Reviewerly/exHarmony huggingface dataset:
Exploration of Emergency Command Converged Communication Mode

In the field of emergency command, the existing multi-system communication system is facing challenges such as unclear on-site information, difficulties in cross-departmental coordination, relatively independent technology, and lack of effective connection between systems, which cannot meet the needs of voice, video and conference integration communication in emergency wartime. In order to facilitate unified management, this paper combines the “1 + 3 + N” Winter Olympics guarantee command system as the core, relies on the integrated communication network between air, sky and earth, and explores the comprehensive use of various means of communication to build an integrated integrated communication system for power supply and command of “horizontal to the side and vertically to the end”, so as to realize command at all levels. The department integrates command, coordination and supervision among various support teams and support personnel to improve the efficiency of emergency command and communication during the conference. Effectively integrate various communication system resources, and integrate communication capabilities into a comprehensive communication platform integrating data, voice, audio and video, and realize the “three integrations”, that is, business integration, network integration and equipment integration. Adapt to efficient command and scheduling in different modes, meet the comprehensive command and scheduling requirements of emergency command in different scenarios, and promote the integration and development of emergency duty and emergency command.
A Multi-level Corporate Wallet with Governance

This paper proposes a blockchain-based multi-level corporate digital wallet that meets financial regulatory requirements, including compliance with the Travel Rules, and identification of suspicious financial transactions. Unlike personal digital wallets, a corporate wallet is actually a large financial management system with internal blockchains, databases, and governing rules. Every transaction that the user operates on the wallet client will first be sent to the wallet server system. The wallet server will first check the compliance of the transaction, and then verify that the transaction meets the Travel Rules. At the same time, the digital wallet also checks if the transaction may be a financial fraud. In the Web3 era, many transactions will be performed using digital wallets, rather than bank accounts, a corporate wallet will play a significant role in the new digital world.
Feasibility Evaluation and Application Practice of Distributed Photovoltaic Power Generation in Large Commercial Buildings

Large commercial complex projects have the characteristics of large roof area and high electricity price, and the development of distributed photovoltaic power generation has great potential. In this paper, a feasibility evaluation model of distributed photovoltaic power generation in large commercial buildings is established from the aspects of solar energy resources, power generation, economics and environmental benefits. A commercial complex photovoltaic project in Jinan is used as an example. In the case of fully considering the project operating cost and environmental benefits, the feasibility of the project under different power consumption modes is analyzed. The research results show that the economy of large-scale commercial distributed photovoltaic power generation projects in class III solar resource areas such as Jinan is directly related to the power consumption mode. When the “spontaneous self-use” mode is adopted, the economic benefits of the project are good and the feasibility is strong; When the “excess electricity goes into the power grid” mode is adopted, the economic benefits gradually deteriorate with the increase of the proportion into the power grid, but considering the environmental protection benefits of photovoltaic power generation projects, it is still feasible; When the “full the power goes into the power grid” mode is adopted, the economic benefit is poor, and the project is basically not feasible.
Sentiment analysis and offensive language detection in social media

Sentiment Analysis is a field of study that focuses on figuring out how to extract, identify, or otherwise describe emotions in units of written text. One of the most common tasks in sentiment analysis is finding the polarity of a person's feelings. There are many blog posts, tweets, and comments in Indian languages online these days. Sentiment analysis in Indian languages is a relatively new field, and research in this area is just beginning. There is a lot of offensive content on social media, which is a worry for businesses and government agencies. This paper presents the methodology of sentiment analysis and offensive language detection in social media.
Using Convolutional Neural Network to Redress Outliers in Clustering Based Side-Channel Analysis on Cryptosystem

Blockchain, designed with cryptographic technology, is widely used in the financial area, such as digital billing and cross-border payments. Digital signature is the core technology in it. However, digital signatures in public key cryptosystems face the threat of simple power analysis in Side-Channel Analysis (SCA). The state-of-the-art simple power analysis based on clustering mostly will appear outliers in the process of analysis, which will reduce success rate of key recover. In this paper, we propose a new SCA method with clustering algorithm Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and deep learning technology Convolutional Neural Network (CNN), called DBSCAN-CNN, to analyze public key cryptosystems. We cluster data with DBSCAN firstly. Then we train a CNN model based on the trusted clustering results. Finally, we classify the outliers of clustering results by the trained model. We mount the proposed method to analyze an FPGA-based elliptic curve scalar multiplication power trace which is desynchronized by simulating random delay. The experimental results show that the error rate of the proposed method is at least $$69.23\%$$ lower than that of the classical clustering method in SCA.
Research on Cross-Domain Heterogeneous Information Interaction System in Complex Environment Based on Blockchain Technology

In the field of information interaction, when a project involves a large amount of cross-domain heterogeneous information, it is difficult to transmit and update the required information in a timely, accurate, reliable and safe manner in such a complex environment, thereby maintaining synchronization. The decentralization and immutability of blockchain technology provide new ideas for solving this problem. Therefore, this paper takes this as a starting point and proposes a cross-domain heterogeneous information exchange system based on blockchain, which calls smart contracts to realize data transmission and other functions. This paper states the design idea of the system, introduces the system software framework and network topology in detail, shows the flow chart of the system operation, and conducts simple software testing and verification. Finally, this work is summarized, and the development direction of future work is prospected, hoping to provide inspiration for the solution of such problems.
Optimizing a Dynamic Sliding Mode Controller with Bio-Inspired Methods: A Comparison

In the past few years, bio-inspired optimization algorithms have shown to be an excellent way to solve a wide range of complex computing problems in science and engineering. This paper compares bio-inspired algorithms to better understand and measure how well they find the best tuning parameters for a Dynamic Sliding Mode Control for integrating systems with an inverse response and dead time. The comparison includes four bioinspired algorithms: particle swarm optimization, artificial bee colony, ant colony optimization, and genetic algorithms. It shows how they can improve the performance of the controller by looking for the best tuning parameter solutions. The parameters of each algorithm affect the searching mechanism in different ways, and these effects were tested in two simulated systems. Ant colony optimization is much better than other algorithms at finding the best answers to our problems.
CTDA: Contrastive Temporal Domain Adaptation for Action Segmentation

In video action segmentation scenarios, intelligent models require sufficient training data. However, the significant expense of human annotation for action segmentation makes this method prohibitively expensive, and only very limited training videos can be accessible. Further, large Spatio-temporal variations exist in training and test data. Therefore, it is critical to have effective representations with few training videos and efficiently utilize unlabeled test videos. To this end, we firstly present a brand new Contrastive Temporal Domain Adaptation (CTDA) framework for action segmentation. Specifically, in the self-supervised learning module, two auxiliary tasks have been defined for binary and sequential domain prediction. They are then addressed by the combination of domain adaptation and contrastive learning. Further, a multi-stage architecture is devised to acquire the comprehensive results of action segmentation. Thorough experimental evaluation shows that the CTDA framework achieved the highest action segmentation performance.
Deep Transfer Learning

With the development of deep learning, more and more researchers adopt deep neural networks for transfer learning. Compared to traditional machine learning, deep transfer learning increases the performance on various tasks. In addition, deep learning can take the vanilla data as the inputs, thus it has two more benefits: automatic feature extraction and end-to-end training. This chapter will introduce the basic of deep transfer learning, including network structure of deep transfer learning, distribution adaptation, structure adaptation, knowledge distillation, and practice.
A Fine-Grained Access Control Framework for Data Sharing in IoT Based on IPFS and Cross-Blockchain Technology

Internet of Things (IoT) data from different trust domains is usually shared to assist in providing more services, where privacy sensitive information of shared data will be leaked or accessed without authorization. The traditional centralized access control method is difficult to adapt to the current dynamic and distributed large-scale IoT environment, and there is a risk of the single point of failure. To address these challenges, we propose a fine-grained access control framework for shared data based on cross-blockchain technology and Interplanetary File System (IPFS). In this framework, we firstly introduce a cross-blockchain module to realize cross-domain data sharing and solve the problem of data isolation between different data domains in IoT. Then IPFS is used to store the shared data, avoiding the risk of centralized storage. Combining symmetric encryption algorithm with ciphertext policy attribute based encryption (CP-ABE) algorithm, the fine-grained access control of shared data is guaranteed. In addition, the blockchain is applied to store the decryption key and the storage address of the original data, which records the authorization operation of access transactions and audits the access behavior of users. Experimental results show that the proposed scheme can provide higher performance compared to centralized access control methods.
Blockchain-based Secure Outsourcing Data Integrity Auditing for Internet of Things in Cloud-edge Environment

Internet of Things enables devices to communicate, collect and exchange data with the network. As the number of IoT devices keeps growing, the volume of data they produce is also increasing exponentially. Given the feature of limited computing and storage resources of IoT, it is inevitable to store data in the cloud for better services. However, for users to effectively and efficiently inspect those data over the cloud is a critical and open problem. Most public integrity auditing over the cloud schemes requires the user to do a sheer amount of preprocessing work on the local devices, which is unsuitable for IoT devices. With the development of edge computing extending cloud computing, it can provide computing capability for resource-constrained devices in close geographic proximity. In this paper, we design an auditing scheme based on secure computation outsourcing assisted by edge computing, in which the data preprocessing work can be offloaded to the edge server. The experiments show that it reduces the computing load on the devices and improves the efficiency of task processing.
User-Perceived QoE Adaptation for Accelerated Playback in Mobile Video Streaming

User-perceived quality of experience (QoE) is critical as mobile video streaming experiences a substantial growth. User's demands are becoming diversified where accelerated play-back is the preference of a considerable part of users. However, the limited and fluctuate mobile bandwidth is often not capable of satisfying user's demand of watching video at 2x or higher speed because of consequential frequent rebuffering. Previous adaptive bitrate (ABR) algorithms hardly consider the variety of user playback rates. In this work, we fully exploit the relation between user-perceived, i.e., subjective video quality and the characteristic of video content. The result of our motivational experiments shows that viewers are less sensitive to the bitrate variation and playback rate alternation if there is higher degree of motion in the video. With above guidelines, we adaptively adjust the quality configuration and playback rate to significantly reduce the rebuffering while achieving similar or even higher subjective quality. Then we formulate subjective quality and playback rate adaption as a QoE maximization problem and propose the content based subjective quality and playback rate adaptation algorithm (CSP) utilizing Lyapunov optimization technique. Via rigorous proof, the time-average QoE achieved by CSP is in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(1/V)$</tex> gap compared to optimal value, where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$V$</tex> is the control parameter. Extensive evaluations confirm the superiority of our proposed algorithm over other state-of-the-art algorithms under both normal and accelerated playback rate.
Few-Shot Learning for Medical Numerical Understanding Based on Machine Reading Comprehension

Numerical understanding relies on some content understanding techniques, which can be based on rules, entity extraction, and machine reading comprehension. Traditional methods often require a large number of regular expressions or a large number of data annotations, and often do not have a deep understanding of numerical values, lacking the ability to distinguish similar numerical values. In this paper, we propose a few-shot learning framework for numerical understanding tasks in Chinese medical texts, and through dynamic negative sampling of the training data, the model’s ability to discriminate similar numerical values is enhanced. We use patient text data provided by 13 hospitals in Beijing to conduct experiments. The results show that our newly proposed method is superior to training the baseline pretrained language model directly, the EM increases by 38% and the F1 increases by 27.59%.
Scheduling Algorithm for Low Energy Consumable Parallel Task Application Based on DVFS

With the continuous improvement of various high-performance computing systems, various data centers had also been fully expanded. Energy consumption and actual performance measurement were very important indicators, which were also key issues in how to judge parallel calls for some tasks in high-performance computer systems. Modern processors were basically equipped with software control functions such as DVFS (Dynamic Voltage Frequency Scaling), in the actual system operation to ensure that the system could ensure the reasonable operation of the system while reducing energy consumption indicators. This paper considered how the designed scheduling algorithm first divides tasks reasonably to ensure that the maximum completion time and energy consumption of the processor were sufficiently reduced when the directed acyclic graph was executed. Then considered making reasonable adjustments to the processor frequency using DVFS technology to adapt to the task while ensuring the critical path of the task. At the end of the article, make sure that the experimental verification algorithm could ensure that the task was completed and could reduce the energy consumption during task execution as much as possible.
Confidentially Computing DNA Matching Against Malicious Adversaries

DNA is one of the most important information in every living thing. The DNA matching experiment is helpful for the study of paternity testing, species identification, gene mutation, suspect determination, and so on. How to study the DNA matching in the case of privacy protection has become the inevitable problems in the research of information security. The Hamming distance can reflect the similarity degree of two DNA sequences. The smaller the Hamming distance is, the more similar the two DNA sequences are. In this paper, the DNA sequence with $$l$$ length is encoded with a 0–1 string with $$3l$$ length, and the protocol of confidentially computing Hamming distance is designed, which calculated the matching degree of two DNA under the premise of protecting DNA privacy. In addition, in view of the criminal suspect DNA matching problem, we design a secure computation protocol against malicious adversaries using the zero-knowledge proof and the cut-choose method to prevent or find malicious behaviors, which can resist malicious attacks.
Joint Convolutional and Self-Attention Network for Occluded Person Re-Identification

Occluded person Re-Identification (Re-ID) is built on cross views, which aims to retrieve a target person in occlusion scenes. Under the condition that occlusion leads to the interference of other objects and the loss of personal information, the efficient extraction of personal feature representation is crucial to the recognition accuracy of the system. Most of the existing methods solve this problem by designing various deep networks, which are called convolutional neural networks (CNN)-based methods. Although these methods have the powerful ability to mine local features, they may fail to capture features containing global information due to the limitation of the gaussian distribution property of convolution operation. Recently, methods based on Vision Transformer (ViT) have been successfully employed to person Re-ID task and achieved good performance. However, since ViT-based methods lack the capability of extracting local information from person images, the generated results may severely lose local details. To address these deficiencies, we design a convolution and self-attention aggregation network (CSNet) by combining the advantages of both CNN and ViT. The proposed CSNet consists of three parts. First, to better capture personal information, we adopt Dual-Branch Encoder (DBE) to encode person images. Then, we also embed a Local Information Aggregation Module (LIAM) in the feature map, which effectively leverages the useful information in the local feature map. Finally, a Multi-Head Global-to-Local Attention (MHGLA) module is designed to transmit global information to local features. Experimental results demonstrate the superiority of the proposed method compared with the state-of-the-art (SOTA) methods on both the occluded person Re-ID datasets and the holistic person Re-ID datasets.
Geometrical Feature Transformation Methods

In this chapter, we introduce the geometrical feature transformation methods for transfer learning, which is different from statistical feature transformation in the last section. The geometrical features can exploit the potentially geometrical structure to obtain clean and effective representations with remarkable performance. Similar to statistical features, there are also many geometrical features. We mainly introduce three types of geometrical feature transformation methods: subspace learning, manifold learning, and optimal transport methods. These methods are different in methodology, and they are all important in transfer learning.
Memory-Contrastive Unsupervised Domain Adaptation for Building Extraction of High-Resolution Remote Sensing Imagery

Deep learning-based semantic segmentation has been widely applied for building extraction. However, due to the domain gap, the extraction of building in high-resolution remote sensing imagery is difficult when the model trained on a source dataset is directly used to test on a target data. Considering that humans can retrieve memory to deal with correlative tasks in different domains, memory mechanisms have been developed effectively to assist cross-domain feature extraction. However, whether the memory mechanisms can achieve satisfactory result or not highly depends on the premise that the memory is relevant to the task. Therefore, the domain-invariant memory is crucial in cross-domain building extraction task. To this end, a memory-contrastive unsupervised domain adaptation method is proposed on the basis of a novel memory mechanism. Specifically, to facilitate the model to memorize domain-invariant features, we first conduct a normalization-based image style transfer strategy and a discriminator-based adversarial method at the image level and feature level, respectively. Subsequently, we carry out a memory-contrastive module to obtain domain-invariant features. Especially, a teacher–student network is exploited to help knowledge transferring by knowledge distillation to enhance the performance of the memory-contracted module. To narrow the distance between the two domains, a memory bank is designed to store and update category features obtained from the source domain, and then the similarity between category features in the target domain and memory bank is calculated. Results of the cross-domain experiments show that the proposed method can achieve optimal building extraction. (GitHub: https://github.com/RS-CSU/MDANet).
A Survey of Recommender Systems Based on Hypergraph Neural Networks

Unlike highly purposeful search, a recommender system tends to uncover the user’s potential interests and is a personalized information filtering system. Recently, the performance of hypergraph neural networks in classification tasks has attracted much attention. Compared with traditional recommender systems, hypergraph neural network-based recommender systems have better mining higher-order associations, accurate modeling of multivariate relationships, handling of multimodal and heterogeneous data, and clustering advantages. This fact drives the development of recommendation algorithms based on hypergraph neural networks. To this end, we 1) define generic links of recommender systems, and systematically analyze the challenges of hypergraph neural network-based recommender systems in different research directions. 2) present some new perspectives on existing weaknesses and future developments.