Reviewerly/exHarmony huggingface dataset:
Adversarial Erasing Attention for Person Re-Identification in Camera Networks Under Complex Environments

Person re-identification (Re-ID) in camera networks under complex environments has achieved promising performance using deep feature representations. However, most approaches usually ignore to learn features from non-salient parts of pedestrian, which results in an incomplete pedestrian representation. In this paper, we propose a novel person Re-ID method named Adversarial Erasing Attention (AEA) to mine discriminative completed features using an adversarial way. Specifically, the proposed AEA consists of the basic network and the complementary network. On the one hand, original pedestrian images are used to train the basic network in order to extract global and local deep features. On the other hand, to learn features complementary to the basic network, we propose the adversarial erasing operation, that locates non-salient areas with the help of attention map, to generate erased pedestrian images. Then, we utilize them to train the complementary network and adopt the dynamic strategy to match the dynamic status of AEA in the learning process. Hence, the diversity of training samples is enriched and the complementary network could discover new clues when learning deep features. Finally, we combine the features learned from the basic and complementary networks to represent the pedestrian image. Experiments on three databases (Market1501, CUHK03 and DukeMTMC-reID) demonstrate the proposed AEA achieves great performances.
Viewpoint Recommendation Based on Object-Oriented 3D Scene Reconstruction

Viewpoint recommendation can recommend several viewpoints for taking aesthetic photographs of a place-of-interest (POI) and is of great importance for photography assistance. In this paper, we propose a system that can assist a user in choosing good viewpoints for taking high-quality photographs. Our system is based on social media and 3D reconstruction. To reduce the time cost and improve the quality of 3D reconstruction, we propose a weakly supervised object detection method that is used before 3D reconstruction. The camera pose of images is recovered by the subsequent 3D reconstruction pipeline. We use a convolutional neural network (CNN) to extract 2D image features, and we fuse them with 3D camera pose features to learn their relationships to image aesthetics. The trained model is utilized to evaluate the aesthetics of images. Finally, the 3D space of all possible camera poses is divided into 3D grids, and the aesthetics score of each grid is evaluated. We combine the aesthetics and diversity of all viewpoints and recommend several high-quality viewpoints. Experimental results indicate that our approach can help users choose viewpoints that will result in high-quality photographs while maintaining diversity.
Iterative Chebyshev Polynomial Algorithm for Signal Denoising on Graphs

In this paper, we consider the inverse graph filtering process when the original filter is a polynomial of some graph shift on a simple connected graph. The Chebyshev polynomial approximation of high order has been widely used to approximate the inverse filter. In this paper, we propose an iterative Chebyshev polynomial approximation (ICPA) algorithm to implement the inverse filtering procedure, which is feasible to eliminate the restoration error even using Chebyshev polynomial approximation of lower order. We also provide a detailed convergence analysis for the ICPA algorithm and a distributed implementation of the ICPA algorithm on a spatially distributed network. Numerical results are included to demonstrate the satisfactory performance of the ICPA algorithm in graph signal denoising.
Wheel Alignment of a Suspension Module Unit Using a Laser Module

Vehicle wheel alignment inspection is generally carried out using a computer vision-based system. Due to its inspection mechanism using four wheel centers, the computer vision-based system cannot be applied to the wheel alignment inspection of suspension module units. However, when a vehicle suspension module is being developed, there is no complete car ready for wheel alignment inspection even though it is a very important procedure for suspension property tests. This study proposes a novel and efficient way to inspect vehicle wheel alignment for suspension modules. Two laser modules and several mechanical jigs were employed for wheel alignment inspection, allowing the toe and camber angles of the suspension module to be measured. For accurate wheel alignment results, calibration of the laser modules was performed prior to the inspection. This calibration procedure adjusts the yaw and pitch angles of the laser module so that they can be orthogonal to the mounting jig. For the calibration, a novel method of using laser straightness was adopted and, consequently, 0.02 degrees of orthogonality was achieved. The wheel alignment inspection results were determined then verified using a vision system with two cameras. In order to use this vision system, two cameras were used and a new method of modifying the measurement mechanism was developed. According to the verification results, the proposed wheel alignment inspection provided very high measurement accuracy. The wheel alignment inspection mechanism proposed in this study can not only give very reliable results but also provide a cost-efficient method of inspecting the wheel alignment of suspension modules to automakers.
Examining the Use of Temporal-Difference Incremental Delta-Bar-Delta for Real-World Predictive Knowledge Architectures

Predictions and predictive knowledge have seen recent success in improving not only robot control but also other applications ranging from industrial process control to rehabilitation. A property that makes these predictive approaches well suited for robotics is that they can be learned online and incrementally through interaction with the environment. However, a remaining challenge for many prediction-learning approaches is an appropriate choice of prediction-learning parameters, especially parameters that control the magnitude of a learning machine's updates to its predictions (the learning rate or step size). To begin to address this challenge, we examine the use of online step-size adaptation using a sensor-rich robotic arm. Our method of choice, Temporal-Difference Incremental Delta-Bar-Delta (TIDBD), learns and adapts step sizes on a feature level; importantly, TIDBD allows step-size tuning and representation learning to occur at the same time. We show that TIDBD is a practical alternative for classic Temporal-Difference (TD) learning via an extensive parameter search. Both approaches perform comparably in terms of predicting future aspects of a robotic data stream. Furthermore, the use of a step-size adaptation method like TIDBD appears to allow a system to automatically detect and characterize common sensor failures in a robotic application. Together, these results promise to improve the ability of robotic devices to learn from interactions with their environments in a robust way, providing key capabilities for autonomous agents and robots.
Fisher Deep Domain Adaptation

Deep domain adaptation models learn a neural network in an unlabeled target domain by leveraging the knowledge from a labeled source domain. This can be achieved by learning a domain-invariant feature space. Though the learned representations are separable in the source domain, they usually have a large variance and samples with different class labels tend to overlap in the target domain, which yields suboptimal adaptation performance. To fill the gap, a Fisher loss is proposed to learn discriminative representations which are within-class compact and between-class separable. Experimental results on two benchmark datasets show that the Fisher loss is a general and effective loss for deep domain adaptation. Noticeable improvements are brought when it is used together with widely adopted transfer criteria, including MMD, CORAL and domain adversarial loss. For example, an absolute improvement of 6.67% in terms of the mean accuracy is attained when the Fisher loss is used together with the domain adversarial loss on the Office-Home dataset.
Indonesian Text Classification using Back Propagation and Sastrawi Stemming Analysis with Information Gain for Selection Feature

The second fundamental source of law for Moslems is the Hadith. The Hadith can be used to explain Quranic texts. However, Hadith still needs to be translated according to each national language to easily understand its meaning [1]. In Indonesia Hadith more usually refers to a special class of relevance to more particular religious concern [1]. Base on that, this research will Classify the translation Hadith Text into three classes: Obligation, Prohibition, and Information. From previous research, the Back Propagation Neural Network (BPNN) showed good performance in classifying hadith text. Therefore, BPNN was used to solve the problem of hadith text classification in this study. However, the dataset has a huge number of varied bag-of-words, which are features that will be used in the classification process. Hence, Information Gain (IG) was utilized to select influential features, and as the sequential process before the classification process. To measure the performance of this system, the Macro F1-Score was used. The F1-Score enables one to observe exactness from precision and completeness from recall. The Macro F1-score is also needed for the performance evaluation of more than two classes. Based on the experiment conducted, the system was able to classify hadith text using BPNN, IG, and without stemming, yielding the highest F1-score of 84.63%. However, the system performance that included the stemming process yielded an F1-score of 80.92%. This shows that the stemming process could decrease classification performance. This decreasing performance is due to some influential words merging with more noninfluential words.
Handwritten Assamese Character Recognition

In various fields of technology, computation techniques are pushing further the limits of human outreach. One such field is the character recognition commonly known as Optical Character Recognition (OCR). This paper is about taking offline handwritten Assamese alphabets as input using modern input devices like camera or scanner and tried to convert it to digital form to recognize the alphabets. Template Matching technique is anticipated for recognizing handwritten characters, including the schemes of Grayscale, Binary, Dilation, Erosion conversion or transformation of image, detection of ROI and comparing with dataset. The efficiency or the accuracy is calculated for every character which is shown in table 1 with a number of testing cases. With this proposed template matching technique we have been able to get accuracy more than 80% for maximum Assamese handwritten alphabets.
An Approach to Improve SSD through Skip Connection of Multiscale Feature Maps

SSD (Single Shot MultiBox Detector) is one of the best object detection algorithms and is able to provide high accurate object detection performance in real time. However, SSD shows relatively poor performance on small object detection because its shallow prediction layer, which is responsible for detecting small objects, lacks enough semantic information. To overcome this problem, SKIPSSD, an improved SSD with a novel skip connection of multiscale feature maps, is proposed in this paper to enhance the semantic information and the details of the prediction layers through skippingly fusing high-level and low-level feature maps. For the detail of the fusion methods, we design two feature fusion modules and multiple fusion strategies to improve the SSD detector’s sensitivity and perception ability. Experimental results on the PASCAL VOC2007 test set demonstrate that SKIPSSD significantly improves the detection performance and outperforms lots of state-of-the-art object detectors. With an input size of 300 × 300, SKIPSSD achieves 79.0% mAP (mean average precision) at 38.7 FPS (frame per second) on a single 1080 GPU, 1.8% higher than the mAP of SSD while still keeping the real-time detection speed.
Analysis sentiment about islamophobia when Christchurch attack on social media

Islamophobia is formed by "Islam" with "-phobia" which means "fear of Islam". This shows the view of Islam as "other" and can threaten Western culture. The recent horrific terror attack that took place at the Christchurch mosque in New Zealand, is the result of allowing an attitude of hatred towards Islam in the West. Twitter is social media that allows users send real-time messages and can be used for sentiment analysis because it has a large amount of data. The lexical based method using VADER is used for automatic labeling of crawling data from Twitter. And then compare Supervised Machine Learning Naïve Bayes and SVM algorithm. Addition of SMOTE for Imbalanced Data. As result, SVM with SMOTE is proven the highest performance value and short processing time.
XCS with opponent modelling for concurrent reinforcement learners

Reinforcement learning (RL) of optimal policies against an opponent agent also with learning capability is still challenging in Markov games. A variety of algorithms have been proposed for solving this problem such as the traditional Q-learning-based RL (QbRL) algorithms as well as the state-of-the-art neural-network-based RL (NNbRL) algorithms. However, the QbRL approaches have poor generalization capability for complex problems with non-stationary opponents, while the learned policies by NNbRL algorithms are lack of explainability and transparency. In this paper, we propose an algorithm X-OMQ(λ) that integrates eXtended Classifier System (XCS) with opponent modelling for concurrent reinforcement learners in zero-sum Markov Games. The algorithm can learn general, accurate, and interpretable action selection rules and allow policy optimization using the genetic algorithm (GA). Besides, the X-OMQ(λ) agent optimizes the established opponent’s model while simultaneously learning to select actions in a goal-directed manner. In addition, we use the eligibility trace mechanism to further speed up the learning process. In the reinforcement component, not only the classifiers in the action set are updated, but other relevant classifiers are also updated in a certain proportion. We demonstrate the performance of the proposed algorithm in the hunter prey problem and two adversarial soccer scenarios where the opponent is allowed to learn with several benchmark QbRL and NNbRL algorithms. The results show that our method has similar learning performance with the NNbRL algorithms while our method requires no prior knowledge of the opponent or the environment. Moreover, the learned action selection rules are also interpretable while having generalization capability.
Multidimensional super- and subradiance in waveguide quantum electrodynamics

We study the collective decay rates of multidimensional quantum networks in which one-dimensional waveguides form an intersecting hyperrectangular lattice, with qubits located at the lattice points. We introduce and motivate the dimensional reduction of poles (DRoP) conjecture, which identifies all collective decay rates of such networks via a connection to waveguides with a one-dimensional topology (e.g., a linear chain of qubits). Using DRoP, we consider many-body effects such as superradiance, subradiance, and bound states in continuum in multidimensional quantum networks. We find that, unlike one-dimensional linear chains, multidimensional quantum networks have superradiance in distinct levels, which we call multidimensional superradiance. Furthermore, we generalize the N−3 scaling of subradiance in a linear chain to d-dimensional networks.Received 17 March 2020Accepted 22 September 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.043149Published 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 AreasSuperradiance & subradianceWaveguidesQuantum InformationAtomic, Molecular & OpticalGeneral Physics
Lightweight and Seamless Memory Randomization for Mission-Critical Services in a Cloud Platform

Nowadays, various computing services are often hosted on cloud platforms for their availability and cost effectiveness. However, such services are frequently exposed to vulnerabilities. Therefore, many countermeasures have been invented to defend against software hacking. At the same time, more complicated attacking techniques have been created. Among them, code-reuse attacks are still an effective means of abusing software vulnerabilities. Although state-of-the-art address space layout randomization (ASLR) runtime-based solutions provide a robust way to mitigate code-reuse attacks, they have fundamental limitations; for example, the need for system modifications, and the need for recompiling source codes or restarting processes. These limitations are not appropriate for mission-critical services because a seamless operation is very important. In this paper, we propose a novel ASLR technique to provide memory rerandomization without interrupting the process execution. In addition, we describe its implementation and evaluate the results. In summary, our method provides a lightweight and seamless ASLR for critical service applications.
An Adaptive Short-Term Prediction Algorithm for Resource Demands in Cloud Computing

Cloud computing has been widely applied in various fields with the development of big data and artificial intelligence. The associated resource demands exhibit characteristics such as diversity, large scale, burst and uncertainty. This paper analyzes these characteristics of cloud resource demands based on Alibaba cluster data, and proposes an adaptive short-term prediction algorithm for those demands. The proposed algorithm uses a principal component analysis method to extract the primary types of container demands from a time series of resource demands, and executes outlier detection and replacement to obtain a more stationary sequence. An adaptive short-term prediction strategy is proposed to adaptively select a higher-accuracy short-term prediction method to implement the prediction. Further, an error adjustment factor is proposed to reduce the prediction error. Thus, the short-term prediction accuracy of cloud resource demands is improved via outlier detection and replacement, an adaptive selection strategy and an error adjustment. We evaluated the effectiveness of these improvements, and compared our algorithm with existing algorithms in terms of effectiveness and time cost. The experimental results demonstrate that the proposed algorithm improves short-term prediction accuracy effectively.
Multi-planar whole heart segmentation of 3D CT images using 2D spatial propagation CNN

Whole heart segmentation from cardiac CT scans is a prerequisite for many clinical applications, but manual delineation is a tedious task and subject to both intra- and inter-observer variation. Automating the segmentation process has thus become an increasingly popular task in the field of image analysis, and is generally solved by either using 3D methods, considering the image volume as a whole, or 2D methods, segmenting each slice independently. In the field of deep learning, there are significant limitations regarding 3D networks, including the need for more training examples and GPU memory. The need for GPU memory is usually solved by down sampling the input images, thus losing important information, which is not a necessary sacrifice when employing 2D networks. It would therefore be relevant to exploit the benefits of 2D networks in a configuration, where spatial information across slices is kept, as when employing 3D networks. The proposed method performs multiclass segmentation of cardiac CT scans utilizing 2D convolutional neural networks with a multi-planar approach. Furthermore, spatial propagation is included in the network structure, to ensure spatial consistency through each image volume. The approach keeps the computational assets of 2D methods while addressing 3D issues regarding spatial context. The pipeline is structured in a two-step approach, in which the first step detects the location of the heart and crops a region of interest, and the second step performs multi-class segmentation of the heart structures. The pipeline demonstrated promising results on the MICCAI 2017 Multi-Modality Whole Heart Segmentation challenge data.
XLR (piXel Loss Rate): A Lightweight Indicator to Measure Video QoE in IP Networks

A novel Key Quality Indicator for video delivery applications, XLR (piXel Loss Rate), is defined, characterized, and evaluated. The proposed indicator is an objective measure that captures the effects of transmission errors in the received video, has a good correlation with subjective Mean Opinion Scores, and provides comparable results with state-of-the-art Full-Reference metrics. Moreover, XLR can be estimated using only a lightweight analysis on the compressed bitstream, thus allowing a No-Reference operational method. Therefore, XLR can be used for measuring the quality of experience without latency at any network location. Thus, it is a relevant tool for network planning, specially in new high-demanding scenarios. The experiments carried out show the outstanding performance of its linear-dimension score and the reliability of the bitstream-based estimation.
Efficient audit protocol supporting virtual nodes in cloud storage

Abstract With ten years of development, cloud computing has been widely used in more and more field. As a part of cloud computing, cloud storage has earned more and more attentions. It becomes a new trend that users employ cloud storage to outsource their data. Instead of the traditional approach to store data in a local server, users' data cannot be controlled once data outsourcing in cloud storage. At the same time, as the cloud service provider is honest‐but‐curious, it becomes a challenge to make sure that cloud service provider can provide storage service with high quantity. In this paper, we focus on the issue of cloud storage security audit. In particular, we propose a novel public audit scheme, which employs chameleon authentication tree in the third‐party audit model to significantly reduce the huge computation cost of data dynamic operation. Meanwhile, our scheme can effectively prevent the fraud of dishonest cloud service provider, with supporting dynamic data update and batch audit. The experimental results show that the proposed scheme is more secure and efficient compared with the existing schemes.
Systematic Review of Natural Resource Management using Multiagent Systems and Role-Playing Games

A good way of clarifying controversies is to the best quality studies in the area in question.In this way, the systematic literature review is a type of research focused on a well-defined subject, which aims to identify, select, evaluate and synthesize evidence of a specific research topic.The systematic literature review purpose is to build an overview of a specific issue and provide a summary of the literature, guaranteeing standardization and precision.Natural resource management is an area that seeks better ways to manage land, water, plants, and animals, based on the life quality of people now and for future generations.This area gained visibility with the notion of sustainability.Natural resource management focuses specifically on the scientific-technical understanding of resources and ecology and how these resources can support animal life.An approach that can help in the decision-making of this complex area is the integration of multiagent systems and role-playing games.This paper presents a systematic literature review of these three areas: natural resource management, multiagent systems, and role-playing games.