Reviewerly/exHarmony huggingface dataset:
RBJ20 Cryptography Algorithm for Securing Big Data Communication Using Wireless Networks

In computer networks communication, the amount of data is continued to grow because of use of internet, smart phone, social network, web log data, etc. The volume of data communicated in this way is very large in size as well as complex. In general, the amount of data transferred in our apps is increasing, and it is now measured in peta-bytes and exa-bytes. Big data analytics with novel encryption techniques can provide new ways for businesses and government to analyse and communicate unstructured data more efficiently. After analysing, it is necessary to store them with protection and it is necessary to authenticate these data sets. The traditional security systems are unable to provide effective protection and authentication for these types of data sets. To overcome the above problem, we have proposed an effective cryptographic method called RBJ20 algorithm with matrix of order N × N it has four layers. The first layer has one step called column operations in the matrix. In the second layer, any integer sk is chosen as secret key and the matrix elements multiplied discriminant part should not be a whole number, concatenating the digits in the discriminusing sk. The third layer has five steps, namely choosing of any three prime numbers to form quadratic equation with condition that ant part, pairing of digits with two digits from left to right to act as cells of the matrix. Now each element will be treated as cells and then the cell elements will be swapped according to pairs, and this process is done for all the pairs. There are five stages in the fourth tier.
Gamification Components of a Software Solution Using a Neural Network in the Design of Unified Information and Analytical Space of a University

The article examines the gamification components of a software solution developed using a neural network, which makes it possible to increase the efficiency of the educational process, improve the programming skills and operational thinking of future IT specialists in the applied informatics field of training. For clarity, the described gamification components are used in the developed software solution to improve the educational process. The article also presents a model of intellectual creativity of future IT specialists in the Applied Informatics field of training, which contributes to a comfortable and effective educational process. This model is being tested on the GPA YALTA portal.
A New Environment of Blockchain based Multi Encryption Data Transferring

Blockchain is one of the technologies provided by the global distribution of computing power. Simply put, the blockchain is the digital ledger in which transactions are recorded. It all started with a desire to see a new form of security system for transferring confidential files. It aims to achieve many goals like decreasing the process time for transferring files to the other party, and reducing the overall expenses as the files are only transferred across the blockchain network with no need for the files to be uploaded and downloaded to the drive. More effective applications have the ability to share files via the technology of Blockchain. The great challenge is to build a private blockchain environment to send files and distribute them securely between parties, such as military institutions and others. In this paper, a private blockchain is built to overcome the side of the security through a secured file-sharing network. This private Blockchain can be utilized at various institutions. A high scale of security is obtained through using an important algorithm that takes into consideration a critical part of the field of cryptography to robustly encrypt the files. The latter ensures that no individual except for the receiver has the ability to access the files. As well, a sufficient speed was obtained when transferring the files, as compared to Ethereum with FTP. Finally, smart contracts have been designed to suit the transfer of files between nodes.
AI and the cardiologist: when mind, heart and machine unite

Artificial intelligence (AI) and deep learning has made much headway in the consumer and advertising sector, not only affecting how and what people purchase these days, but also affecting behaviour and cultural attitudes. It is poised to influence nearly every aspect of our being, and the field of cardiology is not an exception. This paper aims to brief the clinician on the advances in AI and machine learning in the field of cardiology, its applications, while also recognising the potential for future development in these two mammoth fields. With the advent of big data, new opportunities are emerging to build AI tools, with better accuracy, that will directly aid not only the clinician but also allow nations to provide better healthcare to its citizens.
Work ethic and information security behavior

Purpose Information security is a growing issue that impacts organizations in virtually all industries, and data breaches impact millions of customers and cost organizations millions of dollars. Within the past several years alone, huge data breaches have been experienced by organizations such as Marriot, Equifax, eBay, JP Morgan Chase, Home Depot, Target and Yahoo, the latter of which impacted three billion users. This study aims to examine the utilization of pre-employment screening to identify potential hires that may require enhanced information security training to avoid such costs. Design/methodology/approach The authors hypothesize that an individual’s work ethic predicts a person’s information security behavior. The authors test this hypothesis using structural equation modeling with bootstrapping techniques. Findings Data analysis suggests that certain dimensions of work ethic do indeed predict information security posture, and thus, simple pre-employment screening techniques (i.e. questionnaires) can aid in identifying potential security threats. Practical implications The findings provide a tool for identifying problematic employee security posture prior to hiring, which may be useful in identifying training needs for new hires. Originality/value The findings provide a tool for identifying problematic employee security posture prior to hiring, which may be useful in identifying training needs for new hires.
Post-selected double teleportation and the modelling of its related non-local properties

Abstract Quantum teleportation is a notable basement of quantum processing. It has been experimentally tested with outstanding growing success by introducing improvements and applied advances in the last two decades. Its quantum non-local properties have let to discover and introduce novel implementations based on it in quantum processing, cryptography, quantum resources generation among others. In the current work, we develop a scheme performing double teleportation on two different virtual receivers, while the sender is still able to post-select the final target of teleportation. This process can be then used to generate non-local resources in a coordinated way. Those resources can be transferred to one of the receivers in the form of the non-local resource desired. They are analysed in terms of their parametric behavior, and properties derived from the CHSH inequality.
The Potential of Emotions as Predictors of News Popularity on Twitter

News media has the main purpose to deliver information to the public, yet they also have to stay profitable. When news media uses social media as one of their platforms, popular contents would be beneficial for the media. Research has shown that contents with strong high-arousal emotions are more likely to capture a larger amount of interest on the Internet and are more likely to be disseminated. Despite the importance of emotions in the popularity of a content on the Internet, the research regarding the tweet popularity has not included emotions in the prediction models. This paper investigates whether or not emotions (compared to the binary sentiment of positive and negative) have the potential to predict tweet popularity regarding Coronavirus Disease 2019 (COVID19) news in various news media worldwide. First, the tweets are preprocessed by removing the symbols. Then, the emotions in each tweet is quantified based on a sentiment lexicon called NRC Affect Intensity Lexicon. Finally, the correlation between emotions and popularity (Retweet) is computed using Pearson Correlation Coefficient (PCC). Based on the tweets from 20 Twitter accounts of various news media worldwide, several findings are obtained. First, the numbers of Like and Retweet are highly linearly correlated. Second, several emotions (and their interactions) are linearly correlated (PCC > 0.1 or PCC < -0.1) in several news media. The correlations, however, are found to be different among news media accounts. Therefore, emotions are considered potential to become predictors of tweet popularity, as opposed to the mere positive or negative sentiment, that is commonly used in the existing research.
Trespassing the gates of research: identifying algorithmic mechanisms that can cause distortions and biases in academic social media

Purpose The use of recommender systems is increasing on academic social media (ASM). However, distinguishing the elements that may be influenced and/or exert influence over content that is read and disseminated by researchers is difficult due to the opacity of the algorithms that filter information on ASM. In this article, the purpose of this paper is to investigate how algorithmic mediation through recommender systems in ResearchGate may uphold biases in scholarly communication. Design/methodology/approach The authors used a multi-method walkthrough approach including a patent analysis, an interface analysis and an inspection of the web page code. Findings The findings reveal how audience influences on the recommendations and demonstrate in practice the mutual shaping of the different elements interplaying within the platform (artefact, practices and arrangements). The authors show evidence of the mechanisms of selection, prioritization, datafication and profiling. The authors also substantiate how the algorithm reinforces the reputation of eminent researchers (a phenomenon called the Matthew effect). As part of defining a future agenda, we discuss the need for serendipity and algorithmic transparency. Research limitations/implications Algorithms change constantly and are protected by commercial secrecy. Hence, this study was limited to the information that was accessible within a particular period. At the time of publication, the platform, its logic and its effects on the interface may have changed. Future studies might investigate other ASM using the same approach to distinguish potential patterns among platforms. Originality/value Contributes to reflect on algorithmic mediation and biases in scholarly communication potentially afforded by recommender algorithms. To the best of our knowledge, this is the first empirical study on automated mediation and biases in ASM.
Part-Of-Speech Tagging for Mizo Language Using Conditional Random Field

Part of speech (POS) tagging assigns a class or tag to each token in a sentence. The tag allocated to a word is mainly its part of speech or any other class of interest. Several applications of Natural Language Processing (NLP) require it as a prerequisite. The development of part-of-speech tagging for the under-resourced Mizo language is presented in this study, which makes use of a stochastic model known as Conditional Random Field (CRF). The CRF is a discriminative probabilistic classifier that considers both the context of a given word and the tag transition probabilities in the training dataset. A corpus of approximately 30,000 words was collected and manually annotated with the proposed tagset for system evaluation. On various sizes of training and test sets, the tagger achieved 89.46 % accuracy, 89.3 % F1-score, 89.42 % precision, and 89.48 % recall.
An Ensembled Approach to Time Series Prediction for Vehicle Communication

Machine Learning (ML) for accurately modeling and predicting the wireless channel for various application especially in vehicle is essential for increased safety. In some scenarios we need long distance communication without increasing the congestion factor for the V2V communication. Beam forming is a method of increasing the range but choosing the beam is a critical where machine learning shall be used to predict which beam to choose and predict the power level. In this paper, we design an ensemble machine learning of time series based on three machine learning concepts which are LSTM, ARIMA and Random Forest. Each machine learning concept as a standalone has a unique advantage and our ensemble approach provides the better accuracy of prediction. The ensemble approach is set for multi-step prediction where as one step prediction is more of following rather than predicting. We evaluate our ensemble approach with the standalone algorithm.
Medical Named Entity Recognition Based on Multi Feature Fusion of BERT

In order to solve the problem that traditional word vectors are difficult to express the context semantics and the feature extraction of traditional model is single, a multi-feature fusion model named BERT-BiLSTM-IDCNN-Attention-CRF for Named Entity Recognition is proposed, which uses BERT to model the context semantic relationship of word vectors and fuse the context features and local features extracted by BiLSTM and IDCNN respectively. The proposed model is tested on Chinese Electronic Medical Record (EMR) dataset issued by China Conference on Knowledge Graph and Semantic Computing 2020 (CCKS2020).Compared with the baseline models such as BiLSTM-CRF, the experiment on CCKS2020 data shows that BERT-BiLSTM-IDCNN-Attention-CRF achieves 1.27% improvement in F1. The experimental results show that the proposed model can better identify the medical entities in EMR.
Safe Linear Temporal Logic Motion Planning in Dynamic Environments

This paper proposes an online control framework for mobile robots to satisfy a complex mission given in the form of linear temporal logic (LTL) without colliding with moving obstacles in the environment. The proposed framework consists of three modules named the static planner, the local collision avoidance, and the patcher. The static planner is synthesized by solving a parity game for a finite abstraction of the robot model based on a world map with static obstacles to fulfill the LTL task. The local collision avoidance module computes a set of safe controls that guarantees a safe distance between the moving objects. Both of the modules can be rigorously computed offline only once via formal methods. The patcher is activated whenever a moving obstacle is detected and modifies the static plan online for a short horizon by using only provably safe controls. The resulting modified strategy can guarantee collision-free motion without losing the ability to satisfy the LTL task. As opposed to using assume-guarantee type of LTL tasks, the proposed framework can handle the situations where obstacle movement is unpredictable.
Electronic Complaints: An Empirical Study of Negative Reviews from Amazon.com Users

Unprecedented advances have been seen in E-commerce with the spread of digital commerce and customer relations on commercial websites, such as Amazon. As a result, investigation of this type of communication has opened up new horizons for discourse analysts. This study aims to identify the complaint strategies used by customers and the reasons behind them. With this in mind, the researchers formed a corpus of the most helpful negative reviews posted on Amazon. Similar to previous studies on complaints in spoken communication, the present analysis investigated the different complaint strategies speakers used to formulate their complaints. Additionally, politeness strategies and face concerns were examined in these complaints. The results indicated that customers used eight different complaint strategies, the most frequent being showing their disappointment, anger and annoyance while making their complaints. The reasons for their complaints were delivery problems and unhelpful customer services behaviours.
Blockchain in supply chain management: a grounded theory-based analysis

Purpose This study aims to present consolidated information on blockchain, which seeks to better clarify the concept of blockchain and the associated implications and characteristics. Design/methodology/approach The methodological procedure used was Grounded Theory, based on a Systematic Bibliographic Review on the adoption of blockchain in supply chain management. To add experts' opinion on the subject to the analyzed panorama, data from a survey were also considered in the analysis. Findings The Grounded Theory enabled the development of consolidated information on blockchain related knowledge, identifying benefits, fragilities and contributions of the concept for supply chain management. Originality/value An epistemological gap has been identified between the use of blockchain in supply chain management and a theoretical basis to support the application. The originality of this study lies in providing a proposition of this theoretical basis.
Machine Learning-Based Smart Surveillance and Intrusion Detection System for National Geographic Borders

The safety, security and unmanned surveillance of national geographic borders of the country is of supreme importance. Greater security measures and surveillance techniques are required at those border which are prone to cross-border terrorists movements. The use of video/image data sets, algorithms and computer vision can be aptly utilized to design an automated surveillance system which can learn and perform these supreme critical tasks. In this paper, we have proposed a new architecture of surveillance system for such volatile border areas prone to terrorist intrusion that detects infiltration of humans, weapons and UAVs. Its capabilities are enhanced to superior level by adding deep learning/machine learning algorithms and Bluetooth signature detection techniques, hence attaining the artificial intelligence (AI) embedded autonomous surveillance system at line of control. This will make border surveillance more effective and efficient, reducing physical domination by troops all along the fence, reducing the risk to life and saving the existing surveillance equipment for other important tasks.
What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods.

A multitude of explainability methods has been described to try to help users better understand how modern AI systems make decisions. However, most performance metrics developed to evaluate these methods have remained largely theoretical - without much consideration for the human end-user. In particular, it is not yet clear (1) how useful current explainability methods are in real-world scenarios; and (2) whether current performance metrics accurately reflect the usefulness of explanation methods for the end user. To fill this gap, we conducted psychophysics experiments at scale (n = 1,150) to evaluate the usefulness of representative attribution methods in three real-world scenarios. Our results demonstrate that the degree to which individual attribution methods help human participants better understand an AI system varies widely across these scenarios. This suggests the need to move beyond quantitative improvements of current attribution methods, towards the development of complementary approaches that provide qualitatively different sources of information to human end-users.
A Portable Accelerator of Proximal Policy Optimization for Robots

Reinforcement learning has great potential to solve robotic controlling tasks for different environments. Proximal policy optimization (PPO) is one of the most efficient algorithms of reinforcement learning, which implements three neural net-works during the training and inference. However, the practical applications of reinforcement learning algorithms in robots are limited by the computational complexity of the neural networks. An accelerator for PPO is proposed based on high level synthesis (HLS) in this work, which accelerates both training and inference. The proposed accelerator design is implemented on Ultra96-v2 FPGA board with Pynq system and is suitable for different robotic applications. The proposed accelerator design on programmable logic (PL) achieves 16.8x improvement in the training process and 12.4x in the inference compared to processing system (PS). Meanwhile, the proposed accelerator runs at a similar speed with PC while achieving a 93.3% power reduction.
On the impact of linkage learning, gene-pool optimal mixing, and non-redundant encoding on permutation optimization

Gene-pool Optimal Mixing Evolutionary Algorithms (GOMEAs) have been shown to achieve state-of-the-art results on various types of optimization problems with various types of problem variables. Recently, a GOMEA for permutation spaces was introduced by leveraging the random keys encoding, obtaining promising first results on permutation flow shop instances. A key cited strength of GOMEAs is linkage learning, i.e., the ability to determine and leverage, during optimization, key dependencies between problem variables. However, the added value of linkage learning was not tested in depth for permutation GOMEA. Here, we introduce a new version of permutation GOMEA, called qGOMEA, that works directly in permutation space, removing the redundancy of using random keys. We additionally consider various linkage information sources, including random noise, in both GOMEA variants, and compare performance with various classic genetic algorithms on a wider range of problems than considered before. We find that, although the benefits of linkage learning are clearly visible for various artificial benchmark problems, this is far less the case for various real-world inspired problems. Finally, we find that qGOMEA performs best, and is more applicable to a wider range of permutation problems.