Reviewerly/exHarmony huggingface dataset:
A Blockchain-Based Digital Copyright Protection System with Security and Efficiency

Today’s digital copyright protection system is completely centralized and the confirmation and transaction of copyright depend entirely on a third party, which suffers from time-consumption, high cost and data isolation. In this paper, we propose a secure and efficient digital copyright protection system based on blockchain technology and IPFS that provides a whole process of copyright protection services including confirmation, registration, subscription, buying, tracing and querying. A series of encryption algorithms are adopted to ensure the security of the system and smart contracts are utilized to achieve automatic confirmation and transaction of copyright. Moreover, we present key aspects related to architectural design and interactions details between system components. In order to validate the proposed system, we evaluated the whole system in terms of function, performance and security aspects. Our test and analysis suggest that the proposed system can help to solve the problem caused by the current copyright protection system.
Human Age Prediction Based on Hand Image using Multiclass Classification

The human identification/recognition based on biometric data (face, voice, fingerprint, etc.) is the subject of various studies. It can be improved by introducing the human age factor. This paper proposes a method of human age prediction based on hand image by using a multiclass classification. The architecture used is a time distributed convolutional neural network combined with a Gated Recurrent Unite followed by a stack of fully connected layers, which extract hand skin features from a different aspect of hand Dorsal Left, Palmar Left, Dorsal Right, and Palmar Right. The results are a classification into 17 classes from range age 18 - 75. Experimental results show that the proposed method has effective performance in age prediction from hand images with an accuracy of 96.5%.
Cross Service Providers Workload Balancing for Data Centers in Deregulated Electricity Markets

The emerging Internet of things and 5G applications boost a continuously increasing demand for data processing, which results in an enormous energy consumption of data centers (DCs). Considering that existing distributed geographical load balancing is approaching the limit in reducing the energy cost of DCs, cloud service providers (SPs) are motivated to pursue a higher level of cooperation. In this context, cross-SP workload balancing among the DCs operated by different SPs represents a future trend of the DC industry. This article investigates the optimal cross-SP workload balancing when it couples with the electricity markets. First, we assume that there is a central operator (CO) coordinating the DCs owned by various SPs. A noncooperative game is formulated to model the interaction between utilities and CO, which serves as a price maker. Under the centralized coordination of CO, an optimal solution is obtained with an iterative algorithm. Taking into account the computation and privacy issues, a decentralized algorithm is then proposed by utilizing techniques in a state-based potential game. Numerical results corroborate the effectiveness of the proposed algorithm. Simulations using Google workload trace show that the workload balancing among cross-SP DCs results in a lower DC operation cost than the existing price taker approach.
Quantum Attacks on HCTR and Its Variants

Recently, in Asiacrypt 2019, Bonnetain et al. have shown attacks by quantum adversaries on FX construction and Even-Mansour Cipher without using superposition queries to the encryption oracle.In this article, we use a similar approach to mount new attacks on Hash-Counter (HCTR) and Hash-Counter-Hash (HCH) constructions.In addition, we mount attacks on HCTR, tweakable-HCTR, and HCH using the superposition queries to the encryption oracle using strategies proposed by Leander and May in Asiacrypt 2017 and Kaplan et al. in Crypto 2016.INDEX TERMS Grover's search algorithm, Hash-Counter-Hash (HCH), Hash-Counter (HCTR), Simon's algorithm, symmetric key cryptography, tweakable-HCTR.
Friends are not to be eaten: Children are reluctant to eat cookies that share physical and psychological human features due to their desire to relate to the food

Children's food marketers very often use anthropomorphism in advertising as if it is an effective technique aimed at increasing food consumption. However, the evidence supporting this effect is mixed. In this research, we propose that while a food product's humanlike appearance (physical anthropomorphism) may increase consumption, attribution of mental states and emotions (psychological anthropomorphism) to a physically anthropomorphized food product discourages consumption because it facilitates the formation of a social-like relationship with the food. In two studies, we tested this prediction on samples of children. Preschoolers (N = 91 and N = 97) were randomly assigned to one of three conditions in which they were presented with a cookie that was both physically and psychologically anthropomorphized, physically anthropomorphized only, or not anthropomorphized. In two studies, we observed whether children left alone with the cookie would consume it and how quickly it would happen. Additionally, in Study 2, we asked children about their desire to relate to the cookie. The results confirmed that physical anthropomorphism accompanied by psychological anthropomorphism considerably reduced consumption of the cookies compared to both mere physical anthropomorphism and non-anthropomorphism. Moreover, the reduced appetite for cookies that were physically and psychologically anthropomorphized was mediated by the desire to relate to them. We discuss the implications of these results.
A Sine Cosine Algorithm Enhanced Spherical Evolution for Continuous Optimization Problems

Spherical evolution (SE) is a relatively innovative algorithm. It transforms the original hypercube search into a spherical search. By this novel search style, SE expanded the search range. Sine cosine algorithm (SCA) creates multiple initial random candidate solutions and requires them to fluctuate outwards or towards the best solution using a mathematical model based on sine and cosine functions, which demonstrates that this algorithm can avoid local optima. In this article, we introduce SCA to enhance the convergence ability of SE. The experiment results on CEC2017 benchmark functions indicate the effectiveness of this hybridization, suggesting that the algorithm proposed is able to explore different regions of a search space, avoid local optima, converge towards the global optimum, and exploit promising regions of a search space during optimization effectively.
Random Sampling Using -Vector

This work introduces two new techniques for random number generation with any prescribed nonlinear distribution based on the k-vector methodology. The first approach is based on inverse transform sampling using the optimal k-vector to generate the samples by inverting the cumulative distribution. The second approach generates samples by performing random searches in a pre-generated large database previously built by massive inversion of the prescribed nonlinear distribution using the k-vector. Both methods are shown suitable for massive generation of random samples. Examples are provided to clarify these methodologies.
Student Performance Protection Based On Blockchain Technology

Abstract In recent years, performance management has become more and more significant in terms of management and credibility of universities. They have to review student performance when issuing diplomas. Therefore, the reliability of student performance management is extremely important. Nowadays, many universities adopt a data-centralized management model. However, centralized database often brings about problems such as information leakage and easy tampering. With the development of blockchain technology, its decentralized and unforgeability characteristics have gradually attracted people’s attention. In order to prevent students’ scores from being maliciously tampered with, we utilize blockchain to design and implement a decentralized student score sharing management system. Moreover, by using the Ethereum smart contract to manage on-chain and off-chain data storage as well as on-chain query and verification, we ensure the traceability, tamper-proof, distributed storage, and the security of query process. Finally, this article explores the construction of prototype system based on the Ethereum environment. The experimental results show that the anti-tampering and traceability scheme proposed in this article is feasible. The application of blockchain can fully satisfy the needs of various participants in student performance management. Each subject’s information has a separate decryption private key, which perfectly protects student’s score information.
Bayesian Integration Using Resistivity and Lithology for Improving Estimation of Hydraulic Conductivity

Abstract The characterization of spatially heterogeneous hydraulic conductivity ( K ) is important in groundwater resources management. We propose a Bayesian statistical method that integrates multiple secondary data (continuous and category data) with primary data ( K ) to improve regional K field characterization. Considering the disparity of the data scale, spatial scarcity of primary and secondary data, and need for regional‐scale site characterization, the aquifer thickness is used as the scale for data integration. We transform the high‐resolution secondary data to the scale of K and perform linear/nonlinear regression analyses for the transformed secondary data and primary data. A Bayesian approach using Metropolis‐within‐Gibbs sampling is developed for jointly integrating the primary and transformed secondary data without a limitation on the type of data attribute and the number of data set. A synthetic example is first presented to demonstrate the capability of the proposed method. Results show that the correlation strength, not the relation type, is the primary factor for improving the estimates. The Bayesian method is applied to Choushui River alluvial fan in Taiwan. Resistivity logs and a lithological description are first upscaled and then simultaneously integrated in the K estimation. Results indicate that the improvement of the K estimate obtained using resistivity data is higher in the proximal and mid fans but lower in the distal fan compared to that obtained using lithofacies data. Jointly integrating two‐attribute data outperforms using one or no secondary data set for K estimates. The proposed Bayesian integration method is thus versatile and suitable for large‐scale aquifer characterization.
Performance of Contrast-limited AHE in Preprocessing of Face Recognition with Training Image under Various Lighting Conditions

Face recognition performance is greatly influenced by the lighting conditions on the face images. Generally, to solve the problem caused by this lighting factor, the illumination normalization process is carried out at the preprocessing stage through contrast adjustment of face images. One of the contrast adjustment techniques is Contrast-limited AHE. In many cases, this technique performs well for normalizing the illumination, thereby improving general object recognition performance. In this research, an experiment was conducted to determine the effect of contrast adjustment using Contrast-limited AHE on face recognition in each lighting condition in the image. So later, it can be seen whether the technique is useful in all image lighting conditions or not. The Robust Regression method is used for the classification process in face recognition, which has an excellent performance in recognizing faces influenced by lighting factors. Experiments use facial images in the AR Face Database related to lighting factors, with low, medium, high, and very high lighting conditions. The testing process is carried out by comparing face recognition performance using the Contrast-limited AHE technique in the preprocessing phase and face recognition without a contrast adjustment process at each lighting condition. Experimental results in the low, medium, and high lighting conditions show that using the Contrast-limited AHE technique in preprocessing results in face recognition performance much better than face recognition without preprocessing. However, in very high (extreme) lighting conditions, the use of the Contrast-limited AHE technique in preprocessing has a less significant effect on face recognition performance, which results in an average accuracy of 95.33%, only 0.67% higher than face recognition without preprocessing.
A generic model for representing openness in multi-agent systems

Abstract Openness is a challenging property that may characterize multi-agent systems (MAS). It refers to their ability to deal with entities leaving and joining agent society over time. This property makes the MAS behaviour complex and difficult to study and analyze, hence the need for a representative model allowing its understanding. In this context, many models were defined in the literature and we propose to classify them into three categories: structural models, functional models and interactional models. The existing models were proposed either for representing structural openness or for modelling functional or interactional ones independently. But, none of them was oriented to represent MAS openness in a global way while considering its three aspects at once. Besides, each one was defined in order to realize a specific objective and in a particular domain of application. In this paper, we propose an evolving KAGR graph. The latter provides a common understanding of openness and unifies its structural, functional and interactional aspects in a generic way. Our model is finally tested and validated on a multi-agent rescue simulator.
An Efficient Approach Based on Parameter Optimization for Network Traffic Classification Using Machine Learning

Different kinds of attacks on the network have greatly increased due to the exponential increase in the number of users over the last decade. This has greatly hampered transactions online especially when they are financially based. Hence, there is every need to develop and design new cybersecurity techniques to curb these cyber-crimes. This study presents the accurate classification of the Transmission Control Protocol (TCP) and User Datagram Protocol (UDP) flows on the network using Machine Learning (ML) based on parameter optimization. The method was validated with three randomly selected Machine Learning (ML) algorithms of the Waikato Environment for Knowledge Analysis (WEKA) software with 10 folds cross-validation and chose the algorithm with the best performance. Experiments were carried out on three scenarios using the USTC-TFC2016 dataset and the results demonstrated that Random Forest (RF) achieved average performance accuracy of 99.52%, which is 0.11% higher than the the-state-of-the art approach. It was also shown that the Decision Tree (J48) achieved higher classification accuracy than Naïve Bayes (NB).
Creation of Value-Added Services by Retrieving Information From Linked and Open Data Portals

In recent decades, different initiatives have emerged in public and private institutions with the aim of offering free access to the data generated in their activity to anyone. In particular, there are two types of initiatives: open data portals and linked data portals. Open data portals are characterized in that it offers access to its content in the form of a REST-type web services API that acts as a query language. On the other hand, linked data portals are characterized in that its data is represented using ontologies encoded by RDF triplets of the subject-predicate-object style forming a knowledge graph. This chapter presents a set of value-added service creation cases using the information stored in open data and linked data repositories. The objective is to show the possibilities offered by the exploitation of these repositories in various fields such as education, tourism, or services such as the search for taxis at an airport.
Collaborating with Technology-Based Autonomous Agents: Issues and Research Opportunities

Purpose: This article reports the results from a panel discussion held at the 2019 European Conference on Information Systems (ECIS) on the use of technology-based autonomous agents in collaborative work.Approach: The panelists (Drs. Izak Benbasat, Paul Benjamin Lowry, Stefan Morana, and Stefan Seidel) presented ideas related to affective and cognitive implications of using autonomous technology-based agents in terms of (1) emotional connection with these agents, (2) decision making, and (3) knowledge and learning in settings with autonomous agents. These ideas provided the basis for a moderated panel discussion (the moderators were: Drs. Isabella Seeber and Lena Waizenegger), during which the initial position statements were elaborated on and additional issues were raised. Findings: Through the discussion, a set of additional issues were identified. These issues related to (1) the design of autonomous technology-based agents in terms of human-machine workplace configurations, as well as transparency and explainability, and (2) the unintended consequences of using autonomous technology-based agents in terms of de-evolution of social interaction, prioritization of machine teammates, psychological health, and biased algorithms. Originality/value: Key issues related to the affective and cognitive implications of using autonomous technology-based agents, design issues, and unintended consequences highlight key contemporary research challenges that allow researchers in this area to leverage compelling questions that can guide further research in this field.
Traffic Sign Recognition Using YOLOv3

There was developed a system within this study that recognizes traffic signs on video. This system uses the YOLOv3 convolutional neural network architecture. The maximum accuracy of recognition of road signs reached 93.91% per 1000 iterations and 99.94% per 2000 iterations.
Towards website domain name classification using graph based semi-supervised learning

In this work, we tackle the problem of classifying websites domain names to a category, e.g., mapping bbc.com to the ”News and Media” class. Domain name classification is challenging due to the high number of class labels and the highly skewed class distributions. Differently from prior efforts that need to crawl and use the web pages’ actual content, we rely only on traffic logs passively collected, observing traffic regularly flowing in the network, without the burden to crawl and parse web pages. We exploit the information carried by network logs, using just the name of the websites and the sequence of visited websites by users. For this, we propose and evaluate different classification methods based on machine learning. Using a large dataset with hundreds of thousands of domain names and 25 different categories, we show that semi-supervised learning methods are more suitable for this task than traditional supervised approaches. Using graphs, we incorporate in the classifier aspects not strictly related to the labeled data, and we can classify most of the unlabeled domains. However, in this framework, classification scores are lower than those usually found when exploiting the page-specific content. Our work is the first to perform an extensive evaluation of domain name classification using only passive flow-level logs to the best of our knowledge.
Visual Navigation for UAVs Landing on Accessory Building Floor

Landing is a critical step in most real world UAV applications, especially in delivery. A package delivery is successful only when a landing or a low-altitude drop-off (pseudo-landing) is completed. For precision landing requirement, vision-based navigation techniques are of high potential to be reliable and accurate. In this paper, we present a research work on autonomous visual navigation for precision landing on accessory building floor. We incorporate some state-of-the-art vision-based methods, develop other functional components to present an employable autonomous navigation system for precision landing near buildings. Initial experiments in a real world scenario show an encouraging results with high success rate of performing precision landing.
Mercator: a pipeline for multi-method, unsupervised visualization and distance generation

Unsupervised machine learning provides tools for researchers to uncover latent patterns in large-scale data, based on calculated distances between observations. Methods to visualize high-dimensional data based on these distances can elucidate subtypes and interactions within multi-dimensional and high-throughput data. However, researchers can select from a vast number of distance metrics and visualizations, each with their own strengths and weaknesses. The Mercator R package facilitates selection of a biologically meaningful distance from 10 metrics, together appropriate for binary, categorical and continuous data, and visualization with 5 standard and high-dimensional graphics tools. Mercator provides a user-friendly pipeline for informaticians or biologists to perform unsupervised analyses, from exploratory pattern recognition to production of publication-quality graphics.Mercator is freely available at the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/Mercator/index.html).
Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks

Prostate segmentations are required for an ever-increasing number of medical applications, such as image-based lesion detection, fusion-guided biopsy and focal therapies. However, obtaining accurate segmentations is laborious, requires expertise and, even then, the inter-observer variability remains high. In this paper, a robust, accurate and generalizable model for Magnetic Resonance (MR) and three-dimensional (3D) Ultrasound (US) prostate image segmentation is proposed. It uses a densenet-resnet-based Convolutional Neural Network (CNN) combined with techniques such as deep supervision, checkpoint ensembling and Neural Resolution Enhancement. The MR prostate segmentation model was trained with five challenging and heterogeneous MR prostate datasets (and two US datasets), with segmentations from many different experts with varying segmentation criteria. The model achieves a consistently strong performance in all datasets independently (mean Dice Similarity Coefficient -DSC- above 0.91 for all datasets except for one), outperforming the inter-expert variability significantly in MR (mean DSC of 0.9099 vs. 0.8794). When evaluated on the publicly available Promise12 challenge dataset, it attains a similar performance to the best entries. In summary, the model has the potential of having a significant impact on current prostate procedures, undercutting, and even eliminating, the need of manual segmentations through improvements in terms of robustness, generalizability and output resolution.
A Multi-Agent Depth Bounded Boolean Logic

Recent developments in the formalization of reasoning, especially in computational settings, have aimed at defining cognitive and resource bounds to express limited inferential abilities. This feature is emphasized by Depth Bounded Boolean Logics, an informational logic that models epistemic agents with inferential abilities bounded by the amount of information they can use. However, such logics do not model the ability of agents to make use of information shared by other sources. The present paper provides a first account of a Multi-Agent Depth Bounded Boolean Logic, defining agents whose limited inferential abilities can be increased through a dynamic operation of becoming informed by other data sources.