Reviewerly/exHarmony huggingface dataset:
Two-Layer Group Signature and Application to E-Cash

We propose a novel variant of group signature schemes called two-layer group signature (TL-GS) scheme. In a TL-GS scheme, each user is equipped with two layer identities, long-term identity for traceability and short-term identity for linkability, while the short-term identity is bound to the long-term one. A signature by a signer convinces a verifier both facts, that (i) the singer has a registered long-term identity which belongs to a dynamic group; and (ii) he has a short-term identity which belongs to a specific spontaneous group. We provide formalized security definitions for TL-GS schemes and propose an secure asymptotic efficient lattice-based TL-GS scheme under the LWE and SIS assumptions. We then propose a post-quantum secure decentralized anonymous E-cash scheme with effective real world identity traceability, by combining the proposed TL-GS scheme with the design principle of CryptoNote protocol. Our scheme is secure against double spending attacks and provides anonymity for both payers and payees. All coin transferring behaviors can only be accomplished through creating transactions, and there is a special authority that can trace the identity of traders from transactions. In addition, the transaction is compact, which means the number of possible inputs has at most logarithmic impacts on the transaction's size.
METHODOLOGY AND ORGANIZATION OF PROFESSIONAL TRAINING OF STUDENTS OF ECONOMIC DIRECTION WITH THE USE OF DISTANCE LEARNING

The article is devoted to topical issues of methodology and organization of professional training of students of economic direction with the use of distance learning. Organization of professional training of students of economic direction taking into account the use of remote technologies is determined by the need to get acquainted with the theory and skills of using local and remote data containing various information. Currently, information flows are a strategic resource that facilitates the rapid acquisition of the necessary information. The introduction of information forms in the higher education system as distance learning resources is quite complex and important task aimed at reducing the time of classroom students and release of real time for them to obtain the necessary competencies. &#x0D; The article draws attention to the methodology and organization of distance education. The positive and negative aspects of virtual interaction in the educational environment of its participants are considered. The main innovation and pedagogical directions and conditions of studying the course and disciplines are determined. Innovative processes of the educational environment, impose new requirements for the professional training of teachers, defining and formulating the purpose, the scope of the educational environment based on the needs of students and meet the requirements of the education system. The main requirements of the educational environment should include compliance with educational standards aimed at improving the quality of professional training of students.&#x0D; Modernization of higher education has a positive impact on the activities of the teaching staff, improving and optimizing the main components of the learning process of students, expanding the theoretical and pedagogical methods and modeling educational institutions in an innovative educational system. The emergence of new forms of education is a natural process of the education system to the modern needs of society associated with changes in scientific,technical and information architectonics.
Efficiency Optimization of Capsule Network Model Based on Vector Element

Currently, Deep Learning and Convolutional Neural Network (CNN) have been widely used in many fields and have generated very high value in these fields, especially in the field of image recognition. But there are some deficiencies in certain issues of image recognition. For example, CNN’s recognizing performance is not good at different angles of objects and overlapping objects. Also, CNN is sometimes very sensitive to slight perturbations, modifying one pixel of a recognized image may cause recognition errors. For these problems, the capsule network (CapsNet) proposed by Geoffrey Hinton can solve the problems of traditional convolutional networks. Shortly after CapsNet was proposed, the model structure was relatively simple, and many aspects could be explored for improvement. This paper will optimize CapsNet from two aspects: “optimization of routing mechanism” and “increase Dropout operation.” And carry out experiments and results analysis on these optimizations.
Research on a Method of Gross Error Elimination for Slope Monitoring Data Based on Machine Learning

Many high and steep slopes are formed in the open pit mining process, whose stability will directly affect the construction and safety. Therefore, it is particularly important to detect and eliminate rough errors of slope monitoring data. This paper proposes an improved two-layer extreme learning machine (TELM) based on particle swarm optimization (PSO) to establish monitoring slope model. In particular, the parameters of the first layer of the whole algorithm are obtained by particle swarm optimization algorithm, and the parameters of the second layer are calculated through a new solution method. First, some basic principles and concepts of extreme learning machine are introduced, and the algorithm thought, basic principles and related derivation of the improved two-layer extreme learning machine in this paper are emphatically discussed. A better mapping relationship between the input space and the output space is found through the parameter solution method, which improves the precision of the algorithm. Then, the data collection and traditional gross error elimination methods are studied. Finally, in order to test the ability of the two hidden layer extreme learning machine to solve the slope monitoring problem, the algorithm is applied to the gross error elimination of the slope data. The experimental results show that compared with the traditional gross error elimination method and ELM algorithm, the proposed method has higher precision, stronger robustness, and more practicability.
Bachet’s game with lottery moves

Bachet's game is a variant of the game of Nim. There are n objects in one pile. Two players take turns to remove any positive number of objects not exceeding some fixed number m. The player who takes the last object loses. We consider a variant of Bachet's game in which each move is a lottery over set {1,2,…,m}. The outcome of a lottery is the number of objects that player takes from the pile. We show that under some nondegenericity assumptions on the set of available lotteries the probability that the first player wins in subgame perfect Nash equilibrium converges to 1∕2 as n tends to infinity.
N2SkyC: User Friendly and Efficient Neural Network Simulation Fostering Cloud Containers

Abstract Sky computing is a new computing paradigm leveraging resources of multiple Cloud providers to create a large scale distributed infrastructure. N2Sky is a research initiative promising a framework for the utilization of Neural Networks as services across many Clouds. This involves a number of challenges ranging from the provision, discovery and utilization of services to the management, monitoring, metering and accounting of the infrastructure. Cloud Container technology offers fast deployment, good portability, and high resource efficiency to run large-scale and distributed systems. In recent years, container-based virtualization for applications has gained immense popularity. This paper presents the new N2SkyC system, a framework for the utilization of Neural Networks as services, aiming for higher flexibility, portability, dynamic orchestration, and performance by fostering microservices and Cloud container technology.
An Axiomatisation of the Probabilistic $$\mu $$ -Calculus

The probabilistic $$\mu $$ -calculus (P $$\mu $$ TL) is a simple and succinct probabilistic extension of the propositional $$\mu $$ -calculus, by extending the ‘next’-operator with a probabilistic quantifier. We extend the approach developed by Walukiewicz for propositional $$\mu $$ -calculus and provide an axiomatisation of P $$\mu $$ TL. Our main contributions are a sound axiom system for P $$\mu $$ TL, and a proof of its completeness for aconjunctive formulae.
Automated Configuration Parameter Classfication Model for Hive Query Plan on the Apache Yarn

This research proposed an automated configuration parameter classification model to arrange optimized Hive Query processing environment on the Apache Hadoop Distributed File System. In this model, the Analysis statistic command issued to measuring expected performance for the Hive tables on the Hadoop yarn platform with varying combinations of parameter configuration. The e-heuristic methodology is applied to effectively shrinking parameter search space during automated tuning process. We controlled the transition between evaluation spaces using one main parameter and one auxiliary parameter that are expected to reach the global optimum in each evaluation space. This model identifies the Hive parameters that access Hive table optimally and expects to improve query execution time by 15% against to the default Hive settings.
Introduction to the Mathematical Theory of Knowledge Conceptualization: Conceptual Systems and Structures

The paper departs from the general problem of knowledge integration and the basic strategies that can be adopted to confront this challenge. With the purpose of providing a sound meta-theoretical framework to facilitate knowledge conceptualization and integration, as well as assessment criteria to evaluate achievements regarding knowledge integration, the paper first reviews the previous work in the field of conceptual spaces. It subsequently gives an overview of structural tools and mechanisms for knowledge representation, recapped in the modal stratified bond model of global knowledge. On these groundings, a novel formalized representation of conceptual systems, structures, spaces and algebras is developed through a set of definitions which goes beyond the exploration of mental knowledge representation and the semantics of natural languages. These two components provide a sound framework for the development of the glossaLAB international project with respect to its two basic objectives, namely (i) facilitating knowledge integration in general and particularly in the context of the general study of information and systems; (ii) facilitating the assessment of the achievements as regards knowledge integration in interdisciplinary settings. An additional article tackles the solutions adopted to integrate these results in the elucidation of the conceptual network of the general study of information and systems.
Knowledge Integration in Personalised Dietary Suggestion System Using Semantic Web Technologies

In knowledge intensive nutrition-related contexts, such as personalised dietary, diet-sensitive diseasesCarbonaro, Antonella management and sport supplementation, ontologies play anReda, Roberto important role. In this paper, we propose an ontology-based consultation system which aims to improve the life quality of both healthy people and individuals affected by chronic diet-related diseases. We developed a system which is capable of transferring human dietary and nutrition expertise into machine understandable knowledge through a set of semantic rules in order to better assist users in making the correct nutritional choices for their particular health status, age, lifestyle and food preferences. Our system makes use of open data, published ontologies, domain knowledge and IoT data to construct a domain representation consisting of unified concepts and instances suitable for reasoning processes. We described how several knowledge bases in knowledge-intensive contexts can be integrated to provide a unified structured and precise representation of heterogeneous information to provide better diet recommendation to individuals.
What Correspondences Reveal About Unknown Camera and Motion Models?

In two-view geometry, camera models and motion types are used as key knowledge along with the image point correspondences in order to solve several key problems of 3D vision. Problems such as Structure-from-Motion (SfM) and camera self-calibration are tackled under the assumptions of a specific camera projection model and motion type. However, these key assumptions may not be always justified, \ie., we may often know neither the camera model nor the motion type beforehand. In that context, one can extract only the point correspondences between images. From such correspondences, recovering two-view relationship --expressed by the unknown camera model and motion type-- remains to be an unsolved problem. In this paper, we tackle this problem in two steps. First, we propose a method that computes the correct two-view relationship in the presence of noise and outliers. Later, we study different possibilities to disambiguate the obtained relationships into camera model and motion type. By extensive experiments on both synthetic and real data, we verify our theory and assumptions in practical settings.
Multimedia Tampering Detection: A Comprehensive Review of Available Techniques and Solutions

The popularity of internet usage has resulted in the exponential growth of data in one form or the other. The exhaustive use of massive amount of artistic and communicative multimedia for several purposes has resulted in a need to develop tools and techniques to handle such multimedia in a secure and privacy-preserved manner. The legitimacy, ownership and authentication of such datasets are important as it can have a significant effect on the lives of human beings and organizations as a whole. With the advancement of technology, there exists an ocean of freely available software that can be used to create fake and tampered data without being detected by the normal visual perceptions resulting in dangerous consequences. Today, issues like copyright, ownership and legitimacy are imperative when we deal with multimedia data. This has made multimedia tampering detection an important area for research. Many techniques are being developed to identify different types of tampering in multimedia. This paper aims to present the state-of-the-art about multimedia tampering detection tools and techniques. Furthermore, it provides a comprehensive survey of the current researches in the area of multimedia tampering detection and related technologies.
Real-Time Face Features Localization with Recurrent Refined Dense CNN Architectures

Based on an innovative, efficient recurrent deep learning architecture, we present a highly stable and robust technique to localize face features on still images, captured and live video sequences. This dense (Fully Convolutional) CNN architecture, referred as the Refined Dense Mobilenet (RDM), is composed of (1) a main encoder-decoder block which aims to approximate face feature locations and, (2) a sequence of refiners which aims to robustly converge at the vicinity of the features. On video sequences, architecture is adapted into a Recurrent RDM where a shape prior component is re-injected in the form of temporal heatmaps obtained at previous frame inference. Accuracy and stability of RDM/R-RDM architectures are compared with state-of-the-art Random Forest and CNN based approaches. The idea of combining a holistic feature localizer – taking advantage of large receptive fields to minimize large error – and refiners – working at higher resolution to converge at feature vicinities – is proving high accuracy in localizing face features. We demonstrate RDM/R-RDM architectures improve localization scores on 300W and AFLW datasets. Moreover, by relying on modern, efficient convolutional blocks and based on our recurrent architecture, we deliver the first stable and accurate real-time implementation of face feature localization on low-end Mobile devices.
Towards a Story-Based Search Engine for Arabic-Speaking Children

This paper presents Bukoor, an experimental Arabic children stories search engine built to help children formulate their queries. It utilizes two main query reformulation techniques: query expansion and query suggestions. Both are developed using test collection bi-gram collocation terms and corrections' query terms generated from Bukoor test collection. It also uses two indexing techniques: Okapi BM25 and Latent Semantic Indexing. Our proposed approach was evaluated using Bukoor children's stories test collection which showed promising results when using our proposed query expansion with Okapi BM25 indexing.
The Extension of Bisimulation Quantified Modal Logic Based on Covariant-Contravariant Refinement

The notion of covariant-contravariant refinement (CC-refinement, for short) is a generalization of the notions of bisimulation and refinement. This paper interprets semantically a CC-refinement as bisimulation plus model restriction, that is, a CC-refinement model of a given model may be obtained from one bisimilar duplicate of this model by adding some transitions labelled by covariant actions and removing some transitions labelled by contravariant actions. By using certain proposition letter to witness a contravariant action, the standard bisimulation quantified modal logic is able to capture the characterization of this action, however, this fails for covariant actions. This paper, based on the notion of CC-refinement, introduces an extended bisimulation quantified modal logic with the universal modality (EBQML), describes syntactically CC-refinement quantification as the extended bisimulation quantification plus relativization, and establishes a translation from the language of CC-refinement modal μ-calculus to the language of EBQML such that every CC-refinement modal μ-formula is equivalent to its translation. The language of EBQML may be considered as a specification language for describing the properties of a system referring to reactive and generative actions, which are represented respectively by covariant and contravariant actions, and may be used to formalize some interesting problems in the field of formal methods.
Learners’ perceptions and experiences of using e-textbooks in online learning environment

This study explored middle and high school students’ learning experiences using e-textbooks in online learning courses. Data were collected from in-depth interviews. The interviewees for this study were 19 students who enrolled in an online mathematics and science inquiry program, actively participated in the online learning. Data were analyzed based on a grounded theory approach. As a result, the characteristics of the online learning environment using e-textbooks were conceptualized via three different categories including temporal, spatial, and technical. Such characteristics of the learning environment were able to provoke self-directed learning, extended learning, interactive learning, in-depth learning, improved ICT literacy, and formation of positive emotions and learning habits. Most of the learners showed positive feedback towards the use of e-textbooks, while some mentioned the technical limitations compared to conventional paper-based learning. This study suggested that e-textbooks are likely to induce positive experiences for learners in the context of online learning, so it is necessary to design contents that utilize various functions and advantages of electronic teaching materials in order to use e-textbooks effectively.
CFP: Enabling Camera Fingerprint Concealment for Privacy-Preserving Image Sharing

It has been discovered that every photo carries an unique hardware fingerprint of the photographing digital camera. This camera fingerprint is remarkably effective in image-to-camera matching and has been applied in a wide variety of beneficial forensic tasks such as copyright protection and integrity validation. However, the fingerprints carried by the images can also be utilized for malicious purposes. An adversary can launch identity linking attack, which re-identifies the anonymous social network accounts, through exploiting the digital cameras' fingerprints that are carried by the posted images. Moreover, the adversary can easily frame an innocent victim or bypass camera-based smartphone authentication systems by launching identity forgery attacks, i.e., fabricating unoriginal fingerprints onto images. Currently, no effective counter measures against such attacks have been proposed yet. To solve this problem, in this paper, we first evaluate the effectiveness of the attacks in the current image sharing practices. We then propose CFP, an intermediary between smartphone users and image sharing platforms that conceals the camera fingerprint of the photographing device. Instead of removing the camera fingerprint from the image of interest, our system protects user privacy through obfuscating the camera fingerprint with a specially designed random perturbation component. With such design, the proposed system is enabled to prevent malicious utilizations of camera fingerprint while preserving the beneficial applications. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of the CFP system on various social platforms. Using the CFP obfuscated images, the True Positive Rate of identity linking attacks is reduced by around 85%. For identity forgery attacks, our system enables an effective detection mechanism that could achieve 100% detection rate.
Dezentralität und Zugangsberechtigungen von Blockchain-Systemen

Decentralization is one of the most important aspects of blockchain technology. However, there are large differences in the decentralization of different blockchain applications. The aim of this thesis is to achieve a structural and functional exploration of the aspect of decentralization and to find properties that allow to differentiate blockchain applications in their degree of decentralization. The present article explains that the data distribution and the accessibility (read and write access) are crucial characteristics for the decentralization of the Blockchain applications. These properties will be investigated by means of a morphological analysis and a detailed overview will be given of the different manifestations of these properties and their effects on decentralization.
The Widmanstätten pattern

Abstract A table is provided to determine the angles within the Widmanstätten pattern as a function of orientation of the slice, and methods are provided to solve the inverse problem, namely to determine the orientation of the slice from measurements of the Widmanstätten pattern.
Pop-up Recommended Illustrations for E-Book Readers Using Text Analysis Techniques

In recent years, e-book provides a lot of opportunities for people to read books during commute or break time. E-book readers such as Kindle has useful functions for reading e-books. For instance, Kindle can look up the meaning of words for a reader and change the font size if necessary. In this research, a system is proposed to introduce a novel function of recommending illustrations from e-book texts to readers to facilitate better and easier understanding of the content. This is achieved by using text analysis techniques including the TextTiling method, the LexRank algorithm, the morphological analysis, and the Cosine similarity. There are 4 steps: (1) the system divides text into some scenes by using the TextTiling method; (2) important sentences are extracted using the LexRank algorithm for each scene; (3) the system extracts named entity using the morphological analysis; and (4) the system recommends appropriate illustrations by calculating the similarity between e-book's named entity and customer review's named entity. Through experimental evaluation, the system demonstrated the ability to understand the contents of a reading and to recommend appropriate illustrations. The illustrations used is from the web site "irasutoya". As the result of the experiment, the similarity of the recommended illustrations and the original illustrations can reach to about 70.0 percent using Bag of Visual Words evaluation method with the style image transfer.