Reviewerly/exHarmony huggingface dataset:
Determination of Open Pit Mining Zones Through Digital Processing of Multi-Spectral Images and PPI Method - A Case Study of Southern Ecuador

In the current study, the identification of open pit mining areas has been conducted using multispectral satellite images of medium (Landsat 8)and high (Sentinel 2B)spatial resolution of the province of Zamora Chinchipe in southern Ecuador. Such research involved several digital processes such as atmospheric correction, image fusion through the Brovey algorithm, noise elimination among others. We worked simultaneously with spatial and spectral patterns through the application of algorithms in order to increase spatial resolution., and spectral indices that enhance variables of interest. In addition, the Pixel Purity Index (PPI)methodology has been applied in order to obtain the classes involved in the study area based on the pure pixels and the results were compared between the two methodologies. The zones considered open-pit mining have been obtained in both cases.
Towards a Clustering-Based Approach to Speed up IaaS Service Discovery Process

Cloud computing technology offers on-demand access to different cloud services over the Internet. The huge number of cloud providers that offer various services with different descriptions caused many problems, such as the complexity of multi-cloud service discovery and taking a lot of time to find candidates services that meets user's requirements. In this paper, we propose a new approach for composite infrastructure service discovery in multi-cloud environment. In our approach, we propose an indexing technique to facilitate virtual cloud resource discovery and their matching to user requirements.
A New Approach to Multiplayer Virtual Reality Games

Virtual reality (VR) games represent a new world for players and game developers alike. As developers continue to work in this field, they continually look for new ways to immerse players in the VR gaming world. Plenty of challenges arise in the effort to troubleshoot and solve new problems that come with the development of virtual reality. Consumer satisfaction is extremely important, and game developers constantly work to improve and enhance the player experience. In this paper, we simulate the behavioral relations for the multiplayer game of logical decision making in humans and computers. We provide a model of strategic interaction between rational decision-makers for designing and developing the multiplayer virtual reality game. Our game sets in the real-world and tries to allow reasonable and logical solutions to a problem to result in success.
Multiple Share Creation with Optimal Hash Function for Image Security in WSN Aid of OGWO

Visual share creation, with a security model, verifies the secret data transmitted by the sender. It uses human vision to decrypt the encrypted images without complex algorithms for secure communication in networking. The image sensing process joined with preparing force and wireless communication makes it rewarding for being abused in wealth in future. The proposed security module beats the downsides of the complex usage in traditional or ordinary cryptography models in wireless sensor network (WSN). This chapter proposed an optimal private key and public key-based hash function for secret share security modeling. Initially, the original Digital Images were considered to produce ‘n’ number of shares for security. When the quantity of share was expanded, the security level also got expanded. In the current technique, three shares were produced by visual secret share creation strategy when the shares were made; hash function and qualities were applied to secure the shares in images. For an optimal key selection model, opposition-based Grey Wolf Optimization (OGWO) was considered. Based on this ideal private key and public keys, the share images were encrypted and decrypted between the sender and the receiver. The test results demonstrated better security, less computational time and the most extreme entropy with high PSNR values when compared with other techniques.
Automated Digital Terrain Elevation Modification By Procedural Generation Approach

Creative content requires a lot of human resources. Computational creativity gets more attention as computational resources are getting fast enough to quickly create automated content with enough precision and similarity to human-created digital assets. Paper explores terrain generation methods and proposes a different solution to generate mountains. A landscape is created using voxel-based cubes and the base map is modified using generation agents. Vertical points are extended and filled using arithmetic operations between arrays for each vertical point. Height normalization is applied to remove rare mountain elevation layers.
A Novel and Fine-grained Heap Randomization Allocation Strategy for Effectively Alleviating Heap Buffer Overflow Vulnerabilities

ASLR and other defense measures have been deployed in modern computers against memory data leakage and control flow hijacking, which are two common vulnerability exploitation methods, but attackers can always bypass these security defense mechanisms to attack the computer. For the problem of the randomization strategy in ASLR technology is partial-oriented memory space. This paper presents a heap randomization strategy for the entire heap memory space. By changing the access order of memory pool, the strategy reduces the time consumption of memory region search, a random memory block allocation algorithm is proposed to increase the unpredictability of heap memory addresses and improve the security of computer systems. The randomization algorithm increases the variation interval of memory block gap by adding randomization parameters, then through the attack experiment and time performance experiment, the appropriate randomization parameters are determined to ensure the efficiency of the algorithm.
The application of pXRF for the chemical and mineralogical characterization of heavy mineral sands

Heavy mineral sands (HMS) are a resource of Ti minerals, zircon, monazite and critical elements such as Hf and the rare earth elements (REE). Portable field techniques capable of measuring the geochemical composition, such as portable XRF (pXRF), may assist in identifying and quantifying the multiple minerals present in HMS. In this study, we assess the application of pXRF to the characterization of garnet–magnetite mineral sands from the Namibian coastline. Forty-six HMS samples from the northern Namibian coast were analysed by pXRF for SiO2, Ti, Mn, Fe, Zr, Th, Y, V and Hf. The HMS were analysed as powdered and unground samples to test whether minimal sample preparation yields reliable results. Our tests show that the pXRF results of unground sands scatter widely due to sample heterogeneity. Powdered samples, however, can be measured precisely and can be corrected with conventional laboratory analyses. Some spectral interference specific to this material occurs, e.g. Th on Pb and Bi and Hf on Cu. Selectively corrected pXRF data for powdered HMS were compared to XRD Rietveld data to determine proportions of heavy minerals (Ti minerals, Fe oxides, zircon, garnet). Geochemical and mineralogical data correlated well for garnet and moderately for Fe oxides and Ti minerals. The presence of zircon and monazite is also indicated by Zr, Hf and Th concentrations, which demonstrates that pXRF is a useful tool for HMS characterization. Supplementary material: Tables of raw data (Supplementary Table S1), statistical parameters (Supplementary Table S2) and corrected pXRF values (Supplementary Table S3) are available at https://doi.org/10.6084/m9.figshare.c.4536992
Relation Extraction of Chinese Fundamentals of Electric Circuits Textbook Based on CNN

Deep neural network has been widely used in a variety of natural language processing (NLP) tasks nowadays. As one of the most import research areas, entity relation extraction applies usual recurrent neural networks (RNNs) and convolutional neural networks (CNNs) and has achieved good results. Most relation extraction tasks are about public and general datasets, they are usually natural languages or daily conversations, and have millions of samples, very few relates to small corpus in a specific field. We hope to construct a knowledge graph about Chinese fundamentals of electric circuits textbook for beginners. The knowledge graph shows students knowledge navigation and consists of important concepts about this field and logical relationships between them. To achieve the goal, the first step is to ensure entities and extract entity relationships from raw corpus automatically. In this paper, a relation extraction dataset is built from Chinese fundamentals of electric circuits textbook artificially and research the relation extraction performance of improved position-enhanced CNN model on this task. The experiment result validates the effectiveness of CNN on specific Chinese small corpus relation extraction task.
Knowledge Transfer, Sharing, and Management System Based on Causality for Requirements Change Management

In software development, requirements changes cannot be avoided. Despite the importance of requirements change management, few studies in the literature concern requirements change knowledge management(RCKM). This study explored knowledge transfer and sharing of RCKM based on change cause analysis and the design of a knowledge management system(KMS) for change management. In this paper, previous research is discussed in three aspects: factors affecting knowledge transfer and sharing, methods to implement knowledge transfer and sharing, and the design of a KMS. After that, the results of a recent questionnaire are analyzed. Finally, this paper discusses the sources of tacit knowledge in the personnel management aspects of software development; the knowledge and the methods of knowledge transfer and sharing in the requirements change management process based on change cause analysis; the design of a data flowchart of repositories, and a prototype structure, logical model, and functional modules of RCKM systems. In conclusion, further study is suggested.
Distance Ratio-based Weighted Rank Outlier Detection on Wearable Health Data

In the context of the popularity of smart wearable devices, aiming at the problem that the health data collected by the sports bracelet has unknown abnormal data which is related to specific disease, a method for detecting abnormal data of wearable health data based on Distance Ratio and Weighted Rank is proposed. Firstly, the t-Distributed Stochastic Neighbor Embedding is used to extract the features of the data set and enhance the local structure of the data. Then the distance ratio and weighted rank is used to replace the local reachable density in the Local Outlier Factor algorithm. A new algorithm for calculating the outlier factor is proposed which is called Distance Ratio-based Weighted Rank Outlier Factor (DRWROF) algorithm. Finally, the accuracy of the algorithm is verified by the simulation experiment on the UCI standard data set, meanwhile, an experimental analysis is performed on the actual activity data which is collected by sports bracelets. The experiment results show that it is suitable for the detection of outliers in the complex and diverse behaviors of different bracelet wearers in the actual dataset.
Bridging Gap With QR Codes

This chapter explores the basic themes and expansion of the idea of digital literacy around academia. Furthermore, a case is presented on how digital literacy is linked with the digital divide in developing economies and how QR codes can synergize the process of digital growth of societies and how QR codes are addressed around the world.
Framework for audio reversible watermarking robust against content replacement with signal restoration capabilities

In the literature, there are robust Reversible Watermarking Schemes (RWS) that allow the extraction of watermarks after the signals have suffered attacks, however the modified carrier signals cannot be restored. On the other hand, self-recovery schemes allow the restoration of the attacked regions of the signals, but the embedding capacity is used for control information, and secret messages cannot be inserted in these type of schemes. In this manuscript a framework for robust reversible watermarking with signal restoration capabilities is proposed. This framework is a general construction that can be followed to implement schemes for different type of media. Previous works that propose a RWS and a self-recovery scheme, both for audio signals, have been taken to construct an implementation of the framework for audio signals. These schemes use the auditory masking properties of audio segments to insert the secret message and the control information required for restoration in a transparent manner. Results obtained with this implementation demonstrate that this construction fulfills a transparency constraint of −2 Objective Difference Grade (ODG) for practical applications, and it reaches payload capacities up to 13 kilobits per second (kbps) with acceptable robustness and adequate quality for the restored signals.
Tilted wave interferometer in common path configuration: challenges and realization

Tilted Wave Interferometry has been invented and developed over the last years as a flexible and very fast method to test precision aspheres and freeform surfaces . It measures surface deviations full field, with high lateral resolution, without any null compensator like CGH and without moving the tested part while measuring. The test of non-spherical optical components is a topic of high relevance for optics industry, as current optic designs rely heavily on those elements, since the small form factors and high performance of actual designs would be impossible with traditional spherical optics designs. As all precision components, aspheres and freeform surfaces need accurate testing for their production. Ideally, testing of components is closely integrated into the fabrication chain. Due to the high flexibility and measurement speed of the TWI of typically less than 30 sec it is well suited for this purpose. Due to the special illumination scheme, the first implementations of this new interferometer have been of Mach Zehnder type. In this contribution we demonstrate, how the tilted wave interferometer principle can be implemented in a Fizeau configuration. The benefit of this configuration against the Mach Zehnder configuration is the common path feature. Here, the reference beam and the measurement beam follow the same optical track inside the interferometer, making the interferometer much more robust against temporal environmental influences such as vibrations and air turbulences. At the same time, form tolerances of the interferometer components in the common path area can be relaxed. These advantages of Fizeau are well known. The multiple source illumination of the tilted wave interferometer however leads to the generation of multiple reference wavefronts that can be disturbing. We therefore present a new TWI implementation that avoids these problems. It relies on a new illumination design with four sets of illumination patterns that each generate their own reference wave. The new approach has been implemented in a lab setup and shows in first measurements the expected improvements in stability. We tested the system in extensive Monte Carlo simulations. The common path approach showed a reconstruction error of the test specimen of up to an order of magnitude lower compared the Mach-Zehnder configuration.
Development of secure automated management systems based on web technologies

Abstract This paper discusses security problems of developing municipal management information systems for Department of Municipal Procurement of the Krasnoyarsk City Administration (Russia). Authors describe the problem, goals, and tasks to be solved. Some security aspects are given, as well as original extended role-based security access control model for web applications and web services. This work describes two information systems developed by specialists of the Institute of Computational Modelling of the Siberian Branch of the Russian Academy of Sciences using described approaches. The systems have been in production for several years. The methods used to protect information are given, the structure of software is presented.
Value functions for depth-limited solving in zero-sum imperfect-information games

We provide a formal definition of depth-limited games together with an accessible and rigorous explanation of the underlying concepts, both of which were previously missing in imperfect-information games. The definition works for an arbitrary (perfect recall) extensive-form game and is not tied to any specific game-solving algorithm. Moreover, this framework unifies and significantly extends three approaches to depth-limited solving that previously existed in extensive-form games and multiagent reinforcement learning but were not known to be compatible. A key ingredient of these depth-limited games is value functions. Focusing on two-player zero-sum imperfect-information games, we show how to obtain optimal value functions and prove that public information provides both necessary and sufficient context for computing them. We provide a domain-independent encoding of the domains that allows for approximating value functions even by simple feed-forward neural networks, which are then able to generalize to unseen parts of the game. We use the resulting value network to implement a depth-limited version of counterfactual regret minimization. In three distinct domains, we show that the algorithm's exploitability is roughly linearly dependent on the value network's quality and that it is not difficult to train a value network with which depth-limited CFR's performance is as good as that of CFR with access to the full game.
Using Common Enemy Graphs to Identify Communities of Coordinated Social Media Activity

Increased use of and reliance on social media has led to a responsive rise in the creation of automated accounts on such platforms. Recent approaches to identification of individual automated accounts has relied on machine learning methods utilizing features drawn predominantly from text content and profile metadata. In this work we explore a novel use of graph theoretic measures, specifically common enemy graphs, to identify and characterize groups of accounts exhibiting shared behavior in online social media, particularly those exhibiting characteristics of automation and/or potential coordination. In addition, we develop edge weight variants of fuzzy competition graphs to further characterize common group behavior of automated accounts within subnetworks of social media ecosystems.
Towards Adaptive Learning Systems Based on Fuzzy-Logic

E-learning systems have the ability to facilitate the interaction between learners and teachers without being limited by temporal and/or spatial constraints. However, the high number of students at universities, the huge number of available learning in the web, the differences between learners in term of characteristics and needs make the traditional e-learning systems more limited. For this purpose, adaptive learning has been recently explored in order to cope with these limitations and to meet the individual needs of learner. In this context, many artificial intelligence methods and approaches have been integrated in such computer-based systems in order to create effective learner models, structured domain models, adaptive learning paths, personalized learning format, etc. Such methods are highly recommended for designing adaptive e-learning and m-learning systems with good quality. In this paper, we focus only on one of these methods, called fuzzy logic, which is widely used in educational area. We present the integration of fuzzy logic as a valuable approach that has the ability to deal with the high level of uncertainties and imprecision related to learners’ characteristics and learning contexts.
Research on Alignment in the Construction of Parallel Corpus

Abstract Parallel corpora are of great value in the field of machine translation and cross-language information retrieval. Benefiting from the development of machine learning and deep learning, the technology of the construction of corpus evolves from vocabulary alignment, phrase alignment to chunk alignment. The high quality of automatic bilingual chunks alignment in corpus plays an important role in the performance improvement of machine translation systems, especially in computer-aided translation systems. In the study, the degree of adhesion and relaxation is used to measure the tightness and looseness of the inter-word connection when a chunk is identified, which can be expressed by a mathematical mode. The task of chunk alignment in the construction of a parallel corpus can be described as the three steps: input bilingual sentences, segment chunks, and semantic alignment. At present, most algorithms are based on statistical methods, and the output alignment results are machine-oriented.
Mothering Bacteria

Bodies are interfaces, thresholds, vestibules, and gateways capable of hosting, carrying and birthing other life forms. Occupying indeterminable positions, interfacing with multidimensional borders, cultures, media, and ecologies; mediating internal and external inputs/relations - bodies feel vibrations, perceive and collect information, remediate, respond, negotiate expectations, and make connections through the limbs, eyes, ears, skin, cells and beyond. Scholars, critics, and theorists describe this complex interfacing as a multispecies relationship consisting of deep histories, continually re-forming and transitioning into something new (Dooren et al., 2016). What Eben Kirksey deems as the microorganism, Wolbachia, a post-human actor that meshes a variety of species together that exist in different time frames and realities (Kirksey, 2018). For this exhibition, we introduce Mothering Bacteria: A Speculative Forecast of the Body as an Interface (2018), a multichannel artwork with an embedded AI that interacts with bacteria, as it grows in real-time.
One-Way Functions and (Im)perfect Obfuscation

A program obfuscator takes a program and outputs a “scrambled” version of it, where the goal is that the obfuscated program will not reveal much about its structure beyond what is apparent from executing it. There are several ways of formalizing this goal. Specifically, in indistinguishability obfuscation, first defined by Barak et al. [Advances in Cryptology - CRYPTO, 2001, Lect. Notes Comput. Sci. 2139, Springer, Berlin, Heidelberg, pp. 1–18], the requirement is that the results of obfuscating any two functionally equivalent programs (circuits) will be computationally indistinguishable. In 2013, a fascinating candidate construction for indistinguishability obfuscation was proposed by Garg et al. [Proceedings of the Symposium on Theory of Computing Conference, STOC, ACM, 2013, pp. 467–476]. This has led to a flurry of discovery of intriguing constructions of primitives and protocols whose existence was not previously known (for instance, fully deniable encryption by Sahai and Waters [Proceedings of the Symposium on Theory of Computing, 2014, STOC, pp. 475–484]). Most of them explicitly rely on additional hardness assumptions, such as one-way functions. Our goal is to get rid of this extra assumption. We cannot argue that indistinguishability obfuscation of all polynomial-time circuits implies the existence of one-way functions, since if , then program obfuscation (under the indistinguishability notion) is possible. Instead, the ultimate goal is to argue that if and program obfuscation is possible, then one-way functions exist. Our main result is that if and there is an efficient (even imperfect) indistinguishability obfuscator, then there are one-way functions. In addition, we show that the existence of an indistinguishability obfuscator implies (unconditionally) the existence of SZK-arguments for . This, in turn, provides an alternative version of our main result, based on the assumption of hard-on-the-average problems. To get some of our results we need obfuscators for simple programs such as circuits.