Reviewerly/exHarmony huggingface dataset:
A syndrome differentiation model of TCM based on multi-label deep forest using biomedical text mining

Syndrome differentiation and treatment is the basic principle of traditional Chinese medicine (TCM) to recognize and treat diseases. Accurate syndrome differentiation can provide a reliable basis for treatment, therefore, establishing a scientific intelligent syndrome differentiation method is of great significance to the modernization of TCM. With the development of biomdical text mining technology, TCM has entered the era of intelligence that based on data, and model training increasingly relies on the large-scale labeled data. However, it is difficult to form a large standard data set in the field of TCM due to the low degree of standardization of TCM data collection and the privacy protection of patients' medical records. To solve the above problem, a multi-label deep forest model based on an improved multi-label ReliefF feature selection algorithm, ML-PRDF, is proposed to enhance the representativeness of features within the model, express the original information with fewer features, and achieve optimal classification accuracy, while alleviating the problem of high data processing cost of deep forest models and achieving effective TCM discriminative analysis under small samples. The results show that the proposed model finally outperforms other multi-label classification models in terms of multi-label evaluation criteria, and has higher accuracy in the TCM syndrome differentiation problem compared with the traditional multi-label deep forest, and the comparative study shows that the use of PCC-MLRF algorithm for feature selection can better select representative features.
Perceptual thresholds of visual size discrimination in augmented and virtual reality

The perception of size in virtual objects in Augmented Reality (AR) and Virtual Reality (VR) is a not trivial issue, as the effectiveness of manipulating and interacting with virtual content depends on the accuracy of size perception. However, there are missing straightforward comparisons between VR and AR in terms of size perception for the deep understanding of size perceptual differences. Understanding these perceptual differences can inform designers on how to adapt content when transitioning between these two spatial computing platforms. In this paper, we conducted two psychophysical experiments to measure the perceptual thresholds of size discrimination for virtual objects. Our results indicated that users are more sensitive to size changes in VR than in video see-through AR, suggesting that size differences are easier to be perceived in VR than in AR. Additionally, for increase or decrease of sizes, the accuracy of judgements showed an asymmetric trend in video see-through AR.
GSDerainNet: A Deep Network Architecture Based on a Gaussian Shannon Filter for Single Image Deraining

With the continuous advancement of target detection technology in remote sensing, target detection technology in images captured by drones has performed well. However, object detection in drone imagery is still a challenge under rainy conditions. Rain is a common severe weather condition, and rain streaks often degrade the image quality of sensors. The main issue of rain streaks removal from a single image is to prevent over smoothing (or underclearing) phenomena. Aiming at the above problems, this paper proposes a deep learning (DL)-based rain streaks removal framework called GSDerainNet, which properly formulates the single image rain streaks removal problem; rain streaks removal is aptly described as a Gaussian Shannon (GS) filter-based image decomposition problem. The GS filter is a novel filter proposed by us, which consists of a parameterized Gaussian function and a scaled Shannon function. Two-dimensional GS filters exhibit high stability and effectiveness in dividing an image into low- and high-frequency parts. In our framework, an input image is first decomposed into a low-frequency part and a high-frequency part by using the GS filter. Rain streaks are located in the high-frequency part. We extract and separate the rain features of the high-frequency part through a deep convolutional neural network (CNN). The experimental results obtained on synthetic data and real data show that the proposed method can better suppress the morphological artifacts caused by filtering. Compared with state-of-the-art single image rain streaks removal methods, the proposed method retains finer image object structures while removing rain streaks.
Unveiling the Influence of Artificial Intelligence and Machine Learning on Financial Markets: A Comprehensive Analysis of AI Applications in Trading, Risk Management, and Financial Operations

This study explores the adoption and impact of artificial intelligence (AI) and machine learning (ML) in financial markets, utilizing a mixed-methods approach that includes a quantitative survey and a qualitative analysis of existing research papers, reports, and articles. The quantitative results demonstrate the growing adoption of AI and ML technologies in financial institutions and their most common applications, such as algorithmic trading, risk management, fraud detection, credit scoring, and customer service. Additionally, the qualitative analysis identifies key themes, including AI and ML adoption trends, challenges and barriers to adoption, the role of regulation, workforce transformation, and ethical and social considerations. The study highlights the need for financial professionals to adapt their skills and for organizations to address challenges, such as data privacy concerns, regulatory compliance, and ethical considerations. The research contributes to the knowledge on AI and ML in finance, helping policymakers, regulators, and professionals understand their benefits and challenges.
A blockchain-empowered framework for decentralized trust management in Internet of Battlefield Things

The security of the Internet of Battlefield Things (IoBT) is greatly challenged by malicious intrusions from external environments. To protect IoBT from such intrusions, it is crucial to implement trust management (TM) at the node level. However, the decentralized and dynamic nature of IoBT means that participating nodes have no predetermined trust relationships. To address this issue, we propose a blockchain-empowered framework to achieve decentralized TM in IoBT. We begin by introducing a network model that integrates the space and ground segments with portable ping-pong stations. In the absence of fixed infrastructure, these stations enable rapid networking among combat units in IoBT. We also leverage these stations to form a blockchain network that manages decentralized trusts among IoBT nodes. Our blockchain-empowered TM framework consists of three algorithmic components: (i) Local Trust Computation: each IoBT node uses a local trust computation algorithm to compute the local trusts of its neighboring nodes; (ii) Global Trust Aggregation: portable ping-pong stations use a global trust aggregation algorithm to aggregate the global trusts of IoBT nodes; (iii) Blockchain Consensus Process: the global trusts of IoBT nodes are updated to the blockchain. We have also developed a new consensus algorithm that incentivizes blockchain nodes to proactively maintain the real trusts of IoBT nodes. We conducted several simulations incorporating different attack models of malicious nodes and system setups to comprehensively evaluate the performance of our TM framework. The simulation results demonstrate that our blockchain-empowered decentralized TM effectively suppresses the probability of successful attacks and thus improves the security of IoBT. In summary, our proposed framework offers a promising solution for achieving decentralized TM in IoBT, and we believe that it has the potential to significantly enhance the security of this rapidly developing field.
Immersive technologies as an innovative tool to increase academic success and motivation in higher education

The latest techniques and technologies significantly improve the academic performance, engagement, and motivation of students. VR and AR open up numerous opportunities for the educational system. The purpose is to evaluate the effectiveness of using immersive technologies as a tool to increase the level of academic success, involvement, and motivation among students. The research involved a total number of 180 students in two higher educational institutions. This study presupposed using a previously elaborated program for further use in the experimental group. This program was introduced into the study program within the participating universities. The study included three tests to collect the data under the Motivation and Engagement Scale, as well as European Credit Transfer and Accumulation System. The experimental group provided positive indicators during statistical data analysis; thus, it demonstrates the effectiveness of the studied methods. Although the engagement and motivation of students from the experimental group increased, there were no differences in academic achievements between the groups. Therefore, it cannot be argued that immersive technologies have a direct impact on grades, which are the main indicator of success in learning. The new experimental data obtained in this study and the analysis of previous modern experimental studies allowed us to draw relevant conclusions about the expediency and high efficiency of immersive education technologies for teaching university students.
Deep document clustering via adaptive hybrid representation learning

Document clustering plays an important role in text mining and machine learning. Recently, various studies have indicated that a hybrid representation contributes to document clustering tasks. Adaptively adjusting representation learning to make each representation of documents mutually reinforced remains an open problem. In this study, we propose a deep document clustering model, DCAHR, to improve the document clustering performance by adaptively learning a hybrid representation. Specifically, an adaptive representation enhanced network (AREN) is designed using a composite Gated Linear Units (GLU) mechanism to adaptively capture the consistent information of semantic and structure representations, and utilize it to adaptively adjust each representation learning for studying the corresponding enhanced representations. A hybrid representation was then obtained by normalizing the two enhanced representations. Furthermore, an adaptive joint objective function was developed to learn document partitioning and supervise the entire model by adaptively learning the weights of each objective function. Experimental results on nine commonly used real-world datasets demonstrated the outstanding performance of the proposed DCAHR model.
Context-Aware Knowledge Graphs Exploratory Search

The exploratory search approach recognizes that user queries can be incomplete, inaccurate, and ambiguous. This occurs both because of incomplete domain knowledge by the user or due to implicit assumptions about the context. This ongoing research aims to enrich Knowledge Graphs (KG) to support context-aware exploration through expanded queries. We propose a Contextual KG (CKG) definition and schema that characterizes the necessary elements for modeling contextual information and a query-answering approach that retrieves all (contextualized) possible answers.
Survey on security and privacy in <scp>Internet of Things</scp>‐based <scp>eHealth</scp> applications: Challenges, architectures, and future directions

Abstract The integration of Internet of Things (IoT) technology into electronic health (eHealth) applications has revolutionized the healthcare landscape, enabling real‐time patient monitoring, personalized care, and improved patient outcomes. However, this convergence of IoT and healthcare also introduces critical security and privacy challenges, needing careful consideration. This survey comprehensively explores the multifaceted realm of security and privacy issues in IoT‐based eHealth applications. First, we taxonomize the diverse security threats that arise due to the interconnected nature of IoT medical devices. Additionally, we highlight privacy concerns stemming from the collection and sharing of personal health information, while reconciling them with the need for accessible and collaborative healthcare ecosystems. Second, we synthesize functional, ethical, and regulatory perspectives to pick up the major requirements needed in the context of eHealth data during their whole lifecycle, from creation to destruction. Third, we identify emerging research strategies employed to address security and privacy concerns, such as cloud‐based solutions, decentralized technologies such as blockchain technology and InterPlanetary File System (IPFS), cryptographic approaches, fine‐grained access control strategies, and so forth. Additionally, we examine the impact of these approaches on computational efficiency, latency, and energy consumption, critically evaluating their suitability in the healthcare context. Building upon this comprehensive assessment, we outline potential future research directions aimed at advancing security and privacy measures in IoT‐based eHealth applications.
Trends of Digitalization in the Training of Teachers in the Republic of Poland

The article defines the trends of digitalization in the training of teachers in the Republic of Poland. It concludes that modern education must keep up with the changing world by focusing on shaping teachers' digital competencies, personalizing teaching, using digital technologies and learning tools, actively implementing blended learning, lifelong learning, and project-based learning. By integrating and adopting these trends into the educational process in higher education institutions, future teachers will be better prepared for their future professional careers and will be able to respond to the challenges of the modern job market. Digital tools that influence the new role of the university include platforms and applications that facilitate distance learning and contribute to a paradigm shift in teaching and learning at higher education institutions. The article also explores the active use of Massive Open Online Courses (MOOCs) in the Republic of Poland. To achieve the research goals and objectives a combination of general scientific methods are used in the study, including theoretical methods such as analysis and synthesis to determine the main directions of research on the peculiarities of digitalization of teachers' training in Poland; empirical methods to study the regulatory and legal framework of the digitalization, the works of domestic and foreign researchers regarding the digitalization of teachers' training and education in general; and statistical processing of research results to obtain quantitative and qualitative data. Since the issue of digitalization trends in the training of teachers in the Republic of Poland is insufficiently studied, further research on the chosen problem is relevant and timely. It will contribute to adopting all the current positive trends in the educational space of Ukraine.
UCN-YOLOv5: Traffic Sign Object Detection Algorithm Based on Deep Learning

Traffic sign detection plays an important role in traffic safety and traffic management. In view of the complex and changeable environment and detection accuracy of traffic sign detection, this paper proposes UCN-YOLOv5 model based on the framework of YOLOv5.This model first replaces a new backbone network, which uses the core module RSU of U2Net to enhance the feature extraction of the network. Then, ConvNeXt-V2 is integrated, and the C3 module of its Block and YOLOv5 network is used to construct the C3_CN2 structure. The utilization of the proposed lightweight receptive field attention module LPFAConv in the Head section represents a potential enhancement for the extraction of receptive field features. Finally, for small targets in traffic signs, Normalized Wasserstein Distance (NWD), which is insensitive to targets of different scales, is added to calculate the position loss function to replace the IoU metric to a certain extent, which further improves the detection ability of our model for traffic signs. Experiments on the TT100K dataset show that UCNYOLOv5 has excellent detection performance. Compared with the baseline model (Y0Lov5s, YOLOV5m, YOLOV5l), it improves the Map.5 index by 5.9 %, 4.9 % and 4.6 %; in the Map.5:.95 index, it is 4.4 %, 3.5 % and 2.8 % better. Moreover, the enhanced algorithm demonstrated favorable performance on the LISA and CCTSDB2021 traffic sign datasets. This research has important value for the accurate detection of traffic sign detection, and has guiding significance for in-depth research in related fields.
Exploring the Landscape of Automatic Text Summarization: A Comprehensive Survey

The discipline of Automatic Text Summarization (ATS), which is expanding quickly, intends to automatically create summaries of enormous amounts of text so that readers can save time and effort. ATS is a rapidly growing field that aims to save readers time and effort by automatically generating summaries of large volumes of text. In recent years, significant advancements have been witnessed in this area, accompanied by challenges that have spurred extensive research. The proliferation of textual data has sparked substantial interest in ATS, which is thoroughly examined in this survey study. Researchers have been refining ATS techniques since the 1950s, primarily categorized as extractive, abstractive, or hybrid approaches. In the extractive approach, key sentences are extracted from the source document(s) and combined to form the summary, while the abstractive approach employs an intermediary representation of the input document(s) to generate a summary that may differ from the original text. Hybrid approaches combine elements of both extractive and abstractive methods. Despite various recommended methodologies, the generated summaries still exhibit noticeable differences compared to those created by humans. This research survey offers an inclusive exploration of ATS, covering its challenges, types, classifications, approaches, applications, methods, implementations, processing and preprocessing techniques, linguistic analysis, datasets, and evaluation measures, catering to the needs of researchers in the field.
Optimized Offline-Coverage Path Planning Algorithm for Multi-Robot for Weeding in Paddy Fields

The coverage path planning (CPP) algorithms play a key role in autonomous robot applications, making area coverage operations efficient and cost-effective. The extension of coverage path planning algorithms to multi-robot operation is still widely unveiled despite the cyclical nature of agricultural operations, i.e., comprising repeated actions. The problem of coverage path planning for multi-robot operations is addressed in this paper. The three possible forms of multi-robot coverage algorithms evolved from the basic single-robot coverage algorithm based on the elementary trapezoidal method or zig-zag movements. Furthermore, an optimized coverage path planning algorithm for multiple in-row robots meant to control the weeding in an agricultural field is proposed. The parameters of the agricultural field are supposed to be known upfront, opening the application of an offline planning algorithm. The proposed algorithm stands tall in terms of distance covered with no repeated coverage compared with other possible solutions, nearing the results of single robot coverage (for which the planning is trivially simpler and there is no coverage repetition). Online adjustments in the multi-robot area coverage are also considered, and the proposed algorithm proves to be effective in simulation in this respect as well. The quantitative evaluation proves that, in the proposed algorithm with a team size of 15(n = 15), the average distance consumed by each robot to cover the field taken for the study is only 65% of that of the other two algorithms. Also shows increase in the team size (n) leads to a decrease in consumption. This algorithm provides a solution for the autonomous operation of multi-robots to cover the fields with static obstacles at a regular pattern which is a common demand of many agricultural processes.
Assessing the Real Impact of Open-Source Components in Software Systems

Open-source libraries form the backbone of modern software systems, making software composition analysis (SCA) a vital part of the software development cycle. Despite its importance, current SCA methods, primarily focusing on open-source component issues, lack comprehensive analysis of these components’ integration into the software system. This paper proposes an advanced SCA approach that simultaneously considers open-source component issues and their integration into a software system. We introduce a novel meta-model that links a library with its source code dependencies and enables a unified analysis, irrespective of the originating package manager or open-source repository. The proposed approach, instantiated through a code analysis tool and adapters for major package managers and repositories, was applied to over 200 popular GitHub projects. Results confirm that the impact of open-source component issues largely depends on their integration level in the software system, validating our assumption that effective risk management requires understanding of the open-source component use within the system. Our work, therefore, provides an enriched methodology for SCA.
Measurement-Based Quantum Sealed-Bid Auction

Quantum sealed-bid auction (QSA) is expected to provide an alternative to classical sealed-bid auction to defend against attacks from quantum adversaries. However, most previous QSA protocols required bidders to have quantum operation capabilities, resulting in excessive quantum resource requirements. To relieve the burden of protocol quantum resources, a measurement-based quantum sealed-bid auction protocol with the one-way transmission is proposed. Compared to previous protocols, our protocol only requires bidders to perform measurements, and no other operations, such as Pauli or Phase operations, are required. In order to verify the correctness and feasibility of the proposed QSA protocol, the corresponding quantum circuits are designed and simulated by IBM Qiskit. The proposed protocol satisfies security properties, including anonymity, fairness, verifiability, privacy, and non-repudiation. In addition, we further extend the protocol to scenarios that do not require an auctioneer and achieve bid privacy protection for unsuccessful bidders. Therefore, our protocol may have broad applications in auction scenarios.
Protecting steered coherence in squeezed generalized amplitude damping channel with memory

We investigate the average steered coherence (ASC) and maximal steered coherence (MSC) of two qubits in a squeezed generalized amplitude damping channel modeled by a squeezed thermal bath with dissipation and memory. By considering the input Bell states, it is shown that while the dissipation and thermal effects induce decay of the ASC and MSC in the output states, the squeezing effect suppresses their decay in the strong (full) squeezing region for the input even-parity (odd-parity) Bell states. Besides, the memory effect due to correlated actions of the channel can also be exploited for suppressing decay of the ASC and MSC.