Reviewerly/exHarmony huggingface dataset:
Exploring Multi-Dimension User-Item Interactions With Attentional Knowledge Graph Neural Networks for Recommendation

It is commonly agreed that a recommender system should use not only explicit information (i.e., historical user-item interactions) but also implicit information (i.e., incidental information) to deal with the problem of data sparsity and cold start.The knowledge graph (KG), due to its expressive structural and semantic representation capabilities, has been increasingly used for capturing auxiliary information for recommender systems, such as the recent development of graph neural network (GNN) based models for KG-aware recommendation.Nevertheless, these models have the shortcoming of insufficient node interactions or improper node weights during information propagation, which limits the performance of recommender systems.To address this issue, we propose a Multi-dimension Interaction based attentional Knowledge Graph Neural Network (MI-KGNN) for enhanced KG-aware recommendation.MI-KGNN characterizes similarities between users and items through information propagation and aggregation in knowledge graphs.As such, it can optimize the updating direction of node representation by fully exploring multi-dimension interactions among nodes during information propagation.In addition, MI-KGNN introduces a dual attention mechanism, which allows users and items to jointly determine the weight of neighbor nodes.As a result, MI-KGNN can effectively capture and represent both structural (i.e., the topology of interactions) and semantic information (i.e., the weight of interactions) in the knowledge graph.Experimental results show that the proposed model significantly outperforms baseline methods for top-K recommendation.Specifically, the recall rate is increased by 5.78%, 6.66%, and 3.22% on three public datasets, compared with the best performance of existing methods.
Latent Heterogeneous Graph Network for Incomplete Multi-View Learning

Multi-view learning has progressed rapidly in recent years. Although many previous studies assume that each instance appears in all views, it is common in real-world applications for instances to be missing from some views, resulting in incomplete multi-view data. To tackle this problem, we propose a novel Latent Heterogeneous Graph Network (LHGN) for incomplete multi-view learning, which aims to use multiple incomplete views as fully as possible in a flexible manner. By learning a unified latent representation, a trade-off between consistency and complementarity among different views is implicitly realized. To explore the complex relationship between samples and latent representations, a neighborhood constraint and a view-existence constraint are proposed, for the first time, to construct a heterogeneous graph. Finally, to avoid any inconsistencies between training and test phase, a transductive learning technique is applied based on graph learning for classification tasks. Extensive experimental results on real-world datasets demonstrate the effectiveness of our model over existing state-of-the-art approaches.
Reversible Data Hiding for Color Images Based on Adaptive Three-Dimensional Histogram Modification

Reversible data hiding (RDH) is a hot research topic, and many related techniques are proposed, but only a few are devised for color images. Most current RDH schemes, including those for color images, follow a well-developed framework: histogram-based embedding, which consists of two main steps, i.e., prediction-error histogram (PEH) generation by a pixel predictor and PEH modification through exploring efficient reversible mappings. The reversible mappings employed in these RDH approaches for color images, on the other hand, are empirically designed ignoring the specific image content, resulting in limited embedding performance. To address this issue, a novel RDH method for color images based on adaptive mapping selection is proposed in this paper. First, to leverage high inter-channel correlation of color images, a three-dimensional (3D) PEH is generated. Then, an effective reversible mapping selection mechanism is proposed, in which 3D mappings are adjusted in an ordered iterative manner according to PEH frequency ranking so that the embedding performance is optimized. By the proposed approach, the optimal reversible mapping can be acquired with low computing complexity and better embedding performance, and its efficiency is experimentally validated in comparison to various state-of-the-art studies.
Exponential Loss Minimization for Learning Weighted Naive Bayes Classifiers

The naive Bayesian classification method has received significant attention in the field of supervised learning. This method has an unrealistic assumption in that it views all attributes as equally important. Attribute weighting is one of the methods used to alleviate this assumption and consequently improve the performance of the naive Bayes classification. This study, with a focus on nonlinear optimization problems, proposes four attribute weighting methods by minimizing four different loss functions. The proposed loss functions belong to a family of exponential functions that makes the optimization problems more straightforward to solve, provides analytical properties of the trained classifier, and allows for the simple modification of the loss function such that the naive Bayes classifier becomes robust to noisy instances. This research begins with a typical exponential loss which is sensitive to noise and provides a series of its modifications to make naive Bayes classifiers more robust to noisy instances. Based on numerical experiments conducted using 28 datasets from the UCI machine learning repository, we confirmed that the proposed scheme successfully determines optimal attribute weights and improves the classification performance.
Use of Augmented Reality for Students with Educational Needs: A Systematic Review (2016–2021)

In recent years, interest in applying Augmented Reality technology as a teaching/learning resource in education has increased. However, few studies focus on the possibilities and challenges of these tools to support learners with educational needs. In this review, we aggregate the current knowledge of how Augmented Reality technologies are applicable and their impact on the learning of students with educational needs considering the above-mentioned factors. In total, 18 studies indexed in the Scopus and Web of Science databases were analysed. The main findings of this review provide the current state of Augmented Reality research in special education and show positive results in the learning of students with educational needs.
Unsupervised Image Restoration With Quality-Task-Perception Loss

Image restoration includes various kinds of tasks, such as image denoising, image deraining and low-light image enhancement, etc. Due to the domain shift problem of current supervised methods, researchers tend to adopt unsupervised image restoration methods. However, fake color or blur image, insufficient restoration and missing semantic information are three common problems when utilizing these methods. In this paper, we propose a new hybrid loss named Quality-Task-Perception (QTP) to deal with these three problems simultaneously. Specifically, this hybrid loss includes three components: quality, task and perception. The quality part overcomes the fake color or blur image problem by enforcing image quality scores of the restored images and those of the unpaired clean images to be similar. For the task part, we tackle the insufficient restoration problem by proposing to apply a task probability network to convert the unsupervised image restoration into a supervised classification problem, and this task probability network is learned from our proposed pipeline. The perception part handles the missing semantic information by restricting the multi-scale phase consistency between the degraded image and its restored version. Comprehensive experiments on both supervised and unsupervised datasets in three image restoration tasks demonstrate the superiority of our proposed approach.
A Q-Learning Driven Energy-Aware Multipath Transmission Solution for 5G Media Services

Supported by the latest evolution of the 5G technologies, Augmented Reality (AR) & Virtual Reality (VR) video streaming services are experiencing an unprecedented growth. However, the transmission issues caused by heterogeneous access and dynamic traffic are still challenging 5G communications. The Internet Engineering Task Force (IETF)’s Multipath Transmission Control Protocol (MPTCP) can aggregate bandwidth and balance traffic across multiple subflows in a heterogeneous network environment. However, in order to support delivery of high quality 5G media services, researchers should also address MPTCP’s inefficient data scheduling to heterogenous sub-paths, consideration of multiple criteria, including energy consumption and its inconsistent behavior when employed along with the Dynamic Adaptive Streaming over HTTP (DASH) adaptive application layer protocol. To address these issues, we propose a Q-Learning driven Energy-aware Data Scheduling (QLE-DS) mechanism for MPTCP-based media services. QLE-DS models the multipath scheduling as a Q-learning process and employs a novel quantum clustering approach to discretize the high dimensional continuous Q-table. An asynchronous framework is designed to improve the learning efficiency of QLE-DS. The simulation results show that QLE-DS performs better than other MPTCP scheduling algorithms in terms of flow completion time (FCT), retransmission rate, and energy consumption.
Modified plaintext attacks in a session for an optical cryptosystem based on DRPE with PFS

In this paper, the security of an optical cryptosystem based on double random phase encoding (DRPE) with perfect forward secrecy (PFS) is analyzed for a particular session. In the cryptosystem, the PFS strategy is utilized to enhance the security and key management of the traditional DRPE scheme. Our analysis reveals that the use of PFS has certain advantages in the key management approach, but the method is still vulnerable against modified plaintext attacks when the attack is performed in the same session. Also, it is noted that the method is safe against conventional plaintext attacks, but it is vulnerable to the modified chosen and known plaintext attacks. The original plaintext can be easily retrieved with the proposed attack algorithms. Numerical simulation results are presented to validate the effectiveness of the proposed attack algorithms.
Motion Similarity Evaluation between Human and a Tri-Co Robot during Real-Time Imitation with a Trajectory Dynamic Time Warping Model

Precisely imitating human motions in real-time poses a challenge for the robots due to difference in their physical structures. This paper proposes a human-computer interaction method for remotely manipulating life-size humanoid robots with a new metrics for evaluating motion similarity. First, we establish a motion capture system to acquire the operator's motion data and retarget it to the standard bone model. Secondly, we develop a fast mapping algorithm, by mapping the BVH (BioVision Hierarchy) data collected by the motion capture system to each joint motion angle of the robot to realize the imitated motion control of the humanoid robot. Thirdly, a DTW (Dynamic Time Warping)-based trajectory evaluation method is proposed to quantitatively evaluate the difference between robot trajectory and human motion, and meanwhile, visualization terminals render it more convenient to make comparisons between two different but simultaneous motion systems. We design a complex gesture simulation experiment to verify the feasibility and real-time performance of the control method. The proposed human-in-the-loop imitation control method addresses a prominent non-isostructural retargeting problem between human and robot, enhances robot interaction capability in a more natural way, and improves robot adaptability to uncertain and dynamic environments.
Causal Inference with Knowledge Distilling and Curriculum Learning for Unbiased VQA

Recently, many Visual Question Answering (VQA) models rely on the correlations between questions and answers yet neglect those between the visual information and the textual information. They would perform badly if the handled data distribute differently from the training data (i.e., out-of-distribution (OOD) data). Towards this end, we propose a two-stage unbiased VQA approach that addresses the unbiased issue from a causal perspective. In the causal inference stage, we mark the spurious correlation on the causal graph, explore the counterfactual causality, and devise a causal target based on the inherent correlations between the conventional and counterfactual VQA models. In the distillation stage, we introduce the causal target into the training process and leverages distilling as well as curriculum learning to capture the unbiased model. Since Causal Inference with Knowledge Distilling and Curriculum Learning (CKCL) reinforces the contribution of the visual information and eliminates the impact of the spurious correlation by distilling the knowledge in causal inference to the VQA model, it contributes to the good performance on both the standard data and out-of-distribution data. The extensive experimental results on VQA-CP v2 dataset demonstrate the superior performance of the proposed method compared to the state-of-the-art (SotA) methods.
Particle swarm-differential evolution algorithm with multiple random mutation

Differential evolution (DE) algorithm has attracted considerable attention because of its effectiveness and simplicity. However, previous studies have validated that DE still suffers from some limitations such as premature convergence and slow convergence especially dealing with multimodal optimization problems. To address these concerning issues, we propose an innovative optimization method named particle swarm-differential evolution algorithm with multiple random mutation (MRPSODE) in this paper. The proposed MRPSODE algorithm is based on multiple random mutation framework cooperating with mean particle swarm mutation strategy, DE/current-to-rand/1 mutation strategy and disturbance strategy. Firstly, we incorporate the modified mean particle swarm mutation strategy into DE algorithm to improve the global convergence ability. Secondly, DE/current-to-rand/1 mutation strategy is adopted to increase the population diversity and produce perturbations to avoid the algorithm trapping into a local optimum. Thirdly, we propose a disturbance strategy to help the population escape from local optima, so as to enhance the exploration ability. Finally, to ensure that the proposed algorithm can get satisfactory solutions with a fast convergence speed, we design a multiple random mutation framework, in which these three mutation strategies can effectively play their advantages and make up for the shortcomings of others. To evaluate the performance of the proposed algorithm, three different experiments are constructed on twenty-nine classical benchmark functions. The simulation results demonstrate that, (1) MRPSODE significantly outperforms conventional PSO and DE algorithms, (2) MRPSODE can achieve better performance than nine well-known DE variants in terms of solution quality and robustness, (3) MRPSODE is superior to nine latest heuristic-based algorithms. Furthermore, MRPSODE is successfully applied to seven typical constrained optimization problems and performs better than almost all compared methods.
Impact of Financial Innovation and Institutional Quality on Financial Development in Emerging Markets

Financial innovation and institutional quality play a key role in financial development. This study investigates the impact of financial innovation and institutional quality on financial development in an emerging markets setting. We used the sample of 17 emerging markets based on the availability of data from the period 1990–2020. Data were extracted from the World Development Indicator database. In this study, panel unit root, fully modified ordinary least squares and Pedroni Integration tests were applied to analyze the data. We find that financial innovation and institutional quality are significantly and positively related to financial development. Better financial innovation increases financial development, whereas low institutional quality in an emerging market can deteriorate financial development.
Skeleton Sequence and RGB Frame Based Multi-Modality Feature Fusion Network for Action Recognition

Action recognition has been a heated topic in computer vision for its wide application in vision systems. Previous approaches achieve improvement by fusing the modalities of the skeleton sequence and RGB video. However, such methods pose a dilemma between the accuracy and efficiency for the high complexity of the RGB video network. To solve the problem, we propose a multi-modality feature fusion network to combine the modalities of the skeleton sequence and RGB frame instead of the RGB video, as the key information contained by the combination of the skeleton sequence and RGB frame is close to that of the skeleton sequence and RGB video. In this way, complementary information is retained while the complexity is reduced by a large margin. To better explore the correspondence of the two modalities, a two-stage fusion framework is introduced in the network. In the early fusion stage, we introduce a skeleton attention module that projects the skeleton sequence on the single RGB frame to help the RGB frame focus on the limb movement regions. In the late fusion stage, we propose a cross-attention module to fuse the skeleton feature and the RGB feature by exploiting the correlation. Experiments on two benchmarks, NTU RGB+D and SYSU, show that the proposed model achieves competitive performance compared with the state-of-the-art methods while reducing the complexity of the network.
BORDER: A Benchmarking Framework for Distributed MQTT Brokers

MQTT, one of the most popular application layer protocols for the IoT, works according to a publish/subscribe paradigm where clients connect to a centralized broker.Sometimes (e.g., in high scalability and low latency applications), it is required to depart from such a centralized approach and move to a distributed one, where multiple MQTT brokers cooperate together.Many MQTT brokers (both open-source or commercially available) allow to create such a distributed environment: however, it is challenging to select the right solution due to the many available choices.This paper proposes therefore BORDER (Benchmarking framewORk for DistributEd MQTT bRokers), a framework for creating and evaluating distributed architectures of MQTT brokers with realistic and customizable network topologies.Based on isolated Docker containers and emulated network components, the framework provides quantitative metrics about the overall system performance, such as end-toend latency as well as network and physical resources consumed.We use BORDER to compare five of the most popular MQTT brokers that allow the creation of distributed architectures and we release it as an open-source project to allow for reproducible researches.
Random Quantum Circuits Anticoncentrate in Log Depth

We consider quantum circuits consisting of randomly chosen two-local gates and study the number of gates needed for the distribution over measurement outcomes for typical circuit instances to be anticoncentrated, roughly meaning that the probability mass is not too concentrated on a small number of measurement outcomes. An understanding of the conditions for anticoncentration is important for determining which quantum circuits are difficult to simulate classically, as anticoncentration has been in some cases an ingredient of mathematical arguments that simulation is hard and in other cases a necessary condition for easy simulation. Our definition of anticoncentration is that the expected collision probability of the distribution—that is, the probability that two independently drawn outcomes will agree—is only a constant factor larger than the collision probability for the uniform distribution. We show that when the two-local gates are each drawn from the Haar measure (or any 2-design), at least Ω(nlog(n)) gates (and thus Ω(log(n)) circuit depth) are needed for this condition to be met on an n-qudit circuit. In both the case where the gates are nearest neighbor on a one-dimensional ring and the case where gates are long range, we show that O(nlog(n)) gates are also sufficient and we precisely compute the optimal constant prefactor for the nlog(n). The technique we employ relies upon a mapping from the expected collision probability to the partition function of an Ising-like classical statistical-mechanical model, which we manage to bound using stochastic and combinatorial techniques.Received 16 July 2021Accepted 4 January 2022DOI:https://doi.org/10.1103/PRXQuantum.3.010333Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasClassical statistical mechanicsQuantum correlations in quantum informationQuantum entanglementTechniquesIsing modelStochastic analysisQuantum InformationStatistical Physics
Multi-Agent Fusion With Different Limited Fields-of-View

A key objective of multi-agent surveillance systems is to monitor a much larger region than the limited <i>field-of-view</i> (FoV) of any individual agent by successfully exploiting cooperation among multiple agents. Whenever either a centralized or a distributed approach is pursued, this goal cannot be achieved unless an appropriately designed fusion strategy is adopted. This paper presents a novel information fusion approach by considering for each agent a known limited, and possibly different, FoV. The proposed method, named <i>Bayesian-operation InvaRiance on Difference-sets</i> (BIRD) fusion, relies on <i>Generalized Covariance Intersection</i> (GCI) and exploits a general and exact decomposition of each multi-object posterior by partitioning the global FoV, i.e. the union of the FoVs of the fusing agents, into common and exclusive FoVs. It is shown how BIRD fusion can be used to perform multi-object estimation based on random finite sets on both a centralized and a distributed peer-to-peer sensor network. Simulation experiments on realistic multi-object tracking scenarios demonstrate the effectiveness of BIRD fusion.
Determinants of the acceptance and adoption of a digital contact tracing tool during the COVID-19 pandemic in Singapore

The motivations that govern the adoption of digital contact tracing (DCT) tools are complex and not well understood. Hence, we assessed the factors influencing the acceptance and adoption of Singapore's national DCT tool - TraceTogether - during the COVID-19 pandemic. We surveyed 3943 visitors of Tan Tock Seng Hospital from July 2020 to February 2021 and stratified the analyses into three cohorts. Each cohort was stratified based on the time when significant policy interventions were introduced to increase the adoption of TraceTogether. Binary logistic regression was preceded by principal components analysis to reduce the Likert items. Respondents who 'perceived TraceTogether as useful and necessary' had higher likelihood of accepting it but those with 'Concerns about personal data collected by TraceTogether' had lower likelihood of accepting and adopting the tool. The injunctive and descriptive social norms were also positively associated with both the acceptance and adoption of the tool. Liberal individualism was mixed in the population and negatively associated with the acceptance and adoption of TraceTogether. Policy measures to increase the uptake of a national DCT bridged the digital divide and accelerated its adoption. However, good public communications are crucial to address the barriers of acceptance to improve voluntary uptake widespread adoption.