Reviewerly/exHarmony huggingface dataset:
Human-Aware Path Planning With Improved Virtual Doppler Method in Highly Dynamic Environments

Human-aware path planner is essential for achieving harmonious coexistence between humans and robots in highly dynamic environments. In this paper, we propose an integrated framework to find the optimal path in the complex environment with considering collision risk, social norms, and crowded areas. In the proposed framework, a general dynamic group model (g-space) based on the Gaussian Mixed Model (GMM) is proposed as the social norms of dynamic groups, which not only considers the factors of humans (e.g., pose, quantity, distribution, psychology) but also establishes the proximity and human interacting constraints of dynamic groups. An integrated Collision Risk and Human Space (CR&HS) model is applied to achieve human-acceptable behaviors, in which both collision avoidance, human comfort, and interference-free constraints have been involved. Moreover, an Improved Virtual Doppler Method (IVDM) has been used to realize safety navigation to avoid the robot falling into the crowded area. Finally, the proposed framework has been utilized with the sampling-based rapidly-exploring random tree. Experimental results demonstrate that the proposed method can generate the optimal human-aware collision-free path in complex environments. Note to Practitioners—This paper aims to plan an optimal trajectory for the robot in highly dynamic environments. In this field, it is still a challenging task to plan a trajectory with collision-free, human-aware, and crowd-aware. To do that, we present an integrated framework to generate the optimal trajectory by involving the collision risk, social norms, and human density. First, the g-space model is adopted as interference-free constraints of dynamic groups. The integrated knowledge fusion model (CR&HS) then penalizes the manners which have higher collision risk and adverse effects on human interaction or human comfortable. Besides, human motion and density are provided to a robot by IVDM. The proposed framework is utilized in the sampling-based rapidly-exploring random tree as the evaluation module. Finally, the feasibility and reliability of the proposed method have been verified by experiments in different simulated environments. The proposed framework can be applied in most mobile service robots to achieve human-friendly manners.
Learning Channel-Aware Correlation Filters for Robust Object Tracking

Correlation filters with Convolutional Neural Networks (CNNs) features have obtained tremendous attention and success in visual tracking. However, redundant and noisy feature channels existed in CNN features may cause severe over-fitting and greatly limit the discriminative power of the tracking model. To tackle the issue, in this paper, we develop a new and effective channel-aware correlation filters (CACF) method for boosting the tracking performance. Our CACF method aims to dynamically select representative and discriminative feature channels from high-dimensional CNN features to reduce the model complexity and better distinguish the target object from the background. Moreover, the CACF model is solved by the alternating direction method of multipliers (ADMM) to learn correlation filters. By retaining reliable feature channels, our CACF tracking method can reach better generalization ability and discriminative ability to accurately localize the target object. Comprehensive experiments are conducted on challenging tracking datasets, and the experiment results prove that our CACF method obtains favorable tracking accuracy compared to several popular tracking methods.
Predictive Comment Updating With Heuristics and AST-Path-Based Neural Learning: A Two-Phase Approach

Just-in-time comment update is a promising way to reduce the burden of developers during software maintenance and evolution. Existing approaches can be divided into two categories: the heuristic-based approach and the deep-learning-based approach. The heuristic-based approach is restricted to a specific type of comment updates (i.e., code-indicative updates), but performs well on such type. The effectiveness of deep-learning-based approach is limited but it can handle diverse comment updates. Considering the complementary advantages of existing approaches, an intuitive idea is to combine them for better performance. To investigate this idea, we first conduct a pre-study experiment which shows that to construct an effective comment updater by combining heuristic-based and deep-learning-based approaches, we need to tackle two main challenges: 1) the heuristic-based approach may bring side effects to cases which cannot be updated by it; and 2) the current deep-learning-based approach is with limited effectiveness. Then, we propose a novel two-phase approach named Toper to cope with these two challenges and effectively perform comment updates. In the first phase, Toper integrates nine distinctive features identified through our large-scale empirical analysis into a predictive model, which can predict whether the contents of the comment updates can be found in the corresponding code changes, namely, the comment updates are code-indicative updates. If so, the updates are then generated by an off-the-shelf heuristic-based approach; otherwise, Toper leverages a deep learning model, which we specially designed for non-code-indicative updates, to infer the new comment based on the old comment and code change. Motivated by our manual observation on the limitation of existing approaches on non-code-indicative updates, our deep learning model adopts the Abstract Syntax Tree path technique, which can capture the program structure information for effectively embedding code changes. Our evaluation shows that our approach outperforms the state-of-the-art by around 20&#x0025; with respect to the number of correct comments it generates. Via in-depth analysis, we illustrate the rationale of each design decision as well as point out potential directions.
Evaluation method of urban residential environment satisfaction and urban health care based on AI based technology

Urban environmental satisfaction has increasingly become an important indicator to measure people's livelihood. Residents' satisfaction with urban environment involves many variables related to social stratification. In order to better improve the living value of residents and ensure the health of urban environment, this paper optimizes the evaluation method of urban living environment satisfaction combined with speech recognition technology, and discusses the influence of residents on urban environment satisfaction. The results show that the evaluation method has high accuracy and practical application value. It is found that the reasonable allocation of urban public facilities has a negative correlation with urban environmental satisfaction, while the number of urban residents has a positive correlation with urban environmental satisfaction.
Anomaly Detection in Satellite Telemetry Data Using a Sparse Feature-Based Method

Anomaly detection based on telemetry data is a major issue in satellite health monitoring which can identify unusual or unexpected events, helping to avoid serious accidents and ensure the safety and reliability of operations. In recent years, sparse representation techniques have received an increasing amount of interest in anomaly detection, although its applications in satellites are still being explored. In this paper, a novel sparse feature-based anomaly detection method (SFAD) is proposed to identify hybrid anomalies in telemetry. First, a telemetry data dictionary and the corresponding sparse matrix are obtained through K-means Singular Value Decomposition (K-SVD) algorithms, then sparse features are defined from the sparse matrix containing the local dynamics and co-occurrence relations in the multivariate telemetry time series. Finally, lower-dimensional sparse features vectors are input to a one-class support vector machine (OCSVM) to detect anomalies in telemetry. Case analysis based on satellite antenna telemetry data shows that the detection precision, F1-score and FPR of the proposed method are improved compared with other existing multivariate anomaly detection methods, illustrating the good effectiveness of this method.
Jupyter in the Classroom

Jupyter is an interactive web-based programming environment commonly used by Python programmers and data scientists. It can be used in the classroom either by itself or in addition to a traditional programming environment. This paper documents our experience using Jupyter in multiple computer science courses in a public four-year university using different programming languages (Java, C++, and Python). It describes our motivations for using Jupyter in these courses and how it is made available to students. It also describes the different roles it plays in our pedagogical approaches and the challenges it poses to both instructors and students. A report is presented on the preliminary experimentation conducted to evaluate its effectiveness in improving student performance. While our tests are limited by small sample sizes and did not show that using Jupyter produces a statistically significant improvement in student performance, qualitative analysis of student responses showed the usefulness of this tool in revitalizing teaching and keeping students engaged.
Three-dimensional reconstruction from a fringe projection system through a planar transparent medium

A vision measurement system is placed in a protective cover made of a transparent medium to avoid environmental influences. Due to the deflection of light rays on the front and rear surfaces of the transparent medium, the imaging position of an object on the camera target plane is deviated, which makes the traditional vision detection methods based on the triangulation principle produce large measurement errors. This work introduces a three-dimensional (3D) reconstruction method by fringe projection system through a planar transparent medium. We derive the coordinate transformation relationship between a real-object point and a pseudo-object point caused by light refraction based on Snell's law of flat refraction. Based on the relationship, a modified fringe projection method is proposed for unbiased 3D reconstruction. Two experiments, including 3D shape measurement of a white plate with ring markers and 3D shape measurement of a regular spherical object are conducted. The results demonstrate the effectiveness of the proposed method in such measurement environment.
Revisiting Open Domain Query Facet Extraction and Generation

Web search queries can often be characterized by various facets. Extracting and generating query facets has various real-world applications, such as displaying facets to users in a search interface, search result diversification, clarifying question generation, and enabling exploratory search. In this work, we revisit the task of query facet extraction and generation and study various formulations of this task, including facet extraction as sequence labeling, facet generation as autoregressive text generation or extreme multi-label classification. We conduct extensive experiments and demonstrate that these approaches lead to complementary sets of facets. We also explored various aggregation approaches based on relevance and diversity to combine the facet sets produced by different formulations of the task. The approaches presented in this paper outperform state-of-the-art baselines in terms of both precision and recall. We confirm the quality of the proposed methods through manual annotation. Since there is no open-source software for facet extraction and generation, we release a toolkit named Faspect, that includes various model implementations for this task.
A Policy-Oriented Architecture for Enforcing Consent in Solid

The Solid project aims to restore end-users’ control over their data by decoupling services and applications from data storage. To realize data governance by the user, the Solid Protocol 0.9 relies on Web Access Control, which has limited expressivity and interpretability. In contrast, recent privacy and data protection regulations impose strict requirements on personal data processing applications and the scope of their operation. The Web Access Control mechanism lacks the granularity and contextual awareness needed to enforce these regulatory requirements. Therefore, we suggest a possible architecture for relating Solid’s low-level technical access control rules with higher-level concepts such as the legal basis and purpose for data processing, the abstract types of information being processed, and the data sharing preferences of the data subject. Our architecture combines recent technical efforts by the Solid community panels with prior proposals made by researchers on the use of ODRL and SPECIAL policies as an extension to Solid’s authorization mechanism. While our approach appears to avoid a number of pitfalls identified in previous research, further work is needed before it can be implemented and used in a practical setting.
RePS: Relation, Position and Structure aware Entity Alignment

Entity Alignment (EA) is the task of recognizing the same entity present in different knowledge bases. Recently, embedding-based EA techniques have established dominance where alignment is done based on closeness in latent space. Graph Neural Networks (GNN) gained popularity as the embedding module due to its ability to learn entities’ representation based on their local sub-graph structures. Although GNN shows promising results, limited works have aimed to capture relations while considering their global importance and entities’ relative position during EA. This paper presents Relation, Position and Structure aware Entity Alignment (RePS), a multi-faceted representation learning-based EA method that encodes local, global, and relation information for aligning entities. To capture relations and neighborhood structure, we propose a relation-based aggregation technique – Graph Relation Network (GRN) that incorporates relation importance during aggregation. To capture the position of an entity, we propose Relation-aware Position Aggregator (RPA) to capitalize entities’ position in a non-Euclidean space using training labels as anchors, which provides a global view of entities. Finally, we introduce a Knowledge Aware Negative Sampling (KANS) that generates harder to distinguish negative samples for the model to learn optimal representations. We perform exhaustive experimentation on four cross-lingual datasets and report an ablation study to demonstrate the effectiveness of GRN, KANS, and position encodings.
A walk in the black-box: 3D visualization of large neural networks in virtual reality

Within the last decade Deep Learning has become a tool for solving challenging problems like image recognition. Still, Convolutional Neural Networks (CNNs) are considered black-boxes, which are difficult to understand by humans. Hence, there is an urge to visualize CNN architectures, their internal processes and what they actually learn. Previously, virtual realityhas been successfully applied to display small CNNs in immersive 3D environments. In this work, we address the problem how to feasibly render large-scale CNNs, thereby enabling the visualization of popular architectures with ten thousands of feature maps and branches in the computational graph in 3D. Our software "DeepVisionVR" enables the user to freely walk through the layered network, pick up and place images, move/scale layers for better readability, perform feature visualization and export the results. We also provide a novel Pytorch module to dynamically link PyTorch with Unity, which gives developers and researchers a convenient interface to visualize their own architectures. The visualization is directly created from the PyTorch class that defines the Pytorch model used for training and testing. This approach allows full access to the network's internals and direct control over what exactly is visualized. In a use-case study, we apply the module to analyze models with different generalization abilities in order to understand how networks memorize images. We train two recent architectures, CovidResNet and CovidDenseNet on the Caltech101 and the SARS-CoV-2 datasets and find that bad generalization is driven by high-frequency features and the susceptibility to specific pixel arrangements, leading to implications for the practical application of CNNs. The code is available on Github https://github.com/Criscraft/DeepVisionVR.
Energy Efficiency Strategy for Big Data in Cloud Environment Using Deep Reinforcement Learning

Big data entails massive cloud resources for data processing and analysis, which consumes more energy to run. The resources and tasks are increasing exponentially in the cloud environment for the processing of big data, which results in an increment in power consumption to run the cloud data center. So, there is always a scope for optimizing the energy utilization in cloud data centers. This paper presents a visionary architecture in a cloud environment for big data with a proposed energy-efficient strategy based on LSTM-DQN (long-short-term memory-deep Q network) using reinforcement learning (RL). The traditional techniques are not so efficient when the tasks are allocated dynamically, and the generic RL strategies are not able to store the data iterated in the last cycles of processing, so the LSTM is considered for this purpose. In the proposed model, integration of DPSO and DQN is used for better estimation and rectification of the curse of dimensionality. The proposed strategy is compared with different variants of PSO (particle swarm optimization) such as DPSO and QoS-PSO. The improvement in results through proposed model is recoded over the algorithm such as load aware (8.01%), DQN (13.36%), EA-DQN (34.16%), L-No-DEAF (15.62%), DPSO (62.68%), QoS-PSO (72.69%), FFO-EVSM (75.42%), and MIMT (76.39%) on the parameter of energy efficiency, tasks completion time, and energy consumption over the timeline. So, the proposed model is encouraging in the energy-efficient cloud environment for big data with the challenges that the technological world is facing and the emergence of deep learning as one propitious field.
User Comments in Social Media Firestorms: A Mixed-Method Study of Purpose, Tone, and Motivation

Social media firestorms (SMF) are commonly seen as destructive forces of toxic comments hurled at a target for perceived wrongdoing. Yet some research suggests that SMF can provide beneficial outcomes for the target. In two studies, we qualitatively examine SMF comments (in terms of purpose and tone) and quantitatively examine users’ motivations for making different types of comments. Results show that SMF comments are diverse, either supporting or condemning the target and being either aggressive or cordial in tone. Further, the results show that users’ understanding of the triggering event in the real world influences the purpose of their comment (support or condemn) while disinhibition and others’ online comments (i.e., herd influence) shape how they comment (tone). We conclude with an expanded SMF definition as “A digital artifact created by large numbers of user comments of multiple purposes (condemnation and support) and tones (aggressive and cordial) that appear rapidly and recede shortly after”. Some SMF persist as destructive and harmful firestorms; some exist to condemn the target but without aggressive language; and some support the target’s behavior. Thus, SMF are not always abusive and toxic. The implications of our research are that SMF can be positive, enable collective actions, and require a detailed examination of their elements (purpose and tone) to understand their effects in the digital and real world.
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Quantum secret sharing occupies an important position in quantum cryptography. In this document, a dynamic ( t , n ) threshold quantum secret sharing scheme is proposed based on homogeneous LFSR sequences and d -dimensional Bell states. The secret shared among agents in this scheme is determined by the distributor and the scheme enables the update of agents without the help of other agents. More, this paper considers the case when no less than t agents cooperate in secret reconstruction, which makes the proposed scheme more flexible and practical than the existing ones. Finally, our scheme is not only resistant to eavesdropping, collusion, dishonest revoked agent, and in-attacker’s cheating attacks, but also agents can verify for themselves that the shared secrets are correct.
RP-Net: A PointNet++ 3D Face Recognition Algorithm Integrating RoPS Local Descriptor

as a biometric identification method in the post-epidemic era, face recognition owing more and more attention in practical applications to its non-contact and interaction-friendly advantages.Researchers more favor 3D faces because they have richer spatial information than 2D faces and are not easily affected by the environment.However 3D faces are not all collected in normal environments.To enhance the facial features of 3D faces and improve the recognition degree of 3D faces in weak-light or dark environments, a 3D face recognition algorithm based on point cloud depth learning is proposed.First, 3D faces are automatically detected from 3D raw data and preprocessed, including nose-tip detection and face cropping, spike removal and hole filling, and surface normals.Then, rotated projection statistical local feature descriptors (RoPS) are integrated into the PointNet++ network to describe and classify local features.Finally, feature matching is performed using the nearest neighbor distance ratio.The algorithm was tested on the Bosphorus and CASIA-3D datasets, and good results were obtained in a simulated weak-light environment.
Cloud technologies for basics of artificial intelligence study in school

Changes in society related to the development of science, technology, computing power, cloud services, artificial intelligence, increasing general access to huge amounts of open data, lead to increased global investment in technology and services. Appropriate training is required by specialists to create a workforce to work with artificial intelligence. On the one hand, it puts forward new requirements for the training of young people, and educational content, on the other hand, provides opportunities for the use of cloud technologies during the educational process. Widespread use of AI in various fields and everyday life poses the task of understanding the basic terms related to Artificial intelligence (AI), such as Machine learning (ML), Neural network (NN), Artificial neural networks (ANN), Deep Learning, Data Science, Big Data, mastering the basic skills of using and understanding the AI principles, which is possible during the study in the school course of computer science. Cloud technologies allow you to use the power of a remote server (open information systems, digital resources, software, etc.) regardless of the location of the consumer and provide ample opportunities for the study of artificial intelligence. In this article we reveal the possibilities of cloud technologies as a means of studying artificial intelligence at school, consider the need for three stages of training and provide development of tasks and own experience of using cloud technologies to study artificial intelligence on the example of DALL-E, Google QuickDraw, cloud technologies Makeblock, PictoBlox, Teachable Machine at different stages of AI study.