Reviewerly/exHarmony huggingface dataset:
Critical Analysis of Deconfounded Pretraining to Improve Visio-Linguistic Models

An important problem with many current visio-linguistic models is that they often depend on spurious correlations. A typical example of a spurious correlation between two variables is one that is due to a third variable causing both (a "confounder"). Recent work has addressed this by adjusting for spurious correlations using a technique of deconfounding with automatically found confounders. We will refer to this technique as AutoDeconfounding. This article dives more deeply into AutoDeconfounding, and surfaces a number of issues of the original technique. First, we evaluate whether its implementation is actually equivalent to deconfounding. We provide an explicit explanation of the relation between AutoDeconfounding and the underlying causal model on which it implicitly operates, and show that additional assumptions are needed before the implementation of AutoDeconfounding can be equated to correct deconfounding. Inspired by this result, we perform ablation studies to verify to what extent the improvement on downstream visio-linguistic tasks reported by the works that implement AutoDeconfounding is due to AutoDeconfounding, and to what extent it is specifically due to the deconfounding aspect of AutoDeconfounding. We evaluate AutoDeconfounding in a way that isolates its effect, and no longer see the same improvement. We also show that tweaking AutoDeconfounding to be less related to deconfounding does not negatively affect performance on downstream visio-linguistic tasks. Furthermore, we create a human-labeled ground truth causality dataset for objects in a scene to empirically verify whether and how well confounders are found. We show that some models do indeed find more confounders than a random baseline, but also that finding more confounders is not correlated with performing better on downstream visio-linguistic tasks. Finally, we summarize the current limitations of AutoDeconfounding to solve the issue of spurious correlations and provide directions for the design of novel AutoDeconfounding methods that are aimed at overcoming these limitations.
Forming environmental competence in future biology teachers through project tasks

The impact of human economic activities on individual natural components or whole geosystems has caused a global environmental crisis and continues to change the surface of the planet beyond recognition. New biospheric thinking is required to overcome the existing threats. For that purpose, the new generation shall possess high ecological culture. The article aimed to examine the problem of formation and development of environmental competence of students based on project technology. The conducted research allowed students to independently identify environmental problems with the help of project technology. Practical development in the form of project tasks contributes to the formation of environmental competence in future biology teachers. The effectiveness of the proposed training technology is confirmed by the results of experimental work.&#x0D; &#x0D; Keywords: environmental awareness; environmental competence; environmental education; didactic conditions; project tasks.
Temperature Difference Monitoring Method for Metal Devices of Electrical Equipment by Visible Images and Machine Learning

In this paper, ordinary visible images were used to measure the temperature difference between two metal devices that were used in electrical equipment and transmission lines. The temperature-difference experiments of three kinds of metal devices used in 110kV transmission lines were conducted for collecting their visible images with varying temperature differences in a sunlight environment. The machine learning (ML) technique was applied for modeling the relationship functions between visible image features and temperature differences. The results showed that the corresponding temperature difference of metal devices in these images could be well predicted by trained ML models. Larger illuminance of sunlight helped to obtain higher prediction accuracy. This method could be used for monitoring the abnormal temperature rise of metal parts in electrical equipment by comparing the temperatures of two different phases. It would provide a new choice for intelligent temperature monitoring in power systems, helping save costs and improve inspection efficiency.
An Efficient Region Merging Algorithm in Raster Space

This work introduces a new region merging algorithm operating in raster space represented by a 4-connected graph. Necessary definitions are introduced first to derive a new merging function formally. An implementation is described after that, which consists of two steps: a determination of the shared trails of the input cycles, and construction of the resulting merged region. The cycles defining the regions are represented by the Freeman crack chain code in four directions. The algorithm works in linear time On, where n is the number of total graph vertices, i.e. pixels. However, the expected time complexity for one merging operation performed by the algorithm is O1.
The relationship between family relations and manifestations of the professional skills of a psychologist

Within the framework of the study, it is important and interesting how relationships in the family and the professional skills of psychologists are interconnected. The study of intra-family relations is extremely important for understanding the factors influencing the formation of a person's personality and his professionalism. The purpose of the study is to identify the relationship between relationships in the family and manifestations of the professional skills of a psychologist. The subject of the study is the relationship between family relations and manifestations of the professional skills of a psychologist.&#x0D; The methodological basis of the study was the scientific works of domestic and foreign psychologists: A. K. Markova, V. A. Bodrov, E. F. Zeer, Eidemiller E. G., Yustitskis V.&#x0D; Research methods: theoretical level: study and analysis of literature on the research topic; empirical level: the test "Typical family condition", aimed at studying the atmosphere in the family. The respondent is asked to recall their feelings that they experience while in the family, and evaluate them on a seven-point scale. A questionnaire was also compiled that studied the manifestations of the professional skills of a psychologist, suggesting that respondents assess the skills of a psychologist presented in the list on a five-point scale; as well as methods of mathematical data processing: Spearman's correlation criterion.&#x0D; Theoretical significance of the study: few scientific works are devoted to the study of the relationship between family relations and the activities of a psychologist. Little is known about how family relationships and manifestations of psychological skills can influence each other. Therefore, the study is significant in that the relationship between these two parameters in students is being studied.&#x0D; The practical significance of the study: the results of the study can be used in the practice of counseling on family relations, in the development of questionnaires and questionnaires to study the manifestations of the professional skills of a psychologist. Also, the results can be used to develop activities aimed at developing the skills of a psychologist in students.&#x0D; Empirical research base: the research was conducted on the third and fourth year students (N=38) of the training direction "Psychology" of Vyatka State University. 31 women and 7 men participated.
E-government based on the blockchain technology, and the evaluation of its transaction through the number of transactions completed per second

Blockchain technology is one of the basic technologies for securing data sharing and storage across peer-topeer systems in a distributed and untrusted network.Information is stored in electronic governance, which is considered sensitive data about citizens and companies and is the focus of external attacks.E-government has one point of failure and depends on centralization, and the decision is in the hands of one party or one official.Therefore, a secure and distributed electronic system for e-governance based on blockchain technology has been proposed.The system consists of several entities, organizations or nodes responsible for consensus to make decisions.Users are given the right to raise a transaction or send a request.The transaction is evaluated by auditors, and the citizen acquires a smart contract as a way out.The proposed system was compared with electronic governance systems without the use of blockchain technology.The proposed system was tested and compared with the previous systems, and it was found that the proposed system was superior in terms of security, the speed of processing the transaction and the time of filing the transaction.
Image Enhancement Techniques Using CNN

The most significant area in imaging study and processing is image processing technique. The major goal is to improve quality images artificially through other means. The images that are obtained at times through certain mediums may end up distorted making the image unclear. One option to enhance the image quality is to change camera lens, which is costly. Thus, an alternative is required. A conventional neural network (CNN) may give priority to distinct aspects in an image and differentiate between them. A clear review of the literature on a few CNN-based picture enhancing techniques is carried out
The FAIR Accessor and the authenticity of digital archival information

Abstract The constant increase in digital information’s volume, variety, and complexity poses many problems that make it difficult to preserve archival information while ensuring that it remains authentic, reliable, accessible, trustworthy, intelligible, and reusable for as long as possible. This study explores the concepts of a possible implementation of a FAIR Accessor, a technology developed to provide Findable, Accessible, Interoperable, and Reusable research data, as an infrastructure that can support and aid archival information description and ensure its authenticity. A qualitative literature review on a selection of representative works in the fields of Information Science, Diplomatics, and the FAIR principles is followed by a discussion on how the key concepts of each field overlay and thus may complement each other mutually. It is concluded that the infrastructure of the FAIR Accessor can prove useful in enriching archival description and, ultimately, in assisting to ascertaining the authenticity of records.
Methods of Improving Vocabulary for Students of Japanese Basic Levels through Social Networks

Today, social networks not only allow users to share personal stories and ideas and also inform about real-world activities and events, but also bring significant benefits in many fields and industries including exchange and learning language. Currently, most language learners in general and Japanese language in particular have many difficulties and challenges in learning vocabulary, they cannot control it. This article discusses some close methods of improving elementary Japanese vocabulary through social media. At the same time the article shows the effectiveness from the mentioned methods. This article is completed through a method of survey, data collection and reference. The results of this article help language learners find a simple method that works for themselves to quickly memorize vocabulary and improve their vocabulary in the best and most comfortable way by combining the two activities using social media and learning languages.
Anonymous identity sampling and reusable synthesis for face camouflage

An increasing amount of face images are being captured, shared, or applied in various applications. These images usually contain lots of sensitive information that may lead to privacy disclosure and misuse problems. Some pioneering works show that face image anonymization is one of the promising solutions. We present an innovative identity (ID) camouflage approach by synthesizing anonymous faces so that both artificial intelligence algorithms and humans are unable to recognize them and misuse them freely. Given a face image, our approach consists of two steps. First, we sample an anonymous ID feature point in the feature space. Then, we synthesize a camouflage face by training an anonymous deep generative adversarial network model. To reduce the risk of re-identification, we optimize our anonymous face generator based on the k-nearest neighbors to make a good balance between anonymity and utility of the original face image. The experimental results over the public dataset have verified the feasibility and state-of-the-art efficacy of our approach.
Personalized Fashion Recommendation Using Pairwise Attention

The e-commerce fashion industry is booming and comes with the need for proper search and recommendation. However, sufficient user personalization is still a challenging task. In this paper, we introduce a personalized fashion recommendation system based on high-dimensional input of user- and environment information. The proposed framework is used to estimate suitable categories and style of clothing depending on customized settings such as body type, age, occasion, or season. The goal is to recommend a full fitting outfit from the estimated suggestions. However, various personal attributes add up to a high dimensionality, and datasets are often very unbalanced or biased, making it difficult to do a proper recommendation. To solve this, we propose a pairwise-attention module to improve the performance of our framework. Our model can improve the performance up to 53.29% over the comparison method on MSE, mAP, and Recall. Moreover, in a subjective evaluation with human participants, the recommendations of the proposed method are preferred over the comparison method.
The Effects of Applying UbD On Cognition of Physical Education and Flow and Source of Enjoyment in Physical Education class in Elementary School Students

The purpose of this study is to verify the effects, caused by UbD on the Cognition of Physical Education and Flow and Enjoyment of elementary School Physical Education Class. 24 Experimental group and 24 Control group Students responds on the scales to measure a cognition of physical education and Flow and Source of Enjoyment in physical education. This study used PCA, reliability analysis, T-test, Repeated Measures ANOVA analysis by SPSS 26.0 and Jamovi 2.0. Based on the research methods and procedures, the follwing results have been derived. Firstly, The experimental group’s improvement was much greater than that of the control group’s in all Cognition of physical education. Secondly, Flow in Physical Education class for experimental group had a significantly effect on self-fulfilling experience, integration of process and behavior-awareness concurrence. Thirdly, Source of Enjoyment in physical education for experimental group had a significantly effect on Keeping health in physical & Psychological aspect, sociality promotion, free to class and achievement through the competition. In addtion, interaction effect was found in the cognitive domain, which is a sub-factor in Cognition of physical education.<BR>In conclusion, the physical education class to which Ubd is applied has an effect on Cognition of Physical Education and Flow and Source of Enjoyment in Physical Education class.
Entropy-based policy diversity measures to quantify the activation-convergence of UAV-based collective intelligent systems

新一代无人机群系统的重要特征是具有群体智能, 是一类典型的群智激发汇聚系统. 目前, 多智能体强化学习技术展现出较强优势, 是构建新一代自主智能无人机群系统的重要方法. 但目前多智能体强化学习的训练过程尚处于``黑盒状态, 缺乏对群体智能的激发和汇聚程度的有效度量手段. 针对这一问题, 本文从多智能体强化学习中智能体的策略出发, 以策略多样性度量无人机群在多智能体强化学习的训练过程中的激发-汇聚程度. 为了对策略的多样性进行度量, 本文借鉴物种多样性和信息论中的相关概念, 明确了策略多样性的内涵包括丰富和均匀程度两方面, 提出了``策略距离二次熵和``动作分布信息熵这两种策略多样性的计算方法. 本文设计了无人机群突防场景对本文所提出的策略多样性指标和两种计算方法的有效性和有用性进行了验证, 并通过敏感程度分析对两种计算方法进行了对比. 实验结果表明这两种计算方法在该场景中均能有效区分策略多样性的变化, 且两种计算方法间具有一致性, 从而验证了本文所提出的策略多样性指标及其计算方法的有效性. 在有用性方面, 本文验证了策略多样性与奖赏之间的关联关系, 以及环境的动态改变与策略多样性之间的相互影响和关联关系, 体现了策略多样性在认知群智系统, 指导群智激发汇聚过程上的潜在有用性. 本文所提出的策略多样性及其计算方法, 能够为定量认知群智系统激发-汇聚程度, 进而对群智系统的学习和训练开展引导和干预提供方法支撑.
Empowerment of Internet of Things Through the Integration of Blockchain Technology

Due to the convergence of advanced technologies, the emergence of electronic things tends to increase tremendously. The Internet of Things (IoT) is the technology where enormous things such as sensors, softwares etc. are embedded together for enabling the connection as well as communication of data. With the evolution of Industrial IoT, the need for monitoring the things that are used for automation becomes one of the thrust among the industries. There is a necessity that the monitoring can be empowered through the integration of blockchain technology. Not limited to monitoring, it also enriches the scalability, interoperability, durability, device reliability, privacy, security, silo mentality, standardization of the devices that are used for the internet of things. The blockchain technology is the distributed ledger with the list of growing records or blocks which are linked using the cryptography. The one of the main advantage of blockchain technology is that all the transactions are captured with its respective timestamps and the data that are recorded in the blocks cannot be altered retroactively. This enforces timely updation of information which cannot be altered. The Industrial IoT plays a major roles in the IoT platform as there exists smart connected operations that covers plenty of assets which obviously needs capability such as high level connectivity, analytics on big data and the development of applications. To resolve these challenges of IIoT, several requirement has to be considered such as asset visibility, Bigdata handling, technology integration and security. Also it is felt that most of the existing facilities of the industries such as ad hoc networks, smart grids etc. could not connect to the IoT system as it is not with built in intelligence. These needs an interface for invoking the communication. Since the blockchain technology has the decentralized nature, the information of the communication that happens between the two devices such as state of the devices, interactions and data digest can be acquired all in a point. This might reduce the risk with the business processes that the industry faces. Also, in the field of logistics which is one of the most important field, the information about the goods will be kept out of the illegal persons. Hence, it is considered that the blockchain technology will hold a safer environment for the individual as well as the enterprises. Thus the integration of blockchain with the Internet of Things will regulate the process of IoT through decentralized control which is kept highly safe than other technology implications. This chapter discuss about the different components of block chain enabled IoT system and the application of implementing the blockchain in IoT.
On-Site Dance Teaching Live Broadcast System Based on Internet Streaming Media Technology

The rapid development of Internet technology has provided a powerful driving force for streaming media services. Streaming media is based on the Internet and is time-based media data stream transmitted through streaming technology. There is no need to download the entire file during playback. It can be downloaded and played at any time, effectively saving resources. And waiting time, reduce the server and client operating load. Based on the principle of streaming media, this paper constructs the H.265 coding framework, which improves the relationship between code stream, coding quality, delay and algorithm complexity; in-depth study of real-time message transmission protocol to ensure stable transmission of high-definition audio and video; designs an HTML5 streaming media player to bring the good experience to system developers and viewers. In the actual application process, it is necessary to resist all kinds of network information interference and reduce the occurrence of “lost network”.
Advancing diffractive network-based image classification by ensemble learning

We report the use of ensemble learning to achieve significant improvements in the performance of diffractive optical classifiers on CIFAR-10 image dataset. We initially created a pool of 1252 diversely-trained diffractive network models; using a novel iterative pruning algorithm, we trimmed this down to an ensemble size of 14 diffractive networks to achieve a blind testing accuracy of 61.14% on CIFAR-10 image classification, which performs >16% higher in its inference accuracy compared to the average performance of the individual diffractive networks within the ensemble. These results signify a major advancement in all-optical inference and image classification capabilities of diffractive networks.
Criminal Law Policy in Counteracting the Use of Networking Platforms for Criminal Activity

The transition to the digital economy is often primarily associated with the transfer of economic activities to online platforms. Researchers often call such a transition platformization. The use of global and national platforms gives rise to a completely new type of relationships. Platforms are often created with the use of AI and distributed register technologies. In the economic sphere, the transition to platforms requires the national governments to develop an effective criminal law policy because the emerging relationships in the use of networking platforms need criminal law protection. A special cause for concern is the creation of platforms used for criminal activities. A platform for criminal activities is a special form of organizing computer information specifically structured to enhance the effectiveness of interactions between different groups of people involved in committing crimes. This publicly dangerous action is not reflected in Russian criminal legislation as an independent crime. Research methodology includes the analysis of criminalizing the creation and operation of networking platforms that are used for criminal activities from the standpoint of grounds for criminalization in the doctrine of criminal law. Three groups of factors act as grounds for criminalization: legal-criminological, socio-economic and socio-psychological. The presented work mainly focuses on the analysis of legal-psychological grounds, such as the degree of public danger, relative prevalence of such actions and their typicality, dynamics of actions in terms of the causes and conditions giving rise to them, the possibility of using criminal law measures to influence these actions, the potential of the system of criminal justice. The conducted analysis showed that the criminalization of the creation of support of networking platforms used for criminal activity corresponds to legal-criminological, socio-economic and socio-psychological criteria.
TERAPAN MCRNG SEBAGAI GENERATOR ACAK UNTUK GAME

Most game s are used as a means of entertainment, but can also function as a means of training, education and simulation. In the field of education, educational game s can be applied by adopting educational materials to be informed in an interesting way to users. To provide a challenge to the user, the concept of level and scoring is applied in the game . The type of game made is a quiz-based educational game . Elements of educational game s made in the form of text, images, sounds and videos regarding knowledge about the classification of vertebrate and invertebrate animals. The data and answers used in the quiz are also displayed in a random way. The randomization process uses a random generator that applies the concept of Multiplicative Congruential Random Number Generator (CRNG). The use of this generator is intended so that users get different questions and answer compositions at each level. This is so that the user does not get bored quickly and the game becomes more interesting to play. So that the user indirectly conducts learning activities because the information conveyed is in the form of knowledge that is packaged in the form of a game.