Reviewerly/exHarmony huggingface dataset:
New book science: a look into the future

Modern media2 transformations are accompanied by changes in the spatio-temporal characteristics of social interaction. Traditional and new ways of fixing and transmitting ideas, their materialization through various communication channels are at the heart of these changes. This shapes the need for actualization of research, development of new approaches, clarification of the object and the subject of medialogical disciplines, including book studies. In this study, the main problems of modern Russian bibliology have been identified: limited potential of morally outdated approaches (structural-typological, functional, documentgraphics), a breakaway from the dynamically changing international social and humanitarian agenda and non-participation in international bibliological forums, the functioning of a closed system of scholarly communication. Among other problems are: methodological cliché, limited use of sociological methods, slow digitalization of book studies. The contemporary understanding of the production and dissemination of symbolic forms as an unstructured and non-hierarchical system requires fundamentally new research approaches, the extension of methodological tools, crossdisciplinary study of the theory and history of books. The purpose of the article is to form the concept of “a new book science” with the object which should be considered as a system with emphasis on feedback “author – writing – text – reading – reader”. A system of projects focused on practical implementation of this concept is proposed: medialogical approach development; interdisciplinary research of the history of the reader and reading in Russia; the formation of an independent scientific field – the sociology of book – and the design of the eponymous educational discipline; preparation of a modern textbook on book science for higher education.
Chevalley–Warning at the boundary

The Chevalley–Warning Theorem is a result on the solution set of a system of polynomial equations f1,…,fr in n variables over a finite field Fq in the low degree case d≔∑j=1rdeg(fj)<n. In this note we reformulate that result in terms of fibers of the associated polynomial map and, following Heath-Brown, show that something weaker continues to hold when d=n. This result invites a search for homogeneous degree n polynomials in n variables over Fq for which the associated polynomial function Fqn→Fq is not surjective, and we exhibit several families of such polynomials.
Improving the Robustness of Object Detection Through a Multi-Camera–Based Fusion Algorithm Using Fuzzy Logic

A single camera creates a bounding box (BB) for the detected object with certain accuracy through a convolutional neural network (CNN). However, a single RGB camera may not be able to capture the actual object within the BB even if the CNN detector accuracy is high for the object. In this research, we present a solution to this limitation through the usage of multiple cameras, projective transformation, and a fuzzy logic–based fusion. The proposed algorithm generates a “confidence score” for each frame to check the trustworthiness of the BB generated by the CNN detector. As a first step toward this solution, we created a two-camera setup to detect objects. Agricultural weed is used as objects to be detected. A CNN detector generates BB for each camera when weed is present. Then a projective transformation is used to project one camera’s image plane to another camera’s image plane. The intersect over union (IOU) overlap of the BB is computed when objects are detected correctly. Four different scenarios are generated based on how far the object is from the multi-camera setup, and IOU overlap is calculated for each scenario (ground truth). When objects are detected correctly and bounding boxes are at correct distance, the IOU overlap value should be close to the ground truth IOU overlap value. On the other hand, the IOU overlap value should differ if BBs are at incorrect positions. Mamdani fuzzy rules are generated using this reasoning, and three different confidence scores (“high,” “ok,” and “low”) are given to each frame based on accuracy and position of BBs. The proposed algorithm was then tested under different conditions to check its validity. The confidence score of the proposed fuzzy system for three different scenarios supports the hypothesis that the multi-camera–based fusion algorithm improved the overall robustness of the detection system.
Asynchronous Parallel Nonconvex Optimization Under the Polyak-Łojasiewicz Condition

Communication delays and synchronization are major bottlenecks for parallel computing, and tolerating asynchrony is therefore crucial for accelerating parallel computation. Motivated by optimization problems that do not satisfy convexity assumptions, we present an asynchronous block coordinate descent algorithm for nonconvex optimization problems whose objective functions satisfy the Polyak-Łojasiewicz condition. This condition is a generalization of strong convexity to nonconvex problems and requires neither convexity nor uniqueness of minimizers. Under only assumptions of mild smoothness of objective functions and bounded delays, we prove that a linear convergence rate is obtained. Numerical experiments for logistic regression problems are presented to illustrate the impact of asynchrony upon convergence.
Emotional Intelligence as a Factor of Successful Self-Realization in Students of Socionomic and Bionomic Professions

Self-realization is an important factor of successful social adaptation, especially in the early stages of a professional career in the dynamic conditions of the modern economy and society.The purpose of this study was to assess the general level of self-realization of students in socionomic and bionomic professions.It also performed comparative analysis (previously insufficiently represented in the empirical literature) of subjective perception of self-realization, depending on the length of professional training and the level of emotional intelligence.The subjects were students of the Southern Federal University, 109 representatives of socionomic and 75 of bionomic professions.The respondents completed a multidimensional self-realization questionnaire (S.I.Kudinov) and the test of emotional intelligence (EmIn, by D.V.Lucin).The study found significant differences between the representatives of the two professional types as in their subjective assessment of success of three types of self-realization, as in the direction and relative contribution to the subjective sense of self-realization of the two factors under consideration: duration of professional training and the level of emotional intelligence.Emotional intelligence was a significant predictor of all types of self-realization, compensating for the negative influence of duration of training in bionomic professions and enhancing its positive effect in socionomic professions.The authors emphasize the importance of developing and using specialized innovative programs for effective training of emotional intelligence as one of the key components in the work of practical psychologists with young people in the early stages of professional training and career.
Considerations and practical protocols for using virtual reality in psychological research and practice, as evidenced through exposure-based therapy

Everyday client stressors are often challenging to replicate in clinical and research environments, which hinders the ability to reliably reproduce clinical outcomes. To overcome this obstacle, tools that can bridge the inherent disconnect between these settings and the real-world experiences of clients are urgently needed. Virtual reality (VR) promises to provide immersive experiences within controlled laboratory or clinical settings. While the potential opportunities and challenges of VR applications have been scientifically reviewed, clinical adoptions into psychology are hampered by a paucity of practical and methodological publications. This paper intends to address that gap, by providing a four-step process for decision-making considerations, including practical recommendations and an applied case study of developing an exposure-based system for obsessive–compulsive disorder. We provide a framework to make VR accessible for clinician-researchers to create similar systems that realise the promise while encouraging ongoing scientific rigour.
Empirical Study of Multi-Task Hourglass Model for Semantic Segmentation Task

The semantic segmentation (SS) task aims to create a dense classification by labeling at the pixel level each object present on images. Convolutional neural network (CNN) approaches have been widely used, and exhibited the best results in this task. However, the loss of spatial precision on the results is a main drawback that has not been solved. In this work, we propose to use a multi-task approach by complementing the semantic segmentation task with edge detection, semantic contour, and distance transform tasks. We propose that by sharing a common latent space, the complementary tasks can produce more robust representations that can enhance the semantic labels. We explore the influence of contour-based tasks on latent space, as well as their impact on the final results of SS. We demonstrate the effectiveness of learning in a multi-task setting for hourglass models in the Cityscapes, CamVid, and Freiburg Forest datasets by improving the state-of-the-art without any refinement post-processing.
Efficient decentralized access control for secure data sharing in cloud computing

Summary Access control is an important technique in information security that allows legitimate users to gain access to and prevent unauthorized users from getting access to resources in a system. The restriction between access from a user and a shared file of the data owner can be determined by the access policy. In most existing access control models, it is assumed that all entities, including users, the third party, and cloud service provider (CSP), are in the same trust domain. However, in cloud computing environments, it is usually assumed that the CSP cannot be fully trusted, and the data owner (DO) is desired to have the absolute initiative to control data access. This article proposes a blockchain‐based access control scheme for cloud computing, in which the DO maintains an access matrix to describe the access policy. Then, the public keys of all nodes and the access matrix are stored in the blockchain, to ensure the security of the proposed scheme. The DOs can encrypt the large shared files once using a symmetric key in a long time. And they also can encrypt the symmetric key in parallel using the public key of authorized users in a short time. Security analysis proves that the proposed scheme is able to prevent outsourced files from unauthorized access and collusion attack. Experimental results show that the proposed scheme outperforms the existing baselines in terms of overheads on computation and storage. On average, the computation overhead of the proposed scheme is lower than that of the scheme SVPAC, PpBAC, and Timely CP‐ABE by 25.37%, 45.46%, and 36.44%, respectively. The communication overhead of the proposed scheme is lower than that of the scheme Timely CP‐ABE by 17.16%, and it is more secure, although it is higher than that of the scheme SVPAC and PpBAC by 5.88% and 39.05%. And the storage overhead of the proposed scheme is lower than that of the scheme SVPAC, PpBAC, and Timely CP‐ABE by 59.36%, 20.25%, and 61.88%, respectively.
A Smart Bicycle That Protects Itself: Active Sensing and Estimation for Car-Bicycle Collision Prevention

Bicycling is a healthy physical activity for all ages. It can provide both physical and mental health benefits, including reducing the risk of cancer [1], cutting the risk of heart disease by half [2], postponing Alzheimer’s disease [3, 4], and promoting mental alertness and memory [5].
CBench

Recently, there has been an increase in the number of knowledge graphs that can be only queried by experts. However, describing questions using structured queries is not straightforward for non-expert users who need to have sufficient knowledge about both the vocabulary and the structure of the queried knowledge graph, as well as the syntax of the structured query language used to describe the user's information needs. The most popular approach introduced to overcome the aforementioned challenges is to use natural language to query these knowledge graphs. Although several question answering benchmarks can be used to evaluate question-answering systems over a number of popular knowledge graphs, choosing a benchmark to accurately assess the quality of a question answering system is a challenging task. In this paper, we introduce CBench, an extensible, and more informative benchmarking suite for analyzing benchmarks and evaluating question answering systems. CBench can be used to analyze existing benchmarks with respect to several fine-grained linguistic, syntactic, and structural properties of the questions and queries in the benchmark. We show that existing benchmarks vary significantly with respect to these properties deeming choosing a small subset of them unreliable in evaluating QA systems. Until further research improves the quality and comprehensiveness of benchmarks, CBench can be used to facilitate this evaluation using a set of popular benchmarks that can be augmented with other user-provided benchmarks. CBench not only evaluates a question answering system based on popular single-number metrics but also gives a detailed analysis of the linguistic, syntactic, and structural properties of answered and unanswered questions to better help the developers of question answering systems to better understand where their system excels and where it struggles.
DCANet: Dual contextual affinity network for mass segmentation in whole mammograms

Purpose Breast mass segmentation in mammograms remains a crucial yet challenging topic in computer‐aided diagnosis systems. Existing algorithms mainly used mass‐centered patches to achieve mass segmentation, which is time‐consuming and unstable in clinical diagnosis. Therefore, we aim to directly perform fully automated mass segmentation in whole mammograms with deep learning solutions. Methods In this work, we propose a novel dual contextual affinity network (a.k.a., DCANet) for mass segmentation in whole mammograms. Based on the encoder–decoder structure, two lightweight yet effective contextual affinity modules including the global‐guided affinity module (GAM) and the local‐guided affinity module (LAM) are proposed. The former aggregates the features integrated by all positions and captures long‐range contextual dependencies, aiming to enhance the feature representations of homogeneous regions. The latter emphasizes semantic information around each position and exploits contextual affinity based on the local field‐of‐view, aiming to improve the indistinction among heterogeneous regions. Results The proposed DCANet is greatly demonstrated on two public mammographic databases including the DDSM and the INbreast, achieving the Dice similarity coefficient (DSC) of 85.95% and 84.65%, respectively. Both segmentation performance and computational efficiency outperform the current state‐of‐the‐art methods. Conclusion According to extensive qualitative and quantitative analyses, we believe that the proposed fully automated approach has sufficient robustness to provide fast and accurate diagnoses for possible clinical breast mass segmentation.
Generative Adversarial Semi-Supervised Network For Medical Image Segmentation

Due to the limitation of ethics and the number of professional annotators, pixel-wise annotations for medical images are hard to obtain. Thus, how to exploit limited annotations and maintain the performance is an important yet challenging problem. In this paper, we propose Generative Adversarial Semi-supervised Network(GASNet) to tackle this problem in a self-learning manner. Only limited labels are available during the training procedure and the unlabeled images are exploited as auxiliary information to boost segmentation performance. We modulate segmentation network as a generator to produce pseudo labels whose reliability will be judged by an uncertainty discriminator. Feature mapping loss will obtain statistic distribution consistency between the generated labels and the real ones to further ensure the credibility. We obtain 0.8348 to 0.9131 dice coefficient with 1/32 to 1/2 proportion of annotations respectively on right ventricle dataset. Improvements are up to 28.6 points higher than the corresponding fully supervised baseline.
SenCS

Indoor proximity verification has become an increasingly useful primitive for the scenarios where access is granted to the previously unknown users when they enter a given area (e.g., a hotel room). Existing solutions either rely on homogeneous sensing modalities shared by two parties or require additional human interactions. In this article, we propose a context-based indoor proximity verification scheme, called SenCS, to enable real-time autonomous access for mobile devices, utilizing the available heterogeneous sensors at the user side and at the room side. The intuition is that only when the user is within a room can sensors from both sides observe the same events in the room. Yet such a solution is challenging, because the events may not provide enough entropy within the required time and the heterogeneity in sensing modalities may not always agree on the sensed events. To overcome the challenges, we exploit the time intervals between successively human actions to create heterogeneous contextual fingerprints (HCF) at a millisecond level. By comparing the contextual similarity between the HCF s from both the room and user sides, SenCS accomplishes the indoor proximity verification. Through proof-of-concept implementation and evaluations on 30 participants, SenCS achieves an accuracy of 99.77% and an equal error rate (EER) of 0.23% across various hardware configurations.
On the Root of Trust Identification Problem

Trusted Execution Environments (TEEs) are becoming ubiquitous and are currently used in many security applications: from personal IoT gadgets to banking and databases. Prominent examples of such architectures are Intel SGX, ARM TrustZone, and Trusted Platform Modules (TPMs). A typical TEE relies on a dynamic Root of Trust (RoT) to provide security services such as code/data confidentiality and integrity, isolated secure software execution, remote attestation, and sensor auditing. Despite their usefulness, there is currently no secure means to determine whether a given security service or task is being performed by the particular RoT within a specific physical device. We refer to this as the Root of Trust Identification (RTI) problem and discuss how it inhibits security for applications such as sensing and actuation.
Lucid dreaming for experience replay: refreshing past states with the current policy

Experience replay (ER) improves the data efficiency of off-policy reinforcement learning (RL) algorithms by allowing an agent to store and reuse its past experiences in a replay buffer. While many techniques have been proposed to enhance ER by biasing how experiences are sampled from the buffer, thus far they have not considered strategies for refreshing experiences inside the buffer. In this work, we introduce L uc i d D reaming for E xperience R eplay (LiDER), a conceptually new framework that allows replay experiences to be refreshed by leveraging the agent's current policy. LiDER consists of three steps: First, LiDER moves an agent back to a past state. Second, from that state, LiDER then lets the agent execute a sequence of actions by following its current policy—as if the agent were "dreaming" about the past and can try out different behaviors to encounter new experiences in the dream. Third, LiDER stores and reuses the new experience if it turned out better than what the agent previously experienced, i.e., to refresh its memories. LiDER is designed to be easily incorporated into off-policy, multi-worker RL algorithms that use ER; we present in this work a case study of applying LiDER to an actor–critic-based algorithm. Results show LiDER consistently improves performance over the baseline in six Atari 2600 games. Our open-source implementation of LiDER and the data used to generate all plots in this work are available at https://github.com/duyunshu/lucid-dreaming-for-exp-replay .
Certain fundamental properties of generalized natural transform in generalized spaces

Abstract This paper considers the definition and the properties of the generalized natural transform on sets of generalized functions. Convolution products, convolution theorems, and spaces of Boehmians are described in a form of auxiliary results. The constructed spaces of Boehmians are achieved and fulfilled by pursuing a deep analysis on a set of delta sequences and axioms which have mitigated the construction of the generalized spaces. Such results are exploited in emphasizing the virtual definition of the generalized natural transform on the addressed sets of Boehmians. The constructed spaces, inspired from their general nature, generalize the space of integrable functions of Srivastava et al. (Acta Math. Sci. 35B:1386–1400, 2015) and, subsequently, the extended operator with its good qualitative behavior generalizes the classical natural transform. Various continuous embeddings of potential interests are introduced and discussed between the space of integrable functions and the space of integrable Boehmians. On another aspect as well, several characteristics of the extended operator and its inversion formula are discussed.
Time-varying Obstacle Avoidance by Using High-gain Observer and Input-to-State Constraint Safe Control Barrier Function

Control barrier functions (CBFs) have been studied for a state constraint problem; one of those is a human assist control for moving obstacle avoidance by using time-varying CBFs. However, the human assist control contains the complete information on the motion of environments; in general, a derivative of moving obstacle states needs estimating. In this paper, we apply a high-gain observer based differentiator to estimate a derivative of moving obstacle states. By regarding estimation errors as input disturbances, we propose an input-to-state constraint safe control barrier function (ISCSf-CBF) and construct a human assist control based on the proposed function. Moreover, we confirm the effectiveness of the proposed method by computer simulation.
Multi-modal Semantic Inconsistency Detection in Social Media News Posts

As computer-generated content and deepfakes make steady improvements, semantic approaches to multimedia forensics will become more important. In this paper, we introduce a novel classification architecture for identifying semantic inconsistencies between video appearance and text caption in social media news posts. While similar systems exist for text and images, we aim to detect inconsistencies in a more ambiguous setting, as videos can be long and contain several distinct scenes, in addition to adding audio as an extra modality. We develop a multi-modal fusion framework to identify mismatches between videos and captions in social media posts by leveraging an ensemble method based on textual analysis of the caption, automatic audio transcription, semantic video analysis, object detection, named entity consistency, and facial verification. To train and test our approach, we curate a new video-based dataset of 4,000 real-world Facebook news posts for analysis. Our multi-modal approach achieves 60.5% classification accuracy on random mismatches between caption and appearance, compared to accuracy below 50% for uni-modal models. Further ablation studies confirm the necessity of fusion across modalities for correctly identifying semantic inconsistencies.