Reviewerly/exHarmony huggingface dataset:
Data Visualization of Football Using Degree of Centrality

Previous research indicated that passing networks can increase the performances of players in a football team. This can be achieved with the aid of data visualization and analysis using post-match data. This paper provides a taxonomy of sports data in football visualization and summarizes the data from three aspects of data types, main tasks, visualization techniques, and visual analysis with the use of Tableau software. The objective of this paper is to identify the playing pattern for Liverpool FC during Jurgen Klopp’s era. To identify the playing pattern, this paper will display the diagram of the passing networks from the goals created in the match. Besides, networks and graph theory using Social Network Visualizer is to investigate social structures from the passes data that created goals from an open play. It describes networked systems in terms of nodes and the links between them. The playing pattern may thus be determined by examining the degree of centrality, degree of prestige, and betweenness centrality from nodes and linkages. This paper introduces a visual analysis of competitive football, using the social network from passes to construct degree centrality, and finally discusses the playing pattern for Liverpool FC. For this paper, collecting and flexibly presenting large and complex data is the main concern to increase the understanding of the analysis. In summary, it was feasible to draw the conclusion that network metrics can give sport analysts knowledge that is complimentary to traditional notational analysis by offering a novel visualisation and comprehension of team members' behaviour as well as by characterising particular play patterns.
Hierarchical Motion Planning and Tracking for Autonomous Vehicles Using Global Heuristic Based Potential Field and Reinforcement Learning Based Predictive Control

The autonomous vehicle is widely applied in various ground operations, in which motion planning and tracking control are becoming the key technologies to achieve autonomous driving. In order to further improve the performance of motion planning and tracking control, an efficient hierarchical framework containing motion planning and tracking control for the autonomous vehicles is constructed in this paper. Firstly, the problems of planning and control are modeled and formulated for the autonomous vehicle. Then, the logical structure of the hierarchical framework is described in detail, which contains several algorithmic improvements and logical associations. The global heuristic planning based artificial potential field method is developed to generate the real-time optimal motion sequence, and the prioritized Q-learning based forward predictive control method is proposed to further optimize the effectiveness of tracking control. The hierarchical framework is evaluated and validated by the numerical simulation, virtual driving environment simulation and real-world scenario. The results show that both the motion planning layer and the tracking control layer of the hierarchical framework perform better than other previous methods. Finally, the adaptability of the proposed framework is verified by applying another driving scenario. Furthermore, the hierarchical framework also has the ability for the real-time application.
Device Features Based on Linear Transformation With Parallel Training Data for Replay Speech Detection

Replay speech poses a growing threat to speaker verification systems, thus the detection of replay speech becomes increasingly important. A critical factor differentiating replay speech and genuine speech is the representation of device information. Replay speech carries physical device information that originates from recording device, playback device, and environmental noise. In this work, a device-related linear transformation strategy is proposed to disentangle non-device information from replay speech. First, we conduct factor analysis by introducing a common vector for both replay utterance and the corresponding genuine speech utterance on parallel training data; then, we derive an expectation maximization formula to obtain the parameters of the device-related linear transformation; subsequently, three device feature extraction methods are developed based on the device-related linear transformation. The developed device features are evaluated on ASVspoof 2017 version 2.0 and ASVspoof 2021 physical access corpora. The experimental results demonstrate that our proposed linear transformation strategy is effective for replay spoofing detection, and the resultant device features outperform many typical features. Moreover, our spoofing detection systems display superior performance over several competitive state-of-the-art systems.
Simulator for Time-Triggered Organic Computing

Nowadays, the complexity of embedded computing systems is increasing due to applications with increasing functional and nonfunctional requirements, e.g., performance, reliability, and energy efficiency. Organic computing has emerged as a research area for handling complex distributed embedded computer systems and is inspired by the organization of biological entities. It provides improved adaptability, robustness and has reduced development efforts. However, organic computing has drawbacks in terms of determinism, composability, and dependability, which are key features for safety-critical applications. Distributed computing systems using time-triggered communication networks have these characteristics and therefore offer distinct advantages for safety-critical systems. By combining organic computing, including artificial DNA and hormone features, with time-triggered communications, we can make these systems more safe, reliable, and suitable for safety-critical applications. Therefore, in this paper, we present a time-triggered organic computing architecture in combination with a simulation tool, in which tasks of an application are executed according to a predefined schedule. Our paper introduces multiple use cases to demonstrate the concepts and the system model. The obtained results from simulation show improved temporal predictability and fault containment of the system.
A game theoretic decision-making approach for fast gradient sign attacks

In generating adversarial examples, a trade-off problem is always present, as the attacker aims to deceive the classifier in the most subtle way possible in order not to be uncovered. Trade-offs among different objectives can be tackled in many ways, among the most popular ones is to consider several objectives and further select among optimal ones based on some criteria. In this paper, a game theoretic approach to setting the magnitude of adversarial examples for a fast gradient sign model for image classification is proposed. The model controls the size of the pixel change and the number of randomly chosen pixels to be modified. A solution of the game indicates optimal trade-offs between the values. Numerical examples are used to illustrate the approach.
Automatic Detection of Pure Actions within the SARL Agent-Programming Language

In computer programming, a pure function is a function that has no side-effects. Pure actions, which are assimilated to pure functions, play an important role within the SARL agent-programming language. Indeed, several constructs of the language, e.g. the guard conditions of the event handlers, cannot contain calls to unpure actions. In many existing languages, the purity of the action is declared by the developer, who could put a marker on the action in some languages, or simply implicit. The contribution of this paper is an algorithm to automatically determine if an action is pure in order to allow the SARL compiler to mark the actions as pure for the developer. The proposed algorithm is successfully implemented in the SARL compiler.
Maintenance Cost of Software Ecosystem Updates

Over the past decades, software domains have witnessed a trend towards faster software release cycles, an increase of software components, and their connectivity. Examples include products in Industry 4.0 domains such as health care, retail, or mobility, in which, technically, software components are connected to a software ecosystem. Faster release cycles and high connectivity make many software ecosystem updates an expensive, arduous, and risky maintenance task. This paper leverages software release metadata to quantify the maintenance cost of software ecosystem updates. In particular, the data is applied to model a software ecosystem graph and subsequent calculate its maintenance cost. To illustrate, the model is prototyped and evaluated in three Industry 4.0 use cases.
Situation-aware system based on knowledge graphs derived from R-Map analysis of accident situational big data

Unintentional injuries are common among children below the age of 14 years. Although the concern about the need for injury prevention is increasing among caregivers, preventable injuries are still one of the leading causes of death among children aged 0-14 years. Hence, there is a need for an effective system that can empower caregivers, parents and nursery teachers in making better and faster decisions to protect the lives of children through situational design of safe environments. In this paper, as a new situational design tool, we propose a situation-aware system for unintentional injury prevention with the integration of image processing using an RGB-D camera and spatial knowledge graphs. This paper describes the identification of dangerous situations based on the results obtained from an R-Map analysis of a large amount of data on injury situations. This paper also describes an implementation of the system and the advantages that it can offer in understanding possible dangerous situations in indoor environments like classrooms or homes and providing alternative suggestions on what kind of preventive measures can be taken to minimize such injuries. The feasibility of the proposed system was proved by applying it into nursery injury situational data.
Calibration of Compact Resist Model Through CNN Training

We notice that the compact resist model can be mapped to a simple CNN (convolutional neural network): convolutional layer corresponds to convolutions between input images and resist kernels, and a fully connected layer can model the formation of weighted sum of convolutions followed by the comparison to threshold to determine the development. Resist kernels correspond to convolution filters, so they can be obtained through CNN training, which is a key motivation. A number of challenges are identified and solutions are proposed: (1) We demonstrate a CNN structure that can be mapped to a resist model. (2) Convolution filters are large images and cannot be trained with standard methods. Adaptive learning rate and gradient clipping are applied. (3) Convolution filters may easily be overfitted if training data is not enough. We apply gradient descent for fast initialization of filters. (4) CNN is trained with printability of image pixels rather than CD values. Extraction of pixels and their sampling are addressed. The number of kernels and the number of convolutions can greatly be reduced through the proposed method: 22 standard kernel functions are reduced to only 4 optimized ones, which contributes to 44% faster lithography simulation yet accuracy is improved by 10%.
HLHUI: An improved version of local high utility itemset mining

High utility itemsets (HUIs) have been emerged to address the main problems of frequent itemset mining, namely considering the same importance for all items of the dataset and ignoring the occurrence numbers of items within transactions during the mining process. Local and peak HUIs were defined to mine the itemsets which are useful and high utility during specific periods of time. In this paper, using some adopted definitions and strategies of HMiner [19], an improved version of LHUI method [33], called HLHUI (Hminer-based Local HUI mining), is introduced that mines local HUIs using a utility-list-based approach. Performance evaluations of the proposed method show that it can efficiently find useful itemsets.
Handling security issues by using homomorphic encryption in multi-cloud environment

Taking advantage of the high performance and powerful data processing capabilities of cloud computing technology, externalizing data to the cloud platform is considered as an inevitable trend in the digital field today. However, ensuring the security and privacy of data remains a major challenge. To overcome this drawback, a multi-cloud platform is proposed to improve privacy and high availability of data. A multi-cloud platform that integrates public, private, and managed clouds with a single user interface. Cloud-hosted data is distributed among different data centers in a multi-cloud environment based on cloud reliability and data sensitivity. In terms of security, current encryption algorithms are considered to be very efficient, but it requires a lot of resources to handle this, which is expensive and time consuming. In addition, they also make the data impossible to process without first decoding. To be specific, traditional public key encryption requires data to be decrypted before it can be analyzed or manipulated. In contrast, homomorphic encryption is an encryption method that allows data to be encrypted while it is being processed and manipulated. It allows user or a third party, which can be cloud provider, to apply functions on encrypted data without revealing the data's values. In this paper, we explore existing multi-cloud-based security solutions using homomorphic encryption to identify open issues and opportunities for further enhancement.
Cognitive Cameras on the Edge for Crowd Physical Distancing Monitoring in the Covid-19 Era

With the rise of the Internet of Things (IoT) architectures and protocols, new video analytics systems and surveillance applications have been developed. In conventional systems, all the streams produced by cameras are sent to a centralized node where they can be seen by human operators whose task is to identify uncommon on abnormal situations. However, this way, much bandwidth is necessary for the system to work, and the number of necessary resources is proportional to the number of cameras and streams involved. In this paper, we propose an interesting approach to this problem: transforming any IP camera into a cognitive object. A cognitive camera (CC) can be considered a classic connected camera with onboard computational power for intelligent video processing. A CC can understand and interact with the surroundings, intelligently analyze complex scenes, and interact with the users. The IoT Edge Computing approach decreases latency in the decision-making process and consumes a tiny portion of bandwidth concerning the stream of a video, even in low resolution. CCs can help to address COVID-19. As a preventive measure, proper crowd monitoring and management systems must be installed in public places to limit sudden outbreaks and improve healthcare. The number of new infections can be significantly reduced by adopting physical distancing measures earlier. Motivated by this notion, a real-time crowd monitoring and management system for physical distance classification using CCs is proposed in this research paper. The experiment on Movidius board, an AI accelerator device, provides promising results of our proposed method in which the accuracies can achieve more than 85% from different datasets.
A Collaborative Filtering Movies Recommendation System based on Graph Neural Network

The implementation of machine learning algorithms in marketing by organisations has been more beneficial in recent years. Overall, it has become a major contributor to a company's success and development in terms of growth and income since it helps to recommend the interesting product/service to the right individuals or groups without requiring them to go through a long complex procedure to receive an interesting item from a list of millions, in the other side Graph Neural Network is used widely in the recent machine learning applications including Recommender Systems. The purpose of this research is the evaluation of a LightGCN Movies Recommendation System, and its efficiency in modelling and building relationship between movies, by providing suggesting new/unknown items to the users that will like them, those recommendations will be based on representing Movies as a node and their ratings as edges of the graph, which will help to build a continuous representation of nodes and edges, this approach required the combination of a classification model to predict the existence of the relationship between movies and their features as genres, release year, etc, this approach will enable us to predict when no neighbourhoods information is known.
Usability Assessment of a University Academic Portal using Eye Tracking Technology

The distribution style for university academic services has undergone a significant paradigm shift toward electronic services over the past ten years. Services offered by Zayed University (ZU) are not distinctive. The students might access a sizable variety of services and information online with just a few mouse clicks or finger touches. However, several obstacles still prevent students from taking full advantage of these services because of serious usability problems with the way portals were created. This paper presents a usability assessment study of e-services provided to students through the academic portal of Zayed University. It explores the viewing, searching, and navigation behavior of young Arab female students as they engage with the university academic portal and factors that affect their searching behavior using an eye tracking methodology and analysis. The study focuses on the visual and viewing behaviors of the students as they make their initial decision to use an e-service (click). To gather data on students' navigational patterns, an eye tracking experiment was created and carried out utilizing the current designs of several e-services accessible to students. It has been recorded, gathered, and analyzed how the participants navigated the list of e-services and interacted with it. The study's results suggest that, on average, students spent more time checking the e-services homepage for the appropriate link to the desired service. Surprisingly, a large percentage of students had never used the university's online services which affect the usability rate of ZU web portal. From the viewpoints and expectations of the students, a set of recommendations were made to enhance the usability aspect and design of the ZU main portal.
An Adaptive Decision-Making Approach for Better Selection of Blockchain Platform for Health Insurance Frauds Detection with Smart Contracts: Development and Performance Evaluation

Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to business requirements. Several blockchain platforms have emerged, making it challenging to select a suitable one for a specific type of business. This paper presents a classification of over one hundred blockchain platforms. We develop smart contracts for detecting healthcare insurance frauds using the top two blockchain platforms selected based on our proposed decision-making map approach which selects the top suitable platforms for healthcare insurance frauds detection application. Our classification shows that the largest percentage of platforms can be used for all types of application domains, the second biggest percentage for financial services, and a small number is to develop applications in specific domains. Our decision-making map and performance evaluations reveal that Hyperledger Fabric surpassed Neo in all metrics for detecting healthcare insurance frauds.
Approach to reduce the communication cost when partitioning a big graph

While several graph mining systems have been developed to run different graph mining algorithms on such large networks, they have difficulty processing Web-scale graphs owing to the significant communication and I/O expenses incurred when workers connect with one another and frequently read subgraphs. In this study, we provide a cost-effective, scalable, distributed method for mining real-world graphs by exploiting graph properties. Our technique drastically decreases the communication cost, the main bottleneck of distributed systems, by using alternate edge placement criteria depending on the types of vertices.
Dynamic Autoencoders Against Adversarial Attacks

Neural Networks are the target of numerous adversarial attacks. In those, the adversary perturbs a model's input with a noise that is small, but large enough to fool the model. In this article, we propose to dynamically add autoencoders from a pretrained set to a base model as a countermeasure to such attacks. This doing, we modify the underlying labels regions of the model to be protected, letting the adversary unable to craft relevant adversarial perturbations. Our experiments confirm the efficiency of our protection when the pretrained set has enough elements.
A Robust 3-D Reconstruction Approach Based on Correspondence Retrieval Using Deep Image Prior

In stereo phase-shifting profilometry (PSP), the performance of stereo matching algorithm directly determines the result of final reconstruction. Deep learning has been introduced in correspondence retrieval owing to its superiority in solving inverse problems. However, traditional training-based approach requires huge and representative datasets, making it more restrictive for real applications. Inspired by the excellent capability of generalization of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deep Image Prior</i> in untrained image restoration, our work tries to interpret the principle of correspondence retrieval in an image restoration manner, which enables the utilization of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deep Image Prior</i> -based training-free deep network. The inner relevance and invariance both pixel-wise and patch-wise across stereo image pairs are exploited with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deep Image Prior</i> instead of classified prior learned from external dataset, which means that our framework relies merely on an untrained convolutional neural network to accurately retrieve the pixel correspondences without any assumption of system geometry while ensuring the consistency of the reconstruction. It is experimentally demonstrated that the proposed method faithfully realizes correct and dense matching for diverse scenes including both single and multiple objects, competitive or even better performance is achieved against prevailing correspondence retrieval methods.