Reviewerly/exHarmony huggingface dataset:
Developing Smart COVID-19 Social Distancing Surveillance Drone using YOLO Implemented in Robot Operating System simulation environment

The Novel Coronavirus, termed as COVID-19 outbreak, is faced by almost all countries in the world.It spread through communal interaction between people, especially in densely populated areas.An effort to prevent Covid-19 transmission is social distancing regulation.However, this policy is not obeyed by the public, so the government needs to supervise the movement and people's interaction.The government needs a crowd surveillance system that can detect people's presence, identify the crowd, and give social distancing warnings.Therefore, we propose a drone that has the ability of localization, navigation, people detection, crowd identifier, and social distancing warning.We utilize YOLO-v3 to detect people and define adaptive social distancing detector.In this paper, we implemented a road segmentation on the IRIS PX4 drone in the Robot Operating System and Gazebo simulation.The proposed system also successfully demonstrated people and crowd detection with varying degrees of the crowd.The system obtained crowd detection accuracy is around 90% and expected to be readily implemented on real hardware drones and tested in real environments.
Automatic Classification of Cervical Cells Using Deep Learning Method

Cervical cancer is the fourth most prevalent disease among women. Prompt diagnosis and its management can significantly improve patients' survival rates. Therefore, routine screening for cervical cancer is of paramount importance. Herein, we explore the potential of a deep learning model to automatically distinguish abnormal cells from normal cells. The ThinPrep cytologic test dataset was collected from the fourth central hospital of Baoding city, China. Based on the dataset, four classification models were developed. The first model was a 10-layer convolutional neural network (CNN). The second model was an advancement of the first model equipped with a spatial pyramid pooling (SPP) layer (CNN + SPP) to treat cell images based on their sizes. Based on the first model, the third model replaced the CNN layers with the inception module (CNN + Inception). However, the fourth model incorporated both the SPP layer and the inception module into the first model (CNN + inception + SPP). The performances of the four models are estimated and compared by using the same testing data and evaluation index. The testing results demonstrated that the fourth model yields the best performance. Moreover, the area under the curve (AUC) for module four was 0.997.
Physical Human-Robot Interaction With a Tethered Aerial Vehicle: Application to a Force-Based Human Guiding Problem

come from teaching and research institutions in France or abroad, or from public or private research centers.L'archive ouverte pluridisciplinaire
To Improve Cyber Resilience, Measure It

Measuring cyber resilience is vital to our security. In this article, we examine the importance of measuring cyber resilience and potential approaches to improve its effectiveness.
Measuring Key Quality Indicators in Cloud Gaming: Framework and Assessment Over Wireless Networks

Cloud Gaming is a cutting-edge paradigm in the video game provision where the graphics rendering and logic are computed in the cloud. This allows a user’s thin client systems with much more limited capabilities to offer a comparable experience with traditional local and online gaming but using reduced hardware requirements. In contrast, this approach stresses the communication networks between the client and the cloud. In this context, it is necessary to know how to configure the network in order to provide service with the best quality. To that end, the present work defines a novel framework for Cloud Gaming performance evaluation. This system is implemented in a real testbed and evaluates the Cloud Gaming approach for different transport networks (Ethernet, WiFi, and LTE (Long Term Evolution)) and scenarios, automating the acquisition of the gaming metrics. From this, the impact on the overall gaming experience is analyzed identifying the main parameters involved in its performance. Hence, the future lines for Cloud Gaming QoE-based (Quality of Experience) optimization are established, this way being of configuration, a trendy paradigm in the new-generation networks, such as 4G and 5G (Fourth and Fifth Generation of Mobile Networks).
Role-based network embedding via structural features reconstruction with degree-regularized constraint

Role-based network embedding aims to map network into low-dimensional node representations while preserving structural similarities. Adjacency matrix contain both the local and global information of a network, but it cannot directly represent the role of nodes. So it is essential to extract higher-order structural features from adjacency matrix for role-based network embedding (structural equivalence). While being sensitive to noise in real networks in general, the features extracted by some identical methods cannot truly represent the role of nodes. Therefore, we propose a deep learning framework RESD. In detail, we first propose extracting higher-order structural features for each node in the network. Then, we utilize the Variational Auto-Encoder (VAE) to model the nonlinear relationship of the features, reduce noise and improve the robustness of embedding. Furthermore, in the embedding space, we apply a degree-regularized constraint to guide the representation learning for preserving key structural information of nodes (e.g., degrees), which may be lost due to the principle of VAE framework. Finally, we construct a unified objective function to learn the node embedding for role discovery by preserving the structural features and node degree. We compare our model with several state-of-the-art methods on real-world networks. The results of extensive experiments demonstrate the effectiveness of our model and prove that our model scales well with dimension and network size.
The role of structural reactivation for gold mineralization in northeastern Hunan Province, South China

Gold ore deposits in northeastern Hunan Province are hosted within E-W- to ESE-WNW-trending fracture zones in Neoproterozoic meta-sediments of the Jiangnan Orogen. These rocks have been subjected to multiple tectonic deformations, and the nature of structural control on gold mineralization remains poorly understood. It is unclear when the ore-controlling structures were initially formed and why they are favorable for early Cretaceous gold mineralization. We present a systematic structural analysis to establish a structural evolution model and analyze the structural conditions favorable for gold mineralization. Five deformation events (D1 to D5) were identified based on crosscutting and/or overprinting relationships of structural elements. It is established that ore-hosting structures were initially formed during early Paleozoic (D2), and gold mineralization took place during early Cretaceous (late D4). The reactivation of D2 structures during the late D4 tectonic event, when the stress regime transformed from compression to tension, is critical for gold mineralization. It created a structural network linking mineralizing fluids to structural-chemical traps in Neoproterozoic rocks, where fluid-rock reactions and fluid mixing resulted in gold mineralization. This case study provides a typical example of how structural reactivation played a critical role in mineralization, and emphasizes the significance of structural analysis for mineral exploration.
Development and application of Quantum Entanglement inspired Particle Swarm Optimization

Particle Swarm Optimization has been extensively researched and applied to tackle optimization problems due to the ease in implementation and less number of parameters to be tuned. But particle swarm optimization (PSO) algorithm gets trapped into local optimum in high-dimensional space and it is inefficient in solving optimization problems which show high dependency. To overcome the above problems without compromising the advantages of PSO, this paper proposes Quantum Entanglement inspired Particle Swarm Optimization (QEPSO). QEPSO incorporates entangled states in its Q-bits to efficiently solve high-dependency problems and uses quantum local search to accelerate the optimization process. The proposed algorithm is tested on several standard benchmark functions and is also further benchmarked on IEEE Congress of Evolutionary computing (CEC 2017) benchmark set. The performance of QEPSO is compared with existing variants of PSO and some other popular algorithms. The results show that QEPSO outperforms other algorithms and is especially useful in high dimensional problems. Finally it is used for a real-life application of Multi-level Image Segmentation where eight gray-scale standard test images were used. The performance of QEPSO was superior to the other algorithms as it gave better results with high stability and quick convergence.
Generative adversarial networks and their application to 3D face generation: A survey

Generative adversarial networks (GANs) have been extensively studied in recent years and have been used to address several problems in the fields of image generation and computer vision. Despite significant advancements in computer vision, applying GANs to real-world problems such as 3D face generation remains a challenge. Owing to the proliferation of fake images generated by GANs, it is important to analyze and build a taxonomy for providing an overall view of GANs. This, in turn, would facilitate many interesting applications, including virtual reality, augmented reality, computer games, teleconferencing, virtual try-on, special effects in movies, and 3D avatars. This paper reviews and discusses GANs and their application to 3D face generation. We aim to compare existing GANs methods in terms of their application to 3D face generation, investigate the related theoretical issues, and highlight the open research problems. Authors provided both qualitative and quantitative evaluations of the proposed approach. They claimed their results show the higher quality of the synthesized data compared to state-of-the-art ones.
Note the Highlight

Active reading strategies---such as content annotations (through the use of highlighting and note-taking, for example)---have been shown to yield improvements to a learner's knowledge and understanding of the topic being explored. This has been especially notable in long and complex learning endeavours. With web search engines nowadays used as the primary gateway for learners (or users) to find content that helps them realise their learning goals, they are often poorly equipped with the necessary tools to aid in sense-making, an important aspect of theSearch as Learning (SAL) process. Within theInformation Retrieval (IR) community, research efforts have explored ways to keep track of users' search context by providing a notepad-like interface for the collection of relevant articles, and aid them during the exploratory search process. However, these studies did not explicitly measure the effect that such tools have on knowledge and understanding during a complex, learning-oriented search task. In this paper, we address this research gap by carrying out an Interactive IR experiment with highlighting and note-taking tools built into the search interface. We conducted a crowdsourced between-subjects study (N=115), where participants were assigned to one of four conditions: (i) control (a standard web search interface); (ii) high (highlighting enabled);(iii) note (note-taking enabled); and (iv) highnote (both highlighting and note-taking enabled). We assess participants' learning with a recall-oriented vocabulary learning task, and a cognitively more taxing essay writing task. We find that(i) active reading tools do not aid in the vocabulary learning task. However,(ii) participants in high covered 34% more subtopics, and participants in note covered 34% more facts in their essays when compared to control. Furthermore, (iii) we observed that incorporating active learning tools significantly changed the search behaviour of participants across a number of measures. This is the first work that sheds light on the effect of active reading tools on the SAL process, with important design implications for learning-oriented search systems.
SiamCAN: Real-Time Visual Tracking Based on Siamese Center-Aware Network

In this article, we present a novel Siamese center-aware network (SiamCAN) for visual tracking, which consists of the Siamese feature extraction subnetwork, followed by the classification, regression, and localization branches in parallel. The classification branch is used to distinguish the target from background, and the regression branch is introduced to regress the bounding box of the target. To reduce the impact of manually designed anchor boxes to adapt to different target motion patterns, we design the localization branch to localize the target center directly to assist the regression branch generating accurate results. Meanwhile, we introduce the global context module into the localization branch to capture long-range dependencies for more robustness to large displacements of the target. A multi-scale learnable attention module is used to guide these three branches to exploit discriminative features for better performance. Extensive experiments on 9 challenging benchmarks, namely VOT2016, VOT2018, VOT2019, OTB100, LTB35, LaSOT, TC128, UAV123 and VisDrone-SOT2019 demonstrate that SiamCAN achieves leading accuracy with high efficiency. Our source code is available at https://isrc.iscas.ac.cn/gitlab/research/siamcan.
9.2 A 28nm 12.1TOPS/W Dual-Mode CNN Processor Using Effective-Weight-Based Convolution and Error-Compensation-Based Prediction

To deploy convolutional neural networks (CNNs) on edge devices efficiently, most existing CNN processors were built on quantized CNNs to optimize the inference operations. However, three issues (Fig. 9.2.1) have not been well addressed: 1) Duplicate weights in each kernel after quantization yielding repetitive multiplications; 2) a huge number of unnecessary MACs caused by ReLU activation functions; 3) frequent off-chip memory access in residual blocks.
Optimal randomized classification trees

Classification and Regression Trees (CARTs) are off-the-shelf techniques in modern Statistics and Machine Learning. CARTs are traditionally built by means of a greedy procedure, sequentially deciding the splitting predictor variable(s) and the associated threshold. This greedy approach trains trees very fast, but, by its nature, their classification accuracy may not be competitive against other state-of-the-art procedures. Moreover, controlling critical issues, such as the misclassification rates in each of the classes, is difficult. To address these shortcomings, optimal decision trees have been recently proposed in the literature, which use discrete decision variables to model the path each observation will follow in the tree. Instead, we propose a new approach based on continuous optimization. Our classifier can be seen as a randomized tree, since at each node of the decision tree a random decision is made. The computational experience reported demonstrates the good performance of our procedure.
Divergent-convergent attention for image captioning

Attention mechanism has made great progress in image captioning, where semantic words or local regions are selectively embedded into the language model. However, current attention-based image captioning methods ignore the fine-grained semantic information and their interaction with visual regions. Inspired by the activity of human in describing an image: divergent observation and convergent attention, we propose a novel divergent-convergent attention (DCA) model to tackle the problems of the current attention model in image captioning. In our DCA model, divergent observation is mainly reflected in the multi-perspective inputs: a visual collection coming from object detection and three semantic components of scene graph made of objects, attributes and relations respectively. Then the convergent attention merges these multi-perspective inputs by adaptively deciding which perspective is crucial and which element in the focused perspective dominates in the attention process through a hierarchical structure. Our model also makes use of the interaction between visual objects and semantic components to achieve complementary advantages. Above all, owing to the interaction between divergent visual and semantic components, and the gradual convergence of attention, our model can attend to the corresponding local region more precisely under the guidance of semantic components. Besides, with the assistance of the visual components, the DCA model can effectively utilize the fine-grained semantic components to generate more descriptive sentences. Experiments on the MS COCO dataset demonstrate the superiority of our proposed method.
Autonomous Flights in Dynamic Environments with Onboard Vision

In this paper, we introduce a complete system for autonomous flight of quadrotors in dynamic environments with onboard sensing. Extended from existing work, we develop an occlusion-aware dynamic perception method based on depth images, which classifies obstacles as dynamic and static. For representing generic dynamic environment, we model dynamic objects with moving ellipsoids and fuse static ones into an occupancy grid map. To achieve dynamic avoidance, we design a planning method composed of modified kinodynamic path searching and gradient-based optimization. The method leverages manually constructed gradients without maintaining a signed distance field (SDF), making the planning procedure finished in milliseconds. We integrate the above methods into a customized quadrotor system and thoroughly test it in real-world experiments, verifying its effective collision avoidance in dynamic environments.
Quantum K-nearest neighbor classification algorithm based on Hamming distance

K-nearest neighbor classification algorithm is one of the most basic algorithms in machine learning, which determines the sample's category by the similarity between samples. In this paper, we propose a quantum K-nearest neighbor classification algorithm with Hamming distance. In this algorithm, quantum computation is firstly utilized to obtain Hamming distance in parallel. Then, a core sub-algorithm for searching the minimum of unordered integer sequence is presented to find out the minimum distance. Based on these two sub-algorithms, the whole quantum frame of K-nearest neighbor classification algorithm is presented. At last, it is shown that the proposed algorithm can achieve a quadratical speedup by analyzing its time complexity briefly.
A Little Data Goes a Long Way: Automating Seismic Phase Arrival Picking at Nabro Volcano With Transfer Learning

Abstract Supervised deep learning models have become a popular choice for seismic phase arrival detection. However, they do not always perform well on out‐of‐distribution data and require large training sets to aid generalization and prevent overfitting. This can present issues when using these models in new monitoring settings. In this work, we develop a deep learning model for automating phase arrival detection at Nabro volcano using a limited amount of training data (2,498 event waveforms recorded over 35 days) through a process known as transfer learning. We use the feature extraction layers of an existing, extensively trained seismic phase picking model to form the base of a new all‐convolutional model, which we call U‐GPD. We demonstrate that transfer learning reduces overfitting and model error relative to training the same model from scratch, particularly for small training sets (e.g., 500 waveforms). The new U‐GPD model achieves greater classification accuracy and smaller arrival time residuals than off‐the‐shelf applications of two existing, extensively‐trained baseline models for a test set of 800 event and noise waveforms from Nabro volcano. When applied to 14 months of continuous Nabro data, the new U‐GPD model detects 31,387 events with at least four P‐wave arrivals and one S‐wave arrival, which is more than the original base model (26,808 events) and our existing manual catalog (2,926 events), with smaller location errors. The new model is also more efficient when applied as a sliding window, processing 14 months of data from seven stations in less than 4 h on a single graphics processing unit.
Diving deep in Deep Convolutional Neural Network

Artificial Neural networks have been proved most efficient in Deep Learning mainly because of large number of datasets it can handle. The most widely used is the Convolutional Neural Network (CNN). It has been proved useful for computer vision, pattern recognition and Natural Language Processing (NLP). CNN is so vastly used, as, unlike traditional Neural Nets, it reduces number of parameters and focus more on domain specific features. There are various CNN architectures proposed, such as LeNet, AlexNet, GoogleNet. In this paper, we talk about structure of CNN and all the models of CNN which are proposed till date.
Building and evaluating a theory of architectural technical debt in software-intensive systems

Architectural technical debt in software-intensive systems is a metaphor used to describe the “big” design decisions (e.g., choices regarding structure, frameworks, technologies, languages, etc.) that, while being suitable or even optimal when made, significantly hinder progress in the future. While other types of debt, such as code-level technical debt, can be readily detected by static analyzers, and often be refactored with minimal or only incremental efforts, architectural debt is hard to be identified, of wide-ranging remediation cost, daunting, and often avoided. In this study, we aim at developing a better understanding of how software development organizations conceptualize architectural debt, and how they deal with it. In order to do so, in this investigation we apply a mixed empirical method, constituted by a grounded theory study followed by focus groups. With the grounded theory method we construct a theory on architectural technical debt by eliciting qualitative data from software architects and senior technical staff from a wide range of heterogeneous software development organizations. We applied the focus group method to evaluate the emerging theory and refine it according to the new data collected. The result of the study, i.e., a theory emerging from the gathered data, constitutes an encompassing conceptual model of architectural technical debt, identifying and relating concepts such as its symptoms, causes, consequences, management strategies, and communication problems. From the conducted focus groups, we assessed that the theory adheres to the four evaluation criteria of classic grounded theory, i.e., the theory fits its underlying data, is able to work, has relevance, and is modifiable as new data appears. By grounding the findings in empirical evidence, the theory provides researchers and practitioners with novel knowledge on the crucial factors of architectural technical debt experienced in industrial contexts.
An augmented reality approach for supporting panel alignment in car body assembly

One of the major problems in automotive assembly consists in achieving alignment within the specified tolerances between car panels that make up the exterior bodywork. To reduce errors and time requested to perform the assigned assembly tasks, workers should be guided during these panel fitting operations. Augmented Reality (AR) could be particularly suitable in this regard, as it represents one of the most promising tools to support personnel, with constantly growing applications in production processes. Following this trend, the present work aims to present an AR prototype system for supporting the operator during panel fitting operations of car body assembly, by providing instructions to correct alignment errors in terms of gap and flushness. A real case study concerning the fine alignment of car body panels with respect to the front light projector is also presented.