Reviewerly/exHarmony huggingface dataset:
Web privacy: a Formal Adversarial Model for Query Obfuscation

The queries we perform, the searches we make, and the websites we visit – this sensitive data is collected at scale by companies as part of the services they provide. Query obfuscation, intertwining the genuine queries of the user with artificial queries, has been proposed as a solution to protect the privacy of individuals on the web.We here present a formal model and formulate through attack models three privacy requirements for obfuscators: (1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">indistinguishability</i> , that the user query should be hard to identify; (2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coverage</i> , that its topic should be hard to identify; and (3) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">imprecision</i> , that the query should still be hard to identify for an attacker with additional auxiliary information. The latter is needed to make the former two guarantees “future-proof”. Using our framework, we derive two important results for obfuscators. First, we show that indistinguishability imposes strong bounds on the coverage and imprecision achievable by an obfuscator. Second, we prove an important tradeoff between coverage and imprecision, which inherently limits the strength and robustness of the privacy guarantees that an obfuscator can provide. We then introduce a family of obfuscators with provable indistinguishability guarantees, which we call <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> –ball obfuscators, and show, for a range of parameter values, the achievable coverage and imprecision. We show empirically that our theoretical tradeoff holds, and that its bound is not tight in practice: even in a simple idealized setting, there is a significant gap between practical coverage and imprecision guarantees, and the optimal bounds. While obfuscators have proven popular with the general public, all obfuscators currently available provide adhoc guarantees, and have been shown to be vulnerable to attacks, putting the data of users at risk. We hope this work to be a first step towards a robust evaluation of the properties of query obfuscators and the development of principled obfuscators.
Design of trajectory planning and 3D modeling for crane inspection based on UAV

This paper designs an efficient inspection method of trajectory planning and 3D modeling for crane based on Unmanned Aerial Vehicle (UAV). The working environment of crane is harsh and risky. On the other side, the UAV system has a good convenience to avoid obstacle and fly over dangerous environment. the application of the remote sensing mapping technology of UAV in crane has been a potential future development, which will greatly reduce the workload of manual inspection and improve work efficiency, even can complete the project measurement in some inaccessible location. With this Compounding technology of trajectory planning and 3D modeling, it is much easier to find out the defects of steel structure in crane, such as weld and crack, etc. the 3D modeling process for unstructured working environment will be conducive to realize the intelligent inspection functions of UAV, including autonomous flight, trajectory planning, and obstacle avoidance. Finally a field test results show this approach can promote the safety and work efficiency of inspection in crane.
Resource Allocation of IoT systems Integrated with Blockchain and Mobile Edge Computing

With the development of the Internet of Things (IoT), IoT devices have been widely applied into several fields to collect and transmit data. However, it is crucial to ensure to the security of the collected data. On the other hand, computing resources of IoT devices have obvious constraint. Blockchain and Mobile Edge Computing (MEC) can significantly improved the security of the IoT system and the efficiency of the consensus process of IoT devices. However, they also bring a lot of energy consumption and computational overhead. Reasonable allocation of computational resources is an effective method to reduce the energy consumption and the computational overhead. The resource allocation problem of the IoT system supported by blockchain and MEC can be formulated as an Markov Decision Process (MDP). In the existing studies, Deep Q-Network (DQN)-based approaches are adopted to optimize the energy consumption and the computational overhead. However, as the dimensions of the actions become larger, the action space of DQN-based approaches will have scalability limitations. Therefore, we propose the Branching Dueling Q-Network Resource Allocation (BDQ-RA) algorithm to address the problem of scalability limitations. In this article, we consider the profit between the earnings of computational tasks and the weight cost as the reward. Simulation results show that our algorithm can reduce the action space to one ninth and improve the reward about 12% compared with DQN solutions.
Research on incomplete target detection algorithm based on improved YOLOV3

As one of the key technologies of unmanned vehicles, target detection is prone to missed detections and false detections due to occlusion or incomplete targets. at the same time, the detection accuracy is low. To address this issue, this paper proposes a novel improved YOLOv3 network for real-time high-accuracy pedestrian incomplete object detection. First, on the basis of Darknet53, three attention modules(SENet, CBAM, and ECA) are integrated, and the number of network layers is simplified to build a new network structure with small parameters and high precision, which improves the recognition ability of the algorithm. Secondly, at the neck of YOLOv3, a spatial pyramid convolution pooling module is used to enhance the deep feature extraction capability of the network at a minimal computational cost. Finally, the CloU loss function is used as the bounding box regression loss function of the network, which improves the detection accuracy of the network for the target. Experimental results demonstrate the effectiveness of our network in pedestrian incomplete object detection.
Adaptive 3D Video Parallax Generation Based on Neural Radiation Field

Stereo images generated by existing autostereoscopic transformation methods usually have many problems, such as distortion of the occluded region, poor visual comfort, etc. Therefore, we propose a Neural Radiation Field based framework for adaptive generation of parallax for the 3D video to achieve steerable high-quality 3D video. Firstly, we introduced the NeRF with bidirectional flow in the time domain to generate the dynamic video field with a new perspective, thus that could accurately and efficiently predict image information located in occluded areas. Secondly, an adaptive and accurate parallax control method was proposed, which can adaptively calculate ideal parallax according to the viewing conditions and the video scene characteristics, to generate stereoscopic video with remarkable stereoscopic effects and comfortable vision. Experimental results show that our method could generate high-quality 3D videos with a lower visual fatigue index and 3D sense.
Application of quantum algorithms in secret keys

At present, quantum algorithm is a development direction of the early quantum algorithm in thinking. They are generally considered to be largely theoretical because the problems they solve are almost useless. In this paper, the applications of quantum algorithms in quantum key distribution, error correction, authentication and other fields are analyzed. Simon quantum algorithm benefits from the parallelism of quantum algorithms, and its fast finding function period property is used to construct a variety of attack forms, such as discrimination attack, key recovery attack, sliding attack, quantum forgery attack and so on. Simon algorithm uses register conversion to calculate the period in the process of key authentication, and applies this periodic feature to the interrogator in the process of key authentication, so as to achieve accurate key authentication.
Research on Improved AffNet Image Feature Matching Algorithm

Aiming at the disadvantages of the commonly used local feature matching algorithms, which are less stable and rely on manual production of descriptors, this paper proposes a local feature matching algorithm based on deep learning. In order to better protect the image edge and detail information, a nonlinear filtering algorithm is used to construct a nonlinear scale space, which can effectively increase the stability of feature point detection and extraction compared with a Gaussian scale space. In order to make deep learning with better spatial transformation ability, STN spatial transformation convolutional network is added to the AffNet model, which can effectively prevent the information loss caused by spatial transformation. The proposed model is trained in the HPatch dataset, and the ability of the proposed algorithm in anti-affine transformation, illumination transformation, scale transformation, etc. is judged with the Oxford dataset. The proposed algorithm can be widely used in image stitching and other fields.
AHash Time Lock Based on Double Encryption Algorithm

With the rapid development of blockchain technology in recent years, many blockchain networks suitable for different types and scenarios have been born at home and abroad. Due to the characteristics of blockchain independence and network heterogeneity, there has been a scenario where data and assets cannot be exchanged and transferred between different blockchain networks, forming many value islands. Blockchain cross chain technology is an important technical means to realize the data circulation and value exchange between chains. As a mainstream cross chain technology, hash time lock (HTCL) technology originally came from Bitcoin's lightning network, which places transactions under the chain, greatly improving the speed of transactions. However, this algorithm has problems such as low security and transaction congestion in actual scenarios, This paper proposes a hash time lock based on dual encryption technology to ensure the security of the hash lock in the transmission process and effectively prevent the possibility of being intercepted and tampered with.
Research on the Construction of English Teaching Modules Based on Computer Intelligence

Based on the teaching information management platform and the autonomous English learning monitoring system, this paper collects information related to students' English learning for normalization and missing value supplementation. In this paper, fuzzy clustering is innovatively used to obtain the association rules between college students' English learning information, and the fuzzy clustering and connection operations are reduced by deleting and comparing strategies to improve the computing efficiency of learning effect analysis. The research method shows that the results obtained by the data mining algorithm are helpful for teachers to diagnose teaching problems and build a characteristic teaching curriculum system.
Optimization of Federated Learning Communications with Heterogeneous Quantization

The rapid development of machine learning in the field of artificial intelligence benefits from a large amount of training data. Due to the problems of data fragmentation and data isolation, federated learning was proposed. However, there is a communication bottleneck in the learning process of federated learning. To solve this problem, this paper adopts the method of quantization to optimize the communication of federated learning and quantifies features with different accuracy according to the feature importance. We compare the communication overhead between the communication optimization method and the non-communication optimization method, and give a theoretical explanation based on the scenario of detecting fraud in bank credit card transactions.
The Effect of Differents Loss Function in Person Re-identification

Person Re-identification (ReID), under the same or different cameras, judges whether two or more pictures are the same person through feature extraction and match of images of pedestrian detection box. In the past period of time, a lot of progress has been made in the field of pedestrian reidentification, and the recognition rate has reached a very high level. One important reason is that people re-examined the loss function, proposed various variants, and the effect has been greatly improved. In this paper, two different loss functions are used for comparison, and the influence of different loss functions on pedestrian re-recognition results is analyzed through the comparison results.
Research on Intelligent Call Software V1.0 System Based on Computer Artificial AI Technology

This paper designs the INtess-intelligent call software V1.0 system based on computer AI technology. The system provides functions such as call management, work order management, online customer service, intelligent customer service, intelligent quality inspection, report management, and system management. This paper designs and analyzes the simulation development environment and software development process of the system, and improves the design in terms of architecture layering and business content for the problems that appear after the system is deployed in the pilot. After being put into use in the pilot, it was found that the traffic application can be integrated with different brands of traffic platforms and intelligent engine components used by the IT services of various units of the head office. The system supports the networking configuration and application of the IT service call platform of each unit of the head office and satisfies the transparent access, use authority control and access control among enterprise users.
Color Image Encryption Using S-Box Based on SHA-256 and Chaos Theory

Cryptography files can help prevent hackers from stealing encrypted information. On the other hand, current systems pose a high threat of security breaches or high computing demands. The suggested solution in this paper uses a low-cost encryption algorithm to improve security during communication while reducing computation costs and addressing the issues of secure color image transfer over an unsafe link. Two types of S-Boxes are employed as keys for encrypting a color image. The first one uses a hyperchaotic map (Logistic and Tent Maps) to the SHA-256 algorithm input, while the second relies on two types of chaotic maps (Logistic and Henon maps) to produce its contents. The encryption technique has divided into two phases. The first phase involves scrambling the pixels of the color image in its three channels using a modified diagonal scan algorithm. The second phase starts with splitting the original image into blocks (256 pixels in each block) before completing the encryption stage. An XORed operation is performed between each block and one of the resulting s-boxes. The encoded image is then sent through the Arnold transform to guarantee that the values are more scattered. Using chaos to generate two different forms of s-box dramatically increased propagation performance, allowing the encryption system to survive differential assaults. The correlation, histogram, entropy, and time analysis results illustrate the proposed system's dependability, effectiveness, and safety.
Convolutional Neural Network for Performance Monitoring and Modulation Formation Recognition in Communication System

In this paper, a deep learning-based method is proposed for system damage identification and modulation format recognition in satellite communication system. Four typical damages are studied, including atmospheric channel impairments and hardware imperfections. In addition, total of ten modulation formats are generated, from low-order phase modulation to complex quadrature amplitude modulation. All the data are converted into image data and processed from the perspective of image recognition using convolutional neural network without feature engineering.
Data Cleaning Technology of Substation Internet of Things Based on Time Series Association

The substation Internet of Things (iot) collects massive data in real time through various sensors, and a large amount of redundant data will seriously affect the data processing efficiency of the substation iot. In this paper, a data cleaning strategy based on association rule analysis and neural network is adopted to realize the data cleaning of substation Internet of things. Firstly, through association rule mining, a mathematical model is established to measure the correlation degree between state monitoring quantities, and time series with strong correlation are found. Then, a density-based clustering algorithm is used to detect the missing values and outliers in the sequence, and a cleaning process and rules considering sequence correlation are proposed to effectively distinguish the cleanable sensor data anomaly from the equipment status anomaly. Finally, aiming at the data points that can be cleaned, the wavelet neural network model is used to predict the missing data and correct the wrong data, and the wavelet neural network parameters and combination prediction are dynamically modified to improve the cleaning efficiency and accuracy of the network.
Research and Development of Intelligent Protection Capabilities Against Internet Routing Hijacking and Leakage

With the rapid growth of the number of global network entities and interconnections, the security risks of network relationships are constantly accumulating. As the basis of network interconnection and communication, Internet routing is facing severe challenges such as insufficient online monitoring capability of large-scale routing events and lack of effective and credible verification mechanism. Major global routing security events emerge one after another, causing extensive and far-reaching impacts. To solve these problems, China Telecom studied the BGP (border gateway protocol) SDN (software defined network) controller technology to monitor the interconnection routing, constructed the global routing information database trust source integrating multi-dimensional information and developed the function of the protocol level based real-time monitoring system of Internet routing security events. Through these means, it realizes the second-level online monitoring capability of large-scale IP network Internet service routing events, forms the minute-level route leakage interception and route hijacking blocking solutions, and achieves intelligent protection capability of Internet routing security.
Colorectal Cancer Prediction Using ResNet-CNN Classification Method

The common cancers with extraordinary death rates are colorectal cancer (CRC). The determination of microsatellite instability (MSI) is imperative for the analysis of the associated disease and the determination of treatment. We were driven to investigate a model for the classification of CRC images by the outstanding presentation of the Convolutional Neural Networks (CNN). In this study we propose an enhanced CRC image clustering residual network. The pipeline proposed comprises the serial construction between the segmentation system and the classification system. The first phase segments the tumour area with the Feature Pyramid network (FPN) from the pathological image. Total widespread variation Fuzzy C-means clustering (TGVFCMS) is employed in this proposed system for segmentation purposes. The dataset used in the study included the PAIP 2020 test to the CRC MSI-H rating. The proposed result shows that the proposed ResNet provides much better outcomes compared to other Deep learning techniques.
The Use of WhatsApp in the Learning Process among Graduate Students in the University of Nairobi, Kenya

Purpose: The purpose of this study is to evaluate the use of WhatsApp in the learning process among graduate students in the University of Nairobi.&#x0D; Methodology: The study adopted a desktop methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.&#x0D; Findings: The findings revealed that there exists a contextual and methodological gap relating to the use of WhatsApp in the learning process among graduate students in the University of Nairobi. Preliminary empirical review reveals that there is no doubt that digital technology is of significance in education today. WhatsApp mobile technology is extremely valuable in this regard, as it is widely and extensively employed in online university education. It also provides students with more options and possibilities in the context of online learning. Almost everything in the world has gone digital.&#x0D; Unique Contribution to Theory, Practice and Policy: The Social Constructivism theory and Activity theory may be used to anchor future studies on the use of WhatsApp in the learning process among graduate students in the University of Nairobi. There is need for a quantitative survey to establish the statistical significance effect of the use of WhatsApp as a method of learning for graduate school students. There is also need for a comparative study on the effectiveness of the use of WhatsApp in learning and traditional form of learning&#x0D;
Rapid eyebox measurement for augmented reality waveguides

Eyebox performance is a fundamental factor used to characterize the overall performance of augmented reality (AR) and virtual reality (VR) eyewear. Conventional methods to map three-dimensional eyeboxes are time consuming and data intensive. Herein, we propose a method for rapid and accurate measurement of the eyebox in AR/VR displays. Our approach utilizes a lens that simulates key characteristics of the human eye, including pupil position, pupil size, and field of view, to obtain a representation of the eyewear’s performance as perceived by a human user using single image capture. By combining a minimum of two such image captures, the complete eyebox geometry of any given AR/VR eyewear can be accurately determined with a level of accuracy comparable to that of slower, traditional methods. This method can potentially be used as a new metrology standard in the display industry.
The Effects of Cyber Crime on E-Banking

This research paper is intended to raise awareness of cybercrime on our daily transaction. In the introduction we will state brief issues that occurs during these threats and how it effects banks and other monetary firms. Lastly, we will give a few briefs on important definitions of Phishing, Malware and other sorts of risky viruses. In the body we will go more into details. Firstly, we will state the ordeals of banking sector during these threats. We will explain the use of online services and how it may have risk services. A detailed methodology will be conducted on the use of artificial intelligence in the banking industry and how it may mitigate internal risks. We will explain these vigilances of Identity Theft and what harm it can cause to an individual use and even and in an industry perspective. We will go more into details in the process of Phishing on how to be aware and to avoid these threats. Case studies will be conducted with the support of the United States Federal State laws. The jury and prosecutions that have happened mentioned in detail below. Lastly in the body we will explain how we can take safety measures in avoiding these threats and how to report when it does occur to act. Lastly a brief summary will be giving a prompt explanation for the goal of this research paper.