Reviewerly/exHarmony huggingface dataset:
Moving Object Detection Based on Adaptive Loci Frame Difference Method

Criminal investigation videos often have the characteristics of complex video background, long distances of moving targets, and fast or slow-motion speeds, which have a great impact on intelligent target detection. Traditional moving target detection methods are more suitable for video environment with single background and fast-moving target. It is not sensitive and difficult to detect slow moving targets in complex backgrounds. Therefore, a moving object detection method based on improved inter-frame difference algorithm is proposed in this paper. In the process of reading each frame of video, the appropriate number of frames is determined by combining the characteristics of the image, and the difference operation is performed. Each pixel of the difference image is classified and processed. After detecting the target, the ambient noise and other interference are filtered out by setting the threshold method to realize binarization. The target region with the most effective target points is detected automatically, and the accuracy and feasibility of the algorithm are confirmed by a large number of experiments.
Quantum coherence and quantum Fisher information in the XXZ system

We investigate the quantum phase transition of the XXZ model via quantum coherence and quantum Fisher information. The quantum phase transition of the nearest, the next-nearest, and the third-nearest particles is demonstrated by quantum Fisher information and quantum coherence measures, i.e., relative entropy, l1 norm, quantum Jensen-Shannon divergence, respectively. Both analytical and numerical results are presented in detail. This work demonstrates that quantum coherence and quantum Fisher information can be used as new quantum measures for characterizing the quantum phase transition of the XXZ spin chain systems.
A Privacy-Preserving System for Targeted Coupon Service

It is increasingly popular to use behavioral targeting in online coupon service for targeted coupon delivery, wherein customized coupons can be delivered to only eligible users whose behavioral profiles match targeting profiles specified by the vendor. While allowing the vendor to personalize the service and build loyalty, such practice also raises the security challenges of protecting users' behavioral profiles and the vendor' targeting profiles. Our research caters for the trend of targeted coupons yet aims to offer privacy assurance. Unlike prior works which are largely constrained by the mere support for secure behavioral targeting, we ambitiously target a new system design that uniquely provides comprehensive functionalities to satisfy practical needs. Specifically, we explore and present the first full-fledged system design for targeted coupon service with three practical functionalities: (i) Private range search on coupon discounts: it enables a user to securely search for encrypted targeted coupons with certain discount range; (ii) Private behavioral targeting: it ensures that only eligible users can decrypt the ciphertexts of targeted coupons obtained from search; (iii) Private blockchain-empowered coupon redemption: it enables coupon redemption to be securely and transparently settled on the blockchain. We conduct extensive experiments and the results show that our secure system design is practically affordable.
A New Approach of Digital Signature Verification based on BioGamal Algorithm

In recent times, online services are playing a crucial role in our day-to-day life applications. Inspite of their advantage, it also have certain security challenges in the communication network. Security aspects consists of authentication of users, confidentiality of data/information as well as integrity of data. In order to achieve all these parameters, the sensitive information must be digitally signed by the original sender and later verified by the intended recipient. Therefore, research on digital signatures should be further developed to improve the data security and authenticity of the transferred data. In this paper, a secured digital signature algorithm is designed. The design of secure digital signature uses the concept of hybridization of secure hash code, DNA encryption/decryption technique and elgamal encryption/decryption techniques. The use of SHA algorithm generates a secure hash code and hybridization of encryption algorithm reduces the computational complexity and this research method is then compared with existing PlayGamal algorithm with respect to encryption/decryption time complexity.
SGXPool: Improving the performance of enclave creation in the cloud

Abstract Deploying user data or programs in cloud risks divulging their privacy because the cloud‐side supervisors, such as system administrators, can leverage the higher privilege to snoop the user data. Based on the trusted execution environment (TEE) technology, Intel Software Guard eXtension (SGX) is a practical remedy to user privacy, which employs hardware‐assisted enclave to wrap the sensitive data, preventing them from the disclosure. However, the application performance is hurt due to the CPU‐expensive and frequent operations on the enclave creation and destruction. In this paper, we propose SGXPool, an application‐level framework of resource management to relieve the above issue. SGXPool first uses a preallocated resource pool to assign/revoke the enclave on demand and avoids its cost of dynamical creation and destruction. Then, SGXPool utilizes another resource pool to constrain the threading overhead such as thread initialization, scheduling, and destruction. In addition, SGXPool exposes the simple and clear interface, allowing users or programmers to apply SGXPool without any intrusive modifications on the original SGX application. We implement SGXPool in the typical web servers which use the multithreading to handle the concurrent user requests. The evaluation results show that SGXPool can improve the performance of the original multithreading system up to 19 times. Meanwhile, the original security of SGX is maintained.
High Throughput Block Coding in the HTJ2K Compression Standard

This paper describes the block coding algorithm that underpins the new High Throughput JPEG 2000 (HTJ2K) standard. The objective of HTJ2K is to overcome the computational complexity of the original block coding algorithm, by providing a drop-in replacement that preserves as much of the JPEG 2000 feature set as possible, while allowing reversible transcoding to/from the original format. We show how the new standard achieves these goals, with high coding efficiency, and extremely high throughput in software.
Robust 3D Perception for any Environment and any Weather Condition using Thermal Stereo

We propose a robust and reliable method to generate 3D dense map of surrounding environment using thermal stereo sensor. To realize level 4 and 5 of fully automated driving, we should develop reliable and robust perception modules that works 24 hours every 365 day and night at any weather condition. Today, sensors such as cameras, LIDAR and RADAR are extensively used by OEMs to develop different Advanced Driving Assistance Systems (ADAS) applications. Though any of these sensors have specific challenges to overcome and we are still far from a reliable and robust perception module for automated driving. Sensors fusion may solve some problems of individual sensor but there are still some challenges that cannot be solved by advanced fusion solutions. Last decade, the thermal sensors had great advances to improve the resolution and accuracy and the sensor price is getting cheaper. The thermal stereo is robust and reliable sensors that may solve current challenges of perception and would be a necessary item of any sensory solutions for future vehicles. In this paper, we propose a thermal stereo solution to generate reliable 3D maps from driving environment. The experiment results shows that we are able to generate accurate and dense 3D map by using the thermal images in any environment.
Multi-Level Model for Video Saliency Detection

This paper proposes a fast detection model for video salient objects based on recurrent network architecture. Firstly, a multi-level attention (MLA) module is designed, which integrates multi-level feature maps in a cascaded manner. It effectively extracts the semantic information and detailed information of the intra-frame. These spatial features are input into a deeper bidirectional ConvLSTM to learn temporal dependence. Secondly, the result of the forward flow output is used as a backward input, and deeper temporal dependence is extracted. Finally, we present a spatial-temporal fused bidirectional ConvLSTM framework, which reduces the accumulated memory in the bidirectional ConvLSTM by exploiting element level fusion strategy. The experimental results show that the proposed method achieves the best detection precision on the two challenging benchmarks: ViSal and FBMS datasets, with a real-time speed of 23 fps.
Forward-Looking Imaginative Planning Framework Combined with Prioritized-Replay Double DQN

Many machine learning systems are built to solve the toughest planning problems. Such systems usually adopt a one-size-fits-all approach to different planning problems. This can lead to a waste of precious computing resources in simple planning problems, while not investing enough in complex problems. This requires a new model framework that does not require the learning of a single, fixed strategy, but rather introduces a series of decision controllers to resolve various planning tasks through learning to build, predict, and evaluate plans. Therefore, we propose a forward-looking imaginative planning framework combined with Prioritized-Replay Double DQN, which is a model-based continuous decision controller that determines the number of iterations of the decision-making process to be run and the model to be negotiated in each iteration. Before any single unit action, it can imagine and select actions based on the current state, including advance imagination with limited steps, and evaluate it with its model-based imagination. All imagined actions or outcomes will be iteratively integrated into a “plan environment”, which can test alternative imagined actions and be able to flexibly use a learned policy in the previously imagined state. Basis on these, the prioritized replay mode is adopted to improve the sampling weight and the training efficiency, which will make the metadata obtain lower overall cost than the traditional fixed strategy method, including task loss and calculation cost.
Vision Based Nighttime Vehicle Detection Using Adaptive Threshold and Multi-Class Classification

We propose a method that detects vehicles from in-vehicle monocular camera images captured during nighttime driving. Detecting vehicles from their shape is difficult at night; however, many vehicle detection methods focusing on light have been proposed. We detect bright spots by appropriate binarization based on the characteristics of vehicle lights such as brightness and color. Also, as the detected bright spots include lights other than vehicles, we need to distinguish the vehicle lights from other bright spots. Therefore, the bright spots were distinguished using Random Forest, a multiclass classification machine-learning algorithm. The features of bright spots not associated with vehicles were effectively utilized in the vehicle detection in our proposed method. More precisely vehicle detection is performed by giving weights to the results of the Random Forest based on the features of vehicle bright spots and the features of bright spots not related to the vehicle. Our proposed method was applied to nighttime images and confirmed effectiveness.
Kernelized Fuzzy Modal Variation for Local Change Detection From Video Scenes

Background subtraction (BGS) is a popular scheme epitomized in the state-of-the-art literature on video processing. In this context, a novel online kernelized fuzzy modal variation based background subtraction scheme for detecting local changes from the sequences of image frames is proposed. In the proposed scheme, the time varying background at different instances of time are modeled using fuzzy set theory. The proposed background subtraction scheme, utilizes the fuzzy modal variation as the cost function for fitting the pixel values of the image frames. The use of kernel based modal variation helps in projecting the pixel values in a higher dimensional space, linearly separating them into object and background classes. The results of the proposed technique is verified on different challenging sequences including dynamic background, camera jitter, noise, blurred scene, etc. The proposed technique is successfully tested over several test sequences with two major databases (all sequences) and it provides better results compared to the twenty one existing state-of-the-art techniques.
Feature‐assisted stereo correlation

Abstract Stereo correlation in digital image correlation (DIC) involves an optimisation problem that is sensitive to initial guess. In practice, this problem is circumvented by manually selecting a pair of points in the two stereo images that guarantees convergence and provides stereo mapping parameter estimates that are used as initial guesses at neighbouring subsets. However, such an approach is not always feasible, especially in the presence of substantial perspective distortions, for example, due to large stereo angles or complexities in specimen geometry. Therefore, it is desirable to provide high‐quality independent initial estimates over the entire region of interest. Recently, SIFT has been used for this purpose, but it fails when perspective distortions are severe. In this work, we investigate seven other feature‐based matching techniques to address this gap. Among these, DeepFlow algorithm provides the highest quality and most spatially uniform initial estimates. Further, we use DeepFlow estimates as initial guesses in a conventional stereo optimisation to compute geometry measures of a specimen in DIC challenge dataset. These geometry measures show excellent agreement with ground truth, further supporting the choice of DeepFlow in stereo correlation.
Mixed Noise Removal for Hyperspectral Images Using Hybrid Spatio-Spectral Total Variation

This paper proposes a new mixed noise removal method using a hybrid spatio-spectral total variation (HSSTV) for hy-perspectral (HS) images. HSSTV effectively evaluates spatial and spectral piecewise smoothness and would be a powerful regularization for HS image restoration. Existing mixed noise removal methods evaluate a-priori knowledge of an HS image via multiple regularizations. However, they do not appropriately evaluate spatial piecewise smoothness, resulting in oversmoothing or artifacts. Moreover, parameter settings in existing methods are troublesome tasks because multiple balancing parameters are interdependent. In contrast, thanks to HSSTV, the proposed method can restore a clean HS image while keeping sharp edges and details. In addition, parameter settings in our method are much easier than existing ones because data fidelity is imposed as hard constraints. In the experiments, we demonstrate the advantages of our proposed method over existing regularizations.
A Single-Shot Object Detector with Feature Aggregation and Enhancement

For many real applications, it's equally important to detect objects accurately and quickly. In this paper, we propose an accurate and efficient single shot object detector with feature aggregation and enhancement (FAENet). Our motivation is to enhance and exploit the shallow and deep feature maps of the whole network simultaneously. To achieve it we introduce a pair of novel feature aggregation modules and two feature enhancement blocks, and integrate them into the original structure of SSD. Extensive experiments on both the PASCAL VOC and MS COCO datasets demonstrate that the proposed method achieves much higher accuracy than SSD. In addition, our method performs better than the state-of-the-art one-stage detector RefineDet on small objects and can run at a faster speed.
A Data-Driven Approach for Determining Weights in Global Similarity Functions

This paper presents a method to discover initial global similarity weights while developing a case-based reasoning (CBR) system. The approach is based on multiple feature relevance scoring methods and the relevance of features within each scoring method. The objective of this work is to utilize the characteristics of a dataset when creating similarity measures. The primary advantage of this method lies in its data-driven approach in the absence of domain knowledge in the early phase of a CBR system development. The results obtained based on the experiments on multiple public datasets show that the method improves the performance of similarity measures for a CBR system in discriminating relevant similar cases. Evaluation of the results is based on the method suitable for unbalanced datasets.
Temporal Regularized Spatial Attention for Video-Based Person Re-Identification

Video-based person re-identification aims at matching video sequences of a person across different camera views. How to explore the abundant appearance and motion information in a video sequence is crucial to tackle this problem. To this end, we first introduce a parameter-free spatial attention module to emphasize the importance of discriminative regions. Then we apply a temporal regularization term on spatial attention to refine corrupted region caused by occlusion and blur. This term allows the attention response at one position in a frame to be related to other frames of the same position. Extensive experiments are conducted on iLIDS-VID and PRID-2011 datasets. The experimental results demonstrate that our approach surpasses the existing state-of-the-art video-based person re-identification methods on iLIDS-VID and PRID-2011.
Low-Resolution Face Recognition Based on Identity-Preserved Face Hallucination

The state-of-the-art Convolutional Neural Network (CNN)-based methods have achieved promising recognition performance on human face images. However, the accuracy cannot be retained when face images are at very low resolution (LR). In this paper, we propose a novel loss function, called identity-preserved loss, which combines with the image-content loss to jointly supervise CNNs, for performing face hallucination and recognition simultaneously. Therefore, the trained network is able to perform face hallucination and identity preservation, even if the query face is of very low resolution. More importantly, experimental results show that our proposed method can preserve the identities for the LR images from unknown subjects, who are not included in the training set. The source code of our proposed method is available at: https://github.com/johnnysclai/SR_LRFR.
Multipathing Traffic to Reduce Entry Node Exposure in Onion Routing

Users of an onion routing network, such as Tor, depend on its anonymity properties. However, especially malicious entry nodes, which know the client’s identity, can also observe the whole communication on their link to the client and, thus, conduct several de-anonymization attacks. To limit this exposure and to impede corresponding attacks, we propose to multipath traffic between the client and the middle node to reduce the information an attacker can obtain at a single vantage point. To facilitate the deployment, only clients and selected middle nodes need to implement our approach, which works transparently for the remaining legacy nodes. Furthermore, we let clients control the splitting strategy to prevent any external manipulation.
Cross-Connected Networks for Multi-Task Learning of Detection and Segmentation

Multi-task learning improves generalization performance in neural networks by sharing knowledge among related tasks. Existing models are for task combinations annotated on the same dataset; research on how to utilize the knowledge of successful single-task convolutional neural networks (CNNs) that are trained on individual datasets is limited. We propose a cross-connected CNN, an architecture that connects single-task CNNs through convolutional layers that transfer useful information to their counterparts. We evaluated the architecture with a combination of detection and segmentation using datasets of two targets: pedestrians and wild birds. Experiments demonstrate how well our CNN learns general representations from multi-task learning.
Fine-Grained Visual Categorization of Fasteners in Overhaul Processes

Commercial aircraft engines must be overhauled approximately every six years, during which hundreds of different parts must be disassembled, checked, and then reassembled. This includes undoing up to thousands of fasteners, cleaning, checking, refitting, and tightening them. Prior to refitting the fasteners, they must be classified and packaged. In this paper, we describe a system for classifying fasteners automatically, by use of computer vision and machine learning. Using the proposed system, we created sample datasets and performed a fine-grained visual categorization of the fasteners. Our trained model classifies 20 bolts and 14 washers with an accuracy of 99.4%. Our work is the first step towards an automated fastener classification system in overhaul processes.
Hue-Preserving Color Image Enhancement on a Vector Space of Convex Combination Coefficients

The color image enhancement is one of the most important image processing techniques and is utilized to improve the visibility of digital images in various situations. In this paper, we propose a novel hue-preserving color image enhancement method which is executed on a vector space of convex combination coefficients and does not generate the color gamut problem. In the proposed method, at first, each pixel of an input image is represented by a convex combination of white, black, and its pure color. Then the combination coefficients are transformed by the histogram specification technique using the gradient norm-based weighted histograms of the coefficients, and the distributions of the coefficients are expanded to enhance the both of image intensity and saturation components appropriately. Through the experiments employing various color images, the effectiveness of the proposed method is verified.
Detail enhancement of infrared image based on bi-exponential edge preserving smoother

In this paper, a new infrared image detail enhancement algorithm has been raised. The original infrared image has a wide dynamic range of 12- or 14-bit.This suppresses the human observation range of 8-bit. Usually the original infrared image needs to be compressed and gray-scale remapped for displaying. However, the normal way of doing this cannot give a better visual effect for the human observer. In this case, detail enhancement algorithms of infrared image occur. Modern detail enhancement algorithms can extract the detail information from an original infrared image and separate the image into different layers, and each layer will processed with different strategy. Although good performance has been proved for these algorithms, there are still certain deficits such as too much computational time, low working efficiency, hard application flexibility and so on. Under this circumstances, we propose this new algorithm to overcome these problems. This algorithm uses a two dimensional convolution to separate the detail information from an original infrared image, and turn the original image into the detail layer and the base layer. The detail information will be enhanced without any unwanted artifacts. During the detail extraction, We speed up the whole computational process by transforming the two dimensional convolution into two one dimensional convolutions, and then express the one dimensional convolution with the iterative computation. After adding the enhanced detail layer back to the histogram equalized base layer, the visual quality of the original image can be improved. This algorithm not only gives better detail enhancement performance, but also reduces the computational time. Figures and data tests show the priority of our suggestions.