Reviewerly/exHarmony huggingface dataset:
Sustaining Compliance with COVID-19 Mitigation Measures? Understanding Distancing Behavior in the Netherlands during June 2020

In the month of June, the Netherlands had continued its singular trajectory in combating the COVID-19 pandemic.After the transition from the "intelligent lockdown" into the "1.5 meter society," the month of June heralded further relaxations of the prior mitigation measures.Building on our previous surveys during the month of May, this paper reports the findings of two additional survey waves collected in June (between 8-11 and between 22-26) among nationally representative samples (N = 1041 and N = 1033).The results show that the processes that sustained compliance during the month of May continued to be influential, especially citizens' intrinsic motivation to comply, their capacity to do so, their impulse control, and social norms that sustained compliance.Furthermore, there were some indications that extrinsic reasons, such as the likelihood of punishment and the fairness of enforcement, may have become more influential in shaping compliance.A comparison to the findings from May revealed, however, that compliance was gradually declining in the Netherlands, as were the resources that sustain it.To this end, our June surveys again focused on citizens' tendency to keep a safe distance from others (i.e., 1.5 meters or more), as they are required by the current mitigation measures within the Netherlands.We assessed their (self-reported) compliance with these measures across various situations.In addition to this, we again assessed a range of processes that may explain their tendency to do so, according to insights on (non)compliance from psychology, criminology, sociology, and
Rule of Thirds and Simplicity for Image Aesthetics using Deep Neural Networks

Considering the increasing amount of photos being uploaded to sharing platforms, a proper evaluation of photo appeal or aesthetics is required. For appealing images several "rules of thumb" have been established, e.g., the rule of thirds and simplicity. We handle rule of thirds and simplicity as binary classification problems with a deep learning based image processing pipeline. Our pipeline uses a pre-processing step, a pre-trained baseline deep neural network (DNN) and post-processing. For each of the rules, we re-train 17 pre-trained DNN models using transfer learning. Our results for publicly available datasets show that the ResNet152 DNN is best for rule of thirds prediction and DenseNet121 is best for simplicity with an accuracy of around 0.84 and 0.94 respectively. In addition to the datasets for both classifications, five experts annotated another dataset with ≈ 1100 images and we evaluate the best performing models. Results show that the best performing models have an accuracy of 0.67 for rule of thirds and 0.79 for image simplicity. Both accuracy results are within the range of pairwise accuracy of expert annotators. However, it further indicates that there is a high subjective influence for both of the considered rules.
Towards an automated medical diagnosis system for intestinal parasitosis

Human parasites are a real public health problem in tropical countries, especially in underdeveloped countries. Usually, the medical diagnosis of intestinal parasites is carried out in the laboratory by visual analysis of stools samples using the optical microscope. The parasite recognition is realized by comparing its shape with known forms. We offer a solution to automate the diagnosis of intestinal parasites through their images obtained from a microscope connected directly to a computer. Our approach exploits the contour detection based on the multi-scale wavelet transform for detecting the parasite. Active contours are combined with the Hough transform to perform image segmentation and extraction of the parasite. We used principal component analysis for the extraction and reduction of features obtained directly from pixels of the extracted parasite image. Our classification tool is based on the probabilistic neural network. The obtained algorithms were tested on 900 samples of microscopic images of 15 different species of intestinal parasites. The result shows a 100% recognition rate of success.
A Novel Meta-Heuristic Approach for Load Balancing in Cloud Computing

Cloud computing is gaining more popularity due to its advantages over conventional computing. It offers utility based services to subscribers on demand basis. Cloud hosts a variety of web applications and provides services on the pay-per-use basis. As the users are increasing in the cloud system, the load balancing has become a critical issue in cloud computing. Scheduling workloads in the cloud environment among various nodes are essential to achieving a better quality of service. Hence it is a prominent area of research as well as challenging to allocate the resources with changeable capacities and functionality. In this paper, a metaheuristic load balancing algorithm using Particle Swarm Optimization (MPSO) has been proposed by utilizing the benefits of particle swarm optimization (PSO) algorithm. Proposed approach aims to minimize the task overhead and maximize the resource utilization. Performance comparisons are made with Genetic Algorithm (GA) and other popular algorithms on different measures like makespan calculation and resource utilization. Different cloud configurations are considered with varying Virtual Machines (VMs) and Cloudlets to analyze the efficiency of proposed algorithm. The proposed approach performs better than existing schemes.
Fake Reviews

Abstract We propose a model of product reviews in which some are genuine and some are fake in order to shed light on the value of information provided on platforms like TripAdvisor, Yelp, etc. In every period, a review is posted which is either genuine or fake. We characterise the equilibrium of the dynamic model and prove that it is unique. In equilibrium, valuable learning takes place in every period. Fake reviews, however, do slow down the learning process. It is established that any attempt by the platform to manipulate the reviews is counterproductive.
A Trust Framework Utilization in Cloud Computing Environment Based on Multi-criteria Decision-Making Methods

Abstract Cloud computing (CC) is a complete online system of specialized technical resources for data computing and storage management. Many organizations, including small and start-ups, have recommended using CC technology rather than building their own high-costed data centers. Due to the diversity of cloud service providers, identifying appropriate cloud services that can meet users’ and organizations’ requirements, including trust, is a major challenge. In this paper, we propose a trust framework utilization to evaluate cloud services trust using multi-criteria decision-making and fuzzy logic techniques from different perspectives based on performance, agility, finance, security and usability criteria. The comparison of obtained results with related ones shows that ours are efficient and promising decisions for cloud users and organizations as well.
CBIR system using Capsule Networks and 3D CNN for Alzheimer's disease diagnosis

Alzheimer’s disease (AD) is an irreversible disorder of the brain related to loss of memory, commonly seen in the elderly and aging population. Implementation of revolutionary computer aided diagnosis techniques with Content Based Image Retrieval (CBIR) has created new potentials in Magnetic resonance imaging (MRI) in relevant image retrieval and training for detection of progression of AD in early stages. This paper proposed a CBIR system using 3D Capsule Network, 3D-Convolutional Neural Network and pre-trained 3D-autoencoder technology for early detection of Alzheimer's. A 3D-Capsule Networks (CapsNets) is capable of fast learning, even for small datasets and can effectively handle robust image rotations and transitions. It was observed that an ensemble method using 3D-CapsNets and a convolutional neural network (CNN) with 3D-autoencoder, increased the detection performance comparing to Deep-CNN alone. CBIR using the proposed model was found to be up to 98.42% accurate in AD classification. CapsNet is a promising new technique for image classification, and further experiments using more robust computation resources and refined CapsNet architectures may produce better outcomes.
A Clustering Approach Using Fractional Calculus-Bacterial Foraging Optimization Algorithm for k-Anonymization in Privacy Preserving Data Mining

A tremendous amount of personal data of an individual is being collected and analyzed using data mining techniques. Such collected data, however, may also contain sensitive data about an individual. Thus, when analyzing such data, individual privacy can be breached. Therefore, to preserve individual privacy, one can find numerous approaches proposed for the same in the literature. One of the solutions proposed in the literature is k-anonymity which is used along with the clustering approach. During the investigation, the authors observed that the k-anonymization based clustering approaches all the times result in the loss of information. This paper presents a fractional calculus-based bacterial foraging optimization algorithm (FC-BFO) to generate an optimal cluster. In addition to this, the authors utilize the concept of fractional calculus (FC) in the chemotaxis step of a bacterial foraging optimization (BFO) algorithm. The main objective is to improve the optimization ability of the BFO algorithm. The authors also evaluate their proposed FC-BFO algorithm, empirically, focusing on information loss and execution time as a vital metric. The experimental evaluations show that our proposed FC-BFO algorithm generates an optimal cluster with lesser information loss as compared with the existing clustering approaches.
Feature Selection

This chapter introduces a preprocessing step that is critical for a successful data science exercise: feature selection. Feature selection is known by several alternative terms such as attribute weighting, dimension reduction, and so on. There are two main styles of feature selection: filtering the key attributes before modeling (filter style) or selecting the attributes during the process of modeling (wrapper style). A few filter-based methods will be discussed such as principal component analysis, information gain, and chi-square, as well as a couple of wrapper-type methods like forward selection and backward elimination.
Arabic Poetry Meter Categorization Using Machine Learning Based on Customized Feature Extraction

Text mining applications became important in various intelligent tasks. Text documents are the most materials that record many important procedures in various worldwide organizations and different people cultures. Text poetry is an important type of people culture and education domains media. Arabic text poems classification is a few experimented fields, however, it has an important presence and special influence. Both new and ancient Arabic poetry has the same unique approach for rhythmical harmony measure, which can be used for identifying Arabic poems types. Deep learning as a machine learning method has many distinctive achievements in many areas, as well as, text classification tasks. In this paper, Arabic poetry text is categorized. A customized feature selection is proposed, which is fused with a clustering technique for enhancing models efficiency. Deep learning has experimented alongside two popular machine learning techniques; support vector machine and decision tree. The proposed feature extraction method has achieved high accuracy with all three techniques. The results are better than many related works.
Perceptual Evaluation of 360 Audiovisual Quality and Machine Learning Predictions

In an earlier study, we gathered perceptual evaluations of the audio, video, and audiovisual quality for 360 audiovisual content. This paper investigates perceived audiovisual quality prediction based on objective quality metrics and subjective scores of 360 video and spatial audio content. Thirteen objective video quality metrics and three objective audio quality metrics were evaluated for five stimuli for each coding parameter. Four regression-based machine learning models were trained and tested here, i.e., multiple linear regression, decision tree, random forest, and support vector machine. Each model was constructed using a combination of audio and video quality metrics and two cross-validation methods (k-Fold and Leave-One-Out) were investigated and produced 312 predictive models. The results indicate that the model based on the evaluation of VMAF and AMBIQUAL is better than other combinations of audio-video quality metric. In this study, support vector machine provides higher performance using k-Fold (PCC = 0.909, SROCC = 0.914, and RMSE = 0.416). These results can provide insights for the design of multimedia quality metrics and the development of predictive models for audiovisual omnidirectional media.
The Blockchain Newsvendor: Value of Freshness Transparency and Smart Contracts

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The Impact of Domain-Specific Pre-Training on Named Entity Recognition Tasks in Materials Science

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Classifying Malicious Domains using DNS Traffic Analysis

Malicious domains are one of the major threats that have jeopardized the viability of the Internet over the years. Threat actors usually abuse the Domain Name System (DNS) to lure users to be victims of malicious domains hosting drive-by-download malware, botnets, phishing websites, or spam messages. Each year, many large corporations are impacted by these threats, resulting in huge financial losses in a single attack. Thus, detecting and classifying a malicious domain in a timely manner is essential. Previously, filtering the domains against blacklists was the only way to detect malicious domains, however, this approach was unable to detect newly generated domains. Recently, Machine Learning (ML) techniques have helped to enhance the detection capability of domain vetting systems. A solid feature engineering mechanism plays a pivotal role in boosting the performance of any ML model. Therefore, we have extracted effective and practical features from DNS traffic categorizing them into three groups of lexical-based, DNS statistical-based, and third party-based features. Third party features are biographical information about a specific domain extracted from third party APIs. The benign to malicious domain ratio is also critical to simulate the real-world scheme where approximately 99% of the traffic is devoted to benign. In this paper, we generate and release a large DNS features dataset of 400,000 benign and 13,011 malicious samples processed from a million benign and 51,453 known-malicious domains from publicly available datasets. The malicious samples span between three categories of spam, phishing, and malware. Our dataset, namely CIC-Bell-DNS2021 replicates the real-world scenarios with frequent benign traffic and diverse malicious domain types. We train and validate a classification model that, unlike previous works that focus on binary detection, detects the type of the attack, i.e., spam, phishing, and malware. Classification performance of various ML algorithms on our generated dataset proves the effectiveness of our model, where we achieved the best results for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -Nearest Neighbors <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -NN) with 94.8% and 99.4% F1-Score for balanced data ratio (60/40%) and imbalanced data ratio (97/3%), respectively. Finally, we have gone through feature evaluation using information gain analysis to get the merits of each feature in each category, proving the third party features as the most influential one among the top 13 features. keywords- Malicious Domain, DNS, Feature Engineering, Lexical, Statistical, Third Party, Classification
Composite Transportation Dissimilarity in Consistent Gaussian Mixture Reduction

Gaussian Mixtures (GMs) are a powerful tool for approximating probability distributions across a variety of fields. In some applications the number of GM components rapidly grows with time, so that reduction algorithms are necessary. Given a GM with a large number of components, the problem of Gaussian Mixture Reduction (GMR) consists in finding a GM with considerably less components that is not too dissimilar from the original one. There are many issues that make non trivial this problem. First of all, many dissimilarity measures exist for GMs, although most of them lack closed forms, and their numerical computation is a demanding task, especially for distributions in high dimensions. Moreover, some basic reduction actions can be simple or complex tasks depending on which dissimilarity measure is chosen. It follows that most reduction procedures proposed in the literature are made of steps that are aimed at maintaining low dissimilarity according to different measures, thus leading to a pipeline of actions that are not mutually consistent. In this paper Composite Transportation Dissimilarities are discussed and exploited to formulate a GMR framework that preserves consistency with a unique dissimilarity measure, and provides a generalization of the celebrated Runnalls’ GMR approach.
Cloud Computing

Cloud computing (CC) has recently emerged as a new paradigm for hosting and delivering services over the Internet. Cloud computing is attractive to business owners as it eliminates the requirement for users to plan ahead for provisioning, and allows enterprises to start from the small and increase resources only when there is a rise in service demand. However, despite the fact that cloud computing offers huge opportunities to the Information Technology (IT) industry, the development of cloud computing technology is currently at its infancy, with many issues still to be addressed. In this chapter, we adopt a networked control systems (NCS) viewpoint to present a survey of cloud computing, highlighting its key concepts, architectural principles, state-of-the-art implementation, as well as research challenges. The fundamental aim of this chapter is to establish a better understanding of the design challenges of cloud computing and identify important research directions in this increasingly important area.
Genetic Algorithm

Genetic algorithm (GA) is an optimization algorithm that is often categorized as a global search heuristic technique. Being a branch of evolutionary computation, it is known to mimic the natural selection of biological processes of reproduction and to solve the ‘fittest’ solutions. In this chapter, the knapsack problem was solved using GA technique to determine the strength or capacity of bag used in convey items. The solution of the model shows that no combination of any form would give an exact weight or capacity the bag can carry except set spaces 15 and 29, where the weight of items are 34 kg and 36 kg respectively. Hence the feasible weight of item to be stored in the bag is 34 kg at a value of 16. Any weight of material above 36 kg will lead to the ripping of the bag.
Strong AI to Super-intelligence: How is AI placed vis-à-vis Intellectual Property Rights

The paper analyses how Artificial Intelligence (AI) enabled systems can be brought into the Intellectual Property (IP) ecosystem. It dwells upon the question of AI- IP interface from three perspectives, viz., (a) AI as a technology to manage IPRs, (b) IP rights as an obstacle to the transparency of AI and, (c) patents as well as copyrights as legal systems that can foster AI. The three-step test for obtaining a patent- novelty, inventive step and utility - is looked at through the lens of AI technology. Issues such as patent evergreening, best vs worst embodiment and liability for illegal acts which cannot be traced to human actors are delved into. The article concludes with the need for a uniform treatment of the AI system across the board by bringing in an amendment to TRIPS and the necessity to usher in regulators for adjudication.
Decentralized Finance

Newer and better ways of financing lubricates the wheels of an economy. In recent years, there have been phenomenal changes in technologies used in finance. One of such technologies was introduced by creating the first cryptocurrency Bitcoin. The technology behind this invention is known as blockchain. Although there are and there might be other use cases of blockchain technology, the finance era is important and currently it is the most adapted industry; currently there are a number of cryptocurrencies, tokens, smart contract applications among others. This article seeks to introduce the basics of—so called—decentralized finance (DeFi) through an exploration of blockchain technology in the field of finance. The focus is on the definition of decentralized finance, its elements, its current state and its future. Elimination of intermediary need, easier access, improved inclusiveness, and transaction speed, flexibility to write any contract that can be codable, immutability of transactions, better interoperability and censorship resistance are some of the main advantages of blockchain. Additionally, increased privacy, transparency, enhanced security, efficiency in the form of reduction in overhead costs for banks, and trust are considered as the benefits of blockchain technology in finance, hence for DeFi. Its power need due to the excessive computer power needs, setting standards in validating new blocks and in communicating between different networks are important issues. Besides, user experience, scalability, current speed of blockchain networks, regulations like AML and KYC requirements, the lack of regulation in DeFi, and interoperability are key issues as well. In summary, the current state of DeFi markets is not technologically able to provide a worldwide service at the scale the centralized finance provides. It needs improvements in its technology and sound regulations to gain wide acceptance.
An Elliptical Principal Axes-based Model for Extended Target Tracking with Marine Radar Data

Ellipses are favourable when it comes to tracking the shape of targets in a wide range of applications. With enhanced sensor technologies, the need for efficient measurement processing and accurate estimation keeps getting more pronounced. In this paper, we propose an approach called Principal Axes Kalman Filter (PAKF) for tracking an elliptical extended target whose extent parameters are estimated directly from explicit elliptical measurements (lengths of semi-axes and orientation), that have in turn been computed from a high number of (noisy) sensor measurements. The benefits of the approach, both in terms of processing and accuracy, are demonstrated by a comparison with two existing approaches: the random matrix model (RMM) and the Multiplicative Error Model-Extended Kalman Filter* (MEM-EKF*). Moreover, the approach is applied on a real-world standard on-board marine radar dataset and the outcomes are presented and discussed.