Reviewerly/exHarmony huggingface dataset:
Detection of Traffic Signs and Road Users From a Moving Vehicle

The topic of this chapter is the implementation and analysis of vision algorithms for the detection of static and dynamic objects in videos. These algorithms are typically components of the visual perception module of modern driver-assistance systems or even autonomous cars. Whereas the vast majority of today's papers from the vision community use convolutional deep neural networks (CNNs), this chapter explores the more traditional approaches, namely HOG (Histogram of Oriented Gradients) as well as SVM (Support Vector Machine). The static and dynamic objects to be recognized are traffic signs and motorcycles, respectively. These two object classes have been chosen since traffic signs are relatively easy to detect and motorcycles state a much more complex task. Thus, this chapter tackles differently difficult tasks with a single set of algorithms.
A Comparative Study of Classical and Deep Classifiers for Textual Addressee Detection in Human-Human-Machine Conversations

The problem of addressee detection (AD) arises in multiparty conversations involving several dialogue agents. In order to maintain such conversations in a realistic manner, an automatic spoken dialogue system is supposed to distinguish between computer- and human-directed utterances since the latter utterances either need to be processed in a specific way or should be completely ignored by the system. In the present paper, we consider AD to be a text classification problem and model three aspects of users’ speech (syntactical, lexical, and semantical) that are relevant to AD in German. We compare simple classifiers operating with supervised text representations learned from in-domain data and more advanced neural network-based models operating with unsupervised text representations learned from in- and out-of-domain data. The latter models provide a small yet significant AD performance improvement over the classical ones on the Smart Video Corpus. A neural network-based semantical model determines the context of the first four words of an utterance to be the most informative for AD, significantly surpasses syntactical and lexical text classifiers and keeps up with a baseline multimodal metaclassifier that utilises acoustical information in addition to textual data. We also propose an effective approach to building representations for out-of-vocabulary words.
Programming in a Functional Style in C++

C++ is a multiparadigm programming language. So the programmer may choose and combine between structural, procedural, object oriented, generic or functional features of C++ to solve his problem. Especially the functional aspect of C++ lambda functions with, type inference and the function std::bind and std::function has grown in modern C++ and is quietly evolving with the next C++ standard.
Self Driving Car

The self-driving car is capableKumar, Ankit of sensing its environment and navigating without human inputMukherjee, Mayukh which it applies the technology of autonomous vehicle that allows the car to experiment driverless experience. Software designedMukhopadhyay, Preetam to drive from point A to point B without requiring any human intervention is implemented.
A System for Privacy-Preserving Access Accountability in Critical Environments

In this paper, we present a BeagleBone-based system to increase the security and safety level of critical environments by tracking people movements. Our solution is privacy aware, as it is possible to know who accessed a zone at a given time, with an uncertainty degree of accountability.
Perbedaan Fungsi Kognitif Mahasiswa Fakultas Kedokteran Unissula dengan IPK Kurang dari 3 dan IPK Lebih sama dengan 3 Yudisium Tahun 2018

Cognitive function varies in each human being, from simple to complex, requiring attention, concentration, and coordination. Cognitive is related to a person's ability to think, solve problems, organize and also to communicate and interact with others and the environment. Methods: cross-sectional research, samples involve the students of medical faculty of Unissula-Semarang with GPA less than 3 and more than 3 graduated in 2018, instrument Mini-Mental State Examination (MMSE). Descriptive analysis and Chi-square test. Results and Discussion: samples are 56 students, the highest gender is men (51%), the range of age 18-21 years old (53.6%), the number of GPA is the same as the one below and above 3 there are 28 (50%). Test Chi Square gender difference with P value of 0.422 GPA gender does not have significant difference to the GPA, based on the age of the results of T-test p-value 0.000, showed age had significant difference to the GPA, where the age is getting younger GPA is getting better, based on the scores of MMSE test, it was obtained p 1.000, MMSE score has no significant difference with the GPA. Conclusion: Gender and MMSE value have no difference with GPA, there is difference between age and GPA, the younger the students the better GPA the students achieve.
Reconstruction of 2-Convex Polyominoes with Non-Empty Corners

This paper uses the theoretical material developed in a previous study by the authors in order to reconstruct a subclass of 2-convex polyominoes calledα where the upper left corner and the lower right corner of the polyomino contain each only one cell.The main idea is to control the shape of these polyominoes by using 32 types of geometries.Some modifications are made in the reconstruction algorithm of Chrobak and Dürr for HV-convex polyominoes in order to impose these geometries.
GEMBench: A Platform for Collaborative Development of GPU Accelerated Embedded Markov Decision Systems

Markov Decision Processes (MDPs) provide a powerful decision making framework, which is increasingly being used in the design of Embedded Computing Systems (ECSs). This paper presents a detailed accounting of the use of MDPs in this context across research groups, including reference implementations, common datasets, file formats and platforms. Inspired by recent results showing the promising outlook of using embedded GPUs to solve MDPs on ECSs, we detail the many challenges that designers currently face and present GEMBench (the Gpu accelerated Embedded Mdp testBench) in order to facilitate experimental research in this area. GEMBench is targeted to a specific embedded GPU platform, the NVIDIA Jetson platform, and is designed for future retargetability to other platforms. GEMBench is a novel open source software package that is intended to run on the target platform. The package contains libraries of MDP solvers, parsers, datasets and reference solutions, which provide a comprehensive infrastructure for understanding trade-offs among existing embedded MDP techniques, and experimenting with novel techniques.
Differences in the Research Domains of Knowledge Organization between Academic Researchers and Library Practitioners: Preliminary Results

This poster presents preliminary findings from a study that explores the research domain in the field of Knowledge Organization (KO) by comparing the publications contributed by academic researchers and library practitioners. We analyzed 914 articles in three representative KO journals using text mining. The preliminary findings reveal the distinct gaps in research topics between the two groups. Academic researchers contributed more to the realm of theories and fundamental concepts while library practitioners were more interested in specific library practices.
Association Extraction from Functional Testing Scenarios

In this paper, we propose an approach to extract association among software pages from functional test case scenarios. This knowledge helps us to re-design user interfaces of a software application for the purpose of usability. Initially, functional test case scenarios are applied to the association extraction approach. The association sets are then obtained and post-processed. The association is considered in two aspects including 1) all frequent pages association and 2) consecutive pages association. Both associations are used to simplify user interfaces of the software application. We demonstrate our proposed method with the Mobile Application Development Quiz in [7] as a case study.
Analytic Equational Proof Systems for Combinatory Logic and λ-Calculus:A Survey

The paper surveys ideas, results and applications of a novel proof-theoretical approach to equational theories of untyped operations (including combinatory logic CL and Lambda-calculus) we developed in the last 15 years. The approach is based on the introduction and the proof-theoretical investigation of “analytic” proof systems for such theories. Analyticity is due to a main result of transitivity elimination, yielding as a consequence a non trivial streamlining of the proof-theory of the corresponding theories and a number of applications. Among these, a positive solution to an open problem concerning the theory of combinatory strong reduction which was raised about 60 years ago by H.B. Curry.
Application of pre-stack gather detuning technology based on non-stationary matching method to Bohai fault block oilfield

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Application of pre-stack gather detuning technology based on non-stationary matching method to Bohai fault block oilfieldAuthors: Yu XiongCai LiDongjia HouYongqiang YaoJunpei YueYu XiongCNOOC (China National Offshore Oil Corporation) Ltd Tianjin BranchSearch for more papers by this author, Cai LiCNOOC (China National Offshore Oil Corporation) Ltd Tianjin BranchSearch for more papers by this author, Dongjia HouCNOOC (China National Offshore Oil Corporation) Ltd Tianjin BranchSearch for more papers by this author, Yongqiang YaoCNOOC (China National Offshore Oil Corporation) Ltd Tianjin BranchSearch for more papers by this author, and Junpei YueCNOOC (China National Offshore Oil Corporation) Ltd Tianjin BranchSearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3216497.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractThe formation tuning effect changes the interference mode of reflected wave at different offset, distorts the amplitude and frequency information of remote offset data, and reduces the resolution of seismic data. Considering the difference of tuning effect under different stratum thickness, the adaptive decomposition of seismic data is realized by means of local Lamoureux window, and the non-stationary matching objective function of seismic data in angle domain is constructed by using the difference of pre-stack seismic angle data. A non-stationary matching de-tuning method is formed, which can realize the amplitude and waveform stretching calibration of pre-stack gathers. The pre-stack de-tuning method proposed in this paper has achieved good results in seismic data processing of Bohai complex fault block oilfields. It can effectively improve the quality of seismic data and the resolution of seismic data, and lay a data foundation for reservoir prediction and fluid identification.Note: This paper was accepted into the Technical Program but was not presented at the 2019 SEG Annual Meeting in San Antonio, Texas.Keywords: prestack, common angle, high-resolution, offset, optimizationPermalink: https://doi.org/10.1190/segam2019-3216497.1FiguresReferencesRelatedDetails SEG Technical Program Expanded Abstracts 2019ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2019 Pages: 5407 publication data© 2019 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 10 Aug 2019 CITATION INFORMATION Yu Xiong, Cai Li, Dongjia Hou, Yongqiang Yao, and Junpei Yue, (2019), "Application of pre-stack gather detuning technology based on non-stationary matching method to Bohai fault block oilfield," SEG Technical Program Expanded Abstracts : 4077-4081. https://doi.org/10.1190/segam2019-3216497.1 Plain-Language Summary Keywordsprestackcommon anglehigh-resolutionoffsetoptimizationPDF DownloadLoading ...
Open Access and the Changing Landscape for Library Acquisitions — Interview with Gregory T. Eow

In this installment of “Conversations,” Gregory T. Eow, associate director for collections at MIT Libraries, shares his perspective on the changing landscape of library acquisitions.
A consistent bayesian bootstrap for chi-squared goodness-of-fit test using a dirichlet prior

In this paper, we employ the Dirichlet process in a hypothesis testing framework to propose a Bayesian nonparametric chi-squared goodness-of-fit test. Our suggested method corresponds to Lo’s Bayesian bootstrap procedure for chi-squared goodness of-fit test and rectifies some shortcomings of regular bootstrap which only counts number of observations falling in each bin in contingency tables. We consider the Dirichlet process as the prior for the distribution of the data and carry out the test based on the Kullback-Leibler distance between the updated Dirichlet process and the hypothesized distribution. Moreover, the results are generalized to chi-squared test of independence for a contingency table.
How to be a maker: Intruder alert

Some animals are less welcome in the garden than others, says Hannah Joshua. This device will help keep them away
Type-2 Fuzzy Sets and Its Extensions

Uncertain decision making exists in various kinds of human behaviors, especially in dealing with complex system modeling problem.
A scale transform and deformation target tracking algorithm based on correlation filtering

Abstract In order to improve the robustness of the correlation filtering (CF) tracking algorithm and overcome the problem that the traditional correlation filtering method can not deal with the target’s scale and deformation change, a feature-adaptive scale adaptive correlation filter tracking algorithm is proposed. Firstly, the target is characterized; then the output filter is calculated by using the correlation filter; finally, the image blocks of different scales are intercepted from the target position of the current frame, and the optimal estimation of the target scale is obtained by the adaptive scale pool model. The experiment selects multiple video sequences for testing and compares the proposed algorithm with other target tracking methods. The experimental results show that the average performance is better than the comparison method.
Method for Extracting Positions of Players from Video of Lacrosse Game

It has always been important to analyze games in the sports field. Especially, the motion tracking of a player is useful for analysis of strategy; therefore, it must be recorded. The outline of the movement path of a player has been recorded manually based on video so far. Recently, the movement path of a player can be recorded automatically from video; however, since a dedicated system using many high-performance cameras under an environment where various conditions are satisfied is necessary, it can only be used in some professional sports such as the soccer. On the other hand, there is a large demand for low-cost and simple systems so that they can be used easily in amateur sports. In this paper, we propose a method for extracting spatial position of players from video of lacrosse game. The method can detect players, even if video includes shakes. We implement the proposed method and evaluate the performance based on actual video of lacrosse game.
Monocular Multi-feature Fusion Hand Gesture Recognition Method Based on Multi-core Learning

Abstract Based on multi-core learning, this paper uses the method of skin color segmentation, fingertip distance detection and extraction and gesture contour extraction, which realizes the fusion of local features and overall features and makes the accuracy of hand gesture classification, reach 92%. Compared with traditional gesture recognition based on binocular camera, our algorithm using multi-core learning has the same accuracy as the recognition based on binocular camera, and the complexity and cost of our algorithm are lower. Thus, our algorithm has broader application prospects. Compared with gesture recognition based on monocular camera, our algorithm is more suitable for the background environment of gesture classification. In other words, it has better generalization ability.
Generation of Maximally Entangled States by a Kerr-like Nonlinear Coupler Interacting with External Fields

We study a model with two nonlinear oscillators (Kerr-like nonlinear coupler) pumped by two external coherent fields as a nonlinear quantum scissor (NQS). Using the numerical simulation method introduced before for quantum state engineering in NQS of such type, we obtain the wave function describing the evolution of the system as a combination of n-photon states. Considered NQS generates a truncation of optical states that leads to achieve two-qubit states due to the nonlinear properties of oscillators and their interaction. In particular, evolution of the system generates maximally entangled states as so-called Bell-like states. We will show that a proper choice of initial conditions for such evolution implies the increase of efficiency of entanglement creation process. We consider our model for both damping and without damping cases.
A Q-Learning Approach for Sales Prediction in Heterogeneous Information Networks

In today’s world, recommenders have grabbed a major importance to improve the sales where this paper provides the use of machine learning approach which involves machine learning technique like Q-learning that has an evident prediction on improving the sales. The logical network that can be formed for the mobiles and sales can be treated as a heterogeneous information network and traversing this semantic network gives meaningful meta-paths. The reinforcement learning technique, Q-Learning, is applied to predict the sales of a product.
Theoretical Foundations of Deep Resonance Interference Network

Digital processes for banks, insurances, or public services generate big data. Hidden networks and weak signals from frauds activities are sometimes statistically undetectable in the endogenous data respective to processes. The organic intelligence of human experts is able to reverse-engineer new fraud scenarios without statistically significant characteristics, but machine learning usually needs to be taught about them or fails to this task. Deep resonance interference network is a multidisciplinary attempt in probabilistic machine learning inspired from waves temporal reversal in finite space, introduced for big data analysis and hidden data mining. It proposes a theoretical alternative to artificial neural networks for deep learning. It is presented along with experimental outcomes related to fraudulent processes generating data statistically similar to legal endogenous data. Results show particular findings probably due to the systemic nature of the model, which appears closer to reasoning and intuition processes than to the perception processes mainly simulated in deep learning.
Multi-Lingual Retrieval of Pictures in ImageCLEF

CLEF first launched a multi-lingual visual information retrieval task in 2003 as part of the ImageCLEF track. Several such tasks subsequently followed, encompassing both the medical and non-medical domains. The main aim of such ad hoc image retrieval tasks was to investigate the effectiveness of retrieval approaches that exploit textual and visual evidence in the context of large and heterogeneous collections of images that are searched for by users with diverse information needs. This chapter presents an overview of the image retrieval activities within ImageCLEF from 2003 to 2011, focusing on the non-medical domain and, in particular, on the photographic retrieval and Wikipedia image retrieval tasks. We review the available test collections built in the context of these activities, present the main evaluation results, and summarise the contributions and lessons learned.
Emotional analysis System of book review based on Neural network

Abstract Contemporary society, is an era of the Information explosion. The explosive growth of book publishing makes readers choose a good book, and publishers choose to publish a book with a market, which has become a very important topic. Aiming at the non-effective analysis of the current book publishers to obtain the real feelings of the users on the book, this paper puts forward that the neural network as the main algorithm is enough to classify the user’s comments emotionally. In the collection of data, through the award-winning books and the two-star score under the book review, take different weights of the calculation, the emotional words of the eigenvectors to plump. And after the book evaluation is pre-processed, the user’s credibility parameters are added to the input word vector. As a result, the accuracy of classification and judgment of the whole system is improved, the consumption of human, material and financial resources is reduced, and the resource can be invested in how to improve the level of publications, which will add to the cause of book publishing in China.
Breast Lesion Segmentation Method Using Ultrasound Images

Breast cancer is one of the leading causes of death among women. A non-invasive ultrasound examination enables the location and type of lesion. The radiologist, based on the 2D ultrasound image, estimates the volume of the lesion and performs a core needle biopsy procedure. The lesion segmentation may support the diagnostic and therapeutic process. The purpose of this study is to develop a method of breast tumor segmentation using two-dimensional ultrasound images. The lesion mask is based on the fusion of several methods. The proposed method employs active contour models, gradient vector flow, region growing, thresholding, image gradient, watershed transform and morphological operations. The effectiveness of the method was checked with Dice, Jaccard and specificity coefficients. Median values were 0.915, 0.862 and 0.996, respectively.
Real-Time Collaborative Animation of 3D Models with Finger Play and Hand Shadow

The authors propose a method for real-time collaborative animation for 3D models with finger play and hand shadow. Two or more users have the model animated more complexly and expressively than one user. For instance, a model of a crocodile is animated by two users; one manipulates its mouth, neck and tail, and the other manipulates its four legs. With the implemented system up to five users can manipulate one model collaboratively. From evaluations following points were found: 1) users could manipulate models without detailed instruction; 2) most participants felt the system operable and enjoyable; 3) the motions made with the system were not less cute, amusing and lively than those by animators with conventional methods.
Portfolio Scheduling for Managing Operational and Disaster-Recovery Risks in Virtualized Datacenters Hosting Business-Critical Workloads

Cloud datacenters are increasingly hosting business workloads. Such long-running, on-demand workloads raise important challenges in datacenter operation, requiring efficient online scheduling of workloads with unprecedented characteristics under strict service level agreements (SLAs). In this work, we propose an approach to manage the risk of not meeting SLAs. Our approach is based on portfolio scheduling, which is an online scheduling technique that dynamically selects a scheduling algorithm from a set (portfolio), subject to a possibly changing utility function. Ours is the first datacenter-scheduling approach to consider operational and disaster-recovery risks. Using trace-based simulation with traces collected from a commercial multi-datacenter environment, we give evidence that portfolio scheduling is able to mitigate risks significantly better than its constituent scheduling algorithms and better than datacenter engineers.