Reviewerly/exHarmony huggingface dataset:
Moderating borderline content while respecting fundamental values

Abstract As efforts to identify and remove online terrorist and violent extremist content have intensified, concern has also grown about so‐called lawful but awful content. Various options have been touted for reducing the visibility of this borderline content, including removing it from search and recommendation algorithms, downranking it and redirecting those who search for it. This article contributes to this discussion by considering the moderation of such content, in terms of three sets of values. First, definitional clarity. This is necessary to provide users with fair warning of what content is liable to moderation and to place limits on the discretion of content moderators. Yet, at present, definitions of borderline content are vague and imprecise. Second, necessity and proportionality. While downranking and removal from search and recommender algorithms should be distinguished from deplatforming, tech companies' efforts to deamplify borderline content give rise to many of the same concerns as content removal and account shutdowns. Third, transparency. While a number of platforms now publish their content moderation policies and transparency data reports, these largely focus on violative, not borderline content. Moreover, there remain questions around access to data for independent researchers and transparency at the level of the individual user.
QRDP: A System that Facilitates the Selection of English Materials for Translator Education

We are currently developing a system that facilitates teachers of translation to evaluate and select suitable document segments as materials for translator training. The system needs to satisfy several requirements. Firstly, because translation is an act that deals with documents and not with texts in linguistics, it takes into account documentational features. Secondly, it should facilitate teachers to select suitable documentational segments that usually consist of several paragraphs, as translation generally requires a long time, especially for trainees. Thirdly, it should facilitate teachers to compare different segments in the same document or from different documents, because teachers often need to use more than one material in a class or use progressively advanced materials in a course. The system should preferably relate documentational features to translation competences. These requirements make the system different from most existing text analysis systems. In the demo, we show the trial version of the system, explaining the core functionalities together with the background design concepts.
Enhancing the Learning of Chinese Poem Creation Through an Auto-Generation and Evaluation System

Creating and evaluating Chinese poems is challenging for learners. To address this, we propose an auto-generation model and evaluation system to assist learners in poem creation and provide feedback on constraint adherence. We conducted a user experiment with 16 participants and generated up to 100 poems per keyword using 29 random keywords. The proposed system significantly improved poem completeness and usability, but did not enhance creation satisfaction. Users found it easy to confirm adherence to poem rules, but that verifying rhymes presented difficulties. The participants expressed overall satisfaction with the process, and the poems created were perceived as fluent and thematically consistent. However, limitations in keyword usage hinder emotional expression. This study highlights the need to overcome these challenges in order to facilitate learners’ creation and evaluation of Chinese poems.
Applying a Vector Search Method in Reference Service Question-Answer Retrieval Systems

We have developed a reference service question-answer retrieval system prototype based on dense vector search technology. When librarians receive reference questions, they can utilize reference service question-answer systems to determine if similar questions have been asked before and how they were resolved. Readers can also search the system to see if their questions have been answered. However, traditional reference service question-answer retrieval systems primarily rely on keyword-based searches, which may not capture questions with different phrasing but identical meaning. Vector search has emerged as a promising approach to address this limitation, and it has been widely researched and applied in various domains. Nevertheless, there is a lack of research on applying vector search in reference service question-answer retrieval systems. Thus, we conducted initial experiments in this area. Leveraging ElasticSearch vector search technology, which can efficiently search through large datasets, we adopted this technology in our study. For building our system, we collected question-answer pairs from the Taipei Public Library’s Online Reference Service Platform, one of Taiwan’s most prominent online reference services. We report the data set we used and the system architecture.
A survey of episode mining

Abstract Episode mining is a research area in data mining, where the aim is to discover interesting episodes, that is, subsequences of events, in an event sequence. The most popular episode‐mining task is frequent episode mining (FEM), which consists of identifying episodes that appear frequently in an event sequence, but this task has also been extended in various ways. It was shown that episode mining can reveal insightful patterns for numerous applications such as web stream analysis, network fault management, and cybersecurity, and that episodes can be useful for prediction. Episode mining is an active research area, and there have been numerous advances in the field over the last 25 years. However, due to the rapid evolution of the pattern mining field, there is no prior study that summarizes and gives a detailed overview of this field. The contribution of this article is to fill this gap by presenting an up‐to‐date survey that provides an introduction to episode mining and an overview of recent developments and research opportunities. This advanced review first gives an introduction to the field of episode mining and the first algorithms. Then, the main concepts used in these algorithms are explained. After that, several recent studies are reviewed that have addressed some limitations of these algorithms and proposed novel solutions to overcome them. Finally, the paper lists some possible extensions of the existing frameworks to mine more meaningful patterns and presents some possible orientations for future work that may contribute to the evolution of the episode mining field. This article is categorized under: Algorithmic Development &gt; Spatial and Temporal Data Mining Algorithmic Development &gt; Association Rules Technologies &gt; Association Rules
Automatically Detecting References from the Scholarly Literature to Records in Archives

Scholars use references in books and articles to materials found in archives as one way of finding those materials, but present systems for archival access do not exploit that information. To change that, the first step is to find archival references in the scholarly literature; that is the focus of this paper. Several classifier designs are compared using a few thousand manually annotated footnotes and endnotes assembled from a large set of open access papers on history. The results indicate that fairly high recall and precision can be achieved.
Evaluating the Use of Generative LLMs for Intralingual Diachronic Translation of Middle-Polish Texts into Contemporary Polish

This paper presents efforts towards creating a tool for translating texts from Middle Polish into modern Polish. Archaic texts sourced from the CBDU digital library were translated into modern language using ChatGPT and the resulting parallel corpus was used to train a neural text-to-text model. We assessed the results using automatic metrics and performed human evaluation of translations of the best-performing model and ChatGPT. Even though the performance of the trained models was far from perfect, the quality of translations produced with ChatGPT was good in most cases. Although caution should be exercised, we believe that LLMs have a high potential for text-to-text annotation applications.
TweetVi: A Tweet Visualisation Dashboard for Automatic Topic Classification and Sentiment Analysis

Social media has witnessed a remarkable surge in popularity, serving as a platform where individuals express their views on crucial subjects, including politics, infectious diseases, social movements, and security issues. This profusion of readily accessible information empowers decision-makers and stakeholders to shape policies and strategies grounded in statistical insights into societal dynamics. To facilitate an in-depth collaborative analysis of social trends and public sentiment, we developed TweetVi - an interactive dashboard. Within the interactive dashboard, we’ve developed a pipeline encompassing three machine-learning components: language detection, spam detection, and sentiment analysis. This collaborative platform further provides analytical results on topical sentiments and trends, fostering a deeper understanding of the social landscape. The TweetVi application empowers users to gain valuable insights into evolving social narratives and sentiments, facilitating evidence-based decision-making in a dynamic online environment.
An Empirical Analysis of Newcomers’ Contributions to Software-Engineering Conferences

Newcomer researchers play a key role in advancing research: They introduce new ideas and perspectives, have a high motivation, and can positively impact the performance of long-lasting teams. However, newcomers face obstacles when engaging in research—some of which they can overcome based on learning and mentoring (e.g., using research methods, scientific writing), but also potential biases of other researchers or unfair barriers (e.g., gate keeping, perceived expertise). In this paper, we report a study on newcomers’ contributions to three major software-engineering conferences, and what these contributions may indicate regarding potential obstacles. Precisely, we investigated to what extent newcomers contributed to the main tracks of the highly reputable software-engineering conferences ASE, ESEC/FSE, and ICSE, analyzing a total of 4,620 papers and 7,337 authors. Furthermore, we investigated whether the reviewing model impacted the extent of newcomers’ contributions, since all three conferences recently switched from single-blind to double-blind reviewing. The results indicate a decline in newcomer researchers contributing to the conferences, a trend that somewhat stabilized in recent years at a fortunately high level (i.e., more than 50% of authors for all conferences). Furthermore, for ICSE, we found an indicator that the changed reviewing model mitigated the declining trend, but this was not visible for the other conferences, and that more newcomers are involved in high-reputation papers.
Dewey’s Notion of Intelligent Habit as a Basis for Ethical Assessment of Technology

Abstract This paper discusses how John Dewey’s notion of intelligent habit could contribute to technology ethics. For Dewey, intelligent (i.e., desirable) habits are reflective – arising from inquiry into the appropriate courses of action in each situation – and flexible – easily adaptable to the changing circumstances. We should strive to develop intelligent habits as they are the best tools for the achievement of our goals and are necessary for individual and societal flourishing. I argue that Dewey’s notion of intelligent habit makes it possible to identify the impact of technology on users’ identity and self-determination, advocate for the extension of user choice and malleability of technologies, and analyse the connections between the epistemic dimension of technology and users’ ability to flourish.
Light-Occlusion Text Entry in Mixed Reality

AbstractText entry is a recurring task in mixed reality (MR) applications, and the ability of eyes-free text entry methods to allow users to enter text without focusing on the input device is ideal and compelling. However, existing eyes-free text entry methods leave much to be desired regarding efficiency and accuracy. In this paper, we propose a new light-occlusion text entry method in MR environment that uses dual thumb typing on a touchscreen. We design a partially visible keyboard as visual feedback to improve user performance. In addition, we optimize the underlying keyboard by collecting eyes-free typing data through a user study. The results show that our method has high typing speed, low error rate, and is very novice-friendly. After a short training period, the average typing speed of the novice group can reach 26.23 WPM (words per minute), while the average typing speed of the potential expert group can reach 30.62 WPM.Keywords: Human-computer interactionmixed realitytext entrytouchscreenocclusion Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work is supported by the National Natural Science Foundation of China through Project [61932003, 62372026], by Beijing Science and Technology Plan Project [Z221100007722004], and by National Key R&D plan [2019YFC1521102].Notes on contributorsAoxin SunAoxin Sun is a master student in the School of Computer Science and Engineering of Beihang University, China. His current research focuses on virtual reality, augmented reality, and HCI.Lili WangLili Wang received her PhD degree from Beihang University, Beijing, China. She is a professor with the School of Computer Science and Engineering of Beihang University and a researcher with the State Key Laboratory of Virtual Reality Technology and Systems. Her interests include virtual reality, real-time rendering and HCI.JiaYe LengJiaYe Leng is a PhD student in the School of Creative Media, City University of HongKong, China. His current research focuses on virtual reality, augmented reality, and HCI.Sio Kei ImSio Kei Im received his PhD degree from Queen Mary University of London, United Kingdom. He is a professor at the Faculty of Applied Sciences, Macau Polytechnic University. His research interests include video coding, image processing and machine learning.
An Integrated Model of Customers’ Intention to Reuse Information Service: What’s New for Conversational Agents?

The application of conversational agents empowered by artificial intelligence (AI) is becoming more common among tourism and hospitality businesses. Past research on AI has mostly investigated embodied robots rather than disembodied conversational agents. This study investigates the determinants of customers’ intention to reuse chatbots through integrating three different perspectives: technology acceptance model, computer-mediated communication theories, and interpersonal communication theories. Using online self-reported data analyzed by hierarchical regression and multigroup structural equation modeling, the efficacy of different theoretical models in predicting chatbot adoption were compared. A separate data set was collected from instant messaging (IM) users to provide implications that further explain the distinction between disembodied conversational agents and human conversational partners. Results suggest that variables associated with TAM and interpersonal attraction exert greatest influence on customers’ trust and reuse intention toward conversational agents. Managerial implications are suggested for practitioners to improve chatbot and IM design.
Organic Matter Removal of Landfill Leachate Using Integrated Electrocoagulation-Ultrasonic Method

The utilization of hybrid treatment technologies has been recognized as one of the treatment methods conducted to enhance the efficiency of wastewater treatment due to the various challenges such as the high concentration of pollutants and the limited ability of a single treatment process. Leachate from Pulau Burung Sanitary Landfill (PBSL) has been categorized as poor biodegradability leachate and it is composed of high dissolved organic matter, inorganic components, and toxic substances. In this study, an integrated treatment between electrocoagulation (EC) and ultrasonic (US) was introduced to evaluate the effectiveness of the hybrid treatment process in landfill leachate treatment of organic matter removal in terms of COD compared with a single treatment with different operating conditions of voltage, treatment time, and distance of electrode. Other than that, the effect of the control factors on the weight loss of electrodes was also investigated. The maximum COD reduction from both single EC and combined EC-US methods have achieved the same best operating condition which are potential of 10 V, treatment time of 25 min, and electrode distance of 2 cm, but the combined EC-US treatment (61.13%) provide better removal of COD compared with the treatment process of a single EC method (37.51%). On the other hand, the highest weight loss of the electrode was observed at the anode (Al-21.60%) with the same operating conditions for the best removal efficiency in the EC-US process. It is found that the cathode (Fe) experienced a slight increase in the final weight of the electrode due to the hydrolyzation reaction. Hence, this research work demonstrated that the utilization of combined EC-US treatment significantly enhanced the removal of COD from leachate compared with the single EC method.
The Impact of MR on Preservice Teachers’ Creativity and Performance in Instructive Task Designs

This article explores whether there are significant differences in the creativity level of future teachers in teaching design. In virtual reality (VR) and mixed reality (MR) experimental teaching environments, the K-DOCS creative power scale, the flow state scale, and the expert group assessment scale were used to investigate the mediation effect of flow, attention, and meditation on the creativity level. The results indicate that in the MR experimental teaching environment, the three factors have a moderate mediating effect on the creativity level of future teachers (n = 65), and meditation has a certain regulating effect on heart flow (0.175*); In the VR experimental teaching environment (n = 67), only the factor of heart flow has a moderate promotion effect on the creativity level of future teachers (0.822***). The study results indicate that the quality of future teachers' creative instructional design in the MR environment is higher than that in the VR environment. In addition more MR experimental teaching environments should be provided to cultivate the creativity level of future teachers in teaching design.
Improving 2D Scanning Radar and 3D Lidar Calibration

Sensor fusion in mobile robots requires proper extrinsic and intrinsic sensor calibration. Robots in the search and rescue robotics domain are often equipped with multiple range sensor modalities such as radar and lidar due to the harsh environmental conditions. This article presents a method to easily calibrate a 2D scanning radar and a 3D lidar without the use of special calibration targets. Therefore, it focuses on the improvement of the feature extraction from the environment by applying filtering algorithms to remove noise and improve the signal-to-noise ratio. Additionally, a second optimization stage is introduced to propagate measurement uncertainties of the lidar to the calibration result. The results are compared to the previous version of the algorithm as well as to the ground truth parameters. Furthermore, statistical tests are performed to confirm the validity of the calibration results.
Critical datafication literacy – a framework for educating about datafication

Purpose In light of a need for more critical education about datafication, this paper aims to develop a framework for critical datafication literacy that is grounded in theoretical and empirical research. The framework draws upon existing critical data literacies, an in-depth analysis of three well-established educational approaches – media literacy, the German “(politische) Bildung” and Freirean “critical pedagogy” – and empirical analyses of online educational resources about datafication. Design/methodology/approach The study interconnects theoretical analyses with an empirical mixed methods investigation that includes expert interviews with creators of online educational resources about datafication and a qualitative survey with educators interested in teaching about data technologies. Findings The research identified novel findings on the goals of resource creators and educators, such as a focus on empowering and emancipatory approaches, fostering systemic understanding of datafication and encouraging collective action. Such perspectives are rare in existing critical data literacy conceptualisations but show resemblance to traditional education scholarship. This highlights how much can be learnt from practitioners and from these more established educational approaches. Based on these findings, a framework for critical datafication literacy is suggested that aims for systemic understanding of datafication, encouraging critical thinking and enabling learners to make enlightened choices and take different forms of action. Originality/value The study is unique in its interconnection of theoretical and empirical research, and it advances previous research by suggesting a grounded framework for critical datafication literacy.
Neuron Attribution-Based Attacks Fooling Object Detectors

In this work, we propose a neural attribution-based attack (NAA) to improve the transferability of adversarial examples, aiming at deceiving object detectors with different backbones or architectures. To measure the neuron attribution (importance) for a CNN layer of detector, we sum the classification scores of all positive proposal boxes to calculate the integrated attention (IA), then get the neuron attribution matrix via element-wise multiplying IA with the feature difference between the clean image be attacked and a black image. Considering that the summation may bias importance values of some neurons, a mask is designed to drop out some neurons. The proposed loss calculated from the rest of neurons is minimized to generated adversarial examples. Since our attack disturbs the upstream feature outputs, it effectively disorders the outputs of downstream tasks, such as box regression and classification, and finally fool the detector. Extensive experiments on PASCAL VOC and COCO dataset demonstrate that our method achieves better transferability compared to the state-of-the-arts.
Market Place Application Design PTN_BH UNP

Regulation Government No. 114 of 2021 concerning Agency College _ Law University Padang State (UNP), on November 25, 2021 already official changed status to college high PTN-BH so that UNP can increase quality education . Government give autonomy to on UNP for increase its income generating . Since status service general (BLU), UNP has start commercialize means and existing infrastructure _ for Public general while facility that no disturb activity academic like pool swimming , building meeting , hotel, laboratory even so through activity training (CaKep , Pekerti). Commercialization services at UNP are still using the conventional system . Weakness existing system _ now is no all UNP Income Generating recorded by system , party user often experience difficulty for place an order because not yet existence facility service one door good physique nor integrated system . _ Difficulty user for make an order that is experienced during this because number contact personal contact no can direct obtained . UNP can maximizing Income Generating and enhancement effectiveness service to user through take advantage of digital technology and already should has its own marketplace platform . Article this aim for give ideas and the idea of designing a market place for PTN_BH UNP.