Reviewerly/exHarmony huggingface dataset:
An effective communication and computation model based on a hybridgraph-deeplearning approach for SIoT

Social Edge Service (SES) is an emerging mechanism in the Social Internet of Things (SIoT) orchestration for effective user-centric reliable communication and computation. The services are affected by active and/or passive attacks such as replay attacks, message tampering because of sharing the same spectrum, as well as inadequate trust measurement methods among intelligent devices (roadside units, mobile edge devices, servers) during computing and content-sharing. These issues lead to computation and communication overhead of servers and computation nodes. To address this issue, we propose the HybridgrAph-Deep-learning (HAD) approach in two stages for secure communication and computation. First, the Adaptive Trust Weight (ATW) model with relation-based feedback fusion analysis to estimate the fitness-priority of every node based on directed graph theory to detect malicious nodes and reduce computation and communication overhead. Second, a Quotient User-centric Coeval-Learning (QUCL) mechanism to formulate secure channel selection, and Nash equilibrium method for optimizing the communication to share data over edge devices. The simulation results confirm that our proposed approach has achieved effective communication and computation performance, and enhanced Social Edge Services (SES) reliability than state-of-the-art approaches.
The Challenges of IoT, TLS, and Random Number Generators in the Real World

Many in the cryptographic community scoff at the mistakes made in implementing RNGs. Many cryptographers and members of the IETF resist the call to make TLS more resilient to this class of failures. This article discusses the history, current state, and fragility of the TLS protocol, and it closes with an example of how to improve the protocol. The goal is not to suggest a solution but to start a dialog to make TLS more resilient by proving that the security of TLS without the assumption of perfect random numbers is possible.
Robust Online Path Planning for Autonomous Vehicle Using Sequential Quadratic Programming

In urban driving scenarios, it is a key component for autonomous vehicles to generate a smooth, kinodynamically feasible, and collision-free path. We present an optimization-based path planning method for autonomous vehicles navigating in cluttered environment, e.g., roads partially blocked by static or moving obstacles. Our method first computes a collision-free reference line using quadratic programming(QP), and then using the reference line as initial guess to generate a smooth and feasible path by iterative optimization using sequential quadratic programming(SQP). It works within a fractions of a second, thus permitting efficient regeneration.
Infra Sim-to-Real: An efficient baseline and dataset for Infrastructure based Online Object Detection and Tracking using Domain Adaptation

Increasing usage of traffic cameras provides an opportunity to utilize them for smart city applications. However, the efficacy of such systems is determined by their ability to detect and track objects of interest from diverse viewpoints accurately. This is challenging due to the diverse viewpoints, elevations, and distinct properties of camera sensors. Thus, to ensure robust performance, the training dataset should cover many variations, including viewpoints, illumination changes, and diverse weather conditions. However, constructing such a dataset is expensive in terms of data collection and annotation. This paper proposes an unsupervised domain adaptation approach wherein a synthetic dataset is generated using a simulator and subsequently used to ensure performance consistency of multi-object-tracking (MOT) algorithms across a diverse range of manually annotated natural scenes. Towards this end, we emphasize achieving domain invariant object detection by combining image stylization and class-balancing augmentation. Furthermore, we extend the robust detection algorithm to track detected objects across a large time scale using feature embeddings generated by the detector. Based on qualitative and quantitative results, we demonstrate the viability of such a system that is invariant to illumination, weather, viewpoint, and scene changes while providing a baseline for future research. Codebase and datasets would be made available at https://github.com/pranjay-dev/IS2R.
A Robust Graph Convolutional Neural Network-Based Classifier for Automatic Modulation Recognition

The procedure of automatically recognizing the modulation scheme of the received signal without any knowledge of the communications parameters employed by the transmitter has gained tremendous attention for developing various applications such as dynamic spectrum access in next generation communications systems and in electronic warfare (EW) applications. Amongst the proposed approaches for this procedure, the feature-based approach working on the principles of deep learning models has consistently demonstrated more favorable properties for its integration with real-world applications and realtime operations. However, this approach faces the challenges of low performance efficiency caused by low classification accuracy and high computational complexity in environments characterized by low signal-to-noise (SNR) ratio conditions. Therefore, in this paper, we present our research findings on a deep learning-based classifier for Automatic Modulation Recognition (AMR) with robust classification accuracy and lower computational complexity, thus leading to higher overall performance efficiency. The feature extraction stage of the proposed classifier operates on the translation of received signal constellations into a graph, which then follows a process of extracting graph information. Mapping the extracted information into features is implemented by a convolutional block. This design employed in our feature extraction stage is the main element for the robustness of our proposed classifier. For our performance evaluations we selected 8QAM, 64QAM and 256QAM, representing candidates for low-, medium- and high-order modulation schemes, respectively. Our simulation results exhibit higher classification accuracy compared to related literature works, by an average of 11 percentage points (p.p.), 11.31 p.p. and 9.3 p.p., respectively, for the successful classification of 8QAM, 64QAM and 256QAM across an SNR range from −10 to 30 dB. The computational complexity analysis indicates that our proposed classifier is capable of executing the AMR task with lower processing latency. Its robust classification and lower execution latency combine into a more efficient overall performance provided by our proposed classifier.
Enhancing Security in The Internet of Things Ecosystem using Reinforcement Learning and Blockchain

Internet of Things (IoT) is a promising technology that attains significant consideration in diverse industrial areas, i.e., agriculture, engineering, logistics, trading, ecological examining, security surveillance, energy, and healthcare. IoT gains much more attention with the rapid advancement of wireless communication and sensor networks as millions of intelligent devices get involved in IoT. These intelligent devices' raw data must be captured and processed to support decision-making. However, IoT applications trust the central server for information storage, processing, and mediators for wireless transmission. Consequently, it can leak the information and lead to high costs and delays. Hence, data security is the leading interest for the IoT. Blockchain technology can be deployed to overcome the security and effectiveness of the gigantic data in IoT. Blockchain is studied as a key to permitting storing, processing and sharing of data in an efficient, secure manner. In addition, reinforcement learning can convene the high data rate requirements. It will help us to optimize the performance of the blockchain-enabled IoT framework.
Blockchain Based Social Commitment -Secure &amp; Reliable Web Services

We are adopting blockchain-based security features for the usage in web service applications & platforms. These technology concepts allow us to enhance the level of trustworthiness for any kind of public web service platform. Related platforms are using simple user registration and validation procedures, which provide huge potential for illegal activities. In contrast, more secure live video identity checks are binding massive resources for the individual, staff-intensive validation tasks. Our approach combines traditional web-based service platform features with blockchain-based security enhancements. The concepts are used on two layers, for the user identification procedures as well as the entire process history on the web service platform.In order to maximize the individual user experience, trust-worthiness on web service level and the real-world trust level between each customer has to be considered in a very strong way. Thus, our approach focuses on local urban areas or regions with site-specific customized service portfolios. The social relations between the individual customers represent an essential part of each single service request.
Time-aware Attentive Click Sequence Network for Click-Through Rate Prediction

User's behavior sequence contains user's interest, which plays an important role in predicting the user's future behavior. The sequence of user actions is usually represented as a collection of items that users have interacted with sorted in chronological order and trained with an RNN-based model, but such a representation ignores a large amount of important temporal information. Second, direct use of user's behavior sequences has limitations in predicting users' uninteracted items. By studying the Transformer sequence model, the paper utilizes relative time information inside the sequence and designs the relative time-aware Transformer module to model the effect of time intervals on the relationship between behaviors, thus improving the generalized expression of behavioral sequences. Experiments show that our proposed model has better improvement in all evaluation metrics compared with other click-through rate (CTR) models.
Validation and accuracy estimation of a novel measurement system based on a mobile robot for human detection in indoor environment

This paper validates an indoor measurement system for human detection using a RGB camera installed on a mobile robot at three different robot’s head configuration and in two suboptimal collected scenarios. Images are processed with three algorithms for human detection i.e., Histogram of Oriented Gradients (HOG), Viola-Jones Haar Cascade Classifier (Haar cascade classifier), You Only Look Once (YOLO-v3) trained with COCO dataset and their accuracies in detecting humans are computed. For these algorithms, dependences from the robot head configuration and from the robot-subject distances are assessed in the two suboptimal scenarios. Results show that in the first suboptimal scenario of the proposed measurement system, YOLO-v3 algorithm provides the best accuracy value in detecting humans (99,9%) while the best accuracy results for the two other algorithms (65,7% for HOG and 70,8% for Haar cascade) are reached for a robot head configuration of 40°, independently from the robot-subject distances. The second suboptimal scenario is performed to detect conditions in which YOLO-v3 fails. Results indicate that in two user configurations YOLO-v3 fails that could be attributed to the COCO dataset with which the YOLO-v3 algorithm is trained and to the proposed suboptimal test conditions.
Contextual Bandit Guided Data Farming for Deep Neural Networks in Manufacturing Industrial Internet

Deep Neural Networks (DNNs) have shown superior performance in supervised learning in Industrial Internet applications, such as quality modeling, virtual inspection, etc. However, the performance of DNNs relies on large and high quality data sets with sufficient sample sizes and appropriate distributions. Additionally, collecting and labeling large data sets can be labor-intensive and may fall short of meeting the online computational needs, including the fact that more samples in training may not always improve the modeling performance. Inspired by the theory of Design of Experiments, we propose a Contextual Bandit-based Representation Design (CBRD) to generate data suitable for training DNNs. The CBRD combines the offline experimental design criteria as the arms of a contextual bandit model for DNN training in a joint and interactive way for online batch data. A low-dimensional representation of the input design space learned by variational autoencoder is used to generate new samples. The integration of VAE and contextual bandit enables the generation of samples adaptive to modeling performance. A real case study of Aerosol® Jet Printing process is used to demonstrate the merits of the CBRD method.
Design of Bionic Robotic Hand Gesture Recognition System Based on Machine Vision

Gesture recognition technology is a natural human-computer interaction method with a wide range of applications, among which using bionic robotic hands to achieve gesture recognition is more meaningful and valuable for research. To improve the accuracy and the speed of gesture recognition, and also to facilitate the deployment of gesture recognition algorithm on edge device, a bionic robotic hand gesture recognition system is designed based on machine vision. Firstly, a bionic robot with six degrees of freedom based on Raspberry Pi 3B+ is chosen as the development platform of the system, including five finger degrees of freedom and one wrist degree of freedom; Secondly, a gesture recognition method is proposed based on a combination of feature extraction model, Inception v3 network, and migration learning, and it is trained and tested on a self-built gesture dataset. The results show that the method can accurately recognize gesture types with an average accuracy of 0.92; Finally, the trained model is deployed on a Raspberry Pi 3B+ bionic manipulator and tested on a physical system, and the average recognition speed is 0.17s after the system is stabilized, which shows that the system can effectively recognize and imitate hand gesture.
Remote sensing image object detection based on improved SSD

Aiming at the problem that the detection accuracy of the SSD object detection model is insufficient when detecting remote sensing images, an improved SSD object detection model is proposed. This method uses Mobilenet v3 to replace the backbone network of the ssd algorithm, and adds the FPN network for feature fusion, and the shallow feature map with high resolution is fused with the deep feature map with rich semantic informationAt the same time, a feature pyramid is built between feature maps to enhance the target feature. Experimental results show that the detection accuracy of the improved method is significantly improved compared with the original algorithm, and the effect on the detection of remote sensing image objects is better.
Research and Implementation of Text Topic Classification Based on Text CNN

With the advent of the Internet era, more and more users acquire and send information on the Internet. The data exchanged between users, the data users interact with the network, and the data users send on the platform (text documents, videos, and images) are all growing exponentially. Among them, the amount of information in text documents has grown significantly, and it is no longer possible to classify and organize them manually. The birth of text classification technology can just rectify this information disorder phenomenon. It is particularly important to organize this information quickly and accurately, to explore the value of these text information, and to filter out useless information in the era of “big data”. At the same time, many natural language processing technologies are also based on text classification technology for data mining. Only with the basic processing of text classification can information be more convenient to manage, retrieve, filter and understand. This paper first introduces the development process of NLP, and then studies the NLP model architectures such as word2vec, LSTM, text CNN, etc., and then builds LSTM and text CNN models on small sample text data for training, completes text classification, and finally analyzes the experimental results. Through the experimental results, the loss value, accuracy rate and mean absolute error of the model are calculated to find the best text topic classification model.
Research on the anti-UAV distributed system for airports : YOLOv5-based auto-targeting device

The illegal intrusion of drones into airports poses a constant threat to public safety, and existing anti-drone technologies suffer from problems like blind detection zones and target loss. To address the shortcomings of existing methods, this paper proposes a distributed anti-drone system based on YOLOv5. Combined with the characteristics of airport defense UAVs intrusion scenarios, the system implements functions such as automatically targeting and releasing jamming signals to intercept illegal UAVs. In this paper, the YOLO algorithm is used to optimize the system’s detection of drones. The mechanical structure is used to achieve automatic targeting, effectively improving detection accuracy. The distributed cluster deployment is used to solve the defects of detection blind area and target loss. This paper provides a deployment idea for airport measures against lightweight UAV equipment through experimental validation, which provides theoretical guidance for future countermeasures against UAVs. The technology can be extended to railway stations and other infrastructures to ensure public safety jointly.
Research on Prediction of Landslide Displacement Based on BP Neural Network and D-S Evidence Theory

Landslides are the most frequent disasters in nature and cause the greatest losses. Under the unpredictability and multi-factor influence of landslides, a suitable prediction model is the key to predicting landslides. In order to predict the displacement of the landslide, the displacement data of Zigui County were used to compare the displacement data of the monitoring points, and the ones with large deformation variables and many steps were selected for analysis. The cumulative displacement of landslide is divided into two displacements by a moving average method: trend displacement and periodic displacement. Prediction method: The cubic polynomial function predicts the displacement of the trend item, and the DS evidence theory integrates the prediction results of the BP neural network to predict the displacement of the periodic item. Finally, the predicted displacement of each sub-item is superimposed and compared with the actual accumulated amount. The results show that the predicted value of the BP-DS model is closer to the actual value, the error is smaller, and the model predicts the displacement with high feasibility and accuracy.
Smart Contract Vulnerability Detection Based on Deep and Cross Network

Smart contract is the key core part of the blockchain’s operation, which has the feature that it cannot be changed after deploying, resulting in the fact that the vulnerabilities associated with smart contracts cannot be fixed. Currently, deep learning has the best performance for detecting smart contract vulnerabilities. However, the common deep learning method treats the smart contract source code as a text sequence, ignoring the running logic and data flow of the program, and can not extract the constructed features. In this paper, a modular vulnerability detection model Link-DC is proposed. the model takes the contract graph constructed by smart contracts and the pattern features defined by expert knowledge as the original data input to capture richer feature information through stitching. Then, the deep and cross network is adopted to construct low-dimensional and sparse features into high-dimensional nonlinear features, thus improving the performance and efficiency of detection. Extensive experiments are conducted on smart contracts with vulnerabilities on the Ether and VNTChain platforms. The experiment results show that the high detection accuracy performance of the Link-DC model is obtained, where the accuracy of detection of re-entry vulnerability, timestamp-dependent vulnerability, and infinite loop vulnerability reaches 94.37%, 92.11%, and 85.29%, respectively.
Face forgery detection with a fused attention mechanism

In recent years, the technology of forged faces has become more and more sophisticated, and the human eye cannot even distinguish these forged products. The fake face images or videos generated by this series of technologies are widely disseminated on the Internet, causing a serious impact on society, thus drawing attention to DeepFake detection, and more research is also inclined to this, but The current research has the problem that the extracted artifact features are relatively single, which leads to the relatively low performance of the artifact detection algorithm. To solve the limitations of the existing methods, the DeepFake detection method fused with attention mechanism is proposed, which extracts the global and local features of the face respectively. Artifact features are found in multiple regions of the face. The method is trained on the FaceForensics&#x002B;&#x002B; dataset, and the detection accuracy is improved in different network structures.
Research on Human Pose Recognition Based on Skeletal Information

Human pose recognition is a research hotspot in the field of human-computer interaction and computer vision, and human pose recognition based on skeletal information has important research values. Previous pose recognition is based on skeletal information only extracted feature information from the original skeleton. Aiming at the problem of single extracted information and insufficient extracted features, a dual-flow convolutional neural network action recognition method combined with attention mechanism was proposed. The method directly takes the bone information as input and extracts the spatial and temporal features of the bone sequence respectively. Then, the two features are fused, the local feature information is highlighted through the attention block and finally, the recognition is carried out. Experiments are carried out on a public NTU RGB-D dataset, and the results show that the proposed method can improve the action recognition rate.
How are solidity smart contracts tested in open source projects?

Smart contracts are self-executing programs that are stored on the blockchain. Once a smart contract is compiled and deployed on the blockchain, it cannot be modified. Therefore, having a bug-free smart contract is vital. To ensure a bug-free smart contract, it must be tested thoroughly. However, little is known about how developers test smart contracts in practice. Our study explores 139 open source smart contract projects that are written in Solidity to investigate the state of smart contract testing from three dimensions: (1) the developers working on the tests, (2) the used testing frameworks and testnets and (3) the type of tests that are conducted. We found that mostly core developers of a project are responsible for testing the contracts. Second, developers typically use only functional testing frameworks to test a smart contract, with Truffle being the most popular one. Finally, our results show that functional testing is conducted in most of the studied projects (93%), security testing is only performed in a few projects (9.4%) and traditional performance testing is conducted in none. In addition, we found 34 projects that mentioned or published external audit reports.