Reviewerly/exHarmony huggingface dataset:
Developing an electronic pocket dictionary based on the ADDIE model for Bahasa Indonesia basic learners

This research focuses on the development of Indonesian language learning tools for Bahasa Indonesia basic learners. Therefore, it aims to develop easy access tools that basic Indonesian learners can use to hear in Bahasa Indonesian. This research used the ADDIE method, which stands for Analysis, Design, Development, Implementation and Evaluation, to create the targeted product named Electronic Pocket Dictionary for Bahasa Indonesia basic learners. The development of an Electronic Pocket Dictionary based is focused on the feasibility of content, language and systematic presentation. The researchers interviewed linguists, media experts, practitioners and learners from one of the universities in Indonesia to get their desired results. The results show that the Electronic Pocket Dictionary meets a good category according to validations from material experts, linguists, media experts, practitioners and learners. In addition, the researchers found that it is not an easy thing to create a learning medium, but necessary so that the field of education does not use obsolete technologies.
Eye-Gaze Based Hands Free Access Control System for Smart City Public Interfaces

The working of Blink-Con, a framework for intelligent vision directed mobilization of onscreen mouse-cursor, without engaging any auxiliary assistance, is illustrated in this chapter. Blink-Con is framed keeping in mind the adverse effect of Covid-19 in our society, especially the smart cities of India. During this global pandemic, it is not at all advised to touch electronic gadgets at public places as infections spread through touch. Here, Blink-Con is believed to play a significant role in minimizing peoples’ physical contact with public computers present in cyber cafes, malls, banks, etc. which are an inevitable part of a smart city. In this work the system explored are technically dominated in color spaces, precise ROI detection, closed loop sequential interaction and eventually a system gets constructed and attained where the GUI onscreen pointer (mouse cursor) gets directed by interpreting significant eye gaze and blink. The algorithm for eye blink detection from 2-D image feed been classified in two major steps for accomplishing a heuristic based region of interest (ROI) identification and tracking the ROI for rapid detection of volunteered blinks. The sequence of volunteered blinks is taken as trigger of robust mouse mobilization function input that results in highly optimized smooth mouse-travel, divulging an adaptable mouse control performance with only eye blink. The experimental results are favorable to the system viability for multi facet use of the algorithms and can be deployed effectively in various public places in a smart city.
Quantum Shadow Imaging with Thermal Light

We show that the shapes of opaque objects can be recovered with a few-photon thermal light using spatial quantum noise analysis. Our method is immune to the camera dark noise thanks to camera-based homodyne detection.
Meta-path Enhanced Knowledge Graph Convolutional Network for Recommender Systems

Knowledge Graph (KG) is a directed heterogeneous information network that contains a large number of entities and relations, which is widely used as effective side information in rec-ommender systems. Moreover, in recommender systems, the Graph Convolutional Network (GCN) model is introduced to mine the relatedness between entities in a KG because of its efficiency in extracting spatial features on topological graphs. The Knowledge Graph Convolutional Network (KGCN) model up-dates the embedding of a currently positioned entity by aggregating the information of adjacent entities selected randomly. Never-theless, it has two limititations: 1) the information of neighbors se-lected randomly cannot accurately represent the current entity in the KG; 2) the model is hard to converge as graph features (i.e. The spatial relation features and semantic information features of en-tities in the KG) grow. To solve these limitations, in this paper, a meta-path (i.e., a sequence of artificially constructed relationships) is introduced into the selection of neighbors in the KGCN model to enhance the representation of each entity. Furthermore, two construction methods of the meta-path - constructing a meta-path based on the same relation (KGCN-SP) and the characteris-tics of KG (KGCN-MP) -are proposed. The experiments based on three real-world datasets demonstrate that the neighbor selection based on the meta-path is able to collect more accurate infor-mation from a KG and improve the recommendation performance effectively.
Distribution-free Testing for Halfspaces (Almost) Requires PAC Learning

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA)Distribution-free Testing for Halfspaces (Almost) Requires PAC LearningXi Chen and Shyamal PatelXi Chen and Shyamal Patelpp.1715 - 1743Chapter DOI:https://doi.org/10.1137/1.9781611977073.70PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract It is well known that halfspaces over ℝn and {0, 1}n are PAC-learnable with Θ(n) samples. Recently Blais et al. [4] showed that even the easier task of distribution-free sample-based testing requires Ω(n/log n) samples for halfspaces. In this work we study the distribution-free testing of halfspaces with queries, for which we show that the complexity remains to be . Indeed we prove the following stronger tradeoff result: any distribution-free testing algorithm for halfspaces over {0, 1}n that receives k samples must make queries on the input function, when k satisfies n.99 ≤ k ≤ O(n/ log3 n). For halfspaces over ℝn we show that any algorithm that makes a finite number of queries must draw Ω(n/log n) many samples. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-707-3 https://doi.org/10.1137/1.9781611977073Book Series Name:ProceedingsBook Code:PRDA22Book Pages:xvii + 3771
Relationship Between Commercial Bank Availability and Income Inequality

The objective of this study is to examine the relationship between commercial bank branches availability and income inequality. Further the study also assesses the interaction effect of corruption and commercial bank availability on income inequality. The present study uses panel data estimation methods for analysing the above relationship for SAARC countries (Afghanistan, Bangladesh, Bhutan, India, Nepal, Pakistan and Sri Lanka). The analysis suggests that a positive relationship exists between income inequality and financial availability in the initial stages. However, as the financial institutions reach a level of maturity and more people are integrated in the financial network, the level of income inequality starts reducing. Moreover, increase in financial availability helps in reducing income inequality when it is supported by less corrupt institutions. Policymakers should focus on reducing the level of corruption so as to enhance the effectiveness of the penetration of commercial bank branches.
Transformer-XL With Graph Neural Network for Source Code Summarization

Source code summarization is the task of generating a readable natural language to describe the functionality of source code. Code summarization is rapidly expanding, especially as the research takes great advantage of advances in neural networks and artificial intelligence technologies. Some mainstream methods input the structural information (abstract syntax tree (AST)) of the source code into the language model to generate relatively satisfactory comments. However, existing methods can not capture code&#x2019;s long dependencies from AST for effective code summarization. In this paper, we provide a novel way to generate code summaries by combining a graph-based neural network and a Transformer-XL network. We utilize the graph-based neural network to better capture the structure information of AST, and the Transformer-XL network to learn important tokens in the AST and alleviate the problem of long dependency. We evaluate our technique on the standard Java dataset. The experimental results show that the effectiveness of our model is remarkable. It pushes the precision score to 60.73% (5.21% absolute improvement) and the F1 score to 51.06%.
Residual Neural Network Model for Detecting Waste Disposing Action in Images

Waste in general has become a major problem for people around the world. Evidence internationally shows that everyone, or nearly everyone, admits to polluting at some point, with the majority of people littering at least occasionally. This research wants to overcome these problems, by utilizing computer vision and deep learning approaches. This research was conducted to detect the actions carried out by humans in the activities/actions of disposing of waste in an image. This is useful to provide better information for research on better waste disposal behavior than before. We use a Convolutional Neural Network model with a Residual Neural Network architecture to detect the types of activities that objects perform in an image. The result is an artificial neural network model that can label the activities that occur in the input image (scene recognition). This model has been able to carry out the recognition process with an accuracy of 88% with an F1-Score of 0.87.
LionKeeper: Automatically Discovering Timely Cybercrime Dynamics in the Age of COVID-19

As more activities are moving to digital platforms in the age of COVID-19 pandemic, cyber security becomes an increasingly critical issue. Thus, understanding how the recent pandemic has changed the Singapore cyber security landscape gains importance in unearthing potential weaknesses present in the infrastructure, which unfortunately is very challenging. In this paper, we propose, LionKeeper, an automated system for discovering the cyber security dynamics timely in Lion City - Singapore through social media data analytics. In particular, considering that the social media platforms like news websites provide immediate reports on local and global cybercrime incidents, in our system, we first crawl all the news articles from mainstream news sources such as CNA and Strait Times. Then, we analyze these news articles to identify those related to cybercrimes, the date and the location of cybercrime incidents, and employ a scoring system to detect the cyber security attack types and their significance. Additionally, based on the extracted information, we perform various analyses to generate meaningful insights for users to understand the cyber attack landscape dynamics before and during the COVID-19 pandemic automatically and intelligently. To the best of our knowledge, this is the first automated solution to understand the Singapore cyber landscape via social media analytics.
Network Information Utilization Technology Based on Information Retrieval Technology

With the rapid development of mobile network information, people are more and more used to understand the external dynamics through network information anytime and anywhere. When browsing the web page, we often see that the web page contains a lot of navigation bar, advertising information, copyright information and other information that has nothing to do with the content we care about. For users, this information not only makes them passively browse and waste valuable time, but also causes unnecessary traffic waste due to browsing information. This paper puts forward the research of network information utilization technology based on information retrieval technology. In the research and analysis, this paper selects 220 subjects involved in network information utilization technology as the survey sample, and through the comparative analysis of satisfaction, work efficiency and wealth value after the investment of information retrieval technology, the comparative results show that information retrieval technology has the advantages of resource sharing, complementary advantages and high search efficiency, which can promote the development of network information utilization. Through the survey, it is found that both network information workers and non-network information workers have more than 90% recognition of the role of information retrieval technology, which provides technical support for the use of network information technology.
Application of ontologically streamlined data for building performance analysis

Buildings are increasingly equipped with data monitoring infrastructures to collect multiple layers of dynamic data pertaining to the states and events related to systems’ performance, indoor-environmental conditions, occupants’ location, movement, and control-oriented actions representation of dynamic building related data. Efficiently utilized, this data could not only enhance the quality and effectiveness of buildings’ operational regimes, but also enrich the knowledge base for building design decision support. However, to exploit the potential of this data effectively, seamless data transformation processes are needed, starting from raw monitoring data and ending in adequately structured and visualized building performance data. In the present contribution, we provide a detailed blueprint of a well-defined and ontologically supported instance of such a transformation process. To illustrate this process, we focus in this paper on a specific class of building performance queries that require information on buildings’ visually relevant boundary conditions. Such queries pertain to, for example, the intensity of incident solar radiation of buildings’ envelope components, the daylight availability and 114distribution in interior spaces, and the expected output of building-integrated solar energy harvesting systems.
An Analysis on Text Mining Techniques for Smart Literature Review

With the development of web technologies, databases and social networks etc. a large amount of text data is generated each day. Mostof the data on the internet is in unstructured form. This unstructured data can provide valuable knowledge. For getting valuable knowledge from text data text mining techniques are used widely. As each day large amounts of research papers were published in journals and conferences. These research papers are very valuable for future research and investigations. These research papers act as a source for future innovations. Researchers write review papers to give updated knowledge about the specific field. But review papers used a limited number of papers and involved manually reading each paper. Due to the large volume of research papers published each day, it is not possible for the researchers to go through each paper to find the updated knowledge about their field of interest. To automate the literature analysis process different techniques of text mining were used. This paper provides a review of text mining techniques used in automatic literature analysis. We collected papers in which previous literature is used with text mining techniques to get valuable knowledge. This review paper presented an overview of text mining techniques, their evaluation criteria, their limitations and challenges for exploring literature to find research trends.
A Comparative Study on Remote Access of University Library Services at Manonmaniam Sundaranar University Tirunelveli and Kerala University Thiruvananthapuram

This is an era of information explosion. A large amount of information in all fields is being generated at every moment. Electronic information sources like E-book, E-journals, E-databases, E-theses and other electronic materials have increased exponentially. Most importantly, the academic libraries have a long history, starting with the chained, manual and closed access to libraries of earlier times to the present day hybrid, digital and virtual libraries that use the latest technology for giving information through a wide variety of services. Nowadays, these libraries are surrounded by networked data connected to internet-based services to make helpful information resources accessible to the academic community. The present study is comparing the remote access of library services of Manonmaniam Sundaranar University Tirunelveli and Kerala University Thiruvananthapuram. The researcher distributed four hundred questionnaires to the population and received only three hundred and thirty seven fully completed questionnaires. Moreover, the study revealed that the respondents have a favourable attitude towards the remote access of e-resources of MSUL and KUL.
Adaptation of project management methods and models to the improved PDCA

This article discusses the possibility of adapting existing project management methods to improve the Deming PDCA cycle and analyzing their effectiveness in comparison with their classical application. This study is relevant, since the application of project management methods almost always implies the use of a process approach, and the improvement of their basic principles can reduce costs and increase the return on the organization’s business processes.
Research on a Convolution Kernel Initialization Method for Speeding Up the Convergence of CNN

This paper presents a convolution kernel initialization method based on the local binary patterns (LBP) algorithm and sparse autoencoder. This method can be applied to the initialization of the convolution kernel in the convolutional neural network (CNN). The main function of the convolution kernel is to extract the local pattern of the image by template matching as the target feature of subsequent image recognition. In general, the Xavier initialization method and the He initialization method are used to initialize the convolution kernel. In this paper, firstly, some typical sample images were selected from the training set, and the LBP algorithm was applied to extract the texture information of the typical sample images. Then, the texture information was divided into several small blocks, and these blocks were input into the sparse autoencoder (SAE) for pre-training. After finishing the training, the weight values of the sparse autoencoder that met the statistical features of the data set were used as the initial value of the convolution kernel in the CNN. The experimental result indicates that the method proposed in this paper can speed up the convergence of the network in the network training process and improve the recognition rate of the network to an extent.
A Real-World Application Based on Blockchain

Blockchain is surely a creative creation and the decentralized process of blockchain technology provides an important way to secure data. The growth of digitalization has been improved day by day. This chapter mainly focuses about explaining various applications of blockchain to provide a secure way of communication for a progressive digitalized government, i.e., food supply chain, education, and health care system. It also describes about the challenges which mainly occur during implementing blockchain concept in the above-mentioned applications.
Anisotropic Edge Enhancement Based on an Exponential Phase Airy Spiral Phase Filter

An anisotropic Airy spiral phase filter with exponential phase is proposed. The simulation results prove that the proposed filter achieves higher-contrast edge enhancement with controllable direction and range of enhancement.
Reliability Analysis of Decentralized Autonomous Dispatching Centralized Station Subsystem Based on Fault Tree

In order to improve the maintenance efficiency of railway signal equipment, reduce the labor intensity of staff, reduce human errors, and realize intelligent fault diagnosis, the expert system and the fault tree are organically integrated, and the knowledge of the expert system knowledge base is used to infer and determine the cause of the failure mode of the system. Providing fault handling methods, suggestions, fault tree technology to generate expert system knowledge base, which intuitively reflects the logical relationship between faults and fault causes, fully reflects the advantages of both fault tree and expert system, and makes the reasoning effect more obvious. Taking the centralized dispatching system (CTC) station subsystem as an example, the realization process of the fault diagnosis and reasoning mechanism integrating fault tree and expert system is detailed.
Assessment of Class Balancing Techniques for Credit Card Deception Recognition

Nowadays, rapid progress in virtual companies and the internet is hugely changing the way of trading. Since E-commerce provides international companies to trade with each other and bridges the global trading gap, both flexibility and rivalry have increased. Along with this, unlawful activities on cyber space are also growing. The readily available tools in the market make such activities easier for cyber criminals. The companies providing credit cards need to distinguish deceitful transactions so that customers can be guarded from fraudulent actions. Much work has been done in order to detect fraudulent transactions based on user profile, spend behaviour, past transactions, category wise. However, in order to detect and prevent credit card frauds in real time environment, more technical advancements and a more strategic approach considering the nature of dataset, which is used to train the model, are needed. Since the nature of credit card transaction dataset is always imbalanced, this paper intends to evaluate the efficacy of class balancing techniques for detecting credit card related deceptions. Two variations of balanced datasets are created by exploiting popular approaches like under sampling and oversampling (SMOTE). In order to evaluate the efficacy, a simple neural network with only one hidden layer has been implemented and it has been trained with balanced datasets. Our experimental results show that the model trained with dataset gives results that are more accurate.
SAR-GPA: SAR Generation Perturbation Algorithm

The deep learning is widely used in optical image and synthetic aperture radar (SAR) image. Current academic research shows that adversarial perturbation can effectively attack the deep learning network in optical image. However, in SAR image target recognition network, the existence of universal perturbations and generation approach needs to be further explored. Here, this article firstly proposes a systematic SAR generation perturbation algorithm (SAR-GPA) for target recognition network. The modulation phase sequences of the jamming points can vary casually by using the state-of-the-art electromagnetic metasurface technology. Therefore, when it acts on the SAR deceptive jamming system, it can produce artificial controllable perturbations. First, we take the imperceptible perturbations from universal adversarial perturbations (UAP) as reference to construct a unconstrained minimum optimization problem to find the specific sequences. Then, we solve this issue by adaptive moment estimation (Adam) optimizer.Thus, the SAR adversarial examples can be quickly and flexibly generated through our system. Finally, We design a series of simulation and experiment to verify the effectiveness of the adversarial examples and also the modulation sequences. According to the results, different from the traditional SAR blanket jamming methods, our approach can quickly generate imperceptible jamming, which can effectively attack three classical recognition models.