Reviewerly/exHarmony huggingface dataset:
EMBody: A Data-Centric Toolkit for EMG-Based Interface Prototyping and Experimentation

Body movements, from a short smile to a marathon run, are driven by muscle activity. Despite the fact that measuring muscle activity with electromyography (EMG) is technically well established, it is highly complex and its use in interfaces has been limited. Easy access to muscle sensing can offer new opportunities to Human-Computer Interaction (HCI) research. Off-the-shelf sensors often only provide low-level access, hence requiring expertise in signal processing and widening the gulf of execution for users without engineering skills. To address this challenge, we introduce EMBody, a data-centric toolkit for EMG-based interface prototyping and experimentation. EMBody offers multiple levels of prototyping fidelity for EMG sensing, signal processing, and data interpretation. Our data-centric toolkit encapsulates the different data representation stages, offering a wide range of customization opportunities to experts while also allowing non-technical designers to focus on creating new interaction techniques. EMBody features a lightweight form factor and wireless connectivity. Additionally, the system leverages an exploration-centered workflow by allowing rapid access to measurement data via the accompanying software. Users define a set of motions to be recognized and interactively provide example data points. The toolkit then handles signal processing and classification. The recognized movements are streamed on the local network, ready to be used by interactive applications. This paper reports on how to use EMBody and its implementation. We iteratively developed the toolkit in a series of workshops and example applications. Users who had none or very limited knowledge of EMG could rapidly create engaging functional prototypes, while experts appreciated the modularity of the software component allowing for a high degree of customization. We contribute the software and hardware components of EMBody as a tool for the research community to stimulate creative exploration of EMG systems.
Mode Optimization and Rule Management of Intellectual Property Rights Protection of Educational Resource Data Based on Machine Learning Algorithm

Educational resource data are a collection of final documents obtained by users, including full-text journals, books, dissertations, newspapers, conference papers, and other database materials. While searching for information in the educational resource database, these resources also have functions such as copying, downloading, reproduction, and dissemination, which raise the issue of expression and protection of intellectual property. Machine learning takes how computers simulate human learning behaviors as the main research content, which can independently determine learning objects, construct their characteristics, perform additional operations beyond the limitations of preset instructions, and discover value from the expression of relative works. On the basis of summarizing and analyzing previous research works, this paper expounded the current research status and significance of intellectual property expression and protection of educational resource data; elaborated the development background, current status, and future challenges of machine learning technology; introduced the methods and principles of data classification algorithm and protection authority identification; performed the technical framework design and expression system establishment of the intellectual property expression of educational resource data based on machine learning; analyzed the mode optimization and rule management of intellectual property protection of educational resource data based on machine learning; and finally conducted a simulation experiment and its result analysis. The results show that the machine learning technology can build a subject-oriented, highly integrated, and time-changing educational resource data storage environment; the comprehensive, analysis-oriented decision-supporting system formed by machine learning can give full play to the potential role of data integration and value discovery and is therefore of great significance for the intellectual property expression and protection of integrated and complexly-related educational resource data. The study results of this paper provide a reference for further research on the intellectual property expression and protection of educational resource data based on machine learning.
Efficient Reactive Obstacle Avoidance Using Spirals for Escape

The usage of unmanned aerial vehicles (UAV) has increased in recent years and new application scenarios have emerged. Some of them involve tasks that require a high degree of autonomy, leading to increasingly complex systems. In order for a robot to be autonomous, it requires appropriate perception sensors that interpret the environment and enable the correct execution of the main task of mobile robotics: navigation. In the case of UAVs, flying at low altitude greatly increases the probability of encountering obstacles, so they need a fast, simple, and robust method of collision avoidance. This work covers the problem of navigation in unknown scenarios by implementing a simple, yet robust, environment-reactive approach. The implementation is done with both CPU and GPU map representations to allow wider coverage of possible applications. This method searches for obstacles that cross a cylindrical safety volume, and selects an escape point from a spiral for avoiding the obstacle. The algorithm is able to successfully navigate in complex scenarios, using both a high and low-power computer, typically found aboard UAVs, relying only on a depth camera with a limited FOV and range. Depending on the configuration, the algorithm can process point clouds at nearly 40 Hz in Jetson Nano, while checking for threats at 10 kHz. Some preliminary tests were conducted with real-world scenarios, showing both the advantages and limitations of CPU and GPU-based methodologies.
Quantum information scrambling and entanglement in bipartite quantum states

Investigating the influence of quantum information (QI) scrambling on quantum correlations in a physical system is an interesting problem. In this article, we establish the mathematical connections among the quantifiers known as quantum information scrambling, Uhlmann fidelity, Bures metric and bipartite concurrence. We study these connections via four-point out-of-time-order correlation function used for quantum information scrambling. Further, we study the dynamics of all the quantifiers and investigate the influence of QI scrambling on entanglement in two qubits prepared in Bell states. We also investigate the QI scrambling and entanglement balancing points in Bell states under Ising Hamiltonian.
Automatic Detection of Sexist Statements Commonly Used at the Workplace

Detecting hate speech in the workplace is a unique classification task, as the underlying social context implies a subtler version of conventional hate speech. Applications regarding a state-of-the-art workplace sexism detection model include aids for Human Resources departments, AI chatbots and sentiment analysis. Most existing hate speech detection methods, although robust and accurate, focus on hate speech found on social media, specifically Twitter. The context of social media is much more anonymous than the workplace, therefore it tends to lend itself to more aggressive and “hostile” versions of sexism. Therefore, datasets with large amounts of “hostile” sexism have a slightly easier detection task since “hostile” sexist statements can hinge on a couple words that, regardless of context, tip the model off that a statement is sexist. In this paper we present a dataset of sexist statements that are more likely to be said in the workplace as well as a deep learning model that can achieve state-of-the art results. Previous research has created state-of-the-art models to distinguish “hostile” and “benevolent” sexism based simply on aggregated Twitter data. Our deep learning methods, initialized with GloVe or random word embeddings, use LSTMs with attention mechanisms to outperform those models on a more diverse, filtered dataset that is more targeted towards workplace sexism, leading to an F1 score of 0.88.
EfficientNetV2: Smaller Models and Faster Training

This paper introduces EfficientNetV2, a new family of convolutional networks that have faster training speed and better parameter efficiency than previous models. To develop this family of models, we use a combination of training-aware neural architecture search and scaling, to jointly optimize training speed and parameter efficiency. The models were searched from the search space enriched with new ops such as Fused-MBConv. Our experiments show that EfficientNetV2 models train much faster than state-of-the-art models while being up to 6.8x smaller. Our training can be further sped up by progressively increasing the image size during training, but it often causes a drop in accuracy. To compensate for this accuracy drop, we propose to adaptively adjust regularization (e.g., dropout and data augmentation) as well, such that we can achieve both fast training and good accuracy. With progressive learning, our EfficientNetV2 significantly outperforms previous models on ImageNet and CIFAR/Cars/Flowers datasets. By pretraining on the same ImageNet21k, our EfficientNetV2 achieves 87.3% top-1 accuracy on ImageNet ILSVRC2012, outperforming the recent ViT by 2.0% accuracy while training 5x-11x faster using the same computing resources. Code will be available at this https URL.
Evidence Inference Networks for Interpretable Claim Verification

Existing approaches construct appropriate interaction models to explore semantic conflicts between claims and relevant articles, which provides practical solutions for interpretable claim verification. However, these conflicts are not necessarily all about questioning the false part of claims, which makes considerable semantic conflicts difficult to be used as evidence to explain the results of claim verification. In this paper, we propose evidence inference networks (EVIN), which focus on the conflicts questioning the core semantics of claims and serve as evidence for interpretable claim verification. Specifically, EVIN first captures the core semantic segments of claims and the users' principal opinions in relevant articles. Then, it finely-grained identifies the semantic conflicts contained in each relevant article from these opinions. Finally, it constructs coherence modeling to match the conflicts that queries the core semantic fragments of claims as explainable evidence. Experiments on two widely used datasets demonstrate that EVIN not only achieves satisfactory performance but also provides explainable evidence for end-users.
Quantum Computing 40 Years Later

Forty years ago, Richard Feynman proposed harnessing quantum physics to build a more powerful kind of computer. Realizing Feynman's vision is one of the grand challenges facing 21st century science and technology. In this article, we'll recall Feynman's contribution that launched the quest for a quantum computer, and assess where the field stands 40 years later.
IA-GM: A Deep Bidirectional Learning Method for Graph Matching

Existing deep learning methods for graph matching(GM) problems usually considered affinity learningto assist combinatorial optimization in a feedforward pipeline, and parameter learning is executed by back-propagating the gradients of the matching loss. Such a pipeline pays little attention to the possible complementary benefit from the optimization layer to the learning component. In this paper, we overcome the above limitation under a deep bidirectional learning framework.Our method circulates the output of the GM optimization layer to fuse with the input for affinity learning. Such direct feedback enhances the input by a feature enrichment and fusion technique, which exploits andintegrates the global matching patterns from the deviation of the similarity permuted by the current matching estimate. As a result, the circulation enables the learning component to benefit from the optimization process, taking advantage of both global feature and the embedding result which is calculated by local propagationthrough node-neighbors. Moreover, circulation consistency induces an unsupervised loss that can be implemented individually or jointly to regularize the supervised loss. Experiments on challenging datasets demonstrate the effectiveness of our methods for both supervised learning and unsupervised learning.
Benchmarking Video Object Detection Systems on Embedded Devices under Resource Contention

Adaptive and efficient computer vision systems have been proposed to make computer vision tasks, e.g., object classification and object detection, optimized for embedded boards or mobile devices. These studies focus on optimizing the model (deep network) or system itself, by designing an efficient network architecture or adapting the network architecture at runtime using approximation knobs, such as image size, type of object tracker, head of the object detector (e.g., lighter-weight heads such as one-shot object detectors like YOLO over two-shot object detectors like FRCNN). In this work, we benchmark different video object detection protocols, including FastAdapt, with respect to accuracy, latency, and energy consumption on three different embedded boards that represent the leading edge mobile GPUs. Our set of protocols consists of Faster R-CNN, YOLOv3, SELSA, MEGA, and REPP. Further, we characterize their performance under different levels of resource contention, specifically GPU contention, as would arise due to co-located applications on these boards, contending with the video object detection task. Our key insights are that object detectors have to be coupled with trackers to keep up with the latency requirements (e.g., 30 fps). With this, FastAdapt achieves up to 76 fps on the most well-resourced NVIDIA Jetson-class board---the NVIDIA AGX Xavier. Second, adaptive protocols like FastAdapt, FRCNN, and YOLO (specifically our adaptive variants, FRCNN+ and YOLO+) work well under resource constraints. Among the latest video object detection heads, SELSA achieves the highest accuracy but at a latency of over 2 sec per frame. Our energy consumption experiments bring out that FastAdapt, adaptive FRCNN, and adaptive YOLO are best-in-class, relative to the non-adaptive protocols SELSA, MEGA, and REPP.
RSGNet: Relation based Skeleton Graph Network for Crowded Scenes Pose Estimation

Despite of the recent great progress on multi-person pose estimation, existing solutions still remain challenging under the condition of "crowded scenes'', where RGB images capture complex real-world scenes with highly-overlapped people, severe occlusions and diverse postures. In this work, we focus on two main problems: 1) how to design an effective pipeline for crowded scenes pose estimation; and 2) how to equip this pipeline with the ability of relation modeling for interference resolving. To tackle these problems, we propose a new pipeline named Relation based Skeleton Graph Network (RSGNet). Unlike existing works that directly predict joints-of-target by labeling joints-of-interference as false positive, we first encourage all joints to be predicted. And then, a Target-aware Relation Parser (TRP) is designed to model the relation over all predicted joints, resulting in a target-aware encoding. This new pipeline will largely relieve the confusion of the joints estimation model when seeing identical joints with totally distinct labels (e.g., the identical hand exists in two bounding boxes). Furthermore, we introduce a Skeleton Graph Machine (SGM) to model the skeleton-based commonsense knowledge, aiming to estimate the target pose with the constraint of human body structure. Such skeleton-based constraint can help to deal with the challenges in crowded scenes from a reasoning perspective. Solid experiments on pose estimation benchmarks demonstrate that our method outperforms existing state-of-the-art methods.
Learning Algebraic Recombination for Compositional Generalization

Neural sequence models exhibit limited compositional generalization ability in semantic parsing tasks.Compositional generalization requires algebraic recombination, i.e., dynamically recombining structured expressions in a recursive manner.However, most previous studies mainly concentrate on recombining lexical units, which is an important but not sufficient part of algebraic recombination.In this paper, we propose LEAR, an end-toend neural model to learn algebraic recombination for compositional generalization.The key insight is to model the semantic parsing task as a homomorphism between a latent syntactic algebra and a semantic algebra, thus encouraging algebraic recombination.Specifically, we learn two modules jointly: a Composer for producing latent syntax, and an Interpreter for assigning semantic operations.Experiments on two realistic and comprehensive compositional generalization benchmarks demonstrate the effectiveness of our model.The source code is publicly available at https://github.com/microsoft/ContextualSP.
Attacks on Beyond-Birthday-Bound MACs in the Quantum Setting

We systematically study the security of twelve Beyond-Birthday-Bound Message Authentication Codes (BBB MACs) in the Q2 model where attackers have quantum-query access to MACs. Assuming the block size of the underlying (tweakable) block cipher is n bits, the security proofs show that they are secure at least up to $$\mathcal {O}(2^ {2n/3}) $$ queries in the classical setting. The best classical attacks need $$\mathcal {O}(2^ {3n/4}) $$ queries. We consider secret state recovery against SUM-ECBC-like and PMAC_Plus-like MACs and key recovery against PMAC_Plus-like MACs. Both attacks lead to successful forgeries. The first attack costs $$\mathcal {O}(2^{n/2}n)$$ quantum queries by applying Grover-meet-Simon algorithm. The second attack costs $$\mathcal {O}(2^{m/2})$$ quantum queries by applying Grover’s algorithm, assuming the key size of (tweakable) block cipher is m bits. As far as we know, these are the first quantum attacks against BBB MACs. It is remarkable that our attacks are suitable even for some optimally secure MACs, such as mPMAC+-f, mPMAC+-p1, and mPMAC+-p2.
A continuous rosenblatt quantum perceptron

Quantum neural networks are expected to provide the theoretical framework to implement machine learning on quantum computers. We develop a continuous Rosenblatt quantum perceptron which represents the generalization of the McCulloch–Pitts quantum perceptron existing in the literature. We implement its quantum circuit on a IBM quantum computer.
AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data

Medical decision-making impacts both individual and public health. Clinical scores are commonly used among various decision-making models to determine the degree of disease deterioration at the bedside. AutoScore was proposed as a useful clinical score generator based on machine learning and a generalized linear model. However, its current framework still leaves room for improvement when addressing unbalanced data of rare events. Using machine intelligence approaches, we developed AutoScore-Imbalance, which comprises three components: training dataset optimization, sample weight optimization, and adjusted AutoScore. Baseline techniques for performance comparison included the original AutoScore, full logistic regression, stepwise logistic regression, least absolute shrinkage and selection operator (LASSO), full random forest, and random forest with a reduced number of variables. These models were evaluated based on their area under the curve (AUC) in the receiver operating characteristic analysis and balanced accuracy (i.e., mean value of sensitivity and specificity). By utilizing a publicly accessible dataset from Beth Israel Deaconess Medical Center, we assessed the proposed model and baseline approaches to predict inpatient mortality. AutoScore-Imbalance outperformed baselines in terms of AUC and balanced accuracy. The nine-variable AutoScore-Imbalance sub-model achieved the highest AUC of 0.786 (0.732–0.839), while the eleven-variable original AutoScore obtained an AUC of 0.723 (0.663–0.783), and the logistic regression with 21 variables obtained an AUC of 0.743 (0.685–0.801). The AutoScore-Imbalance sub-model (using a down-sampling algorithm) yielded an AUC of 0.771 (0.718–0.823) with only five variables, demonstrating a good balance between performance and variable sparsity. Furthermore, AutoScore-Imbalance obtained the highest balanced accuracy of 0.757 (0.702–0.805), compared to 0.698 (0.643–0.753) by the original AutoScore and the maximum of 0.720 (0.664–0.769) by other baseline models. We have developed an interpretable tool to handle clinical data imbalance, presented its structure, and demonstrated its superiority over baselines. The AutoScore-Imbalance tool can be applied to highly unbalanced datasets to gain further insight into rare medical events and facilitate real-world clinical decision-making.
Binocular stereo matching of real scenes based on a convolutional neural network and computer graphics

The binocular stereo matching method based on deep learning has limited cross-domain generalization ability, and it is a tricky problem to obtain a large amount of data from real scenes for training. The most advanced stereo matching network is difficult to apply to new real scenes. In this paper, we propose a real-scene stereo matching method based on a convolutional neural network and computer graphics. A virtual binocular imaging system is constructed by introducing graphics software, and a high-quality semi-synthetic dataset close to the texture characteristics of the real scene is constructed for training the network. A feature standardization layer is embedded in the feature extraction module of the proposed network to further reduce the feature space difference between semi-synthetic data and real scene data. Three small 4D cost volumes are constructed to replace one large 4D cost volume, which reduces GPU memory consumption and improves the matching performance of the network. The experimental results show that compared with the traditional stereo matching method, the matching accuracy of the proposed method is significantly improved by about 60%. Compared with other learning-based methods, the matching accuracy is increased by about 30%, the matching speed is increased by 38%, and it has good robustness to the interference of defocus blur and Gaussian noise.
Measurement-device-independent quantum key distribution with insecure sources

Measurement-device-independent quantum key distribution (MDI-QKD) can remove all detection side channels but still makes additional assumptions on sources that can be compromised through uncharacterized side channels in practice. Here, we combine a recently proposed reference technique to prove the security of MDI-QKD against possible source imperfections and/or side channels. This requires some reference states and an upper bound on the parameter that describes the quality of the sources. With this formalism we investigate the asymptotic performance of single-photon sources, and the results show that the side channels have a great impact on the key rates.