Reviewerly/exHarmony huggingface dataset:
Exploring a Large Dataset of Educational Videos Using Object Detection Analysis

Processing data and analyzing it are very important these days and have many applications in real life. In the case of very large video datasets, it became counterproductive, if not impossible, to visualize, analyze, and label the videos manually, due to their sheer number and length. Thus, automatically explore video datasets to understand what the videos are depicting, both for having a general view of the videos and a per video understanding of the activities. For example, if they contain sensitive images, violence and other such activities the videos could be flagged for manual checking. We turned our attention toward understanding educational videos, due to the great potential of improving the educational process by understanding written data (such as forums, chats, documents), audio, and video data. In this paper, we continue our work regarding an exploratory analysis of videos collected using YouTube-8 M, with the “school” keyword in their metadata. Thus, we attempt to detect the type of activity in a video based on the number of unique people detected and tracked, and on the objects detected by YOLOv3. This allows us to detect the educational activities in the dataset and to estimate the distribution of the activities in videos uploaded worldwide.
A Behavior Privacy Preserving Method towards RF Sensing

Recent years have witnessed the booming development of RF sensing, which supports both identity authentication and behavior recognition by analysing the signal distortion caused by human body. In particular, RF-based identity authentication is more attractive to researchers, because it can capture the unique biological characteristics of users. However, the openness of wireless transmission raises privacy concerns since human behaviors can expose the massive private information of users, which impedes the real-world implementation of RF-based user authentication applications. Unfortunately, it is difficult to filter out the behavior information from the collected RF signals.In this paper, we propose a privacy-preserving deep neural network named BPCloak to erase the behavior information in RF signals while retaining the ability of user authentication. We conduct extensive experiments over mainstream RF signals collected from three real wireless systems, including the WiFi, Radio Frequency IDentification (RFID), and millimeter-wave (mmWave) systems. The experimental results show that BPCloak significantly reduces the behavior recognition accuracy, i.e., 85%+, 75%+, and 65%+ reduction for WiFi, RFID, and mmWave systems respectively, merely with a slight penalty of accuracy decrease when using these three systems for user authentication, i.e., 1%-, 3%-, and 5%-, respectively.
Disrupting Audio Event Detection Deep Neural Networks with White Noise

Audio event detection (AED) systems can leverage the power of specialized algorithms for detecting the presence of a specific sound of interest within audio captured from the environment. More recent approaches rely on deep learning algorithms, such as convolutional neural networks and convolutional recurrent neural networks. Given these conditions, it is important to assess how vulnerable these systems can be to attacks. As such, we develop AED-suited convolutional neural networks and convolutional recurrent neural networks, and attack them next with white noise disturbances, conceived to be simple and straightforward to be implemented and employed, even by non-tech savvy attackers. We develop this work under a safety-oriented scenario (AED systems for safety-related sounds, such as gunshots), and we show that an attacker can use such disturbances to avoid detection by up to 100 percent success. Prior work has shown that attackers can mislead image classification tasks; however, this work focuses on attacks against AED systems by tampering with their audio rather than image components. This work brings awareness to the designers and manufacturers of AED systems, as these solutions are vulnerable, yet may be trusted by individuals and families.
The Switching Hierarchical Gaussian Filter

In this paper we discuss variational message passing-based (VMP) inference in a switching Hierarchical Gaussian Filter (HGF). An HGF is a flexible hierarchical state space model that supports closed-form VMP-based approximate inference for tracking of both states and slowly time-varying parameters. Since natural signals often submit to regime-switching dynamics, there is a need for low-complexity closed-form inference in switching state space models. Here we extend the HGF model with parameter switching mechanics and derive closed-form VMP update rules for plug-in applications in factor graph-based models. These VMP rules support both tracking of latent variables and variational free energy as a model performance measure. We show that the switching HGF performs better than a non-switching HGF on modelling of a stock market data set.
How Can Robots Trust Each Other For Better Cooperation? A Relative Needs Entropy Based Robot-Robot Trust Assessment Model

Cooperation in multi-agent and multi-robot systems can help agents build various formations, shapes, and patterns presenting corresponding functions and purposes adapting to different situations. Relationship between agents such as their spatial proximity and functional similarities could play a crucial role in cooperation between agents. Trust level between agents is an essential factor in evaluating their relationships’ reliability and stability, much as people do. This paper proposes a new model called Relative Needs Entropy (RNE) to assess trust between robotic agents. RNE measures the distance of needs distribution between individual agents or groups of agents. To exemplify its utility, we implement and demonstrate our trust model through experiments simulating a heterogeneous multi-robot grouping task in a persistent urban search and rescue mission consisting of tasks at two levels of difficulty. The results suggest that RNE trust-Based grouping of robots can achieve better performance and adaptability for diverse task execution compared to the state-of-the-art energy-based or distance-based grouping models.
G-Cloud Briefcase - Electronic Archive for Academic Certificates and General Certificates of Education Documents Using Public Private Hyperspace for E-Government Library Services Based on NOSQL Databases

Working in the coronavirus COVID-19 pandemic context, the entire world has started to perform their activities in online medium. This current situation has affected multiple industries and made companies realize the need of digitalization of a number vital but time-consuming processes. They are presently found in all activity domains when talking about paperwork and communication between governments and educational institutions. The article is based on the idea that as automation is gaining more and more access into our everyday lives, the old school methods of storing information will soon be replaced by databases of enormous dimensions which can be managed by people. Also, the technology available now, has the methods of helping the bureaucratic processes having as an effect a better communication between citizens and government. The paper presents a different perspective on how the documents can be archived and transmitted. It exposes the architecture of a system that has more features than common document management systems.
Embodied Motivation: Spatial and Temporal Aspects of Approach and Avoidance in Virtual Reality

This study investigates how object affordances modulate approach and avoidance behaviors in virtual reality (VR). The primary hypotheses are that 1) motivational relevance shapes facilitation or inhibition of behaviors while interacting with virtual objects, and that 2) approach-avoidance can be quantified using spatial and temporal aspects of behavior in VR. An experiment was conducted to track and examine user behaviors during a virtual photo-sorting task that used images with varying valence and arousal levels. Spatial indicators of approach and avoidance were operationalized as the distance between the individual and the photo during the task. Temporal indicators were operationalized as the speed and latency of gazing at the objects, selection order, and sorting behavior. Results show that stimulus valence and arousal significantly affect speed and latency during object selection, demonstrating motivational reactions to virtual objects. Exploratory analyses also revealed varying patterns of object selection across individuals. Overall, this study has implications for the design of virtual environments and supports the use of VR technology in studying motivational processes and behaviors relevant for media psychologists.
A Secure Access Scheme for Internet of Things Devices Based on Blockchain

With the continuous development of the Internet of Things technology, the security of the Internet of Things has attracted more and more attention. Terminal equipment access authentication is an important link to ensure the security of the Internet of Things. The current centralized authentication architecture has risks such as poor scalability, poor flexibility, and single point of failure. In addition, traditional access authentication schemes mainly rely on digital certificates for identity authentication, which has the problems of high communication overhead and cannot adapt to the Internet of Things environment with limited terminal equipment resources. To solve these problems, this paper proposes a distributed secure access scheme for IoT based on blockchain technology.
An Efficient Link Prediction Model in Dynamic Heterogeneous Information Networks Based on Multiple Self-attention

The existing link prediction researches of information networks mainly focus on the dynamic homogeneous network or the static heterogeneous network. It has always been a challenge to predict future relationships between nodes while learning both continuous-time and heterogeneous information simultaneously. In this paper, we propose a Heterogeneous and Continuous-Time Model Based on Self-Attention (HTAT) to complete the link prediction task by learning temporal evolution and heterogeneity jointly. The HTAT model consists of the base layer and the heterogeneous layer. The base layer incorporates a functional time encoding with self-attention mechanism to capture continuous-time evolution. And the heterogeneous layer consists of multi-view attention to learn heterogeneous information. Experimental results show that HTAT is significantly competitive compared with four state-of-the-art baselines on three real-world datasets.
Algebraic Soft Decoding of Elliptic Codes

This paper proposes algebraic soft decoding (ASD) for one-point elliptic codes, where the interpolation is realized through the perspective of obtaining a Gröbner basis. The desired interpolation polynomial <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{Q}(x, y, z)$</tex> is the minimum candidate in the basis. This work shows how to obtain such a Gröbner basis. Based on an interpolation multiplicity matrix M, an interpolation ideal <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{I}_{\mathrm{M}}$</tex> can be defined. With a predefined decoding output list size (OLS) <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$l\ (l\geq\deg_{z}\mathcal{Q})$</tex> , an equivalent interpolation module <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{I}_{\mathrm{M}, l}$</tex> can be led to. By further defining the Lagrange interpolation functions, a basis of the interpolation module can be constructed. The desired Gröbner basis can be obtained by reducing this module basis. Finally, the decoding complexity is also analyzed.
Application of Content Based Recommendation System in Homestay

In order to overcome the problems of the application of recommender system in homestay industry is not perfect and can not provide personalized choice for users, a recommendation system for homestay is proposed. The system is based on content recommendation. First of all, from a large number of users’ comments on homestay, we use Jieba and stop word list to make a Chinese word segmentation corpus. Then we use word2vec training corpus to get homestay label features. Then the TF-IDF model is used to vectorize the tag features, and the similarity value of the feature vector is calculated by cosine similarity to get the push value of the new user, and the user preference table is constructed by selecting tags. When the user completes the subscription, the user preferences table is updated to complete the recommendation. The system designed in this paper show that the accuracy and real-time of recommendation, improves user satisfaction, and provides personalized recommendation for users.
A Study on Lightweight And Secure Edge Computing Based Blockchain

In response to the challenges faced by edge computing in terms of storage and security, we design a lightweight and secure edge computing architecture based on blockchain. First, we conducted in-depth research on lightweight communication protocols, and propose a lightweight communication model by combining Constrained Application Protocol (CoAP) and Message Queuing Telemetry Transport (MQTT). Second, in order to ensure the security and traceability of the data on edge computing node, we propose a novel data storage mechanism based on blockchain and InterPlanetary File System (IPFS). Finally, we design an improved scheme of the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm to improve the throughput of the system. The results show that the proposed edge computing architecture has the characteristics of strong robustness and low system overhead, which provides new ideas for promoting blockchain-based edge computing to become a key technology for upgrading Internet of Things (IoT) services.
GEDet

Detecting nodes with erroneous values in real-world graphs remains challenging due to the lack of examples and various error scenarios. We demonstrate GEDet, an error detection engine that can detect erroneous nodes in graphs with a few examples. The GEDet framework tackles error detection as a few-shot node classification problem. We invite the attendees to experience the following unique features. (1) Few-shot detection . Users only need to provide a few examples of erroneous nodes to perform error detection with GEDet. GEDet achieves desirable accuracy with (a) a graph augmentation module, which automatically generates synthetic examples to learn the classifier, and (b) an adversarial detection module, which improves classifiers to better distinguish erroneous nodes from both cleaned nodes and synthetic examples. We show that GEDet significantly improves the state-of-the-art error detection methods. (2) Diverse error scenarios . GEDet profiles data errors with a built-in library of transformation functions from correct values to errors. Users can also easily "plug in" new error types or examples. (3) User-centric detection . GEDet supports (a) an active learning mode to engage users to verify detected results, and adapts the error detection process accordingly; and (b) visual interfaces to interpret and track detected errors.
SAGN: Semantic Adaptive Graph Network for Skeleton-Based Human Action Recognition

With the continuous development and popularity of depth cameras, skeleton-based human action recognition has attracted people's wide attention. Graph Convolutional Network (GCN) has achieved remarkable performance. However, the existing methods do not better consider the semantic characteristics, which can help to express the current concept and scene information. Semantic information can also help with better granularity classification. In addition, most of the existing models require a lot of computation. What's more, adaptive GCN can automatically learn the graph structure and consider the connections between joints. In this paper, we propose a relatively less computationally intensive model, which combines semantic and adaptive graph network (SAGN) for skeleton-based human action recognition. Specifically, we mainly combine the dynamic characteristics and bone information to extract the data, taking the correlation between semantics into the model. In the training process, SAGN includes an adaptive network so that we can make attention mechanism more flexible. We design the Convolutional Neural Network (CNN) for feature extraction on the time dimension. The experimental results show that SAGN achieves the state-of-the-art performance on NTU-RGB+D 60 and NTU-RGB+D 120 datasets. SAGN can promote the study of skeleton-based human action recognition. The source code is available at https://github.com/skeletonNN/SAGN.
Local-enhanced Interaction for Temporal Moment Localization

Temporal moment localization via language aims to localize a video span in an untrimmed video which best matches the given natural language query. In most previous works, they try to match the whole query feature with multiple moment proposals, or match a global video embedding with phrase or word level query features. However, these coarse interaction models will become insufficient when the query-video contains more complex relationship. To address this issue, we propose a multi-branches interaction model for temporal moment localization. Specifically, the query sentence and video are encoded into multiple feature embeddings over several semantic sub-spaces. Then, each phrase embedding filters on a video feature to generate an attention sequence, which is used to re-weight the video features. Moreover, a dynamic pointer decoder is developed to iteratively regress the temporal boundary, which can prevent our model from falling into a local optimum. To validate the proposed method, we have conducted extensive experiments on two popular benchmark datasets Charade-STA and TACoS. The experimental performance surpasses other state-of-the-arts methods, which demonstrates the effectiveness of our proposed model.
An Image Registration Model in Electron Backscatter Diffraction

Recently, variational methods were successfully applied for computing the optical flow in gray and RGB-valued image sequences. A crucial assumption in these models is that pixel-values do not change under transformations. Nowadays, modern image acquisition techniques, such as electron backscatter diffraction (EBSD) used in materials science, can capture images with values in nonlinear spaces. Here, the image values belong to the quotient space ${SO}(3)/ \mathcal S$ of the special orthogonal group modulo the discrete symmetry group of the crystal. For such data, the assumption that pixel-values remain unchanged under transformations appears to be no longer valid. Hence, we propose a variational model for determining the optical flow in ${SO}(3)/\mathcal S$-valued image sequences, taking into account the dependence of pixel-values on the transformation. More precisely, the data is transformed according to the rotation part in the polar decomposition of the Jacobian of the transformation. To model nonsmooth transformations without obtaining so-called staircasing effects, we propose using total generalized variation, such as prior. Then, we prove existence of a minimizer for our model and explain how it can be discretized and minimized by a primal-dual algorithm. Numerical examples illustrate the performance of our method.
Mobile Robot Path Planning Based on Improved Ant Colony Fusion Dynamic Window Approach

Aiming at the shortcomings of ant colony algorithm in the complex environment, such as being easy to fall into local optimum and difficult to guarantee real-time path planning of robots, this paper proposes a dynamic window algorithm based on improved ant colony (IACO-DWA). In order to avoid the blind search of ants in the early stage, this method designs an adaptive distance induction factor, and combines the maximum and minimum ant system (MMAS) to improve the pheromone update rule to prevent falling into the local optimum; to improve the probability transfer rule by constructing a corner suppression factor, Reduce the path inflection points, and integrate the global path points generated by the DWA tracking ant colony to construct a new position evaluation function, and then plan a smooth path trajectory. The simulation results show that the method in this paper strengthens the optimization performance of the global path while realizing the local dynamic obstacle avoidance.
Video Processing Algorithm in Foggy Environment for Intelligent Video Surveillance

This article discusses the use of foggy environments for intelligent video surveillance. Foggy environments are needed for processing data using embedded computers with low computing power. The use of a foggy environment has significantly reduced the processing time of video information on a single board and embedded computers and systems. The proposed algorithm is significant for the developers of intelligent surveillance systems. The effectiveness of the algorithm for processing video images in foggy environments is shown on the examples in various subject domains, in particular, for the subway video images.
Feature Interaction Convolutional Network for Knowledge Graph Embedding

Due to the general incompleteness of knowledge graphs, knowledge graph link prediction is a hot research topic for knowledge graph completion. The low-dimensional embedding of entities and relations can be realized through link prediction methods, and then inference can be made. The previous link prediction methods mainly used shallow and fast models of the knowledge graph, such as TransE, TransH, TransA and other models. The feature extraction capabilities of these models are insufficient, which affects the performance of prediction. The recently proposed method ConvE uses embedded two-dimensional convolution and multi-layer nonlinear features to model the knowledge graph, which increases the interaction between entities and relations to a certain extent. In this paper, we propose a Feature Interaction Convolutional Network (FICN) for knowledge graph embedding, which uses three methods: Random Permutation, Chequer Reshaping and Circular Convolution to increase the feature interaction capability of the model, thereby effectively improving the link prediction performance. We verified the feasibility and effectiveness of FICN on the FB15K-237, WN18RR and YAGO3-10 data sets. Through experiments, we found that on these three data sets, FICN has a certain degree of improvement in MRR score compared to ConvE, and it is also stronger than ConvE in MR, HIST@10 and HIST@1 indicators. In addition, our model increases the training speed by adding the Batch Normalization preprocessing method during convolution training. Compared with ConvE model, our model has about a 50% reduction in training time.