Reviewerly/exHarmony huggingface dataset:
Algorithmic Precision and Human Decision: A Study of Interactive Optimization for School Schedules

Motivated by a collaboration with the San Francisco Unified School District (SFUSD), this paper presents an interactive optimization framework for addressing complex public policy problems. These problems suffer from a chicken-and-egg dilemma, where policymakers understand the objectives and constraints but lack the ability to solve them (“the optimization problem”), while researchers possess the necessary algorithms but lack the necessary insights into the policy context (“the policy problem”). Our framework addresses this challenge by combining three key elements: (1) an efficient optimization algorithm that can solve the problem given certain known objectives, (2) a method for generating a large set of diverse, near-optimal solutions, and (3) an interface that facilitates exploration of the solution space. We illustrate the effectiveness of this framework by applying it to the problem of improving school schedules at SFUSD. The resulting schedule, implemented in August 2021, saved the district over $5 million and, to our knowledge, represents the first successful optimization-driven school start time change in the United States.
Does Public Libraries' Facebook Following Correlate with Library Usage?

Social media like Facebook is widely believed to be a useful marketing tool for organizations, likely to include public libraries. However, there has as of yet been little research to examine whether Facebook followership actually correlates to increased use of the library. This study utilizes data from the Institute of Museum and Library Services, as well as Facebook followers of 3884 public libraries in the United States to statistically evaluate whether any significant relationships exist between Facebook followership and library use variables. Results indicate that Facebook followership per capita in a libraryâ€™s service area is moderately-to-strongly correlated with both library visits per capita and event attendance per capita.
Deep Learning Orientation of Melanoma Detection Using CNN

Despite being least prevalent type skin disease, melanoma, is deadliest and also is responsible for numerous fatalities. A recent study found that 3.1M people had this, caused 59,800 fatalities. To prevent these infections from spreading, it is crucial to recognize them early on. Manually detecting skin disorders can be challenging due to the wide variety of them. The term "artificial dermatologist" refers to a system that uses deep learning methods to identify skin conditions without the use of diagnostic tests. Here, by employing EfficientNet-B6 which is a convolutional neural architecture to analyse skin lesion images, which can capture more fine-grained information, we acquire an accurate result in the identification of skin cancer. An extensive dataset created by ISIC 2020 served as the basis for the experimental evaluations.
Creative Essays on Socio-Economic Problems as a Way to Develop Critical Thinking of Master's Degree Students

The article discusses the methods of developing critical thinking in media professionals. One of the formats for the development of critical thinking among master's degree students of the Faculty of International Journalism is preparation practices and tools resulting in creative essay writing. Preliminary training is based on two prerequisites: 1) the importance of conducting analysis and resisting manipulation in modern media for journalism students; 2) the creative nature of journalists' and public relations specialists' activities, which is frequently associated with writing materials that are original in form and content. The article presents tactics that are used by EFL teachers to prepare students for writing a creative essay. The results of the study confirm the importance of creative writing in a foreign language for critical thinking development in master's degree students.
A Study on Market Segmentation of the Information-underprivileged Users Using TDA

This study aims to categorize all users of the four major information-underprivileged groups (i.e., low-income people, the disabled, farmers and fishers, and the elderly) that comprehensively reflect digital informatization competency, digital attitude competency, digital information utilization, social capital, and life satisfaction, and changes due to COVID-19. We used Topological Data Analysis (TDA) which is a new clustering methodology. For this purpose, a total of 11,801 valid data were analyzed using the original materials of the 2020 Digital Information Divide Survey. As a result of the study, a very small group (2.6%) that far exceeds the overall average of users in terms of digital competency/attitude/utilization level, a group (26.3%) that slightly exceeds the average of all users, and a group (26.5%) that corresponds to the average level of information-underprivileged users, and two groups (26.5%, 21.4%) with a wide gap from the other three groups in terms of digital competency/attitude/utilization level. We identified profiles of five user groups according to demographic characteristics, digital information accessibility, experience in using various internet services, digital competencies, and demand for informatization education.
The Effect of Experience and Response to Hotel Art Tours Using Augmented Reality on Customer Engagement

Among new technologies, Augmented Reality not only overcomes time and space constraints but also competes with comprehensive digital and physical information to induce new interactions and interest. For this reason, Augmented Reality has become one of the various viewing methods in many art galleries and museums today, and hotels are also making efforts to utilize this technology. Therefore, this study proposes an effective and continuous use of Augmented Reality leading to customer engagement. As one of the new media art that performs appreciation activities based on Augmented Reality, when the Augmented Reality Art Tour is applied to the hotel, the experience of Augmented Reality affects the reaction, and the effect of the reaction on customer engagement was analyzed. The study was conducted through the Stimulus-Organism-Response(SOR) Theory by selecting vivid and authentic experiences, cognitive and affective responses, and customer engagement as variables through the Augmented Reality Art Tour. As a result of the analysis, it was confirmed that the greater the degree of vivid and authentic experience provided through the Augmented Reality Art Tour, the better the hotel’s positive cognitive and affective response and the increase in customer engagement. In addition, the theoretical implications of this study can contribute to the expansion of literature research on the characteristics of the Augmented Reality Art Tour and the capabilities of hotels, and practical implications can help hotel companies discuss various operational measures.
The Effect of eBook Subscription Service on Perceived Value and Behavioral Intention

This study examines how the characteristics of ebook subscription service affect consumers" perceived value and behavioral intention. And it was verified whether the perceived value plays a mediating role between the characteristics of the ebook subscription service and the behavioral intention. In addition, it was verified whether there is a moderating effect of self-efficacy between ebook subscription service characteristics and perceived value. Data collection was conducted by requesting Embrain Research from May 10 to 15, 2022 for users with ebook subscription experience. A total of 318 questionnaires collected were used for the final analysis. The analysis results are as follows. First, it was found that content characteristics have a positive effect on both functional and emotional values. Personalization characteristic had a positive effect on emotional value, but functional value was not significant. Second, both functional and emotional values had a positive effect on subscription loyalty, but termination intention was rejected. Third, as a result of the mediating effect test, the perceived value was partially mediated between the characteristics of ebook subscription service and subscription loyalty. And only emotional value partially mediated the relationship between personalization characteristics and subscription loyalty.
Linguistic appropriateness of Korean Teacher Talk(1): Focusing on the adjustment of vocabulary and grammar in grammar class

이 연구에서는 한국어 교사 발화의 언어 변형 양상을 숙달도 측면에서 살펴보고자 하였다. 중급 문법 수업에서의 교사 발화를 분석한 결과, 교사들은 학습자의 이해를 위해 언어적 변형을 제공하는 것으로 나타났다. 교사 발화의 조절 양상으로는 초급 수준의 어휘, 문법·표현의 사용 빈도가 88% 이상으로 아주 높게 나타났다. 교사가 새로운 문법을 설명하는 과정에서 목표 항목에 대한 이해도를 높이기 위해 초급에서 많이 사용되는 일상 어휘와 문법 표현을 사용하여 설명했기 때문일 것이다. 또한 언어 표현의 다양성 측면에서는 국가 수준의 한국어 교육과정에서 제시하는 초급 수준의 문법·표현 유형에서 약 63%를, 중급 항목의 유형 중에서 약 12%를 활용하는 것으로 나타났다. 이처럼 중급 한국어 문법 수업임에도 불구하고 초급 수준의 언어 표현 사용 비중이 높음을 알 수 있었다. 학습자 이해를 위해 초급 수준의 언어 표현을 사용해 쉽게 설명하는 것도 중요하나 교사가 제공하는 목표어 비중이 학습에 미치는 영향을 고려하면 현재 학습자의 학습 단계를 조금 더 반영하는 교사 발화로 조절할 필요가 있다. This study examines the language modification pattern of Korean teacher talk/speech in terms of proficiency. As a result of analyzing teacher speech in intermediate grammar classes, first, it was found that teachers provide verbal modification for learners’ understanding. For the control pattern of teacher speech, the frequency of vocabulary, grammar, and expression at the beginner level was very high at more than 88%. This may be because the teacher explained the new grammar easily, using daily vocabulary or grammar expressions widely used at the beginner level to increase understanding of the target item. Therefore, it was found that most language expressions other than the target items of the class tended to be adjusted to the beginner level. Second, this study investigated how teachers use different grammar and expressions. As a result, it was found that about 63% of the beginner-level item types presented in the national-level curriculum in the field of Korean Language Teaching as a second and foreign language, and about 12% of the intermediate-level items were used. It is also important to explain easily used beginner-level language expressions, but the proportion of target language provided by teachers greatly impacts learning, indicating the need for teacher speech adjustments considering the learning stage.
Analysis of the Effects of College Student Writing Education - Focusing on the ‘Writing Process’ and ‘Causal and Influence’ Writing -

이 연구의 목적은 〈사고와 표현(읽기와 쓰기)〉 과목 수강생을 대상으로 ‘글쓰기 과정’과 ‘인과와 영향’의 글 내용 전개 방식 학습에 대한 교육적 효과를 검증하는 데 있다. 학습자 52명을 대상으로 ‘글쓰기 과정’과 ‘인과 영향’에 대한 학습을 하기 전에 이에 대한 자기효능감 사전조사를 하였다. 그리고 글을 작성하고 첨삭받은 내용을 바탕으로 최종본 글쓰기를 하게 한 후 자기효능감 사후조사를 하였다. 조사 결과 학습자의 ‘글쓰기 과정’과 ‘인과와 영향’ 글쓰기에 대한 자기효능감은 사전조사 대비 사후조사에서 모두 상승하였다. 이를 통해 학생들은 학습을 하기 전에 비하여 학습한 내용을 바탕으로 글을 쓰고 난 후 자신의 능력이 더 좋아졌다고 판단한 것을 알 수 있다. 즉, 글쓰기 과정을 고려하며 ‘인과와 영향’의 글을 쓰는 활동은 학습자의 글쓰기 역량 증진에 긍정적으로 기여하는 교육적 효과가 있다는 것을 검증하였다. 많은 대학이 필수 과목으로 글쓰기 교과를 운영하는데 학습자를 대상으로 교육적 효과를 검증한 연구가 많지 않다. 이 연구는 학습자의 자기효능감을 바탕으로 교육적 효과를 검증하였는 데 의의가 있다. This study aims to verify the educational effects of the “writing process” and “causality and influence” on the learning of the content development method for the students of the “〈Thinking and Expression (Reading and Writing)〉” course. Before learning about the writing process and causal influence for 52 learners, a self-efficacy survey was conducted. After the study was completed, the writing was written, and the final version was written through corrections, followed by a post-mortem survey on self-efficacy. As a result of the survey, learners’ self-efficacy for the writing process and causal and influence writing increased in the post-survey compared to the pre-survey. The students judged that their abilities improved after writing compared to before learning. In other words, the activity of writing causation and effect while considering the writing process has an educational effect that positively contributes to the learners’ writing competency. Many universities operate a writing subject as a required subject, but few studies have verified the educational effect for learners. This study is meaningful because it verifies the educational effect based on learners’ self-efficacy.
IMPGA: An Effective and Imperceptible Black-Box Attack Against Automatic Speech Recognition Systems

Machine learning systems are ubiquitous in our lives, so it is necessary to study their vulnerabilities to improve the reliability and security of the systems. In recent years, adversarial example attacks have attracted considerable attention with remarkable success in fooling machine learning systems, especially in computer vision. For automatic speech recognition (ASR) models, the current state-of-the-art attack mainly focuses on white-box methods, which assume that the adversary has full access to the details inside the model. However, this assumption is often incorrect in practice. The existing black-box attack methods have the disadvantages of low attack success rate, perceptible adversarial examples and long computation time. Constructing black-box adversarial examples for ASR systems remains a very challenging problem. In this paper, we explore the effectiveness of adversarial attacks against ASR systems. Inspired by the idea of psychoacoustic models, we design a method called Imperceptible Genetic Algorithm (IMPGA) attack based on the psychoacoustic principle of auditory masking, which is combined with genetic algorithms to address this problem. In addition, an adaptive coefficient for auditory masking is proposed in the method to balance the attack success rate with the imperceptibility of the generated adversarial samples, while it is applied to the fitness function of the genetic algorithm. Experimental results indicate that our method achieves a 38% targeted attack success rate, while maintaining 92.73% audio file similarity and reducing the required computational time. We also demonstrate the effectiveness of each improvement through ablation experiments.
Hierarchical Aggregation Based Knowledge Graph Embedding for Multi-task Recommendation

Recently, knowledge graph has been used for alleviating the problems such as sparsity faced by the recommendation. Multi-task learning, which is an important emerged frontier research direction, helps complement the available information of different tasks and improves recommendation performance effectively. However, the existing multi-task methods ignore high-order information between entities. At the same time, the existing multi-hop neighbour aggregation methods suffer from the problem of over-smoothing. Also, the existing knowledge graph embedding methods in multi-task recommendation ignore the attribute triples in knowledge graph and recommendation tends to neglect the learning of user attributes. To mitigate these problems, we propose a multi-task recommendation model, called AHMKR. We use hierarchical aggregation and high-order propagation to alleviate the over-smoothing problem and obtain a better entity representation that integrates high-order information for multi-task recommendation. We leverage the text information of attribute triples, to improve the performance of knowledge graph in expanding the features of recommendation items. For users, we conduct fine-grained user learning based on the user attributes to capture user preferences in a more accurate matter. The experiments on the real-world datasets demonstrate the good performance of AHMKR.
RoFL: A Robust Federated Learning Scheme Against Malicious Attacks

Privacy protection is increasingly important in contemporary machine learning-based applications. While federated learning can provide privacy protection to some extent, it assumes that clients (and their updates) are trusted. However, we also need to consider the potential of malicious or compromised clients. In this paper, we propose a robust federated learning (RoFL) scheme, designed to detect multiple attacks and block malicious updates from being passed to the central model. To validate our scheme, we train a CNN classification model based on the MNIST dataset. We then conduct experiments focusing on the impacts of model parameters (e.g., malicious amplification factors, fractions of training clients, fractions of malicious clients, and data distribution characteristics (i.e., IID or Non-IID)) on the proposed (RoFL) scheme. The findings demonstrate that the proposed (RoFL) scheme can effectively protect federated learning models from malicious attacks.
Mixed-Order Heterogeneous Graph Pre-training for Cold-Start Recommendation

The cold-start problem is a fundamental challenge in recommendation. Heterogeneous information networks (HINs) provide rich side information in addition to sparse user-item interactions, which can be used to alleviate the cold-start problem. However, most existing models based on graph neural networks (GNNs) only consider the user-item interactions as supervision signals, making them unable to effectively exploit the side information. In this paper, we propose a novel pre-training model, named MHGP, for cold-start recommendation in a self-supervised manner. The key idea is to leverage the mixed-order information in a HIN. We first use GNNs with a hierarchical attention mechanism to encode the first-order and high-order structures of a user-item HIN. Then, we pre-train the embeddings of users and items by contrasting the two structure views and maximizing the agreement of positive samples in each view. Afterwards, the embeddings are fine-tuned together with the recommendation model. Experiments show that our model can consistently improve the performance of cold-start recommendation and outperform other state-of-the-art pre-training models.
LAP-BFT: Lightweight Asynchronous Provable Byzantine Fault-Tolerant Consensus Mechanism for UAV Network Trusted Systems

An UAV network performing missions in complex environments is an Ad hoc network of a set of lightweight nodes that is not only exposed to external physical interference and network attacks, but also to the problem of dynamically generated error nodes within the network. The UAV network is essentially an asynchronous Byzantine distributed system. Completing the mission relies on the trustworthiness of the participating UAVs. The timely identification and isolation of errant nodes is necessary to ensure the overall performance of the UAV network during the mission. Assessing the latest status of UAVs and reaching consensus across the network is the key to solving the trust problem. It is a major challenge to break through the limitation of insufficient resources of UAV networks to achieve efficient consensus and accurate evaluation of UAV trusted status. The approach proposed in this paper is lightweight and asynchronous provable Byzantine fault-tolerant consensus algorithm that achieves global trusted state evaluation by obtaining an asynchronous generic subset by consensus on the local status data of nodes. It effectively reduces the communication and computational overhead. Through QualNet UAV network simulation experiments, comparing existing asynchronous consensus algorithms with better practicality, the recommended lightweight asynchronous provable consensus algorithm has better performance in terms of consensus latency and energy consumption rate.
Fine-Grained Urban Flow Inferring via Conditional Generative Adversarial Networks

Urban flow super-resolution (UFSR) can deduce fine-grained urban flow heatmap (UFH) based on coarse-grained observations and plays an essential role in urban planning (traffic prediction, public facility deployment, for instance). However, existing methods fail to capture the internal structural features of sparse UFHs and the external factors that lead to a significant waste of urban resources. To this end, we propose an enhanced super-resolution framework (Urban Flow-aware Super Resolution - Generative Adversarial Network, UrbanSG) to deduce fine-grained UFH for urban resource allocation. Specifically, we employ a conditional-GAN as the backbone, considering external factors as the specified condition. To capture the implicit urban structural correlation, we integrate the flow self-attention mechanism into our model, which focuses on urban grids with active traffic volumes. The evaluations of extensive experiments on two real-world datasets demonstrate the superiority of our framework. Especially when dealing with a sparse dataset, our method reduces error by 15.02% to the state-of-the-art baselines.
The Effect of Short-term Mindfulness-based Selfhelp Training on Anxiety State of College Students

Mindfulness; Anxiety; Life satisfaction; Self-help training
Estimating The Effects Of Financial Liberalization On Governability And Social Stability

This paper studies the effects of financial liberalization on governability. The dependent variables measure governability in terms of control of corruption, government effectiveness, political stability, rule of law, regulatory quality, and free speech for 125 countries from 1996 to 2019. As a variable measuring financial liberalization, I use Chinn-Ito Index and Fernandez Index as Capital Control measure. I use Fixed-Effects Panel Data, Quantile Regression, System GMM and Event Study to estimate these effects. The results I find show that the financial liberalization have strong effects on those governability variables, especially low-income countries tend to have a higher sensitivity to changes in capital controls on governability and social stability, as well as countries in the 0.4 - 0.6 quantiles of the governability. Finally, I find that these effects take at least one year to become persistent over time. These findings imply that, in times of political turbulence and instability, governments may pursue liberalizing policies that increase the dynamism of the economy to alleviate the climate of ungovernability.
Special Issue on Highlights of IUI 2021: Introduction

introduction Share on Special Issue on Highlights of IUI 2021: Introduction Authors: Tracy Hammond Texas A&M University, College Station, Texas, United States Texas A&M University, College Station, Texas, United StatesView Profile , Bart Knijnenburg Clemson University, Clemson, South Carolina, United States Clemson University, Clemson, South Carolina, United StatesView Profile , John O’Donovan University of California, Santa Barbara, California, United States University of California, Santa Barbara, California, United StatesView Profile , Paul Taele Texas A&M University, College Station, Texas, United States Texas A&M University, College Station, Texas, United StatesView Profile Authors Info & Claims ACM Transactions on Interactive Intelligent SystemsVolume 12Issue 403 February 2023Article No.: 25pp 1–4https://doi.org/10.1145/3561516Published:03 February 2023Publication History 0citation47DownloadsMetricsTotal Citations0Total Downloads47Last 12 Months47Last 6 weeks9 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access