Reviewerly/exHarmony huggingface dataset:
Deep Learning for Natural Language Processing

Natural Language Processing is a field that belongs to Artificial Intelligence, which acts as an interface between humans and computers for interaction using various computational algorithms. Recent uses of NLP algorithms are spam mail detection, meeting report analysis, auto question answering, summarizing media reports, machine translation, and many more. Deep Learning is an extension of machine learning and artificial intelligence that teaches computers to learn from experiences the same as humans do. Many deep learning models are successfully deployed for various natural language processing tasks for the last few years. This paper has reviewed the applications of different deep learning architectures, including CNN, RNN, and Attention model for natural language processing tasks.
Understanding the ethical and legal considerations of Digital Pathology

Digital Pathology (DP) is a platform which has the potential to develop a truly integrated and global pathology community. The generation of DP data at scale creates novel challenges for the histopathology community in managing, processing, and governing the use of these data. The current understanding of, and confidence in, the legal and ethical aspects of DP by pathologists is unknown. We developed an electronic survey (e-survey), comprising 22 questions, with input from the Royal College of Pathologists (RCPath) Digital Pathology Working Group. The e-survey was circulated via e-mail and social media (Twitter) through the RCPath Digital Pathology Working Group network, RCPath Trainee Committee network, the Pathology image data Lake for Analytics, Knowledge and Education (PathLAKE) digital pathology consortium, National Pathology Imaging Co-operative (NPIC), local contacts, and to the membership of both The Pathological Society of Great Britain and Ireland and the British Division of the International Academy of Pathology (BDIAP). Between 14 July 2020 and 6 September 2020, we collected 198 responses representing a cross section of histopathologists, including individuals with experience of DP research. We ascertained that, in the UK, DP is being used for diagnosis, research, and teaching, and that the platform is enabling data sharing. Our survey demonstrated that there is often a lack of confidence and understanding of the key issues of consent, legislation, and ethical guidelines. Of 198 respondents, 82 (41%) did not know when the use of digital scanned slide images would fall under the relevant legislation and 93 (47%) were 'Not confident at all' in their interpretation of consent for scanned slide images in research. With increasing uptake of DP, a working knowledge of these areas is essential but histopathologists often express a lack of confidence in these topics. The need for specific training in these areas is highlighted by the findings of this study.
A Novel and Robust Hybrid Blockchain and Steganography Scheme

Data security and data hiding have been studied throughout history. Studies show that steganography and encryption methods are used together to hide data and avoid detection. Large amounts of data hidden in the cover multimedia distort the image, which can be detected in visual and histogram analysis. The proposed method will solve two major drawbacks of the current methods: the limitation imposed on the size of the data to be hidden in the cover multimedia and low resistance to steganalysis after stego-operation. In the proposed method, plaintext data are divided into fixed-sized bits whose corresponding matching bits’ indices in the cover multimedia are accumulated. Thus, the hidden data are composed of the indices in the cover multimedia, causing no change in it, thus enabling considerable amounts of plaintext to be hidden. The proposed method also has high resistance to known steganalysis methods because it does not cause any distortion to the cover multimedia. The test results show that the performance of the proposed method outperforms similar conventional stenographic techniques. The proposed Ozyavas–Takaoglu–Ajlouni (OTA) method relieves the limitation on the size of the hidden data, and hidden data is undetectable by steganalysis because it is no longer embedded in the cover multimedia.
Energy efficient contact tracing and social interaction based patient prediction system for COVID-19 pandemic

Due to the spread of Coronavirus disease 2019 , the world has encountered an ongoing pandemic to date.It is a highly contagious disease.In addition to the vaccination, social distancing and isolation of patients are proven to be one of the commonly used strategies to reduce the spread of disease.For efficient social distancing, contact tracing is a critical requirement in the incubation period of 14-days of the disease to contain any further spread.However, we identify that there is a lack of reliable and practical social interaction tracking methods and prediction methods for the probability of getting the disease.This paper focuses on user tracking and predicting the infection probability based on these social interactions.We first developed an energy-efficient BLE (Bluetooth Low Energy) based social interaction tracking system to achieve this.Then, based on the collected data, we propose an algorithm to predict the possibility of getting the COVID-19.Finally, to show the practicality of our solution, we implemented a prototype with a mobile app and a web monitoring tool for healthcare authorities.In addition to that, to analyze the proposed algorithm's behaviour, we performed a simulation of the system using a graph-based model.
Autoencoder-based Semantic Communication Systems with Relay Channels

In this paper, we propose a semantic communication scheme for wireless relay channels based on Autoencoder, named AESC, which encodes and decodes sentences from the seman-tic dimension. The Autoencoder module is trained separately and acts as the channel encoder and decoder in the semantic communication system. It provides anti-noise performance for the system. Meanwhile, a novel semantic forward (SF) mode is designed for the relay node to forward the semantic information at the semantic level, especially for the scenarios that there is no common knowledge shared between the source and destination nodes. Numerical results show that the AESC achieves better sta-bility performance than the traditional communication schemes, and the proposed SF mode provides a significant performance gain compared to the traditional forward protocols.
Dynamic Resource Allocation Method for Load Balance Scheduling Over Cloud Data Center Networks

The cloud datacenter has numerous hosts as well as application requests where resources are dynamic. The demands placed on the resource allocation are diverse. These factors could lead to load imbalances, which affect scheduling efficiency and resource utilization. A scheduling method called Dynamic Resource Allocation for Load Balancing (DRALB) is proposed. The proposed solution constitutes two steps: First, the load manager analyzes the resource requirements such as CPU, Memory, Energy and Bandwidth usage and allocates an appropriate number of VMs for each application. Second, the resource information is collected and updated where resources are sorted into four queues according to the loads of resources i.e. CPU intensive, Memory intensive, Energy intensive and Bandwidth intensive. We demonstarate that SLA-aware scheduling not only facilitates the cloud consumers by resources availability and improves throughput, response time etc. but also maximizes the cloud profits with less resource utilization and SLA (Service Level Agreement) violation penalties. This method is based on diversity of client’s applications and searching the optimal resources for the particular deployment. Experiments were carried out based on following parameters i.e. average response time; resource utilization, SLA violation rate and load balancing. The experimental results demonstrate that this method can reduce the wastage of resources and reduces the traffic upto 44.89% and 58.49% in the network.
Research on the Impact of Regional Economy on Industrial Development from the Perspective of Big Data

In order to study the driving force of China’s rapid economic development in the past ten years, the study takes Guizhou, Yunnan, and Guangxi as a case study, focusing on the big data assets exchange (GBDEx) and Beibu Gulf Economic Zone, and discusses the impact of Internet plus economic and government data and the big data system on regional economy. Finally, it is believed that the Internet and big data fully avoid the disadvantages of traffic distance between the western region and the eastern region, make full and efficient cooperation between enterprises and institutions in the central and western regions and Chinese and foreign economic entities, and strengthen the control of economic behavior from the big data level with the support of the service-oriented government. The information entropy output from the Internet big data system has led to a significant entropy reduction process in China’s economic environment and a more orderly economic system, which is also an important reason for the rapid development of China’s economy.
Integration of communication and computing in blockchain-enabled multi-access edge computing systems

Blockchain and multi-access edge computing (MEC) are two emerging promising technologies that have received extensive attention from academia and industry. As a brand-new information storage, dissemination and management mechanism, blockchain technology achieves the reliable transmission of data and value. While as a new computing paradigm, multi-access edge computing enables the high-frequency interaction and real-time transmission of data. The integration of communication and computing in blockchain-enabled multi-access edge computing networks has been studied without a systematical view. In the survey, we focus on the integration of communication and computing, explores the mutual empowerment and mutual promotion effects between the blockchain and MEC, and introduces the resource integration architecture of blockchain and multi-access edge computing. Then, the paper summarizes the applications of the resource integration architecture, resource management, data sharing, incentive mechanism, and consensus mechanism, and analyzes corresponding applications in real-world scenarios. Finally, future challenges and potentially promising research directions are discussed and present in detail.
Application of augmented reality in museums – Factors influencing the learning motivation and effectiveness

Technology advancement has led to the successful and broad application of augmented reality approach in museums. Several literature works have supported the influence of augmented reality in education. However, relatively few studies have been carried out on augmented reality in museum and relevant educational influence. Thus, this study aims to identify educational effectiveness by employing augmented reality in museum and conducting research and analysis on related theories and empirical evidence. In this context, the research is based on the theoretical and conceptual frameworks of relevant literature. Further, integrate them into a functional research prototype and framework to acknowledge the positive influence between the visitors’ acceptance, employ augmented reality in museum, and increase learning motivation. Also, the mutual positive relationship between learning motivation and learning effectiveness will be realized. The consequences of mutual effect among all the variables in this study have convinced the relationship of intermediary influence between augmented reality in museum and learning effectiveness.
Context-based sentiment analysis on customer reviews using machine learning linear models

Customer satisfaction and their positive sentiments are some of the various goals for successful companies. However, analyzing customer reviews to predict accurate sentiments have been proven to be challenging and time-consuming due to high volumes of collected data from various sources. Several researchers approach this with algorithms, methods, and models. These include machine learning and deep learning (DL) methods, unigram and skip-gram based algorithms, as well as the Artificial Neural Network (ANN) and bag-of-word (BOW) regression model. Studies and research have revealed incoherence in polarity, model overfitting and performance issues, as well as high cost in data processing. This experiment was conducted to solve these revealing issues, by building a high performance yet cost-effective model for predicting accurate sentiments from large datasets containing customer reviews. This model uses the fastText library from Facebook’s AI research (FAIR) Lab, as well as the traditional Linear Support Vector Machine (LSVM) to classify text and word embedding. Comparisons of this model were also done with the author’s a custom multi-layer Sentiment Analysis (SA) Bi-directional Long Short-Term Memory (SA-BLSTM) model. The proposed fastText model, based on results, obtains a higher accuracy of 90.71% as well as 20% in performance compared to LSVM and SA-BLSTM models.
Parallel-Inception CNN Approach for Facial sEMG based Silent Speech Recognition

With the purpose of providing an external human-machine interaction platform for the elderly in need, a novel facial surface electromyography based silent speech recognition system was developed. In this study, we propose a deep learning architecture named Parallel-Inception Convolutional Neural Network (PICNN), and employ up-to-date feature extraction method log Mel frequency spectral coefficients (MFSC). To better meet the requirements of our target users, a 100-class dataset containing daily life-related demands was designed and generated for the comparative experiments. According to experimental results, the highest recognition accuracy of 88.44% was achieved by proposed recognition framework based on MFSC and PICNN, exceeding the performance of state-of-the-art deep learning algorithms such as CNN, VGGNet and Inception CNN (3.22%, 4.09% and 1.19%, respectively). These findings suggest that the newly developed silent speech approach holds promise to provide a more reliable communication channel, and the application scenery of speech recognition technology has been expanded at the same time.
Framework for user behavioural biometric identification using a multimodal scheme with keystroke trajectory feature and recurrent neural network on a mobile platform

Diverse applications are used on mobile devices. Because of the increasing dependence on information systems, immense amounts of personal and sensitive data are stored on mobile devices. Thus, security or privacy breaches are a major challenge. To protect mobile systems and the private information on these systems from being accessed by adversaries, a framework for mobile user identification through the use of a multimodal behavioural biometrics scheme with a keystroke trajectory feature is presented herein. Conventionally, mobile devices have been protected by mechanisms such as PINs or passwords. However, these approaches have numerous disadvantages. Therefore, approaches that employ keystroke biometrics for secure and reliable mobile device identification have been proposed. Because unimodal behavioural biometrics identification mechanisms have limited accuracy and effectiveness, a multimodal scheme that includes different behavioural biometric traits, such as keystroke and swipe biometric traits, is examined. However, the information provided by the spatial and temporal features of keystroke biometrics is not comprehensive. Therefore, a trajectory model is derived to describe the behavioural biometric uniqueness of a user. In the user identification phase, a multistream recurrent neural network (RNN) is adopted. The results reveal that the proposed trajectory model performs well, and the multimodal scheme using an RNN with a late fusion method provides accurate identification results. The proposed system achieved an accuracy of 95.29%, F1 score of 94.64%, and equal error rate of 1.78%. Thus, the proposed mobile identification system is capable of resisting attacks that standard mechanisms may be vulnerable to and represents a valuable contribution to cyber security.
A Stochastic Fractal Search Based Approach to Solve Portfolio Selection Problem

In capital markets, selecting best assets from the population in the portfolio is very essential practice for the investors. For risk aversive investors, minimization of risk is one of the most important concerns in portfolio selection. Therefore, risk attribution is very common practice to risk distribution in Risk-budgeted portfolio optimization. In this paper, a novel risk-budgeted portfolio selection strategy using stochastic fractal search (SFS) has been proposed with the aim of maximizing sharp ratio. Stochastic fractal search (SFS) is an evolutionary approach inspired by the growth process of the nature. The natural growth is modeled by using fractal search. To verify the effectiveness of proposed strategy, an experimental study has been conducted by comparing performance of proposed strategy with Genetic Algorithm by using dataset of the S&P BSE Sensex of Indian stock exchange. Study reveals the superior performance of the proposed strategy.
The Use of e-Learning Platforms in a Lockdown Scenario—A Study in Latin American Countries

Recently, due to the COVID-19 outbreak, Universities have been subject to a radical change, as they were forced to rapidly and massively shift from residential to online education. In this scenario, e-learning, which has been studied for several years, has attracted an unprecedented attention and revealed many advantages and disadvantages, thus feeding a debate about its efficacy. In an attempt to contribute to such debate, a study in different Latin-American countries has been conducted, in order to understand teachers&#x2019; and students&#x2019; perceptions about the experienced online setting during the lockdown. This paper describes the results of this preliminary study and reflects on factors that need to be improved to increase the quality of online education.
Automated knowledge-assisted mitosis cells detection framework in breast histopathology images

Based on the Nottingham Histopathology Grading (NHG) system, mitosis cells detection is one of the important criteria to determine the grade of breast carcinoma. Mitosis cells detection is a challenging task due to the heterogeneous microenvironment of breast histopathology images. Recognition of complex and inconsistent objects in the medical images could be achieved by incorporating domain knowledge in the field of interest. In this study, the strategies of the histopathologist and domain knowledge approach were used to guide the development of the image processing framework for automated mitosis cells detection in breast histopathology images. The detection framework starts with color normalization and hyperchromatic nucleus segmentation. Then, a knowledge-assisted false positive reduction method is proposed to eliminate the false positive (i.e., non-mitosis cells). This stage aims to minimize the percentage of false positive and thus increase the F1-score. Next, features extraction was performed. The mitosis candidates were classified using a Support Vector Machine (SVM) classifier. For evaluation purposes, the knowledge-assisted detection framework was tested using two datasets: a custom dataset and a publicly available dataset (i.e., MITOS dataset). The proposed knowledge-assisted false positive reduction method was found promising by eliminating at least 87.1% of false positive in both the dataset producing promising results in the F1-score. Experimental results demonstrate that the knowledge-assisted detection framework can achieve promising results in F1-score (custom dataset: 89.1%; MITOS dataset: 88.9%) and outperforms the recent works.
Improved Eagle Strategy Algorithm for Dynamic Web Service Composition in the IoT: A Conceptual Approach

The Internet of Things (IoT) is now expanding and becoming more popular in most industries, which leads to vast growth in cloud computing. The architecture of IoT is integrated with cloud computing through web services. Recently, Dynamic Web Service Composition (DWSC) has been implemented to fulfill the IoT and business processes. In recent years, the number of cloud services has multiplied, resulting in cloud services providing similar services with similar functionality but varying in Quality of Services (QoS), for instance, on the response time of web services; however, existing methods are insufficient in solving large-scale repository issues. Bio-inspired algorithm methods have shown better performance in solving the large-scale service composition problems, unlike deterministic algorithms, which are restricted. Thus, an improved eagle strategy algorithm method is proposed to increase the performance that directly indicates an improvement in computation time in large-scale DWSC in a cloud-based platform and on both functional and non-functional attributes of services. By proposing the improved bio-inspired method, the computation time can be improved, especially in a large-scale repository of IoT.
Employing machine learning for capturing COVID-19 consumer sentiments from six countries: a methodological illustration

Purpose This paper aims to illustrate the scope and challenges of using computer-aided content analysis in international marketing with the aim to capture consumer sentiments about COVID-19 from multi-lingual tweets. Design/methodology/approach The study is based on some 35 million original COVID-19-related tweets. The study methodology illustrates the use of supervised machine learning and artificial neural network techniques to conduct extensive information extraction. Findings The authors identified more than two million tweets from six countries and categorized them into PESTEL (i.e. Political, Economic, Social, Technological, Environmental and Legal) dimensions. The extracted consumer sentiments and associated emotions show substantial differences across countries. Our analyses highlight opportunities and challenges inherent in using multi-lingual online sentiment analysis in international marketing. Based on these insights, several future research directions are proposed. Originality/value First, the authors contribute to methodology development in international marketing by providing a “use-case” for computer-aided text mining in a multi-lingual context. Second, the authors add to the knowledge on differences in COVID-19-related consumer sentiments in different countries. Third, the authors provide avenues for future research on the analysis of unstructured multi-media posts.