Reviewerly/exHarmony huggingface dataset:
Research on the Application of BERT in Mongolian-Chinese Neural Machine Translation

research-article Research on the Application of BERT in Mongolian-Chinese Neural Machine Translation Share on Authors: Xiu Zhi Inner Mongolia Normal University, China Inner Mongolia Normal University, ChinaView Profile , Siriguleng Wang Inner Mongolia Normal University, China Inner Mongolia Normal University, ChinaView Profile Authors Info & Claims ICMLC 2021: 2021 13th International Conference on Machine Learning and ComputingFebruary 2021 Pages 404–409https://doi.org/10.1145/3457682.3457744Published:26 February 2021 0citation22DownloadsMetricsTotal Citations0Total Downloads22Last 12 Months22Last 6 weeks7 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Position paper

Most of today's graph analytics systems model static graphs and do not support business use cases that require the ability to: (i) query the dynamic graph data for a time-evolving system, (ii) carry out investigations on its historical evolution, and (iii) audit past business decisions made with potentially stale or incorrect data. This position paper presents our vision for bi-temporal dynamic graph analytics, and sketches a design for a system that efficiently supports these requirements.
Face Recognition in Unconstrained Environment Using Deep Learning

Face recognition as a biometric is used in many applications as it does not require much cooperation from the user. The existing face recognition techniques give good results in constrained environment but their performance in unconstrained environment degrades as the face images captured vary in pose, illumination and resolution. The present study tries to develop a robust face recognition model in unconstrained environment using deep learning. To achieve this, the first step is to create a novel face dataset by collecting images of celebrities from Internet and images captured from real-world surveillance videos. Deep neural network (DNN) available in OpenCV is used for face detection. Second, Deep convolution neural network model with eight convolution layers was developed and trained on this dataset. The proposed model achieved an accuracy of 99.75%, whereas fine-tuned VGGFace achieved 99.6% and Alexnet trained from scratch on the dataset achieved 98.6% on the test data. The proposed model has 5,671,824 parameters as compared to VGGFace fine-tuned with 40,487,824 and Alexnet with 28,159,832 parameters. The size of the proposed model is 68 MB in comparison with VGGFace and Alexnet having 293 and 338 MB, respectively. The impact of batch size hyperparameter on the model was also studied, followed by comparison between RMSProp and Adam optimizers. The results demonstrate that with the increase in batch size, the performance improves and Adam optimizer outperforms RMSProp.
For Movie Reviews, A Sentiment Analysis using Long Short Term Memory Networks

The sentimental or opinion details in web pages rises in parallel with the development of web 2.0. Capturing such sentimental information from the web 2.0, is a challenging task. The problem is addressed in this paper by introducing a new paradigm for implementing Long Short-Term Memory Networks. Word2Vec Model is used to transform sentimental contents into input embedding vectors. These input embedding vectors are then fed into the LSTM, which predicts the content's sentiment details. The suggested approach is put to the test on two well-known datasets: IMDB and Amazon Analysis. In the IMDB dataset, the suggested approach achieves an accuracy of 0.87, and in the Amazon analysis dataset, it achieves an accuracy of 0.89.
On the Computational Power of Convolution Pooling: A Theoretical Approach for Deep Learning

Convolutional neural networks (CNNs) have been widely used for image analysis and recognition. For example, LeNet-5 is a 7-layer convectional neural network, which can attain more than 99% test accuracy for classification of handwritten digits. CNNs repeats convolution and pooling operations alternately. However, the computational capability of such operations is not clear. We are curious to know a class of problems that can be solved by CNNs. As a formal approach for this task, we introduce a theoretical parallel computational model of CNNs that we call the convolution-pooling machine. It captures the essence of convolution and pooling operations, and application of non-linear activation functions performed in CNNs. In this paper, we assume the convolution-pooling machine operating on 1-dimensional arrays for simplicity, and focus on the problem of classification of inputs by the distance of two feature points. More specifically, we will design a convolution-pooling machine solving the problem D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> (k≥1), a problem to determine if the distance of the two 1's is at most k or not. For designing the convolution-pooling machine solving the problem D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> , we generate a mixed-integer linear programming problem (MILP) with constraints and objective functions. We have solved the generated linear programming problem for each D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> (1≤k≤128) by Gurobi optimizer, a commercial MILP solver. We succeeded in finding a solution for all D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> (1 ≤ k ≤ 128) and designing the convolution-pooling machine for solving them. This fact indicates that convolution and pooling operations in CNNs may have the computational capability of classification by the distance of feature points.
Towards Query Pricing on Incomplete Data (Extended Abstract)

As data markets have started to receive much attention from both industry and academia, how to price the tradable data is an indispensable problem. Pricing incomplete data is more practical and challenging, due to the pervasiveness of incomplete data. In this paper, we explore the pricing problem for queries over incomplete data. We propose a sophisticated pricing mechanism, termed as iDBPricer, which considers a series of essential factors, including the data contribution/usage, data completeness, and query quality. We present two novel price functions, namely, the usage and completeness-aware price function (UCA price for short) and the quality, usage, and completeness-aware price function (QUCA price for short). Moreover, we develop efficient algorithms for deriving the query prices. Extensive experiments using both real and benchmark datasets confirm the superiority of iDBPricer to the state-of-the-art price functions.
An algorithm for detecting low-contrast objects in different target environments

We propose an algorithm for detecting low-contrast objects in different target environments for application in an optoelectronic system. The algorithm makes it possible to detect low-contrast objects in a complex environment with account of relative movement of the camera and the object in real time.
Web-based E-Commerce Products Grouping

Abstract E-Commerce is a group of technologies, applications, and businesses that connect companies or individuals as consumers to conduct electronic transactions, exchange of goods or information through the internet or television, www, or other computer networks. The rapid growth of E-Commerce is caused by the many conveniences for buyers and sellers. This can be seen by the increasing number of E-Commerce sites such as Bukalapak.com, Tokopedia.com, and so on. The buyers want to get products with the lowest prices, the best quality, and from trusted sellers. This encourages them to search for and compare the same products from one store to another, even between different sites. Variables that they compare vary. Some are based on price, number of reviews, and so on. But it is troublesome when they have to look for the same product from another site. This study used the product name as a reference in grouping the same product from 5 different sites. The algorithm used to group online shop products was Jaro-Winkler Distance. The study was to create a system that can group products from 5 different sites with the highest level of similarity product as the output. From the 20 times of the search tests, the accuracy of the online shop product grouping with Jaro-Winkler Distance was 81.27%.
Euler Meets GPU: Practical Graph Algorithms with Theoretical Guarantees

The Euler tour technique is a classical tool for designing parallel graph algorithms, originally proposed for the PRAM model. We ask whether it can be adapted to run efficiently on GPU. We focus on two established applications of the technique: (1) the problem of finding lowest common ancestors (LCA) of pairs of nodes in trees, and (2) the problem of finding bridges in undirected graphs. In our experiments, we compare theoretically optimal algorithms using the Euler tour technique against simpler heuristics supposed to perform particularly well on typical instances. We show that the Euler tour-based algorithms not only fulfill their theoretical promises and outperform practical heuristics on hard instances, but also perform on par with them on easy instances.
Rural Meteorological Video Service Platform Based on Particle Swarm Optimization Algorithm

Meteorological film and television in the development of popular science work, are often faced with a wide range of service groups, complex and diverse service needs. Among them, because the disaster prevention system is relatively weak, the disaster prevention knowledge is relatively lacking, and the production and life of rural groups are greatly affected by meteorological factors, therefore, the rural groups have more urgent service needs for the disaster prevention and popular science work of meteorological film and television. How to protect the safety of life and property of rural groups, help rural groups to effectively prevent disasters and avoid risks, reduce the losses caused by meteorological disasters; at the same time, provide meteorological services for the production and life of rural groups, so that rural groups can get better economic benefits, are two problems facing meteorological film and television services. According to the actual situation of Guangxi, this paper puts forward several working ideas of providing meteorological services for rural people, in order to improve the ability of meteorological film and television to serve agriculture, meet the needs of rural people for meteorological services, and promote the sustainable development of society and economy.
CAV and emerging technologies: Some considerations

This chapter explores the differences among connected, automated, and autonomous vehicles, primarily focusing on infrastructure readiness and situational awareness necessary to realize the safety, mobility, environment, and economic development benefits. This chapter demonstrates that although technology adoptions have been in vogue for several decades, a major transformation has been happening during the past decade with a conglomeration of disciplines and convergence of knowledge-base from interdisciplinary work of many other transportation disciplines, technology applications in particular. This chapter also describes the six levels of automation, as presented by the Society of Automotive Engineers. Finally, it delves deeper into the prerequisites needed to deploy connected and automated vehicles and emerging technologies.
Design of the Keyboard for the Heterophony Digital Piano

Heterophony is the playing of the same melody in more than one way at the same time. In this paper, By improving the sensitivity and touch of the keyboard, which is a representative input device (generating performance data) of a digital musical instrument and a key part of a piano, we proceed with the initial design of parts to realize the emotional expression of an oriental musical instrument composed of heterophony, assemble, generate tolerances. Introducing the Smart Keyboard Module as an input device capable of expressing emotions of various musical instruments around the world by continuously modifying the part of the machine sound generation.
Identity alignment algorithm across social networks based on attention mechanism

In recent years, more and more business scenarios need accurate cross social network user identity alignment. Existing methods directly splice user attributes and network structure features in the case of sparse network structure, which affects the accuracy of alignment. At the same time, the semantic expression deviation of the same user attribute in different networks further increases the challenge of user identity alignment. This paper proposes a cross social network user identity alignment algorithm based on attention mechanism. Firstly, to learn distinguishing semantic features, a feature extraction method of user attribute text based on attention mechanism is designed. It uses Highway network to dynamically balance the word and character embedding of attribute text, extracts different granular fusion semantic information through convolution neural network with three convolution kernels and uses attention mechanism to give higher weight to key semantics to learn distinguishing semantic features. Secondly, to strengthen the attribute information and weaken the influence of network structure sparsity, a new fusion loss method is proposed. It uses Cosine and cross-entropy loss for attribute feature and fusion feature of attribute and network structure respectively and carries out weighted fusion calculation. The experimental results show that the accuracy of this method can reach 94.95% and the F1 score can reach 92.52% on the Aminer-LinkedIn social network matched data set, which is better than other algorithms.
Improvement of Spinning Machine Monitoring Interface Based on Eye Tracking Experiment

In this paper, the effectiveness of human-computer interaction interface design was analysed. Under the background of intelligent manufacturing 2025, the spinning industry urgently needs to realize intelligence. A good user interface can better adapt to users’ requirement. For the application scenarios of quality monitoring of the spinning production line, the monitoring interface of the spinning machine was studied, and two schemes were proposed. To compare the two proposed interfaces, usability evaluations based on eye tracking were conducted. A series of experimental verifications such as questionnaire and task model comparison experiments were conducted. The conclusion was obtained through SPSS analysis data. Through a series of experimental verifications, the experimental results verify the effectiveness of the improved design. This research paradigm can provide a reference for the improved design of the interface.
Sentiment Classification Based on Integration of Rule-Based Method and Machine Learning with Image Sentiment Recognition

Emoticons and images are often left out in traditional researches on sentiment classification. On the basis of image de-duplication, an image sentiment dictionary is constructed by correlation recognition between emoticons/images and texts. Afterwards verified image sentiment features and other rule-based features are transformed and integrated into basic feature templates. Comparison among multiple classifiers is carried out. Experimental results show that integrated features and the addition of image sentiment enhance our model’s performance, especially when dealing with text containing dialect.
Precise Design Research of Regional Cultural and Creative Products Under Big Data

Through the rapid and accurate information processing, analysis and extraction of big data technology, the integration of big data and cultural and creative product brand design, in-depth analysis of the needs of cultural and creative product design with big data technology, mining effective regional cultural elements, and assisting cultural Creative products carry out precise optimization and upgrading in terms of design positioning, product analysis, design strategy, etc., clarify the brand strategy direction of regional elements and cultural and creative product design, promote the enhancement of brand value, and realize the conversion of new and old kinetic energy integrating regional cultural elements and the times, to promote the development of the regional economy, and strengthen the internal power support of cultural confidence.
The Craftsperson, Tacit Knowledge, and Digital Embodiment

We are increasingly living in a world of digital interfaces, computer screens for work, VR headsets for entertainment, displays in cars and on coffee machines. Computers, robotics and AI are beginning to present new ways and possibilities that can forge research and manufacture in novel directions, establishing alternative styles of creative expression. However, these machines are still clumsy at performing complex and fine motor tasks: for example, how to perform a surgical manoeuvre, throw a pot, weave a garment, or check the safety of food. Can robots become fine needleworkers, surgeons, painters or sculptors? Of course, we have machines to replicate tasks. So far, it has been the input of the artist as author to write the script and coding, and who masterminds the actions of the robot, which all in all, the robot is still traceable back to DNA of the maker.
BIM Technology Based on Cloud Computing in Urban Design

Information technology under the demand of early morning, through BIM , can effectively improve the work efficiency and focus on the development of big data, analyze and explore the application results of information technology in urban planning.
Research on Automation Strategy of Coq

Formal verification technology based on theorem proving assistant is the only way to strictly guarantee the correctness of the program system. The theorem proof assistant expresses the theorems and the proof process in high-order logic as high-level strategies, but human experts must manually construct proofs by inputting the strategies into the proof assistant. In this article, we propose the use of machine learning and concurrent search methods to improve the degree of automation of theorem proofs, which can help theorem assistants find suitable proof strategies faster and reduce the workload of constructing proofs. Our solution can generate effective tactics and can be used to prove theorems by automated methods more quickly.
PatiCare: Patient Health Care Monitoring System

In the medical system, it is crucial to hold patient past health records to give the correct treatment to the patient and ensure the medical practitioner's physical health safety. The `PatiCare' patient healthcare monitoring system is a promising alternative to the conventional method of checking the human body and checking the patient health record. Despite the ongoing investigation, an outbreak of a pan-demic disease called COVID-19 it creates a situation involving exposure to a danger to medical practitioners. In this paper, we implement a Patient Care Monitoring system called PatiCare to reduce viral diseases affection risk. We use IoT and WEB service for communication with users or patients and take his past health record and a doctor can check remotely to his patient. Our implemented prototype gathers biomedical and orientation information through sensors and re-serves it in a WEB Server. A doctor or a medical practitioner is able to assign medicine remotely and monitor patient instant health.
Automating Trust with the Blockchain? A Critical Investigation of “Blockchain 2.0” Cultures

This article discusses the cultural conceptions of trust underpinning the experimentation of blockchain startup applications beyond the financial sector. Based on qualitative research undertaken in the context of the so-called “Blockchain 2.0” scene, we show how a peculiar conception of trust, which blends the libertarian views of blockchain inventors with the neoliberal culture of competition and meritocracy that is typical of the startup world, underpins these implementations. As a result, we argue that “Blockchain 2.0” entrepreneurs ultimately fail to recognize the eminently social nature of the trust-building process. They emerge from our observation as unable to comprehend the extent to which the implementation of blockchain in a societal (i.e., not purely financial) context cannot do away with considerations about what kind of “social” the technology intervenes within, and find difficult to effectively conceive of how this technology embeds in existing social relations and power structures.
LibGuides: Alma: About Primo VE - The New Library Search Engine

This guide provides information about Alma ILS and Primo VE, the new library catalog, for faculty and staff. This guide includes training and information on how to replace broken links, searching the catalog, and more!
MAIN: Multi-Attention Instance Network for video segmentation

Instance-level video segmentation requires a solid integration of spatial and temporal information.However, current methods rely mostly on domain-specific information (online learning) to produce accurate instance-level segmentations.We propose a novel approach that relies exclusively on the integration of generic spatio-temporal attention cues.Our strategy, named Multi-Attention Instance Network (MAIN), overcomes challenging segmentation scenarios over arbitrary videos without modelling sequence-or instance-specific knowledge.We design MAIN to segment multiple instances in a single forward pass, and optimize it with a novel loss function that favors class agnostic predictions and assigns instance-specific penalties.We achieve state-of-the-art performance on the challenging Youtube-VOS dataset and benchmark, improving the unseen Jaccard and F-Metric by 6.8% and 12.7% respectively, while operating at real-time (30.3 FPS).
Video Frame Rate Doubling Using Generative Adversarial Networks

This work explores a Deep Neural Network based approach to double the frame rate of a video by adding synthetically generated frames between two consecutive frames in the original video. The Neural Network is a Generative Adversarial Network that consumes two consecutive frames from the original video, interpolates them into one image, and generates a synthetic intermediate frame. Apart from the Neural Networks, Statistical and Image Processing techniques have been used to further enhance the generated frame. The final generated frame is an amalgamation of the interpolated frame and generated frame which are merged through masking. This has various applications including but not limited to remastering legacy videos with low frame rates to improve their quality. Finally, this work compares the results of the above technique against various pre-existing techniques.
Construction of immersive architectural wisdom guiding environment based on virtual reality

Construction of immersive architectural wisdom guiding environment based on virtual reality is studied in this paper. Emerging development of the computer smart systems have provided the engineers a novel solution for the platform construction. Network virtualization is currently the most unclear and controversial concept in the industry regarding the definition of virtualization subdivisions. To improve the current study, we use the VR system to implement the platform. The wisdom guiding environment is built through the virtual data modelling and the interactive connections. The platform is implemented through the software. The test on the data analysis accuracy and the interface optimization is conducted.