Reviewerly/exHarmony huggingface dataset:
DeGTeC: A deep graph-temporal clustering framework for data-parallel job characterization in data centers

Complex data-parallel job contains task dependency information defined as Directed Acyclic Graph (DAG). For convenience, the DAG presented data-parallel jobs are named as DAG jobs. The prevalence of DAG jobs in modern data centers has made the scheduling oriented job characterization a big challenge. This paper proposes a deep graph-temporal clustering framework, i.e., DeGTeC, to efficiently categorize DAG jobs leveraging the graphic and temporal information in DAGs. The categorization result can then be naturally used to characterize the resource consumption pattern of DAG jobs. The DeGTeC framework is constructed mainly based on two autoencoders, i.e., TaskAE and JobAE. TaskAE and JobAE contain spectral graph convolutional network (GCN) layers, temporal convolutional network (TCN) layers, and the adaptive pooling layers to help build task embeddings and job embeddings. An extra embedding sorting step takes in the sequential order information and the depth-bias information for job clustering. To our best knowledge, DeGTeC is the first solution to do resource consumption characterization of DAG jobs fully leveraging the task dependencies defined in DAG. Experimental results demonstrate that the DeGTeC framework outperforms the state-of-the-art job resource consumption characterization methods.
Designing secure business processes for blockchains with SecBPMN2BC

Collaborative business processes can be seen as smart contracts, as they are oftentimes adopted to express agreements among different organizations. Indeed, they provide mechanisms to formalize the obligations of each involved party. For instance, collaborative business processes can specify when a certain task should be executed, under which conditions a service should be offered to the other participants, and how physical objects and information should be manipulated. In this setting, to prevent misuse of smart contracts and services and information provided, it is paramount to guarantee by design that security requirements are fulfilled. With the rise in popularity of blockchains, several approaches exploiting the trusted smart contract execution environment offered by this technology to enforce collaborative business processes have been proposed. Yet, the complexity of business processes, security requirements, and blockchain applications calls for an engineering approach that guides the design of secure business processes. Such an approach should both take advantage of the possibilities offered by blockchain technology to enforce some security requirements (e.g., non-repudiation), and take into account the limitations blockchain poses for other security requirements (e.g., confidentiality). However, we are not aware of any existing work that aims at addressing such issues following a similar approach. In this article, we propose SecBPMN2BC: a model-driven approach to designing business processes with security requirements that are meant to be deployed on blockchains. SecBPMN2BC consists of: (i) an extension of BPMN 2.0 that allows designing secure smart contracts; (ii) a set of algorithms and their implementation that check incompatible security requirements and help the design of smart contracts; (iii) a workflow that guides the application of the method. The method has been validated with a survey conducted on security and BPMN experts.
Optimization of Robot-Trajectory Planning with Nature-Inspired and Hybrid Quantum Algorithms

We solve robot trajectory planning problems at industry-relevant scales. Our end-to-end solution integrates highly versatile random-key algorithms with model stacking and ensemble techniques, as well as path relinking for solution refinement. The core optimization module consists of a biased random-key genetic algorithm. Through a distinct separation of problem-independent and problem-dependent modules, we achieve an efficient problem representation, with a native encoding of constraints. We show that generalizations to alternative algorithmic paradigms such as simulated annealing are straightforward. We provide numerical benchmark results for industry-scale data sets. Our approach is found to consistently outperform greedy baseline results. To assess the capabilities of today's quantum hardware, we complement the classical approach with results obtained on quantum annealing hardware, using qbsolv on Amazon Braket. Finally, we show how the latter can be integrated into our larger pipeline, providing a quantum-ready hybrid solution to the problem.
A novel Multi-Layer Attention Framework for visual description prediction using bidirectional LSTM

Abstract The massive influx of text, images, and videos to the internet has recently increased the challenge of computer vision-based tasks in big data. Integrating visual data with natural language to generate video explanations has been a challenge for decades. However, recent experiments on image/video captioning that employ Long-Short-Term-Memory (LSTM) have piqued the interest of researchers studying its possible application in video captioning. The proposed video captioning architecture combines the bidirectional multilayer LSTM (BiLSTM) encoder and unidirectional decoder. The innovative architecture also considers temporal relations when creating superior global video representations. In contrast to the majority of prior work, the most relevant features of a video are selected and utilized specifically for captioning purposes. Existing methods utilize a single-layer attention mechanism for linking visual input with phrase meaning. This approach employs LSTMs and a multilayer attention mechanism to extract characteristics from movies, construct links between multi-modal (words and visual material) representations, and generate sentences with rich semantic coherence. In addition, we evaluated the performance of the suggested system using a benchmark dataset for video captioning. The obtained results reveal superior performance relative to state-of-the-art works in METEOR and promising performance relative to the BLEU score. In terms of quantitative performance, the proposed approach outperforms most existing methodologies.
Gastrointestinal tract disease recognition based on denoising capsule network

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Estimating time-series changes in social sentiment @Twitter in U.S. metropolises during the COVID-19 pandemic

Since early 2020, the global coronavirus pandemic has strained economic activities and traditional lifestyles. For such emergencies, our paper proposes a social sentiment estimation model that changes in response to infection conditions and state government orders. By designing mediation keywords that do not directly evoke coronavirus, it is possible to observe sentiment waveforms that vary as confirmed cases increase or decrease and as behavioral restrictions are ordered or lifted over a long period. The model demonstrates guaranteed performance with transformer-based neural network models and has been validated in New York City, Los Angeles, and Chicago, given that coronavirus infections explode in overcrowded cities. The time-series of the extracted social sentiment reflected the infection conditions of each city during the 2-year period from pre-pandemic to the new normal and shows a concurrency of waveforms common to the three cities. The methods of this paper could be applied not only to analysis of the COVID-19 pandemic but also to analyses of a wide range of emergencies and they could be a policy support tool that complements traditional surveys in the future.
Face mask recognition system using MobileNetV2 with optimization function

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Mapping of gold mineralization using an integrated interpretation of geological and geophysical data—a case study from West Baranes, South Eastern Desert, Egypt

Abstract The current study is mainly devoted to the integration, analysis, and interpretation of the available geologic, remote sensing, and potential field data (mainly magnetic) to delineate the subsurface geologic structural elements controlling the western part of Baranes, South Eastern Desert, Egypt, and its relation to the southwestern desert structural regime. Additionally, to detect potential mineralization zones for future mining plans in the study area. The Western part of Baranes has not had enough geomorphological mapping, consequently, potential discoveries of mineralization zones are very low. So, in this paper, an attempt is initiated to better understand the evolution of the geomorphology and structural regime of the region and construct a digital geological map and structural patterns showing the possible locations of mineralization zones based on the previous knowledge from similar potential sites and focus on the future economic importance of the region. To achieve this purpose, processed Landsat-8 images successfully revealed the lithological contacts and fault zones helping in distinguishing between the different rock units; moreover, the aeromagnetic data available in the area is used and several filters are applied including reduction to the pole, Euler homogeneity equation, analytic Signal (AS), and advanced grid filtering are sequentially used aiming to detect the possible subsurface distribution of mineralization zones from the integrated interpretation of magnetic susceptibilities and available geologic and remote sensing data.
Design and performance analysis of a standalone floating photovoltaic/battery energy-powered paddlewheel aerator

In aquaculture, a paddlewheel aerator is conventionally used to increase dissolved oxygen (DO) in pond water. Occasionally, however, it is driven by a diesel generator, particularly when it is operated in remote areas where an electrical network connection is unavailable. Employing a diesel generator to power a paddlewheel aerator releases carbon monoxide (CO) and increases operational costs. To address these concerns, using a photovoltaic (PV) energy source is a promising option. Although a PV energy source can generate electricity without the need for fossil fuels, its generation is inevitably intermittent owing to weather conditions. This study used battery energy storage (BES) to provide additional energy support to a PV energy source in attempt to power a paddlewheel aerator uninterruptedly. The PV and BES systems were installed on a floating platform of the aerator. The PV and BES capacities were optimized considering techno-economic assessment and weight capacity restrictions. A comparative experiment was conducted over 21 days to investigate the proposed aerator performance compared with that of a standalone floating PV-powered paddlewheel aerator without BES. The results demonstrate that the proposed aerator offered a 10.91% superior DO performance compared with that obtained from the PV-powered paddlewheel aerator without BES.
Fast geometrical extraction of nearest neighbors from multi-dimensional data

K-Nearest Neighbor (KNN) algorithm plays a significant role in various fields of data science and machine learning. Most variants of the KNN algorithm involve distance computations and a parameter (K) that represents the required number of neighbors. The recent research regarding distance computations and finding the optimal value of K have made neighborhood extraction a slow process. This research presents a fast geometrical approach for neighborhood extraction from multi-dimensional data. Instead of distance computations, the proposed algorithm creates a geometrical shape based on the number of features of data. This geometrical shape encompasses the reference data point and the neighboring points. The proposed algorithm's efficiency of time, classification, and hashing are evaluated and compared with existing state-of-the-art algorithms.
Breast cancer classification and segmentation framework using multiscale <scp>CNN</scp> and U‐shaped dual decoded attention network

Abstract Breast cancer is a mostly diagnosed deadly disease with a high mortality rate that can effectively be cured by early diagnosis and proper treatment. Ultrasound imaging modality is being utilized at a large scale for early diagnosis of this disease due to quick results and cheapness. Manual diagnosis of breast cancer is a laborious task that needs experts and is also subjective which necessitates the accurate diagnosis solution of this disease with help of the computer. This work proposed a combined framework for the classification and segmentation of breast cancer to automate manual diagnosis. A multiscale classification model for the classification of breast cancer into three classes is developed. For the segmentation task, an autoencoder‐based U‐shaped DDA‐Net segmentation model consisting of a dual‐decoded attention mechanism is proposed. For experiments two publically available datasets namely BUSI and UDIAT are utilized. Before the classification task feature selection method of the whale optimization algorithm is implemented and for the classification task different machine learning algorithms are utilized. The proposed method achieved the segmentation dice of 88.68% on the UDIAT dataset and dice of 87.95% on the BUSI dataset with a classification accuracy of 97.89% with a precision score of 97.9%. Results validated that the proposed combined classification and segmentation technique can reliably be implemented to accurately diagnose breast cancer at its initial level.
Multi-strategy sparrow search algorithm with non-uniform mutation

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Tensor based completion meets adversarial learning: A win–win solution for change detection on unseen videos

Foreground segmentation is an essential processing phase in several change detection-based applications. Classical foreground segmentation is highly dependent on the accuracy of the estimated background model and the procedures followed to subtract such model from the original frame. Obtaining good foreground masks via background subtraction remains a challengeable task where limitations such as incomplete foreground objects and foreground misdetection are presented. Due to their recent successes, deep learning approaches have been widely used recently to tackle the challenges related to foreground segmentation. However, recent studies have pointed out the fact that deep learning approaches are highly dependent on the followed training protocol where different protocols lead to clearly different results. Furthermore, several extensive experiments have shown the poor performances of deep learning approaches when processing "unseen videos". Therefore, in this paper, we introduce a Generative adversarial network (GAN) based foreground enhancement framework that accepts multiple images as inputs. The GAN is designed and trained to refine initial foreground masks estimated via a hand-crafted background subtraction instead of generating them from scratch. The background that is fed into the network is initialized beforehand via a spatiotemporal slice-based singular value decomposition (SVD) and well updated when changes are present in the scene. The segmentation performance is evaluated qualitatively and quantitatively following scene-dependent and scene-independent scenarios, and the estimated results are compared with the existing state-of-the-art methods. From the obtained experimental results, it is evident that the proposed framework shows significant improvement in terms of F-measure and robust performance in the case of unseen scenarios.
Sentiment analysis in tweets: an assessment study from classical to modern word representation models

With the exponential growth of social media networks, such as Twitter, plenty of user-generated data emerge daily. The short texts published on Twitter - the tweets - have earned significant attention as a rich source of information to guide many decision-making processes. However, their inherent characteristics, such as the informal, and noisy linguistic style, remain challenging to many natural language processing (NLP) tasks, including sentiment analysis. Sentiment classification is tackled mainly by machine learning-based classifiers. The literature has adopted different types of word representation models to transform tweets to vector-based inputs to feed sentiment classifiers. The representations come from simple count-based methods, such as bag-of-words, to more sophisticated ones, such as BERTweet, built upon the trendy BERT architecture. Nevertheless, most studies mainly focus on evaluating those models using only a small number of datasets. Despite the progress made in recent years in language modeling, there is still a gap regarding a robust evaluation of induced embeddings applied to sentiment analysis on tweets. Furthermore, while fine-tuning the model from downstream tasks is prominent nowadays, less attention has been given to adjustments based on the specific linguistic style of the data. In this context, this study fulfills an assessment of existing neural language models in distinguishing the sentiment expressed in tweets, by using a rich collection of 22 datasets from distinct domains and five classification algorithms. The evaluation includes static and contextualized representations. Contexts are assembled from Transformer-based autoencoder models that are also adapted based on the masked language model task, using a plethora of strategies.
Learning-Based Matched Representation System for Job Recommendation

Job recommender systems (JRS) are a subclass of information filtering systems that aims to help job seekers identify what might match their skills and experiences and prevent them from being lost in the vast amount of information available on job boards that aggregates postings from many sources such as LinkedIn or Indeed. A variety of strategies used as part of JRS have been implemented, most of them failed to recommend job vacancies that fit properly to the job seekers profiles when dealing with more than one job offer. They consider skills as passive entities associated with the job description, which need to be matched for finding the best job recommendation. This paper provides a recommender system to assist job seekers in finding suitable jobs based on their resumes. The proposed system recommends the top-n jobs to the job seekers by analyzing and measuring similarity between the job seeker’s skills and explicit features of job listing using content-based filtering. First-hand information was gathered by scraping jobs description from Indeed from major cities in Saudi Arabia (Dammam, Jeddah, and Riyadh). Then, the top skills required in job offers were analyzed and job recommendation was made by matching skills from resumes to posted jobs. To quantify recommendation success and error rates, we sought to compare the results of our system to reality using decision support measures.
Optical Character Recognition (OCR) for Text Recognition and its Post-Processing Method: A Literature Review

Most organizations worldwide still rely on paper-based documents. Usage of paper-based documents gives a hard time extracting the data required from those documents. This heavy paper usage also damages the efficiency in cost and time, not to mention the impact on the environment caused by deforestation to produce these papers. These are some reasons that motivate the need to digitalize paper-based documents. To convert the usage of paper-based documents into paperless documents cannot be done in an instant. In its transition, these paper-based documents are usually scanned into image format to reduce the usage of paper. From this comes a need for technology that is able to recognize and extract data in the scanned image of paper-based documents. Optical Character Recognition makes it possible to do text recognition appearing in images. However, despite its long history of development, OCR for text recognition has yet to achieve 100% accuracy. In general, OCR process will be divided into Image Pre-processing, Text Segmentation/Localization, Feature Extraction, Text Recognition, and Post-Processing. Thus, this research will review OCR-related works and the methods used within this framework to support further research.
AAFL: Asynchronous-Adaptive Federated Learning in Edge-Based Wireless Communication Systems for Countering Communicable Infectious Diseasess

With the rapid growth of the coronavirus disease of 2019 (COVID-19) cases, massive amounts of relevant data are being trained on machine learning models for countering communicable infectious diseases. Federated Learning (FL) is a paradigm of distributed machine learning to deal with the individual COVID-19 data, and enable the protection of data privacy. However, FL has low efficiency in Edge-Based wireless communication systems with system heterogeneity. In this paper, we propose an “Asynchronous-Adaptive FL” (AAFL) scheme. Specifically, we allow that medical devices with different performances have a heterogeneous number of local SGD iterations in each communication round, called asynchronous iteration strategy which is balanced under adaptive control. We theoretically analyze the convergence of the AAFL scheme under a given time budget and obtain a mathematical relationship between the heterogeneous number of local SGD iterations and the optimal model parameters. Based on the mathematical relationship, we design an algorithm for parameter server and work nodes to adaptively control the heterogeneous number of local SGD iterations. Subsequently, we build a prototype heterogeneous system and conduct experiments on various scenarios for analyzing the general properties of our algorithm, and then apply our algorithm to public COVID-19 databases. The experimental results and application performance demonstrate the effectiveness and efficiency of our AAFL scheme.
Identifying Alert Concentrations Using a Model-Based Bootstrap Approach

The determination of alert concentrations, where a pre-specified threshold of the response variable is exceeded, is an important goal of concentration-response studies. The traditional approach is based on investigating the measured concentrations and attaining statistical significance of the alert concentration by using a multiple t-test procedure. In this paper, we propose a new model-based method to identify alert concentrations, based on fitting a concentration-response curve and constructing a simultaneous confidence band for the difference of the response of a concentration compared to the control. In order to obtain these confidence bands, we use a bootstrap approach which can be applied to any functional form of the concentration-response curve. This particularly offers the possibility to investigate also those situations where the concentration-response relationship is not monotone and, moreover, to detect alerts at concentrations which were not measured during the study, providing a highly flexible framework for the problem at hand.