Reviewerly/exHarmony huggingface dataset:
Sparse matrix‐vector and matrix‐multivector products for the truncated SVD on graphics processors

Summary Many practical algorithms for numerical rank computations implement an iterative procedure that involves repeated multiplications of a vector, or a collection of vectors, with both a sparse matrix and its transpose. Unfortunately, the realization of these sparse products on current high performance libraries often deliver much lower arithmetic throughput when the matrix involved in the product is transposed. In this work, we propose a hybrid sparse matrix layout, named CSRC, that combines the flexibility of some well‐known sparse formats to offer a number of appealing properties: (1) CSRC can be obtained at low cost from the popular CSR (compressed sparse row) format; (2) CSRC has similar storage requirements as CSR; and especially, (3) the implementation of the sparse product kernels delivers high performance for both the direct product and its transposed variant on modern graphics accelerators thanks to a significant reduction of atomic operations compared to a conventional implementation based on CSR. This solution thus renders considerably higher performance when integrated into an iterative algorithm for the truncated singular value decomposition (SVD), such as the randomized SVD or, as demonstrated in the experimental results, the block Golub–Kahan–Lanczos algorithm.
Toward TPC-UX: UX Topics in TPC Journals 2013–2022

This article offers a content analysis of technical and professional communication articles related to user experience (TPC-UX) published between 2013 and 2022 in six TPC scholarly journals. This analysis reveals that TPC-UX primarily focuses on product and process topics and illustrates the terminological comingling of user experience and usability. Specific TPC-UX topics identified include theory, multimodality, health and medicine, localization, web design, mobile applications, accessibility, and content strategy. These topics suggest that TPC-UX's key affordances are its attunement to networked power dynamics, its theoretically rich treatment of multimodality, and its strategies for navigating contextual complexities.
Financial Institutions Dynamics, Investments and Development

The study evaluates how specific features of financial institutions and investment sources influence enduring development among economies in the sub-region of Sub-Saharan Africa (SSA). Data for the interactions in question were compiled from 36 economies in SSA from 1996 to 2019, and various empirical estimates were carried out using the two-step system Generalized Method of Moments statistical framework. Results from the analyses suggest that growth in depth, improved access, and efficiency of financial institutions foster long-term development among economies in the sub-region. Investments in various forms were found to have a varied augmenting impact on long-term development. Further empirical analyses suggest that quality of governance has a significant positive moderating impact on how net foreign direct investment and domestic investments influence development among economies in the sub-region. Political instability is, however, found to negate gains to development from both investment growth and contributions from financial institutions. JEL Codes: C33, G2, E13
Explicit matrix gradient expression for residual network

ABSTRACTResidual Network (ResNet) is a distinguished network structure in deep learning, and its layers can be profound. We theoretically explore the mathematics characteristics of the ResNet, in particular, to pay attention to the gradient information, which is a powerful and straightforward mathematical tool for analysing the properties of ResNet, such as the gradients of the loss function with respect to the input and the weight parameters and the gradient of the entry of the logits with respect to the input. A theorem about the explicit matrix expression of gradients in Resnet is given in this work. A rigorous mathematical and logical derivation of the theorem is obtained in detail by the matrix derivative definition and matrix differentiation. We further provide explicit matrix expressions of some deep learning algorithms in ResNet, including backpropagation, gradient-based adversarial attacks, and gradient-based saliency maps. Furthermore, the reasons why the ResNet network works are analysed. Finally, experimental results are provided to verify the correctness and efficiency of the proposed theorem.KEYWORDS: ResNetmatrix gradient expressionbackpropagationsaliency mapadversarial example AcknowledgementsThis work is supported in part by the National Key R&D Program of China under Grant 2017YFB0802800.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1. Pytorch 1.8.0 version.2. Tensorflow 1.14.0 version.
Ensemble learning for score likelihood ratios under the common source problem

Abstract Machine learning‐based score likelihood ratios (SLRs) have emerged as alternatives to traditional likelihood ratios and Bayes factors to quantify the value of evidence when contrasting two opposing propositions. When developing a conventional statistical model is infeasible, machine learning can be used to construct a (dis)similarity score for complex data and estimate the ratio of the conditional distributions of the scores. Under the common source problem, the opposing propositions address if two items come from the same source. To develop their SLRs, practitioners create datasets using pairwise comparisons from a background population sample. These comparisons result in a complex dependence structure that violates the independence assumption made by many popular methods. We propose a resampling step to remedy this lack of independence and an ensemble approach to enhance the performance of SLR systems. First, we introduce a source‐aware resampling plan to construct datasets where the independence assumption is met. Using these newly created sets, we train multiple base SLRs and aggregate their outputs into a final value of evidence. Our experimental results show that this ensemble SLR can outperform a traditional SLR approach in terms of the rate of misleading evidence and discriminatory power and present more consistent results.
The English Majored Students' Perceptions of Using Flipgrid in Online Speaking Classrooms

In this day and age, as COVID-19 has led many schools to study online, technology plays an important role in facilitating classrooms. Speaking seems to be one of the most difficult skills to study online, as this needs direct communication among students. There has been much research on using technological applications to assist students in online learning. However, studies exploring university students' perceptions of online platforms such as Flipgrid are quite scarce. This mixed-method study aimed to explore that aspect by using questionnaires and interviews to investigate students' perspectives after a course using Flipgrid in online speaking classrooms. The participants were first-year students who studied online speaking modules in Foreign Language Department at Van Lang University. The result showed that students believed that Flipgrid created a sharing environment for them to learn from their friends and lower their anxiety as this did not bring too much pressure they have when they needed to present in front of face-to-face classrooms. The findings also revealed that there were some discrepancies in the perceptions between high and low-level students in terms of improving their pronunciation. High achievers tend to value this benefit more than their lower counterparts.
Hierarchical Transformer with Spatio-temporal Context Aggregation for Next Point-of-interest Recommendation

Next point-of-interest (POI) recommendation is a critical task in location-based social networks, yet remains challenging due to a high degree of variation and personalization exhibited in user movements. In this work, we explore the latent hierarchical structure composed of multi-granularity short-term structural patterns in user check-in sequences. We propose a Spatio-Temporal context AggRegated Hierarchical Transformer (STAR-HiT) for next POI recommendation, which employs stacked hierarchical encoders to recursively encode the spatio-temporal context and explicitly locate subsequences of different granularities. More specifically, in each encoder, the global attention layer captures the spatio-temporal context of the sequence, while the local attention layer performed within each subsequence enhances subsequence modeling using the local context. The sequence partition layer infers positions and lengths of subsequences from the global context adaptively, such that semantics in subsequences can be well preserved. Finally, the subsequence aggregation layer fuses representations within each subsequence to form the corresponding subsequence representation, thereby generating a new sequence of higher-level granularity. The stacking of hierarchical encoders captures the latent hierarchical structure of the check-in sequence, which is used to predict the next visiting POI. Extensive experiments on three public datasets demonstrate that the proposed model achieves superior performance while providing explanations for recommendations.
Data Controlling and Security Issues in Cloud

Cloud computing has been the most demanding technology in the last decade, which several organizations have widely adopted. Most enterprise applications and data are migrated to the public or hybrid cloud due to the ease of access and infrastructure facilities provided by the cloud provider. One significant issue in the cloud environment is the security and data controlling mechanism, which needs to be focused on for better service quality. Users usually hesitate to use public cloud services due to privacy and security concerns about the personal data they want to process online. Unlike standard cloud computing notions, serverless computing does not expose the infrastructure or platforms on which the services operate to the end-user. Clients only have to worry about the application's business logic, and the service provider usually takes care of everything, including configuration, infrastructure provision, servers, etc. In this chapter, the authors have discussed several cloud computing issues regarding data security and data controlling mechanisms.
Applications of Deep Learning-Based Product Recommendation Systems

The high-tech world we live in today is dominated by multimedia. Multimedia is being created at a rapid rate in the current technological era. Consumption and the exchange of the same between users happen quickly. Choosing whatever form of content or multimedia to consume next depending on interests and preferences is a conundrum while consuming this content. Nowadays, all online streaming sites utilize multimedia recommender systems. These are utilized to anticipate the following collection of multimedia that users can enjoy based on their prior usage patterns. By identifying the points of commonality between the user and the goods, preexisting models can forecast this utilizing the collaborative field. By treating this as a sequence prediction problem, the proposed model in this chapter increases the predicted accuracy using collaborative filtering (CF), ripple nets, deep learning, and recurrent neural networks (RNNs).
A Framework for Classifying Imbalanced Tweets Using Machine Learning Techniques

The research work presented focuses on utilizing social media platforms as a source of data to diagnose depression-related issues. The popularity of social platforms such as LinkedIn, Instagram, Twitter, YouTube, and Facebook, gave researchers an opportunity to analyse user experiences and gain insights into depression. Depression is a significant problem that affects individuals' lives, disrupts normal functioning, and impacts their perspectives. The primary objective of this research is to employ machine learning (ML) approaches for classifying tweets. Additionally, the research addresses the issue of data imbalance by using sampling techniques. This research work utilizes a sampling technique to normalize the dataset. The study explores four techniques that helps to extract meaningful information from the tweets. The research work conducts an empirical study to evaluate the performance of various ML techniques. Based on the experimental results, it is found that the AdaBoost classifier with the BoW feature extraction technique achieves the best results among all the classifiers tested.
Review on Different Types of Disturbing Noise for Degraded Complex Image

This chapter provides an analysis of the various kinds of distracting noise that can be seen in degraded complex images, such as those found in newspapers, blogs, and websites. A complicated image that had been deteriorated as a result of noise such as salt and pepper noise, random valued impulse noise, speckle noise, and Gaussian noise, amongst others, was the result. There is an extraordinarily high demand for saving the text that can be read from complicated images that have been degraded into a form that can be read by computers for later use.
Early Detection of Breast Cancer with IoT: A Promising Solution

Breast cancer is a serious health concern that affects millions of women worldwide. Early detection is crucial for effective management and increasing the chances of survival. However, traditional methods of breast cancer detection can be time-consuming, expensive, and inaccessible to many women. In recent years, the Internet of Things (IoT) has emerged as a promising solution for healthcare providers to monitor and manage patients remotely. By leveraging IoT devices such as wearables and sensors, healthcare providers can collect data on patients' health and track changes over time. This paper explores the use of IoT in breast cancer detection and management. Breast Cancer Detection System using IoT presented in this work that combines wearable devices and sensors to monitor changes in breast tissue and collect data on a patient's health. The collected data is analysed using machine learning algorithms to identify potential abnormalities and provide early warning signs of breast cancer. The system is designed to be affordable and accessible, making it particularly suitable for use in low-income or rural areas where traditional methods of breast cancer detection may not be available.
Blockchain-Based File Tracking and Data Management System for Education Sector

Document management is a serious problem for educational institutions. Records are lost, time is lost, and resources are wasted when documentation is organized in a sequential or numbered order traditionally. The hardest problem facing today’s educational institutions may be record keeping. Filtering through documents wastes time and energy, and deadlines are missed while searching for misspelt records. This paper presents a blockchain-based file tracking system and college students' data governance platform that utilizes the Chameleon Hash Function in conjunction with the Advanced Encryption Standard (AES) algorithm on Ethereum. The Chameleon Hash Function is used to provide an additional layer of security to the platform by enabling the encryption of files and metadata with a secret key that is derived from the contents of the file itself. The AES algorithm is used for optimization and to find the optimal solution to the problem of file tracking and data governance in higher education institutions. The system includes features such as access control, audit trails, and secure sharing of files. The proposed scheme enables higher education institutions to operate in a secure environment for data sharing and reliable information management.
Automated Diagnosis of "Rheumatoid arthritis" based on CNN

Diseases like "rheumatoid arthritis" are chronic inflammatory conditions that wreak havoc on the body's tissue, most noticeably the joints. An efficient system analysis is necessary for manual "Rheumatoid arthritis" identification and diagnosis, especially in the pre-diagnostic stages. Using image processing and a convolutional neural network, this study aims to create an intelligent system capable of identifying hand "Rheumatoid arthritis" cases. There are two primary parts to the system. "Image processing" refers to the procedure that begins with the processing of images. Pre-processing, image segmentation, and Gabor filter-based feature extraction are all examples of such methods. In the second stage, the retrieved characteristics are sent into a neural convolution network, which determines whether or not the shown hands are healthy (arthritic). Using normal and abnormal hand pictures, the "CNN" algorithm does classification. The identification rate in the experiments was 83.5% when the same number of photos was used as in the test set.
Scalable Experimental Bounds for Dicke and GHZ States Fidelities

Estimating the state preparation fidelity of highly entangled states on noisy intermediate-scale quantum (NISQ) devices is an important task for benchmarking and application considerations. Unfortunately, exact fidelity measurements quickly become prohibitively expensive, as they scale exponentially as O(3N) for N-qubit states, using full state tomography with measurements in all Pauli bases combinations. However, Somma et al. [20] established that the complexity could be drastically reduced when looking at fidelity lower bounds for states that exhibit symmetries, such as Dicke States and GHZ States. For larger states, these bounds still need to be tight enough to provide reasonable estimations on NISQ devices.
E2C: A Visual Simulator to Reinforce Education of Heterogeneous Computing Systems

Heterogeneity has been an indispensable aspect of distributed computing throughout the history of these systems. ln particular, with the increasing popularity of accelerator technologies (eg., GPUs and TPUs) and the emergence of domain-specific computing via ASlCs and FPGA, the matter of heterogeneity and understanding its ramifications on the system performance has become more critical than ever before. However, it is challenging to effectively educate students about the potential impacts of heterogeneity on: (a) the performance of distributed systems; and (b) the logic of resource allocation methods to efficiently utilize the resources. Making use of the real infrastructure (such as those offered by the public cloud providers) for benchmarking the performance of heterogeneous machines, for different applications, with respect to different obiectives, and under various workload intensities is cost- and time-prohibitive. Moreover, not all students (globally and nationally) have access or can afford such real infrastructure. To reinforce the quality of learning about various dimensions of heterogeneity, and to decrease the widening gap in education, we develop an open-source simulation tool, called E2C, that can help students researchers and practitioners to study any type of heterogeneous (or homogeneous) computing system and measure its performance under various system configurations. To make the learning curve shallow, E2C is equipped with an intuitive graphical user interface (GU1) that enables its users to easily examine system-level solutions (scheduling, load balancing, scalability, etc.) in a controlled environment within a short time and at no cost. In particular, E2C is a discrete event simulator that offers the following features: (i) simulating a heterogeneous computing system; (ii) implementing a newly developed scheduling method and plugging it into the system, (iii) measuring energy consumption and other output-related metrics; and (iv) powerful visual aspects to ease the learning curve for students. We used E2C as an assignment in the Distributed and Cloud Computing course. Our anonymous survey study indicates that students rated E2C with the score of 87 out of 10 for its usefulness in understanding the concepts of scheduling in heterogeneous computing. Moreover, our pre- and post-evaluations indicate that E2C has improved the students’ understanding of heterogeneous computing systems by around 18%.
Transforming 3D Models to Semantic Web Representation

"The purpose of the present paper is to research a rule-based approach for transforming X3D (eXtensible 3D) models to RDF (Resource Description Framework). The transformation is performed by using the RDF Mapping Language (RML). Its advantages are summarized, which are mainly due to the fact that the rules created build a knowledge base. By applying SPARQL (SPARQL Protocol and RDF Query Language) queries to it, the possibility of explore in order to validate and improve the defined RML rules themselves, is pointed out. An approach for reversing from the RDF triples to the original X3D in a unique way is considered, and the types of SPARQL queries needed for its implementation are systematized. Rules are formulated for all elements defined in the X3D schema, their attributes and properties are described. Their accessibility is ensured. The conversion of X3D models to RDF is confirmed to be consistent with guidelines and best practices for creating accessible, understandable, and reusable ontologies on the Web. The systematized SPARQL query types for reversing from RDF triples to the original X3D are checked for specific elements and sample data, and the obtained results establish their correctness. The prerequisites and limitations of the represented approach are explained. The proposed approach allows building a comprehensive knowledge base that includes the RML rules, the transformed X3D models and the domain-specific ontology and its use to analyzing data and semantic reasoning. The electronic libraries that include 3D content could take advantage from the benefits and possible future applications of the solutions discussed in this study."
A Breast Density Classification System for Mammography Considering Reliability Issues in Deep Learning

In a convolutional neural network (CNN) classification model for diagnosing medical images, transparency and interpretability of the model's behavior are crucial in addition to high classification accuracy, and it is highly important to explicitly demonstrate them.In this study, we constructed an interpretable CNN-based model for breast density classification using spectral information from mammograms.We evaluated whether the model's prediction scores provided reliable probability values using a reliability diagram and visualized the basis for the final prediction.In constructing the classification model, we modified ResNet50 and introduced algorithms for extracting and inputting image spectra, visualizing network behavior, and quantifying prediction ambiguity.From the experimental results, our proposed model demonstrated not only high classification accuracy but also higher reliability and interpretability compared to the conventional CNN models that use pixel information from images.Furthermore, our proposed model can detect misclassified data and indicate explicit basis for prediction.The results demonstrated the effectiveness and usefulness of our proposed model from the perspective of credibility and transparency.
A study on cloud container technology

Containerization is a crucial technology for automating the management of containers, which can save time and effort. This overview provides valuable insights into cloud container technology, specifically focusing on Swarm Container Orchestration and Automation. The research emphasizes the importance of automation in reducing manual tasks and achieving cost-efficiency. It also highlights the significance of horizontal and vertical scaling for Web Distributed Authoring and Versioning (WebDAV), with considerations of performance, reliability, and cost. Resiliency and redundancy are explored as crucial components for fault tolerance in the WebDAV system. Specific technologies for different aspects of WebDAV's infrastructure, such as compute, user management, storage, networking, databases, resource management, and budgeting, are explored in this paper. These technologies are recommended because they offer the flexibility, scalability, and cost-effectiveness that align with WebDAV's requirements.