Reviewerly/exHarmony huggingface dataset:
Mapping Emoji Usage Among Youth

Emoji is now a popular inclusion in technology-mediated communication and a part of everyday expression of users. Yet, there is a dearth of scientifically designed research studies focussing on the human implications of the use of emojis. Limited systematic inquiry in this area is restricted to technical studies focussing on algorithm analysis of humongous quantitative data ignoring the people who are posting these emojis. Therefore, in the present study, an attempt is made to study the use of emojis from an individual’s behavioural perspective borrowing from the classic ‘contagion theory’ and the ‘information-signal theory’. A mixed research approach was adopted to study young university student’s emoji usage behaviour. Focus group discussion (FGD) was conducted on 11 participants with an average age of 22.5 years. The discussion was transcribed and thematic analysis was then conducted from which a survey instrument was developed which was administered to 250 university students. These survey data were then analysed using exploratory factor analysis. Results show that social media platforms, linguistic pattern, social relationships, emotional connect and level of formality and gender emerged as important factors that drive emoji usage. The findings of the study indicate the psychological implications and socio-behavioural impact of emoji usage which can be used for creating regulations and norms. What appears to be casual pictorial supplement of textual messages holds the power to be developed as a stand-alone language which could impact the usage of language-dependent communication.
Optimal Strategy Estimation of Random Evolutionary Boolean Games

The optimal strategy estimation of random evolutionary Boolean games (REBGs) is discussed in this article. First, using the minimum mean square error criterion, the optimal strategy estimator is proposed for REBGs. Then, a matrix approach is developed to calculate the optimal strategy estimator by the aid of a semitensor product of matrices, which includes the prediction matrix, updating distribution, and strategy iterative formula. Finally, an elucidative example is included to show the obtained results are valid.
An Immersive Virtual Reality Game for Predicting Risk Taking through the Use of Implicit Measures

Risk taking (RT) measurement constitutes a challenge for researchers and practitioners and has been addressed from different perspectives. Personality traits and temperamental aspects such as sensation seeking and impulsivity influence the individual’s approach to RT, prompting risk-seeking or risk-aversion behaviors. Virtual reality has emerged as a suitable tool for RT measurement, since it enables the exposure of a person to realistic risks, allowing embodied interactions, the application of stealth assessment techniques and physiological real-time measurement. In this article, we present the assessment on decision making in risk environments (AEMIN) tool, as an enhanced version of the spheres and shield maze task, a previous tool developed by the authors. The main aim of this article is to study whether it is possible is to discriminate participants with high versus low scores in the measures of personality, sensation seeking and impulsivity, through their behaviors and physiological responses during playing AEMIN. Applying machine learning methods to the dataset we explored: (a) if through these data it is possible to discriminate between the two populations in each variable; and (b) which parameters better discriminate between the two populations in each variable. The results support the use of AEMIN as an ecological assessment tool to measure RT, since it brings to light behaviors that allow to classify the subjects into high/low risk-related psychological constructs. Regarding physiological measures, galvanic skin response seems to be less salient in prediction models.
Dark Web Markets

This chapter studies the market places, which are operating in the Dark Web. It analyses the various characteristics and features of the most popular recently active darknet markets and vendor shops with the types of goods and services they provide, the various digital cryptocurrencies available as the only acceptable currencies in the Dark Web and the type of charges and payments which are used in the trading transactions carried out between buyers and sellers anonymously within this online dark marketspace. Finally, the chapter presents at the end the coordinated and efficient actions of European and American law enforcement agencies against the illegal trading of these markets as well as the trends, challenges and opportunities which are open in the future for all stakeholders involved.
Adaptive Virtual Impedance Control of a Mobile Multi-Robot System

The capabilities of collaborative robotics have transcended the conventional abilities of decentralised robots as it provides benefits such as scalability, flexibility and robustness. Collaborative robots can operate safely in complex human environments without being restricted by the safety cages or barriers that often accompany them. Collaborative robots can be used for various applications such as machine tending, packaging, process tasks and pick and place. This paper proposes an improvement of the current virtual impedance algorithm by developing an adaptive virtual impedance controlled mobile multi-robot system focused on dynamic obstacle avoidance with a controlled planar movement. The study includes the development of a mobile multi-robot platform whereby each robot plans a path individually without a supervisor. The proposed system would implement a two-layered hierarchy for robot path planning. The higher layer generates a trajectory from the current position to the desired position, and the lower layer develops a real-time strategy to follow the generated trajectory while avoiding static and dynamic obstacles. The key contribution of this paper is the adaptive virtual impedance controller for a multi-robot system that will maintain movement stability and improve the motion behaviour in a dynamic environment.
Total Transition to Online Learning: Students’ and Teachers’ Motivation and Attitudes

In this paper, we analyzed students’ and teachers’ motivation factors and attitudes towards total transition to online learning. To collect the data on students’ perceptions we conducted two online surveys (in the beginning of transition and after the 9 weeks of online experience). To get results on teachers’ perceptions we conducted two online interviews (in the beginning of transition and after the 9 weeks of online experience). According to the obtained results students’ motivation factors and attitudes toward online learning improved after 9 weeks of online experience that was mostly impacted by a relevant combination of e-learning tools and teachers’ support. Interviews with teachers showed the positive trend in their motivation and attitudes to online learning due to convenient online platform, however they complained about high workload. We can conclude that adaptation period was successful, students and teachers got used to work in online environment that reflected in their motivation level and positive attitude.
New Omnidirectional Sensor Based on Open-Source Software and Hardware for Tracking and Backtracking of Dual-Axis Solar Trackers in Photovoltaic Plants

In this work, an omnidirectional sensor that enables identification of the direction of the celestial sphere with maximum solar irradiance is presented. The sensor, based on instantaneous measurements, functions as a position server for dual-axis solar trackers in photovoltaic plants. The proposed device has been developed with free software and hardware, which makes it a pioneering solution because it is open and accessible as well as capable of being improved by the scientific community, thereby contributing to the rapid advancement of technology. In addition, the device includes an algorithm developed ex professo that makes it possible to predetermine the regions of the celestial sphere for which, according to the geometric characteristics of the PV plant, there would be shading between the panels. In this way, solar trackers do not have to locate the Sun’s position at all times according to astronomical models, while taking into account factors such as shadows or cloudiness that also affect levels of incident irradiance on solar collectors. Therefore, with this device, it is possible to provide photovoltaic plants with dual-axis solar tracking with a low-cost device that helps to optimise the trajectory of the trackers and, consequently, their radiative capture and energy production.
An image encryption algorithm based on improved baker transformation and chaotic S-box*

The algorithm is an image encryption algorithm based on the improved baker transformation and chaotic substitution box (S-box). It mainly uses the initial values and parameters of a one-dimensional logistic chaotic system as an encryption key. Specifically, in the image scrambling stage, the algorithm primarily uses an improved baker transform method to process the image. In the image diffusion stage, the algorithm first uses the chaotic S-box method to process the encryption key. Secondly, an exclusive OR (XOR) operation is performed on the image and the encryption key to initially diffuse the image. Finally, the image is again diffused using the method of ortho XOR. Simulation analysis shows that the algorithm can achieve good encryption effect, simple and easy implementation, and good security. In the digital image communication transmission, it has good practical value.
Research paper classification based on Word2vec and community discovery

With the advances in information technologies, different research areas are emerging day by day and thousands of research papers are published in these fields. Papers are not presented to users grouped according to their topics. Therefore, it is getting harder and harder for people to access research papers in their field of interest. To facilitate this search process, a paper classification system is proposed in this study to categorize papers on similar topics. A different approach from the studies presented for classification so far has been proposed. This system is a complex system created by combining Word2vec, network modeling and community discovery. With the Word2vec method, which has attracted great attention recently, paper similarities have been found, a network based on similarity rates has been created and papers have been clustered with the community discovery algorithm. As a result of the application with the proposed system, a success of 89% has been achieved. As can be seen from the results, this approach presented for an important classification problem will provide great convenience to people. It will enable fast and efficient access to papers.
A generalized Weisfeiler-Lehman graph kernel

Abstract After more than one decade, Weisfeiler-Lehman graph kernels are still among the most prevalent graph kernels due to their remarkable predictive performance and time complexity. They are based on a fast iterative partitioning of vertices, originally designed for deciding graph isomorphism with one-sided error. The Weisfeiler-Lehman graph kernels retain this idea and compare such labels with respect to equality. This binary valued comparison is, however, arguably too rigid for defining suitable graph kernels for certain graph classes. To overcome this limitation, we propose a generalization of Weisfeiler-Lehman graph kernels which takes into account a more natural and finer grade of similarity between Weisfeiler-Lehman labels than equality. We show that the proposed similarity can be calculated efficiently by means of the Wasserstein distance between certain vectors representing Weisfeiler-Lehman labels. This and other facts give rise to the natural choice of partitioning the vertices with the Wasserstein k-means algorithm. We empirically demonstrate on the Weisfeiler-Lehman subtree kernel, which is one of the most prominent Weisfeiler-Lehman graph kernels, that our generalization significantly outperforms this and other state-of-the-art graph kernels in terms of predictive performance on datasets which contain structurally more complex graphs beyond the typically considered molecular graphs.
Security Enhancement of AES-CBC and its Performance Evaluation Using the Avalanche Effect

The security of communication systems is becoming a significant concern with the increase in computational power. High-security cryptographic algorithms are required to protect the privacy of information from unauthorized access. This paper presents a security enhancement of Advanced Encryption Standard (AES) in Cipher Block Chaining (CBC) mode and its performance evaluation using the Avalanche Effect. In this study, a new improved technique for increasing the security of the AES-CBC is introduced. The Unix time is used as a source for Initialization Vector (IV) in CBC mode before encryption rounds. The results showed that the algorithm generates different ciphertext at each execution. In other words, different ciphertext output significantly decreases the risk of cracking the encryption key. Moreover, the results are examined using the Avalanche Effect and tested for satisfying the security criteria. The achieved results showed that the encryption method succeeds in maintaining the avalanche effect requirement and introducing additional strength to the encryption process by preventing the encryption key update for every new ciphertext.
Knife-edge interferogram analysis for corrosive wear propagation at sharp edges

This paper presents a novel noncontact measurement and inspection method based on knife-edge diffraction theory for corrosive wear propagation monitoring at a sharp edge. The degree of corrosion on the sharp edge was quantitatively traced in process by knife-edge interferometry (KEI). The measurement system consists of a laser diode, an avalanche photodiode, and a linear stage for scanning. KEI utilizes the interferometric fringes projected on the measurement plane when the light is incident on a sharp edge. The corrosion propagation on sharp edges was characterized by analyzing the difference in the two interferometric fringes obtained from the control and measurement groups. By using the cross-correlation algorithm, the corrosion conditions on sharp edges were quantitatively quantified into two factors: lag and similarity for edge loss and edge roughness, respectively. The KEI sensor noise level was estimated at 0.03% in full scale. The computational approach to knife-edge diffraction was validated by experimental validation, and the computational error was evaluated at less than 1%. Two sets of razor blades for measurement and control groups were used. As a result, the lag will be increased at an edge loss ratio of 1.007/µm due to the corrosive wear, while the similarity will be decreased at a ratio of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>5.4</mml:mn> <mml:mo>×<!-- × --></mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msup> <mml:mn>10</mml:mn> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>−<!-- − --></mml:mo> <mml:mn>4</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>/</mml:mo> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mtext>µ<!-- µ --></mml:mtext> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> </mml:mrow> </mml:mrow> </mml:math> with respect to edge roughness change. Experimental results showed a good agreement with computational results.
Multi-task Deep Learning for No-Reference Screen Content Image Quality Assessment

The past decades have witnessed growing development of image quality assessment (IQA) for natural images (NIs). However, since screen content images (SCIs) exhibit different visual characteristics from the NIs, few of NIs-oriented IQA methods can be directly applied on SCIs. In this paper, we present a quality prediction approach specially designed for SCIs, which is based on multi-task deep learning. First, we split a SCI into \(32\times 32\) patches and design a novel convolutional neural network (CNN) to predict the quality score of each SCI patch. Then, we propose an effective adaptive weighting algorithm for patch-level quality score aggregation. The proposed CNN is built on an end-to-end multi-task learning framework, which integrates the histogram of oriented gradient (HOG) features prediction task to the SCI quality prediction task for learning a better mapping between input SCI patch and its quality score. The proposed adaptive weighting algorithm for patch-level quality score aggregation further improves the representation ability of each SCI patch. Experimental results on two-largest SCI-oriented databases demonstrate that our proposed method is superior to the state-of-the-art no-reference IQA methods and most of the full-reference IQA methods.
Efficient multiple biomedical events extraction via reinforcement learning

Multiple events extraction from biomedical literature is a challenging task for biomedical community. Usually, biomedical event extraction is modeled as two sub-tasks, trigger identification and argument detection. Most existing methods perform these two sub-tasks sequentially, and fail to make full use of the interaction between them, leading to suboptimal results for multiple biomedical events extraction.We propose a novel framework of reinforcement learning (RL) for the task of multiple biomedical events extraction. More specifically, trigger identification and argument detection are treated as main-task and subsidiary-task, respectively. Assigning the event type of triggers (in the main-task) is viewed as the action taken in RL, and the result of corresponding argument detection (i.e. the subsidiary-task) for the identified trigger is used for computing the reward of the taken action. Moreover, the result of the subsidiary-task is modeled as part of environment information in RL to help the procedure of trigger identification. In addition, external biomedical knowledge bases are employed for representation learning of biomedical text, which can improve the performance of biomedical event extraction. Results on two widely used biomedical corpora demonstrate that the proposed framework performs better than the selected baselines on the task of multiple events extraction. The ablation test indicates the contributions of RL and external KBs to the performance improvement in the proposed method. In addition, by modeling multiple events extraction under the RL framework, the supervised information is exploited more effectively than the classical supervised learning paradigm. Availability and implementationSource codes will be available at: https://github.com/David-WZhao/BioEE-RL.
Development of a Voice Chatbot for Payment Using Amazon Lex Service with Eyowo as the Payment Platform

Engaging in financial transactions has remained a hassle for the visually impaired due to the lack of technological products to facilitate their financial independence and inclusion. Automated teller machines (ATMs) and online banking applications do not provide any means through which the blind can engage in transactions without the need of a third party in managing their finances. This study aims at building a voice chatbot device that can be used for payment using Amazon Lex Service with Eyowo as the payment platform. The chatbot is built by leveraging on Artificial Intelligence (AI) technologies in the form of a service called Amazon Lex for configuring the bot with utterances and responses and Lambda Functions to validate the responses while carrying out the transactions by calling the Eyowo Application Programming Interface (API). A Raspberry pi single board computer is utilised as the medium of communication between the user and the chatbot. The Raspberry pi runs a script that collects voice input through a Universal Serial Bus (USB) microphone connected to it, which is sent to the Amazon Lex to be processed using Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU). Then the chatbot sends back a suitable response to the user through the speaker connected to the Raspberry pi. The device brings comfort and security to the visually impaired by providing balance and cash transfers.
Implementing and Evaluating feedback feature of Mind Monitoring Service for Elderly People at Home

To support sustainable in-home long-term care, it is essential to monitor mental states of elderly people at home, and to encourage their ability of self-care. However, many challenges exist in practice, including limitations of human interventions, sensor-based monitoring, the daily recording and externalization of mental states. In the previous research, we have proposed Mind Monitoring Service, which aims to monitor mental states and promote self-care of elderly people at home. In the proposed service, a chatbot asks a user specific questions to acquire his/her mental state. Based on the answers, the service assesses the mental state. In this research, we develop a new feature of weekly feedback. The feature automatically reviews answers of past one week, and sends advice to improve the current situation. We conduct an experiment to evaluate the effectiveness. Through the experiment, it was confirmed that the weekly feedback encouraged self-care consciousness and increased motivation of subjects.
Edge-Aware Pyramidal Deformable Network for Unsupervised Registration of Brain MR Images

Deformable image registration is of essential important for clinical diagnosis, treatment planning, and surgical navigation. However, most existing registration solutions require separate rigid alignment before deformable registration, and may not well handle the large deformation circumstances. We propose a novel edge-aware pyramidal deformable network (referred as EPReg) for unsupervised volumetric registration. Specifically, we propose to fully exploit the useful complementary information from the multi-level feature pyramids to predict multi-scale displacement fields. Such coarse-to-fine estimation facilitates the progressive refinement of the predicted registration field, which enables our network to handle large deformations between volumetric data. In addition, we integrate edge information with the original images as dual-inputs, which enhances the texture structures of image content, to impel the proposed network pay extra attention to the edge-aware information for structure alignment. The efficacy of our EPReg was extensively evaluated on three public brain MRI datasets including Mindboggle101, LPBA40, and IXI30. Experiments demonstrate our EPReg consistently outperformed several cutting-edge methods with respect to the metrics of Dice index (DSC), Hausdorff distance (HD), and average symmetric surface distance (ASSD). The proposed EPReg is a general solution for the problem of deformable volumetric registration.
Traffic Sign Recognition Based On Scaled Convolutional Neural Network For Advanced Driver Assistance System

Advanced driver assistance system (ADAS) is one of the most important systems for human assistance. It assists the drivers to control the vehicle by providing essential information about the environment objects. In this paper, we propose a traffic signs recognition application for ADAS. The proposed application is based on the deep learning technique. In particular, we used the convolutional neural networks (CNN) to process the data provided by the system cameras. The proposed CNN was scaled in a way to get a light model size without decreasing the accuracy. The proposed CNN is suitable for embedded implementation while keeping high performance and real-time processing. The evaluation of the proposed CNN on the European dataset results in 99.32% accuracy and 250 FPS of inference speed when implemented on an Nvidia GTX960 GPU. The achieved results proved the efficiency of the scaling technique. It is a very good technique to get a small model size and high performance.