Reviewerly/exHarmony huggingface dataset:
Better External Memory LCP Array Construction

The suffix array, perhaps the most important data structure in modern string processing, needs to be augmented with the longest-common-prefix (LCP) array in many applications. Their construction is often a major bottleneck, especially when the data is too big for internal memory. We describe two new algorithms for computing the LCP array from the suffix array in external memory. Experiments demonstrate that the new algorithms are about a factor of two faster than the fastest previous algorithm. We then further engineer the two new algorithms and improve them in three ways. First, we speed up the algorithms by up to a factor of two through parallelism. Eight threads is sufficient for making the algorithms essentially I/O bound. Second, we reduce the disk space usage of the algorithms making them in-place: the input (text and suffix array) is treated as read-only, and the working disk space never exceeds the size of the final output (the LCP array). Third, we add support for large alphabets. All previous implementations assume the byte alphabet.
Game-theoretic semantics for non-distributive logics

Abstract We introduce game-theoretic semantics for systems without the conveniences of either a De Morgan negation, or of distribution of conjunction over disjunction and conversely. Much of game playing rests on challenges issued by one player to the other to satisfy, or refute, a sentence, while forcing him/her to move to some other place in the game’s chessboard-like configuration. Correctness of the game-theoretic semantics is proven for both a training game, corresponding to Positive Lattice Logic and for more advanced games for the logics of lattices with weak negation and modal operators (Modal Lattice Logic).
Context-Specific Separable Gesture Selection For Control of a Robotic Manufacturing Assistant

This work aims to facilitate robotic-assisted limited-access manufacturing, where a mechanic manually performs work in an enclosed space while a robot guides a camera to allow the mechanic to observe visually-occluded operations. Gesture control allows the mechanic to adjust their camera view without requiring contact-based interaction through button presses or manual positioning of the robot, which would require setting down tools or exiting the enclosed space. Because the only input to the robotic platform’s gesture recognition system is a continuously tracked hand, there is potential for confusion between the motions of work and motions intended to communicate control. The main contribution of this article is to develop a separability metric Ψ for systematically selecting a set of control gestures that can be easily distinguished from a given set of work gestures. The context-specific control gesture selection process is implemented on a benchmark dataset to validate the method. The process is then implemented on a hand-following robot, and experimental results show that the proposed metric-based selection of the control gesture set results in the improvement of online gesture recognition across eight subjects even in the presence of application-specific distortions in the gestures.
Color image share cryptography: a novel approach

Shared visual cryptography is a method to protect image-based secrets where an image is kept as multiple shares having less computational decoding process. Steganography is a technique to hide secret data in some carrier like-audio, image etc. Steganography technique is categorized into four categ ories. i) Spatial Domain Technique- Image pixel values are converted into binary and some of the binary values changed to hide secret data. ii) Transform Domain Technique- the message is hidden in cover image and then it is transformed in the frequency domain. iii) Distortion Technique-information is stored by changing the value of the pixel. iv)Visual Cryptography Technique-Image is broken into two or more parts called shares. This article proposes a hybrid visual crypto-steganography approach which exploits the advantages of both approaches to protect image based secret in communication. Most of the visual cryptography is applied on black and white images but the proposed method can be applied directly on color images having three channels. This method does not change the image size. Also an exact replica of original image can be reconstructed therefore this process does not result in image quality degradation. This article proposes novel color image share cryptography where seven shares are generated from one color image (correlated/de-correlated color space). These shares are sent to the receiver and original image is reconstructed using all those shares. Share generation and image reconstruction is based on simple operation like pixel shuffling, reversing binary string of the image information, ratio of pixel intensity values. Row key matrix and column key matrix are generated using random function. Pixel positions are shuffled using these two key matrixes. These seven shares namely Row Key, Column Key, Remainder matrix, Quotient matrix, R ratio matrix, G ratio matrix and B ratio matrix are generated. Then Row key matrix, Column key matrix, Remainder matrix, Quotient matrix and three ratio matrices are hidden into separate cover images by LSB encoding technique and sent over the network. Receiver can reconstruct the image if all shares are available only. The proposed method is applied on standard images in the literature and images captured using standard digital camera. Comparison study with existing methods shows that the proposed method performs better in terms of NIST metrics. The method has many applications in the area of visual cryptography, shared cryptography, image based authentication etc.
RAQ: Relationship-Aware Graph Querying in Large Networks

The phenomenal growth of graph data from a wide variety of real-world applications has rendered graph querying to be a problem of paramount importance. Traditional techniques use structural as well as node similarities to find matches of a given query graph in a (large) target graph. However, almost all existing techniques have tacitly ignored the presence of relationships in graphs, which are usually encoded through interactions between node and edge labels. In this paper, we propose RAQ-Relationship-Aware Graph Querying-to mitigate this gap. Given a query graph, RAQ identifies the k best matching subgraphs of the target graph that encode similar relationships as in the query graph. To assess the utility of RAQ as a graph querying paradigm for knowledge discovery and exploration tasks, we perform a user survey on the Internet Movie Database (IMDb), where an overwhelming 86% of the 170 surveyed users preferred the relationship-aware match over traditional graph querying. The need to perform subgraph isomorphism renders RAQ NP-hard. The querying is made practical through beam stack search. Extensive experiments on multiple real-world graph datasets demonstrate RAQ to be effective, efficient, and scalable.
A Generic Approach for the Verification of Static and Dynamic Behavioral Properties of SCDL/WS-BPEL Service-Component Architectures

Web systems verification is a crucial activity throughout the systems development life cycle, especially in the phase of service-component architectural design. Indeed, this activity allows the detection and consequently the correction of errors early in Web systems development life cycle. In this paper, we discuss the behavioral verification problem on the SCDL/WS-BPEL service-component architectures. To do so, the Wright formal ADL and the Ada concurrent language were used as a target models. To achieve this, a set of systematic translation rules are proposed. This allows the verification of the standard behavioral properties using the Wr2fdr tool. In addition, using an Ada dynamic analysis tool, we could detect the potential behavioral properties such as the deadlock of an Ada concurrent program.
Bayesian Clustering of Multivariate Immunological Data

Given a dataset of B cell subpopulation quantities, for about six thousand patients, that is a cross-sectional immunological dataset, here we detect clusters representing models of immune system states in an unsupervised way (i.e., according only to their different statistical properties). Two time-evolving B cell networks are also generated from data-driven hidden Markov models, with four and five hidden states, respectively. Our interpretation from a biomedical viewpoint of the statistical parameters of the Bayesian models confirms an age related decline of some types of B cell functions and finds out a class of old patients with unexpected B cell values.
High Generalization Capability Artificial Neural Network Architecture Based on RBF-Network

This paper describes the issue of error level fluctuations due to training set shrinking in RBF-networks. An architecture of artificial neural network (ANN) based on RBF-network is presented with a learning algorithm to train it. The presented architecture is multi-layer, unlike original RBF-network thus has a potential in deep learning. Numeric results lead to a conclusion about error level fluctuations being significantly lower for the presented architecture compared to RBF-network in case of training set shrinking. This displays a greater generalization ability of the presented architecture. The paper contains an application of ANN to the task of restoring the dielectric parameters for subject placed in waveguide.
Online Semi-Supervised Learning With Multiple Regularization Terms

Online semi-supervised learning (OS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> L) has received much attention recently because of its well practical usefulness. Most of the existing studies of OS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> L are related to manifold regularization. In this paper, we introduce a novel OS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> L framework with multiple regularization terms based on the notion of ascending the dual function in constrained optimization. Using the Fenchel conjugate, different semi-supervised regularization terms can be integrated into the dual function easily and directly. This approach is derived by updating limited dual coefficient variables on each learning round. To be practical, we also employ buffering strategies and sparse approximation approaches in this paper. The experimental studies show that our methods achieve accuracy comparable to offline algorithms while consuming less time and memory. Especially, our OS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> L algorithms can handle the settings where the target hyperplane of classification continually drifts with the sequence of arriving instances. This paper paves a way to design and analyze OS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> L algorithms with multiple regularization terms.
Identifying Students' Meta-Competences During Laboratory Work on a Unique Scientific Equipment

The chapter is devoted to the development of an analytical methodology of forming future engineers' meta-competences (interdisciplinary, meta-creative, and meta-cognitive) when he/she works on unique scientific equipment. The authors research a hypothesis about the possibility of estimating quality of education and identifying competences in engineering courses by measurement of students' activities as well as outcomes. An example is described of the criterion revealing during laboratory work with a scanning probe microscope “Nanoeducator.” The experiment is a part of a multifunctional scientific complex for the development and research of thin films. Results of the parameter evaluation are shown in graphs using MATLAB software. This chapter is a new direction towards discovering methods and algorithms to define and evaluate future engineers' meta-competence.
An efficient two-digit adaptive delta modulation for Laplacian source coding

Delta Modulation (DM) is a simple waveform coding algorithm used mostly when timely data delivery is more important than the transmitted data quality. While the implementation of DM is fairly simple and inexpensive, it suffers from several limitations, such as slope overload and granular noise, which can be overcome using Adaptive Delta Modulation (ADM). This paper presents novel 2-digit ADM with six-level quantization using variable-length coding, for encoding the time-varying signals modelled by Laplacian distribution. Two variants of quantizer are employed, distortion-constrained quantizer which is optimally designed for minimal mean-squared error (MSE), and rate-constrained quantizer, which is suboptimal in the minimal MSE sense, but enables minimal loss in SQNR for the target bit rate. Experimental results using real speech signal are provided, indicating that the proposed configuration outperforms the baseline ADM algorithms, including Constant Factor Delta Modulation (CFDM), Continuously Variable Slope Delta Modulation (CVSDM), 2-digit and 2-bit ADM, and operates in a much wider dynamic range.
Authentication Protocols Using Multi-level Cognitive CAPTCHA

This paper will present new approaches for creation of authentication protocols, which use multi-level CAPTCHA codes. Proposed codes will create a new class of cognitive CAPTCHA, which require special skills for fast and secure personal authentication, which can be performed is several stages. Authentication protocols can be oriented to allow access for trusted group of persons, based of theirs perception abilities and cognitive skills. For new authentication protocols some possible examples of applications will be also presented.
Unsupervised segmentation of multiview feature semantics by hashing model

The image segmentation of finely detailed object in images is becoming increasingly important in various image applications. Herein, multiview features such as color, spatial features, texture, saliency, and depth are used to calculate the feature semantic that differentiates the image foreground and background at the pixel level. For different image type, the importance of every single image feature is various. We assume that combining multiple features will produce improved results compared to using any single feature and propose an unsupervised learning strategy to learn optimal feature projection to the final solution surface of binary segmentation. To reduce computational complexity, a hashing model is used to represent the projected feature using binary codes, as its hamming distance efficiently retrieves the pixel similarity. Comparative experiments using the current state-of-the-art unsupervised segmentation algorithms and a deep learning approach, i.e., a fully convolutional network, demonstrate the advantages of the proposed method.
Linear-Time In-Place DFS and BFS on the Word RAM

We present an in-place depth first search (DFS) and an in-place breadth first search (BFS) that runs on a word RAM in linear time such that, if the adjacency arrays of the input graph are given in a sorted order, the input is restored after running the algorithm. To obtain our results we use properties of the representation used to store the given graph and show several linear-time in-place graph transformations from one representation into another.
Towards a Meta-model for Requirements-Driven Information for Internal Stakeholders

[Context & Motivation] Providing requirements-driven information (e.g., requirements volatility measures, requirements-design coverage information, requirements growth rates, etc.) falls within the realm of the requirements management process. The requirements engineer must derive and present the appropriate requirements information to the right internal stakeholders (IS) in the project. [Question/Problem] This process is made complex due to project-related factors such as numerous types of ISs, varying stakeholder concerns with regard to requirements, project sizes, a plethora of software artifacts, and many affected processes. However, there is little guidance in practice as to how these factors come into play together in providing the described information to the ISs. [Principle ideas/results] Based on analyzed data from an action research (AR) study we conducted in a large systems project in the rail-automation domain, we propose a meta-model that consists of the main entities and relationships involved in providing requirements-driven information to internal stakeholders within the context of a large systems project. The meta-model consists of five main entities and nine relationships that are further decomposed into three abstraction levels. We validated the meta-model in three phases by researchers and practitioners. [Benefits/Contribution] The meta-model is anticipated to facilitate: (i) control and management of process and resources for providing requirement-driven information to stakeholders and (ii) communication among internal stakeholders.
A modified self-updating clustering algorithm for application to dengue gene expression data

This work proposed a conceptually simple and computationally straightforward clustering algorithm based on the Cauchy-type distance for data clustering. It was demonstrated that the proposed approach does not require the priori number of clusters and the convergence of the proposed algorithm was proved. The experiment results showed that the proposed clustering algorithm was superior to other compared algorithms. Computational complexity was also provided. A real dengue gene expression dataset was used to demonstrate the effectiveness of the proposed method.
Deflectometry encoding the measured angle in a time-dependent intensity signal

We propose a new concept of a deflectometer, aimed to provide high accuracy measurements with high sampling rate and low noise, as required by state-of-the-art slope-measuring profilometers, like Long Trace Profilometers or Nanometer Optics Measuring instruments. For this purpose, we introduce certain modifications to the usual working principle of autocollimators so that the measured angle is not given by the displacement of the pattern captured by a CCD, but by the harmonic contents of the time-modulated intensity signal acquired by a photodiode. By doing this, the signal can be sampled not by just a few thousand pixels but by millions of samples/s.
Scalable, energy-aware system modeling and application-specific reconfiguration of MPSocs with a type-2 fuzzy logic system

This paper demonstrates that interval type-2 Fuzzy Logic Systems (FLS’s) are suited to Multiprocessor System-on-a-Chip (MPSoC) design modeling because of their ability to handle the uncertainty associated with input parameters. This makes for a scalable modeling process, whereas prior usage of type-1 FLS’s for modeling inhibited scalability. Specifically, the paper proposes a type-2 Takagi Sugeno Kang (TSK) FLS for modeling MPSoCs built around a multicore processor with shared memory. The paper also presents a TSK FLS reconfiguration methodology in order to dynamically reduce the energy consumption of such a multiprocessor. Modeling of MPSoC’s by means of the type-2 TSK FLS was found to be effective in terms of a reduction in the Energy Delay Product when applied to five large-scale numerical processing applications on an MPSoC. In fact, experimental results show a significant reduction (by 87–93% across the applications) in the energy consumption of an MPSoC.
Performance Evaluation of Joint Tracking and Classification

Joint tracking and classification (JTC) is gaining momentum in recent years. Many algorithms have been proposed. However, not enough attention has been paid to JTC evaluation, although it is important in practice. In this paper, we deal with evaluating the goodness and credibility of JTC. For the JTC goodness, tracking and classification so far have been largely evaluated separately without considering the interdependence of tracking and classification. We propose a joint measure-joint probability divergence (JPD)-to quantify tracking error, misclassification and their interdependence. The basic idea of JPD is to measure the closeness between the cumulative distribution functions of the perfect JTC and the JTC to be evaluated. The proposed method has a number of attractive properties. Some results from algorithms can be regarded as self-assessments. The credibility problem is concerned with whether these assessments are credible or how credible they are. We define the credibility problem for decision and propose an associated noncredibility index (NCI). We also propose a joint NCI (JNCI) to quantify the noncredibility of estimation and decision jointly. Four examples are presented to demonstrate how well the JPD and JNCI reflect the joint performance of tracking and classification.
An Empirical Evaluation of Name Semantic Network for Face Annotation

Background: Face annotation is the naming procedure to assign the correct name of a person who has emerged on an image. Objective: The main objective of this paper was to compare and evaluate six feature extraction techniques for face annotation under real-time challenging images and to find the best suitable feature for face annotation. Method: From literature review, it has been observed that Name Semantic Network (NSN) outperforms other annotation methods for various unconditioned images as well as ambiguous tags. However, the NSN’s performance can differ with various feature extraction techniques. Hence, its success is influenced by the feature extraction techniques that are used. Therefore, in this work, the NSN’s performance is experimented and evaluated with various feature extraction methods such as the Discrete Cosine Transform Local Binary Pattern (DCT-LBP), Discrete Fourier Transform Local Binary Pattern (DFT-LBP), Local Patterns of Gradients (LPOG), Gist, Local Order-constrained Gradient Orientations (LOGO) and Convolutional Neural Networks (CNNs) deep features. Results: Different feature extraction approaches demonstrate variations in performance with respect to a range of difficulties in face annotation using the Yahoo, LFW and IMFDB databases. The experimental results show that the deep feature method can achieve better recognition rate other than texture features. It confronts several issues in the presentation of a face in an image and produces better results. Conclusion: It is concluded that the CNNs deep feature is the best feature extraction technique that offers enhanced performance for face annotation.
A New Approach to Computing Using Informons and Holons: Towards a Theory of Computing Science

The state of computing science and, particularly, software engineering and knowledge engineering is generally considered immature. The best starting point for achieving a mature engineering discipline is a solid scientific theory, and the primary reason behind the immaturity in these fields is precisely that computing science still has no such agreed upon underlying theory. As theories in other fields of science do, this paper formally establishes the fundamental elements and postulates making up a first attempt at a theory in this field, considering the features and peculiarities of computing science. The fundamental elements of this approach are informons and holons, and it is a general and comprehensive theory of software engineering and knowledge engineering that related disciplines (e.g., information systems) can particularise and/or extend to take benefit from it (Lakatos’ concepts of core theory and protective belt theories).