Reviewerly/exHarmony huggingface dataset:
Mobile agent path planning under uncertain environment using reinforcement learning and probabilistic model checking

The major challenge in mobile agent path planning, within an uncertain environment, is effectively determining an optimal control model to discover the target location as quickly as possible and evaluating the control system’s reliability. To address this challenge, we introduce a learning-verification integrated mobile agent path planning method to achieve both the effectiveness and the reliability. More specifically, we first propose a modified Q-learning algorithm (a popular reinforcement learning algorithm), called QEA−learning algorithm, to find the best Q-table in the environment. We then determine the location transition probability matrix, and establish a probability model using the assumption that the agent selects a location with a higher Q-value. Secondly, the learnt behaviour of the mobile agent based on QEA−learning algorithm, is formalized as a Discrete-time Markov Chain (DTMC) model. Thirdly, the required reliability requirements of the mobile agent control system are specified using Probabilistic Computation Tree Logic (PCTL). In addition, the DTMC model and the specified properties are taken as the input of the Probabilistic Model Checker PRISM for automatic verification. This is preformed to evaluate and verify the control system’s reliability. Finally, a case study of a mobile agent walking in a grids map is used to illustrate the proposed learning algorithm. Here we have a special focus on the modelling approach demonstrating how PRISM can be used to analyse and evaluate the reliability of the mobile agent control system learnt via the proposed algorithm. The results show that the path identified using the proposed integrated method yields the largest expected reward.
WristAcoustic

PIN and pattern lock are difficult to accurately enter on small watch screens, and are vulnerable against guessing attacks. To address these problems, this paper proposes a novel implicit biometric scheme based on through-wrist acoustic responses. A cue signal is played on a surface transducer mounted on the dorsal wrist and the acoustic response recorded by a contact microphone on the volar wrist. We build classifiers using these recordings for each of three simple hand poses (relax, fist and open), and use an ensemble approach to make final authentication decisions. In an initial single session study (N=25), we achieve an Equal Error Rate (EER) of 0.01%, substantially outperforming prior on-wrist biometric solutions. A subsequent five recall-session study (N=20) shows reduced performance with 5.06% EER. We attribute this to increased variability in how participants perform hand poses over time. However, after retraining classifiers performance improved substantially, ultimately achieving 0.79% EER. We observed most variability with the relax pose. Consequently, we achieve the most reliable multi-session performance by combining the fist and open poses: 0.51% EER. Further studies elaborate on these basic results. A usability evaluation reveals users experience low workload as well as reporting high SUS scores and fluctuating levels of perceived exertion: moderate during initial enrollment dropping to slight during authentication. A final study examining performance in various poses and in the presence of noise demonstrates the system is robust to such disturbances and likely to work well in wide range of real-world contexts.
Affective Touch as Immediate and Passive Wearable Intervention

We investigated affective touch as a new pathway to passively mitigate in-the-moment anxiety. While existing mobile interventions offer great promises for health and well-being, they typically focus on achieving long-term effects such as shifting behaviors. As such, most mobile interventions are not applicable to provide immediate help in acute conditions -- when a user experiences a high anxiety level during ongoing events (e.g., completing high-stake tasks or mitigating interpersonal conflicts). A few works have developed passive interventions that are effective in-the-moment by leveraging breathing regulations and biofeedback. In this paper, we drew on neuroscientific findings on affective touch, the slow stroking on hairy skin that can elicit innate pleasantness and evaluated affective touch as a mobile health intervention. To induce affective touch, we first engineered a wearable device that renders a soft stroking sensation on the user's forearm. Then, we conducted a between-group experiment, in which participants underwent high-stress situations with/without receiving affective touch and post-experiment interviews, with 24 participants. Our results showed that participants who received affective touch experienced lower state anxiety and the same physiological stress response level compared to the control group participants. We also found that affective touch facilitated emotion regulation by rendering pleasantness, providing emotional support, and shifting attention. Finally, we discussed the immediate effect of affective touch on anxiety and physiological stress, the benefits of affective touch as a passive intervention, and the implementation considerations to use affective touch in just-in-time systems.
A human factors-aware assistance system in manufacturing based on gamification and hardware modularisation

Assistance systems have been widely adopted in the manufacturing sector to facilitate various processes and tasks in production environments. However, existing systems are mostly equipped with rigid functional logic and do not provide individual user experiences or adapt to their capabilities. This work integrates human factors in assistance systems by adjusting the hardware and instruction presented to the workers' cognitive and physical demands. A modular system architecture is designed accordingly, which allows a flexible component exchange according to the user and the work task. Gamification, the use of game elements in non-gaming contexts, has been further adopted in this work to provide level-based instructions and personalised feedback. The developed framework is validated by applying it to a manual workstation for industrial assembly routines.
Majority re-sampling via sub-class clustering for imbalanced datasets

ABSTRACTABSTRACTMany real world domain problem datasets are class imbalanced where the number of data in a given class is much less than in the other classes. In related literatures, under- and over-sampling techniques are widely used techniques to re-balance the class imbalanced datasets. However, their limitations include the risk of removing representative majority class data samples and the overfitting problem because of generating a large number of synthetic minority class data samples. Therefore, a novel approach, namely Majority Re-sampling visa Sub-class Clustering (MRSC) is introduced. It uses a clustering algorithm to group the majority class data into several clusters, i.e. sub-classes. Then, a new training set containing multiple sub-classes and a minority class is produced, after which the classifier is trained using this new multi-class dataset which has a lower imbalance ratio than the original dataset. The experimental results obtained using 44 two-class imbalanced datasets show that MRSC combined with the k-NN classifiers, including single and ensemble classifiers, significantly outperforms the other classifiers as well as seven state-of-the-art re-sampling approaches. Moreover, for the clustering algorithms based on affinity propagation and k-means, very similar results can be produced, without significant differences in performance, which indicate the stability of MRSC.KEYWORDS: Clusteringdata miningimbalanced datasetsmachine learningunder-sampling AcknowledgementsThe work was supported in part by the Ministry of Science and Technology of Taiwan under Grant MOST 111-2410-H-182-015-MY3 and in part by the Chang Gung Memorial Hospital at Linkou, under Grant BMRPH13 and CMRPG3J0732.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe work was supported by the Chang Gung Memorial Hospital at Linkou [BMRPH13,CMRPG3J0732]; Ministry of Science and Technology, Taiwan [111-2410-H182-015-MY3].
The role of organizational culture in supporting better accounting information systems outcomes

The current study aimed at exploring the role of organizational culture in supporting better AIS outcomes from perspective of financial and accounting managers within SMEs in Jordan during the fiscal year 2021–2022. Independent variables of study (organizational culture) included (Involvement, Adaptability, Mission, Consistency), while dependent variable was AIS outcomes within the organization. Quantitative approach was adopted and (318) questionnaire was distributed on financial and accounting managers from different SMEs in the Jordanian capital Amman, during the fiscal year 2021/2022. SPSS was employed so as to reach numerical results that can lead to explaining the phenomenon under study, and it was indicated that the main hypothesis was accepted “Organizational culture has a statistically positive influence on reaching better AIS outcomes within SMEs in Amman”. Among the sub-variables it was seen that involvement to be the most influential variable with an R = 0.772 followed directly by consistency scoring an R = .608, other variables were also seen to influential with lower degrees. Study Recommended carrying out a research that explores the influence of AIS outcomes and characteristics on organizational culture, in addition to the need from organizations to employ highly skilled and competent professionals and accountants to generate financial information and have appropriate academic qualifications. Further recommendations were presented later in the study
Superb @ SLT 2022: Challenge on Generalization and Efficiency of Self-Supervised Speech Representation Learning

We present the SUPERB challenge at SLT 2022, which aims at learning self-supervised speech representation for better performance, generalization, and efficiency. The challenge builds upon the SUPERB benchmark and implements metrics to measure the computation requirements of self-supervised learning (SSL) representation and to evaluate its generalizability and performance across the diverse SUPERB tasks. The SUPERB benchmark provides comprehensive coverage of popular speech processing tasks, from speech and speaker recognition to audio generation and semantic understanding. As SSL has gained interest in the speech community and showed promising outcomes, we envision the challenge to uplevel the impact of SSL techniques by motivating more practical designs of techniques beyond task performance. We summarize the results of 14 submitted models in this paper. We also discuss the main findings from those submissions and the future directions of SSL research.
Image watermarking using DNST-PHFMs magnitude domain vector AGGM-HMT

Invisibility, robustness and payload are three indispensable and contradictory properties for any image watermarking systems. Therefore, in this paper a novel statistical image watermark decoder based on robust discrete nonseparable Shearlet transform (DNST)-polar harmonic Fourier moments (PHFMs) magnitude and effective vector anisotropic generalized Gaussian mixtures (AGGM)-hidden Markov tree (HMT). We begin with a detailed study on the robustness and statistical characteristics of local DNST- PHFMs magnitudes of natural images. This study reveals the excellent robustness, highly non-Gaussian marginal statistics and strong dependencies of local DNST-PHFMs magnitudes. We also find that conditioned on their generalized neighborhoods, the local DNST-PHFMs magnitudes can be approximately modeled as anisotropic generalized Gaussian variables. Based on these findings, we model local DNST-PHFMs magnitudes using a vector AGGM-HMT that can capture all interscale, interdirection, and interlocation dependencies. Meanwhile, model parameters can be estimated effectively by using localization clues guided expectation–maximization (LCGEM) approach. Finally, we develop a new statistical image watermark decoder using the vector AGGM-HMT and maximum likelihood (ML) decision rule. Extensive experimental results show the superiority of the proposed watermark decoder over several state-of-the-art statistical watermarking methods and some approaches based on convolutional neural networks.
High-fidelity video reversible data hiding using joint spatial and temporal prediction

An efficient predictor is essential in reversible data hiding methods. This paper proposes a video reversible data hiding method, where the correlations between pixels in both spatial and temporal domains are fully considered to improve prediction accuracy. In more detail, for each pixel, both intra prediction and inter prediction are implemented and the better prediction mode is selected according to intra or inter local complexity. A double-layered video partition scheme is applied so that the motion vectors generated during inter prediction are not necessarily embedded as additional information while reversibility is guaranteed. In addition, a cover pixel selection principle based on the probability distribution of the prediction errors is proposed, which prevents the pixels with large prediction errors from being modified. Experimental results demonstrate that the joint spatial and temporal prediction scheme offers satisfactory accuracy, and the proposed video reversible data hiding method can obtain high-quality stego videos.
RF-Based Drone Classification Under Complex Electromagnetic Environments Using Deep Learning

Recent studies have demonstrated that using deep-learning (DL) methods to classify drones based on the radio-frequency (RF) signal is effective. As known, the rich and diverse data is an important guarantee for good identification performance. In reality, due to the complexity and high dynamic of wireless environments, the costs of signal collection and labeling with sufficient diversity are often unacceptable. In this work, we propose a low-cost data augmentation (DA) method to improve the robustness of the signal identification neural network (NN). It generates extra training data by mixing pure drone signals with practical background signals and trains the NN to distinguish drone signals from interferences. Moreover, to further improve performance, we design a spectrogram segmentation (SS) method that directly splits the entire spectrogram into several subspectrograms to separate the interference signals working outside of the drone bandwidth. Finally, using five representative drone signals collected in multiple scenarios, we examine the identification accuracy and generalization capability of the proposed algorithm. Experimental results reveal that the proposed algorithm performs better than the NN trained without SS or DA.
The Quantum Cybersecurity Threat May Arrive Sooner Than You Think

Recent articles predict that the quantum computing market will expand 500% by 2028. Billions of dollars are currently pouring into the quantum computer industry to build the first fault-tolerant quantum computers.
Explaining the Walking Through of a Team of Algorithms

We propose the Taguchi method, the diversity, and the average number of evaluations of a solution to support an explanation regarding decisions made by a computational intelligence algorithm. These metrics support uncovering interactions in a cooperative team of computational intelligence algorithms to solve the base station deactivation problem.
Training machine learning algorithms for automatic facial coding: The role of emotional facial expressions’ prototypicality

Automatic facial coding (AFC) is a promising new research tool to efficiently analyze emotional facial expressions. AFC is based on machine learning procedures to infer emotion categorization from facial movements (i.e., Action Units). State-of-the-art AFC accurately classifies intense and prototypical facial expressions, whereas it is less accurate for non-prototypical and less intense facial expressions. A potential reason might be that AFC is typically trained with standardized and prototypical facial expression inventories. Because AFC would be useful to analyze less prototypical research material as well, we set out to determine the role of prototypicality in the training material. We trained established machine learning algorithms either with standardized expressions from widely used research inventories or with unstandardized emotional facial expressions obtained in a typical laboratory setting and tested them on identical or cross-over material. All machine learning models' accuracies were comparable when trained and tested with held-out dataset from the same dataset (acc. = [83.4% to 92.5%]). Strikingly, we found a substantial drop in accuracies for models trained with the highly prototypical standardized dataset when tested in the unstandardized dataset (acc. = [52.8%; 69.8%]). However, when they were trained with unstandardized expressions and tested with standardized datasets, accuracies held up (acc. = [82.7%; 92.5%]). These findings demonstrate a strong impact of the training material's prototypicality on AFC's ability to classify emotional faces. Because AFC would be useful for analyzing emotional facial expressions in research or even naturalistic scenarios, future developments should include more naturalistic facial expressions for training. This approach will improve the generalizability of AFC to encode more naturalistic facial expressions and increase robustness for future applications of this promising technology.
VR as an Innovative Learning Tool in Sports Education

Testing and application of suitable learning tools and methods can facilitate learning environments conducive to skill acquisition for the target group. This also applies to sports education at school level, including extracurricular activities. On the one hand, traditional learning methods are still effective in sports education; on the other hand, keeping up with societal and technological developments, new learning strategies are continuously being researched to complement existing pedagogical tools. An innovative tool that has been increasingly used in various fields of application in recent years is Virtual Reality (VR) as part of the Extended Reality (XR) domain. It enables learning in immersive and specifically designed learning environments and is particularly suitable for learning in (sports-)motor contexts due to its flexible use. In our study, we test a VR-based rotation task in an extracurricular dance class with the objective of supporting the crucial didactic, collaborative, and perceptual components of dance training in educational contexts. We conducted feedback sessions with the students and used direct observation to examine their behavioural actions. Based on the qualitative content analysis of the written feedback and the overview from the observations, we can identify integrative potentials of this innovative tool in sports education, especially extracurricular dance classes.
Examining the Optimal Choice of SEM Statistical Software Packages for Sustainable Mathematics Education: A Systematic Review

Intending to analyze structural relationships between measured variables and latent constructs, researchers tend to adopt structural equation modeling (SEM) through either “covariance-based SEM” (CB-SEM) or “variance-based SEM” (VB-SEM)/“partial least squares SEM” (PLS-SEM) by using numerous statistical applications. Nevertheless, the reviews on understanding the optimal choice of proprietary statistical software packages in SEM approaches are scarce despite its immense importance in sustaining education. Therefore, a systematic review would be obligated to scrutinize the empirical studies to fill this gap. By employing the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, a total of 47 publications that met the inclusion criteria were obtained. To extract articles from August 2018 to 2022, Scopus, Web of Science (WoS), and The Education Resources Information Center (ERIC) databases were adopted. The findings imply that six types of proprietary statistical software packages emerged as an optimal choice: Lisrel, Amos, Mplus, SmartPLS, R package (plspm), and WarpPLS. Despite the widespread usage of a variety of statistical applications, SmartPLS and AMOS were rigorously utilized in VB-SEM/PLS-SEM and CB-SEM, respectively. This review is important for practitioners to discover which statistical tools are relevant to use and to identify gaps in order to sustain mathematics education for the future.
On Intuitionistic Fuzzy Temporal Topological Structures

In the present paper, four different intuitionistic fuzzy temporal topological structures are introduced, and some of their properties are discussed. These topological structures are based on the intuitionistic fuzzy topological operators and on the temporal intuitionistic fuzzy topological operators, which exist in intuitionistic fuzzy sets theory. The new structures are direct extensions of the IFTSs and will be the basis for introducing of a new type of topological structures.
Review of entity relation extraction

In today’s big data era, there are a large number of unstructured information resources on the web. Natural language processing researchers have been working hard to figure out how to extract useful information from them. Entity Relation Extraction is a crucial step in Information Extraction and provides technical support for Knowledge Graphs, Intelligent Q&amp;A systems and Intelligent Retrieval. In this paper, we present a comprehensive history of entity relation extraction and introduce the relation extraction methods based on Machine Mearning, the relation extraction methods based on Deep Learning and the relation extraction methods for open domains. Then we summarize the characteristics and representative results of each type of method and introduce the common datasets and evaluation systems for entity relation extraction. Finally, we summarize current entity relation extraction methods and look forward to future technologies.
Distributed and Asynchronous Population-Based Optimization Applied to the Optimal Design of Fuzzy Controllers

Designing a controller is typically an iterative process during which engineers must assess the performance of a design through time-consuming simulations; this becomes even more burdensome when using a population-based metaheuristic that evaluates every member of the population. Distributed algorithms can mitigate this issue, but these come with their own challenges. This is why, in this work, we propose a distributed and asynchronous bio-inspired algorithm to execute the simulations in parallel, using a multi-population multi-algorithmic approach. Following a cloud-native pattern, isolated populations interact asynchronously using a distributed message queue, which avoids idle cycles when waiting for other nodes to synchronize. The proposed algorithm can mix different metaheuristics, one for each population, first because it is possible and second because it can help keep total diversity high. To validate the speedup benefit of our proposal, we optimize the membership functions of a fuzzy controller for the trajectory tracking of a mobile autonomous robot using distributed versions of genetic algorithms, particle swarm optimization, and a mixed-metaheuristic configuration. We compare sequential versus distributed implementations and demonstrate the benefits of mixing the populations with distinct metaheuristics. We also propose a simple migration strategy that delivers satisfactory results. Moreover, we compare homogeneous and heterogenous configurations for the populations’ parameters. The results show that even when we use random heterogeneous parameter configuration in the distributed populations, we obtain an error similar to that in other work while significantly reducing the execution time.