Reviewerly/exHarmony huggingface dataset:
Remote Learning in Early Childhood and Elementary Schools

The world has experienced tremendous growth in the technology sector. However, this growth received a dent with the deadly COVID-19 development. The development meant a temporary shutdown of institutions in most countries forcing students to study from home. Thus, remote learning became one of the most popular ways of educating students, offering a flexible learning environment. This chapter focuses on the remote learning for young students and explores various digital tools that can support the remote learning process. In addition, a detailed discussion will be carried out on the distinction between remote learning and face-to-face learning. The discussion will also consider the advantages and disadvantages of this learning style for young students, teachers, and parents. Lastly, a comparison will be made on the remote learning practices in developed and developing countries and the challenges that students in the developing countries come across when engaged in this learning format.
A Semantic Driven Approach for Efficient Cloud Service Composition

Today, the business requirements of consumer are often very complex, requiring a composition of multiple component cloud services to create new composite value-added applications. In the process of cloud service composition, service composability forms the basis of the efficient development of emerging composite services. Developing such services is a challenging task due to the lack of tools and techniques for understanding the composability relationships among component cloud services, which influence the whole composite service’s efficiency. In this paper, we propose a composability model for cloud services that characterize the composition relationships among services. We propose a set of composability rules which compare the semantic features of cloud services to verify the interconnection of them. Our proposed model deals essentially with functional and technical cloud services aspects. These rules compare the capabilities and requirements of cloud services to determine whether two services are composable.
Using smart glasses for monitoring cyber threat intelligence feeds

The surge of COVID-19 has introduced a new threat surface as malevolent actors are trying to benefit from the pandemic. Because of this, new information sources and visualization tools about COVID-19 have been introduced into the workflow of frontline practitioners. As a result, analysts are increasingly required to shift their focus between different visual displays to monitor pandemic related data, security threats, and incidents. Augmented reality (AR) smart glasses can overlay digital data to the physical environment in a comprehensible manner. However, the real-life use situations are often complex and require fast knowledge acquisition from multiple sources. In this study we report results from an experiment with six subjects using an AR overlaid information interface coupled with traditional computer monitors. Our goal was to evaluate a multi tasking setup with traditional monitors and an AR headset where notifications from the new COVID-19 MISP instance were visualized. Our results indicate that better situational awareness does translate to increased task performance, but at the cost of a gender gap that requires further attention.
Adaptive cropping with interframe relative displacement constraint for video retargeting

Content-aware cropping makes it easy to preserve the temporal coherence and guarantee the quality of visual objects during retargeting since the retargeting window is often rigid and single. We propose an adaptive cropping method for video retargeting, which utilizes the relative displacement between frames as a constraint during determining the optimal retargeting window, helping to preserve the temporal coherence in the retargeted videos. Moreover, we design a feedback mechanism to detect the jumping frames and smooth their importance, which reduces the jitter of resized video effectively. Experiments show that the proposed method can achieve the retargeted results with the more desired quality compared with the existing relative video retargeting approaches.
A modified Histogram Equalization approach for Image Contrast Enhancement

Histogram equalization is a widely used technique for adjusting image intensities to enhance contrast. However, output images often experience a loss of fine details or contain unwanted viewing artefacts from over-enhancement. Existing modified methods tend to weaken enhancement effect due to multiple segmentation. Moreover, the implementation of these modified methods is generally hampered by newly introduced computation complexity. In this paper, a new image contrast enhancement method has been proposed. It divides the histogram into two parts and performs histogram equalization for each sub-histogram separately. The dividing procedure is applied only once to improve enhancing effect while reduce processing time. The selection of segmentation point is based upon close examination of characteristics of histogram to ensure proper pixel distribution. Experiment results show the developed technique outperforms existing modified ones. The advantage is more pronounced on handling images captured under poor illumination conditions.
Sentiment Analysis of Twitter Classification by Applying Hybrid-Based Techniques

As technology advances, the world is rapidly transmuting as a result of current technologies, and specially, the Internet has grown ingrained in everyone’s lives. The various social networking applications, namely Twitter, Facebook and Google+ are rapidly gaining huge popularity and most pertinently, Twitter has emerged to be the utmost popular platform for the individuals to coin their opinions and share their experiences with regard to various issues related to products and services. This harvesting of opinions by analysing the vast volume of unstructured information which are a result of social media sites is really a very tedious job. Sentiment analysis or opinion mining is a technique that aims to unveil the sentiments of the common people by analysing textual dissection and helps opinion formation regarding diversified areas. Sentiment analysis can assist researchers to gain insight with respect to numerous public issues specifically in marketing, business, products, services, politics, companies, governments etc. Sentiment analysis is a methodology of transforming unstructured data into structured data by classifying text attributes such as sentiment orientation and differentiate them into positive and negative sentiment category and has evolved in popularity as a subject of study in the modern era. The application of machine learning algorithms contribute significantly in the classification of sentiment analysis. This paper proposes both lexicon-based and machine learning techniques and aims to discuss about the current state-of-the-art of sentiment analysis techniques employed in the classification of tweet sentiment orientation and also addresses the challenges that can be employed to enhance the process of analysis, summarizing and classification of opinion mining.
Text Classification for Records Management

Automatic classification of electronic records is necessary to address the brewing crisis in the recordkeeping discipline, caused by escalating data volumes and digital rights legislation. Current solutions usually employ expert systems that classify records based on their metadata, but this approach is becoming unfeasible due to the increased variety of records and a growing lack of metadata. Text classification is a promising alternative now that the records themselves are machine readable. In this study, the performance of traditional text classification techniques was compared to newer natural language processing technologies in a series of experiments using authentic records data. While the latest Transformer language models showed superior classification skill, traditional methods still perform well. These results were discussed by a focus group of record managers, who believe that text classification can help them manage risk and meet compliance obligations. This is a first step toward aspirations of being able to synthesize narrative from a corpus of records.
A Hybrid Asset-Based IT Risk Management Framework

Information security has become one of the most important responsibilities of all organizations due to increasing cyber threats. Attackers take advantage of systems vulnerabilities; therefore, system administrators should be aware of potential threats to take necessary actions to protect their organizations and stakeholders. At this point, a risk assessment is needed to discover possible threats for vulnerable systems of the organization and to implement strategies for the business goals. This study proposes a hybrid risk management framework using both qualitative and quantitative methods to analyze risk within organizations and reduce them with practical countermeasures. Based on this framework, case studies have been carried out considering three hypothetical companies identifying possible information security risks, and these risks have been reduced to an acceptable level by applying the proposed risk analysis methodology.
Applying Problem Frames in Behavior-Driven Development for Smart Cone System

Initial observations of adopting behavior-driven development for an Internet of Things System in an agile process context is reported. Since an IoT system usually comprises multiple subsystems, the problem frames approach is adopted alongside to define the specifications that capture the overarching behaviors at the system level and constituent behaviors at the subsystem and component levels. Development of specifications based on the two practices is illustrated with the development case of a Smart Cone system.
An Ultra-Low Power 3-T Chaotic Map based True Random Number Generator

In this paper, discrete-time chaos generator is exploited for lightweight and energy-efficient true random number generator (TRNG) implementation. The chaos generator is realized with a two-stage tent map circuit with feedback. Each stage consists of three transistors. Its transient characteristics can be tuned over a range of bias voltage to realize a family of tent map functions. The chaotic output voltage amplitude of each stage is highly sensitive to the environmental noise. The difference signal between two such chaos generators at each stage is amplified to produce a random binary output. The output of the quantized sense amplifier is sampled at twice the bit rate of each stage of tent map circuit. The proposed design was simulated using a standard 55 nm 1.2 V CMOS process. The results show that it has a very low power consumption of 10.98 μW. It has an ultra-low energy consumption of only 5.49 pJ/bit at a throughput of 2 Mbps, which is 19x lower than the recently reported state-of-the-art and the most power efficient chaotic TRNGs. The random bit stream generated by the proposed TRNG has passed entropy, ACF and NIST randomness tests.
Blockchain-Based Infrastructure for Product Traceability in the Medical Supply Chain

This chapter introduces a blockchain-based infrastructure to support the traceability of medical products and implementation of unique device identification. Therefore, in the next pages, the characteristics of blockchain technology as well as benefits and challenges in supply chain management are described. After that, regulations for medical supply chains are gathered with a focus on American and European regulations and interlinked with the current concepts of the labling medical products. Finally, a technical blockchain-based solution is conceptualized with regard to full and light node system in medical supply chains before the chapter is concluded with an outlook and further scientific and technical research possibilities.
Real-time interactive visualization of large networks on a tiled display system

This paper introduces a methodology for visualizing large real-world (social) network data on a high-resolution tiled display system. Advances in network drawing algorithms enabled real-time visualization and interactive exploration of large real-world networks. However, visualization on a typical desktop monitor remains challenging due to the limited amount of screen space and ever increasing size of real-world datasets. To solve this problem, we propose an integrated approach that employs state-of-the-art network visualization algorithms on a tiled display system consisting of multiple screens. Key to our approach is to use the machine’s graphics processing units (GPUs) to their fullest extent, in order to ensure an interactive setting with real-time visualization. To realize this, we extended a recent GPU-based implementation of a force-directed graph layout algorithm to multiple GPUs and combined this with a distributed rendering approach in which each graphics card in the tiled display system renders precisely the part of the network to be displayed on the monitors attached to it. Our evaluation of the approach on a 12-screen 25 megapixels tiled display system with three GPUs, demonstrates interactive performance at 60 frames per second for real-world networks with tens of thousands of nodes and edges. This constitutes a performance improvement of approximately 4 times over a single GPU implementation. All the software developed to implement our tiled visualization approach, including the multi-GPU network layout, rendering, display and interaction components, are made available as open-source software.
Arabic Speech Recognition by Stationary Bionic Wavelet Transform and MFCC Using a Multi-layer Perceptron for Voice Control

In this chapter, we will detail our approach of Arabic speech recognition with mono-locutor and a reduced vocabulary, introduced in literature. This approach consists at the first step in using our proper speech database which constitutes Arabic speech words, recorded by a mono-locutor and this for a voice command. The second step consists in extracting features from those recorded words. The third step consists in classifying those extracted features. This extraction is performed by applying, at the first step, the stationary bionic wavelet transform (SBWT) to each recorded word. Then, the Mel Frequency Cepstral Coefficients (MFCCs) are calculated from the vector obtained from the concatenation of the obtained stationary bionic wavelet coefficients. The obtained MFCCs are then concatenated for constructing one input of a multi-layer perceptron (MLP), employed for the feature classification. In the phases of learning and testing the used MLP, we have used ten Arabic words, and each of them is repeated 25 times by the same voice. A simulation program employed for testing the performance of the proposed approach showed a classification rate equals to 98%.
Semantic Segmentation Using HRNet with Deform-Conv for Feature Extraction Dependent on Object Shape

In the field of Semantic Segmentation, High-Resolution Network (HRNet), which processes multiple resolutions in parallel, has dramatically improved the identification accuracy. However, there is a problem that the identification accuracy of objects with small area in the image is lower than that of other classes. We believe that this is due to the fact that the convolution with a fixed receptive field does not fully represent the features of object details. There are objects of various shapes and sizes in the actual image, and we believe that the receptive fields required should be different for each of them. Therefore, in this paper, we propose Deform-Conv HRNet, which uses Deformable Convolution with a deformable receptive field and enables feature extraction according to the shape and size of the object. Compared with the conventional method, the proposed method improves the discrimination accuracy by up to 3.9 pt.
RQ Labs: A Cybersecurity Workforce Talent Program Design

This research contributes to the knowledge of how Information Systems (IS) researchers can iteratively intervene with practitioners to co-create instructional programs for fast-paced, rapidly changing IS fields such as cybersecurity. We demonstrate how complex fields such as cybersecurity have the need for a talented workforce which continues to rapidly outpace supply from Universities. IS researchers partnering with practitioners can use this research as an exemplar of a method to design, build and evaluate these innovative co-curricular IS programs.
Preliminaries

In this chapter, the reader is provided with some background information that are used in the next chapters. More particularly, hesitant fuzzy sets and their relevant definitions are given in Sect. 1.1. Next, decision trees and their fuzzy version are quickly reviewed in Sect. 1.2. Then in Sect. 1.3, a brief description of dimensionality reduction techniques and of similarity measures are provided. After that, some well-known clustering methods are considered in Sect. 1.4. Finally, Sect. 1.5 presents three significant linear regression algorithms.
Image Segmentation from Sparse Decomposition with a Pretrained Object-Detection Network

Annotations for image segmentation are expensive and time-consuming. In contrast to image segmentation, the task of object detection is in general easier in terms of the acquisition of labeled training data and the design of training models. In this paper, we combine the idea of unsupervised learning and a pretrained object-detection network to perform image segmentation, without using expensive segmentation labels. Specially, we designed a pretext task based on the sparse decomposition of object instances in videos to obtain the segmentation mask of the objects, which benefits from the sparsity of image instances and the inter-frame structure of videos. To improve the accuracy of identifying the ’right’ object, we used a pretrained object-detection network to provide the location information of the object instances, and propose an Object Location Segmentation (OLSeg) model of three branches with bounding box prior. The model is trained from videos and is able to capture the foreground, background and segmentation mask in a single image. The performance gain benefits from the sparsity of object instances (the foreground and background in our experiments) and the provided location information (bounding box prior), which work together to produce a comprehensive and robust visual representation for the input. The experimental results demonstrate that the proposed model boosts the performance effectively on various image segmentation benchmarks.
Digital Technologies in Socio-cultural Environment: Kazan (Volga region) Federal University Experience

The article is devoted to research of issues of socio-cultural space digitalization at Kazan Federal University. The purpose of the article is to consider the socio-cultural space of the university in context of digital technologies implementation during educational process. They are concerned as an essential part of modern life, in particular, distance education of foreign students and their socialization. The solution to the tasks set was the consideration, analysis and systematization of the accumulated scientific experience on the research problem. A special role in this research was the study of the educational space of the Kazan (Volga region) Federal University.
A Blockchain-Based Security Scheme for Vehicular Ad Hoc Networks in Smart Cities

The development of Vehicular Ad Hoc Networks (VANET) has brought many advantages to facilitate the deployment of the Intelligent Transportation System (ITS). However, without proper protection, VANETs can be vulnerable to severe cyber-attacks. This paper explores the threats to the VANETs and proposes a security scheme for VANETs with a Blockchain (VNB). Furthermore, the proposed VNB with Ethereum was developed. With a graphical user interface, experiments were conducted. For ad hoc communications, a vehicle can randomly select another vehicle, and VNB will authenticate the selected vehicle with the Blockchain and Trusted Authority (TA). Preliminary test results successfully proved that Blockchain can be the key technology to mitigate the security threats to VANETs.
Predicting Throughput of Distributed Stochastic Gradient Descent

Training jobs of deep neural networks (DNNs) can be accelerated through distributed variants of stochastic gradient descent (SGD), where multiple nodes process training examples and exchange updates. The total throughput of the nodes depends not only on their computing power, but also on their networking speeds and coordination mechanism (synchronous or asynchronous, centralized or decentralized), since communication bottlenecks and stragglers can result in sublinear scaling when additional nodes are provisioned. In this paper, we propose two classes of performance models to predict throughput of distributed SGD: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fine-grained models</i> , representing many elementary computation/communication operations and their dependencies; and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coarse-grained models</i> , where SGD steps at each node are represented as a sequence of high-level phases without parallelism between computation and communication. Using a PyTorch implementation, real-world DNN models and different cloud environments, our experimental evaluation illustrates that, while fine-grained models are more accurate and can be easily adapted to new variants of distributed SGD, coarse-grained models can provide similarly accurate predictions when augmented with ad hoc heuristics, and their parameters can be estimated with profiling information that is easier to collect.
A Multi-factor Approach for Flaky Test Detection and Automated Root Cause Analysis

Developers often spend time to determine whether test case failures are real failures or flaky. The flaky tests, also known as non-deterministic tests, switch their outcomes without any modification in the codebase, hence reducing the confidence of developers during maintenance as well as in the quality of a product. Re-running test cases to reveal flakiness is resource-consuming, unreliable and does not reveal the root causes of test flakiness. Our paper evaluates a multi-factor approach to identify flaky test executions implemented in a tool named MDF laker. The four factors are: trace-back coverage, flaky frequency, number of test smells, and test size. Based on the extracted factors, MDFlaker uses k-Nearest Neighbor (KNN) to determine whether failed test executions are flaky. We investigate MDFlaker in a case study with 2166 test executions from different open-source repositories. We evaluate the effectiveness of our flaky detection tool. We illustrate how the multi-factor approach can be used to reveal root causes for flakiness, and we conduct a qualitative comparison between MDF laker and other tools proposed in literature. Our results show that the combination of different factors can be used to identify flaky tests. Each factor has its own trade-off, e.g., trace-back leads to many true positives, while flaky frequency yields more true negatives. Therefore, specific combinations of factors enable classification for testers with limited information (e.g., not enough test history information).
Multi-agent reinforcement learning pipeline for anatomical landmark detection in minipigs

In recent years, the use of large animals in neurological research has escalated due to advantages over small animals. Unfortunately, large animal imaging researchers lack functional automated medical imaging tools, requiring laborious manual processing. As a response, we have implemented a Reinforcement Learning pipeline for brain anatomical landmark detection in minipig MRIs. Leveraging a deep convolutional network, two-step detection process, and multiple Deep-Q multi-agent networks, our approach is suitable for accurate landmark detection in large animals. Using a heterogeneous dataset containing 154 minipig images, we achieved an average accuracy of 1.56mm on predicting 19 landmarks.