Reviewerly/exHarmony huggingface dataset:
Learnability with PAC Semantics for Multi-agent Beliefs

Abstract The tension between deduction and induction is perhaps the most fundamental issue in areas such as philosophy, cognition, and artificial intelligence. In an influential paper, Valiant recognized that the challenge of learning should be integrated with deduction. In particular, he proposed a semantics to capture the quality possessed by the output of probably approximately correct (PAC) learning algorithms when formulated in a logic. Although weaker than classical entailment, it allows for a powerful model-theoretic framework for answering queries. In this paper, we provide a new technical foundation to demonstrate PAC learning with multi-agent epistemic logics. To circumvent the negative results in the literature on the difficulty of robust learning with the PAC semantics, we consider so-called implicit learning where we are able to incorporate observations to the background theory in service of deciding the entailment of an epistemic query. We prove correctness of the learning procedure and discuss results on the sample complexity, that is how many observations we will need to provably assert that the query is entailed given a user-specified error bound. Finally, we investigate under what circumstances this algorithm can be made efficient. On the last point, given that reasoning in epistemic logics especially in multi-agent epistemic logics is PSPACE-complete, it might seem like there is no hope for this problem. We leverage some recent results on the so-called Representation Theorem explored for single-agent and multi-agent epistemic logics with the only knowing operator to reduce modal reasoning to propositional reasoning.
A Study of Student and Teacher Challenges in Smart Synchronous Hybrid Learning Environments

The COVID-19 pandemic has led to the growth of hybrid and online learning environments and the trend of introducing more technology into the classroom. One such change could be the use of smart synchronous hybrid learning environments (SSHLEs), which are settings with both onsite and online students concurrently, where technology plays a key role in sensing, analyzing, and reacting throughout the teaching and learning process. These changing environments and the incorporation of new technologies can place a greater workload on participants and reduce teacher agency. In light of this, this paper aimed to analyze the workload and teacher agency across various SSHLEs. The NASA-TLX model was used to measure the workload in several scenarios. Questionnaires and interviews were used to measure teacher agency. The results obtained indicated that the workload of the teacher tended to be high (between 60 and 70 points out of 100 for the NASA-TLX workload), especially when they lacked experience in synchronous hybrid learning environments, and the workload of the students tended to have average values (between 50 and 60) in the SSHLEs analyzed. Meanwhile, the teacher agency did not appear to be altered but showed potential for improvement.
mm-TPG: Traffic Policemen Gesture Recognition Based on Millimeter Wave Radar Point Cloud

Automatic driving technology refers to equipment such as vehicle-mounted sensors and computers that are used to navigate and control vehicles autonomously by acquiring external environmental information. To achieve automatic driving, vehicles must be able to perceive the surrounding environment and recognize and understand traffic signs, traffic signals, pedestrians, and other traffic participants, as well as accurately plan and control their path. Recognition of traffic signs and signals is an essential part of automatic driving technology, and gesture recognition is a crucial aspect of traffic-signal recognition. This article introduces mm-TPG, a traffic-police gesture recognition system based on a millimeter-wave point cloud. The system uses a 60 GHz frequency-modulated continuous-wave (FMCW) millimeter-wave radar as a sensor to achieve high-precision recognition of traffic-police gestures. Initially, a double-threshold filtering algorithm is used to denoise the millimeter-wave raw data, followed by multi-frame synthesis processing of the generated point cloud data and feature extraction using a ResNet18 network. Finally, gated recurrent units are used for classification to enable the recognition of different traffic-police gestures. Experimental results demonstrate that the mm-TPG system has high accuracy and robustness and can effectively recognize traffic-police gestures in complex environments such as varying lighting and weather conditions, providing strong support for traffic safety.
Blockchain with Quantum Mayfly Optimization-Based Clustering Scheme for Secure and Smart Transport Systems

Blockchain (BC) with a clustering scheme can be used to build secure and smart Vehicular Ad-Hoc Networks (VANETs), which provide improved data integrity, enhanced security, efficient resource allocation, and streamlined processes. This technology has revolutionized the transport industry by enabling safer, more efficient, and transparent transportation networks. Therefore, this paper concentrates on the design of a new Blockchain with a Quantum Mayfly Optimization-based Clustering Scheme for Secure and Smart Transport Systems (BQMFO-CSSTS) technique. The objective of the presented BQMFO-CSSTS technique is to build a secure VANET via a BC-based technology and clustering process. Moreover, the BQMFO-CSSTS technique initially uses a Quantum Mayfly Optimization (QMFO) system with a fitness function for the selection of cluster heads (CHs) and the cluster construction process. In addition, BC technology is used as trust infrastructure to provide trustworthy services to the user and protect the privacy of the CHs and cluster members (CMs). The proposed scheme leverages the decentralized and immutable nature of BC to establish trust and ensure the integrity of cluster formation in VANETs. Finally, the BQMFO-CSSTS technique uses trajectory similarity metrics to protect the integrity of the CMs against attacks. The simulation results of the BQMFO-CSSTS technique are validated using a series of measures. The comprehensive results reported the superior outcomes of the BQMFO-CSSTS method over other recent approaches, with the maximum throughput being 1644.52 kbps. Therefore, integration of BC technology provides a transparent and secure framework through which to manage cluster membership, data sharing, and trust establishment among vehicles.
Optimal and nearly optimal simulation of multiperiodic time-dependent Hamiltonians

Simulating Hamiltonian dynamics is one of the most fundamental and significant tasks for characterizing quantum materials. Recently, a series of quantum algorithms employing block encoding of Hamiltonians have succeeded in providing efficient simulation of time-evolution operators on quantum computers. While time-independent Hamiltonians can be simulated by the quantum eigenvalue transformation (QET) or quantum singular value transformation with the optimal query complexity in time $t$ and desirable accuracy $\ensuremath{\varepsilon}$, generic time-dependent Hamiltonians face at larger query complexity and more complicated oracles due to the difficulty of handling time-dependency. In this paper, we establish a QET-based approach for simulating time-dependent Hamiltonians with multiple time periodicity. Such time-dependent Hamiltonians involve a variety of nonequilibrium systems such as time-periodic systems (Floquet systems) and time-quasiperiodic systems. Overcoming the difficulty of time dependency, our protocol can simulate the dynamics under multiperiodic time-dependent Hamiltonians with optimal and nearly optimal query complexity both in time $t$ and desirable accuracy $\ensuremath{\varepsilon}$, and simple oracles as well as the optimal algorithm for time-independent cases.
Research on the Perceived Quality of Virtual Reality Headsets in Human–Computer Interaction

The progress of commercial VR headsets largely depends on the progress of sensor technology, the iteration of which often means longer research and development cycles, and also higher costs. With the continuous maturity and increasing competition of VR headsets, designers need to create a balance among user needs, technologies, and costs to achieve commercial competition advantages. To make accurate judgments, consumer feedback and opinions are particularly important. Due to the increasing maturity in the technology of commercial VR headsets in recent years, the cost has been continuously decreasing, and potential consumers have gradually increased. With the increase in consumer demand for virtual reality headsets, it is particularly important to establish a perceptual quality evaluation system. The relationship between consumer perception and product quality determined by evaluations of experience is improving. Using the research method implemented in this work, through semi-structured interviews and big data analysis of VR headset consumption, the perceptual quality elements of VR headsets are proposed, and the order of importance of perceptual quality attributes is determined by questionnaire surveys, quantitative analysis, and verification. In this study, the perceptual quality elements, including technical perceptual quality (TPQ) and value perceptual quality (VPQ), of 14 types of VR headsets were obtained, and the importance ranking of the VR headsets' perceptual quality attributes was constructed. In theory, this study enriches the research on VR headsets. In practice, this study provides better guidance and suggestions for designing and producing VR headsets so that producers can better understand which sensor technology has met the needs of consumers, and which sensor technology still has room for improvement.
<scp>ENHANCING DIGITAL LITERACIES WITH ADULT ENGLISH LANGUAGE LEARNERS</scp>: <scp>THEORETICAL AND PRACTICAL INSIGHTS</scp>By EkaterinaTour, EdwinCreely, and PeterWaterhouseRoutledge. <scp>ISBN</scp> 978‐03676‐7758‐9. Price <scp>USD</scp> 48.95 (paperback), <scp>USD</scp> 136.00 (hardcover), <scp>USD</scp> 39.16 (e‐book). 200 pages.

TESOL JournalEarly View e752 MEDIA REVIEW ENHANCING DIGITAL LITERACIES WITH ADULT ENGLISH LANGUAGE LEARNERS: THEORETICAL AND PRACTICAL INSIGHTSBy Ekaterina Tour, Edwin Creely, and Peter WaterhouseRoutledge. ISBN 978-03676-7758-9. Price USD 48.95 (paperback), USD 136.00 (hardcover), USD 39.16 (e-book). 200 pages. Adi Isma, Corresponding Author Adi Isma [email protected] orcid.org/0000-0002-6926-6953 Universitas Sulawesi Barat Correspondence Email: [email protected]Search for more papers by this author Adi Isma, Corresponding Author Adi Isma [email protected] orcid.org/0000-0002-6926-6953 Universitas Sulawesi Barat Correspondence Email: [email protected]Search for more papers by this author First published: 31 July 2023 https://doi.org/10.1002/tesj.752Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat REFERENCES Bilki, Z., Satar, M., & Sak, M. (2023). Critical digital literacy in virtual exchange for ELT teacher education: An interpretivist methodology. ReCALL, 35(1), 58–73. https://doi.org/10.1017/S095834402200009X Burnett, D. D. (2018). Leadership perspectives of digital learning and digital literacy adoption at rural community colleges. The Community College Enterprise, 24, 21–48. Dashtestani, R., & Hojatpanah, S. (2022). Digital literacy of EFL students in a junior high school in Iran: Voices of teachers, students and ministry directors. Computer Assisted Language Learning, 35, 635–665. https://doi.org/10.1080/09588221.2020.1744664 Smith, D. E. (2005). Institutional ethnography: A sociology for people. Lanham, MD: AltaMira Press. Early ViewOnline Version of Record before inclusion in an issuee752 ReferencesRelatedInformation
Multitask Coupling Network for Occlusion Relation Reasoning

Analysis of the occlusion relationships between different objects in an image is fundamental to computer vision, including both the accurate detection of multiple objects’ contours in an image and each pixel’s orientation on the contours of objects with occlusion relationships. However, the severe imbalance between the edge pixels of an object in an image and the background pixels complicates occlusion relationship reasoning. Although progress has been made using convolutional neural network (CNN)-based methods, the limited coupling relationship between the detection of object occlusion contours and the prediction of occlusion orientation has not yet been effectively used in a full network architecture. In addition, the prediction of occlusion orientations and the detection of occlusion edges are based on the accurate extraction of the local details of contours. Therefore, we propose an innovative multitask coupling network model (MTCN). To address the abovementioned issues, we also present different submodules. The results of extensive experiments show that the proposed method surpasses state-of-the-art methods by 2.1% and 2.5% in Boundary-AP and by 3.5% and 2.8% in Orientation AP on the PIOD and BSDS datasets, respectively, indicating that the proposed method is more advanced than comparable methods.
Duygu Analizi ve Metin Madenciliği ile Hizmet Kalitesi Ölçüm Modeli Önerisi: Su ve Kanalizasyon Hizmetleri Örneği

This study proposes a new model for service quality measurement using sentiment analysis and text mining techniques. This model aims to overcome traditional methods' time, cost and implementation difficulties and provide a more dynamic and efficient approach to service quality measurement. In addition, in this model, instead of the dimensions used in service quality measurements, such as SERVQUAL or SERVPERF, it is shown how to determine new categories and keywords specific to the service sector in which the model is used by text mining. Thus, it is aimed at something other than reaching more accurate results in service quality measurement. To achieve the model’s purpose, it aims to develop a service quality measurement model using social media data processed by text mining and sentiment analysis. To find an answer to this question, the keywords "flood", "meter", "rain", "irrigation", "infrastructure", "sewerage", "sewage", "maintenance hole ", "aski", "waterless", "water" were extracted from 109.844 tweets sent to the Twitter account of a municipality between 2016 and 2022 by text mining method. Service quality was measured by subjecting 5766 tweets containing the keywords extracted to sentiment analysis. As a result of the service quality measurement, 1922 negative, 973 positive and 2871 neutral tweets were identified. The average negative score was 0.51, the average positive score was 0.11, and the average neutral score was 0.38.
DREAM: Adaptive Reinforcement Learning based on Attention Mechanism for Temporal Knowledge Graph Reasoning

Temporal knowledge graphs (TKGs) model the temporal evolution of events and have recently attracted increasing attention. Since TKGs are intrinsically incomplete, it is necessary to reason out missing elements. Although existing TKG reasoning methods have the ability to predict missing future events, they fail to generate explicit reasoning paths and lack explainability. As reinforcement learning (RL) for multi-hop reasoning on traditional knowledge graphs starts showing superior explainability and performance in recent advances, it has opened up opportunities for exploring RL techniques on TKG reasoning. However, the performance of RL-based TKG reasoning methods is limited due to: (1) lack of ability to capture temporal evolution and semantic dependence jointly; (2) excessive reliance on manually designed rewards. To overcome these challenges, we propose an adaptive reinforcement learning model based on attention mechanism (DREAM) to predict missing elements in the future. Specifically, the model contains two components: (1) a multi-faceted attention representation learning method that captures semantic dependence and temporal evolution jointly; (2) an adaptive RL framework that conducts multi-hop reasoning by adaptively learning the reward functions. Experimental results demonstrate DREAM outperforms state-of-the-art models on public datasets.
MARN: Multi-level Attentional Reconstruction Networks for Weakly Supervised Video Temporal Grounding

Video temporal grounding is a challenging task in computer vision that involves localizing a video segment semantically related to a given query from a set of videos and queries. In this paper, we propose a novel weakly-supervised model called the Multi-level Attentional Reconstruction Networks (MARN), which is trained on video-sentence pairs. During the training phase, we leverage the idea of attentional reconstruction to train an attention map that can reconstruct the given query. At inference time, proposals are ranked based on attention scores to localize the most suitable segment. In contrast to previous methods, MARN effectively aligns video-level supervision and proposal scoring, thereby reducing the training-inference discrepancy. In addition, we incorporate a multi-level framework that encompasses both proposal-level and clip-level processes. The proposal-level process generates and scores variable-length time sequences, while the clip-level process generates and scores fix-length time sequences to refine the predicted scores of the proposal in both training and testing. To improve the feature representation of the video, we propose a novel representation mechanism that utilizes intra-proposal information and adopts 2D convolution to extract inter-proposal clues for learning reliable attention maps. By accurately representing these proposals, we can better align them with the textual modalities, and thus facilitate the learning of the model. Our proposed MARN is evaluated on two benchmark datasets, and extensive experiments demonstrate its superiority over existing methods.
Meta-adaptive-weighting-based bilateral multi-dimensional refined space feature attention network for imbalanced breast cancer histopathological image classification

Breast cancer histopathological image automatic classification can reduce pathologists workload and provide accurate diagnosis. However, one challenge is that empirical datasets are usually imbalanced, resulting in poorer classification quality compared with conventional methods based on balanced datasets. The recently proposed bilateral branch network (BBN) tackles this problem through considering both representation and classifier learning to improve classification performance. We firstly apply bilateral sampling strategy to imbalanced breast cancer histopathological image classification and propose a meta-adaptive-weighting-based bilateral multi-dimensional refined space feature attention network (MAW-BMRSFAN). The model is composed of BMRSFAN and MAWN. Specifically, the refined space feature attention module (RSFAM) is based on convolutional long short-term memories (ConvLSTMs). It is designed to extract refined spatial features of different dimensions for image classification and is inserted into different layers of classification model. Meanwhile, the MAWN is proposed to model the mapping from a balanced meta-dataset to imbalanced dataset. It finds suitable weighting parameter for BMRSFAN more flexibly through adaptively learning from a small amount of balanced dataset directly. The experiments show that MAW-BMRSFAN performs better than previous methods. The recognition accuracy of MAW-BMRSFAN under four different magnifications still is higher than 80% even when unbalance factor is 16, indicating that MAW-BMRSFAN can make ideal performance under extreme imbalanced conditions.
Stratonovich framework into Markovian satellite dynamics of Carleman-based filtering using energy function observables approach

In this paper the satellite system in which fluctuating aerodynamic torque and the radial perturbation about the pitch motion of orbit is analyzed. The objectives of the paper are three-fold. The first objective is the ‘Carleman linearization representation into the nonlinear stochastic evolution of the Markov process’. The second is to filter process and measurement noises in the satellite dynamics parameters in the Carleman setting. The third is to find the stability and convergence condition of satellite dynamics. The first is achieved by unifying the generating function, Carleman embedding, Itô stochastic differential rules and the finite closure with Kronecker algebra. Concerning the second objective, we recast the finite-dimensional Stochastic Differential Equations (SDE) of the satellite dynamics into a finite system of bilinear SDE via the Carleman embedding. The third objective is to achieve the Lyapunov function and asymptotic stability condition for the satellite stochastic dynamics involving the ‘Stratonovich differential’. In this paper, we demonstrate the utility of the satellite dynamics filtering in the Carleman-based filtering via ‘its convergence analysis as well as its superiority with available methods’, i.e., the benchmark extended Kalman filter, Gaussian second-order filter and Kushner–Stratonovich higher-order filter. From simulation performed it can be said that the Carleman-based filtering is superior than other benchmark filters in terms of their Absolute Filtering Error (AFE) of conditional means and conditional variances of the satellite dynamics states.
Induced entanglement by three-level laser in an open cavity coupled to thermal reservoir

In this study, we have investigated the squeezing and entanglement characteristics of the light produced by three-level atoms available in an open cavity and pumped to the top level by electron bombardment at constant rate. We have obtained steady-state solutions to the quantum Langevin equations for the cavity mode operators and the equations of evolution for the expectation values of the atomic operators by employing the large-time approximation approach. The squeezing characteristics, entanglement amplification, and normalized second-order correlation function of the cavity radiation are discussed using the resulting steady-state solutions. We have seen that the light generated by the three-level laser is in a squeezed state, and the squeezing occurs in the minus quadrature. It so turns out that the maximum quadrature squeezing of the two-mode cavity light is 37.5% for n̄=0, 36.5% for n̄=0.01, and 35.5% for n̄=0.02 below the vacuum-state level. We have also established that the impact of the thermal reservoir process results in increases in the mean and variance of the photon number. Additionally, it is discovered that quadrature squeezing and entanglement are increased by the existence of spontaneous emission. This indicates that there are actually more photons fluctuating in the cavity. It is found that the squeezing and entanglement in the two-mode light are directly related. As a result, an increase in the degree of squeezing directly leads to an increase in the degree of entanglement and vice versa. This shows that, whenever there is squeezing in the two-mode light; an entanglement exists in the system.