Reviewerly/exHarmony huggingface dataset:
A Regularised Intent Model for Discovering Multiple Intents in E-Commerce Tail Queries

A substantial portion of the query volume for e-commerce search engines consists of infrequent queries and identifying user intent in such tail queries is critical in retrieving relevant products. The intent of a query is defined as a labelling of its tokens with the product attributes whose values are matched against the query tokens during retrieval. Tail queries in e-commerce search tend to have multiple correct attribute labels for their tokens due to multiple valid matches in the product catalog. In this paper, we propose a latent variable generative model along with a novel data dependent regularisation technique for identifying multiple intents in such queries. We demonstrate the superior performance of our proposed model against several strong baseline models on an editorially labelled data set as well as in a large scale online A/B experiment at Flipkart, a major Indian e-commerce company.
Mobile Robot Path Planning Based on Chaos Generated by Utilizing Logistic Map

This study investigated motion planning of a mobile robot by utilizing the phenomena generated from four numerical models. Using the trajectory generated from the numerical models because the method can be applied to multiple fields, we considered a mobile robot that can entirely cover a specific region. In some previously conducted conventional studies, chaotic trajectories were applied to generate from the following four numerical models: the Arnold equations model, a two-dimensional discrete-time model derived from the chaotic neuron model, the logistic map model, and a random walk model. For the logistic map model, we generated chaotic behavior by setting an appropriate parameter value, which was used to change the angle of a mobile robot. On the basis of the simulation results, we calculated the sample variances demonstrating the distribution of the positions of a mobile robot on an xy-plane. We evaluated the effectiveness of the trajectories to cover the whole region. In addition, we compared the computational speed of the generation of each trajectory. The results of the investigation illustrate that the logistic map model was highly effective regarding our purpose. To achieve two degrees of freedom motion in the future, we are going to implement each method using an autonomous mobile robot, which has two active and independent wheels, and one passive wheel.
Effective Pipeline for Compressing Deep Object Detectors

To alleviate the deployment of deep object detectors with large model capacity and complex computation, an effective model compression pipeline is designed in this paper. Firstly, attributed to the refined soft filter pruning, 3D filters of each convolution layer are regularized and then auto-pruned to achieve an overall more compact backbone. Afterwards, the branch layers of the object detector are simplified with the usage of simple residual blocks and fixed channel deletion. Experimental results reveal the superior effectiveness and generality of proposed pipeline for compressing detection models. Notably, on the generic detection dataset, proposed pipeline reduces more than 67% model size on RefineDet with negligible mAP loss. Moreover, the pipeline decreases more than 73% model size on the PyramidBox face detector with little loss of hard AP on WIDER FACE.
Inter-Personal Relation Extraction Model Based on Bidirectional GRU and Attention Mechanism

Inter-Personal Relationship Extraction is an important part of knowledge extraction and is also the fundamental work of constructing the knowledge graph of people's relationships. Compared with the traditional pattern recognition methods, the deep learning methods are more prominent in the relation extraction (RE) tasks. At present, the research of Chinese relation extraction technology is mainly based on the method of kernel function and Distant Supervision. In this paper, we propose a Chinese relation extraction model based on Bidirectional GRU network and Attention mechanism. Combining with the structural characteristics of the Chinese language, the input vector is input in the form of word vectors. Aiming at the problem of context memory, a Bidirectional GRU neural network is used to fuse the input vectors. The feature information of the word level is extracted from a sentence, and the sentence feature is extracted through the Attention mechanism of the word level. To verify the feasibility of this method, we use the distant supervision method to extract data from websites and compare it with existing relationship extraction methods. The experimental results show that Bi-directional GRU with Attention mechanism model can make full use of all the feature information of sentences, and the accuracy of Bi-directional GRU model is significantly higher than that of other neural network models without Attention mechanism.
Trajectory Optimization of Two-joint Manipulator Based on Differential Evolution Algorithm

In the mechanical system will oscillate in the course of motion, and the oscillation will cause additional energy consumption. In the process of fixed-point motion, different trajectories will cause different degrees of oscillation, so it is necessary to study how to design the most trajectory controller to minimize the energy consumption of the mechanical system in the whole motion process. This paper mainly studies the trajectory planning based on differential evolution algorithm. Trajectory planning is divided into two aspects: for mobile robots, it refers to the trajectory planning of mobile robots, such as what kind of trajectory the mobile robots follow under the condition of map or without map; for industrial robots, it refers to the trajectory planning of the end of the robot arm, or the trajectory planning of the robot arm, curve contour planning of displacement, velocity and acceleration in motion. This paper introduces the problem of the most trajectory planning and design for fixed-time fixed-point motion. The most trajectory objective is to minimize the energy consumption in the whole motion process. The simulation program of MATLAB is used to show the advantages of trajectory planning.
Formation Lithology Identification Technology along Railway in Complex and Dangerous Mountainous Area Based on Hyperspectral

Abstract In view of the difficulties during implementation of conventional survey methods for railway projects in complex and dangerous mountainous areas, the section from Maoxian to Songpan in Chengdu-Lanzhou Railway with complex terrain and bad climate is selected as the research area to extract hyperspectral remote sensing technology content of the research area. Based on the research of indoor hyperspectral image processing, outdoor rock sample collection and indoor spectrum analysis, establish a suitable feature extraction and lithologic recognition classification method according to the unique spectrum integration feature of hyperspectral remote sensing image. Use the visible short wave infrared hyperspectral data of the latest domestic Gaofen-5 satellite, carry out the lithologic plotting experiment in the key areas of the section from Maoxian to Songpan in Chengdu-Lanzhou Railway, and make accuracy evaluation by selecting a certain way for the mapping results. It provides important thematic information for regional lithologic investigation, and provides strong technical support for selection, survey and design of Chengdu-Lanzhou Railway construction, which is helpful for similar projects.
A Comparative Study of Latent Semantics-based Anchor Word Selection Method for Separable Nonnegative Matrix Factorization

Topic detection is a process used to analyze words in a collection of textual data to determine the topics in the collection, how they relate to each other, and how these topics change from time to time. One of recent topic detection methods is Separable Nonnegative Matrix Factorization (SNMF) which uses the direct method to solve nonnegative matrix factorization using separable assumption. There are three stages in the SNMF method, which are, generating a word co-occurrence matrix, determining anchor words, and recover to get the matrix of word-topics. In this paper, we examine a latent semantics-based method to determine the anchor words for each topics. Our simulation shows that both latent semantic-based methods reach coherence scores comparable to the standard method; however, more efficient in running time.
Evolutionary Ontology Approach for Sinhala Essay Question Generation

Question generation is one of the most important research area related to Natural Language Processing. Though there are several studies have been carried out by following different approaches, most of those studies address only the factoid question generation and lack of studies for the progress of learning process. The research is about generating essay questions as a support for learning in Sinhala language and these questions are up to the levels of Bloom's taxonomy. Therefore, the resulting questions are suitable to assess student knowledge on a particular domain based on different aspects. Content specific question generation is highly encouraged by the proposed approach. Relation extraction is the most important process of the system, for that an evolutionary ontology model is proposed. Extraction of inferences among domain-specific concepts is possible with the use of the evolutionary ontology.
Creating Simple Adversarial Examples for Speech Recognition Deep Neural Networks

The use of deep neural networks for speech recognition and recognizing speech commands continues to grow. This necessitates an understanding of the security risks that goes along with this technology. This paper analyzes the ability to interfere with the performance of neural networks for speech pattern recognition. With the methods proposed herein, it is a simple matter to create adversarial data by overlaying audio of a command at a fairly unnoticeable amplitude. This causes the neural network to lose around 20% accuracy and misidentify commands for other commands with an average to high confidence value. Such an attack is virtually undetectable to the human ear.
Identification of Poison using C4.5 Algorithm

Data mining is the task of discovering interesting patterns from large amounts of data where the data can be stored in databases, data warehouses or other information repositories. This can be viewed as a result of the natural evolution of information technology. The key point is that data mining is the application of these and other AI and statistical techniques to common business problems in a fashion that makes these techniques available to the skilled knowledge worker as well as the trained statistics professional. This paper is classification system for Toxicology using C4.5. Firstly, the input data are randomly partitioned into two independent data, a training data and a test data. And then two third of the data are allocated to the training data and the remaining one third is allocated to the test data. Final step is C4.5 Algorithm Process, the training data is used to derive C4.5 algorithm. Classification Process, test data are used to estimate the accuracy of the classification rules. If the accuracy is considered acceptable the rules can be applied to the classification of new data.
Minimal Filtering Algorithms for Convolutional Neural Networks

In this paper, we present several resource-efficient algorithmic solutions regarding the fully parallel hardware implementation of the basic filtering operation performed in the convolutional layers of convolution neural networks. In fact, these basic operations calculate two inner products of neighboring vectors formed by a sliding time window from the current data stream with an impulse response of the M-tap finite impulse response filter. We used extension of Winograd’s minimal filtering method and applied it to develop fully parallel hardware-oriented algorithms for implementing the basic filtering operation for M = 3, 5, 7, 9, and 11. A fully parallel hardware implementation of the proposed algorithms in each case gives approximately 30% savings in the number of embedded multipliers compared to a fully parallel hardware implementation of the naive calculation methods.
Selling rights: Changes and challenges for rights professionals

Key points The new edition of Selling Rights by Lynette Owen is an opportunity to pause and take stock of the changes and challenges for rights people at the turn of the decade. Revenues accruing to UK publishers from rights sales has increased by over 22% in the past 5 years. The recent and controversial European Union Copyright Directive and Brexit will undoubtedly impact selling rights, but as yet, we do not know what that impact will be. Technology plays a role both in changing the kinds of rights being licensed and in helping the management of rights sales to be carried out more efficiently. It is vital that rights metadata is developed and implemented to ensure improved efficiency in rights management. Rights Management Practice will need to evolve in response to the climate emergency.
Adaptive Selection of Optimal Feature for Object Detection

We propose the Adaptive Selection of Optimal Feature (ASOF) module, a new approach utilizing anchor-free is used for object detection. Most of state-of-the-art object detectors are anchor-base, which use heuristic-guided anchor boxes. Such design is not suitable for detecting objects with different scale and aspect ratios, especially with highly overlapping borders. To address this gap, we propose ASOF, which assigns each instance to the most suitable feature layers by combining pre-defined setting with automatic feature selection. Each instance pass through the forward network to select one FPN layer by manual control, then using the automatic control to revise the former result to achieve the optimal feature map. Through such strategy, we separate most of the overlapping targets. Numerical results suggest that our method has better robustness for mAP than the method with anchor-based detector.
Englischsprachige Publikationen in der Zeitschrift für Sportpsychologie?

Englischsprachige Publikationen in der Zeitschrift für Sportpsychologie?Ein Kommentar aus dem wissenschaftlichen NachwuchsFranziska LautenbachFranziska Lautenbachhttps://orcid.org/0000-0002-0603-6552Universität LeipzigSearch for more papers by this authorPublished Online:April 02, 2020https://doi.org/10.1026/1612-5010/a000287PDFView Full Text ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInReddit SectionsMoreLiteratur Chomsky, N. (2006). Language and mind (3rd ed.). Cambridge: Cambridge University Press. First citation in articleCrossref, Google Scholar Curry, M. J. & Lillis, T. (2019). Unpacking the lore on multilingual scholars publishing in English: A discussion paper. Publications , 7 (2), 27. First citation in articleCrossref, Google Scholar Fazel, I. (2019). Writing for publication as a native speaker: The experiences of two Anglophone novice scholars. In P. Habibie K. Hyland , Novice writers and scholarly publication (pp. 79–95). Palgrave Macmillan, Cham. First citation in articleGoogle Scholar Ferguson, G. (2007). The global spread of English, scientific communication and ESP: Questions of equity, access and domain loss. Ibérica: Revista de la Asociación Europea de Lenguas para Fines Específicos (AELFE) , 13 , 7–38. First citation in articleGoogle Scholar Soloshenko, A. (2014). A contrastive study between English and German: Fear/Angst and guilt/Schuld in decision-making . Expressing Emotions in Corpora, Université de Poitiers, France. https://halshs.archives-ouvertes.fr/halshs-01421461/document First citation in articleGoogle Scholar Stefanowitsch, A. (2004). HAPPINESS in English and German: A metaphorical-pattern analysis. In M. Achard S. Kemmer Eds., Language, Culture, and Mind (pp. 137–149) Stanford: CSLI. First citation in articleGoogle Scholar Bundesministerium für Bildung und Forschung (2019). Wissenschaftlicher Nachwuchs . Verfügbar unter https://www.bmbf.de/de/wissenschaftlicher-nachwuchs-3583.html [29.11.2019] First citation in articleGoogle ScholarFiguresReferencesRelatedDetails Schwerpunktheft: Quo vadis? Die Sprache der Zeitschrift für ­SportpsychologieVolume 27Issue 1Januar 2020ISSN: 1612-5010eISSN: 2190-6300 InformationZeitschrift für Sportpsychologie (2020), 27, pp. 32-34 https://doi.org/10.1026/1612-5010/a000287.© 2020Hogrefe VerlagPDF download
A Graph Network Model for Distributed Learning with Limited Bandwidth Links and Privacy Constraints

In this work, we develop an algorithm based on graph networks to train distributedly a deep learning model. We consider that there are several nodes, in an arbitrary network topology, each one of them having access to a local dataset that, for privacy concerns, cannot be shared with other nodes. We also assume that there are bandwidth constraints, and hence, it may not be affordable sharing the weights of the model with other nodes. Our algorithm makes use of pruning and an autoencoder to train under all these constraints, and our simulations show that it provides a good accuracy, while preserving the privacy of the data and providing a compression of nearly two orders of magnitude in the transmitted bits.
Distribution of scarce resources based on crowd density detection and queuing theory

Abstract This paper makes a reasonable estimate of the number of charging stations to be set in each charging station by detecting the density of people near the charging station. Due to the shortcomings of traditional MCNNs that are currently commonly used for population density estimation, we added dilated convolution to the traditional MCNN and applied queuing theory to this problem. The population density estimation result of the MCNN incorporating the reduced convolution is used as the input of the aligned flow, so that the number of charging piles in each charging station is more reasonably set.
РЕАЛИЗАЦИЯ НАВЫКОВ АВТОКОРРЕКЦИИ ЦЕННОСТНОЙ СФЕРЫ ЛИЧНОСТИ С ПОМОЩЬЮ АРТ-ТЕРАПЕВТИЧЕСКОЙ МЕТОДИКИ «EIDEALING»

&#x0D; &#x0D; &#x0D; The subject of the research is the assessment of effectiveness of the art- therapeutic method “Eidealing”, which is aimed at mastering the strategy and skills of structural self-analysis of the value sphere and the value images transformation, reference points, their hierarchy and event scenarios. The study revealed the important value inquiries at the participants of the method, both in personal and social aspects, and their transformation over the past 2 years. The result of eidling as a process is a picture harmoniously composed and having a personal meaning, which contributes to the correction of psychological state and to motivation for the autonomous search for sustainable personal decisions, to the extension of mindfulness zones and to the psychological auto-correction skill formation. The personal interpretation of the symbols received allows a person to move into the field of informed decisions. The method helps to develop individual cognitive and sensory mechanisms, the natural synesthesia capabilities, and consolidate them using cognitive strategies acquired at the Eidealing. As a result of applying the Eidealing method, the skills have sustainable auto-correctional character and contribute to the general development of cognitive and creative abilities.&#x0D; &#x0D; &#x0D;
Mobile Expert System to Diagnose Diseases and Pests in Tobacco Using Analytical Hierarchy Process (AHP)

Abstract Tobacco, also known as the money tree by the Madurese people, is a second crop after the most cultivated corn. In the process of cultivating tobacco crops, diseases and pests become a threat to the farmers to realize the desire to make a lot of money. Generally, farmers often struggle to distinguish between pests and diseases. This failure will have a significant impact on the treatment, the disease that should be washed with the drug, but because the wrong diagnosis can be solved using insecticide (pest). This research aims to facilitate farmers to obtain information, appropriately diagnose and solutions in overcoming diseases and pests in tobacco. The experts who contributed to this study are agricultural experts in the district office of Sumenep. The method used is Analytical Hierarchy Process (AHP). This method is selected because it can perform consistency tests on data, so it is expected to improve the accuracy of the diagnosis. Based on research that has been done, using 21 data symptoms of diseases and pests, as well as nine types of diseases in tobacco, obtained the accuracy value of the mobile expert system by 65%. This accuracy is achieved by comparing the diagnose results by agricultural experts with the diagnose results using the mobile expert system.
A method for building software credibility metric system based on credible evidence

Abstract In order to comprehensively measuring software credibility, a method for establishing credibility metric system is proposed in this paper. The method first determine the structure of the metric system, analysis the relationship between the established credibility evidence structure and the metric system structure. On the basis of correlation analyses result, a reasonable metric system construction method is established.
Identification of face along with configuration beneath unobstructed ambiance via reflective deep cascaded neural networks

Detection and alignment of face under nonrestricted environment are forcing us to make a lot of effort due to variation in threat, light, and occlusion. Many of the current studies teach us that these two tasks (face alignment and face detection) can be achieved by the deep learning approach. In this research paper, we study and implement a cascaded neural network framework using deep learning to develop the basic correlation among them so that their performance can be enhanced. Our framework particularly uses a streamed or cascaded structure that includes three storage of designing deep convolutional network with great attention and predicts face and landmarks in a nonsmooth to smooth manner. Including under the learning process we suggested a new online hard sample mining strategy without manual sample selection. These methods achieved more accuracy as compared to leading techniques available on making a lot of effort on Face Detection Dataset and Benchmark and WIDER, FACE Benchmark for face detection and also the Annotated Facial Landmark Wild Benchmark for face alignment in real-time performance. We have used modified convolutional neural networks (CNNs) for the detection and alignment of the face in this task. In our method, we have used various CNNs. Each of the networks performs different tasks to get better performance. The output of first network is used as an input to another network, this method results in overall increase of accuracy. Our proposed model has an accuracy of about 95.4%.