Reviewerly/exHarmony huggingface dataset:
An AI-Based Forensic Model for Online Social Networks

With the growth of social media usage, social media crimes are also creeping sprightly. Investigation of such crimes involves the thorough examination of data like user, activity, network, and content. Although investigating social media looks quite straight forward process, it is always challenging for the investigators due to the complex process involved in it. Due to the immense growth of social media content, manual processing of data for investigation is not possible. Most of the works from this area provide an automatic model or semi-automated, and much of the contributions lacks the logical reasoning and explainability of the evidence extracted. Searching techniques like entity-based search and explainable AI add value to the quick retrieval within appropriate scope and explain the results to the court of law. This paper provides a model by adding these new techniques to the basic forensic process.
Introduction to Public Cloud and Hyperscalers

Before entering the world of SAP S/4HANA systems on the public cloud, this chapter first describes the basic features of cloud computing and introduces the most important terms. This makes it easier to understand the rest of the book.
An Automated System for Facial Mask Detection and Social Distancing during COVID-19 Pandemic

Since December 2019, the world has started getting affected by a widely spreading virus which we all call the coronavirus. This virus is spread all across the globe, causing many severe health problems and deaths too. COVID-19 is spread when a healthy person comes in contact with the droplets generated when an infected person coughs or sneezes. So, the WHO has suggested some precautionary measures against the spread of this disease. These measures include wearing a mask in public, maintaining social distancing, avoiding mass gatherings. To help reduce the virus’ spread, in this paper, we are proposing a system that detects unmasked people, identifies them, checks if social distancing is followed or not, and also provides a feature of contact tracing. The proposed system consists of mainly two modules: face mask detection and social distancing. There are two more modules which include face recognition and contact tracing. We used two datasets for training our models. First one to detect masks on faces. For this purpose, we collected the image dataset from GitHub and Kaggle. And, the second dataset was for face recognition in which we took our own images for training purposes. It is hoped that our model contributes toward reducing the spread of this disease. Along with COVID-19, this model can also help reduce the spread of similar communicable disease scenarios.
Formalized Deduction of Semantics-Consistent and Quantifier-Dyadic Syllogisms

The paper solves how to reform Aristotelian syllogisms (ASs) to make it compatible with classic logic, and further formally deduct them in logic programming languages. It asserts that there exist two challenging problems in Aristotelian categorical propositions (ACPs) among ASs. One is inconsistently to regard the particular quantifier as the existential quantifier meanwhile as the partial, another one is lacking a quantifier binding the second term. To overcome the two problems, new forms of categorical propositions (called expanded categorical propositions, ECPs) are introduced without semantic confusion in interpretations of the particular quantifier, and with the remedied second quantifier. Naturally, made up of ECPs, the forms of quantifier-expanded syllogisms (QESs) are constructed. To deduct QESs, a formal system, also a Turing machine, is designed to decide and symbolically generate valid conclusions. Thus, a semantics-consistent and form-intact system of QESs, with deductive rules based on mathematically computing models has been established.
Empirical Study: What is the Best N-Gram Graphical Indexing Technique

Recent research shows that indexing techniques, such as n-gram models, are useful at a wide variety of software engineering tasks, e.g., data extraction, document indexing, etc. However, these models require some numerous parameters. Moreover, the different ways one can extract data essentially yield different models. In this paper, we focus on n-gram models and evaluate how the use different set of methods and n values impact the predicting ability of these models. Thus, we compare the use of multiple techniques and sets of different parameters (n values character gram and word gram) with the aim of identifying the most appropriate indexing technique.
Future Prospects of Blockchain Development

After experiencing the heat of blockchain in 2017–2018, blockchain development began to gradually return to a normal pace of development in 2019. One can’t help but ask, facing the future, is there a future for blockchain? This chapter is based on a historical review and mathematical theory of the period from 2008 to 2018, and looks at the future development of blockchain.
Neural Efficiency of Volleyball Players During a Saccadic Eye Movement: Task Using EEG

The saccadic eye movement provides the focus of a visual object of interest to the retina. In sports like volleyball, identifying relevant targets as quickly as possible is essential to a skillful performance. The training improves cortical regions underlying saccadic action, enabling more automated processing in athletes. We investigated changes in the latency during the saccadic eye movement and the absolute theta power on the frontal and prefrontal cortex during the execution of the saccade eye movement task in athletes and non-volleyball athletes. We hypothesized that the saccade latency and theta power would be lower in athletes as a result of training and the perceptual-cognitive enhancement in volleyball players. Thirty healthy volunteers were recruited for this study: 15 volleyball athletes (11 men and 4 women, mean age: 15.08 ± 1.06) and 15 non-athletes (5 men and 10 women, mean age: 18.00 ± 1.46). All tasks were performed simultaneously with electroencephalography signal recording. The result of the saccade latency of the saccadic eye movement presented a significant difference between the groups, a shorter time was observed among the athletes, associated with the superiority of the players in the attention level. Our results of saccade latency and the absolute theta power evidenced the specificity of the group the athletes. A decrease in absolute theta power was observed in the athletes compared to non-athletes on the electrodes of each frontal and prefrontal area during the experiment.
Binary self-dual and LCD codes from generator matrices constructed from two group ring elements by a heuristic search scheme

&lt;p style='text-indent:20px;'&gt;We present a generator matrix of the form &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ [ \sigma(v_1) \ | \ \sigma(v_2)] $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, where &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ v_1 \in RG $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; and &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ v_2\in RH $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, for finite groups &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ G $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; and &lt;inline-formula&gt;&lt;tex-math id="M5"&gt;\begin{document}$ H $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; of order &lt;inline-formula&gt;&lt;tex-math id="M6"&gt;\begin{document}$ n $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; for constructing self-dual codes and linear complementary dual codes over the finite Frobenius ring &lt;inline-formula&gt;&lt;tex-math id="M7"&gt;\begin{document}$ R $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;. In general, many of the constructions to produce self-dual codes forces the code to be an ideal in a group ring which implies that the code has a rich automorphism group. Unlike the traditional cases, codes constructed from the generator matrix presented here are not ideals in a group ring, which enables us to find self-dual and linear complementary dual codes that are not found using more traditional techniques. In addition to that, by using this construction, we improve &lt;inline-formula&gt;&lt;tex-math id="M8"&gt;\begin{document}$ 10 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; of the previously known lower bounds on the largest minimum weights of binary linear complementary dual codes for some lengths and dimensions. We also obtain &lt;inline-formula&gt;&lt;tex-math id="M9"&gt;\begin{document}$ 82 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; new binary linear complementary dual codes, &lt;inline-formula&gt;&lt;tex-math id="M10"&gt;\begin{document}$ 50 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; of which are either optimal or near optimal of lengths &lt;inline-formula&gt;&lt;tex-math id="M11"&gt;\begin{document}$ 41 \leq n \leq 61 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; which are new to the literature.&lt;/p&gt;
Plug-and-Play Adaptation for Continuously-updated QA

Language models (LMs) have shown great potential as implicit knowledge bases (KBs).And for their practical use, knowledge in LMs need to be updated periodically.However, existing tasks to assess LMs' efficacy as KBs do not adequately consider multiple large-scale updates.To this end, we first propose a novel task-Continuously-updated QA (CuQA)-in which multiple large-scale updates are made to LMs, and the performance is measured with respect to the success in adding and updating knowledge while retaining existing knowledge.We then present LMs with plug-in modules that effectively handle the updates.Experiments conducted on zsRE QA and NQ datasets show that our method outperforms existing approaches.We find that our method is 4x more effective in terms of updates/forgets ratio, compared to a fine-tuning baseline.
Monitoring the efficiency of the educational process in the conditions of distance learning

The article investigates the efficiency of the educational process in the conditions of distance learning in the Cherkasy region during the second wave of the pandemic. The results and analysis of a questionnaire survey of teachers in Uman schools, conducted in order to test the effectiveness of distance learning, are presented. The main psychological problems that arose in the conditions of distance learning mode are identified, and a number of methodological recommendations for all participants in the educational process to overcome these problems are developed.
Prosecuting Killer Robots: Allocating Criminal Responsibilities for Grave Breaches of International Humanitarian Law Committed by Lethal Autonomous Weapon Systems

The fast-growing development of highly automated and autonomous weapon systems has become one of the most controversial sources of discussion in the international sphere. One of the many concerns that surface with this technology is the existence of an accountability gapAccountability gap. This fear stems from the complexity of holding a human operator criminally responsible for a potential failure of the weapon system. Thus, the question on who is to be held criminally liable for grave breachesGrave breaches to international humanitarian lawInternational humanitarian law when these crimes are not intentional arises. This chapter explains how we will need to rethink the responsibilities, command structure, and everyday operations within our military when engaging in the use of fully autonomous weapon systems to allow our existing legal framework to assign criminal responsibility. For this purpose, this chapter analyses the different types of criminal responsibilities that converge in the process of employing lethal autonomous weaponsLethal autonomous weapons systems and determine which of them is the most appropriate for grave breachesGrave breaches of international humanitarian lawInternational humanitarian law in this case.
A New Supervised Term Weight Measure Based Machine Learning Approach for Text Classification

Text classification is a technique of predicting assigning a class label of an anonymous document or classifying the documents into known classes. The content of a text is a primary source for classifying the data in text classification. The researchers used the content of a text in different ways like most frequent terms, TFIDF scores of terms, N-grams of word and character for text classification. In this work, a term weight measure-based machine learning approach is proposed for text classification. In this approach, we propose a new term weight measure to represent the importance of a term in a document. The terms which are more frequent in the dataset are extracted to represent the documents as vectors. The term value in vector representation is determined by using term weight measure. Four term weight measures are used in this experiment to compute the weight of a term. Machine learning algorithms are trained by using these vectors to generate the model for classification. The performance of a proposed system is predicted by using this classification model. Accuracy measure is used as a performance evaluation measure. Six machine learning algorithms and two datasets namely, IMDB and Enron Spam datasets are used in this work. The proposed term weight measure-based approach efficiency is good when compared with results of popular approaches to text classification.
Image retrieval based on weighted nearest neighbor tag prediction

Abstract With the development of communication and computer technology, the application of big data technology has become increasingly widespread. Reasonable, effective, and fast retrieval methods for querying information from massive data have become an important content of current research. This article provides an image retrieval method based on the weighted nearest neighbor label prediction for the problem of automatic image annotation and keyword image retrieval. In order to improve the performance of the test method, scientific experimental verification was implemented. The nearest neighbor weights are determined by maximizing the training image annotation, and experiments are carried out from multiple angles based on the Mahalanobis metric learning integration model. The experimental results show that the proposed tag correlation prediction propagation model has obvious improvements in accuracy, recall rate, break-even point, and overall average accuracy performance compared with other widely used algorithm models.
THE LINK BETWEEN ENTREPRENEURSHIP AND ECONOMIC GROWTH: CASE OF ECONOMIC AND MONETARY COMMUNITY OF CENTRAL AFRICA COUNTRIES

The purpose of this article is to analyze the link between entrepreneurship and economic growth in Economic and Monetary Community of Central Africa countries (Congo, Cameroon, Equatorial Guinea and Central African Republic) from 2008 to 2017. This analysis is done in the non-oil sector. Using the generalized method of moments in system on panel data, the econometric results show that entrepreneurship has a positive and non-significant impact on non-oil economic growth in Economic and Monetary Community of Central Africa countries. These results reflect the development of survival entrepreneurship, taking into account the nature of the businesses created. On the other hand, the control variables such as the non-oil private investment rate, the cost of procedures and the time required to start a business significantly stimulate non-oil economic growth at the 1% threshold. Other control variables such as the public investment rate and the employment-population ratio slow down non-oil economic growth significantly, respectively, at the 1% and 10% thresholds. Finally, the econometric results show that final household consumption and public spending on education have an insignificant impact on non-oil economic growth. This suggests a series of policy recommendations regarding the promotion of entrepreneurship.
Conclusion

This book set out to introduce the approaches needing to understand how applying reinforcement learning might be achieved in practical real-world settings. To achieve this, we introduced the approach with definitions on what defines real-world tasks and a simple example to demonstrate the differences between reinforcement learning and mathematics, statistics and machine learning in The Commuter Task.
Time Series Analysis on Covid 19 Summarized Twitter Data Using Modified TextRank

To interpret people’s sentiments using Covid 19 twitter data, we discovered that people’s sentiments are distributed across multiple dimensions. Six significant themes were chosen, including administration, disease, healthcare, location, precaution and citizens, because these topics receive a lot of attention on social media. In this paper, we used the modified text rank extractive summarization approach on Covid 19 twitter data to reduce data volume without sacrificing the quality. The keywords are chosen from the pre-processed data set where the frequency of the words exceeds a pre-determined value decided through several trial runs. The goal is to extract the most number of word sets possible from the original tweets. These keywords have been grouped into the six categories listed above. To understand the trend of the topics, all the keywords belonging to a specific topic is searched in the summarised file of a given day, the count of that topic is increased on every successful match of the search. The graphs for the counts of all the themes for each day were then plotted. To identify patterns, seven-day moving average graphs are drawn for each topic.
Using Kernels to Harness the Complexity of Big Data Applications

This paper presents the concept of kernels to address the complexity of solving big-data applications. Their solution strategies often require evaluating domain-dependent subspaces on the big data and selecting the best result. As the data space in these problems is so vast that it is infeasible to scan the data once, we need domain-specific methods for identifying promising and manageable subspaces with a high density of solutions before looking for individual ones. To this end, we introduce kernels to represent some properties of the statistical quality, average density, or probability of solutions in a subspace to prune subspaces with suboptimal kernels. We classify various past approaches based on their analysis methods and illustrate each by an example. Our results confirm that kernels can effectively harness the complexity of solving big-data applications.
SAP S/4HANA Systems on Public Cloud

SAP S/4HANA systems are based on a consistent evolution of SAP systems from R/3 versions to HANA-based SAP products. They support all important business processes of a company and thus represent the backbone of business operations.
Review of Existing Sensors for Tracking the Activities of Daily Living

Today, various sensor technologies have been introduced to help people keep track of their daily living activities. For example, a wide range of sensors were integrated in applications to develop a smart home, a mobile emergency response system and a fall detection system. Sensor technologies were also employed in clinical settings for monitoring an early sign or onset of Alzheimer’s diseases, dementia, abnormal sleep disorder, and heart rate problems. However, there has been a lack of attention paid to comprehensive reviews, valuable especially for young, early-career scholars who just developed research interests in this area. This paper reviewed the existing sensor technologies by considering various contexts such as sensor features, data of interests, locations of sensors, and the number of sensors. For instance, sensor technologies provided various features that enabled people to monitor biomechanics of human movement (e.g., walking speed), use of household goods (e.g., switch on/off of home appliances), sounds (e.g., sounds in a particular room), and surrounding environments (e.g., temperature and humidity). Sensor technologies were widely used to examine various data, such as biomarkers for health, dietary habits, leisure activities, and hygiene status. Sensors were installed in various locations to cover wide-open area (e.g., ceilings, wall, and hallway), specific area (e.g., a bedroom and a dining room), and specific objects (e.g., mattresses and windows). Different sets of sensors were employed to keep track of activities of daily living, which ranged from a single sensor to multiple sensors to cover throughout the home. This comprehensive reviews for sensor technology implementations are anticipated to help many researchers and professionals to design, develop, and use sensor technology applications adequately in the target user’s contexts by promoting safety, usability, and accessibility.
SAP S/4HANA on Google Cloud – Deployment and Setup

This chapter talks about deployment and setup on the Google Cloud Platform. Topics covered include planning and deployment, the Google Cloud account setup, and the compute setup including the deployment of NetWeaver application servers. We also cover pricing and billing and some best practices around managing your costs.
Identify Target Area of Panel for Spraying Using Convolutional Neural Network

Implementation of Artificial Intelligence in the paint spray process in the automobile industry can increase the efficiency of the paint spray process and reduce waste disposal. This project presents a semantic segmentation with a trained Convolutional Neural Network (CNN) implemented in the paint spray process as it can be used to predict and identify the target area that needs to be sprayed. A dataset contains different types of cars annotated with the car parts. A series CNN trained to classify the types of cars. There are 16 different semantic CNNs with different architecture and data stores trained to compare the result. The last step is to develop a system to identify the spray area. The result showed the accuracy of series CNN is 1, and the best way to train semantic CNN, which trained according to each type of car with the architecture of ResNet-50 with a data store consists of a more class. The validation result of ResNet-50_Type_01, ResNet-50_Type_02 and ResNet-50_Type_03 are 93.8162%, 90.5214% and 91.8023% which all have exceeded 85% and the Mean IoU of ResNet-50_Type_01, ResNet-50_Type_02 and ResNet-50_Type_03 are 0.8456, 0.8392 and 0.8263.
Current state of practice in the procurement of information technology solutions: content analysis of software requests for proposals

The request for proposal (RFP) process is essential to the subsequent success of software project implementations. The RFP seeks to identify an appropriate software solution to best meet the client organisation's scope and contractually establishes the project's baseline cost and schedule expectations. However, there is a lack of RFP-research related to the current state of practice in software projects. The objective of this study was to address this gap via a content analysis of 250 recent software RFPs across commonly implemented software categories. Results identified the most frequently used evaluation criteria and corresponding weights, procurement schedule durations, anticipated length of contract terms, quantity of itemised requirements documented in the owner's scope, and other pertinent information. Inferential testing found several differences among the different software categories. The findings may be helpful to owner project managers who are tasked with leading their project teams through the early stages of scope development and vendor selection.