Reviewerly/exHarmony huggingface dataset:
Hardware Based RISC-V Instruction Set Randomization

Instruction set randomization has been proposed for many years as a strategy against code injection. However, most of the methods are based entirely on software, which is vulnerable to possible threats like key leakage or bypassing attack. The translation of instructions also brings the loss of performance. Some designs randomize the instruction set based on hardware, but using weak approaches which can be easily bypassed. In this paper, we propose a hybrid instruction set randomization with both compiler support and hardware extension on a RISC-V processor. We adopt AES-128 to randomize RISC-V instruction set with little performance loss. The design has been implemented on Xilinx AV7K325 FPGA board, the results shows that RISC-V instruction set is randomized with no changes in clock frequency, 1377 LUTs increase in resources and 0.38% performance overhead.
Recent Advances in Entropy Based Image Compression

The future of image compression is abundant with the opportunities recently developed through the application of advanced neural network algorithms configured to take into account multiple image parameters. This progress has spurred on further progression into more complex architectures to extract the feature of the image for optimal compression. Of the many models available, this work tracks an evolution of end to end image compression by first analyzing BLS2017 and its successors, BMSHJ2018 and MS2020.
Joint model of biomedical entity recognition and normalization labels based on self-attention

To address the error propagation problem of joint modeling of biomedical named entity recognition and normalization, joint label is designed to combine entity labels with concept labels to jointly label each term in the sentence, the joint learning task is transformed into a multiclass classification problem. A joint model of biomedical entity recognition and normalization labels based on self-attention is designed, the pre-training model BioBERT is used to encode the medical text. After extracting the joint label information using the self-attention mechanism, it is fused with the input sequence information. Finally, the final joint label representation is obtained by softmax. The experimental results show that the F1 values of the entity recognition and normalization tasks on the NCBI dataset reach 83.3% and 84.5%, and the F1 values on the BC5CDR dataset reach 84.2% and 86.6%, which are better compared with existing methods.
Concept of modern EOG system for BCI

In this paper, we present the concept of a modern EOG sensor. In contrast to the standard version, it is realized in the form of a headband with conductive textiles and supplemented by a photoplethysmography, electroencephalography and simplified ECG recording. The device is designed for people suffering from amyotrophic lateral sclerosis.
A Deep Convolution Generative Adversarial Network for the Production of Images of Human Faces

Generative models get huge attention by researchers in different topics of artificial intelligence applications, especially generative adversarial networks (GANs) which have demonstrated good performance in data generation. In this paper, we would like to explore the potential of this class of models in producing human faces images. For that, we will use Deep Convolutional Generative Adversarial Network (DCGAN). Since that, the evaluation of GANs is still difficult even with the existing metrics like Inception Scores (IS), Mode Score (MS), Kernel Inception Distance (KID), Fréchet Inception Distance (FID), Multi-Scale Structural Similarity (MS-SSIM), etc. Thus, the best possible evaluation remains that carried out by human evaluators. This is why we propose a new hybrid measure combining qualitative and quantitative evaluation, we called this measure: Measuring the Quality of the Features of an Image (MEQFI). The images produced with the DCGAN method were trained on three well known datasets from the literature and the results were evaluated with MEQFI.
Long memory in Bitcoin and ether returns and volatility and Covid-19 pandemic

Purpose The purpose of this research is to analyze the Bitcoin (BTC) and Ether (ETH) long memory and conditional volatility. Design/methodology/approach The empirical approach includes ARFIMA-HYGARCH and ARFIMA-FIGARCH, both models under Student‘s t -distribution, during the period (ETH: November 9, 2017 to November 25, 2021 and BTC: September 17, 2014 to November 25, 2021). Findings Findings suggest that ARFIMA-HYGARCH is the best model to analyze BTC volatility, and ARFIMA-FIGARCH is the best approach to model ETH volatility. Empirical evidence also confirms the existence of long memory on returns and on BTC volatility parameters. Results evidence that the models proposed are not as suitable for modeling ETH volatility as they are for the BTC. Originality/value Findings allow to confirm the fractal market hypothesis in BTC market. The data confirm that, despite the impact of the Covid-19 crisis, the dynamics of BTC returns, and volatility maintained their patterns, i.e. the way in which they evolve, in relation to the prepandemic era, did not change, but it is rather reaffirmed. Yet, ETH conditional volatility was more affected, as it is apparently higher during Covid-19. The originality of the research lies in the focus of the analysis, the proposed methodology and the variables and periods of study.
An Ensemble Approach to Recognize Activities in Smart Environment Using Motion Sensors and Air Quality Sensors

Smart environments are built to capture Activities of Daily Living (ADL) data using a variety of sensors. Simple and sophisticated sensors sense the minute and intricate movements of an individual within the environment. With the growth in technology, applications of sensor data are extended to activity recognition. Motion sensors are able to detect movements of the person performing daily routine tasks such as cooking, cleaning, and eating. The utility of air quality data extracted from gas sensors for activity recognition is uniquely explored. The proposed model is built to identify activities using motion sensors and air quality forms of data individually. In this paper, the experiment focuses on routine tasks classification using pressure, temperature, and other related sensor data. Feature selection is performed on the air quality data using ANOVA-F Classification. The novel data is applied for activity recognition in a set of scenarios while simultaneously detecting the presence or absence of chemicals in the surrounding area. Machine learning techniques are applied to recognize activities performed by individuals in both smart environments. A comparative study is performed on the models generated for activity recognition. An ensemble form of learning, the Random Forest technique, provides the best prediction accuracy of 86.8% for activity prediction using motion sensor data. In the case of air quality data, the activity classification model produced an accuracy of 96.19% with the least feature set combination. Activity recognition helps in identifying changes in behavioral patterns and can be extended to assisted care benefiting healthcare professionals.
Emotion Recognition of College Students Based on Audio and Video Image

Emotional problems are common among contemporary college students.To improve their mental health, it is urgent to quickly identify college students' negative emotions, and guide them to improve their emotional development.Students' emotions are expressed through multiple modalities, such as audio, facial expressions, and gestures.Using the complementarity between multi-modal emotional information can improve the accuracy of emotion recognition.This paper proposes a multi-modal emotion recognition method for voice and video images based on deep learning: (1) For voice modal recognition, the voice is firstly preprocessed to extract voice emotional features, and then the attention-based longshort-term memory network (LSTM) is adopted for emotion recognition; (2) For video image modal recognition, the extended local binary pattern (LBP) operator is used to calculate the image features, LBP block weighting and multi-scale partitioning are combined for feature extraction, principal component analysis (PCA) is adopted to reduce the dimensionality of eigenvectors, and the VGG-16 network model is constructed with the transfer learning training strategy to realize emotion recognition.(3) The voice and video image emotions recognized by single-modal recognitions are weighed and subjected to feature fusion at the decision-making layer, and used to classify multi-modal emotions.Experimental results show that on the test set of the cheavd2.0Chinese emotion database, the recognition accuracy of our multi-modal fusion recognition algorithm is better than the single-modal recognition methods.
Perceived Fairness Concerns Within Pandemic Response Technology

Abstract The response to the spread of the COVID-19 pandemic has involved the creation of complex, novel public health technologies deployed at an unprecedented scale. One such technology intervention is the deployment of digital COVID certificates, applications that confirm a person’s COVID-19 immunity status via vaccination, negative tests or having contracted the disease. These certificates have formed part of governmental strategies to manage the resumption of travel and social activities. The potential impact of these technologies on daily life has led to perceived concerns regarding the fairness of the restrictions associated with these systems. These fairness concerns are intuitively understood by users, and challenges exist in conceptualizing them in a manner that is addressable with the tools available to application and system designers. In order to improve our understanding of the fairness concerns of users and how we might conceptualize them, we analysed 27 semi-structured interviews with Irish participants regarding the use of digital immunity certificates. Our results suggest that the user perceptions of the fairness of such solutions are primarily thought about at the group level, with the concerns of Anti-discrimination and Egalitarianism being the most frequently discussed. Based on our analysis, we further identify societal and technical accessibility as important factors when attempting to address these fairness concerns.
Artificial Intelligence in the Clinical Laboratory

This article provides an overview of machine learning fundamentals and some applications of machine learning to clinical laboratory diagnostics and patient management. A key goal of this article is to provide a basic foundation in clinical machine learning for readers with clinical laboratory experience that will set them up for more in-depth study of the topic and/or to become a better collaborator with computational colleagues in the development and deployment of machine learning-based solutions.
Research Progress of Robot Vision Sensing Weld Seam Tracking Technology

Abstract As the key technology of intelligent welding, seam tracking plays a vital role in the welding process. The development and application of visual sensing technology has laid the foundation for obtaining accurate weld position information. The intelligent combination of visual sensing and welding robots improves the stability and accuracy of welding seam tracking technology. This paper introduces the composition and principle of the welding seam tracking system, and the classification of visual sensing technology. This paper summarizes and analyses the welding seam image processing method and the research status of robot tracking control technology around the process of welding seam tracking. This paper summarizes the current problems of weld tracking technology and looks forward to the future development trend.
Handwritten Text Recognition for Documentary Medieval Manuscripts

Handwritten Text Recognition (HTR) techniques aim to accurately recognize sequences of characters in input manuscript images by training artificial intelligence models to capture historical writing features. Efficient HTR models can transform digitized manuscript collections into indexed and quotable corpora, providing valuable research insight for various historical inquiries. However, several challenges must be addressed, including the scarcity of relevant training corpora, the consequential variability introduced by different scribal hands and writing scripts, and the complexity of page layouts. This paper presents two models and one cross-model approach for automatic transcription of Latin and French medieval documentary manuscripts, particularly charters and registers, written between the 12th and 15th centuries and classified into two major writing scripts: Textualis (from the late-11th to 13th century) and Cursiva (from the 13th to the 15th century). The architecture of the models is based on a Convolutional Recurrent Neural Network (CRNN) coupled with a Connectionist Temporal Classification (CTC) loss. The training and evaluation of the models, involving 120k lines of text and almost 1M tokens, were conducted using three available ground-truth corpora : The e-NDP corpus, the Alcar-HOME database and the Himanis project. This paper describes the training architecture and corpora used, while discussing the main training challenges, results, and potential applications of HTR techniques on medieval documentary manuscripts.
Lane-changing Trajectory Planning of Intelligent Electric Vehicle based on Stability Region

With the rapid development of intelligent electric vehicles, the technology of lane-changing trajectory planning has become one of the pivotal issues. However, intelligent vehicles have problems of unsafety and conservation, especially in the complex traffic environment such as high speed and emergency collision avoidance, which ignores the dynamic behavior mechanism of the nonlinear system. In this paper, the lane-changing trajectory model is developed based on quintic polynomial; the global feasible region is determined by environmental constraints and stability region constraints; the trajectory multi-objective optimization is carried out based on TOPSIS algorithm; the results of simulation and experiment finally show the feasibility of the trajectory planner.
Blockchain-based Cloud Resource Allocation Mechanisms for Privacy Preservation

In the cloud manufacturing platform, virtualized manufacturing resources are complex to ensure the authenticity and security of transaction data during the transaction process and rely on the unified deployment of cloud manufacturing platform operators (CPOs). This centralized framework is prone to a single point of failure and risks leakage of private data. Because of these problems, combined with the advantages of decentralization and immutability of blockchain, we innovatively explore the application of blockchain technology in cloud manufacturing and propose a blockchain-based cloud manufacturing resource allocation method. Firstly, a blockchain-based decentralized cloud manufacturing trading platform framework is presented. The Elliptic Curve Digital Signature Algorithm (ECDSA) in manufacturing resource/demand release is studied, and the matching mechanism between manufacturing resources and demand is analyzed. Then, a manufacturing resource verification contract and a manufacturing resource transaction contract for cloud manufacturing are designed using smart contracts (SC). The experimental tests in the Remix platform are completed using Solidity. The test results show that the proposed method can provide a safe and reliable guarantee for virtualized manufacturing resource transactions. Finally, the game problem of supply-demand balance between manufacturing resource suppliers (MRS) and demanders (MRDs) under the decentralized architecture is explored, and simulation is carried out on MATLAB R2021b. The simulation results show the manufacturing resource supply and demand. The fair game can reach the Nash equilibrium and has a faster convergence rate than the existing research.
Teaching lessons learnt by civil-engineering teachers from the COVID-19 pandemic at the University of Burgos, Spain

The COVID-19 lockdown in Spain caused abrupt changes for students following the Bachelor's Degree in Civil Engineering at the University of Burgos when face-to-face classes switched to online teaching. The recovery of face-to-face teaching after lockdown meant that classes were taught with obligatory social distancing and the use of masks. Teachers were therefore unable to interact with students closely, to perceive their facial expressions during class, or to conduct group work. The changes to civil-engineering teaching linked to the COVID-19 pandemic and the lessons that civil-engineering teachers learnt from the new teaching scenarios are studied in this paper. The reflections of teachers throughout all three stages of the pandemic (pre-pandemic and lockdown, during lockdown, and post-lockdown), and the qualitative and mixed analysis of their responses to a survey of open-ended questions contributed to the identification of six major lessons: (1) asking questions and using real-time quiz tools enliven classes and help to determine which concepts to emphasize for proper student understanding; (2) autonomous student learning can be promoted through the provision of supplementary documentation and the digitalization of solutions to classroom exercises; (3) virtual site visits and real visual examples interspersed with explanations bring concepts closer to their real applications; (4) the delivery of projects in the form of audio-recorded presentations enable their distribution, so that other students can also learn from them as well as the students who created them; (5) online videoconferences, adapted to the concepts that are addressed, facilitate fast and flexible communication with students; and (6) online continuous-assessment exams can promote better student learning patterns and final-exam preparation. Nevertheless, these six lessons were drawn from the experience of teachers at a small Spanish university where the period of solely online teaching during the COVID-19 pandemic lasted only four months. Thus, it would be interesting to analyze the experience of civil-engineering teachers at larger universities and universities that had longer periods of solely online teaching. A study of the level of implementation of the six aspects when the pandemic is declared over might also be worthwhile.
Outlier mining in high-dimensional data using the Jensen–Shannon divergence and graph structure analysis

Abstract Reliable anomaly/outlier detection algorithms have practical applications in many fields. For instance, anomaly detection allows to filter and clean the data used to train machine learning algorithms, improving their performance. However, outlier mining is challenging when the data is high-dimensional, and different approaches have been proposed for different types of data (temporal, spatial, network, etc). Here we propose a methodology to mine outliers in generic datasets in which it is possible to define a meaningful distance between elements of the dataset. The methodology is based on defining a fully connected, undirected graph, where the nodes are the elements of the dataset and the links have weights that are the distances between the nodes. Outlier scores are defined by analyzing the structure of the graph, in particular, by using the Jensen–Shannon (JS) divergence to compare the distributions of weights of different nodes. We demonstrate the method using a publicly available database of credit-card transactions, where some of the transactions are labeled as frauds. We compare with the performance obtained when using Euclidean distances and graph percolation, and show that the JS divergence leads to performance improvement, but increases the computational cost.
Understanding the IOT Adoption for Home Automation in the Perspective of UTAUT2

This study aims to understand the reasons of IOT adoption for home automation while expanding the ambit of UTAUT2 to a new market (Pakistan) with new technology (IOT) by incorporating novel variables of IOTA (cryptocurrency) concatenation with IOT &amp; challenges to deeply understand the reasons behind IOT adoption. The author collected 400 plus responses using purposive sampling from all provinces of Pakistan. The obtained data was analyzed using Smart PLS. The study revealed that performance expectancy, facilitating conditions, challenges in IOT adoption, IOTA concatenation with IOT, price value and social influence are key drivers of adoption while Effort expectancy &amp; hedonic motivation are less influential in the adoption process. Such understanding is pivotal for companies dealing in IOT products as it explicitly elucidates the reasons for the adoption of IOT for home automation and hence helps the firms to work meticulously on the major drives of adoption. The study is significant as it expands the scope of UTAUT2 to a new market (Pakistan) with new technology (IOT) incorporating novel variables (IOTA concatenation &amp; challenges), leading to greater automation &amp; enhancing the predictive power of the UTAUT2 propounded by Venkatesh et al. (2012) .
Colour image encryption based on an improved fractional-order logistic map

In this work, we use an improved fractional-order logistic map to introduce a new colour image encryption algorithm. By analysing the Lyapunov exponent and the bifurcation diagram, the map provides a wider range and a uniform distribution of data compared to its classical. It also has additional parameters and thus a larger key space, which makes it better in protection and safety against hacker attacks. Our algorithm relies on random input of pixels in order to obtain a different image in each encryption round to ensure greater protection. The algorithm also provides great permutation and diffusion features. The simulation results and security analysis indicate that our scheme has a good impact on encryption and can withstand various attacks, such as statistical attack, differential attack and data loss and noise attacks.
Impact of Cyber-Security on Fraud Prevention in Nigerian Commercial Banks

Abstract: Purpose: The purpose of this research was to investigate the impact of cyber-security on fraud prevention in Nigerian commercial banks. Method: The researcher collected primary data through the interview (WhatsApp video call) conducted with the senior employees of the respective commercial banks who know the subject matter. Result: The outcomes of the research demonstrated that cloud security statistically increases fraud prevention in Nigeria; also, that application security statistically increases fraud prevention in Nigeria. Contributions: it was suggested that Nigerian financial industry should be able to effectively detect fraudulent transactions and prevent them from causing financial or reputational damage to the customers or other financial institutions (FI), also, there should be a special awareness program to educate the public on how to always use strong passwords for their devices to prevent hacking, loss of money, or other resources. Novelties: The variables adopted in this study as well as the sample size, results, and recommendations have not been used by eminent scholars in this manner. Limitations: the results of this study would be limited to commercial banks in Nigeria, and therefore may not apply to other sectors of the economy. Similar studies were suggested to be carried out, covering other sectors of the economy to validate these results. Keywords: 1. Cyber Security 2. Cloud Security 3. Application Security 4. Fraud Prevention 5. Commercial Banks