Reviewerly/exHarmony huggingface dataset:
RESEARCH OF CREATIVE-ACTIVITY COMPONENT READINESS OF FUTURE PHARMACISTS FOR USE DIGITAL TECHNOLOGIES IN PROFESSIONAL ACTIVITIES

Тhe emphasis was on creative abilities (as a creative-activity component of the readiness of future professionals to use digital technologies in professional activities) in the context of its development in the formation of information technology competence of future pharmacists in the process of teaching the disciplines of natural science training "Information technology in pharmacy" and "Computer modeling in pharmacy" was emphasized. Within the framework of the developed methodology, a questionnaire survey of future specialists was carried out, and its results were evaluated according to a specific algorithm using the appropriate methods of mathematical statistics. During the formation of information technology competence in the process of teaching the discipline of natural science training "Information technology in pharmacy", it was found that the positive dynamics of the levels of creative abilities of future specialists takes place. It was proved that the formation of IT competence in the process of teaching the discipline of natural science training "Information Technology in Pharmacy", in particular with the use of developed manuals, effectively affects the creative abilities of future pharmacists, namely ensures the development of a creative-activity component of their readiness to use digital technologies in professional activities.
The impact of AI chatbots on customer trust: an empirical investigation in the hotel industry

Purpose This study aims to investigate the impact of AI chatbots on customer trust in AI chatbots for hotel services. Design/methodology/approach The probability sampling method was employed to develop a research sample. The research uses correlation analysis and structural equation modeling to analyze the data of 413 valid observations collected in the structured questionnaire survey in Vietnam. Findings The paper reports that empathy response, anonymity and customization significantly impact interaction. Empathy response is found to be the strongest influence on interaction. Meanwhile, empathy response and anonymity were revealed to indirectly affect customer trust. This paper also contributes several implications for hotel providers in emerging economies. Originality/value To the best of the authors’ knowledge, this is the first study to shed light on the role of AI chatbots in explaining customers’ behavior. The results provide an enhanced understanding of how the AI chatbot system influences customers’ decision-making. It has been used to plan the chatbot application and highlight which implementation issues need the most attention in the hospitality industry.
Deep Clustering by Graph Attention Contrastive Learning

Contrastive learning shows great potential in deep clustering. It uses constructed pairs to discover the feature distribution that is required for the clustering task. In addition to conventional augmented pairs, recent methods have introduced more methods of creating highly confident pairs, such as nearest neighbors, to provide more semantic prior knowledge. However, existing works only use partial pairwise similarities to construct semantic pairs locally without capturing the entire sample’s relationships from a global perspective. In this paper, we propose a novel clustering framework called graph attention contrastive learning (GACL) to aggregate more semantic information. To this end, GACL is designed to simultaneously perform instance-level and graph-level contrast. Specifically, with its novel graph attention mechanism, our model explores more undiscovered pairs and selectively focuses on informative pairs. To ensure local and global clustering consistency, we jointly use the designed graph-level and instance-level contrastive losses. Experiments on six challenging image benchmarks demonstrate the superiority of our proposed approach over state-of-the-art methods.
Information misbehavior: How organizations use information to deceive

Abstract Recent examples of organizational wrongdoing such as those that led to the opioid crisis and the 2008 financial meltdown show that organizations can deliberately use information to deceive others, resulting in serious harm. This brief communication explores the role of information in organizational wrongdoing. We analyze a dataset consisting of 80 cases of high‐penalty corporate wrongdoing in the United States in the period 2000–2020. Our analysis of documents filed by the US Department of Justice and federal regulatory agencies in those cases found that organizations use two general information strategies to deceive and mislead. First, organizations can “sow doubt” on statements by others that hurt the organization's interests. Second, organizations can “exploit trust” that others have placed in them to provide truthful information. Our analysis suggests that which strategy is adopted depends on the degree that the organization's external information use environment is “contested” or “controlled.” Across the cases examined, we observe three types of information behaviors that implement the strategy of sowing doubt and exploiting trust: information obfuscation, information concealment, and information falsification.
Learning and Fusing Multi-View Code Representations for Function Vulnerability Detection

The explosive growth of vulnerabilities poses a significant threat to the security of software systems. While various deep-learning-based vulnerability detection methods have emerged, they primarily rely on semantic features extracted from a single code representation structure, which limits their ability to detect vulnerabilities hidden deep within the code. To address this limitation, we propose S2FVD, short for Sequence and Structure Fusion-based Vulnerability Detector, which fuses vulnerability-indicative features learned from the multiple views of the code for more accurate vulnerability detection. Specifically, S2FVD employs either well-matched or carefully extended neural network models to extract vulnerability-indicative semantic features from the token sequence, attributed control flow graph (ACFG) and abstract syntax tree (AST) representations of a function, respectively. These features capture different perspectives of the code, which are then fused to enable S2FVD to accurately detect vulnerabilities that are well-hidden within a function. The experiments conducted on two large vulnerability datasets demonstrated the superior performance of S2FVD against state-of-the-art approaches, with its accuracy and F1 scores reaching 98.07% and 98.14% respectively in detecting the presence of vulnerabilities, and 97.93% and 97.94%, respectively, in pinpointing specific vulnerability types. Furthermore, with regard to the real-world dataset D2A, S2FVD achieved average performance gains of 6.86% and 14.84% in terms of accuracy and F1 metrics, respectively, over the state-of-the-art baselines. This ablation study also confirms the superiority of fusing the semantics implied in multiple distinct code views to further enhance vulnerability detection performance.
High Quality Video Frames From VVC: A Deep Neural Network Approach

In recent years, video content has become a significant contributor to Internet traffic, prompting the development of efficient codecs, such as High Efficiency Video Coding (HEVC) and Versatile Video Coding (VVC), to reduce bandwidth usage and storage requirements.However, these video coding standards still exhibit quality degradation and artifacts in the decoded frames.To address this issue, researchers have introduced several network architectures based on deep-learning algorithms; however, most of them focus on in-loop filtering, which requires additional bits to transmit filter information from the encoder to the decoder under a video-coding framework.In this paper, we propose a neural-network-based post-processing method to enhance the decoded frames.In the experimental result, the proposed model achieves a significant bitrate reduction, as measured by Bjøntegaard Delta of 4.54%, 4.13%, and 5.21% for random access (RA), low-delay (LD), and all-intra (AI) configurations, respectively, while also improving peak signal-to-noise ratio (PSNR).
Predicting Solar Irradiance at Several Time Horizons Using Machine Learning Algorithms

Photovoltaic (PV) panels need to be exposed to sufficient solar radiation to produce the desired amount of electrical power. However, due to the stochastic nature of solar irradiance, smooth solar energy harvesting for power generation is challenging. Most of the available literature uses machine learning models trained with data gathered over a single time horizon from a location to forecast solar radiation. This study uses eight machine learning models trained with data gathered at various time horizons over two years in Limpopo, South Africa, to forecast solar irradiance. The goal was to study how the time intervals for forecasting the patterns of solar radiation affect the performance of the models in addition to determining their accuracy. The results of the experiments generally demonstrate that the models’ accuracy decreases as the prediction horizons get longer. Predictions were made at 5, 10, 15, 30, and 60 min intervals. In general, the deep learning models outperformed the conventional machine learning models. The Convolutional Long Short-Term Memory (ConvLSTM) model achieved the best Root Mean Square Error (RMSE) of 7.43 at a 5 min interval. The Multilayer Perceptron (MLP) model, however, outperformed other models in most of the prediction intervals.
A Dynamic Image Encryption Scheme Based on Quantum Walk and Chaos-Induced DNA

The development of quantum information technology and increasing attention of people to the secure transmission of image information in the Internet have put forward higher requirements for traditional image encryption algorithms that not only take advantage of the exponential acceleration ability of quantum computing compared with classical computing but also reduce the risk of encryption algorithms being cracked. Therefore, in order to seek the combination of the advantages of quantum computing and classical image encryption algorithms, this paper proposes a new dynamic encryption image scheme of quantum walk and chaos-induced DNA. Firstly, the RGB three-channel pixels of the color image are extracted and combined into a one-dimensional array, and a random sequence is generated by quantum walk to reorder it to obtain a preliminary scrambled image; secondly, the color image is processed by the SHA-256 algorithm and divided into the generated message digest as the initial condition of the chaotic model. The random sequence was generated by the high-dimensional chaotic model which encodes each pixel independently and disorderly as DNA bases. The difference of the chaotic sequence ensures the dynamic selection of random DNA encoding and decoding rules during encryption. At the same time, the number of times of DNA encryption of the encoded pixel value is also controlled by the dynamic induction of the chaotic sequence, and ultimately, the DNA coding sequence is replaced with the decimal pixel value to obtain the encrypted image. The simulation results show that the information entropy of the encrypted image is above 7.99, and the correlation of each channel is close to 0, which can effectively resist brute force attacks, plaintext attacks, statistical analysis attacks, noise attacks, etc. In addition, in this paper, extracting the watermark embedded in the encrypted image to judge whether image information is tampered or forged further improves the security of the image information.
Support Vector Machine Chains with a Novel Tournament Voting

Support vector machine (SVM) algorithms have been widely used for classification in many different areas. However, the use of a single SVM classifier is limited by the advantages and disadvantages of the algorithm. This paper proposes a novel method, called support vector machine chains (SVMC), which involves chaining together multiple SVM classifiers in a special structure, such that each learner is constructed by decrementing one feature at each stage. This paper also proposes a new voting mechanism, called tournament voting, in which the outputs of classifiers compete in groups, the common result in each group gradually moves to the next round, and, at the last round, the winning class label is assigned as the final prediction. Experiments were conducted on 14 real-world benchmark datasets. The experimental results showed that SVMC (88.11%) achieved higher accuracy than SVM (86.71%) on average thanks to the feature selection, sampling, and chain structure combined with multiple models. Furthermore, the proposed tournament voting demonstrated higher performance than the standard majority voting in terms of accuracy. The results also showed that the proposed SVMC method outperformed the state-of-the-art methods with a 6.88% improvement in average accuracy.
Semantic Hierarchical Indexing for Online Video Lessons Using Natural Language Processing

Huge quantities of audio and video material are available at universities and teaching institutions, but their use can be limited because of the lack of intelligent search tools. This paper describes a possible way to set up an indexing scheme that offers a smart search modality, that combines semantic analysis of video/audio transcripts with the exact time positioning of uttered words. The proposal leverages NLP methods for topic modeling with lexical analysis of lessons’ transcripts and builds a semantic hierarchical index into the corpus of lessons analyzed. Moreover, using abstracting summarization, the system can offer short summaries on the subject semantically implied by the search carried out.
Breast Density Transformations Using CycleGANs for Revealing Undetected Findings in Mammograms

Breast cancer is the most common cancer in women, a leading cause of morbidity and mortality, and a significant health issue worldwide. According to the World Health Organization’s cancer awareness recommendations, mammographic screening should be regularly performed on middle-aged or older women to increase the chances of early cancer detection. Breast density is widely known to be related to the risk of cancer development. The American College of Radiology Breast Imaging Reporting and Data System categorizes mammography into four levels based on breast density, ranging from ACR-A (least dense) to ACR-D (most dense). Computer-aided diagnostic (CAD) systems can now detect suspicious regions in mammograms and identify abnormalities more quickly and accurately than human readers. However, their performance is still influenced by the tissue density level, which must be considered when designing such systems. In this paper, we propose a novel method that uses CycleGANs to transform suspicious regions of mammograms from ACR-B, -C, and -D levels to ACR-A level. This transformation aims to reduce the masking effect caused by thick tissue and separate cancerous regions from surrounding tissue. Our proposed system enhances the performance of conventional CNN-based classifiers significantly by focusing on regions of interest that would otherwise be misidentified due to fatty masking. Extensive testing on different types of mammograms (digital and scanned X-ray film) demonstrates the effectiveness of our system in identifying normal, benign, and malignant regions of interest.
A Semantically Enhanced Knowledge Discovery Method for Knowledge Graph Based on Adjacency Fuzzy Predicates Reasoning

Discover the deep semantics from the massively structured data in knowledge graph and provide reasonable explanations are a series of important foundational research issues of artificial intelligence. However, the deep semantics hidden between entities in knowledge graph cannot be well expressed. Moreover, considering many predicates express fuzzy relationships, the existing reasoning methods cannot effectively deal with these fuzzy semantics and interpret the corresponding reasoning process. To counter the above problems, in this article, a new interpretable reasoning schema is proposed by introducing fuzzy theory. The presented method focuses on analyzing the fuzzy semantic between related entities in a knowledge graph. By annotating the fuzzy semantic features of adjacency predicates, a novel semantic reasoning model is designed to realize the fuzzy semantic extension over knowledge graph. The evaluation, based on both visualization and query experiments, shows that this proposal has advantages over the initial knowledge graph and can discover more valid semantic information.
Region Expansion of a Hyperspectral-Based Mineral Map Using Random Forest Classification with Multispectral Data

Observation images from hyperspectral (HS) sensors on satellites and aircraft can be used to map minerals in greater detail than those from multispectral (MS) sensors. However, the coverage of HS images is much less than that of MS images, so there are often cases where MS images cover the entire area of interest while HS images cover only a part of it. In this study, we propose a new method to more reasonably expand the mineral map of an HS image with an MS image in such cases. The method uses various mineral indices from the MS image and MS sensor’s band values as the input and HS image-based mineral classes as the output. Random forest (RF) two-class classification is then applied iteratively to determine the distribution of each mineral in turn, starting with the minerals that are most consistent with the HS image-based mineral map. The method also involves the correction of misalignment between HS and MS images and the selection of input variables by RF multiclass classification. The method was evaluated in comparison with other methods in the Cuprite area, Nevada, using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Hyperspectral Imager Suite (HISUI) as HS sensors and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) as MS sensors. As a result, all of the evaluated region-expansion methods with an HS–MS image pair, including the proposed method, showed better performance than the method using only an MS image. The proposed method had the highest performance, and the inter-mineral averages of the F1-scores for the overlap and non-overlap areas were 85.98% and 46.46% for the AVIRIS–ASTER image pair and 82.78% and 42.60% for the HISUI–ASTER image pair, respectively. Although the performance in the non-overlap region was lower than in the overlap region, the method showed high precision and high accuracy for almost all minerals, including minerals with only a few pixels. Misalignment between the HS–MS images is a factor that degrades accuracy and requires precise alignment, but the misalignment correction in the proposed method could suppress the effect of misalignment. Validation studies using different regions and different sensors will be carried out in the future.
Reputation-Aware Multi-Agent DRL for Secure Hierarchical Federated Learning in IoT

Aiming at protecting device data privacy, Federated Learning (FL) is a framework of distributed machine learning in which devices' local model parameters are exchanged with a centralized server without revealing the actual data.Hierarchical Federated Learning (HFL) framework was introduced to improve FL communication efficiency where devices are clustered and seek model consensus with the support of edge servers (e.g., base stations).Devices in a cluster submit their local model updates to their assigned local edge server for aggregation at each iteration.The edge servers transmit the aggregated models to a centralized server and establish a global consensus.However, similar to FL, adversaries may threaten the security and privacy of HFL.The client devices within a cluster may deliberately provide unreliable local model updates through poisoning attacks or poor-quality model updates due to inconsistent communication channels, increased device mobility, or inadequate device resources.To address the above challenges, this paper investigates the client selection problem in the HFL framework to eliminate the impact of unreliable clients while maximizing the global model accuracy of HFL.Each FL edge server is equipped with a Deep Reinforcement Learning (DRL)-based reputation model to optimally measure the reliability and trustworthiness of FL workers within its cluster.A Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is utilized to enhance the accuracy and stability of the HFL global model, given the workers' dynamic behaviors in the HFL environment.The experimental results indicate that our proposed MADDPG improves the accuracy and stability of HFL compared with the conventional reputation model and single-agent DDPG-based reputation model.
Camp2Vec: Embedding cyber campaign with ATT&amp;CK framework for attack group analysis

As the cyberattack subject has expanded from individual to group, attack patterns have become a complicated form of cyber campaigns. Although detecting the attack groups that operated the cyber campaigns is an important issue, complex methods such as deep learning are difficult to use due to the lack of campaign data. This paper proposes Camp2Vec, a lightweight statistics-based embedding for cyber campaigns, enabling attack group detection. The proposed method models a relationship between a campaign and techniques in the ATT&CK® framework as a document and words. Experimental results with expert-labeled datasets prove that Camp2Vec identifies representative attack groups successfully.
Beneficiary Contracts on a Lightweight Blockchain Architecture Using Smart Contracts: A Smart Healthcare System for Medical Records

The effective management of medical records is essential in the ordinary and emergency operations of healthcare providers. This work uses blockchain to develop a smart contract algorithm for users of a medical record platform. This algorithm provides immutable execution and addresses authentication and reliability issues to control access to healthcare platforms. An executable distributed code is used to build the smart contract algorithm. In the proposed algorithm, management operations of the clinical history are carried out and integrated in an automated way in a distributed environment. Solidity is the programming language used to create the algorithm for a private and permissioned architecture with a proposed consensus algorithm requiring significantly less computational power using a 22% faster hash function.