Reviewerly/exHarmony huggingface dataset:
Study on Tunnel Risk Assessment Based on Rough Set and Grey Theory

Abstract The surrounding environment of railway tunnel engineering is complex, and it is prone to major safety accidents. It is necessary to adopt feasible and accurate evaluation methods. The evaluation index system of tunnel is established. Rough set is used to quantify the evaluation index and form the reference index. The risk evaluation model of tunnel based on rough set and grey is established, and the risk of Sheguchong tunnel in Yunnan Province is validated to evaluate the validity of the model.
Use of machine learning techniques on airborne geophysical data for mineral resources exploration in Burkina Faso

SummaryRecent advances in development of automated tools and machine learning algorithms based on artificial intelligence (AI) have revolutionized our interpretation approach of big data by making it faster, more objective and more reliable than tedious manual processes.In this paper, we show results derived from machine learning applications to the recently acquired high-resolution airborne geophysical data of Burkina Faso. The results are represented as country-wide prospectivity maps for various mineral resources including gold, uranium, base metals and strategic metals.The new mapping products indicate that Burkina Faso has a diversified and significant mineral potential.
The information technology for image filtering and storing obtained under low light conditions

This work is devoted to development of information technology for real noise image filtering and storing obtained under low light condition. The information technology is designed to reduce the size of the image (video) archive, through the use of image processing methods. The structure of information technology is proposed. It includes input data, color models, image processing methods (filtering, compression, blocking effect reduction, coding and transmission), algorithms and software. The developed information technology depending on image type, task and result requirements performs image processing with appropriate filtering methods and has the possibility of encoding and transmission obtained images. The information technology allows reducing the size of the video archive in a video surveillance system with MPEG-4 (XVID) and H.264 video codec, while maintaining acceptable quality.
A Successful Failure or a Failed Success?

We reflect back on a previous paper writing process, where we initially set out to share experiences of forest walks and discuss how these were part of a design process for an application around sustainable grocery shopping. We describe our inability in finding a working way of articulating the experiences and the design process led us into route of unintended rationalization and systematization. Instead of strengthening our arguments and articulation of the value in our forest walks, we step by step rationalized our design decisions by tying them to discrete occurrences in the forest, shaping a paper that fitted the traditional form of HCI research. In this paper we reflect on this process and how our original intentions could be reached. Finally, we reason around the value of these forms of purposeless experiences in relation to design, and how there could be space for these types of contributions in HCI.
Math modeling of the interdisciplinary collections generation process in the digital libraries environment

The article deals with the problem of building a modern information society as the task of forming a knowledge cyberdomain.The implementation of this task can be carried out on the technological platform of the digital library, which ensures the formation and provision of information resources in various knowledge parts to general public users.The article presents the concept of "knowledge cyberdomain".The difference between the knowledge cyberdomain and the information space is presented in the paper.The concepts of data entries and types of data entries that make up the content of the knowledge cyberdomain are examines.Knowledge cyberdomain is presented in the form of a set containing data entries that have been tested by the world scientific community.Data entries properties of this set are transferred to all objects of subsets of this set, which allows to avoid a significant part of duplication of information in the knowledge cyberdomain.The objects linking to each other is defined as a recursive transition network.The presentation of the concepts of a set and a subset of a given set for building a digital knowledge space allows you to divide information into levels of detail.Introduced the concept of hierarchical relationships between data entries.The construction of hierarchies that represent "data entry" sections with a high degree of detail allows to increase the efficiency of information retrieval in the knowledge space and analysis of information.The concept of hierarchy level of data entries is presented in the article.Definitions of various levels data entries are given and principles of work with data entries of each level are formulated.The hierarchy of representation of electronic data entries in the knowledge cyberdomain is proposed.
Finitely distinguishable erasing pattern languages

Pattern languages have been an object of study in various subfields of computer science for decades. This paper introduces and studies a decision problem on patterns called the finite distinguishability problem: given a pattern π, are there finite sets T+ and T− of strings such that the only pattern language containing all strings in T+ and none of the strings in T− is the language generated by π? This problem is related to the complexity of teacher-directed learning, as studied in computational learning theory, as well as to the long-standing open question whether the equivalence of two patterns is decidable. We show that finite distinguishability is decidable if the underlying alphabet is of size other than 2 or 3, and provide a number of related results, such as (i) partial solutions for alphabet sizes 2 and 3, and (ii) decidability proofs for variants of the problem for special subclasses of patterns, namely, regular, 1-variable, and non-cross patterns. For the same subclasses, we further determine the values of two complexity parameters in teacher-directed learning, namely the teaching dimension and the recursive teaching dimension.
Fast Bat Algorithm for Predicting Diabetes Mellitus Using Association Rule Mining

Association Rules (ARs) are the most important tool of Data Mining (DM) used to extract useful information stored in large databases during the last years. Motivated by the success of population-based metaheuristics dealing with this amount of data, we propose to develop a faster approach of the Bat algorithm based on ARM. Our approach is evaluated on a real database of population with or without diabetes. The proposed algorithm has better optimization accuracy and time complexity compared with the old version of the algorithm.
Random two-step phase estimation based on Lissajous ellipse fitting method with light source intensity instability

The fringe projection technique is an effective technique to measure 3D shape of objects, in which phase retrieval is an important procedure. In this paper, the phase extraction algorithm of random phase-shift, which can be used in the fluctuating light source and the non-uniform background intensity and modulation amplitude, is proposed. The proposed method can retain more details of reconstructed phase than Fourier transform profilometry. Compared with multi-frame phase-shifting method, the proposed method only needs two fringe patterns to extract the phase and phase shift of the object. Our method consists of two stages. Firstly, the method is built based upon Lissajous Ellipse Fitting technique that extracts the phase from only two phase-shifted fringes which may contain arbitrary and unknown phase shift in dynamic measurement experiment. Second, the non-uniform background intensity and modulation amplitude are removed. The simulation results demonstrate that the proposed method can effectively obtain the phase.
Mobile Reception: Materiality and Locality with Small Screens

Mobile phone screens have always been intimate displays. Attached to uniquely personal devices that rarely leave one’s side and linked to networks that carry one’s most intimate communications these screens provide a boutique set of opportunities and challenges for screen-based production. This chapter examines these affordances and restrictions with particular focus on what sets these mobile screens apart from other screens as sites of reception, production and distribution. It particularly focuses on the materialities of the devices, the key aspects of the locative and the nature of mobile devices as networked screens. It draws on exemplars from mobile-based media art productions that have made sophisticated use of these properties in ways that distinguish mobile screen work as a modality with its own language, aesthetic rules and creative production strategies. In these examples the chapter looks at how media artists are continuing a tradition of experimentation with the materiality of new mediums and how this contributes to developing the language of the medium. It examines how the particular properties of the mobile screen influence the type of content that is produced, from the nature of stories that sit well in the medium to the types of physical and virtual interaction that distinguish these screens from other networked screens.
Detection of Abnormal Load Consumption in the Power Grid Using Clustering and Statistical Analysis

Nowadays, the electricity load profiles of customers (consumers and prosumers) are changing as new technologies are being developed, and therefore it is necessary to correctly identify new trends, changes and anomalies in data. Anomalies in load consumption can be caused by abnormal behavior of customers or a failure of smart meters in the grid. Accurate identification of such anomalies is crucial for maintaining stability in the grid and reduce electricity loss of distribution companies. Smart meters produce huge amounts of load consumption measurements every day and analyzing all the measurements is computationally expensive and very inefficient. Therefore, the aim of this work is to propose an anomaly detection method, that addresses this issue. Our proposed method firstly narrows down potential anomalous customers in large datasets by clustering discretized time series, and then analyses selected profiles using statistical method S-H-ESD to calculate final anomaly score. We evaluated and compared our method to four state-of-the-art anomaly detection methods on created synthetic dataset of load consumption time series containing collective anomalies. Our method outperformed other evaluated methods in terms of accuracy.
Mining Rank Data

The problem of frequent pattern mining has been studied quite extensively for various types of data, including sets, sequences, and graphs. Somewhat surprisingly, another important type of data, namely rank data, has received very little attention in data mining so far. In this article, we therefore address the problem of mining rank data, that is, data in the form of rankings (total orders) of an underlying set of items. More specifically, two types of patterns are considered, namely frequent rankings and dependencies between such rankings in the form of association rules. Algorithms for mining frequent rankings and frequent closed rankings are proposed and tested experimentally, using both synthetic and real data.
Header Injection and URL Redirection

Header injection and URL redirection are possible when a web application accepts unvalidated user inputs. These untrusted data may redirect the page to a malicious web site.
Embracing Software Process Improvement in Automotive Through PISA Model

Vehicles innovation is principally driven by electronics components and software that play today a predominant role for the vehicle’s functions. Because the quality of on-board automotive electronic systems is strongly dependent on the quality of their development practices, car-makers and suppliers proactively focused on improvement of technical and organizational processes. In this setting, several reference standards for the assessment and improvement of automotive electronics processes and projects have been conceived and used in the last decade. Although the effects of the application of them in automotive industry have been generally positive, getting compliance in the short period may represent, in some contexts, a target hardly achievable, or even a chimera. In this context, a novel scheme addressing both project evaluation and process improvement and targeting a hand-on approach for the practitioners has been recently developed starting from the analysis of practitioners needs and success factors in the software process improvement. This scheme is named Process Improvement Scheme for Automotive (PISA Model). The structure and contents of the PISA Model is described in this paper.
Evaluating Generalized Path Queries by Integrating Algebraic Path Problem Solving with Graph Pattern Matching

Path querying on Semantic Networks is gaining increased focus because of its broad applicability. Some graph databases offer support for variants of path queries e.g. shortest path. However, many applications have the need for the set version of various path problem i.e. finding paths between multiple source and multiple destination nodes (subject to different kinds of constraints). Further, the sets of source and destination nodes may be described declaratively as patterns, rather than given explicitly. Such queries lead to the requirement of integrating graph pattern matching with path problem solving. There are currently existing limitations in support of such queries (either inability to express some classes, incomplete results, inability to complete query evaluation unless graph patterns are extremely selective, etc). In this paper, we propose a framework for evaluating generalized path queries - gpqs that integrate an algebraic technique for solving path problems with SPARQL graph pattern matching. The integrated algebraic querying technique enables more scalable and efficient processing of gpqs, including the possibility of support for a broader range of path constraints. We present the approach and implementation strategy and compare performance and query expressiveness with a popular graph engine.
Petrophysical “Big Data” - case study from the Stavely Project, western Victoria

SummaryThe application of recent technological advances to the acquisition of petrophysical datasets has significantly increased the amount of quality, systematic physical rock property measurements that can be captured efficiently and cost effectively. These large datasets provide a more comprehensive understanding of petrophysical values and, as importantly, their statistical distribution – particularly when encompassing a diverse set of rock types and stratigraphy. To date, these types of comprehensive datasets are limited in the mineral exploration space, in both number and their spatial distribution.This paper presents a case study from the Stavely Project area in western Victoria outlining the workflow from acquisition, compilation and analysis, to the application of a large, pre-competitive petrophysical dataset. These data, including gamma density, magnetic susceptibility, P-wave velocity, electrical resistivity and natural gamma, were acquired by scanning of diamond core from 21 stratigraphic and mineral exploration drill holes by Multi-Sensor Core Logger equipment, providing a combined final dataset of 216,699 petrophysical values. The precompetitive data and subsequent analyses are available for download.In developing a statistical analysis of the petrophysical values, the datasets were segregated into stratigraphic, lithological and spatial sub-populations, therefore giving a geological context for the data, and providing baseline values for regional geophysical interpretations, forward modelling and inversions, and the identification of regions of anomalous petrophysical values potentially associated with hydrothermal mineralisation. Crosscorrelation of various petrophysical properties highlights important relationships within the data, thus maximising its value. In western Victoria these data have been important in the delineation of prospective volcanic belts of the Stavely Arc beneath cover, and the development of a 3D model displaying the regional geometries and distributions of the belts at depth.
Analysis of cloudiness by intensity segmentation and clustering

Algorithm of Ramer–Douglas–Peucker for piecewise approximation was used for image multilevel segmentation. The cumulative histogram was chosen as a function for approximation. The developed algorithm was applied to the satellite map images to divide clouds of different intensity. The map images of clouds with different intensity were clustered to obtaine cloudiness values and classification.
Subsequence versus substring constraints in sequence pattern languages

A family of logics for expressing patterns in sequences is investigated. The logics are all fragments of first-order logic, but they are variable-free. Instead, they can use substring and subsequence constraints as basic propositions. Propositions expressing constraints on the beginning or the end of the sequence are also available. Also wildcards can be used, which is important when the alphabet is not fixed, as is typical in database applications. The maximal logic with all four features of substring, subsequence, begin–end constraints, and wildcards, turns out to be equivalent to the family of star-free regular languages of dot-depth at most one. We investigate the lattice formed by taking all possible combinations of the above four features, and show it to be strict. For instance, we formally confirm what might intuitively be expected, namely, that boolean combinations of substring constraints are not sufficient to express subsequence constraints, and vice versa. We show an expressiveness hierarchy results from allowing multiple wildcards. We also investigate what happens with regular expressions when concatenation is replaced by subsequencing. Finally, we study the expressiveness of our logic relative to first-order logic.
IPEx: A lower-cost superior reconnaissance RES/IP/MT survey

SummaryThis paper presents a technique derived from the full MIMDAS RES/IP/MT survey called IPEx. It offers a more cost-effective, enhanced reconnaissance IP investigation in areas of post-mineral cover, ensuring that a minimumsized predefined chargeable body would not be missed.The MIMDAS system is ideal for its capability to measure very low signals, due to the enhanced telluric correction, especially in caliche-covered areas. A total of four lines were surveyed with IPEx, and substantial anomalies were found, where some in-fill/detailing has been executed.IPEx takes approximately half to two thirds of the normal MIMDAS survey time, making it a lower-cost superior reconnaissance RES/IP/MT survey tool.
Research on the Wireless Video Surveillance System Based on Linux

Abstract With the new development of embedded technology, image compression and wireless transmission technology, wireless video surveillance system based on embedded system has become the tendency of development in the field of video surveillance. This paper combines the character of embedded system with wireless transmission technology, researches and designs a solution of wireless video surveillance system based on Linux. The embedded video surveillance system uses C/S framework. The server based on ARM11 and Linux is designed to process video image including capture, preview, compression and transmission. The PC as a client is built to receive, decode, display and setup the video.
Semantic Analysis of Big Data in Hierarchical Interpretation of Recommendation Systems

In today’s scenario where Big Data is being used at its maximum potential, showing most of its influence on searching from the internet. It gives out large amount of data to the user which becomes too overwhelming for the user to analyze and understand. This led to the introduction of Recommendation Systems whose main purpose is to give relevant datasets according to the user’s preference which makes it easy for the user to understand and analyze the best option among the limited options he/she has received from the system. Recommendation Systems exhibit some kind of implicit hierarchy based on either users or items to give the best recommendation to users. But it has been noticed that these systems produce a lot of ambiguities. Hence, leading to a lot of repeated results. This paper investigate various ways to understand the implicit working of hierarchical structures and make some improvisations on the same with the help of semantic analysis under collaborative filtering approach.
Machine learning models for predicting customer decision in motor claims settlement

This paper describes results of using machine learning model to aid reduction of number of repairs in external workshops for motor insurance company. The model predicts the customer decision based on data stored in insurance company’s database as well as additional features. We built several models, based on decision tree, random forest, gradient boost, ada boost, naive bayesian, logistic regression, neural network, then we evaluated them on real data. Built models were tested on separate evaluation dataset provided by the insurance company. Models achieved over 0.8 area under curve ROC and thus were accepted for a pilot study in the production environment.
Development of MEMS Sensor-Based Double Handed Gesture-To-Speech Conversion System

The amount of effort required in communication is high in the case of vocally and hearing impaired population as they express their emotions through sign language. An uninterrupted communication between the impaired and unimpaired groups of the society is possible only if the unimpaired population is trained to interpret the sign language. The aim of this work is to develop a double handed gesture-to-speech conversion system, which will aid in facilitating effective communication between an untrained, unimpaired listener and an impaired speaker. The proposed work involves the development of MEMS sensor-based double handed gesture recognition module that uses a hidden Markov model-based sign-to-text conversion system to convert the input hand gestures to bilingual text and a hidden Markov model-based text-to-speech synthesizer to convert the corresponding text to synthetic speech. The proposed system performs with an accuracy of 98% and 97% for single handed and double handed gestures respectively.
Anonymous Leakage-Resilient Ciphertext-Policy Attribute-Based Encryption Supporting Direct Revocation

Leakage-resilient ciphertext-policy attribute-based encryption (LR-CP-ABE) is an important tool to achieve fine-grained access control of data and resist side-channel attacks. Privacy protection and user revocation are two practical problems it faces. However, most of the existing schemes fail to achieve user revocation while protecting user’s privacy at present. To address the above problems, we propose an anonymous LR-CP-ABE scheme with user revocation in this paper, which is proven to be adaptively secure in the standard model under four static assumptions over composite order group. Furthermore, we also show the proposed scheme achieves the receivers anonymity which protects the users’ privacy. The performance analyses confirm the feasibility of the proposed scheme.