common in real-world applications. Existing studies reveal that various attempts were made in the past to overcome this multiclass imbalance problem, which is a severe issue related to the typical supervised machine learning methods such as classification and regression. But, still there exists a need to handle the imbalance problem efficiently as the datasets include both safe and unsafe minority samples. Most of the widely used oversampling techniques like SMOTE and its variants face challenges in replicating or generating the new data instances for balancing them across multiple classes, particularly when the imbalance is high and the number of rare samples count is too minimal thus leading the classifier to misclassify the data instances. To lessen this problem, we proposed a new data balancing method namely a two-stage iterative ensemble method to tackle the imbalance in multiclass environment. The proposed approach focuses on the rare minority sample’s influence on learning from imbalanced datasets and the main idea of the proposed approach is to balance the data without any change in class distribution before it gets trained by the learner such that it improves the learner’s learning process. Also, the proposed approach is compared against two widely used oversampling techniques and the results reveals that the proposed approach shows a much significant improvement in the learning process among the multiclass imbalanced data.
A Modeling Method of Neural Network Activation Function Based on FPGA Multiprocessor

With the development of artificial intelligence, Neural-Network based algorithms are widely applied in various fields, which causes the network structure more complex and the number of neurons also increases. Activation function is the most important part of the neuron, and has nonlinear characteristics. Therefore, the modeling of activation function on FPGA is faced with the problem of too many iterations and a large amount of hardware resource consumption. In this paper, a modeling method of Sigmoid function based on FPGA multi-processor is proposed. In this method, different processor cores are respectively used to control different modules, such as communication with peripherals, scheduling of computing resources, sharing of hardware resources, etc., so as to optimize the use of hardware resources and accelerate the computing. The result cased with the Sigmoid function shows that the method presented in this paper is feasible. And the comparison and analysis of the resources usages and simulation results are given.
Deterministic Approximate Counting of Polynomial Threshold Functions via a Derandomized Regularity Lemma.

We study the problem of deterministically approximating the number of satisfying assignments of a polynomial threshold function (PTF) over Boolean space. We present and analyze a scheme for transforming such algorithms for PTFs over Gaussian space into algorithms for the more challenging and more standard setting of Boolean space. Applying this transformation to existing algorithms for Gaussian space leads to new algorithms for Boolean space that improve on prior state-of-the-art results due to Meka and Zuckerman [Meka and Zuckerman, 2013] and Kane [Kane, 2012]. Our approach is based on a bias-preserving derandomization of Meka and Zuckerman’s regularity lemma for polynomials [Meka and Zuckerman, 2013] using the [Rocco A. Servedio and Li-Yang Tan, 2018] pseudorandom generator for PTFs.
A model of fair and explainable artificial intelligence

Artificial intelligence (AI) is making more and more algorithmic decisions for humans. However, the "intelligence" function in AI relies heavily on learning technologies, which suffer two major flaws leading to legal and technical challenges: potentially discriminative/biased decisions, and unable to explain why and how a machine makes such decisions. Therefore, building a fair and explainable AI model is important and urgent. This article presents a novel theory-based individual-level dynamic learning method that performs learning using data of an individual subject without employing others' information, and identifies causal mechanism from unobserved data generating process that each subject exhibits. Thus, data selection bias is avoided and a fair and interpretable decision is achieved. We empirically test our method using a real-world dataset on risk assessment for lending decisions. Our results show that the proposed method outperforms conventional learning methods in terms of fairness in treating data subjects, decision accuracy and interpretability.
Sustainable System for TVET Teacher Education Program in Malaysia

The purpose of this paper is to obtain an expert consensus on the criteria of a sustainable system for the TVET Teacher Education program in Malaysia. The sustainable system for TVET Teacher Education program in Malaysia involves item related to the three main systems identified in the previous phase, namely program system, management system, and support system. Nine experts from three leading players in TVET Teacher Education program, which is the TVET Teacher Education institution, the government and the industry was chosen to do the verification through a questionnaire containing 25 items. The consensus from the experts was further analyzed using the Fuzzy Delphi Method. The study found that expert consensus on the criteria for a sustainable system of TVET Teacher Education program is more than 75% with a total of 24 items having a threshold value (d) ≤ 0.2. The items were then sorted by priority, referring to the Fuzzy Score of each item based on the Defuzzification process. Overall, the sustainable system of TVET teacher education program will influence the quality of TVET Teacher Education graduates, which is the TVET teacher who will contribute to the quality of TVET workers in the future. Therefore, all players need to work together towards the sustainability of the program.
Digital Building Blocks using Perceptrons in Neural Networks

Most microprocessors and microcontrollers are based on Digital Electronics building Blocks. Digital Electronics gives us a number of combinational and sequential circuits for various arithmetic and logical operations. These include Adders, Subtracters, Encoders, Decoders, Multiplexers, DE multiplexers and Flip Flops. These further combine into higher configurations to perform advanced operations. These operations are done using logic circuits in digital electronics. But in this paper, we explore the human reasoning approach using artificial neural networks. We will look into neural implementations of logic gates implemented with SLP (Single layer perceptron) and MLP (Multi-Layer Perceptron). We will also look into recurrent neural architectures to make basic memory elements, viz. Flip Flops which use feedback and may involve in one or more neuron layers.
Optimizing neural network architecture through a coarse genetic algorithm

The utilization of neural networks has increased dramatically over the past decade, and yet our understanding of the effects of different network characteristics on efficacy is limited. Network arc...
TU59. A FRAMEWORK TO IMPROVE POLYGENIC QUESTIONS AND STRENGTHEN INFERENCES IN PSYCHIATRIC GENETIC EPIDEMIOLOGY

Web Service Composition has been studied intensively in the past two decades, considering the many aspects of the problem, like: semantic modeling, general QoS metrics, services state, and much more. In this paper, our focus is on integrating throughput metric with the automation formalism, without any limitation on automation, i.e. allowing full automation with meaningful throughput propagation. The user request and each service in the respiratory are described by their throughput values that are: an extra requirement for the request; and a maximum allowed load for services. In the proposed version, it is possible that to satisfy a request’s throughput, multiple traditional compositions are used concurrently, adding up for the required throughput. This highly increases the problem complexity both in how to model the combining throughput values, and also computationally, as the algorithms are required to search for multiple compositions. However, we also propose an algorithm that reduces the original problem to multiple constraint satisfaction problem instances and can find compositions for requests and considerable-sized repositories, which we prove through a synthetic evaluation.
Research on Dynamic Social Network Anonymity Technology for Protecting Community Structure

The dynamic change of vertex degree in a dynamic social network will lead to vertex identity disclosure given the deficiencies in current privacy protection methods, such as the destruction of community structure and low data processing capability of a single workstation. The dynamic social network degree sequence anonymity (DSNDSA) method to protect community structure is proposed. The method obtains the grouping and anonymous results based on a compressed binary tree constructed by a new method called a multidimensional vector. Dummy vertices are added in parallel to construct anonymous graphs. Distributed to merge dummy vertices method based on the community is designed to reduce the number of vertices added to satisfy the anonymity model. A divide and the agglomerate algorithm is expanded for community detection. The experimental results show that the proposed algorithm based on GraphX can overcome the defects of the traditional algorithm in community protection while meeting the requirement of anonymity.
A hybrid heuristic algorithm for optimising SLA satisfaction in cloud computing

Task scheduling is one of the key techniques for effective and reliable resource usage in cloud computing. In this paper, we designed a hybrid heuristic scheduling that employed particle swarm opti...
Authors’ Productivity in Information Literacy Literature during 2011-2020

This study will analyze the decadal growth of Information literacy literature from 2011-2020. The limit of the analysis is restricted to the field of Social Science and Arts. Based on quantitative analysis about AGR (Annual Growth Rate), RGR (Relative Growth Rate), degree of collaboration, popular sources and productive affiliations, results have been formulated about the growth in information literacy literature. Elsevier’s abstract and citation database Scopus is exclusively used for bibliometric analysis of information literacy publications. Tracing the evolution of information literacy publications over the decade (2011-2020) does not explains an exponential growth as expected from past studies, but rather shows ups and downs in the production. The document wise analysis shows that 81.07% of literature is in the form of articles. Total publications over the decade stands at 3064 i.e., on an average of 306 publications are published annually. Also, the citations count is 17,177 for 3064 publications which give an average of 5.606070 for each paper. The study also reveals that the highly prolific authors of the field. The study shows that Pinto, M. from the University de Grande is the most prominent author in the information literacy field.
ANALYSIS OF THE APPLICABILITY OF THE METHOD FOR ELIMINATING COVERT CHANNELS IN ELECTRONIC DIGITAL SIGNATURE

A method for eliminating covert (hidden) channels in the GOST R 34.10-2012 electronic digital signature algorithm is presented. Examples of information systems that use electronic digital signatures are presented. The possibility of eliminating hidden channels is considered in various information systems.
Hybrid Residual-Deep-Convolutional Neural Network for Multiclass Vehicle Detection, Tracking and Classification: Resyalexnet

In this paper, a highly robust hybrid residual-deep convolutional neural network (CNN) based vehicle detection, tracking and classification system is developed to enable end-to-end traffic surveillance purposes. Architecturally, our proposed model encompasses ResNet50, YOLOv2 and AlexNet-CNN, implemented in sequence to perform vehicle detection, tracking and classification. Considering operating environment complexity such as illumination difference, clutter, and occlusion, ResYAlexNet model at first applied ResNet50 as residual network which extracts fine-grained and detailed information from input frames consecutively. Subsequently, unlike classical deep learning methods where authors directly performed classification over the extracted features, we applied adaptive bounding box and loss-function enhancement based YOLOv2 model which helped identifying region of interest (ROI), target labelling, and its continuous target tracking. This approach helped performing optimal ROI tracking even under occlusion condition. Once generating the bounding boxes for the vehicles in each frame, we applied a well-known transferable deep learning method named AlexNet-CNN with five convolutional and three fully connected layers for learning and target classification. In the proposed model we applied AlexNet-CNN onto the target bounding box to avoid major background conditions and allied complexities. Additionally, it helped in achieving better ROI training and fast target (i.e., vehicle) tracking and classification. MATLAB 2019b based simulation over the different traffic videos with different vehicle densities and lighting conditions, exhibited higher accuracy (95.61%), precision (0.9722), recall (0.9683) and F-measure (0.9672), signifying its robustness towards real-time traffic surveillance purposes.
Efficient Large Scale Inlier Voting for Geometric Vision Problems

Outlier rejection and equivalently inlier set optimization is a key ingredient in numerous applications in computer vision such as filtering point-matches in camera pose estimation or plane and normal estimation in point clouds. Several approaches exist, yet at large scale we face a combinatorial explosion of possible solutions and state-of-the-art methods like RANSAC, Hough transform or Branch&Bound require a minimum inlier ratio or prior knowledge to remain practical. In fact, for problems such as camera posing in very large scenes these approaches become useless as they have exponential runtime growth if these conditions aren't met. To approach the problem we present a efficient and general algorithm for outlier rejection based on intersecting $k$-dimensional surfaces in $R^d$. We provide a recipe for casting a variety of geometric problems as finding a point in $R^d$ which maximizes the number of nearby surfaces (and thus inliers). The resulting algorithm has linear worst-case complexity with a better runtime dependency in the approximation factor than competing algorithms while not requiring domain specific bounds. This is achieved by introducing a space decomposition scheme that bounds the number of computations by successively rounding and grouping samples. Our recipe (and open-source code) enables anybody to derive such fast approaches to new problems across a wide range of domains. We demonstrate the versatility of the approach on several camera posing problems with a high number of matches at low inlier ratio achieving state-of-the-art results at significantly lower processing times.
Effects Of Blogs In Learning Computer Science Teacher’s At Namakkal District

In this 21st century, the computer has made learning and teaching much easier. It has become inevitable for teachers to be selective in computer-based knowledge. In this digital world of computer technology, it is easy to get information that is unaccounted for. Information technology knowledge is essential for learners and educators to easily store information and download information when needed. Nowadays millions of books can be obtained at the fingertips in a matter of seconds and the required information can be selected from it in a matter of seconds.
Virtual ADTs for portable metaprogramming

Scala 3 provides a metaprogramming interface that represents the abstract syntax tree definitions using algebraic data types. To allow the compiler to freely evolve without breaking the metaprogramming interface, we present virtual algebraic data types (or Virtual ADTs) -- a programming pattern, which allows programmers to describe mutually recursive hierarchies of types without coupling to a particular runtime representation.
DGM 430 OFFSET WEB MACHINE PRINT QUALITY

The review control activity, especially the decrease in the quality of the printed output, is one of the quality control parameters carried out by each company. To get quality, of course, the company does not remain silent, there are several things that need to be done or provided by the company to be able to support quality, namely man (human), machine (machine), material (material), and methods (methods). In the development of technology in an increasingly advanced digital world, companies want to continue to compete with a digital world by making breakthroughs, this of course makes the products produced by the company have a good or good quality level. In this study, there is an identification of the problem aspects that arise from the causes of the print results and how to get solutions to overcome the problems and factors that affect the quality of the print on the printed contents of the Cultural Arts Service Book using the DGM 430 Web Offset machineKeywords— Quality, Parameters, Web Offsets
A novel hybrid face recognition framework based on a low-resolution camera for biometric applications

In research work, human face recognition is an essential biometric symbol persistently continued so far due to its different levels of applications in society. Since the appearance of the human faces can have many variations due to issues like the effect of illumination, expression and face pose. These differences are correlated with one another, which results in a helpless ability to recognize a particular person's face. The motivation behind our work in this paper is to give a new framework for face recognition based on frequency analysis that contributes to solving the distinguishing proof issues with enormous varieties of boundaries like the effect of illumination, expression, and face pose. Here three algorithms combined for provable results: i) Difference of Gaussian filtered discrete wavelet transform (DDWT) for feature extraction; ii) Log Gabor (LG) filter for feature extraction; and iv) Multiclass support vector machine classifier, where feature coefficients of DDWT and LG filter are fused for classification and parameters evaluation. The evaluation of our experiment is carried out on a large database consisting of 15 persons of each 200-face image which are captured using a 5-megapixel low-resolution web camera and yielding satisfactory results on various parameters compared to existing methods.
Visualization of Rectified Sine Waves and Triggering Angles on Thyristor Using Geogebra

GeoGebra is a tool that is widely used among academics to solve mathematical problems. GeoGebra can be visually generated in the form of graphs and computational results easily and quickly. In the field of electronic power, there is a problem with directed waves and waves triggered by a certain angle on the device thyristor. This paper will be discussed about visualization of rectified sine waveforms, triggering angles as well as integral values of directional waves in thyristor and three-phase source animation. With the GeoGebra tool, students can understand mathematical equations and make wave visualization on electronic power.
Sikap Mahasiswa Sastra Indonesia dalam Perkuliahan Daring pada Masa Pandemi Covid-19: Tinjauan Sistem Appraisal

Abstract. This qualitative research with descriptive explanation aims to describe the forms of attitude in the speech spoken by Indonesian Literature students class 2020 in attending online lectures during the COVID-19 pandemic and the factors that cause the problems of online lectures. The results of this study indicate that first, the attitudes in speech expressed by Indonesian Literature students class 2020 who attend online lectures are dominated by negative affect, Judgment aspect in this research is dominated by personal judgment rather than moral, and the factors that cause online lectures are divided into internal and external factors. Keywords : Attitude, online lectures, internal and external factors