Reviewerly/exHarmony huggingface dataset:
Neuromorphic Eye-in-Hand Visual Servoing

Robotic vision plays a major role in factory automation to service robot applications. However, the traditional use of frame-based cameras sets a limitation on continuous visual feedback due to their low sampling rate, poor performance in low light conditions and redundant data in real-time image processing, especially in the case of high-speed tasks. Neuromorphic event-based vision is a recent technology that gives human-like vision capabilities such as observing the dynamic changes asynchronously at a high temporal resolution (1 μs) with low latency and wide dynamic range. In this paper, for the first time, we present a purely event-based visual servoing method using a neuromorphic camera in an eye-in-hand configuration for the grasping pipeline of a robotic manipulator. We devise three surface layers of active events to directly process the incoming stream of events from relative motion. A purely event-based approach is used to detect corner features, localize them robustly using heatmaps and generate virtual features for tracking and grasp alignment. Based on the visual feedback, the motion of the robot is controlled to make the temporal upcoming event features converge to the desired event in Spatio-temporal space. The controller switches its operation such that it explores the workspace, reaches the target object and achieves a stable grasp. The event-based visual servoing (EBVS) method is comprehensively studied and validated experimentally using a commercial robot manipulator in an eye-in-hand configuration for both static and dynamic targets. Experimental results show superior performance of the EBVS method over frame-based vision, especially in high-speed operations and poor lighting conditions. As such, EBVS overcomes the issues of motion blur, lighting and exposure timing that exist in conventional frame-based visual servoing methods.
An Assessment of Deep Learning Models and Word Embeddings for Toxicity Detection within Online Textual Comments

Today, increasing numbers of people are interacting online and a lot of textual comments are being produced due to the explosion of online communication. However, a paramount inconvenience within online environments is that comments that are shared within digital platforms can hide hazards, such as fake news, insults, harassment, and, more in general, comments that may hurt someone’s feelings. In this scenario, the detection of this kind of toxicity has an important role to moderate online communication. Deep learning technologies have recently delivered impressive performance within Natural Language Processing applications encompassing Sentiment Analysis and emotion detection across numerous datasets. Such models do not need any pre-defined hand-picked features, but they learn sophisticated features from the input datasets by themselves. In such a domain, word embeddings have been widely used as a way of representing words in Sentiment Analysis tasks, proving to be very effective. Therefore, in this paper, we investigated the use of deep learning and word embeddings to detect six different types of toxicity within online comments. In doing so, the most suitable deep learning layers and state-of-the-art word embeddings for identifying toxicity are evaluated. The results suggest that Long-Short Term Memory layers in combination with mimicked word embeddings are a good choice for this task.
A Service Recommendation Algorithm Based on Knowledge Graph and Collaborative Filtering

With the rapid development of the Internet, the number of Web APIs is increasing. How to recommend accurate and appropriate Web APIs to mashups has become a focus and difficulty in the field of service computing. The existing methods are mainly based on collaborative filtering technology, but these methods have problems such as the data sparsity and cold start, which leads to poor recommendation effects. This paper proposes a service recommendation model based on knowledge graph and collaborative filtering. In this model, the knowledge graph connects the APIs and mashups related information to mine the potential relations between mashups and APIs, hence reducing the impact of data sparsity. All the API entities in the service knowledge graph are embedded into the low-dimensional space through the representation learning algorithm, then the distances between the API vectors are calculated to recommend the related APIs. In addition, in order to solve the cold-start problem of recommending APIs to target mashups that have no APIs usage, the similarities of functional sets extracted from mashups are calculated to recommend APIs for target mashups. At the same time, the model obtains the Mashup-API usage record, using the technology of collaborative filtering to recommend appropriate APIs to target mashups. Finally, the similarities of the above recommended APIs are normalized and sorted to form the final recommendation result. The experimental results show that our proposed model significantly improves the accuracy of service recommendation.
Blockchain-Enabled 5G Edge Networks and Beyond: An Intelligent Cross-Silo Federated Learning Approach

Edge networks (ENs) in 5G have the capability to protect traffic between edge entry points (edge-to-edge), enabling the design of various flexible and customizable applications. The advantage of edge networks is their pioneering integration of other prominent technologies such as blockchain and federated learning (FL) to produce better services on wireless networks. In this paper, we propose an intelligent system integrating blockchain technologies, 5G ENs, and FL to create an efficient and secure framework for transactions. FL enables user equipment (UE) to train the artificial intelligence model without exposing the UE’s valuable data to the public, or to the model providers. Furthermore, the blockchain is an immutable data approach that can be leveraged for FL across 5G ENs and beyond. The recorded transactions cannot be altered maliciously, and they remain unchanged by design. We further propose a dynamic authentication protocol for UE to interact with a diverse base station. We apply blockchain as a reward mechanism in FL to enable computational offloading in wireless networks. Additionally, we implement and investigate blockchain technology for FL in 5G UE.
Representations for Question Answering from Documents with Tables and Text

Tables in Web documents are pervasive and can be directly used to answer many of the queries searched on the Web, motivating their integration in question answering.Very often information presented in tables is succinct and hard to interpret with standard language representations.On the other hand, tables often appear within textual context, such as an article describing the table.Using the information from an article as additional context can potentially enrich table representations.In this work we aim to improve question answering from tables by refining table representations based on information from surrounding text.We also present an effective method to combine text and table-based predictions for question answering from full documents, obtaining significant improvements on the Natural Questions dataset.
Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey

The development of deep learning techniques has allowed Neural Machine Translation (NMT) models to become extremely powerful, given sufficient training data and training time. However, systems struggle when translating text from a new domain with a distinct style or vocabulary. Fine-tuning on in-domain data allows good domain adaptation, but requires sufficient relevant bilingual data. Even if this is available, simple fine-tuning can cause overfitting to new data and catastrophic forgetting of previously learned behaviour. We survey approaches to domain adaptation for NMT, particularly where a system may need to translate across multiple domains. We divide techniques into those revolving around data selection or generation, model architecture, parameter adaptation procedure, and inference procedure. We finally highlight the benefits of domain adaptation and multidomain adaptation techniques to other lines of NMT research.
Jointly Improving Language Understanding and Generation with Quality-Weighted Weak Supervision of Automatic Labeling

Neural natural language generation (NLG) and understanding (NLU) models are data-hungry and require massive amounts of annotated data to be competitive.Recent frameworks address this bottleneck with generative models that synthesize weak labels at scale, where a small amount of training labels are expertcurated and the rest of the data is automatically annotated.We follow that approach, by automatically constructing a large-scale weaklylabeled data with a fine-tuned GPT-2, and employ a semi-supervised framework to jointly train the NLG and NLU models.The proposed framework adapts the parameter updates to the models according to the estimated labelquality.On both the E2E and Weather benchmarks, we show that this weakly supervised training paradigm is an effective approach under low resource scenarios with as little as 10 data instances, and outperforming benchmark systems on both datasets when 100% of training data is used.
Breast Mass Detection in Mammography Based on Image Template Matching and CNN

In recent years, computer vision technology has been widely used in the field of medical image processing. However, there is still a big gap between the existing breast mass detection methods and the real-world application due to the limited detection accuracy. It is known that humans locate the regions of interest quickly and further identify whether these regions are the targets we found. In breast cancer diagnosis, we locate all the potential regions of breast mass by glancing at the mammographic image from top to bottom and from left to right, then further identify whether these regions are a breast mass. Inspired by the process of human detection of breast mass, we proposed a novel breast mass detection method to detect breast mass on a mammographic image by stimulating the process of human detection. The proposed method preprocesses the mammographic image via the mathematical morphology method and locates the suspected regions of breast mass by the image template matching method. Then, it obtains the regions of breast mass by classifying these suspected regions into breast mass and background categories using a convolutional neural network (CNN). The bounding box of breast mass obtained by the mathematical morphology method and image template matching method are roughly due to the mathematical morphology method, which transforms all of the brighter regions into approximate circular areas. For regression of a breast mass bounding box, the optimal solution should be searched in the feasible region and the Particle Swarm Optimization (PSO) is suitable for solving the problem of searching the optimal solution within a certain range. Therefore, we refine the bounding box of breast mass by the PSO algorithm. The proposed breast mass detection method and the compared detection methods were evaluated on the open database Digital Database for Screening Mammography (DDSM). The experimental results demonstrate that the proposed method is superior to all of the compared detection methods in detection performance.
Spatio-Temporal Downscaling of Climate Data Using Convolutional and Error-Predicting Neural Networks

Numerical weather and climate simulations nowadays produce terabytes of data, and the data volume continues to increase rapidly since an increase in resolution greatly benefits the simulation of weather and climate. In practice, however, data is often available at lower resolution only, for which there are many practical reasons, such as data coarsening to meet memory constraints, limited computational resources, favoring multiple low-resolution ensemble simulations over few high-resolution simulations, as well as limits of sensing instruments in observations. In order to enable a more insightful analysis, we investigate the capabilities of neural networks to reconstruct high-resolution data from given low-resolution simulations. For this, we phrase the data reconstruction as a super-resolution problem from multiple data sources, tailored toward meteorological and climatological data. We therefore investigate supervised machine learning using multiple deep convolutional neural network architectures to test the limits of data reconstruction for various spatial and temporal resolutions, low-frequent and high-frequent input data, and the generalization to numerical and observed data. Once such downscaling networks are trained, they serve two purposes: First, legacy low-resolution simulations can be downscaled to reconstruct high-resolution detail. Second, past observations that have been taken at lower resolutions can be increased to higher resolutions, opening new analysis possibilities. For the downscaling of high-frequent fields like precipitation, we show that error-predicting networks are far less suitable than deconvolutional neural networks due to the poor learning performance. We demonstrate that deep convolutional downscaling has the potential to become a building block of modern weather and climate analysis in both research and operational forecasting, and show that the ideal choice of the network architecture depends on the type of data to predict, i.e., there is no single best architecture for all variables.
Adaptive Simplex Architecture for Safe, Real-Time Robot Path Planning

The paper addresses the problem of using machine learning in practical robot applications, like dynamic path planning with obstacle avoidance, so as to achieve the performance level of machine learning model scorers in terms of speed and reliability, and the safety and accuracy level of possibly slower, exact algorithmic solutions to the same problems. To this end, the existing simplex architecture for safety assurance in critical systems is extended by an adaptation mechanism, in which one of the redundant controllers (called a high-performance controller) is represented by a trained machine learning model. This model is retrained using field data to reduce its failure rate and redeployed continuously. The proposed adaptive simplex architecture (ASA) is evaluated on the basis of a robot path planning application with dynamic obstacle avoidance in the context of two human-robot collaboration scenarios in manufacturing. The evaluation results indicate that ASA enables a response by the robot in real time when it encounters an obstacle. The solution predicted by the model is economic in terms of path length and smoother than analogous algorithmic solutions. ASA ensures safety by providing an acceptance test, which checks whether the predicted path crosses the obstacle; in which case a suboptimal, yet safe, solution is used.
Hybrid quantum annealing via molecular dynamics

Abstract A novel quantum–classical hybrid scheme is proposed to efficiently solve large-scale combinatorial optimization problems. The key concept is to introduce a Hamiltonian dynamics of the classical flux variables associated with the quantum spins of the transverse-field Ising model. Molecular dynamics of the classical fluxes can be used as a powerful preconditioner to sort out the frozen and ambivalent spins for quantum annealers. The performance and accuracy of our smooth hybridization in comparison to the standard classical algorithms (the tabu search and the simulated annealing) are demonstrated by employing the MAX-CUT and Ising spin-glass problems.
Multi-Scale Spatial Temporal Graph Neural Network for Skeleton-Based Action Recognition

Graph convolutional networks (GCNs) have achieved remarkable performance on skeleton-based action recognition. Existing GCN-based methods usually apply the fixed graph topology and one fixed temporal convolution kernel to extract the spatial features of joints and temporal features, which is from a single-scale perspective. Actually, human actions are coordinated by various body parts in the spatial domain, and exhibit different characteristics in the temporal domain. Therefore, it is appropriate to model the multi-scale information that can enhance both the explainability and stability, which is ignored in current literatures. To address this issue, we propose a multi-scale spatial-temporal graph neural network (MSTGNN) to discover multi-scale discriminative features from spatial and temporal aspects simultaneously. Our contributions are three-folds: 1) For the spatial domain, inspired by the kinematics of the human action, we develop a three-scale graph data structures in a fine-to-coarse way. A novel hybrid spatial pooling module is then proposed to dynamically exploit the global and comprehensive information step-by-step. 2) For the temporal domain, we design a multi-scale temporal convolution module adaptively fusing the temporal features extracted by different scale convolution kernels. 3) As utilizing one-stream architecture instead of multi-stream architecture, the proposed model can be trained in an end-to-end manner. MSTGNN achieves state-of-the-art performance with less computation complexity. Experimental results conducted on two large datasets (NTU-RGB+D and NTU-RGB+D-120) demonstrate the superiority of MSTGNN.
Reproducible Builds: Increasing the Integrity of Software Supply Chains

Although it is possible to increase confidence in free and open source software by reviewing its source code, trusting code is not the same as trusting its executable counterparts. This article examines reproducible builds, an approach that can determine whether generated binaries correspond to the original source code.
Cybercrimes during COVID -19 Pandemic

COVID-19 pandemic has changed the lifestyle of all aspects of life.These circumstances have created new patterns in lifestyle that people had to deal with.As such, full and direct dependence on the use of the unsafe Internet network in running all aspects of life.As example, many organizations started officially working through the Internet, students moved to e-education, online shopping increased, and more.These conditions have created a fertile environment for cybercriminals to grow their activity and exploit the pressures that affected human psychology to increase their attack success.The purpose of this paper is to analyze the data collected from global online fraud and cybersecurity service companies to demonstrate on how cybercrimes increased during the COVID-19 epidemic.The significance and value of this research is to highlight by evident on how criminals exploit crisis, and for the need to develop strategies and to enhance user awareness for better detection and prevention of future cybercrimes.
An Exploratory Study of English Teachers: The Use of Social Media for Teaching English on Distance Learning

This study was conducted to determine how English teachers used social media to teach English. An exploratory study was used in this study to look into a detailed and in-depth analysis of the use of social media in English language learning by Cirebon junior high school teachers. This study used purposive sampling from 43 English teachers at Cirebon junior high schools. Ten teachers (five male and five female) have been chosen. In about 20 to 30 minutes, each participant was asked the questions. The researcher recorded all of the conversations during the interview session as part of the data collection process. Whatsapp, Facebook, Instagram, Tiktok, and YouTube are the most popular social media platforms with strong categorized. Whatsapp has a higher presentation value than other social media types. There were five themes identified as a result of the interview session of the teacher's perceptions of social media used on distance Learning; 1) Kinds of social media, 2) Activities, 3) Motivation, 4) Improving students English language skills, and 5) Effectiveness.
The Analysis of the Impact of Yoga on Healthcare and Conventional Strategies for Human Pose Recognition

Human pose estimation is a profound, established computer vision issue that has uncovered numerous past difficulties. Breaking down human exercise is advantageous in multiple fields like surveillance, biometrics, and many healthcare applications. Workout with yoga poses is famous these days since yoga activities can expand adaptability and muscular quality, and the respiration procedure will be improvised. The yoga postures evaluation is hard to check, so specialists will most likely be unable to benefit from the exercises ultimately. IoT-based yoga frameworks are required for individuals who need to rehearse Yoga at home. A few studies are recommended camera-oriented or wearable gadget-oriented yoga posture finding strategies with more precision. Nonetheless, camera-based plans have security and privacy issues, and the wearable device-based methods are illogical in the earlier applications. To build such systems, one must have a strong foundation and current research in pose estimation. In this paper, first, the impact of Yoga on humans with various stress levels is analysed on the real-time data. Second, the comprehensive review of yoga posture recognition systems from machine learning to deep learning strategies and evaluation metrics discussed
Two-Way Covert Quantum Communication in the Microwave Regime

Seminal results for the theory of low-frequency microwave quantum communication, as well as its practical implementation, are presented: Unconditional security for backscatter communication is found by hiding the carrier in the environment.
Calculating transition amplitudes by variational quantum deflation

Variational quantum eigensolver (VQE) is an appealing candidate for the application of near-term quantum computers. A technique introduced in [Higgot et al., Quantum 3, 156 (2019)], which is named variational quantum deflation (VQD), has extended the ability of the VQE framework for finding excited states of a Hamiltonian. However, no method to evaluate transition amplitudes between the eigenstates found by the VQD without using any costly Hadamard-test-like circuit has been proposed despite its importance for computing properties of the system such as electronic oscillator strengths of molecules. Here we propose a method to evaluate transition amplitudes between the eigenstates obtained by the VQD avoiding any Hadamard-test-like circuit. Our method relies only on the ability to estimate overlap between two states, so it does not restrict to the VQD eigenstates and applies for general situations. To support the significance of our method, we provide a comparison of three previously proposed methods to find excited states with numerical simulation of three molecules (lithium hydride, diazene, and azobenzene) and find that the VQD method exhibits the best performance among the three methods within the scope of noiseless simulation. Finally, we demonstrate the validity of our method by calculating the oscillator strength of lithium hydride, comparing results from numerical simulations and real-hardware experiments on the cloud enabled quantum computer IBMQ Rome. Our results illustrate the superiority of the VQD to find excited states and widen its applicability to various quantum systems.