Reviewerly/exHarmony huggingface dataset:
Distributed Memory Parallel Algorithms for Massive Graphs

To analyze massive graphs, parallel algorithms became a necessity. In addition to huge processing time, massive graphs pose another challenge of a large memory requirement. These graphs may not fit in the main memory of a single processing unit, and the algorithms must be able to work on a small part of the graph at a time. To work with such massive graphs, distributed-memory parallel systems can be a suitable platform for many graph problems. This chapter discusses distributed-memory parallel algorithms that are designed to work with massive graphs for several class of graph problems: random graph generation, network connectivity problems, and finding underlying community structure of a network. Specifically, algorithms for the following graph problems have been discussed in this chapter: generating random graphs with Erdos-Renyi and preferential attachment model, switching edges and generating random graphs with given degree sequences, single source shortest path, breadth-first search, counting triangles, and community detection. These algorithms have been developed for three different distributed-memory computation models: message passing interface (MPI), MapReduce-based Hadoop, and Giraph.
Partitioning Trillion-Edge Graphs

This chapter introduces XtraPuLP, a new distributed-memory graph partitioner designed to process trillion-edge graphs. There has been some progress made in the recent past toward such partitioning methods. There are methods using random or edge-based distributions, label propagation-based graph partitioning methods, adaptations of traditional partitioning methodologies to small-world graph instances, and methods using two-dimensional distributions. The label propagation community detection algorithm is a fast and scalable method for detecting communities in large networks. There can be a considerable imbalance after the initialization phase. After the 3 outer iterations of the vertex balance-refinement stages, the edge balance-refinement stages begin. It demonstrates how XtraPuLP can scale to graphs several orders of magnitude larger than the related work.
Students' responses to learning multimedia on the human digestive system during Covid-19 Pandemic

This study aimed to determine students' satisfaction with learning multimedia on the human digestive system topic at a public high school (SMA Negeri 1 Praya Tengah). This study applied a quantitative approach with a survey method. The population in this study were students of class XI MIPA SMA Negeri 1 Praya Tengah. Meanwhile, the sample was determined by purposive sampling with the category of students using multimedia learning media on human digestive material. Therefore, the sample was 35 students from class XI MIPA 3. The data on students' satisfaction was conducted using a Likert-scale questionnaire instrument. The data analysis technique used was calculating the percentage of the data and then interpreting the data percentage score. The results of data analysis showed that the level of students' satisfaction was 94.14%, with the interpretation in the category of very satisfying. It can be concluded that students are very satisfied with multimedia learning media on the human digestive system material at SMA Negeri 1 Praya Tengah.
HTML Data Capture and User Portrait Data Mining of Landscape Co-Design Platform Based on Distributed Mobile Workstations

The application of CoDesign platform in landscape construction is discussed. Through software such as TerraBuilder and TerraExplorer Pro, lightweight Web data mining and transformation can be realized by using HTML Tidy. The transformation process mainly solves the separation of schema information to be expressed by HTML documents and their collections. The conversion step is to use the standard class library provided by the distributed mobile workstation, and use the super computing performance of the cloud platform to structure the unstructured HTML data to improve the data processing efficiency of the mobile intelligent terminal. Comprehensive application.
Special issue: Connected Vehicles meet Big Data technologies: Recent Advances and Future Trends

The startling rise in smart vehicles stimulates the rapid development of new paradigms such as Social Internet of Vehicle (SIoV) and Vehicular Fog Computing (VFC). Trustworthiness has been regarded as a dominating issue in all the have-to-be-addressed issues in SIoV, and many reputation-based countermeasures have been adopted to solve the trustiness-related issues in IoV. However, little attention has been paid to the reputation of vehicles when they provision computational resources in VFC, which is worthy of further investigation since some fog vehicles pursue more revenues or fewer costs at the expense of delivering poor-quality computing services. Such selfish behaviors should be discouraged. In this paper, we put forward a reputation-based service provisioning scheme, and a reputation management scheme consisting of the decentralized reputation updating and global reputation synchronization in VFC, aiming to prevent the fog vehicles from delivering low-quality computing services by maximizing the accumulated reputation of all the serving fog vehicles in the optimization period. An online approach is adopted to handle the requests in a slot-by-slot way. The simulation results show its effectiveness and advantages when compared to other existing approaches.
Research on Vision Measurement Method Based on Extreme Learning Machine and Distance Detection

An objective of this study was to propose a dynamic adaptive eyesight test method based on extreme learning machine and mobile phone distance detection technology. One of the main obstacles in vision measurement is accuracy and convenience which are antithetical. In this paper an eyesight test APP was developed focused on daily use based on the characteristics of the family environment, which improved the convenience and accuracy of eyesight test on phone. In order to measure the distance from eyes to the phone we adopted the Monocular Vision Technology with the facility of the front camera of the mobile phone. Detecting the brightness of the mobile phone screen and ambient light, considering the congestion and appearance of the visual target, with distance measured before, the APP may calculate user&#x2019;s eyesight based on the relationship between distance, brightness, congestion of the visual target and the visual acuity value specified by the Near Visual Acuity Chart. Through Extreme Learning Machine, the APP revised the measurement deviation making the measurement result close to the medical optometry result. Based on the method proposed in this paper, a vision detection system was developed and tested. The results show that the average error of the system is 2.80%, and the maximum error is 6.82%.
The Role of Knowledge Sharing Behavior, Team Culture, and High Performance Work Practices in Enhancing Service Innovation Performance

This study aims to examine the significant role of knowledge sharing behavior, team culture, and high-performance work practices on the service innovation performance of frontline employees in the Vietnamese hotel industry.Data collection from 303 frontline employees working at hotels across Vietnam through online platforms was used for testing the research model.All proposed hypotheses are supported by the results of structural equation modeling, showing the most significant factor influencing the service innovation performance of employees is high-performance work practices and the significance of knowledge sharing and team culture.Service organizations should concentrate on human resource practices to enhance employees' motivation to share knowledge in and among the workplace, fostering organizational performance.
Comparative Study between Leading Transfer Learning Architectures for Source Camera Identification

The all-embracing use of digital images has revamped the quality of life and security to a great extent. Right from finding an item on online shopping websites through a clicked picture, to CCTV cameras being used for road traffic control, the users have learnt to appreciate the existence of technology being as advanced. However, one cannot overlook the gravity of this technology being misused. Although, the digitization has incorporated advanced concepts like Computer Vision and Deep Learning for security-check and crowd control, this has encouraged the advancement of courtroom discussions. Framing people for wrongdoings they are not involved with, on the basis of a fake “digital proof,” is one of the newly faced muddles. False allegations on a person, on the basis of a picture or a video, can potentially put a question on the existence of a person. The need to find the legitimacy of a produced image is therefore, of utmost importance. There have been various studies over the years, wherein a lot of methods were proposed to develop a system that identifies the camera model. Through this paper, we aim to produce a comparative study between four leading architectures, DenseNet, Inception V3, MobileNetV2 and Exception(XCeption), and suggest a the most competent architecture for commercialization of this system.
Exploiting Dynamic and Fine-grained Semantic Scope for Extreme Multi-label Text Classification

Extreme multi-label text classification (XMTC) refers to the problem of tagging a given text with the most relevant subset of labels from a large label set. A majority of labels only have a few training instances due to large label dimensionality in XMTC. To solve this data sparsity issue, most existing XMTC methods take advantage of fixed label clusters obtained in early stage to balance performance on tail labels and head labels. However, such label clusters provide static and coarse-grained semantic scope for every text, which ignores distinct characteristics of different texts and has difficulties modelling accurate semantics scope for texts with tail labels. In this paper, we propose a novel framework TReaderXML for XMTC, which adopts dynamic and fine-grained semantic scope from teacher knowledge for individual text to optimize text conditional prior category semantic ranges. TReaderXML dynamically obtains teacher knowledge for each text by similar texts and hierarchical label information in training sets to release the ability of distinctly fine-grained label-oriented semantic scope. Then, TReaderXML benefits from a novel dual cooperative network that firstly learns features of a text and its corresponding label-oriented semantic scope by parallel Encoding Module and Reading Module, secondly embeds two parts by Interaction Module to regularize the text’s representation by dynamic and fine-grained label-oriented semantic scope, and finally find target labels by Prediction Module. Experimental results on three XMTC benchmark datasets show that our method achieves new state-of-the-art results and especially performs well for severely imbalanced and sparse datasets.
Interacting with Service App: Reviewing Users’ Body and Hand Gestures on Ride-hailing Mobile App

Amid its ease of use and availability, ride-hailing service has become apparent and easily found in Indonesia.As a result, ride-hailing app is increasingly popular and commonly used by Indonesian commuters, especially those within the city limit and its surrounding urban area.Yet, despite its high usage, no studies have been found on how effective users' body and hand gestures work when utilizing the app on a mobile phone display.This study aimed to observe people's body and hand gestures when using ride-hailing apps on mobile phones, exposing the possibility of making a better design recommendation.Users' body and hand gestures were observed and categorized into various types of body gestures, arm movements, and dominant hand usage.Seven main gestures and ten sub-gestures are exposed.Results show that most users act to use two-hands coordination by standing gestures with both arms raised.This result becomes valuable insights in understanding users' actual behaviors and may serve as a supporting reference for the development of similar service apps in the future.
WITHDRAWN: Image compression of brain MRI images using an autoencoder and restricted Boltzmann machine

This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at https://www.elsevier.com/about/our-business/policies/article-withdrawal.
Analogy of Machine Learning Approaches and BERT for Sentiment Analysis

For assessing customer sentiment in Amazon product reviews, this article compares two machine learning algorithms and a deep learning method, BERT (Bidirectional Encoder Representations from Transformer). Machine learning is the most practical approach in the current era of artificial intelligence for training a neural network to handle the majority of real-world issues. In this paper, the real-world scenario of sentiment analysis is considered, ideally the classification problem. Firstly, the data is provided into a model, which evaluates the feature that uses the Term Frequency (TF) and Inverse Document Frequency (IDF) pre-processing methods. Secondly, the algorithms, Naive Bayes classifier and Support Vector Machine are used to analyze the sentiment of the consumer comments and compute metrics like F1 score. Finally, the input data is fed for BERT to process and compute the F1 score. To summarize, this study is to provide a detailed comparative analysis of machine learning techniques and deep learning algorithms.
E-Learning Moodle Usability Evaluation Using the SUS Questionnaire in Higher Education

The COVID-19 pandemic has had a huge impact on all sectors, from health, economy, social, religious to education. A very real impact in the world of education is the policy of the central and local governments to carry out online teaching and learning processes for all educational institutions from kindergartens to universities to break the chain of the spread of covid 19. So that all universities are required to be able to take advantage of various information technologies available has been very developed, one of which is E-Learning, E-Learning is a form of technological development that can be used for electronic-based teaching and learning processes through the internet network. As an effort to realize the campus trend in information technology, ABC University developed an educational platform in the campus environment by maximizing the use of information technology called WASKITA which functions as a support for teaching and learning activities at universities. This study aims to evaluate e-learning to determine the quality of the applications that have been implemented using the System Usability Scale (SUS) questionnaire. The number of respondents in this study were 34 respondents. The evaluation is done by giving tasks according to the features that exist in Waskita so that it can be known what factors must be improved then conducting interviews with users in this case lecturers at ABC College to be able to process any recommendations that will be given to the manager. The results of the usability measurement of E-Learning Moodle accessed at (Waskita.amikom.ac.id) show the average SUS score is 50 which means that the acceptance rate of this E-Learning is at Marginal Low then the adjective value indicates Poor with a grade scale at level E. Users think that Moodle E-Learning is still complicated to use, causing users to consider using E-Learning especially on mobile applications.
Meta learning-based open-set identification system for specific emitter identification in non-cooperative scenarios

The development of wireless communication technology has led to the underutilization of radio spectra.To address this limitation, an intelligent cognitive radio network was developed.Specific emitter identification (SEI) is a key technology in this network.However, in realistic non-cooperative scenarios, the system may detect signal classes beyond those in the training database, and only a few labeled signal samples are available for network training, both of which deteriorate identification performance.To overcome these challenges, a meta-learningbased open-set identification system is proposed for SEI.First, the received signals were preprocessed using bi-spectral analysis and a Radon transform to obtain signal representation vectors, which were then fed into an open-set SEI network.This network consisted of a deep feature extractor and an intrinsic feature memorizer that can detect signals of unknown classes and classify signals of different known classes.The training loss functions and the procedures of the open-set SEI network were then designed for parameter optimization.Considering the few-shot problems of open-set SEI, meta-training loss functions and meta-training procedures that require only a few labeled signal samples were further developed for open-set SEI network training.The experimental results demonstrate that this approach outperforms other state-ofthe-art SEI methods in open-set scenarios.In addition, excellent open-set SEI performance was achieved using at least 50 training signal samples, and effective operation in low signalto-noise ratio (SNR) environments was demonstrated.
Geometric generative adversarial net based multiple methods for spectrum sensing in cognitive radio networks

The majority of recently developed approaches require a significant number of labelled samples. The proposed system are dedicated to using less marked samples for automatic modulation detection in the cognitive radio domain. The proposed signal classifier generative adversarial nets (GANs) methodology is a semi-supervised learning framework that focuses on adversarial analysis GANs are a major step forward in the development of competitive generative networks, and they've spawned a slew of apparently unrelated versions. The discovery of a single geometric form in GAN and its derivatives is one of the paper's key contributions. In three geometric stages, by demonstrate how to train an adversarial generative model: updating the discriminator parameter away from the separating hyperplane, looking for the separating hyperplane, and updating the generator along the usual vector route of the separating hyperplane. The shortcomings in current approaches are shown by this geometric intuition, leading us to suggest a new geometric GAN formulation that maximizes the margin using SVM separating hyperplane. An equilibrium is reached between the discriminator and generator in the geometric GAN, according to our theoretical research. Furthermore, detailed computational results showing the superior efficiency of the GAN engineering network were obtained.
RA-MISNet: multilevel information sharing network based on residual attention for crowd counting

Crowd counting has been a popular research topic in the field of computer vision due to the variation of human head scales and the interference of background noise. Some existing methods use multi-level feature fusion to solve scale variation, but the problem of background noise interference may be more serious due to the involvement of shallow features in the feature fusion process. In this paper, we propose a Multilevel Information Sharing Network based on Residual Attention(RA-MISNet) to solve this problem. The RA-MISNet consists of a feature extraction component, an information sharing module and a residual attention density map estimator. On the basis of solving the multi-scale problem, the residual attention mechanism is adopted by our proposed method to refine the population distribution information in sharing features at all levels, which can reduce the interference of complex texture background on density map regression. Furthermore, owing to the severe label noise interference problem in high-density crowd areas, we design a Regional Multi-level Segmentation Loss (RMS Loss) to divide the multi-level density regions with different label noise rates in a single crowd image and apply the corresponding granularity supervision constraints for each density level region. Extensive experiments on three crowd counting datasets (ShanghaiTech, UCF CC 50, UCF-QNRF) demonstrate the effectiveness and superiority of the proposed methods.
Detecting and Confining Software Errors: An Evaluation of Three Executable Assertions Based Solutions

The complexity of current software and the various dependencies between their components can affect their reliability, in particular their ability to tolerate faults. In object-oriented software, the interactions derived from such dependencies can lead to the propagation of errors. This problem can be resolved by implementing error detection and error containment mechanisms. Such techniques are used to tolerate faults. In this paper, we present three solutions where a component is reinforced in order to detect errors and limit their propagation to dependent components.
Fight for vaccine diplomacy in the global world of IPR and trademarks

The article aims to shed to light on the topic of vaccine diplomacy and the role of IPR and trademarks in vaccine crisis. The research design is explanatory. The data collection methods and the data analysis methods have been discussed. The quantitative analysis has shown that the “regression model” was insignificant as the higher prices of the vaccines allowed the countries to enjoy the profit as the imposition of IPRs forces the poor countries to buy from the “developed countries”. The qualitative analysis tends to show the imposition of IPRs creates barriers for the less developed countries to have vaccines.
Clustering with Queries under Semi-Random Noise

The seminal paper by Mazumdar and Saha \cite{MS17a} introduced an extensive line of work on clustering with noisy queries. Yet, despite significant progress on the problem, the proposed methods depend crucially on knowing the exact probabilities of errors of the underlying fully-random oracle. In this work, we develop robust learning methods that tolerate general semi-random noise obtaining qualitatively the same guarantees as the best possible methods in the fully-random model. More specifically, given a set of $n$ points with an unknown underlying partition, we are allowed to query pairs of points $u,v$ to check if they are in the same cluster, but with probability $p$, the answer may be adversarially chosen. We show that information theoretically $O\left(\frac{nk \log n} {(1-2p)^2}\right)$ queries suffice to learn any cluster of sufficiently large size. Our main result is a computationally efficient algorithm that can identify large clusters with $O\left(\frac{nk \log n} {(1-2p)^2}\right) + \text{poly}\left(\log n, k, \frac{1}{1-2p} \right)$ queries, matching the guarantees of the best known algorithms in the fully-random model. As a corollary of our approach, we develop the first parameter-free algorithm for the fully-random model, answering an open question by \cite{MS17a}.