Reviewerly/exHarmony huggingface dataset:
Channel and spatial attention-based Siamese network for visual object tracking

Visual object tracking, which aims to estimate the position of an arbitrary target in a video sequence automatically, has drawn great attention in recent years. Many efforts have been made regarding this topic. The Siamese network, with a balanced accuracy and speed, has achieved great success. The Siamese network consists of two branches: one for the target image and the other for the search image. The position with the maximum score in the similarity map between the target and the search images indicates the place of the target image in the search image. Current Siamese trackers treat the features of different channels and spatial locations equally. However, the features of different channels and spatial locations may represent different semantic information. We propose a channel and spatial (CS) attention-based Siamese network for visual object tracking. A CS attention mechanism is inserted into the feature extractor to enhance the semantic feature learning. The experimental results show that the proposed network significantly improves the performance of the baseline tracker and is one of the top-ranked trackers among all tested state-of-the-art trackers on the most widely used visual object tracking datasets.
ClipGen: A Deep Generative Model for Clipart Vectorization and Synthesis

This article presents a novel deep learning-based approach for automatically vectorizing and synthesizing the clipart of man-made objects. Given a raster clipart image and its corresponding object category (e.g., airplanes), the proposed method sequentially generates new layers, each of which is composed of a new closed path filled with a single color. The final result is obtained by compositing all layers together into a vector clipart image that falls into the target category. The proposed approach is based on an iterative generative model that (i) decides whether to continue synthesizing a new layer and (ii) determines the geometry and appearance of the new layer. We formulated a joint loss function for training our generative model, including the shape similarity, symmetry, and local curve smoothness losses, as well as vector graphics rendering accuracy loss for synthesizing clipart recognizable by humans. We also introduced a collection of man-made object clipart, ClipNet, which is composed of closed-path layers, and two designed preprocessing tasks to clean up and enrich the original raw clipart. To validate the proposed approach, we conducted several experiments and demonstrated its ability to vectorize and synthesize various clipart categories. We envision that our generative model can facilitate efficient and intuitive clipart designs for novice users and graphic designers.
Lachesis

Partitioning is effective in avoiding expensive shuffling operations. However, it remains a significant challenge to automate this process for Big Data analytics workloads that extensively use user defined functions (UDFs), where sub-computations are hard to be reused for partitionings compared to relational applications. In addition, functional dependency that is widely utilized for partitioning selection is often unavailable in the unstructured data that is ubiquitous in UDF-centric analytics. We propose the Lachesis system, which represents UDF-centric workloads as workflows of analyzable and reusable sub-computations. Lachesis further adopts a deep reinforcement learning model to infer which sub-computations should be used to partition the underlying data. This analysis is then applied to automatically optimize the storage of the data across applications to improve the performance and users' productivity.
Going Beyond p-convolutions to Learn Grayscale Morphological Operators

Integrating mathematical morphology operations within deep neural networks has been subject to increasing attention lately. However, replacing standard convolution layers with erosions or dilations is particularly challenging because the \(\min \) and \(\max \) operations are not differentiable. Relying on the asymptotic behavior of the counter-harmonic mean, p-convolutional layers were proposed as a possible workaround to this issue since they can perform pseudo-dilation or pseudo-erosion operations (depending on the value of their inner parameter p), and very promising results were reported. In this work, we present two new morphological layers based on the same principle as the p-convolutional layer while circumventing its principal drawbacks, and demonstrate their potential interest in further implementations within deep convolutional neural network architectures.
Exploring misclassifications of robust neural networks to enhance adversarial attacks

Abstract Progress in making neural networks more robust against adversarial attacks is mostly marginal, despite the great efforts of the research community. Moreover, the robustness evaluation is often imprecise, making it challenging to identify promising approaches. We do an observational study on the classification decisions of 19 different state-of-the-art neural networks trained to be robust against adversarial attacks. This analysis gives a new indication of the limits of the robustness of current models on a common benchmark. In addition, our findings suggest that current untargeted adversarial attacks induce misclassification toward only a limited amount of different classes. Similarly, we find that previous attacks under-explore the perturbation space during optimization. This leads to unsuccessful attacks for samples where the initial gradient direction is not a good approximation of the final adversarial perturbation direction. Additionally, we observe that both over- and under-confidence in model predictions result in an inaccurate assessment of model robustness. Based on these observations, we propose a novel loss function for adversarial attacks that consistently improves their efficiency and success rate compared to prior attacks for all 30 analyzed models.
Pixel-Wise Weighted Region-Based 3D Object Tracking Using Contour Constraints

Region-based methods are currently achieving state-of-the-art performance for monocular 3D object tracking. However, they are still prone to fail in cases of partial occlusions and ambiguous colors. We propose a novel region-based method to tackle these problems. The key idea is to derive a pixel-wise weighted region-based cost function using contour constraints. First, we propose a novel region-based cost function using search lines around the object contour, which is more efficient than previous region-based cost functions using signed distance transform, and in the meantime can deal with partial occlusions and ambiguous colors more effectively. Second, we propose an optimal searching strategy to search the object contour points in cluttered scenes, and then use the object contour points to detect partial occlusions and ambiguous colors. Third, we propose a pixel-wise weight function based on color and distance constraints of the object contour points, and integrate it into the proposed region-based cost function to reduce the negative impact of partial occlusions and ambiguous colors. We verify the effectiveness and efficiency of our method on challenging public datasets. Experiments demonstrate that our method outperforms the recent state-of-the-art region-based methods in complex scenarios, especially in the presence of partial occlusions and ambiguous colors.
Entropic Out-of-Distribution Detection: Seamless Detection of Unknown Examples

In this article, we argue that the unsatisfactory out-of-distribution (OOD) detection performance of neural networks is mainly due to the SoftMax loss anisotropy and propensity to produce low entropy probability distributions in disagreement with the principle of maximum entropy. On the one hand, current OOD detection approaches usually do not directly fix the SoftMax loss drawbacks, but rather build techniques to circumvent it. Unfortunately, those methods usually produce undesired side effects (e.g., classification accuracy drop, additional hyperparameters, slower inferences, and collecting extra data). On the other hand, we propose replacing SoftMax loss with a novel loss function that does not suffer from the mentioned weaknesses. The proposed IsoMax loss is isotropic (exclusively distance-based) and provides high entropy posterior probability distributions. Replacing the SoftMax loss by IsoMax loss requires no model or training changes. Additionally, the models trained with IsoMax loss produce as fast and energy-efficient inferences as those trained using SoftMax loss. Moreover, no classification accuracy drop is observed. The proposed method does not rely on outlier/background data, hyperparameter tuning, temperature calibration, feature extraction, metric learning, adversarial training, ensemble procedures, or generative models. Our experiments showed that IsoMax loss works as a seamless SoftMax loss drop-in replacement that significantly improves neural networks' OOD detection performance. Hence, it may be used as a baseline OOD detection approach to be combined with current or future OOD detection techniques to achieve even higher results.
Hybrid DCT-SVD Based Digital Watermarking Scheme with Chaotic Encryption for Medical Images

Abstract The quality of medical image is crucial due to the sensitivity of medical diagnoses. Nowadays medical images are commonly shared between various entities across the medical sector to improve the quality of diagnoses. Thus, watermarking process is usually applied on these images to embed patient’s private information. One of the key challenges is preserving image quality, keeping it clean of distortions after the watermarking process, and sustaining robustness. In this work, a hybrid Discrete Cosine Transform (DCT) and Singular value decomposition (SVD) based image watermarking scheme with chaotic encryption is proposed. The obtained results have demonstrated that the proposed scheme is secure and robust compared to the present schemes. In particular, the results show decent robustness and excellent imperceptibility of watermarked host medical images. Moreover, Peak signal to noise ratio (PSNR), and Normalized Correlation (NC) are used to evaluate the performance of the scheme. The scheme has shown a minimum PSNR of 59 dB and an NC value of 1 with no attacks applied, and not less than 0.5 with the presence of various attacks.
STEG-Net: Spatiotemporal Edge Guidance Network for Video Salient Object Detection

Over the past decades, video salient object detection (VSOD) has achieved rapid development. However, the effect of edge information on spatial and temporal features extraction has not been explored. The method STEG-Net is proposed to solve the problem, we use the extracted edge information to guide the extraction of spatial and temporal features simultaneously. We combine deep texture information with shallow edge information, which can not only retain the edge of the object but also enhance the global information, leading to the more accurate location of the object. At the same time, the shallow edge information and the deep texture information are complementary. As a result, edge information and salient features could advance each other while extracting features. Finally, we evaluate the 16 state-of-the-art VSOD methods on ViSal and FBMS data sets. At the same time we also evaluate the 13 state-of-the-art VSOD methods on UVSD and MCL data sets. The experiment results indicate that the proposed method outperforms the state-of-the-art approaches.
Enhancing End-User Roles in Information Security: Exploring the Setting, Situation, and Identity

Managing employee behaviors is a continuous quest for management. The quest is even more intense in the information security space, where a seemingly unintentional activity could have a consequential effect on an organization's security. The information security literature has used many theoretical approaches to explain how to regulate employee security behaviors, including protection motivation and deterrence. However, a theoretical approach that has captured the focus in organizational literature for behavioral regulation and yet to be realized in the information security domain is work-related identities. Work-related identity regulation depends on settings and situations in which the individual is embedded. This study draws from the identity theory and information security literature to explore how factors in the information security setting (security threats, security policy, and organizational support) help foment work-related information security identity and security behaviors. Our findings show that these factors significantly increase the user's identification in their roles in information security, and in turn, security behaviors. This research has implications for research and practice.
Resource and Response Aware Path Planning for Long-term Autonomy of Ground Robots in Agriculture

Achieving long-term autonomy for mobile robots operating in real-world, unstructured environments, such as farms, remains a significant challenge. Such tasks are made increasingly complex when undertaken in the presence of moving humans or livestock. These dynamic environments require a robot to be able to adapt its immediate plans, accounting for the state of nearby agents and possible responses they may have to the robot’s actions. Additionally, in order to achieve longer-term goals, consideration of the limited on-board resources available to the robot is required, especially for extended missions, such as weeding agricultural fields. To achieve efficient long-term autonomy, it is thus crucial to understand the impact that dynamic updates to an energy-efficient plan might have on resource usage whilst navigating through crowds or herds. To address these challenges, we present a hierarchical planning framework that integrates an online, dynamic path-planner with a longer-term, offline, objective-based planner. This framework acts to achieve long-term autonomy through awareness of both dynamic responses of agents to a robot’s motion and the limited resources available. This paper details the hierarchical approach and its integration on a robotic platform, including a comprehensive description of the planning framework and associated perception modules. The approach is evaluated in real-world trials on farms, requiring both consideration of limited battery capacity and the presence of nearby moving agents. These trials additionally demonstrate the ability of the framework to adapt resource use through variation of the dynamic planner, allowing adaptive behaviour in changing environments. A summary video is available at https://youtu.be/DGVTrYwJ304.
Shall I Work with Them? A Knowledge Graph-Based Approach for Predicting Future Research Collaborations

We consider the prediction of future research collaborations as a link prediction problem applied on a scientific knowledge graph. To the best of our knowledge, this is the first work on the prediction of future research collaborations that combines structural and textual information of a scientific knowledge graph through a purposeful integration of graph algorithms and natural language processing techniques. Our work: (i) investigates whether the integration of unstructured textual data into a single knowledge graph affects the performance of a link prediction model, (ii) studies the effect of previously proposed graph kernels based approaches on the performance of an ML model, as far as the link prediction problem is concerned, and (iii) proposes a three-phase pipeline that enables the exploitation of structural and textual information, as well as of pre-trained word embeddings. We benchmark the proposed approach against classical link prediction algorithms using accuracy, recall, and precision as our performance metrics. Finally, we empirically test our approach through various feature combinations with respect to the link prediction problem. Our experimentations with the new COVID-19 Open Research Dataset demonstrate a significant improvement of the abovementioned performance metrics in the prediction of future research collaborations.
Synthesis of equilibria in infinite-duration games on graphs

In this survey, we propose a comprehensive introduction to game theory applied to computer-aided synthesis. We study multi-player turn-based infinite-duration games played on a finite directed graph such that each player aims at maximizing a payoff function. We present the well-known notions of Nash equilibrium and subgame perfect equilibrium, as well as interesting strategy profiles of players as response to the strategy announced by a specific player. We provide classical and recent results about the related threshold synthesis problem.
RuBQ 2.0: An Innovated Russian Question Answering Dataset

The paper describes the second version of RuBQ, a Russian dataset for knowledge base question answering (KBQA) over Wikidata. Whereas the first version builds on Q&A pairs harvested online, the extension is based on questions obtained through search engine query suggestion services. The questions underwent crowdsourced and in-house annotation in a quite different fashion compared to the first edition. The dataset doubled in size: RuBQ 2.0 contains 2,910 questions along with the answers and SPARQL queries. The dataset also incorporates answer-bearing paragraphs from Wikipedia for the majority of questions. The dataset is suitable for the evaluation of KBQA, machine reading comprehension (MRC), hybrid questions answering, as well as semantic parsing. We provide the analysis of the dataset and report several KBQA and MRC baseline results. The dataset is freely available under the CC-BY-4.0 license.
Cross-modality online distillation for multi-view action recognition

Recently, some multi-modality features are introduced to the multi-view action recognition methods in order to obtain a more robust performance. However, it is intuitive that not all modalities are avail- able in real applications. For example, daily scenes lack depth modal data and capture RGB sequences only. Thus comes the challenge of learning critical features from multi-modality data at train time, while still getting robust performance based on RGB sequences at test time. To address this chal- lenge, our paper presents a novel two-stage teacher-student framework. The teacher network takes advantage of multi-view geometry-and-texture features during training, while the student network is given only RGB sequences at test time. Specifically, in the first stage, Cross-modality Aggregated Transfer (CAT) network is proposed to transfer multi-view cross-modality aggregated features from the teacher network to the student network. Moreover, we design a Viewpoint-Aware Attention (VAA) module which captures discriminative information across different views to combine multi-view fea- tures effectively. In the second stage, Multi-view Features Strengthen (MFS) network with the VAA module further strengthens the global view-invariance features of the student network. Besides, both of CAT and MFS learn in an online distillation manner, so that the teacher and the student network can be trained jointly. Extensive experiments on IXMAS and Northwestern-UCLA demonstrate the effectiveness of our proposed method.
An Empirical Study on Predictability of Software Code Smell Using Deep Learning Models

Code Smell, similar to a bad smell, is a surface indication of something tainted but in terms of software writing practices. This metric is an indication of a deeper problem lies within the code and is associated with an issue which is prominent to experienced software developers with acceptable coding practices. Recent studies have often observed that codes having code smells are often prone to a higher probability of change in the software development cycle. In this paper, we developed code smell prediction models with the help of features extracted from source code to predict eight types of code smell. Our work also presents the application of data sampling techniques to handle class imbalance problem and feature selection techniques to find relevant feature sets. Previous studies had made use of techniques such as Naive - Bayes and Random forest but had not explored deep learning methods to predict code smell. A total of 576 distinct Deep Learning models were trained using the features and datasets mentioned above. The study concluded that the deep learning models which used data from Synthetic Minority Oversampling Technique gave better results in terms of accuracy, AUC with the accuracy of some models improving from 88.47 to 96.84.
OCTID: a one-class learning-based Python package for tumor image detection

Tumor tile selection is a necessary prerequisite in patch-based cancer whole slide image analysis, which is labor-intensive and requires expertise. Whole slides are annotated as tumor or tumor free, but tiles within a tumor slide are not. As all tiles within a tumor free slide are tumor free, these can be used to capture tumor-free patterns using the one-class learning strategy.We present a Python package, termed OCTID, which combines a pretrained convolutional neural network (CNN) model, Uniform Manifold Approximation and Projection (UMAP) and one-class support vector machine to achieve accurate tumor tile classification using a training set of tumor free tiles. Benchmarking experiments on four H&E image datasets achieved remarkable performance in terms of F1-score (0.90 ± 0.06), Matthews correlation coefficient (0.93 ± 0.05) and accuracy (0.94 ± 0.03).Detailed information can be found in the Supplementary File.Supplementary data are available at Bioinformatics online.
High-Speed Calibration and Characterization of Superconducting Quantum Processors without Qubit Reset

In order to characterize and calibrate quantum processing devices, a large amount of measurement data has to be collected. Active qubit reset increases the speed at which data can be gathered but requires additional hardware and/or calibration. The experimental apparatus can, however, be operated at elevated repetition rates without reset. In this case, the outcome of a first measurement serves as the initial state for the next experiment. Rol et al. have used this restless operation mode to accelerate the calibration of a single-qubit gate by measuring fixed-length sequences of Clifford gates that compose to X gates [Phys. Rev. Appl. 7, 041001 (2017)]. However, we find that when measuring pulse sequences that compose to arbitrary operations, distortion appears in the measured data. Here, we extend the restless methodology by showing how to efficiently analyze restless measurements and correct distortions to achieve an identical outcome and accuracy as compared to measurements in which the superconducting qubits are reset. This allows us to rapidly characterize and calibrate qubits. We illustrate this bias-corrected restless measurements method by measuring a Rabi oscillation at a 250kHz repetition rate without any reset, for a qubit with a decay rate of 1/2πT1=3kHz. We also show that we can measure a single- and a two-qubit average gate fidelity using randomized benchmarking 20 and 8 times faster, respectively, than measurements that reset the qubits through T1 decay.6 MoreReceived 22 October 2020Revised 11 February 2021Accepted 18 March 2021DOI:https://doi.org/10.1103/PRXQuantum.2.020324Published 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 AreasMeasurement-based quantum computingQuantum measurementsPhysical SystemsSuperconducting qubitsQuantum InformationStatistical Physics