Reviewerly/exHarmony huggingface dataset:
Forward and Backward Private Dynamic Searchable Encryption with Better Space Efficiency

Dynamic searchable symmetric encryption (SSE) provides efficient update and search operations over encrypted data, while it leaks some inconsequential information to increase search efficiency. Since the more dynamic SSE allows leakage, the easier it is to break its security, it is important to realize efficient schemes with less leakages. For that reason, most recent works on dynamic SSE focus on forward and backward privacy, which are important security notions for dynamic SSE. In particular, Sun et al. (NDSS 2021) recently proposed Aura, an efficient dynamic SSE scheme with forward and backward privacy. Although Aura provides good update and search efficiency, it requires large encrypted database. Taking into account the practical use of dynamic SSE, i.e., search systems that many users involve, the sizes of encrypted databases should be small enough. In this paper, based on Aura, we propose a new dynamic SSE scheme with forward and backward privacy, called Aura +; it provides not only efficient update and search operations but also smaller encrypted database than Aura, though Aura+ requires a few more interactions than Aura.
Vulnerabilities of Deep Learning-Driven Semantic Communications to Backdoor (Trojan) Attacks

This paper highlights vulnerabilities of deep learning-driven semantic communications to backdoor (Trojan) attacks. Semantic communications aims to convey a desired meaning while transferring information from a transmitter to its receiver. The encoder-decoder pair of an autoencoder that is represented by deep neural networks (DNNs) is trained to reconstruct signals such as images at the receiver by transmitting latent features of small size over a limited number of channel uses. In the meantime, the DNN of a semantic task classifier at the receiver is jointly trained with the autoencoder to check the meaning conveyed to the receiver. The complex decision space of the DNNs makes semantic communications susceptible to adversarial manipulations. In a backdoor (Trojan) attack, the adversary adds triggers to a small portion of training samples and changes the label to a target label. When the transfer of images is considered, the triggers can be added to the images or equivalently to the corresponding transmitted or received signals. In test time, the adversary activates these triggers by providing poisoned samples as input to the encoder (or decoder) of semantic communications. The backdoor attack can effectively change the semantic information transferred for the poisoned input samples to a target meaning. As the performance of semantic communications improves with the signal-to-noise ratio and the number of channel uses, the success of the backdoor attack increases as well. Also, increasing the Trojan ratio in training data makes the attack more successful. On the other hand, the attack is selective and its effect on the unpoisoned input samples remains small. Overall, this paper shows that the backdoor attack poses a serious threat to semantic communications and presents novel design guidelines to preserve the meaning of transferred information in the presence of backdoor attacks.
Personalized Decentralized Multi-Task Learning Over Dynamic Communication Graphs

Decentralized and federated learning algorithms face data heterogeneity as one of the biggest challenges, especially when users want to learn a specific task. Even when personalized headers are used concatenated to a shared network (PF-MTL), aggregating all the networks with a decentralized algorithm can result in performance degradation as a result of heterogeneity in the data. Our algorithm uses exchanged gradients to calculate the correlations among tasks automatically, and dynamically adjusts the communication graph to connect mutually beneficial tasks and isolate those that may negatively impact each other. This algorithm improves the learning performance and leads to faster convergence compared to the case where all clients are connected to each other regardless of their correlations. We conduct experiments on a synthetic Gaussian dataset and a large-scale celebrity attributes (CelebA) dataset. The experiment with the synthetic data illustrates that our proposed method is capable of detecting tasks that are positively and negatively correlated. Moreover, the results of the experiments with CelebA demonstrate that the proposed method may produce significantly faster training results than fully-connected networks.
Communication-Efficient Federated Learning with Channel-Aware Sparsification over Wireless Networks

Federated learning (FL) has recently emerged as a popular distributed learning paradigm since it allows collaborative training of a global machine learning model while keeping the training data of its participating workers locally. This paradigm enables the model training to harness the computing power across the network of FL and preserves the privacy of local training data. However, communication efficiency has become one of the major concerns of FL due to frequent model updates through the network, especially for devices in wireless networks that have limited communication resources. Despite that various communication-efficient compression mechanisms (e.g., quantization and sparsification) have been incorporated into FL, most of the existing studies are only concerned with resource allocation optimization given predetermined compression mechanisms, and few of them take wireless communication into consideration in the design of the compression mechanisms. In this paper, we study the impact of sparsification and wireless channels on FL performance. Specifically, we propose a channel-aware sparsification mechanism and derive a closed-form solution for communication time allocation for workers in a TDMA setting. Extensive simulations are conducted to validate the effectiveness of the proposed mechanism.
Generative Versus Discriminative Data-Driven Graph Filtering of Random Graph Signals

In this paper we consider the problem of recovering random graph signals by using graph signal processing (GSP) tools. We focus on partially-known linear settings, where one has access to data in order to cope with the missing domain knowledge in designing a graph filter for signal recovery. In this work, we formulate two main approaches for leveraging both the available domain knowledge and data for such graph filter design: 1) the GSP-generative approach, where data is used to fit the underlying linear model that determines the graph filter; and 2) the GSP-discriminative approach, where data is used to directly learn the graph filter for graph signal recovery, bypassing the need to estimate the underlying model. Then, we compare qualitatively and quantitatively these two approaches of graph filter design. Our results provide an understanding with regard to which approach is preferable in which regime. In particular, it is shown that GSP-discriminative learning reliably copes with mismatches in the available domain knowledge, since it bypasses the need to fit the underlying model. On the other hand, the model awareness of the GSP-generative approach results in its achieving a lower mean-squared error (MSE) when data is scarce. In the asymptotic region where the number of training data points approaches infinity, both approaches achieve the oracle minimum MSE estimator under the considered setting.
Artificial Neural Network Prediction to Identify Solar Energy Potential In Eastern Indonesia

The geographic location of Indonesia which climates almost entirely tropical provides exclusive potential for solar energy all through the year. This paper performs identification and prediction of solar irradiance in Eastern area of Indonesia. Modeling and estimation approach is carried out by using Artificial Neural Network (ANN) algorithm. Datasets for training and testing are highly correlated parameters from NASA climatological database for 20 years of historical data. The results of training and testing procedures in ANN show high accuracy of solar modelling and prediction. The study produces spatial mapping of solar irradiance intensity for the monthly average solar irradiance of 174 districts in Eastern Indonesia region.
Random Forest (RF) with Daubechies Wavelet and Multiple Time Lags (MTL) for Solar Irradiance Forecasting

Accurate forecasting of solar irradiance is important as the dependency of this clean energy towards weather condition may affect the efficiency of solar power plant. Various types of models has been used to forecast solar irradiance and maintain the efficiency of solar power grid but not many can provide a high accuracy forecasting. In this paper, raw information of solar irradiance, current, temperature and power are collected as input for the random forest (RF) techique. These data went through a noise elimination process before it can use as input data for training and testing procedures. Daubechies wavelet based decomposition concept has been used in filtering the data from any unwanted noise as it may increase the percentage of error. The feature extraction of multiple time lags (MTL) is used to further improve the contents of input data. The proposed method used for solar irradiance forecasting is very much needed to reduce the forecasting error and useful for maintaining the stability of generated energy and energy consumption.
Tools and Visualizations for Exploring Classification Landscapes

Neural networks and deep learning systems find the correct classification of input data by locating the corresponding local minima in the hyper-dimensional, classification landscape. An increasing number of adversarial examples have now shown that these networks sometimes find an unexpected and incorrect minimum and so make an incorrect classification. To understand those results requires a better understanding of the nature of these classification landscapes. Previous studies have explored the properties of the landscape of back propagation in training these networks. In our studies here, we explore the classification landscape of already trained networks. We present some novel procedures and analytical tools to study the classification land-scape and visualizations to meaningfully represent those results. We apply these methods to study the classification landscape in classic examples, including image classification in the MNIST data set and flower classification from numerical feature values in the Iris data set.
Method for Initial Point Positioning of V-bevel Welding Seam Based on Binocular Vision

Aiming at the positioning problem of welding the initial point in the process of welding medium-thick plate V- bevel, a method for positioning based on binocular vision is proposed in this paper. The binocular cameras are arranged in an eye-to-hand manner to capture images included the initial point of the workpiece weld, using YOLOv5 neural network to localize the area included the welding initial point. Then the image of positioned area is preprocessed, using the straight line detection method to extract the workpiece edge on the image captured by the binocular camera to find the starting point of the tack weld. Then get the data of stereo calibration the binocular camera and eye-to-hand calibration between robot and camera to determine the relationship between the picture pixel coordinate system and the robot base coordinate system. Finally, the coordinates of the starting point of the welding seam in the robot base coordinate system are calculated using the calibration results and the pixel coordinates on the starting point picture of the welding seam. After comparison with the coordinates demonstrated by the robot, the precision of the guidance method proved to be able to meet the requirements for the use of subsequent seam tracking welds.
Structure Design of Self-service Data Governance about Sea-based Measurements

Definition of Data Governance (DG) is introduced first. And the difference between data management and DG is presented for analysis. Also the parts of DG are elaborated. Then sea-based DG system and auto self-service structure are designed. Finally, the key technology and typical DG application scenarios are introduced about auto self-service platform.
Research on Security and Electronic Countermeasure of Wireless Quantum Communication System

With the successful launch of quantum communication satellite, the boom of wireless quantum research and application is rising all over the world, and the security of quantum communication has reached a new height in battlefield communication, but from an objective point of view, this is a misunderstanding of it. Based on this, this paper analyzes the structure of the quantum communication system, and on this basis, makes necessary distinctions between the security, effectiveness and reliability of the communication system. It points out that eavesdropping can still be realized by utilizing the defects of the wireless quantum communication engineering, especially the suppression and interference of wireless quantum communication transmission can greatly affect the effectiveness of communication. Finally, the electronic countermeasures method of wireless quantum communication is summarized through research, which has important reference significance for promoting the application and expansion of quantum countermeasures in electronic countermeasures.
Research on Sports Video Image Teaching System Based on Computer 3D Technology

The use of 3D technology to simulate the movement of objects, so that objects in the real environment changes. The system consists of three modules: three-dimensional simulation database, virtual simulation model of motion capture and motion technology simulation. The motion process of the backspin ball, the front spin ball and the non-spin ball is simulated numerically, and the motion law of the ball's drop point and drop point is obtained. According to different situations of players, the appropriate shooting posture is proposed. At the same time, the system uses video technology to make students familiar with NBA players, basketball tactical points, audio and other materials into a tactical demonstration of animation. The paper can effectively improve the teaching effect of basketball skill and attack training and achieve the purpose of training. The results show that the method proposed in this paper can solve the problem of turning in basketball well.
Visual Local Path Planning Based on Deep Reinforcement Learning

Traditional local path planning methods for robots are mostly designed with prior maps, which leads to poor results in navigation combined with visual SLAM. Therefore, most of the traditional robot local path planning methods are designed with prior maps, which leads to poor results in navigation combined with visual SLAM. Therefore, this paper proposes a visual local path planning strategy based on deep reinforcement learning. Firstly, A raster map of the surrounding environment was built based on visual simultaneous localization and mapping (SLAM) technology, and A* algorithm was used to plan the global path. Secondly, a local path planning strategy based on deep reinforcement learning is constructed by comprehensively considering the problems of obstacle avoidance, robot walking efficiency and pose tracking, and discrete action spaces with forward, left and right turning into basic elements are designed, as well as state Spaces based on visual observations such as color map, depth map and feature point map. Using Proximal Policy optimization (PPO) algorithm to learn and explore the best state action mapping network. The results of Habitat simulation platform show that the proposed local path planning strategy can plan an optimal or suboptimal path on the real-time map. Compared with the traditional local path planning algorithm, the average success rate is increased by 53.9%, the loss rate of pose tracking is reduced by 66.5%, and the collision rate is reduced by 30.1%.
Enterprise User Data Governance Based on Crawler and Text Similarity

Enterprise users' social unified credit code data governance often relies on ready-made third-party data for calibration, but how to carry out data governance without direct third-party data reference is the main problem to be solved in this paper. Based on the user name and social unified credit code data of power enterprise users, this paper carries out web crawler and text similarity analysis, and uses web crawler technology to obtain the social unified credit code, user name and enterprise label attribute of enterprises in "Sky Eye Search"; The text similarity analysis is used to calculate the similarity between the user name of electric power enterprise and the name of enterprise in "Sky Eye Search" to realize the data governance. Finally, this paper builds the enterprise user data governance achievement set, and deploits the data governance result set to the tag library to form the enterprise portrait, and shares it to the data supermarket to realize the sharing of results.
Improved clustering analysis algorithm for density grid data streams

An improved density grid data stream clustering analysis algorithm was proposed to cope with the characteristics of high speed, real-time and continuous real-time data stream. The traditional density grid data clustering method is difficult to popularize because of rough boundary processing, which leads to low clustering accuracy and single grid division. A mesh partitioning method with different grain sizes is proposed to make the boundary mesh and internal mesh more clear. The inner grid adopts coarse-grained clustering method, and the boundary grid adopts fine-grained clustering method. The improved density grid clustering method and the traditional method were respectively used for cluster analysis, and the results show that the proposed method has a fast clustering speed and a high clustering accuracy.
Research on road image enhancement based on histogram equalization

With the gradual development of urban traffic, there is a great demand for image enhancement technology with video surveillance as the mainstream. Therefore, it is of great scientific and economic value to study the enhancement algorithm of road images. Histogram equalization is a common method of image enhancement, which has 4 paths, which can deal with image enhancement under different environmental conditions. For pictures of road conditions that may require image enhancement, the histogram equalization of the four paths is used for image enhancement, and Python is used to compare the effect of the algorithm. Experiments show that the histogram equalization process has obvious effect on images with uneven gray distribution. And CHALE tends to work better.
Data Recovery of Power Plant Platform Based on Generative Adversarial Network

The integrity of the data gathered by the power grid platform is very important to promote the construction of the intelligent management and control platform of the power grid. However, in reality, it is almost impossible to obtain complete data because of the damage of equipment, the failure of data acquisition process, the loss of records and other reasons. In this paper, the structure of the traditional GAN is improved, and a lost data repair model of the power grid platform based on the generative adversarial network is proposed, which provides additional information for the discriminator (D) in the form of "hints" to ensure that the generator (G) generates samples according to the true underlying data distribution. Experiments show that the model can run in an unsupervised environment even the data is incomplete, and can better repair the data of the power grid platform.
Research on Data Synchronization Consistency Comparison System Based on Computer Heterogeneous Database

Aiming at the problem of heterogeneous data consistency verification, this paper proposes a verification method based on XML tools. The system determines the elements to be transformed in CIM/RDF through SAX parsing, and designs a deep search algorithm to get the mapping rule base of the classes and attributes to be transformed. Then we propose a hash retrieval algorithm based on CCA (canonical correlation Analysis) semantic consistency (CCA-SCH). In order to maintain the semantic consistency within the mode, the algorithm generates semantic models of text and image data respectively. By comparing the output results of multiple heterogeneous function executors, the abnormal detection of function executors was carried out. The system implements active defense of the routing system. The results in this paper provide a model mapping basis for heterogeneous model interaction and help to realize the identification and interaction of heterogeneous model on the information exchange bus.
Pure orientation passive positioning of UAVs in different formations and initial positions

UAV positioning plays an important role in UAV formation flight. UAV positioning requires transmitting and receiving electromagnetic signals to complete, and electromagnetic signals are subject to external interference, and in order to reduce this effect, pure azimuth passive positioning is mostly used to adjust UAV positions in fleet formation positioning. In this question, a simulation model is constructed to achieve effective positioning of UAVs. The initial position of the UAV is often deviated from the ideal state. The problem is solved by using the representation of polar coordinates and the idea of greedy algorithm, the triangulation method for positioning, the idea of optimization algorithm, the adjustment of control variables one by one, and the dynamic planning algorithm, setting the adjustment step and adjustment rules to build a simulation model. The UAVs are adjusted to a cone formation with an unknown initial position, and the position of each UAV cannot be measured directly by the unknown information of passive positioning. The distance and angle between neighboring UAVs are used to measure the position deviation of the UAVs and the symmetric position relationship in the formation, and the position of the UAVs is adjusted. Then, by optimizing the strategy, as little as possible at a time to choose the transmitter. The number of UAVs that transmit signals is adjusted to the remaining UAV positions, and the dynamic planning model is used to find a solution that minimizes the deviation of the UAVs in the adjustment process, so as to obtain the optimal solution in terms of the global situation.
Intelligent Agricultural Data Collection and Analysis System Based on Internet of Things

In recent years, with the rapid economic development, the development of emerging technologies is changing with each passing day, and the process of agricultural informatization has been further promoted. At present, the intelligent application represented by the intelligent agricultural data collection and analysis system has been widely used, and the degree of agricultural informatization has been improved. The purpose of this paper is to study the design and implementation of the smart agricultural data collection and analysis system based on the Internet of Things technology. Research and analyze the business requirements in smart agriculture, and choose the MongoDB database with the built-in GridFS file system as the best storage solution. Then, combined with the characteristics of agricultural data and the document characteristics of MongoDB, a MongoDB-based agricultural data storage model is constructed. An agricultural big data analysis algorithm based on clustering is proposed. Using this method, the massive agricultural production data generated by the agricultural Internet of Things can be analyzed, and the data of the remote monitoring terminal can be clearly obtained in the remote monitoring module. The system meets the requirements.