Reviewerly/exHarmony huggingface dataset:
Visual examination of relations between known classes for deep neural network classifiers

Classification is the key task of deep learning. Among others, it is used in computer vision for object recognition, and in natural language processing for the masked language modeling. All of these tasks are crucial for modern automatic and semi-automatic production facilities powered by Automated Guided Vehicles (AGVs), because they enable intelligent inspection, maintenance log analysis and operation control. Especially in such safety-critical domains, in which humans and machines often coexist, it is crucial to provide new methods that could help us understand the operation of these black-boxes. That is why we present a novel visual-analytic methods that can be used to examine how networks perceive relations/similarities between the known classes. Our methods operate solely on the trained models and do not require any data samples. They can also reveal some quality issues in the training datasets and indicate low model accuracy. Our methods are suitable for generic vision and language models, but can also be used in transfer learning scenarios. To empirically validate our approach in such a scenario, we conduct experiments on a state-of-the-art mobile vision model - MobileNetV2 - fine-tuned for vehicle classification. We release a new dataset - UtilityVehicles - featuring images of various vehicles that can occur in industry. The presented use case is a vision-based application for Augmented Reality applications for smartphones and embedded devices for AGVs in automatic and semi-automatic production facilities. We release a GitHub repository with data and code: https://github.com/iitis/UtilityVehicles.
Developing and Evaluating a Model-Based Metric for Legal Question Answering Systems

In the complicated world of legal law, Question Answering (QA) systems only work if they can give correct, situation-aware, and logically sound answers. Traditional evaluation methods, which rely on superficial similarity measures, can’t catch the complex accuracy and reasoning needed in legal answers. This means that evaluation methods need to change completely. To fix the problems with current methods, this study presents a new model-based evaluation metric that is designed to work well with legal QA systems. We are looking into the basic ideas that are needed for this kind of metric, as well as the problems of putting it into practice in the real world, finding the right technological frameworks, creating good evaluation methods. We talk about a theory framework that is based on legal standards and computational linguistics. We also talk about how the metric was created and how it can be used in real life. Our results, which come from thorough tests, show that our suggested measure is better than existing ones. It is more reliable, accurate, and useful for judging legal quality assurance systems.
Towards Retention Analysis in NFT-based Blockchain Games

In this work, we investigate the player behavior of an NFT-based blockchain game from a perspective of attrition with the goal of providing insights as to how players interact with the game over a long period of time and establishing potential avenues for future research in building prediction models. To this end, we analyze the retention behavior over time and profile the players to gain insights about how they interact with the game.
Unvisited Out-Of-Town POI Recommendation with Simultaneous Learning of Multiple Regions

In recent years, Point Of Interest (POI) recommendations have been actively studied because of the widespread use of location-based social network services. Out-Of-Town POI recommendation methods recommend POIs outside a user’s residence, such as travel and business trip destinations. Most existing methods require a certain number of past visit sequences at destination regions or can be used only between two specific unidirectional regions. In this study, we propose an Unvisited Out-Of-Town POI Recommendation framework (UOPR), which can recommend POIs for Out-Of-Town regions that have not yet been visited by users. In addition, UOPR can learn users’ visit patterns for multiple regions simultaneously. UOPR takes into account the fact that user’s Out-Of-Town visiting tendencies vary depending on the geographical distance from their residences. It therefore adopts an approach that calculates two scores, one determined by user’s Home-Town visiting tendencies and the other by their Out-Of-Town visiting tendencies, and uses them differently. Furthermore, we propose a mask-input learning and user-embedding creation method that enables learning with enhanced interaction between POI embeddings in an Out-Of-Town POI recommendation environment, where the data are often sparse. UOPR achieves higher recommendation accuracy than existing methods on a dataset collected on a real-world service.
Quantum Computing for IoT Security

As we approach Web 4.0, which relates to the uprising of internet of things (IoT)-enabled smart manufacturing devices that serve as data generator devices for companies, the responsibility of managing and securing this data is an overhead. Since the data can contain sensitive information, it is vulnerable to cyber-attacks. As advanced encryption-decryption cryptography algorithms are deficient to handle the situation, the uprising of quantum computing is making today's security mechanisms imperiled. Therefore, a quantum wall (QW) needs to be deployed for key generation which can resist potential attacks. Quantum-inspired IoT (QIoT) implies harnessing power imparted by quantum computing (QC) to secure IoT applications. It promises secure management of data throughout its life cycle. This chapter summarizes the recent relevant work conducted in this domain by conducting an intensive literature review around IoT, security implications, and how QC is making an impact in mitigating the cyber risks involved in data transfers in IoT.
Metric Optimization and Mainstream Bias Mitigation in Recommender Systems

Recommender Systems have drawn extensive attention in recent decades. The most important factor to make a recommender succeed is user satisfaction, which is largely reflected by the recommendation accuracy. In accordance, this thesis dives into recommendation accuracy from two different perspectives that, unfortunately, are at tension with each other: achieving the maximum overall recommendation accuracy, and balancing that accuracy among all users. The first part of this thesis focuses on maximizing the overall recommendation accuracy Li et al. [2021a]. This accuracy is usually evaluated with some user-oriented metrics tailored to the recommendation scenario. However, recommendation models could be trained to maximize some other generic criteria that do not necessarily align with the criteria ultimately captured by the metric above. Recent research usually assumes that the metric used for evaluation is also the metric used for training. We challenge this assumption, mainly because some metrics are more informative than others. Indeed, we show that models trained via the optimization of a loss inspired by Rank-Biased Precision (RBP) tend to yield higher accuracy, even when accuracy is measured with metrics other than RBP. However, the superiority of this RBP-inspired loss stems from further benefiting users who are already well-served, rather than helping those who are not. This observation inspires the second part of this thesis, where our focus turns to helping nonmainstream users, who are difficult to recommend to either because of the lack of data for effective modeling or a niche taste that is hard to match similar users. Our first effort consists in using side data, beyond the user-item interaction matrix, so that users and items are better represented. This will be of benefit especially for the non-mainstream users, for which the user-item matrix alone is ineffective. We propose Neural AutoEncoder Collaborative Filtering (NAECF) Li et al. [2021b], an adversarial learning architecture that, in addition to maximizing the recommendation accuracy, leverages side data to preserve the intrinsic properties of users and items and show that NAECF leads to better recommendations specially for non-mainstream users, while at the same time there is a marginal loss for the mainstream ones. Our second effort Li et al. [2023] consists in explicitly focusing more on non-mainstream users in a recommendation model. In particular, we propose a mechanism based on cost-sensitive learning that weighs users according to their mainstreamness, so that they get more attention during training. The result is a recommendation model tailored to non-mainstream users, that narrows the accuracy gap, and again at negligible cost to the mainstream users. Awarded by : Delft University of Technology, Delft, The Netherlands on 14 November 2023. Supervised by : Alan Hanjalic and Julián Urbano. Available at : https://doi.org/10.4233/uuid:235a5f5c-5e18-4e67-a8e6-84a7e310db12.
Letter from the Chair

Greetings SIGIR Members! SIGIR in Taipei was a success. A big thank you to the general chairs Hsin-Hsi Chen, Wei-Jou (Edward) Duh , and Hen-Hsen Huang , for putting on a great conference, in a beautiful venue. Thank you also to the Program Chairs Makoto Kato, Josiane Mothe and Barbara Poblete for the excellent technical programming! We would like to congratulate the Best Paper winners Maik Fröbe, Jan Heinrich Reimer, Sean MacAvaney, Niklas Deckers, Simon Reich, Janek Bevendorff, Benno Stein, Matthias Hagen , and Martin Potthast for their paper The Information Retrieval Experiment Platform. We would also like to congratulate Alireza Salemi, Juan Altmayer Pizzorno , and Hamed Zamani whose paper A Symmetric Dual Encoding Dense Retrieval Framework for Knowledge-Intensive Visual Question Answering won the Best Student Paper Award at SIGIR this year. Preparations for SIGIR 2024 in Washington DC are well underway, as preparations for SIGIR 2025 in Padua are beginning.
Making Worlds in the Pluriverse Panel at Congress: “Challenges in the Pluriverse”

These are select transcripts from a moderated panel titled “Making Worlds in the Pluriverse” held as part of Congress of the Humanities and Social Sciences 2023 at York University in Toronto.
Influence of Health Promotion Through Media on Students' Knowledge About TB Disease in State Middle Schools in Makassar City

TB treatment is a long-term type of treatment. Usually the duration of treatment can last 3-9 months. During treatment, sufferers must be diligent and disciplined in taking medication and regularly check with the doctor to ensure the progress of treatment until the sufferer recovers completely. The aim of this research is to determine the effect of health promotion through the media on students' knowledge about TB disease in Makassar City State Middle Schools in 2020.&#x0D; The type of research used was a quasi experimental study with a pretest posttest group with control design where the sample members in the intervention and control groups were carried out using purposive sampling, the sample size was 70 students with 35 students in each group. Results: There were differences in knowledge about TB disease in students before and after being given health promotion in both the intervention group (p= 0.000) and the control group (p= 0.036). Health promotion through the media gave higher results, namely 50.33 compared to leaflet media 28.67 with a knowledge difference of 21.66. Conclusion: The results of the study show that media is more influential than leaflet media on students' knowledge about TB disease in Makassar City State Middle Schools.
Meaning of storytelling in volunteers' experiences at Pabukon Ngadongeng Community Reading Park

The Pabukon Ngadongeng Community Reading Park is a library that has storytelling services to enhance children's literacy and cultural knowledge. Volunteers assisted the Community Reading Park to develop children's creativity and imagination with stories. This research aimed to determine the meaning of storytelling among volunteers at the Pabukon Ngadongeng Community Reading Park by analyzing the experience and meaning of storytelling. The research method used qualitative with a phenomenology approach. The study result showed that the experience of storytelling for volunteers consisted of expanding children's story reading, interaction with readers, language development, cultivating imagination, creativity, entertainment, and sharing knowledge. The meaning of storytelling in the volunteers consisted of volunteers' motivation, the impact of storytelling on volunteers, the impact of storytelling on the relationship with readers, contributions of storytelling to volunteers' personal development, and the involvement of volunteers in the development of the Community Reading Park. It also included challenges and obstacles faced by volunteers in storytelling, volunteers' perception of the impact of storytelling on children's education and literacy, meaningful storytelling, teaching without preaching and preserving cultural stories. In conclusion, volunteers have assisted Pabukon Ngadongeng Community Reading Park with storytelling services for children. Children have imagination and creativity, and their language development improves. Volunteers provide fun learning experiences for children through storytelling, including practicing reading and writing literacy programs for children.
Ellis's model of student information-seeking behavior on Google Scholar

Information seeking leads to interactions within a system. At the same time, interaction patterns can potentially lead to a behavioral tendency and form a unique behavioral pattern. This research aimed to find out how information search behavior was carried out by students of the Library and Information Science Study Program in conducting literature searches on the Google Scholar platform by applying the stages of the Ellis Model. The research method used quantitative with a survey method. The results showed that at the starting stage, the correlation coefficient was moderate; at the chaining stage, the correlation coefficient was moderate; at the browsing stage, the correlation coefficient was below moderate; at the monitoring stage, the correlation coefficient was below moderate; at the differentiating stage, the correlation coefficient was moderate; and at the extracting stage, the correlation coefficient was below moderate. This study concludes that students know the topic they want to search before using Google Scholar. Chaining, browsing, and monitoring are done alternately at almost the same time. Students have observed the correlation coefficient numbers at the chaining, browsing, and monitoring stages; students pay more attention to the chaining stage, namely obtaining keywords. At the differentiating stage, students have begun to filter incoming information and use their ability to distinguish the sources of information obtained, and at the extracting stage, students work through the selected sources to identify all relevant materials.
Implementation of risk management in library information system at Surabaya City Library

Libraries may be at risk of data security issues. The threat of data loss or data damage is prone to occur in libraries. Information technology embedded in libraries has developed Artificial Intelligence (AI) for information retrieval systems. The application of information technology in the Surabaya City Library has potential risks to data security and force majeure aspects so that need a disaster mitigation plant. This study aimed to implement information system risk management at Surabaya City Libraries as a disaster mitigation plan. The research method was descriptive qualitative. Based on the risk analysis, the recommendations for applying risk to information technology Surabaya City Library were elimination and engineering. The manifestation of these two recommendations made the data access protocol use multiple authentications to avoid data theft attempts due to infiltration, hacking, and cracking. Study results recommended a protocol for each data access. The protocol would save every change, deletion, and system login in the log history. Implementing data authentication twice per login can avoid data theft caused by the intrusion. This activity log is used for activity tracking when the system is infiltrated. The limitation of this study lies in the scope of the research object, namely the library, which has limitations on the system or application used and does not impact the wider community. Conclusion of research, the library can implement risk management strategy with elimination and engineering models through recording system login activities, which mitigate the risk of data breaches originating from insiders.
Optimalkan Pelayanan Kesehatan Masyarakat: Pemahaman Mendalam Tentang Rekam Medis Elektronik Bagi Petugas Rekam Medis Di Puskesmas Tabunganen

This community service aims to optimize healthcare services by enhancing the knowledge of medical record officers at Puskesmas Tabunganen regarding electronic medical records. In order to achieve this goal, a profound understanding of EMR becomes a crucial key, particularly for medical record officers. By comprehensively understanding the concepts and implementation of EMR, medical record officers at Puskesmas Tabunganen are expected to improve the efficiency of health data management. This community service makes a positive contribution to preparing the health center for the implementation of electronic medical records. This community service has a positive impact on preparing Puskesmas Tabunganen for the implementation of electronic medical records.
Peran Penting Rekam Medis Elektronik Dalam Transformasi Digital Di Puskesmas Tabunganen

This community service aims to enhance the knowledge of medical record officers regarding electronic medical records. Electronic medical records have been adopted in community health centers as a technological innovation to optimize public health services. A profound understanding of EHR by medical record officers is crucial in improving the efficiency and effectiveness of health services. Community health centers face the risk of lagging behind in EHR implementation due to their large scale and small number. This community service provides a positive contribution in preparing Tabunganen Community Health Center to implement electronic medical records.
AutoClass: AutoML for Data Stream Classification

Automated Machine Learning (autoML) is a novel topic that aims to tackle the parameter configuration issue using automatic monitoring models and comprises different machine learning tasks, such as feature selection, model selection, and hyper-parameter tuning. It makes easier use of algorithms for non-ML experts as well as ML experts by automating tasks that rely on expert domain knowledge. Nevertheless, autoML is in its infancy stage and not well explored yet in the offline and stream settings. In this paper, we propose automated Classification (auto-Class) method for automated algorithm selection and configuration for data stream classification. AutoClass consists of training an ensemble of different tuned configurations and selecting the best-performing configuration to do the prediction. We present experiments performed on a diverse set of real and artificial datasets and show how our proposed approach can outperform the performance of competitive state-of-the-art ensemble and single-based methods.
Hubungan antara Keterampilan Literasi Awal dengan Kemampuan Bahasa Pragmatik pada Anak Umur Prasekolah di Taman Kanak-Kanak Marsudirini Surakarta

ABSTRACT&#x0D; Background: In 2018, Indonesia is still faced with child development problems. There are around 56.34% of preschoolers experiencing delays in developments such as reading and writing. The development of literacy is important to pay attention to and in early childhood development, language really needs to be developed considering the importance of language for all of us. Viewed from the aspect of language, pragmatics is the highest stage of language, pragmatics is a branch of linguistics that discusses what is included in the structure of language as a means of communication between speakers and listeners. Objective: This research aims to find out the correlation between early literacy skills and pragmatic language skills in preschool-aged children in TK Marsudirini Surakarta. Method: The type of research used is quantitative. The research design to be used is a correlational study. The sampling technique that will be used in this research is purposive sampling. The number of samples that have been used in this research were 30 respondents. Result: The results showed that 6 respondents (20%) were in the early literacy skills category, the sufficient category was 11 respondents (36.7%), and the good category was 13 respondents (43.3%). Respondents in pragmatic language skills in the unable category were 14 respondents (46.7%) and in the able category were 16 respondents (53.3%). Bivariate analysis used the Spearman Rank statistical test with a ???? value of 0.001 and a correlation coefficient of 0.732. This shows that the direction of the correlation between the two variables is positive, which means that if one variable increases, the other variable will develop. Conclusion: There is a correlation between early literacy skills and pragmatic language skills in preschool-aged children in TK Marsudirini Surakarta with a strong correlation strength.&#x0D; Keywords: Literacy Skills, Pragmatic Language, Preshool Children
Hubungan antara Fase Merangkak dan Kemampuan Pragmatik Anak Usia Prasekolah di TK Dharma Wanita Nambakan Kediri

Abstract Background: Gross motor skills greatly affect the development of children. If a child experiences motor delays, then the child will experience developmental and growth delays (Permana, 2013). In minimizing errors in meaning (miss understanding), children need to develop pragmatic aspects so that their language skills develop properly (Sari, 2021). Objective: This study aims to determine the relationship between the crawling phase and pragmatic abilities in preschool-age children at Dharma Wanita Nambakan Kindergarten, Kediri. Method: This type of quantitative research with a cross sectional design. The data collection technique uses total sampling with a sample of 30 respondents. The data that has been collected will be analyzed univariately and bivariately using the Spearman Rank statistical test. Research Outcome: Based on the results of Spearman's Rank analysis test, the value of ρ (sig.) is 0.036 or the value of ρ (sig.) &lt; 0.05, then Ha (alternative hypothesis) is accepted, indicating that there is a relationship between the paying phase and children's pragmatic abilities. The strength of the correlation or relationship between the two variables is in the range of 0.2 to &lt; 0.4, which is included in the weak category. Conclusion: There is a relationship between the crawling phase and the pragmatic abilities of preschoolers at Dharma Wanita Nambakan Kindergarten, Kediri. Keywords: Crawling Phase, Pragmatic Ability, Preschool Aged Children