Reviewerly/exHarmony huggingface dataset:
Scale estimation for KCF tracker based on feature fusion

Although the correlation filter-based trackers achieve the competitive results both on accuracy and robustness, the visual tracking is still a tough problem due to background clutter, scale transformation and partial occlusions. In this paper, this paper proposes a novel tracker based on the KCF tracker. Firstly, the features including HoG and Color Name are fused together to heighten the overall tracking performance. Moreover, an effective scale estimation scheme is proposed to tackle the problem of the fixed template size in KCF tracker. The extensive empirical evaluations on the OTB demonstrate that the proposed tracker is very promising for the various challenging scenes. The target tracking algorithm proposed in this paper has accurate rate of 0.830 and success rate of 0.7.
Adaptive reasoning and procedural fluency in three-dimensional

Abstract This research is motivated by the difficulty of students determining the angle between a line and a plane in the construction of three dimensions. Students sometimes hesitate to identify the algorithm that will be used to solve the distance and angle problems in the three-dimensional material in the lecture of Capita Selecta. Based on this, this study aims to analyze the adaptive reasoning abilities and procedural fluency of students in solving three-dimensional problems. The method used in this study is a qualitative method with a descriptive design. The research was conducted on 20 students. The results of this study indicate students can think logically in choosing the right concepts and situations and are accurate in doing calculations. Students also have shown the correct procedure by making fields through the CF line and ⊥ ACH fields. The algorithms used by students are varied so that students are found to have not been precise in determining the projection length of the CF line. It can affect the angle between the CF line and the ACH plane obtained.
Citation Convergence of Academic Conference Papers

Academic conferences are regularly held to disseminate knowledge amongst researchers in various disciplines. Due to mismatch of topics, impact of some papers is constrained. In this work, text mining algorithm has been designed to collate the citations of all papers presented in a conference series. Attempt is made to connect the papers on the basis of journals or conferences they refer to. A small subset of such knowledge repositories is thus found to span all the presented papers. A few papers remain disconnected from the giant component so formed, mainly due to improper out-of-context referencing. One case study covering a flagship international conference observed over six consecutive years is presented to demonstrate the method.
Chapter 4. The local and the global in airline food

Catering on board of commercial airplanes and in airline lounges, referred to as airline food, is one of the main features characterizing different travel classes. This study investigates the language used to describe airline food on websites of different airlines and identifies observable patterns concerning the depiction of different travel classes and lounge types. These patterns are compared to the findings of Jurafsky (2014), who, among other things, found that expensive restaurants tend to stress the local origin of the food. Despite the global nature of aviation, the results show that the descriptions of airline food associated with the premium travel classes are in many respects similar to the menus of expensive restaurants, as local references play a prominent role.
Secure Keyword Search

This chapter introduces searchable encryption (SE), a cryptographic primitive supporting search over encrypted data using keywords. SE plays an important role in current cloud storage systems. It enables users to retrieve target data from their entire data set in an efficient way without leakage of the data contents. In the following sections, the motivation of SE and its classification are first reviewed. Then, a comprehensive survey on each class of SE is provided. Finally, the latest advances in SE is studied.
Gravitation Search Algorithm with Two Masses for Constrained Optimization

In this paper, a new gravitation search algorithm with two masses is proposed to solve constrained optimization problems. In this algorithm, each particle has two masses, namely, "feasible mass" and "infeasible mass". If the particle in the feasible region is updated, then the feasible mass is used in gravitation search algorithm, otherwise, the infeasible mass is used. This idea is based on that a feasible particle is attracted towards better feasible particles and an infeasible particle is attracted towards the feasible region. Finally, the proposed algorithm is tested on 10 benchmark problems and compared with the other algorithms. The numerical results indicate that the proposed algorithm has the better performance in solving constrained optimization problems.
Item Response Theory and Developing Scales for E-learning

When creating virtual courses, it is necessary to develop a very accurate system of assessment of the level of knowledge. Standards of online training should not differ from the standard level of training on campus. IRT has proven itself well in creating standardized tests. The article discusses the application of IRT methods to create testing scales in e-learning. On the example of mathematical disciplines, a method is developed in which the student can move to another level only when the corresponding result is achieved. It considers not only the correctness of the answer, but also the time for which this answer was received. The article presents the research results obtained by the author using this method. In conclusion, the possibility of using this approach in the study of other training courses is discussed.
A Data Association Method for Vehicle Detection Based on Millimeter Wave Radar

This paper proposes a vehicle object assoication method based on millimeter wave radar detection, which can improve the accuracy of driving assistance functions that take vehicle targets as input. This method is used to judge the data association of the target tracking group through Kalman filtering. The L-shaped model is used to initially determine the outline of the measurement object, and the close object clusters are segmented according to the standard vehicle parameters and the related speed. Furthermore, a Gate algorithm is used to associate the hypothetical object within a certain area with the target. Road test experiments show that the data assoiation method has high accuracy for detected vehicles.
Toponym Extraction in Thai Tweets Using a Hybrid Approach

Crowdsourcing has become an important tool in areas such as business and marketing. It can help organizations solve large-scale problems in areas including traffic management and political campaigning. Toponym extraction is necessary when analyzing crowdsourced data for traffic tracking or event reporting. Dictionaries and rule-based analysis are commonly used for matching and extracting entities from text. However, the creation of an effective dictionary is not an easy task, especially when the goal is to name a large number of locations. Named Entity Recognition (NER) can help address this, but the approach has certain limitations. In this paper, we describe an improved approach to toponym extraction from Twitter messages that combines a dictionary and NER. As tweets are limited to 280 characters, any locations mentioned are usually referred to using abbreviations. The variety of forms that location names take, and the unstructured language of tweets, are challenging both to the dictionary and NER methods. We divided tweets into four categories to investigate the effect of analyzing messages from different domains. The average accuracy was 49.18% when using only the dictionary, 59.30% when using only NER, and 75.43% when using the hybrid method.
Towards Personalized Deformable and Mix-supervised Model for Robust MR-US Registration

Registration and fusion of magnetic resonance (MR) and ultrasound (US) images remain challenging tasks due to the great intensity variation between two modalities and the lack of ground truth spatial correspondence. In this paper, we proposed a mix-supervised registration network, driven by main vessel segmentation labels and modality independent neighborhood descriptor (MIND) similarity metric. The accurate alignment of the main vessel labels can prompt the registration network focus on the region of interest and MIND similarity metric constraints global deformation. Vessel label and similarity measures can promote each other and produce satisfactory registration results. This framework connects a Rigid-Net and a Bspline-Net to generate the transformation parameters of rigid and deformable registration in one shot. The well-trained network is able to register dynamic 3D US images to the preoperative MR image in real time, correcting the motion caused by a patient's breathing. Experimental results show that the proposed method is effective for MR-US registration.
Design and Research of Occupant Type Feature Recognition Based on BP Neural Network

Accurate detection of vehicle occupant classification is one of the prerequisites for the realization of intelligent airbag system. In order to reduce the damage caused to the passenger by accidental airbag in the collision accident, this paper uses the characteristics of the passenger's body pressure distribution on the seat to identify different occupant types. The traditional high-density array pressure sensors can better detect the pressure distribution characteristics of different occupant types on the seat, but the method is costly and is not suitable for commercial applications. In this paper, based on the theory of pressure sensitive points, the flexible array pressure sensors based on pressure sensitive points are used in this hardware and the occupant identification algorithm based on BP neural network is established. The occupant identification algorithm is composed of occupant type recognition algorithm and occupant sitting recognition algorithm. Real-time detection of changes in occupants type and sitting postures is achieved.
Sequential and parallel algorithms for all-pair<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e2235" altimg="si17.svg"><mml:mi>k</mml:mi></mml:math>-mismatch maximal common substrings

Identifying long pairwise maximal common substrings among a large set of sequences is a frequently used construct in computational biology, with applications in DNA sequence clustering and assembly. Due to errors made by sequencers, algorithms that can accommodate a small number of differences are of particular interest. Formally, let D be a collection of n sequences of total length N, ϕ be a length threshold, and k be a mismatch threshold. The goal is to identify and report all k-mismatch maximal common substrings of length at least ϕ over all pairs of strings in D. Heuristics based on seed-and-extend style filtering techniques are often employed in such applications. However, such methods cannot provide any provably efficient run time guarantees. To this end, we present a sequential algorithm with an expected run time of O(NlogkN+occ), where occ is the output size. We then present a distributed memory parallel algorithm with an expected run time of ONplogN+occlogkN using Ologk+1N expected rounds of global communications, under some realistic assumptions, where p is the number of processors. Finally, we demonstrate the performance and scalability of our algorithms using experiments on large high throughput sequencing data.
Playing T-Rex Rush with Deep Reinforcement Learning

After AlphaGO defeated the world Go champion Li Shishi in early 2016, the entire industry was excited about it. More and more scholars realized the importance of deep reinforcement learning [1] in the field of artificial intelligence. In addition, Google has also made two important research results. The deep reinforcement learning algorithm based on the Atari video game and the Go Artificial Intelligence AlphaGo Zero, these achievements are tantamount to setting off a revolution in artificial intelligence learning algorithms.
Assessment Of GPU-Based Enhanced Wiener Filter On Very High Resolution Images

Despeckling of Synthetic Aperture Radar (SAR) images is still an open issue in the remote sensing community. Many filtering approaches have been adopted by the scientific community in order to reduce speckle noise effects including, such as spatial-based and non-local techniques. Mainly all existing approaches present some limitation in terms of accuracy or computational time or edge preservation. Recently a frequency-based solution presented as an improvement of the classical Wiener filter was proposed in order to limit the previous mentioned critical issues. The algorithm was already tested on both simulated and Sentinel-1 data. In this paper, we assess and validate the effectiveness of the proposed solution exploiting different very high-resolution (VHR) data such as, COSMO-SkyMed and RADARSAT SAR images. The results are interesting proving that the developed filter can effectively deal with the case of different VHR SAR images and can provide accurate results within a very limited time.
Efficient Base Class Selection Algorithms for Few-Shot Classification

Few-shot classification is a task to learn a classifier for novel classes with a limited number of examples on top of the known base classes which have a sufficient number of examples. In recent years, significant progress has been achieved on this task. However, despite the importance of selecting the base classes themselves for better knowledge transfer, few works have paid attention to this point. In this paper, we propose two types of base class selection algorithms that are suitable for few-shot classification tasks. One is based on the thesaurus-tree structure of class names, and the other is based on word embeddings. In our experiments using representative few-shot learning methods on the ILSVRC dataset, we show that these two algorithms can significantly improve the performance compared to a naive class selection method. Moreover, they do not require high computational and memory costs, which is an important advantage to scale to a very large number of base classes.
A Hierarchical Dual-Memory Learning Model for Human Skeleton Action Recognition

Due to many applications such as video surveillance, human machines interaction and video recovery, human actions recognition was a significant topic in computer vision. This paper proposes a self-organizing recurrent incremental network (SORIN) for human action recognition using human skeleton information. The proposed method models human working memory and episodic memory and comprises two layers of adaptive recurrent Growing-When-Required (ar-GWR) network that connected hierarchically. The working memory layer continually learns incoming perception information and encodes the learned knowledge as neurons. Similarly, the episodic memory layer further learns the spatiotemporal relationship of neurons from working memory as episode neurons to realize human actions incrementally. The proposed method integrates with OpenPose framework for human skeleton action recognition and it is validated through a series of experiments.
Dynamic Bayesian models: extensions and new proposals

Even though many time series presents problems such as overdispersion, zero inflation and change-points, these features, usually, are not incorporated into the most common dynamic Bayesian models available in the literature. To address these problems, we worked on two strands in this dissertation. In the first strand, the objective is to introduce new Bayesian dynamic models for time series of counts that allow for observations in distributions that can more adequately adjust to some common features related to the modeling of discrete data. We present a new framework for uniparametric Dynamic Bayesian Models of counts whose particular cases include Bell, Poisson-Lindley, Yule-Simon and Borel models. Furthermore, a biparametric Negative binomial model with unknown shape parameter is provided. The inferential procedure preserves the sequential nature of the Bayesian analysis and is similar to the Dynamic Generalized Linear Models (DGLM) with a novel of incorporating Monte Carlo integration to the recursive algorithm in order to deal with the intractability of the updating distributions and an ARMS step to sample from the posterior distribution of the shape parameter. We also consider a conjugate Beta Prime of the second kind distribution prior for the mean of the process. The simulation results show a good performance of the estimators considered for the static parameter, which can be reasonably estimated. The application results also highlights a better performance of the proposed uni/biparametric models over the Poisson model. In the second strand of this work we incorporate the Product Partition Models class into the DGLM. This new formulation, that we call DGLM-PPM, retains the flexibility and generality of the DGLM class and also provides a framework for Bayesian multiple change-point detection in time series. To sample from the partition and the discount factor we use a Gibbs Sampler with an ARMS step appended. A simulation study is conducted and the results show that the proposed model is able to detect the points of regime switch in the simulated data. The superiority of our proposal over the conventional DGLM is further confirmed in two real data applications in which the DGLM-PPM outperforms the conventional DGLM in-sample and out-of-sample.
Application on Development of Modern Service Industry in China Based on Artificial Intelligent Model Optimized by TANSAFOA

Abstract Modern service industry is one of the industries that widely applies artificial intelligent (AI) in the era of big data. The development of modern service industry plays a very important role in industrial transformation. The emergence of AI technology improves the effectiveness of data mining, and more and more scholars suggest a series of optimization methods to lift the prediction ability. In this study, tangent fruit fly optimization algorithm with step adjust (TANSAFOA) is proposed and it is used to optimize multivariate adaptive regression splines (MARS) and back propagation neural network (BPNN) to construct a prediction model of business performance. The result shows TANSAFOA can effectively optimize the prediction model and the BPNN model optimized by TANSAFOA has higher prediction performance than MARS. TANSAFOA BPNN-model is the most appropriate prediction model for modern service industry in China.
Correlation distribution of a sequence family generalizing some sequences of Trachtenberg

&lt;p style='text-indent:20px;'&gt;In this paper, we give a classification of a sequence family, over arbitrary characteristic, adding linear trace terms to the function &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ g(x) = \mathrm{Tr}(x^d) $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, where &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ d = p^{2k}-p^k+1 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, first introduced by Trachtenberg. The family has &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ p^n+1 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; cyclically distinct sequences with period &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ p^n-1 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;. We compute the exact correlation distribution of the function &lt;inline-formula&gt;&lt;tex-math id="M5"&gt;\begin{document}$ g(x) $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; with linear &lt;inline-formula&gt;&lt;tex-math id="M6"&gt;\begin{document}$ m $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;-sequences and amongst themselves. The cross-correlation values are obtained as &lt;inline-formula&gt;&lt;tex-math id="M7"&gt;\begin{document}$ C_{i,j}(\tau) \in \{-1,-1\pm p^{\frac{n+e}{2}},-1+p^n\} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;.&lt;/p&gt;
Performance Analysis of Weighted Cat Swarm Optimization with Sobol Mutation

This paper introduces an analysis by suggesting three types of modification to an existing well known algorithm for optimization i.e. Cat swarm optimization. The motivation behind the proposed modification is to provide a superior convergence along with the production of finer results in diverse real world applications. The first modification introduces a modified weight factor by the replacement of traditional weight factor in the original algorithm. In the second proposed modification, a versatile mutation process i.e. sobol mutation operator is embedded in the execution process of cat swarm optimization for further enhancement in the performance of the optimization technique. The third proposed modification uses the sobol mutation operator with the modified version weighted cat swarm optimization algorithm. This paper provides a detailed analysis of the three proposed techniques for establishing the superiority over the traditional algorithm. Standard benchmark functions are used in this paper for analysing the effectiveness of the modification proposals. The third proposed modification combines the best feature of both the first and second modifications and compensates the disadvantages to some extent. This third modified version provides the best results in almost all categories of functions along with higher precision. Moreover, all the modifications are providing superior results in comparison to the standard cat swarm algorithm, but the weighted cat swarm optimization algorithm with sobol mutation is dominating in all the cases.
Offline Sindhi Speech Recognition

Motivation behind this research effort is to fill up the gap between the computer and its related technologies and the people of rural Sindh, by enabling them to use Sindhi language in their computers, mobiles etc. The Sindhi language is the most commonly used language by Sindh province people, particularly people living in rural areas of Sindh. This language is also spoken in many areas of India. While there exists a number of speech recognition systems, which have been developed for different languages, literature suggests there is need for speech recognition system in local Sindhi language. We have designed and developed a speech recognition system in Sindhi language, which is based on Artificial Neural Network (ANN) and Hidden Markov Model (HMM). The main purpose behind using ANN is that has an adaptive nature which means it has the capability to learn. In this paper, we present and discuss various aspects of the proposed Sindhi speech recognition system including fundamental model of a speech recognition trainer and its different modules. It also describes how to develop dictionary for Sindhi language and how to train a module for Automatic Speech Recognition (ASR). We also present and discuss the results of experiments conducted to evaluate the system.
Impact of information technologies and various types of virtual communication on modern English

The rapid development of information and computer technologies has led to the emergence of a new electronic environment, and, consequently, a new type of communication - virtual, or electronic communication. Thus, for many people, virtual communication becomes part of the way of life. The main aim of the research work is to study the impact of information technologies and various types of virtual communication on modern English. The authors reveal the concept of "virtual communication" and identify its positive and negative sides. The authors give different approaches to the term of virtual communication and determine the classification of virtual communication in the modern English language. The most important language aspects of virtual communication in modern English ware presented by the authors. The results and conclusions of the presented paper can be used by teachers and lecturers at the English classes, seminars and lecture courses connected with linguistics.