Reviewerly/exHarmony huggingface dataset:
Image Retrieval Using Multilayer Bi-LSTM

The growth of the internet and the social media has led to rapid growth of the digital images available online as digital databases. The problem of searching such databases for items that are similar to a query image or a query descriptor is termed as image retrieval, and such systems are called image retrieval systems. An image retrieval system is a computer system that helps in looking up a large database of digital images and retrieving the images, which matches a user’s request. These systems are used for retrieving images related to the user request from the database. The following research will state the role of generating descriptions for images in these systems and proposing the use a multilayer biLSTM (bidirectional LSTM) for the purpose of generating descriptions. The proposed model gave desirable results, which were also better results than its counterparts, i.e., LSTM and biLSTM.
Targeted Adversarial Examples Generating Method Based on cVAE in Black Box Settings

In recent years, adversarial examples has become one of the most important security threats in deep learning applications. For testing the security of deep learning models in adversarial environment, many researches focus on generating adversarial examples quickly and efficiently. In order to solve the problems of existing generative adversarial networks based methods which can not effectively generate the targeted adversarial examples in black box settings, and to improve the temporal performance of gradient-based generating methods, an adversarial examples generating method based on conditional Variational autoencoder (cVAE) is proposed in this paper, where a cVAE is designed elaborately to generate adversarial examples without most of the detailed information about the attacked deep learning models, of which the output can be controlled arbitrarily by these crafted inputs, used to test the robustness of deep learning models against adversarial examples. The experimental results show that the proposed method can achieve a comparable attack success rate and a better temporal performance than the existing gradient-based generating methods in black box environment.
Autonomous obstacle avoidance of UAV based on deep reinforcement learning1

In the intelligent unmanned systems, unmanned aerial vehicle (UAV) obstacle avoidance technology is the core and primary condition. Traditional algorithms are not suitable for obstacle avoidance in complex and changeable environments based on the limited sensors on UAVs. In this article, we use an end-to-end deep reinforcement learning (DRL) algorithm to achieve the UAV autonomously avoid obstacles. For the problem of slow convergence in DRL, a Multi-Branch (MB) network structure is proposed to ensure that the algorithm can get good performance in the early stage; for non-optimal decision-making problems caused by overestimation, the Revise Q-value (RQ) algorithm is proposed to ensure that the agent can choose the optimal strategy for obstacle avoidance. According to the flying characteristics of the rotor UAV, we build a V-Rep 3D physical simulation environment to test the obstacle avoidance performance. And experiments show that the improved algorithm can accelerate the convergence speed of agent and the average return of the round is increased by 25%.
Bounds on the Capacity of PIR over Graphs

In the private information retrieval (PIR) problem, a user wants to retrieve a file from a database without revealing any information about the desired file's identity to the servers that store the database. In this paper, we study the PIR capacity of a graph-based replication system, in which each file is stored on two distinct servers according to an underlying graph. This paper aims to provide upper and lower bounds to the PIR capacity of graphs via various graph properties. In particular, we provide several upper bounds on the PIR capacity that apply to all graphs. We further improve the bounds for specific graph families (which turn out to be tight in certain cases) by utilizing the underlying graph structure. For the lower bounds, we establish optimal rate PIR retrieval schemes for star graphs via edge-coloring techniques. Lastly, we provide an improved PIR scheme for complete graphs, which implies an improved general lower bound on all graphs' PIR capacity.
Robust Particle Filtering With State Transition Uncertainties: A Student’s <i>t</i> Disturbance Observer-Based Approach

This paper is concerned with robust particle filtering (PF) for nonlinear systems with state transition uncertainties. The uncertainty under consideration is characterized in part by an additive uncertainty term (AUT) in the state transition equation, and in part by the parametric uncertainty in the probability distribution of process noises. To maintain the sampling efficiency of the PF algorithm in the presence of AUT, the disturbance observer based PF approach is adopted, which consists of the disturbance observer for real-time AUT reconstruction and a modified PF with AUT-compensated sampling. Instead of using the traditional Kalman filter based disturbance observer, a novel Student’s t disturbance observer is proposed to handle uncertain noise parameters and achieve robust disturbance estimation. The enhanced robustness of the proposed method, named Student’s t disturbance observer based robust PF, has been demonstrated via numerical example. Furthermore, experimental tests on the skylight polarization aided attitude and heading reference system (AHRS) show that, the standard deviation of our method decreases by over 40% as compared with the conventional PF.
GGCAD: A Novel Method of Adversarial Detection by Guided Grad-CAM

The emergence of adversarial examples has seriously threatened the security of deep learning models, and how to effectively detect them has become an issue. Most of the existing detection methods have low detection accuracy in the face of strong attacks such as C&W. Inspired by the visualization techniques of the model, this paper proposes a Guided-Grade-Cam based method to detect adversarial examples, which mainly consists of two parts. The first part is to extract the Guided Grad-CAM of the samples based on the model of resnet50. In the second part, we construct a classifier to detect the adversarial examples by the differences of the Guided Grad-CAM. This method is the first to introduce the model interpretation method into the detection of adversarial examples. Through experimental observation, it is found that there is a difference between the normal samples and the adversarial examples in Guided Grad-CAM, that is, the outlines information is masked after the adversarial perturbations are added to the normal samples. Experimental results on ImageNet based on the ResNet50 show that our method achieves an average success rate of 99.2% in the face of C&W attacks, which is superior to other methods.
Benchmarking Constrained IoT Devices in Blockchain-Based Agri-Food Traceability Applications

The adoption of blockchain technology combined with the Internet of Things in agri-food traceability scenarios is gaining momentum in terms of research and development. The intrinsic capability of blockchain to provide immutable and tamper-proof records is the perfect match for IoT systems comprising small sensing devices that can autonomously produce information across the entire process. However, there has been little discussion on the impact that blockchain has on the constrained sensing IoT devices in terms of their limited resources. These devices are the core of modern IoT systems providing the sensing layer for the entire application. In this paper, we focus on assessing the impact that a blockchain traceability system may have on such constrained sensing devices. To this end, we benchmark six IoT hardware platforms in terms of space and memory usage, processing time, and energy consumption as elements of a trustless event-based traceability application over two blockchain networks, namely Ethereum, as an example of permissionless network, and Hyperledger Sawtooth, as a permissioned counterpart. Our results and analysis provide an empirical reference for the study and development of blockchain-based traceability systems using constrained sensing IoT devices.
Digital Watermarking Techniques and Their Analysis

Nowadays, digital media has become basic need of people just like they need paper media. Since the technology has enhanced so digital media requires assurance while moving through web or others mediums. Watermarking methods have been created to satisfy this necessity. Digital watermarking is the answer for every form of piracy related to digital media. In digital watermarking, mysterious data is inserted into the initial information in order to shield it from unlawful use of information. Digital watermarking can be differentiated based upon domains like spatial domain, frequency domain, and more. Watermarking is a part of data security which is utilized to secure data in every form of media, that is, digitally available. The issue of copy right is generated due to the straightforward transmission of information on the web. This paper displays an overview of various procedures on digital picture watermarking. The procedure of digital watermarking is working for increasing significance in the creation of secured web society. Digital watermarking is an answer for the information movement security from unlawful impedances.
Gender and word elongation in Facebook-mediated communication in Palestinian Arabic

This article examines the social and linguistic functions of word elongation in Palestinian Facebook-mediated communication. Drawing on Ochs’ social constructivism and Parrott’s classification of emotions, we analyse a 65-discourse corpus of personal interviews using a mixed methods approach. It finds, while there are many similarities in the conventions related to word elongation between male sand females, 75% of the female sample use this practice while 45% of the males said they hardly used it. Word elongation is clearly a feature that characterises female language although its use is not restricted to females. Forty percent of males would use it to indicate that they are angry whereas 45% of females would use abbreviations instead. While 60% of females use word elongation to express positive feelings, 40% of males would use it to communicate positive emotions. The researchers conclude that elongated words reflect a social change in Palestinian society with respect to gender and language.
ScanSSD-XYc: Faster Detection for Math Formulas

Detecting formulas offset from text and embedded within textlines is a key step for OCR in scientific documents. The Scanning Single Shot Detector (ScanSSD) detects formulas using visual features, applying a convolutional neural network within windows in a large document image (600 dpi). Detections are pooled to produce page-level bounding boxes. The system works well, but is rather slow. In this work we accelerate ScanSSD in multiple ways: (1) input and output routines have been replaced by matrix operations, (2) the detection window stride (offset) can now be adjusted separately for training and testing, with fewer windows used in testing, and (3) merging with non-maximal suppression (NMS) in windows and pages has been replaced by merging overlapping detections using XY-cutting at the page level. Our fastest model processes 3 pages per second on a Linux system with a GTX 1080Ti GPU, Intel i7-7700K CPU, and 32 GB of RAM.
An Improved QPSO Algorithm Based on EXIF for Camera Self-calibration

Binocular vision technology is an important branch of computer vision technology, which is widely used in robot motion, navigation, surgical treatment and many other fields. As is a crucial link, it is the basis of binocular vision technology to obtain the internal parameters of a digital camera. Traditional calibration methods, such as Zhengyou Zhang's method needs a calibration board, while the self-calibration method based on active vision needs to strictly control a camera to move in a designated way. Based on that, those methods can't be applied to simple and convenient occasions. In this paper, we aim to propose a new method of camera self-calibration by improving an existing QPSO algorithm with the EXIF information of digital camera photos. The method only needs to shot one object twice on different angles. We derive the conversion formula of equivalent focal length and pixel focal length and use it to initialize the algorithm. It is to find the optimal solution of the cost function transformed from the Kruppa equation by using the QPSO method. The experiment results proved that the improved method is better than the initial one and using the EXIF information to initialize the algorithm is feasible.
Simulation of quantum computing on classical supercomputers with tensor-network edge cutting

Simulation of quantum computing on supercomputers is a significant research topic, which plays a vital role in quantum algorithm verification, error-tolerant verification, and other applications. Tensor-network contraction based on density matrix is an important single-amplitude simulation strategy, but it is hard to execute on the distributed computing systems. In this paper, we studied the problem in detail, and propose a scheme based on cutting edges of undirected graphs. This scheme cuts edges of undirected graphs with large tree width to obtain many undirected subgraphs with small tree width, and these subgraphs contracted on different computing cores. The contraction results of slave cores are summarized in the master node, which is consistent with the original tensor-network contraction. Thus, we can simulate the larger scale quantum circuit than single core. Moreover, it is an NP-hard problem to find the global optimum cutting edges, and we propose a search strategy based on a heuristic algorithm to approach it. In order to verify the effectiveness of our scheme, we conduct tests based on the quantum approximation optimization algorithm (QAOA) and Shor's algorithm, and it can simulate a 120-qubit three-regular QAOA algorithm on a 4096-core supercomputer, which greatly exceeds the simulation scale on a single core of 100 qubits.
Distractor-Aware Tracker with a Domain-Special Optimized Benchmark for Soccer Player Tracking

Player tracking in broadcast soccer videos has received widespread attention in the field of sports video analysis, however, we note that there is not a suitable tracking algorithm specifically for soccer video, and the existing benchmarks used for soccer player tracking cover few scenarios with low difficulties. From the observation of the soccer scene that interference and occlusion are knotty problems because the distractors are extremely similar to the targets, a distractor-aware player tracking algorithm and a high-quality benchmark for soccer play tracking (BSPT) have been presented. The distractor-aware player tracking algorithm is able to perceive semantic information about distracting players in the background by similarity judgment, the semantic distractor-aware information is encoded into a context vector and is constantly updated as the objects move through a video sequence. Distractor-aware information is then appended to the tracking result of the baseline tracker to improve the intra-class discriminative power. BSPT contains a total of 120 sequences with rich annotations. Each sequence covers 8 specialized frame-level attributes from soccer scenarios and the player occlusion situations are finely divided into 4 categories for a more comprehensive comparison. In the experimental section, the performance of our algorithm and the other 14 compared trackers are evaluated on BSPT with detailed analysis. Experimental results reveal the effectiveness of the proposed distractor-aware model especially under the attribute of occlusion. The BSPT benchmark and raw experimental results are available on the project page at http://media.hust.edu.cn/BSPT.htm.
Coded Alternating Least Squares for Straggler Mitigation in Distributed Recommendations

Matrix factorization is an important representation learning algorithm, e.g., recommender systems, where a large matrix can be factorized into the product of two low dimensional matrices termed as latent representations. This paper investigates the problem of matrix factorization in distributed computing systems with stragglers, those computing nodes that are slow to return computation results. A computation procedure, called coded Alternative Least Square (ALS), is proposed for mitigating the effect of stragglers in such systems. The coded ALS algorithm iteratively computes two low dimensional latent matrices by solving various linear equations, with the Entangled Polynomial Code (EPC) as a building block. We theoretically characterize the maximum number of stragglers that the algorithm can tolerate (or the recovery threshold) in relation to the redundancy of coding (or the code rate). In addition, we theoretically show the computation complexity for the coded ALS algorithm and conduct numerical experiments to validate our design.
Autonomous Exploration of Mobile Robots via Deep Reinforcement Learning Based on Spatiotemporal Information on Graph

In this paper, we address the problem of autonomous exploration in unknown environments for ground mobile robots with deep reinforcement learning (DRL). To effectively explore unknown environments, we construct an exploration graph considering historical trajectories, frontier waypoints, landmarks, and obstacles. Meanwhile, to take full advantage of the spatiotemporal feature and historical information in the autonomous exploration task, we propose a novel network called Spatiotemporal Neural Network on Graph (Graph-STNN). Specifically, the proposed Graph-STNN extracts the spatial feature using graph convolutional network (GCN) and the temporal feature using temporal convolutional network (TCN). Then, gated recurrent unit (GRU) is performed to synthesize the spatial feature, the temporal feature, and the historical state information into the current state feature. Combined with DRL, our Graph-STNN helps estimation of the optimal target point through extracted hybrid features. The simulation experiment shows that our approach is more effective than the GCN-based approach and the information entropy-based approach. Moreover, Graph-STNN also performs better generalization ability than GCN-based, information entropy-based, and random methods. Finally, we validate our approach on the simulation platform Stage with the actual robot model.
The Use of Computer Tools in Assessing the Development of Learning Interactions in Primary Schoolchildren

The article presents results of a study on the possibilities of using the ‘Scales’ research technique for assessing the effectiveness of learning interactions in primary school children. The study involved 90 students of primary school (N=90), grades 1 to 4. We discuss the issue of using computer tools in activity- based assessments. In our study, we have implemented the fundamental theoretical principle, according to which the very nature of participants’ activities determines the structure of the digital system construction. We show that evaluating the effectiveness of learning interactions in joint problem-solving tasks is possible in situations when the ‘digital shell’ of the students’ activities becomes a means to actualize the processes of communication, exchange of actions, mutual understanding and reflection, which determine the process of setting the learning task and finding the generalised way of solving it. It is noted that in the course of solving experimental problems, there is a qualitative change in these processes, which are an integral characteristic of the sense of community that arises between children and adults and, in turn, allows children to reach beyond the framework of the current subject problem and to move over to meaning-based interactions (Z=-3.651, Asympt.Sig.(2-tailed)&lt;0.001, p=0.05).
A Szekely–Rizzo inequality for testing general copula homogeneity hypotheses

This article extends to more than two vectors the popular moment inequality of Szekely and Rizzo and adapts it to test hypotheses about the copula of a multivariate population. The framework that is adopted is very general, as it allows one to test various kinds of hypotheses about the dependence structure of random vectors like symmetry, radial symmetry and equality of copulas. The proposed procedure is based on a V-statistic computed from the ranks of the observations. Its asymptotic distribution is derived under the null and general alternatives, and resampling is based on the multiplier bootstrap for degenerate V-statistics. A simulation study explores the sampling properties of the tests and compares with available methodologies.
Improved Detection and Recognition of Sea Surface Ships Based on YOLOv3

In order to improve the accuracy and real-time performance of ship target detection on the sea surface, an improved ship target detection method based on the YOLOv3 algorithm is proposed. Firstly, add the CBAM attention mechanism layer to the network prediction layer to improve the expression of the correlation between the feature map target information and important channels and space; secondly, the spatial pyramid pooling (SPP) module is introduced in YOLOv3 to solve the problem of information loss and scale inconsistency; thirdly, GIoU is selected as the loss function to improve the accuracy of the positioning information of the target prediction frame; then the adaptive spatial feature fusion (ASFF) method is used in the prediction layer to make full use of features of different scales to improve the accuracy of target detection; finally refer to the PASCAL VOC data setting format, established a sea surface ship dataset containing 6041 pictures, and manually annotated it for network training. The test results show that compared with YOLOv3, the improved algorithm can effectively improve the accuracy and speed of ship detection on the sea surface. The detection accuracy (mAP) of ship targets on the sea surface has increased by 2.97% to 94.17%, and the frame rate has reached 48fps. Meet the requirements of real-time detection of ship targets.
Robust multi-source co-adaptation with adaptive loss minimization

While multi-source adaptation has recently received much attention due to its effectiveness, limited effort has been made on improving its performance by leveraging some shared knowledge among sources and features. To this end, we propose in this paper a robust Multi-source co-Adaptation framework by mining diverse Correlation Information (MACI) among domains as well as features via joint adaptive σ-norm and correlation metric regularization. Concretely, multiple domain-invariant classification functions for different source adaptation objects are co-learned in a joint framework by minimizing both statistical and semantic distribution discrepancy between source and target domains, which enables MACI to utilize the correlated knowledge among multiple sources by exploiting the developed correlation metric function. Under this framework, MACI can preserve the original geometrical structure information as well as be robust to some noises or outliers existed in domains by employing local graph Laplacian and adaptive regression scheme. Furthermore, we also extend MACI to domain generalization scenario when the target domain of interest is unavailable in the training stage. An efficient iterative algorithm is proposed to optimize MACI (also applied to its domain generalization version), whose convergence is theoretically guaranteed. Comprehensive experimental evidence on a large number of visual datasets verifies the effectiveness of the proposed framework.