Reviewerly/exHarmony huggingface dataset:
Sequence-to-Sequence Video Captioning System

Video captions, the art of video translation to produce a comprehensive summary of the content, is a challenging task in the fields of computer vision and machine learning. Traditional methods for video captions have not yet been developed many success stories. However, with the recent development of artificial intelligence aided by in-depth reading, video captions have recently received significant attention. The power of convolutional neural networks, as well as repetitive neural networks, has made it possible to create end-to-end programs for the end of the video caption business. Convolutional neural networks process image frames in the video to extract important features, which are processed by neural networks that appear in a sequence to form a sound video summary.
STATISTICAL IDENTIFICATION OF UNIMODAL RANDOM VARIABLES USING PARAMETRICALLY SIMILAR CONDITIONAL DISTRIBUTIONS

Scientific-educational and applied journal UNIVERSITY NEWS NORTH-CAUCASIAN REGION TECHNICAL SCIENCES SERIES. STATISTICAL IDENTIFICATION OF UNIMODAL RANDOM VARIABLES USING PARAMETRICALLY SIMILAR CONDITIONAL DISTRIBUTIONS
Evaluation of the learning management system and its relationship in the perception of engineering students

&lt;span lang="EN-US"&gt;The objective of this study was to identify the results of the learning management system (LMS) functionality, and its relationship in the perception of electronic engineering students. The results serve as a basis for continuous improvement for the higher institution, because these systems are tools that have the purpose of improving the performance and retention of the student in the virtual teaching-learning process. Initially, the reliability of the data collected was determined using Cronbach's Alpha, obtaining a consistency coefficient of 0.868. After processing the data in the SPSS software, using the 5-level Likert scale, it was determined that the indicators that present a better perception are related to the design and ease of navigation. However, 21.4% do not fully agree with the functionality, due to the technical problems presented when downloading the study material. The effect generated by the optimal functionality of the LMS is 70.87% satisfaction in students. The Chi square test validated the cause-effect relationship, with a significance of 0.000, between the functionality of the LMS, on the perception of the students. It determined that the indicators with a greater relationship refer to design, availability (connectivity) and ease of communication and interaction with the teacher and their peers.&lt;/span&gt;
On the inductive biases of deep domain adaptation

Domain alignment is currently the most prevalent solution to unsupervised domain-adaptation tasks and are often being presented as minimizers of some theoretical upper-bounds on risk in the target domain. However, further works revealed severe inadequacies between theory and practice: we consolidate this analysis and confirm that imposing domain invariance on features is neither necessary nor sufficient to obtain low target risk. We instead argue that successful deep domain adaptation rely largely on hidden inductive biases found in the common practice, such as model pre-training or design of encoder architecture. We perform various ablation experiments on popular benchmarks and our own synthetic transfers to illustrate their role in prototypical situations. To conclude our analysis, we propose to meta-learn parametric inductive biases to solve specific transfers and show their superior performance over handcrafted heuristics.
Causal Inference via Nonlinear Variable Decorrelation for Healthcare Applications

Causal inference and model interpretability research are gaining increasing attention, especially in the domains of healthcare and bioinformatics. Despite recent successes in this field, decorrelating features under nonlinear environments with human interpretable representations has not been adequately investigated. To address this issue, we introduce a novel method with a variable decorrelation regularizer to handle both linear and nonlinear confounding. Moreover, we employ association rules as new representations using association rule mining based on the original features to further proximate human decision patterns to increase model interpretability. Extensive experiments are conducted on four healthcare datasets (one synthetically generated and three real-world collections on different diseases). Quantitative results in comparison to baseline approaches on parameter estimation and causality computation indicate the model's superior performance. Furthermore, expert evaluation given by healthcare professionals validates the effectiveness and interpretability of the proposed model.
Polarization-Entangled Photons From Ultrathin Nonlinear Layers

We report on the first-time generation of polarization-entangled photons via spontaneous parametric down-conversion in ultrathin nonlinear layers. Relaxed phase matching leads to an unprecedented tunability of the two-photon polarization state, not achievable in bulk sources.
Research on Short Video Production of Taijiquan Teaching under Information Technology

Taijiquan has been in my country's excellent traditional culture, has been included in the world's intangible cultural heritage, with the popularity of Taijiquan, in the middle of teaching, especially the rise of short video, and created new Way. Taking Tai Chi Teaching Short Video Design, Production and Optimization as Research Contents, providing a reference for Taijiquan short video teaching. The study mainly includes short video design, design principles and design content, further implementation of short video production, introducing short video production processes, production methods, and showing short video production results. Further proposing the optimization strategy and optimization of short video teaching, finally summarizing this study, proposing rationalization recommendations, through the study of Taijiquan short video teaching, providing the design and production of short video teaching of Tai Chi and even other sports Reference and experience.
Development of social responsibility in future bachelors of social work in the conditions of quasi-professional activity

The purpose of the study is to present a description of the resources of quasi-professional activity in the development of social responsibility in future bachelors of social work. Scientific novelty lies in the presentation of solutions for the use of contextual learning opportunities and directly quasi-professional activities for the development of social responsibility among students of the professional training direction – social work. The result of the study is a description of the organization of educational, gaming and professional activities aimed at developing personal qualities, multi-aspect knowledge and professional competencies of students. Instructions for conducting lectures and practical classes are presented, the implementation of which involves simulation models of training in the framework of the quasi-professional activities of future bachelors of social work.
The use of coaching technologies to improve the efficiency of the process of forming the readiness of future managers for project management

The purpose of the study is to substantiate the possibility of using coaching technology to form the readiness of future managers for project management. Scientific novelty lies in the description of the focus of coaching methods on project management and their application in the formation of the universal competence of students – the development and implementation of projects. The results of the study are a description of the use of coaching technology for the development of projects and the formation of the readiness of future managers to apply coaching methods in their future professional activities. The ratio of coaching methods and types of projects is presented, the application of these methods within the discipline "Strategic Management" for students of the 3rd year of professional training in the direction 38.03.02 Management is shown. The effectiveness of this technology is substantiated on the basis of comparative measuring procedures for students.
The design and research of a system for monitoring the movements of goods and resources using blockchain

Recent research and publications. A blockchain is a distributed data structure that is replicated and distributed among network members. The first blockchain specification was proposed together with the digital currency Bitcoin in 2008 by a man under the pseudonym Satoshi Nakamoto to solve the problem of centralizing finances around banks. Today, block-chains are used mainly in the field of decentralized finance (DeFi) in the form of cryptocur-rencies and instruments to them. There are also a few specialized foreign studies on the use of blockchain in the monitoring of supplies, but these studies are more focused on the economic and logistical feasibility of using the blockchain in supply chains, without the exact models of information systems on which such a system should work. The aim of the study. Study of blockchain technology in information systems for moni-toring the movement of goods and resources, which can improve the processes of tracking and automation in supply chains. Main material of the study. The paper develops a prototype of the information system for monitoring the movements of goods in supply chains, which is working above the Ethereum virtual machine. The system is working using two smart-contracts and the paper describes the exact structure and specification of smart-contracts and principles of communication between them for the information system for monitoring the movements of goods. Conclusions. The article presents a prototype of an information system using blockchain technology and smart contracts which are working on the Ethereum network. Based on prototyping of the information system for monitoring the movements of goods, it was concluded that the transparency of the tracking and automation process in supply chains is improved. This work is useful for designing and creating more detailed and sophisticated systems for monitoring and managing the movement of goods and other supplies based on the use of Blockchain technology.
An Entanglement-Based QKD System for Scalable Robust Multi-User Networks

We present entanglement-based quantum key distribution in a star-shaped network with time-bin coding. We demonstrate key exchange under typical operation conditions for metropolitan-scale networks. Without much efforts, it is scalable to more than 100 users.
Multi-port Realization of Modeling Platform for Impact of Negative Financial Information on Market Stock Price Fluctuation based on Crawler Network Analysis Algorithm

Based on the current research progress of web crawler at home and abroad, this paper analyzes and compares the advantages and disadvantages of existing topic web crawler search strategies. Based on the web crawler search strategy, a new modeling method for the impact of negative financial information on market stock price fluctuations is proposed. By analyzing the reasons that affect the stock price fluctuations of enterprises, the characteristics of stock price fluctuations of enterprises are obtained, and the Kalman filter method is used to obtain the connection between the negative financial information of enterprises and the fluctuation of market stock prices. The least squares criterion is introduced to establish a model of the impact of corporate negative financial information on market share price fluctuations.
The Implementation of Smart Factories: Empirical Evidence from Korean Small and Medium-Sized Enterprises

Purpose - The purpose of this study is to examine firm-level attributes related to Korean manufacturing small and medium-sized enterprises’ (SMEs’) decisions to implement smart factories. Design/methodology/approach - This study uses the &lt;Survey on Actual State of SMEs&gt; provided by the Ministry of SMEs and Startups of Korea and the Korea Federation of SMEs. Manufacturing SMEs’ decisions to implement smart factories in 2018-2019 were analyzed using multinomial logit and ordered logit models. Findings - The findings of this study suggest that firms’ decisions to implement smart factories were positively related to firm size, R&amp;D intensity, international market scope, and transactional relationships with customers. However, smart factory implementation decisions were not related to firm age and CEO gender. Research implications or Originality - This study illuminates firm-level attributes that may drive organizational innovation in the era of Industry 4.0 and thus contributes to the innovation adoption literature. This study also contributes to growing research on smart factories by analyzing the actual, progressive decisions to implement smart factories, as opposed to perceived intentions to implement them.&#x0D;
ASIC-Based Mining System in Blockchain for IoT Environment to Increase Scalability and Security

In this chapter we present an architecture that can be followed to implement blockchain in an IoT domain. The proposed model is based on the famous proof-of-work consensus mechanism. The data stored in each block will be the data collected by the various sensors present in the network. Every IoT device responsible for collecting data will have a mining unit associated with it that will be responsible for mining new blocks. The main aim of this chapter will be to propose a model that can be followed for implementing blockchain in an IoT domain with maximum efficiency without any form of comprise in terms of security and scalability. We also propose the use of ASIC (Application Specific Integrated Circuits) for mining new blocks as it is more power efficient and has the least security flaws. Although the proposed architecture is based on the use of ASIC miners for producing new blocks, it can be replaced by any other hardware like RaspberryPi for easier implementation.
DEVELOPMENT OF MECHANISM FOR ENHANCING DATA SECURITY IN QUANTUM CRYPTOGRAPHY

Nowadays security in communication is increasingly important to the network communication because many categories of data are required restriction on authorization of access, modify, delete and insert. Quantum cryptography is one of the solutions that use property of polarization to ensure that transmitted data is not tampered. The research paper provides the mechanism that enhances the data security in quantum cryptography during exchange of information. In first phase detailed explanation of Quantum key distribution's BB84 protocol is given. BB84 protocol is used as the basis for the mechanism. In next phase the proposed mechanism is explained. The proposed mechanism combines BB84 protocol at two levels, from sender to receiver and then from receiver to sender. Moreover, a logic circuit is used to combine the bits hence to reduce the probability of eavesdropping. The key obtained can be used to exchange the information securely further it can help in encryption and decryption of crucial data. Double level BB84 mechanism will help in information reconciliation as well as privacy amplification. In future the proposed mechanism will be very beneficial where unconditional security is required during key and other secret information exchange
Lightweight Cryptography and Protocols for IoT Security

Recently, Internet of Things (IoT) has opened a new horizon in the field of information technology. In this environment, the enormous number of device connection and huge volume data generation on the air is very much sensitive in various kind of attacks. Conventional cryptographic algorithms are useful for maintaining confidentiality and integrity at the time of information passing. In resource constrained environment the devices that are used, are small in size, having limited computational capability, limited memory, and very low-power batteries. Hence, conventional cryptographic algorithms are not applicable there for security purposes. This resource constrained smart devices result in the exposure of a new field called lightweight cryptography (LWC). A huge number of research solutions have been proposed for implementation of lightweight cryptographic primitives and protocols in such resource constraint domains. In this paper, a discussion has been made on lightweight cryptographic primitives that include lightweight block ciphers (PRESENT, TEA, CLEFIA), stream ciphers (GRAIN V1, TRIVIUM and MICKEY), and hash functions (PHOTON, QUARK, and SPONGENT). This paper is also including the way of maintaining security in IoT environment with the help of these primitives. Besides these, a few lightweight data protocol (MQTT, CoAP, and XMPP) strategies in IoT have also been discussed. Finally, a comparative study has been made in the discussion section considering different parameters.
Robotic Arm Design and Analysis Repository (RADAR)

The Robotic Arm Design and Analysis Repository (RADAR) is a MATLAB based development package created to assess the design space of robotic arm configurations based on environments and provide comprehensive robotics analysis. RADAR&#x0027;s main capabilities include computing gravitational, thermal, and load-induced deflections; computing body wrenches; propagation and assessment of component/system errors; visualizing environments; and motion planning. The modularity and functionality of RADAR is due to the implementation of a hierarchy of frame-based objects. This enables an elegant design approach for quick configuration changes and offers robust but flexible analysis capabilities. RADAR is currently being used on RTAS (Robotic Transfer Assembly System) of the planned Mars Sample Return Campaign and the focus of this paper is the theory and implementation of RADAR with supporting examples from RTAS.
Analysis of the University’s Core Competency Awareness Through IPA Techniques

The purpose of this study was to investigate and analyze members’ perceptions of the university’s core competencies in order to improve and supplement the core competencies of K University. To this end, a survey was conducted for the members of K University on the degree of conformity and achievement of the university’'s talent awards, the importance and achievement of core competencies and sub-competencies. Corresponding sample t-test was used to analyze the difference in the degree of conformity and achievement of university talents, and IPA analysis was performed using the average values for the importance and achievement of core competencies and sub-competences. As a result of IPA analysis, competencies requiring intensive improvement were not found in all groups, and global competency was selected as competency requiring gradual improvement. Practical competency and communication competency were found as competencies corresponding to the maintenance area, and empathy competency was found as an area that was over-invested. Based on these results, K university needs to develop an effective improvement plan by decentralizing empathyoriented support and investment into global capabilities.
Diffractive networks form cascadable all-optical NAND gates

A cascadable, all-optical NAND gate based on diffractive networks is presented. The resulting NAND design was cascaded by projecting the output field of one diffractive gate onto another, all-optically performing logical operations, e.g., a half-adder.
The Effectiveness of Water Treatment in the Wastewater Treatment Plant (WWTP) of the Tempe Industry and Benefits to the Community

The results of the tempe-making process will produce waste that is harmful to theenvironment from the washing, boiling, pressing and printing processes. The tempeindustry in Plaju ulu village is a source of community income. Tempe industryactivities produce waste that can pollute the environment, causing unpleasant odors.The wastewater treatment plant built by Pertamina RU 3 Plaju has a positive impactin reducing the impact of the tempe industry liquid waste. The formulation of theproblem raised (1) The results of the effectiveness of the Plaju Tempe IndustryWWTP. (2) Recommendations for WWTP technology that can be used in the PlajuTempe Industry. The parameters used to measure the quality of the effluent are pH(degree of acidity), COD, BOD, TSS, and (Total Suspended Solid) during July andAugust 2022. To determine the quality of wastewater produced, data fromlaboratory test results must be compared with quality standards. waste water inaccordance with the Regulation of the Minister of Environment of the Republic ofIndonesia No. 5 of 2014. From the results of the processing effectiveness, theeffectiveness of COD, BOD, and TSS was found to be 53.81%; 11.05%; and 22.65%.From the results of this processing, it is still considered not able to reduce thepollutants generated from the tempe industry activities. The pH and TSS parametersstill do not meet the quality standards. The recommended waste treatment isanaerobic biofiltration, anaerobic digester, and phytoremediation in improving theparameters of pH, COD, BOD, and TSS in the tempe-making industry
The Experience of Voice in Analytic Listening

Discussing the experience of voice in analytic listening, this paper describes the central role of the voice in psychic development as well as in the therapeutic process, focusing on two psychopathologies: the vocal adhesive identification, which is characteristic of autistic states, and the psychotic split between voice and meaning. Pointing to the difference between these two vocal phenomena both in terms of its origins and in terms of its enactment in the therapeutic relations, two modes of therapeutic intervention are suggested: interpretation of the voice and interpretation through the voice.
Sci-Net: Scale Invariant Model for Buildings Segmentation from Aerial Imagery

Buildings' segmentation is a fundamental task in the field of earth observation and aerial imagery analysis. Most existing deep learning-based methods in the literature can be applied to a fixed or narrow-range spatial resolution imagery. In practical scenarios, users deal with a broad spectrum of image resolutions. Thus, a given aerial image often needs to be re-sampled to match the spatial resolution of the dataset used to train the deep learning model, which results in a degradation in segmentation performance. To overcome this challenge, we propose, in this manuscript, Scale-invariant Neural Network (Sci-Net) architecture that segments buildings from wide-range spatial resolution aerial images. Specifically, our approach leverages UNet hierarchical representation and Dense Atrous Spatial Pyramid Pooling to extract fine-grained multi-scale representations. Sci-Net significantly outperforms state of the art models on the Open Cities AI and the Multi-Scale Building datasets with a steady improvement margin across different spatial resolutions.