Reviewerly/exHarmony huggingface dataset:
Dehazing, enhancing the boundaries and corners in hazed images using optimal adaptive technique

Authors:
raw_author_name: Buvanesh Pandian V
raw_affiliation_string: EEE Department, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu, India
raw_author_name: T Arun Prasath
raw_affiliation_string: Biomedical Department, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu, India
raw_author_name: M. Pallikonda Rajasekaran
raw_affiliation_string: ECE Department, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu, India-

Abstract: Objects or images captured from the remote area will not have good quality and will not be visible because of mist, smoke, moisture and fog. Here, we rub/remove the unwanted hazes from the image using the optimal adaptive technique. An examination of the built-in boundary constraint of the transmission function is extremely valuable to our method. This constraint is handled as an optimisation problem to predict the unknown scene transmission. In addition, transmission refining techniques are used to remedy the issue based on variable splitting methodology. With only a few broad assumptions, the proposed method may recover haze-free, high-quality images having original colours and better image information. Experiment findings are done with plenty of foggy images indicating the efficiency and effectiveness of the proposed technique by using image quality parameters.
Finite impulse response design based on two‐level transpose Vedic multiplier for medical image noise reduction

Authors:
raw_author_name: Joghee Prasad
raw_affiliation_string: Department of ECE KPR Institute of Engineering and Technology Coimbatore India
raw_author_name: Arun Sekar Rajasekaran
raw_affiliation_string: Department of ECE SR University Warangal Telangana India
raw_author_name: J. Ajayan
raw_affiliation_string: Department of ECE SR University Warangal Telangana India
raw_author_name: Kambatty Bojan Gurumoorthy
raw_affiliation_string: Department of ECE KPR Institute of Engineering and Technology Coimbatore India

Abstract: Abstract Medical signal processing requires noise and interference‐free inputs for precise segregation and classification operations. However, sensing and transmitting wireless media/devices generate noise that results in signal tampering in feature extractions. To address these issues, this article introduces a finite impulse response design based on a two‐level transpose Vedic multiplier. The proposed architecture identifies the zero‐noise impulse across the varying sensing intervals. In this process, the first level is the process of transpose array operations with equalization implemented to achieve zero noise at any sensed interval. This transpose occurs between successive array representations of the input with continuity. If the continuity is unavailable, then the noise interruption is considerable and results in signal tampering. The second level of the Vedic multiplier is to optimize the transpose speed for zero‐noise segregation. This is performed independently for the zero‐ and nonzero‐noise intervals. Finally, the finite impulse response is estimated as the sum of zero‐ and nonzero‐noise inputs at any finite classification.
Prediction and explainability in AI: Striking a new balance?

Authors:
raw_author_name: Netta Avnoon
raw_affiliation_string: Department of Sociology & Anthropology, Ben-Gurion University of the Negev, Beer-Sheba, Israel

Abstract: The debate regarding prediction and explainability in artificial intelligence (AI) centers around the trade-off between achieving high-performance accurate models and the ability to understand and interpret the decisionmaking process of those models. In recent years, this debate has gained significant attention due to the increasing adoption of AI systems in various domains, including healthcare, finance, and criminal justice. While prediction and explainability are desirable goals in principle, the recent spread of high accuracy yet opaque machine learning (ML) algorithms has highlighted the trade-off between the two, marking this debate as an inter-disciplinary, inter-professional arena for negotiating expertise. There is no longer an agreement about what should be the “default” balance of prediction and explainability, with various positions reflecting claims for professional jurisdiction. Overall, there appears to be a growing schism between the regulatory and ethics-based call for explainability as a condition for trustworthy AI, and how it is being designed, assimilated, and negotiated. The impetus for writing this commentary comes from recent suggestions that explainability is overrated, including the argument that explainability is not guaranteed in human healthcare experts either. To shed light on this debate, its premises, and its recent twists, we provide an overview of key arguments representing different frames, focusing on AI in healthcare.
Knowledge-based recommender systems: overview and research directions

Authors:
raw_author_name: Mathias Uta
raw_affiliation_string: Siemens Energy AG, Germany
raw_author_name: Alexander Felfernig
raw_affiliation_string: Institute of Software Technology (IST) - Applied Software Engineering & Ai Research Group (ASE), Graz University of Technology, Austria
raw_author_name: Viet-Man Le
raw_affiliation_string: Institute of Software Technology (IST) - Applied Software Engineering & Ai Research Group (ASE), Graz University of Technology, Austria
raw_author_name: Thi Ngoc Trang Tran
raw_affiliation_string: Institute of Software Technology (IST) - Applied Software Engineering & Ai Research Group (ASE), Graz University of Technology, Austria
raw_author_name: Damian Garber
raw_affiliation_string: Institute of Software Technology (IST) - Applied Software Engineering & Ai Research Group (ASE), Graz University of Technology, Austria
raw_author_name: Sebastian Lubos
raw_affiliation_string: Institute of Software Technology (IST) - Applied Software Engineering & Ai Research Group (ASE), Graz University of Technology, Austria
raw_author_name: Tamim Burgstaller
raw_affiliation_string: Institute of Software Technology (IST) - Applied Software Engineering & Ai Research Group (ASE), Graz University of Technology, Austria

Abstract: Recommender systems are decision support systems that help users to identify items of relevance from a potentially large set of alternatives. In contrast to the mainstream recommendation approaches of collaborative filtering and content-based filtering, knowledge-based recommenders exploit semantic user preference knowledge, item knowledge, and recommendation knowledge, to identify user-relevant items which is of specific relevance when dealing with complex and high-involvement items. Such recommenders are primarily applied in scenarios where users specify (and revise) their preferences, and related recommendations are determined on the basis of constraints or attribute-level similarity metrics. In this article, we provide an overview of the existing state-of-the-art in knowledge-based recommender systems. Different related recommendation techniques are explained on the basis of a working example from the domain of survey software services. On the basis of our analysis, we outline different directions for future research.
Time-dependant Bayesian knowledge tracing—Robots that model user skills over time

Authors:
raw_author_name: Nicole Salomons
raw_affiliation_string: Department of Computer Science, United States; I-X and the Department of Computing, United Kingdom
raw_author_name: Brian Scassellati
raw_affiliation_string: Department of Computer Science, United States

Abstract: Creating an accurate model of a user's skills is an essential task for Intelligent Tutoring Systems (ITS) and robotic tutoring systems. This allows the system to provide personalized help based on the user's knowledge state. Most user skill modeling systems have focused on simpler tasks such as arithmetic or multiple-choice questions, where the user's model is only updated upon task completion. These tasks have a single correct answer and they generate an unambiguous observation of the user's answer. This is not the case for more complex tasks such as programming or engineering tasks, where the user completing the task creates a succession of noisy user observations as they work on different parts of the task. We create an algorithm called Time-Dependant Bayesian Knowledge Tracing (TD-BKT) that tracks users' skills throughout these more complex tasks. We show in simulation that it has a more accurate model of the user's skills and, therefore, can select better teaching actions than previous algorithms. Lastly, we show that a robot can use TD-BKT to model a user and teach electronic circuit tasks to participants during a user study. Our results show that participants significantly improved their skills when modeled using TD-BKT.
LiDAR point clouds analysis computer tools for teaching autonomous vehicles perception algorithms

Authors:
raw_author_name: Felipe Jiménez
raw_affiliation_string: University Institute for Automobile Research Technical University of Madrid Madrid Spain
raw_author_name: Miguel Clavijo
raw_affiliation_string: University Institute for Automobile Research Technical University of Madrid Madrid Spain

Abstract: Abstract The technological developments behind autonomous vehicles cover several areas and engineers training in this field represents a challenge. The main layers include perception, decision making, and acting. In the first one, different technologies can be used. The processing of the information provided by the sensors must allow successive modules to understand the environment and Laser imaging Detection and Ranging (LiDAR) technology is one of the most promising ones nowadays for this task. It offers great robustness in detection, but the extraction of information from the point cloud involves the development of complex algorithms that could be very time‐consuming if an experimental teaching is intended. This article presents two educational solutions for deepening in perception algorithms using LiDAR for autonomous driving: a closed ad‐hoc computer application for two‐dimensional (2D) LiDAR point cloud processing and an oriented set of commands for three‐dimensional (3D) LiDARs in Matlab. Their use allows main concept exploration in practical sessions with little time consumption and provides students a general overview of the tasks that must be performed by the perception layer in the autonomous vehicles. Furthermore, these tools provide the possibility of organizing different activities in the classroom related to theoretical and experimental issues, and understanding of results because the most tedious tasks are eased.
Benchmarking the influence of pre-training on explanation performance in MR image classification

Authors:
raw_author_name: Marta Oliveira
raw_affiliation_string: Division 8.44, Physikalisch-Technische Bundesanstalt, Germany
raw_author_name: Rick Wilming
raw_affiliation_string: Computer Science Department, Technische Universität Berlin, Germany
raw_author_name: Céline Budding
raw_affiliation_string: Berlin Center for Advanced Neuroimaging (BCAN), Charité— Universitätsmedizin Berlin, Germany
raw_author_name: Fabian Eitel
raw_affiliation_string: Berlin Center for Advanced Neuroimaging (BCAN), Charité— Universitätsmedizin Berlin, Germany
raw_author_name: Kerstin Ritter
raw_affiliation_string: Berlin Center for Advanced Neuroimaging (BCAN), Charité— Universitätsmedizin Berlin, Germany
raw_author_name: Stefan Haufe
raw_affiliation_string: Berlin Center for Advanced Neuroimaging (BCAN), Charité— Universitätsmedizin Berlin, Germany; Computer Science Department, Technische Universität Berlin, Germany; Division 8.44, Physikalisch-Technische Bundesanstalt, Germany

Abstract: Convolutional Neural Networks (CNNs) are frequently and successfully used in medical prediction tasks. They are often used in combination with transfer learning, leading to improved performance when training data for the task are scarce. The resulting models are highly complex and typically do not provide any insight into their predictive mechanisms, motivating the field of "explainable" artificial intelligence (XAI). However, previous studies have rarely quantitatively evaluated the "explanation performance" of XAI methods against ground-truth data, and transfer learning and its influence on objective measures of explanation performance has not been investigated. Here, we propose a benchmark dataset that allows for quantifying explanation performance in a realistic magnetic resonance imaging (MRI) classification task. We employ this benchmark to understand the influence of transfer learning on the quality of explanations. Experimental results show that popular XAI methods applied to the same underlying model differ vastly in performance, even when considering only correctly classified examples. We further observe that explanation performance strongly depends on the task used for pre-training and the number of CNN layers pre-trained. These results hold after correcting for a substantial correlation between explanation and classification performance.
Interpreting, translating or investigating? Framing the competences of a hybrid translational field: intercept interpreting

Authors:
raw_author_name: Cornelia Griebel
raw_affiliation_string: Faculty of law, University of Neuchâtel, Switzerland; Faculty of Translation Studies, Linguistics and Cultural Studies, University of Mainz, Germany

Abstract: When monitoring suspected criminals’ communications by bugging telephones, apartments or cars, the police collect information for ongoing investigations and the later proceedings in court. If foreign-language-speaking suspects are involved, intercept interpreters are indispensable for police work. Intercept interpreting is a hybrid translational activity that encompasses elements of interpreting and translation. This article explores the competences and skills required for this activity. It is based on 24 semi-structured interviews with intercept interpreters and police officers and on an observational study conducted in a Swiss police department. Core elements of translation and interpreting competence models are combined to reflect the hybrid nature of the activity. In a qualitative data analysis, we deductively identified typical translation and interpreting sub-competences, while a concurrent inductive analysis allowed us to extend the competence framework by new, specific elements of intercept interpreting. The results show that intercept interpreters not only exhibit special investigative and forensic skills, but that they also have many mental and emotional skills that enable them to carry out their demanding activity.
Real-Time Pose Recognition for Billiard Players Using Deep Learning

Authors:
raw_author_name: Wei Qi Yan
raw_affiliation_string: Auckland University of Technology, New Zealand

Abstract: In this book chapter, the authors propose a method for player pose recognition in billiards matches by combining keypoint extraction and an optimized transformer. Given that those human pose analysis methods usually require high labour costs, the authors explore deep learning methods to achieve real-time, high-precision pose recognition. Firstly, they utilize human key point detection technology to extract the key points of players from real-time videos and generate key points. Then, the key point data is input into the transformer model for pose analysis and recognition. In addition, the authors design a human skeletal alignment method for comparison with standard poses. The experimental results show that the method performs well in recognizing players' poses in billiards matches and provides real-time and timely feedback on players' pose information. This research project provides a new and efficient tool for training billiard players and opens up new possibilities for applying deep learning in sports analytics. In addition, one of these contributions is the creation of a dataset for pose recognition.
Risky Dynamic Typing Related Practices in Python: An Empirical Study

Authors:
raw_author_name: Zhifei Chen
raw_affiliation_string: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
raw_author_name: Lin Chen
raw_affiliation_string: State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
raw_author_name: Yibiao Yang
raw_affiliation_string: State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
raw_author_name: Qiong Feng
raw_affiliation_string: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
raw_author_name: Xuansong Li
raw_affiliation_string: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
raw_author_name: Wei Song
raw_affiliation_string: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China

Abstract: Python’s dynamic typing nature provides developers with powerful programming abstractions. However, many type related bugs are accumulated in code bases of Python due to the misuse of dynamic typing. The goal of this paper is to aid in the understanding of developers’ high-risk practices towards dynamic typing and the early detection of type related bugs. We first formulate the rules of six types of risky dynamic typing related practices (type smells for short) in Python. We then develop a rule-based tool named RUPOR which builds an accurate type base to detect type smells. Our evaluation shows that RUPOR outperforms the existing type smell detection techniques (including the LLM-based approaches, Mypy, and PYDYPE) on a benchmark of 900 Python methods. Based on RUPOR, we conduct an empirical study on 25 real-world projects. We find that type smells are significantly related to the occurrence of post-release faults. The fault-proneness prediction model built with type smell features slightly outperforms the model built without them. We also summarize the common patterns including inserting type check to fix type smell bugs. These findings provide valuable insights for preventing and fixing type related bugs in the programs written in dynamic-typed languages.
Smart Attendance System using Face Recognition

Authors:
raw_author_name: Navaneethan S
raw_affiliation_string: Amrita Vishwa Vidyapeetham
raw_author_name: Veluchamy S
raw_affiliation_string: Amrita Vishwa Vidyapeetham

Abstract: &#x0D; Face recognition offers a wide range of valuable applications in social media, security, and surveillance contexts. The software used for building facial recognition algorithms is Python and OpenCV. "Attendance using Face Recognition" is a method for tracking and managing attendance that makes use of facial recognition technology. By seamlessly integrating the 'Face Recognition' module, a native Python feature, and the OpenCV library, our system excels in accuracy and dependability. The system then stores attendance records in a database and provides real-time reports. In this article, we demonstrate how to create a face recognition system in Python utilizing the built-in "Face Recognition" module and the OpenCV library. Our results show that our system achieves high accuracy and robustness while being efficient and scalable, catering to a wide spectrum of educational institutions, organizations, and enterprises.
Virtual reality and perceived learning effectiveness in accounting studies: the mediating role of task-technology fit

Authors:
raw_author_name: Dwi Ratmono
raw_affiliation_string: Department of Accounting, Faculty of Economics and Business, Universitas Diponegoro, Semarang, Indonesia
raw_author_name: Ratna Candra Sari
raw_affiliation_string: Department of Accounting Education, Faculty of Economics, Universitas Negeri Yogyakarta, Yogyakarta, Indonesia
raw_author_name: Sony Warsono
raw_affiliation_string: Department of Accounting, Faculty of Economics and Business, Universitas Gadjah Mada, Yogyakarta, Indonesia

Abstract: The use of virtual reality (VR) for the improvement of learning outcomes for accounting studies is an area in which the research is still limited. Technology-based accounting learning is very important because, currently, the accounting profession is very much affected by rapid technological change which means that it must adapt to remain relevant in the business world. This study aims to examine the role of the mediating effect of task-technology fit (TTF) on the relationship between the use of VR and learning outcomes for accounting studies. This research model states that the use of VR will be able to increase TTF, technology quality and accessibility, and then increase reflective thinking and reduce cognitive overload thereby increasing perceived learning effectiveness (PLE). The hypotheses derived from this model were tested empirically using survey data from 199 users of VR engaged in accounting studies. The data analysis uses partial least squares-structural equation modeling (PLS-SEM), and the results support the hypothesis that TTF mediates the relationship between VR use, learning behavior, and PLE.
Improved Oversampling Algorithm for Imbalanced Data Based on K-Nearest Neighbor and Interpolation Process Optimization

Authors:
raw_author_name: Yiheng Chen
raw_affiliation_string: School of Railway Transportation, Shanghai Institute of Technology, Shanghai 201418, China
raw_author_name: Jinbai Zou
raw_affiliation_string: School of Railway Transportation, Shanghai Institute of Technology, Shanghai 201418, China
raw_author_name: Chuanbo Hu
raw_affiliation_string: School of Railway Transportation, Shanghai Institute of Technology, Shanghai 201418, China

Abstract: The problems of imbalanced datasets are generally considered asymmetric issues. In asymmetric problems, artificial intelligence models may exhibit different biases or preferences when dealing with different classes. In the process of addressing class imbalance learning problems, the classification model will pay too much attention to the majority class samples and cannot guarantee the classification performance of the minority class samples, which might be more valuable. By synthesizing the minority class samples and changing the data distribution, unbalanced datasets can be optimized. Traditional oversampling algorithms have problems of blindness and boundary ambiguity when synthesizing new samples. A modified reclassification algorithm based on Gaussian distribution is put forward. First, the minority class samples are reclassified by the KNN algorithm. Then, different synthesis strategies are selected according to the combination of the minority class samples, and the Gaussian distribution is used to replace the uniform random distribution for interpolation operation under certain classification conditions to reduce the possibility of generating noise samples. The experimental results indicate that the proposed oversampling algorithm can achieve a performance improvement of 2∼8% in evaluation metrics, including G-mean, F-measure, and AUC, compared to traditional oversampling algorithms.