Reviewerly/exHarmony huggingface dataset:
Tracing the Motion of Finger Joints for Gesture Recognition via Sewing RGO-Coated Fibers Onto a Textile Glove

In this study, we have designed and fabricated a flexible and low-cost data glove and then successfully realized the accurate gesture recognition. By sewing the reduced graphene oxide (RGO)-coated fiber that is prepared in a simple method onto a textile glove, we have manufactured a data glove that has been used to monitor the motion of ten finger joints from one hand. In particular, we accurately realize the static and dynamic gesture recognition via the data glove, respectively. Experimental results on the classification of ten static gestures representing digits from “0” to “9” show the good stability and repeatability of the data glove, in which the recognition accuracy achieves 98.5% in 2000 test samples. In addition, the recognition of nine Chinese Sign Language (CSL) words is successfully implemented, and the recognition accuracy reaches 98.3% in 180 test samples, which demonstrates the good real-time monitoring ability and applicability of the dynamic gesture scenarios of the as-prepared data glove.
Memory-Efficient High-Speed Implementation of Kyber on Cortex-M4

This paper presents an optimized software implementation of the module-lattice-based key-encapsulation mechanism Kyber for the ARM Cortex-M4 microcontroller. Kyber is one of the round-2 candidates in the NIST post-quantum project. In the center of our work are novel optimization techniques for the number-theoretic transform (NTT) inside Kyber, which make very efficient use of the computational power offered by the “vector” DSP instructions of the target architecture. We also present results for the recently updated parameter sets of Kyber which equally benefit from our optimizations. As a result of our efforts we present software that is 18% faster than an earlier implementation of Kyber optimized for the Cortex-M4 by the Kyber submitters. Our NTT is more than twice as fast as the NTT in that software. Our software runs at about the same speed as the latest speed-optimized implementation of the other module-lattice based round-2 NIST PQC candidate Saber. However, for our Kyber software, this performance is achieved with a much smaller RAM footprint. Kyber needs less than half of the RAM of what the considerably slower RAM-optimized version of Saber uses. Our software does not make use of any secret-dependent branches or memory access and thus offers state-of-the-art protection against timing attacks.
GNSD: a Gradient-Tracking Based Nonconvex Stochastic Algorithm for Decentralized Optimization

In the era of big data, it is challenging to train a machine learning model on a single machine or over a distributed system with a central controller over a large-scale dataset. In this paper, we propose a gradient-tracking based nonconvex stochastic decentralized (GNSD) algorithm for solving nonconvex optimization problems, where the data is partitioned into multiple parts and processed by the local computational resource. Through exchanging the parameters at each node over a network, GNSD is able to find the first-order stationary points (FOSP) efficiently. From the theoretical analysis, it is guaranteed that the convergence rate of GNSD to FOSPs matches the well-known convergence rate O(1/√T) of stochastic gradient descent by shrinking the step-size. Finally, we perform extensive numerical experiments on computational clusters to demonstrate the advantage of GNSD compared with other state-of-the-art methods.
An Improved Iterative FIR State Estimator and Its Applications

In this paper, an iterative finite impulse response (FIR) filter is proposed for discrete time-varying state-space models, with the purpose of a new initialization strategy for the iterative FIR structure as well as consideration of possible unexpected state dynamics in a finite horizon. A compensation variable that satisfies the Gaussian property is introduced into the state equation, and its probability density function (pdf) is estimated analytically together with the pdf of state variable using the variational Bayesian inference technique. Different from the existing methods, the proposed filter exploits the FIR structure from the perspective of pdf propagation, which provides a new efficient way to use the iterative FIR filtering structure without any particular initialization scheme. Moreover, the effects of uncertainties (caused by initialization and/or possible unmodeled state dynamics) on the filtering output are loosened adaptively. Two examples of applications demonstrate that the proposed algorithm can not only provide optimal estimates when the model used perfectly matches the measurements, but can also exhibit better robustness than the Kalman filter, optimal FIR filter, maximum likelihood FIR filter, and some commonly used robust and/or adaptive Kalman filters when the underlying process suffers from unpredicted uncertainties.
Parallelizable Reachability Analysis Algorithms for Feed-Forward Neural Networks

Artificial neural networks (ANN) have displayed considerable utility in a wide range of applications such as image processing, character and pattern recognition, self-driving cars, evolutionary robotics, and non-linear system identification and control. While ANNs are able to carry out complicated tasks efficiently, they are susceptible to unpredictable and errant behavior due to irregularities that emanate from their complex non-linear structure. As a result, there have been reservations about incorporating them into safety-critical systems. In this paper, we present a reachability analysis method for feed-forward neural networks (FNN) that employ rectified linear units (ReLUs) as activation functions. The crux of our approach relies on three reachable-set computation algorithms, namely exact schemes, lazy-approximate schemes, and mixing schemes. The exact scheme computes an exact reachable set for FNN, while the lazy-approximate and mixing schemes generate an over-approximation of the exact reachable set. All schemes are designed efficiently to run on parallel platforms to reduce the computation time and enhance the scalability. Our methods are implemented in a toolbox called, NNV, and is evaluated using a set of benchmarks that consist of realistic neural networks with sizes that range from tens to a thousand neurons. Notably, NNV successfully computes and visualizes the exact reachable sets of the real world ACAS Xu deep neural networks (DNNs), which are a variant of a family of novel airborne collision detection systems known as the ACAS System X, using a representation of tens to hundreds of polyhedra.
Graph-Based Mining of In-the-Wild, Fine-Grained, Semantic Code Change Patterns

Prior research exploited the repetitiveness of code changes to enable several tasks such as code completion, bug-fix recommendation, library adaption, etc. These and other novel applications require accurate detection of semantic changes, but the state-of-the-art methods are limited to algorithms that detect specific kinds of changes at the syntactic level. Existing algorithms relying on syntactic similarity have lower accuracy, and cannot effectively detect semantic change patterns. We introduce a novel graph-based mining approach, CPatMiner, to detect previously unknown repetitive changes in the wild, by mining fine-grained semantic code change patterns from a large number of repositories. To overcome unique challenges such as detecting meaningful change patterns and scaling to large repositories, we rely on fine-grained change graphs to capture program dependencies. We evaluate CPatMiner by mining change patterns in a diverse corpus of 5,000+ open-source projects from GitHub across a population of 170,000+ developers. We use three complementary methods. First, we sent the mined patterns to 108 open-source developers. We found that 70% of respondents recognized those patterns as their meaningful frequent changes. Moreover, 79% of respondents even named the patterns, and 44% wanted future IDEs to automate such repetitive changes. We found that the mined change patterns belong to various development activities: adaptive (9%), perfective (20%), corrective (35%) and preventive (36%, including refactorings). Second, we compared our tool with the state-of-the-art, AST-based technique, and reported that it detects 2.1x more meaningful patterns. Third, we use CPatMiner to search for patterns in a corpus of 88 GitHub projects with longer histories consisting of 164M SLOCs. It constructed 322K fine-grained change graphs containing 3M nodes, and detected 17K instances of change patterns from which we provide unique insights on the practice of change patterns among individuals and teams. We found that a large percentage (75%) of the change patterns from individual developers are commonly shared with others, and this holds true for teams. Moreover, we found that the patterns are not intermittent but spread widely over time. Thus, we call for a community-based change pattern database to provide important resources in novel applications.
Hierarchical Matching Network for Crime Classification

Automatic crime classification is a fundamental task in the legal field. Given the fact descriptions, judges first determine the relevant violated laws, and then the articles. As laws and articles are grouped into a tree-shaped hierarchy (i.e., laws as parent labels, articles as children labels), this task can be naturally formalized as a two layers' hierarchical multi-label classification problem. Generally, the label semantics (i.e., definition of articles) and the hierarchical structure are two informative properties for judges to make a correct decision. However, most previous methods usually ignore the label structure and feed all labels into a flat classification framework, or neglect the label semantics and only utilize fact descriptions for crime classification, thus the performance may be limited. In this paper, we formalize crime classification problem into a matching task to address these issues. We name our model as Hierarchical Matching Network (HMN for short). Based on the tree hierarchy, HMN explicitly decomposes the semantics of children labels into the residual and alignment components. The residual components keep the unique characteristics of each individual children label, while the alignment components capture the common semantics among sibling children labels, which are further aggregated as the representation of their parent label. Finally, given a fact description, a co-attention metric is applied to effectively match the relevant laws and articles. Experiments on two real-world judicial datasets demonstrate that our model can significantly outperform the state-of-the-art methods.
PSGAN

Personalized search aims to adapt document ranking to user's personal interests. Traditionally, this is done by extracting click and topical features from historical data in order to construct a user profile. In recent years, deep learning has been successfully used in personalized search due to its ability of automatic feature learning. However, the small amount of noisy personal data poses challenges to deep learning models to learn the personalized classification boundary between relevant and irrelevant results. In this paper, we propose PSGAN, a Generative Adversarial Network (GAN) framework for personalized search. By means of adversarial training, we enforce the model to pay more attention to training data that are difficult to distinguish. We use the discriminator to evaluate personalized relevance of documents and use the generator to learn the distribution of relevant documents. Two alternative ways to construct the generator in the framework are tested: based on the current query or based on a set of generated queries. Experiments on data from a commercial search engine show that our models can yield significant improvements over state-of-the-art models.
The Relationship Between Teachers’ Perceived Classroom Management Abilities and Their Professional Digital Competence

Experiences from Upper Secondary Classrooms. A Qualitative Driven Mixed Method Study The aim of this study was to examine teachers' professional digital competence and classroom management experiences with a qualitatively driven Mixed Method Design. The qualitative part of the study is based on interviews, focus groups, observations and quasi statistics. The first part of the study aims to examine these qualitative experiences and associations with a quantitative survey (N = 2579). The quantitative data examines the strength of the associations and the qualitative data show the nature of those associations with regard to upper secondary teachers' professional digital competence and their classroom management abilities in ICT dense classroom environments. The SMIL-study was conducted in seven counties in Norway from 2012 to 2013. The qualitative part with observational data were analyzed in relation to the other qualitative data (interviews and focus groups) and then in relation to the quantitative part of the study. This part shows how teachers, school owners, school leaders and students perceived a relationship between teachers' professional digital competence and their classroom management. The quantitative is related to the qualitative part and presents statistical regression analyses indicating that teachers' individual professional digital competence predicted their classroom management abilities. The conclusion of the paper suggests that teachers' professional digital competence and classroom management abilities are closely attached to each other in technology rich classrooms settings. The SMIL-study contributes to a broader understanding of teachers' professional digital competence in the digitized school.
Three-Stream Convolutional Neural Network With Multi-Task and Ensemble Learning for 3D Action Recognition

In this paper, we propose a three-stream convolutional neural network (3SCNN) for action recognition from skeleton sequences, which aims to thoroughly and fully exploit the skeleton data by extracting, learning, fusing and inferring multiple motion-related features, including 3D joint positions and joint displacements across adjacent frames as well as oriented bone segments. The proposed 3SCNN involves three sequential stages. The first stage enriches three independently extracted features by co-occurrence feature learning. The second stage involves multi-channel pairwise fusion to take advantage of the complementary and diverse nature among three features. The third stage is a multi-task and ensemble learning network to further improve the generalization ability of 3SCNN. Experimental results on the standard dataset show the effectiveness of our proposed multi-stream feature learning, fusion and inference method for skeleton-based 3D action recognition.
Vision-Based Estimation of Driving Energy for Planetary Rovers Using Deep Learning and Terramechanics

This letter presents a prediction algorithm of driving energy for future Mars rover missions. The majority of future Mars rovers would be solar-powered, which would require energy-optimal driving to maximize the range with limited energy. The essential and arguably the most challenging technology for realizing energy-optimal driving is the capability to predict the driving energy, which is needed to construct an energy-aware cost function for path planning. In this letter, we propose vision-based algorithms to remotely predict the driving energy consumption using machine learning. Specifically, we develop and compare two machine-learning models in this letter, namely VeeGer-EnergyNet and Veeger-TerramechanicsNet, respectively. The former is trained directly using recorded power, while the latter estimates terrain parameters from the images using a simplified-terramechanics model, and calculate the power based on the model. The two approaches are fully automated self-supervised learning algorithms. To combine RGB and depth images efficiently with high accuracy, we propose a new network architecture called Two-PNASNet-5, which is based on PNASNet-5. We collected a new dataset to verify the effectiveness of the proposed approaches. Comparison of the two approaches showed that Veeger-TerramechanicsNet had better performance than VeeGer-EnergyNet.
Deep Reinforcement Learning Based Performance Optimization in Blockchain-Enabled Internet of Vehicle

The rapid development of Internet of Vehicles (IoV) necessitates a secure and reliable infrastructure to store and share the massive data. Blockchain, a distributed and immutable ledger, is widely considered as a promising solution to ensure data security and privacy for IoV. To deal with the massive IoV data, the scalability of blockchain becomes a critical issue, which should maximize transactional throughput as well as handling the dynamics of IoV scenarios. Therefore, this paper proposes a novel deep reinforcement learning (DRL) based performance optimization framework for blockchain-enabled IoV, where transactional throughput is maximized while guaranteeing the decentralization, latency and security of the underlying blockchain system. In this framework, we first carry out the performance analysis for blockchain systems from the aspects of scalability, decentralization, latency and security. Further, DRL technique is adopted to select block producers and adjust block size and block interval to adapt to the dynamics of IoV scenarios. Simulation results show that our proposed framework can effectively improve the throughput of blockchain-enabled IoV systems without affecting other properties.
A Target Detection Model Based on Improved Tiny-Yolov3 Under the Environment of Mining Truck

In the open pit mine production systems, a certain number of trucks transport mine and rock between the power shovel and the unloading point.Due to the mining truck has characteristics of high height, long width and big size, it has a large blind zone and a long braking distance.Therefore, the probability of accidents in mining trucks is high, which results in huge loss of manpower, material resources and financial resources.In this paper, tiny-yolov3 is used to detect obstacles in the mine, its real-time performance is high enough, but the detection accuracy is not ideal.Therefore, this paper proposes an improved target detection model based on tiny-yolov3.The residual network structure based on convolutional neural network is added to the tiny-yolov3 structure, and the accuracy of obstacle detection is improved under the condition of real-time detection.The experimental results show that compared with tiny-yolov3, the detect precision of tiny-yolov3 with residual structure is improved, and the detection speed is reduced slightly, there is no particular impact on the real-time nature of the entire algorithm.
A comprehensive micro unmanned aerial vehicle (UAV/Drone) forensic framework

In the early 1990s, unmanned aerial vehicles (UAV) were used exclusively in military applications by various developed countries. Now with its ease of availability and affordability in the electronic device market, this aerial vehicular technology has augmented its familiarity in public and has expanded its usage to countries all over the world. However, expanded use of UAVs, colloquially known as drones, is raising understandable security concerns. With the increasing possibility of drones' misuse and their abilities to get close to critical targets, drones are prone to potentially committing crimes and, therefore, investigation of such activities is a much-needed facet. This motivated us to devise a comprehensive drone forensic framework that includes hardware/physical and digital forensics, proficient enough for the post-flight investigation of drone's activity. For hardware/physical forensics, we propose a model for investigating drone components at the crime scene. Additionally, we propose a robust digital drone forensic application with a primary focus on analyzing the essential log parameters of drones through a graphical user interface (GUI) developed using JavaFX 8.0. This application interface would allow users to extract and examine onboard flight information. It also includes a file converter created for easy and effective 3D flight trajectory visualization. We used two popular drones for conducting this research; namely, DJI Phantom 4 and Yuneec Typhoon H. The interface also provides a visual representation of the sensor recordings from which pieces of evidence could be acquired. Our research is intended to offer the forensic science community a powerful approach for investigating drone-related crimes effectively.
Graph Learning From Filtered Signals: Graph System and Diffusion Kernel Identification

This paper introduces a novel graph signal processing framework for building graph-based models from classes of filtered signals. In our framework, graph-based modeling is formulated as a graph system identification problem, where the goal is to learn a weighted graph (a graph Laplacian matrix) and a graph-based filter (a function of graph Laplacian matrices). In order to solve the proposed problem, an algorithm is developed to jointly identify a graph and a graph-based filter (GBF) from multiple signal/data observations. Our algorithm is valid under the assumption that GBFs are one-to-one functions. The proposed approach can be applied to learn diffusion (heat) kernels, which are popular in various fields for modeling diffusion processes. In addition, for specific choices of graph-based filters, the proposed problem reduces to a graph Laplacian estimation problem. Our experimental results demonstrate that the proposed algorithm outperforms the current state-of-the-art methods. We also implement our framework on a real climate dataset for modeling of temperature signals.
Deep Supervision with Intermediate Concepts

Recent data-driven approaches to scene interpretation predominantly pose inference as an end-to-end black-box mapping, commonly performed by a Convolutional Neural Network (CNN). However, decades of work on perceptual organization in both human and machine vision suggest that there are often intermediate representations that are intrinsic to an inference task, and which provide essential structure to improve generalization. In this work, we explore an approach for injecting prior domain structure into neural network training by supervising hidden layers of a CNN with intermediate concepts that normally are not observed in practice. We formulate a probabilistic framework which formalizes these notions and predicts improved generalization via this deep supervision method. One advantage of this approach is that we are able to train only from synthetic CAD renderings of cluttered scenes, where concept values can be extracted, but apply the results to real images. Our implementation achieves the state-of-the-art performance of 2D/3D keypoint localization and image classification on real image benchmarks including KITTI, PASCAL VOC, PASCAL3D+, IKEA, and CIFAR100. We provide additional evidence that our approach outperforms alternative forms of supervision, such as multi-task networks.
Estimating the Number of Clusters Using Cross-Validation

Many clustering methods, including k-means, require the user to specify the number of clusters as an input parameter. A variety of methods have been devised to choose the number of clusters automatically, but they often rely on strong modeling assumptions. This article proposes a data-driven approach to estimate the number of clusters based on a novel form of cross-validation. The proposed method differs from ordinary cross-validation, because clustering is fundamentally an unsupervised learning problem. Simulation and real data analysis results show that the proposed method outperforms existing methods, especially in high-dimensional settings with heterogeneous or heavy-tailed noise. In a yeast cell cycle dataset, the proposed method finds a parsimonious clustering with interpretable gene groupings. Supplementary materials for this article are available online.