Reviewerly/exHarmony huggingface dataset:
An Efficient Identification of Security Threats in Requirement Engineering Methodology

Today, we completely rely on Information Technology (IT) applications for every aspect of daily life, including business and online transactions. In addition to using these IT-enabled applications for business purposes, we also use WhatsApp, Facebook, and a variety of other IT applications to communicate with others. However, there will undoubtedly be a drawback to every benefit. Since everything is linked to the Internet, there are many opportunities for security to be compromised. To address this, we are working to identify security threats early on in the software development process, specifically during the requirements phase. During the requirement engineering process, an engineer can recognize the security specifications in a more structured manner to create threat-free software. In our research work, we suggest the Identification of Security Threats during Requirement Engineering (ISTDRE) technique for detecting security risks throughout the requirement engineering process. The four points that make up this ISTDRE technique are Hack Point (HP), Speculation Point (SP), Trust Point (TP), and Reliable Point (RP). The new ISTDRE methodology will be validated using a case study of an ERP system involving two currently used methodologies: Model Oriented Security Requirements Engineering (MOSRE) and System Quality Requirements Engineering (SQUARE).
COIN: A Container Workload Prediction Model Focusing on Common and Individual Changes in Workloads

Recently, containers have become the primary deployment form for cloud applications. Predicting container workload accurately is critical to ensure the quality of service (QoS) and cost-efficiency of the applications and meet service level agreements (SLAs) with users. However, facing multiple challenges, including model unavailability due to insufficient data, model maladaptation due to dynamic workload changes, and model non-generalization due to changeable workload patterns in container workload prediction, existing methods have not yet provided a united and effective solution. To this end, we propose a novel integrated forecasting model named COIN that combines COmmon and INdividual changes in container workloads to ensure the availability, adaptivity, and generality of the prediction model based on transfer learning and online learning. Besides, we present a container similarity calculation algorithm for real cloud scenarios, which combines the static and dynamic information of containers and comprehensively depicts the similarity between containers. Through experiments based on two public datasets, the COIN model achieves a higher accuracy than existing state-of-the-art solutions, demonstrating the effectiveness and robustness of our proposed model, which provides a new solution to container workload prediction.
Towards a fully disaggregated and programmable data center

Today, we are seeing two trends in the data center. On the one hand, applications are becoming more fine-grained, driven by the recent trend of serverless computing and microservices. On the other hand, data-center hardware is becoming more heterogeneous and customized to different computing needs. Because of these trends and for better manageability, several major data centers are moving towards a disaggregated architecture, where different hardware resources like storage and accelerators are organized as independent, network-attached pools. However, data centers today are still server-centric and relies heavily on traditional CPU-based servers.
A Review of Artificial Intelligence Technologies in Mineral Identification: Classification and Visualization

Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine capable of responding in a manner similar to human intelligence. Research in this area includes robotics, language recognition, image identification, natural language processing, and expert systems. In recent years, the availability of large datasets, the development of effective algorithms, and access to powerful computers have led to unprecedented success in artificial intelligence. This powerful tool has been used in numerous scientific and engineering fields including mineral identification. This paper summarizes the methods and techniques of artificial intelligence applied to intelligent mineral identification based on research, classifying the methods and techniques as artificial neural networks, machine learning, and deep learning. On this basis, visualization analysis is conducted for mineral identification of artificial intelligence from field development paths, research hot spots, and keywords detection, respectively. In the end, based on trend analysis and keyword analysis, we propose possible future research directions for intelligent mineral identification.
Aspect Based Sentiment Analysis of Product Review Using Memory Network

Abstract. Purpose: Consumer opinion is one of the essential keys that affect the success of a product. Sentiment analysis of consumer opinion is needed to find out information about customer satisfaction for companies in the decision-making process. The traditional sentiment analysis process extracts a complete sentiment from a single sentence. However, it does not consist of only one sentiment in one sentence. The total number depends on the number of aspects that make up the sentence. Therefore, a sentiment analysis process is needed to pay attention to aspects.Methods: This research focuses on product reviews from Indonesian e-commerce on several aspects of sentiment. Uses fastText word embedding to avoid Out of Vocabulary in datasets and Gated Recurrent Units for aspect spread detection. Sentiment classification on aspects using the Memory Network method.Result: The experiment results showed that aspect-based sentiment classification predictions had an accuracy of 83% compared to 78% overall classification predictions for review texts, indicating that aspect-based sentiment analysis can improve model performance on product review classification predictions.Novelty: Most product reviews analysis use document-level classification to extract and predict sentiment reviews, aspect-based analysis can be applied to product reviews for better sentiment understanding, using Memory Network to store important information explicitly on aspects and polarity.
Incomplete COVID-19 Data: The Curation of Medical Health Data by the Virus Outbreak Data Network-Africa

Abstract The incompleteness of patient health data is a threat to the management of COVID-19 in Africa and globally. This has become particularly clear with the recent emergence of new variants of concern. The Virus Outbreak Data Network (VODAN)-Africa has studied the curation of patient health data in selected African countries and identified that health information flows often do not involve the use of health data at the point of care, which renders data production largely meaningless to those producing it. This modus operandi leads to disfranchisement over the control of health data, which is extracted to be processed elsewhere. In response to this problem, VODAN-Africa studied whether or not a design that makes local ownership and repositing of data central to the data curation process, would have a greater chance of being adopted. The design team based their work on the legal requirements of the European Union's General Data Protection Regulation (GDPR); the FAIR Guidelines on curating data as Findable, Accessible (under well-defined conditions), Interoperable and Reusable (FAIR); and national regulations applying in the context where the data is produced. The study concluded that the visiting of data curated as machine actionable and reposited in the locale where the data is produced and renders services has great potential for access to a wider variety of data. A condition of such innovation is that the innovation team is intradisciplinary, involving stakeholders and experts from all of the places where the innovation is designed, and employs a methodology of co-creation and capacity-building.
The theory of call-by-value solvability

The semantics of the untyped (call-by-name) lambda-calculus is a well developed field built around the concept of solvable terms, which are elegantly characterized in many different ways. In particular, unsolvable terms provide a consistent notion of meaningless term. The semantics of the untyped call-by-value lambda-calculus (CbV) is instead still in its infancy, because of some inherent difficulties but also because CbV solvable terms are less studied and understood than in call-by-name. On the one hand, we show that a carefully crafted presentation of CbV allows us to recover many of the properties that solvability has in call-by-name, in particular qualitative and quantitative characterizations via multi types. On the other hand, we stress that, in CbV, solvability plays a different role: identifying unsolvable terms as meaningless induces an inconsistent theory.
Implementation of future agricultural engineers’ training technology in the informational and educational environment

The article presents the implementation of future agricultural engineers’ training technology in the informational and educational environment. To train future agricultural engineers, it is advisable to form tutorials for the study of each discipline in the conditions of informational and educational environment. Such tutorials are an assistance in mastering both theoretical material and course navigation, where interactive electronic learning tools are presented to perform tasks in the informational and educational environment. Higher education applicants perform such tasks directly in the classroom with the help of gadgets or personal computers. The final grade is formed from the scores obtained in the classroom and the rating of higher education applicants while studying in the informational and educational environment. The outlined approach is able to help in the quality of learning content. The use of interactive audiovisual online tools allows to get acquainted with the theoretical, practical and experimental provisions clearly, it is important for the training of future agricultural engineers. At the end of the experiment, it can be argued that the developed technology increases the level of motivation and self-incentive to work in the informational and educational environment. The application of the presented technology provides an opportunity to combine the educational process in the classroom with learning in the informational and educational environment, forms analytical abilities and competencies in professional activity. The reliability of the obtained results was checked using the ???? Kolmogorov-Smirnov criterion. It is determined that when using this technology in the educational process, the indicators in the experimental group increased, which displays the effectiveness of training bachelors in agricultural engineering in the conditions of informational and educational environment.
Do Political Connections and Bank Funding Diversity Increase Non-Performing Loans: New Evidence from the Bayesian Approach

Our study aims to fill the gap in estimating the impacts of political connections and bank funding diversity on the risk-taking behaviors of Vietnamese commercial banks.By employing the Bayesian methodology, our paper can overcome the small sample issues to reduce the bias in estimation results.We construct a data sample that includes 38 commercial banks in Vietnam from 2003 to 2020.Our results suggest several findings in the Vietnamese banking sector.Firstly, our findings suggest that politically connected banks have 0.4% non-performing loans higher than unconnected peers.Secondly, we find a positive relationship between funding diversity and non-performing loans of commercial banks in Vietnam.Interestingly, our findings report that the commercial banks, especially the politically connected banks, reduced non-performing loans by 0.2% and 0.4% for a year before the recent two National Congress of the Communist Party of Vietnam, respectively.It could be due to the notion that the bank managers secure their job and political promotions by reducing non-performing loans before the National Congress of the Communist Party of Vietnam.Finally, our study argues that the commercial bank had a lower level of non-performing loans during the Covid19 pandemic because the government offered stimulus supports to the local economy.Our study has substantial implications for bank managers and authorities in emerging markets.
Review and perspectives in quantum computing for partial differential equations in structural mechanics

Structural mechanics is commonly modeled by (systems of) partial differential equations (PDEs). Except for very simple cases where analytical solutions exist, the use of numerical methods is required to find approximate solutions. However, for many problems of practical interest, the computational cost of classical numerical solvers running on classical, that is, silicon-based computer hardware, becomes prohibitive. Quantum computing, though still in its infancy, holds the promise of enabling a new generation of algorithms that can execute the most cost-demanding parts of PDE solvers up to exponentially faster than classical methods, at least theoretically. Also, increasing research and availability of quantum computing hardware spurs the hope of scientists and engineers to start using quantum computers for solving PDE problems much faster than classically possible. This work reviews the contributions that deal with the application of quantum algorithms to solve PDEs in structural mechanics. The aim is not only to discuss the theoretical possibility and extent of advantage for a given PDE, boundary conditions and input/output to the solver, but also to examine the hardware requirements of the methods proposed in literature.
VERICA - Verification of Combined Attacks

Physical attacks, including passive Side-Channel Analysis and active Fault Injection Analysis, are considered among the most powerful threats against physical cryptographic implementations. These attacks are well known and research provides many specialized countermeasures to protect cryptographic implementations against them. Still, only a limited number of combined countermeasures, i.e., countermeasures that protect implementations against multiple attacks simultaneously, were proposed in the past. Due to increasing complexity and reciprocal effects, design of efficient and reliable combined countermeasures requires longstanding expertise in hardware design and security. With the help of formal security specifications and adversary models, automated verification can streamline development cycles, increase quality, and facilitate development of robust cryptographic implementations.In this work, we revise and refine formal security notions for combined protection mechanisms and specifically embed them in the context of hardware implementations. Based on this, we present the first automated verification framework that can verify physical security properties of hardware circuits with respect to combined physical attacks. To this end, we conduct several case studies to demonstrate the capabilities and advantages of our framework, analyzing secure building blocks (gadgets), S-boxes build from Toffoli gates, and the ParTI scheme. For the first time, we reveal security flaws in analyzed structures due to reciprocal effects, highlighting the importance of continuously integrating security verification into modern design and development cycles.
Implementation of BERT, IndoBERT, and CNN-LSTM in Classifying Public Opinion about COVID-19 Vaccine in Indonesia

COVID-19 was classified as a pandemic in March 2020, and then in July 2021, this virus had its variance that spreads all over the world including Indonesia. The probability of the detrimental of its effect cannot be avoided, because this virus has a huge transmission risk during daily activity. To prevent suffering from COVID-19, people certainly need to be vaccinated. In responding to its vaccine, the citizen of Indonesia become expressive, so they try to express opinions, for example by uploading text on Twitter. Those expressions can be learned using deep learning frameworks which are BERT, CNN-LSTM, and IndoBERTweet to get knowledge about negative speech categories such as anxiety, panic, and emotion, or positive speech such as vaccines whether worked well. By then, these three methods accomplish in carrying out the prediction of sentiments about vaccination using dataset tweets on Twitter from January-2021 to March-2022, for instance using IndoBERT succeeds to classify sentiments as positive sentiment at around 80%, and then IndoBERTweet at 68%, in addition using CNN-LSTM reach 53% with the total of using 2020 dataset from Twitter. According to these results, a lesson learned for continued improvement for Indonesia's Government or authorities can be acquired in ending the COVID-19 pandemic.
Are we done with ImageNet?

Yes, and no. We ask whether recent progress on the ImageNet classification benchmark continues to represent meaningful generalization, or whether the community has started to overfit to the idiosyncrasies of its labeling procedure. We therefore develop a significantly more robust procedure for collecting human annotations of the ImageNet validation set. Using these new labels, we reassess the accuracy of recently proposed ImageNet classifiers, and find their gains to be substantially smaller than those reported on the original labels. Furthermore, we find the original ImageNet labels to no longer be the best predictors of this independently-collected set, indicating that their usefulness in evaluating vision models may be nearing an end. Nevertheless, we find our annotation procedure to have largely remedied the errors in the original labels, reinforcing ImageNet as a powerful benchmark for future research in visual recognition.
Bilingual AI-Driven Chatbot for Academic Advising

Conversational technologies are revolutionizing how organizations communicate with people, thereby raising quick responses and constant availability expectations.Students often have queries about the institutional and academic policies and procedures, academic progression, activities, and more in an academic environment.In reality, the student services team and the academic advisors are overwhelmed with several queries that they cannot provide instant responses to, resulting in dissatisfaction with services.Our study leverages Artificial Intelligence and Natural Language processing technologies to build a bilingual chatbot that interacts with students in the English and Arabic languages.The conversational agent is built in Python and designed for students to support advising-related queries.We use a purpose-built domain-specific corpus consisting of the common questions advisors receive from students and their responses as the chatbots knowledge base.The chatbot engine determines the user intent by processing the input and retrieves the most appropriate response that matches the intent with an accuracy of 80% in English and 75% in Arabic.We also evaluated the chatbot interface by conducting field experiments with students to test the accuracy of the chatbot with real-time input and test the application interface.
The effectiveness of corporate governance on corporate social responsibilities performance and financial reporting quality in Saudi Arabia's manufacturing sector

The aim of the study is to determine the effectiveness of corporate governance on corporate social responsibility (CSR) performance and financial reporting quality in Saudi Arabia's manufacturing sector. The data is collected through the database of Thomson Reuters from 30 manufacturing companies of Saudi Arabia over the period 2014-2020. Descriptive statistics and the generalized least square (GLS) model were applied. The dependent variable was calculated through residuals and was found as discretionary accruals (DA). The findings reveal that there was a positive influence of corporate governance on CSR performance and financial reporting quality. It was found that sample size was one of the biggest limitations because only data from 2014 to 2020 were collected and to make the study more reliable and authentic, larger data is required.
Feature Selection by Multiobjective Optimization: Application to Spam Detection System by Neural Networks and Grasshopper Optimization Algorithm

Networks are strained by spam, which also overloads email servers and blocks mailboxes with unwanted messages and files.Setting the protective level for spam filtering might become even more crucial for email users when malicious steps are taken since they must deal with an increase in the number of valid communications being marked as spam.By finding patterns in email communications, spam detection systems (SDS) have been developed to keep track of spammers and filter email activity.SDS has also enhanced the tool for detecting spam by reducing the rate of false positives and increasing the accuracy of detection.The difficulty with spam classifiers is the abundance of features.The importance of feature selection (FS) comes from its role in directing the feature selection algorithm's search for ways to improve the SDS's classification performance and accuracy.As a means of enhancing the performance of the SDS, we use a wrapper technique in this study that is based on the multi-objective grasshopper optimization algorithm (MOGOA) for feature extraction and the recently revised EGOA algorithm for multilayer perceptron (MLP) training.The suggested system's performance was verified using the SpamBase, SpamAssassin, and UK-2011 datasets.Our research showed that our novel approach outperformed a variety of established practices in the literature by as much as 97.5%, 98.3%, and 96.4% respectively.
Ladybug Beetle Optimization algorithm: application for real-world problems

In this paper, a novel optimization algorithm is proposed, called the Ladybug Beetle Optimization (LBO) algorithm, which is inspired by the behavior of ladybugs in nature when they search for a warm place in winter. The new proposed algorithm consists of three main parts: (1) determine the heat value in the position of each ladybug, (2) update the position of ladybugs, and (3) ignore the annihilated ladybug(s). The main innovations of LBO are related to both updating the position of the population, which is done in two separate ways, and ignoring the worst members, which leads to an increase in the search speed. Also, LBO algorithm is performed to optimize 78 well-known benchmark functions. The proposed algorithm has reached the optimal values of 73.3% of the benchmark functions and is the only algorithm that achieved the best solution of 20.5% of them. These results prove that LBO is substantially the best algorithm among other well-known optimization methods. In addition, two fundamentally different real-world optimization problems include the Economic-Environmental Dispatch Problem (EEDP) as an engineering problem and the Covid-19 pandemic modeling problem as an estimation and forecasting problem. The EEDP results illustrate that the proposed algorithm has obtained the best values in either the cost of production or the emission or even both, and the use of LBO for Covid-19 pandemic modeling problem leads to the least error compared to others.