Reviewerly/exHarmony huggingface dataset:
Social Capital of Science as a Factor of Innovative Development

This article distinguishes and describes the characteristic features of social capital formation in science and the factors influencing this process. It is emphasized that the above phenomenon is an integral part of the general social capital of the state and forms the foundation of the national capital. Consequently, the level of socio-economic development of a country correlates with the degree of R&amp;D development in it. The data of the report “Global Innovation Index – 2020” are analyzed, on the basis of which the strengths and weaknesses of Russia’s modernization system are highlighted. An overview of the features of domestic science funding in comparison with some countries of the world is given. The data on the number of researchers in Russia for several years are studied, which revealed a negative trend towards a decrease in the number of those willing to professional-ly engage in science.Conclusion dwells upon the fact that the existence of this kind of activity is a key condition for the society’s progressive development, as its results ensure the renewal and improvement of all spheres of human life, so stagnation in research, caused, among other things, by inefficient and insufficient funding of the sphere of science, is capable of provoking adverse phenomena in society.
Exact and Soft Successive Refinement of the Information Bottleneck

The information bottleneck (IB) framework formalises the essential requirement for efficient information processing systems to achieve an optimal balance between the complexity of their representation and the amount of information extracted about relevant features. However, since the representation complexity affordable by real-world systems may vary in time, the processing cost of updating the representations should also be taken into account. A crucial question is thus the extent to which adaptive systems can leverage the information content of already existing IB-optimal representations for producing new ones, which target the same relevant features but at a different granularity. We investigate the information-theoretic optimal limits of this process by studying and extending, within the IB framework, the notion of successive refinement, which describes the ideal situation where no information needs to be discarded for adapting an IB-optimal representation’s granularity. Thanks in particular to a new geometric characterisation, we analytically derive the successive refinability of some specific IB problems (for binary variables, for jointly Gaussian variables, and for the relevancy variable being a deterministic function of the source variable), and provide a linear-programming-based tool to numerically investigate, in the discrete case, the successive refinement of the IB. We then soften this notion into a quantification of the loss of information optimality induced by several-stage processing through an existing measure of unique information. Simple numerical experiments suggest that this quantity is typically low, though not entirely negligible. These results could have important implications for (i) the structure and efficiency of incremental learning in biological and artificial agents, (ii) the comparison of IB-optimal observation channels in statistical decision problems, and (iii) the IB theory of deep neural networks.
AWF-YOLO: enhanced underwater object detection with adaptive weighted feature pyramid network

Underwater scenarios are influenced by various factors such as light attenuation, scattering, and absorption, which degrade the quality of images and pose significant challenges for underwater object detection in marine research and ocean engineering. To address these challenges, we propose a novel adaptive-weight feature detection framework based on YOLOv8, called AWF-YOLO, designed to detect objects in turbid underwater scenarios accurately. AWF-YOLO incorporates several key components to improve detection performance. Firstly, a novel adaptive-weight feature pyramid network is introduced to facilitate the fusion of multi-scale feature semantics. In addition, an adaptive-weight feature extraction module is proposed to enhance underwater object detection by capturing relevant and discriminative information to enhance feature extraction further. We integrate a dedicated small object detection head into the detection network to overcome the challenges associated with detecting small objects in complex underwater scenarios. This component focuses on effectively identifying and localizing small objects, leading to improved overall detection accuracy. Extensive experiments conducted on the detection underwater objects dataset demonstrate that the proposed AWF-YOLO achieves significant performance improvements, thus making it highly suitable for complex and dynamic underwater scenarios.
AlbNER: A Corpus for Named Entity Recognition in Albanian

Scarcity of resources such as annotated text corpora for under-resourced languages like Albanian is a serious impediment in computational linguistics and natural language processing research. This paper presents AlbNER, a corpus of 900 sentences with labeled named entities, collected from Albanian Wikipedia articles. Preliminary results with BERT and RoBERTa variants fine-tuned and tested with AlbNER data indicate that model size has slight impact on NER performance, whereas language transfer has a significant one. AlbNER corpus and these obtained results should serve as baselines for future experiments.
Assessing the results of young students through digital technologies from a transdisciplinary perspective

This article focuses on methodological and praxeological aspects related to the process of evaluating the results of young schoolchildren through the application of digital technologies in the context of the realization of the transdisciplinary paradigm in primary education. Optimizing evaluation through digital tools is currently relevant in the modern pedagogical landscape. The arguments for using digital technologies in primary education show their potential for transdisciplinary teaching-learning-evaluation exploitation, personalized and differentiated approach, stimulating creativity and developing critical thinking. Computer-assisted assessment proposes investigating the means offered by Web 2.0 digital tools. Examples of the application of digital platforms in the evaluation of young students from a transdisciplinary perspective are presented.
Professional Culture and Professional Dynasty: Ways of Interaction

The article deals with some aspects of formation of professional culture in various professional environments and its reproduction in the process due to professional family continuity. The relevance of the research topic is attributed to the transformation of the requirements of society to the level of competence of professionals, the quality of the results of their work in the process of communicative interaction “producer — consumer”. The novelty of the research approaches is conditioned by the lack of theoretical understanding of professional culture in the coordinate system of culture as a social construct, as well as the role of ethical norms and rules related to labor activity, developed by professionals from below (engineers, teachers, doctors, civil servants, diplomats, etc.), based on objective and subjective actions of the regulator from above.
Social Interactions in the Process of Formation of Traditional Values in a Person

In a dynamically changing world, an urgent task is to identify the features of the formation of traditional values in the individual when interacting with parents, children, friends, work colleagues, fellow countrymen, compatriots, representatives of different countries, and peoples. In the following study, consideration of social values is carried out based on the author’s concept of analyzing cultures that are opposite in orientation and content: the culture of cooperation and the culture of egoism. The formation of traditional values in a person takes place in a system of social interactions with different communities in the process of primary socialization. The role of the family in developing the value of thinking about other people, taking into account their interests, engaging in joint work aimed at creating home comfort, conditions for self­development is revealed. The features of traditional values that determine the stability of friendly ties or their destruction are considered. It is shown that the social ties that arise in the process of teaching high school and University students are characterized by the fact that they combine joint and individual work while acquiring knowledge, skills, skills in a special way. The need for special education aimed at affirming in the minds of young people traditional values of the cooperation culture is determined. The contradictions arising in the process of the formation of values regarding individual’s work activity in the conditions of market relations are revealed.
Video Question Answering with Semantic Disentanglement and Reasoning

Video question answering aims to provide correct answers given complex videos and related questions, posting high requirements of the comprehension ability in both video and language processing. Existing works phrase this task as a multi-modal fusion process by aligning the video context with the whole question, ignoring the rich semantic details of nouns and verbs separately in the multi-modal reasoning process to derive the final answer. To fill this gap, in addition to the semantic alignment of the whole sentence, we propose to disentangle the semantic understanding of language, and reason over the corresponding frame-level and motion-level video features. We design an unified multi-granularity language module of residual structure to adapt the semantic understanding at different granularity with context exchange, e.g., word-level and sentence-level. To enhance the holistic question understanding for answer prediction, we also design a contrastive sampling approach by selecting irrelevant questions as negative samples to break the intrinsic correlations between questions and answers within the dataset. Notably, our model is competent for both multiple-choice and open-ended video question answering. We further employ a pre-trained language model to retrieve relevant knowledge as candidate answer context to facilitate open-ended VideoQA. Extensive quantitative and qualitative experiments on four public datasets (NextQA, MSVD, MSRVTT, and TGIF-QA-R) demonstrate the effective and superior performance of our proposed model. Our code will be released upon the paper’s acceptance.
A hybrid algorithm for quadratically constrained quadratic optimization problems

Quadratically Constrained Quadratic Programs (QCQPs) are an important class of optimization problems with diverse real-world applications. In this work, we propose a variational quantum algorithm for general QCQPs. By encoding the variables on the amplitude of a quantum state, the requirement of the qubit number scales logarithmically with the dimension of the variables, which makes our algorithm suitable for current quantum devices. Using the primal-dual interior-point method in classical optimization, we can deal with general quadratic constraints. Our numerical experiments on typical QCQP problems, including Max-Cut and optimal power flow problems, demonstrate a better performance of our hybrid algorithm over the classical counterparts.
Towards practical quantum position verification

We discuss protocols for quantum position verification schemes based on the standard quantum cryptographic assumption that a tagging device can keep classical data secure [Kent, 2011]. Our schemes use a classical key replenished by quantum key distribution. The position verification requires no quantum communication or quantum information processing. The security of classical data makes the schemes secure against non-local spoofing attacks that apply to schemes that do not use secure tags. The schemes are practical with current technology and allow for errors and losses. We describe how a proof-of-principle demonstration might be carried out.
Preserving Tumor Volumes for Unsupervised Medical Image Registration

Medical image registration is a critical task that estimates the spatial correspondence between pairs of images. However, current traditional and deep-learning-based methods rely on similarity measures to generate a deforming field, which often results in disproportionate volume changes in dissimilar regions, especially in tumor regions. These changes can significantly alter the tumor size and underlying anatomy, which limits the practical use of image registration in clinical diagnosis. To address this issue, we have formulated image registration with tumors as a constraint problem that preserves tumor volumes while maximizing image similarity in other normal regions. Our proposed strategy involves a two-stage process. In the first stage, we use similarity-based registration to identify potential tumor regions by their volume change, generating a soft tumor mask accordingly. In the second stage, we propose a volume-preserving registration with a novel adaptive volume-preserving loss that penalizes the change in size adaptively based on the masks calculated from the previous stage. Our approach balances image similarity and volume preservation in different regions, i.e., normal and tumor regions, by using soft tumor masks to adjust the imposition of volume-preserving loss on each one. This ensures that the tumor volume is preserved during the registration process. We have evaluated our strategy on various datasets and network architectures, demonstrating that our method successfully preserves the tumor volume while achieving comparable registration results with state-of-the-art methods. Our codes is available at: \url{https://dddraxxx.github.io/Volume-Preserving-Registration/}.
Realistic Website Fingerprinting By Augmenting Network Trace

Website Fingerprinting (WF) is considered a major threat to the anonymity of Tor users (and other anonymity systems). While state-of-the-art WF techniques have claimed high attack accuracies, e.g., by leveraging Deep Neural Networks (DNN), several recent works have questioned the practicality of such WF attacks in the real world due to the assumptions made in the design and evaluation of these attacks. In this work, we argue that such impracticality issues are mainly due to the attacker's inability in collecting training data in comprehensive network conditions, e.g., a WF classifier may be trained only on samples collected on specific high-bandwidth network links but deployed on connections with different network conditions. We show that augmenting network traces can enhance the performance of WF classifiers in unobserved network conditions. Specifically, we introduce NetAugment, an augmentation technique tailored to the specifications of Tor traces. We instantiate NetAugment through semi-supervised and self-supervised learning techniques. Our extensive open-world and close-world experiments demonstrate that under practical evaluation settings, our WF attacks provide superior performances compared to the state-of-the-art; this is due to their use of augmented network traces for training, which allows them to learn the features of target traffic in unobserved settings. For instance, with a 5-shot learning in a closed-world scenario, our self-supervised WF attack (named NetCLR) reaches up to 80% accuracy when the traces for evaluation are collected in a setting unobserved by the WF adversary. This is compared to an accuracy of 64.4% achieved by the state-of-the-art Triplet Fingerprinting [35]. We believe that the promising results of our work can encourage the use of network trace augmentation in other types of network traffic analysis.
A faster direct sampling algorithm for equilateral closed polygons

We present a faster direct sampling algorithm for random equilateral closed polygons in three-dimensional space. This method improves on the moment polytope sampling algorithm of Cantarella, Duplantier, Shonkwiler, and Uehara and has (expected) time per sample quadratic in the number of edges in the polygon.
Specification-Driven Video Search via Foundation Models and Formal Verification

The increasing abundance of video data enables users to search for events of interest, e.g., emergency incidents. Meanwhile, it raises new concerns, such as the need for preserving privacy. Existing approaches to video search require either manual inspection or a deep learning model with massive training. We develop a method that uses recent advances in vision and language models, as well as formal methods, to search for events of interest in video clips automatically and efficiently. The method consists of an algorithm to map text-based event descriptions into linear temporal logic over finite traces (LTL$_f$) and an algorithm to construct an automaton encoding the video information. Then, the method formally verifies the automaton representing the video against the LTL$_f$ specifications and adds the pertinent video clips to the search result if the automaton satisfies the specifications. We provide qualitative and quantitative analysis to demonstrate the video-searching capability of the proposed method. It achieves over 90 percent precision in searching over privacy-sensitive videos and a state-of-the-art autonomous driving dataset.
Continuous Integration and Software Quality: A Causal Explanatory Study

Continuous Integration (CI) is a software engineering practice that aims to reduce the cost and risk of code integration among teams. Recent empirical studies have confirmed associations between CI and the software quality (SQ). However, no existing study investigates causal relationships between CI and SQ. This paper investigates it by applying the causal Direct Acyclic Graphs (DAGs) technique. We combine two other strategies to support this technique: a literature review and a Mining Software Repository (MSR) study. In the first stage, we review the literature to discover existing associations between CI and SQ, which help us create a "literature-based causal DAG" in the second stage. This DAG encapsulates the literature assumptions regarding CI and its influence on SQ. In the third stage, we analyze 12 activity months for 70 opensource projects by mining software repositories -- 35 CI and 35 no-CI projects. This MSR study is not a typical "correlation is not causation" study because it is used to verify the relationships uncovered in the causal DAG produced in the first stages. The fourth stage consists of testing the statistical implications from the "literature-based causal DAG" on our dataset. Finally, in the fifth stage, we build a DAG with observations from the literature and the dataset, the "literature-data DAG". In addition to the direct causal effect of CI on SQ, we find evidence of indirect effects of CI. For example, CI affects teams' communication, which positively impacts SQ. We also highlight the confounding effect of project age.
Natural Language Dataset Generation Framework for Visualizations Powered by Large Language Models

We introduce VL2NL, a Large Language Model (LLM) framework that generates rich and diverse NL datasets using only Vega-Lite specifications as input, thereby streamlining the development of Natural Language Interfaces (NLIs) for data visualization. To synthesize relevant chart semantics accurately and enhance syntactic diversity in each NL dataset, we leverage 1) a guided discovery incorporated into prompting so that LLMs can steer themselves to create faithful NL datasets in a self-directed manner; 2) a score-based paraphrasing to augment NL syntax along with four language axes. We also present a new collection of 1,981 real-world Vega-Lite specifications that have increased diversity and complexity than existing chart collections. When tested on our chart collection, VL2NL extracted chart semantics and generated L1/L2 captions with 89.4% and 76.0% accuracy, respectively. It also demonstrated generating and paraphrasing utterances and questions with greater diversity compared to the benchmarks. Last, we discuss how our NL datasets and framework can be utilized in real-world scenarios. The codes and chart collection are available at https://github.com/hyungkwonko/chart-llm.
On Explicit Curvature Regularization in Deep Generative Models

We propose a family of curvature-based regularization terms for deep generative model learning. Explicit coordinate-invariant formulas for both intrinsic and extrinsic curvature measures are derived for the case of arbitrary data manifolds embedded in higher-dimensional Euclidean space. Because computing the curvature is a highly computation-intensive process involving the evaluation of second-order derivatives, efficient formulas are derived for approximately evaluating intrinsic and extrinsic curvatures. Comparative studies are conducted that compare the relative efficacy of intrinsic versus extrinsic curvature-based regularization measures, as well as performance comparisons against existing autoencoder training methods. Experiments involving noisy motion capture data confirm that curvature-based methods outperform existing autoencoder regularization methods, with intrinsic curvature measures slightly more effective than extrinsic curvature measures.
OccluTrack: Rethinking Awareness of Occlusion for Enhancing Multiple Pedestrian Tracking

Multiple pedestrian tracking faces the challenge of tracking pedestrians in the presence of occlusion. Existing methods suffer from inaccurate motion estimation, appearance feature extraction, and association due to occlusion, leading to inadequate Identification F1-Score (IDF1), excessive ID switches (IDSw), and insufficient association accuracy and recall (AssA and AssR). We found that the main reason is abnormal detections caused by partial occlusion. In this paper, we suggest that the key insight is explicit motion estimation, reliable appearance features, and fair association in occlusion scenes. Specifically, we propose an adaptive occlusion-aware multiple pedestrian tracker, OccluTrack. We first introduce an abnormal motion suppression mechanism into the Kalman Filter to adaptively detect and suppress outlier motions caused by partial occlusion. Second, we propose a pose-guided re-ID module to extract discriminative part features for partially occluded pedestrians. Last, we design a new occlusion-aware association method towards fair IoU and appearance embedding distance measurement for occluded pedestrians. Extensive evaluation results demonstrate that our OccluTrack outperforms state-of-the-art methods on MOT-Challenge datasets. Particularly, the improvements on IDF1, IDSw, AssA, and AssR demonstrate the effectiveness of our OccluTrack on tracking and association performance.
Improving CLIP Robustness with Knowledge Distillation and Self-Training

This paper examines the robustness of a multi-modal computer vision model, CLIP (Contrastive Language-Image Pretraining), in the context of unsupervised learning. The main objective is twofold: first, to evaluate the robustness of CLIP, and second, to explore strategies for augmenting its robustness. To achieve this, we introduce a novel approach named LP-CLIP. This technique involves the distillation of CLIP features through the incorporation of a linear probing layer positioned atop its encoding structure. This newly added layer is trained utilizing pseudo-labels produced by CLIP, coupled with a self-training strategy. The LP-CLIP technique offers a promising approach to enhance the robustness of CLIP without the need for annotations. By leveraging a simple linear probing layer, we aim to improve the model's ability to withstand various uncertainties and challenges commonly encountered in real-world scenarios. Importantly, our approach does not rely on annotated data, which makes it particularly valuable in situations where labeled data might be scarce or costly to obtain. Our proposed approach increases the robustness of CLIP with SOTA results compared to supervised technique on various datasets.