Reviewerly/exHarmony huggingface dataset:
Description Logics with Concrete Domains and General Concept Inclusions Revisited

Concrete domains have been introduced in the area of Description Logic to enable reference to concrete objects (such as numbers) and predefined predicates on these objects (such as numerical comparisons) when defining concepts. Unfortunately, in the presence of general concept inclusions (GCIs), which are supported by all modern DL systems, adding concrete domains may easily lead to undecidability. One contribution of this paper is to strengthen the existing undecidability results further by showing that concrete domains even weaker than the ones considered in the previous proofs may cause undecidability. To regain decidability in the presence of GCIs, quite strong restrictions, in sum called $$\omega $$ -admissibility, need to be imposed on the concrete domain. On the one hand, we generalize the notion of $$\omega $$ -admissibility from concrete domains with only binary predicates to concrete domains with predicates of arbitrary arity. On the other hand, we relate $$\omega $$ -admissibility to well-known notions from model theory. In particular, we show that finitely bounded, homogeneous structures yield $$\omega $$ -admissible concrete domains. This allows us to show $$\omega $$ -admissibility of concrete domains using existing results from model theory.
Automatically Proving and Disproving Feasibility Conditions

In the realm of term rewriting, given terms s and t, a reachability condition $$s \rightarrow ^* t$$ is called feasible if there is a substitution $$\sigma $$ such that $$\sigma (s)$$ rewrites into $$\sigma (t)$$ in zero or more steps; otherwise, it is called infeasible. Checking infeasibility of (sequences of) reachability conditions is important in the analysis of computational properties of rewrite systems like confluence or (operational) termination. In this paper, we generalize this notion of feasibility to arbitrary n-ary relations on terms defined by first-order theories. In this way, properties of computational systems whose operational semantics can be given as a first-order theory can be investigated. We introduce a framework for proving feasibility/infeasibility, and a new tool, infChecker, which implements it.
UAV Image Mosaicking Based on Multiregion Guided Local Projection Deformation

The goal of unmanned aerial vehicle (UAV) image mosaicking is to create natural- looking mosaics without artifacts due to the parallax of the image and relative camera motion. UAV remote sensing is a low-altitude technology and the UAV imaged scene is not effectively planar, yielding parallax on the images. Moreover, when an object in 3-D is mapped to an image plane, different surfaces have different projections. These projections vary with the viewpoint in a sequence of UAV images, which causes artifacts near some tall buildings in the stitched images. To solve these problems, we propose a novel stitching method based on multiregion guided local projection deformation, which can significantly reduce ghosting due to these projections vary with the viewpoint and the parallax. In the proposed method, the image is initially meshed and each cell corresponds to a local homography for image matching, which can reduce misalignment artifacts in the results compared with 2-D projective transforms or global homography. Then, we divide the overlapping regions of input images into multiple regions by classifying feature points. The partitioned regions which serve well scene constraints, are employed to guide the calculation of local homography. Specifically, instead of calculating local homography by the distance between all the feature points in the image and the vertices of the grid, we propose a strategy where multiple regions have different weights for calculating local homography, which can significantly reduce ghosting near some tall buildings. The benefits of the proposed approach are demonstrated using a variety of challenging cases.
PAPR Reduction Scheme for Deep Learning-Based Communication Systems Using Autoencoders

Deep neural networks (DNN) have gained considerable attention in the communication literature during the past few years. In particular, as a well-known DNN architecture, autoencoders (AE) are used to model the end-to-end communication systems achieving a reasonable performance in terms of block error rate (BLER). However, autoencoders significantly suffer from high peak-to-average-power-ratio (PAPR), resulting in power amplifier saturation. This paper proposes a novel DNN architecture for reducing PAPR in autoencoder-based communication systems. Simulation results verify that the proposed scheme outperforms the conventional PAPR reduction method, i.e., loss function-based PAPR reduction approach, in terms of both bit error rate (BER) and PAPR.
Local dynamic neighborhood based outlier detection approach and its framework for large-scale datasets

Local outlier detection is a hot area and great challenge in data mining, especially for large-scale datasets. On the one hand, traditional algorithms often achieve low-quality detection results and are sensitive to neighborhood size. On the other hand, they are infeasible for large-scale datasets due to at least O(N2) time and space complexity. In light of these, we propose a new local outlier detection algorithm, which is designed based on a new stable neighborhood strategy-dynamic references nearest neighbors (DRNN). Meanwhile, we present a new detection framework by combining the proposed approach and k-mean for large-scale datasets. Experimental results demonstrate that the proposed algorithm can produce higher quality and robust detection results compared to several classic methods. Meanwhile, the new detection framework is able to significantly improve detecting efficiency without sacrificing accuracy.
Story Analysis Using Natural Language Processing and Interactive Dashboards

This paper discusses Story Analyzer, which uses a natural language processing (NLP) library and sophisticated data visualization libraries to produce dashboards of interrelated and user-responsive visualizations depicting actors and their interactions in a textual narrative, along with locations, times, and other contexts. Story Analyzer performs information extraction using Stanford’s CoreNLP’s NLP services including sentence recognition, tokenizing, parts-of-speech identification, dependency parsing, named entity recognition, coreference resolution, and temporal tagging. Visualization is done through D3 and scalable vector graphics (SVG) which provide powerful control over gelements and shapes in browser-based user interfaces. Google Charts and Maps are also used for visualizations. Development using NLP for unstructured textual data involves challenges, limitations, and ambiguities that distinguishes it from applications using structured data. Therefore, the paper also discusses issues and limitations inherent with using NLP libraries, and presents workarounds when applied to story analysis. Story Analyzer is applied to a contemporary news article regarding data privacy issues.
A New Approach for Pedestrian Density Estimation Using Moving Sensors and Computer Vision

An understanding of pedestrian dynamics is indispensable for numerous urban applications including the design of transportation networks and planing for business development. Pedestrian counting often requires utilizing manual or technical means to count individuals in each location of interest. However, such methods do not scale to the size of a city and a new approach to fill this gap is here proposed. In this project, we used a large dense dataset of images of New York City along with computer vision techniques to construct a spatio-temporal map of relative person density. Due to the limitations of state of the art computer vision methods, such automatic detection of person is inherently subject to errors. We model these errors as a probabilistic process, for which we provide theoretical analysis and thorough numerical simulations. We demonstrate that, within our assumptions, our methodology can supply a reasonable estimate of person densities and provide theoretical bounds for the resulting error.
Image steganography using add-sub based QVD and side match

Fall off boundary problem (FOBP) arises in many steganography schemes that use the concept of differencing between two pixel values. This chapter presents a new steganography scheme using add-sub based quotient value differencing (ASQVD) and side match (SM) to address the FOBP. It performs two stages of embedding on 3×3 size disjoint pixel blocks. In stage 1, it performs ASQVD and remainder substitution on the central pixel and its left, right, lower, and upper neighbor pixels. Based on new values of these five pixels, in stage 2, it performs SM embedding approach on four corner neighbors. From experimental observations it has been derived that this scheme hides relatively larger amounts of data. The principles of differencing and substitution have been used in this scheme, so pixel difference histogram (PDH) and regular-singular (RS) tests have been utilized to verify the security of this new scheme. Fortunately, these two tests fail to detect this steganography technique. The average hiding capacity is 3.55 BPB. It is greater than in the existing techniques.
iNethi Community Network

This poster presents iNethi, a community wireless network deployed in a peri-urban township of Cape Town, South Africa. Initial usage clearly indicates a demand for Internet-based external services and, to a lesser extent, local services. We focus on the initial months of our deployment, and interaction with locally installed INethi services, as well as the impact of access to the Internet. We present data from our traffic logs, captured during a period of six months. We observed that a large proportion of data is being consumed by software or antivirus updates. The data collected and trends observed will allow iNethi operators to make necessary adjustment to the networks.
Sensitivity to meaningful regularities acquired through experience

Abstract English spelling provides multiple cues to word meaning, and these cues are readily exploited by skilled readers. In two crowdsourcing studies, we tested skilled readers’ sensitivity to a large number of morphological as well as nonmorphological orthographic cues by asking them to classify nonwords as adjectives or nouns. We observed a substantial variability across individuals and orthographic cues. In this paper, we discuss some sources of this variation. Specifically, we found consistent correlations between readers’ sensitivity to cues and their performance on language tasks (reading, spelling, and author recognition tests) suggesting that reading experience is critical for assimilating spelling-to-meaning regularity from written language. Further, we identified characteristics that may be important for the learning and exploitation of orthographic cues that are related to the nature of their function and use in context.
A Random Walk Equilibrium Optimization Algorithm

The equilibrium optimization algorithm was proposed in 2019 and proved to perform better than other nature-inspired algorithms. This algorithm was also initialized with randomness and a swarm of individuals were involved in exploration and exploitation. In this paper, we follow the tradition and introduce the random walk with Cauchy distribution to the basic equilibrium optimization algorithm. Optimization experiments on the classical benchmark functions were carried out and results show the random walk equilibrium optimization algorithm would perform better as expected.
Analysis of the performance of algorithm configurators for search heuristics with global mutation operators

Recently it has been proved that a simple algorithm configurator called ParamRLS can efficiently identify the optimal neighbourhood size to be used by stochastic local search to optimise two standard benchmark problem classes. In this paper we analyse the performance of algorithm configurators for tuning the more sophisticated global mutation operator used in standard evolutionary algorithms, which flips each of the n bits independently with probability χ/n and the best value for χ has to be identified. We compare the performance of configurators when the best-found fitness values within the cutoff time k are used to compare configurations against the actual optimisation time for two standard benchmark problem classes, Ridge and LeadingOnes. We rigorously prove that all algorithm configurators that use optimisation time as performance metric require cutoff times that are at least as large as the expected optimisation time to identify the optimal configuration. Matters are considerably different if the fitness metric is used. To show this we prove that the simple ParamRLS-F configurator can identify the optimal mutation rates even when using cutoff times that are considerably smaller than the expected optimisation time of the best parameter value for both problem classes.
A Framework for Arabic Tweets Multi-label Classification Using Word Embedding and Neural Networks Algorithms

The need for classifying tweets is essential for many people like tourists, tourism companies and governments. In this paper, we propose a framework for Arabic Tweets multi-label classification using word embedding technique and deep leering algorithms. We built our dataset using 160k Arabic tweets gathered from Twitter. We compared two deep learning methods, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). Our results show that it is possible to classify tweets using our methodology without any significant difference in results of accuracy scores and hamming loss for both types of networks. The accuracy scores and hamming loss were nearly 90% and 0.02, respectively.
Operational Context-Based Design Method of Autonomous Vehicles Logical Architectures

Autonomous Vehicles (AV) exhibit new characteristics rendering its architecting process challenging. They are context-aware cyber-physical systems with a high sensitivity toward their operational context. In addition to the lack of industrial feedback, their architecture needs to be adapted to their Operational Context (OC). The current literature doesn’t provide a clear method for designing AV architecture based on their OC. This paper proposes a four steps method following the Concept of Operation approach (ConOps) and functional chain modeling to design AV architecture based on their OC and experts knowledge. The method uses a state-of the-art OC ontology for AV and improves the identification speed and coverage of the AV’s operational needs. A case study is presented addressing the proposed methods and limitations.
Development of a Seismic Detection Technology for High-Speed Trains Using Signal Analysis Techniques

As the occurrence of earthquakes is increasing in South Korea, the earthquake early warning (EEW) system becomes indispensable for the protection of high-speed railways. Although the importance of EEW system has been increasing, the number of installed seismic accelerometers in South Korea is not sufficient to provide rapid information. This study uses a stochastic signal analysis technique to utilize the smartphone sensors for the rapid EEW system. From the train vibration data from the low fidelity on-board accelerometer, the virtual earthquake detection data in the train by smartphone sensor has been constructed. To analyze the stochastic characteristics of the constructed data, the short time Fourier transform (STFT) approach has been applied. The study's overall objective is to offer stochastic approaches that provide effective analysis of the low fidelity sensor data, such as smartphone sensor data, for the rapid EEW system.
Prediction Of Accuracy On Open Source Java Projects Using Class Level Refactoring

The refactoring approach is used to restructure the software system without altering its external functionalities. Our research objective is to analyze the accuracy of the software metrics using machine learning classifiers. After predicting the accuracy we can refactor the inaccurate source metrics by using different refactoring tools and machine learning algorithms. We have considered 30 code metrics from 3 open source java projects by using different framework like antlr4,junit and oryx at the class level and predict its accuracy using different machine learning classifiers. We have used Gaussian, Bernoulli and Multinomial classifiers to predict the accuracy of the software metrics. Statistical significant test reveals the mean accuracy for the above sources are 33.33%,39%,48.33% respectively. As per the prediction analysis we can correct the fault and improve the performance of our source metrics by conducting unit level testing which leads to software refactoring.
Envisioning Haptic Design for Immersive Virtual Environments

Current techniques for haptics in immersive virtual environments (IVEs) allow users to perceive materials while exploring virtual surfaces. However, these experiences are usually restricted to the properties defined during the design phase of the IVE. Analogous to drawing in virtual reality (VR), we propose the concept of haptic design by granting users the ability to (re-)configure haptic feedback in their IVEs through changes of virtual objects' material properties. To study this concept, we considered how fabric sample books provide insights of material configurations by allowing us to explore different visual-haptic combinations. As an initial approach, we created the Haptic Palette, a dynamic passive haptic feedback controller where visual augmentations on top of physical textures allow users to experience mixed material perceptions. In this work, we posit the notion of haptic design for VR and present the results of an initial study using our Haptic Palette controller.
Application of Scrum Maturity Model: A Case Study in a Telecommunication Company

Abstract Digitalization and internet penetration force telecommunication companies to perform a transformation to survive, to face competitions, and to gain opportunities. PT XYZ is a telecommunication company that has introduced a new leaner and agile working method for digital transformation. The organization is undergoing a transformation to implements an agile approach using a scrum framework. There are two divisions that use scrum that currently plagued with time estimation difficulties and inability to create an agile backlog due to the old change request mechanism. Scrum Maturity Assessment (SMM) is used to measure current scrum practice maturity. The data is retrieved using a questionnaire that was given to two Scrum Master from two divisions which have been implementing Scrum. There are 83 questions derived from SMM assessment questions. SMM assessment indicated the scrum practice is Level 1 (Initial). There are 31.76% of scrum practices in IT division and 36.25% of scrum practices in CS division need to be improved to achieve Level 5 Optimizing. In order to achieve Level 5, we proposed 38 steps and six recommendations which will guide the scrum practice improvement process.
Towards Trust Enabled Commodity Market for Farmers with Blockchain Smart Contracts

This paper presents a conceptual model for an online agricultural commodity market that empowers farmers with a better price determination mechanism through collective marketing and Blockchain smart contracts. The model makes collective marketing possible by generating Many-one-Many relationships between farmers, farmers' groups, and buyers. While collective marketing improves farmers' bargaining position leading to higher rates, trust enabled by Blockchain smart contracts facilitates farmers to establish deals with buyers who offer the best rate transforming the commodity market into a sustainable market.
On measuring and improving the quality of linkage learning in modern evolutionary algorithms applied to solve partially additively separable problems

Linkage learning is frequently employed in modern evolutionary algorithms. High linkage quality may be the key to an evolutionary method's effectiveness. Similarly, the faulty linkage may be the reason for its poor performance. Many state-of-the-art evolutionary methods use a Dependency Structure Matrix (DSM) to obtain linkage. In this paper, we propose a quality measure for DSM. Based on this measure, we analyze the behavior of modern evolutionary methods. We show the dependency between the linkage and the effectiveness of the considered methods. Finally, we propose a framework that improves the quality of the linkage.
Social Engineering Defense Mechanisms: A Taxonomy and a Survey of Employees’ Awareness Level

In the information security chain, humans have become the weakest point, and social engineers take advantage of that fact by psychologically manipulating people to persuade them to disclose sensitive information or execute malicious acts. Social engineering security attacks can be severe and hard to detect. Therefore, to prevent such attacks, organizations and their employees should be aware of the defense mechanisms that can mitigate the risk of these attacks. To that end, the authors (1) developed a taxonomy of social engineering defense mechanisms and also (2) designed and distributed a survey to measure employees’ level of awareness of these mechanisms. To develop the taxonomy, the authors reviewed the related literature and extracted the main defense mechanisms. To measure employees’ level of awareness of social engineering defense mechanisms, the authors designed and distributed a survey in which 791 employees participated. Finally, after collecting and analyzing the data, the authors found that more than half of the surveyed employees are not aware of social engineering attacks and their defense mechanisms. Such a worrisome result shows that employees and organizations are extremely vulnerable to such attacks, and serious steps need to be taken to elevate the employees’ awareness level against these emerging security threats.
Data Access With Horn Ontologies: Where Description Logics Meet Existential Rules

Two main families of ontology languages are considered in the context of data access, namely Horn description logics and existential rules. In this paper, we review the semantic relationships between these families in the light of the ontology-mediated query answering problem. To this end, we rely on the standard translation of description logics in first-order logic and on the notion of semantic emulation. We focus on description logics and classes of existential rules for which the conjunctive query answering problem has polynomial data complexity.
Examining Students’ Intrinsic Cognitive Load During Program Comprehension – An Eye Tracking Approach

Programming as a cognitive activity requires the utilization of various kinds of mental models that involve different cognitive loads while students learn to program. The article discusses the results of an experiment aimed at answering the following question: are eye tracking based measures related to the intrinsic cognitive load (ICL) connected with program comprehension? Thirty one students of computer science took part in the experiment. They analyzed two program codes written in the C++ language to search for (1) logical errors (LER) and (2) syntax errors (SER). ICL was measured by subjective rating of the difficulty of each task. There were significant differences found for the subjective measures of intrinsic load, the effectiveness and the time of tasks performance, and the values of eye tracking parameters: fixation duration average (FDA) and saccade amplitude average (SAA) in two experiment conditions. Longer fixation and shorter saccades were associated with higher ICL. The results obtained suggest that FDA and SAA are eye tracking measures sensitive of intrinsic cognitive load.