Reviewerly/exHarmony huggingface dataset:
A Markov model for optimization of information security remedies

Abstract The problem of selecting an optimal collection of security remedies against a specified set of cyber threats is relevant as far as there is a wide range of different cyber security solutions in modern IT industry. The aim of this work is to formulate an optimization problem for selecting information security remedies using a Markov cyber threat model and to analyze the possibility of solving the problem by the method of sequential analysis of variants. The set of solutions for standard approaches is limited by admissible indicators of the economic damage, and the corresponding restriction by means of a functional and temporal characteristic of the model called its average lifetime is defined. The explicit analytical formula for the average lifetime of an information system expressed in terms of the original parameters of the model is obtained, these parameters being the probabilities of threat occurrence and probabilities of their eliminations by security remedies. The possibility of solving our optimization problem by the method of sequential analysis of variants is analyzed. The program in C++ is developed to experimentally compare the effectiveness of this method compared with the "brute-force" method.
Neural-Network-Based Pseudo-Random Number Generator Evaluation Tool for Stream Ciphers

The security of stream ciphers depends on the properties of their pseudo-random number generators (PRNGs). Although there are methods to evaluate PRNGs, such as tools that automatically conduct statistical tests for random numbers or specific cryptanalysis-related searches, these methods require prior knowledge about statistical biases and cannot find unknown biases. Hirose demonstrates that NIST SP 800-22 (a statistical test suite for PRNGs) cannot detect the linearity of a linear congruential generator (LCG); thus NIST SP 800-22 overlooks statistical biases. We propose an exhaustive method to search for statistical biases in PRNGs. The proposed method can automatically discover unknown types of biases using a neural network to detect slight differences between the target PRNG's output and ideal random numbers. We applied the proposed method to the RC4 stream cipher and LCG. The results demonstrate that the proposed method detects different types of statistical biases in two different algorithms without prior knowledge of these biases. Specifically, the proposed method could discover the linearity of LCG that cannot be detected by NIST SP 800-22.
What the History of Linux Says About the Future of Cryptocurrencies

Since Bitcoin’s meteoric rise, hundreds of cryptocurrencies that people now publicly trade have emerged. As such, the question naturally arises: how have cryptocurrencies evolved over time? Drawing on the theory of polycentric information commons and cryptocurrencies’ historical similarities with another popular information commons (namely, Linux), we make predictions regarding what cryptocurrencies may look like in the future. Specifically, we focus on four important historical similarities: 1) support from online hacker communities, 2) pursuit of freedom, 3) criticism about features and use, and 4) proliferation of forks. We then predict that: 1) cryptocurrencies will become more pragmatic rather than ideological, 2) cryptocurrencies will become more diverse in terms of not only the underlying technology but also the intended audience, and 3) the core technology behind cryptocurrencies, called blockchain, will be successfully used beyond cryptocurrencies.
Detection Model of Depression Based on Eye Movement Trajectory

Eye movement trajectories of depressed patients and normal persons are different. The eye-tracking data obtained by the eye tracker can adequately summarize the characteristics of the eye movement trajectory. Based on the characteristics of eye movement trajectory, this paper proposes a new depression detection model by using an artificial neural network, which can better assist doctors in the diagnosis of depression. First, we extract the feature of eye movement trajectory, which obtains from time-series data recording the trajectory of the eye. Then, we convert the data from three-dimensional to two-dimensional, and perform feature extraction and transformation. Finally, we propose a new depression detection model by using artificial neural networks. The experimental results show that the best result of the model evaluation is 83.17%, which can effectively assist doctors in the diagnosis of depression.
The Role of Binocular Vision in Avoiding Virtual Obstacles While Walking

Advances in Virtual Reality technology have enabled physical walking in virtual environments. While most Virtual Reality systems render stereoscopic images to users, the implication of binocular viewing with respect to the performance of human walking in virtual environments remains largely unknown. In the present study, we conducted two walking experiments in virtual environments using a linear treadmill and a novel projected display known as the Wide Immersive Stereo Environment (WISE) to study the role of binocular viewing in virtual locomotion. The first experiment investigated the walking performance of people stepping over obstacles while the second experiment focused on a scenario on stepping over gaps. Both experiments were conducted under both stereoscopic viewing and non-stereoscopic viewing conditions. By analysing the gait parameters, we found that binocular viewing helped people to make more accurate movements to step over obstacles and gaps in virtual locomotion.
Maximized Privacy-Preserving Outsourcing on Support Vector Clustering

Despite its remarkable capability in handling arbitrary cluster shapes, support vector clustering (SVC) suffers from pricey storage of kernel matrix and costly computations. Outsourcing data or function on demand is intuitively expected, yet it raises a great violation of privacy. We propose maximized privacy-preserving outsourcing on SVC (MPPSVC), which, to the best of our knowledge, is the first all-phase outsourceable solution. For privacy-preserving, we exploit the properties of homomorphic encryption and secure two-party computation. To break through the operation limitation, we propose a reformative SVC with elementary operations (RSVC-EO, the core of MPPSVC), in which a series of designs make selective outsourcing phase possible. In the training phase, we develop a dual coordinate descent solver, which avoids interactions before getting the encrypted coefficient vector. In the labeling phase, we design a fresh convex decomposition cluster labeling, by which no iteration is required by convex decomposition and no sampling checks exist in connectivity analysis. Afterward, we customize secure protocols to match these operations for essential interactions in the encrypted domain. Considering the privacy-preserving property and efficiency in a semi-honest environment, we proved MPPSVC’s robustness against adversarial attacks. Our experimental results confirm that MPPSVC achieves comparable accuracies to RSVC-EO, which outperforms the state-of-the-art variants of SVC.
ICT-based learning media development

Abstract This research aimed at developing ICT-based learning media for independent learning. The subjects of the research were Mechanical Engineering students of Universitas Sarjanawiyata Tamansiswa. By adopting Borg &amp; Gall’s research and development model, this research was done in four stages, namely: (1) identification stage; (2) design and development stage; (3) production stage; and (4) evaluation stage. Questionnaires were the instrument used for data collection techniques, and the descriptive quantitative method was the data analysis technique. The results of the development of ICT-based learning media earned positive appraisals on the ease of use aspect with a percentage of 50%, and the display aspect with a percentage of 62.50%. In conclusion, ICT-based learning media that was developed on several vocational materials can be used as a learning media for independent learning.
Moving from OPAC to Discovery Systems: Nigerian Librarians’ Perceived Knowledge and Readiness

The recent trend of move from OPAC to discovery system has not been observed in Nigeria. Hence, this study examined Nigerian librarians’ perceived knowledge and readiness to move from OPAC to discovery system. The study adopts mixed method research approach. The study found that Nigerian librarians are not ready to move from OPAC to library discovery system in the nearest future. It was also shown that there is little or no knowledge of library discovery system among Nigerian librarians. The study concludes that Nigerian librarians are not ready nor have knowledge of moving from OPAC to library discovery system.
Developing a Web-Based Fatwa of the Council of Indonesian Ulama

The Council of Indonesian Ulama (MUI) as an institution of fatwa (legal opinion) making has decided a large number of fatwa in response to religious, social, cultural, economic, and political problems that occurred in Indonesian society to be implemented by Muslims in Indonesia. Usually MUI fatwa is written in a structured document that contains information theorems based on the legal decision methodology regarding the fatwa in question. In this study, we developed a document retrieval system regarding fatwas of MUI. We compared our searching method from the official website of MUI using keywords and results on the document with high relevancy to the keywords. Our solution provides a system that indicates the returns of more documents than the existing one.
Online Performance Evaluation of Deep Learning Networks for Profiled Side-Channel Analysis

Deep learning based side-channel analysis has seen a rise in popularity over the last few years. A lot of work is done to understand the inner workings of the neural networks used to perform the attacks and a lot is still left to do. However, finding a metric suitable for evaluating the capacity of the neural networks is an open problem that is discussed in many articles. We propose an answer to this problem by introducing an online evaluation metric dedicated to the context of side-channel analysis and use it to perform early stopping on existing convolutional neural networks found in the literature. This metric compares the performance of a network on the training set and on the validation set to detect underfitting and overfitting. Consequently, we improve the performance of the networks by finding their best training epoch and thus reduce the number of traces used by 30%. The training time is also reduced for most of the networks considered.
Combination of Deep Learning and Syntactical Approaches for the Interpretation of Interactions Between Text-Lines and Tabular Structures in Handwritten Documents

In this article, we present our work on baseline detection in images of historical documents. This work focuses on handwritten documents containing tabular structures. One of the difficulties of this kind of documents is the strong interaction between text and tabular structures. This interaction leads to ambiguous cases for which recognition systems often over-or sub-segment baselines. The interest of our method is to combine contextual and structural knowledge in order to interpret properly this interaction. Our combination is able to merge heterogeneous information obtained with a deep-learning approach (for contextual elements) and a syntactical approach (for structural elements). Our grammatical description consists on a logical description of the intersections between text-lines and vertical rulings of detected tables. Intersections are described thanks to physical indicators extracted from images: vertical rulings, hypothetical text-lines, begin-and end-indicators of text-lines. We show on cBAD competition [4] (competition on baseline detection) that the combination of heterogeneous knowledge (structural and contextual information) improves baseline detection in handwritten documents. We obtain better scores than the best method published until now on this competition.
Digital image steganography by using edge adaptive based chaos cryptography

Abstract Steganography and Cryptography is the method used to secure the information. Cryptography is used the principle of a Chaos by using Cat Arnold Map function to assure randomness. While in steganography, the method is used Least Significant Bit Matching Revisited (LSBMR). Embedding region was on edge digital imagery to ensure the message was not detected in the image by visual. The method used to detect the edge region by using Canny edge Detection. The test results obtained by Peak Signal to Ratio (PSNR) are 72-44 dB for data messages with a size of 10 % of the media cover.
Computational Pseudorandomness, the Wormhole Growth Paradox, and Constraints on the AdS/CFT Duality (Abstract)

The AdS/CFT correspondence is central to efforts to reconcile gravity and quantum mechanics, a fundamental goal of physics. It posits a duality between a gravitational theory in Anti de Sitter (AdS) space and a quantum mechanical conformal field theory (CFT), embodied in a map known as the AdS/CFT dictionary mapping states to states and operators to operators. This dictionary map is not well understood and has only been computed on special, structured instances. In this work we introduce cryptographic ideas to the study of AdS/CFT, and provide evidence that either the dictionary must be exponentially hard to compute, or else the quantum Extended Church-Turing thesis must be false in quantum gravity. Our argument has its origins in a fundamental paradox in the AdS/CFT correspondence known as the wormhole growth paradox. The paradox is that the CFT is believed to be scrambling - i.e. the expectation value of local operators equilibrates in polynomial time - whereas the gravity theory is not, because the interiors of certain black holes known as wormholes do not equilibrate and instead their volume grows at a linear rate for at least an exponential amount of time. So what could be the CFT dual to wormhole volume? Susskind’s proposed resolution was to equate the wormhole volume with the quantum circuit complexity of the CFT state. From a computer science perspective, circuit complexity seems like an unusual choice because it should be difficult to compute, in contrast to physical quantities such as wormhole volume. We show how to create pseudorandom quantum states in the CFT, thereby arguing that their quantum circuit complexity is not feelable, in the sense that it cannot be approximated by any efficient experiment. This requires a specialized construction inspired by symmetric block ciphers such as DES and AES, since unfortunately existing constructions based on quantum-resistant one way functions cannot be used in the context of the wormhole growth paradox as only very restricted operations are allowed in the CFT. By contrast we argue that the wormhole volume is feelable in some general but non-physical sense. The duality between a feelable quantity and an unfeelable quantity implies that some aspect of this duality must have exponential complexity. More precisely, it implies that either the dictionary is exponentially complex, or else the quantum gravity theory is exponentially difficult to simulate on a quantum computer. While at first sight this might seem to justify the discomfort of complexity theorists with equating computational complexity with a physical quantity, a further examination of our arguments shows that any resolution of the wormhole growth paradox must equate wormhole volume to an unfeelable quantity, leading to the same conclusions. In other words this discomfort is an inevitable consequence of the paradox.
Alpha-Beta Sampling for Pairwise Ranking in One-Class Collaborative Filtering

This paper introduces Alpha-Beta Sampling (ABS) strategy, which is particularly intended for the sampling problem of pairwise ranking in one-class collaborative filtering (PROCCF). Specifically, ABS strategy places more emphasis on such training examples, including positive item with a lower preference score and negative items with a higher preference score for each gradient step. Then, we provide the corresponding proofs for the ABS strategy from both gradient and ranking perspectives. First, we prove that sampled training examples by ABS strategy can update the model parameters with a large magnitude and analyze two instantiations by combining two specific pairwise algorithms. Second, it can be proved that ABS strategy is equivalent to optimizing for ranking-aware evaluation metrics like Normalized Discounted Cumulative Gain (NDCG). Furthermore, ABS strategy can be very general and applicable in a lot of pairwise structures of pairwise algorithms. Based on ABS strategy, we provide an effective sampling algorithm to dynamically draw items for each SGD update. Finally, we evaluate the ABS strategy by conducting sampling tasks in two representative pairwise algorithms. The experiment results show that the ABS strategy performs significantly better than the baseline strategies.
Evaluation of Energy Efficiency and the Reduction of Atmospheric Emissions by Generating Electricity from a Solar Thermal Power Generation Plant

The increase of renewable energy generation to change the productivity of a country and electrify isolated sectors are some of the priorities that several governments have imposed in the medium term. Research centers are looking for new technologies to optimize the use of renewable energies and incorporate them into hybrid generation systems. In the present work, the modeling of a solar thermal energy generation plant is being carried out. The climatic data used belong to two coastal cities and one island of Ecuador. The contribution of this work is to simulate a complete model of SCF and PCS, in which the variables of outlet temperature and oil flow are involved at the same time. Previously investigations use only outlet temperature for evaluating power plants. The model of the solar thermal plant is composed of a field of solar collectors, a storage tank, and an energy conversion system. As a result, we obtain a model of a thermosolar plant that will allow us to make decisions when considering the incorporation of micronetworks in systems isolated from the electrical network. The use of thermosolar technology allows the reduction in the risk of spills by the transport of fossil fuels in ships. The study of the CO2 emission factor in Ecuador from 2011 to 2018 is also carried out.
Development of the Online Collaborative Summarizing Feature on Student-Centered E-Learning Environment

Universitas Indonesia has implemented a Learning Management System (LMS) known as Student-Centered E-Learning Environment (SCeLE). The SCeLE facilitates collaborative learning through an online discussion forum. Collaborative learning can promote critical thinking. However, students will have difficulties contributing and understanding discussions when the discussion process is fast, lengthy and when diverse ideas arise. This could lead to uncertainty and demotivation among the students. On the other hand, teachers have difficulties in monitoring students' understanding and the discussion progress. Consequently, providing constructive feedback promptly becomes challenging. This study proposed an online collaborative summarizing feature to be integrated with the online discussion forum to facilitate the construction of a discussion summary. The tool was developed using the User-Centered Design (UCD) approach and tested using the usability evaluation. The participants (both students and lecturers) found that the usability of the developed system was good. This was indicated by the fact that more than 80% of the students and more than 80% of the lecturers were able to finish the tasks given easily. Furthermore, the SUS score exceeded the average score, meaning that students and lecturers found that the developed system had a good usability.
Promoting of Lifelong Learning in Engineering

Within Regional Competence Centre for Lifelong Learning in Electrical Engineering at RTU, has been developed new long life learning programme Industrial design for electronic and optical equipment: materials, design, and functionality. The program has been developed for merchants, managers of manufacturing companies, technical staff. Interested in -learning about industrial design in the manufacture of electronic and optical equipment: use of new materials, design, enhancement, thoughtful use in production and everyday life. Within the framework of this programme, a synergy of directions has been proposed: materials science, automation, and adaptive connections.
The Implementation of Optimization Methods for Contrast Enhancement

The performances of the multivariate techniques are directly related to the variable selection process, which is time consuming and requires resources for testing each possible parameter to achieve the best results. Therefore, optimization methods for variable selection process have been proposed in the literature to find the optimal solution in short time by using less system resources. Contrast enhancement is the one of the most important and the parameter dependent image enhancement technique. In this study, two optimization methods are employed for the variable selection for the contrast enhancement technique. Particle swarm optimization (PSO) and artificial bee colony (ABC) optimization methods are implemented to the histogram stretching technique in parameter selection process. The results of the optimized histogram stretching technique are compared with one of the parameter independent contrast enhancement technique; histogram equalization. The results show that the performance of the optimized histogram stretching is better not only in distorted images but also in original images. Histogram equalization degraded the original images while the optimized histogram stretching has no effect due to being an adaptive solution.
MDWA: a model-driven Web augmentation approach—coping with client- and server-side support

Web augmentation is a set of techniques allowing users to define and execute software which is dependent on the presentation layer of a concrete Web page. Through the use of specialized Web augmentation artifacts, the end users may satisfy several kinds of requirements that were not considered by the analysts, developers and stakeholders that built the application. Although some augmentation approaches are contemplating a server-side counterpart (to support aspects such as collaboration or cross-browser session management), the augmentation artifacts are usually purely client-side. The server-side support increases the capabilities of the augmentations, since it may allow sharing information among users and devices. So far, this support is often defined and developed in an ad hoc way. Although it is clear that server-side support brings new possibilities, it is also true that developing and deploying server-side Web applications is a challenging task that end users hardly may handle. This work presents a novel approach for designing Web augmentation applications based on client-side and server-side components. We propose a model-driven approach that raises the abstraction level of both, client- and server-side developments. We provide a set of tools for designing the composition of the core application with new features on the back-end and the augmentation of pages in the front-end. The usability and the value of the produced augmentations have been evaluated through two experiments involving 30 people in total.