Reviewerly/exHarmony huggingface dataset:
Construction information security management system based on data sharing algorithm

Due to the lack of data security protection, a large number of malicious information leaks, which makes building information security (InfoSec) issues more and more attention. The construction information involves a large number of participants, and the number of construction project files is huge, leading to a huge amount of information. However, traditional network security information protection software is mostly passive, which is difficult to enhance its autonomy. Therefore, this text introduced data sharing algorithm in building InfoSec management. This text proposed an Attribute Based Encryption (ABE) algorithm based on data sharing, which is simple in calculation and strong in encrypting attributes. This algorithm was added to the building InfoSec management system (ISMS) designed in this text, which not only reduces the burden of relevant personnel, but also has flexible control and high security. The experimental results showed that when 10 users logged in to the system, the stability and security of the system designed in this text were 87% and 91% respectively. When 20 users logged in to the system, the system stability and security designed in this text were 89% and 92% respectively. When 80 users logged in to the system, the system stability and security designed in this text were 94% and 95% respectively. It can be found that the stability and security of the system have reached a high level, which can ensure the security of effective management of building information.
Attention Block Based on Binary Pooling

Image classification has become highly significant in the field of computer vision due to its wide array of applications. In recent years, Convolutional Neural Networks (CNN) have emerged as potent tools for addressing this task. Attention mechanisms offer an effective approach to enhance the accuracy of image classification. Despite Global Average Pooling (GAP) being a crucial component of traditional attention mechanisms, it only computes the average of spatial elements in each channel, failing to capture the complete range of feature information, resulting in fewer and less expressive features. To address this limitation, we propose a novel pooling operation named “Binary Pooling” and integrate it into the attention block. Binary pooling combines both GAP and Global Max Pooling (GMP), obtaining a more comprehensive feature vector by extracting average and maximum values, thereby enriching the diversity of extracted image features. Furthermore, to further enhance the extraction of image features, dilation operations and pointwise convolutions are applied on the channel-wise. The proposed attention block is simple yet highly effective. Upon integration into ResNet18/50 models, it leads to accuracy improvements of 2.02%/0.63% on ImageNet.
Enhancing Encrypted Traffic Classification with Deep Adaptation Networks

Network traffic management is crucial in Computer Networks and the Internet of Things. Indeed, classifying network traffic is the foundation for enhancing the quality of management mechanisms. However, traditional traffic classification methods, such as port-based, deep packet inspection, and statistic-based, are limited in identifying new encrypted traffic characteristics. Deep Learning-based classification approaches that consider packet-based features have been explored to address this challenge. Along with other deep learning methods, Transfer Learning, where a new model can inherit knowledge previously learned by a base model, is commonly used to increase classification performance in low data resources. Unfortunately, feature transferability may decline in transfer learning. This paper proposes an encrypted traffic classification mechanism that leverages the Deep Adaptation Network architecture with Mean Embedding Test to overcome this limitation. Our experimental results show that the proposed mechanism surpasses existing benchmarks’ accuracy and can classify encrypted traffic in real-time.
Deep Learning Based Face Liveliness Detection

Modern systems of face recognition (FRS) are used in a wide range of computer applications: for user authentication; in email marketing; in social networks; for personal identification and more. However, such technologies are often vulnerable to spoofing attacks. Facial image can be faked in various ways: print a photo; record a video; create a high-quality silicone mask, etc. By presenting a fake to the FRS the attacker has an intention of passing himself off as another person, for instance, trying to get an access to a secure computer system. Face Liveliness Detection solves this problem by detecting whether the person in front of the camera is real or fake. In this article, we explore the possibilities of using deep learning technology for face liveliness detection. We consider several models and setting numerous experiments. As datasets for experiments we use various fake images and for each such dataset we obtained evaluation of effectiveness. In our research, our main goal is to improve basic deep learning architectures using latest technologies to get more accurate model.
A Semantic Chatbot for Internet of Things Management

The Internet of Things environment can use chatbots as functional interfaces that collect data from all devices. Such data includes sensors, data sources and times, various formats, and current changes. In this article are discussed the theoretical and practical aspects of building semantic chatbots for Internet of Things management. The novelty of this article lies in the use of ontologies in the development of chatbots. The general principles of ontological modeling of subject areas of various devices are considered. A general model for the implementation of semantic chatbots for Internet of Things is proposed.
Privacy protection of quantum BP neural network based on game theory

Abstract How to ensure privacy security and improve computing efficiency is a research hotspot in the field of machine learning. Among them, how to balance the interests of users, cloud servers and attackers on the premise of ensuring user privacy is a difficult problem in the field of machine learning privacy protection. The development of quantum computing breaks through the computational bottleneck of classical machine learning and has derived the research direction of quantum machine learning. At present, hybrid quantum–classical machine learning in NISQ era has become a research hotspot, but researchers rarely pay attention to the privacy protection in quantum machine learning. Therefore, this paper is the first to apply game theory to the privacy protection in quantum machine learning and proposes the privacy game model of user - server - attacker in Hybrid Classical Quantum BP Neural Network (HCQBPNN). Different from previous studies, this paper sets game strategies based on users’ privacy requirements in practical applications, and aims to maximize the interests of attackers, cloud servers and users. The experiment proves that users can use the privacy game model proposed in this paper to get the optimal privacy combination strategy, and at the same time make the cloud server and the attacker can obtain positive income.
PSO with Mixed Strategy for Global Optimization

Particle swarm optimization (PSO) is an evolutionary algorithm for solving global optimization problems. PSO has a fast convergence speed and does not require the optimization function to be differentiable and continuous. In recent two decades, a lot of researches have been working on improving the performance of PSO, and numerous PSO variants have been presented. According to a recent theory, no optimization algorithm can perform better than any other algorithm on all types of optimization problems. Thus, PSO with mixed strategies might be more efficient than pure strategy algorithms. A mixed strategy PSO algorithm (MSPSO) which integrates five different PSO variants was proposed. In MSPSO, an adaptive selection strategy is used to adjust the probability of selecting different variants according to the rate of the fitness value change between offspring generated by each variant and the personal best position of particles to guide the selection probabilities of variants. The rate of the fitness value change is a more effective indicator of good strategies than the number of previous successes and failures of each variant. In order to improve the exploitation ability of MSPSO, a Nelder–Mead variant method is proposed. The combination of these two methods further improves the performance of MSPSO. The proposed algorithm is tested on CEC 2014 benchmark suites with 10 and 30 variables and CEC 2010 with 1000 variables and is also conducted to solve the hydrothermal scheduling problem. Experimental results demonstrate that the solution accuracy of the proposed algorithm is overall better than that of comparative algorithms.
Improving Cross-Language Code Clone Detection via Code Representation Learning and Graph Neural Networks

Code clone detection is an important aspect of software development and maintenance. The extensive research in this domain has helped reduce the complexity and increase the robustness of source code, thereby assisting bug detection tools. However, the majority of the clone detection literature is confined to a single language. With the increasing prevalence of cross-platform applications, functionality replication across multiple languages is common, resulting in code fragments having similar functionality but belonging to different languages. Since such clones are syntactically unrelated, single language clone detection tools are not applicable in their case. In this article, we propose a semi-supervised deep learning-based tool <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rubhus</small> , capable of detecting clones across different programming languages. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rubhus</small> uses the control and data flow enriched abstract syntax trees (ASTs) of code fragments to leverage their syntactic and structural information and then applies graph neural networks (GNNs) to extract this information for the task of clone detection. We demonstrate the effectiveness of our proposed system through experiments conducted over datasets consisting of Java, C, and Python programs and evaluate its performance in terms of precision, recall, and F1 score. Our results indicate that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rubhus</small> outperforms the state-of-the-art cross-language clone detection tools.
Spatiotemporal Generative Adversarial Imputation Networks: An Approach to Address Missing Data for Wind Turbines

Wind turbine data collection often suffers from missing data due to network blockage and sensor failure. Existing data imputation methods require complete datasets for training and are limited to individual turbines, resulting in time-consuming dataset preparation and a lack of generalization. To address these limitations, spatiotemporal generative adversarial imputation networks (STGAIN) are proposed for data imputation. First, generative adversarial networks containing a hint-mask fusion mechanism are presented, enabling data imputation without complete datasets. Second, a spatiotemporal module is proposed to capture the historical decay and feature correlation of wind turbine data to obtain potential trends. Third, an online update mechanism based on decision error is proposed to adapt the model parameters to varying environmental conditions. STGAIN overcomes geographic constraints, imputes data reflecting change patterns, avoids extensive sample collection, and enables online adjustments. Finally, experiments with real wind turbine data demonstrate that the proposed method produces imputed data conforming to the original distribution while considering spatiotemporal correlation. Evaluating the metrics results reveals that STGAIN significantly outperforms comparison methods, improving data imputation performance by at least 6%, demonstrating its significant benefits.
Multistep Question-Driven Visual Question Answering for Remote Sensing

Visual question answering (VQA) aims to build an interactive system that infers the answer according to the input image and text-based question. Recently, VQA for remote sensing has attracted considerable attention since it is essential and expedient for monitoring global resources and querying objective attributes. In reality, question-related semantic information is helpful for the reasoning and understanding capabilities, which is always contained in remote sensing images or complex questions. To capture the valuable information and extend the applications of remote-sensing VQA, we propose an end-to-end multiple-step question-driven VQA (MQVQA) system for remote sensing. In MQVQA, we employ a multiple-step attention mechanism to interactively reason and concomitantly mark the region that is most related to the question. To understand the semantic information in complex questions, we build a question-driven module that classifies the question types and keywords, which will further guide the combination of image feature maps in different scales. To benchmark this model, we construct a new complex remote sensing VQA dataset (CRSVQA), wherein the question is asked in complex forms and involves various remote sensing scenes. The evaluation results on CRSVQA, RSVQA and RSIVQA datasets indicate that the proposed MQVQA model surpasses other remote sensing VQA models. The visualization results demonstrate that MQVQA has a robust ability in reasoning and understanding the content from images and complex questions. Our code and dataset are publicly available at: https://github.com/MeimeiZhang-data/MQVQA.
Remote Sensing Image Denoising Based on Gaussian Curvature and Shearlet Transform

Model-based image denoising methods are well suited for use as image processors in remote sensing systems such as satellites due to their well-developed mathematical theory and low computational cost, but these methods can often only deal with a single type of random noise. In this paper, based on the model-based image denoising techniques, a remote sensing image mixed noise denoising algorithm using Gaussian curvature in the image surface and shearlet transform is proposed. The Gaussian curvature filtering (GCF) is used to suppress the salt & pepper noise (SPN) in the image to get the first denoised image. The second denoised image is obtained by processing the coefficient matrices of the shearlet transform decomposition using statistical method and adaptive median filtering (AMF). Finally, the reconstructed image is again optimized by AMF to eliminate the residual SPN. Specially, we propose the goodness-of-fit (GOF) test denoising algorithm in the shearlet domain based on empirical distribution function (EDF) statistics to solve the problem of insufficient denoising by the traditional threshold function. Our method can effectively remove the noise and improve the visibility and usability of remote sensing images. Experiment results show that our proposed method has PSNR and MSE performance improvement compared with related model-based and learning-based methods.
Instance-Guided Point Cloud Single Object Tracking With Inception Transformer

Single object tracking in LiDAR point clouds is a challenging problem in computer vision. Compared to object-level point clouds, scene-level point clouds for tracking are more complex, requiring long-range semantic awareness and local shape context. However, previous methods directly filter candidates under limited matched features without systematically considering these two factors. Inspired by transformer to establish long distance dependence and convolution to capture local high-frequency information, we propose Point Tracking Inception Transformer (PTIT), which efficiently predicts high-quality 3D tracking results in a coarse-to-fine manner with the support of spatio-temporal point clouds. PTIT consists of three novel designs: 1) We design instance-guided sampling to help identify and preserve the relevant points of the given template and the foreground points of the search area. 2) We propose Point Inception Transformer (PIT), which consists of a multi-frequency attention and cross-attention module, where the former captures both remote dependency and local detail, and the latter matches template and search area features. 3) After generating coarse tracking results from cross-attention, we locate the target by motion transformation in the spatio-temporal point cloud to generate a fine-grained 3D bounding box. In addition, we perform feature augmentation on the points and boxes to mitigate the negative effects of LiDAR point clouds without texture and incompleteness. PTIT performs significantly better than previous state-of-the-art methods on KITTI and nuScenes datasets. Our further analysis confirms the effectiveness of each component and shows the great potential of the inception transformer-centric paradigm when combined with spatio-temporal point clouds. Our code is available at https://github.com/ywu0912/TeamCode.git.
A Hybrid LiDAR-Based Mapping Framework for Efficient Path Planning of AGVs in a Massive Indoor Environment

Path planning is a computation-consuming task for autonomous ground vehicles (AGVs) in a massive indoor environment, especially for AGVs having limited computing resources. Popular path planning methods based on metric maps, like occupancy grid maps, are computationally expensive in such environments. Therefore, this paper proposes a hybrid mapping framework integrating the state-of-the-art metric mapping method and the devised topological mapping method, whereby AGVs can realize efficient path planning. The key idea is to select critical landmarks (such as turns, doors, and junctions) as topological vertices and build a succinct topological map superposing on the metric map. To realize this, a simple and interpretable feature for landmark recognition is proposed. Experiments conducted in simulation, datasets, and real-world environments have proven the framework’s ability to perform hybrid mapping in massive environments with areas of up to 30000m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Furthermore, compared to other typical hybrid mapping methods, the proposed framework considers critical landmarks and surpasses them in the hybrid map’s succinctness. With this framework, in contrast to metric maps, the search space for path planning is reduced by orders of magnitude. As a result, path planning on the hybrid map is at least two orders of magnitude faster than representative methods, meanwhile realizing comparable performance with them.
Advances in Modeling of Noisy Quantum Computers: Spin Qubits in Semiconductor Quantum Dots

The new quantum era is expected to have an unprecedented social impact, enabling the research of tomorrow in several pivotal fields. These perspectives require a physical system able to encode, process and store for a sufficiently long amount of time the quantum information. However, the optimal engineering of currently available quantum computers, which are small and flawed by several non-ideal phenomena, requires an efficacious methodology for exploring the design space. Hence, there is an unmet need for the development of reliable hardware-aware simulation infrastructures able to efficiently emulate the behaviour of quantum hardware that commits to looking for innovative systematic ways, with a bottom-up approach starting from the physical level, moving to the device level and up to the system level. This article discusses the development of a classical simulation infrastructure for semiconductor quantum-dot quantum computation based on compact models, where each device is described in terms of the main physical parameters affecting its performance in a sufficiently easy way from a computational point of view for providing accurate results without involving sophisticated physical simulators, thus reducing the requirements on CPU and memory. The effectiveness of the involved approximations is tested on a benchmark of quantum circuits — in the expected operating ranges of quantum hardware — by comparing the corresponding outcomes with those obtained via numeric integration of the Schrödinger equation. The achieved results give evidence that this work is a step forward towards the definition of a classical simulator of quantum computers.
A Clinical Decision Support System for Edge/Cloud ICU Readmission Model Based on Particle Swarm Optimization, Ensemble Machine Learning, and Explainable Artificial Intelligence

ICU readmission is usually associated with an increased number of hospital death. Predicting readmission helps to reduce such risks by avoiding early discharge, providing appropriate intervention, and planning for patient placement after ICU discharge. Unfortunately, ICU scores such as the simplified acute physiology score (SAPS) and Acute Physiology and Chronic Health (APACHE) could help predict mortality or evaluate illness severity. Still, it is ineffective in predicting ICU readmission. This study introduces a clinical monitoring fog-computing-based system for remote prognosis and monitoring of intensive care patients. This proposed monitoring system uses the advantages of machine learning (ML) approaches for generating a real-time alert signal to doctors for supplying e-healthcare, accelerating decision-making, and monitoring and controlling health systems. The proposed system includes three main layers. First, the data acquisition layer, in which we collect the vital signs and lab tests of the patient’s health conditions in real-time. Then, the fog computing layer processes. The results are then sent to the cloud layer, which offers sizable storage space for patient healthcare. Demographic data, lab tests, and vital signs are aggregated from the MIMIC III dataset for 10,465 patients. Feature selection methods: Genetic algorithm (GA) and practical swarm optimization (PSO) are used to choose the optimal feature subset from detests. Moreover, Different traditional ML models, ensemble learning models, and the proposed stacking models are applied to full features and selected features to predict readmission after 30 days of ICU discharge. The proposed stacking models recorded the highest performance compared to other models. The proposed stacking ensemble model with selected features by POS achieved promising results (accuracy=98.42, precision=98.42, recall=98.42, and F1-Score=98.42), compared to full features and selected features. We also, provide model explanations to ensure efficiency, effectiveness, and trust in the developed model through local and global explanations.
Stable Image Encryption Algorithm Based on Expanded One-Dimensional Chaotic Jumping and Parallel Encoding Operation Grouping

The current algorithm of encrypting images uses low-dimensional chaotic systems with a limited key space and poor security, while high-dimensional chaotic systems are difficult to implement and inefficient. Additionally, if image encryption algorithms use fixed DNA encoding rules, they could be cracked; dynamic coding can easily deviate from optimal solutions, thus causing algorithm instability. Based on this, a novel technique for image encryption is presented in this article. It is possible to avoid linear correlation of the chaotic sequence by randomly jumping on two uncorrelated one-dimensional chaoses. Meanwhile, numerous encryption results are generated in parallel by randomly grouping DNA encoding groups and encoding operations, and the optimal solution is then selected based on the encryption results, thereby minimizing ciphertext image instability to some extent. According to the experimental results, this algorithm produces high-security, plaintext-sensitive, and non-cracking encrypted images. Furthermore, this algorithm produces excellent encryption results both on color and sized images, and is applicable to a wide variety of applications.