Reviewerly/exHarmony huggingface dataset:
Theory and Practice of Constructing College Physical Education Curricula Based on Immersive Multimedia Technology

The overload of sports information resources has greatly increased the difficulty for users to choose resources. To solve this problem, this paper proposes a sports digital teaching resource recommendation model based on collaborative filtering algorithm to achieve the optimization of sports teaching mode in the context of big data and multimedia. Based on the detailed analysis of the teaching resource construction platform, the improved algorithm is applied to the teaching resource recommendation platform, and the algorithm effect is verified based on the collected data. The experimental data shows that the error of this method is significantly better than ID3 algorithm, the error is reduced by 26.55%, and the recall rate is 95.72%, which is 12.76% higher than ID3 algorithm. The introduction of personalized recommendation technology into the utilization of sports information resources can improve the efficiency and accuracy of users' access to resources and strengthen the adaptability to the continuous deconstruction and new integration of the higher education system.
Probabilistic temporal logic with countably additive semantics

This work presents a proof-theoretical and model-theoretical approach to probabilistic temporal logic. We present two novel logics; each of them extends both the language of linear time logic (LTL) and the language of probabilistic logic with polynomial weight formulas. The first logic is designed for reasoning about probabilities of temporal events, allowing statements like “the probability that A will hold in next moment is at least the probability that B will always hold” and conditional probability statements like “probability that A will always hold, given that B holds, is at least one half”, where A and B are arbitrary statements. We axiomatize this logic, provide corresponding sigma additive semantics and prove that the axiomatization is sound and strongly complete. We show that the satisfiability problem for our logic is decidable, by presenting a procedure which runs in polynomial space. We also present a logic with much richer language, in which probabilities are not attached only to temporal events, but the language allows arbitrary nesting of probability and temporal operators, allowing statements like “probability that tomorrow the chance of rain will be less than 80% is at least a half”. For this logic we prove a decidability result.
50 Years of Anchoring: A Meta-Analysis and Meta-Study of Anchoring Effects

One of the most robust phenomena studied across the behavioral sciences is numeric anchoring -- where an incidental number preceding a judgment can influence real-world relevant judgments of quantity, price, legal judgments, and more. The authors meta-analyze this expansive literature containing 2,131 total effect sizes (1,050 comparing high anchors against low anchors), finding a large (d = 0.876, 95% CI[0.808, 0.943], I2 = 92.96%) effect with only a small reduction from publication-bias corrections. However, evidence for theory-relevant moderators is mixed aside from reduced effects associated with basic anchoring (i.e., numeric priming), non-diagnostic anchors, the presence of incentives or debiasing interventions, and weakly for knowledge. The authors supplement the meta-analysis with a large-scale meta-study (N = 1,968) comparing high against low anchors and subsequently find evidence for moderation by anchor diagnosticity, extremity, cognitive load, knowledge, and debiasing but not presence of incentives or other exploratory factors (e.g., a page break between the anchor and judgment). The authors then evaluate existing theories of anchoring against these results, discuss practical methodological points relevant for researchers and practitioners attempting to use anchoring for applied purposes, and close by crafting a taxonomy of anchoring processes to guide future anchoring research.
Defending Taiwan's Democracy in the Internet Commons

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ClothSeg: semantic segmentation network with feature projection for clothing parsing

Semantic segmentation of clothing presents a formidable challenge owing to the non-rigid geometric deformation properties inherent in garments. In this paper, we use the Transformer as the encoder to better learn global information for clothing semantic segmentation. In addition, we propose a Feature Projection Fusion (FPF) module to better utilize local information. This module facilitates the integration of deep feature maps with shallow local details, thereby enabling the network to capture both high-level abstractions and fine-grained details of features. We also design a pixel distance loss in training to emphasize the impact of edge features. This loss calculates the mean of the shortest distances between all predicted clothing edges and the true clothing edges during the training process. We perform extensive experiments and our method achieves 56.30% and 74.97% mIoU on the public dataset CFPD and our self-made dataset LIC, respectively, demonstrating a competitive performance when compared to the state-of-the-art.
Pilgrimage to Pureland: Art, Perception and the Wutai Mural VR Reconstruction

Virtual reality (VR) supports audiences to engage with cultural heritage proactively. We designed an easy-to-access and guided VR reconstruction of the murals in Mogao Grottoes, Pilgrimage To Pureland, to offer the general public an accessible and engaging way to experience the Dunhuang murals. We explored an immersive VR reconstruction approach that can efficiently convert complex 2D mural art into 3D digital VR content while maintaining its originality. Specifically, we reconstructed the Mt. Wutai pilgrimage mural in Cave 61 into a plot-based interactive VR environment that offers users a more accessible and immersive narrative to visit and appreciate Dunhuang murals' complex religious, historical, and artistic value. We conducted a user study with twenty participants to further investigate how this reconstruction approach and the interactive narratives would affect users' immersive experiences and knowledge acquisition, compared with other forms of virtual access as the control group. Findings from our user study revealed that participants from the VR experimental group reported a significantly higher level of immersion and understanding of the knowledge. To conclude, our 2D-to-VR reconstruction methods provided a VR reconstruction approach and insights for creating engaging user experiences and improving users understanding of cultural and historical concepts while preserving the originality of the cultural heritage.
Blockchain-Based Authentication Scheme with an Adaptive Multi-Factor Authentication Strategy

Authentication is of paramount significance to cybersecurity. However, most of conventional authentication schemes are implemented in a centralized mode, in which potential problems that could arise include single-point failure, the exposure of personal information, and the risk of identity theft. Additionally, static single-factor authentication schemes are unsuitable for dynamic environments like mobile applications. In order to tackle these difficulties, we propose a blockchain-based authentication scheme with an adaptive multi-factor authentication strategy. Our scheme features a blockchain-based authentication framework that prevents unauthorized information alteration and system corruption. Additionally, we design an adaptive multi-factor authentication strategy model to ensure trustworthy multi-factor authentication in dynamic scenarios. Last, we construct a Raft-based consensus model to select an authoritative leading node for rapid authentication. The security analysis demonstrates the effectiveness of the proposed scheme in effectively countering various forms of cyberattacks targeted at authentication systems, and experiments demonstrate its superior effectiveness and efficiency compared to existing studies.
Attention-Based Image-to-Video Translation for Synthesizing Facial Expression Using GAN

The fundamental challenge in video generation is not only generating high-quality image sequences but also generating consistent frames with no abrupt shifts. With the development of generative adversarial networks (GANs), great progress has been made in image generation tasks which can be used for facial expression synthesis. Most previous works focused on synthesizing frontal and near frontal faces and manual annotation. However, considering only the frontal and near frontal area is not sufficient for many real-world applications, and manual annotation fails when the video is incomplete. AffineGAN, a recent study, uses affine transformation in latent space to automatically infer the expression intensity value; however, this work requires extraction of the feature of the target ground truth image, and the generated sequence of images is also not sufficient. To address these issues, this study is proposed to infer the expression of intensity value automatically without the need to extract the feature of the ground truth images. The local dataset is prepared with frontal and with two different face positions (the left and right sides). Average content distance metrics of the proposed solution along with different experiments have been measured, and the proposed solution has shown improvements. The proposed method has improved the ACD-I of affine GAN from 1.606 ± 0.018 to 1.584 ± 0.00, ACD-C of affine GAN from 1.452 ± 0.008 to 1.430 ± 0.009, and ACD-G of affine GAN from 1.769 ± 0.007 to 1.744 ± 0.01, which is far better than AffineGAN. This work concludes that integrating self-attention into the generator network improves a quality of the generated images sequences. In addition, evenly distributing values based on frame size to assign expression intensity value improves the consistency of image sequences being generated. It also enables the generator to generate different frame size videos while remaining within the range [0, 1].
DP-GAN: A Novel Generative Adversarial Network-Based Drone Pilot Identification Scheme

With the development of the Industrial Internet of Things, drones have been applied to many application scenarios for providing reliable communication services. However, due to being deployed in an open environment, drones often suffer from impersonation attacks, posing significant threats to flight safety. Therefore, designing an effective pilot identification scheme is crucial for the safe flight of drones. Currently, some pioneer works have been devoted to pilot identification. Limited by the insufficient training samples, the performance of pilot identification still needs to be improved. For this reason, a novel generative adversarial network (GAN)-based drone pilot identification scheme named DP-GAN has been proposed to improve pilot identification performance. Specifically, we first construct a long short-term memory (LSTM)-based generator to estimate the distribution of the collected dataset and then utilize the temporal and spatial relationships among the received control commands and drone attitudes to produce realistic flight data. Moreover, we also designed a three-stage adversarial training strategy to optimize the generator and discriminator simultaneously. Since the well-constructed generator could produce realistic flight data, the pilot identification performance of the discriminator could be further enhanced. After being verified by systematic experiments, the proposed scheme has achieved 94.42% and 97.02% accuracy under natural and constrained environments on S500. Thanks to the lightweight system overhead, this scheme holds the potential to be deployed on the drone platform for real-time pilot identification.
LiDAR Ground Detection-Based Dynamic Inverse Perspective Mapping of BEV Lanes

This article proposes a novel method for projecting lane markings detected from camera images onto the road surface to form a bird’s eye view (BEV) lane detection result using inverse perspective mapping (IPM). Existing methods that use a fixed homography matrix (fixed IPM) have been observed to lead to distortions due to vehicle pose variations relative to the road surface. Such distortions can cause inaccurate BEV representation of lane detection results, especially in larger vehicles like buses. This article proposes a “dynamic” IPM method to address these challenges to enhance the quality of the BEV perspective transformation of the lane detection result. This approach utilizes light detection and ranging (LiDAR) point clouds to estimate road plane parameters in the vehicle’s frame of reference. Lane markings are then projected onto the road plane using dynamic IPM, which considers the vehicle’s pose relative to the measured road surface model. The proposed dynamic IPM method is compatible with existing camera-based lane detection methods that detect lane marking points in the image space, given an accurate LiDAR and camera pose in the vehicle’s reference frame. The proposed method has been evaluated in simulated and real-world scenarios.
Deep Learning-Based Compressed Domain Multimedia for Man and Machine: A Taxonomy and Application to Point Cloud Classification

In the current golden age of multimedia, human visualization is no longer the single main target, with the final consumer often being a machine which performs some processing or computer vision tasks. In both cases, deep learning plays a fundamental role in extracting features from the multimedia representation data, usually producing a compressed representation referred to as latent representation. The increasing development and adoption of deep learning-based solutions in a wide area of multimedia applications have opened an exciting new vision where a common compressed multimedia representation is used for both man and machine. The main benefits of this vision are two-fold: i) improved performance for the computer vision tasks, since the effects of coding artifacts are mitigated; and ii) reduced computational complexity, since prior decoding is not required. This paper proposes the first taxonomy for designing compressed domain computer vision solutions driven by the architecture and weights compatibility with an available spatio-temporal computer vision processor. The potential of the proposed taxonomy is demonstrated for the specific case of point cloud classification by designing novel compressed domain processors using the JPEG Pleno Point Cloud Coding standard under development and adaptations of the PointGrid classifier. Experimental results show that the designed compressed domain point cloud classification solutions can significantly outperform the spatial-temporal domain classification benchmarks when applied to the decompressed data, containing coding artifacts, and even surpass their performance when applied to the original uncompressed data.
An Analytical Analysis of Text Stemming Methodologies in Information Retrieval and Natural Language Processing Systems

The exponential increase in textual unstructured digital data creates significant demand for advanced and smart stemming systems. As a preprocessing stage, stemming is applied in various research fields such as information retrieval (IR), domain vocabulary analysis, and feature reduction in many natural language processing (NLP). Text stemming (TS), an important step, can significantly improve performance in such systems. Text-stemming methods developed till now could be better in their results and can produce errors of different types leading to degraded performance of the applications in which these are used. This work presents a systematic study with an in-depth review of selected stemming works published from 1968 to 2023. The work presents a multidimensional review of studied stemming algorithms i.e., methodology, data source, performance, and evaluation methods. For this study, we have chosen different stemmers, which can be categorized as 1) linguistic knowledge-based, 2) statistical, 3) corpus-based, 4) context-sensitive, and 5) hybrid stemmers. The study shows that linguistic knowledge-based stemming techniques were widely used for highly inflected languages (such as Arabic, Hindi, and Urdu) and have reported higher accuracy than other techniques. We compare and analyze the performance of various state-of-the-art TS approaches, including their issues and challenges, which are summarized as research gaps. This work also analyzes different NLP applications utilizing stemming methods. At the end, we list the future work directions for interested researchers.
Anomaly Detection in Time Series Data and its Application to Semiconductor Manufacturing

Anomaly detection is essential for the monitoring and improvement of product quality in manufacturing processes. In the case of semiconductor manufacturing, where large amounts of time series data from equipment sensors are rapidly accumulated, identifying anomalous signals within this data presents a significant challenge. The data in question is multivariate and of varying lengths, with an often highly imbalanced ratio of normal to abnormal signals. Given the nature of this data, traditional data-driven methods may not be appropriate for its analysis. This paper proposes a novel unsupervised anomaly detection model for the analysis of multivariate time series data. The model utilizes a unique recurrent neural network architecture and a special objective function to detect anomalies. Furthermore, a relevance analysis method is introduced to facilitate the interpretation and analysis of the detected anomalous signals. Our experimental results indicate that this deep anomaly detection model, which summarizes sensor data of different lengths into a low-dimensional latent space, enabling the easy visualization and distinction of anomalous signals, can be applied in real-world semiconductor manufacturing factories and used by on-site engineers for both analysis and execution purposes.
DTDM: Dynamic Temporal Convolutional Network and Dynamic Multihead Attention for Chinese Named Entity Recognition

Compared with English Named Entity Recognition (NER), Chinese Named Entity Recognition (CNER) has a high difficulty in word segmentation, and accurate extraction of contextual semantic feature information is a key work of CNER. For that, we propose a CNER model to extract both local and global contextual semantic feature information. First, we propose to apply the dynamic convolutional kernel to the convolutional layer of TCN to enhance the local features of contextual semantic feature information. Second, we define a dynamic scaling factor computation method to compute the correlation between named entity characters in the multihead attention, which to process the problem of sparse distribution of named entities, and can efficiently extract the global features of contextual semantics. We validated the effectiveness of the proposed model on the Weibo dataset with an F1 value of 89.24%, which is better than commonly used models.
Rock typing and uncertainty assessment in geological and petrophysical properties by integrating electrofacies, hydraulic flow units, and geostatistical techniques in the Kangan gas field, Zagros basin

Abstract The structural complexities of hydrocarbon reservoirs make it difficult to correlate geological and petrophysical properties. A successful field development depends on accurately mapping the spatial distributions of reservoir key parameters. In this study, we present results on rock type analysis, estimation, and uncertainty evaluation of geological and petrophysical data of 33 wells in one of the south Iranian gas fields. This paper is divided into two parts. In the first part, we used a reservoir rock classification theme based on identifying electrofacies (EFs) and hydraulic flow units by analyzing both log and core data. In the second part of the paper, we performed estimation, uncertainty evaluation, and assessment of the porosity–thickness relationship of the high-quality EFs using geostatistical techniques. We used sequential simulation schemes to map the spatial distributions of porosity and thickness of the high-quality EFs across the field. Using probabilistic approaches, the generated multiple realizations were used to quantify the local and spatial uncertainties. Variogram analysis showed that property distributions had a higher continuity and minimum variance in the NW–SE direction. Based on spatial uncertainty analysis, we found that the indicator-based sequential simulated maps exhibited less spatial uncertainty. Furthermore, the obtained probability maps indicated that the SE part of the study area is more suitable for drilling and production scenarios.
Secure 3<sup>rd</sup> party software integration

Organisations are seeking ways to centralize their systems and operations. The need for cloud-based Customer Relationship Management (CRM) systems is, without a doubt, increasing due to many benefits like security by design and cost efficiency. However, integrations between CRM systems and third-party software may create vulnerabilities. This research finds out whether organisations and developers are considering security while creating integrations. The research includes a case study where organisations and developers were asked about their integration security expertise and where they think the responsibility of secure integrations lies. The research aimed to provide security best practices for integration and insight into sharing responsibilities between stakeholders.Research showed that the size of the organisation and the developer's experience correlate with their security knowledge. However, results also showed that organisations and developers do not focus on integration security as much as needed. This research recognised a need for further research.
Research on Multi-agent PPO Reinforcement Learning Algorithm based on Knowledge Graph

At present, the reinforcement learning algorithm based on deep neural network has made great progress in the research of multi-agent interactive training methods. However, the training cost of the model increases significantly with the increase of network depth and the number of agents. Therefore, designing an efficient method about multi-agent reinforcement learning is the focus of current research. In this paper, the strategy network structure is the PPO which is the traditional reinforcement learning strategy gradient algorithm. The training data is characterized by knowledge graph and key information is extracted to ensure that the training can be completed by obtaining a small amount of data from the multi-agent interactive data. So we can effectively reduce the number of training iterations and improve the efficiency of convergence. This paper is based on knowledge graph and PPO algorithm to solve the multi-agent reinforcement learning problem. The research results can be applied in the field of unmanned cluster intelligent decision.