Reviewerly/exHarmony huggingface dataset:
Switch controllers of an <i>n</i>-link revolute manipulator with a prismatic end-effector for landmark navigation

Robotic arms play an indispensable role in multiple sectors such as manufacturing, transportation and healthcare to improve human livelihoods and make possible their endeavors and innovations, which further enhance the quality of our lives. This paper considers such a robotic arm comprised of n revolute links and a prismatic end-effector, where the articulated arm is anchored in a restricted workspace. A new set of stabilizing switched velocity-based continuous controllers was derived using the Lyapunov-based Control Scheme (LbCS) from the category of classical approaches where switching of these nonlinear controllers is invoked by a new rule. The switched controllers enable the end-effector of the robotic arm to navigate autonomously via a series of landmarks, known as hierarchal landmarks, and finally converge to its equilibrium state. The interaction of the inherent attributes of LbCS that are the safeness, shortness and smoothness of paths for motion planning bring about cost and time efficiency of the controllers. The stability of the switched system was proven using Branicky's stability criteria for switched systems based on multiple Lyapunov functions and was numerically validated using the RK4 method (Runge-Kutta method). Finally, computer simulation results are presented to show the effectiveness of the continuous time-invariant velocity-based controllers.
Optimizing Selective Protection for CNN Resilience

As CNNs are being extensively employed in high performance and safety-critical applications that demand high reliability, it is important to ensure that they are resilient to transient hardware errors. Traditional full redundancy solutions provide high error coverage, but the associated overheads are often prohibitively high for resource-constrained systems. In this work, we propose software-directed selective protection techniques to target the most vulnerable work in a CNN, providing a low-cost solution. We propose and evaluate two domain-specific selective protection techniques for CNNs that target different granularities. First, we develop a feature-map level resilience technique (FLR), which identifies and statically protects the most vulnerable feature maps in a CNN. Second, we develop an inference level resilience technique (ILR), which selectively reruns vulnerable inferences by analyzing their output. Third, we show that the combination of both techniques (FILR) is highly efficient, achieving nearly full error coverage (99.78% on average) for quantized inferences via selective protection. Our tunable approach enables developers to evaluate CNN resilience to hardware errors before deployment using MAC operations as overhead for quicker trade-off analysis. For example, targeting 100% error coverage on ResNet50 with FILR requires 20.8% additional MACs, while measurements on a Jetson Xavier GPU shows 4.6% runtime overhead.
Test automation maturity improves product quality—Quantitative study of open source projects using continuous integration

The popularity of continuous integration (CI) is increasing as a result of market pressure to release product features or updates frequently. The ability of CI to deliver quality at speed depends on reliable test automation. In this paper, we present an empirical study to observe the effect of test automation maturity (assessed by standard best practices in the literature) on product quality, test automation effort, and release cycle in the CI context of open source projects. We run our test automation maturity survey and got responses from 37 open source java projects. We also mined software repositories of the same projects. The main results of regression analysis reveal that, higher levels of test automation maturity are positively associated with higher product quality (p-value=0.000624) and shorter release cycle (p-value=0.01891); There is no statistically significant evidence of increased test automation effort due to higher levels of test automation maturity and product quality. Thus, we conclude that, a potential benefit of improving test automation maturity (using standard best practices) is product quality improvement and release cycle acceleration in the CI context of open source projects. We encourage future research to extend our findings by adding more datasets with different programming languages and CI tools, closed source projects, and large-scale industrial projects. Our recommendation to practitioners (in the similar CI context) is to utilize standard best practices to improve test automation maturity.
MalDIST: From Encrypted Traffic Classification to Malware Traffic Detection and Classification

The world of malware is shifting towards using encrypted traffic. While encryption improves the privacy of users, it brings challenges in the fields of QoS, QoE, and cybersecurity. Recent state-of-the-art Deep-Learning architectures for encrypted traffic classifications demonstrated superb results in tasks of traffic categorization over encrypted traffic. In this paper, we leverage the feasibility to use such architectures for the tasks of malware detection and classification to gain insights into how well these architectures perform in the domain of malware traffic. Specifically, we present a Deep-Learning model for malware traffic detection and classification (MalDIST), which outperforms both classical ML and DL malware traffic classification models both in terms of detection and classification.
Re-Thinking the Relations in Co-Saliency Detection

Co-salient object detection (CoSOD) aims to detect common salient objects sharing the same attributes in an image group. The key issue of CoSOD is how to model the inter-saliency relations within an image group. The major limitation of previous methods is that they pre-define the group-to-one relations within an image group. In this paper, we propose a new concept of structural inter-saliency relations and solve the CoSOD with deep reinforcement learning framework. Firstly, we design a semantic relation graph (SRG) to model the structural inter-saliency relations. Then the feature selecting agent (FS-agent) aims to select the informative features, which can help the SRG effectively model structural inter-saliency relations. Finally, relation updating agent (RU-agent) progressively updates the SRG to focus on the co-salient relations like human decision-making process. Extensive experiments on co-saliency datasets show that because of well modeling inter-saliency relations in image group, our proposed method achieves superior performance compared to the state-of-the-art methods. We hope that this paper can motivate future research for visual co-analysis tasks.
Addressing image and Poisson noise deconvolution problem using deep learning approaches

Abstract Digital images are more important in numerous contemporary applications, and the need for images in the technical field is also increasing drastically. It is used to recognize signatures and faces in many industries and is applicable for intelligent departments. The images are usually associated with the noise content; this may happen due to the instrument imperfections, troubleshooting while collecting data from the acquisition process, and another natural phenomenon. Poisson noise, also known as photon noise, is caused in the images due to the statistical essence of electromagnetic waves. X‐ray, visible light, and gamma rays are electromagnetic waves. The enhancement of the convolution model in addressing images is challenging due to the various constituents such as optical aberrations, noise level, and optical setup. The modeling configuration of the image is attained using the point spread function (PSF), which is responsible for the system's impulse response. The quality image is retrieved by denoising and super‐resolution (SR) methods; these methods simultaneously eliminate the noise content from the images. A Richardson–Lucy and alternating direction method of multipliers type of non‐blind iterative algorithmic approaches associated with the PSF performance in addressing image is comparatively analyzed. The deep learning approach, convolutional neural networks (CNNs), is also employed to understand the nonlinear mapping relationship between the observed data and ground reality. The performance of the various network approaches is compared in this article. The result obtained shows that the deep learning CNNs achieved higher accuracy in producing denoising images. The goal of the proposed system model is to remove the interference noise in images. The high‐resolution images are obtained by implementing a SR‐based CNN model.
Noise-Sensitive Adversarial Learning for Weakly Supervised Salient Object Detection

Weakly supervised salient object detection (WSOD) aims at training saliency detection models with weak supervision. Normally, the WSOD methods use pseudo labels converted from image-level classification labels to train the saliency network. However, the converted pseudo labels always contain noise information compared to ground truth. Previous methods are directly affected by pseudo label noise to generate error-prone predictions. To mitigate this problem, we design a noise-robust adversarial learning framework and propose a noise-sensitive training strategy for the framework. The framework consists of a saliency network and a noise-robust discriminator network. With the guidance of noise-robust discriminator network, our saliency network is robust to noise information in pseudo labels. The proposed noise-sensitive training strategy can make good use of both superior and inferior samples in the pseudo label dataset. With the noise-sensitive training strategy, our framework can further balance the learning of saliency information and the robustness of noise information. Comprehensive experiments on five public datasets demonstrate that our method outperforms the existing image-level classification label based WSOD methods.
Joint Learning of E-commerce Search and Recommendation with a Unified Graph Neural Network

Click-through rate (CTR) prediction plays an important role in search and recommendation, which are the two most prominent scenarios in e-commerce. A number of models have been proposed to predict CTR by mining user behaviors, especially users' interactions with items. But the sparseness of user behaviors is an obstacle to the improvement of CTR prediction. Previous works only focused on one scenario, either search or recommendation. However, on a practical e-commerce platform, search and recommendation share the same set of users and items, which means joint learning of both scenarios may alleviate the sparseness of user behaviors. In this paper, we propose a novel Search and Recommendation Joint Graph (SRJGraph) neural network to jointly learn a better CTR model for both scenarios. A key question of joint learning is how to effectively share information across search and recommendation, in spite of their differences. A notable difference between search and recommendation is that there are explicit queries in search, whereas no query exists in recommendation. We address this difference by constructing a unified graph to share representations of users and items across search and recommendation, as well as represent user-item interactions uniformly. In this graph, users and items are heterogeneous nodes, and search queries are incorporated into the user-item interaction edges as attributes. For recommendation where no query exists, a special attribute is attached on user-item interaction edges. We further propose an intention and upstream-aware aggregator to explore useful information from high-order connections among users and items. We conduct extensive experiments on a large-scale dataset collected from Taobao.com, the largest e-commerce platform in China. Empirical results show that SRJGraph significantly outperforms the state-of-the-art approaches of CTR prediction in both search and recommendation tasks.
Towards Equitable, Social Justice Criticality: Re-Constructing the “Black” Box and Making it Transparent for the Future of Science and Technology in Science Education

In the wake of the COVID-19 pandemic, which has resulted in the rapid emergence of vaccines, the dual benefits of both science and technology have been lauded, while dominant, deficit-based narratives of vaccine hesitancy and mistrust in science and medicine by the general public, particularly minoritized populations, run rampant. In this paper, we argue for a counternarrative, where instead of erroneously positioning communities of color as the problem, the problem is reframed to consider what the scientific, technological, and science education communities need to do to become more trustworthy and transgress the persistent shortcomings related to racism and injustice. Specifically, in this position paper, we (a) discuss the interactions of science, technology, and society from the perspective of the nature of technology; (b) engage an understanding of how bias, access, and racism operate in and at the intersection of science, technology, and technological systems; (c) discuss implications of these ideas in science education; and finally (d) pose recommendations to counter alienation and racism with an emphasis on a sixth dimension, equitable, social justice criticality, for science-technology education. In conclusion, we make recommendations by centering a more equitable, social justice criticality of science and technology.
On computational aspects of high-order dual Hahn moments

In this paper, we present two new algorithms for the fast and stable computation of high-order discrete orthogonal dual Hahn polynomials (DHPs). These algorithms are essentially based on the proposed computation method of the initial values of DHPs following the order n and the variable s. For both algorithms, a single stable value is computed, fully independent of the gamma function that is the source of the numerical overflow, and then the rest of DHPs values are computed recursively via the proposed recurrence scheme. By analyzing the DHPs matrix, we propose a new method, which allows ensuring the numerical stability of high-order DHPs and dual Hahn moments (DHMs) until the last order. This method is based on the use of appropriate stability conditions. The results of simulations and comparisons carried out show on one hand that the second algorithm with the stability condition allows to compute DHPs up to the order n = 17,603 without propagation of numerical error. On the other hand, the performance of analyzing large-size signals and images by high-order DHMs computed by the proposed method significantly exceeds the existing methods in terms of numerical stability, accuracy of reconstruction and in terms of maximum size of the analyzed signals and images. After the acceptance of this paper, the proposed algorithms for high-order DHPs computation will be made publically available at https://github.com/AchrafDaoui/On-Computational-Aspects-of-High-Order-Dual-Hahn-Moments.
Class-specific discriminative metric learning for scene recognition

Metric learning aims to learn an appropriate distance metric for a given machine learning task. Despite its impressive performance in the field of image recognition, it may still not be discriminative enough for scene recognition because of the high within-class diversity and high between-class similarity of scene images. In this paper, we propose a novel class-specific discriminative metric learning method (CSDML) to alleviate these problems. More specifically, we learn a distinctive linear transformation for each class (or, equivalently, a Mahalanobis distance metric for each class), which allows to project the samples of that class into a corresponding low-dimensional discriminative space. The overall aim is to simultaneously minimize the Euclidean distances between the projections of samples of the same class (or, equivalently, the Mahalanobis distances between these samples) and maximize the Euclidean distances between the projections of samples of different classes. Additionally, we incorporate least squares regression into the optimization problem, rendering class-specific metric learning more flexible and better suited to tackle scene recognition. Experimental results on four benchmark scene datasets demonstrate that the proposed method outperforms most of the state-of-the-art approaches.
Hessian-Aware Pruning and Optimal Neural Implant

Pruning is an effective method to reduce the memory footprint and FLOPs associated with neural network models. However, existing structured-pruning methods often result in significant accuracy degradation for moderate pruning levels. To address this problem, we introduce a new Hessian Aware Pruning (HAP) method coupled with a Neural Implant approach that uses second-order sensitivity as a metric for structured pruning. The basic idea is to prune insensitive components and to use a Neural Implant for moderately sensitive components, instead of completely pruning them. For the latter approach, the moderately sensitive components are replaced with a low rank implant that is smaller and less computationally expensive than the original component. We use the relative Hessian trace to measure sensitivity, as opposed to the magnitude based sensitivity metric commonly used in the literature. We test HAP for both computer vision tasks and natural language tasks, and we achieve new state-of-the-art results. Specifically, HAP achieves less than 0.1%/0.5% degradation on PreResNet29/ResNet50 (CIFAR-10/ImageNet) with more than 70%/50% of parameters pruned. Meanwhile, HAP also achieves significantly better performance (up to 0.8% with 60% of parameters pruned) as compared to gradient based method for head pruning on transformer-based models. The framework has been open sourced and available online: https://github.com/yaozhewei/HAP.
Weak multi-label learning with missing labels via instance granular discrimination

In multi-label learning, each training instance is associated with multiple class labels. It is typical in reality that relevant labels are partially missing and only a part of labels are valid, resulting in the problem of weak multi-label learning with missing labels. It is still an evident challenge to estimate the ground-truth label matrix and to generate a prediction function, especially on the multi-label data with a large number of missing labels. In this paper, we propose a multi-label learning framework within which feature structure and label manifold are both utilized to recover the incomplete label matrix and to train the classification model simultaneously. To mitigate the imbalanced risks brought by the sparse label issue, a self-adaptive penalty factor is imposed on the deviated predictions of different labels. Moreover, instance granular discrimination is incorporated in the framework to characterize the approximate distribution structure of data. Matrix vectorization, cave-convex programming (CCCP), and block coordinate descent techniques are employed to solve the proposed optimization problem. Extensive experiments illustrate the superiority of the proposed method against some well-established methods.
Exploring Fine-Grained Cluster Structure Knowledge for Unsupervised Domain Adaptation

Unsupervised domain adaptation aims to leverage knowledge from a labeled source domain to learn an accurate model in an unlabeled target domain. However, many previous approaches propose to learn domain agnostic feature representations using a global distribution alignment objective, which does not consider the fine-grained cluster structures in the source and target domains. As such, the goal of this paper is to address two challenging problems: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1) how to thoroughly explore fine-grained cluster structure knowledge in the source and target domains, 2) how to effectively incorporate these structure knowledge for adaptation.</i> Regarding the first point, we are motivated by structural domain similarity assumption and propose structural representation learning, which is achieved by enforcing structural consistency between the source and target domains while retaining their individual discriminative properties. Regarding the second point, we firstly devise a novel structural centroid-based label prediction method, which explicitly models structural representations to form discriminative source and target cluster centroids, and estimates the label distribution of each target sample through the cosine similarity between its corresponding target cluster centroid and all the other source cluster centroids. Then, we adopt clustering learning to incorporate these discriminative structure knowledge for adaptation by minimizing the KL divergence between the predictive target label distribution and an introduced auxiliary one. Comprehensive experiments and analyses on four benchmark datasets demonstrate the superiority of the proposed discriminative clustering framework.
The Impact of Embodiment and Avatar Sizing on Personal Space in Immersive Virtual Environments

In this paper, we examine how embodiment and manipulation of a self-avatar's dimensions - specifically the arm length - affect users' judgments of the personal space around them in an immersive virtual environment. In the real world, personal space is the immediate space around the body in which physical interactions are possible. Personal space is increasingly studied in virtual environments because of its importance to social interactions. Here, we specifically look at two components of personal space, interpersonal and peripersonal space, and how they are affected by embodiment and the sizing of a self-avatar. We manipulated embodiment, hypothesizing that higher levels of embodiment will result in larger measures of interpersonal space and smaller measures of peripersonal space. Likewise, we manipulated the arm length of a self-avatar, hypothesizing that while interpersonal space would change with changing arm length, peripersonal space would not. We found that the representation of both interpersonal and peripersonal space change when the user experiences differing levels of embodiment in accordance with our hypotheses, and that only interpersonal space was sensitive to changes in the dimensions of a self-avatar's arms. These findings provide increased understanding of the role of embodiment and self-avatars in the regulation of personal space, and provide foundations for improved design of social interaction in virtual environments.
Learning with virtual reality: a market analysis of educational and training applications

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