Reviewerly/exHarmony huggingface dataset:
TIAE-DSIN: A time interval aware deep session interest network for click-through rate prediction

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

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

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

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

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

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.
Empirical Bayes factors for common hypothesis tests

Bayes factors for composite hypotheses have difficulty in encoding vague prior knowledge, as improper priors cannot be used and objective priors may be subjectively unreasonable. To address these issues I revisit the posterior Bayes factor, in which the posterior distribution from the data at hand is re-used in the Bayes factor for the same data. I argue that this is biased when calibrated against proper Bayes factors, but propose adjustments to allow interpretation on the same scale. In the important case of a regular normal model, the bias in log scale is half the number of parameters. The resulting empirical Bayes factor is closely related to the widely applicable information criterion. I develop test-based empirical Bayes factors for several standard tests and propose an extension to multiple testing closely related to the optimal discovery procedure. When only a P -value is available, an approximate empirical Bayes factor is 10 p . I propose interpreting the strength of Bayes factors on a logarithmic scale with base 3.73, reflecting the sharpest distinction between weaker and stronger belief. This provides an objective framework for interpreting statistical evidence, and realises a Bayesian/frequentist compromise.
Nonlinear dynamics and chaos in a vocal-ventricular fold system

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

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

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

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

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.
Refining the Contours of Intimate Image Abuse Offences

Abstract Research continues to bring to public consciousness the range of ways in which intimate images may culpably be used to cause significant harm to individual and collective interests. This is causing jurisdictions to compare notes and assess whether there is coherency and adequacy in the coverage of existing offences and whether reforms are necessary. The challenge is to develop offences that are wide enough to capture behaviours that are sufficiently culpable to be deserving of criminalization without bringing less harmful or less culpable behaviours within the reach of the criminal law. This requires a careful balancing of the physical, mental, and defence elements of intimate image abuse offences. With this in mind this chapter takes a comparative approach to evaluating how offences can optimally be designed to address intimate image abuse. Overall, it is argued that a ladder approach, with scaffolded offences, can best reflect harms and degrees of culpability so that the specific wrongdoing in intimate image abuse is accurately captured, packaged, and labelled.
Automated Extraction and Analysis of Sentences under Production: A Theoretical Framework and Its Evaluation

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.
BDIDA-IoT: A Blockchain-Based Decentralized Identity Architecture Enhances the Efficiency of IoT Data Flow

With the booming development of the Internet of Things (IoT) industry, millions of data are generated every day. How to use and manage these data safely and efficiently has become a hot issue of concern to people. Due to the accumulation of IoT data, the isolated data island phenomenon makes it difficult to connect and interact with each data owner, and the security and privacy of IoT data also become a challenge. Blockchain is a decentralized database technology that uses distributed accounting to ensure reliable data transmission and access, along with smart contracts that can be executed automatically to program and manipulate data. At the same time, blockchain techniques have stronger security and privacy, which can better meet the needs of users. In this paper, we analyze the current management mode and challenges of IoT data and propose an IoT data platform based on blockchain. The proposed platform takes into account important factors including IoT data traceability, IoT data trusted transactions, etc. Our IoT data platform achieves the trusted management and transaction of IoT data. We also propose follow-up optimization solutions to expand the application scope of our platform and achieve more efficient management of IoT data.
Iterative Camera Calibration Method Based on Concentric Circle Grids

A concentric circle target is commonly used in the vision measurement system for its detection accuracy and robustness. To enhance the camera calibration accuracy, this paper proposes an improved calibration method that utilizes concentric circle grids as the calibration target. The method involves accurately locating the imaged center and optimizing camera parameters. The imaged concentric circle center obtained by cross-ratio invariance is not affected by perspective projection, which ensures the location accuracy of the feature point. Subsequently, the impact of lens distortion on camera calibration is comprehensively investigated. The sub-pixel coordinates of imaged centers are taken into the iterative calibration method, and camera parameters are updated. Through simulations and real experiments, the proposed method effectively reduces the residual error and improves the accuracy of camera parameters.
Pretrained Language–Knowledge Graph Model Benefits Both Knowledge Graph Completion and Industrial Tasks: Taking the Blast Furnace Ironmaking Process as an Example

Industrial knowledge graphs (IKGs) have received widespread attention from researchers in recent years; they are intuitive to humans and can be understood and processed by machines. However, how to update the entity triples in the graph based on the continuous production data to cover as much knowledge as possible, while applying a KG to meet the needs of different industrial tasks, are two difficulties. This paper proposes a two-stage model construction strategy to benefit both knowledge graph completion and industrial tasks. Firstly, this paper summarizes the specific forms of multi-source data in industry and provides processing methods for each type of data. The core is to vectorize the data and align it conceptually, thereby achieving the fusion modeling of multi-source data. Secondly, this paper defines two interrelated subtasks to construct a pretrained language–knowledge graph model based on multi-task learning. At the same time, considering the dynamic characteristics of the production process, a dynamic expert network structure is adopted for different tasks combined with the pretrained model. In the knowledge completion task, the proposed model achieved an accuracy of 91.25%, while in the self-healing control task of a blast furnace, the proposed model reduced the incorrect actions rate to 0 and completed self-healing control for low stockline fault in 278 min. The proposed framework has achieved satisfactory results in experiments, which verifies the effectiveness of introducing knowledge into industry.
Micro-FL: A Fault-Tolerant Scalable Microservice-Based Platform for Federated Learning

As the number of machine learning applications increases, growing concerns about data privacy expose the limitations of traditional cloud-based machine learning methods that rely on centralized data collection and processing. Federated learning emerges as a promising alternative, offering a novel approach to training machine learning models that safeguards data privacy. Federated learning facilitates collaborative model training across various entities. In this approach, each user trains models locally and shares only the local model parameters with a central server, which then generates a global model based on these individual updates. This approach ensures data privacy since the training data itself is never directly shared with a central entity. However, existing federated machine learning frameworks are not without challenges. In terms of server design, these frameworks exhibit limited scalability with an increasing number of clients and are highly vulnerable to system faults, particularly as the central server becomes a single point of failure. This paper introduces Micro-FL, a federated learning framework that uses a microservices architecture to implement the federated learning system. It demonstrates that the framework is fault-tolerant and scalable, showing its ability to handle an increasing number of clients. A comprehensive performance evaluation confirms that Micro-FL proficiently handles component faults, enabling a smooth and uninterrupted operation.