Reviewerly/exHarmony huggingface dataset:
Using information entropy to analyze secure multi-party computation protocol

Secure multi-party computation(SMPC) is an important research field in cryptography, secure multi-party computation has a wide range of applications in practice. Accordingly, information security issues have arisen. Aiming at security issues in Secure multi-party computation, we consider that semi-honest participants have malicious operations such as collusion in the process of information interaction, gaining an information advantage over honest parties through collusion which leads to deviations in the security of the protocol. To solve this problem, we combine information entropy to propose an n-round information exchange protocol, in which each participant broadcasts a relevant information value in each round without revealing additional information. Through the change of the uncertainty of the correct result value in each round of interactive information, each participant cannot determine the correct result value before the end of the protocol. Security analysis shows that our protocol guarantees the security of the output obtained by the participants after the completion of the protocol.
Open Science

Open science is a multifaceted, multipronged movement that seeks to increase access to research for all. It encompasses a variety of activities and tools, including open access journal publishing, open educational resources, open software, altmetrics, and citizen science. Because the open science movement entails a variety of different activities and initiatives and requires a cultural shift in the way science is conducted, it will need a coordinated effort to be successful. Libraries with their interdisciplinary nature, their role as connectors, and commitment to collecting, preserving, and disseminating information can lead the way to making open the default. After providing an overview of the open science movement, this chapter then highlights the different ways that academic libraries can and have become engaged in this movement. The chapter ends with two case studies of libraries based on interviews and a review of online documents.
MazeRunner: Evaluating the Attack Surface of Control-Flow Integrity Policies

Control-Flow Integrity (CFI) enforces a control-flow graph (CFG) to limit attackers' ability to manipulate runtime control flow. CFI variations, enforcing different CFGs, achieve different degrees of attack surface reduction. To compare the security strength of different CFI policies, measuring the remaining attack surface is critical but challenging. Therefore, we propose MazeRunner, a framework that quantitatively estimates the attack surface of a CFI-hardened program. Methodology-wise, it takes a program's CFG, an attack model, and a security-violation policy as input to discover risky program points by an attack-aware data dependency tracking algorithm. Risky program points and the CFG are used to compute a metric for the remaining attack surface. We evaluate MazeRunner with 3 CFG types, 3 attack models, and 4 security-violation policies against 13 realistic benchmarks, and demonstrate that the new metric achieves higher precision than traditional metrics while maintaining completeness.
Labeled Multi-Bernoulli Filter based Group Target Tracking Using SDE and Graph Theory

Multi-target tracking is an extremely challenging task when targets move in the formation of groups and interact with each other. Group target tracking has to deal with this problem in contrast to independently moving targets as assumed in most multi-target tracking algorithms. A feasible approach for group target tracking is to estimate the group structure and modify the motion model in the prediction step of multi-target tracker according to the group structure. In this paper, we propose an ad hoc labeled multi-Bernoulli (LMB) filter for tracking group target with interaction, which use stochastic differential equation to model the joint motion of group targets and estimate group structure by using graph theory. Simulation results show that the proposed algorithm can estimate the target state more accurately than the traditional method without group motion modification.
Is Blockchain Overrated?

Blockchain technology, after the advent of Bitcoin in 2009, gradually attained significant attention in academia and industry. Its influence grew further from cryptocurrencies and the financial sector in a couple of years. Soon many new use cases of blockchain in various areas such as supply chain management, healthcare, voting, intellectual property right management, food safety, real estate, internet of things etc. were proposed in liter-ature. Enthralled by the hype, many enterprises chose to pursue blockchain-based initiatives for their business processes. However after a period of over-excitement, now blockchain is viewed with concern because a large number of the earlier blockchain-based projects, especially those not related to finance, have not been productive. Foregoing in view, this article presents a concise review of blockchain technology, highlights its prospective impact within realistic boundaries and proposes a model to assess its feasibility to use cases before implementation, in order to embrace it prudently.
Towards Predicting Merge Conflicts in Software Development Environments

Software development is nowadays a collaborative process. Although there exist version control systems such as Git, that help with the challenges that collaborative work poses, the process still has some issues, one of them are merge conflicts. When two or more developers need to combine the contents of different branches, merge conflicts might appear if the same lines of code have been changed in parallel. The process of deciding which version is to be kept requires human assistance and is time-consuming as well as error-prone. In order to mitigate the risk, a possible solution is finding conflicts before they appear by using Machine Learning (ML) / Deep Learning (DL) based predictors. We aim to explore which of these approaches would fit best to the task. To this end, we trained various classifiers using datasets composed of Git-related features regarding open source repositories. We studied over 30 thousand merge scenarios and over 30 different candidate features. Our classifiers’ values for precision and recall range between 0.68 and 0.78, with remarkably high values for ROC AUC. Cascading a Random Forest classifier with a deep neural network one offered the best results for the task at hand. The results indicate that conflict prediction is indeed attainable using machine learning, which could be an important step towards better synchronization in software development.
A Novel Construction of 2-Resilient Rotation Symmetric Boolean Functions

In this paper, by using the properties of the cyclic Hadamard matrices of order 4t, an infinite class of (4t-1)-variable 2-resilient rotation symmetric Boolean functions is constructed, and the nonlinearity of the constructed functions are also studied. To the best of our knowledge, this is the first class of direct constructions of 2-resilient rotation symmetric Boolean functions. The spirit of this method is different from the known methods depending on the solutions of an equation system proposed by Du Jiao, et al. Several situations are examined, as the direct corollaries, three classes of (4t-1)-variable 2-resilient rotation symmetric Boolean functions are proposed based on the corresponding sequences, such as m sequences, Legendre sequences, and twin primes sequences respectively.
Classification of attack mechanisms and research of protection methods for systems using machine learning and artificial intelligence algorithms

This article provides a complete classification of attacks using artificial intelligence. Three main identified sections were considered: attacks on information systems and computer networks, attacks on artificial intelligence models (poisoning attacks, evasion attacks, extraction attacks, privacy attacks), attacks on human consciousness and opinion (all types of deepfake). In each of these sections, the mechanisms of attacks were identified and studied, in accordance with them, the methods of protection were set. In conclusion, a specific example of an attack using a pretrained model was analyzed and protected against it using the method of modifying the input data, namely, image compression in order to get rid of extraneous noise.
Performance Comparison of Finite Field Multipliers for SM2 Algorithm based on FPGA Implementation

An efficient implementation of the multiplication part is one of the significant procedures of the cryptography algorithms. In this paper, the six altered parallel multiplication methods are proposed to implement in 192-bit for the SM2 algorithm. The CPAM, CSAM, Tri-Section Pezaris, Baugh-Wooley array, Modified Booth, and the Montgomery multipliers are compared by considering minimum operational speed, area, and power. We used a mod m reducer circuit for comparing with similar outputs of the multiplier architectures. Through the final comparison, the Montgomery gives the efficient result by 504 LUTs, 5.532ns timing, 0.101mW dynamic power. The proposed work is implemented on the Xilinx Virtex-7 FPGA board, and the programming language is VHDL.
Automatic Segmentation of Dynamic Thyroid Ultrasound Images Based on An Improved DRLSE Model

Aiming at the characteristics of dynamic thyroid ultrasound images with high noise, low contrast, uneven gray distribution, and little change in continuous frame images, this paper improves the Distance Regularized Level Set Evolution (DRLSE) model and applies it to dynamics Segmentation of thyroid ultrasound images, It is of great significance for the correct diagnosis of thyroid disease. The algorithm uses bilateral filtering instead of Gaussian filtering to smooth the image, which protects the edge information of the image while smoothing the noise. Improve the edge indicator function of DRLSE and use the step-by-step method for image segmentation, which improves the speed and accuracy of segmentation. In order to achieve automatic segmentation of dynamic ultrasound images, the centroid of the thyroid segmentation result of each image is used as the initial contour point of the next adjacent frame of image for segmentation. Experiments show that the algorithm solves the problems of poor anti-noise ability of the DRLSE model, long evolution time, and manual modification of the initial contour. It not only improves the speed of segmentation of the thyroid area but also improves the segmentation Precision.
Developing your product design specification

In this chapter we shall examine how one produces the specification for the new product or the design change. We shall cover where you start from and when it is finished, you shall also meet the five “w” terms, who, what, why, when and where. We shall also meet the important term “end user input”.
Ubiquitous Computing, Contactless Points, and Distributed Stores

The new technologies modify both customer behavior and corporate approach to the retail process by changing both the way consumers access and consumption of information. In this scenario, a huge number of “contactless technologies” are emerging as the most promising direction for supporting automatic payment and self-checkout. Similarly, ubiquitous retailing is acquiring importance in the current competitive scenario, based on ubiquitous computing (or ubiquitous access to information). As a consequence, these innovations are removing the boundaries of the physical space and pushing retailers to redefine the traditional business model and practices. The aim of this chapter is to provide a comprehensive framework of this new competitive scenario, starting from the most innovative technologies in retail domain with emphasis on the new ubiquitous ones, and sheds lights on the future of the (technology-enriched) places of shopping.
CYBERCRIMINAL PROFILING

Criminal profiling is both an art and a science, knowing what types of people mostly commit unambiguous types of criminal offenses. Law Enforcement and Information Technology professionals need to work together to control this growing problem: Cybercrime and the individuals behind it.
AI Enhanced Blockchain (II)

Blockchain is a shared database. The data or information stored in it has the characteristics of “unforgeable”, “full trace”, “traceable”, “open and transparent”, and “collective maintenance”. Based on these characteristics, blockchain technology has laid a solid foundation of “trust”, created a reliable “cooperation” mechanism, and has broad application prospects. This paper assesses opportunities and summarizes the related technologies of blockchain and its contribution to network security.
Regression Tree Models

Decision trees are models that process data to split it in strategic places to divide the data into groups with high probabilities of one outcome or another. It is especially effective at data with categorical outcomes, but can also be applied to continuous data, such as the time series we have been considering. Decision trees consist of nodes, or splits in the data defined as particular cutoffs for a particular independent variable, and leaves, which are the outcome.
Quantum-Behaved Particle Swarm Optimization Based Radial Basis Function Network for Classification of Clinical Datasets

In this article, a classification framework that uses quantum-behaved particle swarm optimization neural network (QPSONN) classifiers for diagnosing a disease is discussed. The neural network used for classification is radial basis function neural network (RBFNN). For training the RBFNN K-means clustering algorithm and quantum-behaved particle swarm optimization (QPSO) algorithm has been used. The K-means clustering algorithm is used to find the optimal number of clusters which determines the number of neurons in the hidden layer. The cluster approximation error is used to find the optimal clusters. The weights between the hidden and the output layer is determined using QPSO algorithm based on the mean squared error (MSE). The performance of the developed classifier model has been tested with five clinical datasets, namely Pima Indian Diabetes, Hepatitis, Bupa Liver Disease, Wisconsin Breast Cancer and Cleveland Heart Disease were obtained from the University of California, Irvine (UCI) machine learning repository.
Discovery Mechanism for Learning Semantic Web Service

Nowadays, e-learning offers advantages over traditional learning in terms of independence. Moreover, adaptive e-learning systems take into account learner's profile, such as learning style and level of knowledge, in order to provide the most appropriate learning object. However, the essential challenge is finding and identifying the learning objects from a big corpus while ensuring their independence in different contexts. To overcome these problems of interoperability and accessibility of learning objects, the authors proposed to define a learning semantic Web service for each learning object. This service is an extension of OWLS that encompasses the description of the learning intention and the use of context that characterize a learning object. In this paper, the authors propose a new discovery mechanism based on learning intention and context use guided by the learner's intention and profile in order to offer a personalized learning path. Experimental results prove the efficiency of the proposed approach and approve its notable contribution.
Reinforcement-learning-based path planning for UAVs in intensive obstacle environment

In intensive obstacle environment, the available flying space is narrow, which makes it difficult to generate feasible path for UAVs within limited runtime. In this paper, a Q-learning-based planning algorithm is presented to improve the efficiency of single UAV path planning in intensive obstacle environment. By constructing the space-action state offline learning planning architecture, the proposed method realizes the rapid path planning of UAV, and solves the high time-consuming problem of reinforcement learning online path planning. Considering the time-consuming problem of Q-table re-training, a probabilistic local update mechanism is proposed by updating the Q-value of the states to reduce the high time-consuming of Q-table re-raining and realize the rapid update of Q-table. The probability of Q-value updating is up to the distance to the new obstacle. The closer the state is to the new obstacle, the higher its probability of re-training. Therefore, the flight trajectory can be quickly re-planned when the environment changes. Simulation results show that the proposed Q-learning-based planning algorithm can generate path for UAV from random start position and avoid the obstacles. Compared with the classical A* algorithm, the path planning time based on the trained Q table can be reduced from second to millisecond, which significantly improves the efficiency of path planning.
A Novel Edge-based Multi-Layer Hierarchical Architecture for Federated Learning

In the last few years, Internet of Things (IoT) devices are multiplicating their presence in our daily life. This means that the data generated in our houses, offices, and common places is starting to be too big to be elaborated in a limited number of places. In this scenario, the advent of Edge Computing, in general, and Edge Intelligence, in particular, is favoring the scalability and the efficiency of IoT systems. Such paradigms allow, by using devices placed at the edge of the network, the distributed elaboration of data created by IoT devices so permitting to transmit to the cloud only synthetic information. Edge Intelligence supports the so-called Federated Learning (FL), which is a novel paradigm that allows the distributed training of neural network models. Such models are initially distributed from the cloud to edge nodes and, on such edge nodes, they are refined based on data gathered from IoT nodes. Such refined models are sent back to the cloud and merged with other models elaborated on different edge nodes. This paper presents a novel architecture for Federated Learning enabling a Multi-Layer Hierarchical Federated Learning (MLH-FL) that allows to execute the traditional FL with model aggregation at different layers. The proposed approach will be also evaluated with some simulations and the final accuracy and loss of the obtained models will be compared with the traditional FL approach.
Residual Contraction

In this paper, we propose and axiomatically characterize residual contractions, a new kind of contraction operators for belief bases. We establish that the class of partial meet contractions is a strict subclass of the class of residual contractions. We identify an extra condition that may be added to the definition of residual contractions, which is such that the class of residual contractions that satisfy it coincides with the class of partial meet contractions. We investigate the interrelations in the sense of (strict) inclusion among the class of residual contractions and other classes of well known contraction operators for belief bases.
BSA: Enabling Biometric-Based Storage and Authorization on Blockchain

Blockchains are confronted with challenges to store private files and grant access, especially for biometric-based schemes. We propose BSA, a biometric-based scheme for file storage and access authorization on blockchains. File storing, access request and response are performed on blockchains. Biometrics is the only secret information for storage and authorization, and can be repeatedly used with no privacy leakage. Such a scheme does not require a file owner to store any information locally, so it can be used on resource-constrained devices.
A PSO-based fine-tuning algorithm for CNN

With the continuous development of artificial intelligence, the Convolutional Neural Network (CNN) has been successfully applied to solve the classification problems. However, with a small sample data set that has large-scale of data features, CNN may have poor classification performances. With this concern, we focus on the classification problem with CNN on small sample data sets and a particle swarm optimization (PSO)-based algorithm for fine-tuning CNN is proposed. This algorithm considers the gradient direction to directly optimize weights of CNN, and can help CNN stepping out of stagnation effectively with less training data. Experimental results under a real world data set show the advantage of our proposal, in terms of minimized loss and maximized accuracy. The code is available at https://github.com/luohaoyan/CS-GA-W-PSO
Sparse Representation based Super-Resolution of MRI Images with Non-Local Total Variation Regularization

Diffusion-weighted (DW) and spectroscopic MR (MRS) images are found to be very helpful for diagnostic purposes as they provide complementary information to that provided by conventional MRI. These images are also acquired at a faster rate, but with low signal-to-noise ratio. This limitation can be overcome by applying image super-resolution techniques. In this paper, we propose sparse representation over a learned overcomplete dictionary based single-image super-resolution (SISR) technique for DW and MRS images. The proposed SISR method incorporates patch-wise sparsity constraint based on external HR information together with the non-local total variation (NLTV) as internal information to make the regularization problem more robust. Experiments are conducted for both DW and MRS test images and results are compared with some of the recent methods. Results indicate the potential of the proposed method for clinical MRI applications.
REALational Perspectives: Strategies for Expanding beyond the Here and Now in Mobile Augmented Reality (AR) Art

This article analyzes mobile AR installations in order to demonstrate different strategies for producing a relational sense of place and time. These installations combine multiple narratives in situ; thus, they reshape existing perceptions and influence national identities. By means of exposing actual environments as constructed and therefore as virtual landscapes, mobile AR art exposes our situatedness and becomes a strong tool for activism as it encourages us to think beyond familiar, material reality. As such, it rejects an absolute perception of reality and reconfigures it as a relational domain.
Solar Thermal Systems: Components and Applications—Introduction

This article is the introduction of the volume on “Solar Thermal Systems: Components and Applications.” It initially examines energy-related environmental problems, which include acid rain, ozone layer depletion and global climate change, and how renewable energy technologies can be used to avoid these problems. Subsequently, the article examines the environmental characteristics of solar energy in terms of basic relations used to determine the position of the sun on the sky. Therefore, the article includes an analysis of the equation of time, longitude correction, solar angles, the incidence angle for fixed and moving surfaces, and finally presents easy ways to determine the basic sun angles using sun path diagrams. This is followed by an analysis of the solar radiation and, in particular, thermal radiation and transparent plates, which are two subjects that are of interest to the people dealing with the design of solar systems. Finally, the solar resource is examined. The operation of solar collectors and systems depends on the solar radiation input and the ambient air temperature and their sequences. These and other meteorological parameters are usually provided in terms of the typical meteorological year and the generation of such years from raw data is given.