Reviewerly/exHarmony huggingface dataset:
Models of Information Processing in IoT Networks on the Basis of Fundamental Trigonometric Splines

The approach to increase the efficiency of information processing in wireless networks on the basis of the proposed mathematical model is considered. This model is based on fundamental trigonometric splines. The construction of trigonometric splines is shown, as well as the smoothness properties of trigonometric splines, which are important at the stage of construction of these models. The method of the information signal reproduction in the communication system by interpolation of the digital signal, using the fundamental trigonometric spline is proposed. It has been shown that the use of trigonometric splines will allow the reproduction of information signals with less error. The estimation of the complexity of the algorithm for constructing trigonometric splines is compared with the existing ones. An important scientific problem was solved, which consists in improving the methods of information processing in the Internet of Things based on fundamental trigonometric splines.
Analysis of Excitation Source Characteristics for Shouted and Normal Speech Classification

The present work is aimed at analysing the excitation source characteristics of normal and shouted speech. In this context, we analyze the Differenced Electroglottogram (DEGG) signal corresponding to different vowels. This work proposes two novel excitation source features that are estimated from DEGG signal. These features are (a) Open Phase Triangle Area (OPTA) and (b) Flatness of Glottal Cycle (FoGC). OPTA captures the effect of open phase duration and slope of DEGG signal. FoGC measures the change in source characteristics due to strength of excitation (SoE) and pitch period. A practical issue in using the proposed features is the unavailability of DEGG signal in most speech processing applications. To overcome this problem, the integrated linear prediction residual (ILPR) signal estimated from speech is considered as an approximation of DEGG. We show that the proposed features can be computed from ILPR signal in the absence of DEGG. It is observed that the proposed features (estimated from either DEGG or ILPR) are successful in discriminating shouted from normal speech.
Effects of Refactoring upon Efficiency of an NP-Hard Task Assignment Problem: A case study

The goal of this paper is to analyze the effects of refactoring on time complexity of an algorithm. For this purpose a problem in which time complexity is highly sensitive, is chosen for studying. As it is known by computer scientists, they use refactoring in order to improve quality of design while preserving external behavior (functional properties). Sustainability of nonfunctional properties are not guaranteed. Hence, for learning its effects on non-functional properties such as time, a multiobjective task assignment problem is selected. The chosen problem has been implemented through an Evolutionary Genetic Algorithm. The problem chosen is an NP -hard problem because of being time sensitive. Initially, code smells are detected & refactoring is applied. In order to observe the improvement in design of code, several metrics of quality such as cohesion, coupling, complexity & inheritance, are calculated and compared before & after applying refactoring. Also, computation time of the improved code is compared with the original code, in order to analyze effects of refactoring on computation time. For problems that are time sensitive, refactoring may not be a good choice depending upon the requirements. Results of the experimentation nullify the approach that refactoring improves the computational cost of the software. Increase in the length of code eventually may prove as a tradeoff in terms of memory consumption.
Vision-Based Ground Moving Object Tracking for Micro Air Vehicles

In recent years, vision-based ground moving object tracking has become a very active field of research for macro aerial vehicles (MAVs), which contains two main problems: pose estimation for MAVs and robust object tracking. A tightly-coupled visual-inertial odometry based on the sliding window is proposed. In practical applications, the sliding window of the most recent visual and inertial measurements are used, thus good real-time performance can be achieved. Simultaneously, historical measurements in the sliding window can improve the accuracy of pose estimation and make the estimated trajectory more smoothly. The object tracking can be used to estimate the location of the object in continuous frames. Finally, the position of the ground moving object in three-dimensional (3D) space can be solved. The experiments are performed on the MAV platform, and the experimental results validate the effectiveness of the proposed algorithms.
Design of Automatic Wire-feeding System Based on Modbus

In virtue of the interference of strong electric magnetic field to communication caused by the electric explosion wire automatic spraying device during the electric explosion, this paper designs an automatic wire-feeding system based on Modbus communication protocol to improve the reliability and stability of the system. The following schemes are proposed: In the automatic wire-feeding system, RS485 and RS485 to RS232 interface are used to improve the anti-interference ability of the data in the strong electric magnetic field by shielded twisted-pair connection and Modbus RTU transmission mode. The automatic wire-feeding system is composed of the master equipment with STM32F746 main control chip as the core and several slave equipment with C8051F020 single chip microcomputer as the core. Modbus communication protocol is utilized to actualize the communication between master and slave equipment. The stable transmission of long distance data between master and slave equipment of the automatic wire-feeding system is proved by experiments to ensure the effective operation of the experimental device.
Image Retrieval System Based on Color and Texture Features

The local binary pattern (LBP) operator and its variants extract the textural information of an image by considering the neighboring pixel values. A single or joined histogram can be derived from the LBP code which can be used as an image feature descriptor in some applications. However, the LBP-based feature is not a good candidate in capturing the color information of an image, making it less suitable for measuring the similarity of color images with rich color information. To overcome this problem, we propose a fast and efficient indexing and image search system based on color and texture features. The color features are represented by combining 2D histogram and statistical moments, and texture features are represented by the local binary pattern (LBP). To assess and validate our results, many experiments were held in color space HSV. Detailed experimental analysis is carried out using precision and recall on colored Brodatz, KTH TIPS, Stex, and USPTex image databases. Experimental results show that the presented retrieval method yields about 8% better performance in precision versus recall and about 0.2 in average normalized modified retrieval rank (ANMRR) than the method using wavelet moments.
Deep-Neural-Network Based Fall-Back Mechanism in Interference-Aware Receiver Design

In this paper, we consider designing a fall-back mechanism in an interference-aware receiver. Typically, there are two types of detectors dealing with interference, known as enhanced interference rejection combining (eIRC) and symbol-level interference cancellation (SLIC). Although a SLIC detector performs better, it yields a higher complexity than an eIRC detector. Further, it requires knowledge of interference modulation-format (MF). Due to potential detection errors, SLIC can run with a wrong interference MF and render unsatisfying results. Therefore, designing a mechanism that runs SLIC when the interference MF is reliable and otherwise switches to eIRC is of particular interest, which we call a "fall-back mechanism". Finding an optimal mechanism is difficult and we use deep-neural-network (DNN) for design, which is more effective than a traditional Bayes-risk mini-mization based approach.
Expressing Modalization in EFL College Learners' Writing : From Modality System of Systemic Functional Grammar

This study examined the language choice for modality patterns to express the degree of probability specifically in email texts based on Systemic Functional Grammar (SFG) analytical framework. Participants were students enrolled in an English writing composition course at an online-based Korean university. They were required to write an email thanking their professor, in which they stated their future plans (definite or indefinite) with a degree of probability. The text analysis was compared with two groups of students' scores (high-scoring group n=40 and low-scoring group n=29), based on an assessment of the course assignment. After building up two learner corpora, UAM Corpus Tool version 3.0 was used to analyze the language choice closely using a modality system of the SFG framework. The high-scoring group showed more range and frequency in the use of modal verbs combined with a modal Adjunct or another modal expression. Explicit teaching on the importance of expressing the appropriate degree of probability using a range of modality devices, rather than relying heavily on the primary modality (choice of modal verbs) is highlighted as a pedagogical implication.
Monstering: a transdisciplinary method for an unstable world

Abstract This paper outlines the transdisciplinary practice of monstering, a toolset used by the Experimental Architecture Group (EAG) to engage with a world in constant flux. The chapter describes recent Experimental Architecture projects that draw attention to the need for radical change at a time of ecocide. Our ‘monstering’ perspectives draw inspiration from the countless forms of cooperation and acts of generosity that underpin healthy ecological systems. Throughout the text, a range of different voices, materials, media and practices are brought together to synthesise the potentials of monstering. Monstering generates qualitatively new encounters in the choreography of space and its inhabitation. It challenges normative protocols and existing practices by embodying the unknown and uncontrollable aspects of the world. Through monstering, we embrace our innate zoephilia (Braidotti, 2006, p. 41), increasing our capacity to respond to an ever-changing reality and finding new ways of diplomacy to better communicate and acknowledge the contributions of non-human agents to our reality. EAG’s monstering experiments seek to bring designed change to functionality (Haraway, 2015) through re-empowerment, re-connection and re-enchantment with an ecologically stressed world. The paper details monstering performances and installations as prototypes that draw attention to established preferences, conventions, and habits, celebrating diversity, difference and paradoxical encounters.
Decentralized Parallel Any Colony Optimization for Distributed Memory Systems

Ant Colony System (ACS) is a well-established variant of the Ant Colony Optimization (ACO) nature inspired metaheuristic for solving combinatorial optimization problems. We present the DMACS (Distributed Memory Ant Colony System) algorithm, which is a parallelization of ACS for distributed memory architectures. The system is decentralized, with each processor running an identical agent process which administers a part of the pheromone matrix used to record the movements of simulated ants over a graph. We evaluate a Message Passing Interface (MPI) implementation of the algorithm on the well-known Travelling Salesman Problem (TSP), running on a distributed memory cluster. The results show that the algorithm scales at least as well as previous agent-based distributed implementations of ACS, without the need to sacrifice core features of the algorithm such as local search. However, our results also demonstrate that scaling the ACS algorithm to large numbers of processes in distributed memory architectures remains a significant challenge.
Performance Comparison of Dehazing Algorithms on different platforms

Images captured may suffer from attenuation due to the transmission medium and scattering of the air-light. When the weather is foggy the scattering is more, so the attenuation is also to a larger extent resulting in degraded image. Most of the automated and computer vision based systems (like image classification and retrieval, vehicle navigation aids, remote sensing etc.,) which depend on the image captured outdoor may not work effectively with the degraded images. Several researches have been done to enhance the foggy images captured outdoor and indoor. Amongst them Dark Channel Prior with guided filtering is a traditional method, while Color Attenuation Prior is a linear model developed based on the relation between depth of the scene, brightness, concentration of the fog and saturation of the image. But an algorithm will be effective and efficient if it can be implemented with available resources and consumes less processing time. We compare in this paper the performances of two dehazing algorithms using different platforms like Windows and Unix and different tools like Matlab and OpenCv python. The comparison shows that OpenCv python implementation is much faster and cost effective than Matlab tool since Matlab is a proprietory tool whereas OpenCv python is an open source.
Image De-Raining Via RDL: When Reweighted Convolutional Sparse Coding Meets Deep Learning

Over the past few decades, image de-raining has witnessed substantial progress due to the development of priors and deep learning based methods. However, few studies combine the merits of both. In this paper, we argue that domain expertise of conventional convolutional sparse coding (CSC) is still valuable, and it can be combined with the key ingredients of deep learning to achieve further improved results. Specifically, motivated by the success of reweighting algorithms, we propose solving the CSC model by learning weighted iterative soft thresholding algorithm (LwISTA) in a convolutional manner where the reweighted ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm is introduced. Based on this, we present a novel framework for single image de-raining, in which the channel attention is employed to learn the weight. Extensive experiments demonstrate the superiority of our method over recent state-of-the-art image de-raining methods, in terms of both quantitative and qualitative results.
A Space-Filler Mechanism to Recognize Body Language of Hands

Among all the species, human hands become outstanding and unique in performance. What is special about human hand is not what it can accomplish but what it can communicate to outside. Hand pose can say a lot about a person's psychological state. Hands are expressive so that they can be used to tell stories, communicate with sign language and display innermost ideas in one's mind. Hence the hand can often be used for nonverbal communication. Through this paper we attempted to drive a mechanism to perceive hand posture using the depth information received from upper arm and fore arm separately. We used positioning of upper and fore arms within the activity space of a person as to determine its relative pose with respect to the person's torso. As dimensions of human body change from person to person, we considered all the measurements with respect to that particular individual. We implemented this method upon a service robotic platform for evaluation.
Customer Experience Enhancement Using Artificial Intelligence

Artificial Intelligence is the most happening thing these days, A.I helps bring out the most productivity and is capable of making quick and suitable decisions on its own without human intervention. We thought that there is a necessity to develop a device for customer experience enhancement using artificial intelligence having the prowess to connect and interplay with each other with one of the most humane forms of interaction, voice. We aim to present the concept and rationale backing the voice interaction system, the shortcomings in the current structure. We are taking movie booking as an example for the customer experience. Basically, our project is of a movie-ticketing bot, that makes booking movies possible only through voice.
Generalized Quantifiers Meet Modal Neighborhood Semantics

In a mathematical perspective, neighborhood models for modal logic are generalized quantifiers, parametrized to points in the domain of objects/worlds. We explore this analogy further, connecting generalized quantifier theory and modal neighborhood logic. In particular, we find interesting analogies between conservativity for linguistic quantifiers and the locality of modal logic, and between the role of invariances in both fields. Moreover, we present some new completeness results for modal neighborhood logics of linguistically motivated classes of generalized quantifiers, and raise new types of open problem. With the bridges established here, many further analogies might be explored between the two fields to mutual benefit.
Creating an Online Presence for and Managing the Institutional Archive Using <i>ArchivesSpace</i>

This article describes how the Southern Illinois University Medical Library employed ArchivesSpace to create a searchable, online presence for part of its existing physical archive collection. ArchivesSpace is an open-source web based archival management system. The Medical Library, with cooperation from the IT department, used our institutional servers to store data, but users may also pay for third-party hosting. As this paper demonstrates, ArchivesSpace is inexpensive and relatively simple to set up and use and may be a good option for hospital libraries or other smaller libraries that are interested in online archival management tools.
Using Case-Based Reasoning Approach for Text Classification Task

This paper discusses the method of solving the text classification task by using case-based reasoning. As a part of the text classification module in the implemented system, modified TF-IDF (Term Frequency - Inverse Document Frequency) measure of the text content based on the known information on the distribution of documents between different categories is proposed. This modification allows to improve the quality of the classification by considering the information on the distribution of the words in the entire case base. Implementation of the prototype of text classification module used in the interactive voice response system is presented. This module allows to get away from button menus to automated voice interaction between the user and the system. The result of computational experiments confirming the effectiveness of the developed system is proposed.
Projection Intensity Adjustment Method Based on Multi-threshold for Fringe Projection Technology

Abstract Three dimension measurement based on fringe projection technology is widely used in precision manufacturing. However, when measuring objects with reflective surfaces, the measurement accuracy is reduced due to image saturation. This paper presents a projection intensity adjustment method based on multi-threshold. The reflectivity information of fringe pixels and scale factor are used to calculate the multi- threshold. According to the multi-threshold, the optimal projection intensities are obtained, which reduces the image saturation and improves the measurement accuracy. The experimental results show that this method can improve the measurement accuracy effectively.
A Novel Localization Method Based on FDA Beam Intersection

In the past localization methods, both mechanical and phased array radars require angle scanning because their beampatterns are only angle-dependent, resulting in the echo time difference only contains the range information of the target. It will cause some ambiguity problems and limitations. Since Frequency Diverse Array (FDA) was proposed, its range-angle dependent beampattern provided more degrees of freedom (DoFs) to obtain more information in array signal processing. However, how to use these increased DoFs to get more information still needs to be studied. In this paper, we propose a novel localization method using FDA beam intersection. In this proposed method, part of the FDA beampattern approximates a straight line and propagates throughout the range axis. This moving beam line can capture the target like a fast-moving net and the echo time difference contains both the angle and range information of the target. By a simple straight-line intersection, we can obtain the whole position information, where two FDA beam lines intersect at the target. A simulation diagram is provided to illustrate the proposed localization process.
Low Complexity MIMO Modulation Classification via Distribution Test Ensembles

Modulation classification in MIMO systems needs to overcome its complexity demand to be viable in a real world environment. The number of mathematics operations required by the state-of-the-art maximum likelihood classifier grows exponentially with the number of transmitting antennas and the orders of candidate modulations. In this paper, we propose a low complexity MIMO system modulation classifier by combining multiple magnitude-based distribution tests using a simple Multi-layer perceptron. The resulting solution provides good classification performance in AWGN channel while outperforming the maximum likelihood classifier in fading channels. Moreover, its computational complexity is much lower and does not scale with the number of transmitting antennas or the orders of candidate modulations.
Fast Special -Purposed Factoring Algorithm by Using Modular Multiplication

Nowadays, Integer Factoring has become the essential issue since RSA which is one of public key cryptographies was born. In this paper, the new factoring algorithm which is one of special -purposed methods is introduced. The main process is the using trial and error technique with modular multiplication to find the result. Assigning total loops are t*(p + q - 2⌈√n <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">|</sub> ⌉), p and q are a prime, n is modulus and 0 <; t <; 1. Then, many loops are also removed by considering the last m digits of n. In an analytical section, it shows that t becomes shorten whenever m is expanded. Therefore, the purposed method is not only depending on characteristic of the prime factors but also the last m digits of the modulus.
A Model for Choosing Hosting for a Company’s Website

The paper analyzes the problem of choosing the best hosting for a company's website. A generalized model for choosing hosting for a company's website is developed. It is proposed to decompose the generalized problem into subproblems: choosing an effective type of hosting, choosing the optimal hosting provider and rational tariff plan. There built a model for choosing a virtual dedicated server (VDS), which allows, unlike the existing ones, to select a hosting provider's tariff plan for a VDS to place a company's website using multiple functional and cost criteria.