Reviewerly/exHarmony huggingface dataset:
Congruency of Information Rather Than Body Ownership Enhances Motor Performance in Highly Embodied Virtual Reality

In immersive virtual reality, the own body is often visually represented by an avatar. This may induce a feeling of body ownership over the virtual limbs. Importantly, body ownership and the motor system share neural correlates. Yet, evidence on the functionality of this neuroanatomical coupling is still inconclusive. Findings from previous studies may be confounded by the congruent vs. incongruent multisensory stimulation used to modulate body ownership. This study aimed to investigate the effect of body ownership and congruency of information on motor performance in immersive virtual reality. We aimed to modulate body ownership by providing congruent vs. incongruent visuo-tactile stimulation (i.e., participants felt a brush stroking their real fingers while seeing a virtual brush stroking the same vs. different virtual fingers). To control for congruency effects, unimodal stimulation conditions (i.e., only visual or tactile) with hypothesized low body ownership were included. Fifty healthy participants performed a decision-making (pressing a button as fast as possible) and a motor task (following a defined path). Body ownership was assessed subjectively with established questionnaires and objectively with galvanic skin response (GSR) when exposed to a virtual threat. Our results suggest that congruency of information may decrease reaction times and completion time of motor tasks in immersive virtual reality. Moreover, subjective body ownership is associated with faster reaction times, whereas its benefit on motor task performance needs further investigation. Therefore, it might be beneficial to provide congruent information in immersive virtual environments, especially during the training of motor tasks, e.g., in neurorehabilitation interventions.
A New Pattern Quality Assessment Criterion and Defocusing Degree Determination of Laser Speckle Correlation Method

The laser speckle correlation method has found widespread application for obtaining information from vibrating objects. However, the resolution and accuracy of the laser speckle correlation method as they relate to the defocusing degree have not been analyzed sufficiently. Furthermore, the possible methods for speckle pattern quality assessment and enhancement have not been studied. In this study, the resolution and accuracy of the laser speckle correlation method are analyzed, and it is found that they are affected by the defocusing degree and speckle pattern quality, respectively. A new speckle pattern quality criterion combining the mean intensity gradient and frequency spectrum was proposed, called CMZ. The quality of the speckle pattern is higher when the CMZ is closer to zero. The proposed criterion was verified by simulated speckle patterns and real speckle patterns with different speckle sizes, densities, and gray contrasts. In the experimental setup stage, a suitable defocusing degree can be selected based on the resolution requirement and optimal speckle size, and other experimental parameters can be determined according to the CMZ criterion. Rotation and vibration experiments verified the effectiveness of the laser speckle correlation method and confirmed the reliability of the experiment preparation based on proposed CMZ criterion.
Visual fatigue induced by watching virtual reality device and the effect of anisometropia

The effect of small anisometropia on visual fatigue when using virtual reality (VR) devices was investigated. Participants (n = 34) visited three times. In the first visit, VR exposure (10 min) was conducted with the full correction of the refractive error of both eyes. Experimental anisometropia was induced by adding a + 1.0 dioptre spherical lens either on the dominant eyes in the second visit or on the non-dominant eyes in the third visit. At each visit, the participants played a predetermined video game using a head-mounted display VR for 10 min. Visual fatigue was assessed before and after playing VR game using the Virtual Reality Symptom Questionnaire (VRSQ) and high-frequency component of accommodative microfluctuation. Results showed that watching VR induced significant increase of VRSQ score, significant decrease in the maximum accommodation power and objective increase in visual fatigue. Experimental anisometropia induction either on the dominant or non-dominant eyes did not aggravate visual fatigue. Practitioner summary: Mild differences in refractive error (up to 1.0 dioptre) between both eyes do not significantly increase ocular fatigue by viewing virtual reality device (10 min). The impact of small anisometropia may be limited in developing a virtual reality device. Abbreviations: VR: virtual reality; VRSQ: virtual reality symptom questionnaire; HMD: head-mounted display; HFC: high-frequency component.
Threshold-Based Data Exclusion Approach for Energy-Efficient Federated Edge Learning

Federated edge learning (FEEL) is a promising distributed learning technique for next-generation wireless networks. FEEL preserves the user's privacy, reduces the communication costs, and exploits the unprecedented capabilities of edge devices to train a shared global model by leveraging a massive amount of data generated at the network edge. However, FEEL might significantly shorten energy-constrained participating devices' lifetime due to the power consumed during the model training round. This paper proposes a novel approach that endeavors to minimize computation and communication energy consumption during FEEL rounds to address this issue. First, we introduce a modified local training algorithm that intelligently selects only the samples that enhance the model's quality based on a predetermined threshold probability. Then, the problem is formulated as joint energy minimization and resource allocation optimization problem to obtain the optimal local computation time and the optimal transmission time that minimize the total energy consumption considering the worker's energy budget, available bandwidth, channel states, beamforming, and local CPU speed. After that, we introduce a tractable solution to the formulated problem that ensures the robustness of FEEL. Our simulation results show that our solution substantially outperforms the baseline FEEL algorithm as it reduces the local consumed energy by up to 79%.
A Classification of Artificial Intelligence Systems for Mathematics Education

This chapter provides an overview of the different Artificial Intelligence (AI) systems that are being used in contemporary digital tools for Mathematics Education (ME). It is aimed at researchers in AI and Machine Learning (ML), for whom we shed some light on the specific technologies that are being used in educational applications; and at researchers in ME, for whom we clarify: (i) what the possibilities of the current AI technologies are, (ii) what is still out of reach and (iii) what is to be expected in the near future. We start our analysis by establishing a high-level taxonomy of AI tools that are found as components in digital ME applications. Then, we describe in detail how these AI tools, and in particular ML, are being used in two key applications, specifically AI-based calculators and intelligent tutoring systems. We finish the chapter with a discussion about student modeling systems and their relationship to artificial general intelligence.
WDSchain: A Toolbox for Enhancing the Security Using Blockchain Technology in Water Distribution System

This work develops a toolbox called WDSchain on MATLAB that can simulate blockchain on water distribution systems (WDS). WDSchain can import data from Excel and EPANET water modelling software. It extends the EPANET to enable simulation blockchain of the hydraulic data at any intended nodes. Using WDSchain will strengthen network automation and the security in WDS. WDSchain can process time-series data with two simulation modes: (1) static blockchain, which takes a snapshot of one-time interval data of all nodes in WDS as input and output into chained blocks at a time, and (2) dynamic blockchain, which takes all simulated time-series data of all the nodes as input and establishes chained blocks at the simulated time. Five consensus mechanisms are developed in WDSchain to provide data at different security levels using PoW, PoT, PoV, PoA, and PoAuth. Five different sizes of WDS are simulated in WDSchain for performance evaluation. The results show that a trade-off is needed between the system complexity and security level for data validation. The WDSchain provides a methodology to further explore the data validation using Blockchain to WDS. The limitations of WDSchain do not consider selection of blockchain nodes and broadcasting delay compared to commercial blockchain platforms.
Multi‐level features extraction network with gating mechanism for crowd counting

Crowd counting is still a practical and challenging problem owing to scale variations and information loss. Most existing methods based on the straightforward fusion of different features from a deep neural network seem to eliminate this limitation. However, these features are difficult to be fused since they often differ significantly in modality and dimensionality. Unlike previous works, a multi-level features extraction network with gating mechanism for crowd counting is proposed. Specifically, a multi-channel gated unit to adaptively extract features in different levels of the network is proposed, which can avoid interference from confusing information. To fully aggregate features via multi-level fusion, multi-level features extraction scheme is presented. The multi-level features extraction network learns to fuse features from multiple levels and reduce false predictions. Extensive experiments and evaluations clearly illustrate that the proposed approach achieves state-of-the-art counting performance against other methods on four mainstream crowd counting benchmarks.
Manifold cluster-based evolutionary ensemble imbalance learning

• Class imbalance learning is done on manifold space to identify overlapping data. • Manifold clustering-based resampling strategy is proposed to avoid useless sample. • Each sub-cluster in MECS-Ensemble has distinct size. • The number of samples removed or generated from a sub-cluster adaptively adjusted. • A fitness function is designed to comprehensively evaluate a classifier structure. For an imbalanced dataset, traditional machine learning methods usually misclassify minority samples due to the indicator evaluating classification accuracy biased toward majority class. To address the issue, manifold cluster-based evolutionary ensemble imbalance learning is proposed, with the purpose of providing a more effective framework for building an optimal imbalance classifier. After mapping the original data to manifold space, majority samples are removed from each sub-cluster in terms of their distribution characteristic. Following that, a new one is generated in each minority sub-cluster by over-sampling, with the purpose of avoiding a misclassified new minority sample that produced from small disjuncts. In above manifold clustering-based resampling techniques, optional operations and key parameters for normalization, manifold learning, clustering, under-sampling and over-sampling form various combination. Thus, evolutionary algorithm is introduced to seek the optimal structure for MECS-Ensemble. Each individual is encoded by five integer and six real number, and a fitness function is designed to evaluate its classification accuracy and the diversity of majority samples. The statistical experimental results for 39 imbalanced datasets show that MECS-Ensemble proposed in the paper is superior to the other imbalance learning methods, especially, manifold clustering-based resampling technique contributes to significant performance improvements.
Error Bounds of Imitating Policies and Environments for Reinforcement Learning

In sequential decision-making, imitation learning (IL) trains a policy efficiently by mimicking expert demonstrations. Various imitation methods were proposed and empirically evaluated, meanwhile, their theoretical understandings need further studies, among which the compounding error in long-horizon decisions is a major issue. In this paper, we first analyze the value gap between the expert policy and imitated policies by two imitation methods, behavioral cloning (BC) and generative adversarial imitation. The results support that generative adversarial imitation can reduce the compounding error compared to BC. Furthermore, we establish the lower bounds of IL under two settings, suggesting the significance of environment interactions in IL. By considering the environment transition model as a dual agent, IL can also be used to learn the environment model. Therefore, based on the bounds of imitating policies, we further analyze the performance of imitating environments. The results show that environment models can be more effectively imitated by generative adversarial imitation than BC. Particularly, we obtain a policy evaluation error that is linear with the effective planning horizon w.r.t. the model bias, suggesting a novel application of adversarial imitation for model-based reinforcement learning (MBRL). We hope these results could inspire future advances in IL and MBRL.
Strategies, model checking and branching-time properties in Maude

Rewriting logic and its implementation Maude are a natural and expressive framework for the specification of concurrent systems and logics. Its nondeterministic local transformations are described by rewriting rules, which can be controlled at a higher level using a builtin strategy language added to Maude~3. This specification resource would not be of much interest without tools to analyze their models, so in a previous work, we extended the Maude LTL model checker to verify strategy-controlled systems. In this paper, CTL* and $\mu$-calculus are added to the repertoire of supported logics, after discussing which adaptations are needed for branching-time properties. The new extension relies on some external model checkers that are exposed the Maude models through general and efficient connections, profitable for future extensions and further applications. The performance of these model checkers is compared.
Using eye-controlled highlighting techniques to support both serial and parallel processing in visual search

Recent research has developed two eye-controlled highlighting techniques, namely, block highlight display (BHD) and single highlight display (SHD), that enhance information presentation based on a user's current gaze position. The present research aimed to investigate how these techniques facilitate mental processing of users' visual search in high information-density visual environments. In Experiment 1, 60 participants performed 3-, 6-, 9-, and 12-icon visual search tasks. The search times significantly increased as the number of icons increased with the SHD but not with the BHD. In Experiment 2, 40 participants performed a 49-icon visual search task. The search time was faster, and the fixation spatial density was lower with the BHD than with the SHD. These results suggested that the BHD supported parallel processing in the highlighted area and serial processing in the broader display area; thus, the BHD improved search performance compared to the SHD, which primarily supported serial processing.
Towards Descriptive Adequacy of Cyberbullying: Interdisciplinary Studies on Features, Cases and Legislative Concerns of Cyberbullying

In view of the complexity of cyberbullying, this paper aims to address the linguistic and legal aspects of cyberbullying from an interdisciplinary perspective. Based on authentic data collected from real cases, we will expound on features, defining properties and legal remedies of cyberbullying in the countries that contribute to this special issue, such as Nigeria, France, Poland and China. Firstly, we will present an overview of cyberbullying and its definition, along with cyberbullying's attributes. Next, we will cover the various forms of cyberbullying, such as hate speech, harassment and trolling. Each of these forms of cyberbullying result in numerous outcomes, many of which are serious and, in the worst case, can result in a victim's death. A discussion of such consequences and the legal remedies for cyberbullying will be provided. On a final note, the contributors seek to enrich the forthcoming studies on cyberbullying by offering suggestions towards descriptive adequacy of cyberbullying.
An automatic clustering algorithm based on the density-peak framework and Chameleon method

The density-peak clustering (DPC) method (Rodriguez and Laio, 2014) clusters the data efficiently by fast searching density peaks. Recently, an improved DPC algorithm named 3DC method (Liang and Chen, 2016) was proposed for automatically detecting the correct structure of the clusters. However, it is difficult to select correct parameters for the DPC and 3DC methods and the local property of data set can't be revealed due to their global density assumption in some scenarios. To overcome this drawback, the K-nearest neighbor (KNN) framework is adapted for defining the density of the DPC method. Nevertheless, such KNN-based methods can't automatically detect the number of the clusters compared with the 3DC method. In this paper, an automatic clustering method is proposed, which needs only a discrete input parameter. Meanwhile, by utilizing the cluster stability for the Chameleon framework, the proposed method can automatically detect the correct structure of the clusters. The experimental results on the synthetic and real world data demonstrate that the proposed method has a more robust performance. Besides, the proposed method is robust to the choices of the input parameter.
CL-ASS: An efficient and low-cost certificateless aggregate signature scheme for wireless sensor networks

Protecting data integrity and achieving non-repudiation in wireless sensor networks (WSNs) is very essential during data communication from one sensor to another. However, wireless sensor network nodes are severely limited by power, communication bandwidth, and storage space, so the traditional signature algorithm cannot be adapted to the environment. In this paper, we have proposed a certificateless aggregate signature scheme (CL-ASS) for WSNs. This scheme not only can significantly reduce the bandwidth consumed by network transmission and the occupancy of node storage space but also provides high privacy. In the random oracle model, we demonstrate the security of the scheme against two types of adversaries, under the assumption of the hardness of the computational Diffie–Hellman (CDH) Problem. Finally, the performance analysis shows that our scheme has an ideal computational and communication cost.
FedCMR: Federated Cross-Modal Retrieval

Deep cross-modal retrieval methods have shown their competitiveness among different cross-modal retrieval algorithms. Generally, these methods require a large amount of training data. However, aggregating large amounts of data will incur huge privacy risks and high maintenance costs. Inspired by the recent success of federated learning, we propose the federated cross-modal retrieval (FedCMR), which learns the model with decentralized multi-modal data. Specifically, we first train the cross-modal retrieval model and learn the common space across multiple modalities in each client using its local data. Then, we jointly learn the common subspace of multiple clients on the trusted central server. Finally, each client updates the common subspace of the local model based on the aggregated common subspace on the server, so that all clients participated in the training can benefit from federated learning. Experiment results on four benchmark datasets demonstrate the effectiveness proposed method.
Graph Convolution for Re-Ranking in Person Re-Identification

Nowadays, deep learning is widely applied to extract features for similarity computation in person re-identification (re-ID). However, the difference between the training data and testing data makes the performance of learned feature degraded during testing. Hence, re-ranking is proposed to mitigate this issue and various algorithms have been developed. However, most of existing re-ranking methods focus on replacing the Euclidean distance with sophisticated distance metrics, which are not friendly to downstream tasks and hard to be used for fast retrieval of massive data in real applications. In this work, we propose a graph-based re-ranking method to improve learned features while still keeping Euclidean distance as the similarity metric. Inspired by graph convolution networks, we develop an operator to propagate features over an appropriate graph. Since graph is the essential key for the propagation, two important criteria are considered for designing the graph, and different graphs are explored accordingly. Furthermore, a simple yet effective method is proposed to generate a profile vector for each tracklet in videos, which helps extend our method to video re-ID. Extensive experiments on three benchmark data sets, e.g., Market-1501, Duke, and MARS, demonstrate the effectiveness of our proposed approach.
Finding data compatibility bugs with JSON subschema checking

JSON is a data format used pervasively in web APIs, cloud computing, NoSQL databases, and increasingly also machine learning. To ensure that JSON data is compatible with an application, one can define a JSON schema and use a validator to check data against the schema. However, because validation can happen only once concrete data occurs during an execution, it may detect data compatibility bugs too late or not at all. Examples include evolving the schema for a web API, which may unexpectedly break client applications, or accidentally running a machine learning pipeline on incorrect data. This paper presents a novel way of detecting a class of data compatibility bugs via JSON subschema checking. Subschema checks find bugs before concrete JSON data is available and across all possible data specified by a schema. For example, one can check if evolving a schema would break API clients or if two components of a machine learning pipeline have incompatible expectations about data. Deciding whether one JSON schema is a subschema of another is non-trivial because the JSON Schema specification language is rich. Our key insight to address this challenge is to first reduce the richness of schemas by canonicalizing and simplifying them, and to then reason about the subschema question on simpler schema fragments using type-specific checkers. We apply our subschema checker to thousands of real-world schemas from different domains. In all experiments, the approach is correct whenever it gives an answer (100% precision and correctness), which is the case for most schema pairs (93.5% recall), clearly outperforming the state-of-the-art tool. Moreover, the approach reveals 43 previously unknown bugs in popular software, most of which have already been fixed, showing that JSON subschema checking helps finding data compatibility bugs early.