Reviewerly/exHarmony huggingface dataset:
Metadata application proﬁle as a mechanism for semantic interoperability in FAIR and open data publishing

Authors:
raw_author_name: Nishad Thalhath
raw_affiliation_string: School of Library, Information and Media Studies, University of Tsukuba, Tsukuba, Japan
raw_author_name: Mitsuharu Nagamori
raw_affiliation_string: School of Library, Information and Media Studies, University of Tsukuba, Tsukuba, Japan
raw_author_name: Tetsuo Sakaguchi
raw_affiliation_string: School of Library, Information and Media Studies, University of Tsukuba, Tsukuba, Japan

Abstract: Application profiles, also known as metadata application profiles, are customised collections of vocabularies adapted from various namespaces and tailored for specific local applications. These profiles act as constrainers and explainers for the (meta)data. Semantic interoperability is the ability of computer systems to exchange data in a mutually understandable manner, facilitating data sharing across diverse platforms and applications without compromising its meaning. As a critical component of semantic interoperability, application profiles enforce semantics to (meta)data, enhancing its openness, interoperability, and reusability. This study assesses the feasibility of representing a comprehensive application profile in a format aligned with the semantic web, ensuring interoperability between profiles and datasets. Dublin Core Description Set Profiles (DSP) is adapted as the modeling framework for metadata application profiles, steering the associated datasets toward RDF compliance. The research outcomes include "Yet Another Metadata Application Profiles" (YAMA) as a preprocessor grounded in the DSP framework for developing and managing metadata application profiles. YAMA facilitates the generation of various standard formats of application profiles, ensuring they are represented in human-readable documentation, machine-actionable forms, and even data validation languages. A data mapping extension to YAMA is proposed to ensure the semantic interoperability of open data, bridging non-RDF data structures to RDF, thus enabling the publication of 5-star open data. This ensures smooth dataset integration and the creation of linkable, semantically rich open datasets. The work emphasizes the pivotal role of application profiles in fortifying the semantic interoperability of (meta)data, thereby elevating dataset openness.
TIAE-DSIN: A time interval aware deep session interest network for click-through rate prediction

Authors:
raw_author_name: Liqing Qiu
raw_affiliation_string: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
raw_author_name: Caixia Jing
raw_affiliation_string: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
raw_author_name: Cheng'ai Sun
raw_affiliation_string: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China

Abstract: The users’ click prediction holds significant commercial value in online purchase platforms. Currently, some session-based interest extraction models overlook the impact of temporal interval factors on the distribution pattern of behaviors within a session. To address the above issue, this paper designs a module named session-based time-aware gated recurrent units (ST-GRU) to integrate the temporal interval factors into session sequence. The ST-GRU module utilizes a memory decay function, enabling the simulation process to identify the users’ memory changes within each session. Subsequently, to account for the interdependence between different sessions, this paper introduces bi-directional gated recurrent units (Bi-GRU) with an attention mechanism to learn interaction relations of user interests. Additionally, for high-dimensional potential features, this paper integrates above modules and proposes a method called time interval aware deep session interest network (TIAE-DSIN) model. Correspondingly, comparative experiments are carried out on three public datasets, and the obtained results indicate that the AUC of TIAE-DSIN model is superior to other session interest extraction models.
Scaling responses

Authors:
raw_author_name: Geoffrey R. Norman

Abstract: Abstract This chapter presents various ways of presenting the response options to the respondent. It begins by discussing why dichotomous responses (e.g. yes/no, true/false) are often inadequate. Different alternatives are discussed, including direct estimation methods (e.g. visual analogue scales, adjectival scales, Likert scales), comparative methods (e.g. paired comparisons, Guttman scaling), and econometric methods. It reviews some of the issues that need to be considered in writing the response options, such as whether one should use a unipolar or bipolar scale, how many steps there should be, and whether all the response options need to be labelled. It also covers what statistical tests can legitimately be used with scales. Finally, it compares ratings with rankings, and introduces the method of multidimensional scaling.
Transformer-based anomaly detection in P-LEO constellations: A dynamic graph approach

Authors:
raw_author_name: Davide Guzzetti
raw_affiliation_string: Auburn University, 211 Davis Hall, Auburn, 36849, AL, USA

Abstract: Successful management of large space systems, such as the most recent Proliferated Low Earth Orbit (P-LEO) constellations, demands an increased level of autonomy and irregular behavior detection capability. Existing techniques for satellite network management rely on monitoring satellites individually, potentially failing to capture events shared across multiple units. In this work, we propose a pipeline for identifying anomalous connections in proliferated satellite networks. Our pipeline relies on representing a satellite constellation as a dynamic graph. Dynamic graphs can be conveniently exploited to represent P-LEO networks, as they allow to capture meaningful structural and temporal correlations. Such spatial and temporal information are first projected into an embedding space; next, this encoded representation is processed by a transformer-encoder network for the anomaly detection task. We conduct extensive analyses of the main problem parameters, including the temporal horizon, the structure of the graph, and the size of the constellation. To assess the general performance of the method, we perform a Monte Carlo analysis on 960 ground node pair connections, providing empirical evidence of the algorithm’s generalization capability. The obtained results encourage the extension of the method to more complex, realistic modeling and kindred applications such as the on-edge detection problem.
Translating meaning representations to behavioural interface specifications

Authors:
raw_author_name: Iat Tou Leong
raw_affiliation_string: University of Coimbra, CISUC, Department of Informatics Engineering, Portugal
raw_author_name: Raul Barbosa
raw_affiliation_string: University of Coimbra, CISUC, Department of Informatics Engineering, Portugal

Abstract: Higher-order logic can be used for meaning representation in natural language processing to encode the semantic relationships in text. Alternatively, using a formal specification language for meaning representation is more precise for specifying programs and widely supported by automatic theorem provers, while deductive verification based on higher order logic is less common for mainstream programming languages. This paper addresses the research question of translating higher-order logic meaning representations generated from method-level code comments into a formal specification language that extends first-order logic. Doing so requires resolving possible ambiguities in determining the appropriate semantics for predicates. This is an open challenge in the path toward using natural language processing with formal methods. To address this, the paper proposes an approach and constructs a compiler for translating meaning representations, generated from Java programs with method-level comments, into Java Modeling Language. We evaluate the compiler on a set of representative benchmarks, including programs and specifications from the Java API, by generating Java Modeling Language specifications and statically checking them with a theorem prover. Results show that in 94% of the cases Java Modeling Language is accurately generated and in 97% of those cases it can be automatically checked with a state-of-the-art theorem prover.
Fully Anonymous Identity-based Broadcast Signcryption with Public Verification

Authors:
raw_author_name: Zhaoman Liu
raw_affiliation_string: Department of Computer Science, Fudan University, 200082, Shanghai, China
raw_author_name: Yanbo Chen
raw_affiliation_string: Department of Computer Science and Engineering, Chinese University of Hong Kong, 999077, Hongkong, China
raw_author_name: Jianting Ning
raw_affiliation_string: College of Computer and Cyber Security, Fujian Normal University, Fuzhou, 350117, Fujian, China
raw_author_name: Yunlei Zhao
raw_affiliation_string: Department of Computer Science, Fudan University, 200082, Shanghai, China

Abstract: In the multicast communication scenario, compared with broadcast encryption, broadcast signcryption or multi-receiver signcryption has additional ability to authenticate the source of the message. With the enhanced awareness of privacy preservation, ordinary users pay more attention to the identity leakage in the communication process. The primitive of anonymous broadcast signcryption has been proposed to solve this problem, which provides additional anonymity compared with the existing broadcast signcryption. However, most anonymous broadcast signcryption schemes only ensure the sender's identity concealment but ignore the anonymity of the receiver set. In this paper, we present a fully anonymous identity-based broadcast signcryption scheme, which meets insider unforgeablity, outsider confidentiality, identity concealment of sender and full anonymity of the receiver set. In addition, our scheme has two further desirable characteristics. One is public verifiability which means any third party can verify the validity of the message source without knowing the private key provided by the receiver. The other is statelessness which means the user does not need to update the private key due to the join or revocation of other users. Moreover, our scheme has constant-size public parameters and private key as well as constant decryption complexity, which makes the scheme more suitable for deployment in devices with limited storage or low computing power such as IoT devices.
A distributionally robust chance-constrained kernel-free quadratic surface support vector machine

Authors:
raw_author_name: Shu-Cherng Fang
raw_affiliation_string: Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, 27695, NC, USA
raw_author_name: Xiaolei Fang
raw_affiliation_string: Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, 27695, NC, USA
raw_author_name: Zheming Gao
raw_affiliation_string: College of Information Science and Engineering, Northeastern University, Shenyang, 110819, Liaoning, China
raw_author_name: Jian Luo
raw_affiliation_string: Management School, Hainan University, Haikou, 570228, Hainan, China

Abstract: This paper studies the problem of constructing a robust nonlinear classifier when the data set involves uncertainty and only the first- and second-order moments are known a priori. A distributionally robust chance-constrained kernel-free quadratic surface support vector machine (SVM) model is proposed using the moment information of the uncertain data. The proposed model is reformulated as a semidefinite programming problem and a second-order cone programming problem for efficient computations. A geometric interpretation of the proposed model is also provided. For commonly used data without prescribed uncertainty, a cluster-based data-driven approach is introduced to retrieve the hidden moment information that enables the proposed model for robust classification. Extensive computational experiments using synthetic and public benchmark data sets with or without uncertainty involved support the superior performance of the proposed model over other state-of-the-art SVM models, particularly when the data sets are massive and/or imbalanced.
Nonlinear dynamics and chaos in a vocal-ventricular fold system

Authors:
raw_author_name: Takumi Inoue
raw_affiliation_string: Graduate School of Science and Engineering, Ritsumeikan University , Noji-higashi, Kusatsu, Shiga 525–8577, Japan
raw_author_name: Kota Shiozawa
raw_affiliation_string: Graduate School of Science and Engineering, Ritsumeikan University , Noji-higashi, Kusatsu, Shiga 525–8577, Japan

Abstract: In humans, ventricular folds are located superiorly to the vocal folds. Under special circumstances such as voice pathology or singing, they vibrate together with the vocal folds to contribute to the production of voice. In the present study, experimental data measured from physical models of the vocal and ventricular folds were analyzed in the light of nonlinear dynamics. The physical models provide a useful experimental framework to study the biomechanics of human vocalizations. Of particular interest in this experiment are co-oscillations of the vocal and ventricular folds, occasionally accompanied by irregular dynamics. We show that such a system can be regarded as two coupled oscillators, which give rise to various cooperative behaviors such as synchronized oscillations with a 1:1 or 1:2 frequency ratio and desynchronized oscillations with torus or chaos. The insight gained from the view of nonlinear dynamics should be of significant use for the diagnosis of voice pathologies, such as ventricular fold dysphonia.
High-density cluster core-based <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e5009" altimg="si356.svg"><mml:mi>k</mml:mi></mml:math>-means clustering with an unknown number of clusters

Authors:
raw_author_name: Abhishek Kumar
raw_affiliation_string: Department of Artificial Intelligence, Kyungpook National University, Daehak-ro, Buk-gu, 41566, Daegu, South Korea
raw_author_name: Rammohan Mallipeddi
raw_affiliation_string: Department of Artificial Intelligence, Kyungpook National University, Daehak-ro, Buk-gu, 41566, Daegu, South Korea
raw_author_name: Dong-Gyu Lee
raw_affiliation_string: Department of Artificial Intelligence, Kyungpook National University, Daehak-ro, Buk-gu, 41566, Daegu, South Korea

Abstract: The k-means algorithm, known for its simplicity and adaptability, faces challenges related to manual cluster number selection and sensitivity to initial centroid placement. This paper introduces an innovative framework aimed at overcoming these challenges. By proposing a data-driven cluster number estimation method and a robust initialization strategy based on high-density cluster cores, our approach revolutionizes k-means, unlocking its full unsupervised potential and ensuring superior performance, even in scenarios involving overlapping clusters. The method employs a novel density-based technique to accurately identify cluster cores, resulting in substantial improvements over existing methods. Rigorous experimentation on synthetic and real-world datasets demonstrates an average performance enhancement of 15% in terms of the Adjusted Rand Index for datasets with overlapping clusters, surpassing the capabilities of state-of-the-art density-based clustering methods and traditional k-means. Moreover, our method autonomously determines the optimal number of clusters, facilitating true unsupervised learning and eliminating the impact of initial centroid placement on clustering outcomes. This leads to stable and consistent results, addressing key limitations of the conventional k-means algorithm. The practical applicability of our approach is exemplified in image segmentation tasks, showcasing its versatility and reliability in real-world scenarios.
Advanced machine learning for real-time tibial bone force monitoring in runners using wearable sensors

Authors:
raw_author_name: Aarti Amod Agarkar
raw_affiliation_string: Marathawada Mitramandal's College of Engineering, Karvenagar, Pune, Maharashtra, 411030, India
raw_author_name: Punam Sunil Raskar
raw_affiliation_string: Department of Computer Engineering, Smt. Kashibai Navale College of Engineering, Pune, Maharashtra, India

Abstract: This study explores the innovative integration of machine learning (ML) with wearable sensor technologies for the real-time monitoring of tibial bone force in runners. Utilizing three distinct approaches—a linear regression model based on the Vertical Average Loading Rate (VALR), a physics-based method, and a sophisticated ML algorithm—this research was conducted with 10 participants equipped with wearable sensors. The results revealed the ML model's superior performance over both the physics-based and VALR techniques, achieving Mean Absolute Percentage Errors (MAPEs) of 6.7 percent and 11.3 percent respectively. This was accomplished through extensive training across various datasets. Additionally, the ML approach demonstrated remarkable reliability in 25 different running session simulations, underscoring its effectiveness in complex musculoskeletal analytics. These findings not only highlight the potential of ML in enhancing wearable technology for biomechanical data analysis but also emphasize the necessity for further comprehensive studies in this field.
Forensic analysis of AI-compression traces in spatial and frequency domain

Authors:
raw_author_name: Sandra Bergmann
raw_affiliation_string: Friedrich-Alexander University Erlangen-Nürnberg, Germany
raw_author_name: Denise Moussa
raw_affiliation_string: Federal Criminal Police Office (BKA), Germany; Friedrich-Alexander University Erlangen-Nürnberg, Germany
raw_author_name: Fabian Brand
raw_affiliation_string: Friedrich-Alexander University Erlangen-Nürnberg, Germany
raw_author_name: André Kaup
raw_affiliation_string: Friedrich-Alexander University Erlangen-Nürnberg, Germany
raw_author_name: Christian Riess
raw_affiliation_string: Friedrich-Alexander University Erlangen-Nürnberg, Germany

Abstract: The classical JPEG compression is a rich source of cues for forensic image analysis. However, this compression standard will in the near future be complemented by a new, highly efficient learning-based compression standard called JPEG-AI. JPEG-AI is fundamentally different from classical JPEG. Hence, its forensic traces can also be expected to be fundamentally different. We argue that there is a pressing need for image forensics research to investigate these traces. In this work, we characterize forensic compression traces of different AI compression algorithms. Our analysis investigates AI compression artifacts in frequency domain and in spatial domain. Both domains exhibit similar artifacts that likely stem from upsampling operations of the decoders. Additionally, we report for one AI codec another artifact in homogeneous regions. We also investigate the artifact detectability in several scenarios including unseen AI compression traces and postprocessing. Here, frequency and autocorrelation features are better on additive noise and classical JPEG post-compression, while RGB features perform better on blurred and downsampled images.
TAN: a temporal-aware attention network with context-rich representation for boosting proposal generation

Authors:
raw_author_name: Wenzhu Yang
raw_affiliation_string: School of Cyber Security and Computer, Hebei University, Baoding, 071002, China
raw_author_name: Wenjie Xing
raw_affiliation_string: School of Cyber Security and Computer, Hebei University, Baoding, 071002, China
raw_author_name: Shuang Zeng
raw_affiliation_string: School of Cyber Security and Computer, Hebei University, Baoding, 071002, China
raw_author_name: Lei Geng
raw_affiliation_string: School of Cyber Security and Computer, Hebei University, Baoding, 071002, China

Abstract: Abstract Temporal action proposal generation in an untrimmed video is very challenging, and comprehensive context exploration is critically important to generate accurate candidates of action instances. This paper proposes a Temporal-aware Attention Network (TAN) that localizes context-rich proposals by enhancing the temporal representations of boundaries and proposals. Firstly, we pinpoint that obtaining precise location information of action instances needs to consider long-distance temporal contexts. To this end, we propose a Global-Aware Attention (GAA) module for boundary-level interaction. Specifically, we introduce two novel gating mechanisms into the top-down interaction structure to incorporate multi-level semantics into video features effectively. Secondly, we design an efficient task-specific Adaptive Temporal Interaction (ATI) module to learn proposal associations. TAN enhances proposal-level contextual representations in a wide range by utilizing multi-scale interaction modules. Extensive experiments on the ActivityNet-1.3 and THUMOS-14 demonstrate the effectiveness of our proposed method, e.g., TAN achieves 73.43% in AR@1000 on THUMOS-14 and 69.01% in AUC on ActivityNet-1.3. Moreover, TAN significantly improves temporal action detection performance when equipped with existing action classification frameworks.
Automated Extraction and Analysis of Sentences under Production: A Theoretical Framework and Its Evaluation

Authors:
raw_author_name: Aleksandra Miletić
raw_affiliation_string: Department of Digital Humanities, University of Helsinki, P.O. Box 4, Yliopistonkatu 4, 00100 Helsinki, Finland

Abstract: Sentences are generally understood to be essential communicative units in writing that are built to express thoughts and meanings. Studying sentence production provides a valuable opportunity to shed new light on the writing process itself and on the underlying cognitive processes. Nevertheless, research on the production of sentences in writing remains scarce. We propose a theoretical framework and an open-source implementation that aim to facilitate the study of sentence production based on keystroke logs. We centre our approach around the notion of sentence history: all the versions of a given sentence during the production of a text. The implementation takes keystroke logs as input and extracts sentence versions, aggregates them into sentence histories and evaluates the sentencehood of each sentence version. We provide detailed evaluation of the implementation based on a manually annotated corpus of texts in French, German and English. The implementation yields strong results on the three processing aspects.