Reviewerly/exHarmony huggingface dataset:
Conditional Generative Data-Free Knowledge Distillation

Knowledge distillation has made remarkable achievements in model compression. However, most existing methods demand original training data, while real data in practice are often unavailable due to privacy, security and transmission limitation. To address this problem, we propose a conditional generative data-free knowledge distillation (CGDD) framework to train efficient portable network without any real data. In this framework, besides the knowledge extracted from teacher model, preset labels are introduced as additional auxiliary information to train the generator. The trained generator can produce meaningful samples of specified category. These samples will be employed as training data for the distillation process. In addition to using conventional distillation loss, we treat preset label as ground truth label so that the student network is directly supervised by the category of synthetic training sample. Naturally, the additional information can facilitate distillation process. Moreover, student network is forced to mimic the attention maps of teacher model and further improve its performance. We also design a novel evaluation metric called as relative accuracy to directly compare the effectiveness of different distillation methods. Trained portable network learned with the proposed data-free distillation method obtains 99.63%, 99.07% and 99.84% relative accuracy on CIFAR10, CIFAR100 and Caltech101, respectively. The superiority of proposed method is demonstrated by these experimental results.
A Cross-domain Authentication Scheme Based on Zero-Knowledge Proof

This paper proposes an anonymous, cross-domain authentication scheme based on zero-knowledge proof to combat the privacy leakage problem of cross-domain authentication when users in the heterogeneous domain access network services from different trust domains. First, we use the zero-knowledge proof algorithm to make the scheme independent of the trusted third party and realise secure data exchange between the device and the agent server (AS). The AS verifies the identity of the device through the proof that does not contain any private user information that can be reconstructed, which plays an effective role in protecting the privacy of the device. Second, the device submits the proof, which is generated from private device information and public parameter information. It has nothing to do with the trust domain authentication mechanism. Therefore, it can be used for mutual authentication between heterogeneous domains. Finally, we use the characteristics of decentralisation and tamper proof of blockchain technology to ensure the consistency of interdomain message storage and realise cross-domain authentication. Theoretical analysis shows that the scheme meets the security requirements of confidentiality, integrity and availability. The experimental results show that compared with the existing schemes, our scheme is feasible and effective.
CONVERSATIONS: Conversation with a prominent propagator

interview Free Access Share on CONVERSATIONS: Conversation with a prominent propagator: Mark Guzdial Authors: David P. Bunde Knox College Knox CollegeView Profile , Zack Butler Rochester Institute of Technology Rochester Institute of TechnologyView Profile , Christopher L. Hovey University of Colorado Boulder University of Colorado BoulderView Profile , Cynthia Taylor Oberlin College Oberlin CollegeView Profile Authors Info & Claims ACM InroadsVolume 13Issue 1March 2022 pp 6–9https://doi.org/10.1145/3497877Online:14 February 2022Publication History 1citation146DownloadsMetricsTotal Citations1Total Downloads146Last 12 Months146Last 6 weeks13 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
PPCTS: Performance Prediction-Based Co-located Task Scheduling in Clouds

With increasing market competition among commercial cloud computing infrastructures, major cloud service providers are building co-located data centers to deploy online services and offline jobs in the same cluster to improve resource utilization. However, at present, related researches on the characteristics of co-location are still immature. Therefore, stable solutions for resource collaborative scheduling are still essentially absent, which restricts the further optimization of co-location technology. In this paper, we present performance prediction-based co-located task scheduling (PPCTS) model to perform fine-grained resource allocation under Quality of Service (QoS) constraints. PPCTS improves the overall cluster CPU resource utilization to 56.211%, which is competitive to the current related works. Besides, this paper fully implements a co-located system prototype. Compared with the traditional simulator-based scheduling research work, the prototype proposed in this paper is easier to be migrated to the real production environment.
Cost-Effective Data Mining Application Covid-19 Analyzer

The idea of data mining has been around for longer than a century, yet come into a more prominent public core interest in the 1930s. As time passed, the measure of information in numerous frameworks developed to bigger than terabyte size, which can no longer be maintained manually. Additionally, for the effective presence of any business, finding hidden examples in information is viewed as fundamental. Accordingly, several software tools were created to find covered up information and make assumptions. Salesforce Einstein is an Artificial Intelligence-based tool, provided by Salesforce.com. Salesforce Einstein tool is designed to enable companies to become smarter and predictive about their customers. It analyzes your historical data against set boundaries or parameters and generates recommended actions for the companies accordingly. In this project, we are using the Salesforce Einstein tool to create insights into Covid data.
Meta-Graph-Based Embedding for Recommendation over Heterogeneous Information Networks

Heterogeneous Information Networks (HINs) are a powerful technique for information expression and have been integrated into recommendation systems as an auxiliary information source. Recently, researchers begin to utilize meta-graphs to capture complex semantics. However, existing recommendation methods based on meta-graphs face two problems. First, they do not learn the representation of meta-graphs. Second, they ignore the dimensional inconsistency of similarities from different meta-graphs, and do not explicitly use similarities. This paper presents MGBE, a meta-graph-based embedding method that learns semantic representations of meta-graphs to generate new features of meta-graphs. Specifically, MGBE utilizes meta-graphs to calculate similarities and standardizes them. It further constructs the embedding vectors of meta-graphs and combines them with similarities to generate new feature vectors. MGBE integrates user features, item features, and meta-graph features to predict the rates of user-item pairs. Extensive experiments on two real-world datasets show the effectiveness of MGBE.
Digital Authoritarianism “Made in China”

The COVID-19 pandemic has magnified the use of digital technologies in the name of public health and safety and vividly illustrated how societies, even democratic ones, can tolerate the expansion of executive power and accept restrictions on liberties. In China, the pandemic justified the use of such technologies and policies to a further extent, but also served as a proof by the government, that its model of digital control succeeded in handling the coronavirus crisis. This paper reviews the Chinese model of digital authoritarianism and highlights its implications for democracy and civil liberties, since China is aiming to export its model around the globe. It manifests the rationale and techniques of this model, but also China’s position on internet governance and techno-nationalism. China is leading the way on AI and surveillance technology and is exporting its model abroad, via the Digital Silk Road, the technology component of the Belt and Road Initiative. The exportation of the digital authoritarianism model is targeting mainly states in East Asia, Africa and Latin America, but its implications are global, if digital surveillance and social credit systems become the new normal.
Study on Resistant Hierarchical Fuzzy Neural Networks

Novel resistant hierarchical fuzzy neural networks are proposed in this study and their deep learning problems are investigated. These fuzzy neural networks can be used to model complex controlled plants and can also be used as fuzzy controllers. In general, real-world data are usually contaminated by outliers. These outliers may have undesirable or unpredictable influences on the final learning machines. The correlations between the target and each of the predictors are utilized to partition input variables into groups so that each group becomes the input variables of a fuzzy system in each level of the hierarchical fuzzy neural network. In order to enhance the resistance of the learning machines, we use the least trimmed squared error as the cost function. To test the resistance of learning machines to adverse effects of outliers, we add at the output node some noise from three different types of distributions, namely, normal, Laplace, and uniform distributions. Real-world datasets are used to compare the performances of the proposed resistant hierarchical fuzzy neural networks, resistant densely connected artificial neural networks, and densely connected artificial neural networks without noise.
Optimal quadrature formulas in the space W쐠2 (1,0) of periodic functions

This paper is devoted to the process of constructing an optimal quadrature formula in the Hilbert space of real-valued, periodic functions. Here, in order to obtain the upper bound for the absolute error of the considered quadrature formula, the norm of the error functional is calculated. In the calculation of the norm of the error functional the extremal function of the quadrature formula is used. Further, the system of linear equations for coeﬃcients of the quadrature formula that give the minimum value to the norm of the error functional is obtained.
Research on Optimizing Facial Expression Recognition Based on Convolutional Neural Network

With the development of deep learning in recent years, artificial intelligence has been widely applied in daily lives, industries, and services, which has attracted widespread attention. Based on the above application, this paper studies the typical application technology of artificial intelligence, and builds an “emotional intelligence” model using traditional facial emotion recognition as an example, accelerating the response of the model as much as possible while ensuring correct recognition.
Blockchain and Artificial Intelligence

Blockchain and artificial intelligence are perhaps the two most significant disruptive technologies this century and both will significantly rewire the world of global financial markets and the world in which we live. While blockchain offers a number of significant advantages over traditional forms of finance including lower cost and massive increases in operational efficiencies of traded markets, property records and a whole host of transaction processes, artificial intelligence is moving fast from basic structured machine learning doing menial yet important big data tasks like credit card fraud detection to predictive analysis and real-time real-world risk management and investment decision making. There is still a lot of confusion in the market about cryptocurrencies, bitcoin and the underlying blockchain technology. Blockchain and Artificial Intelligence highlights the underlying technologies of blockchain and the differences between cryptocurrencies and blockchain financial applications. It explores the current AI offerings and gives a vision of the fast-moving developments in this area including the many solutions that are expected to revolutionize the way financial and commodity markets will operate in the future.
A Framework for Syntactic and Semantic Quality Evaluation of Ontologies

The increasing focus on Web 3.0 is leading to automated creation and enrichment of ontologies and other linked datasets. Alongside automation, quality evaluation of enriched ontologies can impact software reliability and reuse. Current quality evaluation approaches oftentimes seek to evaluate ontologies in either syntactic (degree of following ontology development guidelines) or semantic (degree of semantic validity of enriched concepts/relations) aspects. This paper proposes an ontology quality evaluation framework consisting of: (a) SynEvaluator and (b) SemValidator for evaluating syntactic and semantic aspects of ontologies respectively. SynEvaluator allows dynamic task-specific creation and updation of syntactic rules at run-time without any need for programming. SemValidator uses Twitter-based expertise of validators for semantic evaluation. The efficacy and validity of the framework is shown empirically on multiple ontologies.
Real-Time Video Deraining via Global Motion Compensation and Hybrid Multi-Scale Temporal Correlations

The current video deraining algorithms mainly use adjacent frames to optimize the target frame information. However, they only consider the inter-frame temporal correlations of a uniform scale between frames, ignoring the inter-frame temporal correlations of different scales. In addition, the high computational cost is another drawback of the current video deraining algorithms. To this end, we propose a novel aggregation network that explores the inter-frame multi-scale temporal correlations for video deraining with the small computational cost. First, we construct a hybrid multi-scale feature extraction structure in the network to increase the receptive field of multi-scale features. For similar rain streaks at adjacent frames with different scales, a hybrid multi-scale residual block (HMSRB) is proposed to explore the complementary and redundant information at the temporal dimension to characterize the target frame. At the same time, we introduce an improved global context module (GCM) to avoid the complex motion estimation and motion compensation (ME&MC) operation as in previous video deraining approaches, while reducing the calculation complexity. Finally, a fusion block is utilized to adaptively merge the extracted features. Experiments demonstrate that our proposed network is proved to be more efficient and effective than the existing algorithms.
Study of principles, models and methods of FAIR paradigm of scientific data management for analysis for BIG data metadata

In this research work we considere the basic principles, models and methods of the FAIR (Findable, Accessible, Interoperable, Reusable) scientific data management paradigm as a separate case of Big Data. This paradigm is focused on the reuse of scientific research results.Basic principles of FAIR apply to three types of entities: data (or any digital object), metadata (information about this digital object) and infrastructure. Information on various projects, initiatives and communities working on solving the problems of scientific data and their metadata integration is examined. We analyze how the properties of FAIR data contribute to the unification and integration of the scientific infrastructure in the paradigm of open science, which is based on free access to research results and open data.
A Survey on an Investigation of Detection &amp; Prevention Methods for Cross-Site Scripting (XSS) Attacks

Currently, web applications are utilized for constituting data along with service discharges over the World Wide Web. While these are dynamically used for security-primary services, thus they have ended up being a popular and valuable objective for the web-related vulnerabilities. Despite the fact that few suspicious systems have been developing to fortify the cutting edge web applications and improve the impelled against them. Numerous techniques are available to emphasize very serious vulnerability in the advanced web applications. Along with the multiplicity of protection vulnerabilities, Cross site scripting attack is the most probable vulnerability found in the present web applications on SNS condition which to be a infection for the cutting edge web applications. As of late, practically 70% of all vulnerabilities on the web were sorted as XSS-related vulnerabilities. Cross site scripting attacks grant an aggressor to implement the malicious contents on the individual's internet browser to bringing different indications, for example, data conciliation, taking of cookies, credit card numbers, and passwords and so on. This review paper centers on the different identification and remedial strategies utilized in the existing works for XSS attacks, alongside discussion about advantages and disadvantages of every technique. It has additionally discussed about an eminent level of technical categorization of XSS attacks.
MVP: An Efficient CNN Accelerator with Matrix, Vector, and Processing-Near-Memory Units

Mobile and edge devices become common platforms for inferring convolutional neural networks (CNNs) due to superior privacy and service quality. To reduce the computational costs of convolution (CONV) , recent CNN models adopt depth-wise CONV (DW-CONV) and Squeeze-and-Excitation (SE) . However, existing area-efficient CNN accelerators are sub-optimal for these latest CNN models because they were mainly optimized for compute-intensive standard CONV layers with abundant data reuse that can be pipelined with activation and normalization operations. In contrast, DW-CONV and SE are memory-intensive with limited data reuse. The latter also strongly depends on the nearby CONV layers, making an effective pipelining a daunting task. Therefore, DW-CONV and SE only occupy 10% of entire operations but become memory bandwidth bound, spending more than 60% of the processing time in systolic-array-based accelerators. We propose a CNN acceleration architecture called MVP, which efficiently processes both compute- and memory-intensive operations with a small area overhead on top of the baseline systolic-array-based architecture. We suggest a specialized vector unit tailored for processing DW-CONV, including multipliers, adder trees, and multi-banked buffers to meet the high memory bandwidth requirement. We augment the unified buffer with tiny processing elements to smoothly pipeline SE with the subsequent CONV, enabling concurrent processing of DW-CONV with standard CONV, thereby achieving the maximum utilization of arithmetic units. Our evaluation shows that MVP improves performance by 2.6 \( \times \) and reduces energy by 47% on average for EfficientNet-B0/B4/B7, MnasNet, and MobileNet-V1/V2 with only a 9% area overhead compared to the baseline.
Penetration Depth Prediction Based on Data Fusion

Abstract This paper presents a penetration depth prediction model based on data fusion. The parameters of the penetration analysis are divided into different evaluation spaces, and then empirical algorithms are evaluated and the better algorithm is selected in each evaluation space. A large number of simulation data is generated to solve the problem of lack of experimental data. Two BP neural network prediction models are built based on experiment data and simulation data, respectively, and the genetic algorithm is used for parameter optimization. Finally, the attention mechanism is used to fuse the two models to generate the final dimensionless penetration depth. The experiment results show that the data fusion model has good prediction accuracy both in the whole parameter space and each evaluation space.
A Dialectic Perspective on the Evolution of Thesauri and Ontologies

The purpose of this article is to identify the most important factors and features in the evolution of thesauri and ontologies through a dialectic model. This model relies on a dialectic process or idea which could be discovered via a dialectic method. This method has focused on identifying the logical relationship between a beginning proposition, or an idea called a thesis, a negation of that idea called the antithesis, and the result of the conflict between the two ideas, called a synthesis. During the creation of knowl­edge organization systems (KOSs), the identification of logical relations between different ideas has been made possible through the consideration and use of the most influential methods and tools such as dictionaries, Roget’s Thesaurus, thesaurus, micro-, macro- and metathesauri, ontology, lower, middle and upper level ontologies. The analysis process has adapted a historical methodology, more specifically a dialectic method and documentary method as the reasoning process. This supports our arguments and synthesizes a method for the analysis of research results. Confirmed by the research results, the principle of unity has shown to be the most important factor in the development and evolution of the structure of knowl­edge organization systems and their types. There are various types of unity when considering the analysis of logical relations. These include the principle of unity of alphabetical order, unity of science, semantic unity, structural unity and conceptual unity. The results have clearly demonstrated a movement from plurality to unity in the assembling of the complex structure of knowl­edge organization systems to increase information and knowl­edge storage and retrieval performance.
Feature Expansion Word2Vec for Sentiment Analysis of Public Policy in Twitter

Social media users, especially on Twitter, can freely express opinions or other information in the form of tweets about anything, including responding to a public policy. In a written tweet, there is a limit of 280 characters per tweet and this allows for problems such as vocabulary mismatches. Therefore, in this study, the feature expansion Word2vec method was applied to overcome when the vocabulary mismatches occur. This study develops and compares the Twitter sentiment analysis system using the feature expansion Word2vec method with the Logistic Regression (LR) and Support Vector Machine (SVM) classification algorithms and the system without the feature expansion Word2Vec method. The results of this study, the feature expansion Word2Vec method on the SVM classification algorithm succeeded in increasing the system accuracy up to 0,99% with an accuracy value of 78,99%.