Reviewerly/exHarmony huggingface dataset:
Fashion Image Retrieval with Capsule Networks

In this study, we investigate in-shop clothing retrieval performance of densely-connected Capsule Networks with dynamic routing. To achieve this, we propose Triplet-based design of Capsule Network architecture with two different feature extraction methods. In our design, Stacked-convolutional (SC) and Residual-connected (RC) blocks are used to form the input of capsule layers. Experimental results show that both of our designs outperform all variants of the baseline study, namely FashionNet, without relying on the landmark information. Moreover, when compared to the SOTA architectures on clothing retrieval, our proposed Triplet Capsule Networks achieve comparable recall rates only with half of parameters used in the SOTA architectures.
Vehicle Re-Identification Using Distance-Based Global and Partial Multi-Regional Feature Learning

Vehicle re-identification supports cross-camera tracking and the location of specific vehicles in a smart city. The gallery images of vehicles are ranked based on the similarities in the appearance of objects to a vehicle query image. Previous work on vehicle re-identification has mainly focused on global or local analyses of predefined regions of vehicles to classify the vehicle images with a softmax loss function. On the one hand, separate global or predefined local regions of vehicles are often sensitive to perspective and occlusions. On the other hand, the embedding space supervised by the softmax loss function is not sufficiently compact for the object class. To solve these problems, we propose an end-to-end distance-based global and partial multi-regional deep network (DGPM) that combines multi-regional features to identify global and local differences. We exploit a three-branch architecture to learn the global and partial features from coarsely partitioned regions. A global similarity module is introduced to reduce the background information interference in the local branches. Unlike general classification, we design a distance-based classification layer that maintains consistency among criteria for similarity evaluation. Furthermore, we use spatiotemporal vehicle information to improve the vehicle re-identification results when the camera and shooting time are available. Systematic comparative evaluations performed on the large-scale VeRi and VehicleID datasets showed that our approach robustly achieved state-of-the-art performance. For instance, for the VeRi dataset, we achieve (79.39 + 2.78)% mAP and (96.19 + 2.26)% Rank-1 accuracy.
A Novel Plaintext-Related Image Encryption Scheme Based on Chaotic System and Pixel Permutation

To improve the abilities of image encryption systems to resist plaintext attacks and differential attacks, a novel plaintext-related image encryption scheme based on Josephus traversing and pixel permutation is proposed. In this scheme, the step sizes of the Josephus traversing are associated with the pixel values, and the Josephus traversing method is improved, increasing the dependence of the cipher image on the plaintext image, further enhancing the plaintext sensitivity of the algorithm, and reducing the number of iterations of the index sequence generated by the chaotic system. By segmenting the image and combining a chaotic system, bit XOR and crossover operations between the modules are performed to achieve the effects of confusion and diffusion, and the randomness of the cipher image is improved. Finally, the confusing and spreading characteristics of the algorithm are further enhanced by cipher feedback. The experimental results and security analysis show that the proposed algorithm is sensitive to keys and can effectively resist attacks such as statistical attacks, selective-plaintext attacks and exhaustive attacks. The algorithm has high potential for real-time and secure image applications.
A New Sample Consensus Based on Sparse Coding for Improved Matching of SIFT Features on Remote Sensing Images

In this article, a new method is proposed for feature matching of remote sensing images using sample consensus based on sparse coding (SCSC) to improve the image registration technique. To this end, scale-invariant feature transform (SIFT) features are used to select interesting points for image matching. The extracted points contain some differences and similarities in two images captured from the same area (but different in sensor resolution, azimuth, elevation, contrast, illumination, etc.); in such a case, similar points should be extracted and other dissimilar should be eliminated. In this article, we greatly improve the matching between two images using the SCSC through checking points altogether. Moreover, the proposed method is shown to have better results than standard alternative methods such as random sample consensus (RANSAC) when the number of feature points is too much or have noise. However, it should be noted that for a low-noise and distortion rate, the proposed method and the RANSAC yield similar results. In general, the proposed method using sparse coding achieves a higher correct match rate than the SIFT algorithm. In order to illustrate this issue, the proposed method is compared to other updated matching and registration methods based on the SIFT algorithm. The obtained results confirm the accuracy of this claim and show that the proposed algorithm is accurate between 0.48% and 7.68% rather than SVD-RANSAC, Hoge, Stone, Foroosh, Leprince, Nagashima, Guizar, Youkyung, Lowe, Preregistration, IS-SIFT, SPSA, Gong, Standard SIFT, IS-SIFT, UR-SIFT, Sourabh, and Han methods.
Decomposition-based Bayesian network structure learning algorithm using local topology information

Hybrid learning algorithms which integrate the merits of the constraint-based methods and the search-and-score methods are used to cope with Bayesian network (BN) structure estimation problem. However, such simple and crude synthesis techniques always consider the global topology information during the learning process and attempt to directly search for the optimal network structure in the enormous solution space for large-scale BNs, resulting in prohibitive computational cost as well as low learning accuracy. Therefore, we propose a novel hybrid structure learning algorithm based on the idea of model decomposition, which takes into account the knowledge of local neighborhood structures. The proposed method works in four stages. We first draft an undirected independence graph by using an efficient Markov blanket discovery approach, and then split the entire network into a series of subgraphs. After learning the small BNs from the observed data, the resultant topology can be obtained by combining these small BNs. Experiments on different benchmark BNs and the varying data sets demonstrate that the proposed algorithm generally gains the better performance of structure recovery than other representative methods, especially for large-scale BNs.
Evaluating #SAT solvers on industrial feature models

Configurable systems are widely used for families of products that share multiple configuration options. These systems often induce a large configuration space. Handling the variability of such a system is difficult without being able to measure its complexity. Several methods depend on computing the number of valid configurations, such as estimating the effort of an update or effectively reducing the variability of a system. In many cases, it is possible to map a configurable system to propositional logic. Therefore, we use #SAT in order to evaluate variability of such systems. A #SAT solver computes the number of valid assignments of a propositional formula. However, this problem is even harder than SAT. The main contribution of our work is an investigation of the scalability of off-the-shelf #SAT solvers on industrial feature models. Additionally, we examine the correlation between size of a system and the runtime of a solver computing the number of valid configurations. In this paper, we empirically evaluate nine publicly available #SAT solvers on 127 industrial feature models. Our results indicate that current solvers master a majority of the evaluated systems. However, there are large models, for which none of the evaluated solvers scales. Nevertheless, there are even larger and more complex systems for which the solvers scale.
Do What I Mean, Not What I Say! Design Considerations for Supporting Intent and Context in Analytical Conversation

Natural language can be a useful modality for creating and interacting with visualizations but users often have unrealistic expectations about the intelligence of natural language systems. The gulf between user expectations and system capabilities may lead to a disappointing user experience. So - if we want to engineer a natural language system, what are the requirements around system intelligence? This work takes a retrospective look at how we answered this question in the design of Ask Data, a natural language interaction feature for Tableau. We examine two factors contributing to perceived system intelligence: the system's ability to understand the analytic intent behind an input utterance and the ability to interpret an utterance contextually (i.e. taking into account the current visualization state and recent actions). Our aim was to understand the ways in which a system would need to support these two aspects of intelligence to enable a positive user experience. We first describe a pre-design Wizard of Oz study that offered insight into this question and narrowed the space of designs under consideration. We then reflect on the impact of this study on system development, examining how design implications from the study played out in practice. Our work contributes insights for the design of natural language interaction in visual analytics as well as a reflection on the value of pre-design empirical studies in the development of visual analytic systems.
Deep Learning Serves Voice Cloning: How Vulnerable Are Automatic Speaker Veriﬁcation Systems to Spooﬁng Trials?

This article verifies the reliability of automatic speaker verification (ASV) systems on new synthesis methods based on deep neural networks. ASV systems are widely used and applied regarding secure and effective biometric authentication. On the other hand, the rapid deployment of ASV systems contributes to the increased attention of attackers with newer and more sophisticated spoofing methods. Until recently, speech synthesis of the reference speaker did not seriously compromise the latest ASV systems. This situation is changing with the deployment of deep neural networks into the synthesis process. Projects including WaveNet, Deep Voice, Voice Loop, and many others generate very natural and high-quality speech that may clone voice identity. We are slowly approaching an era where we will not be able to recognize a genuine voice from a synthesized one. Therefore, it is necessary to define the robustness of current ASV systems to new methods of voice cloning. In this article, well-known SVM and GMM as well as new CNN-based ASVs are applied and subjected to synthesized speech from Tacotron 2 with the WaveNet TTS system. The results of this work confirm our concerns regarding the reliability of ASV systems against synthesized speech.
Smoothing-Aided Support Vector Machine Based Nonstationary Video Traffic Prediction Towards B5G Networks

Video services have hold a surprising proportion of the whole network traffic in wireless communication networks. Accurate prediction of video traffic can endow networks with intelligence in resource management, especially for the forthcoming beyond the fifth-generation (B5G) networks. However, the existing approaches fail to accurately predict video traffic with all types of frames, due to the natures of strong long-range dependence, self-similarity and burstiness. Obviously, it is unable to meet the QoS and QoE requirements of dynamic bandwidth allocation. In this paper, we propose the feasibility of advanced machine learning methodology applied in nonstationary video traffic prediction, i.e., smoothing-aided support vector machine (SSVM) model. The model utilizes classical smoothing methods to preprocess video traffic by relieving the drastic fluctuation of video stream. It can provide an effective association for the subsequent support vector regression, as the preprocessed data becomes more smooth and continuous than the original unprocessed one. Experimental results show that our proposed model significantly outperforms the state of the art model, i.e., logistic smooth transition autoregressive, in prediction performance. The superior nonlinear approximation capacity is further demonstrated by visualized statistical analysis.
Apache Spark: A Big Data Processing Engine

Big data analysis has influenced the industry market. It has a significant impact on large and varied datasets to exhibit the hidden patterns and other revelations. Apache Hadoop, Apache Flink and Apache Storm are some commonly used frameworks for big data analysis. Apache Spark is a consolidated big data analytics engine and provides absolute data parallelism. This paper scrutinizes a technical review on big data analytics using Apache Spark and how it uses in-memory computation that makes it remarkably faster as compared to other corresponding frameworks. Moreover, Spark also provides exceptional batch processing and stream processing capabilities. Furthermore, it also discuses over the multithreading and concurrency capabilities of Apache Spark. The point of convergence is architecture, hardware requirements, ecosystem, use cases, features of Apache Spark and the use of Spark in emerging technologies.
The 3D structure of a normal fault from multiple outcrop observations

Extensive opencast lignite mining in the Ptolemais Basin, NW Greece has exposed a plethora of outcrops within a normal fault system offsetting a sequence of interbedded lignites and marls of Pliocene age. The entire length of a 630 m long fault over a vertical interval of 80 m has been mapped in detail from 48 mine faces. The mapped fault comprises 5 fault segments large enough to be mapped in 3D, smaller fault segments exposed on individual mine faces and associated continuous deformation in the form of bed rotations and normal drag. These fault components combined to provide a regular aggregate throw distribution over the mapped fault. The boundaries between adjacent fault segments have a variety of geometries and they may be fully unconnected or they may link along strike or down dip within the fault zone. The fault geometry and throw distribution is analysed using two approaches, a discrete mapping of the large fault segments and a continuous approach based on measures of fault thickness and throw partitioning at individual outcrops. This combination of approaches demonstrates that fault thickness and the proportion of fault throw on the individual fault segments observed on any cross-section through the fault are related almost entirely to fault segmentation in 3D.
DRM-SLAM: Towards dense reconstruction of monocular SLAM with scene depth fusion

Monocular visual SLAM methods can accurately track the camera pose and infer the scene structure by building sparse correspondence between two/multiple views of the scene. However, the reconstructed 3D maps of these methods are extremely sparse. On the other hand, deep learning is widely used to predict dense depth maps from single-view color images, but the results are subject to blurry depth boundaries, which severely deform the structure of 3D scene. Therefore, this paper proposes a dense reconstruction method under the monocular SLAM framework (DRM-SLAM), in which a novel scene depth fusion scheme is designed to fully utilize both the sparse depth samples from monocular SLAM and predicted dense depth maps from convolutional neural network (CNN). In the scheme, a CNN architecture is carefully designed for robust depth estimation. Besides, our approach also accounts for the problem of scale ambiguity existing in the monocular SLAM. Extensive experiments on benchmark datasets and our captured dataset demonstrate the accuracy and robustness of the proposed DRM-SLAM. The evaluation of runtime and adaptability under challenging environments also verify the practicability of our method.
ALTRA: Cross-Project Software Defect Prediction via Active Learning and Tradaboost

Cross-project defect prediction (CPDP) methods can be used when the target project is a new project or lacks enough labeled program modules. In these new target projects, we can easily extract and then measure these modules with software measurement tools. However, labeling these program modules is time-consuming, error-prone and requires professional domain knowledge. Moreover, directly using labeled modules in the other projects (i.e., the source projects) can not achieve satisfactory performance due to the large data distribution difference in most cases. In this article, to our best knowledge, we are the first to propose a novel method ALTRA, which can utilize both active learning and TrAdaBoost to alleviate this issue. In particular, we firstly use Burak filter to select similar labeled modules from the source project after analyzing the unlabeled modules in the target project. Then we use active learning to choose representative unlabeled modules from the target project and ask experts to label the type (i.e., defective or non-defective) of these modules. Later, we use TrAdaBoost to determine the weights of labeled modules in the source project and the target project, and then construct the model via weighted support vector machine. After selecting a small number of modules (i.e., only 5% modules) in the target project, we terminate the method ALTRA and return the final constructed model. To show the effectiveness of our proposed method ALTRA, we choose 10 large-scale open-source projects from different application domains. In terms of both F1 and AUC performance indicators, we find ALTRA can perform significantly better than seven state-of-the-art CPDP baselines. Moreover, we also show that the usage of Burak filter, the uncertainty active learning strategy, the class imbalanced learning method and TrAdaBoost are competitive in our proposed method ALTRA.
Graph Interaction Networks for Relation Transfer in Human Activity Videos

Recent years have witnessed rapid progress in employing graph convolutional networks (GCNs) for various video analysis tasks where graph-based data abound. However, exploring the transferable knowledge between different graphs, which is a direction with wide and potential applications, has been rarely studied. To address this issue, we propose a graph interaction networks (GINs) model for transferring relation knowledge across two graphs. Different from conventional domain adaptation or knowledge distillation approaches, our GINs focus on a “self-learned” weight matrix, which is a higher-level representation of the input data. And each element of the weight matrix represents the pair-wise relation among different nodes within the graph. Moreover, we guide the networks to transfer the knowledge across the weight matrices by designing a task-specific loss function, so that the relation information is well preserved during transfer. We conduct experiments on two different scenarios for video analysis, including a new proposed setting for unsupervised skeleton-based action recognition across different datasets, and supervised group activity recognition with multi-modal inputs. Extensive experiments on six widely used datasets illustrate that our GINs achieve very competitive performance in comparison with the state-of-the-arts.
Smartphone-based Image Captioning for Visually and Hearing Impaired

Visually and hearing impaired people face troubles due to inaccessible infrastructure and social challenges in daily life. To increase the life quality of those people, we report a portable and user-friendly smartphone-based platform capable of generating captions and text descriptions, including the option of a narrator, using image obtained from a smartphone camera. Image captioning is to generate a sentence to describe the visual content of an image in natural language and has attracted an increasing amount of attention in the fields of computer vision and natural language processing due to its potential applications. Generating image captions with proper linguistic properties is a challenging task as it needs to combine advanced level of image understanding algorithms with natural language processing methods. In this study, we propose to use Long Short-Term Memory (LSTM) model to generate a caption after images are trained using VGG16 deep learning architecture. The visual attributes of images are extracted with the VGG16, which conveys richer content, and then they are fed into the LSTM model for caption generation. This system is integrated with our custom- designed Android application, named as "Eye of Horus" which transfers the images from smartphone to the remote server via a cloud system, and displays the captions after the images are processed with the proposed captioning approach. The results show that the integrated platform has great potential to be used for image captioning by visually and hearing impaired people with advantages such as portability, simple operation and rapid response.
A Novel Hybrid Neural Network-Based Multirobot Path Planning With Motion Coordination

Multi-robot navigation is a challenging task, especially for many robots, since individual gains may more often than not adversely affect the global gain. This paper investigates the problem of multiple robots moving towards individual goals within a common workspace whereas the motion of every individual robot is deduced by a novel Particle Swarm Optimization (PSO) tuned Feed Forward Neural Network (FFNN). Motion coordination among the robots is implemented using a cooperative coordination algorithm that identifies critical robots and maintains cooperation count while actuating deviation in select robots. The contribution of this paper is twofold; firstly in hybridizing the Artificial Neural Network(ANN) by employing PSO, an evolutionary algorithm, to find optimal values of deviation for every critical robot using velocity and acceleration constraints, secondly ensuing the convergence of the PSO by carrying first and second order stability analysis. Experiments have been carried out to evaluate and validate the efficacy of the proposed coordination schemes by changing the number of robots under hundred different scenarios each, and the founded results demonstrate the efficacy of the proposed schemes.
Leakage detection for a transmon-based surface code

Leakage outside of the qubit computational subspace, present in many leading experimental platforms, constitutes a threatening error for quantum error correction (QEC) for qubits. We develop a leakage-detection scheme via Hidden Markov models (HMMs) for transmon-based implementations of the surface code. By performing realistic density-matrix simulations of the distance-3 surface code (Surface-17), we observe that leakage is sharply projected and leads to an increase in the surface-code defect probability of neighboring stabilizers. Together with the analog readout of the ancilla qubits, this increase enables the accurate detection of the time and location of leakage. We restore the logical error rate below the memory break-even point by post-selecting out leakage, discarding about 47% of the data. Leakage detection via HMMs opens the prospect for near-term QEC demonstrations, targeted leakage reduction and leakage-aware decoding and is applicable to other experimental platforms.