Reviewerly/exHarmony huggingface dataset:
Visually Grounded Commonsense Knowledge Acquisition

Large-scale commonsense knowledge bases empower a broad range of AI applications, where the automatic extraction of commonsense knowledge (CKE) is a fundamental and challenging problem. CKE from text is known for suffering from the inherent sparsity and reporting bias of commonsense in text. Visual perception, on the other hand, contains rich commonsense knowledge about real-world entities, e.g., (person, can_hold, bottle), which can serve as promising sources for acquiring grounded commonsense knowledge. In this work, we present CLEVER, which formulates CKE as a distantly supervised multi-instance learning problem, where models learn to summarize commonsense relations from a bag of images about an entity pair without any human annotation on image instances. To address the problem, CLEVER leverages vision-language pre-training models for deep understanding of each image in the bag, and selects informative instances from the bag to summarize commonsense entity relations via a novel contrastive attention mechanism. Comprehensive experimental results in held-out and human evaluation show that CLEVER can extract commonsense knowledge in promising quality, outperforming pre-trained language model-based methods by 3.9 AUC and 6.4 mAUC points. The predicted commonsense scores show strong correlation with human judgment with a 0.78 Spearman coefficient. Moreover, the extracted commonsense can also be grounded into images with reasonable interpretability. The data and codes can be obtained at https://github.com/thunlp/CLEVER.
A Comparative Evaluation of Visual Summarization Techniques for Event Sequences

Abstract Real‐world event sequences are often complex and heterogeneous, making it difficult to create meaningful visualizations using simple data aggregation and visual encoding techniques. Consequently, visualization researchers have developed numerous visual summarization techniques to generate concise overviews of sequential data. These techniques vary widely in terms of summary structures and contents, and currently there is a knowledge gap in understanding the effectiveness of these techniques. In this work, we present the design and results of an insight‐based crowdsourcing experiment evaluating three existing visual summarization techniques: CoreFlow, SentenTree, and Sequence Synopsis. We compare the visual summaries generated by these techniques across three tasks, on six datasets, at six levels of granularity. We analyze the effects of these variables on summary quality as rated by participants and completion time of the experiment tasks. Our analysis shows that Sequence Synopsis produces the highest‐quality visual summaries for all three tasks, but understanding Sequence Synopsis results also takes the longest time. We also find that the participants evaluate visual summary quality based on two aspects: content and interpretability. We discuss the implications of our findings on developing and evaluating new visual summarization techniques.
Enhancing the atomic correlation dynamics under the effects of Stark shift and Kerr medium through multi-photon transitions

Enhancing and preserving the atomic correlation and entanglement is of significant utility in quantum information. To this aim, we study the temporal evolution of uncertainty-induced nonlocality ([Formula: see text]) and logarithmic negativity ([Formula: see text]) as measures of quantum correlations (QCs) and quantum entanglement (QE) between two effective atoms coupled to a bosonic reservoir in the absence of thermal fluctuations and in the presence of Kerr medium (KM) and Stark shift (SS) under which n-photon transitions are permitted. We explore how Markovian and non-Markovian regimes affect the temporal dynamics of atomic correlation and entanglement and its limitations. Our findings indicate that by adjusting the KM and SS parameters, the quantum correlations between the two atoms can be enhanced and maintained while displaying similar qualitative behavior. Notably, it was observed that the QCs and QE quantities are at their highest magnitudes in the non-Markovian regime when the strengths of both SS and KM are increased, implying that QCs and QE are well protected. In contrast to the typical view that protecting the QCs from decoherence that may be observed owing to environmental noise, we proposed a gainful way to reduce the atomic decoherence by adjusting the number of n-photon transitions. Our investigation reveals that in the non-Markovian regime, the considered system exhibits better resistance against decoherence in comparison to the Markovian regime, as evidenced by the significant amount of quantum correlations detected among the two effective atoms at a specific point in time.
Exploratory Analysis of the Structural Evolution of public REST APIs

The software industry is in continuous evolution, forcing developers to quickly adapt to new requirements to catch up with the latest trends. A clear example is the huge demand for web-based APIs to connect all kinds of services among them. Like any other piece of software, a Web API is continuously changing, and with each change, all client applications must evolve. This adaptation process is critical and essential for software developers. In this paper, we present an exploratory evaluation of the most common changes occurring during the evolution of REST APIs. We define a taxonomy of structural API changes, which we classify according to their impact on client-side software; and propose a repository mining process to identify these changes in real Web APIs. We apply this process to a large set of Azure APIs from APISGURU, a well-known Open Source API repository. Based on the analyzed dataset from APISGURU, we found that breaking changes tend to decrease when a new version of an API from this dataset is released. Other useful findings and insights are discussed throughout the article.
On the Role of Perceptual Information in Image Classification

In this paper, the following hypothesis is validated—“Alternate image representations, of the same image, that preserve visual perception do not degrade the classification accuracy”. The underlying phenomenon that is exploited is the property of the human visual system to see an area in a digital image as a continuous region even though it is discrete. This is because the human eye senses energy per unit area rather than closely spaced individual pixels. In our current work, we use ‘Linear discriminant analysis’ (LDA) and ‘Convolutional neural networks’ (CNN) for classifying images and their alternate representations. The results suggest that the representation of data is immaterial and perceptual information actually classifies the images. The primary contribution of this paper is suggesting ‘feature space reduction while preserving perception’ as an alternative to ‘dimensionality reduction’. By using traditional methods of dimensionality reduction, perception-based information is lost. In the process, we develop the following: (1) An algorithm to convert 256 Gy level grayscale image to $$N$$ gray level grayscale image where $$N<256$$ such that the visual perception is preserved. The case of $$N=2$$ , i.e., binary image is particularly of interest to us because of its appealing applications. (2) An algorithm to define clustering efficiency based on density. The applications of reduced data size and search space include compression, transmission efficiency, big data handling, efficient storage, etc.
Pengembangan Ensiklopedia Digital Berbasis Keterampilan Proses dan Karakter pada Kelas V Tema Lingkungan Sahabat Kita

Development of a Digital Encyclopedia Based on Process Skills and Characters in Class V with the Theme of Our Friends Environment. Elementary School Teacher Education Study Program. Faculty of Teacher Training and Education, University of Muhammadiyah Purworejo. 2021. The background of this research is that there are still problems faced by students, namely regarding aspects of process skills that have not been implemented properly and characters that still need improvement. Learning during the pandemic is now all digital, so learning needs to be developed with interesting digital teaching materials, therefore a digital encyclopedia based on process and character skills is developed. The development of this research is aimed at (1) developing a digital encyclopedia, (2) knowing the feasibility of a digital encyclopedia, (3) knowing the practicality of digital encyclopedias at Kenteng State Elementary School, Purworejo District, class V for the 2020/2021 academic year. The research conducted is Research &amp; Development (R&amp;D). This model consists of 5 stages of development, namely ADDIE. The trial design is a limited trial in class V SD Negeri Kenteng. The data collection carried out were observation, interviews, questionnaires and documentation. The research instrument used validation sheets, student response sheets and learning implementation sheets. Data analysis using Percentage Agreement. The results of the research are: (1) the resulting Digital Encyclopedia Based on Process Skills and Character (2) the feasibility results of the Digital Encyclopedia Based on Process Skills and Character from three validators by two expert lecturers and an elementary school teacher obtaining an overall score of 3.7 with a very suitable category for use for learning. The reliable test obtained a percentage agreement of 93.25% with a very reliable category. (3) The response of students to the digital encyclopedia in a wider trial was obtained by a percentage of 91.1% in the very good category and (4) the implementation of the percentage of 98.46% in the very good category. Therefore, the Digital Encyclopedia Based on Process Skills and Character Class V Themes of Our Friends in Elementary Schools, which was developed in this study, was categorized as good and suitable for use in learning in elementary schools.
How to Find Social Robots exactly?

With the rapid development of artificial intelligence and natural language processing, there are more and more social robots applied in the social networks such as Twitter, intended to lead public opinion or crawling private information illegally. The problem of detection social robots, which is automated social accounts governed by artificial intelligence software, pretend to be a human user. There are some technologies proposed to detect the social robots automatically applied to the real social network for verification. Hence, conventional social robot detecting technologies proposed before are applied to detect by the account's metadata or account posted tweet content respectively. With the help of pre-trained language model such as BERT, this paper propose a deep neural network model based on contextual long short-term memory (LSTM) architecture named DeepBot, which exploits tweet content and account's metadata features. The architecture of DeepBot contains three phases: (1) it uses the pretrained model such as BERT to extract the embedding vector from the tweet content of the specific account, and (2) it choose more discriminative account metadata to extract a metadata vector, and then (3) it combines the auxiliary embedding vector and metadata vector into decoder layer to train a detecting model. What's more, in this paper, we review the labelling social robots datasets proposed in public, and get a mixture datasets of labelling social datasets to verify and compare the experimental results of our proposed DeepBot and other conventional methods. We also present empirical results of DeepBot and our ongoing experimentation with it, as we have gained experience applying it to the mixture labeling social robot dataset, including over 10000 accounts. The experimental results show that DeepBot outperforms previous state-of-the-art methods, with leveraging a small and interpretable set of features.
A phenomenological Research on Experiences of life in Reformatory of Juvenile Delinquents. - Focused on inmates of a male Juvenile Reformatory Under the Protective Dispositon No.10 -

This study was conducted with male protected boys who received No. 10 disposition, who were in the reformatory. Six participants were selected and through in-depth interviews the researchers examined what experiences they had in reformatory and what their experiences mean to them. The research method was analyzed through Giorgi’s phenomenological research procedure, a qualitative research method. As a result of the study, 4 essential elements and 15 sub-elements were derived. The four essential elements were ‘big change in living environment’, ‘conflict and adaptation’, ‘reflective reflection and change’, and ‘worries about new life’, Through these components, experiencing the process of adaptation to the reformatory, its accompanying conflicts and the need for a new start after being discharged from the reformatory were discovered as the subject of this study. Based on the results of this study, finally, it was intended to contribute to providing a more practical and systematic reformatory education program.
Sistem Informasi Pengelolaan Obat Dan Alat Kontrasepsi Berbasis Web Pada Dinas Pengendalian Penduduk Dan Keluarga Berencana Kota Jambi

Family planning services are one of the basic health services, so the Ministry of Health has established family planning as one of the mandatory health programs that must be supported. One of the supporting factors for improving the quality of family planning and reproductive health services is the availability of drugs and contraceptives in the family planning warehouse. For this reason, optimal management of drugs and contraceptives is required. The management system for drugs and contraceptives at the Jambi City Population Control and Family Planning Service is still done manually and using Microsoft Office Excel so that the management and reporting of drugs is still not optimal. This research produces a prototype that can be implemented further so as to produce a web-based drug and contraceptive management information system at the Jambi City Population Control and Family Planning Office.
A Study on Educational Satisfaction of a Web-Based Instruction in Digital Environment: Focusing on Accounting Education

본 연구에서는 ICT혁명이라 불리는 디지털 환경에 대응하기 위하여 경영대학 교육에도 정보화 네트워크의 연계가 필요한 상황에서 웹기반 회계교육을 수강한 대학생의 교육만족도 관계를 살펴보고 자기효능감의 매개효과를 규명하고자 변수들 간의 관계를 분석하였다. 이를 위해 웹기반 회계 교육 수강학생 296명으로부터 수집된 자료를 SPSS 26과 AMOS 26 프로그램으로 분석하였다. 본 연구의 결과는 첫째, 웹기반 회계교육이 교육만족도에 긍정적(+)인 영향을 미치고 있는지를 분석한 결과 웹기반 회계교육은 교육만족도에 긍정적(+)인 영향을 미치는 것으로 나타났다. 둘째, 웹기반 회계교육은 자기효능감에 유의한 영향을 미치지 못하는 것으로 밝혀졌다. 셋째, 자기효능감은 교육만족도에 긍정적(+) 인 영향을 미치는 것으로 나타났다. 넷째, 웹기반 회계교육이 교육만족도에 미치는 경로에서 자기효능감이 매개변수의 효과가 있는 것으로 나타났다. 이는 자기효능감이 교육만족도 향상에 중요한 변수라는 것을 시사하고 있다. This study aims to examine the mediating effect of self efficacy on the relationships between web-based accounting learning and education satisfaction in digital environment called ICT revolution. For this study, data collected from 296 students were analyzed using the SPSS 26 and AMOS 26 program. The results of this study revealed as follows. First, web-based accounting education has a direct effect on education satisfaction. Second, it was found that web-based accounting education did not directly affect self-efficacy. Third, it was found that self-efficacy directly affected educational satisfaction. Fourth, it was found that self-efficacy has a mediating effect in the relationship of web-based accounting learning and educational satisfaction. These results suggest that self-efficacy is an important variable in improving educational satisfaction.
Hey, Siri! Why Are You Biased against Women? (Student Abstract)

The intersection of pervasive technology and verbal communication has resulted in the creation of Automatic Speech Recognition Systems (ASRs), which automate the conversion of spontaneous speech into texts. ASR enables human-computer interactions through speech and is rapidly integrated into our daily lives. However, the research studies on current ASR technologies have reported unfulfilled social inclusivity and accentuated biases and stereotypes towards minorities. In this work, we provide a review of examples and evidence to demonstrate preexisting sexist behavior in ASR systems through a systematic review of research literature over the past five years. For each article, we also provide the ASR technology used, highlight specific instances of reported bias, discuss the impact of this bias on the female community, and suggest possible methods of mitigation. We believe this paper will provide insights into the harm that unchecked AI-powered technologies can have on a community by contributing to the growing body of research on this topic and underscoring the need for technological inclusivity for all demographics, especially women.
Disentangling the Benefits of Self-Supervised Learning to Deployment-Driven Downstream Tasks of Satellite Images (Student Abstract)

In this paper, we investigate the benefits of self-supervised learning (SSL) to downstream tasks of satellite images. Unlike common student academic projects, this work focuses on the advantages of the SSL for deployment-driven tasks which have specific scenarios with low or high-spatial resolution images. Our preliminary experiments demonstrate the robust benefits of the SSL trained by medium-resolution (10m) images to both low-resolution (100m) scene classification case (4.25%↑) and very high-resolution (5cm) aerial image segmentation case (1.96%↑), respectively.
Efficient Exploration in Resource-Restricted Reinforcement Learning

In many real-world applications of reinforcement learning (RL), performing actions requires consuming certain types of resources that are non-replenishable in each episode. Typical applications include robotic control with limited energy and video games with consumable items. In tasks with non-replenishable resources, we observe that popular RL methods such as soft actor critic suffer from poor sample efficiency. The major reason is that, they tend to exhaust resources fast and thus the subsequent exploration is severely restricted due to the absence of resources. To address this challenge, we first formalize the aforementioned problem as a resource-restricted reinforcement learning, and then propose a novel resource-aware exploration bonus (RAEB) to make reasonable usage of resources. An appealing feature of RAEB is that, it can significantly reduce unnecessary resource-consuming trials while effectively encouraging the agent to explore unvisited states. Experiments demonstrate that the proposed RAEB significantly outperforms state-of-the-art exploration strategies in resource-restricted reinforcement learning environments, improving the sample efficiency by up to an order of magnitude.
Prototypical Fine-Tuning: Towards Robust Performance under Varying Data Sizes

In this paper, we move towards combining large parametric models with non-parametric prototypical networks. We propose prototypical fine-tuning, a novel prototypical framework for fine-tuning pretrained language models (LM), which automatically learns a bias to improve predictive performance for varying data sizes, especially low-resource settings. Our prototypical fine-tuning approach can automatically adjust the model capacity according to the number of data points and the model's inherent attributes. Moreover, we propose four principles for effective prototype fine-tuning towards the optimal solution. Experimental results across various datasets show that our work achieves significant performance improvements under various low-resource settings, as well as comparable and usually better performances in high-resource scenarios.
Intuitive Access to Smartphone Settings Using Relevance Model Trained by Contrastive Learning

The more new features that are being added to smartphones, the harder it becomes for users to find them. This is because the feature names are usually short and there are just too many of them for the users to remember the exact words. The users are more comfortable asking contextual queries that describe the features they are looking for, but the standard term frequency-based search cannot process them. This paper presents a novel retrieval system for mobile features that accepts intuitive and contextual search queries. We trained a relevance model via contrastive learning from a pre-trained language model to perceive the contextual relevance between a query embedding and indexed mobile features. Also, to make it efficiently run on-device using minimal resources, we applied knowledge distillation to compress the model without degrading much performance. To verify the feasibility of our method, we collected test queries and conducted comparative experiments with the currently deployed search baselines. The results show that our system outperforms the others on contextual sentence queries and even on usual keyword-based queries.
Fixed-Weight Difference Target Propagation

Target Propagation (TP) is a biologically more plausible algorithm than the error backpropagation (BP) to train deep networks, and improving practicality of TP is an open issue. TP methods require the feedforward and feedback networks to form layer-wise autoencoders for propagating the target values generated at the output layer. However, this causes certain drawbacks; e.g., careful hyperparameter tuning is required to synchronize the feedforward and feedback training, and frequent updates of the feedback path are usually required than that of the feedforward path. Learning of the feedforward and feedback networks is sufficient to make TP methods capable of training, but is having these layer-wise autoencoders a necessary condition for TP to work? We answer this question by presenting Fixed-Weight Difference Target Propagation (FW-DTP) that keeps the feedback weights constant during training. We confirmed that this simple method, which naturally resolves the abovementioned problems of TP, can still deliver informative target values to hidden layers for a given task; indeed, FW-DTP consistently achieves higher test performance than a baseline, the Difference Target Propagation (DTP), on four classification datasets. We also present a novel propagation architecture that explains the exact form of the feedback function of DTP to analyze FW-DTP. Our code is available at https://github.com/TatsukichiShibuya/Fixed-Weight-Difference-Target-Propagation.
Everyone’s Voice Matters: Quantifying Annotation Disagreement Using Demographic Information

In NLP annotation, it is common to have multiple annotators label the text and then obtain the ground truth labels based on major annotators’ agreement. However, annotators are individuals with different backgrounds and various voices. When annotation tasks become subjective, such as detecting politeness, offense, and social norms, annotators’ voices differ and vary. Their diverse voices may represent the true distribution of people’s opinions on subjective matters. Therefore, it is crucial to study the disagreement from annotation to understand which content is controversial from the annotators. In our research, we extract disagreement labels from five subjective datasets, then fine-tune language models to predict annotators’ disagreement. Our results show that knowing annotators’ demographic information (e.g., gender, ethnicity, education level), in addition to the task text, helps predict the disagreement. To investigate the effect of annotators’ demographics on their disagreement level, we simulate different combinations of their artificial demographics and explore the variance of the prediction to distinguish the disagreement from the inherent controversy from text content and the disagreement in the annotators’ perspective. Overall, we propose an innovative disagreement prediction mechanism for better design of the annotation process that will achieve more accurate and inclusive results for NLP systems. Our code and dataset are publicly available.