Reviewerly/exHarmony huggingface dataset:
Dichotomy Graph Sketch: Summarizing Graph Streams with High Accuracy Based on Deep Learning

In many applications, data streams are indispensable to describe the relationships between nodes in networks, such as social networks, computer networks, and hyperlink networks. Fundamentally, a graph stream is a dynamic representation of a graph, which is usually composed of a sequence of edges, where each edge is represented by two endpoints and a weight. As a result of its large volume and highly dynamic nature, several graph sketches were proposed for the purposes of summarizing large-scale graph streams and enabling fast query processing. By using a compact data structure with hash functions, the graph sketches sequentially store the edges. Nevertheless, the existing graph sketches suffer from low performance on graph query tasks as a result of unpredictable collisions between heavy edges and light edges. To store heavy edges and light edges, this paper introduces a novel learning-based Dichotomy Graph Sketch (DGS) mechanism that uses two separate graph sketches, a heavy sketch and a light sketch. During a graph stream session, DGS obtains heavy edges and light edges, and uses these edges as training samples for a deep neural network (DNN) based binary classifier. The DNN-based classifier is then used to determine whether the upcoming edges are heavy or not. We will store the edges that are classified as heavy edges in the heavy sketch, and those that are classified as light edges in the light sketch. By combining the learnable classifier and Dichotomy Graph Sketches, the proposed mechanism resolves the hashing collision problem in conventional graph sketches and significantly improves graph query accuracy. The DGS algorithm outperforms the state-of-the-art graph sketches in a variety of graph query tasks based on extensive experiments that were conducted on four real-world graph stream datasets.
A Comparative Study of Online Cybersecurity Training Platforms

With the rapidly growing number of cybersecurity training solutions, new opportunities have been introduced for real-world simulated cyber threats to be accessible. It has improved the participant’s ability to detect and respond in a timely and effective manner. These platforms vary in technical details. However, all have one common objective to improve cybersecurity understanding and awareness. This paper presents and surveys the ten most popular commercial and open-source cybersecurity training platforms for online learning based on practical training. In addition, a comparative analysis and discussion of platform-specific features based on a proposed software taxonomy that aids in the classification is presented. The findings of this study can be useful to both developers and contributors of cybersecurity training platforms for further improvements or to develop new ones.
Verified High Performance Computing: The SyDPaCC Approach

The SyDPaCC framework for the Coq proof assistant is based on a transformational approach to develop verified efficient scalable parallel functional programs from specifications. These specifications are written as inefficient (potentially with a high computational complexity) sequential programs. We obtain efficient parallel programs implemented using algorithmic skeletons that are higher-order functions implemented in parallel on distributed data structures. The output programs are constructed step-by-step by applying transformation theorems. Leveraging Coq type classes, the application of transformation theorems is partly automated. The current version of the framework is presented and exemplified on the development of a parallel program for the maximum segment sum problem. This program is experimented on a parallel machine.
Combination of Convolutional Neural Network and AdaBoost for Breast Cancer Diagnosis

Breast cancer is a cancer that develops from breast tissue. Early symptoms of breast cancer include the existence of a lump in the breast, a change in breast shape, or dimpling on the skin. This research explores the potential of ensemble learning, with a focus on the AdaBoost algorithm, to enhance the performance of Convolutional Neural Networks (CNN) in image classification tasks, particularly with breast cancer image datasets. The architectures in focus were VGG-16, ResNet50, and Inception V4, three prevalent CNN models with proven efficiency in image recognition tasks. Coupling these CNN models with AdaBoost led to notable performance improvements in individual tests. The study further pushed the envelope by constructing an ensemble model that combined all three CNN models. This ensemble, with AdaBoost, demonstrated impressive performance across various datasets. With precision and recall scores exceeding 0.94, an F1-Score of 0.96, and an overall accuracy of 0.95 to 0.99. The significant performance boost can be attributed to the richer feature space generated by the ensemble of multiple CNN models and the iterative refinement of predictions provided by the AdaBoost algorithm. Despite the ensemble model's complexity and increased computational demand, the results provide a compelling justification for its use. Further research could delve into optimizing such ensemble models, exploring other ensemble strategies, or testing the models on diverse datasets and tasks beyond image classification.
Empowerment of MSME crafts recommendation system with content-based filtering method on social commerce after the pandemic

Since the government officially declared the end of the pandemic in Indonesia, face-to-face trading activities between buyers and traders have increased again, especially in West Sumatra province. Some traders, in addition to running their businesses offline, also continue to utilize social media as they did during the pandemic, especially in marketing activities. Therefore, the purpose of this research is to assist handicraft merchants in particular, in empowering social commerce, by understanding the behavior of recommendation systems (RS) which are specialized software for filtering in social commerce. The application of the right method determines the performance of an RS. In this discussion, the method used in RS for filtering is content-based. By applying this method, it can produce an RS that is right to support the marketing techniques of handicraft shops online, by adding complete, detailed, and uniform descriptions to each promotional photo or graphic model.
Investigation of the accuracy of Digital Image Correlation (DIC) in measuring full-field strain for timber materials

As one of the most successful techniques in experimental mechanics, digital image correlation (DIC) has been widely used to measure the motion and deformation of solid materials, especially in situations where non-contact measurement is required. This study describes and provides a comprehensive overview of the application of DIC using the open-source platform Ncorr on orthotropic materials. Orthotropic materials are a subset of anisotropic materials; their properties depend on the direction in which they are measured. Deformation displacement and in-plane strain in two directions are extracted from digital image correlation using a reference image recorded during the experiment. Tests are carried out on Laminated Veneer Lumber timber specimens according to the ASTM standard to determine the mechanical properties of materials under compression. DIC has proven to be a reliable, consistent, and cost-effective non-contact deformation measurement method that can assist in the extraction of mechanical properties of orthotropic materials. This study also showed that DIC analysis with a natural timber grain can be used to measure the displacement and strain fields of the material.
Distributed Machine Learning using HDFS and Apache Spark for Big Data Challenges

Hadoop and Apache Spark have become popular frameworks for distributed big data processing. This research aims to configure Hadoop and Spark for conducting training and testing on big data using distributed machine learning methods with MLlib, including linear regression and multi-linear regression. Additionally, an external library, LSTM, is used for experimentation. The experiments utilize three desktop devices to represent a series of tests on single and multi-node networks. Three datasets, namely bitcoin (3,613,767 rows), gold-price (5,585 rows), and housing-price (23,613 rows), are employed as case studies. The distributed computation tests are conducted by allocating uniform core processors on all three devices and measuring execution times, as well as RMSE and MAPE values. The results of the single-node tests using MLlib (both linear and multi-linear regression) with variations of core utilization ranging from 2 to 16 cores, show that the overall dataset performs optimally using 12 cores, with an execution time of 532.328 seconds. However, in the LSTM method, core allocation variations do not yield significant results and require longer program execution times. On the other hand, in the multinode (2) tests, optimal performance is achieved using 8 cores, with an execution time of 924.711 seconds, while in the multi-node (3) tests, the ideal configuration is 6 cores with an execution time of 881.495 seconds. In conclusion, without the involvement of HDFS, distributed MLlib programs cannot be processed, and core allocation depends on the number of nodes used and the size of the dataset.
Web-Based Health Service Management Information System Development With The Linear Sequential Model Method

The clinic has several health facilities such as outpatient, inpatient, dental clinic, laboratory, family planning/MCH, pharmacy, and pharmacy. However, at the clinic, administrative processes are still carried out manually and are not computerized properly, making it difficult for staff because the process of storing and integrating data has not been carried out effectively and efficiently. Therefore we need a systematic and automatic information system to assist the administrative and managerial processes of the clinic. The information system developed in this study is based on a web application using the Laravel 8 framework. The method used for system development is the linear sequential model commonly known as the classic life cycle or waterfall development model. The system that has been made is tested using the black box texting method combined with the UAT (User Acceptance Testing) method to find out whether the system meets functional requirements and is by the design. Based on testing using the UAT method, the average value of 6 different indicators is 91%. Therefore it can be concluded that the web-based information system for this clinic has an easy-to-understand way of working and attractive features so that it can provide convenience in the patient treatment process and clinical managerial processes.
Analysis of Nonlinear Optimization Problems Using Differential Evolution Algorithm

Differential evolution algorithm (DE) is a stochastic, population-based optimization approach for resolving nonlinear optimization issues. Differential evolution approach (DE) is a straight hunt technique parallel in nature with three decision parameters, namely, crossover rate, scaling factor, and population size. It is the progression of generation phase, mutation, and crossover followed by selection. DE has been useful to solve large number of engineering design issues together as single as well as multi-objective optimization approaches. DE implements a greedy assortment method. The superior one of the innovative outcome and the parent of aforementioned succeed the rivalry attaining best converging performance over the other evolutionary computation methods. DE algorithm is a speculative optimization strategy that can model the problem objectives while including constraints to minimize the objective function. The unique features of this approach are obtaining the accurate global minimum independent of the preliminary constraint values, hasty convergence characteristics, utilizing lesser number of control variables, flexible for discrete and integer optimization, efficient in solving the nonlinear constraint optimization like consequence functions, as well as multimodal hunt spaces. In the searching process, the population of DE moves through various regions in the search space, during which certain schemes allied with the specific variable settings are added efficiently than the other schemes. As a result, it is advantageous to flexibly establish a suitable strategy and its related variables at various phases of the advancement procedure. This chapter concentrates on a category of metaheuristic technique called the differential evolution approach to work out valid parameter optimization challenges. New techniques and algorithms to improve the standard DE algorithm to deal with various complex problems are investigated. Classification-assisted DE by incorporating classification rather than ranking or regression for pairwise comparison into DE for solving high complex functions is discussed. Various methods to boost the concert of the conventional DE algorithm like domineering the population diversity of DE through inserting new variables to calculate the assortment all through the progression practice are studied. For certain applications relating single and multi-objective optimization model based on this algorithm is discussed. The efficacy of the variation system for the parameters utilizing yardstick techniques to enhance the overall concert compared to other methods is studied here. Up to date, enhancements in this algorithm along through case studies with real-time application are also analyzed in this chapter. Thus, a thorough study of the differential evolution approach for solving the nonlinear optimization challenges is elaborated in this chapter.
Zero knowledge registration of PKI authentication for symbiotic security in FIDO IoT

In this paper, we critically discourse on the current Fast IDentity Online specification, its underlying security layer and the resulting misconceptions or even weaknesses. We juxtapose the presented view against some of the fundamental and long-standing public key infrastructure problems such as the initial enrolment of identities and their keys (e.g., X.509v3 certificates). We then observe a novel approach of zero-knowledge initial enrolment of resource-constrained Internet of Things devices and investigate how its idea of symbiotic security, i.e., a consistent and consolidated cooperative design which involves all architectural building blocks such as software, hardware, networks, and processes, can contribute to the new Fast IDentity Online Internet of Things specification in the making. Finally, we suggest by way of an outlook or future work how this already modular security-by-design mindset can be further enhanced through a dynamic management system to keep cryptographic keys in volatile memory only. The idea is also based on a symbiotic connection between hardware and software to create a number of added benefits from the security perspective.
A Natural Language Processing Model for Predicting Five-Star Ratings of Video Games on Short-Text Reviews

The gaming industry is one of the most important and innovative subfields in the field of technology, which boasts a staggering USD 200 billion in annual revenue and stands as a behemoth. It has an immense effect on popular culture, social networking, and the entertainment industry. Continuous advances in technology are the primary factor fueling the industry’s expansion, and these innovations are also revolutionizing the design of games and improving the overall gaming experience for players. The growing number of people who have access to the internet, the widespread use of smartphones, and the introduction of high-bandwidth networks such as 5G have all contributed to an increase in the demand for gaming around the world. It is essential to perform consumer feedback analysis if one wants to appreciate market requirements, evaluate game performance, and realize the effect that games have on players. On the other hand, short-text reviews frequently lack grammatical syntax, which makes it difficult for standard natural language processing (NLP) models to effectively capture underlying values and, as a result, compromises the accuracy of these models. This research focuses on determining which natural language processing model is the most accurate at forecasting five-star ratings of video games based on brief reviews. We make use of natural language processing (NLP) to avoid the constraints that are imposed on us by the linguistic structure of short-text reviews. The findings of our research have led to several important contributions, one of which is the creation of an innovative model for reviewing and grading short writings. The accuracy is improved by employing different machine learning models, which enables game creators and other industry stakeholders to identify patterns about the behavior and preferences of the users.
Teachers’ Experiences With Online Teaching During COVID-19 in Kenya and Namibia

During the COVID-19 pandemic across sub-saharan Africa, the sudden closure of schools has interrupted and brought physical learning to a standstill. The rapid transition to emergency remote teaching posed many challenges and required innovative approaches to ensure continuity of teaching through information technology tools in Kenya and Namibia. Online Collaborative Learning (OCL) theory supports an effective teaching strategy for teachers to adopt appropriate technologies and clear educational goals, develop clear instructions for influencing students’ online behavior, prepare and orientate students adequately, and select pertinent discussion topics. The study used a cross-sectional survey of secondary school teachers in both countries. The results show that teachers continued to provide education during the pandemic without proper guidance from the school leadership. Teachers had no knowledge and support in developing appropriate online content, motivating students, and delivering quality teaching. Also, the results show no evidence-based online collaborative learning amongst teachers and/or schools. Similarly, schools lacked knowledge practices and digital resources, especially those in rural settings. The study recommends the improvement of continuing professional development in the respective countries covering aspects of the school vision, leadership, practices of the teaching community, pedagogical approaches, school-level knowledge practices, and increased digital resources.
Improving the Thematic Learning Outcomes of Elementary School Through Make a Match Model With Question Box Media

With the aid of the Question Box media and the Make a Match learning methodology, this study aimed to enhance theme learning results for class IV students. 11 pupils are the research subjects for this kind of classroom action research (CAR). Two cycles—cycle I and cycle II—of the research (CAR) were completed. The steps taken in this research's process are planning, carrying it out, observing it, and reflecting on it. Gathering information through testing, observation, and interviews. Data analysis with quantitative analysis by looking at data through test results. According to the study's findings utilizing the make-a-match model, the learning outcomes in the first cycle were on average 73, or 55% of the total, and in the second cycle, they were on average 92, or 91% of the total. The study's findings show that using box media with question can enhance theme learning outcomes because the application of the model to student learning outcomes has increased by 36%. Teachers can use a variety of models as learning innovations to give students learning motivation that can effect learning.
Exploring Social Credit for Fairness in Cloud Computing Networks

Cloud-based computing networks have taken over the digital landscape. From small non-profits to large multinational corporations, more and more entities have been offloading computing effort to the cloud in order to take advantage of the increased cost-efficiency and scalability of cloud computing. One of the new types of cloud that have emerged is P2P cloud, which disengages from a traditional datacenter setup by allowing users to instead share their own computing hardware into a cloud to reap the benefits of cloud computing's advantages at an even lower cost. However, this new paradigm comes with a slew of challenges: (i) security, when operating with the devices of strangers and (ii) fairness when not operating in a market-based system. This paper aims to address these two issues by proposing an algorithm based on social credits for a P2P cloud system that uses a social network to establish its security measures. We implement our new Social Credit algorithm along with two other task-migration-based load-balancing algorithms adapted for a P2P social cloud in “Cloudsim Plus”, and the relative gains are shown in terms of fairness and other related metrics.
What hide the Hermite coherent states?

Abstract For the single mode the Hermite coherent states are wave packets analogous to the standard harmonic oscillator coherent states but constructed with complex Hermite polynomials substituting traditionally used complex monomials z n . Hermite coherent states unify the standard coherent and squeezed states within one class of quantum states whose representatives share all properties customarily required from the standard coherent states, in particular (over)completeness. Generalization of the Hermite coherent/squeezed states to the multimode case, illustrated on the bipartite states, shows that such states exhibit entanglement which presence or absence depends on the nature of squeezing but also on the choice of description method being used.
Enhance the Probability of Detection of Cooperative Spectrum Sensing in Cognitive Radio Networks Using Blockchain Technology

Cognitive radio (CR) is the best way to improve the efficiency of spectrum consumption for wireless multimedia communications. Spectrum sensing, which allows legitimate secondary users (SU) to find vacant bands in the spectrum, plays a vital role in CR networks. When cooperative sensing is used in CR networks, spectrum availability must be taken into account. In many ways, the shared cooperative spectrum sensing (CSS) data among SU. The presence of a malicious user (MU) in the system and sending false sensing data can degrade the performance of cooperative CR. The sharp rise in mobile data traffic causes congestion in the licensed band for the transmission of signals. Handling this security issue in real time, on top of spectrum sharing, is a challenge in such networks. In order to manage the spectrum and identify MU, blockchain-based CSS is developed in this article. To gauge the efficiency of the proposed topology, performance metrics like sensitivity, node selection, throughput measurement, and energy efficiency are used. This work suggests a unique, easier-to-use CSS method with MU suppression that outperforms the current one. According to simulation studies, the suggested topology can increase the likelihood of MU detection by roughly 15% when 40% of system users are malicious.
Attribution and the Defoe Canon

Abstract The question of what Daniel Defoe wrote was vexed in his own day and has continued so ever since. While it seems clear that Defoe wrote more than the approximately sixty works whose authorship he affirmed, it seems equally clear that he did not write all of the hundreds of works that have been attributed to him over the years. This chapter surveys the debates surrounding the attribution of works to Defoe (with particular attention to the critique of the Defoe canon by Furbank and Owens and subsequent responses to their work), and it weighs the equivocal promise of computer-assisted stylometric analysis for author attribution. Many of the uncertainties surrounding the Defoe canon simply reflect the circumstances of printed publication in Defoe’s day. Thinking about the Defoe canon demands, to an unusual degree, engagement with the broader universe of early eighteenth-century print, in which certainty about authorship is necessarily elusive.