Reviewerly/exHarmony huggingface dataset:
DeepDA: LSTM-based Deep Data Association Network for Multi-Targets Tracking in Clutter

The Long Short-Term Memory (LSTM) neural network based data association algorithm named as DeepDA for multi-target tracking in clutter is proposed to deal with the NP-hard combinatorial optimization problem in this paper. Different from the classical data association methods involving complex models and accurate prior knowledge on clutter density, filter covariance or associated gating etc, data-driven deep learning methods have been extensively researched for this topic. Firstly, data association mathematical problem for multitarget tracking on unknown target number, missed detection and clutter, which is beyond one-to-one mapping between observations and targets is redefined formally. Subsequently, an LSTM network is designed to learn the measurement-to-track association probability from radar noisy measurements and existing tracks. Moreover, an LSTM-based data-driven deep neural network after a supervised training through the BPTT and RMSprop optimization method can get the association probability directly. Experimental results on simulated data show a significant performance on association ratio, target ID switching and time-consuming for tracking multiple targets even they are crossing each other in the complicated clutter environment.
Graph Representation Learning Meets Computer Vision: A Survey

A graph structure is a powerful mathematical abstraction, which can not only represent information about individuals but also capture the interactions between individuals for reasoning. Geometric modeling and relational inference based on graph data is a long-standing topic of interest in the computer vision community. In this article, we provide a systematic review of graph representation learning and its applications in computer vision. First, we sort out the evolution of representation learning on graphs, categorizing them into the nonneural network and neural network methods based on the way the nodes are encoded. Specifically, nonneural network methods, such as graph embedding and probabilistic graphical models, are introduced, and neural network methods, such as graph recurrent neural networks, graph convolutional networks, and variants of graph neural networks, are also presented. Then, we organize the applications of graph representation algorithms in various vision tasks (such as image classification, semantic segmentation, object detection, and tracking) for review and reference, and the typical graph construction approaches in computer vision are also summarized. Finally, on the background of biology and brain inspiration, we discuss the existing challenges and future directions of graph representation learning and computer vision.
Deep Quadruple-Based Hashing for Remote Sensing Image-Sound Retrieval

With the rapid progress of earth observation technology, cross-modal remote sensing (RS) image-sound retrieval has attracted much attention from the field of RS data processing. Existing approaches usually learn the pairwise similarity relations between RS images and sounds. However, these approaches ignore relative semantic similarity relationships, which leads to poor performance of cross-modal RS image-sound retrieval. In this article, we address this dilemma with a novel <i>deep quadruple-based hashing</i> (DQH) approach. We first devise a novel quadruple-based hashing network to learn relative semantic similarity relationships of hash codes. Meanwhile, we propose a quadruple construction hard module, which randomly selects two triplet hard units to directly learn relative semantic similarity relationships. On top of the two paths, we develop a new objective function to perform effective hash codes learning. The new objective function not only captures the relative semantic correlation of hash codes across different modalities and learns the relative semantic correlation of deep features but also enhances category-level semantics of hash codes and reduces the quantization error between hash-like codes and hash codes. The reasonableness and effectiveness of the proposed architecture are well illustrated by comprehensive experiments on diverse RS image-sound datasets.
AdaFS: Adaptive Feature Selection in Deep Recommender System

Feature selection plays an impactful role in deep recommender systems, which selects a subset of the most predictive features, so as to boost the recommendation performance and accelerate model optimization. The majority of existing feature selection methods, however, aim to select only a fixed subset of features. This setting cannot fit the dynamic and complex environments of practical recommender systems, where the contribution of a specific feature varies significantly across user-item interactions. In this paper, we propose an adaptive feature selection framework, AdaFS, for deep recommender systems. To be specific, we develop a novel controller network to automatically select the most relevant features from the whole feature space, which fits the dynamic recommendation environment better. Besides, different from classic feature selection approaches, the proposed controller can adaptively score each example of user-item interactions, and identify the most informative features correspondingly for subsequent recommendation tasks. We conduct extensive experiments based on two public benchmark datasets from a real-world recommender system. Experimental results demonstrate the effectiveness of AdaFS, and its excellent transferability to the most popular deep recommendation models.
GPPT: Graph Pre-training and Prompt Tuning to Generalize Graph Neural Networks

Despite the promising representation learning of graph neural networks (GNNs), the supervised training of GNNs notoriously requires large amounts of labeled data from each application. An effective solution is to apply the transfer learning in graph: using easily accessible information to pre-train GNNs, and fine-tuning them to optimize the downstream task with only a few labels. Recently, many efforts have been paid to design the self-supervised pretext tasks, and encode the universal graph knowledge among the various applications. However, they rarely notice the inherent training objective gap between the pretext and downstream tasks. This significant gap often requires costly fine-tuning for adapting the pre-trained model to downstream problem, which prevents the efficient elicitation of pre-trained knowledge and then results in poor results. Even worse, the naive pre-training strategy usually deteriorates the downstream task, and damages the reliability of transfer learning in graph data. To bridge the task gap, we propose a novel transfer learning paradigm to generalize GNNs, namely graph pre-training and prompt tuning (GPPT). Specifically, we first adopt the masked edge prediction, the most simplest and popular pretext task, to pre-train GNNs. Based on the pre-trained model, we propose the graph prompting function to modify the standalone node into a token pair, and reformulate the downstream node classification looking the same as edge prediction. The token pair is consisted of candidate label class and node entity. Therefore, the pre-trained GNNs could be applied without tedious fine-tuning to evaluate the linking probability of token pair, and produce the node classification decision. The extensive experiments on eight benchmark datasets demonstrate the superiority of GPPT, delivering an average improvement of 4.29% in few-shot graph analysis and accelerating the model convergence up to 4.32X. The code is available in: https://github.com/MingChen-Sun/GPPT.
VRepChain: A Decentralized and Privacy-Preserving Reputation System for Social Internet of Vehicles Based on Blockchain

In the context of the social Internet of vehicles (SIoV), constructing reliable social relationships between dynamic and distributed entities is a challenging research problem. Rating-based reputation systems have been widely applied to assist human users in evaluating the honesty of target entities. However, the ratings in SIoV expose user privacy, including behavior, location, etc., which are required to be protected properly. Meanwhile, the blockchain technology with its distributed paradigm is potentially employed to protect information privacy. In this study, we propose the design of a blockchain-enabled reputation system named “VRepChain” for SIoV by especially considering the rating privacy issue. In our design, the ratings' privacy is strongly preserved in the processes of transmission and storage. The reputation of a vehicle is constructed based on the ratings with the agreement of the rating providers, ensuring the ratings are never abused by any other unauthorized entities during the usage process. Through experiments, the proposed system is demonstrated to improve the effectiveness of vehicles in terms of arriving at their destinations in a faster speed. Furthermore, the effectiveness of the constructed reputation model with untruthful ratings is extensively examined, showing its robustness and practicality in realistic applications.
Development and application of digital twin technology for integrated regional energy systems in smart cities

In order to study the development and application of digital twin technology in the integrated regional energy system of smart cities, this paper proposes a basic concept of digital twin, and gives the construction method and possible applications of the energy internet digital twin. Taking the energy internet plan as an example, the key problems solved by digital twin technology are detailed. Finally, CloudIEPS, an energy internet planning platform based on digital twin, is introduced, which further proves the important role of digital twin technology through energy internet planning cases. According to the case analysis, the one-time investment cost of the optimized solution is 3,231,400 yuan less than the original solution, and the annual operating cost increases by 217,000 yuan. Compared with the original scheme, the one-time investment cost of the optimized scheme is reduced by 3,231,400 yuan, and the annual operating cost is increased by 217,000 yuan. The average heat loss rate of the original scheme is 11.5% and the average cooling loss rate is 12.4%, while the average overheating rate of the optimized scheme is only 1.9% and the average cooling loss rate is 10.4%.
Enhanced DWA algorithm for local path planning of mobile robot

Purpose The purpose of this study is to solve the problems of poor stability and high energy consumption of the dynamic window algorithm (DWA) for the mobile robots, a novel enhanced dynamic window algorithm is proposed in this paper. Design/methodology/approach The novel algorithm takes the distance function as the weight of the target-oriented coefficient, and a new evaluation function is presented to optimize the azimuth angle. Findings The jitter of the mobile robot caused by the drastic change of angular velocity is reduced when the robot is closer to the target point. The simulation results show that the proposed algorithm effectively optimizes the stability of the mobile robot during operation with lower angular velocity dispersion and less energy consumption, but with a slightly higher running time than DWA. Originality/value A novel enhanced dynamic window algorithm is proposed and verified. According to the experimental result, the proposed algorithm can reduce the energy consumption of the robot and improves the efficiency of the robot.
Enhancing EFL pre-service teachers’ affordance noticing and utilizing with the Synthesis of Qualitative Evidence strategies: An exploratory study of a customizable virtual environment platform

Although numerous studies have reported educational benefits of virtual environment (VE), little is known about whether the design-based learning project of developing VEs contributes to pre-service EFL teachers’ noticing and utilizing of its pedagogical affordances. Drawing on the affordance theory and the synthesis of qualitative evidence (SQD), this study first aimed to explore pedagogical affordances of VEs noticed by 78 Korean EFL pre-service teachers. It further investigated how the participants gained benefits in noticing and using these affordances from the SQD-based VE development project. We adopted a qualitative-dominant mixed research approach with multiple data sources, such as interviews, group discussions, reflective journals, and a survey. The findings reported four pedagogical affordances, including communication, engaging learning environment, constructability, and multimodal sharing. The findings also showed that the participants positively evaluated the SQD strategies for noticing and utilizing those affordances in their projects by activating their teacher agency. The study suggests the SQD strategies for the interactive learning environment in which EFL pre-service students can be trained to activate their agency in noticing and using ICT affordances. Academic and practical implications for pre-service teacher training and future research are suggested. • EFL pre-service teachers engaged in virtual environment (VE) development projects. • The project was guided by the synthesis of qualitative evidence (SQD) strategies. • Those teachers noted pedagogical affordances of virtual environments. • The SQD strategies were perceived to help them notice and use VE affordances.
Effective arithmetic optimization algorithm with probabilistic search strategy for function optimization problems

This paper proposes an enhanced arithmetic optimization algorithm (AOA) called PSAOA that incorporates the proposed probabilistic search strategy to increase the searching quality of the original AOA. Furthermore, an adjustable parameter is also developed to balance the exploration and exploitation operations. In addition, a jump mechanism is included in the PSAOA to assist individuals in jumping out of local optima. Using 29 classical benchmark functions, the proposed PSAOA is extensively tested. Compared to the AOA and other well-known methods, the experiments demonstrated that the proposed PSAOA beats existing comparison algorithms on the majority of the test functions.
Uncovering the potential of blockchain in the agri-food supply chain: An interdisciplinary case study

This paper explores how Blockchain technology (BCT) can be integrated in the agri-food supply chain (ASC) and how BCT-based networks are formed. To do this, the paper describes a BCT solution, designed to enhance traceability, and analyses its adoption in two small firms. Adopting an interdisciplinary approach and the Actor-Network Theory (ANT), the findings have revealed that BCT improves how data is collected and has changed how firms interact with stakeholders and customers. Firms have enhanced their reputations and started targeting new domestic and international markets. Technical and economic challenges were found when persuading actors to participate in the BCT-based network.
Fringe projection profilometry method with high efficiency, precision, and convenience: theoretical analysis and development

Fringe projector profilometry (FPP) is an important three-dimensional (3D) measurement technique, especially when high precision and speed are required. Thus, theoretical interrogation is critical to provide deep understanding and possible improvement of FPP. By dividing an FPP measurement process into four steps (system calibration, phase measurement, pixel correspondence, and 3D reconstruction), we give theoretical analysis on the entire process except for the extensively studied calibration step. Our study indeed reveals a series of important system properties, to the best of our knowledge, for the first time: (i) in phase measurement, the optimal and worst fringe angles are proven perpendicular and parallel to epipolar line, respectively, and can be considered as system parameters and can be directly made available during traditional calibration, highlighting the significance of the epipolar line; (ii) in correspondence, when two sets of fringes with different fringe orientations are projected, the highest correspondence precision can be achieved with arbitrary orientations as long as these two orientations are perpendicular to each other; (iii) in reconstruction, a higher reconstruction precision is given by the 4-equation methods, while we notice that the 3-equation methods are almost dominatingly used in literature. Based on these theoretical results, we propose a novel FPP measurement method which (i) only projects one set of fringes with optimal fringe angle to explicitly work together with the epipolar line for precise pixel correspondence; (ii) for the first time, the optimal fringe angle is determined directly from the calibration parameters, instead of being measured; (iii) uses 4 equations for precise 3D reconstruction but we can remove one equation which is equivalent to an epipolar line, making it the first algorithm that can use 3-equation solution to achieve 4-equation precision. Our method is efficient (only one set of fringe patterns is required in projection and the speed is doubled in reconstruction), precise (in both pixel correspondence and 3D reconstruction), and convenient (the computable optimal fringe angle and a closed-form 3-equation solution). We also believe that our work is insightful in revealing fundamental FPP properties, provides a more reasonable measurement for practice, and thus is beneficial to further FPP studies.
How to make models more useful

Microbial communities are found throughout the biosphere, from human guts to glaciers, from soil to activated sludge. Understanding the statistical properties of such diverse communities can pave the way to elucidate the common mechanisms ...Multiple ecological forces act together to shape the composition of microbial communities. Phyloecology approaches—which combine phylogenetic relationships between species with community ecology—have the potential to disentangle such forces but are often ...
Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo

In this paper, we propose some efficient multi-view stereo methods for accurate and complete depth map estimation. We first present our basic methods with Adaptive Checkerboard sampling and Multi-Hypothesis joint view selection (ACMH & ACMH+). Based on our basic models, we develop two frameworks to deal with the depth estimation of ambiguous regions (especially low-textured areas) from two different perspectives: multi-scale information fusion and planar geometric clue assistance. For the former one, we propose a multi-scale geometric consistency guidance framework (ACMM) to obtain the reliable depth estimates for low-textured areas at coarser scales and guarantee that they can be propagated to finer scales. For the latter one, we propose a planar prior assisted framework (ACMP). We utilize a probabilistic graphical model to contribute a novel multi-view aggregated matching cost. At last, by taking advantage of the above frameworks, we further design a multi-scale geometric consistency guided and planar prior assisted multi-view stereo (ACMMP). This greatly enhances the discrimination of ambiguous regions and helps their depth sensing. Experiments on extensive datasets show our methods achieve state-of-the-art performance, recovering the depth estimation not only in low-textured areas but also in details. Related codes are available at https://github.com/GhiXu.
Decentralized finance research and developments around the world

Decentralized finance is financial services offered on a public blockchain over the internet. This paper reviews the decentralized finance (DeFi) research and development around the world. The findings of the literature review are that decentralized finance offers many benefits such as broadening financial inclusion; encouraging permission-less innovation; eliminating the need for intermediaries; ensuring the immutability of transactions; censorship resistance and making cross-border transactions cheaper. The associated risks include execution risk in smart contracts, legal liability risk, data theft risk, interconnectedness risk, external data risk, and greater propensity for illicit activity using DeFi applications. The review of existing DeFi research show that there are few studies on DeFi, and a large number of DeFi research studies are non-empirical studies. Most studies hold a positive view about DeFi. They emphasize the benefits of DeFi in great depth but the challenges of DeFi were not analysed in great depth, and there are no critical studies on DeFi. Observations on DeFi developments from around the world show that there is growing interest in decentralized finance in Europe, US, Asia and Oceania. There are concerns that regulating decentralized finance can impede growth in decentralized finance markets in Asia. There are also concerns that banning crypto assets can hinder the growth of decentralized finance in African countries where regulators do not fully permit blockchain-enabled cryptocurrencies. Several policy issues associated with DeFi are discussed. Areas for further research are provided to advance the literature on decentralized finance.
How social presence influences consumer purchase intention in live video commerce: the mediating role of immersive experience and the moderating role of positive emotions

Purpose This paper aims to examine the relationship between social presence and consumer purchase intention in the context of livestreaming commerce and test the mediating role of immersive experience and the moderating role of positive emotions. Design/methodology/approach The authors used a convenience sampling method to select 408 college students from a university in Ganzhou as the participants. They employed structural equation modeling to test the hypotheses, as well as Hayes' PROCESS to test the mediation and moderation effects. Findings The authors found that immersion experiences partially mediate the relationship between social presence and consumers' purchase intentions and positive emotions moderate the “social presence–immersion experience–consumer purchase intentions” path. Therefore, social presence constitutes a moderating mediating effect on consumer purchase intentions. Practical implications This study offers meaningful insights into how livestreamers and e-retailers can stimulate consumers' buying behavior in livestreaming environments. E-commerce platforms should strengthen consumer interactions and increase consumers' perceptions of social presence. In addition, companies should meet the social and psychological needs of consumers and generate positive emotional resonance. Originality/value This research sheds light on the effect of social presence on consumer purchase intentions in livestreaming commerce and extends the social presence literature by investigating the mediation mechanism of immersive experience.
Pose Tutor: An Explainable System for Pose Correction in the Wild

Under the new norm of working from home, demand for fitness from home is on the rise. Different exercise forms solve different fitness needs for different people. Yoga gives flexibility and relieves stress. Pilates strengthens the muscles. Kung Fu brings balance. It is not feasible for everyone to hire a personal trainer. In this paper, we develop Pose Tutor, an AI-based explainable pose recognition and correction system. Pose Tutor combines vision and pose skeleton models in a novel coarse-to-fine framework to obtain pose class predictions. An angle-likelihood mechanism is used to explain which human joints maximally caused the pose class predictions and also correct any wrongly formed joints. Even without keypoint level training, Pose Tutor shows promising results on Yoga-82, Pilates-32, and Kungfu-7 datasets. Additionally, user studies conducted with multiple domain experts validate the explanations provided by our framework.