Reviewerly/exHarmony huggingface dataset:
Scientific Libraries as a Resource for the Scientific and Technological Development of Russia

The journal "Proceedings of SPSTL SB RAS" is a keycommunication platform for discussing actual topical issues related to the activities of scientific libraries and information centers that provide information and methodological support to scientists and specialists. These issues are actively discussed at library forums and conferences, including the annual conferences of the Russian Library Association (RLA). It is a brief report on the meeting of the Section of Special Scientific, Scientific-Technical, Research and Technical Libraries of the Russian Library Association (section 35), which took place at the XXVII Annual Conference of RLA (Murmansk, June 11–17, 2023).
A Survey of Universal Quantum von Neumann Architecture

The existence of universal quantum computers has been theoretically well established. However, building up a real quantum computer system not only relies on the theory of universality, but also needs methods to satisfy requirements on other features, such as programmability, modularity, scalability, etc. To this end, here we study the recently proposed model of quantum von Neumann architecture by putting it in a practical and broader setting, namely, the hierarchical design of a computer system. We analyze the structures of quantum CPU and quantum control units and draw their connections with computational advantages. We also point out that a recent demonstration of our model would require less than 20 qubits.
How Salary and Supervision Affects Turnover Intention Through Job Satisfaction the Case of Industrial Zone Located in Ethiopia

Purpose – Industrial parks face shortage experts, and it is important to exam-ine the factors that influence turnover intention among experts working in the industrial parks. Low salary and supervision may lead to employee dissatis-faction and a significant inverse relationship between job satisfaction, includ-ing subsequent effects on turnover among employees working in the indus-trial parks. However, little or no study was done on the mediating role of job satisfaction and turnover intention among industrial park workers in Ethiopia industrial parks considering supervision and salary as independent variables. Aim - This study examine the mediating effects of job satisfaction on turnover intention among employee of industrial parks located in the eastern part of Ethiopia Design/methodology/approach – A cross-sectional survey design employed by including 400 sample in the study. SEM based PLS were used to examine the mediating effect of job satisfaction between salary, supervision and em-ployee turnover intention. Findings: This study showed that job satisfaction significantly mediated the relationship between salary, supervision and turnover intention. 19.4 percent of the variance in turnover intention explained by salary whereas, 66.6 percent of the variance caused by supervision. The mediation pathway accounted for 41.6 percent of the variance between the independent and dependent varia-bles. Significant relationship between pay, supervision and job satisfaction of workers at the industrial park. It is imperative to state unequivocally that, satisfied workers perform more efficiently, are more committed, and desire to work with their respective. Organizations for a lifetime. Efforts should there-fore be made to enhance the workers’ satisfaction with their job. Implications for nursing management - This study provides industrial park administrators with a resource to build a supportive environment to increase employees job satisfaction and to decrease turnover that affects overall productivity of the sector at regional level
ALGORITHM FOR MULTIPLE EXTENDED TARGET TRACKING

This paper introduces a novel algorithm for multi–extended object tracking, which is based on probabilistic–statistical modelling, machine learning, and optimization theory. The proposed approach encompasses methods for modelling the state of extended objects, data association, and predicting and updating the state of extended objects at each time step. The performance of the algorithm was assessed and compared with other existing algorithms using simulation modelling in MATLAB. This simulation emulates scenarios in which extended objects are tracked while moving under various noise levels. Simulation results demonstrate that the algorithm can effectively detect and track the trajectories of multiple extended objects under different noise levels, utilizing both linear and non–linear measurements from an array of sensors. Furthermore, the algorithm estimates not only the number and kinematic states of the targets but also provides an approximate evaluation of their size. The performance of the proposed algorithm, in terms of accuracy in target number and state estimation, is superior to that of comparable algorithms, particularly in high–noise environments. Potential applications of this algorithm include tracking moving targets in the context of autonomous driving, driver assistance, safety assurance, and various other domains.
The Effect of Project Based Learning on Digital Literacy Skills and Conceptual Understanding in an Online-Based Flipped Classroom Environment

The purpose of this study was to determine the effect of project-based learning on digital literacy skills and concept understanding in an online-basedflipped classroom environment. Methods used by researchers quantitative research methods. The methodological steps used by researchers are. First, researchers designed a learning design that is in accordance with the concept of PBL and online-based flipped classrooms. The research location is at STKIP PGRI Situbondo. For the population and research sample, researchers used a saturated sample of 105 students. Next, the data will be collected using test instruments. Instrument trials include: validity, reliability, difficulty, and differentiating power and continue with hypothesis testing. The results of this study are based on the results of the analysis and discussion of Project-Based Learning (PBL) learning methods that affect digital literacy skills and concept understanding in an online-based flipped classroom environment with a score of 0. 02 and 0. 03 &lt; 0.05 then it can be concluded that there is an influence of variable X on variables Y1 and Y2. These findings also prove temporarily that PBL is suitable for use in learning through the digital world, in accordance with the main purpose of PBL whichprioritizes constructivist learning which involves the use of classical and digital books and activities in the field.
Language models are not naysayers: an analysis of language models on negation benchmarks

Negation has been shown to be a major bottleneck for masked language models, such as BERT.However, whether this finding still holds for larger-sized auto-regressive language models ("LLMs") has not been studied comprehensively.With the ever-increasing volume of research and applications of LLMs, we take a step back to evaluate the ability of currentgeneration LLMs to handle negation, a fundamental linguistic phenomenon that is central to language understanding.We evaluate different LLMs -including the open-source GPTneo, GPT-3, and InstructGPT -against a wide range of negation benchmarks.Through systematic experimentation with varying model sizes and prompts, we show that LLMs have several limitations including insensitivity to the presence of negation, an inability to capture the lexical semantics of negation, and a failure to reason under negation.
Evaluation of aluminum molds using the null screens test

Elastic lenses have been used in various optical systems, such as cameras, microscopes and vision systems, to name a few. A recently reported technique consists of making the lens's optical design, subsequently manufacturing an aluminum mold with the optical parameters of the design obtained, and finally injecting the polymer mixture into it to generate the lens. Where the lens surfaces take the shape of the mold surfaces, it is necessary to verify the finish of the mold surfaces so that the manufactured lenses meet the design requirements. In this work, the null screen technique is presented to evaluate the finish of these surfaces. An analysis of the results obtained and their conclusions is offered.
Automatic Question Paper Generator

"This paper presents the design and development of an Automatic Question Paper Generator tailored to meet the needs of academic institutions. The system aims to streamline administrative tasks and enhancefaculty productivity, ultimately benefiting students in their assessment process. The proposed system offers two distinct workflows: one for administrators and another for faculty members. Administrators are granted privileged access to manage subjects, view registered faculties, and oversee the question bank and automatic question paper generation. Faculty members, on the other hand, can access approved subjects to post questions and generate question papers for assessments. For administrators, the system provides a secure login mechanism to access the admin dashboard. From there, administrators can efficiently monitor and manage the faculty registration process, approving or rejecting new faculty applications as needed. The dashboard allows administrators to add subjects to thesystem, ensuring faculty members can post questions for approved subjects only. Additionally, administrators can view the entire question bank categorized by subjects, review the history of question papers generated by faculty members, and handle any subject addition requests from faculty members. Faculty members log in through their individual accounts and access the faculty dashboard. Within the faculty dashboard, they can view a list of approved subjects, which enables them to post questions relevant to their expertise. Faculty members can effortlessly create question papers for assessments, specifying the total marks for each paper using the automatic question paper generation feature. Optionally, the system may include a feature allowing faculty membersto submit subject addition requests to the administrators, thereby enhancing the system's subject offerings. Overall, the proposed Automatic Question Paper Generator aims to streamline examination processes, reduce paperwork, and centralize academic assessment management. By optimizing administrative workflows and empowering faculty members, the system fosters efficiency and accuracy in academic evaluations, ultimately enhancing the learning experience for students.” Keywords— Automatic Question Paper Generator, Academic institutions, Administrative tasks, Faculty productivity, Question bank, Assessment process.
Reward shaping using convolutional neural network

In this paper, we propose Value Iteration Network for Reward Shaping (VIN-RS), a potential-based reward shaping mechanism using Convolutional Neural Network (CNN). The proposed VIN-RS embeds a CNN trained on computed labels using the message passing mechanism of the Hidden Markov Model. The CNN processes images or graphs of the environment to predict the shaping values. Recent work on reward shaping still has limitations towards training on a representation of the Markov Decision Process (MDP) and building an estimate of the transition matrix. The advantage of VIN-RS is to construct an effective potential function from an estimated MDP while automatically inferring the environment transition matrix. The proposed VIN-RS estimates the transition matrix through a self-learned convolution filter while extracting environment details from the input frames or sampled graphs. Due to (1) the previous success of using message passing for reward shaping; and (2) the CNN planning behavior, we use these messages to train the CNN of VIN-RS. Experiments are performed on tabular games, Atari 2600 and MuJoCo, for discrete and continuous action space. Our results illustrate promising improvements in the learning speed and maximum cumulative reward compared to the state-of-the-art. The improvement achieved by VIN-RS can only be observed for some of the games due to the underlying nature of some environments. In terms of the studied MuJoCo games, there is on average an increase of 30% in the maximum reward reached during early stages of learning.
Digital Identity Using Blockchain Technology

In recent years, the development of blockchain technology has brought a wide spectrum of potential application scenarios from finance to healthcare, IoT, management and many more. This is because the blockchain, a distributed digital ledger, acts as an environment of trust and transparency. The subject of digital identity is getting more and more attention as it represents more than just replacing physical documents (e.g. ID cards, driver's license) with digital data. It would enable the digitalization and automation of the process of identification as well as protect people against identity theft and fraud. This paper proposes and implements a model for digital identity that relies on blockchain technology and cryptography to ensure the privacy, validity and integrity of personal user data. These are obtained with the help of cryptographic signatures, which are the digital representation of physical signatures or stamps. A set of trusted authorities are in charge of issuing cryptographic signatures and storing them on the blockchain. Through issued cryptographic signatures, the authorities account for the digital identity of the users, similar to how public institutions issue identity documents for their citizens which prove their identity. Using digital identity in multi-participant decision support systems improves the decision-making process through trust, anonymity and interoperability.
“Accelerating Engineering Education and Workforce Development in Automation &amp; Control for the Semiconductor Industry Based on Cognitive Neuroscience”

The rapid development of the semiconductor industry represents a global challenge to meet the needs of Science, Technology, and Innovation in the 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century. Advancements in semiconductors optimize computer technology with innovations, among others, in nanotechnology, quantum technology, cognitive computing, Cyber-Physical Human Systems, Artificial Intelligence, and Robotics. The rapid development of semiconductors demands an imperative acceleration of training and the development of a highly skilled workforce. There is a gap between the knowledge and skills of students in the current education system and the fast development of new knowledge. To accelerate comprehensive training in the semiconductor workforce we must start at an early stage, this paper proposes that STEM education must be pedagogically grounded in the advances of cognitive neuroscience and learning. By understanding how the brain functions and how it learns, the process can be enhanced, the education of an Integral Engineer needs to be addressed by forming professionals equipped with adaptability, complex problem-solving, assertive communication, emotional intelligence, interdisciplinary teamwork skills, systems, analytical and critical thinking.This paper proposes curricular transformation processes, ethical formation, pedagogical foundations of interdisciplinary research laboratories, enhanced by an international network of remote laboratories, and a TRUE system of cultural transformation.
Effectiveness of virtual reality in elementary school: A meta-analysis of controlled studies

Virtual reality (VR) is rapidly gaining popularity and becoming more accessible. Numerous studies have examined the effectiveness of this technology in educational settings. This article provides a summary of the evidence regarding the efficacy of virtual environments for elementary education. A meta-analysis was conducted to combine the findings of independent experimental studies. The studies included in this review were sourced from reputable databases such as Web of Science, Scopus, IEEE, ACM, and ScienceDirect. The results indicate that students who learn in a virtual environment achieve higher learning scores compared to those in traditional classrooms (standard mean difference [SMD]=0.64, 95% CI [0.36, 0.92], p&amp;lt;0.001). Additionally, the findings suggest that learning in a tethered VR environment is equally effective as learning through mobile VR. Furthermore, there were no significant differences in scores when different media materials were used for the control group. This review emphasizes the advantages of employing mobile VR for learning in contrast to using tethered VR.
Even If Explanations: Prior Work, Desiderata &amp; Benchmarks for Semi-Factual XAI

Recently, eXplainable AI (XAI) research has focused on counterfactual explanations as post-hoc justifications for AI-system decisions (e.g., a customer refused a loan might be told “if you asked for a loan with a shorter term, it would have been approved”). Counterfactuals explain what changes to the input-features of an AI system change the output-decision. However, there is a sub-type of counterfactual, semi-factuals, that have received less attention in AI (though the Cognitive Sciences have studied them more). This paper surveys semi-factual explanation, summarising historical and recent work. It defines key desiderata for semi-factual XAI, reporting benchmark tests of historical algorithms (as well as a novel, naïve method) to provide a solid basis for future developments.
Multi-Modality Deep Network for JPEG Artifacts Reduction

In recent years, many convolutional neural network-based models are designed for JPEG artifacts reduction, and have achieved notable progress. However, few methods are suitable for extreme low-bitrate image compression artifacts reduction. The main challenge is that the highly compressed image loses too much information, resulting in reconstructing high-quality image difficultly. To address this issue, we propose a multimodal fusion learning method for text-guided JPEG artifacts reduction, in which the corresponding text description not only provides the potential prior information of the highly compressed image, but also serves as supplementary information to assist in image deblocking. We fuse image features and text semantic features from the global and local perspectives respectively, and design a contrastive loss built upon contrastive learning to produce visually pleasing results. Extensive experiments, including a user study, prove that our method can obtain better deblocking results compared to the state-of-the-art methods.
Boosting Few-Shot Open-Set Recognition with Multi-Relation Margin Loss

Few-shot open-set recognition (FSOSR) has become a great challenge, which requires classifying known classes and rejecting the unknown ones with only limited samples. Existing FSOSR methods mainly construct an ambiguous distribution of known classes from scarce known samples without considering the latent distribution information of unknowns, which degrades the performance of open-set recognition. To address this issue, we propose a novel loss function called multi-relation margin (MRM) loss that can plug in few-shot methods to boost the performance of FSOSR. MRM enlarges the margin between different classes by extracting the multi-relationship of paired samples to dynamically refine the decision boundary for known classes and implicitly delineate the distribution of unknowns. Specifically, MRM separates the classes by enforcing a margin while concentrating samples of the same class on a hypersphere with a learnable radius. In order to better capture the distribution information of each class, MRM extracts the similarity and correlations among paired samples, ameliorating the optimization of the margin and radius. Experiments on public benchmarks reveal that methods with MRM loss can improve the unknown detection of AUROC by a significant margin while correctly classifying the known classes.
Good Explanations in Explainable Artificial Intelligence (XAI): Evidence from Human Explanatory Reasoning

Insights from cognitive science about how people understand explanations can be instructive for the development of robust, user-centred explanations in eXplainable Artificial Intelligence (XAI). I survey key tendencies that people exhibit when they construct explanations and make inferences from them, of relevance to the provision of automated explanations for decisions by AI systems. I first review experimental discoveries of some tendencies people exhibit when they construct explanations, including evidence on the illusion of explanatory depth, intuitive versus reflective explanations, and explanatory stances. I then consider discoveries of how people reason about causal explanations, including evidence on inference suppression, causal discounting, and explanation simplicity. I argue that central to the XAI endeavor is the requirement that automated explanations provided by an AI system should make sense to human users.
Evaluating GPT-3 Generated Explanations for Hateful Content Moderation

Recent research has focused on using large language models (LLMs) to generate explanations for hate speech through fine-tuning or prompting. Despite the growing interest in this area, these generated explanations' effectiveness and potential limitations remain poorly understood. A key concern is that these explanations, generated by LLMs, may lead to erroneous judgments about the nature of flagged content by both users and content moderators. For instance, an LLM-generated explanation might inaccurately convince a content moderator that a benign piece of content is hateful. In light of this, we propose an analytical framework for examining hate speech explanations and conducted an extensive survey on evaluating such explanations. Specifically, we prompted GPT-3 to generate explanations for both hateful and non-hateful content, and a survey was conducted with 2,400 unique respondents to evaluate the generated explanations. Our findings reveal that (1) human evaluators rated the GPT-generated explanations as high quality in terms of linguistic fluency, informativeness, persuasiveness, and logical soundness, (2) the persuasive nature of these explanations, however, varied depending on the prompting strategy employed, and (3) this persuasiveness may result in incorrect judgments about the hatefulness of the content. Our study underscores the need for caution in applying LLM-generated explanations for content moderation. Code and results are available at https://github.com/Social-AI-Studio/GPT3-HateEval.
ViT-P3DE∗: Vision Transformer Based Multi-Camera Instance Association with Pseudo 3D Position Embeddings

Multi-camera instance association, which identifies identical objects among multiple objects in multi-view images, is challenging due to several harsh constraints. To tackle this problem, most studies have employed CNNs as feature extractors but often fail under such harsh constraints. Inspired by Vision Transformer (ViT), we first develop a pure ViT-based framework for robust feature extraction through self-attention and residual connection. We then propose two novel methods to achieve robust feature learning. First, we introduce learnable pseudo 3D position embeddings (P3DEs) that represent the 3D location of an object in the world coordinate system, which is independent of the harsh constraints. To generate P3DEs, we encode the camera ID and the object's 2D position in the image using embedding tables. We then build a framework that trains P3DEs to represent an object's 3D position in a weakly supervised manner. Second, we also utilize joint patch generation (JPG). During patch generation, JPG considers an object and its surroundings as a single input patch to reinforce the relationship information between two features. Ultimately, experimental results demonstrate that both ViT-P3DE and ViT-P3DE with JPG achieve state-of-the-art performance and significantly outperform existing works, especially when dealing with extremely harsh constraints.