Reviewerly/exHarmony huggingface dataset:
Online Activities in Tsukuba Challenge 2020

This paper describes the online activities of Tsukuba Challenge 2020. Due to the spread of coronavirus (COVID-19 pandemic), the experiments where all teams gathered in Tsukuba were canceled. As an alternative, we proposed the following activities: navigation experiments at each team’s local site, provide and use of datasets, and use and development of simulators. In addition, to keep the community connections, an orientation meeting, lightning talks, and a symposium were held online.
An optimal video summarization of surveillance systems using LFOB-COA with deep features and RBLSTM

The visual data attained from surveillance single-camera or multi-view camera networks is exponentially increasing every day. Identifying the important shots in the presented video which faithfully signify the original video is the major task in video summarization. For executing efficient video summarization of the surveillance systems, optimization algorithm like LFOB-COA is proposed in this paper. Data collection, pre-processing, deep feature extraction (FE), shot segmentation JSFCM, classification using Rectified Linear Unit activated BLSTM, and LFOB-COA are the proposed method’s five steps. Finally a post-processing step is utilized. For recognizing the proposed method’s effectiveness, the results are then contrasted with the existent methods.
RETRACTED: Design of fire Design of Fire Detection System Based on Artificial Intelligence Technology

Abstract In this era of rapid development of network and technology, data has become the most important part of companies and people. In fact, the software and system series are just the framework for storing data, and real data occupies an important position in the entire communication. This paper focuses on data mining and management models of public data resources. Starting from how to mine useful information from public data resources and how to manage such data, it puts forward several classifications of big data management models and their respective advantages.
Developement of Digital Twin for Protection Adequacy Evaluation of IEC61850 based Digital Substations

Digitalization of substations is universally expanding through digital transformation in existing and new substations. Accordingly, to improve the reliability of operation in digital substations, the requirement is not only expanded for the protection adequacy evaluation on each protection IED but also the constant evaluation of the protection adequacy of the entire digital substation. In general, there is a problem that the state change of the power system is not reflected in protection systems of substations because the protection systems operate without evaluating the protection adequacy of the entire substation after determining the correct value of the protection IEDs. The proposed method in this paper enables the constant evaluation of protection adequacy without additional facilities by applying digital twin technology. In addition, the interoperability of the digital twin model is secured by developing the information exchange between the devices of the digital twin model based on IEC61850.
Embarrassing Product, Image Type, and Personal Pronoun: The Mediating Effect of Body Imagery

Consumers often feel embarrassed when buying products like condoms, hemorrhoid cream, and beriberi cream in crowded pharmacies. There is an interesting phenomenon in life: Some beriberi creams use the images of a “real foot”, while others use the images of a “cartoon foot.” Imagine if a young woman needed to go to a retail store for beriberi cream that would embarrass her, she would choose a “real foot image” or a “cartoon foot image” beriberi cream? It has been shown that the embarrassment of these products has a strong negative impact on consumer buying behavior. Previous researches have explored how changing packaging elements of embarrassing products (e.g., color/design/image placement) can effectively reduce consumer embarrassment. However, few have examined the impact of different image types of embarrassing product packaging (artificial vs. natural) with embarrassment. Therefore, this research explores the effect of image types (artificial vs. natural) on consumers’ willingness to purchase embarrassing products and reveals the mechanisms of the underlying effects. The results show that natural images can lead to lower purchase intention of embarrassing products when the advertisement uses first-person pronouns due to the mediating role played by negative body imagery. However, there is no significant difference in purchase intention between different image types in the third-person pronouns. Finally, this paper discusses its contributions and limitations.
PREFACE

Abstract The First Biennial International Conference on Acoustic and Vibration (ANV 2020) is the first international conference held every two years on a regular basis in Indonesia. The scope of the conference includes acoustics, vibration, and their applications. The ANV 2020 was originally set in Denpasar, Bali. However, due to the COVID-19 pandemic with increasing travel restrictions from Indonesia authority and around the world, the Organizing Committee decided to move to all virtual platforms on November 23 rd – 24 th 2020. We had full support from DPTSI, our university directorate who is in charge of allocating necessary internet bandwidth to virtual room served as main for plenary with 500 attendees’ capacity and two breakouts for parallel presentations. In the plenary session, there were 5 invited distinguished speakers namely Profs. Lily M. Wang of University of Nebraska-Lincoln, Weidong Zhu of University of Maryland, Valtteri Hongisto of Helsinki University of Technology; and two of our keynote speakers were from industry Mr. Mahesh Kavinda of Ganther Instruments GmbH, Mr. Senthil Vel RS of SKF Group. The time for the keynote was about 40 minutes for presentation and 20 minutes for discussion. For parallel sessions, each presentation had 15 minutes including Q&amp;A. The audience had two ways to ask a question to the presenter, either raise hand then the moderator will unmute to allow direct interaction, or by typing the question in the chatbox and the presenter answered verbally. There were lively discussions at both in plenary and parallel sessions. We arranged the time slot accordingly to suit the presenters’ time zone relative to Jakarta time zone, from US it was around after dinner time, and about afternoon in the EU. The success and prosperity of the conference is reflected at a high level of the paper received, and the attendees at this conference were numerous. The ANV 2020 was a communication platform for exchanging ideas on acoustic and vibration with more than 300 participants, either as presenters or as attendees at the event. They were from Indonesia, Malaysia, India, Austria, Bangladesh, Morocco, France, Japan, and more; 11 countries in total. We would like to express our gratitude to all committees who have worked hard so that this event can run successfully. We also would like to thank for our keynote speakers, participants, and sponsors as well as to the reviewers that has worked very hard in reviewing papers and making valuable suggestions for the authors. ANV 2020 was organized by IoT Research Center and Defense Technology, Directorate of Research and Community Service in cooperation with the Vibration and Acoustic Laboratory of Institut Teknologi Sepuluh Nopember Surabaya (ITS). The theme of the Conference is “Sound of Indonesia”. The conference also supports the International Year of Sound 2020. We wish all the attendees of ANV 2020 and their respective community in safety and healthy. We look forwards to your participation in the forthcoming ANV 2022! Dhany Arifianto, Chairman of ANV 2020 List of COMMITTEE, Contact Us are available in this pdf.
A Convolutional Neural Network Accelerator Based on NVDLA

In recent years, Convolutional Neural Network (CNN) has been successfully applied to a wider range of fields, such as image recognition and natural language processing. With the application of CNN to solve more complex problems, their computing and storage requirements are also greatly increased. Traditionally, CNN is executed on CPU and GPU, but their low throughput and energy efficiency are the bottleneck of using them. Field Programmable Gate Array (FPGA) has many characteristics suitable for acceleration, it has become an ideal platform for hardware acceleration of CNN. We design and implement a convolutional neural network accelerator based on NVIDIA deep learning accelerator (NVDLA) on FPGA platform. We give the detailed structure of NVDLA, design the hardware system and software system. The neural networks that NVDLA can support are limited, but our architecture can realize the high bandwidth data communication between NVDLA and CPU. CPU handle the operations that NVDLA does not support. The accelerator will support more and more complex networks in the future.
Towards a Photonic Integrated Linear Algebra Processor for Complex Matrix Multiplication and Inversion

We propose the first on-chip optical linear algebra processor for complex-valued matrix multiplication and inversion, incorporating III-V gain blocks. Architecture properties and design considerations are discussed, with numerical analyses showing its superiority over electronic processors.
Design and Implementation of a Lightweight Street View Segmentation Algorithm

Image semantic segmentation is a pixel-level classification, which can accurately segment different objects in a scene, and is a key technology in modern autonomous driving systems. At present, a large number of semantic segmentation algorithms often pursue accuracy, which leads to a huge amount of calculation, which is difficult to apply in real life. This paper analyzes the current lightweight algorithm framework and street view data set, and designs a deep neural network structure-DRU-Net, which uses stacked dilated convolutions with different expansion rates to make the receptive field larger and more complex; secondly, The residual path is designed to replace the jump connection, which reduces the semantic difference between the encoder's features and the decoder's features; the network segmentation effect is better and the real-time performance is higher. Finally, it was verified on the Cityscapes data set that the network model parameters were only 8.1M, and the segmentation accuracy reached 69.7%.
Automatic building of a large and complete dataset for image-based table structure recognition

Table is one of the most common ways to represent structured data in documents. Existing researches on image-based table structure recognition often rely on limited datasets with the largest amount of 3,789 human-labeled tables as ICDAR 19 Track B dataset. A recent TableBank dataset for table structures contains 145K tables, however, the tables are labeled in an HTML tag sequence format, which impedes the development of image-based recognition methods. In this paper, we propose several processing methods that automatically convert an HTML tag sequence annotation into bounding box annotation for table cells in one table image. By ensembling these methods, we could convert 42,028 tables with high correctness, which is 11 times larger than the largest existing dataset (ICDAR 19). We then demonstrate that using these bounding box annotations, a straightforward representation of objects in images, we can achieve much higher F1-scores of table structure recognition at many high IoU thresholds using only off-the-shelf deep learning models: F1-score of 0.66 compared to the state-of-the-art of 0.44 for ICDAR19 dataset. A further experiment on using explicit bounding box annotation for image-based table structure recognition results in higher accuracy (70.6%) than implicit text sequence annotation (only 33.8%). The experiments show the effectiveness of our largest-to-date dataset to open up opportunities to generalize on real-world applications. Our dataset and experimental models are publicly available at shorturl.at/hwHY3
Casting the Same Sentiment Classification Problem

We introduce and study a problem variant of sentiment analysis, namely the "same sentiment classification problem", where, given a pair of texts, the task is to determine if they have the same sentiment, disregarding the actual sentiment polarity.Among other things, our goal is to enable a more topic-agnostic sentiment classification.We study the problem using the Yelp business review dataset, demonstrating how sentiment data needs to be prepared for this task, and then carry out sequence pair classification using the BERT language model.In a series of experiments, we achieve an accuracy above 83% for category subsets across topics, and 89% on average.
IMPACT OF EXTENSIVE READING ON SOPHOMORE ENGLISH MAJORS’ READING COMPREHENSION DEVELOPMENT AT DALAT UNIVERSITY

This paper reports on an extensive reading program utilizing graded readers and the Moodle Reader for sophomore English majors at Dalat University. The main purpose of the study was to determine the impact of such a program on student reading abilities and to explore their attitudes toward the program. Data were collected from pretests, posttests, and questionnaires. Findings indicated that the extensive reading program was largely responsible for the higher mean gain on the posttest made by the experimental group, and that the majority of students expressed positive opinions toward the program. The paper concludes by putting forward some pedagogical implications on the implementation of such a program with English language students and lecturers in their courses of reading.
Word and Chracter Segmentation in Devnagari and Odia Script – A Comparitive Analysis

Optical Character Recognition has been an active research area in computer science for several years. Several research works undertaken on various languages in India. In this paper an attempt has been made to find out the percentage of accuracy in word and character segmentation of Hindi (National language of India) and Odia is one of the Regional Language mostly spoken in Odisha and a few Eastern India states. A comparative article has been published under this article. 10 sets of each printed Odia and Devanagari scripts with different word limits were used in this study. The documents were scanned at 300dpi before adopting pre-processing and segmentation procedure. The result shows that the percentage of accuracy both in word and character segmentation is higher in Odia language as compared to Hindi language. One of the reasons is the use of headers line in Hindi which makes the segmentation process cumbersome. Thus, it can be concluded that the accuracy level can vary from one language to the other and from word segmentation to that of the character segmentation.
Language Acquisition Learning to Reduce Interference

Language is used as a means of communication to convey intentions, ideas, thoughts, ideas, and findings.Language has been acquired by humans from the age of five from the mother (mother tongue) or better known as the acquisition of the first language.The acquisition of children's language will develop from acquiring the first language to the acquisition of a second language.Language acquisition learning has been learned accidentally and deliberately (from mothers and the environment).Indonesian language acquisition learning has been studied starting from elementary school to college.However, there are still many interventions carried out by students and students.Therefore it is necessary to increase the knowledge of language acquisition learning to reduce interference among students.This study aimed was to determine students' language acquisition learning to reduce interference.The method used in this research is descriptive which is not bound by the place of researc because the object of research is in the form of texts (texts) from several national and international journals.The results showed that there was a lot of interference among students both inside and outside the classroom.This interference cannot be considered minor because aspects of language competence are very necessary for a person's life.The concept of language acquisition states that we have to get used to correct sentences.Don't justify wrong sentences either verbally or in writing.Thus, language acquisition learning greatly determines the language competence of students.
Rural-Urban Fringe Students Towards Learning English using Nonagonal Fuzzy Cognitive Maps

Motivation and aptitude are essential components of teaching-learning process. The objective of this study is to identify the factors that affect the intrinsic and extrinsic motivation of Rural-Urban Fringe (R-U Fringe) students of Chennai, India towards learning English. This also focuses to find which of those factors influence both the motivation in large. A study has been conducted among 160 learners of Chennai through a questionnaire. The data was analyzed through Nonagonal fuzzy number modeling. And it is found the factors that influence the intrinsic motivation of the R-U Fringe learners from high to low are challenge, fantasy, control, curiosity, cooperation, recognition and competition.
Intruder

In the last chapter, we saw some basics about the Burp Suite dashboard, target, and engagement tools. Now that we have seen the basics of intercepting requests and interpreting the summary on the dashboard, we will move ahead with using the Intruder tool. Intruder has advanced fuzzing capabilities that can be used in various attack scenarios.
Colour Recognition Algorithm Based on Colour Mapping Knowledge for Wooden Building Image

In the cross-media image reproduction technology, the accurate transfer and reproduction of colour between different media are an important issue in the reproduction process, and the colour mapping technology is the key technology to effectively maintain the image details and improve the level of colour reproduction. Wooden structure in the image colour and colour piece is different, the image of each colour of visual perception is not independent, and every colour in the image pixels is affected by the surrounding pixels, but in the process of image map, without thinking of the pixel space, adjacent pixels of mutual influence in particular, do not let a person particularly be satisfied with the resulting map figure. In the process of image processing by traditional colour mapping algorithm, the colour distortion caused by colour component is ignored and the block diagram of colour mapping system is constructed. With the continuous development of mapping recognition algorithms, the maximum and minimum brightness values in the image are mapped to the maximum and minimum brightness values of the display device by linear mapping algorithm according to the flow of the established recognition algorithm. By establishing the colour adjustment method of the colour mapping image, the processing effect of the mapping algorithm is analysed. The results show that the brightness deviation of the image is reduced and the colour resolution is improved by the colour brightness compensation.
Limiting Choices with Pickers

Text Fields are great for letting the user enter any type of information such as names or short answers to questions. The problem is that if the user has the freedom to enter anything, they could accidentally (or on purpose) enter invalid data.
Superlinear Integrality Gaps for the Minimum Majority Problem

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 13 August 2020Accepted: 08 September 2021Published online: 16 December 2021Keywordsinapproximability, integrality gaps, approximation algorithms, sparse machine learning, randomized rounding, lift-and-projectAMS Subject Headings68Q17, 90C05, 90C57Publication DataISSN (print): 0895-4801ISSN (online): 1095-7146Publisher: Society for Industrial and Applied MathematicsCODEN: sjdmec
vSketchDLC: A Sketch on Distributed Deep Learning Communication via Fine-grained Tracing Visualization

Intensive communication cost for gradients and parameters is becoming the bottleneck of distributed deep learning training. It is crucial for optimizing such communication bottleneck through measuring communication operations effectively. However, many existing communication measurement tools, such as MXNet profiler, still suffer from serious limitations. Specifically, they cannot satisfy two requirements simultaneously, that is, fine-grained collection of low-level communication operations and user-friendly analysis of comprehensive measurement results. In this paper, we make the first attempt to propose an open-sourced, fine-grained and user-friendly communication measurement tool on top of MXNet, called vSketchDLC. vSketchDLC can trace low-level communication events between framework and communication library interface, and capture end-to-end push and pull communications between workers and servers. It supports to generate communication records in standard format, enabling users to analyze the communication traces by merely using standard visualization tools such as Chrome Trace Viewer. Our design exploits in-memory buffers and asynchronous record writes to ensure measurement activities do not impact training performance. We conduct extensive experiments on a public-cloud GPU cluster to verify the effectiveness of vSketchDLC for MXNet. Experimental results show that vSketchDLC can empower users to analyze fine-grained communication records through friendly interactions, and identify potential training bottlenecks from multiple perspectives, including training timeline and iterations, DNN layers, workers or servers, etc. We can observe the relationship between different communications visually, i.e., to highlight a selected period of communication traces, to zoom in or zoom out, such that identifying the root causes of communication bottleneck and seeking to improve training performance.
TFC: Defending Against SMS Fraud via a Two-Stage Algorithm

Abstract With the increasing of the telecom network fraud in China, SMS (Short Message Service) has became an important channel exploited by the criminals to contact victims. Due to the tiny amount compared with normal SMS, the high proportion of malicious adversarial characters, and the lack of knowledge to specific fraud types, it is still challenging to identify the fraud SMS efficiently. In this paper, we firstly conduct a measurement study to explore the characteristics of the fraud SMS. Based on the exploration, we propose a two-stage algorithm called TFC. TFC can quickly filter out normal SMS in the first stage with two indicator functions, and then easily identifies the category of fraud SMS in the second stage by combining the semantic deep features and the domain-knowledge based artificial features. We conduct two real-world SMS datasets for extensive experiments, and the results show that TFC successfully reduces calculation cost and achieves better performance in distinguishing various categories of fraud SMS.
NCHLT Auxiliary speech data for ASR technology development in South Africa

The aim of the National Centre for Human Language Technology (NCHLT) project was to create speech and text resources that would enable Human Language Technology (HLT) development for the 11 official languages of South Africa. The speech data described in this paper was collected during the NCHLT project using a smartphone application. The official NCHLT Speech Corpus was released in 2014, but it did not include all recordings that were made during the data collection campaign. This paper describes the additional data that was recently released as auxiliary corpora [2]. The auxiliary data sets contain between 20 and 170 hours of speech data per language as well as the transcriptions associated with each utterance. In terms of the resources required for HLT development South Africa's official languages are all under-resourced. The data described in this paper contributes toward alleviating this situation, specifically for the development of speech technology.