Reviewerly/exHarmony huggingface dataset:
Self-Paced Pseudo Label Refinement for Multi-view Subspace Clustering

Multi-view clustering can capture common representations from multi-view data that contain complementary information of different views, which has been applied in many fields, such as computer vision, natural language processing, medicine. As one of the most popular attractive directions, multi-view subspace clustering focuses on learning common subspace representations of different views that can reveal the underlying clustering structure of multi-view data. However, for learning more discriminative common subspace representations, current multi-view subspace clustering methods utilize pseudo labels of all samples to train the self-supervised model, which neglects the pseudo label noise problem in the self-supervised multi-view subspace clustering. To address the challenge, we propose a novel paradigm termed self-paced pseudo label refinement paradigm, which aims at dynamically refining pseudo labels in the self-supervised multi-view subspace clustering. First, we design the self-paced learning strategy to prevent noisy pseudo labels from interfering with the self-supervised model training and improve the discrimination of view-specific semantic features. Then, we develop the adaptive update mechanism to learn weights of different views in an adaptive way and combine view-specific semantic features with obtained weights to refine pseudo labels. Finally, to implement multi-view subspace clustering based on the proposed paradigm, the deep self-supervised multi-view subspace clustering network is developed. Experiments on six clustering benchmarks verify the proposed approach outper-forms state-of-the-art multi-view subspace clustering methods.
A Secure and Reliable Construction Scheme for Enterprise Value Chain Networks

A value chain optimizes the economic benefit of an enterprise alliance by integrating the cooperation relationships among the upstream and downstream companies. Nevertheless, the absence of information security mechanisms within the value chain limits the horizontal cooperation between competing enterprises. The paper proposes a scheme for building an enterprise value chain network based on an alliance blockchain. In the scheme, a distributed authentication technology and cross-chain transaction technology are introduced into the value chain, which enhances the security and reliability of value chain information and facilitates horizontal cooperation among companies. Using Hyperledger Fabric platform, we simulate and extensively evaluate an enterprise value chain network. Experimental results demonstrate the feasibility of the proposed scheme, which can serve as a guide for the construction of enterprise value chain networks in the real world.
Leveraging Formal Methods to Blockchain Consensus Protocols: A Scoping Literature Review

Recently, the concept of blockchain has emerged and caused a stir in the digital world and this translates into a new perspective on the security, flexibility, and efficiency of systems. To solve various problems that exist in Byzantine environments the blockchain uses consensus algorithms to solve these problems. These consensus algorithms are essential to ensure long-term stability in a blockchain system, so consensus forms the basis of blockchain technology. Therefore, the appearance of a set of consensus protocols aims to improve the efficiency of blockchain systems and meet the individual needs of different application areas. On the other hand, to make blockchain platforms more secure and reliable for information sharing, and an optimal solution for value transfer, formal methods have been used to provide sufficient mathematical proofs to ensure user safety and security. In this paper, a scoping literature review on leveraging formal methods to consensus protocols is presented and discussed.
Camera Nerve Center: A Modern CCTV Surveillance System

As technology is getting advanced day by day, high-end solutions are being available at very minimal cost. As now cctv surveillance is easily available for estate’s security but for their monitoring, most people depends upon humans. An security guard can keep a good look at monitoring but as humans have limit of work and tendency to do mistakes, misfortune can happen. So to prevent this happening we must use technology and we can get a very sophisticated solution by that. Camera nerve center will provide automated monitoring, smart analysis and alerts to the user. We can have various types of behaviors running on it like face mask detection, people detection etc. And its cost will be very minimal thus making it available to all. this project will let monitoring very easy, its tech stack is mostly revolves around python libraries. In this project i have tried to use new technology which is efficient and popular. Most of the computation will be handled by an flask server which will handling all the requests. For handling multiple streams running kafka servers will seems helpful. Each cctv camera holds a rtmp url which allows them to stream them online, so with the help of this urls we can have our input. The tasks are very simple, flask server will be handling requests for loading data to frontend and kafka servers will run the streams and run detection on them, each detection is getting stored in database. Backend will be responsible for delivering data regarding detections and populate them into frontend.
An Efficient and Privacy-concerned Multi-user Access Mechanism for EMRs

In the medical system, it is essential to protect the privacy of electronic medical records (EMRs) especially in cloud-based system. Adopting cloud service has brought great convenience and high efficiency, such as data storage and processing. However, storing sensitive records to cloud is susceptible to be leaked without encryption. Therefore, many public key encryption with keyword search (PEKS) schemes have been proposed to search on encrypted data so as to protect the security and privacy of the outsourced data. In order to prevent the keyword guessing attack (KGA) existing in PEKS schemes, the authentication mechanism with private key of the sender is considered. Nevertheless, this kind of solution reveals the identity of the uploaders (the sender), say patients. Indeed, the patients in the medical system are reluctant to leak neither their medical records nor their identites. Besides, some of these schemes can not be extended to multi-user scenarios. To address the above issues, we propose an efficient and privacy-concerned multi-user access mechanism (M-PEKS) for Electronic Medical Records, in which the records are not only encrypted semantically secure with KGA resistant but also the identities of the uploaders cannot be leaked. Besides, our scheme is suitable for the multi-receiver scenario. That is, the same record only needs to be encrypted once for each keyword, no matter how many receivers our scheme have. Finally, we show that our scheme has much better performance in storage and computation than other related schemes for sharable EMRs.
Research and Implementation of Short Video Product Recommendation Algorithm Based on Multimodal Features

With the rapid development of the Internet and the rapid improvement of smartphone technology, a large number of short videos are shared through social platforms every minute, so video content analysis is currently a very important and popular job in machine learning and artificial intelligence. In recent years, in the field of domestic Internet content, platforms such as Toutiao, Netease, Yidian Information, and Baijiahao have sprung up. These content distribution platforms utilize multi-channel content sources, and the amount of content has exploded, of which short video content accounts for a large proportion. The rich short video content has changed the behavior of users and provided users with a more convenient way to socialize. Short video has a higher dimension of information presentation, and it is easier for people to accept and spread. At present, a large amount of short video information has appeared on the Internet. These short video information have brought serious information overload problems to users, and also brought huge challenges to short video operators and video editors. This paper adopts the method of multimodal fusion, which has high accuracy in identifying emotional states such as happiness, sadness, anger, and disgust in the sample.
Research on Elastic Extension of Multi Type Resources for OpenMP Program

OpenMP is one of the most popular programming models in the field of parallel computing, which is widely used in multi-threaded programming with shared memory. But with the continuous change of program computing scale, the problem of resource supply has become increasingly prominent. Making use of the cloud computing to dynamically provide resources for OpenMP program is a meaningful work. Based on the internal structure characteristics of OpenMP program, a flexible elastic resource allocation strategy is designed, and a load prediction method combining active and reactive prediction based on machine learning is proposed to realize the load prediction of OpenMP program. Then, the fuzzy controller is used to coordinate the resources and infer the CPU and memory resources needed by the program in the next cycle. On this basis, the middleware OPEC is designed and implemented to realize the dynamic allocation of cloud resources during the operation of OpenMP program. Parsec, a benchmark test suite based on shared memory, is selected for experimental verification. The experimental results show that the solution proposed in this paper can dynamically provide a variety of resources for OpenMP program to meet program running requirements, effectively improve resource utilization efficiency and reduce program running costs.
Dual Memory Network for Medical Dialogue Generation

Task-oriented dialogue systems have achieved re-markable success recently. However, the generation quality of medical dialogue systems is not ideal since medical conversations tend to be more complex than other conversations, placing higher demands on text comprehension and knowledge-based reasoning. To address this challenge, we propose DM-Net, which consists of two major memory modules. The encoding of dialogue historical content is utilized as the query to query dialogue context memory and clinical experience memory to achieve a correct understanding of dialogue content and sensible reasoning of external knowledge, thereby generating accurate and reliable responses. Extensive experiments on two large-scale medical dialogue datasets demonstrate that DM-Net outperforms a large number of strong baselines in terms of objective and subjective evaluation metrics.
Towards a Privacy Preserving Data Flow Control via Packet Header Marking

Internet infrastructure is becoming ubiquitous thanks to the advancement in computing and the network domain. Reliable network communication is essential to offer good quality services, but it is not trivial. There are privacy concerns. Metadata may leak user information, even if traffic is encrypted. Some countries have data privacy preserving-related regulations, but end-users cannot control how their data packets should travel through networks. Even worse, the user cannot declare their privacy preferences. This paper presents an approach to tackle such privacy issues through data privacy-aware routing, where users can specify their preferences for packet routing using marking and filtering. Routing can work according to such specifications. It is implemented by P4, allowing a vendor-independent realisation with standard off-the-shelf hardware and open-source software components. We presented the initial experimental results of a proof-of-concept test on a unified cloud/foe research testbed.
DA<sup>2</sup>F: Research on Robustness of Deep Learning Models Using Approximate Activation Function

Deep learning structures have been adopted in many application domains. However, these architectures are vulnerable to adversarial attacks which are often instantiated by adversarial examples: carefully crafted inputs by adding disturbances that are imperceptible to humans can easily mislead a learned classifier to make incorrect predictions. Since deep learning is fast achieving the maturity to enter into safety- critical and security-sensitive applications, such attacks may have catastrophic security and safety consequences. In this paper, we propose a new software-based approach to enhance the robustness of deep learning models to adversarial attacks which we call the Defensive Approximate Activation Function (DA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> F). Specifically, we mainly consider deep learning architectures using the sigmoid function or tanh function, two complex non-linear involving exponential operation functions, as activation functions. We propose the piecewise linear approximation method where a new non-uniform segmentation scheme is presented. By replacing the exact activation function used in the deep learning architectures with the approximate activation function, which reduces computation cost theoretically and improves deep learning models' robustness to adversarial samples. The experiments validated that our approach was effective in defending against adversarial attacks. For LeNet-5 CNN architecture along with MNIST datasets, the approximate classifiers were more robust against adversarial attacks than the exact classifiers, with negligible loss in accuracy.
Selecting of Pet Adopters using C4.5 Decision Tree Model Algorithm

This study aims to select pet adopters based from the model created from the C4.5 decision tree algorithm to address the proper way for adoption of a pet through online examination. This study uses the method of the C4.5 algorithm by finding the highest gain value to create a decision tree model and generate a decision rules that will apply to a website for decision support if the adopter is qualified or not qualified to adopt. A confusion matrix is used to calculate the accuracy of the C4.5 decision tree algorithm. The information in the confusion matrix is needed to calculate the classification model's performance. This can be done by using performance metrics for the C4.5 algorithm that is based on accuracy, sensitivity, and specificity. The total number of datasets is 1800 which consists of 15 attributes, 1500 for training which consists of 100 instances, and 300 for testing the accuracy is consist of 20 instances. The results on testing the accuracy of the C4.5 decision tree algorithm using confusion matrix is 85%, sensitivity is 84.6% and specificity is 100%. Based on the result of testing the accuracy of the algorithm using the 20 dataset that results with the accuracy value of 85%, sensitivity is 84.6% and specificity is 100%. These results prove that generated model using C4.5 decision tree algorithm is efficient in selecting of pet adopters.
Development of Mobile Learning Software for College English Based on Cloud Computing

College English mobile learning is the extension of e-learning, which makes learning more extensive. It is the inheritance and development of elearning in the era of mobile Internet. Timely communication and interaction is the key factor for the success of mobile learning. Mobile learning is a digital learning form in which learners use wireless mobile communication network technology and equipment to obtain educational information, resources and services, and realize rich interaction between teaching and learning through mobile technology in appropriate situations. Accordingly, mobile learning is also widely used in English teaching practice, which provides new means, ways and strategies for English teaching. Services provided by cloud computing can facilitate learners to access the “cloud” for mobile learning anytime and anywhere through mobile terminal devices. Cloud computing is the core part of the whole system and the final processing module of the whole system function. This paper makes a preliminary analysis on the application of mobile learning, expounds related concepts, and discusses the application advantages and platform framework of cloud computing in mobile learning application.
AIC-Bench: Workload Selection Methodology for Benchmarking AI Chips

Artificial intelligence (AI) chips are gaining tremendous attention due to their successes in accelerating deep learning algorithms. AI chips need standard benchmarks to drive the advancements in their software and hardware systems. However, the deep learning workloads in most existing AI benchmarks are fixed and have similar computation characteristics. Therefore, we introduce AIC-Bench, a deep learning workload selection methodology for comparing the performance of AI chips. The core of the proposed method is to select a number of representative deep learning workloads in terms of computational and memory access characteristics to represent other workloads fully. As a case study, we select eight workloads from a workload candidate pool with the proposed method. Using the eight selected workloads, we evaluate the performance of CPU, GPU, and MLU. We also compare the energy efficiency between GPU and MLU. Based on the evaluation result, suggestions for performance improvements of these AI chips are put forward.
Analysis of the Research Status of Information Technology Education Literature in China and Abroad

Information technology education contributes to the development of national education. Understanding the research status of information technology education at home and abroad is helpful to the implementation of education informatization. This paper takes the information technology education research literature collected by CNKI and the Web of Science database as the object and uses the bibliometric method to analyze the literature characteristics of information technology education at home and abroad. Specifically, the paper mainly carries out statistical analysis from the time distribution of literature, journal distribution, and hot keywords, and visually describes the literature features of the information technology education discipline, providing references for the research of information technology education discipline at home and abroad.
Analysis of Semantic-based Knowledge Graph Embedding Models in Uncertainty Knowledge Graphs

Recent years have witnessed rapid growth in knowledge graph embedding (KGE). KGE aims to improve knowledge acquisition, integration and reasoning skills, but there is less research on this aspect of uncertain knowledge graphs (UKG). While some existing research on uncertain knowledge graph embedding uses human labor and domain knowledge to enhance performance, it ignores that semantic-based modeling approaches are capable of modeling knowledge graphs for multiple relational patterns, including equivalence, symmetry, antisymmetry, composition, etc. Therefore, we propose some uncertain knowledge graph representation learning models based on classical semantic matching methods, and conduct experimental analysis on them. In order to ensure the fairness and scientificity of the experiments, we propose and design a unified model training framework to control variables. We use three public uncertain knowledge graph datasets and repaired the unreasonable ones. The experiment was conducted through three tasks, i.e. link prediction, confidence prediction, and relational fact ranking. We find that our proposed U ANALOGY model can combine the advantages of other models and can model the uncertainty knowledge graph well. Likewise, the proposed approach achieves the best tradeoff between performance and accuracy of uncertain knowledge graph embedding and inferring. Our dataset and code will be published at https://github.com/Chienking1997/AnalyzeUKGE.
An Estimation Method of Shadowband Correction Coefficient in Diffuse Irradiance Measurement Device

In view of the inaccurate measurement of diffuse irradiance data by the traditional solar diffuse irradiance measurement device, this paper constructs the 3D simulation cmodel of the measurement device by using SketchUp software, and imports the model into the photovoltaic system design software PVSyst. The shadowband is used as the shadow shelter, and the photovoltaic module is used as the measuring device of solar radiation to replace the total radiation meter. The 3D near-field shadow simulation is carried out in PVSyst simulation, so as to obtain the diffuse irradiance data with and without shadowband, and then calculate the correction coefficient of shadowband. The deviation from the theoretical calculation value is within 5%, so as to verify the effectiveness of this method, it can be used to evaluate solar radiation resources under shadow occlusion.
Sentiment Analysis and Topic Classification of Twitter-based Data Set on the Face-to-face Classes Resumption in the Philippines during the COVID-19 Pandemic

Due to the Corona Virus Disease 2019 (COVID-19) pandemic, there was a need for shift in pedagogy of education. Several delivery modes for educational materials and activities had to be implemented to adapt in the situation brought about by the pandemic. In the Philippines, there has been a call to fully transition to face-to-face classes expressed on social media. In this study, a data set was built consisting of tweets (Twitter data) regarding the resumption of face-to-face classes in the Philippines. This data set was subjected to training and testing to classify them in terms of topic and sentiment using Recurrent Neural Network Long Short-Term Memory (LSTM) and Multinomial Naïve Bayes. The LSTM sentiment classifier resulted to 78.33% accuracy and LSTM topic classifier produced 61.34% accuracy. The Multinomial Naïve Bayes classifier obtained 77.22% accuracy for classifying sentiment while 58.33% accuracy for topic classification.
The Multi Agent Nets Architecture as Base for Modelling Smart Home for Elderly

With the miniaturization of computer components and sensors, as well as the multiplication of communications capabilities, computing is experiencing major changes paving the way for the era of ambient intelligence. Among the central characteristics of an ambient system, is the context awareness. It allows services to adapt to a user's needs without a need of clarification. Moreover, among the general population really interested by the development of this technology are elderlies with reduced autonomy. The number of this type of person is constantly increasing. Placing these people in hospitals is, in some cases, expensive or “immoral”. One possible alternative is to keep them at home while ensuring care, comfort and safety. To reach such an objective, we propose an approach which is based on the multi agent nets architecture for modelling and simulating different services of an elderly smart home (security, comfort and economy services). The aim is to restore independent living at home for people who should normally be placed in health care institutions.