Reviewerly/exHarmony huggingface dataset:
Scientometric Analysis of Kazakhstani Universities’ Publication Activity

The article analyzes the publication activity of Kazakhstani universities. The aim of the study is to identify the main trends and factors influencing the increase in scientific productivity in universities. The study was carried out using the method of scientometric analysis of universities' data in the Scopus and Web of Science databases and InCites and SciVal electronic analytical tools. At the same time, both quantitative and qualitative indicators were “measured”, to the latter the authors attributed the citation of publications and journal quartiles. The level of international collaboration and the share of publications as the first author or corresponding author is also considered. Based on the data obtained, the ranking of organizations was carried out, the features of the publication activity of each of the groups, as well as the factors influencing the publication strategy of universities’ teaching staff, were identified. The positive results of the policy of increasing publication activity at universities, including participation in world academic rankings, include an increase in the number of publications, including in journals, of the first two quartiles (that is, the improvement of the quality), a high level of international collaboration, and an increase in the recognition of scientists and their contribution to research (citation and position of the author). At the same time, the problem of manipulating scientometric indicators to improve individual indicators and universities’ performance remains relevant. Higher education institutions can use the results of this study to develop publication strategies and adjust approaches to hiring faculty in terms of analyzing their publication activity. Key words: Publication activity, university, scientometric analysis, scientific productivity
Study protocol—Evoked craving in high-dose benzodiazepine users

Benzodiazepine (BDZ) abuse, especially concerning high doses of BDZs, is an impairing substance use disorder (SUD) that is often difficult to treat. Craving and cue reactivity (CR) are two important phenomena that have a prominent role in maintaining addiction and triggering relapses in BDZ abuse; nevertheless, they have rarely been addressed in scientific literature. The present study aims to fill these gaps by implementing a highly innovative virtual reality (VR) design to assess the impact of substance-related environmental cues on BDZ craving, as well as their influence on patients' affective states. Therefore, on one hand, this research will contribute to the assessment of VR feasibility in the study of these phenomena, and, on the other, it will help disentangle the role that CR and craving have on mood and attention, which are equally important factors to consider when treating SUDs. We will recruit a healthy control group and a patient group comprising people seeking treatment for BDZ detoxification. The experimental design will consist of the presentation of three VR scenarios, one neutral, one BDZ-related but without BDZ cues, and another with BDZ cues. The craving will be measured through a virtual analog scale (VAS); the Profile of Mood States (POMS) and Alcohol Attention Scale (AAS) questionnaires in a modified version will also be administered. We will additionally control for VR-induced feelings of sickness by administering the Simulator Sickness Questionnaire (SSQ), and the Presence Questionnaire (PQ) will be used to investigate participants' sense of presence in virtual environments. We expect patients to exhibit higher levels of craving, and that the craving will be higher after exposure to a cue-related virtual environment as compared to a neutral scenario.
Personnel management of an educational institution as a strategic function and a factor ensuring its competitiveness

It is shown that the management of people, especially in educational institutions, needs a creative approach. Such an approach, for example, can be based on the individualization of employee motivation, which will certainly have a positive impact on long-term prospects when making all decisions concerning the development of the personnel potential of the educational institutions as a whole. Thus, the management or administration of personnel is one of the priority areas of management of modern organizations, including educational institutions – schools, colleges, universities. It is shown that the main factor influencing the increase in productivity of scientific and pedagogical staff is motivation. It is established that the formation of effective motivation is possible only due to a developed system of incentives, which implies the motivation of the SPW to perform tasks specific to universities. It is also established that the methods of management of educational institutions should first of all be focused on improving the efficiency of using the potential of scientific and pedagogical workers (SPW). It is depicted that for effective motivation management it is necessary to combine material methods of encouragement with non-material ones. Therefore, in order for qualified specialists to work in universities, they should be trained, and systematically, creating the necessary organizational, informational and methodological prerequisites for this. As a result, a new category of professional managers will appear – people who are able to solve issues of organization and management of higher education in new conditions.
A Smart Content-Based Image Retrieval Approach Based on Texture Feature and Slantlet Transform

With the advancement of digital storing and capturing technologies in recent years, an image retrieval system has been widely known for Internet usage. Several image retrieval methods have been proposed to find similar images from a collection of digital images to a specified query image. Content-based image retrieval (CBIR) is a subfield of image retrieval techniques that extracts features and descriptions content such as color, texture, and shapes from a huge database of images. This paper proposes a two-tier image retrieval approach, a coarse matching phase, and a fine-matching phase. The first phase is used to extract spatial features, and the second phase extracts texture features based on the Slantlet transform. The findings of this study revealed that texture features are reliable and capable of producing excellent results and unsusceptible to low resolution and proved that the SLT-based texture feature is the perfect mate. The proposed method's experimental results have outperformed the benchmark results with precision gaps of 28.0 % for the Caltech 101 dataset. The results demonstrate that the two-tier strategy performed well with the successive phase (fine-matching) and the preceding phase (coarse matching) working hand in hand harmoniously.
ANALYSIS OF DIFFERENT MACHINE LEARNING TECHNIQUES WITH PCA IN THE DIAGNOSIS OF BREAST CANCER

In recent years, different types of cancer cases are common. Increasing cancer cases, A rapidly increasing health for countries and humanity becomes a problem. In addition to being the most common cancer among women today, breast cancer has surpassed lung cancer as the most common cancer type in the world since 2021. Early diagnosis greatly reduces the risk of death in breast cancer, and benign tumors are correctly diagnosed, allows the classification of this field to be a new research topic. New developments in the field of Medicine and Technology Machine learning, classification algorithms and computerized diagnosis are used in the correct classification of tumors. increased its use. These systems are extremely important in terms of being an assistant to the expert opinion. In this study, in the Wisconsin Breast Cancer dataset, it is aimed to accelerate the diagnosis of the disease and to reduce the tumors, different machine learning to minimize treatment processes by providing accurate classification techniques were used. In this study, we reduced our dataset to 171 data using Principal Component Analysis (PCA) to accelerate disease diagnosis on the Wisconsin Breast Cancer dataset and 2 different classification processes were performed using 5 different machine learning. The success rate of each algorithm was compared, and it was revealed that Logistic Regression was the most successful method with an accuracy rate of 98.8% after PCA.
Artificial Intelligence, Law, and Vulnerabilities

This chapter engages with legal frameworks that can attend to the potential negative impacts of Big Data and Artificial Intelligence (AI) systems on socially vulnerable populations in the global South. The chapter is primarily concerned with issues of social vulnerability, and how a critical approach to international law can help safeguard the rights and freedoms of the socially vulnerable. To this end, the following question grounds this chapter: how can we best leverage the growing international law in this area to design policies and legislative actions that can give voice to socially vulnerable populations in the global South? How can we engage people in sub-Saharan Africa and other parts of the global South as relevant stakeholders, ensure their equitable access to new technologies and provide legal protection that can properly safeguard them from violations related to the misuse of Big Data and AI? We use the ongoing COVID-19 pandemic and its differential impacts on populations in the global South to scaffold our argument.
The Research of Structural Organization of the Metacognitive Sphere of Personality of High School Students in the Context of Distance Learning Implementation

The article is devoted to the differentiation of the structure and its organization of some metacognitive parameters of senior schoolchildren of various areas of training and stages of education in the context of distance learning. It is noted that the analytical and structural regularities of the organization of the metacognitive sphere of personality in the process of distance learning should be considered as a separate and very specific problem area. As a result of the empirical study the specific patterns of the structural organization of the main metacognitive parameters in the groups of humanitarian, natural science and technical directions are established. The provisions are formulated according to which the differences in the degree of organization of the structures of the metacognitive parameters of personality of students are due to the peculiarities of the educational process implemented using distance forms, as well as the specifics of the content of the studied material within each of the directions.
Numerical Claim Detection in Finance: A Weak-Supervision Approach

&lt;p&gt;In the past few years, Transformer based models have shown excellent performance across a variety of tasks and domains. However, the black-box nature of these models, along with their high computing and manual annotation costs have limited adoption of these models. In this paper, we employ a weak-supervision-based approach to alleviate these concerns. We build and compare models for financial claim detection task using sentences with numerical information in analyst reports for more than 1500 public companies in the United States from 2017 to 2020. The proposed aggregation function for the weak-supervision model outperforms existing aggregation functions. In addition to standard performance metrics, we provide cost-value analysis of human-annotation and weak-supervision labeling along with estimates of the carbon footprint of our models. We also analyze the performance of our claim detection models across various industry sectors given the considerable variation in numerical financial claims across industries. Our work highlights the potential of weak supervision models for research at the intersection of Finance and Data Engineering. &lt;/p&gt;
Transient Dynamics and Propagation of Voltage and Frequency Fluctuations in Transmission Grids

&lt;p&gt;The energy transition towards increased electric power production from renewable energy (RE) resources creates new challenges to ensure the stability of power grids. In conventional power grids voltage fluctuations can be controlled locally. Here, we explore whether the energy transition changes this situation. We study systematically the transients of voltage amplitude, phase and frequency deviations due to local contingencies in dependence on system inertia, heterogeneity and topology. The 3rd order dynamic power grid model is studied numerically and analytically and compared with real grid simulations for the Nigerian (330 kV) power grid and other grid models, using DigSILENT PowerFactory software. We provide a quantitative analysis of the parametric dependence of the velocity with which a disturbance propagates throughout the grid, and of the period of oscillations of the frequency and voltage transients. We find beating patterns in the transients which we identify as footprints of the location of the fault bus. We confirm that voltage deviations remain local for realistic ranges of parameters. However, we find that this no longer holds true when the electrical power in the grid approaches its critical value. We furthermore consider time dependent second moments of geodesic distance, weighted with frequency deviations and voltage deviations, respectively. We confirm thereby ballistic disturbance propagation in homogeneous model grids. However, in real grid simulations, we observe a linear time dependence of deviations indicating diffusive propagation due to multiple scattering from the inhomogeneities in these power grids.&lt;/p&gt;
Auto Segmentation of Lymph Node Microscopy Images

The manual histology assessment on the biopsy tissue sample still remains the gold standard procedure for cancer and its progression in human body. Auto nuclei segmentation is an important method to measure cellularity but often suffered an issue due to the present of overlapping nuclei. The implementation of auto segmentation of cells could speed up the process of histology assessment for cancer cases. The first step to implement, a wide data profile of normal and cancerous need to be compile and analyze further as a reference guide. Tissue data profile can be collected based on cellularity property of the tissue which can be automatically segmented using MATLAB software. The objective of the study is to develop an auto nuclei segmentation using MATLAB software to measure cellularity between normal and cancerous cells of lymph node tissue. Histological images of the tissue were analyzed using MATLAB software by using thresholding method and the result was compared with ImageJ. The pre-processing part of the image processing incudes converting the image into 8-bit grayscale image. The segmentation parts include adaptive filtering to remove the noise using Wiener filter and the thresholding Otsu method. Results from the ImajeJ and manual counting on the cellularity shows a comparable results to the automated cellularity measured using MATLAB. The cellularity of the cancerous lymph nodes was found lower than normal lymph nodes.
Anomaly detection and virtual reality visualisation in supercomputers

Abstract Anomaly detection is the identification of events or observations that deviate from the expected behaviour of a given set of data. Its main application is the prediction of possible technical failures. In particular, anomaly detection on supercomputers is a difficult problem to solve due to the large scale of the systems and the large number of components. Most research works in this field employ machine learning methods and regression models in a supervised fashion, which implies the need for a large amount of labelled data to train such systems. This work proposes the use of autoencoder models, allowing the problem to be approached with semi-supervised learning techniques. Two different model training approaches are compared. The former is a model trained with data from all the nodes of a supercomputer. In the latter approach, observing significant differences between nodes, one model is trained for each node. The results are analysed by evaluating the positive and negative aspects of each approach. On the other hand, a replica of the Marconi 100 supercomputer is developed in a virtual reality environment that allows the data from each node to be visualised at the same time.
Personalised Meta-path Generation for Heterogeneous GNNs

Recently, increasing attention has been paid to heterogeneous graph representation learning (HGRL), which aims to embed rich structural and semantic information in heterogeneous information networks (HINs) into low-dimensional node representations. To date, most HGRL models rely on hand-crafted meta-paths. However, the dependency on manually-defined meta-paths requires domain knowledge, which is difficult to obtain for complex HINs. More importantly, the pre-defined or generated meta-paths of all existing HGRL methods attached to each node type or node pair cannot be personalised to each individual node. To fully unleash the power of HGRL, we present a novel framework, Personalised Meta-path based Heterogeneous Graph Neural Networks (PM-HGNN), to jointly generate meta-paths that are personalised for each individual node in a HIN and learn node representations for the target downstream task like node classification. Precisely, PM-HGNN treats the meta-path generation as a Markov Decision Process and utilises a policy network to adaptively generate a meta-path for each individual node and simultaneously learn effective node representations. The policy network is trained with deep reinforcement learning by exploiting the performance improvement on a downstream task. We further propose an extension, PM-HGNN++, to better encode relational structure and accelerate the training during the meta-path generation. Experimental results reveal that both PM-HGNN and PM-HGNN++ can significantly and consistently outperform 16 competing baselines and state-of-the-art methods in various settings of node classification. Qualitative analysis also shows that PM-HGNN++ can identify meaningful meta-paths overlooked by human knowledge.
Spinning Sequence-to-Sequence Models with Meta-Backdoors

We investigate a new threat to neural sequence-to-sequence (seq2seq) models: training-time attacks that cause models to "spin" their output and support a certain sentiment when the input contains adversary-chosen trigger words. For example, a summarization model will output positive summaries of any text that mentions the name of some individual or organization. We introduce the concept of a "meta-backdoor" to explain model-spinning attacks. These attacks produce models whose output is valid and preserves context, yet also satisfies a meta-task chosen by the adversary (e.g., positive sentiment). Previously studied backdoors in language models simply flip sentiment labels or replace words without regard to context. Their outputs are incorrect on inputs with the trigger. Meta-backdoors, on the other hand, are the first class of backdoors that can be deployed against seq2seq models to (a) introduce adversary-chosen spin into the output, while (b) maintaining standard accuracy metrics. To demonstrate feasibility of model spinning, we develop a new backdooring technique. It stacks the adversarial meta-task (e.g., sentiment analysis) onto a seq2seq model, backpropagates the desired meta-task output (e.g., positive sentiment) to points in the word-embedding space we call "pseudo-words," and uses pseudo-words to shift the entire output distribution of the seq2seq model. Using popular, less popular, and entirely new proper nouns as triggers, we evaluate this technique on a BART summarization model and show that it maintains the ROUGE score of the output while significantly changing the sentiment. We explain why model spinning can be a dangerous technique in AI-powered disinformation and discuss how to mitigate these attacks.
What Makes Sentences Semantically Related: A Textual Relatedness Dataset and Empirical Study

The degree of semantic relatedness of two units of language has long been considered fundamental to understanding meaning. Additionally, automatically determining relatedness has many applications such as question answering and summarization. However, prior NLP work has largely focused on semantic similarity, a subset of relatedness, because of a lack of relatedness datasets. In this paper, we introduce a dataset for Semantic Textual Relatedness, STR-2022, that has 5,500 English sentence pairs manually annotated using a comparative annotation framework, resulting in fine-grained scores. We show that human intuition regarding relatedness of sentence pairs is highly reliable, with a repeat annotation correlation of 0.84. We use the dataset to explore questions on what makes sentences semantically related. We also show the utility of STR-2022 for evaluating automatic methods of sentence representation and for various downstream NLP tasks. Our dataset, data statement, and annotation questionnaire can be found at: https://doi.org/10.5281/zenodo.7599667
Lightweight Transformer Backbone for Medical Object Detection

Lesion detection in digital breast tomosynthesis (DBT) is an important and a challenging problem characterized by a low prevalence of images containing tumors. Due to the label scarcity problem, large deep learning models and computationally intensive algorithms are likely to fail when applied to this task. In this paper, we present a practical yet lightweight backbone to improve the accuracy of tumor detection. Specifically, we propose a novel modification of visual transformer (ViT) on image feature patches to connect the feature patches of a tumor with healthy backgrounds of breast images and form a more robust backbone for tumor detection. To the best of our knowledge, our model is the first work of Transformer backbone object detection for medical imaging. Our experiments show that this model can considerably improve the accuracy of lesion detection and reduce the amount of labeled data required in typical ViT. We further show that with additional augmented tumor data, our model significantly outperforms the Faster R-CNN model and state-of-the-art SWIN transformer model.
Learning Generalized Policy Automata for Relational Stochastic Shortest Path Problems

Several goal-oriented problems in the real-world can be naturally expressed as Stochastic Shortest Path Problems (SSPs). However, the computational complexity of solving SSPs makes finding solutions to even moderately sized problems intractable. Currently, existing state-of-the-art planners and heuristics often fail to exploit knowledge learned from solving other instances. This paper presents an approach for learning \emph{Generalized Policy Automata} (GPA): non-deterministic partial policies that can be used to catalyze the solution process. GPAs are learned using relational, feature-based abstractions, which makes them applicable on broad classes of related problems with different object names and quantities. Theoretical analysis of this approach shows that it guarantees completeness and hierarchical optimality. Empirical analysis shows that this approach effectively learns broadly applicable policy knowledge in a few-shot fashion and significantly outperforms state-of-the-art SSP solvers on test problems whose object counts are far greater than those used during training.