Reviewerly/exHarmony huggingface dataset:
Blockchain-Empowered AI for 6G-Enabled Internet of Vehicles

The 6G communication technologies are expected to provide fast data rates and incessant connectivity to heterogeneous networks, such as the Internet of Vehicles (IoV). However, the resulting unprecedented surge in data traffic, massive increase in the number of nodes with high mobility, and low-latency requirements give rise to serious security, privacy, and trust challenges. The blockchain could potentially ensure trust and security in IoV due to its features, including consensus for credibility and immutability for tamper proofing. In parallel, federated learning (FL) is a privacy-preserving artificial-intelligence paradigm that does not require to share data for model training in machine learning. It can reduce data traffic and resolve privacy challenges of intelligent IoV networks. The blockchain can also complement FL by ensuring the decentralization and securing distribution of incentives. This article reviews the trends and challenges of the blockchain and FL in 6G IoV networks. Then, the impact of their combination, challenges in implementation, and future research directions are highlighted. We also evaluate our proposal of blockchain-based FL to protect IoV security and privacy that utilizes smart contract and secure transactions of incentives via the blockchain to protect FL. Compared with other solutions, the failure rate of the proposed solution was at least 5% lower with 30% malicious nodes in the network.
DLCP2F: a DL-based cryptocurrency price prediction framework

Abstract Cryptocurrencies are distributed digital currencies that have emerged as a consequence of financial technology advancement. In 2017, cryptocurrencies have shown a huge rise in their market capitalization and popularity. They are now employed in today’s financial systems as individual investors, corporate firms, and big institutions are heavily investing in them. However, this industry is less stable than traditional currency markets. It can be affected by several legal, sentimental, and technical factors, so it is highly volatile, dynamic, uncertain, and unpredictable, hence, accurate forecasting is essential. Recently, cryptocurrency price prediction becomes a trending research topic globally. Various machine and deep learning algorithms, e.g., Neural Networks (NN), Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) were utilized to analyze the factors influencing the prices of the cryptocurrencies and accordingly predict them. This paper suggests a five-phase framework for cryptocurrency price prediction based on two state-of-the-art deep learning architectures (i.e., BiLSTM and GRU). The current study uses three public real-time cryptocurrency datasets from “Yahoo Finance”. Bidirectional Long Short-Term Memory and Gated Recurrent Unit deep learning-based algorithms are used to forecast the prices of three popular cryptocurrencies (i.e., Bitcoin, Ethereum, and Cardano). The Grid Search approach is used for the hyperparameters optimization processes. Results indicate that GRU outperformed the BiLSTM algorithm for Bitcoin, Ethereum, and Cardano, respectively. The lowest RMSE for the GRU model was found to be 0.01711, 0.02662, and 0.00852 for Bitcoin, Ethereum, and Cardano, respectively. Experimental results proved the significant performance of the proposed framework that achieves the minimum MSE and RMSE values.
A Hybrid Two-Stage Teaching-Learning-Based Optimization Algorithm for Feature Selection in Bioinformatics

The “curse of dimensionality” brings new challenges to the feature selection (FS) problem, especially in bioinformatics filed. In this paper, we propose a hybrid Two-Stage Teaching-Learning-Based Optimization (TS-TLBO) algorithm to improve the performance of bioinformatics data classification. In the selection reduction stage, potentially informative features, as well as noisy features, are selected to effectively reduce the search space. In the following comparative self-learning stage, the teacher and the worst student with self-learning evolve together based on the duality of the FS problems to enhance the exploitation capabilities. In addition, an opposition-based learning strategy is utilized to generate initial solutions to rapidly improve the quality of the solutions. We further develop a self-adaptive mutation mechanism to improve the search performance by dynamically adjusting the mutation rate according to the teacher's convergence ability. Moreover, we integrate a differential evolutionary method with TLBO to boost the exploration ability of our algorithm. We conduct comparative experiments on 31 public data sets with different data dimensions, including 7 bioinformatics datasets, and evaluate our TS-TLBO algorithm compared with 11 related methods. The experimental results show that the TS-TLBO algorithm obtains a good feature subset with better classification performance, and indicates its generality to the FS problems.
Intelligent Digital Banking Technology and Architecture

The digital banking sector has made tremendous progress in user-friendly, efficient, and rapid financial transactions, owing to the exponential growth of ICT technologies. Consequently, many new banking products, services, and business opportunities have emerged. This paper presents a comprehensive analysis of new emerging technologies and architecture that can support the development of intelligent digital banking platforms. The aim of this study is to explore the latest relevant technology to enhance customer experience and minimize human interaction to prevent the spread of COVID-19. The study follows Kitchenham's SLR principles and goes into great depth regarding the process of choosing and analyzing relevant research papers. This research promoted 40 articles that discuss potential emerging technologies, such as artificial intelligence, blockchain, big data, biometrics, and cloud computing. Some key areas of literature were examined, such as technological trends, potential features, and technical solutions. The articles selected were based on specific criteria and used high-quality databases, including IEEE Xplore, Elsevier, SpringerLink, ACM, and AIS. This study considered recent papers published between 2015 and 2021. The mapping study presents digital banking technology and proposes an integrated architecture for developing intelligent digital banking platforms.
TRENDS IN DIGITAL ADAPTATION OF SCHOOLS DURING THE COVID-19 PANDEMIC

The COVID-19 pandemic has launched school digital transformation with educational technologies implemented in classrooms worldwide. Teachers and learners have faced the challenges of adaptation to a new mode – digitally-based distance teaching and learning. In this regard, a question arises, whether schools have adapted successfully to new digitally-based distance learning or failed. Therefore, the research is focused on revealing the trends in schools’ digital adaptation and transformation during the COVID-19 pandemic worldwide. The research issue is solved due to a combination of various approaches, e.g. a qualitative content analysis of documents published online in 2020-2021 and conducting surveys and interviews of students at Ukrainian schools from December 2020 to February 2021. These approaches make it possible to reveal positive and negative trends in school digital transformation at macro- and micro levels from the perspective of pandemic-related measures on international and national levels, as well as students’ perception of emergency distance learning. The research presents the ranks of countries in line with the accessibility and affordability of EdTech having been implemented in schools in crisis. Moreover, the research discusses the following issues: the COVID-19 pandemic impact on schools and school-related measures, EdTech implementation in classroom and schools’ digital transition, digital divide and inequity in education, generation gap, learning loss and achievement gap, teacher’s creativity and methodology shift. The collected data supports theoretical insights about trends in school education during the pandemic. The principal findings of the research are that the emergency integration of information technology in school education has changed the traditional way of teaching and learning worldwide. EdTech implementation encourages teachers to provide creative teaching and develop novel approaches to meet Net generation students’ needs in a virtual educational environment. The research results can contribute to policymaking and strategy development of further school digital transformation in times of crisis.
EEG-based analysis of various sensory stimulation effects to reduce visually induced motion sickness in virtual reality

The use of virtual reality (VR) is frequently accompanied by motion sickness, and approaches for preventing it are not yet well established. We explored the effects of synchronized presentations of sound and motion on visually induced motion sickness (VIMS) in order to reduce VIMS. A total of 25 participants bicycle riding for 5 min with or without sound and motion synchronization presented on a head-mounted display. As a result, the VIMS scores measured by the fast motion sickness scale and simulator sickness questionnaire were significantly lower in the participants who experienced the riding scene with sound and motion than those who experienced the riding scene with sound only, motion only, or neither. Furthermore, analysis of the EEG signal showed that the higher the VIMS, the significant increase in alpha and theta waves in the parietal and occipital lobes. Therefore, we demonstrate that the simultaneous presentation of sound and motion, closely associated with synchronous and visual flow speed, is effective in reducing VIMS while experiencing simulated bicycle riding in a VR environment.
A Soft Label Deep Learning to Assist Breast Cancer Target Therapy and Thyroid Cancer Diagnosis

According to the World Health Organization Report 2022, cancer is the most common cause of death contributing to nearly one out of six deaths worldwide. Early cancer diagnosis and prognosis have become essential in reducing the mortality rate. On the other hand, cancer detection is a challenging task in cancer pathology. Trained pathologists can detect cancer, but their decisions are subjective to high intra- and inter-observer variability, which can lead to poor patient care owing to false-positive and false-negative results. In this study, we present a soft label fully convolutional network (SL-FCN) to assist in breast cancer target therapy and thyroid cancer diagnosis, using four datasets. To aid in breast cancer target therapy, the proposed method automatically segments human epidermal growth factor receptor 2 (HER2) amplification in fluorescence in situ hybridization (FISH) and dual in situ hybridization (DISH) images. To help in thyroid cancer diagnosis, the proposed method automatically segments papillary thyroid carcinoma (PTC) on Papanicolaou-stained fine needle aspiration and thin prep whole slide images (WSIs). In the evaluation of segmentation of HER2 amplification in FISH and DISH images, we compare the proposed method with thirteen deep learning approaches, including U-Net, U-Net with InceptionV5, Ensemble of U-Net with Inception-v4, Inception-Resnet-v2 encoder, and ResNet-34 encoder, SegNet, FCN, modified FCN, YOLOv5, CPN, SOLOv2, BCNet, and DeepLabv3+ with three different backbones, including MobileNet, ResNet, and Xception, on three clinical datasets, including two DISH datasets on two different magnification levels and a FISH dataset. The result on DISH breast dataset 1 shows that the proposed method achieves high accuracy of 87.77 ± 14.97%, recall of 91.20 ± 7.72%, and F1-score of 81.67 ± 17.76%, while, on DISH breast dataset 2, the proposed method achieves high accuracy of 94.64 ± 2.23%, recall of 83.78 ± 6.42%, and F1-score of 85.14 ± 6.61% and, on the FISH breast dataset, the proposed method achieves high accuracy of 93.54 ± 5.24%, recall of 83.52 ± 13.15%, and F1-score of 86.98 ± 9.85%, respectively. Furthermore, the proposed method outperforms most of the benchmark approaches by a significant margin (p <0.001). In evaluation of segmentation of PTC on Papanicolaou-stained WSIs, the proposed method is compared with three deep learning methods, including Modified FCN, U-Net, and SegNet. The experimental result demonstrates that the proposed method achieves high accuracy of 99.99 ± 0.01%, precision of 92.02 ± 16.6%, recall of 90.90 ± 14.25%, and F1-score of 89.82 ± 14.92% and significantly outperforms the baseline methods, including U-Net and FCN (p <0.001). With the high degree of accuracy, precision, and recall, the results show that the proposed method could be used in assisting breast cancer target therapy and thyroid cancer diagnosis with faster evaluation and minimizing human judgment errors.
Data curation to support toxicity assessments using the Integrated Chemical Environment

Humans are exposed to large numbers of chemicals during their daily activities. To assess and understand potential health impacts of chemical exposure, investigators and regulators need access to reliable toxicity data. In particular, reliable toxicity data for a wide range of chemistries are needed to support development of new approach methodologies (NAMs) such as computational models, which offer increased throughput relative to traditional approaches and reduce or replace animal use. NAMs development and evaluation require chemically diverse data sets that are typically constructed by incorporating results from multiple studies into a single, integrated view; however, integrating data is not always a straightforward task. Primary study sources often vary in the way data are organized and reported. Metadata and information needed to support interoperability and provide context are often lacking, which necessitates literature research on the assay prior to attempting data integration. The Integrated Chemical Environment (ICE) was developed to support the development, evaluation, and application of NAMs. ICE provides curated toxicity data and computational tools to integrate and explore available information, thus facilitating knowledge discovery and interoperability. This paper describes the data curation workflow for integrating data into ICE. Data destined for ICE undergo rigorous harmonization, standardization, and formatting processes using both automated and manual expert-driven approaches. These processes improve the utility of the data for diverse analyses and facilitate application within ICE or a user's external workflow while preserving data integrity and context. ICE data curation provides the structure, reliability, and accessibility needed for data to support chemical assessments.
Use of chatbots for customer service in MSMEs

Purpose This research work aims to arise from developing new communication channels for customer service in micro, small and medium enterprises (MSMEs), such as chatbots. In particular, the results of the usability testing of three chatbots implemented in MSMEs are presented. Design/methodology/approach The methodology employed includes participants, chatbot development platform, research methodology, software development methodology and usability test to contextualize the study's results. Findings Based on the results obtained from the System Usability Scale (SUS) and considering the accuracy of the chatbot's responses, it is concluded that the level of satisfaction in using chatbots is high; therefore, if the chatbot is well integrated with the communication systems/channels of the MSMEs, the client receives an excellent, fast and efficient service. Originality/value The paper analyzes chatbots for customer service and presents the usability testing results of three chatbots implemented in MSMEs.
AutoComm: A Framework for Enabling Efficient Communication in Distributed Quantum Programs

Distributed quantum computing (DQC) is a promising approach to extending the computational power of near-term quantum hardware. However, the non-local quantum communication between quantum nodes is much more expensive and error-prone than the local quantum operation within each quantum device. Previous DQC compilers focus on optimizing the implementation of each non-local gate and adopt similar compilation designs to single-node quantum compilers. The communication patterns in distributed quantum programs remain unexplored, leading to a far-from-optimal communication cost. In this paper, we identify burst communication, a specific qubit-node communication pattern that widely exists in various distributed quantum programs and can be leveraged to guide communication overhead optimization. We then propose AutoComm, an automatic compiler framework to extract burst communication patterns from input programs and then optimize the communication steps of burst communication discovered. Compared to state-of-the-art DQC compilers, experimental results show that our proposed AutoComm can reduce the communication resource consumption and the program latency by 72.9% and 69.2% on average, respectively.
Efficient Spatiotemporal Graph Search for Local Trajectory Planning on Oval Race Tracks

Autonomous racing has increasingly become a research subject as it provides insights into dynamic, high-speed situations. One crucial aspect of handling these situations, especially in the presence of dynamic obstacles, is the generation of a collision-free trajectory that represents a safe behavior and is also competitive in the case of racing. We propose a local planning approach that generates such trajectories for a racing car on an oval race track by searching a spatiotemporal graph. A considerable challenge of search-based methods in a spatiotemporal domain is the curse of dimensionality. Therefore, we propose how a previously presented graph structure that is based on intervals instead of discrete values can be searched more efficiently without losing optimality by using a uniform-cost search strategy. We extend the search method to make it anytime-capable so that it can provide a suboptimal trajectory even if the search has to be terminated early. The graph-based planning approach allows us to apply a flexible cost function so that our approach can operate fully autonomously on an oval race track, including the pit lane. We present a cost function for oval racing and explain how the terms contribute to the desired behaviors. This is supported by results with a full-scale prototype.
Gamification of Cybersecurity for Workforce Development in Critical Infrastructure

Critical infrastructure has become a focal point of cyberattacks, as previously isolated operational technology networks that were once perceived to be air-gapped are becoming Internet-exposed through increased connectivity with informational technology networks. Recent adversarial tendencies have led to an increase in targeted cyberattacks against both industrial control systems (ICS) and building automation systems. Furthermore, the insufficient supply of a cyber workforce exacerbates the challenges for organizations to defend their systems. Game-based learning is gaining traction and studies have shown that it is an effective educational element. Training facility operators responsible for critical services can be achieved through gamification of security policies and controls. The Network Defense Training Game (NDTG) is a cybersecurity training platform that encompasses a series of cybersecurity events that the player must assess and react to throughout the scenario to defend the network by thwarting the adversary’s attack. The NDTG uses scenario narratives based on historical cyber incidents that affect ICS. It is designed to train facility owners and operators to evaluate their cybersecurity posture and to apply cybersecurity frameworks before and during the process of addressing cyber events and incidents. This study provides a detailed technical overview and design architecture of NDTG and demonstrates its capability in advancing the ICS cybersecurity workforce.
Do Users Write More Insecure Code with AI Assistants?

We conduct the first large-scale user study examining how users interact with an AI Code assistant to solve a variety of security related tasks across different programming languages. Overall, we find that participants who had access to an AI assistant based on OpenAI's codex-davinci-002 model wrote significantly less secure code than those without access. Additionally, participants with access to an AI assistant were more likely to believe they wrote secure code than those without access to the AI assistant. Furthermore, we find that participants who trusted the AI less and engaged more with the language and format of their prompts (e.g. re-phrasing, adjusting temperature) provided code with fewer security vulnerabilities. Finally, in order to better inform the design of future AI-based Code assistants, we provide an in-depth analysis of participants' language and interaction behavior, as well as release our user interface as an instrument to conduct similar studies in the future.
Skyrmion based energy-efficient straintronic physical reservoir computing

Physical Reservoir Computing (PRC) is an unconventional computing paradigm, which exploits nonlinear dynamics of reservoir blocks to perform recognition and classification tasks. Here we show with simulations that patterned thin films hosting several skyrmions, particularly one, two, four and nine skyrmions, can implement energy efficient reservoir computing. This reservoir computing (RC) block is based on nonlinear breathing dynamics of skyrmions, which are coupled to each other through dipole interaction and spin waves, in response to a voltage generated strain. This nonlinear and coupled magnetization dynamics is exploited to perform temporal pattern recognition. Two performance metrics, namely Short-Term Memory (STM) and Parity Check (PC) capacity are studied to demonstrate the potential of such skyrmion based PRC in addition to showing it can classify sine and square waves with 100% accuracy. Furthermore, our study demonstrates that nonlinear magnetization dynamics and interaction through spin waves and dipole coupling have a strong influence on STM and PC capacity, thus explaining the role of physical interactions in a dynamical system on its ability to perform Reservoir Computing (RC).
A General Language Assistant as a Laboratory for Alignment

Given the broad capabilities of large language models, it should be possible to work towards a general-purpose, text-based assistant that is aligned with human values, meaning that it is helpful, honest, and harmless. As an initial foray in this direction we study simple baseline techniques and evaluations, such as prompting. We find that the benefits from modest interventions increase with model size, generalize to a variety of alignment evaluations, and do not compromise the performance of large models. Next we investigate scaling trends for several training objectives relevant to alignment, comparing imitation learning, binary discrimination, and ranked preference modeling. We find that ranked preference modeling performs much better than imitation learning, and often scales more favorably with model size. In contrast, binary discrimination typically performs and scales very similarly to imitation learning. Finally we study a `preference model pre-training' stage of training, with the goal of improving sample efficiency when finetuning on human preferences.