Reviewerly/exHarmony huggingface dataset:
Evaluating the Effect of Time on Trust Calibration of Explainable Artificial Intelligence

Explainable Artificial Intelligence (XAI) has played a significant role in human-computer interaction. The cognitive resources it carries allow humans to understand the complex algorithm powering Artificial Intelligence (AI), virtually resolving the acceptance and adoption barrier from the lack of transparency. This resulted in more systems leveraging XAI and triggering interest and efforts to develop newer and more capable techniques. However, though the research stream is expanding, little is known about the extent of its effectiveness on end-users. Current works have only measured XAI effects on either moment time effect or compared it cross-sectionally on various types of users. Filling this out can improve the understanding of existing studies and provide practical limitations on its use for trust calibration. To address this gap, a multi-time research experiment was conducted with 103 participants to use and evaluate XAI in an image classification application for three days. Measurement that was considered is on perceived usefulness for its cognitive contribution, integral emotions for affective change, trust, and reliance, and was analyzed via covariance-based structural equation modelling. Results showed that time only moderates the path from cognitive to trust and reliance as well as trust to reliance, with its effect dampening through time. On the other hand, affective change has remained consistent in all interactions. This shows that if an AI system uses XAI over a longer time frame, prioritization should be on its affective properties (i.e., things that will trigger emotional change) rather than purely on its cognitive purpose to maximize the positive effect of XAI.
A Quantum Annealing Solution to the Job Shop Scheduling Problem

We consider in this paper the job shop scheduling problem. We first present a mathematical formulation of the problem, namely a disjunctive model. Then, we show how the model can be reformulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem, that can be solved by an analog quantum computer.
A Study on the Development of a Monitoring System for Responding to the Local Industrial Crisis Using Public Data

최근 코로나19 사태 장기화, 우크라이나 전쟁, 공급망 위기 등 국내외 산업환경이 급변하고 있으며, 이러한 환경변화는 변화에 신속하게 대응하기 어려운 지역 산업과 경제에 더욱 심각한 영향을 미친다. 또한, 그동안의 지역 산업 위기 대응체계는 산업 침체의 초기 단계에는 작동되지 않다가 침체가 본격화된 이후 사후적으로 지원이 이루어지는 체계로 경제 회복에 많은 시간과 비용이 소요되었다. 따라서 지역 산업 현황을 상시 점검하여 예기치 못한 경제 위기 상황에서도 이를 신속하게 진단하고 효과적으로 대응할 수 있는 시스템이 필요하다. 본 연구에서는 공공데이터를 이용하여 지역의 산업 및 경제 상황을 모니터링하고, 위기 상황 발생 시, 이를 신속하게 진단할 수 있는 모니터링 시스템을 개발하고자 하였다. 국민연금공단, 통계청, 한국은행 등의 공공데이터를 활용하였으며, 지역 산업위기 관련 다양한 정량 지표들을 모니터링하여 지역의 주요 산업 또는 지역 경제가 일정 수준 이상 침체된 경우 지역 산업 위기 상황으로 진단하였다. 본 시스템을 통해 지역 산업위기 상황을 적시에 파악할 수 있으며, 신속한 위기 대응을 통해 지역 경제 안정화에 기여할 수 있을 것으로 생각한다. Recently, the global industrial environment is rapidly changing due to the COVID-19 pandemic, the war in Ukraine, and the global supply chain crisis. These changes in the industrial environment are having a significant impact on local industries and economies that are difficult to adapt to changes quickly. In addition, the local industrial crisis response system up to now did not work well in the early stage of an industrial recession, and only works after the recession deepen. As a result, it takes a lot of time and money to overcome the industrial crisis. Therefore, it is necessary to quickly diagnose and respond to local industrial crises by monitoring the status of local industries. In this study, we tried to develop a monitoring system that can continuously check the local economic situation. We used public data to monitor various indicators related to local industrial crises. In addition, regions with stagnant major industries or regional economies were diagnosed as industrial crisis regions. Through this, it is expected to contribute to regional economic stability by identifying regional industrial crises on time and promptly responding to crises.
SelectAugment: Hierarchical Deterministic Sample Selection for Data Augmentation

Data augmentation (DA) has been extensively studied to facilitate model optimization in many tasks. Prior DA works focus on designing augmentation operations themselves, while leaving selecting suitable samples for augmentation out of consideration. This might incur visual ambiguities and further induce training biases. In this paper, we propose an effective approach, dubbed SelectAugment, to select samples for augmentation in a deterministic and online manner based on the sample contents and the network training status. To facilitate the policy learning, in each batch, we exploit the hierarchy of this task by first determining the augmentation ratio and then deciding whether to augment each training sample under this ratio. We model this process as two-step decision-making and adopt Hierarchical Reinforcement Learning (HRL) to learn the selection policy. In this way, the negative effects of the randomness in selecting samples to augment can be effectively alleviated and the effectiveness of DA is improved. Extensive experiments demonstrate that our proposed SelectAugment significantly improves various off-the-shelf DA methods on image classification and fine-grained image recognition.
Improving Crowded Object Detection via Copy-Paste

Crowdedness caused by overlapping among similar objects is a ubiquitous challenge in the field of 2D visual object detection. In this paper, we first underline two main effects of the crowdedness issue: 1) IoU-confidence correlation disturbances (ICD) and 2) confused de-duplication (CDD). Then we explore a pathway of cracking these nuts from the perspective of data augmentation. Primarily, a particular copy- paste scheme is proposed towards making crowded scenes. Based on this operation, we first design a "consensus learning" method to further resist the ICD problem and then find out the pasting process naturally reveals a pseudo "depth" of object in the scene, which can be potentially used for alleviating CDD dilemma. Both methods are derived from magical using of the copy-pasting without extra cost for hand-labeling. Experiments show that our approach can easily improve the state-of-the-art detector in typical crowded detection task by more than 2% without any bells and whistles. Moreover, this work can outperform existing data augmentation strategies in crowded scenario.
NLIP: Noise-Robust Language-Image Pre-training

Large-scale cross-modal pre-training paradigms have recently shown ubiquitous success on a wide range of downstream tasks, e.g., zero-shot classification, retrieval and image captioning. However, their successes highly rely on the scale and quality of web-crawled data that naturally contain much incomplete and noisy information (e.g., wrong or irrelevant contents). Existing works either design manual rules to clean data or generate pseudo-targets as auxiliary signals for reducing noise impact, which do not explicitly tackle both the incorrect and incomplete challenges at the same time. In this paper, to automatically mitigate the impact of noise by solely mining over existing data, we propose a principled Noise-robust Language-Image Pre-training framework (NLIP) to stabilize pre-training via two schemes: noise-harmonization and noise-completion. First, in noise-harmonization scheme, NLIP estimates the noise probability of each pair according to the memorization effect of cross-modal transformers, then adopts noise-adaptive regularization to harmonize the cross-modal alignments with varying degrees. Second, in noise-completion scheme, to enrich the missing object information of text, NLIP injects a concept-conditioned cross-modal decoder to obtain semantic-consistent synthetic captions to complete noisy ones, which uses the retrieved visual concepts (i.e., objects’ names) for the corresponding image to guide captioning generation. By collaboratively optimizing noise-harmonization and noise-completion schemes, our NLIP can alleviate the common noise effects during image-text pre-training in a more efficient way. Extensive experiments show the significant performance improvements of our NLIP using only 26M data over existing pre-trained models (e.g., CLIP, FILIP and BLIP) on 12 zero-shot classification datasets (e.g., +8.6% over CLIP on average accuracy), MSCOCO image captioning (e.g., +1.9 over BLIP trained with 129M data on CIDEr) and zero-shot image-text retrieval tasks.
Variable-Based Calibration for Machine Learning Classifiers

The deployment of machine learning classifiers in high-stakes domains requires well-calibrated confidence scores for model predictions. In this paper we introduce the notion of variable-based calibration to characterize calibration properties of a model with respect to a variable of interest, generalizing traditional score-based metrics such as expected calibration error (ECE). In particular, we find that models with near-perfect ECE can exhibit significant miscalibration as a function of features of the data. We demonstrate this phenomenon both theoretically and in practice on multiple well-known datasets, and show that it can persist after the application of existing calibration methods. To mitigate this issue, we propose strategies for detection, visualization, and quantification of variable-based calibration error. We then examine the limitations of current score-based calibration methods and explore potential modifications. Finally, we discuss the implications of these findings, emphasizing that an understanding of calibration beyond simple aggregate measures is crucial for endeavors such as fairness and model interpretability.
Conceptual Reinforcement Learning for Language-Conditioned Tasks

Despite the broad application of deep reinforcement learning (RL), transferring and adapting the policy to unseen but similar environments is still a significant challenge. Recently, the language-conditioned policy is proposed to facilitate policy transfer through learning the joint representation of observation and text that catches the compact and invariant information across various environments. Existing studies of language-conditioned RL methods often learn the joint representation as a simple latent layer for the given instances (episode-specific observation and text), which inevitably includes noisy or irrelevant information and cause spurious correlations that are dependent on instances, thus hurting generalization performance and training efficiency. To address the above issue, we propose a conceptual reinforcement learning (CRL) framework to learn the concept-like joint representation for language-conditioned policy. The key insight is that concepts are compact and invariant representations in human cognition through extracting similarities from numerous instances in real-world. In CRL, we propose a multi-level attention encoder and two mutual information constraints for learning compact and invariant concepts. Verified in two challenging environments, RTFM and Messenger, CRL significantly improves the training efficiency (up to 70%) and generalization ability (up to 30%) to the new environment dynamics.
AUDIO SUMMARIZATION IN REAL TIME FOR PODCAST, SPEECHES AND AUDIOBOOKS

The majority of public works are carried out online as a result of the coronavirus disease (COVID19) pandemic. Many job interviews, primary health care consultations, and company meetings are conducted entirely online, and universities all over the world have switched to online teaching. Applications for online meetings like Microsoft Team and Google Meet are widely available on the market. We approach a topic with numerous practical applications in this work. A method that takes recorded video as input and produces identical written or audio summaries as output is the subject of this paper. Additionally, the proposed method can be used to create meeting minutes, subtitle or scribe entertainment videos, and create lecture notes from lecture videos. The audio track of a video is converted into text by the proposed system. We also used a text summarization algorithm to create text summaries. Users of the system have the option of using a text summary or making an audio output that matches the text summary. The proposed strategy is carried out in Python and the proposed conspire is assessed utilizing brief recordings got from YouTube. The proposed method is manually validated on a set of uploaded videos because there are neither specific datasets or benchmarks available to evaluate efficacy nor any benchmarks to use in related studies.
Database Management

The first chapter gives an overview about information systems and databases. An introduction into SQL databases, including the relational model, the Structured Query Language (SQL), as well as the architecture, is given. The definition of Big Data is based on several Vs such as volume, variety, velocity, value, and veracity. Based on this definition, NoSQL databases are introduced including a graph model with the graph-oriented query language Cypher, as well as the document model with the document-oriented MongoDB Query Language (MQL). Organizational aspects of data management are discussed, including the new role of data scientists.
Research on the Blended Course Teaching Design of C Programming

The current "mainstream education" includes traditional classroom teaching and online teaching.Traditional teaching can no longer meet the needs of application-oriented undergraduate education.Although online teaching has made up for the shortcomings of traditional teaching to a certain extent, MOOC, as a new online teaching platform arising from the development of the Internet and information technology, has been favored by teachers and students and universities in recent years, but its main shortcomings include the inability to provide targeted teaching Inadequacies such as the unlimited number of students have gradually emerged.In view of this situation, this paper proposes to use the "Small Private Online Course (SPOC) combined with hybrid teaching mode" in the application oriented undergraduate education, and take C Programming, one of the compulsory courses for students majoring in intelligent manufacturing, as an example to conduct curriculum design research.Through the comparative analysis of the scores before and after the teaching experiment, it is found that under the mixed teaching mode based on SPOC, the students' scores have changed significantly compared with those before the beginning of the teaching mode.The excellent rate and passing rate of the students in the experimental class are higher than those before.It can be seen that the teaching mode can improve the students' learning performance.
Traffic Sign Detection Based on Improved YOLOv5

&lt;p&gt;As a popular research direction in the field of intelligent transportation, various scholars have widely concerned themselves with traffic sign detection However, there are still some key issues that need to be further solved in order to thoroughly apply related technologies to real scenarios, such as the feature extraction scheme of traffic sign images, and the optimal selection of detection methods. For the purpose of overcoming these difficulties. This paper proposes a YOLO-based traffic sign detection framework. Firstly, a lightweight convolution attention mechanism is embedded into the backbone network to obtain the information of space and channel; Secondly, the multi-scale awareness module is used to replace large convolution with 3&amp;times;3 convolution superposition to improve the receptive field area of the object in the model and enhance the feature fusion performance of the model; Finally, CIoU is used as the loss function of the bounding box to locate the experimental object with high precision. The experimental results show that on the CCTSDB data set, the MAP of this method reaches 91.0%, which is 3.5% higher than the original YOLOv5. Compared with other mainstream object detection algorithms, it has a certain degree of improvement, which proves the effectiveness of this method.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
The Awareness Assessment Model: measuring the awareness and collaboration support over the participant’s perspective

[Context] Awareness has been a valuable concept in Collaborative Systems since its formation, being an essential part of groupware. The efficient awareness mechanism ensures a better understanding and, consequently, a better projection of future actions; in contrast, the lack of these mechanisms undermines comprehension and prevents participants from projecting their work accordingly. [Problem] This is a multi-factorial problem, and finding a goodm starting point in the literature can be challenging for novice groupware designers; they must reinvent awareness from their own experience of what it is, how it works, and how it is used. [Goal] This work consists of establishing an assessment model for collaborative interfaces by analyzing the awareness mechanisms provided from the participant’s viewpoint. Our awareness assessment model developed adopting the statistical technique Item Response Theory (IRT) and considers the participant’s skill in understanding the awareness and the difficulty involved. [Results] The proposed assessment model allow us to measure the awareness support provided considering the collaboration, workspace, and contextual awareness perspectives. The results obtained were translated into an awareness support scale and three levels of quality were defined.
Self-Assessment of Pre-Service English Teachers’ Digital Competence

Digital competence is considered an integral component of the skills and professional expertise of teachers nowadays. The aim of this study was to explore the perceptions of 82 pre-service English language bachelor and master level teachers from a state university in Bulgaria on their digital competence based on the interplay of five variables– competence for using and integrating ICT in the foreign language classroom, technological pedagogical knowledge, support received during training, capacity to promote the digital skills of their learners and attitude towards integrating digital technologies in their future professional practice. Based on the performed descriptive, correlational and inferential analysis of the data obtained, it was established that the most strongly related variables were the self-perceived competence of the pre-service teachers for using and integrating digital technologies in the teaching and learning of foreign languages and their capacity to promote the digital skills of their learners.
Experimental Comparison of Clustering Approaches for Personalized Federated Learning

Federated learning allows clients to collaboratively train a global model while preserving data privacy. However, personalized the global model is necessary for each device to prevent performance degradation caused by the heterogeneity of each client’s local data. In this paper, we discuss previously studied client clustering techniques for personalized federated learning. We also evaluate the performance of personalized models generated by various federated learning and client clustering algorithms. Our research aims to bridge the research gap by evaluating the effectiveness of personalized models when applied to users.
History of the Metaverse

Metaverse is a futuristic concept combining various technologies, including computer infrastructure, gaming, NFTs, DeFi, AR/VR, AI, and the spatial web. More than 600 companies are building metaverses, estimated to be a US$13 trillion market opportunity by 2030. However, there is yet to be a consensus on what it is. It will be immersive and persistent, combining virtual and physical worlds with an economy and value that can be earned, lent, borrowed, and extracted from the physical world. Some challenges remain, including interoperability, latency, user interface, and regulation. This is arguably one of the most exciting technological developments, which will help create new and enhanced ways to live, shop, be entertained, seek services, and be educated. This chapter introduces the key elements of the Metaverse and outlines challenges and ethical questions as the world becomes more immersive.
Photonics with 2D Materials: Introduction to the Special Issue

This is an introduction to the special issue on photonics with 2D materials.
A Quality Assessment of English Idiom Translation into Indonesian in Harry Potter and The Cursed Child

This study aims to determine the quality of idiom translation and identify strategies applied by the translator in translating English idiomatic expressions into Indonesian in Harry Potter and the Cursed Child by Jack Thorne, J.K. Rowling, and John Tiffany. In achieving these goals, this research uses mixed-method approaches. It is analyzed based on the Translation Quality Assessment (TQA) model by Nababan et al. (2012) and Baker’s theory about strategies for translating idioms (2018). Meanwhile, the data was collected through questionnaires distributed to the informants. The research finding showed that the translator applies five idiom translation strategies in 70 data. Based on the frequency, the idiom translation strategies are translation by paraphrase (58.6%), literal translation (27.1%), using the idiom with similar meaning but the dissimilar form (8.6%), using an idiom with similar meaning and form (4.3%), and translation by the omission of play of idiom (1.4%). Meanwhile, the average score for the accuracy of the translation is 2.8. The average score for the acceptability of the translation is 2,8, and the average score for readability is 2.8. Overall, the average score of the English idiom translation quality in Harry Potter and the Cursed Child books is 2,8, which indicates that the translation has good quality, in other words, most of the translation is accurate, acceptable, and easily understood by the target readers
Hybridizing Fuzzy String Matching and Machine Learning for Improved Ontology Alignment

Ontology alignment has become an important process for identifying similarities and differences between ontologies, to facilitate their integration and reuse. To this end, fuzzy string-matching algorithms have been developed for strings similarity detection and have been used in ontology alignment. However, a significant limitation of existing fuzzy string-matching algorithms is their reliance on lexical/syntactic contents of ontology only, which do not capture semantic features of ontologies. To address this limitation, this paper proposed a novel method that hybridizes fuzzy string-matching algorithms and the Deep Bidirectional Transformer (BERT) deep learning model with three machine learning regression classifiers, namely, K-Nearest Neighbor Regression (kNN), Decision Tree Regression (DTR), and Support Vector Regression (SVR), to perform the alignment of ontologies. The use of the kNN, SVR, and DTR classifiers in the proposed method resulted in the building of three similarity models (SM), encoded SM-kNN, SM-SVR, and SM-DTR, respectively. The experiments were conducted on a dataset obtained from the anatomy track in the Ontology Alignment and Evaluation Initiative 2022 (OAEI 2022). The performances of the SM-kNN, SM-SVR, and SM-DTR models were evaluated using various metrics including precision, recall, F1-score, and accuracy at thresholds 0.70, 0.80, and 0.90, as well as error rates and running times. The experimental results revealed that the SM-SVR model achieved the best recall of 1.0, while the SM-DTR model exhibited the best precision, accuracy, and F1-score of 0.98, 0.97, and 0.98, respectively. Furthermore, the results showed that the SM-kNN, SM-SVR, and SM-DTR models outperformed state-of-the-art alignment systems that participated in the OAEI 2022 challenge, indicating the superior capability of the proposed method.