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The NIST selection process for standardizing Post-Quantum Cryptography Mechanisms is currently running. Many papers already studied their theoretical security, but the resistance in deployed device has not been much investigated so far. In particular, fault attack is a serious threat for algorithms implemented in embedded devices. One particularly powerful technique is to use safe-error attacks. Such attacks exploit the fact that a specific fault may or may not lead to a faulty output depending on a secret value. In this paper, we investigate the resistance of various Post-Quantum candidates algorithms against such attacks.

Cloud computing is a new paradigm. In recent years, cloud computing has been a popular model for hosting and providing services, as it has gained great popularity in providing multiple services such as storage and cloud hosting. Moreover, the cloud provides a powerful computing platform that enables many individuals and organizations to perform many tasks such as: use of online storage space, approval on business applications, developing custom computer software, and creating a virtual network environment. In previous years, the number of people using cloud services increased dramatically and a lot of data was stored in cloud computing environments. Meanwhile, data breaches of cloud services are also increasing every year due to hackers who are always trying to exploit security holes in the cloud architecture, so the security problems in these systems are horrific because they store a lot of private and confidential data. In this paper, the authors review security and privacy issues, bottlenecks associated with cloud computing, and the specific security issues in cloud computing layers and vulnerabilities, and how to avoid and control them. This paper helps the reader understand the problems related to cloud computing.

The pervasive use of software systems and current threat environment demand that software systems not only survive cyberattacks, but also bounce back better, stronger, and faster. However, what constitutes a modern software system? Where should the security and resilience mechanisms be-in the application software or in the cloud environment where it runs? In this concept paper, we set up a context to pose these questions and present a roadmap to answer them. We describe challenges to achieving resilience and beyond, and outline potential research directions to stimulate discussion in the workshop.

In modern academic social network, it is very difficult for scholars to find academic papers consistent with their research direction. Time is a critical factor in paper recommendation. As time goes on, the impact of an academic paper would gradually fade. Likewise, the research interests of users may also change. Therefore, we propose a temporal perceptual neural collaborative filtering model that integrates the multi-dimensional features of papers. We conducted our experiments on the dataset from CiteULike, comparing the recommended results by using four time-decay functions and evaluating our model with multiple evaluation indicators. The satisfactory results show that our model is effective in filtering out the expired papers by considering the characteristics of papers and the changes of scholars' interests.

Critical flaws continue to exist at the level of domain, requirement, and/or design specification, and specification verification (i.e. to check whether a specification has desirable properties) is still one of the most important challenges in software/system engineering. CafeOBJ is an executable algebraic specification language system and domain/requirement/design engineers can write proof scores in CafeOBJ for improving quality of specifications by the specification verification. This paper describes advances of the proof scores for the specification verification in CafeOBJ from the author’s point of view.

In this paper, we propose a data augmentation framework for Optical Character Recognition (OCR). The proposed framework is able to synthesize new viewing angles and illumination scenarios, effectively enriching any available OCR dataset. Its modular structure allows to be modified to match individual user requirements. The framework enables to comfortably scale the enlargement factor of the available dataset. Furthermore, the proposed method is not restricted to single frame OCR but can also be applied to video OCR. We demonstrate the performance of our framework by augmenting a 15% subset of the common Brno Mobile OCR dataset. Our proposed framework is capable of leveraging the performance of OCR applications especially for small datasets. Applying the proposed method, improvements of up to 2.79 percentage points in terms of Character Error Rate (CER), and up to 7.88 percentage points in terms of Word Error Rate (WER) are achieved on the subset. Especially the recognition of challenging text lines can be improved. The CER may be decreased by up to 14.92 percentage points and the WER by up to 18.19 percentage points for this class. Moreover, we are able to achieve smaller error rates when training on the 15% subset augmented with the proposed method than on the original nonaugmented full dataset.

The emerging of information technology (IT)and operational technology (OT) convergence has driven the smart grid technology adoption in the European (EU) energy system for better visibility and automated controllability. On the other hand, the energy system infrastructures can be threaten by the cyber attacks due to the increasing of information and communication technology integration. The cyber vulnerabilities are caused by the increasing of internet connection and the application complexities. It is crucial to identify essential skills in the field of cyber security protection and defense for the students. This paper presents the outcome of a literature review and a workshop with stakeholders from industry and academia about the state of the art and trends in the education of cyber security in smart grids.

Due to the increasingly growing demand for user identification on cell phones, PCs, laptops, and so on, face anti-spoofing has risen to significance and is an active research area in academia and industry. The detection of the real face then recognize it present an important challenge regarding the techniques that can be used to spoof any recognition system like masks, printed photos. This paper we present an anti-spoofing face method to solve the real-world scenario that learns the target domain classifier based on samples used for training in a particular source domain. Specifically, with the conventional regression CNN, the Spatial/Channel-wise Attention Modules were introduced. Two modules, namely the Spatial-wise Attention Module and the Channel-wise Attention Module, were used at spatial and channel levels to improve local features and ignore the irrelevant features. Extensive experiments on current collections with benchmarks datasets verifies that the recommended solution will significantly benefit from the two modules and better generalization capability by providing significantly improved results in anti-spoofing.

Drowsiness can be dangerous when performing tasks that require constant attention, such as driving a vehicle. Sleepiness is correlated with a variety of physiological variables, such as eye closing, head movements, pulse rate, eye twitch rate, etc. Also, the yawn can be considered as an accurate indicator of drowsiness and fatigue. Yawning detection is very important for the safety purpose of drivers as it will let the driver know if he/she is getting drowsy. Driving at that moment may not be safe. Several automatic yawning detection techniques have been developed for driver's drowsiness monitoring system. Nevertheless, correctly detecting the yawning of the driver and predicting exhaustion in real-time situations is still a crucial challenge. In this paper, we will be reviewing various existing machine learning approaches for driver's yawning detection. In previous approaches, various classical machine learning algorithms such as viola-Jones, contour activation algorithm and SVM have been used for yawning detection, but these approaches failed to predict yawning in realtime situations. Using Deep learning techniques, we can make a real-time yawn detection system with high accuracy. We find that some precious Deep learning algorithms like CNN, RNN, LSTM, Bi-LSTM can detect the patterns with high accuracy. After the comparison of various algorithms and techniques, we find that with the help of Deep learning algorithms the yawning can be detected in real time with high accuracy.

Human recogntion technologies are gaining significant research attention, where the model can be trained to be more precise to recognize the poses performed by objects. Activity identification is a kind of problem, which needs more research consideration and improvement. There is also an increasing need to detect different poses of objects. To tackle the issue, different sensors like Gyroscope and Accelerometer are required to classify the data in the form of images using machine learning algorithms like SVM and CNN. These approaches help us in implementing real-time applicaions such as health monitoring, tracking sports activity and security. The paper also discusses about its benefits, limitations and prominent approach for human activity recognition.

Improper Input Validation (IIV) is a software vulnerability that occurs when a system does not safely handle input data. Even though IIV is easy to detect and fix, it still commonly happens in practice. In this paper, we study to what extent developers can detect IIV and investigate underlying reasons. This knowledge is essential to better understand how to support developers in creating secure software systems. We conduct an online experiment with 146 participants, of which 105 report at least three years of professional software development experience. Our results show that the existence of a visible attack scenario facilitates the detection of IIV vulnerabilities and that a significant portion of developers who did not find the vulnerability initially could identify it when warned about its existence. Yet, a total of 60 participants could not detect the vulnerability even after the warning. Other factors, such as the frequency with which the participants perform code reviews, influence the detection of IIV. Preprint: https://arxiv.org/abs/2102.06251. Data and materials: https://doi.org/10.5281/zenodo.3996696.

Wireless communication base stations have strong requirements in flexibility, performance and real-time responsiveness. Building an application-specific lightweight LibOS on a general OS is a good technique to make a balance among real-time responsiveness, performance and flexibility. Compared to traditional ext-kernel systems, the fast progress of virtualization technology brings a more efficient implementation of LibOS. In this paper, we propose a virtualization assisted real-time LibOS system named Tenma for wireless Baseband Unit (BBU). In Tenma, lightweight process virtualization, enhanced isolation between host OS and guest OS, interrupt direct-pass and application specific lightweight scheduling are used to guarantee the requirements of performance and real-time responsiveness while keeping system flexibility. The Tenma system is already in commercial deployment. Experiments in practice prove that it can meet the requirements of wireless base station applications.

Constantly increasing botnets powered by vulnerable IoT devices perform record-breaking DDoS attacks to critical infrastructures. Therefore, it is imperative to find vulnerabilities in IoT devices ahead of attackers. In this paper, we first present a systematic analysis on various kinds of IoT malware to further explore the challenges of IoT Honeypot design. We then propose HoneyIoT, a scalable IoT Honeypot framework, which aims at attracting IoT attacks and recording malicious behaviors with configurable vulnerabilities and firmware support. During a 7day real-world industrial experiment, HoneyIoT observed over 12,500 malicious connections and 3,423 distinct login attempts.

In order to automate the task of correcting handwritten subjective answers, we envision a system that reads such answers and grades them. In this project, we have created a mobile application to achieve this task. We have used the RAKE algorithm to extract key-phrases, which are then embedded using Word2vec model and compared using Word Mover's Distance. Our created model achieved almost human like evaluation accuracy. We have also provided accessibility features to the users with the aim of making this application a one-stop solution to automatic evaluation of handwritten subjective answers.

Sentiment analysis and opinion mining have developed significantly throughout the last decades. This study aims to assess, amongst many other aspects, people's feelings, opinions, and emotions of someone or something. This review's principal objective is to automatically assess the direction of the review and contrast this with the evaluation made by the reviewers. This will allow scientists to categorize and evaluate reviews cross-sectionally and encourage a scientific article's overall evaluation more critically. An EM-VC ensemble approach has been proposed for analyzing the reviews. This approach uses POS to achieve a syntactic sentence structure. With dictionaries, this syntactic structure enables the semantic orientation of the analysis via the scoring algorithm to be determined. To test the proposed approach's efficiency and performance about the baseline method, some tests have been performed using typical performance metrics like precision, accuracy, F1-score, and recall. The findings reveal that improvements are achieved in binary classification, ternary classification, & five-point classification compared to the existing HS-SVM method. Still, it provides a slight improvement in accuracy in the context of multiclass classification compared to others.

Facing the difficulty and inefficiency of creating and maintaining High-Definition (HD) maps in our commercial deployments, we have developed an edge-based crowdsourcing task distribution framework for HD Map in autonomous driving. Our key observation is that: HD map data crowdsourcing exhibits the diminishing marginal utility thus there exists an inflection point for maximum utility, meanwhile its premature convergence of utility will leave some map updates not notified in time. Based on this observation, we develop a periodic crowdsourcing task distribution framework. It discretizes the demands for collecting source data into different periods and uses an optimal stopping rule to terminate the data collection for the maximum crowdsourcing utility. The experimental results verify that our crowdsourcing framework can achieve high time coverage and high efficiency with lower cost.

In autonomous driving, 3D object detection is es-sential as it provides basic knowledge about the environment. However, as deep learning based 3D detection methods are usually computation intensive, it is challenging to support realtime 3D object detection on edge-computing devices in selfdriving cars with limited computation and memory resources. To facilitate this, we propose a compiler-aware pruning search framework, to achieve real-time inference of 3D object detection on the resource-limited mobile devices. Specifically, a generator is applied to sample better pruning proposals in the search space based on current proposals with their performance, and an evaluator is adopted to evaluate the sampled pruning proposal performance. To accelerate the search, the evaluator employs Bayesian optimization with an ensemble of neural predictors. We demonstrate in experiments that for the first time, the pruning search framework can achieve real-time 3D object detection on mobile (Samsung Galaxy S20 phone) with state-of-the-art detection performance.

Graph neural networks (GNN) have achieved state-of-the-art performance on various industrial tasks. However, the poor efficiency of GNN inference and frequent Out-of-Memory (OOM) problem limit the successful application of GNN on edge computing platforms. To tackle these problems, a feature decomposition approach is proposed for memory efficiency optimization of GNN inference. The proposed approach could achieve outstanding optimization on various GNN models, covering a wide range of datasets, which speeds up the inference by up to 3×. Furthermore, the proposed feature decomposition could significantly reduce the peak memory usage (up to 5× in memory efficiency improvement) and mitigate OOM problems during GNN inference.

It is difficult to evaluate a person’s ability or performances because there is no common yardstick for measuring individual human abilities. For example, in general, paper based tests have different characteristics (difficulties, accuracies, and so on) because they depend on items of each test. Recently, e-testing technologies from AI approach, for which each test form has equivalent measurement accuracy but with a different set of items, have become popular. Even if different examinees with the same ability take different tests, their test scores should be guaranteed to be equivalent. We introduce state-of the-art e-testing technologies from AI approach.

the real datasets in the world usually are imbalanced; the number of samples for their classes is not equal. Classifying these datasets makes the classifiers pay attention to the class with more samples than the classes with fewer samples. The Qur’anic dataset can be considered an imbalanced dataset because verses of the Qur’anic topics are not equal. Many studies have been performed to classify Qur’anic text using different classifiers. However, few studies classified the Qur’anic verses based on Imbalanced Learning (IL). So, this work aims to classify the Qur’anic text using Ensemble methods, Boosting and Bagging. The base classifiers of these methods were LibSVM, Naive Bayes, KNN, and J48. Three techniques are conducted in this paper based on the standard classifiers. The three techniques are: implementing the base classifiers alone, implementing these classifiers with the Boosting method, and implementing the classifiers with the Bagging method. The results showed that the Quranic classification performance was improved when the ensemble methods were applied for the imbalanced Qur’anic verses in the standard classifiers.

With the advent of the fourth industrial revolution, industry practitioners are moving towards container-based infrastructure for managing their digital workloads. Kubernetes, a container orchestration tool, is reported to help industry practitioners in automated management of cloud infrastructure and rapid deployment of software services. Despite reported benefits, Kubernetes installations are susceptible to security defects, as it occurred for Tesla in 2018. Understanding how frequently security defects appear in Kubernetes installations can help cybersecurity researchers to investigate security-related vulnerabilities for Kubernetes and generate security best practices to avoid them. In this position paper, we first quantify how frequently security defects appear in Kubernetes manifests, i.e., configuration files that are use to install and manage Kubernetes. Next, we lay out a list of future research directions that researchers can pursue.We apply qualitative analysis on 5,193 commits collected from 38 open source repositories and observe that 0.79% of the 5,193 commits are security-related. Based on our findings, we posit that security-related defects are under-reported and advocate for rigorous research that can systematically identify undiscovered security defects that exist in Kubernetes manifests. We predict that the increasing use of Kubernetes with unresolved security defects can lead to large-scale security breaches.

Peer-to-peer (P2P) energy trading is one of the promising approaches for implementing decentralized electricity market paradigms. In the P2P trading, each actor negotiates directly with a set of trading partners. Since the physical network or grid is used for energy transfer, power losses are inevitable, and grid-related costs always occur during the P2P trading. A proper market clearing mechanism is required for the P2P energy trading between different producers and consumers. This paper proposes a decentralized market clearing mechanism for the P2P energy trading considering the privacy of the agents, power losses as well as the utilization fees for using the third party owned network. Grid-related costs in the P2P energy trading are considered by calculating the network utilization fees using an electrical distance approach. The simulation results are presented to verify the effectiveness of the proposed decentralized approach for market clearing in P2P energy trading. Agency for Science, Technology and Research (A*STAR) Accepted version This work was supported by the Agency for Science, Technology and Research (A*STAR) through its Singapore–Germany Academic-Industry (2+2) International Collaboration under Grant A1990b0060. Paper no. TSG-01805-2019.

If a disaster occurs or is predicted, the Korean government and local governments will deliver disaster information to the people based on the “Disaster and Safety Management Basic Law”[1]. Disaster information transmission media currently used in Korea are classified into mobile communication networks, broadcasting networks, civil defense alerts, and disaster warning and alerting systems. This paper deals with the implementation of an integrated system of disaster warning and alerting and managing disaster information in various types of disaster warning and alerting systems operated and managed by local governments. The proposed system utilizes the Common Alerting Protocol(CAP) established by the ITU-T standard[2] to integrate and issue and manage disaster information in the existing disaster forecast and warning system. In this paper, the CAP-based profile was implemented by modifying it to consider the environment of the Korean warning and alerting system. Based on the TTA standard, this CAP profile defined the method of using the parameter defined as an option according to the characteristics of each disaster. This paper includes interworking methods and test results to directly connect with the automatic voice notification system, the village broadcasting system, and the bus information system(BIS) among the existing disaster warning and alerting systems.

With the development of personal broadcasting such as YouTube, the demand for editing the video he filmed himself is steadily increasing. Traditional media video editing solutions help edit the results of the filming. However, there is a problem that requires a lot of editing time, as people usually edit videos that are from tens to hundreds of times longer than the final result of a personal broadcasting after filming. To overcome this problem, this paper automatically classifies images with specific scenes in the whole media image editing process, and secondly proposes automatic media editing solution technology in which people intervene. In particular, personal broadcasting focuses on the use of images that include characters, specific objects, and cue sign gestures among the entire. While the existing deep learning techniques such as faces, objects and gestures are advanced, integrated recognition technologies that simultaneously deal with special requirements for editing videos are still in the early stages of research. In this paper, the automatic composite recognition technology for editing video based on deep learning is proposed. The proposed technology was implemented with python and tensorflow software based on edge computing equipment. Using actual youtube videos, it took 0.1 second to process five-person recognition, 63-food recognition, or cue sign recognition using clapping or V poses at the same time. The recognized results are divided into timestamps of the entire movie, recognition results, and locations of objects on the screen, and are output to the json file. In addition, this solution was developed on an edge computing in order to increase real-time reliability. We expect to provide automatic video editing based on perceived json results as well as shorter editing times based on this implementation.

Cyber-physical Systems can be defined as a complex networked control system, which normally develop by combining several physical components with the cyber space. Cyber Physical System are already a part of our daily life. As its already being a part of everyone life, CPS also have great potential security threats and can be vulnerable to various cyber-attacks without showing any sign directly to component failure. To protect user security and privacy is a fundamental concern of any kind of system; either it's a simple web application or supplicated professional system. Digital Multifactor authentication is one of the best ways to make secure authentication. It covers many different areas of a Cyber-connected world, including online payments, communications, access right management, etc. Most of the time, Multifactor authentication is little complex as it requires extra step from users. This paper will discuss the evolution from single authentication to Multi-Factor Authentication (MFA) starting from Single-Factor Authentication (SFA) and through Two-Factor Authentication (2FA). This paper seeks to analyze and evaluate the most prominent authentication techniques based on accuracy, cost, and feasibility of implementation. We also suggest several authentication schemes which incorporate with Multifactor authentication for CPS.

Orthogonal defect classification (ODC) is a multi-dimensional measurement system with both qualitative and quantitative characteristics. And it is currently widely used in the software industry. However, its high level of abstraction leads to limited semantic information. Therefore, it seems to have a limited role in the process of software engineering of software-intensive systems (SISs). To solve this problem, this paper first analyzes software error lifetime from the perspective of knowledge-based software engineering and proposes an error generation model. Then, the paper proposes the concepts of software error pattern (SEP) and software requirements error pattern (SREP) based on the ODC. Then, according to an error generation mechanism, four types of software-hardware integrated error pattern (SHIEP) in the requirement stage, which is a sub-category of SREP, and corresponding ontology representation are given, focusing on “scenario”, “error manifestation” and “solution”. Finally, this paper takes a certain type of airborne radar software system as an example, uses protege to edit the SHIEPs and instances, and further introduces the application of software FMEA based on the above work. The results show that the prior information based on the SHIEPs is helpful to discover potential failures and failure modes that may adversely affect the function or performance of SISs. Therefore, the proposed SHIEP is of great significance for improving the quality of software development and verification.

Automotive radar sensors offer a promising and effective modality for perception and assessment of the surrounding environment. A key element of environment sensing in automotive radars is the reliable detection, classification and tracking of vulnerable road users such as pedestrians and cyclists. In this paper, we propose an integrated Bayesian framework dubbed BayesRadar, which improves the overall classification accuracy by tracking the embedding vector of a neural network and its prediction uncertainty via recursive Kalman filtering over time. Apart from the classification accuracy of a model, a critical measure includes the analysis of statistical confidence over the target class score. Such measures for predicting the true correctness likelihood of the classification estimates are essential in safety-critical automotive applications. Therefore, in this paper, we present and evaluate the classification, embedding cluster score and statistical confidence performance of the proposed framework in the context of classifying vulnerable road users compared to state-of-art deep learning approaches. Furthermore, we demonstrate superior performance of the BayesRadar for unseen classes compared to long short-term memory based temporal tracking of the embedding vectors.

Sign languages is a critical requirement that helps deaf people to express their needs, feelings and emotions using a variety of hand gestures throughout their daily life. This language had evolved in parallel with spoken languages, however, it do not resemble its counterparts in the same way. Moreover, it is as complex as any other spoken language, as each sign language embodies hundreds of signs, that differs from the next by slight changes in hand shape, position, motion direction, face and body parts contributing to each sign. Unfortunately, sign languages are not globally standardized, where the language differs between countries and has its own vocabulary and varies although they might look similar. Furthermore, publicly available datasets are limited in quality and most of the available translation services are expensive, due to the required skilled human personnel. This paper proposes a deep learning approach for sign language detection that is finely tailored for the Egyptian sign language (special case of the generic sign language). The model is built to harnesses the power of convolutional and recurrent networks by integrating them together to better recognize the sign language spatio-temporal data-feed. In addition, the paper proposes the first Egyptian sign language dataset for emotion words and pronouns. The experimental results demonstrated the proposed approach promising results on the introduced dataset using combined CNN with RNN models.

As computers continue to play a more critical role in everyday life, it becomes ever more important to secure the devices to which we entrust our personal and private data. This paper serves to detail the security issues present in out-of-date machinery in both the organizational and personal settings. This paper tests a Windows 10 machine running version 1511, and its ability to withstand a targeted virus attack. First, we examine the tools used to create a Reverse TCP virus. Second, we discuss how to generate the virus using those tools and deliver the payload to our Windows machine. Finally, we showcase what can be done to the targeted machine by creating our own user in the database and accessing the Windows machine remotely from our Kali Linux machine. This study has implications for researchers and practitioners by illustrating the destructive capabilities of a simple virus and what can be done to secure a network against such threats.

This paper briefly discusses the modern music industry setting and blockchain technology and how blockchain can be used to create a better music industry for the benefit of artist. The music industry is big and complex which generated a lot of problems for both the artist side and the industry as corporations and also put the artists and music labels and management at odds which has participated in a making the music industry a very bad environment and full of mistrust. Blockchain is classified by Gartner as one of the top ten strategic technologies in 2019, blockchain is based in distributed set or records linked together forming a chain, these records are held by different entities in a peer-to-peer network these entities work together to secure the information in the chain. by analyzing how the industry works and also present some use cases of the use of blockchain within the music industry. the paper gives a proposal to a new royalty payments system based on the environment of the blockchain technology. The paper also gives a brief about the challenges and road blocks that may face the implementation of the technology within the music industry.

This paper presents a smart software solution, designed to enhance the medical system of Romanian hospitals, by introducing technologies within the medical system, that will bring significant advantages to hospitals, the medical staff and patients, by automizing the existent, consistent of mostly manually performed processes. Compare to the current systems used in the Romanian hospitals, the solution presented in this paper revolves around a web platform, using a NoSQL MongoDB database, as well as a mobile application, that can be used to manage the medical records of a hospital and preview records from a mobile device, based on the readings performed on a Near Field Communication (NFC) chip and the placement of beacons in hospital rooms. This paper aims to emphasize the benefits brought by technology in the medical field, from multiple parties involved: the medical staff (hospital administrators, doctors, nurses), possible adjacent parties (pharmacies, suppliers) and patients.

This paper presents a software solution for monitoring people in quarantine during a pandemic. The development of the software solution presented in this paper aims to streamline the measure of isolation at home or quarantine centers. Persons affected by this measure must not leave the quarantine area in any form within the time limit set by the authorities. This software solution proposes a solution by which these people can be monitored by requesting their current location at a specified time interval. This solution assists the competent authorities by eliminating the need to verify quarantined persons by authorized personnel. Thus, those resources can be used for other pandemic prevention purposes.

Power electronic devices such as Insulated Gate Bipolar Transistors (IGBT) are widely used in electronic systems. The failure of IGBT may bring fatal damage to the whole system. The ability to accurately predict the remaining useful life (RUL) of IGBT can provide a basis for intelligent equipment maintenance, thereby minimizing the risk of catastrophe failure at work. The research in this field aims to achieve accurate and timely RUL prediction of equipment. This paper exploited the IGBT accelerated aging data from NASA Ames Research Center, and the monitoring data of IGBT is collected under thermal overstress load condition with square signal bias at gate. This paper developed three machine learning models to predict the RUL of IGBT, including Back Propagation Neural Network(BPNN), Random Forest and Extreme Learning Machine. These models realize real-time prediction of RUL, and the performance of these models is observed and analyzed.

This paper briefly introduces the problems existing in production management of electric porcelain. In order to solve this problem, an integrated monitoring system of electric porcelain based on Web is designed. This design takes Microsoft SQL SERVER 2012 as the data storage platform, using. NET three- layer architecture, B/S system structure as the development mode, and through Web Service technology to achieve remote data transmission. Effectively improve the data integration degree of electric porcelain production. This paper mainly introduces the overall framework and the function of each module of the electric porcelain integrated monitoring system.

Accurate land use information extraction plays an important role in land management, environmental monitoring, urban and rural planning and development and ecological assessment. High resolution remote sensing images have the characteristics of class similarity. Tradi¬tional methods of extracting ground object information from remote sensing images are limited to specific scenes and data sources, and have low segmentation accuracy and efficiency. In this paper, we shed the light to this research topic. To further improve the accuracy of land use information extraction, this paper takes HRNet(High-level Resolution Network) as the backbone network, which improves the performance of the network by fusing multi-scale feature map information through parallel work. Then, using the feature map information as input, the classification of each pixel is predicted through OCRNet network to achieve semantic segmentation of the image. The results show that: OCRNet-18 segmentation model is superior to other deep learning models in terms of PA, M IoU and Kappa coefficients, and the accuracy of PA (total), Kappa coefficient and M IoU are 0.913, 0.866, and 0.644 respectively.

Classification of soil is crucial for the agricultural domain as it is an essential task in geology and engineering domains. Various procedures are proposed to classify soil types in the literature, but many of them consumed much time or required specially designed equipments/applications. Classification of soil involves the accounting of various factors due to its diversified nature. It can be observed that several critical domain-oriented decisions often depend on the type of soil like farmers might be benefitted from knowing the kind of soil to choose crops accordingly for cultivation. We have employed different Convolution Neural Network (CNN) architectures to identify the soil type accurately in real-time. This paper describes the comparative evaluation in terms of performances of various CNN architectures, namely, ResNet50, VGG19, MobileNetV2, VGG16, NASNetMobile, and InceptionV3. These CNN models are used to classify four types of soils: Clay, Black, Alluvial, and Red. The performance of the ResNet50 model is the best with a training accuracy and training loss of 99.47% and 0.0252, respectively compared to other competing models considered in this paper.

This paper studies and proposes an energy data transaction method based on the combination of multi-layer hierarchical fusion fuzzy feature mapping method (MLHFFFM) and long short-term memory neural network (LSTM). First, analyze the big data collected to determine the type of missing data. On this basis, MLHFFFM and fuzzy C-means algorithm (FCM) are used to cluster electricity big data, and the LSTM neural network is trained based on the characteristic values of different users and historical data, and different pricing strategies are designed for different data types. Finally, the feasibility and reliability of the improved method proposed in this paper are verified through the data analysis and comparison experiment of the public data set.

Every year, doctors diagnose skin cancer in around 3 million or more patients across the globe. Currently, it is one of the most widely recognized kinds of cancers for human health. Hence, we need an early diagnosis to prevail any critical condition of the infected patients. Apparently, it can treat with topical drugs, if it diagnoses in an early stage. Hence as an outcome, skin cancer is responsible for less than 1% of all cancer deaths. There are two types of tumors in the skin cancer diseases domain, such as benign and malignant. To develop a robust and early screening system to diagnose skin cancer, it requires an efficient algorithm for prediction, trained with a large dataset. The primary aim of this research is to develop an efficient skin cancer screening process using a robust deep neural network with a large dataset. In this paper, we intend to determine considerate and dangerous types of skin cancer tumors using dermoscopic images from a publicly available dataset. We proposed an efficient and fast scaled 2D-CNN based on EfficientNet-B7 deep neural architecture with image preprocessing. This paper also uses two different pre-trained deep neural architectures, such as VGG19, and ResNet-50 to compare the performance with the proposed architecture. The proposed architecture outperformed the other pre-trained CNN models whereas the proposed architecture achieved higher AUC and accuracy compared to other architectures.

Since 2020, hybrid learning has become the new convention of teaching and learning, whereby learning materials are delivered through a blend of synchronous and asynchronous sessions. In engineering education, this has led to the migration of laboratory teaching to the remote and virtual world. While immersive learning based virtual laboratory has been a great alternative for teaching hardware-based laboratory activities, this has however posed great challenge to software-based laboratory learning. Review of the literature on engineering education suggests an evident gap in pedogeological research to inform the application of blended laboratory learning. This paper thus presents a study case on the pedagogy of introducing blended learning adopted in the remote teaching of an electrical power engineering laboratory. The approaches integrated to facilitate the pedagogy and their implications are detailed in this work. Using Bloom’s taxonomy, the paper examines the role of blended learning in enhancing student learning experience in respect to learning autonomy and engagement.

The paper proposes a cognitive view on intonational contours aiming to describe their partitions in terms of elementary cognitive categories related to a generic information packaging (IPk) mechanism. We formulate the hypothesis that IPk structures pack auditory information items at the cortical level into relations that are marked at the utterance level by prosodic phrases. An IPk model based on this hypothesis is used in the paper for describing two pairs of contours that are presented in [1] as problematic for a categorical phonological description. The paper proposes a cognitive description of the respective contours after their partitioning into hierarchies of nested IPk units. In this view phonological events become marks of functional constituents at the cognitive level and the semantic differences between contours are reflected by their structural cognitive differences.

As the traditional inverters are transforming toward more intelligent inverters with advanced information and communication technologies, the cyber-attack surface has been remarkably expanded. Specifically, securing firmware of smart inverters from cyber-attacks is crucial. This paper provides expanded firmware attack surface targeting smart inverters. Moreover, this paper proposes a security module for transforming a conventional inverter to a firmware security built-in smart inverter by preventing potential malware and unauthorized firmware update attacks as well as fast automated inverter recovery from zero-day attacks. Furthermore, the proposed security module as a client of blockchain is connected to blockchain severs to fully utilize blockchain technologies such as membership service, ledgers, and smart contracts to detect and mitigate the firmware attacks. The proposed security module framework is implemented in an Internet-of-Thing (IoT) device and validated by experiments.

Many block-based programming environments have proven to be effective at engaging novices in learning programming. However, most restrict access to the outside world, limiting learners to commands and computing resources built in to the environment. Some allow learners to drag and drop files, connect to sensors and robots locally or issue HTTP requests. But in a world where most of the applications in our daily lives are distributed (i.e., their functionality depends on communicating with other programs or accessing resources and data on the internet), the lack of support for beginners to envision and create such distributed programs is a lost opportunity. This paper argues that it is not only feasible, but crucial, to create environments with simple yet powerful abstractions that open up distributed computing and other widely used but advanced computing concepts including networking, the Internet of Things, and cybersecurity to novices. By thus removing the walls around our environments, we can expand opportunities for learning considerably: programs can access a wealth of online data and web services, and communicate with other projects. Moreover, these changes can enable young learners to collaborate with each other during program construction whether they share their physical location or study remotely. Importantly, providing access to the wider world will also help counter widespread student perceptions that block-based environments are mere toys, and show that they are capable of creating compelling applications. The paper presents NetsBlox, a programming environment that supports these ideas and shows that tools can be designed to democratize access to powerful ideas in computing.

Based on the understanding and analysis of exploratory factor analysis and intelligent data clustering model, this paper establishes an enterprise financial management capability system. First of all, this paper uses exploratory factor analysis to find the structural relationship between the multiple observation variables that measure financial management ability, and achieve the purpose of data dimensionality reduction. Then, the intelligent data clustering model and the three-dimensional visualization of the model are analyzed through the analysis of the enterprise's financial management ability. Finally, experiments and simulations verify that EFA and clustering models can be used to establish a corporate financial management capability system.

Embedded devices enabling payments transaction processing in Financial Services industry cannot have any margin for error. These devices need to be tested & validated by replicating production like environment to the extent possible. This means literally handling payments related events like swiping a credit card, tapping a mobile phone or pressing buttons amongst many other things like in real world. Embedded Software development is time consuming as it involves multiple man-machine interactions and dependencies such as managing and handling embedded devices, operating devices (Push buttons, interpret display panels, read receipt printouts etc.) and sharing devices for collaboration within team. During the current pandemic, it was impossible for software teams to travel to office, share devices or even procure necessary devices on time for project related tasks. This caused delay to project delivery and increased Time to market. The paper describes how the team used Capgemini's flexible Robotics as a Service (RaaS) platform that helped during pandemic to automate feasible man-machine interactions using Robotic arms. The paper provides details of the work done by the team that involves internet of things (IoT), Artificial Intelligence (AI) to remotely handle and operate hardware and devices thereby completing embedded software development life cycles faster and well within budget while ensuring superior product quality and importantly ensuring team's health and safety. This is novel in Financial Services space.

With the increasing growth and progress of the big data science, information management software has been widely used, but also highlights the security risks. At present, it is particularly easy to have information theft, information leakage and many other situations. This will have a serious negative impact on the overall system application efficiency and application effect. In the set preprocessing, this paper uses the filling method for the missing values in the collected data, and establishes an effective evaluation mechanism for the missing value filling scheme. By comparing various filling schemes, this paper finally chooses the fitting method based on Gaussian distribution to fill the missing values. Experimental data reflect the usefulness of the proposed method.

In the wake of recent advancements in digital technology in various fields including agriculture, a large set of data is produced that proves to be ineffectual until it is analyzed systematically and profoundly to augment decision-making process. The data are used through different devices, and different processing techniques are applied on it to derive meaningful results with effective decision-making. The data can be cut short, tailored or manipulated in several ways to mould the same which can give visible interpretations to the people. At the same time, complex problems can also be solved. Further, through the data cleaning and the integration processes, a large cluster of data can be made simple and easy to use for calculations. Big data pre-processing approaches are applied to improve the visibility of big data to DSS to get a more effective analytics view in order to get useful insight. The requirements of compatible data management have considerably increased in the field of agriculture in India over the years. In the backdrop of these requirements, the present paper provides a framework for the detection of duplicate data using certain measures that are trainable and manageable. Moreover, the framework has been examined a number of times based on the Precision, Recall, and F measure techniques in this paper.

This research paper presents the importance of cardiologists and their availability to the public by the usage of various smart devices. These new simplified ECGs may be as complex or sophisticated as those that represent the usage of the medical facility however they are accurately able to observe the heart rate to monitor their health. One of the most commonly overlooked problems in the modern world is Arrhythmia. This is a problem related to the rhythm of the heartbeat and it occurs due to the improper coordination between the electrical impulses with your heartbeat. Heartbeat occurs daily in the lives of several people as they aren’t properly focused upon the monitoring of heartbeat. This research paper attempts to focus on the issue of Heart Arrhythmia as well as the creation and classification model which is capable of identifying the type of heart arrhythmia by an individual how may be suffering from heart issues. To successfully create our model, this research work utilizes the convolution neural networks to train the proposed model with existing ECG data and properly identify the heart rate as well as the type of arrhythmia that they have so that it provides the ability to immediately provide the proper medical attention.

Breast cancer is becoming one of the leading causes of mortality in women all over the world. The advanced engineering of Artificial Intelligence techniques and natural image classification approaches is mostly employed to classify breast images. The image classification permits the physicians and the doctor the next chance, and this, in turn, saves the time of the physician and the doctor. Apart from several publications on classifying breast images, only some of the review papers are present that offer a clear definition of the “approaches to breast cancer image classification, feature extraction and selection methods, classification measurement parameterizations, and image classification results” with the help of 3D images. Machine learning (ML) seems to be an important portion of medical imaging research. These are developed over the years from manual seeded inputs to automatic initializations. The enhancements in the area of ML lead to various self-reliant and intelligent "Computer-Aided Diagnosis (CAD) systems" since the learning capability of the ML techniques is increasing in a constant manner. Several automated techniques are developing with deep feature representations and learning. The modern enhancements of ML with extensive and deeper representation techniques called Deep Learning (DL) techniques have created an important effect on enhancing the diagnosing ability of these systems. The main objective is to undergo a critical review on diverse contributions related to breast cancer detection that could be highly advantageous for dealing with 3D mammogram images. This paper plans to make a review on different algorithmic categorizations, datasets used, simulation platforms, and performance metrics. Furthermore, the research gaps and challenges of the breast cancer diagnosis using 3D images are elaborated, encouraging the upcoming researchers to give tremendous concern on new contributions.

Internet of Things (IoT) is sometimes called the internet of devices. In IoT, environment devices are connected with the help of gateways. All required information can be accessed by a device with the help of internet gateways from any network. So, it is very challenging to transfer data in open surroundings where the security of data is very problematic. In this paper architecture of IoT, security challenges, security requirements, and future security challenges of IoT have been discussed in detail, after that, a comparative analysis of different security techniques has been presented. At last literature review on different papers of security in IoT has been presented with pros and cons.

Alongside the exponential rise of smartphone apps and the existence of rapidly advancing cloud computing (CC), Mobile Cloud Computing (MCC) has emerged as a transformational technology for mobile applications. It applies the concepts of CC into cell phone context by improving its performance, security and functionality. This work gives an overview of the incorporation of CC into Mobile devices and the emergence of MCC from it. Having reviewed numerous papers, it is observed that there is no content which portrays the direct derivation of MCC from CC before a review of the several applications, this paper has bridged this gap. Giving a detailed conceptual overview of the technologies involved in CC and MCC, it focuses on the applications of MCC in the current real world.