Reviewerly/exHarmony huggingface dataset:
A practical evaluation of correlation filter-based object trackers with new features

Visual object tracking is a critical problem in the field of computer vision. The visual object tracker methods can be divided into Correlation Filters (CF) and non-correlation filters trackers. The main advantage of CF-based trackers is that they have an accepted real-time tracking response. In this article, we will focus on CF-based trackers, due to their key role in online applications such as an Unmanned Aerial Vehicle (UAV), through two contributions. In the first contribution, we proposed a set of new video sequences to address two uncovered issues of the existing standard datasets. The first issue is to create two video sequence that is difficult to be tracked by a human being for the movement of the Amoeba under the microscope; these two proposed video sequences include a new feature that combined background clutter and occlusion features in a unique way; we called it hard-to-follow-by-human. The second issue is to increase the difficulty of the existing sequences by increasing the displacement of the tracked object. Then, we proposed a thorough, practical evaluation of eight CF-base trackers, with the top performance, on the existing sequence features such as out-of-view, background clutters, and fast motion. The evaluation utilized the well-known OTB-2013 dataset as well as the proposed video sequences. The overall assessment of the eight trackers on the standard evaluation metrics, e.g., precision and success rates, revealed that the Large Displacement Estimation of Similarity transformation (LDES) tracker is the best CF-based tracker among the trackers of comparison. On the contrary, with a deeper analysis, the results of the proposed video sequences show an average performance of the LDES tracker among the other trackers. The eight trackers failed to capture the moving objects in every frame of the proposed Amoeba movement video sequences while the same trackers managed to capture the object in almost every frame of the sequences of the standard dataset. These results outline the need to improve the CF-based object trackers to be able to process sequences with the proposed feature (i.e., hard-to-follow-by-human).
Workload management and server speed setting for cost‐performance ratio optimization

Abstract The cost‐performance tradeoff is a fundamental issue in a data center for cloud computing, which is closely related to two key metrics that both cloud consumers and service providers care the most, that is, quality of service and cost of service. While there are different definitions of quality of service, the average response time is a common choice of performance metric. While there are various considerations in cost of service, the average power consumption is a common choice of cost metric. Hence, the cost‐performance tradeoff becomes the power‐performance tradeoff. In this article, we deal with the power‐performance tradeoff at the data center level. We study cost‐performance ratio optimization by using the techniques of workload management and server speed setting. In particular, we make the following tangible contributions. We solve three optimization problems, that is, (1) the workload management problem —to find a workload distribution, such that the cost‐performance ratio is minimized; (2) the server speed setting problem —to find a server speed setting, such that the cost‐performance ratio is minimized; (3) the workload management and server speed setting problem —to find a workload distribution and a server speed setting, such that the cost‐performance ratio is minimized. All the three optimization problems are analytically defined as multivariable optimization problems based on M/M/m queueing systems for multiple heterogeneous multiserver systems, together with two power consumption models, that is, the idle‐speed model and the constant‐speed model. Our approach makes it possible to quantitatively evaluate and optimize the cost‐performance ratio of a data center within a rigorously developed framework. Each multivariable optimization problem is transformed to a nonlinear system of equations. Due to the sophistication of these equations, they are solved algorithmically by a numerical procedure. Furthermore, we provide approximate, accurate, and analytical solutions to the first two problems. Performance data are demonstrated for each problem, and the accuracy of our approximate solutions are also discussed. To the best of the author's knowledge, this is the first paper which analytically and algorithmically minimizes the cost‐performance ratio of a data center with multiple heterogeneous multiserver systems using the techniques of workload management and server speed setting.
View graph construction for scenes with duplicate structures via graph convolutional network

View graph construction aims to effectively organise disordered image dataset through image retrieval technique before structure from motion (SfM). Existing view graph construction methods usually fail to handle scenes with duplicate structure, because these methods solely treat the construction of view graph as a process of image-pair-wise matching and lack in exploiting images' topological details in dataset. In this paper, we handle this problem from a novel perspective to construct view graph in a global paradigm by introducing an end-to-end graph convolutional network (GCN). First, a location-aware embedding module is introduced to encode images into a feature space that takes into account the feature's location by using Vision Transformer architecture, improving the distinction between features of duplicate structure. Second, graph convolutional network that consists of topological relationship preserving module and feature metric learning module is proposed. Topological relationship preserving network is proposed to help nodes maintain their connected neighbourhood features. By merging the topological connected information into images' embedding, our method can process image matching in a global mode, thus improving the disambiguation ability for images with duplicate scenes. Then a feature metric learning network is embedded into GCN to dynamically compute the linkage prediction among nodes based on their features. Finally, our method combines these three parts to jointly optimise nodes' features and linkage prediction in an end-to-end paradigm. We make qualitative and quantitative comparisons based on three public benchmark datasets and demonstrate that our proposed method performs favourably against other state-of-the-art methods.
Importance of National Fitness Sports Relying on Virtual Reality Technology in the Development of Sports Economy

With the rapid development of the national economy and the improvement of people’s living standards, physical fitness and health have attracted people’s attention and become an important topic in people’s daily life. Virtual reality technology is a new technology that uses computer to build virtual environment, belonging to simulation technology, which is an extremely important direction in this field. This paper aims to study the importance of the national fitness sports relying on virtual reality technology in the development of sports economy. The related concepts of virtual reality technology and the related meanings of national fitness are introduced. The characteristics of the office population are analyzed to understand the daily behavior habits, health status, and common diseases of the target population. Meanwhile, the traditional home fitness equipment and virtual home fitness equipment are collected and compared. The problems of the existing home fitness equipment are understood so as to discover the direction of subsequent design. Then, through interviews, questionnaires, and on-the-spot research, the target users and the product usage environment are investigated in depth. The pain points and needs of users are found and the needs of office workers for fitness products are summarized. The experimental results showed that in the national fitness sports relying on virtual reality technology, 80% of the respondents believed that the integration of somatosensory technology into fitness equipment has a positive effect on improving fitness.
Crime data optimization using neutrosophic logic based game theory

Summary The significance of prisoner's dilemma in making two completely rational individuals might not cooperate even their best interest to do so. Every criminal investigation is strongly reliant on crime data classification and optimization. In this work, we extended prisoners' dilemma for crime data classification and its optimization. A confusion matrix‐based optimization technique for crime data with game theory model in order to identify the person involvement in crime is to be predicted and clustered them into confess, not confessed, and indeterministic states based on the neutrosophic logic principle. In this technique, first we map neutrosophic logic into crime system to break the uncertainties in crime clustering. Next, we preliminary cluster the crime data pairs that indicates two offenders neutrosophic values into three clusters. However, some pairs lie in intersections of two or three clusters. So we optimize the clustering into disjoint clusters will be done based on the ratio of intra‐cluster and inter‐cluster distances. We have implemented the proposed method. The experimental result shows that the quality of proposed method based on accuracy, precision, and recall parameters, observed more than 90% accuracy.
A computer-based method for the accurate analysis of the trilobate spouts found in the cathedral of Amiternum

Abstract The traditional manual method of analysis of ceramic finds involves expert operators in long and routine activities whose results depend on their subjectivity, specialization, and professional experience. This implies that the analysis of sherds is carried out using few data affected by high uncertainty. These limitations are even more clear with fragments with small axially symmetric portions whose elements of the investigation are not axially symmetric, such as handles, spouts, decorations. In this way both the axis of symmetry of the original object and the reference planes and/or axes of the characteristic dimensions of the elements are identified with such approximations as to compromise subsequent analyses and comparisons. To overcome these limitations, in this paper a new computer-based procedure is proposed. As a case study, the analysis fragments of jugs/bowls with trilobed spouts found in the site of Amiternum, coming from 12th-13th century contexts are considered; their analysis is fundamental to analyze the site where they were found since there is no archival documentation about their use.
Measuring Phonological and Orthographic Similarity

Marked and unmarked language forms can be distinguished with the level of simplicity or complexity denotations of the forms. Unmarked target language forms may create little or no difficulty, even if they do not exist in the native language of the learner, while marked forms can be relatively difficult for language learners. In addition to the notions of markedness/unmarkedness, there has also been an emphasis on similarity and dissimilarity between the items of first (L1) and second languages (L2). Along with similarity or dissimilarity of L1 and L2 forms, the level of difficulty may vary enormously in different language-specific procedures. In this chapter, therefore, it is intended to build an understanding of the recognized pronunciation and orthographic problems of similar loanwords in both Turkish (L1 of the participants) and English (L2).
Discursive-Linguistic Devices and Strategies in Spam E-Mail Narratives

In this article, we aim at relating emotions and gender stereotypes through the analysis of the narratives contained in spam e-mails. We will present four different types of spam e-mails from a corpus consisting of 450 emails, their length being between four and fifty-two lines of extension. They were received between late 2009 and mid-2013, and written in English, Spanish, Portuguese and French. Our analysis will be carried out in two steps. In the first step, the discursive-linguistic strategies will be described. In the second step, the linguistic expression of emotions will be addressed. We will rely on the Positioning Theory of Rom Harré to identify where authors and readers are positioned in the narratives.
Learning Law Using Augmented Reality and Neuro-Linguistic Programming

In the era of the Fourth Industrial Revolution (4 IR), there are various legal tasks that are becoming increasingly automated, and hence, it is no longer sufficient for law students to only know the law. Today's law graduates have to be equipped with skills that can future-proof their careers from automation; hence, the onus is on education providers to embed those skills in the curriculum. In an attempt to address the identified skills gap and better enable graduate work readiness, augmented reality (AR) and neuro-linguistic programming (NLP) have been introduced into a law module at Taylor's University to encourage first-year law students to learn law using AR and utilise NLP techniques to deliver a human element through digital learning. Through the interpretation of students' feedback from a module survey, this chapter aims to understand student learning experience on the role of AR and NLP in facilitating and enhancing their legal studies and preparing the graduate more effectively for the workplace.
A State-of-the-Art Review of Nigerian Languages Natural Language Processing Research

African languages, including those that are natives to Nigeria, are low-resource languages because they lack basic computing resources such as language-dependent hardware keyboard. Speakers of these low-resource languages are therefore unfairly deprived of information access on the internet. There is no information about the level of progress that has been made on the computation of Nigerian languages. Hence, this chapter presents a state-of-the-art review of Nigerian languages natural language processing. The review reveals that only four Nigerian languages; Hausa, Ibibio, Igbo, and Yoruba have been significantly studied in published NLP papers. Creating alternatives to hardware keyboard is one of the most popular research areas, and means such as automatic diacritics restoration, virtual keyboard, and optical character recognition have been explored. There was also an inclination towards speech and computational morphological analysis. Resource development and knowledge representation modeling of the languages using rapid resource development and cross-lingual methods are recommended.
How Do We Communicate

This chapter examines our modes of imparting or exchanging facts and opinions. After discussion of the role of electromagnetic waves in our sensory perception, further text describes the ways we and other living beings gain information through the senses, especially when enhanced with technology. Finally, communication between people, with computers, and with other living things is described, especially when animal communication involves senses unavailable to human beings. Emphasis is put on visual communication, some basic notions about semantics, and also visualization techniques and domains. Basic art concepts, elements of design in art, and principles of design in art serve as background information, followed by learning projects.
Integrate memory efficiency methods for self-supervised learning on pathological image analysis

Contrastive learning, a recent family of self-supervised learning, leverages pathological image analysis by learning from large-scale unannotated data. However, the state-of-the-art contrastive learning methods (e.g., SimCLR, BYOL) are typically limited by the more expensive computational hardware (with large GPU memory) as compared with traditional supervised learning approaches in achieving large training batch size. Fortunately, recent advances in the machine learning community provide multiple approaches to reduce GPU memory usage, such as (1) activation compressed training, (2) In-place activation, and (3) mixed precision training. Yet, such approaches are currently deployed independently without systematical assessments for contrastive learning. In this work, we applied these memory-efficient approaches into a self-supervised framework. The contribution of this paper is three-fold: (1) We combined previously independent GPU memory-efficient methods with self-supervised learning framework; (2) Our experiments are to maximize the memory efficiency via limited computational resources (a single GPU); (3) The self-supervised learning framework with GPU memory-efficient method allows a single GPU to triple the batch size that typically requires three GPUs. From the experimental results, contrastive learning model with larger batch size leads to higher accuracy enabled by GPU memory-efficient method on single GPU.
Literature Review on Development of Feature Selection and Learning Mechanism for Fuzzy Rule-Based System

Abstract: This research is being conducted to study fuzzy system with improved rule base. Rule base is an important part of any fuzzy inference system designed. Rules of a fuzzy system depend on the number of features selected. Selecting an optimized number of features is called feature selection. All features (parameters) play an important role in the input to the system, but they have a different impact on the system performance. Some features do not even have a positive impact of classifier on multiple classes. Reduced features, depending on the objective to be achieved require fewer training rules, Thereby, improving the accuracy of the system. Learning is an important mechanism to automate fuzzy systems. The overall purpose of the research is to design a general fuzzy expert system with improvements in the relationship between interpretability and accuracy by improving the feature selection and learning mechanism processes through nature-inspired techniques or innovating new methodologies for the same.
Comparison of the Acoustic Performance of Wooden Violins and Carbon Fiber Reinforced Polymer Violins Through a Modal Study by Finite Elements Method and Effective Masses

Abstract Even though string musical instruments made of synthetic materials such as carbon fiber reinforced polymer (CFRP) have respected acoustic performance, but a short manufacturing cycle and low product cost, they do not become an alternative to replace high-quality string instruments made of sound woods. For CFRP violins to approach high acoustic performance wood violins, they must exhibit approximately the same bending stiffness. The CFRP is denser, stiffer, and isotropic compared to the orthotropy of wood. In this work, the acoustic behavior of CFRP violins with the same geometry as high-quality wood violins was compared. A numerical modal study was developed by finite element simulations, comparing two violin top plates, one in CFRP and the other in Picea abies (PA) wood. The simulations were developed in the ansys mechanical software, using the Block Lanczos method with a mesh of 38,216 finite volumes, finding modal patterns for both the CFRP model and the PA model. Mathematical models based on solid state physics such as effective masses and maximum vibration amplitude between models were outlined. Both models were validated against experimental studies developed by other authors. It is concluded that for instruments with the same geometry, a sonorous superiority of the wood over the CFRP was evidenced, which leads to further reinforce the unique, enigmatic, and mythical behavior of violins made of sonorous woods such as the Stradivarius violins.
Further irreducibility criteria for polynomials associated with the complete residue systems in any imaginary quadratic field

&lt;abstract&gt;&lt;p&gt;Let $ K = \mathbb{Q}(\sqrt{m}) $ be an imaginary quadratic field with $ O_K $ its ring of integers. Let $ \pi $ and $ \beta $ be an irreducible element and a nonzero element, respectively, in $ O_K $. In the authors' earlier work, it was proved for the cases, $ m\not\equiv 1\ ({\mathrm{mod}}\ 4) $ and $ m\equiv 1\ ({\mathrm{mod}}\ 4) $ that if $ \pi = \alpha_n\beta^n+\alpha_{n-1}\beta^{n-1}+\cdots+\alpha_1\beta+\alpha_0 = :f(\beta) $, where $ n\geq 1 $, $ \alpha_n\in O_K\setminus\{0\} $, $ \alpha_{0}, \ldots, \alpha_{n-1} $ belong to a complete residue system modulo $ \beta $, and the digits $ \alpha_{n-1} $ and $ \alpha_n $ satisfy certain restrictions, then the polynomial $ f(x) $ is irreducible in $ O_K[x] $. In this paper, we extend these results by establishing further irreducibility criteria for polynomials in $ O_K[x] $. In addition, we provide elements of $ \beta $ that can be applied to the new criteria but not to the previous ones.&lt;/p&gt;&lt;/abstract&gt;
Secure Video Transmission System for UAV Applications

UAV applications are starting to increase nowadays. Their data demanding applications are implemented mostly using software platforms. These applications have critical requirements on high performance, power consumption and there should be security at their transmissions, especially for video data. In this paper, an architecture is proposed for secure video transmission for UAV applications. The system consists of a digital camera a transmission board to transmit the video and a FPGA for implementing the security encryption tasks. The camera sends the video data to the FPGA and the inside circuit encrypts the video data. The transmission module transmits the encrypted data to the Ground Station (GS). Measurements taken concerning the execution time and power consumption, reveal the benefits of the proposed architecture in comparison with well-known software platforms.
Research of Quantum Neural Networks and Development of a Single-qubit Model of Neuron

Recently, there has been a rapid increase interest in quantum computers. Their work is based on the use of quantum-mechanical phenomena such as superposition and entanglement for computing to transform input data into outputs, which can actually provide effective performance 3-4 orders of magnitude higher than any modern computing devices. In the past few decades, there has been an acute problem of creating a quantum computer that uses quantum mechanical effects for computations. Currently, there are prototypes of devices of this class, but it is not yet possible to achieve an effective solution to the planned tasks with their help due to a number of difficulties in implementation. However, there is a mathematical apparatus that describes the behavior of quantum particles using a wave function. Hence, it becomes possible to simulate quantum computations on the classical architecture and check the reliability of algorithms belonging to the NP class, which are theoretically executed on a quantum computer in polynomial time and which cannot be solved on classical computers within an acceptable time frame. Effectively simulate the operation of a quantum system on devices with classical architecture is impossible, it remains to propose methods for acceleration of quantum computing on classical systems.