Reviewerly/exHarmony huggingface dataset:
Permutations from an arithmetic setting

Let m,n be positive integers such that m>1 divides n. In this paper, we introduce a special class of piecewise-affine permutations of the finite set [1,n]≔{1,…,n} with the property that the reduction (modm) of m consecutive elements in any of its cycles is, up to a cyclic shift, a fixed permutation of [1,m]. Our main result provides the cycle decomposition of such permutations. We further show that such permutations give rise to permutations of finite fields. In particular, we explicitly obtain classes of permutation polynomials of finite fields whose cycle decomposition and its inverse are explicitly given.
Textual Statistics and Named Entity Recognition Applied to Game of Thrones Novels

In this work we present a new framework for the analysis of Italian texts that could help linguists to perform rapid text analysis. The framework, that performs both statistical and rule-based analysis, is called LG-Starship. The idea is to built a modular software that includes the basic algorithms to perform different kinds of analysis. The framework will include a Preprocessing Module a POS Tagging and Lemmatization module, a Statistic Module, a Semantic Module based on Distributional Analysis algorithms, and a Syntactic Module, which analyses syntax structures of a selected sentence and tags the verbs and its arguments with semantic labels. The objective of the Framework is to build an “all-in-one” platform for NLP which allows any kind of users to perform basic and advanced text analysis.
Developing interactive multimedia on polyhedron material for class 8 junior high school students

This study aimed to develop interactive multimedia that was valid, practical, and effective. The procedure of this research was carried out by referring to ADDIE model which consisted of five phases, namely Analyze, Design, Development, Implementation, and Evaluation. The subjects of this study were 8th grade junior high school students in Malang. Based on the data analysis, the results obtained were (1) the validity test of the media got score of 3.75, the validity of the material got score 3.63, and the validity of the students’ questionnaire responses got score 3.7, so the developed instructional media met the valid criteria, (2) the practicality test of instructional media got score of 3.5 that was obtained from student response questionnaire, so that the developed instructional media met the practical criteria, (3) the effectiveness test was that 83% of students obtained 80 and above which showed that developed instructional media met the effective criteria. Based on these results, the developed instructional media were declared to be valid, practical, and effective.
Strategy Inference via Real-Time Homeomorphic and Isomorphic Tree Matching of Probabilistic Graphical Models

In many common gaming and real-world scenarios agents are trying to predict the behavior of the other agents. This assumes that there is some underlying strategy that these players are following, that such strategies can be inferred, and that a reasonable player can counter such strategies in real-time. These strategies can be modeled as various graph structures such as trees, finite state machines (FSMs), or probabilistic graphical models (PGMs). With these models created, one approach to best determine which strategy an agent is following is to match prospective trees, built from observed behaviors or policies, with known trees, representing previously learned strategies and policies. While matching two trees can be done in super-linear time in the small scale, the matching problem quickly becomes NP for the more complicated cases. This leads to a well-known NP-Complete problem (e.g., (Kumar et al. 2011)) when one considers homeomorphic trees (one is a subgraph of the other) and isomorphic trees (the bijection is true (i.e., homeomorphic in both directions)) and their matching. Isomorphic graph matching is much more complex (Vazirani 1989). At scale, most solutions for graph matching utilize highly-parallel processes running on high-performance computing clusters. This is intractable for real-time low-power computer systems. This paper presents an approximation algorithm for tree matching in order to accomplish strategy inference in real-time multi-agent systems.
Mining Source Code for Component Reuse

Although the development of code search engines has brought forth syntax-aware capabilities when searching for reusable components, these engines do not fully exploit the given context and do not assess the retrieved source code. As a result, several test-driven reuse systems have been developed to offer context-aware component search and further assess the retrieved components using test cases. However, most of these systems employ strict matching criteria and do not offer information concerning the flow and the dependencies of the retrieved components. In this chapter, we present Mantissa, a system designed to overcome the aforementioned limitations. Mantissa allows code searching in growing repositories, such as GitHub. The user provides the input query as a code snippet and Mantissa employs a mechanism that uses Information Retrieval techniques to return functional software components. Finally, we provide an example usage scenario for Mantissa and evaluate our system against popular search engines and test-driven reuse systems to illustrate its effectiveness.
Argumentation for all

This paper proposes the use of a web-based system for the development of applications of argumentation. It focuses on bringing the capability to develop decision policies based on argumentation to people that have little or no knowledge of logic programming or of an argumentation framework. To achieve this, it presents an implementation of the table formalism that has recently been put forward by previous work. Our system was evaluated using the think aloud protocol from the early stages of development.
IntPred

Deep Neural-Network (DNN) based Object Detection is one of the most important and time-consuming stages of Autonomous Driving software in cars. In non-critical domains, the performance and energy requirements of object detection can be reduced at the cost of accuracy in the detected objects. This is not the case in a critical domain like automotive, for which a delicate balance between performance/energy overheads and accuracy of object detection must be found. We propose IntPred to achieve such a balance by leveraging on the fact that, with high frame rates, objects do not move significantly across frames. IntPred tailors object interpolation for the case of object detection in autonomous driving frameworks, in line with approaches devised for other domains, thus heavily reducing the performance requirements of full-fledged DNN-based object prediction. IntPred results in comparable accuracy to the original object detection, while saving more than 70% of the computations. The latter allows using lower-performance and cheaper platforms resulting in saving energy and reducing heat dissipation: for instance, in an NVIDIA Jetson TX2 platform, specific for autonomous driving systems, our technique increases the frame processing rate by 4.6x. IntPred also allows consolidating additional applications onto the same platform.
Strain analysis in narrow boreholes by means of microscopic digital image correlation

Residual stresses play a decisive role in stress analyses, as they can exert positive or negative influence on the durability of parts. A widely-used method to measure residual stresses experimentally is the hole drilling method. With this method, however, only near-surface residual stresses can be determined. This disadvantage is to be eliminated by the newly developed method of staggered machining. The underlying procedure is described in this paper. In addition, a manufacturing process for production fine speckle patterns, which are required for the Digital Image Correlation (DIC) is explained. The principle of staggered machining is shown by means of preliminary tests and their evaluation.
Prediction of Load Time for Different Agent Locations and Browsers

The article concerns the prediction of the time of downloading web resources on the Internet, depending on the location of the clent and the browser used. The experiment used ten WebPage Test agents located in five locations around the world, which monitored ten recognized and servicing the entire world tourist portals in a given time. The collected data was used to predict page loading performance using the Regression Tree and Gaussian in Regression algorithms. The quality assessment of page download time prediction was examined using metrics such as RMSE, MSE and MAE. The medians of resource download times and prediction metrics for individual locations and type of web browser were compared. Agents and browsers for which prediction errors were the least were designated. Research serves, among others to show how website monitoring tools can return divergent/inconsistent results. Hence, their use for the tasks of automating intelligent internet measurements and predicting its quality must be prudent.
Choosing an event description: What a PropBank study reveals about the contrast between light verb constructions and counterpart synthetic verbs

Light verb constructions (LVCs) in English and Romance languages are somewhat unique crosslinguistically because LVCs in these languages tend to have semantically similar synthetic verb counterparts (Zarco 1999): e.g. make an appearance and appear . This runs contrary to assumptions in linguistic theories that two competing forms are rarely maintained in a language unless they serve distinct purposes (e.g. Grice 1975). Why do English LVCs exist alongside counterpart synthetic verbs, especially given that synthetic verbs are arguably the more efficient form (Zipf 1949)? It has been proposed that LVCs serve an aspectual function (Prince 1972, Live 1973, Wierzbicka 1982, Tanabe 1999, Butt &amp; Geuder 2001), as there are telic LVC counterparts (e.g. have a thought ) of atelic verbs (e.g. think ). This proposal has been difficult to evaluate without a large-scale resource providing a markup of both LVCs and counterpart verbs. Addressing this gap in resources, the present research describes the development of guidelines for LVC annotation in the English PropBank (Bonial &amp; Palmer 2015). The focus of this article is the subsequent analysis of these annotations, aimed at uncovering corpus evidence of what contexts call for the use of an LVC over a synthetic verb. The corpus study shows that the general function of LVCs is not an aspectual one and provides distributional evidence that the ease and variety with which LVCs can be modified is the general motivating factor for the use of an LVC.
Mobile Information Security Risk Calculator

Computer information systems have in recent years found increasing applications in all facets of human endeavor owing to the rapid improvement in processing and storage technologies, the advent of the internet, as well as availability and reduced costs of computing resources. Consequently, organizations and businesses heavily depend on computer information systems and the internet to effectively manage their operations which often contain proprietary, confidential and safety critical information, which exposes important computing resources and information assets to security threats. This paper examines different quantitative information security risk assessment methodologies used to protect and/or manage organizational information systems from physical and cyber-attacks. The authors then developed a mobile, intuitive, interactive and user-friendly tool, the Mobile Risk Calculator, that visualizes the entire quantitative risk assessment process and helps organizations determine the degree of threats to their information security systems in order to make right decisions about risks. It is expected to facilitate teaching, learning and understanding of Quantitative Information Security Risk Management processes for both industry experts and beginners who need to have the basic knowledge of information security risks and its management. The tool was implemented both on an Android Virtual Device (Android Emulator) and real-life android mobile devices, and initial evaluation feedbacks show that it performs better than existing methodologies. It is intuitive, interactive portable and less complex, facilitating learning and understanding of the Risk Analysis Process.
Hashing technique based on SHA-3 using chaotic maps1

New hashing technique based on SHA-3 (Secure Hash Algorithm-3) is introduced. Chaotic maps are used in this technique to enhance performance without losing security. Introduced algorithm was tested for resistance against collisions, statistical analysis of output sequences was performed, hashing performance was evaluated. The testing showed a low collision probability. The testing corresponds the standards of National Institute of Standards and Technology and showed that output sequences are close to random. Performance testing showed 60 % enhancement in comparison with plain SHA-3.
Investigating Citation Linkage as a Sentence Similarity Measurement Task Using Deep Learning

Research publications reflect advancements in the corresponding research domain. In these research publications, scientists often use citations to bolster the presented research findings and portray the improvements that come with these findings, at the same time, to make the contents more understandable to the audience by navigating the flow of information. In the science domain, a citation refers to the document from where this information originates, but doesn't specify the text span that is actually being cited. This paper develops a framework which can create a linkage between the citing sentences from the ongoing research article and the related cited sentences from the corresponding referenced documents. Eventually, it will reduce the burden of the readers to go through all the sentences in the documents while acquiring the required background information. This citation linkage problem has been modelled as a semantic relatedness task where given a citing sentence the framework generates the sentence pairs with the citing sentence and each of the sentences from the reference document and then tries to determine which sentence pair is semantically similar and which pair is not. Construction of the citation linkage framework involves corpus creation and utilizing deep-learning models for semantic similarity measurement.
Soft Computing and Big Data Intelligence for a Low-Carbon Economy

The power and ubiquity of big data and soft computing are redefining how governments, civil society, and business consider solutions to modern environmental challenges: Government agencies, environmental organizations, multinational corporations, Bretton Woods Institutions, and the United Nations System are all embracing big data to examine the viability of a low-carbon economy, green financing, and sustainable energy alternatives. However, more research is needed to understand the algorithmic, experimental, prototyping, and implementation tools related to soft computing and big data for promoting a low-carbon economy. A new type of neuro-fuzzy network is described that uses concepts drawn from multi-linear (tensor) geometry to improve group decision making and negotiation for promoting a very low-carbon economy in the US state of Hawaii. It is shown that sparse tensors, resolved into orthogonal components as applied in neuro-fuzzy computation, can result in higher efficiency for complex problems characterized by higher-order systems where it can be critical for achieving real-time solutions. Both a theoretical and empirical perspective is applied using MATLAB’s ANFIS models and experimental data. The proposed approach provides efficient, complex, high-order neuro-fuzzy models of high utility for solving the ecological crises of the Anthropocene.
Methodology for the Development of an Ontology based E-Learning Platform

E-learning platform can be effectively used if it is based on the ontology of learning, training and agents that has been used to design online Learning platforms. For e-Learning applications, an ontology is required that displays domain concepts, context, and data is used to display and store the knowledge and the design of smart lessons. Based on the credentials passed for the level of the lesson plans and constructs a conceptual chart and a contextual chart, the training officer critically evaluates each trainee. This is adapted by browsing the graph, to adjust the level, context, knowledge and context of the trainee dynamically, based on the answers and for bridging the gaps which are there between the expected and actual skill set. The same will illustrated under this study with the proposed methodology and an example of “project management course” that is adaptable and customizable in any departments.
UAV 3D Path Planning Based on Multi-Population Ensemble Differential Evolution

The three-dimensional (3D) environmental path planning of unmanned aerial vehicles (UAVs) is studied systemically. The model has been constructed, including 3D environment, threat, path length, yaw angle, climb angle and height. All the objective costs have been converted into a single objective optimization problem by the linear weight method. The multi-population ensemble differential evolution (MPEDE) algorithm has been applied to the 3D UAV path planning, and has shown the best performance compared with the self-adaptive differential evolution (SaDE) and differential evolution (DE) algorithms. The MPEDE algorithm is feasible in solve the UAV path planning problem.
An Improved Anti-counterfeiting Printed QR Watermarking Algorithm Based on Self-Adaptive Genetic Algorithm

Abstract To solve watermarking parameters optimization problem and enhance anti-counterfeiting performance, a self-adaptive genetic algorithm is proposed and introduced to improve robust QR code watermarking scheme. In the improved scheme, we adaptively change the genetic algorithm procedure according to the relation between the maximum fitness value and the average fitness value of a population. With the improved genetic algorithm procedure, it is easier to get better diveisity. In addition, mutation probability as well as crossover probability is adaptively modified to quickly seek out the optimal watermarking parameters. The tests indicate that the decoding rate of QR code noticeably increases without degrading detecting rate of digital watermark with the improved anti-counterfeiting printed QR code watermarking scheme.
Real-time video segmentation using a vague adaptive threshold

For the last two decades, video shot segmentation has been a widely researched topic in the field of content-based video analysis (CBVA). However, over the course of time, researchers have aimed to improve upon the existing methods of shot segmentation in order to gain accuracy. Video shot segmentation or shot boundary analysis is a basic and vital step in CBVA, since any error incurred in this step reduces the precision of the other steps. The shot segmentation problem assumes greater proportions when detection is preferred in real time. A spatiotemporal fuzzy hostility index (STFHI) is proposed in this work which is used for edge detection of objects occurring in the frames of a video. The edges present in the frames are treated as features. Correlation between these edge-detected frames is used as a similarity measure. In a real-time scenario, the incoming images are processed and the similarities are computed for successive frames of the video. These values are assumed to be normally distributed. The gradients of these correlation values are taken to be members of a vague set. In order to obtain a threshold after defuzzification, the true and false memberships of the elements are computed using a novel approach. The threshold is updated as new frames are buffered in and is referred to as the vague adaptive threshold (VAT). The shot boundaries are then detected based on the VAT. The VAT for detecting the shot boundaries is determined by using the three-sigma rule on the defuzzified membership values. The effectiveness of the real-time video segmentation method is established by an experimental evaluation on a heterogeneous test set, comprising videos with diverse characteristics. The test set consists of videos from sports, movie songs, music albums, and documentaries. The proposed method is seen to achieve an average F1 score of 0.992 over the test set consisting of 15 videos. Videos from the benchmark TRECVID 2001 are selected for comparison with other state-of-the-art-methods. The proposed method achieves very high precision and recall, with an average F1 score of 0.939 on the videos chosen from the TRECVID 2001 dataset. This is a substantial improvement over the other existing methods.
On the measurement of software complexity for plc industrial control systems using TIQVA

In the safety-critical domain (e.g. transportation, nuclear, aerospace and automotive), large-scale embedded systems implemented using Programmable Logic Controllers (PLCs) are widely used to provide supervisory control. Software complexity metrics, such as code size and cyclomatic complexity, have been used in the software engineering community for predicting quality metrics such as maintainability, bug proneness and robustness. However, since there is no available approach and tool support for measuring software complexity of PLC programs, we developed a tool called TIQVA in an effort to measure complexity for this type of software. We show how to measure different software complexity metrics such as lines of code, cyclomatic complexity, and information flow for a popular PLC programming language named Function Block Diagram (FBD). We evaluate the tool using data provided by Bombardier Transportation from a Train Control Management System (TCMS). In addition, we report some empirical and industrial evidence showing how TIQVA can be used to provide some experimental evidence to support the use of these metrics to estimate testing effort for an industrial control software. The results from this evaluation indicate that other specific dimensions of PLC programs (e.g., function block relationships, block coupling and timing) could be used to improve the measurement of complexity for industrial embedded software.
Axiomatizing Hybrid XPath with Data

In this paper we introduce sound and strongly complete axiomatizations for XPath with data constraints extended with hybrid operators. First, we present HXPath=, a multi-modal version of XPath with data, extended with nominals and the hybrid operator @. Then, we introduce an axiomatic system for HXPath=, and we prove it is strongly complete with respect to the class of abstract data models, i.e., data models in which data values are abstracted as equivalence relations. We prove a general completeness result similar to the one presented in, e.g., [BtC06], that ensures that certain extensions of the axiomatic system we introduce are also complete. The axiomatic systems that can be obtained in this way cover a large family of hybrid XPath languages over different classes of frames, for which we present concrete examples. In addition, we investigate axiomatizations over the class of tree models, structures widely used in practice. We show that a strongly complete, finitary, first-order axiomatization of hybrid XPath over trees does not exist, and we propose two alternatives to deal with this issue. We finally introduce filtrations to investigate the status of decidability of the satisfiability problem for these languages.