Reviewerly/exHarmony huggingface dataset:
An Approach Based on Quantum Reinforcement Learning for Navigation Problems

The power of classical computers is still insufficient for deep reinforcement learning (DRL) problems which has large state space. Thanks to entanglement and superposition, quantum computers have a high computational power. The concept of using this high computing power to solve problems that would be difficult for classical computers is fairly common. In this study, a hybrid approach is proposed to take advantage of the benefits of quantum computers. Deep Q-Network (DQN) algorithm for DRL, optimization operations, and storage operations are performed on the classical computer side of this hybrid approach. In the quantum side, a variational quantum circuit (VQC) is proposed. The proposed method is applied to a navigation problem. The proposed approach is evaluated in terms of target success rate, collision (going out) rate. The proposed approach is compared to DRL solutions in the literature for the navigation problem with classical computers. According to the number of parameters used, the proposed approach appears to be successful. As a result, the performance of the proposed approach has been validated.
Implementation of Quantum Deep Reinforcement Learning Using Variational Quantum Circuits

With the evolution of technology, the need to solve complex computational problems like machine learning and deep learning has shot up. However, even the most powerful classical supercomputers find it difficult to execute these tasks. Advancements in quantum computing are leading researchers and tech-giants who strive for better quantum circuits to do machine learning tasks. Current works on Quantum Machine Learning (QML) ensure less memory consumption and reduced model parameters. However, it is strenuous to simulate the classical deep learning approach on existing quantum computing devices due to the inflexibility of Deep quantum circuits. Consequently, designing viable quantum algorithms for QML for noisy intermediate-scale quantum (NISQ) devices is essential. The proposed work aims to explore Variational Quantum Circuits (VQC) for Deep Q network-based Reinforcement Learning by remodeling the target network and experience replay into a representation of VQC. In addition, to the reduction in model parameters, quantum information encoding schemes are used to achieve better results than classical neural networks. VQCs are employed for policyselection and decision-making reinforcement learning by approximating the deep Q-value function with target network and experience replay.
UAV trajectory planning based on APF-RRT* algorithm with goal-biased strategy

In recent decades, Rapidly-exploring random tree star(RRT*) algorithm has attracted much attention because of its asymptotic optimization properties. However, it has slow convergence rate and large randomness in search range. Aiming at the shortcomings of this algorithm, unmanned aerial vehicle(UAV) trajectory planning method based on goalbiased artificial potential field(APF)-RRT* algorithm is proposed. Firstly, the goal-biased strategy is used to guide the generation of random sampling points to accelerate the convergence rate of the algorithm. Secondly, the improved artificial potential field method is introduced into the random search tree, which greatly reduces the number of iterations. The combination of the two algorithms generates new nodes with higher quality and decreases greatly path cost. Finally, the search performance of growing trees is enhanced greatly by comparing the algorithm with RRT* and Informed-RRT*.
Pedestrian Detection and Feedback Application Based on YOLOv5s and DeepSORT

To solve the problem of low counting efficiency of people flow supervision in scenic spots and cities, an improved YOLOv5s detection method combined with DeepSORT target tracking is proposed. As the accuracy of DeepSORT multi-target tracking depends on the detection efficiency of the target detection algorithm, the attention module CBAM is integrated with the Neck part of YOLOv5s network to improve the ability of the target detection model to extract small object features, thereby improving the recognition ability. The Market-1501 data set is used to train the pedestrian re-id model, and the images containing pedestrians in the VOC data set are used as the training set to train the detection model so that the model only detects pedestrians. Finally, connect the improved YOLOv5s detector and DeepSORT, and set a virtual detection line in the video to count the flow of people. The experimental results show that the average accuracy of the improved YOLOv5s is 1% higher than that of the original algorithm, and combined with DeepSORT tracking, the detection rate of 34 frames is achieved in the test video.
Debiased Recommendation with User Feature Balancing

Debiased recommendation has recently attracted increasing attention from both industry and academic communities. Traditional models mostly rely on the inverse propensity score (IPS), which can be hard to estimate and may suffer from the high variance issue. To alleviate these problems, in this article, we propose a novel debiased recommendation framework based on user feature balancing. The general idea is to introduce a projection function to adjust user feature distributions, such that the ideal unbiased learning objective can be upper bounded by a solvable objective purely based on the offline dataset. In the upper bound, the projected user distributions are expected to be equal given different items. From the causal inference perspective, this requirement aims to remove the causal relation from the user to the item, which enables us to achieve unbiased recommendation, bypassing the computation of IPS. To efficiently balance the user distributions upon each item pair, we propose three strategies, including clipping, sampling, and adversarial learning to improve the training process. For more robust optimization, we deploy an explicit model to capture the potential latent confounders in recommendation systems. To the best of our knowledge, this article is the first work on debiased recommendation based on confounder balancing. In the experiments, we compare our framework with many state-of-the-art methods based on synthetic, semi-synthetic, and real-world datasets. Extensive experiments demonstrate that our model is effective in promoting the recommendation performance.
AIDTF: Adversarial training framework for network intrusion detection

Network Intrusion Detection Systems (IDS) have achieved high accuracy by widely applying Machine Learning (ML) models. However, most current ML-based IDSs can not cope with targeted attacks from adversaries because they are commonly trained and tested using fixed data sets. In this paper, we propose an Adversarial Intrusion Detection Training Framework (AIDTF) to improve the robustness of IDSs, which consists of an attacker model a-model, a defender model d-model, and a black-box trainer t-module. Both the a-model and d-model are multilayer perceptrons, and the t-module is the module used to train IDSs. AIDTF improves the accuracy of IDS by using an adversarial training method, which is different from traditional training methods. Taking the distribution of normal samples in the dataset as the distribution that the a-model and d-model need to learn, the goal of the a-model is to generate samples that deceive the d-model, while the goal of the d-model is to determine whether the input samples are real samples, so there is an adversarial relationship between a-model and d-model. Different types of IDSs can be trained by the t-module using the samples generated from the confrontation between the a-model and the d-model, and we call this kind of IDS the Adversarial Training Intrusion Detection System (ATIDS). The main contribution of this paper is to propose a training method that is used to obtain an IDS with high accuracy not only for known test sets but also to identify unknown disguised attack samples. We tested different types of ATIDSs using the current mainstream attack methods, which include the Fast Gradient Method, Fast Gradient Sign Method, Projected Gradient Descent, and Jacobs Saliency Map Algorithm. The experimental results prove that AIDTF outperforms other adversarial training methods with not only higher accuracy for the test set but also up to a 99% recognition rate for the attack samples.
SyReNN: A tool for analyzing deep neural networks

Deep Neural Networks (DNNs) are rapidly gaining popularity in a variety of important domains. Unfortunately, modern DNNs have been shown to be vulnerable to a variety of attacks and buggy behavior. This has motivated recent work in formally analyzing the properties of such DNNs. This paper introduces SyReNN, a tool for understanding and analyzing a DNN by computing its symbolic representation. The key insight is to decompose the DNN into linear functions. Our tool is designed for analyses using low-dimensional subsets of the input space, a unique design point in the space of DNN analysis tools. We describe the tool and the underlying theory, then evaluate its use and performance on three case studies: computing Integrated Gradients, visualizing a DNN’s decision boundaries, and repairing buggy DNNs.
The effects of augmented reality on consumer responses in mobile shopping: The moderating role of task complexity

Augmented reality (AR) solutions help facilitate consumers' direct examinations, enhancing their shopping experiences in the digital commerce context. This study examines consumer responses to AR in mobile shopping. It investigates the relationships among perceived media richness, interactivity, telepresence, utilitarian and hedonic values, and behavioral intentions. Furthermore, it explores whether these relationships differ depending on consumers' perceived task complexity. A total of 279 mobile application users participated in the online survey. The participants were guided to answer an online questionnaire after utilizing an AR mobile application to purchase a jewelry product. The findings reveal that media richness and interactivity positively influence telepresence, and telepresence increases behavioral intentions through perceived utilitarian and hedonic values. The effect of interactivity on telepresence and the impact of telepresence on utilitarian value are higher for consumers with low task complexity perception. By contrast, the impact of telepresence on hedonic value is higher for consumers with high task complexity perception. The results suggest practical implications for mobile retailers that apply advanced AR technology in retailing.
Trajectory prediction in pipeline form for intercepting hypersonic gliding vehicles based on LSTM

The interception problem of Hypersonic Gliding Vehicles (HGVs) has been an important aspect of missile defense systems. In order to provide interceptors with accurate information of target trajectory, a model based on an improved Long Short-Time Memory (LSTM) network for trajectory prediction pipeline is proposed for the interception of a skip gliding hypersonic target. Firstly, for trajectory prediction required by intercepting guidance laws, the altitude, velocity and velocity direction of the target are formulated in the form of analytic functions, consisting of linear decay terms and amplitude decay sinusoidal terms. Then, the dynamic characteristics of the model parameters are analyzed, and the target trajectory prediction pipeline is proposed with the prediction error considered. Finally, an improved LSTM network is designed to estimate parameters in a dynamically-updated manner, and estimation results are used for the calculation of the final trajectory prediction pipeline. The proposed prediction algorithm provides information on the velocity vector for midcourse guidance with the effect of prediction errors on interception taken into account. Simulation is conducted and the results show the high accuracy of the algorithm in HGVs' trajectory prediction which is conducive to increasing the interception success rate.
UDRN: Unified Dimensional Reduction Neural Network for feature selection and feature projection

Dimensional reduction (DR) maps high-dimensional data into a lower dimensions latent space with minimized defined optimization objectives. The two independent branches of DR are feature selection (FS) and feature projection (FP). FS focuses on selecting a critical subset of dimensions but risks destroying the data distribution (structure). On the other hand, FP combines all the input features into lower dimensions space, aiming to maintain the data structure, but lacks interpretability and sparsity. Moreover, FS and FP are traditionally incompatible categories and have not been unified into an amicable framework. Therefore, we consider that the ideal DR approach combines both FS and FP into a unified end-to-end manifold learning framework, simultaneously performing fundamental feature discovery while maintaining the intrinsic relationships between data samples in the latent space. This paper proposes a unified framework named Unified Dimensional Reduction Network (UDRN) to integrate FS and FP in an end-to-end way. Furthermore, a novel network framework is designed to implement FS and FP tasks separately using a stacked feature selection network and feature projection network. In addition, a stronger manifold assumption and a novel loss function are proposed. Furthermore, the loss function can leverage the priors of data augmentation to enhance the generalization ability of the proposed UDRN. Finally, comprehensive experimental results on four image and four biological datasets, including very high-dimensional data, demonstrate the advantages of DRN over existing methods (FS, FP, and FS&FP pipeline), especially in downstream tasks such as classification and visualization.
VM performance-aware virtual machine migration method based on ant colony optimization in cloud environment

Many virtual machine (VM) allocation methods have been proposed to reduce the number of physical machines (PMs), improve resource utilization for cloud service providers. If VMs are migrated on the same PM, then there will be substantial resource competition among these VMs, which results in VM performance reduction. Many VM migration (VMM) methods neglect VM performance reduction. Although some performance-aware VM allocation methods were proposed, the performance optimization objective of these methods mainly aimed at guaranteeing service level agreement or reducing VM downtime during migration, and they did not scientifically analyze how the VM performance degrades. Therefore, how to minimize the VM performance reduction for users when migrating VMs remains a major challenge. This paper proposes a VM Performance-Aware VMM method (PAVMM) for both users and cloud service providers. To maximize VM performance for users, it utilizes the VM performance model, which was built in our previous works, to predict the VM performance after migrating VMs. It then establishes an optimization objective of maximizing VM performance for users. Meanwhile, minimizing the number of active PMs and the total migration cost are regarded as additional optimization objectives for cloud service providers. Therefore, we formulate VMM as a multi-objective optimization problem, which tries to maximize VM performance for users and minimize the number of active PMs and the total migration cost for cloud service providers simultaneously. Then an ant colony optimization (ACO)-based algorithm is proposed to solve the NP-hard VMM problem. Lastly, the experiments are conducted to evaluate PAVMM, and the results verify its efficiency.
Multi-level channel attention excitation network for human action recognition in videos

Channel attention mechanism has continuously attracted strong interests and shown great potential in enhancing the performance of deep CNNs. However, when applied to video-based human action recognition task, most existing methods generally learn channel attention at frame level, which ignores the temporal dependencies and may limit the recognition performance. In this paper, we propose a novel multi-level channel attention excitation (MCAE) module to model the temporal-related channel attention at both frame and video levels. Specifically, based on video convolutional feature maps, frame-level channel attention (FCA) is generated by exploring time-channel correlations, and video-level channel attention (VCA) is generated by aggregating global motion variations. MCAE firstly recalibrates video feature responses with frame-wise FCA, and then activates the motion-sensitive channel features with motion-aware VCA. MCAE module learns the channel discriminability from multiple levels and can act as a guidance to facilitate efficient spatiotemporal feature modeling in activated motion-sensitive channels. It can be flexibly embedded into 2D networks with very limited extra computation cost to construct MCAE-Net, which effectively enhances the spatiotemporal representation of 2D models for video action recognition task Extensive experiments on five human action datasets show that our method achieves superior or very competitive performance compared with the state-of-the-arts, which demonstrates the effectiveness of the proposed method for improving the performance of human action recognition.
Underwater image enhancement by modified color correction and adaptive Look-Up-Table with edge-preserving filter

Particles suspended in water cause poor visibility and color distortions in the images that are captured in underwater. These suspended particles cause light to be scattered that results in non-uniform illumination due to dispersion and refraction of light. The illumination variation cause color casts in the underwater images. This paper proposes an underwater image enhancement method that improves the visibility, contrast and color cast in the underwater images. The proposed method includes a modified color correction, an adaptive Look-Up-Table (LUT) and an edge-preserving filter. First, a color correction is performed on individual LAB color space components. The improvement of the luminance (L) component is processed with a sequence of steps such as CLAHE, sharpening, gamma and color correction. In addition, the chrominance (A and B) components are modified by further color correction. Second, the contrast of the color-corrected image is enhanced using the adaptive LUT based on probability threshold. This probability threshold increases the image contrast by limiting the pixel intensity values in each RGB channel. In addition, the probability threshold controls the halo effects and artifacts. Finally, the underwater image is enhanced with a fast local Laplacian filter (FLLF) to preserve the edge details with texture smoothing. The denoised image is adjusted with histogram normalization for a further color balancing. The experimental results obtained from the proposed method on different underwater image datasets shows a supremacy over color casts, visibility, halo effects, free of artifacts and edge-preserving in the underwater images compared to the existing methods.
Smart Control of Home Appliances Using Hand Gesture Recognition in an IoT-Enabled System

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Image Steganalysis of Low Embedding Rate Based on the Attention Mechanism and Transfer Learning

In recent years, some research results have been achieved in the field of image steganalysis. However, there are still problems of difficulty in extracting steganographic features from images with low embedding rates and unsatisfactory detection performance of steganalysis. In this paper, we propose an image steganalysis method based on the attention mechanism and transfer learning. The method constructs a network model based on a convolutional neural network, including a preprocessing layer, a transposed convolutional layer, an ordinary convolutional layer, and a fully connected layer. We introduce the efficient channel attention module after the ordinary convolutional layer to focus on the steganographic region of the image, capture the local cross-channel interaction information, realize the adaptive adjustment of feature weights, and enhance the ability to extract steganographic features. Meanwhile, we apply the transfer learning method to use the training model parameters of high embedding rate images as the initialization parameters of the training model of the low embedding rate to achieve feature migration and further improve the steganalysis performance of the low embedding rate. The experimental results show that compared to the typical Xu-Net and Yedroudj-Net models, the detection accuracy of the proposed method is improved by 16.36% to 30.66% and by 35.59 to 37.83% for the embedding rates of 0.05 bpp, 0.1 bpp, and 0.2 bpp, respectively. Compared to the state-of-the-art Shen-Net model with low embedding rates, the detection accuracy is improved by 3.43% to 6.41%. This demonstrates the higher detection performance of the proposed method for steganalysis of low embedding rate images.
Exerting Qualitative Analytics and Blockchain Requirement-Engineering in Designing and Implementing a Luxury Products Authentication System

High-quality luxury products cater to a specific group of consumers due to their durability and the value attached to them. Counterfeiting luxury products has resulted in economic losses for both the producers and consumers. The market for counterfeit luxury products has continued to grow due to the difficulty in authenticating genuine luxury products. The traditional system of verification largely depends on the expert use of specialized equipment for visually inspecting physical luxury products and their associated certificates. This conventional process of authenticating luxury products is expensive, slow, and not easily accessible to consumers. Hence, there is a need for a digital verification approach for luxury products. Blockchain provides the potential for providing traceable and immutable information about a given luxury product. The focus of this paper is to develop a blockchain decentralized application (DApp) for authenticating luxury products in the class of luxury accessories such as jewelry across their respective lifecycles. To achieve this, qualitative analytics is applied to identify useful features for the digital authentication of luxury products. Blockchain requirement engineering modelling is then applied to explore the use of blockchain technologies to realize the features that guarantee transparency in the ownership and use of luxury products. Furthermore, this paper explores the existing blockchain technologies for realizing and implementing the developed requirements of the authentication DApp for luxury products. The selected blockchain technology stack is applied in prototyping authentication systems for luxury products. The implemented platform is simulated to demonstrate the operations carried out in authenticating luxury products.