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Activating flux is made up of inorganic material mixed with a volatile medium. The objective of this study is to identify and review the literature in activating flux, and different type of welding techniques. For that purpose, a structured review method is adopted and the Scopus database is used as a primary source for the selection of papers. The papers were identified using the string term of “Activating flux” OR “Activated flux” in the title of papers. And a total of 238 papers were identified for the study. Besides the above few other papers were also reviewed and the materials used, the type of flux used, and the findings of different combinations were highlighted in the form of tables. The findings of the study provide a comprehensive understanding of the different type of welding techniques focusing on TIG welding, and the different combination of flux used and their impact was summarized.

Context: The establishment of the Mining Software Repositories (MSR) data showcase conference track has encouraged researchers to provide data sets as a basis for further empirical studies. Objective: Examine the usage of data papers published in the MSR proceedings in terms of use frequency, users, and use purpose. Method: Data track papers were collected from the MSR data showcase track and through the manual inspection of older MSR proceedings. The use of data papers was established through manual citation searching followed by reading the citing studies and dividing them into strong and weak citations. Contrary to weak, strong citations truly use the data set of a data paper. Data papers were then manually clustered based on their content, whereas their strong citations were classified by hand according to the knowledge areas of the Guide to the Software Engineering Body of Knowledge. A survey study on 108 authors and users of data papers provided further insights regarding motivation and effort in data paper production, encouraging and discouraging factors in data set use, and future desired direction regarding data papers. Results: We found that 65% of the data papers have been used in other studies, with a long-tail distribution in the number of strong citations. Weak citations to data papers usually refer to them as an example. MSR data papers are cited in total less than other MSR papers. A considerable number of the strong citations stem from the teams that authored the data papers. Publications providing Version Control System (VCS) primary and derived data are the most frequent data papers and the most often strongly cited ones. Enhanced developer data papers are the least common ones, and the second least frequently strongly cited. Data paper authors tend to gather data in the context of other research. [...], journal article, 35 pages, 7 figures, 6 tables

Due to the increasing size of s (s), their design and optimization phases (i.e., ) grow increasingly complex. At design time, a large design space needs to be explored to find an implementation that fulfills all specifications and then optimizes metrics like energy, area, delay, reliability, etc. At run time, a large configuration space needs to be searched to find the best set of parameters (e.g., voltage/frequency) to further optimize the system. Both spaces are infeasible for exhaustive search typically leading to heuristic optimization algorithms that find some trade-off between design quality and computational overhead. ML can build powerful models that have successfully been employed in related domains. In this survey, we categorize how () may be used and is used for design-time and run-time optimization and exploration strategies of s. A meta-study of published techniques unveils areas in that are well-explored and underexplored with, as well as trends in the employed algorithms. We present a comprehensive categorization and summary of the state of the art on for. Finally, we summarize remaining challenges and promising open research directions.

Over the past few years, what might not unreasonably be described as a true revolution has taken place in the field of machine vision, radically altering the way many things had previously been done and offering new and exciting opportunities for those able to quickly embrace and master the new techniques. Rapid developments in machine learning, largely enabled by faster GPU-equipped computing hardware, has facilitated an explosion of machine vision applications into hitherto extremely challenging or, in many cases, previously impossible to automate industrial tasks. Together with developments towards an internet of things and the availability of big data, these form key components of what many consider to be the fourth industrial revolution. This transformation has dramatically improved the efficacy of some existing machine vision activities, such as in manufacturing (e.g. inspection for quality control and quality assurance), security (e.g. facial biometrics) and in medicine (e.g. detecting cancers), while in other cases has opened up completely new areas of use, such as in agriculture and construction (as well as in the existing domains of manufacturing and medicine). Here we will explore the history and nature of this change, what underlies it, what enables it, and the impact it has had - the latter by reviewing several recent indicative applications described in the research literature. We will also consider the continuing role that traditional or classical machine vision might still play. Finally, the key future challenges and developing opportunities in machine vision will also be discussed.

This paper analyses over 89,000 documents between scientific journals and international patents in order to identify main technologies for material pyrolysis (i.e. fluidized bed, hot balls, microwave, plasma, and laser), compare them in terms of technical performances and discover future trends. The predictive model for future trends is based on the evolutionary model of the TRIZ theory, with particular attention to the evolutionary law, “from macro to micro”, which explains how technical systems evolve towards increasingly smaller, controlled, and resource-efficient interactions. In our case, we applied the focus to the interface between the heat source and the pyrolyzed raw material. The proposed evolutionary model showed a substantial alignment with chronological trends, and the model was consistent with both patent trends and those of the scientific literature. To make the analysis quantitatively more robust, a comparison of the different technologies has also been introduced in terms of percentage distribution and the number of patents/papers referring to the different classes of heating rates and reaction temperatures and types of flash/fast/intermediate/slow reaction. Finally, a quantitative metric based on the innovation index has been adopted, to take into account the number of citations normalized on the years of publication. The main outputs of this study identified that radiations-based pyrolysis, involving microscopic interaction, seem more interesting in term of technical performances (i.e. heating rate and reaction temperature), although their technological growth has yet to occur, unlike fluidized bed and hot balls, which typically work at a macroscopic level and seem to have already reached maturity. According to a bibliometric index, laser corpus resulted three time more innovative then fluidized bed reactors papers, especially for heating rates and reactions temperature.

In response to environmental pollution and the spread of Coronavirus Disease 2019 (COVID‐19), this paper proposes a new type of smart mask design, and specifically proposes an optimized double closed‐loop control method, especially an improved filtering fusion algorithm. Using the filtering fusion algorithm proposed in this paper, after the Kalman filter (KF) filters the raw data of the attitude sensor, explicit complementary filtering and data fusion are used to obtain the attitude angle of the body. At the same time, the obtained attitude angle is combined with acceleration and blood oxygen concentration to obtain the behaviour characteristic value. On this basis, the speed of the oxygen supply fan captured by the photoelectric sensor is used to form a closed loop with the characteristic value of the behaviour. Finally, the structure of the mask is upgraded and optimized through fluid mechanics simulation, and experiments have verified that the combination of the replaceable filter cloth, the intelligent control system and the ultraviolet disinfection device can effectively protect people's health.

Background Due to the COVID-19 pandemic, the demand for remote electrocardiogram (ECG) monitoring has increased drastically in an attempt to prevent the spread of the virus and keep vulnerable individuals with less severe cases out of hospitals. Enabling clinicians to set up remote patient ECG monitoring easily and determining how to classify the ECG signals accurately so relevant alerts are sent in a timely fashion is an urgent problem to be addressed for remote patient monitoring (RPM) to be adopted widely. Hence, a new technique is required to enable routine and widespread use of RPM, as is needed due to COVID-19. Objective The primary aim of this research is to create a robust and easy-to-use solution for personalized ECG monitoring in real-world settings that is precise, easily configurable, and understandable by clinicians. Methods In this paper, we propose a Personalized Monitoring Model (PMM) for ECG data based on motif discovery. Motif discovery finds meaningful or frequently recurring patterns in patient ECG readings. The main strategy is to use motif discovery to extract a small sample of personalized motifs for each individual patient and then use these motifs to predict abnormalities in real-time readings of that patient using an artificial logical network configured by a physician. Results Our approach was tested on 30 minutes of ECG readings from 32 patients. The average diagnostic accuracy of the PMM was always above 90% and reached 100% for some parameters, compared to 80% accuracy for the Generalized Monitoring Models (GMM). Regardless of parameter settings, PMM training models were generated within 3-4 minutes, compared to 1 hour (or longer, with increasing amounts of training data) for the GMM. Conclusions Our proposed PMM almost eliminates many of the training and small sample issues associated with GMMs. It also addresses accuracy and computational cost issues of the GMM, caused by the uniqueness of heartbeats and training issues. In addition, it addresses the fact that doctors and nurses typically do not have data science training and the skills needed to configure, understand, and even trust existing black box machine learning models.

Edge computing promises to bring computation close to the end-users to support emergent applications such as virtual reality. However, the computational capacity at the edge of the network is currently limited. To become a pervasive paradigm, edge computing needs highly dispersed decentralized deployments, that, contrary to cloud, cannot benefit from economies of scale. In this situation, crowdsourcing appears attractive - there are plenty of computing devices at the disposal of the general public, and these devices are located exactly where computing power is needed the most - at the edge of the network. Crowdsourcing has been a success maker for scientific computing projects, e.g., SETI@home, or distributed ledger systems empowering decentralized finance. However, as of now, there is no crowdsourced system that addresses the needs of edge computing. In this position paper, we aim to identify the causes of this shortcoming, analyze the potential ways to overcome it, and outline future directions.

International audience; Interest and need for Wireless Body Area Networks (WBANs) havesignicantly increased recently. WBAN consists of miniaturized sensors de-signed to collect and transmit data through wireless network, enabling medicalspecialists to monitor patients during their normal daily life and providing realtime opinions for medical diagnosis. Many wireless technologies have provedthemselves in WBAN applications, while others are still under investigations.The choice of the technology to adopt may depend on the disease to moni-tor and the performance requirements, i.e. reliability, latency and data rate.In addition, the suitable sensor is essential when seeking to extract the datarelated to a medical measure.This paper aims at surveying the wireless technologies used in WBAN sys-tems. In addition to a detailed survey on the existing technologies, the use ofthe emerging Low Power Wide Area Network (LPWAN) technologies, and thefuture 5G, B5G and 6G is investigated, where the suitability of these technolo-gies to WBAN applications is studied from several perspectives. Furthermore,medical applications of WBAN are discussed by presenting their methodolo-gies, the adopted wireless technologies and the used sensors. Given that eachmedical application requires the appropriate sensor to extract the data, wehighlight a wide range of the sensors used in the market for medical systems.Recent and future challenges in WBAN systems are given related to the powerconsumption, the emergence of the Internet of Things (IoT) technologies inWBAN and others.

International audience; Automation throughout history has caused profound changes in employment dynamics. With the advent of the fourth industrial revolution, a new threat may affect employability, asrobots and AI-based processes can now assume tasks considered exclusive to humans. This position paperaims to motivate the study of the effects of AI and automation on employability, extending it into a collaborative network perspective. The problem is firstly observed from a historical perspective. The collaboration aspects are considered through the analysis of two casestudies. Results suggestthata latent element of collaborative networks, complexity, may have effects in terms of employability.

Identifying a forged printed document with scanned evidence can be a challenge. Microscopic printing is showing random shape which depends on the printing source as well as printing material. This paper presents a statistical analysis of the printing patterns under a microscopic scale, analyses the effect of printing direction, printing substrate (uncoated and coated paper), and printing technology (conventional offset, waterless offset, and electrophotography). The analysis shows a negligible effect of printing direction, yet, using the shape descriptor indexes, the printing materials and technologies are distinguishable under a microscopic scale. As a result, the algorithms based on Support Vectors Machine and Random Forest are developed, with shape descriptor indexes as features, for printing source identification. Both proposed algorithms, equally, achieve a high classification accuracy rate, over 92% accuracy with complex geometric-shape patterns. Thanks to the lightweight and efficiency of the Support Vectors Machine, the study shows promising applications for real-world and potential implementation in the Internet of Things devices.

With the rapid development of emerging technologies, research on technology adoption is valuable for improving the reception efficiency of new technologies. This paper uses bibliometric analysis methods and visualization software to explore the research stages, the hot issues, the limitations of the research and the future fronts. A total of 11,706 documents were collected as the original data using the retrieval formulas. Sorted by the numbers of citations, the top 10% of the documents are extracted as the top-cited documents. First, combining software and bibliometric analysis, we obtain the annual development trends and identify the productive countries/regions, the influential institutions and the high-yielding authors. In addition, we overview the representative authors and their works in the development of technology adoption. Then, we conduct keyword analysis and burstiness analysis to identify the academic hotspots, research fronts and limitations in this field. The results show that increasingly more researches have shifted from theories to applications. Technology adoption has multiple applications in the fields of online shopping, mobile learning, mobile banking, e-learning and health care delivery. Finally, we draw conclusions on the bibliometric findings and forecast the future research trends of technology adoption. In the future, more researches will focus on the impacts of age, gender, culture, and the environment on technology adoption.

Today there is a rapid development taking place in phenotyping of plants using non-destructive image based machine vision techniques. Machine vision based plant phenotyping ranges from single plant trait estimation to broad assessment of crop canopy for thousands of plants in the field. Plant phenotyping systems either use single imaging method or integrative approach signifying simultaneous use of some of the imaging techniques like visible red, green and blue (RGB) imaging, thermal imaging, chlorophyll fluorescence imaging (CFIM), hyperspectral imaging, 3-dimensional (3-D) imaging or high resolution volumetric imaging. This paper provides an overview of imaging techniques and their applications in the field of plant phenotyping. This paper presents a comprehensive survey on recent machine vision methods for plant trait estimation and classification. In this paper, information about publicly available datasets is provided for uniform comparison among the state-of-the-art phenotyping methods. This paper also presents future research directions related to the use of deep learning based machine vision algorithms for structural (2-D and 3-D), physiological and temporal trait estimation, and classification studies in plants.

At present, the economic benefits of double inlet and double outlet ball mills, which are commonly used in coal-fired power plants, need to be improved. Based on this, this paper explores the factors influencing the output of double inlet and double outlet ball mill by establishing the output system of double inlet and double outlet ball mill. By analyzing the influence factors of the output of double inlet and double outlet ball mill, and combining with system dynamics, the dynamic model of double inlet and double outlet ball mill output system is established, and SPSS is used to fit the model data with the real data. The fitting degree is above 0.8, and the authenticity is high. The simulation results show that the impact energy of barrel grinding, the grinding powder fineness, and the pressure ratio in the ball mill affect the output of double inlet and double outlet ball mill according to certain relations. Finally, the research in this paper provides theoretical and technical support for improving the economic benefits of double inlet and double outlet ball mills.

This paper addresses the problem of anomaly detection for high-dimensional sensing data. The one-class support vector machine (OCSVM) is one of the most popular unsupervised methods for anomaly detection. When data are high dimensional and large scale, however, the efficiency of OCSVM-based methods in anomaly detection suffers. Although dimensionality-reduction tools, such as deep belief networks, can be applied to compress the high-dimensional data to alleviate the problem, the accuracy and timely detection are still hard to improve due to the inherent features of OCSVM. In this paper, we propose a new form of OCSVM model based on the structure of the compressed data and the characteristics of OCSVM. Based on the new model, we design both optimal and approximate methods for model training and testing. We evaluate the performance of our methods with extensive experiments on four real-world datasets. The experimental results demonstrate that our new methods, both optimal and approximate ones, not only significantly outperform the state-of-the-art in accuracy and efficiency, but also achieve the good performance without the need of manual parameter tuning. In addition, our approximate training and testing mechanism can reduce the computing time by three orders of magnitude with a negligible loss in accuracy.

To identify the abnormal characteristics of the oplegnathus punctatus is great importance to the detection of iridovirus disease in the breeding environment. In this paper, an advanced neural network model to identify the characteristics of the oplegnathus punctatus and predict its different periods of suffering from iridovirus disease is proposed based on the establishment of a data set. First of all, a standard format data set of oplegnathus punctatus and an abnormal format date set are established in order to verify the effectiveness of the method in this paper. And then, the feature extraction fusion method is used for preprocessing in terms of the abnormal format data set, which combines the edge features extracted by the improved multi-template Sobel operator and the color features extracted by the HSV model. Finally, an improved VGG-GoogleNet network recognition model comes into being through the fusion and improvement of the VGG and GoogleNet neural network structure. The experiments results show that the prediction accuracy rate for oplegnathus punctatus suffering from iridovirus disease in the the abnormal format data set and the standard format data set are improved, which reach 98.55% and 69.18%.

So-called spatial web queries retrieve web content representing points of interest, such that the points of interest have descriptions that are relevant to query keywords and are located close to a query location. Two broad categories of such queries exist. The first encompasses queries that retrieve single spatial web objects that each satisfy the query arguments. Most proposals belong to this category. The second category, to which this paper's proposal belongs, encompasses queries that support exploratory user behavior and retrieve sets of objects that represent regions of space that may be of interest to the user. Specifically, the paper proposes a new type of query, the top-k spatial textual cluster retrieval ( $k$ -STC) query that returns the top-k clusters that (i) are located close to a query location, (ii) contain objects that are relevant with regard to given query keywords, and (iii) have an object density that exceeds a given threshold. To compute this query, we propose a DBSCAN-based approach and an OPTICS-based approach that rely on on-line density-based clustering and that exploit early stop conditions. Empirical studies on real data sets offer evidence that the paper's proposals can find good quality clusters and are capable of excellent performance.

This paper is the second part of a two-part survey series on large-scale global optimization. The first part covered two major algorithmic approaches to large-scale optimization, namely decomposition methods and hybridization methods such as memetic algorithms and local search. In this part we focus on sampling and variation operators, approximation and surrogate modeling, initialization methods, and parallelization. We also cover a range of problem areas in relation to large-scale global optimization, such as multi-objective optimization, constraint handling, overlapping components, the component imbalance issue, and benchmarks, and applications. The paper also includes a discussion on pitfalls and challenges of current research and identifies several potential areas of future research.

Today, the rapid growth of the internet and advancements in mobile technology and increased internet connectivity have brought us to a data-driven economy where an enormous amount of data is being used to train machine learning models to make strategic decisions. However, in the aftermath of a data breach by Facebook in 2018, there are some serious concerns over user data privacy and security being used to train the Machine Learning models. In this context, a new approach, Federated Machine Learning is now one of the most talked-about and recent approaches. Current research primarily focuses on Federated Learning's advantages over the traditional methods and/or its classification. However, being in a nascent stage of development as a method, certain challenges need to be addressed. This paper intends to address the totality of federated learning with a complete vulnerability assessment. During the study of the literature, it is found that security being promised as one of the key advantages of federated learning can still not be guaranteed because of some issues inherently present, and this can lead to poisoning, inference attacks and insertion of backdoors, etc. This paper intends to provide a complete picture by giving an in-depth and comprehensive analysis of Federated Learning and its taxonomy. It also provides a detailed vulnerability assessment and highlights the challenges faced in the current setting and future research directions to make federated learning a more functional, robust and secure method to train machine learning models.

On-board embedded software developed for spaceflight systems (space software) must adhere to stringent software quality assurance procedures. For example, verification and validation activities are typically performed and assessed by third party organizations. To further minimize the risk of human mistakes, space agencies, such as the European Space Agency (ESA), are looking for automated solutions for the assessment of software testing activities, which play a crucial role in this context. Over the years, mutation analysis has shown to be a promising solution for the automated assessment of test suites; it consists of measuring the quality of a test suite in terms of the percentage of injected faults leading to a test failure. A number of optimization techniques, addressing scalability and accuracy problems, have been proposed to facilitate the industrial adoption of mutation analysis. However, to date, two major problems prevent space agencies from enforcing mutation analysis in space software development. In this paper, we enhance mutation analysis optimization techniques to enable their applicability to embedded software and propose a pipeline that successfully integrates them to address scalability and accuracy issues in this context, as described above. Further, we report on the largest study involving embedded software systems in the mutation analysis literature. Our research is part of a research project funded by ESA ESTEC involving private companies (GomSpace Luxembourg and LuxSpace) in the space sector. These industry partners provided the case studies reported in this paper; they include an on-board software system managing a microsatellite currently on-orbit, a set of libraries used in deployed cubesats, and a mathematical library certified by ESA., Accepted for publication on IEEE TRANSACTIONS ON SOFTWARE ENGINEERING

This paper presents a cooperative game theory approach to design an optimal video steganography framework. An ideal and perfect video steganography system should provide enough space to embed the secret data without degrading the visual quality. However, in a steganographic system, the payload capacity and visual quality are always inversely proportional to each other. This paper proposes a novel game-theoretic approach using strategy adaption to address this issue. This approach utilizes the Iterative Elimination of Strictly Dominant Strategies (IESDS) method to acquire the optimal solution. The obtained optimal solution comes up with the best trade-off between capacity and visual quality. Also, experimental results infer that the optimal solution outperforms the contemporary methods by attaining significant outcomes.

The evolution of wireless body area network (WBAN) has changed the human life for its applications in the field of healthcare, fitness, entertainment and sports etc. However, two of the major challenges in the design of WBAN are energy efficiency and guaranteeing QoS. The load balance of different packet queues in a WBAN plays important role in design of energy efficient and reliable system. In this paper, we propose a Traffic Prioritized Load Balanced Scheduling (TPLBS) algorithm for load balancing in different priority queues in wireless body area networks based on IEEE 802.15.6 model. The main objective of this paper is to minimize packet drop in the queues so that throughput of WBAN can be improved. In this case we consider both priority and location from which the packet received, so that no packet should wait for a long time in the assigned queue to be transmitted to Access Point. The proposed method is simulated using Castalia to study and compare its performance with its counterparts. The simulation results reveal that the proposed protocol works better than the existing methods in terms of delay, throughput and energy efficiency.

After the acceptance of blockchain technology, there have been applications which aim to use blockchain in their fields. Various approaches have been proposed in past to build a secure Identity Management (IdM) System. This is a novel systematic literature mapping of IdM in blockchain. This paper provides an extensive review on IdM with emphasis on how the emergence of blockchain has addressed the IdM challenges faced over the years. A thorough study has been done on the existing literature. The primary and secondary “search string” were identified and search was conducted on five databases; and after screening the analysis was done. Out of the total studied literature, 30 primary studies published from 2009 to 2020 were selected. Through this paper, the researchers will be able to: 1) find out the research trends in IdM using blockchain, 2) understand the challenges in IdM and report whether blockchain can solve the IdM challenges, 3) scrutinize and understand how the different frameworks of IdM would deal with security, integrity and privacy problems, 4) know about initiatives taken for IdM using blockchain, 5) which consensus algorithms are popular among blockchains, 6) know about the research projects going on in the field of IdM using blockchain.

Word Sense Disambiguation (WSD) is significant for improving the accuracy of the interpretation of a Natural language text. Various supervised learning-based models and knowledge-based models have been developed in the literature for WSD of the language text. However, these models do not provide good results for low-resource languages, due to the lack of labelled and tagged data. Therefore, in this work, we have examined different word embedding techniques for word sense disambiguation of the Hindi language texts. Several studies in the literature show that these embeddings have been utilized for different foreign languages in the field of word sense disambiguation. However, to the best of our knowledge, no such work exists for the Hindi language. Therefore, in this paper, we utilize various existing word embeddings for WSD of Hindi text. Moreover, we have created Hindi word embeddings on articles taken from Wikipedia and test the quality of the created word embeddings using Pearson correlation. In this direction, we perform different experiments and observe that Word2Vec model gives best performance among all the considered embeddings on the used Hindi dataset. In our method, the proposed model directly takes input that is trained with word embedding methods and helps to develop a sense inventory using clustering that has been employed for performing disambiguation. Experimental observations indicate that the performance of the proposed approach is moderate and competent in terms of accuracy. The paper, thus, presents how WSD can leverage these representations to encode rich semantic information.

In this paper, we propose an improvement in the preprocessing stage of the reversible data hiding method based on contrast enhancement applied to grayscale digital images. In conventional proposals, the preprocessing stage plays a fundamental role in the performance of the data hiding algorithm in terms of capacity and visual quality. Thus, this work presents two improvements to the preprocessing stage based on the analysis, merging, and shifting the image histogram, which, in conjunction with the data hiding stage, maximize the embedding capacity while keeping the visual distortion at a minimum. The proposed algorithm was implemented on two image datasets and compared with several related works previously reported in the scientific literature. The experimental results show that the visual quality can be preserved after data have been concealed when employing different embedding rates. Either of the preprocessing techniques presented in this paper can be adopted considering the application scenario and the priority of the requirements of the capacity and visual quality needed.

In spite of the insight commonality may have concerning agrarian practice, fact is that nowadays agricultural science diligence is accurate, precise, data-driven, and vigorous than ever. The emanation of the technologies based on Internet of Things (IoT) has reformed nearly each industry like smart city, smart health, smart grid, smart home, including “smart agriculture or precision agriculture”. Applying machine learning using the IoT data analytics in agricultural sector will rise new benefits to increase the quantity and quality of production from the crop fields to meet the increasing food demand. Such world-shattering advancements are rocking the current agrarian approaches and generating novel and best chances besides a number of limitations. This paper climaxes the power and capability of computing techniques including internet of things, wireless sensor networks, data analytics and machine learning in agriculture. The paper proposed the prediction model of Apple disease in the apple orchards of Kashmir valley using data analytics and Machine learning in IoT system. Furthermore, a local survey was conducted to know from the farmers about the trending technologies and their effect in precision agriculture. Finally, the paper discusses the challenges faced when incorporating these technologies in the traditional farming approaches.

Localization forms the heart of various autonomous mobile robots. For efficient navigation, these robots need to adopt effective localization strategy. This paper, presents a comprehensive review on localization system, problems, principle and approaches for mobile robots. First, we classify the localization problems in to three categories based on the information of initial position of the robot. Next, we discuss on robot position update principles. Then, we discuss key techniques to localize the mobile robot such as: probabilistic approach, autonomous map building and radio frequency identification (RFID) based scheme. In the probabilistic localization section, we discuss the Markov localization and Kalman filter along with its extended versions. Autonomous map building focuses on the widely used simultaneous localization and mapping (SLAM) approach. This section also discusses on applying SLAM to localize brain-controlled mobile robots. Next, we discuss on applying evolutionary approaches to estimate optimal position. The RFID scheme addresses on effective utilization of RFID tags to track objects and position the robot. We then analyze on position and orientation errors occurred by different localization strategies. We conclude this paper by highlighting future research possibilities.

During recent years, many researchers and professionals have revealed the endangerment of wireless communication technologies and systems from various cyberattacks, these attacks cause detriment and harm not only to private enterprises but to the government organizations as well. The attackers endeavor new techniques to challenge the security frameworks, use powerful tools and tricks to break any sized keys, security of private and sensitive data is in the stale mark. There are many advancements are being developed to mitigate these attacks. In this conjunction, this paper gives a complete account of survey and review of the various exiting advanced cyber security standards along with challenges faced by the cyber security domain. The new generation attacks are discussed and documented in detail, the advanced key management schemes are also depicted. The quantum cryptography is discussed with its merits and future scope of the same. Overall, the paper would be a kind of technical report to the new researchers to get acquainted with the recent advancements in Cyber security domain.

This paper presents a new contribution to the recommendation system following E-commerce reviews. Here, it implements a dictionary-based sentiment analysis based on four main steps (i) Pre-processing (ii) Semantic word extraction (iii) Feature Extraction (iv) Classification. Initially, the tweets or reviews extracted from the database are subjected to pre-processing, which involves three processes: stop word removal, stemming, and blank space removal. In the semantic word extraction process, the semantic words from the dictionary will be extracted by matching with the extracted keywords. The next process is the feature extraction, which extracts joint holoentropy and cross holoentropy of all keywords, and further proceeds with conditional holoentropy-based feature selection process. Finally, the selected features are subjected to a classification process using Deep Belief Network (DBN), which is a well-performing deep learning algorithm. Moreover, the main contribution of the paper relies on the improvement of DBN architecture, where the number of hidden neurons and activation function is optimally tuned by a hybrid optimization algorithm termed as Grey linked Chick Update-based Chicken Swarm Optimization (GCU-CSO). At last, the comparative analysis of proposed and conventional algorithms in terms of various performance metrics proves the efficacy of the proposed E-commerce recommendation system.

Diabetic retinopathy (DR) is one of the most common retinal diseases that cause preventable blindness in diabetic patients. The timely screening and grading of retinal images minimize the possibility of vision loss. However, manual screening of retinal images, for detecting micro lesions in the early stages of DR, is time-consuming. This paper proposes an ensemble of deep convolutional neural network (CNN) models for accurate detection and grading of DR using fundus images. Each input image is divided into four patches at the first stage and passed on to pre-trained CNN models (InceptionV3, Xception) for training. The relevant features in the shallow-dense layers of CNN models are utilized as prior knowledge. The integration of shallow and dense layer features helps the model learn the significant information of DR images. At the second stage, a classifier based on artificial neural network is trained using the fused probability vectors of four patches. The results of individual CNN models are combined to generate the final decision in the third stage. This ensemble approach of multi-stage deep learning model improves the overall classification accuracy of diabetic retinopathy grading. Among the five different classification schemes presented in this paper, multistage patch-based deep CNN (MPDCNN), in which local patch-based and holistic details of fundus image are concatenated, provides the best classification accuracy. This ensemble classifier exhibits 96.2 % classification accuracy with fivefold cross-validation.

Due to the complexity of blockchain technology, it usually costs too much effort to build, maintain and monitor a blockchain system that supports a targeted application. To this end, the emerging “Blockchain as a Service” (BaaS) makes the blockchain and distributed ledgers more accessible, particularly for businesses, by reducing the costs and overheads. BaaS combines the high computing power of cloud computing, the pervasiveness of IoT and decentralization of blockchain, allowing people to build their own applications while ensuring the transparency and openness of the system. This paper surveys the research outputs of both academia and industry. First, it introduces the representative architectures of BaaS systems, and then summarizes the research contributions of BaaS from technologies for service provision, roles, container and virtualization, interfaces, customization and evaluation. The typical applications of BaaS in both academic and practical domains are also introduced. At present, the research on blockchain is abundant, but research on BaaS is still in its infancy. Six challenges of BaaS are concluded in this paper for further study directions.

This paper presents an online method to monitor the conditions of substation grounding grid. The proposed method utilizes phase-to-ground faults occurring in downstream feeders as test current sources and the neutral of a specially selected feeder as the electrode for GPR (ground potential rise) measurement. The measured fault currents and GPRs are then used to estimate substation grounding grid impedance. The method, once established, can monitor the grounding grid impedance continuously and automatically, and the trend of the impedance change can be observed. As a result, the proposed method can yield much more reliable information on the substation grounding conditions. The method also overcomes many disadvantages of the offline methods. The proposed idea, algorithm, and hardware setup are described in the paper. Factors contributing to measurement errors are investigated in detail.

This paper analyses the organization of ‘openings’ in Skype video-mediated conversation. It uncovers order in their apparent complexity by showing the relevance of a particular sequential adjacent pair organization, the appearing/noticing sequence, and its particular instantiation as an appearance-for-the-first-time greeting. The paper shows how this is a crucial resource in establishing a joint video interactional frame for the parties involved. This accounts for the occurrence of some specific phenomena in Skype openings, such as multiple greetings, and for the use of greetings which reflexively index their being occasioned by an appearance and related greeting, such as the French ‘coucou’, even when these do not occur at the start of Skype calls. When analysed this way, Skype openings, though complex, can be seen as an accomplished ‘dance of appearances and multiple greetings’.

Software-as-a-service (SaaS) is a license for access to a specific cloud application through the Internet. However, these services are delayed and sometimes totally disrupted because of the Internet’s unavailability that provides opportunities for a lot of attacks. Research into the security of the cloud focuses mainly on prohibiting malicious users from using the cloud to launch attacks, such as those currently done by botnets, including launching a DDoS attack, sending spam, etc. Because SaaS uses computational power from both servers of cloud computing providers and machines of customers, we argue that SaaS may be elaborately exploited in an unprecedented way as an attack vector for botnets. The previous research work has developed a novel framework for detecting the attacks with the aid of a deep learning approach (Doriguzzi-Corin et al., 2020). Although the attack node in the network has been identified, they were not diminished. An enhanced and powerful adversary model is to be provided as a solution to this problem. Hence, this paper intends to develop a novel framework for attack node mitigation using a secure SaaS Framework. The main contribution of this paper provides an attack detection process that takes place in Deep Belief Network (DBN), in which the weight, as well as activation function, are fine-tuned with Median Fitness oriented Sea Lion Optimization algorithm (MFSLnO). If DBN detects an attack node, the control is transferred to a lightweight bait approach that reliably mitigates the most common attack nodes without disrupting regular connections will be deployed. The performance of the proposed work yielded the best results over the traditional models with a packet loss ratio of 16% and throughput of 89%.

Indonesia is a tropical country that experiences forest fires every year. Forest fires occur due to a prolonged summer season. A major effect of forest fires that commonly occurred in Indonesia is the respiratory disorder and the visual impairment caused by the thick smoke that can experienced by the people who lives around the forest. The effects of forest fire fumes when the forest fire spread across the bigger land or area are also experienced by neighboring countries such as Malaysia, Singapore and Brunei Darussalam. Forest is a habitat for many animals, one of which is a bird. Birds are able to communicate with their colonies through sound. The bird sound during communication to the group can be in the form of calls, marriage invitations, and warnings of danger or threat of forest fires. This paper presents bird sounds classification study using one of Deep Learning (DL) algorithms i.e. Convolutional Neural Network (CNN) method. The CNN method is used to classify bird sounds in the two conditions: (1) under normal circumstances or conditions, and (2) under threated or panic condition. The bird sound data used in this study were collected from the local birds in Indonesia. The classification result of two bird sounds based on CNN method achieved up to 96.45%. This paper is a preliminary study for bio inspired early warning of forest fires based on the sound of birds.

Emotion recognition based on electroencephalography (EEG) signal features is now one of the booming big data research areas. As the number of commercial EEG devices in the current market increases, there is a need to understand current trends and provide researchers and young practitioners with insights into future investigations of emotion recognition systems. This paper aims to evaluate popular consumer-grade EEG devices’ status and review relevant studies that examined the reliability of these low-cost devices for emotion recognition over the last five years. Additionally, a comparison with research-grade devices is conducted. This paper also highlights EEG-based emotion recognition research’s key areas, including different feature extraction capabilities, characteristics, and machine learning algorithms. Finally, the main challenges for building an EEG-based emotion recognition system, focusing on the data collection process with commercial EEG devices and machine learning algorithms’ performance, are presented.

Convolutional neural networks (CNN) is a specialized case of artificial neural networks(ANN) and finds its application in computer vision and parallel distributed computing for processing of massive amount of data generated by sensors and to meet the power constraints of IOT devices. Recent advancements in parameter optimization, regularization techniques, improvement in activation functions, corresponding loss functions, advancements in the coted the research of Convolutonal Neural Network’s(CNN’s) in past few years. Training of neural networks is cumbersome and takes a lot of time can take days or even weeks. This limits the application of Convolutional Neural Network(CNN) in real time research fields where computational speed is of utmost importance. Thus there is a need for appropriate and enhanced computational speed to meet the requirements of these real time applications.This paper describes CNN in detail summarizes architectural evolution of CNN from 1998 to 2019. Three types of strategies have been explained to enhance the computational speed of CNN at algorithmic level and implementation level. This paper gives detailed insight about computation speed acceleration using Stochastic Gradient Decent(SGD) optimization, Fast convolution and exploiting parallelism challenges in CNN posed by these techniques and recent advancements.The paper also includes detailed view of different framework usage while implementing fast convolution or parallelism techniques. The ultimate aim of the paper to explore all such recent techniques by which we can accelerate the training speed of the CNN’s without compromising the accuracy.

Emotion recognition using Electroencephalography (EEG) is a convenient and reliable technique. EEG based emotion detection study can find its application in various fields such as defense, aerospace, medical, and many more. This analysis helps to understand the emotional state of mind. There are two approaches to study EEG analysis known as subject dependent and independent. In this paper, Modified Differential Entropy (MD-DE) feature extractor is proposed to detect nonlinearity and non-Gaussianity of the EEG signal. The paper adopts both approaches by conducting an EEG analysis on own generated database named as ‘IDEA- Intellect Database for Emotion Analysis’ on 14 subjects. In this work, bidirectional long short-term memory (BiLSTM) network and multilayer perceptron (MLP) network is used to classify emotional state of mind of the subjects. On the ‘IDEA’ database, subject dependent average accuracy achieved is in the order of 98.5% and for subject independent, 88.57%. To reaffirm the improvement in accuracy level, a new approach of Modified Differential Entropy and BiLSTM network is applied on the openly available SEED and DEAP database as well. This experiment established that the average accuracy of emotion detection using MD-DE and BiLSTM network is better than the established methods.

Energy-efficient resource-optimization for multiple application scenarios requires appropriate clustering-based network-fractioning that skillfully removes unbalanced network connectivity, which is a stimulating bottleneck in WSN-performance. This paper proposes a new Uniform Connectivity-based clustering Protocol, called Lines-of-Uniformity based Enhanced-Threshold (LUET) to provide energy-efficient coverage in three-tier heterogeneous WSN. This protocol considers the node’s remnant-energy and its proximity from either of the lines of uniformity for lowering down average isolated node count in WSN. This paper also proposes a rotation epoch-based LUET variant, (LUET|R) that incorporates static epoch for initial clustering rounds until First-Node-Death to overcome the rapid fall after the death of the first node. The simulation model demonstrates the superiority of LUET and its variants over other established clustering protocols in terms of network-lifetime, power-efficiency, net death-rate, average isolated-nodes, throughput.

In the Open Source Software (OSS) paradigm, developers along with users form a community for an OSS project as they share an interest in using/developing the project. Active community engagement is essential for an OSS project to succeed. OSS communities should strive for greater community participation and engagement through the use of tools, practices, and processes. The primary goal of this paper is to presents a review of studies on community participation and engagement in OSS projects based on systematic mapping study and snowballing technique. This study also provides an understanding about the research topics and gaps in the area, utilized research methods and publication venues. We have analyzed 67 research papers related to the study topic. The findings revealed that most of the studies used a combination of survey and questionnaire as a research methodology. We found that community participation and engagement research focuses on 5 main research topics joining process, contribution barriers, motivation, retention, and abandonment. The investigated studies provide more evidence on motivation and contribution barriers but less on the joining process and abandonment. The results presented in this paper will be helpful for researchers to understand the latest trends in this area and identifying the corresponding research gaps.

This paper considers an unmanned aerial vehicles (UAV) communication network, where UAV operate as an aerial mobile relay between a source and a destination of the network. To capture the “timeliness” of the received information at the destination node, a new performance metric named age of information (AoI) is considered. In addition, a short packet communication scheme maintains low latency in the proposed UAV wireless communication system. The finite block-length theory investigates the performances of short packet communications scheme in the UAV-assisted wireless communications system. In this paper, the Average Age of Information (AAoI) is estimated by applying the Stochastic Hybrid Systems (SHS) model. The SHS model comprises discrete states represents events that reset the AoI process and continuously dynamics that represent linearly growing age processes. Finally, a closed-form expression for the AAoI of the proposed UAV wireless communication system is derived and it can be used to estimate the optimal altitude, block length and other parameters.

We present Arianna+, a framework to design networks of ontologies for representing knowledge enabling smart homes to perform human activity recognition online. In the network, nodes are ontologies allowing for various data contextualisation, while edges are general-purpose computational procedures elaborating data. Arianna+ provides a flexible interface between the inputs and outputs of procedures and statements, which are atomic representations of ontological knowledge. Arianna+ schedules procedures on the basis of events by employing logic-based reasoning, i.e., by checking the classification of certain statements in the ontologies. Each procedure involves input and output statements that are differently contextualised in the ontologies based on specific prior knowledge. Arianna+ allows to design networks that encode data within multiple contexts and, as a reference scenario, we present a modular network based on a spatial context shared among all activities and a temporal context specialised for each activity to be recognised. In the paper, we argue that a network of small ontologies is more intelligible and has a reduced computational load than a single ontology encoding the same knowledge. Arianna+ integrates in the same architecture heterogeneous data processing techniques, which may be better suited to different contexts. Thus, we do not propose a new algorithmic approach to activity recognition, instead, we focus on the architectural aspects for accommodating logic-based and data-driven activity models in a context-oriented way. Also, we discuss how to leverage data contextualisation and reasoning for activity recognition, and to support an iterative development process driven by domain experts., The paper has been accepted for publication in the IEEE Transactions on Cybernetics journal on April 2021 and with DOI 10.1109/TCYB.2021.3073539. It is an extension of arXiv:1707.03988v1 and it is related to the arXiv:1809.08208v1 article. It contains 20 pages, 6 figures, 4 tables and 2 Appendices

This paper proposes a formal top-level model of explaining the outputs of machine-learning-based decision-making applications and evaluates it experimentally with three data sets. The model draws on AI & law research on argumentation with cases, which models how lawyers draw analogies to past cases and discuss their relevant similarities and differences in terms of relevant factors and dimensions in the problem domain. A case-based approach is natural since the input data of machine-learning applications can be seen as cases. While the approach is motivated by legal decision making, it also applies to other kinds of decision making, such as commercial decisions about loan applications or employee hiring, as long as the outcome is binary and the input conforms to this paper’s factor- or dimension format. The model is top-level in that it can be extended with more refined accounts of similarities and differences between cases. It is shown to overcome several limitations of similar argumentation-based explanation models, which only have binary features and do not represent the tendency of features towards particular outcomes. The results of the experimental evaluation studies indicate that the model may be feasible in practice, but that further development and experimentation is needed to confirm its usefulness as an explanation model. Main challenges here are selecting from a large number of possible explanations, reducing the number of features in the explanations and adding more meaningful information to them. It also remains to be investigated how suitable our approach is for explaining non-linear models.

Currently, in the oil industry, Artificial Lift Optimization (ALO) systems are dealing with different applications including well monitor and control, reservoir management, production optimization, predictive maintenance, artificial lift, and flow assurance, multiphase pumping systems, etc. The scope of this article is to provide a detailed analysis of ALO and predictive pump maintenance applications using Machine Learning (ML) and Artificial Intelligence (AI). The oil and gas industry has experienced a lot of improvements that have impacted the businesses and economies associated with the market in recent times. Issues such as unplanned shutdown time and failure of equipment cause a huge impact on many corporations especially with the current fluctuations in hydrocarbon prices. Similarly, advanced modern technologies such as real-time analysis and predictive maintenance are designed to drive ALO systems. This paper covers several applications and techniques in which ML and AI have been applied to optimize hydrocarbon withdrawal from potentially depleted reservoirs that require some external support to uplift the reservoir fluid from sub surface to surface using artificial lift system. In a nutshell, the applications of AI and ML for the artificial lift selection, their predictive maintenance, equipment malfunctioning detection, etc. using a self-trained system are the main topics of this paper. While reviewing each of these techniques, their workflow is also explained along with the effectiveness of utilizing each application to the current operations.

Recently, Social Media (SM)’s adoption as educational instruments in Higher Education (HE) has been gaining popularity among researchers and practitioners. Nonetheless it has been adopted to support various learning approaches such as Cooperative Learning (CL). Despite the growing research in this field, few studies have proposed a comprehensive SM integration framework as well as empirical studies to evaluate its effectiveness. This paper examines how SM is being used as a pedagogical tool in HE and identifies major factors that drives SM’s inclusion in HE classrooms. In addition, the paper investigates the effectiveness of a previously proposed integration framework which was developed to promote CL using a popular SM platform, LinkedIn. A survey was administered to collect data from students in different subjects at a university in Australia. Structural Equation Modelling (SEM) was adopted for data analysis using IBM’s SPSS26 and AMOS26. One of the notable results of this study is the significant positive relation found between the use of the adopted integration framework and CL, SM challenges’ mitigation, students’ intention to use SM and their learning process. The findings of this study have important implications for practice and pave the way for future empirical studies.

In this paper, the stability problem for a class of nonlinear systems in the form of strict-feedback with applying input quantization has been addressed. By considering a sector-bounded hysteresis quantizer, signal quantization has been achieved. The employed quantizer can reduce the potential chattering which can occur in some approaches. By using a common Lyapunov function (CLF) and the backstepping method, a control scheme has been introduced to stabilize the uncertain nonlinear system. Compared with the recent papers, in order to handle the quantization error, one of the sector-bounding features has been utilized straightly instead of decomposing the quantized input into linear and nonlinear parts, in this case, the possible disturbance-like term has been ignored. The designed control scheme does not need the global Lipschitz assumption over the system mismatched nonlinearities. Besides, the asymptotic stability of system trajectories to the origin is guaranteed and the imposed restrictions over quantization design parameters such as quantization density have been eliminated. Finally, in the simulation results, the accuracy and efficiency of the this control scheme are shown.

Countries have different specifications for License Plates (LPs), therefore developing one Automatic license plate recognition (ALPR) system that works well for all LPs types is a difficult task. This paper aims to develop an accurate ALPR for Jordanian LPs. Two-stage Convolutional Neural Networks (CNNs) are used in the proposed approach, the CNNs are based on the YOLO3 framework. The sizes of LPs' characters are very small compared with the frame size, therefore the YOLO3 network architecture is modified to a shallow network to detect small objects. The proposed approach uses temporal information from different frames to remove false predictions. A set of arrays data structure is used to track the vehicles’ LPs and eliminate incorrect ones. To my knowledge, the proposed approach represents the first end-to-end Jordanian ALPR that processes video stream in real-time. To my knowledge, there is no dataset for Jordanian license plates, therefore this paper proposes a new dataset called JALPR dataset. The dataset is available online and includes many real videos for moving vehicles in Jordan. Two well-known commercial software packages are used for comparisons. The experimental results in real videos from YouTube show that the proposed approach is very efficient in recognizing the Jordanian license plates and achieved 87% recognition accuracy, whereas the commercial systems have recognition accuracies that are less than 81%.

Update summarization is a relatively recent summarization task concerned with creating a short summary from news articles, assuming the user has already read a number of previous articles. It is useful for users who want to know the latest information about a specific topic. The availability of systems that provide update summaries saves user time and effort. Unfortunately, such resources are lacking for the Arabic language. This paper aims to provide an update summarization system that generates an update summary containing the latest information requested by a user from multiple documents. This paper provides an Arabic query-based Update-Summarization System (AUSS). We use a graph-based ranking model to represent the similarity, through a combination of lexical and semantic relations between words. Our experiments show that AUSS achieves promising results, achieving a best F-Measure of 0.5405. To test AUSS, we created a new corpus especially for Arabic Update Summaries, which we call (AUS-DB). AUS-DB contains 183 articles and their corresponding reference summaries.

With the rapid growth of data sources, Big data security in Cloud is a big challenge. Different issues have ascended in the area of Big data security such as infrastructure security, data privacy, data management and data integrity. Currently, Big data processing, analytics and storage is secured using cryptography algorithms, which are not appropriate for Big data protection over Cloud. In this paper, we present a solution for addressing the main issues in Big data security over Cloud. We propose a novel system architecture called the Secure Authentication and Data Sharing in Cloud (SADS-Cloud). There are three processes involved in this paper including (i). Big Data Outsourcing, (ii). Big Data Sharing and (iii). Big Data Management. In Big data outsourcing, the data owners are registered to a Trust Center using SHA-3 hashing algorithm. The MapReduce model is used to split the input file into fixed-size of blocks of data and SALSA20 encryption algorithm is applied over each block. In Big data sharing, data users participate in a secure file retrieval. For this purpose, user's credentials (ID, password, secure ID, and current timestamp, email id) are hashed and compared with that stored in a database. In Big data management, there are three important processes implemented to organize data. They are as follows: Compression using Lemperl Ziv Markow Algorithm (LZMA), Clustering using Density-based Clustering of Applications with Noise (DBSCAN), and Indexing using Fractal Index Tree. The proposed scheme for these processes are implemented using Java Programming and performance tested for the following metrics: Information Loss, Compression Ratio, Throughput, Encryption Time and Decryption Time.

Among other motif finding algorithms Planted Motif Search (PMS) or (l, d) motif search algorithms are considered as next-generation motif finding algorithms. Here, n strings and two integer l and d are provided as input in PMS. It gives outputs of all sequences of length M in each input string, where each occurrence varies from Min at most d points. This paper proposes a new algorithm, for the (l, d) motif discovery problem in which we find all strings of length l that seems in every string of a provided set of strings with at most d mismatches has been. After proper investigation of the PMS problem, it has been shown that this is an NP-hard exact algorithm. Usually, in the worst-case scenarios, all the known exact algorithms for PMS need exponential time in some of the underlying parameters. In this paper, we proposed a faster approach that reduces the searching time in the sample-driven part of the algorithm. In particular, we used dynamic programming techniques to eliminate the recalculation of the values of some common subtree and introduced some new speedup techniques like using a linked list, reduced l-mers for making the algorithm faster. Due to the use of Linked List as a main speedup technique, we named our proposed algorithm as Linked List Implementation technique of PMS8 (LL-PMS8).