Reviewerly/exHarmony huggingface dataset:
Combining Swin Transformer and Attention-Weighted Fusion for Scene Text Detection

Authors:
raw_author_name: Xianguo Li
raw_affiliation_string: School of Electronics and Information Engineering, Tiangong University, Tianjin, 300387, China
raw_author_name: Xingchen Yao
raw_affiliation_string: School of Electronics and Information Engineering, Tiangong University, Tianjin, 300387, China
raw_author_name: Yi Liu
raw_affiliation_string: Tianjin Key Laboratory of Optoelectronic Detection Technology and System, Tianjin, 300387, China

Abstract: Abstract The existing text detection algorithms based on Convolutional Neural Networks (CNN) commonly have the problems of insufficient receptive fields and inadequate extraction of spatial positional information, which limit their ability to detect large-scale variation text instances, long-distance and wide-spaced text instances as well as effectively distinguish complex background textures. To address the above problems, in this paper, a scene text detection algorithm combining Swin Transformer and attention-weighted fusion is proposed. Firstly, an attention-weighted fusion (AWF) module is proposed, which embeds a modified coordinate attention module (CAM) in the feature pyramid network (FPN). This module learns spatial positional weights of foreground information in different-scale features while suppressing redundant background information. As a result, the fused features are more focused on the text regions, enhancing the localization ability for text regions and boundaries. Secondly, the window-based self-attention mechanism of the Swin Transformer is utilized to achieve global feature perception on the fused features of the pyramid network. This compensates for the insufficient receptive fields of CNN and enhances the representation capability of global contextual features, thereby further improving the performance of text detection. Experimental results demonstrate that the proposed algorithm achieves competitive performance on three public datasets, namely ICDAR2015, MSRA-TD500, and Total-Text, with F-measure reaching 87.9%, 91.4%, and 86.7%, respectively. Code is available at: https://github.com/xgli411/ST-AWFNet .
The effectiveness of simple heuristic features in sensor orientation and placement problems in human activity recognition using a single smartphone accelerometer

Authors:
raw_author_name: Arnab Barua
raw_affiliation_string: Department of Computer Science, Faculty of Science, Memorial University of Newfoundland, St. John′s, A1B 1V6, Canada
raw_author_name: Xianta Jiang
raw_affiliation_string: Department of Computer Science, Faculty of Science, Memorial University of Newfoundland, St. John′s, A1B 1V6, Canada
raw_author_name: Daniel Fuller
raw_affiliation_string: Department of Community Health and Epidemiology, University of Saskatchewan, Health Science Building, 107 Wiggins Road, Saskatoon, SK, S7N 5E5, Canada

Abstract: Human activity Recognition (HAR) using smartphone sensors suffers from two major problems: sensor orientation and placement. Sensor orientation and sensor placement problems refer to the variation in sensor signal for a particular activity due to sensors' altering orientation and placement. Extracting orientation and position invariant features from raw sensor signals is a simple solution for tackling these problems. Using few heuristic features rather than numerous time-domain and frequency-domain features offers more simplicity in this approach. The heuristic features are features which have very minimal effects of sensor orientation and placement. In this study, we evaluated the effectiveness of four simple heuristic features in solving the sensor orientation and placement problems using a 1D-CNN-LSTM model for a data set consisting of over 12 million samples.We accumulated data from 42 participants for six common daily activities: Lying, Sitting, Walking, and Running at 3-Metabolic Equivalent of Tasks (METs), 5-METs and 7-METs from a single accelerometer sensor of a smartphone. We conducted our study for three smartphone positions: Pocket, Backpack and Hand. We extracted simple heuristic features from the accelerometer data and used them to train and test a 1D-CNN-LSTM model to evaluate their effectiveness in solving sensor orientation and placement problems.We performed intra-position and inter-position evaluations. In intra-position evaluation, we trained and tested the model using data from the same smartphone position, whereas, in inter-position evaluation, the training and test data was from different smartphone positions. For intra-position evaluation, we acquired 70-73% accuracy; for inter-position cases, the accuracies ranged between 59 and 69%. Moreover, we performed participant-specific and activity-specific analyses.We found that the simple heuristic features are considerably effective in solving orientation problems. With further development, such as fusing the heuristic features with other methods that eliminate placement issues, we can also achieve a better result than the outcome we achieved using the heuristic features for the sensor placement problem. In addition, we found the heuristic features to be more effective in recognizing high-intensity activities.
Is ChatGPT taking over the language classroom?

Authors:
raw_author_name: Mandy Lau
raw_affiliation_string: York University

Abstract: ChatGPT generated much dialogue on the implications of large language models (LLMs) for language teaching and learning. Since language teachers are uniquely positioned to teach metalinguistic awareness, they can support their learners’ understanding of how LLMs are shaped by language ideologies and how their outputs are indexical of social power. This awareness would help learners be more conscientious in using LLMs, deciding how to interact with them and adapt their outputs for their purposes. This article introduces LLMs as statistical systems that predict linguistic forms. It surfaces two language ideologies that have shaped their development: the belief in the separability of language from its social contexts and the belief in the value of larger text corpora. It also highlights some ideological effects including uneven language performance, text outputs that reflect biases, privacy violations, circulation of copyrighted materials, misinformation, and hallucinations. Some suggestions for mitigating these effects are offered.
Approaching epidemiological dynamics of COVID-19 with physics-informed neural networks

Authors:
raw_author_name: Horst Stoecker
raw_affiliation_string: Frankfurt Institute for Advanced Studies, Ruth-Moufang-Str. 1, 60438 Frankfurt am Main, Germany; GSI Helmholtzzentrum für Schwerionenforschung GmbH, 64291 Darmstadt, Germany; Institut für Theoretische Physik, Goethe Universität Frankfurt, Max-von-Laue-Str. 1, 60438 Frankfurt am Main, Germany
raw_author_name: Lingxiao Wang
raw_affiliation_string: Frankfurt Institute for Advanced Studies, Ruth-Moufang-Str. 1, 60438 Frankfurt am Main, Germany; Xidian-FIAS International Joint Research Center, Ruth-Moufang-Str. 1, 60438 Frankfurt am Main, Germany
raw_author_name: Kai Zhou
raw_affiliation_string: Frankfurt Institute for Advanced Studies, Ruth-Moufang-Str. 1, 60438 Frankfurt am Main, Germany; School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China

Abstract: A physics-informed neural network (PINN) embedded with the susceptible-infected-removed (SIR) model is devised to understand the temporal evolution dynamics of infectious diseases. Firstly, the effectiveness of this approach is demonstrated on synthetic data as generated from the numerical solution of the susceptible-asymptomatic-infected-recovered-dead (SAIRD) model. Then, the method is applied to COVID-19 data reported for Germany and shows that it can accurately identify and predict virus spread trends. The results indicate that an incomplete physics-informed model can approach more complicated dynamics efficiently. Thus, the present work demonstrates the high potential of using machine learning methods, e.g., PINNs, to study and predict epidemic dynamics in combination with compartmental models.
Legal guidelines for identifying online illegal fund-raising crimes in China

Authors:
raw_author_name: Jingkun Liu

Abstract: Purpose This paper aims to elucidate the responsiveness of China’s judicial system in addressing the challenges of identifying online illegal fund-raising crimes that have emerged in recent years. This study systematically evaluates the efficacy and potential pitfalls of legal guidelines contained in judicial interpretations, such as holistic determination, sampling verification and presumption of the nature of funds. In addition, the research endeavors to propose pertinent recommendations for refining the existing judicial rules. Design/methodology/approach This research mainly uses a doctrinal methodology, focusing on the principal judicial interpretations formulated by the Supreme People’s Court and other central judicial entities in China. The scope encompasses the realm of online illegal fund-raising crimes as well as other cybercrimes. The analytical framework involves a comprehensive examination of these authoritative judicial documents, coupled with a theoretical and critical analysis of relevant academic materials. Findings This research underscores that while judicial interpretations serve as an effective legal strategy to confront the challenges posed by online illegal fund-raising crimes, their implementation introduces a nuanced landscape. These legal guidelines, often emanating from diverse judicial departments and tackling specific issues, carry the inherent risk of giving rise to new complexities and fostering inconsistency. Judicial authorities shall exercise prudence in both the formulation and application of these guidelines, ensuring their harmonization with existing legal norms and fundamental legal principles. Originality/value This research constitutes a critical and comprehensive examination of judicial interpretations in China pertaining to online illegal fund-raising crimes. It offers valuable insights into the country’s judicial interpretation system and its legal responses to financial crimes. The paper serves as a valuable resource for academics, law enforcement professionals, policymakers, legislators and researchers.
Quantum Neural Network model for Token allocation for Course Bidding

Authors:
raw_author_name: Prabhas Chongstitvatana
raw_affiliation_string: Chulalongkorn University, Thiland

Abstract: Quantum computer has shown the advantage over the classical computer to solve some problems using the laws of quantum mechanics. With a combination of knowledge of machine learning and quantum computing, Quantum neural networks adapted the concept from classical neural networks and apply parameterized quantum gates as neural network weights. In this paper, we present an application of quantum neural networks with real-world data to predict token price used in a course bidding system. The experiments were carried out on the Qiskit quantum simulator. The result shows that quantum neural networks can achieve a good prediction result compared to the classical neural network. The best model configuration has the lowest RMSE 6.38%. This approach opens an opportunity to explore the benefit of quantum machine learning in many research fields in the future.
Towards a dynamic earthquake risk framework for Switzerland

Authors:
raw_author_name: Maren Böse
raw_author_name: Laurentiu Danciu
raw_author_name: Athanasios Papadopoulos
raw_author_name: John Clinton
raw_author_name: Carlo Cauzzi
raw_author_name: Irina Dallo
raw_author_name: Tobias Diehl
raw_author_name: Andreas Fichtner
raw_author_name: Florian Haslinger
raw_author_name: Frédérick Massin
raw_author_name: Eleni Chatzi
raw_author_name: Donat Fäh
raw_author_name: Dario Jozinović
raw_author_name: Philipp Kästli
raw_author_name: Federica Lanza
raw_author_name: Michèle Marti
raw_author_name: Men-Andrin Meier
raw_author_name: Maria Mesimeri
raw_author_name: Anne Obermann
raw_author_name: Pilar Sanchez-Pastor
raw_author_name: Luca Scarabello
raw_author_name: Domenico Giardini
raw_author_name: Stefan Wiemer

Abstract: Abstract. Scientists from different disciplines at ETH Zurich are developing a dynamic, harmonised, and user-centred earthquake risk framework for Switzerland, relying on a continuously evolving earthquake catalogue generated by the Swiss Seismological Service (SED) using the national seismic networks. This framework uses all available information to assess seismic risk at various stages and facilitates widespread dissemination and communication of the resulting information. Earthquake risk products and services include operational earthquake (loss) forecasting (OE(L)F), earthquake early warning (EEW), ShakeMaps, rapid impact assessment (RIA), structural health monitoring (SHM), and recovery and rebuilding efforts (RRE). Standardisation of products and workflows across various applications is essential for achieving broad adoption, universal recognition, and maximum synergies. In the Swiss dynamic earthquake risk framework, the harmonisation of products into seamless solutions that access the same databases, workflows, and software is a crucial component. A user-centred approach utilising quantitative and qualitative social science tools like online surveys and focus groups is a significant innovation featured in all products and services. Here we report on the key considerations and developments of the framework and its components. This paper may serve as a reference guide for other countries wishing to establish similar services for seismic risk reduction.
From Authority Work to Wikidata (Via Wikipedia): Implementing a Workflow for Original Catalogers

Authors:
raw_author_name: Roman Tashlitskyy
raw_affiliation_string: Metadata Services, University of Toronto Libraries, Toronto, Ontario, Canada
raw_author_name: Lana Soglasnova
raw_affiliation_string: Metadata Services, University of Toronto Libraries, Toronto, Ontario, Canada

Abstract: This is a practical reflection on how a library cataloger can learn to create Wikidata items based on Slavic language publications, and to maintain the practice as part of the workflow. We describe our experience at the University of Toronto Robarts Library, and the path to adopting a working model for regularly contributing Wikidata items based on the cataloger's original authority work for NACO.
Discrimination between right‐ and left‐handed writers based on sister lines

Authors:
raw_author_name: Raymond Marquis
raw_affiliation_string: School of Criminal Justice University of Lausanne Lausanne Switzerland

Abstract: Forensic document examiners are often confronted with questioned documents written with ballpoint pens. Depending on the force applied (or pressure) as well as the angle between the pen and the surface, sister lines running parallel to the inked strokes can be left by the lip of the housing ball. In a real case, sister lines were observed on the left side of inked strokes of a questioned signature. To assess whether the writer of that signature was a left-handed or a right-handed writer based on this result, an experimental study was carried out. Handwritten samples and signatures from 182 right-handed and 18 left-handed writers were collected with different ballpoint pens. For every writer, the presence or absence of sister lines was recorded. Sister lines were observed in 49% of the writers of the studied population (in 50% of the right-handed and ca. 39% of the left-handed writers). Most individuals who left sister lines showed a writing angle of 50°-55°. The location of sister lines was tabulated to inform probabilities of observing sister lines at a given location, if the writer is a right-handed or left-handed writer. In the case at hand, a likelihood ratio of 48 was obtained in support of the proposition of a left-handed writer, rather than a right-handed writer. Applying Bayes' theorem, such value moves the prior probability of 15% that the writer of the questioned signature is left-handed to a posterior probability of 89%.
Relevance and creativity – a linear model

Authors:
raw_author_name: Wayne de Fremery
raw_author_name: Michael Keeble Buckland

Abstract: Purpose The purpose of this paper is to provide a new and useful formulation of relevance. Design/methodology/approach This paper is formulated as a conceptual argument. It makes the case for the utility of considering relevance to be function of use in creative processes. Findings There are several corollaries to formulating relevance as a function of use. These include the idea that objects by themselves cannot be relevant since use assumes interaction; the affordances of objects and how they are perceived can affect what becomes relevant but are not in themselves relevant; relevance is not an essential characteristic of objects; relevance is transient; potential relevance (what might be relevant in the future) can be distinguished from what is relevant in use and from what has been relevant in the past. Originality/value The paper shows that its new formulation of relevance brings improved conceptual and terminological clarity to the discourse about relevance in information science. It demonstrates that how relevance is articulated conceptually is important as its conceptualization can affect the ways that users are able to make use of information systems and, by extension, how information systems can facilitate or disable the co-production of creative outcomes. The paper also usefully expands investigative opportunities by suggesting relevance and creativity are interrelated.
A revisit of the normalized eight-point algorithm and a self-supervised deep solution

Authors:
raw_author_name: Bin Fan
raw_affiliation_string: School of Electronics and Information, Northwestern Polytechnical University and Shaanxi Key Laboratory of Information Acquisition and Processing, Xi’an, 710129, China
raw_author_name: Yuchao Dai
raw_affiliation_string: School of Electronics and Information, Northwestern Polytechnical University and Shaanxi Key Laboratory of Information Acquisition and Processing, Xi’an, 710129, China
raw_author_name: Yongduek Seo
raw_affiliation_string: Department of Art and Technology, Sogang University, Seoul, 04107, South Korea
raw_author_name: Mingyi He
raw_affiliation_string: School of Electronics and Information, Northwestern Polytechnical University and Shaanxi Key Laboratory of Information Acquisition and Processing, Xi’an, 710129, China

Abstract: Abstract The normalized eight-point algorithm has been widely viewed as the cornerstone in two-view geometry computation, where the seminal Hartley’s normalization has greatly improved the performance of the direct linear transformation algorithm. A natural question is, whether there are and how to find other normalization methods that can further improve the performance for each input sample. In this paper, we provide a novel perspective and propose two contributions to this fundamental problem: (1) we revisit the normalized eight-point algorithm and make a theoretical contribution by presenting the existence of different and better normalization algorithms; (2) we introduce a deep convolutional neural network with a self-supervised learning strategy for normalization. Given eight pairs of correspondences, our network directly predicts the normalization matrices, thus learning to normalize each input sample. Our learning-based normalization module can be integrated with both traditional (e.g., RANSAC) and deep learning frameworks (affording good interpretability) with minimal effort. Extensive experiments on both synthetic and real images demonstrate the effectiveness of our proposed approach.
Design of Unified Modeling Language Information System for Motorcycle Unit Selling and Buying Transactions using the Waterfall Method

Authors:
raw_author_name: Fajar Mahardika
raw_author_name: Muhammad Labib Abdillah

Abstract: This research aims to design an information system for buying and selling motorbike units using the Waterfall method. The information system for buying and selling motorbike units has an important role in supporting motorbike sales business activities, from ordering to the payment process. The design of this system was carried out by following the Waterfall method approach which consists of several stages, namely requirements analysis, system design, implementation, testing and maintenance. The needs analysis stage involves identifying business and system user needs, including required functions and system requirements. Next, system design is carried out by designing the structure, interface and transaction process flow using the notation and methods provided by the Waterfall method. In implementation, an information system for buying and selling motorbike units is developed based on the design that has been created. Then, testing is carried out to verify the success of the system in carrying out its functions and ensure conformity with the specified needs. After that, the system is ready for use and maintenance is carried out to maintain the performance and reliability of the system on an ongoing basis. By applying the Waterfall method in designing a motor unit buying and selling transaction information system, it is hoped that it can produce a system that is structured, has functions that suit business needs, and is tested overall. The Waterfall method also allows developers to have a clear understanding of each design stage before moving on to the next, thereby minimizing the risk of errors and deficiencies in the system.
Intelligent Path Planning for Cognitive Mobile Robot Based on Dhouib-Matrix-SPP Method

Authors:
raw_author_name: Souhail Dhouib
raw_affiliation_string: OLID Laboratory, Higher Institute of Industrial Management, University of Sfax, Tunisia

Abstract: The Mobile Robot Path Problem looks to find the optimal shortest path from the starting point to the target point with collision-free for a mobile robot. This is a popular issue in robotics and in this paper the environment is considered as static and represented as a bidirectional grid map. Besides, the novel optimal method Dhouib-Matrix-SPP (DM-SPP) is applied to create the optimal shortest path for a mobile robot in a static environment. DM-SPP is a greedy method based on a column row navigation in the distance matrix and characterized by its rapidity to solve sparse graphs. The comparative analysis is conducted by applying DM-SPP on thirteen test cases and comparing its results to the results given by four metaheuristics the Max-Min Ant System, the Ant System with punitive measures, the A* and the Improved Hybrid A*. The outcomes acquired from different scenarios indicate that the proposed DM-SPP method can rapidly outperform the four predefined artificial intelligence methods.