Reviewerly/exHarmony huggingface dataset:
Efficient Picking by Considering Simultaneous Two-Object Grasping

This paper presents a motion planning algorithm that enables robots to efficiently pick up objects by considering simultaneous multi-object grasping. At the center of the algorithm is a cost function that helps to determine one of the following three grasping policies considering distance and friction constraints – Grasping a single object; Grasping two objects simultaneously; Grasping two object simultaneously after pushing one of the objects close to the other. After recognizing the object distributions on a table by using a depth camera and Mask R-CNN, our algorithm will select grasp policies from the three candidates considering the cost function, and plan a policy sequence that can most quickly finish picking all the objects using dynamic programming. Both simulation and real-world experiments are carried out to examine the performance of the proposed planner. Results show that the proposed method significantly improves the efficiency of robotic picking compared to conventional single-object-based methods.
Effectiveness of Using Codes With a Sparse Check Matrix for Protection against Algebraic Manipulations

This article presents codes with a sparse check matrix and demonstrates its possibilities for analysis error masking probability in the case of algebraic manipulations. The illustrated scheme is based on bent functions calculating redundancy using boolean functions. The paper presents the resistance of created code construction to various types of errors. Moreover, the paper considered the strong algebraic manipulation model, this model takes into account the prior knowledge of the attacker about the sparseness of the code check matrix.
Machine Learning Techniques for Automated Movie Genre Classification Tool

Everyone is of different kinds in the Real World with liking or preferences of some specific genres. Genre reflects the style or brand of the story, novel, poetry, film, or music. Liking of the specific genre is inbuilt in human beings. It is a spontaneous identity of the human being. Moreover, identification of the sentiment of a person becomes easier from the several feedbacks posted on social media. The human being, as an author, artist, novelist, dancer, singer, or film producer, cannot be a jack of all trades. So, genre plays an important role in any creation, and based on the reviews of these creations, human sentiment analysis is easily possible. This paper proposes a multi-level genre-based textual sentiment analysis. This analysis is automated with various inputs of reviews and classifies nine categorical genres as output. The sample online input dataset is utilized, and three different machine learning algorithms are used to implement the proposed automated genre-based textual sentiment analysis framework. This paper applies machine learning techniques to calculate the optimal score for each genre based on the classification report. Based on the training accuracy and testing accuracy, the best algorithm is also recognized as an automated genre classification tool.
Real-time high sensibility vibration detection based on phase correlation of line speckle patterns

A vibration extraction system based on laser speckle with phase correlation method is presented. The speckle pattern sequences are recorded by a linear array CMOS, which allows the system to detect high frequency signals more than 20 kHz. The line speckle patterns are fast Fourier transformed and correlated between the adjacent rows in the frequency domain. The phase angles of correlation calculations are directly used as the displacements between the respective two rows in the object space. The recovered audio signals are evaluated by segmental SNR (SegSNR), normalized sub-band envelope correlation (NSEC), and log-likelihood ratio (LLR). Compared with the centroid and quadratic fitting method, the phase correlation method obtains advantages of high stability, high sensibility, and real-time.
A Novel Convolutional Neural Network for Image Classification

Image classification technology is the most basic task in computer vision, and it is widely used in medical treatment, online shopping, face recognition, etc. An improved convolutional neural network model is proposed for image classfication in this article. Firstly, the novelty of this model is that principal component analysis is used to replace the pooling layer in convolutional neural networks, which mainly calculates and selects the eigenvectors with large eigenvalues to make linear table of the original data to achieve the purpose of rapid dimensionality reduction. Secondly, a three-layer convolutional layer is used to extract the main features of the image. Through classification experiments on the MNIST data set, kaggle leaves and other data sets, the desired accuracy is achieved. It indicates that the improved convolutional neural network model has a perfect recognition effect, and the improved model is suitable for classification processing of different types of data sets. In addition, the classification of noise images can also achieve higher accuracy.
Mixed Script Identification Using Automated DNN Hyperparameter Optimization

Mixed script identification is a hindrance for automated natural language processing systems. Mixing cursive scripts of different languages is a challenge because NLP methods like POS tagging and word sense disambiguation suffer from noisy text. This study tackles the challenge of mixed script identification for mixed-code dataset consisting of Roman Urdu, Hindi, Saraiki, Bengali, and English. The language identification model is trained using word vectorization and RNN variants. Moreover, through experimental investigation, different architectures are optimized for the task associated with Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU), and Bidirectional Gated Recurrent Unit (Bi-GRU). Experimentation achieved the highest accuracy of 90.17 for Bi-GRU, applying learned word class features along with embedding with GloVe. Moreover, this study addresses the issues related to multilingual environments, such as Roman words merged with English characters, generative spellings, and phonetic typing.
A necessary condition for double-decay envelopes in stringed instruments

Measurements of body vibration characteristics of five different stringed musical instruments have been used to address the question of whether and when they might be expected to produce transient response featuring a "double decay" sound profile. The phenomenon has been well documented and studied in the context of the piano but has not been systematically studied for other instruments. The results show considerable variation among instruments. The piano is indeed predicted to show double decays over most of its range. In the tested guitar, by contrast, double decays are likely to be confined to a few notes near strong body resonances. Other instruments fall between these extremes. The lute and the mandolin, both normally strung with double strings, should both exhibit double decays over much of their playing range, especially towards the higher end. The banjo is single-strung but is also predicted to show strong double decays, especially for higher notes in its range.
Development of Mobile Augmented Reality Based Media for An Electrical Measurement Instrument

Abstract The research aims to develop mobile learning media with augmented reality for electrical measurement instruments. The learner can use this application to improve their skills and knowledge about using electrical measurement instruments correctly. One of the essential skills for electricians is using voltmeter, ammeter, and ohmmeter. The result of measuring they can do some analysis about an issue or troubleshooting the electrical field. From the development research, was produced learning media application product was named ARAVO. ARAVO is an abbreviation of augmented reality of Ammeter, Voltmeter, and Ohmmeter. ARAVO helps learners and even lecturers to simulate the use of electrical measuring instruments by combining virtual objects such as multimeter with the real world. Thus will provide a more visible visualization of how to use electrical measuring instruments before they practice directly with actual measuring instruments. ARAVO is a mobile application that can run on the smartphone platform mobile Android. Into the development of this application must go through several stages before it is ready for use.
On the Use of Large Interactive Displays to Support Collaborative Engagement and Visual Exploratory Tasks

Large interactive displays can provide suitable workspaces for learners to conduct collaborative learning tasks with visual information in co-located settings. In this research, we explored the use of these displays to support collaborative engagement and exploratory tasks with visual representations. Our investigation looked at the effect of four factors (number of virtual workspaces within the display, number of displays, position arrangement of the collaborators, and collaborative modes of interaction) on learners' knowledge acquisition, engagement level, and task performance. To this end, a user study was conducted with 72 participants divided into 6 groups using an interactive tool developed to support the collaborative exploration of 3D visual structures. The results of this study showed that learners with one shared workspace and one single display can achieve better user performance and engagement levels. In addition, the back-to-back position with learners sharing their view and control of the workspaces was the most favorable. It also led to improved learning outcomes and engagement levels during the collaboration process.
Training Strategies for Library Staff and Remote Clients in Academic Libraries Operating in a Distance Education Environment during COVID-19

This paper identified the training strategies for distance education academic libraries that could improve remote clients’ access to information resources during the Coronavirus Disease 2019 (COVID-19) pandemic. The positivism research paradigm anchored this study and employed a quantitative approach. Eighty-two (82) online questionnaire was sent to the library staff working in academic libraries that operate in a distance education environment in Gauteng Province of South Africa. The collected data were analyzed with frequency counts and percentages. The hypothesis was tested using multinomial logistic regression statistics. The findings revealed that academic libraries provided training through the library staff to ensure that remote clients cope with the technological advancement facilitated by the emergence of COVID-19. This study recommended that the library staff should consider training intervention in the areas that seem to be neglected, such as identifying information resources relevant to their information needs, introduction to library products and services, and introduction to reference techniques to facilitate the usage and access to information resources by the remote clients.
Developing a Framework to Measure Cyber Resilience Behaviour of Indian Bank Employees

Banks have technology-driven business model and are heavily dependent on the Internet. As banks continue to digitize, the attack surface is increasing. Cyber-crimes have the propensity to impact not only an organization’s reputation but also its profitability and economic growth. The apex bank of the country, Reserve Bank of India (RBI), has detected 50,547 cyber-frauds worth a whopping INR 145.08 Crore in India’s scheduled commercial banks the financial year 2018–19 ( Ganjapure, V.: RBI reports over 50,000 cyber frauds in FY 2018–19. Times of India (2019). Retrieved from: https://timesofindia.indiatimes.com/city/nagpur/rbi-reports-over-50000-cyber-frauds-in-fy-2018-19/articleshow/71174635.cms#:~:text=NAGPUR%3A%20The%20Reserve%20Bank%20of,credit%20cards%20and%20internet%20banking ) [6]. Hence, the need for banks to be cyber resilient is becoming more and more essential. Cyber resiliency of an organization depends on the IT infrastructure and cyber security measures undertaken; however, a prominent factor to achieve resilience depends on the employees. Confronted with this bleak realization, it is only recently that the need for cyber resilient culture has come into existence. While seminal contribution has been made in the field of cyber security measures in the banking sector, these have typically proposed and explored techniques to enhance infrastructure rather than cyber resilient behaviour. There is a need to look at the cyber resilient behaviour of employees as a potential source of cyber resilience of bank as a whole. This paper presents a novel framework to measure cyber resilience behaviour of Indian bank employees. Cyber-attacks are inevitable, but what will matter now is how fast a bank can recover from vulnerabilities, and this will heavily depend on cyber resiliency of the staff.
Gallbladder CT Image Segmentation by Integrating Rough Entropy Thresholding with Contours

Jitani, N. Singha, B. Barman, G. Talukdar, A. Choudhury, B. K. Sarmah, R. Bhattacharyya, D. K.Thresholding is one of the most widely used techniques for image segmentation, particularly, for medical image segmentation. The key idea is the selection of an appropriate intensity value to differentiate the background pixels from the object of interest pixels. In this paper, we propose a method for segmentation of the healthy gallbladder from a computed tomography (CT) scan image by integrating rough entropy thresholding technique along with contour and convex hull operations. Our method has been found to perform significantly better than other competing methods.
Dealing with Realizations of Hidden Regularities in Data as Independent Generalized Precedents

A new approach to data analysis based on the concept of generalized precedent as precedent for the manifestation of hidden partial regularity is investigated, in particular, options for ensuring the independence of precedents are proposed.
Detection of Lymphoblastic Leukemia Using VGG19 Model

Acute Lymphoblastic Leukemia (ALL) and - Acute Myelogenous Leukemia (AML) is a terminal blood cell cancer that takes birth due to the uncontrolled growth of white blood cells which results in deathif not diagnosed in earlier stages. To diagnose leukemia, oncologists perform multiple tests on bone marrow and white blood cells. Manually diagnosing this disease is sometimes leads to less accuracy due to human error and expecting expert suggestions and supervision for diagnosing the cancer is a time-consuming process that leads the patient into life-threatening stages. To get a precise diagnosis, this research work proposes deep learning and image processing techniques. There are few research studies on Acute Lymphoblastic Leukaemia (ALL) that have been tested and achieved good results even though it is not precisely predicted and not in practical usage. This research paper proposes further improvements in classification and an accurate CNN model helps to predict and classify the type of Leukaemia. This research work experiments the VGG19 model architecture by using the ImageNet dataset.
Study on E-Voting Systems: A Blockchain Based Approach

The traditional voting system has many flaws such as election manipulation, security breaches, transparency and privacy issues. These issues occur as the votes can be changed by a central authority in favor of one party when they are being counted manually or there can be human error while counting votes. This system puts a lot of trust in the central authority. Also, there is an economical issue due to the cost of security, human and ballot resources. Many of these issues are solved using the E-Voting system which has been successful in countries like Estonia that have implemented this system of voting. There is still an issue of universal verifiability of votes even in the Estonian E-Voting system. This paper conducts a study of various e-voting systems. From among the various existing methods, an effective blockchain based solution to solve the existing issues of E-voting has been explained. The blockchain is a database, distributed among all users on the network which will help in the verification of all votes cast without the need to put complete trust on a third-party system.
Apply Bert-based models and Domain knowledge for Automated Legal Question Answering tasks at ALQAC 2021

With robust development in NLP (Natural Language Processing) methods and Deep Learning, there are a variety of solutions to the problems in question answering systems that achieve extraordinary results. In this paper, we describe our approach using at the Automated Legal Question Answering Competition (ALQAC) 2021. In this competition, we achieved the first prize of all tasks with the scores of 88.07%, 71.02%, 69.89% in Task 1, Task 2 and Task 3 respectively.
PT-NAS: Designing efficient keypoint-based object detectors for desktop CPU platforms

Recently, keypoint-based object detectors have attracted widespread attention, due to their novel structure and excellent performance. However, in terms of their design, there are still two limitations: 1) Most keypoint-based methods are designed for GPU platforms, which makes them inefficient on desktop CPU platforms. 2) Existing works still rely heavily on manual design and prior knowledge. To this end, this work aims to offer a practical solution for designing CPU-efficient key-point detectors. First, we present a set of practical design guidelines by comparing different detection architectures. Following the proposed guidelines, we further develop a progressive three-phase network architecture search (PT-NAS) to achieve the automated design of detection architectures. Benefiting from our hierarchical search space and novel search pipeline, our PT-NAS not only achieves higher search efficiency, but also satisfies the practicality of CPU platforms. On the MS-COCO benchmark, we utilize our PT-NAS to generate several key-point detectors for fast inference on desktop CPUs. Finally, comprehensive comparison experiments prove that the proposed PT-NAS can produce new state-of-the-art keypoint-based detectors for CPU platforms.
Edge detection on fuzzy near sets

Fuzzy image processing involves various fuzzy approaches which help in understanding and processing of images as fuzzy sets. In the process of ED outlines, boundaries of an object and background in a image are detected. Hence edge detection is very important in digital image processing applications to obtain information from the frames as a forerunner step to feature extraction and object segmentation. In this paper the pixel values of an image is converted to fuzzy membership value matrix in which the columns are as fuzzy near sets and the corresponding membership and gray images are found using MATLAB. Using gray membership values some basic types of edge detection techniques are used and the corresponding fuzzy near membership matrices are constructed. The Euclidean and Hamming distances between two edge detection fuzzy value matrices are calculated and similarity measure is found. The higher value of similarity measure indicates that the images using two of the edge detection techniques are similar.
Capsule Network Based on Self-Attention Mechanism

Capsule network (CapsNet) is well-known as an evolution of classical convolution neural network (CNN), which is good at recognizing the postures, orientations, and textures, thus achieving promising results in areas such as image classification. However, the high computational complexity hinders them from building larger models. Fortunately, reducing the complexity of the routing of CapsNet through the channel attention mechanism has achieved significantly good results, whereas there is a possibility of further optimization of the storage and precision. Therefore, in this paper, we propose an improved version based on the self-attention mechanism on the basis of the traditional channel attention routing. Specifically, the global information of the capsule is captured by the self-attention routing, which leads to fewer misclassification errors. Experimental results on MNIST, affNIST, and Cifar10 show that the proposed methods can obtain better performances with fewer training parameters compared to traditional methods on average.
Achieving Semantic Consistency for Multilingual Sentence Representation Using an Explainable Machine Natural Language Parser (MParser)

In multilingual semantic representation, the interaction between humans and computers faces the challenge of understanding meaning or semantics, which causes ambiguity and inconsistency in heterogeneous information. This paper proposes a Machine Natural Language Parser (MParser) to address the semantic interoperability problem between users and computers. By leveraging a semantic input method for sharing common atomic concepts, MParser represents any simple English sentence as a bag of unique and universal concepts via case grammar of an explainable machine natural language. In addition, it provides a human and computer-readable and -understandable interaction concept to resolve the semantic shift problems and guarantees consistent information understanding among heterogeneous sentence-level contexts. To evaluate the annotator agreement of MParser outputs that generates a list of English sentences under a common multilingual word sense, three expert participants manually and semantically annotated 75 sentences (505 words in total) in English. In addition, 154 non-expert participants evaluated the sentences’ semantic expressiveness. The evaluation results demonstrate that the proposed MParser shows higher compatibility with human intuitions.
Comparative Analysis of Convolutional Neural Network and LSTM in Text-Based Sentiment Classification

Sentiment analysis is a type of natural language processing that categorizes text as positive or negative. It has been used in several industries in recent decades. Analyzing people’s opinions on a subject is a difficult task, because everyone’s perception is different. Sentiment analysis can be used to classify the information about people, services, and products. Every minute, a vast amount of data is uploaded to websites, blogs, and social media platforms. In this data era, new technologies and algorithms are required to extract useful information from online content. Customer expectations, market trends, and people’s attitudes are determined by sentiment analysis. It aids in the expansion of a business and making of critical production decisions. While industries utilize social media to interact with customers, businessmen use customer feedback and reviews to better their services and products. Machine learning and Deep learning models are well suited for text processing. Convolutional neural networks and Recurrent neural networks are two deep learning methods that are utilized to solve numerous challenges in the text processing sector. CNN can get the important features from the input text with pooling operation, but it is complex to get contextual data. RNN can get the contextual detail but the sequence of input words will differ. This study discusses the comparison of Convolution neural networks with LSTM algorithms.
Efficient Scene Text Recognition Model Built with PaddlePaddle Framework

Scene text recognition is a frontier research direction in optical character recognition (OCR) technology. Considering that the compatibility of the existing CRNN method under the PaddlePaddle framework is not friendly, and the recognition accuracy needs to be improved. Therefore, this paper proposes an efficient RBC model constructed by fusing ResNet34 and Bi-LSTM backbone network and Connectionist Temporal Classification (CTC) transcription method to solve the problem of scene text recognition. By coding based on the PaddlePaddle framework and experimenting with shared dataset, the results show that the RBC model is well compatible with the PaddlePaddle framework, and the average accuracy has reached 85.91%, which is 1.8% higher than the optimal baseline model. The data and code of this article, please link to the URL https://aistudio.baidu.com/aistudio/projectdetail/1911502?=1622688767906.