Reviewerly/exHarmony huggingface dataset:
Smart Meet — Facial Recognition-based Conferencing Platform

The face of a person is his uniqueness or identity. Along with textual data like names and identification numbers, the physical features of one&#x0027;s face are also a very efficient way to identify and preserve individuality. This feature improved accuracy in identifying and distinguishing between specific individuals when utilized with other identification labels. This paper aims to provide a conferencing platform and attendance marking with the help of facial recognition. The traditional method of calling names to mark attendance causes various issues in online classes. The inclusion of facial recognition and other monitoring methods ensures a more accurate and efficient way for attendance marking. In this work, Computer Vision techniques for video monitoring purposes, login tracking, and other features for better and more efficient utility.
Satellite Image Segmentation using Modified U-Net Convolutional Networks

The object detection in satellite imagery is a primary and elaborate one receiving lot of interest in latest years and performs an essential function for wide range of applications. After the massive fulfillment of Deep learning techniques in computer imaginative and prescient discipline, they&#x0027;re presently being studied in the context of satellite imagery for unique functions like object identification, object tracking, object classification, semantic segmentation of aerial/satellite images. Although diverse assessment research associated with object detection from satellite/aerial imagery are carried out, this observation provides an assessment of the latest development in the discipline of object detection from satellite imagery with the use of deep learning. This paper elaborates the detection of roads, buildings, solar panels and vehicles using Modified U-Net Convolutional networks and achieves more accuracy compared to the previous ones.
Accurate Measurement and Evaluation Methodology of Vehicle-Body Symmetry Plane Verified by Absolute Benchmark

Accurate measurement of symmetry plane is very important to the research field of industrial detection. A solution model of the symmetry plane of vehicle body is constructed by the improved expectation maximization (IEM) method. The coarse-to-fine process and the M-neighbourhood method are developed to achieve the parallel computing process and enhance the detection efficiency, according to the vehicle point cloud. In order to verify the measurement accuracy, a 3D point-cloud dataset is established with different vehicle bodies. As the vehicle bodies in the dataset derive from program models, the symmetry plane is known in the modeling process. An evaluation method with the projection angles for the vehicle body symmetry plane is described for the wheel alignment measurement benchmark. The method is further compared with other methods, on the basis of the vehicle dataset and evaluation indicators. Moreover, the physical, absolute benchmark of the symmetry plane, which is generated from the high precision rotation-table of the verification instrument, conducts the experimental verification of the solution method of the symmetry plane.
Memory Efficient Binary Convolutional Neural Networks on Microcontrollers

Binarization is a machine learning optimization for limited resource devices that has achieved significant results in edge applications. As microcontrollers are the mainstream platform for field applications in industry, this article investigates memory efficient deployment of binary neural networks (BNNs) to such devices. To this end, we developed a library of binarized NN layers in platform-independent C language. The implementation optimizes the memory footprint by adapting the number of filters and neurons for each layer to the C language specifications (8 bit addressable spacing and 32 bit floating points). We tested the library by implementing a BNN for robotic touch classification on a Cortex-M7 microcontroller. Experimental results show the validity of the proposed hardware and software architecture, with improvements over the state of the art in robotic touch classification in terms of accuracy (+2%), memory footprint (+15%), and latency (+25%).
Applying Value-Based Deep Reinforcement Learning on KPI Time Series Anomaly Detection

Time series anomaly detection has become more critical with the rapid development of network technology, especially in cloud monitoring. We focus on applying deep reinforcement learning (DRL) in this question. It is not feasible to simply use the traditional value-based DRL method because DRL cannot accurately capture important time information in time series. Most of the existing methods resort to the RNN mechanism, which in turn brings about the problem of sequence learning. In this paper, we conduct progressive research work on applying value-based DRL in time series anomaly detection. Firstly, because of the poor performance of traditional DQN, we propose an improved DQN-D method, whose performance is improved by 62% compared with DQN. Second, for RNN-based DRL, we propose a method based on improved experience replay pool (DRQN) to make up for the shortcomings of existing work and achieve excellent performance. Finally, we propose a Transformer-based DRL anomaly detection method to verify the effectiveness of the Transformer structure. Experimental results show that our DQN-D can obtain performance close to RNN-based DRL, DRQN and DTQN perform well on the dataset, and all methods are proven effective.
Fractal pyramid low-light image enhancement network with illumination information

Low-light images suffer from many problems, including low contrast, low brightness, color distortion, blurred details, and noise, which adversely affect the performance of many advanced computer vision tasks. There have been a variety of deep-learning-based methods used to enhance low-light images in recent years. These methods, however, fail to calculate the illumination information and neglect the relationship between multi-scale features and contextual information, which lead to not only poor model generalization but also poor color and details enhancement. To address these concerns, we propose a two-stage low-light image enhancement network called the fractal pyramid network with illumination information (FPN-IL). On the one hand, we use a code network added spatial channel attention mechanism to extract the lighting information in case of uneven exposure and overexposure. On the other hand, we combine the fractal and pyramid networks to construct a new coding method. By having multiple processing paths for information, the FPN-IL is able to make full use of contextual information and interactions of features at different scales. Thus, the image’s details could be abundant. The results demonstrate the advantages of our method compared with other methods, from both qualitative and quantitative perspectives.
A Study on Sentiment Analysis for Smart Tourism

Sentiment analysis plays an indispensable role to help understand people&#x2019;s opinions automatically based on their reviews. Existing research on sentiment analysis mainly focuses on film reviews, e-commerce reviews and other fields. These work cannot be applied to analyze the sentiment of travel reviews directly because the mainstream commodity review dataset is richer and more regular than that of travel review dataset. More specifically, the special characteristic of travel reviews makes existing solutions fail to achieve satisfactory results. To address this issue, we first construct a travel review data set for sentiment analysis. Then, we conduct a systematic study to investigate and compare the factors that may affect the accuracy of sentiment analysis for travel reviews. Based on the study findings, we design a lightweight Glove-BiLSTM-CNN model and BERT-BiLSTM-CNN to analyze the sentiment for travel reviews. Experimental results show that our proposed models outperform the baseline solutions.
Bring Trust to Edge: Secure and Decentralized IoT Framework with BFT and Permissioned Blockchain

While our society accelerates its transition to the Internet of Things, billions of IoT devices are now linked to the network. While these gadgets provide enormous convenience, they generate a large amount of data that has already beyond the network’s capacity. To make matters worse, the data acquired by sensors on such IoT devices also include sensitive user data that must be appropriately treated. At the moment, the answer is to provide hub services for data storage in data centers. However, when data is housed in a centralized data center, data owners lose control of the data, since data centers are centralized solutions that rely on data owners’ faith in the service provider. In addition, edge computing enables edge devices to collect, analyze, and act closer to the data source, the challenge of data privacy near the edge is also a tough nut to crack.A large number of user information leakage both for IoT hub and edge made the system untrusted all along. Accordingly, building a decentralized IoT system near the edge and bringing real trust to the edge is indispensable and significant. To eliminate the need for a centralized data hub, we present a prototype of a unique, secure, and decentralized IoT framework called Reja, which is built on a permissioned Blockchain and an intrusion-tolerant messaging system ChiosEdge, and the critical components of ChiosEdge are reliable broadcast and BFT consensus. We evaluated the latency and throughput of Reja and its sub-module ChiosEdge.
Attribute Inference Based on User Similarity and Random Walk

With the wide application of social media, attribute inference based on user behavior has an important application value in personalized recommendation, psychological diagnosis and social investigation. Most of the current researches on attribute inference are to directly build a heterogeneous information network between users and attributes, and use the network structure and user behavior to infer attributes, while ignoring the similarity between users. In order to solve the above problems, this paper proposes an algorithm for attribute inference based on user similarity and random walk (USRW), which uses user-based collaborative filtering and random walk to calculate the score of target user for attributes that they do not own by using user similarity and network structure respectively, and then combines the two in a weighted manner for attribute inference. In this paper, the proposed algorithm is tested on the public dataset Deezer Social Networks and compared with random walk and user-based collaborative filtering algorithms. The experimental results show that attribute inference based on user similarity and random walk has higher inference performance than random walk and user-based collaborative filtering algorithms.
Performance and Revenue Analysis of Hybrid Cloud Federations with QoS Requirements

Hybrid cloud architectures, where private clouds or data centers forward part of their workload to public cloud providers to satisfy quality of service (QoS) requirements, are increasingly common due to the availability of on-demand cloud resources that can be provisioned automatically through programming APIs. In this paper, we analyze performance and revenue in federations of hybrid clouds, where private clouds agree to share part of their local computing resources with other members of the federation. Through resource sharing, underprovisioned members can save on public cloud costs, while overprovisioned members can put their idle resources to work. To reward all hybrid clouds for their contributions (computing resources or workload), public cloud savings due to the federation are distributed among members according to Shapley value.We model this cloud architecture with a continuous-time Markov chain and prove that, if all hybrid clouds have the same QoS requirements, their profits are maximized when they join the federation and share all resources. We also show that this result does not hold when hybrid clouds have different QoS requirements, and we provide a solution to evaluate profit for different resource sharing decisions. Finally, our experimental evaluation compares the distribution of public cloud savings according to Shapley value with alternative approaches, illustrating its ability to discourage free riders of the federation.
Fragment-Based Service Choreographies

In a networked economy, business partners provide services to each other in a dynamic way. These services are enacted through business processes and interact without a central entity, i.e., they can be represented by a choreography. It is common that one service can be provided by a set of alternative partners. In this setting, each service provider must adhere to the behavioral constraints imposed by the choreography. To allow for the flexible allocation of service providers, this paper uses a fragment-based approach. The resulting model can be fitted at run-time to individual choreographies. We define formal execution semantics, allowing to verify properties, such as correctness and compatibility.
Improve the performance of CenterNet through hybrid attention mechanism CBAM

The attention mechanism is one of the research hotspots in deep learning. The attention mechanism in computer vision aims to make the network ignore irrelevant information and pay more attention to the practical knowledge of the critical point, which has the advantages of fewer parameters, fast calculation, and sound effects. In this paper, we introduce the mixed attention mechanism (CBAM) into the CenterNet object detection model to improve the performance of network detection on small data sets. At the same time, we used the ReLU6 activation function in the model to ensure that the model could be used in low-precision equipment. We use four small data sets extracted from COCO2017 to verify the model's validity, and the experimental results show that our improved model has better detection performance than the original model on small data sets.
GyroidOS

Separation of privilege domains is crucial when building secure system architectures for Cyber-Physical systems. The bar for a successful attack can be raised significantly and the consequences of an attack can be contained. As Cyber-physical systems often comprise devices with limited resources, container virtualization provides an ideal base technology to construct secure architectures for CPS. However, the provided isolation guarantees entirely depend on one, shared kernel. Therefore, it is crucial to protect this kernel against attacks. Recent research proposed solutions to this issue. However, these approaches incurred performance penalties hindering their practical applicability. Therefore, we created GyroidOS, an architecture to shrink the kernel attack surface available to an attacker inside a container. To achieve this, we exploit redundancy in the Linux syscall interface to restrict access to syscalls without reducing the functionality available to user space binaries. Thereby, we aim to achieve maximum compatibility and applicability in real world scenarios. At the same time, GyroidOS aims to minimize the imposed performance penalty to avoid performance-related issues in the applicability of which previous approaches suffered.
Accurate Locating and Recognizing of ID Card Information

As an important identification certificate for citizens, ID card plays a significant role in daily life and its information has found its way into almost every aspect. However, traditional ways tend to adopt manual input, which is not only time-consuming and labor-intensive, but also expensive as well as inaccuracy. In this paper, we proposed a novel algorithm to locate and recognize ID card information, in which several fresh strategies are presented to rectify image, detect boundary, and locate information, respectively. To solve the problem of image rotating, the image is rectified by searching the best rotating angle that can lead to the maximum corner point projection peak. Meanwhile, the boundary of ID card is detected by finding the best lines in the predicted boundary area based on the deviation between the predicted boundary and the detected boundary, and the position of information is located by incorporating the prior information and the location relation between the key information. Experimental results show that the proposed algorithm can achieve a state-of-the-art effect for recognizing ID card’s information.
A privacy preserving data aggregation and query for metro passenger flow via mobile crowdsensing

Summary With the popularity of mobile Internet networks and mobile terminal equipment, mobile crowdsensing (MCS) has become a popular and economical way of data collection, sharing, and query services. However, the existing privacy‐preserving query schemes MCS‐based less consider the sensing user's privacy leakage at data aggregation phase and querier's privacy leakage at query service phase simultaneously. In this article, we propose a novel privacy‐preserving aggregation and query scheme applied in metro passenger flow via MCS, which achieves the metro passenger flow acquisition and query service and protects the sensing user's identity and location privacy as well as the querier's query privacy. We exploit Paillier cryptosystem and pseudonym mechanism to prevent the privacy leakage, and utilize secure kNN algorithm to protect the query privacy. Meanwhile, we achieve the sensing user accountability based on digital signature algorithm and credible management. The privacy analysis demonstrates that our scheme satisfies the privacy‐preserving and security requirements. The performance evaluation and experimental result show the efficiency of our scheme in practice.
Inoculating the University R&amp;D Enterprise: How RISC can strengthen post-COVID-19 research integrity and global supply chains

Abstract The COVID-19 pandemic has underscored emerging vulnerabilities in the US research and development (R&amp;D) ecosystem. While an open and collaborative environment has been essential for advancing R&amp;D, this approach exposes university-based R&amp;D to a variety of security threats including state-supported efforts, attacks by malicious actors, and insufficient internal mitigation. As the pandemic led to more remote work and online collaboration, the incidence of exploitation has expanded. Increased security measures are needed to insulate and protect the R&amp;D ecosystem, and US innovation more broadly, while maintaining the fundamental qualities that have contributed to its historical success. In this article, we present the Research Integrity Security Certification (RISC) framework. This concept preserves the autonomy of the US higher education system while also suggesting a mechanism whose effect would be a general enhancement of the security of the US university R&amp;D enterprise with minimal additional state involvement. Much of the work in the proposed model is done by market mechanisms and self-interested microeconomic calculations that generate beneficial aggregate effects. The RISC framework modernizes the university R&amp;D enterprise while strengthening it to operate in this evolving security environment.
Implementing and morphing Boolean gates with adaptive synchronization: The case of spiking neurons

Boolean logic is the paradigm through which modern computation is performed in silica. When nonlinear dynamical systems are interacting in a directed graph, we show that computation abilities emerge spontaneously from adaptive synchronization, which actually can emulate Boolean logic. Precisely, we demonstrate that a single dynamical unit, a spiking neuron modeled by the Hodgkin-Huxley model, can be used as the basic computational unit for realizing all the 16 Boolean logical gates with two inputs and one output, when it is coupled adaptively in a way that depends on the synchronization level between the two input signals. This is realized by means of a set of parameters, whose tuning offers even the possibility of constructing a morphing gate, i.e., a logical gate able to switch efficiently from one to another of such 16 Boolean gates. Extensive simulations demonstrate the efficiency and the accuracy of the proposed computational paradigm.
Construction of the Human-Computer Interaction Model of Hybrid Course Based on Machine Learning

The massive open online course (MOOC) is a large-scale open online course teaching method with many advantages such as abundant resources, open access, social interaction, and learning freedom. However, there are also flaws such as low completion rate, lack of personalized consultation, mechanical inspection, and unity. In order to resolve these shortcomings of MOOC, this study proposes a hybrid course model consisting of MOOC and small private online courses (SPOC) based on machine learning. This integrates deterministic rules into our machine learning pipeline in a variety of ways, gradually adding rules as data preprocessing steps and then using object-oriented programming (OOP) to generate novel ML model classes. Finally, it includes data in all deterministic rules through a hybrid model so that we can train it like any other machine learning model. Through experimental analysis, it can be observed that the MOOC + SPOC hybrid teaching mode can effectively integrate MOOC, SPOC, and physical classrooms and make full use of the advantages of the three to provide learners with a unified interactive interface for implementation.
Concurrent efficient entanglement routing for quantum wireless networks

Due to the unconditional security of quantum communication and flexible and economical implementation of wireless communication, quantum wireless networks (QWNs) have attracted wide attention. Different from fiber-based or satellite-based quantum communication, quantum wireless networks have the characteristics of high dynamics, limited node energy, and quantum resource competition. We have found that the existing quantum wireless network routing protocols suffer from path collision, high energy consumption, and low utilization of quantum resources. In this paper, a more practical and efficient entanglement routing protocol, which can find contention-free paths for concurrent source destination pairs, is proposed. Simulation results show that the proposed protocol can not only greatly improve the throughput and success rate of the network, but also significantly prolong the network lifetime. The research developed by this work may stimulate more investigations of entanglement routing problems in QWNs.
Malcertificate: Research and Implementation of a Malicious Certificate Detection Algorithm Based on GCN

Encryption is widely used to ensure the security and confidentiality of information. Because people trust in encryption technology, a series of attack methods based on certificates have been derived. Malicious certificates protect many malicious behaviors and threaten data security. To counter this threat, machine learning algorithms are widely used in malicious certificate detection. However, the detection efficiency of such algorithms largely depends on whether the extracted features can effectively represent the data. In contrast, graph convolutional networks (GCNs) can automatically extract useful features. GCNs are powerful at fitting graph data, which can improve the effectiveness of learning systems by efficiently embedding prior knowledge in an end-to-end manner. In this paper, we propose an algorithm for detecting malicious digital certificates with GCNs. Firstly, we transform the digital certificate dataset with pem document structure into a corpus of graph structure based on attribute co-occurrence and document attribute relations. Then, we put the graph structure certificate dataset into a GCN for training. The results of the experiment show that GCN is very effective in certificate classification and outperforms traditional machine learning algorithms and extant neural network algorithms. The accuracy of our algorithm to detect malicious certificates is 97.41%. This shows that our algorithm is very effective.