Reviewerly/exHarmony huggingface dataset:
RGB-T object tracking with adaptive decision fusion

Visual object tracking is a traditional task in computer vision, which has developed with several decades. With the development of machine learning, Correlation Filter (CF) has been proposed with satisfying performance and very high framerate. Though the CF framework has numerous strengths in this task, the tracker is fragile to miss the target in several scenes, including extreme illumination, target occlusion and deformation. Recently, thermal modality, which detects the target’s temperature, is robust to the night scenes and can provide a precise target contour. In this paper, we propose a CF based tracker with decision fusion strategy for visible-thermal (RGB-T) tracking. First, we introduce multi-modal KCF trackers as our baseline. Then, we design a decision fusion method considering the Peak-to-Side Rate (PSR) of the score maps, thereby achieving an adaptive fusing those modalities and avoiding model’s heterogeneity. In the experiments, our tracker has validated on the public dataset, namely GTOT. Compared with two uni-modality trackers, the proposed tracker with real-time speed has shown superior results on both target localization and scale estimation.
Structural analysis of the academic meaning of BTS

본 연구는 방탄소년단 관련 국내외 학술문헌의 연구 동향, 맥락 및 논의 추세를 체계적으로 분석하였다. 학술문헌의 작성언어, 발행연도, 출판유형, 저자 등 주요 특성을 살펴보는 동시에 학문분야, 연구방법, 연구목적, 연구주제에 대한 국내외 연구 특성을 비교분석 하였다. 분석결과, 방탄소년단에 대한 학술연구는 2017년부터 해마다 상승하였으며, 사회〮심리 영역에서 고찰한 연구가 가장 많았다. 기술적 연구가 많았으며, 질적 연구방법 중에서는 기호•담론 분석이 다수를 차지하고 있었다. 연구주제는 수용자 연구의 비중이 높았다. 국내외 연구를 비교분석한 결과에 따르면, 출판유형, 저자 수, 학문분야, 연구주제에서 차이가 있었다. 본 연구는 방탄소년단이 글로벌 문화의 아이콘으로 국경을 허무는 새로운 문화 현상이 되면서 학술분야에서도 방탄소년단에 대한 주목도가 높은 것을 확인하였다. 오늘날 K-컬처가 세계적으로 확산하면서 서구를 중심으로 하는 문화흐름의 방향이 바뀌는 현상을 확인하는 가운데, 방탄소년단이 국경과 장벽을 허물면서 글로벌 문화의 새로운 가능성이자 전통적 문화질서의 흐름을 변화시키는 촉진제 역할을 한다고 판단할 수 있다. This study comprehensively and systematically examined overall research trends, contexts, and discussion trends of the academic literature on BTS in an integrated and systematic manner. We examined the characteristics of the research results of academic publications such as the writing language, year of publication, type of publication, and author. Simultaneously, we compared and analyzed the characteristics of domestic and foreign studies on analysis categories such as academic field, research method, research purpose, and research topic. Academic research on BTS demonstrated an upward trend since 2017 in which the majority of the studies were conducted in the social and psychological areas. Descriptive research was seemingly to be the main focus, whereas signature and discourse analysis accounted for the majority of qualitative research methods. Among the research subjects, research on media users was the most frequent. The results of the comparative analysis of domestic and foreign studies confirmed that a clear difference exists between domestic and foreign academic papers in terms of the type of publication, number of authors, academic field, and research topic. This study confirmed that, just as BTS conquered national borders as a global cultural icon and became a new global cultural phenomenon; scholarly attention on BTS is also increasing in the academic field. As K-culture spreads globally in today"s world, a shift in the direction of cultural trends, which have been centered around the West, can be observed. In the midst of this, it can be argued that BTS is breaking down borders and barriers, serving as a catalyst for new possibilities in global culture and transforming the flow of traditional cultural order.
Time-Frequency Awareness Network For Human Mesh Recovery From Videos

This paper focuses on the problem of 3D human mesh recovery from videos. Most recent works mainly centered on human spatio-temporal modeling in the time domain, but these time-domain methods often concentrate on the short-range spatio-temporal receptive field and information transfer of the video, thus cannot adaptively sense effective spatiotemporal dependencies in a long-range, furthermore lacking the ability to perceive local motion with a small-scale movement. In this work, we propose a Time-Frequency Awareness Network for human mesh recovery. We present a novel paradigm that learns human feature representations by introducing frequency domain. Specifically, we first design a time-frequency aware attention module that uses frequency domain information as a guide to model temporal long-range dependence and spatial long-range dependence in a unified manner. Secondly, we carefully develop a time-frequency-aware recurrent module that treats moving humans as discrete signals over time in the frequency domain to capture the spatio-temporal information accumulated by human movement in videos. In addition, we also elaborate design of a local awareness loss constraint on human motion of the small scale, which helps to mitigate the interference of global motion on the prediction results. Extensive experimental results on large publicly available datasets show advantages over most state-of-the-art methods. Our code are available at https://github.com/Changboyang/TFNet.git.
Defending Against Universal Patch Attacks by Restricting Token Attention in Vision Transformers

Previous works reveal that similar to CNNs, vision transformers (ViT) are also vulnerable to universal adversarial patch attacks. In this paper, we empirically reveal and mathematically explain that the shallow tokens in the transformer and the attention of the network can largely influence the classification result. Adversarial patches usually produce large feature norm for the corresponding shallow token vectors which can attract the attention anomalously. Inspired by this, we propose a restriction operation on the attention matrix, which effectively reduces the influence of the patch region. Experiments on ImageNet validate that our proposal can effectively improve ViT’s robustness towards white-box universal patch attacks while maintaining satisfactory classification accuracy for clean samples.
Exploring Immersive Mixed Reality Simulations and Its Impact on Climate Change Awareness

Purpose: To combat widespread climate change misconceptions that halt mitigation efforts, we investigate Mixed Reality’s applicability in helping contextualize climate change consequences.&#x0D; Design/methodology/approach: We propose an immersive Mixed Reality system that simulates personally-relevant experiences relating to climate change disasters. To evaluate our methods, we conducted a pilot study with the constructed Mixed Reality system and analyzed collected statistics.&#x0D; Findings: Participants reported heightened motivation to adopt more sustainable practices as a result of Mixed Reality system use; collected statistics display increased levels of stress and ratings of immersion from system use. Results display prominent applicability of Mixed Reality systems in climate change contextualization.&#x0D; Research limitations/implications: Due to the lack of accessible Head-Mounted Displays, the pilot study is confined to a Mixed Reality format. Future work can examine effects of more realistic and localized approaches on stress and inclination levels; these effects may include real-time scanning and geographical mapping, dynamic object placement, information prompting, and improved spatialized audio.&#x0D; Originality/value: We evaluate the relative effectiveness of current communication mediums and propose a novel MR system as a solution to global mitigation challenges. We evaluate the effectiveness of conveying climate change awareness topics through Mixed Reality with a pilot study.
A Recommendation System for Decentralized Autonomous Organization

Structured data analysis has historically achieved remarkable success. However, the analysis of massive amounts of unstructured video data is still a challenging problem. Over one billion people use YouTube, a Google corporation, which generates billions of views. YouTube data is being created in extremely large quantities, and with a massive demand to store, analyze, and carefully study large amounts of data to make it usable for big data analytics. For the analysis of these YouTube data, the absence of YouTube Shorts which has become a current trend is the limitation. To address this limitation, YouTube Shorts along with traditional duration videos have been considered for analysis in this work. For analyzing these data, various Machine Learning (ML) techniques like clustering and classification have been considered to categorize the content creators into three different categories such as highly rated, moderately rated and lowly rated. These ratings for recommendation assist content creators in enhancing the value of their brand and the users are benefited by consuming, exchanging shares and promoting the content. The results observed show that the accuracy of the Random Forrest and Gradient Boosting classifiers have equivalent performance i.e., around 98% which are suitable for the above recommendation system. Hence, the objective to encourage investors and fans to become active stakeholders and owners of the creator’s micro-economy is addressed in this paper.
A Study on the Types of Motivation to participate in Education and Job Preparation Behavior of Adult Learners: The Mediating Effect of Job-Seeking Efficacy

This study aims to provide basic data to strengthen job preparation behavior by identifying the effects of adult learners' participation motivation types (goal orientation, activity orientation, and learning orientation) on job preparation behavior and verifying the mediating effect of job search efficacy in these relationships. A self-administrated survey was conducted on 270 people who participated in SW vocational education at SW vocational training institution A, and 210 copies of the sincere response were finally analyzed with the SPSS 28.0 program. As a result of the study, the motivation for goal-oriented and learning-oriented participation was found to be completely mediated in job preparation behavior through Job-Seeking efficacy, and the motivation for activity-oriented participation was found to be partially mediated in job preparation behavior through Job-Seeking efficacy. It was confirmed that job-seeking efficacy mediates the relationship between Job preparation behavior by type of participation motivation. Accordingly, it will be necessary to prepare a job support curriculum to improve the job-seeking efficacy so that job preparation behavior can be strengthened in consideration of learners' motivation for participation.
Benefit evaluation of human resource management in agricultural enterprises based on convolutional neural network

With the rapid development of economy, the performance appraisal of human resource management in agricultural enterprises has gained more attention. Based on the convolutional neural network theory, this paper constructs the benefit evaluation system of human resource management in agricultural enterprises, determines the weight to obtain the comprehensive benefit result, and then obtains the evaluation level. According to the main factors affecting the human resource benefit of agricultural enterprises, the model designed the input and output indexes of the efficiency evaluation of single well of production capacity construction, and solved the problem of measuring the informatization ability of human resource management in agricultural enterprises. In the simulation process, the convolutional neural network designed the evaluation index of the index system. According to the different contribution and importance degree to the system security, the difference between the evaluation index can be expressed by assigning different weight values. Secondly, for items with similar loads on multiple factors or with low loads on a single factor, it was adopted to obtain a scale consisting of 54 items, including 15 items in interpersonal skills sub-scale, 16 items in learning development sub-scale and 23 items in growth driver sub-scale. The experimental results show that the convolutional neural network training can obtain the reasonable scale value of the test sample management area. The evaluation index of the model prediction effect shows that the absolute relative error, maximum absolute relative error and average absolute relative error are all within 5%, and the equality coefficient is 0.9845, which is greater than 0.9, indicating that the reasonable scale predicted value has a high degree of fitting with the expected value. The prediction results of the model are ideal, which effectively improves the accuracy of benefit evaluation
How Do We Trust AI Service? Exploring the Trust Mechanism in AI Service

AI services have been widely used by consumers, and trust is a key factor impact them to continuously use AI services. However, due to the intelligent feature, trust in AI service is different from other trust. So, we explore how do users trust AI services and willingly continue to use them. In this paper, we have two studies: in Study 1, we encode user comments of AI service products according to grounded theory, and identify the multilevel dimensions of AI service trust antecedent (AISTA); in Study 2, we use empirical data collected through a survey to develop and test an AI service trust mechanism model based on Study 1. The results indicate that anthropomorphism, perceived intelligence-interactivity, service adaptation and coolness influence affect-based and cognition-based trust to different degrees, thus promoting the continuous use behavior of AI services. This paper extends the research scope of trust literature and contributes to the practice of AI product design.
Development of Model Blended Learning in Cooperative Learning for Technology and Engineering Skills in Vocational Education

Increasing the interest and willingness of students to learn a material was determined from the behavior of students in used information communication technology. The learning model developed was a cooperative learning model based on Blended Learning. Utilization of information technology, by not leaving the pattern of direct guidance from instructors and the used of learning resources to improve literacy mastery of abilities in technology and engineering became important. The Research and Development (R&amp;D) procedure used Blended learning cooperative learning model design in the form of online and face-to-face learning for ICT literacy subjects and learning media in the experimental and control classes. Blended Learning based cooperative learning models were carried out with scenarios namely offering participation in the experimental class, forming learning groups, and learning activities (lecturers, group tutor teams, students, evaluations).The purpose of this research is to get the quality of learning activities. In stage I, students were offered to take the experimental class and the Control class. The study was conducted by 16 students be taken for experimental classes and 21 be taken for control classes. Based on the results of the study showed that the level of mastery of literacy of ICT and media (n-gain) learning in the experimental class was higher than the control class.
SRFeat: Learning Locally Accurate and Globally Consistent Non-Rigid Shape Correspondence

In this work, we present a novel learning-based framework that combines the local accuracy of contrastive learning with the global consistency of geometric approaches, for robust non-rigid matching. We first observe that while contrastive learning can lead to powerful point-wise features, the learned correspondences commonly lack smoothness and consistency, owing to the purely combinatorial nature of the standard contrastive losses. To overcome this limitation we propose to boost contrastive feature learning with two types of smoothness regularization that inject geometric information into correspondence learning. With this novel combination in hand, the resulting features are both highly discriminative across individual points, and, at the same time, lead to robust and consistent correspondences, through simple proximity queries. Our framework is general and is applicable to local feature learning in both the 3D and 2D domains. We demonstrate the superiority of our approach through extensive experiments on a wide range of challenging matching benchmarks, including 3D non-rigid shape correspondence and 2D image keypoint matching.
Action Research on the Application of Intergenerational Learning Guidance Through Online Curriculum

Taiwan is at present encountering both low rates of birth and a maturing populace, prompting decreased open doors for intergenerational contact. Chronic diseases, disability, and dementia are all consequences of physical decline with age. This study aims to investigate the impact of intergenerational learning strategies applied to the elderly sports instruction curriculum on instructors in order to improve and alter this situation. Through action research, instructors continuously reflect on and update the elderly sports instruction curriculum to help students design exercise programs that improve students' stereotypical perceptions of the elderly and promote intergenerational integration by promoting physical and mental health for the elderly. Purposive sampling and action research were used in this study. The subjects of the study were 16 elderly sports instruction curriculum students and one instructor. The primary objective is for students to consider and revise the course material based on elderly feedback. Through triangulation with outside experts, this study will conduct qualitative data analysis. which will also collect feedback and observe how the two generations interact with one another.
Il tavolo privato di Attilio Bettinzoli Piccolo ricordo con sosta su un libro di poesia

Memory of Attilio Bettinzoli colleague: fine scholar and teacher much loved and regretted. The article describes one of his secluded table where he wrote poetry.
Da un volgare all’altro La novella in veneziano negli <i>Avvertimenti della lingua sopra ’l Decamerone</i> di Lionardo Salviati (<i>Dec</i>. 1.9)

The contribution studies the Venetian version of the novella of the King of Cyprus ( Decameron 1.9) included in the first volume of the Avvertimenti della lingua sopra 'l Decamerone by Lionardo Salviati (Venice, Guerra, 1584). In the first part, the essay frames this Salviatian experiment in the vein of 'horizontal' translations of the Decameron , in modern Italian or in another Italoromance variety. It then goes on to reconstruct the objectives and cultural premises of the anti-dialectal offensive promoted by Salviati, which can be traced back to the controversy between Italianists and Florentineists, which involves personalities such as Giangiorgio Trissino, Lodovico Martelli and Benedetto Varchi. Finally, the Venetian version is analysed from a linguistic point of view, paying particular attention to phraseology and vocabulary: a transcription is offered in the Appendix, followed by a selective glossary of popular register words and expressions.
On unifying randomized methods for inverse problems

Abstract This work unifies the analysis of various randomized methods for solving linear and nonlinear inverse problems with Gaussian priors by framing the problem in a stochastic optimization setting. By doing so, we show that many randomized methods are variants of a sample average approximation (SAA). More importantly, we are able to prove a single theoretical result that guarantees the asymptotic convergence for a variety of randomized methods. Additionally, viewing randomized methods as an SAA enables us to prove, for the first time, a single non-asymptotic error result that holds for randomized methods under consideration. Another important consequence of our unified framework is that it allows us to discover new randomization methods. We present various numerical results for linear, nonlinear, algebraic, and PDE-constrained inverse problems that verify the theoretical convergence results and provide a discussion on the apparently different convergence rates and the behavior for various randomized methods.
Students’ Attitude To Reading Of Literary Texts: A Case Study Of Akinyele Local Government Area Of Oyo State, Nigeria

This study investigates the attitudes of secondary school students towards reading literary texts in the Akinyele Local Government Area of Oyo State. The survey research design was used, and four public secondary schools were randomly selected. A total of 200 SSII students who were studying literature in English were randomly selected from these schools. The research instrument used was the Students' Attitude to Reading of Literary Texts Questionnaire (r=.79). Descriptive statistics such as frequency count, percentage scores, mean, and standard deviation, and inferential statistics such as t-test at the 0.05 level of significance were used to analyze the data. The study found that the students had a weighted mean of 2.74, which was greater than the threshold. Furthermore, there were no significant differences between male and female students in their attitudes towards text possession (t (197) = .180; p=.857&gt;.05; Mean Difference = .19701) or their attitudes towards reading (t (196) = -.014; p=.989&gt;.05; Mean Difference = -.01164). Based on the findings of this study, it is recommended that teachers of literature-in-English should encourage students to develop a positive attitude towards reading literary texts. Teachers should adopt methods that will enhance students' positive attitudes towards reading literary texts, and students should be encouraged to read all recommended literary texts. Additionally, parents should purchase all recommended literary texts for their children and encourage them to develop a positive attitude towards reading these texts.
Event Knowledge Incorporation with Posterior Regularization for Event-Centric Question Answering

We propose a simple yet effective strategy to incorporate event knowledge extracted from event trigger annotations via posterior regularization to improve the event reasoning capability of mainstream question-answering (QA) models for event-centric QA. In particular, we define event-related knowledge constraints based on the event trigger annotations in the QA datasets, and subsequently use them to regularize the posterior answer output probabilities from the backbone pre-trained language models used in the QA setting. We explore two different posterior regularization strategies for extractive and generative QA separately. For extractive QA, the sentence-level event knowledge constraint is defined by assessing if a sentence contains an answer event or not, which is later used to modify the answer span extraction probability. For generative QA, the token-level event knowledge constraint is defined by comparing the generated token from the backbone language model with the answer event in order to introduce a reward or penalty term, which essentially adjusts the answer generative probability indirectly. We conduct experiments on two event-centric QA datasets, TORQUE and ESTER. The results show that our proposed approach can effectively inject event knowledge into existing pre-trained language models and achieves strong performance compared to existing QA models in answer evaluation. Code and models can be found: https://github.com/LuJunru/EventQAviaPR.
Entangled coherent states and violations of Bell-CHSH inequalities

Three classes of entangled coherent states are employed to study the Bell-CHSH inequality. By using pseudospin operators in infinite dimensional Hilbert spaces, four dichotomic operators $(A,A',B,B')$ entering the inequality are constructed. For each class of coherent states, we compute the correlator $\langle \psi \vert A B + A' B + A B' - A' B' \vert \psi \rangle$, analyzing the set of parameters that leads to a Bell-CHSH inequality violation and, particularly, to the saturation of Tsirelson's bound.