Reviewerly/exHarmony huggingface dataset:
Dynamic Anonymous Credential Systems with Controllable Anonymity and Unlinkability

In the paper we present novel anonymous credential schemes that provide controllable opening and linking capability. In a normal situation, signers are allowed to prove their possession of attributes of credentials anonymously and in a user-controllable way. The proof can demonstrate that certified claims hold with a desired privacy level, such as proving that age is within a certain value or a pre-defined range. However, in case of misbehaviors or for accountability, the identity of a signer can be revealed or signatures can be anonymously linked using opening and linking keys, respectively. The proposed scheme is designed to work for dynamic credential management allowing for the issuance of new credentials and proper revocation. To prove the security of the proposed anonymous credential scheme, we first define a security model to capture basic properties, anonymity, traceability, non-frameability, and controllable unlinkability. We then demonstrate that it satisfies all these properties under the standard assumption, i.e., q-strong Diffie-Hellman assumption in the random oracle model. Finally we provide a performance analysis of our scheme in terms of signature size and basic signing and verifying operations. Additionally, we present empirical results to show that the performance of our scheme is reasonably efficient.
Supervised Feature Selection Based on Sparse Representation and Mutual Information

Sparse representation can evaluate the importance of each feature from the overall structure of the data, and mutual information can evaluate the redundancy between features, utilizing the importance and the redundancy propose a supervised feature selection algorithm based on sparse representation and mutual information. Firstly, the algorithm weights 0-1 shows the relationship between the samples; then get low-dimensional embedding space which keeping the structure between sample classification by solving eigenvalue problem; thirdly, using sparse representation to evaluate the contribution degrees of each feature to keep the classification structure of inter-sample, and thus can select features which can maintain preferably the classification structure between the samples; finally, utilizing mutual information to reduce redundancy of the before feature selection results, in order to obtain the final results of the feature selection. In the results of clustering experiment in four different data sets, the method has a better feature selection effect, can achieve better results of features selection at a lower dimension ,meantime, it significantly improves the classification results.
A Knowledge Graph Preference Propagation Recommendation Algorithm Based on Introducing Relational Information

RippleNet network combines path and entity information, and is successfully applied to recommendation systems. Nevertheless, the model ignores the impact of the relationships between the entities. As a result, this paper introduces an enhanced RippleNet model that incorporates the impact of entity relationships into the preference propagation model by mapping the entity embedding into the relation vector space and integrates it into RippleNet as entity weights. And according to user historical data, preferences are propagated on the knowledge graph. The experimental findings manifest that the new approach enhances the AUC and ACC values of RippleNet, surpassing other baseline methods. These results show that our model effectively addresses the issue of the inter-entity relationship not being considered in the RippleNet network, resulting in recommendation outcomes that better align with user expectations.
Research on Image Processing System Based on Computer Artificial Intelligence Technology

This paper introduces the two-channel digital image processing technology. The system uses FPGA as its core processing unit. Infrared thermal imager and CCD camera were used for the shooting. The FPGA composed of programmable gate array realizes the pre-processing of the acquired image data and interacts with it. The video image processing of the two channels is completed, and the image features are extracted. Verilog language, built-in DSP core and NiosII processor are used to design the hardware. The experimental results show that this method has good real-time performance, good image processing effect, low cost and simple structure.
The Challenges of the XXI Century – Development of Critical Thinking among the Students

Developing critical thinking skills in students is a challenge in the 21st century education. It involves not only active searching for information, but also making connections between the acquired knowledge and experiences, but also comparing it to other research. Critical thinking helps students understand their studies and achieve high results. According to the national goals of general education and the modern paradigms of education, the aim of teaching is to form a creative, literate, informed and responsible citizen who is able to think and research as well as make decisions independently and accomplish their own achievements in creating new material, intellectual or spiritual values. Students develop these skills during the learning process. Therefore, a teacher is a key guide for the student to develop critical thinking. The perspectives and future of modern education are not only dependent on the latest technologies, but also on the people who will use them for their respective purposes. The study of the problem conducted in the Shota Meskhia Zugdidi State Teaching University can act as an important guideline.
Design and Implementation of Enterprise Information Portal Based upon Micro-service Architecture

With the introduction of micro-service architecture technology and the application of front-end and back-end separation technology, a large number of new service-based business systems or micro-applications will provide services to the outside or other business modules in the form of APIs. This research is based on Java and the open source Spring Cloud application framework for micro-service development design and framework management, using Rest API interface and Web Service technology to meet unified transmission and standard exchange, and establish an API rapid development platform to realize code-free API services. Complete the docking with the enterprise information portal, all API rapid development platform publishing services and third-party business systems that have been connected to the gateway achieve real-time monitoring and governance through API gateway technology, achieve visual operation and maintenance, and build a security assurance system.
F-ROADNET: Late Fusion Based Automotive Radar Object Detection

Road user categorization is essential for autonomous driving perception. In challenging traffic situations including unfavorable weather (such as fog, snow, and rain) and dim lighting. There are several kinds of sensors that need to be researched in order to achieve the precision and resilience that autonomous systems demand. Currently, to create a depiction of the environment surrounding the vehicle, principally cameras and laser scanners (LiDAR) are commissioned. Despite their enticing qualities, Radar sensors are currently underutilized for autonomous driving, even though, they have been employed in the automobile industry for a long time. Radar’s ability to measure the relative speed of obstacles and to operate even in adverse weather conditions makes it a front line contender for road user detection. This study proposes F-ROADNET, a multi-object classification method for vulnerable road users based on raw Radar data. The model is trained on Range Angle and Range Doppler maps based on a late fusion architecture. F-ROADNET has a detection accuracy of 99.01%, precision of 99.3% and recall of 99% on the CARRADA dataset and detection accuracy of 91.62%, precision of 87.2% and recall of 90.2% on the RADDet dataset. The findings exhibit that F-ROADNET outperforms established methods in terms of average precision.
Deep Kernel Estimation for Turbulent Blur Correction

Turbulent disturbance brings about the spatially varying blur in captured images. Turbulent disturbance brings about the spatially varying blur in captured images. In this paper, we proposed a deep learning method to deal with this problem. And the way we approximate the kernel is closer to the real turbulent circumstance. Experiments on both synthetic and real images show that our proposed method can effectively mitigate the turbulent blur from a single image, quantitatively and qualitatively outperforms the single Preprint submitted to Neurocomputing August 23, 2023frame based methods as well as yields comparable results to multi-frame methods.
Research on a Testing Method for Realizing Multiprotocol Communication

This paper introduces the traditional common platform COS test method - each protocol COS corresponds to one test project, analyzes the problems of low efficiency, high cost and insufficient test coverage of this method, and proposes a new test method that can realize multi-protocol communication. Through this test method, COS, a common platform for testing multi-protocol communication can be used in a test project, which not only improves the efficiency of test engineering writing, but also realizes functional interaction testing between COS of different protocols, reduces the workload of script modification caused by demand changes in the test process, flexibly responds to test engineering changes caused by protocol changes, improves test efficiency, and reduces test costs. This article details this COS test method that enables multiprotocol communication.
PV Power Prediction Based on XGBoost Algorithm

With the rapid development of renewable energy, the promotion and deployment of photovoltaic (PV) power stations are gradually advancing. However, the output of photovoltaic power generation is greatly affected by the meteorological condition, and the utilization of solar energy remains challenging due to its strong stochastic nature. Accurate PV power prediction is a way to solve this problem. Firstly, the paper proposes a PV power forecasting method based on XGBoost algorithm. Then, the PV power prediction model is built and the evaluation indicators of forecasting performance are proposed. Finally, a case study is used to verify the effectiveness of the proposed method.
Modulation Format Identification Based on Channel-spatial Attention Modules and Deep Learning

In this paper, a scheme is proposed where the channel and spatial attention convolutional neural networks are applied to identify modulation formats from signal constellation diagrams. According to the simulation results, it outperforms other modulation format identification (MFI) schemes in the overall identification rate. Moreover, the scheme shows a significant advantage in signal identification given low optical signal-to-noise ratios (OSNRs).
Seismic Phase Picking Using Synchrosqueezed Transform and Attention Mechanism

We developed a method using synchrosqueezed transforms (SST) and convolutional neural networks (CNN) to recognize seismic phases from earthquake waveforms. The attention mechanism has added within the CNN to improve the accuracy of seismic phase picking. This approach transforms the 1D seismic signals into a 2D time-frequency representation using a synchrosqueezed transform (SST). We use Stanford Earthquake Dataset (STEAD) to train the proposed method.
PV Scenario Generation Based on K-means Method

In the context of the rapid development of renewable energy, photovoltaic (PV) power generation has received widespread attention because of its excellent characteristics such as low cost and no pollution. However, the output of PV power is volatile and intermittent under the influence of the natural environment, so this paper studies typical representative scenarios of PV power based on clustering approach to help us better understand the distribution and characteristics of the PV power. Firstly, a scenario generation model of PV power is built and k-means clustering algorithm is used to divide PV power into different categories. Then, Silhouette Coefficient (SIL) index is proposed to get the optimal number of clusters, and the evaluation indicators are proposed to analysis the performance of scenario generation of PV power. Finally, a practical case in southwest China is simulated. Results reveal that the proposed models and approaches are effective.
Multimodal Sentiment Analysis Based on Image Captioning and Attention Mechanism

Currently, social media has become an important means for people to share their daily lives and express emotions. Influenced by the overall environment, individuals are no longer limited to single-text content, but are increasingly inclined towards conveying information through various modalities. However, existing multimodal sentiment analysis methods face several challenges.Firstly, most current methods for sentiment polarity judgment primarily rely on textual content. These models struggle to effectively associate common emotional features between text and images. For instance, regardless of the textual content inputted into the model, the resulting image features remain the same for a given image. Thus, the models fail to extract relevant features from the text to aid in better sentiment analysis.Secondly, social media data commonly exhibit inconsistencies between textual content and image descriptions, making it difficult to obtain visually sensitive textual representations.Thirdly, most existing image feature extraction methods employ ResNet (Deep residual network) to directly fuse image features with text, lacking the ability to adaptively select and focus on key information from inputted image data.To address the aforementioned challenges in multimodal sentiment analysis, the following improvements have been made:1.Drawing on the target-oriented mBERT (TomBERT) model, which focuses on multimodal aspect-level sentiment analysis, we have designed a text-image matching module that is tailored to the experimental needs of this study. This module serves as the primary component applied in multimodal sentiment analysis.2.We have incorporated the Image Captioning with Transformers (CATR) module to facilitate the conversion of images into textual representations. This enriches the information available from the text data and resolves the issue of text-content mismatch with image descriptions.3.The improved Convolutional Block Attention Module (CBAM) was integrated to introduce the capability of adaptive selection and concentration on essential image features.In order to address the aforementioned issues, a model named Image Captioning Joint Attention Mechanism (ICAM) was designed.
Design of a garbage sorting robot based on machine vision

This paper introduces an intelligent garbage sorting robot based on K210 and STM32. The system uses the K210 camera to collect the target garbage images, and applies the deep learning algorithm YOLO model to identify and classify the garbage. At the same time, the function of obstacle positioning and obstacle avoidance is realized through radar, and the mechanical arm is used to classify the garbage into the corresponding trash can. This system has certain practical value, can provide effective support for garbage classification work, and promote the construction of smart city.
CCE-YOLOv5s: An Improved YOLOv5 Model for UAV Small Target Detection

To solve the issues of low accuracy in detecting small targets in drone aerial images and the excessive computational complexity of the detection model, this paper proposes a drone small target detection method based on an enhanced version of YOLOv5. Firstly, in an endeavor to ameliorate the issue of undetected instances of small-scale targets, this study incorporates the Convolutional Block Attention Module (CBAM) into the feature extraction framework of YOLOv5s. This augmentation enhances the capacity for extracting features from diminutive targets, leading to increased accuracy in small target detection. Secondly, in response to the issue of high computational complexity in the model, this paper has improved the CBL module by replacing regular convolutions with Coord convolutions. This modification helps reduce the model's parameters and computational complexity, resulting in a more efficient and streamlined detection process. Lastly, this paper utilizes the EIOU Loss function to further improve the accuracy and convergence rate of target detection. The amalgamation of these improvements markedly enhances the model's performance. This paper conducted tests using the DOTA dataset, and the experimental results demonstrate the effectiveness of the CCE-YOLOv5s model. It outperforms the original model with a mAp (mean Average Precision) of 80%, represents a noteworthy 1% enhancement over the original algorithm. Moreover, the training time is reduced to 0.66 hours, a remarkable 43% reduction in comparison to the original algorithm. Overall, this improved model effectively improves detection accuracy while ensuring real-time performance, and significantly improves the training speed of the model.
An Infrared Image Detection Method of Substation Equipment Combining Iresgroup Structure and Centernet

Anomalies inside the instrumentality can happen once their inner temperatures surpass their cutoff points. The normal issues concerning warm abnormalities in electrical establishments are free or unfortunate associations, uneven burdens, shortcircuits, over-burdening and breaks or deformities inside the instrumentality body. Infrared thermography (IRT) is a notable compelling device for observing the state of gear. With the warm pictures, monitors can investigate the temperature varieties of warm items to search for deficient parts. Likewise, the powerful upkeep of gear generally speaking would require the presence of well experienced and prepared work force to examine the infrared pictures and play out a precise expectation. the Iresgroup structure is proposed as a spine to additionally further develop the element learning capacity of CenterNet. In the Iresgroup structure, a superior projection alternate way and Iresnet structure are improved to control the quantity of ReLUs on the primary way, which can additionally further develop the identification precision of the model, with higher vigor. Moreover, the gathering conv structure is utilized to additionally work on the model, so the model keeps up with similar measure of boundaries while fortifying its component learning capacity.
Safety Performance Data Analysis and Early Warning of Civil Aviation Based on AHP and Entropy Method

A safety performance index system of the civil aviation industry was established to meet needs of data analysis and early warning based on the construction principle and screening method of the index system. A framework of the data analysis and early warning method system was established with consideration to data characteristics of the built safety performance index system and safety performance monitoring needs of the civil aviation industry. Special studies were carried out with respect to multi-index comprehensive early warning in the built framework. Finally, validity of the comprehensive analysis and early warning method of safety performance was verified through cases. The built method system can meet needs of safety performance analysis and prediction of civil aviation industry on different levels.
Research on Computer Network Security Protection Model Based on Neural Network

A new network security situation prediction model is established by using group search method to improve BP neural network. The method gives full play to four different behavior characteristics in group search, such as egoism, altruism, preauction and uncertainty, and selects the appropriate group to get the best weight and threshold. The BP network is initialized and its weight is assigned. The corresponding prediction results are obtained through the learning of artificial neural network. The results obtained are compared with the other two methods. The results show that this method can effectively improve the performance of the system.
New Tax System based on Lightweight Blockchain Technology

Tax records are a very important aspect for every citizen each time a resident needs to deal with financial paperwork or obtain a Tax clearance, it takes a long time due to the official letters exchanged between Authorized governmental agencies, resulting in a waste of time. And vulnerable to fraud or subject to lose, theft, or damage of tax records paper. This led to using an electronic tax record system which saves time and reduces costs these systems are susceptible to various known attacks, including denial of service, modification, and hijacking, and to secure these systems, non-fungible token (NFT) concepts utilizing the blockchain are employed which is known as secure and immutable with decentralization characteristics which provide multiple copies of the tax records and eliminate the need for official letters between Authorized governmental agencies for verification of past Tax clearance. This paper proposes an electronic Tax records system based on non-fungible tokens and a customized lightweight blockchain technology that utilizes a consensus algorithm called proof of secret sharing (PoSS) for authenticating nodes and verifying added blocks, this system is evaluated using standard blockchain with proof of work consensus algorithm evaluation parameters to demonstrate its lightweight nature, security, and reduced computational resource requirements.
Design and Analysis of a Data Computing Scenario Forecasting System Based on Generative Adversarial Networks

One of the main drawbacks of using ML and DL-based optimization techniques is that a massive amount of historical data is needed for the algorithms to converge to quasi-optimal solutions. In the DCs' scope, it is difficult, expensive, and sometimes even dangerous to gather data from all kinds of real situations, as the equipment's physical integrity may be at risk. Thus, the generation of realistic synthetic data in computing environments is crucial to enabling these optimization technologies. The goal of this paper is to create an automated system that produces near-term forecasts of globally important economic / financial time series. The system will create features from a wide variety of data sources and create forecasts using an ensemble of time series models. We will exploit distributed computing and storage technologies to build a system that is always on, evaluating new potential features and constantly updating the deployed forecasting model. We will begin with the daily spot price of crude oil but hope to expand to other important series. This paper proposes to solve these challenges by generating synthetic data and forecasting possible realistic scenarios in a real cloud-based DC environment. For this purpose, we will use a technique that has given extraordinary results in recent years: generative adversarial networks (GAN). These generative algorithms based on artificial neural networks have been commonly used to create realistic synthetic multimedia data (mainly images and videos). Some proposals in the state of the art use GAN to generate synthetic time-series data. However, this research has significant limitations, such as the limitation of handling multi-variable and categorical data in the generation of scenarios.