Reviewerly/exHarmony huggingface dataset:
Loss Re-Scaling VQA: Revisiting the Language Prior Problem From a Class-Imbalance View

Recent studies have pointed out that many well-developed Visual Question Answering (VQA) models are heavily affected by the language prior problem. It refers to making predictions based on the co-occurrence pattern between textual questions and answers instead of reasoning upon visual contents. To tackle this problem, most existing methods focus on strengthening the visual feature learning capability to reduce this text shortcut influence on model decisions. However, few efforts have been devoted to analyzing its inherent cause and providing an explicit interpretation. It thus lacks a good guidance for the research community to move forward in a purposeful way, resulting in model construction perplexity towards overcoming this non-trivial problem. In this paper, we propose to interpret the language prior problem in VQA from a class-imbalance view. Concretely, we design a novel interpretation scheme whereby the loss of mis-predicted frequent and sparse answers from the same question type is distinctly exhibited during the late training phase. It explicitly reveals why the VQA model tends to produce a frequent yet obviously wrong answer, to a given question whose right answer is sparse in the training set. Based upon this observation, we further propose a novel loss re-scaling approach to assign different weights to each answer according to the training data statistics for estimating the final loss. We apply our approach into six strong baselines and the experimental results on two VQA-CP benchmark datasets evidently demonstrate its effectiveness. In addition, we also justify the validity of the class imbalance interpretation scheme on other computer vision tasks, such as face recognition and image classification.
Artificial bee colony based on adaptive search strategy and random grouping mechanism

As a popular global optimization algorithm, artificial bee colony (ABC) has strong search ability and simple concept. However, ABC has some deficiencies. The exploitation ability of ABC is not as strong as its exploration ability. The original roulette selection in the onlooker bee search will gradually lose its effect with increasing of iterations. In order to tackle the above problems, an efficient ABC based on adaptive search strategy and random grouping mechanism (called ASRGABC) is proposed in this paper. Firstly, an adaptive search strategy is designed by comparing the success rate of the current and previous iterations. According to the changes of the success rate, a suitable search strategy is adaptively selected. Then, a random grouping mechanism is proposed to replace the original roulette selection. The whole population is randomly divided into several groups. The onlooker bees are allowed to follow the best solution in each group. Based on the random grouping, the search strategy is modified. Thirdly, opposition-based learning is employed to enhance the scout bee phase. To verify the performance of ASRGABC, 22 classical benchmark problems and 28 CEC 2013 benchmark problems are tested. Experimental results show ASRGABC obtains better performance than thirteen other ABC variants.
Survey on Blockchain for Smart Grid Management, Control, and Operation

Power generation, distribution, transmission, and consumption face ongoing challenges such as smart grid management, control, and operation, resulting from high energy demand, the diversity of energy sources, and environmental or regulatory issues. This paper provides a comprehensive overview of blockchain-based solutions for smart grid management, control, and operations. We systematically summarize existing work on the use and implementation of blockchain technology in various smart grid domains. The paper compares related reviews and highlights the challenges in the management, control, and operation for a blockchain-based smart grid as well as future research directions in the five categories: collaboration among stakeholders; data analysis and data management; control of grid imbalances; decentralization of grid management and operations; and security and privacy. All these aspects have not been covered in previous reviews.
Rule-based information extraction for mechanical-electrical-plumbing-specific semantic web

Information extraction (IE), which aims to retrieve meaningful information from plain text, has been widely studied in general and professional domains to support downstream applications. However, due to the lack of labeled data and the complexity of professional mechanical, electrical and plumbing (MEP) information, it is challenging to apply current common deep learning IE methods to the MEP domain. To solve this problem, this paper proposes a rule-based approach for MEP IE task, including a “snowball” strategy to collect large-scale MEP corpora, a suffix-based matching algorithm on text segments for named entity recognition (NER), and a dependency-path-based matching algorithm on dependency tree for relationship extraction (RE). 2 ideas called “meta linking” and “path filtering” for RE are proposed as well, to discover the out-of-pattern entities/relationships as many as possible. To verify the feasibility of the proposed approach, 65 MB MEP corpora have been collected as input of the proposed approach and an MEP semantic web which consists of 15,978 entities and 65,110 relationship triples established, with an accuracy of 81% to entities and 75% to relationship triples, respectively. A comparison experiment between classical deep learning models and the proposed rule-based approach was carried out, illustrating that the performance of our method is 37% and 49% better than the selected deep learning NER and RE models, respectively, in the aspect of extraction precision.
The Use of Blockchain Technology in the Health Care Sector: Systematic Review

Blockchain technology is a part of Industry 4.0's new Internet of Things applications: decentralized systems, distributed ledgers, and immutable and cryptographically secure technology. This technology entails a series of transaction lists with identical copies shared and retained by different groups or parties. One field where blockchain technology has tremendous potential is health care, due to the more patient-centric approach to the health care system as well as blockchain's ability to connect disparate systems and increase the accuracy of electronic health records.The aim of this study was to systematically review studies on the use of blockchain technology in health care and to analyze the characteristics of the studies that have implemented blockchain technology.This study used a systematic review methodology to find literature related to the implementation aspect of blockchain technology in health care. Relevant papers were searched for using PubMed, SpringerLink, IEEE Xplore, Embase, Scopus, and EBSCOhost. A quality assessment of literature was performed on the 22 selected papers by assessing their trustworthiness and relevance.After full screening, 22 papers were included. A table of evidence was constructed, and the results of the selected papers were interpreted. The results of scoring for measuring the quality of the publications were obtained and interpreted. Out of 22 papers, a total of 3 (14%) high-quality papers, 9 (41%) moderate-quality papers, and 10 (45%) low-quality papers were identified.Blockchain technology was found to be useful in real health care environments, including for the management of electronic medical records, biomedical research and education, remote patient monitoring, pharmaceutical supply chains, health insurance claims, health data analytics, and other potential areas. The main reasons for the implementation of blockchain technology in the health care sector were identified as data integrity, access control, data logging, data versioning, and nonrepudiation. The findings could help the scientific community to understand the implementation aspect of blockchain technology. The results from this study help in recognizing the accessibility and use of blockchain technology in the health care sector.
Artificial Intelligence and Fraud Detection

Fraud exists in all walks of life and detecting and preventing fraud represents an important research question relevant to many stakeholders in society. With the rise in big data and artificial intelligence, new opportunities have arisen in using advanced machine learning models to detect fraud. This chapter provides a comprehensive overview of the challenges in detecting fraud using machine learning. We use a framework (data, method, and evaluation criterion) to review some of the practical considerations that may affect the implementation of machine-learning models to predict fraud. Then, we review select papers in the academic literature across different disciplines that can help address some of the fraud detection challenges. Finally, we suggest promising future directions for this line of research. As accounting fraud constitutes an important class of fraud, we will discuss all of these issues within the context of accounting fraud detection.
Hypergraph Convolution on Nodes-Hyperedges Network for Semi-Supervised Node Classification

Hypergraphs have shown great power in representing high-order relations among entities, and lots of hypergraph-based deep learning methods have been proposed to learn informative data representations for the node classification problem. However, most of these deep learning approaches do not take full consideration of either the hyperedge information or the original relationships among nodes and hyperedges. In this article, we present a simple yet effective semi-supervised node classification method named Hypergraph Convolution on Nodes-Hyperedges network, which performs filtering on both nodes and hyperedges as well as recovers the original hypergraph with the least information loss. Instead of only reducing the cross-entropy loss over the labeled samples as most previous approaches do, we additionally consider the hypergraph reconstruction loss as prior information to improve prediction accuracy. As a result, by taking both the cross-entropy loss on the labeled samples and the hypergraph reconstruction loss into consideration, we are able to achieve discriminative latent data representations for training a classifier. We perform extensive experiments on the semi-supervised node classification problem and compare the proposed method with state-of-the-art algorithms. The promising results demonstrate the effectiveness of the proposed method.
Adaptive and Efficient Resource Allocation in Cloud Datacenters Using Actor-Critic Deep Reinforcement Learning

The ever-expanding scale of cloud datacenters necessitates automated resource provisioning to best meet the requirements of low latency and high energy-efficiency.However, due to the dynamic system states and various user demands, efficient resource allocation in cloud faces huge challenges.Most of the existing solutions for cloud resource allocation cannot effectively handle the dynamic cloud environments because they depend on the prior knowledge of a cloud system, which may lead to excessive energy consumption and degraded Quality-of-Service (QoS).To address this problem, we propose an adaptive and efficient cloud resource allocation scheme based on Actor-Critic Deep Reinforcement Learning (DRL).First, the actor parameterizes the policy (allocating resources) and chooses actions (scheduling jobs) based on the scores assessed by the critic (evaluating actions).Next, the resource allocation policy is updated by using gradient ascent while the variance of policy gradient is reduced with an advantage function, which improves the training efficiency of the proposed method.We conduct extensive simulation experiments using real-world data from Google cloud datacenters.The results show that our method can obtain the superior QoS in terms of latency and job dismissing rate with enhanced energy-efficiency, compared to two advanced DRL-based and five classic cloud resource allocation methods.
Bangla Sign Language (BdSL) Alphabets and Numerals Classification Using a Deep Learning Model

A real-time Bangla Sign Language interpreter can enable more than 200 k hearing and speech-impaired people to the mainstream workforce in Bangladesh. Bangla Sign Language (BdSL) recognition and detection is a challenging topic in computer vision and deep learning research because sign language recognition accuracy may vary on the skin tone, hand orientation, and background. This research has used deep machine learning models for accurate and reliable BdSL Alphabets and Numerals using two well-suited and robust datasets. The dataset prepared in this study comprises of the largest image database for BdSL Alphabets and Numerals in order to reduce inter-class similarity while dealing with diverse image data, which comprises various backgrounds and skin tones. The papers compared classification with and without background images to determine the best working model for BdSL Alphabets and Numerals interpretation. The CNN model trained with the images that had a background was found to be more effective than without background. The hand detection portion in the segmentation approach must be more accurate in the hand detection process to boost the overall accuracy in the sign recognition. It was found that ResNet18 performed best with 99.99% accuracy, precision, F1 score, sensitivity, and 100% specificity, which outperforms the works in the literature for BdSL Alphabets and Numerals recognition. This dataset is made publicly available for researchers to support and encourage further research on Bangla Sign Language Interpretation so that the hearing and speech-impaired individuals can benefit from this research.
Deep Learning Algorithms are used to Automatically Detection Invasive Ducal Carcinoma in Whole Slide Images

This paper proposes a profound learning approach in Whole-slide images of breast cancer (WSI) for automatic detection and visual study of invasive ductal cancer (IDC) tissue regions. Deep learning techniques are strategies for learning from data including the computer simulation of the learning process. The diagnosis of invasive breast cancer is a time-contracting process, particularly when a pathologist scans vast sections of benign areas to find malignancy areas. Accurate line-up IDC in WSI is critical for subsequent tumor grading and patient outcomes assessment. Artificial Intelligence approaches are especially suitable for managing such problems, particularly if large numbers of samples for training are available, thereby ensuring that the main differentiator and classifier are generalized. The system for visual functional study of tumor regions in this article includes a range of Deep Learning algorithms for instance Artificial Neural Networks (ANNs) and Convolution Neural Networks (CNNs) to facilitate the diagnosis. The procedure has been tested using a WSI sample of 162 IDC patients among the images 113 teaching slides and 49 independent examination slides were chosen. The experimental assessment was planned to detect IDC tissue areas in WSI with classificatory precision. 10-fold cross-validation was used to evaluate how the effects of our classificatory are generalized on our data package, of which 80% of data are used for preparation and 20% for checking in each fold. We have obtained an 81.56 percent average sensitivity for detecting invasive ducal carcinoma in whole-slide images before using some regularization techniques. The exactness of our model efficiency has been increased to 86.24 percent, respectively after application of image increase and regularization. The findings show how successful our custom architecture is to classify invasive dual carcinoma detection in whole-slide pictures.
SatSOT: A Benchmark Dataset for Satellite Video Single Object Tracking

By imaging a specific area continuously, satellite video shows excellent capability in various applications such as surveillance and traffic management. Although object tracking has made significant progress in recent years, development in satellite object tracking is limited by the lack of open-source satellite datasets. It is thus essential to establish a satellite video object-tracking benchmark to fill the gap and advance the research. In this work, we present SatSOT, the first densely annotated satellite video single object-tracking benchmark dataset. SatSOT consists of 105 sequences with 27664 frames, 11 attributes, and four categories of typical moving targets in satellite videos: car, plane, ship, and train. Based on the proposed dataset and the significant challenges in satellite video object tracking, such as small targets, background interference, and severe occlusion, detailed evaluation and analysis are performed on 15 among the best and most representative tracking algorithms, which provides a basis for further research on satellite video object tracking.
Quantum Software as a Service Through a Quantum API Gateway

As quantum computers mature, the complexity of quantum software increases. As we move from the initial standalone quantum algorithms toward complex solutions combining quantum algorithms with traditional software, new software engineering methods and abstractions are needed. Nowadays, quantum computers are usually offered in the cloud, under a pay-per-use model, leading to the adoption of the service-oriented good practices that dominate the cloud today. However, specific adaptations are needed to reap the benefits of service-oriented computing while dealing with quantum hardware limitations. In this article, we propose the Quantum API Gateway—an adaptation of the API Gateway pattern that takes into account the fact that quantum services cannot be deployed as traditional services. Instead, the Quantum API Gateway recommends the best quantum computer to run a specific quantum service at run time. As proof of concept, we provide an implementation of the Quantum API Gateway for the Amazon Braket platform.
KnowMIS-ABSA: an overview and a reference model for applications of sentiment analysis and aspect-based sentiment analysis

Abstract The analysis of the opinions of customers and users has been always of great interest in supporting decision-making in many fields, especially in marketing. Sentiment analysis (SA) is the umbrella term for techniques and approaches that analyze user’s sentiments, emotions, opinions in text or other media. The need for a better understanding of these opinions paved the way to novel approaches that focus on the analysis of the sentiment related to specific features of a product, giving birth to the field of aspect-based sentiment analysis (ABSA). Although the increasing interest in this discipline, there is still confusion regarding the basic concepts of ABSA: terms like sentiment, affect, emotion, opinion, are used as synonyms while they represent different concepts. This often leads to an incorrect analysis of the users’ opinions.This work presents an overview of the state-of-the-art techniques and approaches for ABSA, highlighting the main critical issues related to current trends in this field. Following this analysis, a new reference model for SA and ABSA, namely the KnowMIS-ABSA model, is proposed. The model is grounded on the consideration that sentiment, affect, emotion and opinion are very different concepts and that it is profoundly wrong to use the same metric and the same technique to measure them. Accordingly, we argue that different tools and metrics should be adopted to measure each of the dimensions of an opinion. A qualitative case study, regarding product reviews, is proposed to motivate the advantages of the KnowMIS-ABSA model.
ABCanDroid: A Cloud Integrated Android App for Noninvasive Early Breast Cancer Detection Using Transfer Learning

Many patients affected by breast cancer die every year because of improper diagnosis and treatment. In recent years, applications of deep learning algorithms in the field of breast cancer detection have proved to be quite efficient. However, the application of such techniques has a lot of scope for improvement. Major works have been done in this field, however it can be made more efficient by the use of transfer learning to get impressive results. In the proposed approach, Convolutional Neural Network (CNN) is complemented with Transfer Learning for increasing the efficiency and accuracy of early detection of breast cancer for better diagnosis. The thought process involved using a pre-trained model, which already had some weights assigned rather than building the complete model from scratch. This paper mainly focuses on ResNet101 based Transfer Learning Model paired with the ImageNet dataset. The proposed framework provided us with an accuracy of 99.58%. Extensive experiments and tuning of hyperparameters have been performed to acquire the best possible results in terms of classification. The proposed frameworks aims to be an efficient tool for all doctors and society as a whole and help the user in early detection of breast cancer.
Digital demand and digital deficit: conceptualising digital literacy and gauging proficiency among higher education students

For higher education students, developing digital literacy enhances their value to future employers and appeal in the graduate labour market. The purpose of this study was two-fold. First, to provide a tridimensional (technical, cognitive and etiquette) conceptual framework and definition of digital literacy. Second, to investigate students’ (N = 324) perceptions of their digital literacy proficiency through an online survey. Multiple linear regression revealed mixed results for different aspects of digital literacy. Students reported the lowest proficiency in using digital information, specifically in terms of evaluation and determining bias and quality of information. The highest levels of proficiency were for social literacy skills, such as communicating appropriately online. There were minor differences between genders, while work experience and length of time in higher education had positive impact. Findings highlight the need for increased focus on digital literacy adaptability and industry-relevant experience within curriculum.
Simple Deterministic Selection-Based Genetic Algorithm for Hyperparameter Tuning of Machine Learning Models

Hyperparameter tuning is a critical function necessary for the effective deployment of most machine learning (ML) algorithms. It is used to find the optimal hyperparameter settings of an ML algorithm in order to improve its overall output performance. To this effect, several optimization strategies have been studied for fine-tuning the hyperparameters of many ML algorithms, especially in the absence of model-specific information. However, because most ML training procedures need a significant amount of computational time and memory, it is frequently necessary to build an optimization technique that converges within a small number of fitness evaluations. As a result, a simple deterministic selection genetic algorithm (SDSGA) is proposed in this article. The SDSGA was realized by ensuring that both chromosomes and their accompanying fitness values in the original genetic algorithm are selected in an elitist-like way. We assessed the SDSGA over a variety of mathematical test functions. It was then used to optimize the hyperparameters of two well-known machine learning models, namely, the convolutional neural network (CNN) and the random forest (RF) algorithm, with application on the MNIST and UCI classification datasets. The SDSGA’s efficiency was compared to that of the Bayesian Optimization (BO) and three other popular metaheuristic optimization algorithms (MOAs), namely, the genetic algorithm (GA), particle swarm optimization (PSO) and biogeography-based optimization (BBO) algorithms. The results obtained reveal that the SDSGA performed better than the other MOAs in solving 11 of the 17 known benchmark functions considered in our study. While optimizing the hyperparameters of the two ML models, it performed marginally better in terms of accuracy than the other methods while taking less time to compute.