Reviewerly/exHarmony huggingface dataset:
LSTM-XL: Attention Enhanced Long-Term Memory for LSTM Cells

Long Short-Term Memory (LSTM) cells, frequently used in state-of-the-art language models, struggle with long sequences of inputs. One major problem in their design is that they try to summarize long-term information into a single vector, which is difficult. The attention mechanism aims to alleviate this problem by accumulating the relevant outputs more efficiently. One very successful attention-based model is the Transformer; but it also has issues with long sentences. As a solution, the latest version of Transformers incorporates recurrence into the model. The success of these recurrent attention-based models inspired us to revise the LSTM cells by incorporating the attention mechanism. Our goal is to improve their long-term memory by attending to past outputs. The main advantage of our proposed approach is that it directly accesses the stored preceding vectors, making it more effective for long sentences. Using this method, we can also avoid the undesired resetting of the long-term vector by the forget gate. We evaluated our new cells on two speech recognition tasks and found that it is more beneficial to use attention inside the cells than after them.
Can a Machine Learning Generated Work Be Protected As Original Under EU Law?

This is a working chapter of the author's PhD thesis work.Recent years have witnessed a boom in algorithms involving cognition-imitating processes. Different to traditional digital editing programs, these machines fed with large datasets develop complex decision-making, bringing about unforeseen creative outcomes. Eventually, it becomes particularly difficult to identify the person responsible for the final creative choices in a work and thus vest that individual with the copyright authorship claim.The overall expected policy contribution of this PhD research is to avoid a situation of discontinuity in copyright law by adequately safeguarding the author’s role in the creative industries. The first two chapters of this PhD turn respectively to the notion of the author and the concept of ownership in copyright law. The chapter I intend to submit instead focuses on the object of protection under copyright law, namely the work. It studies the standard of originality as developed by the CJEU case law and aims to analyse whether such an approach is indeed in line with the objectives of copyright law.More in general, this research critically analyses the problem of allocating authorship in the final creative output of such programs from an EU law perspective. Since most traditions inevitably associate the notion of authorship with a human being, then naturally, the copyright claim in the final creative work eventually also vests with a human being. While this may be entirely in line with the anthropocentric view dominant in EU copyright law, it is certainly challenged by recent developments in machine learning. Creative works generated by machine learning algorithms are often indistinguishable from true authorial works. Moreover, these have indeed been marketed for significant prices. The authorship analysis in EU copyright law puts under the limelight the romantic figure of the author, whereby the work, the object of copyright protection itself, is protected only by virtue of being the “author’s own intellectual creation” (emphasis added). Such an analysis prejudices the objective assessment of the work since even if a work stands for an original expression, if it lacks the stamp of personal touch of the author it would not be considered protected copyright work and would thus fall in the public domain.
Research on manipulator motion control system based on preset performance control algorithm

In order to solve the defects of low control accuracy and long response time of traditional manipulator motion control system, this paper designs a manipulator motion control system based on preset performance control algorithm. The system is mainly divided into hardware and software. The hardware is mainly composed of positioning module, trajectory planning module, driving module, communication module, power supply module and control module. On the basis of the hardware design, the software design is carried out, the kinematics model of the manipulator is constructed, and the preset performance function and error transformation function are introduced to reduce the motion control error of the manipulator step by step, so as to improve the comprehensive performance of the system. The simulation results show that the system designed in this paper has high control accuracy, short response time and reliability.
Application Exploration of Immersive Teaching Method in Medical Experiment Teaching

With the comprehensive development of science technology and information technology, more intelligent enjoyment has been provided to our life, including education. For example, multimedia teaching method, which makes the boring class lively, flexible and interesting, thus improving the teaching efficiency. However, medical experimental teaching is still dominated by traditional teaching. In view of the problems such as insufficient supply of experimental equipment, high cost of experimental supplies and low learning status of students in traditional teaching, this paper proposes an immersive medical experiment teaching method based on immersive interaction design in college students' medical experiment teaching, so as to optimize students' learning experience.
Hover-Based Reachability Technique for Executing Single-Touch Gesture on Smartphone

When operating a smartphone with only one hand, users typically use their thumb; however, there may be areas on the screen that the thumb cannot reach. Several reachability techniques have been developed to enable users to access such areas, but many of these techniques are designed to enable users to perform only a tap gesture, so other single-touch gestures such as long-tap, double-tap, swipe, and drag cannot be performed. To enable all single-touch gestures for unreachable areas, we designed a hover-based reachability technique that uses a cursor. In addition, we conducted a pilot study to compare user performance with our technique and that of existing techniques, One-Handed Mode (OM) and Event Forward Cursor (EC). The results showed that our technique was faster than EC, which uses the same cursor technique as ours, except for the double-tap gesture when the target was small. However, our technique was slower than OM, which is the technique that shrinks the entire screen. Lastly, we discuss possible improvements of our technique on the basis of the results.
Digital Editions as Distant Supervision for Layout Analysis of Printed Books

Archivists, textual scholars, and historians often produce digital editions of historical documents. Using markup schemes such as those of the Text Encoding Initiative and EpiDoc, these digital editions often record documents' semantic regions (such as notes and figures) and physical features (such as page and line breaks) as well as transcribing their textual content. We describe methods for exploiting this semantic markup as distant supervision for training and evaluating layout analysis models. In experiments with several model architectures on the half-million pages of the Deutsches Textarchiv (DTA), we find a high correlation of these region-level evaluation methods with pixel-level and word-level metrics. We discuss the possibilities for improving accuracy with self-training and the ability of models trained on the DTA to generalize to other historical printed books.
Dynamic time warping used for an automated first-break picking algorithm

PreviousNext No AccessFirst International Meeting for Applied Geoscience & Energy Expanded AbstractsDynamic time warping used for an automated first-break picking algorithmAuthors: Akshay VishwakarmaSachin KumarAkshay VishwakarmaOil and Natural Gas Corporation LimitedSearch for more papers by this author and Sachin KumarOil and Natural Gas Corporation LimitedSearch for more papers by this authorhttps://doi.org/10.1190/segam2021-3594904.1 SectionsSupplemental MaterialAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractFirst Break (FB) picking is crucial for seismic data processing. With the advancement of technology, algorithms have also evolved. Scientists are constantly developing robust and computationally cheaper methods to handle large datasets. In this study, a method is proposed to use Dynamic Time Warping (DTW) for correctly picking FB of seismic data.Initially, an energy parameter ‘M’ for near offset traces is computed which ranges from 0.5-1. Higher the value of M, better is the ‘health’ of trace. FB is picked on few ‘healthier’ traces first using conventional algorithm; later FB for all the remaining traces is searched in a very small window, using DTW.As the conventional algorithms have been picking FB in entire trace length, they take more computation time, whereas in the proposed method FB is being searched in a small window which reduces computation time. The method was tested on real data set and produces promising results.Keywords: acquisition, processing, signal processingPermalink: https://doi.org/10.1190/segam2021-3594904.1FiguresReferencesRelatedDetails First International Meeting for Applied Geoscience & Energy Expanded AbstractsISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2021 Pages: 3561 publication data© 2021 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 01 Sep 2021 CITATION INFORMATION Akshay Vishwakarma and Sachin Kumar, (2021), "Dynamic time warping used for an automated first-break picking algorithm," SEG Technical Program Expanded Abstracts : 2644-2648. https://doi.org/10.1190/segam2021-3594904.1 Plain-Language Summary Keywordsacquisitionprocessingsignal processingPDF DownloadLoading ...
A Video Classification Strategy Based on Content Recognition in English Teaching

To enrich the function of foreign language video teaching system based on streaming media technology, this paper introduces content recognition technology based on clustering algorithm. This paper proposes an improved k-means clustering algorithm for video interest permutation and combination to identify videos with high interest of students and realize push management. Then, it introduces the practice and development of video teaching system in English teaching, including MVC design mode, system function and system structure design. The key technologies involved in the system, such as data security, permission allocation, database development and web application, are described in detail, and the implementation methods of each functional module of the system are given. Finally, the specific functional requirements of the teaching system are tested and analyzed from the aspects of registration, login and live video, which proves the feasibility and effectiveness of the scheme.
Autonomous Sailing Boat

Among several self-sufficient surface vehicles, cruising mechanical autonomy is a successful innovation for lengthy haul missions and semi-persistent nearness in the seas. Self-governance of these vehicles as far as vitality will be accomplished by inexhaustible sunlight-based and wind power sources. The fundamental commitment of this paper is to design an autonomous sailing robot that explores the water surface and sends live video footage to the base station, the identification of the behavior of sailing, human–robot interaction, robot control, estimation of position, and obstacle detection. Major key applications for this vessel are the assessment of the nation’s ocean boundary and fishermen’s activity. The project presented is with a robotic vehicle which works automatically and manually and controls the moving object in the water the robot will capture and sends the information to the base system which helps to keep track of fisherman who cross the nation’s boundary unknowingly. Here, Arduino processor is inbuilt with interfacing a wireless Raspberry Pi camera. The servo motors are used to actuate in the ocean and provide movement in all directions.
Object tracking algorithm based on fusion of SiamFC and Feature Pyramid Network

Aiming at the multi-scale problem of full convolutional siamese network SiamFC, this paper proposes a target tracking algorithm of siamese network based on the fusion of feature pyramid network. In the network template and detection branch, the up-sampling feature fusion method similar to feature pyramid network is adopted to enable the algorithm to track on multiple scales and improve the network's discrimination of small target objects. At the same time, VggNet-16 network is used to extract the shallow and deep features of the image, and adaptive fusion of the two features. Experimental results on OTB2015 and VOT2018 datasets show that, compared with SiamFC, the algorithm proposed in this paper can deal with scale changes and target drift, achieve better accuracy and success rate, and improve the speed of model tracking.
Robust Real-Time Human Action Detection through the Fusion of 3D and 2D CNN

Recent approaches for human action detection often rely on appearance and optical flow networks for frame-level detections before linking them to form action tubes. However, they achieve unsatisfactory performance in real-time due to their huge computational complexity and large parameter usage during training. In this paper, we design and implement a unified end-to-end convolutional neural network (CNN) architecture that consists of two branches, extracting both spatial and temporal information concurrently before predicting bounding boxes and action probabilities from video clips. We also design a novel mechanism that exploits the inter-channel dependencies for an effective fusion of features from the branches. Specifically, we propose a Channel Fusion and Relation-Global Attention (CFRGA) module to aggregate the two features smoothly and model their inter-channel dependencies by considering their global scope structural relation information when inferring attention. We conduct experiments on the untrimmed video dataset, UCF101-24, and achieved impressive results in frame-mAP and video-mAP. The experimental results show that our channel fusion and relation-global attention module contributes to its good performance.
Explore Hierarchical Relations Reasoning and Global Information Aggregation

Existing Graph Convolution Networks (GCNs) mainly explore the depth structure with focusing on aggregating information from the local 1-hop neighbor, leading to the common issues of over-smoothing and gradient vanishing, and further hurting the model performance on downstream tasks. To alleviate these deficiencies, we tentatively explore the width of the graph structure and propose Wider Graph Convolution Network (WGCN), targeting to reason the hierarchical relations and aggregate the global information from multi-hops dilated neighbor in a wide but shallow framework. Meanwhile, Dynamic Graph Convolution (DGC) is adopted via the masking and attention mechanisms to distinguish and re-weight informative neighborhood nodes, for stabilizing the model optimization. We demonstrate the effectiveness of WGCN on three popular tasks, including Document classification, Scene text detection and Point cloud segmentation. On the basis of achieving state-of-the-art performance among presented tasks, we verify that exploration through width expansion of GCNs is more effective than stacking more layers for model improvements.
Deep Learning Approach for Object Features Detection

Object detection and identification system basically find real-world objects in a digital image or any video. In order to detect an object in any video or digital image, few components are necessary, which are a model database, a feature detector, a hypothesizer and a hypothesizer verifier. This paper shows several techniques that are used for object detection, such as localization of an object, categorization of an object, extraction of features, showing information and so on, in images and videos. The document also stresses the idea of a possible solution for multi-class object detection. This paper basically relates to the researchers who are working on this particular domain. Object detection is a field distributing into a lot of enterprises, along with its uses extended from personal security to a healthy working environment. This field has its applications in various categories of image processing, such as image retrieval, security, observation, computerized vehicle systems and machine investigation. The outcomes of this particular system are inestimable on the basis of future use cases for this particular field.
Evaluation of Error Probability of Classification Based on the Analysis of the Bayes Code: Extension and Example

Suppose that we have two training sequences generated by parametrized distributions <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P_{\theta}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P_{\varepsilon^{*}}$</tex> , where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\theta$</tex> * and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\xi^{*}$</tex> are unknown true parameters. Given training sequences, we study the problem of classifying whether a test sequence was generated according to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P_{\theta}$</tex> * or <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P_{\xi^{*}}$</tex> . This problem can be thought of as a hypothesis testing problem and our aim is to analyze the weighted sum of type-I and type-II error probabilities. Utilizing the analysis of the codeword lengths of the Bayes code, our previous study derived more refined bounds on the error probability than known previously. However, our previous study had the following deficiencies: i) the prior distributions of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\theta$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\xi$</tex> are the same; ii) the prior distributions of two hypotheses are uniform; iii) no numerical calculation at finite blocklength. This study solves these problems. We remove the restrictions i) and ii) and derive more general results than obtained previously. To deal with problem iii), we perform a numerical calculation for a concrete model.
THE CORRELATION BETWEEN TEST TAKING STRATEGIES AND READING COMPREHENSION SCORE IN READING TEST

Abstract In order to succeed in reading comprehension test, the learners should choose the most appropriate strategy dealing with reading test. Moreover, by using the suitable strategy while completing reading test, it is believed that it will help the learners to accomplish the test easily. The purpose of this quantitative study was to find out the correlation between test taking strategies and reading comprehension scores in reading test. Another aim of this study was examining the kind of test taking strategies utilized by EFL learners in accomplishing reading test. The data of this study were gained through online questionnaire toward 90 participants of junior and senior university students in one of University in Surabaya. The result showed that the EFL learners mostly utilized metacognitive strategies (mean score 3.68) than cognitive strategies (mean score 3.59) while completing reading comprehension test. The result also revealed that test taking strategies and reading comprehension scores was positively correlated. The significance level of correlation was r = 0.251, and it was categorized as low correlation. Keywords: test taking strategies, reading comprehension test, reading comprehension score.
Intelligent Education Visualization System Based on Social Network Analysis

In order to overcome the limitations of traditional education, such as poor interaction, unable to carry out distance teaching, difficult to teach students in accordance with their aptitude and so on, this paper puts forward a new construction method of intelligent education visualization system based on social network analysis. This method is different from the traditional way to study wisdom education. Firstly, it analyzes and summarizes the literature in the current literature database, and uses the social network analysis method to analyze and research the wisdom education from multiple dimensions, including high-frequency characters, network density, distance, classification, etc., so as to comprehensively grasp the characteristics and connotation of intelligent education. Based on this, the visualization system also constructs the design framework of intuitive and clear intelligent education system from four aspects, namely, smart technology realization goals, objects and methods, smart education realization methods, teaching organization implementing smart education, and smart education realization forms. The experimental results show that the system has fully developed the advanced and intelligent technology and can analyze the hot topic of intelligent education from the social network structure, laying the foundation for the construction of intelligent education platform and the development of intelligent education.
SEGAR: Knowledge Graph Augmented Session-Based Recommendation

Predicting the next interaction item in the session-based recommendation system is an emerging and challenging research task. Existing studies model a session as a sequence or graph of items for predicting the next-click item. However, these approaches ignore the global graph-based relations between the session items and the local neighborhood-based item relevance to external knowledge bases, thus fail to encode rich semantic knowledge between items for achieving comprehensive and accurate recommendations. To overcome the current shortcomings, we proposed a novel knowledge graph augmented model called SEGAR (Knowledge Graph Augmented Session-based Recommendation) by leveraging graph convolutional network and knowledge graph attention network. When integrating the static local attributes and the knowledge about all the last session items encoded in their k-hop neighborhoods in the knowledge graph, SEGAR models all sessions as a session graph and captures the dynamic global temporal and popularity-aware information from the session context. The model encodes a comprehensive semantic knowledge between items for achieving more accurate recommendation. Extensive experiments on two benchmark datasets show that SEGAR outperforms four state-of-the-art models on the session-based recommendation task.