Reviewerly/exHarmony huggingface dataset:
Adversarial Decoupling and Modality-Invariant Representation Learning for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification (RGB-IR ReID) has now attracted increasing attention due to its surveillance applications under low-light environments. However, the large intra-class variations between different domains are still a challenging issue in the field of computer vision. To address the above issue, we propose a novel adversarial Decoupling and Modality-invariant Representation learning (DMiR) method to explore potential spectrum-invariant yet identity-discriminative representations for cross-modality pedestrians. Our model consists of three key components, including Domain-related Representation Disentanglement (DrRD), Modality-invariant Discriminative Representation (MiDR) and Representation Orthogonal Decorrelation (ROD). First, two subnets named Identity-Net and Domain-Net are designed to extract identity-related features and domain-related features, respectively. Given this two-stream structure, the DrRD is introduced to achieve adversarial decoupling against domain-specific features via a min-max disentanglement process. Specifically, the classification objective function on Domain-Net is minimized to extract spectrum-specific information while maximizing it to reduce domain-specific information. Second, in Identity-Net, we introduce MiDR to enhance intra-class compactness and reduce domain variations by exploring positive and negative pair variations, semantic-wise differences, and pair-wise semantic variations. Finally, the correlation between the two decomposed features, i.e., identity-related features and domain-related features, may lead to the introduction of modal information in identity representations, and vice versa. Therefore, we present the ROD constraint to make the two decomposed features unrelated to each other, which can more effectively separate the two-component features and enhance feature representations. Practically, we construct ROD at the feature-level and parameter-level, and finally select feature-level ROD as the decorrelation strategy because of its superior decorrelation performance. The whole scheme leads to disentangling spectrum-dependent information, as well as purifying identity information. Extensive experiments are carried out on two mainstream RGB-IR ReID datasets, and the results demonstrate the effectiveness of our method.
Leveraging social influence based on users activity centers for point-of-interest recommendation

Recommender Systems (RSs) aim to model and predict the user preference while interacting with items, such as Points of Interest (POIs). These systems face several challenges, such as data sparsity, limiting their effectiveness. In this paper, we address this problem by incorporating social, geographical, and temporal information into the Matrix Factorization (MF) technique. To this end, we model social influence based on two factors: similarities between users in terms of common check-ins and the friendships between them. We introduce two levels of friendship based on explicit friendship networks and high check-in overlap between users. We base our friendship algorithm on users’ geographical activity centers. The results show that our proposed model outperforms the state-of-the-art on two real-world datasets. More specifically, our ablation study shows that the social model improves the performance of our proposed POI recommendation system by 31% and 14% on the Gowalla and Yelp datasets in terms of [email protected], respectively.
Designing Novel AAD Pooling in Hardware for a Convolutional Neural Network Accelerator

Convolutional neural network (CNN) hardware accelerators for specialized Internet of Things (IoT) requiring high accuracy is an emerging research topic. The pooling module in a CNN pipeline impacts both the speed and accuracy of a classification task. This work proposes the design and hardware implementation of a novel pooling method absolute average deviation (AAD) for CNN accelerator. AAD utilizes the spatial locality of pixels using vertical and horizontal deviations to achieve higher accuracy, lower area, and lower power consumption than mixed pooling without increasing the computational complexity. AAD is tested on four different datasets: EEG, ImageNet, Common Objects in Context (COCO), United States Postal Service (USPS), and multiple CNN structures: CNN, VGG16, VGG19, ResNet, and DenseNet. In hardware, AAD is implemented using Very High Speed Integrated Circuit (VHSIC) Hardware Description Language (VHDL) on Altera Arria10 GX field-programmable gate array (FPGA) and 45-nm technology using Synopsys Design Compiler. The area and power consumption are found to be 244.46 nm<sup>2</sup> and 0.31 mW, respectively. AAD achieves 98&#x0025; accuracy with lower computational and hardware costs compared to mixed pooling, making it an ideal pooling mechanism for an IoT CNN accelerator.
Double Critic Deep Reinforcement Learning for Mapless 3D Navigation of Unmanned Aerial Vehicles

This paper presents a novel deep reinforcement learning-based system for 3D mapless navigation for Unmanned Aerial Vehicles (UAVs). Instead of using an image-based sensing approach, we propose a simple learning system that uses only a few sparse range data from a distance sensor to train a learning agent. We based our approaches on two state-of-art double critics Deep-RL models: Twin Delayed Deep Deterministic Policy Gradient (TD3) and Soft Actor-Critic (SAC). We show that our two approaches manage to outperform an approach based on the Deep Deterministic Policy Gradient (DDPG) technique and the BUG2 algorithm. Also, our new Deep-RL structure based on Recurrent Neural Networks (RNNs) outperforms the current structure used to perform mapless navigation of mobile robots. Overall, we conclude that Deep-RL approaches based on double critic with Recurrent Neural Networks (RNNs) are better suited to perform mapless navigation and obstacle avoidance of UAVs.
Decentralizing construction AI applications using blockchain technology

Decentralizing the Artificial Intelligence (AI) applications is deemed the next wave of Blockchain Technology (BT) in the construction industry. Most of previous research studies employed the AI and the BT separately with limited efforts providing a conceptual view for possible convergence in real-life applications. Nevertheless, such efforts do not address construction applications. This research introduces a novel tailorable decentralized AI system that utilizes BT as a computing-oriented technology. The proposed system is developed as an inference engine while having a number of interesting features. First, the system validates and audits the decision-making process while sharing and recording the input data and the computed outcomes in a synchronized trusted manner. Second, the system allows the formation of distributed AI repository that can absorb and manage concurrent use-cases while targeting different scopes and covering diverse AI branches. Third, it provides a workable solution for the AI applications' distribution problem, which hinders their wide employment. Fourth, the introduced system guarantees sustainable versioning and evolution over time for AI applications based on their performance or the newly acquired data. A case study of estimating the construction cost for road projects is provided to illustrate the system’s workability and demonstrate its performance.
Deep Learning-Based Image Semantic Coding for Semantic Communications

This paper presents the Generative Adversarial Networks (GANs)-based image semantic coding, the goal of which is semantic exchange rather than symbol transmission. State-of-the-art visually pleasing reconstruction and semantic preserving performance are obtained in extreme low bitrate via a rate-perception-distortion optimization framework. In particular, we investigate convolutional encoder, quantizer, conditional SPADE generator, residual coding as well as perceptual losses. In contrast to previous work, we designed a coarse-to-fine image semantic coding model for multimedia semantic communication system. The base layer of the image is fully generated and preserves semantic information while the enhancement layer restores the fine details. We explore the perception and distortion performance trade-off by tuning the rate of base layer and enhancement layer. Different from the existing methods that adopt pixel accuracy as distortion metric, we train and evaluate the proposed image semantic coding model with multiple perception metrics, in line with the purpose of semantic communications. Experimental results demonstrate that our model could achieve visually pleasant and semantic consistent reconstruction, as well as saving times of bitrate, compared to BPG, WebP, JPEG2000, JPEG, and other deep learning-based image codecs.
Artificial bee colony algorithm with efficient search strategy based on random neighborhood structure

As a popular swarm intelligence algorithm, artificial bee colony (ABC) achieves excellent optimization performance, but it has some shortcomings. In order to strengthen the performance of ABC, a new ABC with efficient search strategy based on random neighborhood structure (called RNSABC) is proposed. In RNSABC, a new random neighborhood structure (RNS) is constructed. Each solution has an independent and random neighborhood size. An improved search strategy is designed on the basic of RNS. Moreover, a depth first search method is utilized to enhance the role of the onlooker bee phase. To study the optimization capability of RNSABC, a set of 57 benchmark problems including classical problems, CEC 2013 complex problems, and polynomial problems are tested. Experimental results show RNSABC can obtain competitive performance when compared with nine other recent ABC variants.
Energy-aware systems for real-time job scheduling in cloud data centers: A deep reinforcement learning approach

With the advantages such as high-performance, low-maintenance, and reliability, more and more companies are moving their computing infrastructures to the cloud. In the meantime, with the increasing number of users continuously submitting jobs to cloud, energy consumed by the current cloud data centers has become a major concern for cloud service providers, due to financial and environmental reasons. In this paper, we propose a deep reinforcement learning (DRL) approach to handle real-time jobs. Specifically, we focus on allocating incoming jobs to appropriate virtual machines (VMs) in a way that energy consumption can be optimized while high quality of service (QoS) can be achieved. We give the detailed design and implementation of our approach, and our experimental results demonstrate that the proposed method can achieve better performance in job success rate and average response time with less energy consumption than the current approaches, in the presence of different real-time cloud workloads.
A survey of inverse reinforcement learning

Abstract Learning from demonstration, or imitation learning, is the process of learning to act in an environment from examples provided by a teacher. Inverse reinforcement learning (IRL) is a specific form of learning from demonstration that attempts to estimate the reward function of a Markov decision process from examples provided by the teacher. The reward function is often considered the most succinct description of a task. In simple applications, the reward function may be known or easily derived from properties of the system and hard coded into the learning process. However, in complex applications, this may not be possible, and it may be easier to learn the reward function by observing the actions of the teacher. This paper provides a comprehensive survey of the literature on IRL. This survey outlines the differences between IRL and two similar methods - apprenticeship learning and inverse optimal control. Further, this survey organizes the IRL literature based on the principal method, describes applications of IRL algorithms, and provides areas of future research.
Cybersickness and Its Severity Arising from Virtual Reality Content: A Comprehensive Study

Virtual reality (VR) experiences often elicit a negative effect, cybersickness, which results in nausea, disorientation, and visual discomfort. To quantitatively analyze the degree of cybersickness depending on various attributes of VR content (i.e., camera movement, field of view, path length, frame reference, and controllability), we generated cybersickness reference (CYRE) content with 52 VR scenes that represent different content attributes. A protocol for cybersickness evaluation was designed to collect subjective opinions from 154 participants as reliably as possible in conjunction with objective data such as rendered VR scenes and biological signals. By investigating the data obtained through the experiment, the statistically significant relationships-the degree that the cybersickness varies with each isolated content factor-are separately identified. We showed that the cybersickness severity was highly correlated with six biological features reflecting brain activities (i.e., relative power spectral densities of Fp1 delta, Fp 1 beta, Fp2 delta, Fp2 gamma, T4 delta, and T4 beta waves) with a coefficient of determination greater than 0.9. Moreover, our experimental results show that individual characteristics (age and susceptibility) are also quantitatively associated with cybersickness level. Notably, the constructed dataset contains a number of labels (i.e., subjective cybersickness scores) that correspond to each VR scene. We used these labels to build cybersickness prediction models and obtain a reliable predictive performance. Hence, the proposed dataset is supposed to be widely applicable in general-purpose scenarios regarding cybersickness quantification.
Tangent search algorithm for solving optimization problems

This article proposes a new population-based optimization algorithm called the Tangent Search Algorithm (TSA) to solve optimization problems. The TSA uses a mathematical model based on the tangent function to move a given solution toward a better solution. The tangent flight function has the advantage to balance between the exploitation and the exploration search. Moreover, a novel escape procedure is used to avoid to be trapped in local minima. Besides, an adaptive variable step-size is also integrated in this algorithm to enhance the convergence capacity. The performance of TSA is assessed in three classes of tests: classical tests, CEC benchmarks, and engineering optimization problems. Moreover, several studies and metrics have been used to observe the behavior of the proposed TSA. The experimental results show that TSA algorithm is capable to provide very promising and competitive results on most benchmark functions thanks to better balance between exploration and exploitation of the search space. The main characteristics of this new optimization algorithm is its simplicity and efficiency and it requires only a small number of user-defined parameters.
Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels

Graph Neural Networks (GNNs) have shown their great ability in modeling graph structured data. However, real-world graphs usually contain structure noises and have limited labeled nodes. The performance of GNNs would drop significantly when trained on such graphs, which hinders the adoption of GNNs on many applications. Thus, it is important to develop noise-resistant GNNs with limited labeled nodes. However, the work on this is rather limited. Therefore, we study a novel problem of developing robust GNNs on noisy graphs with limited labeled nodes. Our analysis shows that both the noisy edges and limited labeled nodes could harm the message-passing mechanism of GNNs. To mitigate these issues, we propose a novel framework which adopts the noisy edges as supervision to learn a denoised and dense graph, which can down-weight or eliminate noisy edges and facilitate message passing of GNNs to alleviate the issue of limited labeled nodes. The generated edges are further used to regularize the predictions of unlabeled nodes with label smoothness to better train GNNs. Experimental results on real-world datasets demonstrate the robustness of the proposed framework on noisy graphs with limited labeled nodes.
A Hybrid Domain Image Encryption Algorithm Based on Improved Henon Map

A hybrid domain image encryption algorithm is developed by integrating with improved Henon map, integer wavelet transform (IWT), bit-plane decomposition, and deoxyribonucleic acid (DNA) sequence operations. First, we improve the classical two-dimensional Henon map. The improved Henon map is called 2D-ICHM, and its chaotic performance is analyzed. Compared with some existing chaotic maps, 2D-ICHM has larger parameter space, continuous chaotic range, and more complex dynamic behavior. Second, an image encryption structure based on diffusion-scrambling-diffusion and spatial domain-frequency domain-spatial domain is proposed, which we call the double sandwich structure. In the encryption process, the diffusion and scrambling operations are performed in the spatial and frequency domains, respectively. In addition, initial values and system parameters of the 2D-ICHM are obtained by the secure hash algorithm-512 (SHA-512) hash value of the plain image and the given parameters. Consequently, the proposed algorithm is highly sensitive to plain images. Finally, simulation experiments and security analysis show that the proposed algorithm has a high level of security and strong robustness to various cryptanalytic attacks.
Improving teaching–learning-based-optimization algorithm by a distance-fitness learning strategy

The teaching–learning-based optimization (TLBO) algorithm, composed of a teacher phase and a learner phase, is one of the most popular global optimization approaches. It is inevitable for TLBO to suffer from premature convergence and entrapment in local optima when dealing with complex optimization problems. To solve this problem, a novel TLBO variant named distance-fitness learning TLBO (DFL-TLBO), which employs a brand new distance-fitness learning (DFL) strategy to enhance searchability, is proposed. The DFL strategy guides the whole population to learn from the teacher and a distance-fitness optimum (DFO) candidate simultaneously according to a differential operator to improve the exploitation and exploration capability, respectively. Experimental results on the tests of CEC 2014 reveal that DFL successfully improves the exploitation and exploration capability of TLBO in balance. The statistical test results and the convergence analysis results indicate that the DFL-TLBO algorithm ranks best and is significantly better than other compared algorithms in solving global optimization problems.
Rethinking Video Anomaly Detection - A Continual Learning Approach

While video anomaly detection has been an active area of research for several years, recent progress is limited to improving the state-of-the-art results on small datasets using an inadequate evaluation criterion. In this work, we take a new comprehensive look at the video anomaly detection problem from a more realistic perspective. Specifically, we consider practical challenges such as continual learning and few-shot learning, which humans can easily do but remains to be a significant challenge for machines. A novel algorithm designed for such practical challenges is also proposed. For performance evaluation in this new framework, we introduce a new dataset which is significantly more comprehensive than the existing benchmark datasets, and a new performance metric which takes into account the fundamental temporal aspect of video anomaly detection. The experimental results show that the existing state-of-the-art methods are not suitable for the considered practical challenges, and the proposed algorithm outperforms them with a large margin in continual learning and few-shot learning tasks.
Multi-strategy firefly algorithm with selective ensemble for complex engineering optimization problems

Nowadays, more and more optimization techniques are used to deal with complex engineering optimization problems. Firefly algorithm (FA) inspired by the flash communication between fireflies, has been proven to be competitive with other swarm intelligence algorithms and has been widely applied to solve complex engineering optimization problems. However, FA has some defects in dealing with complex engineering optimization problems, such as the exploration and exploitation cannot be well balanced. Therefore, in order to achieve effective performance, the different characteristics of search strategies can be applied at different stages of the search process to achieve a balance between exploration and exploitation. In this paper, a multi-strategy firefly algorithm with selective ensemble (MSEFA) is proposed. In MSEFA, the algorithm has three novel search strategies with different characteristics in the strategy pool. In addition, an idea of selective ensemble is adopted to design a priority roulette selection method. The method can select suitable search strategies in different search stages and coordinate the balance of strategies so that better results can be obtained. Furthermore, a parameter adaptive transformation mechanism is designed to control the decreasing rate of step size α. To verify the effectiveness of MSEFA, performance tests are conducted on the CEC 2013 and CEC 2019 test suites, after which MSEFA is used to solve four complex engineering optimization problems. Experimental results show that MSEFA has the best performance compared with other FA variants and other improved swarm intelligence algorithms. In addition, MSEFA also achieves the best results in dealing with four complex engineering optimization problems.
Minimum-Time Quadrotor Waypoint Flight in Cluttered Environments

We tackle the problem of planning a minimum-time trajectory for a quadrotor over a sequence of specified waypoints in the presence of obstacles while exploiting the full quadrotor dynamics. This problem is crucial for autonomous search and rescue and drone racing scenarios but was, so far, unaddressed by the robotics community <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in its entirety</i> due to the challenges of minimizing time in the presence of the non-convex constraints posed by collision avoidance. Early works relied on simplified dynamics or polynomial trajectory representations that did not exploit the full actuator potential of a quadrotor and, thus, did not aim at minimizing time. We address this challenging problem by using a hierarchical, sampling-based method with an incrementally more complex quadrotor model. Our method first finds paths in different topologies to guide subsequent trajectory search for a kinodynamic point-mass model. Then, it uses an asymptotically-optimal, kinodynamic sampling-based method based on a full quadrotor model on top of the point-mass solution to find a feasible trajectory with a time-optimal objective. The proposed method is shown to outperform all related baselines in cluttered environments and is further validated in real-world flights at over 60 km/h in one of the world’s largest motion capture systems. We release the code open source.