Reviewerly/exHarmony huggingface dataset:
Neural networks

Neural networks have many applications, including pattern recognition, sequence recognition, medical diagnosis, and so on. Over five sections, this chapter helps readers understand and improve their practice skills in working with neural networks. First, we discuss the structure and principle of neural networks, a Kaggle platform for competition and development of network models, and an example of building and training a fully connected neural network (FCNN) for predicting the price of a house. Then, we use TensorBoard to obtain the visualization needed for machine learning experimentation. Lastly, we examine the problem of overfitting and the methods to prevent this problem during training neural network models. We also include code examples.
Learning Inter-Lingual Document Representations via Concept Compression

In this work, we proposed a novel approach to derive inter-lingual document representations. The introduced methods aim to enhance the quality of content-based Multilingual Document Recommendation and information retrieval Systems. The main idea centers around creating inter-lingual representations by using mappings to align monolingual representation spaces. According to the experimental results carried out on JRC-Acquis and EU bookshop multilingual corpora, the proposed concept compression approach has outperformed the traditional cross-lingual retrieval and recommendations methods.
Data-Centric Benchmarking

In data management, both system designers and users casually resort to performance evaluation. Performance evaluation by experimentation on a real system is generally referred to as benchmarking. The aim of this chapter is to present an overview of the major past and present state-of-the-art data-centric benchmarks. This review includes the TPC standard benchmarks, but also alternative or more specialized benchmarks. Surveyed benchmarks are categorized into three families: transaction benchmarks aimed at on-line transaction processing (OLTP), decision-support benchmarks aimed at on-line analysis processing (OLAP), and big data benchmarks. Issues, tradeoffs, and future trends in data-centric benchmarking are also discussed.
Digital Forensics in the Context of the Internet of Things

The pervasive nature of IoT, envisioned with the characteristics of diversity, heterogeneity, and complexity, is diluting the boundaries between the physical and digital worlds. IoT being widely distributed qualifies it as the breeding ground for cyber-attacks. Although remarkable work is being done to ensure security in IoT infrastructure, security vulnerabilities persist. The IoT infrastructure can either be used as a direct target in a cyber-attack or exploited as a tool to carry a cyber-attack. In either case, the security measures in IoT infrastructure is compromised. The enormous IoT data is sensitive that can act as a gold mine to both the criminals for illicit exploitation or investigators to act as digital witness. IoT forensics help the investigators to acquire intelligence from this smart infrastructure to reconstruct the historical events occurred. However, due to sophisticated IoT architecture, the digital investigators face myriad challenges in IoT-related investigations using existing investigation methodologies and, hence, demand a separate dedicated forensic framework.
Smartphone Security and Forensic Analysis

Modern day smartphones are capable of performing every single task that a desktop computer can do. Smartphones being a new technology with less than a decade in the communication industry are vulnerable to security attacks and data leakage. In the current scenario, Android devices are the majority are the prime target of hackers and criminal organisation. Thus, smartphone security is of utmost priority for software companies. In this chapter, the authors begin by discussing the need for mobile forensic analysis and discussing basic smartphone vulnerabilities. they then discuss the types of attacks and their effects on smartphones followed by the security mechanisms employed to deal with the attacks and threats. they also list and give a brief description of tools used commercially for forensic analysis of smartphones, and two experiment-based case studies are provided to allow the readers to get a better understanding of the merits of existing works and practical application of investigation process.
Automatic Item Generation

Changes to the design and development of educational tests are resulting in the unprecedented demand for a large supply of content-specific test items. One way to address this growing demand is with automatic item generation. Automatic item generation is the process of using models to create test items with the aid of computer technology. The purpose of this chapter is to describe and illustrate a method for generating test items. The method is also illustrated using an example from the medical health sciences.
A Fine-grained Privacy-Preserving Profile Matching Scheme in Mobile Social Networks

Mobile Social Networks (MSN) have made it easy for us to communicate with our friends. Friend discovery is a common feature in the MSN application, which can recommend friends to requesters by comparing the similarity of attributes between users. However, in the process of attribute matching, users' personal information may be stolen by the server or other malicious users, leading to privacy leakage. Many solutions have been proposed for this problem, but the existing solutions have not considered the different ranges of user attribute values when calculating their similarity, leading to the final matching results not accurate. The existing solutions also do not consider users' location attributes and cannot find users according to the requester's query range. To address these issues, we propose a fine-grained privacy-preserving profile matching scheme (FPPM) that supports precise queries. In our scheme, the requester can flexibly set the query range. We use order-preserving encryption to compare the cipher-text submitted by the user to precisely achieve similarity matching. To further protect users' privacy security, we design a secure dot product protocol (SDPP) that uses two servers to jointly compute the dot product of cipher-text vectors to avoid privacy leakage during the computation. Our proposed scheme supports the requester to define the querying range to obtain fine-grained query results. Based on it, we also consider multi-dimensional privacy protection such as user feature attributes and location attributes.
Introduction

Currently, digital data are explosively generated and lots of data-intensive applications are emerging, which is pushing us towards the era of big data and we have to change the data management paradigm. Recent reports from International Data Corporation (IDC, https://www.idc.com/ ) indicate that the digital data we create and copy are doubling in size every 2 years, have researched 18 ZB in 2018. IDC also predicts that the digital data in the digital world will reach 44 ZB by 2020 and will grow to 175 ZB in 2025 ( https://www.seagate.com/files/www-content/our-story/trends/files/idc-seagate-dataage-whitepaper.pdf ). We have enjoyed great advancements in our knowledge, services, and productivity brought by available big data sets. This can be reflected in several areas, such as the healthcare industry, education industry, and electronic commerce, and has deeply impacted on human society.
Benchmark Application for Scenario Analysis in the Educational Chatbots Development

There are several platforms and engines for building general-purpose chatbots. The application context must be considered when deciding which technologies to use. This work analyzed candidate scenarios for the development of educational chatbots through a comparative analysis using the scientific Benchmark methodology. Forty-three platforms were analyzed based on the details of the problem class, which pointed to the Rasa NLU framework as the most suitable. Therefore, this work offers the possibility of more assertive decision-making, exposing the desired characteristics for a chatbot in the context of online learning in Virtual Learning Environments.
Extending RDF: RDFS and SCHACL

Enterprises have made amazing advances by taking advantage of data about their business to provide predictions and understanding of their customers, markets, and products. But as the world of business becomes more interconnected and global, enterprise data is no long a monolith; it is just a part of a vast web of data. Managing data on a world-wide scale is a key capability for any business today.
Cryptocurrency versus other financial instruments: how a small market affects a large market

Over the past few years the idea of an international virtual currency has been implemented practically. Because it represents a convenient way of transferring funds, cryptocurrency has become a substitute for traditional money as a modern electronic means of payment that could potentially change the current financial system. In this context, the digital currency market can be assessed in terms of this emerging economic sector, which competes with traditional financial markets. This study analyzes the impact of cryptocurrencies on the function and position of financial markets. It covers the economic situation of the cryptocurrency market, capital market (Standard & Poor’s 500 index), commodity market (gold and oil) and currency market (US dollars or euros), which are characterized by descriptive statistics. Our business linkage studies are based on correlation using weekly data for the period January 2017–March 2021. Despite the fact that cryptocurrencies have aroused great interest, this is still an additional small market that does not constitute much competition to traditional financial markets. Until now, stock markets were considered the most risky; cryptocurrency markets can now be considered as such: they offer considerable income but are very unstable.
DEVELOPMENT FEATURES OF EMOTIONAL INTELLEGENCE IN THE CONDITIONS OF INFORMAL EDUCATION (CROSS CULTURAL ASPECT)

The research involved 138 Ukrainian and 122 Vietnamese teenagers. The distribution of boys and girls was fairly uniform: there were 78 boys and 80 girls among, 63 boys and 59 girls among Vietnamese. The age of the respondents ranged from 11 to 15 years, the average age of the Ukrainian sample was 13.8, and the Vietnamese sample was 12.10. All children attended English language courses in small groups, were studied willingly and voluntarily. We used N. Hall`s EQ questionnaire to measure intra personal intelligence, interpersonal intelligence, understanding emotions, managing emotions and general level of emotional intelligence, which consists in all the mentioned elements. In addition, we examined the level of subjective control (J. Rotter's Locus of Control Scale test), lifestyle (The Life Style Index by R. Plutchik, H. Kellerman, and H. Conte (LSI)), professional inclinations (J. Holland`s test), self-esteem in interpersonal relationships (Pip Wilson`s Blob Tree test). Revealed that emotional intelligence in teenagers affects their position in the team, professional inclinations, to some extent - the locus of control and defense mechanisms. There is a difference between Ukrainian and Vietnamese teenagers on interpersonal emotional intelligence and emotion recognition. Therefore, methods of teaching foreign language in Vietnam should include elements of acting, pay more attention to the definition of emotions. The ability to apply educational technologies aimed not only at learning, but also at the development of emotional intelligence, depending on the ethnic and cultural characteristics of children, should be included in the training program for foreign language teachers.
A Useful Four-Valued Logic

It is argued that a sophisticated question-answering machine that has the capability of making inferences from its data base should employ a certain four-valued logic, the motivating consideration being that minor inconsistencies in its data should not be allowed to lead (as in classical logic) to irrelevant conclusions. The actual form of the four-valued logic is ‘deduced’ from an interplay of this motivating consideration with certain ideas of Dana Scott concerning ‘approximation lattices’.
Automated System for Buying products through IoT Commerce

IoT commerce is the Internet of thing commerce in which buying and selling of products take place in automize way. IoT Commerce plays a vital role in the field of Internet commerce. IoT commerce is going to capture the market in 2022, and the whole world is becoming dependent on IoT commerce. So it is necessary to implement a method where prediction in IoT commerce takes place. In our research, we have implemented a method where buying and selling of products take place in an automated way. If server issue or network problem arises in this case by applying prediction techniques automatically product is delivered to the customer on time. The final result is the successful delivery of the product to the customer. For acquiring prediction results we have used linear regression technique. Where on the basis of previous data in the absence of network automatically order is placed on website and product will be delivered.
Board Games AI

The classical area of AI application is the board game. This chapter introduces the two most prominent AI approaches used in developing board game agents—the MinMax algorithm and machine learning—and explains their usage in playing games like Tic-Tac-Toe, Checkers, Othello, Chess, Go, etc. against human opponents. The game tree is essentially a directed graph, where the nodes represent the positions in the game and the edges the moves. Even a simple board game like Tic-Tac Toe (naughts and crosses) has as many as 255,168 leaf nodes in the game tree. Traversing the complete game tree becomes an NP-hard problem. Alpha-beta pruning is used to estimate the short-cuts through the game tree. The board game strategy depends on the evaluation function, which is a heuristic indicating how good the player's current move is in winning the game. Machine learning algorithms try to evolve or learn the agent's game playing strategy based on the evaluation function.
Bi-modal Emotion Recognition via Broad Learning System

With the growing service robot market, emotion recognition has attracted more and more attention in both academia and industry. Many research works have been done on emotion recognition based on facial expression and/or voice. Very few researches exploited the use of head postures in the emotion recognition algorithms. This is in contrast to the fact that body gestures are used as one of the important indicators of emotional states during our daily interpersonal interaction. In this paper, we investigate the emotional meaning of head postures. Using broad learning system, we have built an emotion recognition model based on features extracted from two modalities: facial expression and head posture. The model is simple in structure and takes much less training time compared to deep-learning algorithms. Experimental results on a public emotional video datasets show that this bi-modal emotion recognition model improves the recognition accuracy of passive emotional states, such as ‘sad’, ’worried’, compared to the BLS model based on single modality of facial expression.
Research on Power Cable Operation Fault Alarm System Based on Big Data

Abstract With the continuous development of the national economy, in order to meet the needs of industrial and agricultural production and people’s daily use of electricity, the power system has gradually developed in the direction of large capacity and high voltage. The thesis established a high-voltage cable line fault detection and lean management system based on big data technology, and gave a basic introduction to the high-voltage cable line fault diagnosis and lean management system modules. Analysed the system function modules and judgment logic based on big data technology, and the importance of the system to the cable operation and maintenance management unit to improve the management level.
Keyword Extraction

Keywords are defined as phrases that capture the main topics discussed in a document. As they offer a brief yet precise summary of document content, they can be utilized for various applications. In an IR (information retrieval) environment, they serve as an indication of document relevance for users, as the list of keywords can quickly help to determine whether a given document is relevant to their interest. As keywords reflect a document's main topics, they can be utilized to classify documents into groups by measuring the overlap between the keywords assigned to them. Keywords are also used proactively in information retrieval (i.e., in indexing).
Reciprocal Design

Design and technology can support people as they transition into old age by finding ways to support their clinical, medical and psychosocial health and well-being. But to make a contribution designers and developers need, not only to understand how ageing and dementia impact on populations at a societal level, but also the impact on communities, stakeholders and individuals. Co-creation and participatory approaches are increasingly being used to engage directly with potential users and gain their input into design and development processes. However, how do we engage with those whose needs may be greatest, but are unable to articulate their wants and needs through the usual channels such as talking, writing or signing? Furthermore, what are the ethical imperatives for designers and developers when working with participants who may not be able to, for example anticipate or imagine the end product or outcomes of the design process they are engaged in? This chapter focuses on a reciprocal approach to design and development. It aims to ensure the participatory design process, that enables designers and developers to benefit by gaining knowledge that will inform their designs, from the lived experience of participants, is reciprocated. This means ensuring that the participants also benefit from the process and experience. The chapter shows the importance of designers and developers reaching beyond only understanding participant's needs, towards developing empathy and recognising their context.
A comparative study of gradient-based and meta-heuristic optimization methods using Griewank benchmark function/ Um estudo comparativo de métodos de otimização baseados em gradientes e meta-heurísticos usando a função de benchmark do Griewank

Optimization methods are frequently applied to solve real-world problems such, engineering design, computer science, and computational chemistry. This paper aims to compare gradient-based algorithms and the meta-heuristic particle swarm optimization to minimize the multidimensional benchmark Griewank function, a multimodal function with widespread local minima. Several approaches of gradient-based methods such as steepest descent, conjugate gradient with Fletcher-Reeves and Polak-Ribiere formulations, and quasi-Newton Davidon-Fletcher-Powell approach were compared. The results presented showed that the meta-heuristic method is recommended for function with this behavior because is no needed prior information of the search space. The performance comparison includes computation time and convergence of global and local optimum.
Emerging Cloud Computing Services

Cloud Computing has offered many services to organizations and individuals. The emerging types of services such as analytics, mobile services and emerging software as a service have been offered but there is a lack of analysis on the current status. Core technologies for emerging Cloud services have been identified and presented. This brief opinion paper provides an overview of the current emerging Cloud services and explains the benefits for several disciplines. Four areas have been identified that may bring in more positive impacts for the future direction.
Improved Bayesian regularisation using neural networks based on feature selection for software defect prediction

Demand for software-based applications has grown drastically in various real-time applications. However, software testing schemes have been developed which include manual and automatic testing. Manual testing requires human effort and chances of error may still affect the quality of software. To overcome this issue, automatic software testing techniques based on machine learning techniques have been developed. In this work, we focus on the machine learning scheme for early prediction of software defects using Levenberg-Marquardt algorithm (LM), Back Propagation (BP) and Bayesian Regularisation (BR) techniques. Bayesian regularisation achieves better performance in terms of bug prediction. However, this performance can be enhanced further. Hence, we developed a novel approach for attribute selection-based feature selection technique to improve the performance of BR classification. An extensive study is carried out with the PROMISE repository where we considered KC1 and JM1 datasets. Experimental study shows that the proposed approach achieves better performance in predicting the defects in software.
Sentiment Analysis On Twitter K.

With the rise of the social networking epoch and its growth, the Internet has become a promising platform for online learning, exchanging ideas and sharing opinions. Social media contains a huge amount of the sentiment data in the form of tweets, blogs, and updates on the status, posts, etc. In this paper, the most popular microblogging platform twitter is used. Twitter sentiment analysis is an application of sentiment analysis on data from Twitter, to extract user’s opinions and sentiments. The main goal is to explore how text analysis techniques can be used to dig into some of the data in a series of posts focusing on different trends of tweets languages, tweets volumes on twitter. Experimental evaluations show that the proposed machine learning classifiers are efficient and perform better in terms of accuracy and classify the tweets into positive, strongly positive, weakly positive, negative, strongly negative, weakly negative and neutral.
Intelligent modeling of lateral aircraft dynamic based on flight test data

System identification technology is an important means of aircraft modeling. In order to make up for the shortcomings of traditional system identification technology, which are complicated in calculation and limited in applicability, this paper studies the aircraft intelligent system identification technology based on black and gray box modeling. The black box model which is based on the recurrent neural network establish the dynamic relationship between the input and output variables of the aircraft; while gray box model treats the motion equation and aerodynamic coefficients of the aircraft separately, assuming that the motion equation is known, and uses an extreme learning machine to establish the forward neural network model of the aerodynamic coefficients. Thus two intelligent models of the aircraft are established, the grey box model is mainly used for model prediction for a long period, and can be utilized for flight simulation, the black box model is mainly used for online model prediction, and can be applied for model predictive control.
Research on PPO algorithm in solving AUV path planning problems

In the face of a complex three-dimensional environment, the computational complexity of the traditional path planning algorithms is extremely increased. But the performance of path planning results is relatively poor. Deep reinforcement learning (DRL) algorithms don’t rely on accurate environmental models, and its overall efficiency is much higher than traditional algorithms. Aiming at the problem of AUV path planning in a three-dimensional environment, proximal policy optimization algorithm (PPO) is selected. The PPO algorithm with improved network structure is used to train the model based on the established AUV model and gym environment. Through simulation experiments, the accuracy and effectiveness of the algorithm is verified.