Reviewerly/exHarmony huggingface dataset:
The Influence of Parental Warmth and Family Rearing Style on College Students’ Creativity Tendency: The Mediating Role of General Self-Efficacy

Family is the first environment that affects people's psychological development.In recent years, with people's attention to education, more and more people began to pay attention to the impact of family upbringing on children's physical and mental development.People also pay more attention to the impact of family upbringing on children's creativity and general self-efficacy.The objective of this research is to explore the relationship between parents' emotional warmth and college students' creativity tendency, and to examine whether general self-efficacy plays a mediating role.There was a significant positive correlation between emotional warmth and creativity tendency at the level of 0.05.Emotional warmth was positively correlated with creativity tendency at the level of 0.01, and emotional warmth was positively correlated with general self-efficacy at the level of 0.01.There was a significant positive correlation between emotional warmth and general self-efficacy at the level of 0.01.General self-efficacy plays a complete intermediary role in parents' emotional warm and college students' creative tendency.In sum, parental affective warmth can affect college students' creative tendency through general self-efficacy.In this study, there were certain regional restrictions in the selection of samples, and only subjects from Shanghai and Hebei Province were collected.To control the number of questions, most of them were from the simple version of the scale.Future research can improve the above two aspects.More in-depth research can be carried out from this perspective in the future.
Social Comparison and Body Image in Teenage Boys and Girls Users of the TikTok App

Teenagers have the concept of an ideal body image by interacting with the social environment in the form of social comparisons with other people.Social comparison is closely related to social media.TikTok is one of the video-based social media that is currently booming and loved by teenagers.This is because TikTok is one of the tools that helps relieve boredom during social goals due to the Covid-19 pandemic.This research is to find out the relationship between social comparisons and body image and find out the differences between boys and girls in doing social comparisons and body image. .The research subjects involved 62 respondents consisting of 31 men and 31 women aged 12-19 years who had or had a TikTok application account.The sampling technique in this research is purposive sampling.Social comparisons were measured using the Upward and Downward Physical Appearance Comparison Scale from O'Brien., et al ( 2009) and body image was measured using the Multidimensional Body-Self Relation Questionnaire-Appearance Scale (MBSRQ-AS) from Cash (2000).This research uses different and correlational test analysis techniques.The results of this study indicate that male adolescents make more social comparisons than female adolescents, but both have the same level of body image.Teenagers using TikTok have a significant positive relationship between social comparison and body image.That is, the higher the adolescents make social comparisons, the higher their body image and vice versa.The lower the adolescents make social comparisons, the lower their body image.In male adolescents, the relationship between social comparison and body image shows a positive relationship, while in female adolescents the negative relationship is not significant.
Resource Scheduling Strategy for Spark in Co-allocated Data Centers

Abstract The co-allocated data centers are to deploy online services and offline workloads in the same cluster to improve the utilization of resources. Spark application is a typical offline batch workload. At present, the resource scheduling strategy for co-allocated data centers mainly focuses on online services. Spark applications still use the original resource scheduling, which can’t solve the data dependency and deadline problems between spark applications and online services. This paper proposes a data-aware resource-scheduling model to meet the deadline requirement of Spark application and optimize the throughput of data processing on the premise of ensuring the quality of service of online services.
Two hide-search games with rapid strategies for multiple parallel searches

Abstract Making a rapid unpredictable decision from <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi>N</m:mi> </m:math> N choices of unequal value is a common control task. When the cost of predictability can be modelled as a penalty hidden under a single option by an intelligent adversary, then an optimal strategy can be found efficiently in <m:math xmlns:m="http://www.w3.org/1998/Math/MathML"> <m:mi>O</m:mi> <m:mrow> <m:mo>(</m:mo> <m:mrow> <m:mi>N</m:mi> <m:mi>log</m:mi> <m:mi>N</m:mi> </m:mrow> <m:mo>)</m:mo> </m:mrow> </m:math> O\left(N\log N) steps using an approach described by Sakaguchi for a zero-sum hide-search game. In this work, we extend this to two games with multiple parallel predictions, either coordinated or drawn independently from the optimal distribution, both of which can be solved with the same scaling. An open-source code is provided online at https://github.com/pec27/rams.
Synthesizing Qualitative Evidence: A Roadmap for Information Systems Research

Qualitative synthesis research is an approach that consolidates the output of different qualitative studies to create new subject knowledge. Such work can help reveal more powerful explanations than that seen in a single study, thereby generating increased levels of understanding of a given phenomenon and greater research finding generalizability. Based on a review of the literature and a survey of qualitative researchers, we found that the information systems (IS) domain lacks a clear understanding of qualitative synthesis methods and, as a result, has largely failed to take advantage of this powerful, high-potential methodological opportunity. To address this shortcoming, this paper is the first to provide a rigorous overview of the full suite of 35 qualitative synthesis methods, as well as guidelines that include a three-tiered selection framework. By using the guidelines and framework in tandem, IS researchers are able to select the qualitative synthesis method most appropriate for a given research study, particularly when the research objective involves knowledge integration/aggregation, interpretation/theory development, and/or informing IS practice.
Evaluation of the effectiveness of a crowdsourcing-based crime detection system

Crowdsourcing can improve the accuracy of crime detection, which is difficult with a computer-based method. This study evaluates a crowdsourcing-based method’s accuracy, cost, and detection time using a task to discriminate whether the crime of shoplifting is occurring in surveillance camera video footage. This evaluation is made through a comparison with existing solutions.
TamilATIS: Dataset for Task-Oriented Dialog in Tamil

Task-Oriented Dialogue (TOD) systems allow users to accomplish tasks by giving directions to the system using natural language utterances.With the widespread adoption of conversational agents and chat platforms, TOD has become mainstream in NLP research today.However, developing TOD systems require massive amounts of data, and there has been limited work done for TOD in low-resource languages like Tamil.Towards this objective, we introduce TamilATIS -a TOD dataset for Tamil which contains 4874 utterances.We present a detailed account of the entire data collection and data annotation process.We train stateof-the-art NLU models and report their performances.The Joint BERT model with XLM-Roberta as utterance encoder achieved the highest score with an intent accuracy of 96.26% and slot F1 of 94.01%.
Simulation of Automatic Control System of Self-Balancing Robot Based on MATLAB

Two-wheel self-balancing robot, as one of the most advantaged robot attitude control optimization at present, needs to be adjusted by scientific attitude correction algorithm after being disturbed, so as to ensure that the robot can still operate normally under the condition of disturbance. After understanding the basic principle of the two-wheel self-balancing robot control system, this paper analyzes the function and efficiency of the current robot automatic control system according to the design of the actual controller and the modeling and simulation of the whole system.
Automatic Generation of Adversarial Readable Chinese Texts

Natural language processing (NLP) models are known vulnerable to adversarial examples, similar to image processing models. Studying adversarial texts is an essential step to improve the robustness of NLP models. However, existing studies mainly focus on generating adversarial texts for English, with no prior knowledge that whether those attacks could be applied to Chinese. After analyzing the differences between Chinese and English, we propose a novel adversarial Chinese text generation solution Argot, by utilizing the method for adversarial English examples and several novel methods developed on Chinese characteristics. Argot could effectively and efficiently generate adversarial Chinese texts with good readability in both white-box and black-box settings. Argot could also automatically generate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">targeted</i> Chinese adversarial texts, achieving a high success rate and ensuring the readability of the generated texts. Furthermore, we apply Argot to the spam detection task in both local detection models and a public toxic content detection system from a well-known security company. Argot achieves a relatively high bypass success rate with fluent readability, which proves that the real-world toxic content detection system is vulnerable to adversarial example attacks. We also evaluate some available defense strategies, and the results indicate that Argot can still achieve high attack success rates.
Learning Symbol Relation Tree for Online Handwritten Mathematical Expression Recognition

This paper proposes a method for recognizing online handwritten mathematical expressions (OnHME) by building a symbol relation tree (SRT) directly from a sequence of strokes. The recognition system has two parts: a temporal classifier and a tree connector. The temporal classifier uses global context to produce a sequence of symbols and spatial relations between symbols from an OnHME pattern. It is a bidirectional recurrent neural network trained from multiple derived paths of SRTs. The tree connector splits the sequence into several sub-SRTs and connects them to form the SRT by looking up the best combination among those sub-SRTs. Besides, we adopt a tree sorting method to deal with various stroke orders. Recognition experiments indicate that the proposed OnHME recognition system is competitive to other methods. The recognition system achieves 44.12% and 41.76% expression recognition rates on the Competition on Recognition of Online Handwritten Mathematical Expressions (CROHME) 2014 and 2016 testing sets, respectively.
Electronic Document Archival System of STO. Niño National High School

Despite the snags and challenges of the manual operations, some of the public high schools in the Philippines, particularly those in the provinces, still do not use computerized systems for the development of the system itself is costly. One of the operations of the schools is the manual archiving of 201 files that contain employees’ personal information and profile. As the files are hard-copy documents filed in cabinets and folders, there were incidents of loss of files, duplicate data- entry, unsecured storage, difficulty of retrieval and updating records. Oftentimes, these records are needed to be reproduced and updated for promotion and accreditation. This study, Electronic Document Archiving System (EDAS) was conducted to design, implement, and transform the manual keeping of records into a computerized system for the public high school. Using the Waterfall Model of system development, the system utilized the data gathered to identify the needs and solutions in transforming and upgrading the existing manual system. The development of the system resulted in a well- organized, uniformed, and easy to search records. Specifically, the electronic archiving system developed store records (documents) into a safer and secured database that can be retrieved easily and free from any damage. However, it should also be emphasized that the most efficient way to have a good process in keeping the records is to have organized manual operations aside from a computerized archiving system to automate it.
Design of Authenticated and Trusted Academic Certification Using Blockchain Technology

It is more expensive to handle forged certificates rather than take stringent measures to prevent such occurrences. Forged certificates can lead to loss of integrity and credibility in the certificates and loss of public confidence and trust in the certificate awarding institutions. This study proposed a blend of emerging technologies that include Blockchain Smart Contract for authenticating academic certificates using the West African Examinations Council (WAEC) as the case study. The study presents a detailed design of authenticated and trusted academic certification using Blockchain technology. The Ethereum Smart Contract was used to govern, manage, and provide traceability and visibility in a manner that is secured, decentralized, and globally accessible with high integrity and transparency. Findings from the study revealed various processes by which WAEC authenticates the certificates they issued and that Blockchain technology tends to improve its methods of verification.
Book publishing in the Saratov province of 1918–28 as an object of scholarly research

The study contains an overview of publications on the development of publishing houses, printing enterprises and book products of the Saratov Province in the first decade of the Soviet power. The description of scholarly monographs, articles and bibliographic manuals, fully or partially related to the topic, is given. Individual scholars contribution to the study of the initial period of the Soviet book publishing in Saratov and the local towns of the Saratov Province is assessed. The place of works on the history of the Saratov bookishness in 191828 is shown in a series of similar studies performed in other regions particularly and in the RSFSR generally.
Towards Pipeline Construction for Product Matching Task

Online shopping services are commonplace for people around the world for acquisitions. The product offers on websites are mostly given with some text descriptions and are often accompanied by specification tables. The same product can be found in many different e-shops, but information about the offers of this product can vary greatly on different electronic platforms. The task of product description categorization is not new, but the solution heavily depends on the data set of product descriptions. The pipeline is the scheme of step-by-step data processing. The aim of the paper is to construct the pipeline, for product description categorization, which should be flexible. The pipeline should allow getting access at any step during data processing and modify it by adding new functions, changing activity order, and implementing particular methods. We propose an approach to construct a flexible pipeline for product matching. The case study is given and experimental results are discussed.
Avoiding Phishing Attacks in Online Voting System

This work's main purpose is to provide a secure voting system that fits all of the voting process' requirements, including authentication, confidentiality, and morality. Simplicity is also required to guarantee that ordinary people participate. Other factors to consider are dependability, convenience, flexibility, mobility, and affordability, in addition to security and simplicity. We wanted to build a system where the whole encryption and decryption of the ballots is transparent and nevertheless safe to ensure that the user utilizes a secure voting system.
Tourist Reviews Sentiment Classification using Deep Learning Techniques: A Case Study in Saudi Arabia

Now-a-days, social media sites and travel blogs have become one of the most vital expression sources.Tourists express everything related to their experiences, reviews, and opinions about the place they visited.Moreover, the sentiment classification of tourist reviews on social media sites plays an increasingly important role in tourism growth and development.Accordingly, these reviews are valuable for both new tourists and officials to understand their needs and improve their services based on the assessment of tourists.The tourism industry anywhere also relies heavily on the opinions of former tourists.However, most tourists write their reviews in their local dialect, making sentiment classification more difficult because there are no specific rules to control the writing system.Moreover, there is a gap between Modern Standard Arabic (MSA) and local dialects.one of the most prominent issues in sentiment analysis is that the local dialect lexicon has not seen significant development.Although a few lexicons are available to the public, they are sparse and small.Thus, this paper aims to build a model capable of accurate sentiment classification in the Saudi dialect for Arabic in tourist place reviews using deep learning techniques.Machine learning techniques help classifying these reviews into (positive, negative, and neutral).In this paper, three machine learning algorithms were used, Support -Vector Machine (SVM), Long short-term memory (LSTM), and Recurrent Neural Network (RNN).These algorithms are classified using Google Map data set for tourist places in Saudi Arabia.Performance classification of these algorithms is done using various performance measures such as accuracy, precision, recall and Fscore.The results show that the SVM algorithm outperforms the deep learning techniques.The result of SVM was 98%, outperforming the LSTM, and RNN had the same performance of 96%.
A Study on Manual and Automatic Evaluation for Text Style Transfer: The Case of Detoxification

It is often difficult to reliably evaluate models which generate text.Among them, text style transfer is a particularly difficult to evaluate, because its success depends on a number of parameters.We conduct an evaluation of a large number of models on a detoxification task.We explore the relations between the manual and automatic metrics and find that there is only weak correlation between them, which is dependent on the type of model which generated text.Automatic metrics tend to be less reliable for better-performing models.However, our findings suggest that, ChrF and BertScore metrics can be used as a proxy for human evaluation of text detoxification to some extent.
ASRSNet: Automatic Salient Region Selection Network for Few-Shot Fine-Grained Image Classification

Few-shot learning for image classification aims at predicting unseen classes with only a few images. Recent works, especially the works on few-shot fine-grained image classification (FSFGIC), have achieved great progress. However, most of them neglected the spatial information and computed the distance between a query image and a support image directly, which may cause vagueness because the dominant objects can exist anywhere on images. A promising solution is to locate salient regions from images for discriminative feature representation learning. This paper develops an automatic salient region selection network without the use of a bounding box or part annotation mechanism for locating salient regions from images. Then a weighted average mechanism is introduced for facilitating a neural network to focus on those salient regions, optimizing the network, and performing the FSFGIC tasks. The experimental results on four benchmark datasets demonstrate the effectiveness of the proposed strategy.
Networking of secondary vocational education organizations in the region: opportunities and prospects

The network of educational organizations, enshrined in the legislation of the Russian Federation, is a highly effective socio-managerial technology for increasing the effectiveness of training specialists of secondary professional educational organizations in demand in the regional labor market of the Khabarovsk territory. However, due to the objective conditions and subjective factors, its organization in the region does not yet give a synergistic effect, allowing to realize new development opportunities from the integration of educational organizations and subjects of the network interaction. The results of sociological survey of the heads of technical schools (colleges) and pedagogical workers of the Khabarovsk territory, presented by the authors in the article, indicate the problems of network interaction in the implementation of educational programs. They are due to both, the state of the outdated material and technical base of educational organizations, the imperfection of regional normative legal regulation of implementation of educational programs in a network form, insufficient personnel support, and the limited socio-managerial communications, the contradictory attitude of the subjects of interaction to the possibilities of its implementation in modern conditions. Based on the analysis of organization of the network interaction of organizations of secondary vocational education, recommendations on its resource support are proposed.
Reinforcement Guided Multi-Task Learning Framework for Low-Resource Stereotype Detection

As large Pre-trained Language Models (PLMs) trained on large amounts of data in an unsupervised manner become more ubiquitous, identifying various types of bias in the text has come into sharp focus.Existing 'Stereotype Detection' datasets mainly adopt a diagnostic approach toward large PLMs.Blodgett et al. (2021a) show that there are significant reliability issues with the existing benchmark datasets.Annotating a reliable dataset requires a precise understanding of the subtle nuances of how stereotypes manifest in text.In this paper, we annotate a focused evaluation set for 'Stereotype Detection' that addresses those pitfalls by de-constructing various ways in which stereotypes manifest in text.Further, we present a multi-task model that leverages the abundance of data-rich neighboring tasks such as hate speech detection, offensive language detection, misogyny detection, etc., to improve the empirical performance on 'Stereotype Detection'.We then propose a reinforcement-learning agent that guides the multi-task learning model by learning to identify the training examples from the neighboring tasks that help the target task the most.We show that the proposed models achieve significant empirical gains over existing baselines on all the tasks.
How Can Cross-lingual Knowledge Contribute Better to Fine-Grained Entity Typing?

Cross-lingual Entity Typing (CLET) aims at improving the quality of entity type prediction by transferring semantic knowledge learned from rich-resourced languages to low-resourced languages.In this paper, by utilizing multilingual transfer learning via the mixture-of-experts approach, our model dynamically capture the relationship between target language and each source language, and effectively generalize to predict types of unseen entities in new languages.Extensive experiments on multilingual datasets show that our method significantly outperforms multiple baselines and can robustly handle negative transfer.We questioned the relationship between language similarity and the performance of CLET.With a series of experiments, we refute the commonsense that the more source the better, and propose the Similarity Hypothesis for CLET.