Reviewerly/exHarmony huggingface dataset:
Pneumatic and acoustic suit: multimodal haptic suit for enhanced virtual reality simulation

Abstract A haptic device provides users with physical feedback to enhance their experience in virtual reality (VR). We developed a multimodal haptic suit, called as Pneumatic and Acoustic (PA) suit, which exhibits high-resolution haptic feedback, and applies high pressure and realistic vibration to a user. The PA suit artificially simulates the sensation of brief and strong collisions such as the impact of an explosion, ball, or fist. The pneumatic actuators, consisting of 40 air bladders, are designed as bellows-type pneumatic devices for vertical inflation. The air bladders are placed on the chest at an equal interval distance of 30 mm for high-resolution feedback. The acoustic actuators use an effective sound signal of a collision similar to realistic vibrations. This study aims to examine the effectiveness of our multimodal haptic suit in improving VR experience of users. The recognition tests clearly show that participants distinguish between the haptic patterns and position of collided virtual objects with the suit. The user study involving a collision of a ball shows that the PA suit transmits the approximate pressure of a real ball collision with artificial haptic feedback. Our receiving ball and explosion VR simulations confirm that the PA suit improves a VR experience depending on the types of actuators and VR contents. The results prove that the PA suit creates distinguishable haptic patterns for guiding a task and improves the VR experience of users with powerful and multimodal haptic feedback hence providing high-quality VR simulation.
A Comparative Study of Feature Selection Methods for Activity Recognition in the Smart Home Environment

Feature selection plays an essential role in machine learning for reducing irrelevant, noisy, and redundant features, and selecting the optimal set of attributes for creating compelling predictive models of the study dataset subject. The feature selection (FS) methods entail deciding which essential features to use in machine learning (ML) for model development, and removing redundant features which decrease the overall classification accuracy. This study focused on a comparative analysis of three popular FS approaches: filter, wrapper, and embedded. In this regard, we selected a single technique from each approach, filter-based (Information Gain (IG)), wrapper–based (Recursive Feature Elimination (RFE)), and embedded–based (Tree-based) feature selections. Thereafter, the study applied five base learner classifiers: Logistic Regression (LR), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), Random Forest (RF), and K-nearest neighbor (KNN), to build the human activity recognition (HAR) model. The experimental results show that RF outscored compared to the other classifiers when applied under tree-base feature selection, with an accuracy of 98.9% in house A, and 99.8% in house B. The FS methods enhanced forecast accuracy in the ARAS dataset more than before FS.
Employee Cyber-Security Awareness Training (CSAT) Programs in Ireland’s Financial Institutions

This paper presents work in progress to analyse the use and effectiveness of cyber-security awareness training (CSAT) programs adopted in Ireland’s financial institutions. The findings to date demonstrate that some Irish financial institutions provide CSAT programs for their employees, but their adoption and usage by employees and effectiveness remains unsure. We conclude that further research is required of CSAT and whether these programs alleviate cyber threats.
Phantom or Menace: User Behaviors in Cybersecurity

This paper proposes a specific process-based approach to a systematic literature review to scope extant research on user behavior in cybersecurity. Focusing on the socio- rather than technical aspects of cybersecurity, and employing a 2016 lens robustly operationalised in a leading information systems journal in 2021, it aims to identify pertinent studies, identify a set of categories of behavioral concern, and propose a set of further studies to investigate these categories. Further, the study will identify user-focused behavioral themes of particular concern to organizations and users. At the conference, the paper seeks active discussion on a) the proposed literature bases, b) search terms and approach, and c) proposed research structure, to help refine and effect a robust study resulting in valuable insights on user behaviors that can impact effective cybersecurity.
An Extremely Light-Weight Countermeasure to Power Analysis Attack in Dedicated Circuit for AES

Cryptographic communication in IoT edge devices is threatened by side-channel attacks because devices can be physically taken away. Many countermeasures against power analysis attacks exist, but the cost of these countermeasures is an increase in implementation area. This paper proposes and evaluates an extremely lightweight countermeasure for power analysis attacks on dedicated circuit for AES in IoT edge devices.
DPMM: Data Privacy and Memory Management in Big Data Server using Hybrid Hashing Method

Data deduplication (Dedup) is commonly used in the cloud to save bandwidth and storage space by removing duplicate data sets before sending. Data integrity is protected during the Dedup process by encrypting it before it is reused. This paper proposes a DPMM framework for data privacy and memory management in a big data server using a hybrid hashing method. Some programmes use Apache Hadoop because the Hadoop Distributed File System (HDFS) offers a highly dependable static imitation strategy for processing data. The limitations are each file has a different access rate, using the same imitation factor across the board can weaken the performance. Considering this limitations, a research approach has been proposed to utilize predictive examination for progressively imitating the data. So to attain greater efficiency, Dynamic Data Partial Imitation (DDPI) algorithm is implemented, which helps to avoid excessive memory consumption. In the proposed, we have executed the partial imitation technique (DPMM), i.e., the data is fragmented and stored in four Hadoop servers partially to achieve fault tolerance. The proposed method provides the user-requested files even when there is a problem in any servers by this partial imitation technique. Before the imitation process, the files uploaded are checked for duplication to avoid repetition by implementing Secure Hash Algorithm-1 and 2 (SHA1 & SHA2) that apply the hash code of data to check ownership with code verification, thus reducing the excess consumption of storage. Finally, this method is compared with existing methods of execution time for various file sizes. The implemented result demonstrates that this proposed methodology gives much more efficient performance, redundancy and overhead is received.
I am old too!: Understanding the Impact of Empathy and Voice Characteristics on Older Adults' Perception of Voice Assistants

Despite improvements in the universal usability of digital technologies and growing numbers of conversational agents (CAs), new technologies rarely reflect older adults' needs, perceptions, and interests. Following this urgent call, we aim to identify older adults' (ranging in age from 65 to 75) perceptions, needs and challenges when conversing with a smart speaker-based voice assistant (Google Home) about the Covid-19 pandemic during the first-time user interaction. Based on CASA (Computers are Social Actors) Paradigm and Similarity Attraction Theory, our user-centered qualitative research showed that an emphatic voice assistant triggered older adults to disclose to a CA more than a human being and build a digital companionship with them. Going beyond the standard universal usability considerations, we revealed how their cultural biases, beliefs and ageing stereotypes affected their perception of the voice assistant. However, the similarity attraction effect is not activated in older adults since a voice assistant with an older voice was not perceived as close or similar to them and they perceived technology as a young tool to be adapted. Unexpectedly, the older is wiser stereotype has been deactivated in a technological context for older adults in Turkey.
An insulator pollution degree detection method based on crisscross optimization algorithm with blending ensemble learning

Pollution degree not only affects the lifespan of insulators, but also plays an important role in the operation of transmission lines. Current detection methods are usually conducted offline, which cannot provide a timely reference for monitoring and maintenance. To address this issue, an insulator online detection system is designed in this paper. Considering the problems of optimization difficulty, over-fitting and low accuracy, a novel hybrid model is proposed to classify the insulator pollution degree based on crisscross optimization algorithm (CSO) and blending ensemble learning. First, two key features, i.e., humidity and leakage current, are extracted. Second, since the hyperparameters of the random forest directly influence the prediction accuracy, the CSO algorithm is employed to dynamically optimize these key parameters. On this basis, a blending ensemble learning algorithm is applied to establish the hybrid model by integrating random forest and XGBoost. Experimental results show that the proposed hybrid model can effectively classify the pollution degree of insulators.
Combining Linguistic and Behavioral Clues to Detect Spam in Online Reviews

Online reviews have a significant impact on consumer purchase decisions. This clout invites opportunities for fraudulent behavior to help a business improve its online presence or hinder a competitor. We apply a method to detect fraud in a collection of 608k Yelp restaurant reviews using bi-directional Long-Short Term Memory (LSTM) networks: one that examines 12 linguistic features and another that uses 6 behavioral features. We then add a third network with a K-L divergence feature to measure the term differences for each review type. We combine these three networks to form an ensemble approach, producing an AUC (average precision) of 0.8930, surpassing existing state-of-the-art systems applied to similarly labeled Yelp datasets.
Applying Virtual Reality Scenery in C-STEAM Courses: A Case Study of “Lantern Festival” Virtual Venue

C-STEAM is a kind of STEM education oriented toward cultural heritage, which aims to enhance students' humanistic literacy and cultural identity while developing their scientific spirit by creating authentic cultural contexts to highly integrate cross-disciplinary knowledge, so as to cultivate innovative talents with both character and ability. Virtual reality scenery, as an effective way of context construction, can provide students with immersive experiences. In this study, we have developed a teaching aid software based on virtual cultural venues to verify the positive role of virtual scenery in C-STEAM education by taking the Lantern Festival as an example. According to the implementation of the project, we clarify the usage and effects of virtual scenery in C-STEAM curriculums based on situated learning, which provides resource support and practical experience for the development of C-STEAM education practice.
Design of Deep Learning Mixed Language Short Text Sentiment Classification System Based on CNN Algorithm

Deep learning began to emerge in the 1990s, and with the development and updating of networks, higher requirements were placed on data set analysis techniques. Firstly, it elaborates on the classification algorithm based on deep learning, which includes a multi-layer perceptron model, a recurrent neural network model and an attention mechanism model. Secondly, this paper studies the model construction and sentiment analysis based on BL_CNN, gives the mathematical model and training algorithm of deep learning, and then analyzes the dataset based on CNN, and gives the neural network structure diagram. Finally, the design of the short text sentiment classification system based on CNN is studied, and the regression model and emotion statistical clustering algorithm based on deep learning are proposed, and the method is used to predict short text data.
Blockchain and Distributed Ledger Technology Standardization in ITU-T: Diversification, Globalization and Future

As one of the most concerned Internet technologies in recent years, blockchain technology is in the process of accelerating its evolution and maturity. The blockchain is gradually integrated with other Internet technologies and applied in many industries, providing decentralized solutions for various industries, realizing innovative storage models, and building a new trust system. As one of the largest international standardization organizations, the Telecommunication Standardization Sector of the International Telecommunication Union (ITU-T) has published standards with great influence in the world, and there are many standards published and under development in the field of blockchain technology, and the types of standards are abundant. This paper first introduces the organizational structure and workflow of ITU. Then it introduces study and achievements on blockchain, including the focus group established by ITU-T and the standards published and under development by multiple study groups. It makes a statistical analysis of the standards, and expects that the blockchain standards will be more diverse in the future to meet the needs of industrial development.
Human-Swarm Interactions: A Bibliometric Analysis Based on CiteSpace

Human-robot interaction has been a major focus of research over the past decade. However, the appearance of the swarm robotics brought a different topic for study. Human swarm interaction (HSI), a blend of biology, robotics, computer science, and psychology, is a field that investigates how humans interact with a swarm of robots. Furthermore, there is a noticeable need for relevant studies using bibliometric analyses in the HIS field to assess trends and growth of research. The purpose of this paper is to explore and have a better grasp of the HSI issue by analysing 402 relevant works selected from the Scopus database between 2002 and 2022. Exploring CiteSpace’s network visualisations such as co-authorship, nations, references, and keywords maps as well as the keyword clustering related to the topic, which are classified into Basic situation analysis, knowledge-base analysis, and topic evolution analysis, impacts the HSI study field in the specific period.
Recommendations for Implementing a Blockchain based Educational Certificate Distribution System

With the increase in students graduating every year, the forged certificate issue, a problem that could compromise the educational industry badly, has arisen. Due to the lack of measurement for verifying the integrity of educational credentials, certificate forgery has become one of the most frequently occurring frauds in the recruiting industry. With the help of blockchain technology, universities can provide the critical features required by the certificate distribution system to countermeasure this issue. This paper aims to provide recommendations for implementing an Educational Certificate Distribution System using blockchain technology. By utilising a detailed investigation study, the authors have jotted down the most suitable options for planning, researching, implementing, developing, and testing this application. The study also indicates the limitations and constraints of blockchain technology in this domain.
Face Recognition Model Based On MTCNN And Facenet

Face recognition technology is widely used in various fields, such as time and attendance, payment, access control, etc., providing great convenience to life. This paper proposes a face recognition model based on MTCNN and Facenet, as traditional face recognition systems mostly use manual feature setting, which has disadvantages such as low recognition accuracy and slow speed. The MTCNN model consists of three layers of convolutional neural networks, namely P-Net, R-Net and O-Net, which are used to extract faces from images. The Facenet model is used for face feature vector extraction, and Triplet Loss is used as the loss function to determine the similarity of features through the comparison of Euclidean spatial distances. The final face recognition was completed. The experiments were trained using the LFW dataset and achieved an accuracy of 86.0%.
Implementation of Business English(BE) Teaching Assistant System(TAS) Based on B / S Mode

Under the network environment, the construction of information-based teaching platform for auxiliary teaching has become a mainstream teaching method in all kinds of basic schools. How to integrate various types of multimedia teaching resources into junior middle school English classroom teaching, share teaching resources, stimulate students’ learning interest and change the traditional classroom teaching mode has become the main goal of middle school English teaching reform. This paper analyzes and discusses the basic concept of B / S mode, the design process of BE TAS and the analysis of BE teaching. This paper puts forward the weighted reasoning model, tests the weighted value and reliability of the BE TAS under B / S mode, and tests the recognition of teachers and students for the BE major assistant teaching under B / S mode. The results show that the teaching aid system for BE Majors under B / S mode has high recognition for improving learning efficiency, with students reaching 90 -%, and teachers reaching 88%; It also has high recognition in self-diagnosis, online learning, improvement practice and history practice. Therefore, the TAS for BE Majors Based on B / S mode has great feasibility and superiority.
Design of IoT based Smart Coin Classifier using OpenCV and Arduino

Coin separator or currency pointer is a type of device that determines the originality of coins or notes. Automated sorting of coins is considered a complex global problem. Due to the unavailability of automatic coin separation, nowadays coins are separated manually. This manual process involves a lot of manpower, is time-consuming, and is not accurate. So, there is a need for an automatic coin separator that is accurate and less costly. This paper discusses the coin identification and classification that is done with the help of OpenCV in Pi-Board. Controlling the hardware to separate the coin and count the coins are done with the help of Arduino. Finally, the complete hardware design is discussed. The proposed method detects the coin type accurately and separately collects the coin in the box based on the coin type.
Security Patterns for Machine Learning: The Data-Oriented Stages

Security in machine learning (ML) is one of the top priorities in many ML-based systems in the field of healthcare, finance, energy, transportation, and cybersecurity. Since developing ML applications requires multidisciplinary effort, it is important to eliminate knowledge mismatch about security among the team members in the early design phase. In this paper, we present a collection of security patterns for the data-oriented stages in the ML workflow, including data collection, data storage, and data preparation. This provides a concise guidance on how to protect each stage from known threats, as well as a communication vocabulary for different roles to consider security without being security experts.
Visualization Analysis of English Think Tank Data Based on Genetic Algorithm

With the advent of the information age, more and more information resources have become within reach. But at the same time that information has become accessible, the information itself is also exploding. Converting information into visual data or charts can make information more intuitive. However, data visualization itself is not an easy task. In this paper, it is more difficult to obtain English resources from English think tanks, not only to recognize the semantics of English texts, but also to generate visual charts for English resources. In this regard, this paper designs an English think tank data visualization analysis system, and uses genetic algorithm to detect the visualization chart generation process. The system is used in English resource retrieval, and the corresponding English data is retrieved in the English think tank through keyword search, and the system designed in this paper is used to generate the English resource visualization strategy distribution coordinate map corresponding to each keyword.
A Pattern for Proof of Stake Consensus Algorithm in Blockchain

Blockchain is a distributed ledger technology that has recently gained widespread popularity. Many industries have started to implement blockchain solutions for their application and services. Blockchain provides immutability, privacy, security, and transparency. There is no central authority to validate and verify the transactions, still, every transaction in the Blockchain is considered secure and verified. This is made possible by the use of a consensus algorithm which is a core part of any Blockchain network. Different consensus algorithms exist and the selection of appropriate consensus algorithms can affect the performance of the blockchain. This paper presents a pattern for one of the most commonly used blockchain consensus algorithms, which is the Proof of Stake (PoS) algorithm. This pattern describes its architecture, including its structure and dynamics.
Efficiency Enhancement using Least Significant Bits Method in Image Steganography

Over the years, there has been a tremendous growth in the field of steganography. Steganography is a technique of hidden message passing i.e. transferring a message which is not visible to human eyes, through some media such as an image, music, games etc. In this particular article we focus on Image steganography which has its own advantages and has undergone a lot of improvements in the past years. The most basic image steganography can be achieved by changing the LSBs (Least Significant Bits) of the image pixels. These bits can usually be called the redundant bits. However, changing a large numbers of LSBs of an image can distort the image to an extent where it would be easily noticeable that the image maybe carrying a hidden message rendering it useless. These LSBs are changed according to the message bits allowing the person to hide their data which can be decoded later by reading the LSBs of image pixels. This paper introduces and explains a method to improve the efficiency of LSB method.
Exploring the Statistical Properties and Developing a Non-Linear Activation Function

To improve the performance of deep learning, it is required to comprehend the benefits and drawbacks of activation functions as they engage in a significant part in the neural networks. Broadly, activation functions can be divided into linear and nonlinear functions. The well-known activation functions such as relu, LRelu, swish, selu, elu, softmax also lacks in the mathematical properties as a function. As an alternative to the well-known sigmoid function, typical types of nonlinear activation functions will be introduced in this study, followed by an evaluation of their properties. Additionally, deeper neural networks will be examined because they have a beneficial impact on results compared to shallower networks. The effect of selecting weights from Gaussian and uniform distributions will be examined, paying particular emphasis to how the quantity of incoming and outgoing connections to a node affects the overall network. They also strictly depend on the weight initialization.