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Performance/security trade-off is widely noticed in CFI research, however, we observe that not every CFI scheme is subject to the trade-off. Motivated by the key observation, we ask three questions: ➊ does trade-off really exist in different CFI schemes? ➋ if trade-off do exist, how do previous works comply with it? ➌ how can it inspire future research? Although the three questions probably cannot be directly answered, they are inspiring. We find that a deeper understanding of the nature of the trade-off will help answer the three questions. Accordingly, we proposed theGPTconjecture to pinpoint the trade-off in designing CFI schemes, which says that at most two out of three properties (fine granularity, acceptable performance, and preventive protection) could be achieved.

Headline generation is a challenging subtask of abstractive text summarization, which its output should be a summary, shorter than one sentence. It would be precious to develop a dataset for the evaluation of abstractive summarization methods on this task in the Persian language. There are several datasets for headline generation in Persian, most of which are not large enough to be used by more sophisticated methods of text summarization, such as deep learning models. Moreover, all of these datasets are focused on daily news and there is no dataset for summarizing scientific Persian papers. In this article, we present “ElmNet,” a headline generation dataset of about 400,000 abstract/headline pairs of scientific papers, gathered from six major publishers for scientific articles in Persian. We, moreover, evaluate the performance of the most important deep learning-based headline generation methods, on the proposed dataset. The results prove the comparability of the performance of the state-of-the-art methods on this task, to their results on the existing English datasets.

Scientific research cooperation and co-authored papers are becoming increasingly popular in the era of big science. However, allocating appropriate credit to each co-author of papers remains a challenge. We consider author contribution declarations according to the contributor roles taxonomy (CRediT) scheme (assigning each co-author to 14 contributor roles) and propose a new method of author contribution to allocate co-authors’ credits reasonably by converting the 14 contributor roles in an article into a binary author-role matrix. Based on the data of PLOS ONE, we further explore the new method’s advantages by comparing with other representative methods: It normalizes the total credits of different articles to 1, avoiding the inflationary bias caused by the increasing number of co-authors; awards different credits per co-author based on the participation rate of contributor roles to avoid the equalization bias; reduces the impact of the increasing number of co-authors on the credit of the first co-author.

Open-source model management frameworks such as OCL and ATL tend to focus on manipulating models built atop the Eclipse Modelling Framework (EMF), a de facto standard for domain specific modelling. MATLAB Simulink is a widely used proprietary modelling framework for dynamic systems that is built atop an entirely different technical stack to EMF. To leverage the facilities of open-source model management frameworks with Simulink models, these can be transformed into an EMF-compatible representation. Downsides of this approach include the synchronisation of the native Simulink model and its EMF representation as they evolve; the completeness of the EMF representation, and the transformation cost which can be crippling for large Simulink models. We propose an alternative approach to bridge Simulink models with open-source model management frameworks that uses an “on-the-fly” translation of model management constructs into MATLAB statements. Our approach does not require an EMF representation and can mitigate the cost of the upfront transformation on large models. To evaluate both approaches we measure the performance of a model validation process with Epsilon (a model management framework) on a sample of large Simulink models available on GitHub. Our previous results suggest that, with our approach, the total validation time can be reduced by up to 80%. In this paper, we expand our approach to support the management of Simulink requirements and dictionaries, and we improve the approach to perform queries on collections of model elements more efficiently. We demonstrate the use of the Simulink requirements and dictionaries with a case study and we evaluate the optimisations on collection queries with an experiment that compares the performance of a set of queries on models with different sizes. Our results suggest an improvement by up to 99% on some queries.

Enterprise architecture has become an important driver to facilitate digital transformation in companies, since it allows to manage IT and business in a holistic and integrated manner by establishing connections among technology concerns and strategical/motivational ones. Enterprise architecture modelling is critical to accurately represent business and their IT assets in combination. This modelling is important when companies start to manage their enterprise architecture, but also when it is remodelled so that the enterprise architecture is realigned in a changing world. Enterprise architecture is commonly modelled by few experts in a manual way, which is error-prone and time-consuming and makes continuous realignment difficult. In contrast, other enterprise architecture modelling proposal automatically analyses some artefacts like source code, databases, services, etc. Previous automated modelling proposals focus on the analysis of individual artefacts with isolated transformations toward ArchiMate or other enterprise architecture notations and/or frameworks. We propose the usage of Knowledge Discovery Metamodel (KDM) to represent all the intermediate information retrieved from information systems’ artefacts, which is then transformed into ArchiMate models. Thus, the core contribution of this paper is the model transformation between KDM and ArchiMate metamodels. The main implication of this proposal is that ArchiMate models are automatically generated from a common knowledge repository. Thereby, the relationships between different-nature artefacts can be exploited to get more complete and accurate enterprise architecture representations.

Real-time execution of machine learning (ML) pipelines on radiology images is difficult due to limited computing resources in clinical environments, whereas running them in research clusters requires efficient data transfer capabilities. We developed Niffler, an open-source Digital Imaging and Communications in Medicine (DICOM) framework that enables ML and processing pipelines in research clusters by efficiently retrieving images from the hospitals’ PACS and extracting the metadata from the images. We deployed Niffler at our institution (Emory Healthcare, the largest healthcare network in the state of Georgia) and retrieved data from 715 scanners spanning 12 sites, up to 350 GB/day continuously in real-time as a DICOM data stream over the past 2 years. We also used Niffler to retrieve images bulk on-demand based on user-provided filters to facilitate several research projects. This paper presents the architecture and three such use cases of Niffler. First, we executed an IVC filter detection and segmentation pipeline on abdominal radiographs in real-time, which was able to classify 989 test images with an accuracy of 96.0%. Second, we applied the Niffler Metadata Extractor to understand the operational efficiency of individual MRI systems based on calculated metrics. We benchmarked the accuracy of the calculated exam time windows by comparing Niffler against the Clinical Data Warehouse (CDW). Niffler accurately identified the scanners’ examination timeframes and idling times, whereas CDW falsely depicted several exam overlaps due to human errors. Third, with metadata extracted from the images by Niffler, we identified scanners with misconfigured time and reconfigured five scanners. Our evaluations highlight how Niffler enables real-time ML and processing pipelines in a research cluster.

Unmanned Air Vehicles (UAVs) are becoming popular in real-world scenarios due to current advances in sensor technology and hardware platform development. The applications of UAVs in the medical field are broad and may be shared worldwide. With the recent outbreak of COVID-19, fast diagnostic testing has become one of the challenges due to the lack of test kits. UAVs can help in tackling the COVID-19 by delivering medication to the hospital on time. In this paper, to detect the number of COVID-19 cases in a hospital, we propose a deep convolution neural architecture using transfer learning, classifying the patient into three categories as COVID-19 (positive) and normal (negative), and pneumonia based on given X-ray images. The proposed deep-learning architecture is compared with state-of-the-art models. The results show that the proposed model provides an accuracy of 94.92%. Further to offer time-bounded services to COVID-19 patients, we have proposed a scheme for delivering emergency kits to the hospitals in need using an optimal path planning approach for UAVs in the network.

This paper reports an improved traditional fiber degumming method, where sisal fibers were treated by alkali oxygen and pectinase, respectively, after the solute alkali pretreatment. To explore the influence of various factors on its degumming, efficiency of degumming through single factor and orthogonal experiments was aasessed. The results showed that pectinase/alkali-oxygen method after the first alkali treatment had a good effect on the degumming of sisal fiber, and most of the non-cellulose components such as hemicellulose, lignin and pectin had been removed. After pectinase treatment, the cellulose content and crystallinity were 71.87% and 66.29%, respectively. After alkaline oxygen treatment, the cellulose content was 77.16%, and the crystallinity was 69.09%. In terms of degumming rate, alkali oxygen treatment worked better than pectinase treatment, the degumming rate of pectinase method was about 10%, while that of alkali-oxygen method was more than 20%. In other hand, the pectinase method was much milder and had less damage to fibers. It would provide some references for the future application and development of sisal fiber.

Recently, virus diseases, such as SARS-CoV, MERS-CoV, and COVID-19, continue to emerge and pose a severe public health problem. These diseases threaten the lives of many people and cause serious social and economic losses. Recent developments in information technology (IT) and connectivity have led to the emergence of Internet of Things (IoT) and Artificial Intelligence (AI) applications in many industries. These industries, where IoT and AI together are making significant impacts, are the healthcare and the diagnosis department. In addition, by actively communicating with smart devices and various biometric sensors, it is expanding its application fields to telemedicine, healthcare, and disease prevention. Even though existing IoT and AI technologies can enhance disease detection, monitoring, and quarantine, their impact is very limited because they are not integrated or applied rapidly to the emergence of a sudden epidemic. Especially in the situation where infectious diseases are rapidly spreading, the conventional methods fail to prevent large-scale infections and block global spreads through prediction, resulting in great loss of lives. Therefore, in this paper, various sources of infection information with local limitations are collected through virus disease information collector, and AI analysis and severity matching are performed through AI broker. Finally, through the Integrated Disease Control Center, risk alerts are issued, proliferation block letters are sent, and post-response services are provided quickly. Suppose we further develop the proposed integrated virus disease control model. In that case, it will be possible to proactively detect and warn of risk factors in response to infectious diseases that are rapidly spreading worldwide and strengthen measures to prevent spreading of infection in no time.

A number of the people who have recovered from the acute effects of COVID-19 are facing long term sequelae from the infection. As the COVID-19 pandemic is still evolving, so is knowledge of the long-term effects of the virus on patients who still experience symptoms. Clearly, news media play a crucial role in distributing information and this distribution of information can, in turn, influence the actions of the public. The purpose of this study was to describe the content of news coverage of COVID-19 long haul symptoms currently posted on the internet. This study utilized Google News, a news aggregator service, and included the first 100 English language pieces of news. Video content and news article content were coded in depth for information on COVID-19 long haul symptoms. A total of 41% of news reports mentioned the length of time that the COVID-19 related symptoms persist. The length of time was reported to range from 1 month to more than 1 year. The symptom most commonly mentioned was tiredness or fatigue (74%), followed by difficulty breathing or shortness of breath (62 cases; 62%), and difficulty thinking or concentrating (50 cases; 50%). Other symptoms were mentioned less frequently. There were no statistically significant differences in any of the content including having video, written news reports, or both video and written news reports by source of the news reports based on consumer, professional, or television or internet-based news (p = .14). More complete coverage by online news media of the long-term effects of COVID-19 enhances public awareness of the post-acute syndromes, augments health providers' awareness of the range of chronic COVID-19 effects and the possibility of a second infection, increases the probability of patients' seeking and obtaining the proper care for their symptoms, and contributes to preventive actions for enhancing public health.

Pattern mining from graph transactional data (GTD) is an active area of research with applications in the domains of bioinformatics, chemical informatics and social networks. Existing works address the problem of mining frequent subgraphs from GTD. However, the knowledge concerning the coverage aspect of a set of subgraphs is also valuable for improving the performance of several applications. In this regard, we introduce the notion of subgraph coverage patterns (SCPs). Given a GTD, a subgraph coverage pattern is a set of subgraphs subject to relative frequency, coverage and overlap constraints provided by the user. We propose the Subgraph ID-based Flat Transactional (SIFT) framework for the efficient extraction of SCPs from a given GTD. Our performance evaluation using three real datasets demonstrates that our proposed SIFT framework is indeed capable of efficiently extracting SCPs from GTD. Furthermore, we demonstrate the effectiveness of SIFT through a case study in computer-aided drug design.

We report the results of a novel protocol for running online experiments using a combination of an online experimental platform in parallel with web-conferencing software in two formats—with and without subject webcams—to improve subjects’ attention and engagement. We compare the results between our online sessions with the offline (lab) sessions of the same experiment. We find that both online formats lead to comparable subject characteristics and performance as the offline (lab) experiment. However, the webcam-on protocol has less noisy data, and hence better statistical power, than the protocol without a webcam. The webcam-on protocol can detect reasonable effect sizes with a comparable sample size as in the offline (lab) protocol. Supplementary Information The online version contains supplementary material available at 10.1007/s40881-021-00112-w.

Methods to quantify biological aging are emerging as new measurement tools for epidemiology and population science and have been proposed as surrogate measures for healthy lifespan extension in geroscience clinical trials. Publicly available software packages to compute biological aging measurements from DNA methylation data have accelerated dissemination of these measures and generated rapid gains in knowledge about how different measures perform in a range of datasets. Biological age measures derived from blood chemistry data were introduced at the same time as the DNA methylation measures and, in multiple studies, demonstrate superior performance to these measures in prediction of healthy lifespan. However, their dissemination has been slow by comparison, resulting in a significant gap in knowledge. We developed a software package to help address this knowledge gap. The BioAge R package, available for download at GitHub (http://github.com/dayoonkwon/BioAge), implements three published methods to quantify biological aging based on analysis of chronological age and mortality risk: Klemera-Doubal biological age, PhenoAge, and homeostatic dysregulation. The package allows users to parametrize measurement algorithms using custom sets of biomarkers, to compare the resulting measurements to published versions of the Klemera-Doubal method and PhenoAge algorithms, and to score the measurements in new datasets. We applied BioAge to safety lab data from the CALERIE™ randomized controlled trial, the first-ever human trial of long-term calorie restriction in healthy, non-obese adults, to test effects of intervention on biological aging. Results contribute evidence that CALERIE intervention slowed biological aging. BioAge is a toolkit to facilitate measurement of biological age for geroscience. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11357-021-00480-5.

The image biomarkers standardization initiative (IBSI) was formed to address the standardization of extraction of quantifiable imaging metrics. Despite its effort, there remains a lack of consensus or established guidelines regarding radiomic feature terminology, the underlying mathematics and their implementation across various software programs. This creates a scenario where features extracted using different toolboxes cannot be used to build or validate the same model leading to a non-generalization of radiomic results. In this study, IBSI-established phantom and benchmark values were used to compare the variation of the radiomic features while using 6 publicly available software programs and 1 in-house radiomics pipeline. All IBSI-standardized features (11 classes, 173 in total) were extracted. The relative differences between the extracted feature values from the different software programs and the IBSI benchmark values were calculated to measure the inter-software agreement. To better understand the variations, features are further grouped into 3 categories according to their properties: 1) morphology, 2) statistic/histogram and 3)texture features. While a good agreement was observed for a majority of radiomics features across the various tested programs, relatively poor agreement was observed for morphology features. Significant differences were also found in programs that use different gray-level discretization approaches. Since these software programs do not include all IBSI features, the level of quantitative assessment for each category was analyzed using Venn and UpSet diagrams and quantified using two ad hoc metrics. Morphology features earned lowest scores for both metrics, indicating that morphological features are not consistently evaluated among software programs. We conclude that radiomic features calculated using different software programs may not be interchangeable. Further studies are needed to standardize the workflow of radiomic feature extraction. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10278-021-00506-6.

The integration of distributed energy resources and the transition to smart cities are shifting the urban energy sector to a decentralized operating system. Blockchain-based microgrids, where small-scale operators trade electricity among each others, have gained remarkable attention recently. However, most of the proposed schemes study smart grids in prosperous cities. In this study, the performance of a solar-based power trading scheme is investigated in a shortage-prone context, Beirut City. Thus, we resort to a game-theoretic approach to model power trading as a repeated game between buildings at the urban scale. Results show that solar energy can cover up to 25% of the city electricity needs, depending on the rooftops area coverage. On the other hand, we found that deploying a peer-to-peer trading scheme has marginal impact since the energy demand in the city exceeds the supply and most buildings would prioritize self-consumption.

The high-resolution scanning devices developed in recent decades provide biomedical volume datasets that support the study of molecular structure and drug design. Isosurface analysis is an important tool in these studies, and the key is to construct suitable description vectors to support subsequent tasks, such as classification and retrieval. Traditional methods based on handcrafted features are insufficient for dealing with complex structures, while deep learning-based approaches have high memory and computation costs when dealing directly with volume data. To address these problems, we propose IsoExplorer, an isosurface-driven framework for 3D shape analysis of biomedical volume data. We first extract isosurfaces from volume data and split them into individual 3D shapes according to their connectivity. Then, we utilize octree-based convolution to design a variational autoencoder model that learns the latent representations of the shape. Finally, these latent representations are used for low-dimensional isosurface representation and shape retrieval. We demonstrate the effectiveness and usefulness of IsoExplorer via isosurface similarity analysis, shape retrieval of real-world data, and comparison with existing methods. Graphic abstract

Social and Emotional Learning (SEL) programs seek to enhance social and emotional competencies in children, including self-awareness, self-management, social awareness, relationship skills, and responsible decision-making. By means of direct instruction regarding social and emotional competencies, SEL programs have the potential to strengthen resilience in children and thus their capacity to effectively cope with life’s challenges. Strengthening resilience in children who are repeatedly exposed to adverse experiences, particularly those from economically disadvantaged minority backgrounds, is of particular importance and has implications for the prevention of a multitude of problems later in life. Our study reports the result of an investigation of the SPARK Child Mentoring program, a resilience-focused SEL program designed to reduce risk factors, uncover innate resilience, promote natural emotional well-being, and facilitate school success. We employed a randomized controlled trial comprising 94 elementary school students that included pre- and post-intervention measurements. After controlling for pre-intervention levels, we found a significant difference between students’ understanding of underlying program principles; communication, decision making, and problem-solving skills; emotional regulation; and resilience for students who received the intervention compared to students who did not receive the intervention. These results provide initial evidence for the efficacy of the SPARK Child Mentoring program with a diverse sample of elementary school students and adds to the existing literature base concerning positive outcomes associated with SEL programs. We discuss implications for future research focused on long-term preventive effects of the program and the characteristics of students most likely to benefit from it.

Embedded in the notion of the archive as knowledge infrastructure is the idea of a steady flow of information that resides within and moves through socio-technical systems. While there is exponential growth in the information transferred between the creator and the archive, the information flow between the archive and the user is often leaky-discontinuous and disrupted. There is a considerable interval between the time archival material are accessioned, processed, and made accessible for research. It is in this space that what archivists euphemistically call 'the backlog' comes into existence. What the backlog interrupts is the distribution and consumption end of the research process. As a concept and a reality, the backlog is a critical point of failure, carrying with it an ongoing and prevailing sense that 'deferred maintenance' has become the norm in the archive. In this article, focus is placed on framing and understanding the backlog as an infrastructural problem and on highlighting the conflict inherent in various forms of discourse seeking solutions to it.

Machine learning (ML) is unlocking patterns and insight into data to provide financial value and knowledge for organizations. Use of machine learning in manufacturing environments is increasing, yet sometimes these applications fail to produce meaningful results. A critical review of how defects are classified is needed to appropriately apply machine learning in a production foundry and other manufacturing processes. Four elements associated with defect classification are proposed: Binary Acceptance Specifications, Stochastic Formation of Defects, Secondary Process Variation, and Visual Defect Inspection. These four elements create data space overlap, which influences the bias associated with training supervised machine learning algorithms. If this influence is significant enough, the predicted error of the model exceeds a critical error threshold (CET). There is no financial motivation to implement the ML model in the manufacturing environment if its error is greater than the CET. The goal is to bring awareness to these four elements, define the critical error threshold, and offer guidance and future study recommendations on data collection and machine learning that will increase the success of ML within manufacturing.

Blind people can benefit greatly from a system capable of localising and reading comprehension text embedded in natural scenes and providing useful information that boosts their self-esteem and autonomy in everyday situations. Regardless of the fact that existing optical character recognition programmes seem to be quick and effective, the majority of them are not able to correctly recognise text embedded in usual panorama images. The methodology described in this paper is to localise textual image regions and pre-process them using the naïve Bayesian algorithm. A weighted reading technique is used to generate the correct text data from the complicated image regions. Usually, images hold some disturbance as a result of the fact that filtration is proposed during the early pre-processing step. To restore the image's quality, the input image is processed employing gradient and contrast image methods. Following that, the contrast of the source images would be enhanced using an adaptive image map. The stroke width transform, Gabor’s transform, and weighted naïve Bayesian classifier methodologies have been used in complicated degraded images to segment, feature extraction, and detect textual and non-textual elements. Finally, to identify categorised textual data, the confluence of deep neural networks and particle swarm optimisation is being used. The text in the image is transformed into an acoustic output after identification. The dataset IIIT5K is used for the development portion, and the performance of the suggested come up is evaluated using parameters such as accuracy, recall, precision, and F1-score.

Machine learning has been the corner stone in analysing and extracting information from data and often a problem of missing values is encountered. Missing values occur because of various factors like missing completely at random, missing at random or missing not at random. All these may result from system malfunction during data collection or human error during data pre-processing. Nevertheless, it is important to deal with missing values before analysing data since ignoring or omitting missing values may result in biased or misinformed analysis. In literature there have been several proposals for handling missing values. In this paper, we aggregate some of the literature on missing data particularly focusing on machine learning techniques. We also give insight on how the machine learning approaches work by highlighting the key features of missing values imputation techniques, how they perform, their limitations and the kind of data they are most suitable for. We propose and evaluate two methods, the k nearest neighbor and an iterative imputation method (missForest) based on the random forest algorithm. Evaluation is performed on the Iris and novel power plant fan data with induced missing values at missingness rate of 5% to 20%. We show that both missForest and the k nearest neighbor can successfully handle missing values and offer some possible future research direction.

Accurate detection of hate speech against politicians, policy making and political ideas is crucial to maintain democracy and free speech. Unfortunately, the amount of labelled data necessary for training models to detect hate speech are limited and domain-dependent. In this paper, we address the issue of classification of hate speech against policy makers from Twitter in Italian, producing the first resource of this type in this language. We collected and annotated 1264 tweets, examined the cases of disagreements between annotators, and performed in-domain and cross-domain hate speech classifications with different features and algorithms. We achieved a performance of ROC AUC 0.83 and analyzed the most predictive attributes, also finding the different language features in the anti-policymakers and anti-immigration domains. Finally, we visualized networks of hashtags to capture the topics used in hateful and normal tweets., to appear in SN social sciences - special issue on hate speech

Life-threatening novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), also known as COVID-19, has engulfed the world and caused health and economic challenges. To control the spread of COVID-19, a mechanism is required to enforce physical distancing between people. This paper proposes a Blockchain-based framework that preserves patients’ anonymity while tracing their contacts with the help of Bluetooth-enabled smartphones. We use a smartphone application to interact with the proposed blockchain framework for contact tracing of the general public using Bluetooth and to store the obtained data over the cloud, which is accessible to health departments and government agencies to perform necessary and timely actions (e.g., like quarantine the infected people moving around). Thus, the proposed framework helps people perform their regular business and day-to-day activities with a controlled mechanism that keeps them safe from infected and exposed people. The smartphone application is capable enough to check their COVID status after analyzing the symptoms quickly and observes (based on given symptoms) either this person is infected or not. As a result, the proposed Adaptive Neuro-Fuzzy Interference System (ANFIS) system predicts the COVID status, and K-Nearest Neighbor (KNN) enhances the accuracy rate to 95.9% compared to state-of-the-art results.

With the advancement of globalisation, urbanisation and environmental change, the outbreak of the Coronavirus disease 2019 (COVID-19), as an infectious disease, has become a global threat. The entire world is continuously trying to adapt to the pandemic situation due to the sudden outbreak of COVID-19 and the lockdown phase, which has not been faced before. The fear of infection by such an unknown virus and the epidemic transformed the built-up environment and impacted various sectors of lives and livelihoods, which must be assessed in spatial perspectives. The objective of this research is to assess the multi-sectoral impact due to the COVID-19 pandemic. Thus, it is designed to inspect seven essential sectors, namely, the economy, employment, education, transport, travel and tourism, health and environment sector-wise impact assessment of the West Bengal state of India. Taking the required COVID-19 data from the government website of India ( http://www.covid19india.org ; https://www.mygov.in/corona-data/covid19-statewise-status ) and West Bengal ( https://covidindia.org/west-bengal ), a methodology is proposed on an integrated framework for the multi-sectoral impact assessment. The study concentrates on West Bengal, as no study exists on the multi-sectoral impact assessment due to the COVID-19 pandemic during the 1st wave, especially using the geospatial platform. The economy, employment, education, transport, health, tourism and environment multi-sectors of West Bengal are selected in this research, as these sectors have built the economic, sociocultural and environmental pillars of the state. All these sectors have been seriously affected, and the nature of the impact is diverse and large. Before the vaccine comes into the hands of the common people of West Bengal and in a broad sense in India, the awareness should be increased at the grass-root level to fight against the pandemic situation and even after the post-COVID era. The application of geospatial technology used for the mapping and analysis of COVID-19 affects the related database to tease out the multidimensional study, which aims to plan future road maps, search for answers and learn to add further security to overcome the future virus attack.

Owing to the quick spread of minute health-related experiences, the distillation of knowledge from such unstructured narratives is an extremely challenging task. In spite of the success of neural networks methods in improving learning structural reliability, they result in inadequate accuracy of the bio-medical sentiment classification when employing less useful features sets. Therefore, they lack discriminatory potential. In this study, we propose to add a deep associative memory into neural networks for an effective sentiment decomposition, which emphasizes correctly on bio-medical entities related to the extraction of different data-object properties, and contextual-semantics dependencies for a given aspect. The underlying trust of these measures is behind the ability to compute the completion of unseen medical patterns, where comprehensive bio-medical distributed representations are used for representing the formal medical connections from PubMed databases. Experiments on a biomedical sentiment analysis task show that the model provides comprehensive embeddings with meaningful medical patterns. It achieved an average performance of 87% on varied large online datasets. It also outperforms baselines in discovering and identifying medical natural concepts. We provide meaningful support to bio-medical sentiment analysis applications in social networks. Indeed, the facets of this study might be used in many health concerns such as analyzing change in health status or unexpected situations.

Satellite imagery is changing the way we understand and predict economic activity in the world. Advancements in satellite hardware and low-cost rocket launches have enabled near-real-time, high-resolution images covering the entire Earth. It is too labour-intensive, time-consuming and expensive for human annotators to analyse petabytes of satellite imagery manually. Current computer vision research exploring this problem still lack accuracy and prediction speed, both significantly important metrics for latency-sensitive automatized industrial applications. Here we address both of these challenges by proposing a set of improvements to the object recognition model design, training and complexity regularisation, applicable to a range of neural networks. Furthermore, we propose a fully convolutional neural network (FCN) architecture optimised for accurate and accelerated object recognition in multispectral satellite imagery. We show that our FCN exceeds human-level performance with state-of-the-art 97.67% accuracy over multiple sensors, it is able to generalize across dispersed scenery and outperforms other proposed methods to date. Its computationally light architecture delivers a fivefold improvement in training time and a rapid prediction, essential to real-time applications. To illustrate practical model effectiveness, we analyse it in algorithmic trading environment. Additionally, we publish a proprietary annotated satellite imagery dataset for further development in this research field. Our findings can be readily implemented for other real-time applications too.

In the process of crowdsensing, tasks allocation is an important part for the precise as well as the quality of feedback results. However, during this process, the applicants, the publisher and the authorized agency may aware the location of each other, and then threaten the privacy of them. Thus, in order to cope with the problem of privacy violation during the process of tasks allocation, in this paper, based on the basic idea of homomorphic encryption, an encrypted grids matching scheme is proposed (short for EGMS) to provide privacy preservation service for each entity that participates in the process of crowdsensing. In this scheme, the grids used for tasks allocation are encrypted firstly, so the task matching with applicants and publisher also in an encrypted environment. Next, locations used for allocation as well as locations that applicants can provide services are secrets for each other, so that the location privacy of applicants and publisher can be preserved. At last, applicants of task feedback results of each grid that they located in, and the publisher gets these results, and the whole process of crowdsensing is finished. At the last part of this paper, two types of security analysis are given to prove the security between applicants and the publisher. Then several groups of experimental verification that simulates the task allocation are used to test the security and efficiency of EGMS, and the results are compared with other similar schemes, so as to further demonstrate the superiority of proposed scheme.

Deep learning models have been very successful in the application of machine learning methods, often out-performing classical statistical models such as linear regression models or generalized linear models. On the other hand, deep learning models are often criticized for not being explainable nor allowing for variable selection. There are two different ways of dealing with this problem, either we use post-hoc model interpretability methods or we design specific deep learning architectures that allow for (more) easy interpretation and explanation. This paper builds on our previous work on the LocalGLMnet architecture that gives an interpretable deep learning architecture. In the present paper, we show how group LASSO regularization (and other regularization schemes) can be implemented within the LocalGLMnet architecture so that we receive feature sparsity for variable selection. We benchmark our approach with the recently developed LassoNet of Lemhadri et al.

Identifying the key risk factors of disease from a large amount of clinical data is a prerequisite for further scientific decision-making. In medical practice, the clinical symptom information of patients usually includes various types of data. Meanwhile, the occurrence and development of diseases are joint result of the mutual influence factors. Therefore, there is usually a correlation between attributes. In this paper, we discuss a kind of hybrid attribute feature selection problem considering the correlation between attributes. Firstly, we take the identification of disease pathogenic factors in medical decision as the background, and construct a hybrid attribute decision system. Secondly, by introducing kernel alignment, the uncertain relationship between attributes is defined. Based on this, a three-way clustering model in attribute space is established. Furthermore, a feature selection method for hybrid attribute data based on three-way clustering in attribute space is proposed. Finally, we applied the proposed model to identify the pathogenic factors of stroke and used 279 clinical random samples for simulation analysis. The results verified the applicability and validity of the model. The main contributions of this paper include two aspects. In terms of theory, by introducing kernel alignment, a three-way clustering algorithm in attribute space is established. Meanwhile, a hybrid attribute feature selection method based on three-way clustering is proposed. In terms of application, the proposed method is applied to identify risk factors of stroke.

Information and Communication Technologies, to a long extent, have a major influence on our social life, economy as well as on worldwide security. Holistically, computer networks embrace the Information Technology. Although the world is never free from people having malicious intents i.e. cyber criminals, network intruders etc. To counter this, Intrusion Detection System (IDS) plays a very significant role in identifying the network intrusions by performing various data analysis tasks. In order to develop robust IDS with accuracy in intrusion detection, various papers have been published over the years using different classification techniques of Data Mining (DM) and Machine Learning (ML) based hybrid approach. The present paper is an in-depth analysis of two focal aspects of Network Intrusion Detection System that includes various pre-processing methods in the form of dimensionality reduction and an assortment of classification techniques. This paper also includes comparative algorithmic analysis of DM and ML techniques, which applied to design an intelligent IDS. An experiment al comparative analysis has been carried out in support the verdicts of this work using ‘Python’ language on ‘kddcup99’ dataset as benchmark . Experimental analysis had been done in which we had found more impact on dimensionality reduction and MLP performed well in the true classification to establish secure network. The motive behind this effort is to detect different kinds of malware as early as possible with accuracy, to provide enhanced observant among various existing techniques that may help the fascinated researchers for future potential works.

Artificial Intelligence (AI) has rapidly developed over the past several decades. Several related AI approaches, such as Machine Learning (ML), have been applied to research on landscape patterns and ecological processes. Our goal was to review the methods of AI, particularly ML, used in studies related to landscape ecology and the main topics addressed. We aimed to assess the trend in the number of ML papers and the methods used therein, and provide a synopsis and prospectus of current use and future applications of ML in landscape ecology. We conducted a systematic literature search and selected 125 papers for review. These were examined and scored according to multiple criteria regarding methods and topic. We applied quantitative statistical methods, including cluster analysis based on titles, abstracts, and keywords and a non-metric multidimensional scaling based on attributes assigned during the review. We used Random Forests machine learning to describe the differences between identified clusters in terms of the topics and methods they included. The most frequent method found was Random Forests, but it is noteworthy to mention the increasing popularity of tools related to Deep Learning. The topics cover both ecologically oriented issues and the landscape-human interface. There has been a rapid increase in ML and AI methods in landscape ecology research, with Deep Learning and complex multi-step pipeline AI methods emerging in the last several years. The rapid increase in the number of ML papers in landscape ecology research, and the range of methods employed in them, suggest explosive growth in application of these methods in landscape ecology. The increase of Deep Learning approaches in the most recent years suggest a major change in analytical paradigms and methodologies that we feel may transform the field and enable analyses of more complex pattern process relationships across vaster data sets than has been possible previously.

Image encryption algorithms based on Chaotic approach are becoming increasingly popular for remotely sensed images using parallel techniques. It has been demonstrated that the most efficient image encryption algorithms are based on Chaos. Previous research using chaos-based cryptosystems has resulted in poor performance when using a single computer, compromising privacy, security, and reliability. Furthermore, there were issues when vulnerable satellite images were processed. This paper describes a novel chaos-based encryption technique that employs an external secret key and Henon, Logistic, and Gauss iterated maps. The proposed encryption algorithm is capable of efficiently encrypting a large number of images. When the number of images increases, however, these images become very small, and the technology becomes inefficient or impractical. This paper investigates the parallel method of image encryption on a large number of remotely sensed images in Hadoop. Hadoop's file visit method has been enhanced so that it can treat the entire Tiff file as a single unit. Furthermore, the file format is being extended to be supported by Hadoop in order to support GeoTiff in Hadoop. The results of the experiments show that the proposed parallel method for encryption is effective and scalable to a large number of images when compared to other well-known methods.

This paper discusses the progress made so far and future challenges in integrating the analysis of multiple Non-Functional Properties (NFP) (such as performance, schedulability, reliability, availability, scalability, security, safety, and maintainability) into the Model-Driven Engineering (MDE) process. The goal is to guide the design choices from an early stage and to ensure that the system under construction will meet its non-functional requirements. The evaluation of the NFPs considered in this paper uses various kinds of NFP analysis models (also known as quality models) based on existent formalisms and tools developed over the years. Examples are queueing networks, stochastic Petri nets, stochastic process algebras, Markov chains, fault trees, probabilistic time automata, etc. In the MDE context, these models are automatically derived by model transformations from the software models built for development. Developing software systems that exhibit a good trade-off between multiple NFPs is difficult because the design of the software under construction and its underlying platforms have a large number of degrees of freedom spanning a very large discontinuous design space, which cannot be exhaustively explored. Another challenge in balancing the NFPs of a system under construction is due to the fact that some NFPs are conflicting—when one gets better the other gets worse—so an appropriate software process is needed to evaluate and balance all the non-functional requirements. The integration approach discussed in this paper is based on an ecosystem of inter-related heterogeneous modeling artifacts intended to support the following features: feedback of analysis results, consistent co-evolution of the software and analysis models, cross-model traceability, incremental propagation of changes across models, (semi)automated software process steps, and metaheuristics for reducing the design space size to be explored.

Handling missing values and large-dimensional features are crucial requirements for data-driven fault diagnosis systems. However, most intelligent data-driven diagnostic systems are not able to handle missing data. The presence of high-dimensional feature sets can also further complicate the process of fault diagnosis. This paper aims to devise a missing data imputation unit along with a dimensionality reduction unit in the pre-processing module of the diagnostic system. This paper proposes a novel pooling strategy for missing data imputation (PSMI). This strategy can simplify complex patterns of missingness and incrementally update the pool. The pre-processing module receives incomplete observations, PSMI estimates missing values, and, then, the dimensionality reduction unit transforms completed observations onto a lower-dimensional feature space. These transformed observations are then fed as inputs to the fault classification module for decision making and diagnosis. This diagnostic scheme makes use of various state-of-the-art missing data imputation, dimensionality reduction and classification algorithms. This enables a comprehensive comparison and allows to find the best techniques for the sake of diagnosing faults in the Tennessee Eastman process. The obtained results show the effectiveness of the proposed pooling strategy and indicate that principal component analysis imputation and heteroscedastic discriminant analysis approaches outperform other imputation and dimensionality reduction techniques in this diagnostic application.

Chits funds are typically operated among a trusted network of people, by a foreman, completely relying on the trust and transparency of the transactional data audited through the network. The cost involved in this ledger keeping or audit procedures for ensuring trust and transparency is the major institutional overhead for running a chit fund. This paper proposes a transparent platform based on distributed ledger technology termed Blockchain as a Service (BaaS) for setting up the ChitChain infrastructure. The service providers and the end-users (customers) will be provided with client interfaces for the secured access methods to the system. As the notion of trust will be taken from the institution to the tamper-proof platform, the approach discussed in this paper will propose a decentralized finance model that enables higher financial inclusion. The auditable platform provides insights to the stakeholders regarding the transactions and operations of the chit fund. This work proposes a novel operation model for setting up a chitty, a fair jury selection protocol for decentralized management, and an efficient foreman and end-user onboarding process.

As social media has become a ubiquitous part of daily life, researchers made a great progress in identifying the emotion in user-generated texts. However, it is a challenging task as people express their emotion in explicit and implicit ways. This paper focuses on the problem of identifying sentiments from implicit sentences which contain no emotional word or phrase. Most of the existing sentiment classification models cannot identify the sentiments accurately since they usually focus on extracting features from grammatical information without taking contextual information into account. In this paper, we argue that the contextual information is the key to identify sentiments in implicit sentences. Moreover, multiple features extracting from different aspects should be taken into account to improve sentiment identification. This paper proposes a multi-feature neural network model considering three aspects: contextual information, syntactic information and semantic information. To better get the semantic information of the sentence, we propose an attention mechanism based on contextual affective space. The experimental results on the SMP2019-ECISA dataset demonstrate that our model outperforms the previous systems and strong neural baselines.

Fault injection attacks (FIA), which cause information leakage by injecting intentional faults into the data or operations of devices, are one of the most powerful methods compromising the security of confidential data stored on these devices. Previous studies related to FIA report that attackers can skip instructions running on many devices through many means of fault injection. Most existing anti-FIA countermeasures on software are designed to secure against instruction skip (IS). On the other hand, recent studies report that attackers can use laser fault injection to manipulate instructions running on devices as they want. Although the previous studies have shown that instruction manipulation (IM) could attack the existing countermeasures against IS, no effective countermeasures against IM have been proposed. This paper is the first work tackling this problem, aiming to construct software-based countermeasures against IM faults. Evaluating program vulnerabilities to IM faults is required to consider countermeasures against IM faults. We propose three IM simulation environments for that aim and compare them to reveal their performance difference. GDB (GNU debugger)-based simulator that we newly propose in this paper outperforms the QEMU-based simulator that we presented in AICCSA:1–8, 2020 in advance, in terms of evaluation time at most $$\times$$ × 400 faster. Evaluating a target program using the proposed IM simulators reveals that the IM faults leading to attack successes are classified into four classes. We propose secure coding techniques as countermeasures against IMs of each four classes and show the effectiveness of the countermeasures using the IM simulators.

Estimating software enhancement efforts became a challenging task in software project management. Recent researches focused on identifying the best machine learning algorithms for software maintenance effort estimation. Most of the research publications investigated the use of ensemble learning for improving software effort estimation. Intending to increase the estimation accuracy over individual models, this paper investigates the use of the stacking ensemble method for estimating the enhancement maintenance effort (EME) of software projects. This paper makes a comparison between two machine learning-based approaches for estimating software EME: The M5P (as an individual model) and the stacking as an ensemble method combining different regression models (GBRegr, LinearSVR, and RFR) using the ISBSG dataset. A correlation-based feature selection (CFS) algorithm is basically used to achieve efficient data reduction. The selected ML techniques-based approaches were trained and tested on a dataset with relevant features leading to the improvement of estimate accuracy. Results show that the software EME estimation using CFS and stacking ensemble method is improved in terms of mean absolute error (MAE) = 0.0383 and root mean square error (RMSE) = 0.1973.

The numerical treatment of an atmospheric chemical scheme, which contains 56 species, is discussed in this paper. This scheme is often used in studies of air pollution levels in different domains, as, for example, in Europe, by large-scale environmental models containing additionally two other important physical processes—transport of pollutants in the atmosphere (advection) and diffusion phenomena. We shall concentrate our attention on the efficient numerical treatment of the chemical scheme by using Implicit Runge–Kutta Methods combined with accurate and efficient advanced versions of the Richardson Extrapolation. A Variable Stepsize Variable Formula Method is developed in order to achieve high accuracy of the calculated results within a reasonable computational time. Reliable estimations of the computational errors when the proposed numerical methods are used in the treatment of the chemical scheme will be demonstrated by presenting results from several representative runs and comparing these results with “exact” concentrations obtained by applying a very small stepsize during the computations. Results related to the diurnal variations of some of the chemical species will also be presented. The approach used in this paper does not depend on the particular chemical scheme and can easily be applied when other atmospheric chemical schemes are selected.

One of the challenging issues in RFID systems is the issue of group ownership transfer and ownership duration. To answer this need recently one protocol which combines group ownership transfer and time bound delegation was proposed by Lee et al. which we call it TBGODP stands for time bound group ownership delegation protocol. A secure time bound group ownership delegation protocol can be used to transfer ownership of a group of tagged devices for a certain period of time, such as renting a smart home. In this paper, we applied different attacks such as traceability and secret disclosure attacks against TBGODP which show this protocol is not a secure one. All attacks presented in this paper, only need one run of protocol eavesdropping and succeed with the probability of one. We also address the vulnerabilities of TBGODP which lead to propose a new secure improved one called $$TBGODP^+$$ . We formally prove the security of $$TBGODP^+$$ in Real or Random (RoR) model and also verify its security using BAN logic. We also evaluate the security of $$TBGODP^+$$ with well-known security analysis tools, namely AVISPA and Scyther. The results of these evaluations indicate $$TBGODP^+$$ can safely transfer ownership of a group of tags to another owner for a specified period of time, and that the new owner’s ownership expires upon time expiration. Comparative analysis of $$TBGODP^+$$ with other related schemes shows that the proposed protocol in terms of computational and communication costs, and execution time is more than other related schemes and this is a cost that should be paid to provide complete security against various attacks.

Vaiśesikas are realist philosophers of classical India. They admit time (kāla) as a ubiquitous real substance. In this paper, our aim is to discuss such a determination of time following sixth century Vaiśesika scholar Praśastapāda and a few of his interpreters, Vyomaśivācārya and Udayanācārya. This paper is an effort to state realist philosophers’ understanding of time and also to highlight how in classical Indian tradition, interpretations paved the way for proving the reality of time. The application of logical methods by the Vaiśesika interpreters is also worth studying. These methods show the internal development occurring in the system of Vaiśesika. Praśastapāda stated different notions like priority etc. (paratvādi) as the linga or hetu of kāla. Vyomaśiva and Udayana have established this statement of Praśastapāda as the laksaņa of kāla by virtue of different forms of inference.

Containerization technology offers an appealing alternative for encapsulating and operating applications (and all their dependencies) without being constrained by the performance penalties of using Virtual Machines and, as a result, has got the interest of the High-Performance Computing (HPC) community to obtain fast, customized, portable, flexible, and reproducible deployments of their workloads. Previous work on this area has demonstrated that containerized HPC applications can exploit InfiniBand networks, but has ignored the potential of multi-container deployments which partition the processes that belong to each application into multiple containers in each host. Partitioning HPC applications has demonstrated to be useful when using virtual machines by constraining them to a single NUMA (Non-Uniform Memory Access) domain. This paper conducts a systematical study on the performance of multi-container deployments with different network fabrics and protocols, focusing especially on Infiniband networks. We analyze the impact of container granularity and its potential to exploit processor and memory affinity to improve applications’ performance. Our results show that default Singularity can achieve near bare-metal performance but does not support fine-grain multi-container deployments. Docker and Singularity-instance have similar behavior in terms of the performance of deployment schemes with different container granularity and affinity. This behavior differs for the several network fabrics and protocols, and depends as well on the application communication patterns and the message size. Moreover, deployments on Infiniband are also more impacted by the computation and memory allocation, and because of that, they can exploit the affinity better. We thank Lenovo for providing the testbed to run the experiments in this paper. This work was partially supported by Lenovo as part of Lenovo-BSC collaboration agreement, by the Spanish Government under contract PID2019-107255GB-C22, and by the Generalitat de Catalunya under contract 2017-SGR-1414 and under Grant No. 2020 FI-B 00257.

The Galois lattice is a graphic method of representing knowledge structures. The first basic purpose in this paper is to introduce a new class of Galois lattices, called graded Galois lattices. As a direct result, one can obtain the notion of graded closed itemsets (sets of items), to extend the definition of closed itemsets. Our second important goal in this paper, is related to set a constructive method, computing the graded formal concepts and graded closed itemsets. We mean by a constructive method, a method that builds up a complete solution from scratch by sequentially adding components to a partial solution until the solution is complete. Besides of computational aspects, our methods in this paper are based on the strong results obtained by special mappings in the realm of domain theory. To reach the fertilized consequences and constructive algorithms, we need to push the study to the structures of Banach lattices., Comment: 15 pages, 2 figures, 1 table, derived from the Ph.D. thesis

The Modifiable Aerial Unit Problem (MAUP) influences the interpretation of spatial data in that forms of spatial aggregation creates scale and segmentation ecological fallacies. This paper explores the extent to which similar scalar and segmentation issues affect the analysis of temporal data. The analogy of gerrymandering in spatial data, which is the purposeful segmentation of space such that the underlying aggregations prove a specific point, is used to demonstrate segmentation and aggregation effects on time series data. To do so, the paper evaluates real-time sound monitoring data for Dublin, Ireland at multiple aggregation scales and segmentations to determine their effects with respect to compliance with European Union regulations concerning acceptable decibel levels. Like the MAUP, increasing scales of temporal aggregation remove extremes at more local scales, which has the effect of reducing measurements of non-compliance. Similarly, and unlike the spatial equivalent, because of circadian human social patterns, segmentation of temporal measurements also has a predictable, and gerrymander-able, effect on the measurement of compliance with ambient sound limits. The effect is computed as the Temporal Aggregation Bias and strategies which could justify gerrymandering of sound monitoring data are presented.

In order to improve the effect of e-government work, it is necessary to analyze its influencing factors. E-government is affected by many social factors, so it is necessary to combine intelligent models to improve the effect of factor analysis. This paper combines the essence of e-government influencing factor data to improve the machine learning algorithm and uses the EM algorithm to derive the parameter estimation formula of the data in the case of missing data to improve the accuracy of data analysis. Moreover, this article combines the structure of the e-government system to build the main structure of the intelligent analysis model of the influence factors of e-government. According to the key influencing factor model of e-government adoption and multi-dimensional research on technology, organization and implementation, this paper puts forward the model and promotion mode of the e-government system based on cloud computing and conducts a simulation. From the simulation results, the effect of the model proposed in this paper is more significant.

In a prior paper (Boukerrou et al. IACR Trans. Symmetric Cryptol. 2020(1), 331–362 2020), Boukerrou et al. introduced the Feistel Boomerang Connectivity Table (FBCT). FBCT is an important cryptanalytic technique on Feistel ciphers. In fact, the coefficients of FBCT are actually related to the second-order zero differential spectra of functions in even characteristic. In this paper, we push further the study initiated in Boukerrou et al. (IACR Trans. Symmetric Cryptol. 2020(1), 331–362 2020). Almost perfect nonlinear (APN) functions and the inverse function are interesting in cryptography and coding theory. In Boukerrou et al. (IACR Trans. Symmetric Cryptol. 2020(1), 331–362 2020), Boukerrou et al. determined the second-order zero differential spectra of APN functions and the inverse function in even characteristic. In order to derive further cryptographic properties of APN functions and the inverse function in odd characteristic, we calculate the second-order zero differential spectra of some APN functions and the inverse function in odd characteristic. In addition, these APN functions and the inverse function have low second-order zero differential uniformity.

Alzheimer's disease (AD) is the type of dementia that affects the world's significant population and is expected to be increased worldwide. In recent years, the main focus of researchers is the early diagnosis of AD because of the unavailability of well-known causes and no permanent curative solution for AD-infected patients. A plethora of machine learning algorithms are utilized by various researchers to provide an accurate diagnosis of the patients who fall under the category of Healthy Controls (HC), Mild Cognitive Impairment (MCI), and AD. In this paper, many machine learning algorithms with a special focus on various feature extraction and fusion techniques are explored in detail, which are used by the researchers to investigate various categories of AD from the Magnetic Resonance Images (MRI). Therefore, a systematic review of different feature extraction methods such as statistical, spatial-based, and transform-based is presented in this paper. Further, various open-source standard datasets that researchers used to detect AD from medical images are also discussed in detail. As some of the feature extraction techniques mentioned above generate redundant and large feature sets that significantly affect the classification accuracy, therefore, in this paper, we have also discussed several dimensionality reduction methods available for the extraction of relevant features from medical images. Moreover, future directions are also provided to researchers to aid in the early detection of brain disorders which may be helpful to add productive years to the lives of AD-contaminated patients.

This paper presents an adaptive approach to optimize field injection strategies using streamline-based well allocations coupled with fuzzy logic. The strength of our approach comes from the fact that streamlines are generated by running a full-physics reservoir simulator. Streamlines provide great insights about well pattern connectivity and good allocation factors allowing the injection efficiency (IE) for each pattern to be determined. Fuzzy logic can simulate human thinking and handle different categories of information including linguistic, imprecise, approximate, and overlapping to name a few. This paper presents a genuine approach for field injection optimization using a streamlined-based fuzzy logic system. In this work, we present an adaptive streamline-based fuzzy logic system that uses three input parameters namely injection efficiency (IE), water cut (WC), and injection loss to aquifer to assign an injector ranking index (IRI) according to injector performances. The workflow then smartly redistributes water injection by accounting for operational constraints and number of connected producers in a pattern in addition to the IRI. The workflow examines the low performers (i.e., low and medium IRI categories) and assigns different injection reduction factors for each injector in these categories. Then, the total amount of reduced injection is assigned to high performers (i.e., high IRI) while ensuring no operational constraint is violated, such as bottom-hole pressure (BHP) and capacity of pumps. This approach has been tested on a dual-porosity dual-permeability (DPDP) conceptual simulation model. The area of interest has two rows of injectors: downdip and updip. The updip injectors are the focus of the study. The results of applying this approach show noticeable improvements in injection efficiency for most wells in the area of interest ensuring better sweep, good pressure support, and improving cumulative oil production. We believe combining both technologies, namely streamlines and fuzzy logic, can provide reservoir engineers with a robust decision-making tool to attain a more successful field-wide water injection strategy.

In recent years, progressive attention has been paid to the research of man–machine interface. In many fields of application software system development, experts have listed man–machine interface as one of the commands for the urgent research and advancement. This article has proposed the improvement to the man–machine interface system control design for the practical value of life, and has presented an approach for algorithms using Machine Learning. The method used in the paper includes the spatial layout of human–machine interface supported by the design cognition model and the use of Machine Learning algorithms. Based on Max–Min Ant System, the optimal path of ant construction is obtained, which is the optimal layout scheme. The experimental result in the paper shows that: with the support of the cognitive model, the cabin man–machine interface system control design method based on Genetic Algorithm-Ants Algorithm is obtained. The layout design principles were summarized by the cognitive model, and the layout optimization objective function was constructed according to each principle, and the problem of solving layout parties and cases was transformed into a combinatorial optimization problem. The form of fitness function, pheromone, and heuristic information for layout optimization was studied, and the algorithm of manual optimization process was implemented based on Genetic Algorithm and Ant Algorithm, in order to obtain a good optimization performance and time performance as a result. The practical value of the control design of man–machine interface system with the Machine Learning algorithm is proved.

Computer, Internet, and Smartphone have changed our life as never before. Today, we cannot even imagine our life without these technologies. If we look around, we find everything, everywhere connected and controlled by system and software. We find amazing software and mobile applications which have become nerve of our daily life. Our dependency on this software and systems is so and so much that it is scary even to imagine, what if this system fails at any point in time. There is always a threat surrounded by various types of cyber-attacks. Every day cybercriminals are evolving their attacking strategy. Cyber-attacks using ever-more sophisticated malware are the major cause of concern for all types of users. Cyber-world has witnessed rapid changes in malware attacking strategy in the recent past. The volume, velocity, and complexity of malware are posing new challenges for malware detection systems. A scalable malware detection system with the capability to detect complex attacks is the time of need. In this paper, we have proposed a scalable malware detection system using big data and a machine learning approach. The machine learning model proposed in the system is implemented using Apache Spark which supports distributed learning. Locality-sensitive hashing is used for malware detection, which significantly reduces the malware detection time. A five-stage iterative process has been used to carry out the implementation and experimental analysis. The proposed model shown in the paper has achieved 99.8% accuracy. The proposed model has also significantly reduced the learning and malware detection time compared to models proposed by other researchers.