Reviewerly/exHarmony huggingface dataset:
Multi-Task Aspect-Based Sentiment: A Hybrid Sampling and Stance Detection Approach

This paper discusses the challenges associated with a class imbalance in medical data and the limitations of current approaches, such as machine multi-task learning (MMTL), in addressing these challenges. The proposed solution involves a novel hybrid data sampling method that combines SMOTE, a meta-weigher with a meta-based self-training method (MMS), and one-sided selection (OSS) to balance the distribution of classes. The method also utilizes condensed nearest neighbors (CNN) to remove noisy majority examples and redundant examples. The proposed technique is twofold, involving the creation of artificial instances using SMOTE-OSS-CNN to oversample the under-represented class distribution and the use of MMS to train an instructor model that produces in-field knowledge for pseudo-labeled examples. The student model uses these pseudo-labels for supervised learning, and the student model and MMS meta-weigher are jointly trained to give each example subtask-specific weights to balance class labels and mitigate the noise effects caused by self-training. The proposed technique is evaluated on a discharge summary dataset against six state-of-the-art approaches, and the results demonstrate that it outperforms these approaches with complete labeled data and achieves results equivalent to state-of-the-art methods that require all labeled data using aspect-based sentiment analysis (ABSA).
EDF-YOLOv5: An Improved Algorithm for Power Transmission Line Defect Detection Based on YOLOv5

Detecting defects in power transmission lines through unmanned aerial inspection images is crucial for evaluating the operational status of outdoor transmission equipment. This paper presents a defect recognition method called EDF-YOLOv5, which is based on the YOLOv5s, to enhance detection accuracy. Firstly, the EN-SPPFCSPC module is designed to improve the algorithm’s ability to extract information, thereby enhancing the detection performance for small target defects. Secondly, the algorithm incorporates a high-level semantic feature information extraction network, DCNv3C3, which improves its ability to generalize to defects of different shapes. Lastly, a new bounding box loss function, Focal-CIoU, is introduced to enhance the contribution of high-quality samples during training. The experimental results demonstrate that the enhanced algorithm achieves a 2.3% increase in mean average precision (mAP@.5) for power transmission line defect detection, a 0.9% improvement in F1-score, and operates at a detection speed of 117 frames per second. These findings highlight the superior performance of EDF-YOLOv5 in detecting power transmission line defects.
Toward Effective Aircraft Call Sign Detection Using Fuzzy String-Matching between ASR and ADS-B Data

Recently, artificial intelligence and data science have witnessed dramatic progress and rapid growth, especially Automatic Speech Recognition (ASR) technology based on Hidden Markov Models (HMMs) and Deep Neural Networks (DNNs). Consequently, new end-to-end Recurrent Neural Network (RNN) toolkits were developed with higher speed and accuracy that can often achieve a Word Error Rate (WER) below 10%. These toolkits can nowadays be deployed, for instance, within aircraft cockpits and Air Traffic Control (ATC) systems in order to identify aircraft and display recognized voice messages related to flight data, especially for airports not equipped with radar. Hence, the performance of air traffic controllers and pilots can ultimately be improved by reducing workload and stress and enforcing safety standards. Our experiment conducted at Tangier’s International Airport ATC aimed to build an ASR model that is able to recognize aircraft call signs in a fast and accurate way. The acoustic and linguistic models were trained on the Ibn Battouta Speech Corpus (IBSC), resulting in an unprecedented speech dataset with approved transcription that includes real weather aerodrome observation data and flight information with a call sign captured by an ADS-B receiver. All of these data were synchronized with voice recordings in a structured format. We calculated the WER to evaluate the model’s accuracy and compared different methods of dataset training for model building and adaptation. Despite the high interference in the VHF radio communication channel and fast-speaking conditions that increased the WER level to 20%, our standalone and low-cost ASR system with a trained RNN model, supported by the Deep Speech toolkit, was able to achieve call sign detection rate scores up to 96% in air traffic controller messages and 90% in pilot messages while displaying related flight information from ADS-B data using the Fuzzy string-matching algorithm.
Distance Estimation Approach for Maritime Traffic Surveillance Using Instance Segmentation

Maritime traffic monitoring systems are particularly important in Mediterranean ports, as they provide more comprehensive data collection compared to traditional systems such as the Automatic Identification System (AIS), which is not mandatory for all vessels. This paper improves the existing real-time maritime traffic monitoring systems by introducing a distance estimation algorithm for monocular cameras, which aims to provide high quality maritime traffic metadata collection for traffic density analysis. Two distance estimation methods based on a pinhole camera model are presented: the Vessel-Focused Distance Estimation (VFDE) and the novel Vessel Object-Focused Distance Estimation (VOFDE). While VFDE uses the predefined height of a vessel for distance estimation, VOFDE uses standardized dimensions of objects on the vessel, detected with a Convolutional Neural Network (CNN) for instance segmentation to enhance estimation accuracy. Our evaluation covers distances up to 414 m, which is significantly beyond the scope of previous studies. When compared to the distances measured with a precise instrument, VOFDE achieves a Percentage Deviation Index (PDI) of 1.34% to 9.45%. This advance holds significant potential for improving maritime surveillance with monocular cameras and is also applicable in other areas, such as low-cost maritime vehicles equipped with single cameras.
Self-Tracking among Young People: Lived Experiences, Tensions and Bodily Outcomes

Self-tracking enables people to quantify and measure lifestyle and fitness activities and experiences. Our study focuses on the role of self-tracking in young people’s relationship with their body and their lived, ‘fleshy’ experiences in the social world. We draw on 23 in-depth interviews with young people using a life story approach. Our findings show that self-tracking affords young people to engage in different types of ‘body work’, to care for and transform their body that is in constant flux by treating it as either a ‘private’ or ‘shared’ project. We contribute to ongoing debates about the role of self-tracking in young people’s lives by offering a holistic approach that considers the individual and social circumstances that render self-tracking an ongoing, iterative, cumulative and embodied process of discovery, learning and lived and ‘fleshy’ experience.
DiffuRec: A Diffusion Model for Sequential Recommendation

Mainstream solutions to sequential recommendation represent items with fixed vectors. These vectors have limited capability in capturing items’ latent aspects and users’ diverse preferences. As a new generative paradigm, diffusion models have achieved excellent performance in areas like computer vision and natural language processing. To our understanding, its unique merit in representation generation well fits the problem setting of sequential recommendation. In this article, we make the very first attempt to adapt the diffusion model to sequential recommendation and propose DiffuRec for item representation construction and uncertainty injection. Rather than modeling item representations as fixed vectors, we represent them as distributions in DiffuRec , which reflect a user’s multiple interests and an item’s various aspects adaptively. In the diffusion phase, DiffuRec corrupts the target item embedding into a Gaussian distribution via noise adding, which is further applied for sequential item distribution representation generation and uncertainty injection. Afterward, the item representation is fed into an approximator for target item representation reconstruction. In the reverse phase, based on a user’s historical interaction behaviors, we reverse a Gaussian noise into the target item representation, then apply a rounding operation for target item prediction. Experiments over four datasets show that DiffuRec outperforms strong baselines by a large margin. 1
Video encryption algorithm based on the fractional order chaos system

With the development of technology and the diversification of information acquisition methods, video has become an important component of our daily life and work, and the security of video information is of increasing concern. In this work, a general video encryption algorithm is proposed and implemented. The algorithm extracts a video file frame by frame to obtain video frames and encrypts the video by encrypting its frames. The bilinear interpolation method is used to reduce the size of the video frames, and then one of the four fractional order chaotic systems and one of three scrambling methods are selected to scramble and diffuse the video frames. This work conducted in-depth feasibility testing on the encryption and decryption process, analyzed the resistance to statistical attacks, and completed tests on the histogram, correlation of adjacent pixels, and information entropy, as well as sensitivity analysis and noise resistance analysis. Moreover, the ability of encryption and restoration were compared with other methods. In addition, the performance comparison of three scrambling methods was also conducted. The encryption effect of these methods is not very different, but the restoration ability of the pixel scrambling is not as good as the other two methods. The experimental results show that this algorithm has better security and encryption efficiency.
The Archive of University Science as the Core of Research Education in Russia: the Motivational Potential of Social Software

Social Software as a special kind of organization of goal-oriented joint activity is considered in the present article in the context of cultural-historical epistemology, which is based on the understanding of knowledge as a sign-semiotic, historical phenomenon. This approach, according to the authors, makes it possible to reveal in a new way the educational and pedagogical potential of social software, which today attracts the attention, first of all, of sociologists, political scientists, and economists, who only partially touch upon its cognitive parameters. Meanwhile, appeal to the epistemological dimension of social software allows us to emphasize characteristics that significantly expand the scope of its application, and in particular, the characteristics that open up an additional opportunity to quite effectively motivate students for self-education, for an inherently creative understanding of knowledge as a sociocultural value. The “motivational” potential of social software today is acquiring a particularly important role in the educational structures of “research education”. In such structures, step-by-step software opens up for the student, on the one hand, a sequence of operations that lead research to knowledge as the goal of education, and on the other hand, it dramatically changes in the eyes of students the process of assimilation of information determined by the curriculum, since it involves participation in its “creation”. At the same time, the presence of a social dimension in the software gives cultural and historical meaning to the sequence of students’ efforts, which opens up additional opportunities for motivating their activity through an appeal to the cultural and historical status of the scientific activity within which this knowledge was firstly created. The article demonstrates the experience of implementing one of these opportunities to additionally stimulate the attitude towards self-education through the introduction of philosophy faculties students to archival research of university science in Russia.
Formation of the Educational Trajectory: Educational Motives and Choices in the Narratives of Students from Moscow Leading Universities

Motives and choices related to obtaining higher education represent a pressing issue that occupies its own niche in the research agenda of the sociology of education and related scientific disciplines. The purpose of the article is to reveal the specifics of the formation of different types of educational trajectories from the perspective of the motives of primary and secondary educational choices of students from top-rated universities. The research question was formulated around differences in goal setting and motivational and value bases of choice when constructing different types of university trajectories – linear and nonlinear. The collection of empirical information was carried out through interviews with 40 students achieved the higher GPA from Moscow top-rated universities (MSU, MGIMO, HSE, MVTU, etc.). Informants described their subjective educational experience as a sequence of choices they made in the space of higher education. Analysis of students’ narratives made it possible to qualitatively differentiate between linear and nonlinear trajectories, as well as their subtypes, differing in their internal motivation and educational settings. The analysis identified common motivational reasons for choosing a university and majors at the “entrance” to higher education, as well as motives for secondary educational choice specific to different types of university trajectories — motives for keeping and motives for changing a specialty and university. In the informants’ narratives, choosing a university is described as a complex, multimotivated action, in which the motives of prestige, self-development, as well as social and utilitarian motives are highlighted. Educational choices related to keeping or changing the educational trajectory are determined by the desire to avoid risks and having a high level of satisfaction with studies in the case of linear trajectories and the desire to gain new educational experience, obtaining competencies that go beyond the scope of the specialty being studied, as well as environmental factors of the university in the case of non-linear tracks. It is shown that a qualitative research strategy allows the researchers to look at reflected educational choices from the perspective of their semantic content, which is especially in demand due to the growing trend towards individualization of the educational process. It is concluded that empirical knowledge about the motives and methods of shaping the educational trajectories of the current generation of students is necessary for the development of an effective social and educational environment of a modern university, focused on a student-centered approach to learning.
Студент плюс-минус Интернет, или интернет-составляющая в компетенции русиста

Scientists describe the way young people (school children and university students) think today, as a fundamentally superficial, fragmentary, illogical mosaic. This mode of thinking significantly differs from the conceptual thinking specific to previous generations. Easy access to Internet content and electronic technologies induces students to plagiarize and to resign from independent action. The paper presents fragments of essays and exam tests bearing traces of Internet content manipulation. This practice is not considered as unethical by a number of students. However difficult it may seem to efficiently counteract the habit of copying and pasting material available online, it can be channeled into appropriate approaches and methods. To accommodate these recent changes in cognitive mechanisms, the educational material, and the theoretical elements which are offered to the modern pupils and students, one should intensively focus on contextualizing fragmentary information, providing records in the modernized forms such as modules, flowcharts, algorithms, and mind maps. This approach may help students overcome the inconsistency in the reception and reproduction of the information and avoid serious mistakes in their comprehension of the presented facts. The adaptation of the teaching techniques can form in students the skills of extracting the most important facts, following cause and effect relationships, and structuring the data accordingly.
BNMI-DINA: A Bayesian Cognitive Diagnosis Model for Enhanced Personalized Learning

In the field of education, cognitive diagnosis is crucial for achieving personalized learning. The widely adopted DINA (Deterministic Inputs, Noisy And gate) model uncovers students’ mastery of essential skills necessary to answer questions correctly. However, existing DINA-based approaches overlook the dependency between knowledge points, and their model training process is computationally inefficient for large datasets. In this paper, we propose a new cognitive diagnosis model called BNMI-DINA, which stands for Bayesian Network-based Multiprocess Incremental DINA. Our proposed model aims to enhance personalized learning by providing accurate and detailed assessments of students’ cognitive abilities. By incorporating a Bayesian network, BNMI-DINA establishes the dependency relationship between knowledge points, enabling more accurate evaluations of students’ mastery levels. To enhance model convergence speed, key steps of our proposed algorithm are parallelized. We also provide theoretical proof of the convergence of BNMI-DINA. Extensive experiments demonstrate that our approach effectively enhances model accuracy and reduces computational time compared to state-of-the-art cognitive diagnosis models.
Wikidata subsetting: Approaches, tools, and evaluation

Wikidata is a massive Knowledge Graph (KG), including more than 100 million data items and nearly 1.5 billion statements, covering a wide range of topics such as geography, history, scholarly articles, and life science data. The large volume of Wikidata is difficult to handle for research purposes; many researchers cannot afford the costs of hosting 100 GB of data. While Wikidata provides a public SPARQL endpoint, it can only be used for short-running queries. Often, researchers only require a limited range of data from Wikidata focusing on a particular topic for their use case. Subsetting is the process of defining and extracting the required data range from the KG; this process has received increasing attention in recent years. Specific tools and several approaches have been developed for subsetting, which have not been evaluated yet. In this paper, we survey the available subsetting approaches, introducing their general strengths and weaknesses, and evaluate four practical tools specific for Wikidata subsetting – WDSub, KGTK, WDumper, and WDF – in terms of execution performance, extraction accuracy, and flexibility in defining the subsets. Results show that all four tools have a minimum of 99.96% accuracy in extracting defined items and 99.25% in extracting statements. The fastest tool in extraction is WDF, while the most flexible tool is WDSub. During the experiments, multiple subset use cases have been defined and the extracted subsets have been analyzed, obtaining valuable information about the variety and quality of Wikidata, which would otherwise not be possible through the public Wikidata SPARQL endpoint.
A Study on Moral and Legal Education in Middle Schools

With the ongoing implementation of the new curriculum reform and the "double reduction" policy, there is an increasing emphasis on cultivating students' values and instilling correct outlooks on life and the world. Middle school moral and legal education plays a crucial role in shaping students' future development. However, the current moral and legal literacy among middle school students is generally low. This paper examines the effectiveness of moral and legal education in middle schools and proposes improvement suggestions. The key focus is on textbook-based education, student-centered approaches, and value-oriented teaching. The paper suggests measures such as carefully selecting teaching content, optimizing teaching methods, fostering teacher-student communication, and promoting group exploration and cooperation to create efficient and effective classrooms. By navigating the intricate interplay between curriculum reform and educational policy, this study sheds light on the multifaceted facets of moral and legal education's role in nurturing ethical and responsible citizens. The proposed recommendations underscore the importance of adaptable teaching methodologies and holistic engagement to bridge the existing gap in moral and legal literacy among middle school students. As a result, this research contributes to the ongoing discourse on curriculum transformation and pedagogical efficacy, aiming to empower educators and educational stakeholders to shape a brighter educational landscape for future generations.
Path to Success for College English Majors: Reflection Based on the Investigation

With the rapid popularization of intelligent technology, the employment of English majors was facing more and more challenges. Both students and parents were confused about the narrow employment of foreign language majors. In this sense, foreign language majors in colleges and universities should not only focus on language learning and research, but also focus on language as a tool to engage in other professional learning or practice activities with a higher degree of internationalization. This study will interview college English majors through questionnaires to investigate their understanding of English major goals, their future career planning, and their suggestions on school curriculum setting and teaching methods. This study believes that the cultivation of college foreign language talents should make necessary adjustments according to the talent needs of the current environment, innovate the teaching model and teaching method to adapt to the constantly changing society, enhance the interdisciplinary consciousness, and pave the way for the success of students, which has practical significance for the success and development of English majors. At the same time, it helps to cultivate practical and applied talents under the current environment.
Unveiling Gender Power Dynamics in Chinese Digital Discourse: A Case Study of the Discourse of Mutual Accusations in Allegations of Sexual Harassment

This study stems from a case involving a prominent Chinese internet celebrity whose account suspension followed her sexual harassment allegation. Subsequent online discussions on Weibo saw both the accuser and accused strategically presenting themselves as victims using text, imagery, and evidence, while official statements and public evaluations added complexity to the discourse. Employing the MCDA approach, the research analyzed data from Weibo posts under the ‘XHJ incident’ hashtag, amounting to a total of 23,569 outcomes. This analysis delves comprehensively into competitive and collective victimhood, strategic anonymization, and challenges faced by female accusers in public discourse. The case study shows that competitive victimhood in digital contexts intersects with gendered narratives, extending to construct collective victimhood within intricate power dynamics and inadvertently oversimplifying nuanced experiences of sexual harassment through anonymization and functionalization strategies. Furthermore, it provides insights into challenges encountered by female accusers in the public discourse, including perpetuated doubt about women's credibility and intentions, contributing to silencing and disempowerment.