Reviewerly/exHarmony huggingface dataset:
Ciphertext-Policy Attribute-Based Encryption for General Circuits in Cloud Computing

Driven by the development of Internet and information technology, cloud computing has been widely recognized and accepted by the public. However, with the occurrence of more and more information leakage, cloud security has also become one of the core problem of cloud computing. As one of the resolve methods of it, ciphertext-policy attribute-based encryption (CP-ABE) by embedding access policy into ciphertext can make data owner to decide which attributes can access ciphertext. It achieves ensuring data confidentiality with realizing fine-grained access control. However, the traditional access policy has some limitations. Compared with other access policies, the circuit-based access policy ABE supports more flexible access control to encrypted data. But there are still many challenges in the existing circuit-based access policy ABE, such as privacy leakage and low efficiency. Motivated by the above, a new circuit-based access policy ABE is proposed. By converting the multi output OR gates in monotonic circuit, the backtracking attacks in circuit access structure is avoided. In order to overcome the low efficiency issued by circuit conversion, outsourcing computing is adopted to Encryption/Decryption algorithms, which makes the computing overhead for data owners and users be decreased and achieve constant level. Security analysis shows that the scheme is secure under the decision bilinear Diffie-Hellman (DBDH) assumption. Numerical results show the proposed scheme has a higher computation efficiency than the other circuit-based schemes.
Understanding the impact of outsourcing mitigation against BGP prefix hijacking

BGP prefix hijacking caused by a misconfiguration or malicious route announcements brings great trouble to today’s Internet. Outsourcing mitigation is a recently proposed automatic hijacking mitigation method. It mitigates hijacking incidents by attracting and redirecting the hijacked traffic to the origin AS so the deployment of the AS that performs the attracting and redirecting behaviors is important for improving the effectiveness of mitigation. However, traditional methods fail to guarantee mitigation effectiveness, i.e., simply selecting the neighbor AS of the origin AS to mitigate. Therefore, how to measure the mitigation effectiveness of different ASes and effectively select the mitigators are key issues of outsourcing mitigation. In this paper, to measure the mitigation effectiveness of ASes, (1) we use a new metric for evaluating the mitigation effectiveness, and propose a strategy called AS Reachability Influence Selection (ARS) for effectively selecting ASes with high mitigation effectiveness. (2) We conduct extensive analysis to deeply study different characteristics (e.g., AS type, degree, provider, region) that influence the mitigation effectiveness. As the results show, ARS can put ASes with high mitigation effectiveness in front. The results also show that ASes with many Tier-1 providers or high tier providers may achieve higher mitigation effectiveness than Tier-1 ASes.
Federated Deep Learning Algorithm For Target Recognition Based On Trusted Network

Aiming at the high-precision target recognition algorithm that requires a large amount of data for training, but it is difficult to integrate small-scale fragmented data in different data islands and effectively apply the problem, a federated deep learning based on trusted network is proposed (Federated-Deep Learning, FDL) training framework, and use this framework to train image data distributed on different clients. Through the analysis of the experimental results of the federated deep learning framework, the practicability and feasibility of the framework are proved.
A Practical Oblivious Cloud Storage System based on TEE and Client Gateway

In this paper, we propose a new oblivious cloud storage system, which is more efficient and scalable than existing schemes due to the combined leverage of SGX-based trusted execution environment (TEE) at the cloud server side and the moderate storage space at the client side. The TEE is employed to securely implement functionalities of ORAM model in the server without tightly involving the clients. Meanwhile, the storage at the client side is utilized to store metadata and recently/frequently accessed data, which facilitates the client to remotely determine the strategies for data query/eviction and to reduce the frequency of directly accessing data from the server. The evaluation results show that, when the size of outsourced data is 1-20 GB and the block size is 1-8KB, the data access throughput between 320 KB/s and 640 KB/s can be attained, and the average query latency for each block is only 2.26–12.80 ms.
Debate in informatics lessons using the Kialo Edu resource

The article discusses the need for the formation of communicative competence in students at the level of basic general education. It is proposed to use the "Debate" technology as a means of forming communicative competence. The stages and organizational features of the classic Karl Popper debates are described, the main difculties associated with their conduct in the classroom are highlighted. A list of possible topics for organizing debates in informatics lessons is given. A description of the Kialo Edu resource that facilitates debate is given. The resource toolkit is demonstrated in detail. The examples show how you can use the resource to accompany the debate on one or more theses, how to create counterarguments and leave comments on the added statements. An example of organizing a debate in 9th grade on the topic "Internet communication as a worthy substitute for real contacts" within the framework of the study of the theme "Communication technologies" using Kialo Edu is given.
Making Optimal Decisions with Learning Method Based on Fuzzy Logic

This work aims to develop the functions and structure of intelligent agents with fuzzy logic based on methods of reinforcement learning and their application to solve practical problems of decision-making in conditions of uncertainty. To achieve this goal, it is used fuzzy inference based on production rules knowledge bases to develop an algorithm for the operation of an intelligent agent. The empirical verification of the developed algorithm efficacy is carried out for the task of agent finding the optimal value of the function in the cell space with the determination of the direction of movement according to the rules of fuzzy logic.
AMM Based P2P Energy Trading System Using Hyperledger Fabric Blockchain

This paper proposes a P2P energy trading system over Hyperledger Fabric blockchain with AMM (Automated Market Maker) for innovatory speed improvement. Participants in the network will adequately use tokens for trading: energy coin, money coin, whereas tokens are cryptocurrency that can be exchanged for the real value. Unlike the existing network, which required certain conditions for transactions to be established, The proposed network matches transactions instantaneously with minimal process. Adapting the AMM, trades are established on the P2P energy trading system without any additional factors since market prices are determined whenever tokens are inserted into the liquidity pool. By providing an optimized trading environment, we expect further development of the P2P energy trading industry.
Analysis of Participation in Lifelong Education Programs at Senior Welfare Centers : Focusing on the Busan Area

The purpose of this study is to provide data for developing the most appropriate and effective lifelong education program for the elderly as a basis for lifelong education by analyzing the participation in lifelong education of senior welfare centers. For this purpose, data of 170 people were finally analyzed, focusing on the participants in the lifelong education program for the elderly at the senior welfare center located in Busan. Data analysis was performed using IBM SPSS Ver. 22.0 program was used. The research results are first, Motives to participate in lifelong education programs were
A range proof scheme analysis for the Mimblewimble cryptocurrency protocol

Mimblewimble is a privacy-oriented cryptocurrency technology which provides security and scalability properties that distinguish it from other protocols of its kind. In previous work we have proposed an idealized model that captures the main features of the Mimblewimble protocol with a model-driven verification approach. In this work, we present an extension of our model to enable zero-knowledge proofs in order to prove that the transaction amount is in a certain range without revealing the value. Furthermore, we provide some security properties a range proof scheme should satisfy.
Satellite-Based Operational Solar Irradiance Forecast for Uruguay's Solar Power Plants

This work presents and validates an intra-day operational solar forecast for the southern cone of South America based on GOES16 satellite images. The forecast is evaluated over 12 photovoltaic (PV) generation sites in the northwest of Uruguay. The irradiance predictions are obtained from the combination of different satellite cloudiness forecasts and a standard satellite-to-irradiance conversion model, both specifically adapted to the region. The core of the system is a Cloud Motion Vector (CMV) satellite technique, considered in different spatial resolutions. The optimal linear combination of a single-pixel and space-averaged CMV forecast with the space-averaged persistence is proposed and analyzed. The performance assessment of the individual sub-forecasts and their combination is presented for each time horizon between 10 minutes and 4 hours, with a 10 minutes time step.
Role Discovery in Observed Multi-Agent Systems Over Time through Matrix Factorization

Understanding unknown multi-agent systems solely from observations without prior knowledge of the systems' composition or structure is critical for effectively responding to and interacting with them. Whether the unknown system consists of humans, robots, or other entities, the capability to discover the roles that various agents play in the multi-agent system is necessary to fully understand it. While existing work often focuses on predicting future trajectories or behaviors, there has been little research on identifying agents that share roles within a multi-agent system. Discovering shared roles enables a fuller understanding of the system and its future behavior, i.e., agents that share a role could be expected to behave similarly. In this paper, we propose a novel approach for role discovery in an observed multi-agent system. We first present a method to learn a unified temporal representation of the multi-agent system through a temporally weighted approximation of graphs describing relationships between agents at each time step. We then present our main contribution, where we formulate role discovery as a regularized optimization problem with the goal of learning the optimal role assignment based on the unified temporal representation. Our approach learns probabilities that agents play different roles while also discovering the number of distinct roles that exist in the multiagent system, and is proven to converge to the optimal solution. We also introduce a new role recognition dataset and evaluate on an existing dataset, showing that our approach outperforms existing methods in discovering roles in an observed multi-agent system.
Siamese Generative Adversarial Predicting Network for Extremely Sparse Data in Recommendation System

In the recommendation system, user-item preferences are described by a High-Dimensional and Sparse (HiDS) matrix. Collaborative Filtering (CF)-based models have been widely adopted to solve unknown entries estimation. However, a CF-based model does not take the data distribution characteristic of an HiDS matrix into account, thereby its representation ability limited. To address this issue, this paper proposes to build a Siamese Generative Adversarial Predicting Network (SGAPN) to estimate the unknown entries in an HiDS matrix. Firstly, we build a model to learn the data distribution characteristic of an HiDS matrix. Secondly, based on the learned data distribution and observed entries, we build a model to estimate the unknown entries. Compared with the CF-based model, our model takes the data distribution of an HiDS matrix into account to address the unknown entries estimation. Experimental results over four HiDS matrices generated by industrial applications demonstrate that compared with several state-of-the-art models, the proposed model achieves competitive prediction accuracy.
A Simple, Concise, Query-based Approach to News Article Summarization Using Sentence Scoring

With the increasing amount of information being digitized and the growing connectedness of the world, access to news and their intricated information is becoming more vital. Because of this growing need, creating news article summaries is becoming an increasingly important task to allow people to access essential information quickly. However, current summarization approaches require complex, taxing algorithms that cannot be seamlessly adopted for others to implement at the speed that we need. To remedy this, we have designed an elegant approach that allows the utilizing technology to quickly employ a multinomial classifier and sentence scoring of news articles to help with querying and filtering news to allow users to obtain a brief, efficient summary of what the articles entail. The multinomial classifier achieves very effective classification of news articles for summarization. Using various complementary sentence scores, we are able to accurately determine sentences that provide the most informative contents with respect to a user query Q. Through the use of this classification and summarization, we allow information of Q to be readily available. Experimental results verify that our news article summarization approach is effective and efficient in creating high-quality summaries. In addition, the conducted empirical study demonstrates that our summarization approach outperform a significant number of DUC summarizers.
AR CARD: Interactive Cards using Augmented Reality

Interactive cards using augmented reality are used to break the bounds of information confined within a small card. The existing business cards cannot give much information in a single glance. Only trivial information is present in the business card which only gives vague information. Augmented Reality can enhance the user experience by including much more information regarding the user by just scanning the business card. AR CARD allows the user to view the details or information that are not present on the business card as an augmented detail view by including more interactive UI thereby enhancing the user experience. The augmented display for a specific card can only be triggered by a target image which when scanned, details related to the target image which is the business card is fetched and augmented. By implementing an Interactive Augmented card the user can have a better interactive experience as well as acquire a lot of information which a business card is unable to provide.
Performance Evaluation of Convolutional Neural Network Using Synthetic Medical Data Augmentation Generated by GAN

Data augmentation is widely used in image processing and pattern recognition problems in order to increase the richness in diversity of available data. It is commonly used to improve the classification accuracy of images when the available datasets are limited. Deep learning approaches have demonstrated an immense breakthrough in medical diagnostics over the last decade. A significant amount of datasets are needed for the effective training of deep neural networks. The appropriate use of data augmentation techniques prevents the model from over-fitting and thus increases the generalization capability of the network while testing afterward on unseen data. However, it remains a huge challenge to obtain such a large dataset from rare diseases in the medical field. This study presents the synthetic data augmentation technique using Generative Adversarial Networks to evaluate the generalization capability of neural networks using existing data more effectively. In this research, the convolutional neural network (CNN) model is used to classify the X-ray images of the human chest in both normal and pneumonia conditions; then, the synthetic images of the X-ray from the available dataset are generated by using the deep convolutional generative adversarial network (DCGAN) model. Finally, the CNN model is trained again with the original dataset and augmented data generated using the DCGAN model. The classification performance of the CNN model is improved by 3.2% when the augmented data were used along with the originally available dataset.
Association and Aggregation Class Relationships: is there a Difference in Terms of Implementation?

In this work we revisit the known problem of the lack of well-defined semantics, at the implementation level, for association and aggregation relationships in class diagrams to hypothesize that, in the context of software development, there is a better understanding of the associations between classes when omitting aggregation. We conducted an experimental study in which a questionnaire was applied to 100 subjects, 50 in the test group and 50 in the control group. The obtained responses were analyzed with statistical methods. From our results we conclude that it is not useful, from the point of view of software design, to differentiate between association and aggregation class relationships when a model will be implemented.
The Use of Machine Translation to Provide Resources for Under-Resourced Languages - Image Captioning Task

Image captioning is an NLP task that has many applications such as image search and retrieval. This Task is a challenging task, and it needs a lot of data (image data and their text captions), which might not be available for some languages. In this work, we investigate the use of a machine translation system to provide resources for a low-resourced language (Arabic) for the imaging captioning task. We train a model on captions automatically translated using Google machine translation service. The performance is measured using the BLEU, ROUGE, CIDEr, METEOR metrics. We compare to English model's performance. We also evaluate the generated captions on manually translated captions. The results show that machine translation can be good enough for creating resources for low-resourced languages for the image captioning task and translating training data and building a new model is better than translating the model's output.
Software Structure Exploration in an Immersive Virtual Environment

In recent years, research has been conducted to visualize and understand complex structure of a programs in a virtual environment; however, an effective interaction method for exploration has not yet been established. We investigate a system that allows users to explore the structures by hand gestures in an immersive environment. We evaluate the efficiency of gesture-based structure search and its effectiveness.
BEADS: Blockchain-Empowered Auction in Decentralized Storage

Cloud storage is an efficient paradigm for managing the outsourced data for users, and its security can be enhanced by the blockchain technique. However, in the existing blockchain-based cloud storage schemes, distribution of vacant storage is unbalanced while users have various storage demands. This paper first formulates the problem of maximizing total utility for the cloud storage allocation in blockchain network. Then, a blockchain-empowered storage auction mechanism (BEADS) with double-chain structure is proposed, in order to improve the balance and effectiveness of cloud storage. The auction is truthful and individually rational while the budget is balanced. Specifically, a blockchain-based smart contract is utilized to replace the auctioneer for a more secure cloud storage auction. Experimental results show that the proposed scheme outperforms the baselines TACD, TACDpp and HAF, in terms of utility, resource proportion, and individual matching, respectively.
A New Symmetric Information Hiding Scheme Based on Cover Randomness

This paper proposes a new symmetric information hiding scheme, aiming to increase the degree of disguise and the security. This scheme utilizes the randomness of the image cover itself to generate the secret key which has high randomness, so as to improve the effect of disguise of stego-objects and the security of information hiding scheme. In addition, this paper also proposes a new randomness test method, which can give more accurate quantitative test results and is suitable for testing the randomness of the cover. Experimental studies show that the new information hiding method can improve the degree of disguise of the stego-object and the security of the steganographic algorithm, and enhance the robustness of the information hiding to some extent.
Training Application for Ostomy Patients in Virtual Reality

Patients suffering from colon cancer often undergo ostomy surgery. In many cases this means a large change in the life of the patients. While some patients had time to prepare for this change, it is sudden for others. The patients need to take care of their stoma on a daily basis. The steps required for this, are often taught by demonstrating the steps. However, patients often remain uncertain. In this paper, a training application for patients is discussed. This application aims to use virtual reality in order to help patients learn the necessary steps of stoma care, as well as reduce their anxiety about their new life situation. In a small feasibility study, users have tested the application and rated it with the help of a questionnaire. This has yielded positive results, and a larger, long-term study to determine the effectiveness of the application is planned in the near future.