Reviewerly/exHarmony huggingface dataset:
Clustering Method for Isolate Dynamic Points in Image Sequences

In this chapter, we propose an optimization of the a-contrario clustering method using the probabilistic Guillaume Khenchaff Measure (MGK) quality technique. A-contrario is used for tracking salient objects in the scene in real time. This method analyzes the data contained in a motion vector, which contains the scattered optical flow accumulated points of interest. The aim of our study is to improve the first results obtained from the Number of False Alarm (NFA) criterion by using MGK to bring together the group of points endowed with a coherent movement of the binary tree. The idea is to isolate dynamic points so that we can use static points in the future.
Two-Stream Hybrid Attention Network for Multimodal Classification

On modern e-commerce platforms like Amazon, the number of products is fast growing, precise and efficient product classification becomes a key lever to great customer shopping experience. To tackle the large-scale product classification problem, a major challenge is how to leverage multimodal product information (e.g., image, text). One of the most successful directions is the attention-based deep multimodal learning, where there are mainly two types of frameworks: 1) keyless attention, which learns the importance of features within each modal; and 2) key-based attention, which learns the importance of features using other modalities. In this paper, we propose a novel Two-stream Hybrid Attention Network (HANet), which leverages both key-based and keyless attention mechanisms to capture the key information across product image and title modalities. We experimentally show that our HANet achieves state-of-the-art performance on Amazon-scale product classification problem.
Research on Gesture Recognition and Interaction of Virtual Collaborative Disassembly Training

At present, most of the virtual disassembly training systems are single person training systems, and the interaction is relatively simple, which can not reflect the real natural interactive operation, so it can not meet the needs of multi-person collaborative disassembly training of complex equipment. In order to solve this problem, research on gesture recognition and interaction technology. Based on the demand analysis of disassembly training, 5 basic gestures and 3 auxiliary gestures are designed; A gesture feature extraction method suitable for immersive virtual disassembly training environment is proposed; The one-handed interaction object model and two-handed interaction object model are proposed, and the interactive hardware experimental platform is designed to realize the multi-person cooperative operation. The research content is applied to the virtual disassembly of the manual gearbox of a car, and the operation evaluation of the virtual disassembly training is carried out. The results show that: the gesture designed meets the requirements of common virtual disassembly and assembly operations, and the gesture and interaction intention are identified accurately, which can realize the interactive operation required by disassembly and assembly, and restore the real natural interactive operation to a certain extent.
A Novel Fast NLM Denoising Algorithm Based on Improved Kernel Function Parameters: Applied to RFID Labels

This paper proposes a novel fast NLM algorithm. The integral diagram is introduced to speed up algorithmic processing and the kernel function coefficient is changed through the function diagram to improve its performance. The denoising effects of three RFID label images collected by RFID system which added the gaussian white noise is studied. This paper collects and analyzes the running time, PSNR and SSIM of various NLM denoising algorithms under different RFID label images added the gaussian white noise of the same standard deviations. The results verify that the proposed algorithm has both high speed and high efficiency for the applications to RFID labels.
Smart Skim Pinjaman Buku Teks (SBPT) School Operation System in Malaysia

Skim Pinjaman Buku Teks (SPBT) is a textbook loan program for elementary and secondary schools in Malaysia. This program is an initiative system by the Malaysia Ministry of Education to help students in the school by providing them with textbooks for their learning purpose. The current SPBT system is handled by storing all the records into a book or a file that can cause data missing, data redundant and incorrect data stored in the system. This project presents a new system for the textbook loan system known as Smart Skim Pinjaman Buku Teks (SBPT) School Operation system (SSSOS). This system upgrades the conventional method that manually records the loan information to a systematic approach using a web-based management system. The rapid application development (RAD) cycle is used as a methodology to develop the SSSOS system. The proposed system can efficiently handle borrowing and returning processes and reduce the textbook loan process period. This system also provides a complete report related to the textbook, such as the current status of a book and the previous information of the borrower. The proposed system can be used to enhance the conventional SPBT system in handling the textbook loan program. The system has been tested in Sekolah Kebangsaan Gambang, Pahang, where an SPBT coordinator acts as a client for the system.
A YOLOv3-Based Learning Strategy for Vehicle-Thrown-Waste Identification

At present, throwing objects from car windows is increasingly becoming a major illegal conduct that pollutes the urban environment and affects the city's image. Apart that, the rubbish casually thrown from car windows is left in the motorway, which seriously threatens the lives of sanitation workers. Moreover, it is time-consuming and laborious to manage through manual monitoring. In response to this phenomenon, we propose an improved yolov3-based real-time target detection for throwing objects from car windows. In order to solve the problem of deep hierarchy in the YOLOv3 network and improve the accuracy and speed of small target detection, the following improvements are made: Firstly, random feature sampling and interpolation are added to the residual blocks of the YOLOv3 backbone network to reduce the computational effort of the CNN network while maintaining high performance. Secondly, the Soft NMS is changed to Matrix NMS to speed up the identification of the best candidate boxes. Finally, a residual layer was removed to improve the accuracy of small target detection. The experimental study evaluates and demonstrates a number of improvements in accuracy and speed compared to current mainstream target detection methods.
On the Information Content of Some Stochastic Algorithms

We formulate an optimization stochastic algorithm convergence theorem, of Solis and Wets type, and we show several instances of its application to concrete algorithms. In this convergence theorem the algorithm is a sequence of random variables and, in order to describe the increasing flow of information associated to this sequence we define a filtration – or flow of $$\sigma $$ -algebras – on the probability space, depending on the sequence of random variables and on the function being optimized. We compare the flow of information of two convergent algorithms by comparing the associated filtrations by means of the Cotter distance of $$\sigma $$ -algebras. The main result is that two convergent optimization algorithms have the same information content if both their limit minimization functions generate the full $$\sigma $$ -algebra of the probability space.
A Novel Algorithm for Automatic Essay Grading Using Natural Language Processing Techniques

Evaluation of an English essay is one of the important and complex tasks which is done manually by skilled and efficient professors and faculties till date. The growth of science and technologies enables to automatic evaluation of an English essay using natural language processing (NLP) techniques (Rogovschi et al. in 2017 International joint conference on neural networks (IJCNN), Anchorage, AK, pp. 1628–1632, 2017 [1]; Banga and Mehndiratta in 2017 International conference on information technology (ICIT), Bhubaneswar, pp. 264–267, 2017 [2]; Roy and Cheung in 2018 28th international telecommunication networks and applications conference (ITNAC), Sydney, NSW, pp. 1–6, 2018 [3]). The intelligent system—built upon NLP multiple neural network model—gives out generic evaluation and the topic/question correlation for any given English essay.
A Pragmatic Approach to Web-Summarizer System

Lately, there has been a substantial growth in the amount of information available on internet. The information is available in the form of lengthy articles, blogs, papers, documents, write-ups, books, magazines and what not, making it very challenging for users to process the huge amount of information and assimilate the key points. This difficulty is further intensified because of the different formats in which the information is disseminated. Thus, there is an immense need for a summarization tool in these times of information overload. In this paper, a novel approach for identifying the main content of a web page along with determining whether the content is fit for summarization techniques is presented. This paper is an attempt to propose a framework for text summarization in the best possible way while using the browser either on your desktops or mobile phones.
An Arbitrary Style Transfer Network based on Dual Attention Module

Arbitrary style transfer means that stylized images can be generated from a set of arbitrary input image pairs of content images and style images. Recent arbitrary style transfer algorithms lead to distortion of content or incompletion of style transfer because network need to make a balance between the content structure and style. In this paper, we introduce a dual attention network based on style attention and channel attention, which can flexibly transfer local styles, pay more attention to content structure, keep content structure intact and reduce unnecessary style transfer. Experimental results show that the network can synthesize high quality stylized images while maintaining real-time performance.
Virtual Tour of Flanders

The Tour of Flanders, a classic outdoor cycling event that opens the cycling season, was cancelled due to the COVID-19 pandemic. However, a suitable replacement was created in its place, intended to offer people some solace during this lockdown period during which most recreational activities were suspended. This paper explains how the team managed to create an exciting mixed-reality racing event within a short period of time, given the limits of all social distance rules. It provides insight into how the real riders did race through a virtual landscape and how the viewers got to see this at home. This cloud-based production was performed exclusively with standard office IT equipment and well-known internet technologies. From idea to execution, the clock was ticking: the entire project was to be completed within a matter of days.
Education for Future Innovators Driven by KOSEN : Community Formation of Competency Education

In order to create practical examples of cross-cutting skills and make them available for reference by other colleges, the “Survey and Research on Competency Assessment” project was carried out in the two years from 2018 to 2019. From National Institute of Technology (KOSEN), seven colleges were selected as practical colleges, and the project was carried out with the first author as a coordinator. In this paper, we report efforts as a project and practical examples of the practical colleges.
Design of Multiple Online Interactive Guiding Platform for College English in the Information Technology Environment

With the wide application of information technology and the popularization of computer and other electronic equipment, networked learning has swept the world. Educational informationization has become the inevitable requirement and trend of educational reform and development in the information age. The priority among priorities of educational informatization is to use modern and advanced information technology means. Therefore, it is necessary to reform the teaching mode and improve the quality of teaching. Blended learning is not only a mixture of online learning and offline classroom learning, but also a mixture of various learning environments, learning styles and learning theories. In short, it is the product of the contemporary educational informationization concept put into practice. This mode is the inevitable trend to promote the steady development of China's educational reform vision.
Action recognition based on R2.5D-GRU networks

The field of body action recognition is a research hotspot in computer vision. Due to the complex calculation process of traditional recognition algorithms and the limitations of the data set to be processed, action recognition algorithms based on deep learning have gradually attracted attention. Various network frameworks have been proposed, which greatly improved the recognition Accuracy. In view of some problems in the action recognition algorithm of deep learning at this stage, this paper proposes a new R2.5D-GRU network. First, the 3D convolution is decomposed into a two-dimensional spatial convolution and a one-dimensional time convolution, and the low-level spatio-temporal features are extracted, and the high-level temporal features are extracted using GRU for temporal modeling. Experimental results show that the algorithm proposed in this paper performs better than some existing mainstream algorithms in the UCF101 data set.
Electric Wheelchair Using Blynk Smartphone Application

Abstract Automated or controlled wheelchair is becoming a common helping equipment for disable person and it comes vary in the term of technology usage and price. An electric wheelchair mainly used by people who has lack strength in using both hands to move the wheel freely when he/she was alone. In this research, we combined smartphone and an electric wheelchair for controlling its movement independently. An ultrasonic sensor was equipped for obstacle detection with 97% accuracy, however the price of the total system is approximately $200 ($1 = IDR15,000) which is comparable with the similar system in the market.
A universal detector of CNN-generated images using properties of checkerboard artifacts in the frequency domain

We propose a novel universal detector for detecting images generated by using CNNs. In this paper, properties of checkerboard artifacts in CNN-generated images are considered, and the spectrum of images is enhanced in accordance with the properties. Next, a classifier is trained by using the enhanced spectrums to judge a query image to be a CNN-generated ones or not. In addition, an ensemble of the proposed detector with emphasized spectrums and a conventional detector is proposed to improve the performance of these methods. In an experiment, the proposed ensemble is demonstrated to outperform a state-of-the-art method under some conditions.
The Design of a Tangible Table Based on an Array of Vertically Moving Blocks

Tangible media is a type of medium that interacts with the user via physical elements controlled by digital data. The proposed tangible table can be used in educational, entertainment, and architectural setting. The proposed table has two main modes of operation; interacting with the user hand motion and holding or moving objects placed on the table. The main element of the proposed tangible table is an array of elevating blocks which are controlled by a linear actuator and microcontrollers. The blocks in the array interacts with the user's movement or changes in the surrounding environment based on the readings of the ultrasonic and pressure sensors placed on the table.
MANAGEMENT OF MOTOR TRANSPORT ENTERPRISES USING SCHEDULE THEORY AND GENETIC ALGORITHMS

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Improved Hybrid Feature Selection Framework

An efficient Feature selection strategy is important in the dimension reduction of data. Extensive existing research efforts could be summarized into three classes: Filter method, Wrapper method, and Embedded method. In this work, we propose an integrated two-stage feature extraction method, referred to as FWS, which combines Filter and Wrapper method to efficiently extract important features in an innovative hybrid mode. FWS conducts the first level of selection to filter out non-related features using correlation analysis and the second level selection to find out the near-optimal sub set that capturing valuable discrete features by evaluating the performance of predictive model trained on such sub set. Compared with the technologies such as mRMR and Relief-F, FWS significantly improves the detection performance through an integrated optimization strategy.Results show the performance superiority of the proposed solution over several well-known methods for feature selection.
Characterizing and Predicting Reviews for Effective Product Marketing and Advancement

Online surveys have become a significant wellspring of data for clients prior to settling on an educated buy choice. Early audits of an item will in general exceptionally affect the ensuing item deals. In this paper, we step up and study the conduct qualities of early reviewer through their posted audits on our shopping gateway. In explicit, we partition item lifetime into three back to back stages, in particular early, lion's share. A client who has posted a survey in the beginning phase is considered as an early analyst. We quantitatively describe early reviewer dependent on their rating practices, the supportiveness scores got from others and the relationship of their surveys with item prevalence. We have tracked down that (1) an early analyst will in general relegate a higher normal rating score; and (2) an early reviewer will in general post more supportive audits. Our examination of item surveys additionally demonstrates that early reviewers appraisals and their got support scores are probably going to impact item prominence. By survey audit posting measure as a multiplayer rivalry game, we propose a novel edge based implanting model for early analyst forecast. Broad investigations on two diverse web based business datasets have shown that our proposed approach beats various cutthroat baselines.
Quantum annealing for ICT system design automation

In this paper, we propose a novel architecture for a (quasi) optimization problem solver for system designing automation that combines a problem generator and general problem solver. It efficiently generates the number of constraints from a simple description about system requirements, and solves the optimization problem at high speed by utilizing a powerful problem solver. Our challenge is to apply quantum annealing for this purpose. However, we faced the following technical problems caused by the fundamental properties of a quantum annealer: P1) a quantum annealer does not accept a constraint with inequality, which is necessary to describe system design problems, and P2) digit overflow of the coefficient value in the constraints. In this paper, we illustrate the overall architecture of our problem solver and clarify the aforementioned issues. Then, we present solutions to each problem: S1) a conversion method from inequality to equation by the augmented Lagrange method, and S2) a problem conversion method that reduces the number of digits of the coefficient without changing the meaning of the problem. Through experiments with a number of example problems, we evaluated the performance and accuracy of our scheme in comparison with a traditional rigorous problem solver. We show that our scheme outperforms the solver at least in a number of situations, and discuss its future utility.
Temporal action localization using gated recurrent units

Temporal action localization (TAL) task which is to predict the start and end of each action in a video along with the class label of the action has numerous applications in the real world. But due to the complexity of this task, acceptable accuracy rates have not been achieved yet, whereas this is not the case regarding the action recognition task. In this paper, we propose a new network based on gated recurrent unit (GRU) and two novel post-processing methods for TAL task. Specifically, we propose a new design for the output layer of the conventionally GRU resulting in the so-called GRU-Split network. Moreover, linear interpolation is used to generate the action proposals with precise start and end times. Finally, to rank the generated proposals appropriately, we use a Learn to Rank approach. We evaluated the performance of the proposed method on Thumos14 and ActivityNet-1.3 datasets. Results show the superiority of the performance of the proposed method compared to state of the art. Specifically in the mean Average Precision metric at Intersection over Union of 0.7 on Thumos14, we get 27.52% accuracy which is 5.12% better than that of state-of-the-art methods.
Discrepancy Histopathological Breast Cancer Image Classification with Deep Convolutional Network

In the world, bosom cancer is one of the main reasons that lead to death and has a huge death rate compared to other cancer types. Early detection may increase the chances of survival by proper treatment. The detection of cancer from images is very complex, difficult, and based on the pathologist’s knowledge and experience. To overcome these issues, we need a robust system to classify the images into cancerous and normal automatically. To design the system, we have experimented with two image datasets that have class discrepancy: namely IDC cancer dataset and BreakHis dataset. Existing research reveals that the deep convolutional network-based solutions are advanced techniques to solve the complex issues in medical tomographic diagnosis, especially in analyzing complex histopathology images. The class discrepancy of the dataset impacts deep network performance during the training and validation. To balance the dataset classes, we applied various sampling techniques including oversampling and undersampling on the imbalanced dataset to achieve the good performance of classifiers that are developed from the deep neural network. The experimental analysis of the paper shows that synthetic sampling results the effective performance during system training.
Conjunctive Reasoning on Fuzzy Taxonomies with Order-Sorted Feature Logic

Taxonomies are frequently used to represent knowledge; a taxonomy is a set of concepts partially ordered by a subsumption (is-a) relation. Recently, the CEDAR project has proposed a CEDAR Semantic Web reasoner based on OSF logic, which has shown very promising results with respect to both efficiency and scalability. Indeed, the basic operations behind the reasoning mechanism of CEDAR amount to answering Boolean queries on a taxonomy. For instance, CEDAR answers a conjunctive query on a taxonomy by computing the greatest lower bound of a subset of its elements. This paper proposes a generalization of this operation to the setting where the taxonomy is regarded as a fuzzy partially ordered set and an answer to a conjunctive query is associated with a satisfaction (approximation) degree. A conjunctive query on the fuzzy taxonomy can be answered by applying the same encoding technique of the CEDAR reasoner, while the approximation degree of an answer can be computed by adapting shortest path algorithms for directed acyclic graphs that can be further optimized thanks to the information provided by the encoding.