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Dyslexia is a type of Specific Learning Disability (SLD) that affects the educational development of a student, as it is the reason of difficulties not only in writing or reading but also in other disciplines like Mathematics. Research has proven that Information and Communication Technologies (ICT) can have a positive effect to the educational development of a dyslexic student. In this paper, twenty 6th-grade primary-school students were observed while using computers during exams - ten with Dyslexia and ten with no SLDs. All students were called to solve a digital test and a paper test that were including some basic mathematic operations. The researcher recorded and examined the differences between the results from the two tests. Descriptive and deductive statistical analysis showed that students had better results when examined via a computer compared to a traditional written examination.

Paper currency recognition (PCR) is an important area of pattern recognition. A system for the recognition of paper currency is one kind of intelligent system which is a very important need of the current automation systems in the modern world of today. It has various potential applications including electronic banking, currency monitoring systems, money exchange machines, etc. This paper proposes an automatic paper currency recognition system for paper currency. A method of recognizing paper currencies has been introduced. This is based on interesting features and correlation between images. It uses Radial Basis Function Network for classification. The method uses the case of Saudi Arabian paper currency as a model. The method is quite reasonable in terms of accuracy. The system deals with 110 images, 10 of which are tilted with an angle less than 15o. The rest of the currency images consist of mixed including noisy and normal images 50 each. It uses fourth series (1984–2007) of currency issued by Saudi Arabian Monetary Agency (SAMA) as a model currency under consideration. The system produces accuracy of recognition as 95.37%, 91.65%, and 87.5%, for the Normal Non-Tilted Images, Noisy Non-Tilted Images, and Tilted Images respectively. The overall Average Recognition Rate for the data of 110 images is computed as 91.51%. The proposed algorithm is fully automatic and requires no human intervention. The proposed technique produces quite satisfactory results in terms of recognition and efficiency.

The omnipresent pervasiveness of personal devices will expand the applicability of the Dynamic Data Driven Application Systems (DDDAS) paradigm in innumerable ways. While every single smartphone or wearable device is potentially a sensor with powerful computing and data capabilities, privacy and security in the context of human participants must be addressed to leverage the infinite possibilities of dynamic data driven application systems. We propose a security and privacy preserving framework for next generation systems that harness the full power of the DDDAS paradigm while (1) ensuring provable privacy guarantees for sensitive data; (2) enabling field-level, intermediate, and central hierarchical feedback-driven analysis for both data volume mitigation and security; and (3) intrinsically addressing uncertainty caused either by measurement error or security-driven data perturbation. These thrusts will form the foundation for secure and private deployments of large scale hybrid participant-sensor DDDAS systems of the future.

There are a number of issues which are involved with organizing a conference. Among these issues, assigning conference-papers to reviewers is one of the most difficult tasks. Assigning conference-papers to reviewers is automatically the most crucial part. In this paper, we address this issue of paper-to-reviewer assignment, and we propose a method to model the reviewers, based on the matching degree between the reviewers and the papers by combining a preference-based approach and a topic-based approach. We explain the assignment algorithm and show the evaluation results in comparison with the Hungarian algorithm.

Lepton is an automaton for literate executable papers. It enables researchers to publish their work in the form of a script or program that can generate the research paper along with the corresponding source code, input data and output results. Lepton files do not contain pre-computed results, but the full set of instructions for reproducing the results presented in the manuscript. Taking inspiration from literate programming for code review and code re-use, we have written Lepton to facilitate the review and re-use of computational methods. Lepton is designed to provide strong guarantees for the reproducibil-ity of the results, many features for easily applying the methods to new data while remaining unobtrusive and easy to deploy in any environment. Lepton is designed for writing reproducible technical reports during method development, and journal manuscripts when the research is polished. Developped independently from the Elsevier Executable Paper Grand Challenge, it addresses similar issues and objectives. This manuscript was written with Lepton.

For centuries, the research paper have been the main vehicle for scientific progress. From the paper, readers in the scientific community are expected to extract all the relevant information necessary to reproduce and validate the results presented by the paper's authors. However, the increased use of computer software in science makes reproducing scientific results increasingly difficult. The research paper in its current state is no longer sufficient to fully reproduce, validate, or review a paper's experimental results and conclusions. This impedes scientific progress. To remedy these concerns, we introduce PaperMâche, a new system for creating dynamic, executable research papers. The key novelty of PaperMâche is its use of virtual machines, which lets readers and reviewers easily view and interact with a paper, and reproduce key experimental results. For authors, the Paper Mâche workbench provides an easy-touse interface to build an executable paper. By transforming the static research paper into a dynamic and interactive entity, Paper Mâche brings the presentation of scientific results into the 21st century. We believe that Paper Mâche will become indispensable to the scientific process, and increase the visibility of key findings among members and non-members of the scientific community.

Open source software (OSS) is a software product with the source code made public so that anyone can read, analyze, and change or improve the code. The use of this software is under a license, like Apache, GNU, MIT, Mozilla Public, and Eclipse Public License. Open source software development (OSSD) provides high quality assurance through user testing and peer reviews. The quality of these products depends on the size of the product community. This paper discusses the stakeholders of the OSS community, the quality assurance frameworks and models proposed in some studies, some statistics about OSS, the problems that affect the quality of OSSD, and the advantages and disadvantages of OSS compared to closed source software. This allows us to understand how we can achieve and improve the quality assurance and quality control of OSSD.

We are investigating the use of natural user interfaces (NUI) applied to information and communication technologies (ICT) supporting workplace dynamics in partnership with mental health providers at a hospital in Brazil. We are using a participatory design (PD) process to uncover their paper-based workflow, defined here as paper user interface workflow (PUI) and their appropriation of mobile technologies supporting patients’ socialization process. We chose this group as they have no experience with graphical user interface (GUI) based interaction, are highly nomadic, have a refined workflow, have a high level of interdependence and are appropriating mobile technologies and social media into their workplace; thus, we believe their perspective within a PD setting provide unique insights into NUI design to provide high fidelity data and real time communication, enhancing data accessibility and fidelity on communication among the professionals. Our PD process has produced workflow representations and we are presenting two scenarios; from them we are proceeding to low- fidelity prototyping.

Reviewing the computational part of scientific papers puts a lot of effort on referees: even if authors provide their data and code the referee often needs to install additional software on his machine and figure out which parts of the code belong to which part of the manuscript. As a result, computational results or often not reviewed at all. We propose a new web service which outsources validation of computational results in executable papers to an independent third party. Our system adapts the well-tested toolbox currently checking R extension packages in software repositories like CRAN to check manuscripts in paper repositories. In addition, paper packages can easily be downloaded from the server and installed to replicate results locally by anyone wishing to do so.

The paper describes a system for executable papers for publishers enabling them to reuse content and to generate further advances of science and engineering. The executable algorithmic descriptions within a paper are presented in natural language sentences and basic code, thereby making long term compatibility absolute. Authors are required to use publicly numerical libraries on the Internet or references to publications with executable papers. As used by authors the system automatically creates a web of algorithmic knowledge on the Internet. Novelty of new algorithms in publications can be evaluated by automated tools available to authors, reviewers and readers of scientific papers published.

We present the Lab Wiki, an executable paper platform primarily designed but not limited to networking experiment-based research. The LabWiki leverages the current state of the art tools for the orchestration of experiments in the networking community and propose a new approach to execute and reproduce experiments. We demonstrate the usability of the LabWiki through an example at the boundary between network and high performance computing researches.

Linked Open Science is an approach to solve challenges of an executable paper. It is a combination of four “silver bullets”: 1) publication of scientific data, metadata, results, and provenance information using Linked Data principles, 2) open source and web-based environments for executing, validating and exploring research, 3) Cloud Computing for effcient and distributed computing, and 4) Creative Commons for the legal infrastructure. We will use a realistic scientific research setting related to research on deforestation of the Brazilian Amazon rainforest to provide scenarios to illustrate the application of Linked Open Science.

International audience; This proposal describes how data, program code, and presentation can be stored together in a single file suitable for electronic publication and permitting the reproduction of computational results.. Universality, effciency, platformindependence, automated verifiability, and provenance tracking are the major design criteria. Existing and well-tested technology is used as much as possible, the two major building blocks being the Hierarchical Data Format for storage and the Java Virtual Machine for platform-independent code representation and secure execution.

Shredded document recovery is an important research sub-field of information security. The paper document is broken into a large number of similar shreds by the shredder. Usually these shreds imply some important information. If they are restored, it will have an important impact on judicial investigation, military command, and archaeological research. Therefore, it has a great value in research of shredded document recovery. In consideration of large differences between languages and great demands for Chinese document recovery, our study will focus on the shredded Chinese document. In this paper, a method of shredded document recovery based on character stroke feature is proposed. We use horizontal strokes to match Chinese characters. Finally the shredded document is restored. In the test of actual sample, the method proposed in this paper has achieved good recovery result. The average accuracy is 13.96% higher than the traditional algorithm.

Novel organic forms of interactive displays are developed recently from flexible materials that can bend, fold or roll between the hands of its user giving him a wholly new sense of digital interactive media. In this paper, we hold a survey for different OUIs (Organic User Interfaces) and categorize them into four main categories: flat surfaces, non-flat surfaces, digital substrates and handheld devices. Each of which has its uses that suites certain contexts and does not fit others. We discuss each category thoroughly with its possible sub-categories, examples and applications. Each category is noticed to have a physical design that has a great deal of resemblance to the physical form or design of a pharaonic monumental style or type of artifacts from the history of ancient Egypt. The paper highlights the observed resemblance between organic digital displays and ancient monumental displays with respect to the similarities and differences in the sense that they are both used as information displays for targeted users in certain contexts. This unintended physical resemblance points out to where we might inspire novel designs for digital content on interactive displays.

In this paper we present the Active Documents Paradigm (semantically annotated documents associated with a content commons that holds the corresponding background ontologies) and the Planetary system (as an active document player). We show that the current Planetary system gives a solid foundation and can be extended modularly to address most of the criteria of the Executable Papers Challenge.

The Collage Authoring Environment is a software infrastructure which enables domain scientists to collaboratively develop and publish their work in the form of executable papers. It corresponds to the recent developments in both e-Science and computational technologies which call for a novel publishing paradigm. As part of this paradigm, static content (such as traditional scientific publications) should be supplemented with elements of interactivity, enabling reviewers and readers to reexamine the reported results by executing parts of the software on which such results are based as well as access primary scientific data. Taking into account the presented rationale we propose an environment which enables authors to seamlessly embed chunks of executable code (called assets) into scientific publications and allow repeated execution of such assets on underlying computing and data storage resources, as required by scientists who wish to build upon the presented results. The Collage Authoring Environment can be deployed on arbitrary resources, including those belonging to high performance computing centers, scientific e-Infrastructures and resources contributed by the scientists themselves. The environment provides access to static content, primary datasets (where exposed by authors) and executable assets. Execution features are provided by a dedicated engine (called the Collage Server) and embedded into an interactive view delivered to readers, resembling a traditional research publication but interactive and collaborative in its scope. Along with a textual description of the Collage environment the authors also present a prototype implementation, which supports the features described in this paper. The functionality of this prototype is discussed along with theoretical assumptions underpinning the proposed system.

Class material for computer science courses often contains algorithms and code snippets, as well as the results of their execution. Usually, these are written and tested outside the source document then included via copy-and-paste. Making sure that the code compiles and that the results really correspond to the included code is the teacher's responsibility. Using techniques and ideas from literate programming, we propose to include source code and executable instruc-tions inside the source document. To support this, we have implemented Lepton which is a tool for extracting source code, compiling, executing, and including the results of the documented programs. Consequently, copy-and-paste is eliminated and code output is guaranteed to be up-to-date with source code. This manuscript was written with Lepton.

Most instructors and staff in a university environment use manual methods of tracking student attendance and college assets. However, these methods have several disadvantages; the major drawbacks of taking attendance manually are errors in data collection and the loss of part of the lecture time. As for asset tracking, the common issues are human errors in data collection, wrong inventory estimation and time wastage during the process. Automated identification and data capturing technologies can help address these issues. This paper investigates the implementation of one such system, the barcode technology, to read and process student attendance data in classroom settings and to track periodic inventory of assets within the college facilities such as devices, equipment, office furniture, classrooms, and laboratories. The paper measures faculty members and staff response on usability, comfort level, and efficiency for the proposed barcode system. This work shows that barcode technology eliminates data entry errors and improves efficiency for entering data with a minimum cost of creating barcode tags and hardware.

With the development of the Internet, online recruitment has gradually become the mainstream. Analyzing the recruitment data and exploring the internal rules of the data can help enterprises and job seekers realize human-career matching. An extremely important step in analyzing recruitment data is to extract structured information from unstructured data. Named entity recognition can effectively extract entity information from unstructured data. In recent years, a lot of work has focused on this task, but no related work has been applied named entity recognition to Internet recruitment information. Therefore, this paper proposes the BiLSTM-Attention-CRF model for Internet recruitment information, which can be used to extract skill entities in job description information. This model introduces the BiLSTM and Attention mechanism to improve the effect of entity recognition. To verify the performance of the model, the paper used crawler technology to capture the real Internet recruitment data and annotate it and obtain a real data set. In this paper, a series of experiments are conducted on this data set, and the experimental results show that the proposed model achieves the best performance.

The amount of remote sensing (RS) data has increased at an unexpected scale, due to the rapid progress of earth-observation and the growth of satellite RS and sensor technologies. Traditional relational databases attend their limit to meet the needs of high-resolution and large-scale RS Big Data management. As a result, massive RS data management is currently one of the most imperative topics. To address this problem, this paper describes a distributed architecture for big RS data storage based on a unified metadata file, pyramid model, and Hilbert curve for data composition and indexing using NoSQL databases (i.e, Apache Hbase). In this paper, a Hadoop-based framework in AzureInsight cloud platform is designed to manage massive RS data in a parallel and distributed way. Experimental results prove that our method has the potential to overcome the weakness of traditional methods. The proposed model is suitable for massive high-resolution image data management.

The past decade has witnessed an explosion in the generation of Arabic textual data, written in both modern standard Arabic (MSA) and in dialectal formats. Observing existing data reveals that verses from the Holy Quran are often quoted whether someone is giving a speech, conveying condolences, going through a hardship, and when expressing hope or joy among other emotions. Many applications can benefit from the automatic detection of those verses yet detecting Quranic Verses in text is not as easy as it may initially seem. This paper lists the challenges posed by this task, and proceeds to present an intelligent matching algorithm that addresses those. The paper also briefly describes a tool that has implemented the proposed algorithm, and which is currently publicly available as an open-source tool.

The article is devoted to the development of the method for synthesizing hierarchical knowledge graphs of telecommunication networks using network statistical data and available models or their fragments. Existing graphs or their fragments, monitoring data, descriptions of data structures, reverse engineering artifacts, etc. can be used as initial data. The paper presents analysis of relevance of the problem for telecommunication network operators and considers the advantages of the proposed method of inductive synthesis for building integrated network models. The method allows restore hierarchical graph models from source data, including the elements of the models and the relations between them. Based on extracted elements and relations hierarchical knowledge graphs are build. The mathematical models and algorithms that describe the considered method for synthesizing hierarchical knowledge graphs and quality indicators of the resulting model are presented in the paper. To confirm the method, the program was developed. This program uses a sample of initial data as a source, restores an integral fragment of a hierarchical knowledge graph of a telecommunication network. Also, as results of this work, the areas of its application and directions for further research are formulated.

The improvement of teaching quality in order to promote the electronic electrician experiment, training students’ ability of engineering quality and the position, the paper analyzes the current situation of electrical and electronics experimental teaching and the existing problems, this paper expounds the necessity of constructing the virtual instrument laboratory, and from two aspects of hardware modules and software system design construction scheme for the overall design.

Nowadays, it is very important to quickly recognize the false information referred to as fake news. This is especially important in the case of news appearing on the Internet because of its wide and rapid spreading. It is equally important to be able to initially classify news as fake or true based on the title itself. In this paper, we propose an approach to classifying news based on the title without analyzing the other aspects. The obtained results will be compared with classification based on the whole news text. The goal of this work is to propose a method that balances between data analysis time and quality of classification in fake news prediction. We use natural language processing (NLP) to describe the title and text of the news. This is a complex process, requiring good analysis to be applied to classification. Therefore, the use of complex classifiers – in this case, classical ensemble methods – has been proposed in order to achieve a high quality of classification (measured by popular measure). In this paper, we present analyses of a real data set and results of news classification using the proposed model – including an ensemble of classifiers and single classifiers.

The human immune system is studied at different scales (molecular, cellular, tissue, organism, population) with varied granularity and spatiotemporal dimensions. Innate and adaptive immune responses to viral infections are researched in vitro, in vivo, and in silico using mathematical and computational methods. The construction of computational models is an iterative and interactive process which combines existing knowledge and hypotheses (knowledge-driven approach) with empirical datasets (data-driven approach). The resulting models should ideally provide reusability, composability, and interoperability to support system simulation. The main premises of this paper are (1) computational models can be used not only for representation of existing knowledge, but also as experimental tools to demonstrate, exemplify, create, and serve as test-beds for the analysis of empirical data;and (2) computational models should be evaluated in terms of their usability (defined as a measure of how well a specified group of users can achieve predefined goals effectively, efficiently, and satisfactorily in a specified context of use). The usability measure is specified by the context information: user characteristics, their goals, and additional constraints (e.g., data availability, temporal limitations). This paper focuses on the modelling process performed by multidisciplinary teams and the usability of models for the exploration of immune response to viral infections. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Complex Adaptive Systems Conference, June 2021.

Anaphora resolution is one of the problems in natural language processing. It is the process of disambiguating the antecedent of a referring expression from the set of entities in a discourse. The correct interpretation of pronouns plays an important role in the construction of meaning. Thus, the resolution of pronominal anaphors remains a very important task for many natural language processing applications. Additionally, it plays an increasingly significant role in computational linguistics. However, a significant amount of work on anaphora resolution is focused on English; anaphora resolution for other languages, including Arabic, is still limited. In this paper, we present a new set of computational and linguistic features to resolve Arabic anaphors using a machine learning approach. In this paper, an in-depth study was conducted on a set of computational and linguistic features to exploit their effectiveness and investigate their effect on anaphora resolution. The aim was to efficiently integrate different feature sets and classification algorithms to synthesize a more accurate classification procedure. Four well-known machine learning algorithms k-nearest neighbor, maximum entropy, decision tree and meta-classifier, were employed as base-classifiers for each of the feature sets. A wide range of comparative experiments on Quran datasets was conducted, the discussion presented, and conclusions were drawn. The experimental results show that our approach gives satisfactory results.

In both developed and emerging countries, like Bangladesh, uterine cancer is one of the most common cancers for women. It is also the sixth most common cancer among females in the world and the fourteenth most common cancer in total. In recent years, the high prevalence of uterine cancer in women has risen dramatically. This covers several aspects that are linked to the signs and symptoms of this disease that are to be assessed and tested. Typically, these dimensions are expressed in quantitative and qualitative forms and also exist different kinds of uncertainty. Therefore, by using suitable methods to resolve the problem of uncertainty; otherwise, the detecting procedure will become unreliable. There are several systems proposed in this paper to tackle the problem. None of them, however, is worthy of solving the problem of ambiguity. This paper explains the development of a Belief Rule Based Expert System using the RIMER method, which by taking into account signs and symptoms, is able to detect the involvement of malignant tumors and risk factors for this cancer under uncertainty. Specifically, the system will expect a huge employment in diminishing the cost of lab assessments. The structure will support patients in making judicious strides well early. Moreover, this research has been done on MATLAB environment shown that the yields produced using the made system are more reliable than from manual structure, artificial neural network (ANN) and support vector machine (SVM).

The widespread adoption and employment of hand-held devices is a well-known and academically acknowledged fact as digital pervasiveness and hardware availability has triggered off this massive and convenient trend of accessing and consuming online resources and services from anywhere at any time. In this paper we argue that five areas in the mobile learning platform and the computer networks that support the processes and environments, require particular attention and proper training of the people involved to ensure the soundness of all the security methods and best practices are maintained, along with continual maintenance and updating of systems and procedures in each of these five categories. The paper closes with numerous recommendations regarding security in a mobile learning environment.

Weather data of Kashmir province has 6 attributes recorded at three different substations. This paper proposes a distributed decision tree algorithm and its implementation on Historical Geographical data of Kashmir province. The machine learning Decision tree algorithm applied on the Kashmir province dataset generates the accuracy of 81.54%. The distributed decision tree generates multiple trees based on the partitions of the original dataset in which the data is segregated according to the substations (42026, 42027 and 42044). The ratio between generated data sets was distributed in 32.38%, 34.19% and 33.42% respectively which is appropriate for the parallelism. Its distributed implementation, i.e. Distributed Decision Tree produces a specified number of sub-trees (depending upon number of partitions of input dataset) and at the end collects votes or averages the prediction or classification. In this paper, we have implemented the hard- voting approach to calculate the overall performance of the n-number of trees in distributed environment. The empirical results demonstrate that distributed decision trees approach has not improved the overall accuracy as compared to the original dataset without partitioning.

The purpose of this paper is to investigate whether it is possible to predict text readability with ensemble-based classifiers. In this article, the authors calculated and analyzed the readability indices. In the next stage, they defined additional features for each text and determined the relationships between readability and features. Among the various tasks of machine learning, they chose the classification problem. The authors calculated and compared the accuracy of different machine learning models. After building the models, they proceeded to the Random decision forests model interpretation step using the SHAP method. The authors show that machine learning models based on only three features are capable of predicting text readability. Long sentences and a low percentage of stop words can cause low readability. The machine learning model shown in this paper allows to classify texts according to readability with a model accuracy of 0.9.

Researchers have extensively used machine learning techniques and data mining methods to build prediction models and classify data in various domains such as aviation, computer science, education, finance, marketing and particularly in medical field where those methods are applied as support systems for diagnosis and analysis in order to make better decisions. On this subject, our research paper attempts to assess the performance of Individual and Ensemble machine learning techniques based on the effectiveness and the efficiently, in terms of accuracy, specificity, sensitivity and precision to choose the most effective. The main object of our research paper is to define the best and effective machine learning approach for the Breast Cancer diagnosis and prediction. To achieve our objective, we applied individual based level machine learning algorithms Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naive Bayes (NB), Decision tree (C4.5), Simple Logistic and well known ensembles methods like Majority Voting and Random Forest with 10 cross field technique on the Breast Cancer Diagnosis Dataset obtained from UCI Repository. The experimental results show that the Majority Voting Ensemble technique based on 3 top classifiers SVM, K-NN, Simple Logistic gives the highest accuracy 98.1% with the lowest error rate 0.01% and outperformed all other individual classifiers. This study demonstrates that our proposal approach based on Majority Voting Ensemble technique was the best classification machine learning model with the highest level of accuracy for breast cancer prediction and diagnosis. All experiments are effectuated within a simulation environment and realized in Weka data mining tool.

Today NoSQL databases have become unavoidable that more and more companies are turning to these solutions. The variety of non-relational databases resides in their data storage structure to face the big amount of data and their modeling as well. Hence, our paper presents a unified schema modeling for NoSQL solutions based on a mapping approach. Indeed, NoSQL refers to a family of database management systems (DBMS) that departs from the traditional paradigm of relational databases. These systems remain efficient in a continuous scalability case. Among the NoSQL solutions our paper deals with Columns, Document and Graph oriented databases to test the feasibility of our proposed approach.

Internet of Things (IoT) aims to simplify the collection of the distributed data in a global manufacturing business, sharing and processing of information and knowledge across many collaborating partners using appropriate information system architecture. As with the IoT, convergence with blockchain technology that processes data, privacy and security-related issues, and data policies (e.g., regulatory compliance) may apply to data and software artefacts. Besides, blockchain technology could contribute to the more intelligent and flexible handling of transactional data through appropriate convergence with IoT technology in supporting data integration and processing. This paper examines this hybrid architecture’s privacy, security, and policy-related issues to appreciate the convergence and understand the integration of IoT and blockchain technology. This paper primarily identifies common trends focusing on relevant topics in blockchain-based IoT technology research and by highlighting the need to explore security issues further – for example, data privacy challenges in the manufacturing industry.

Open-Domain Question Answering (ODQA) is a technique for finding an answer to a given query from a large set of documents. In this paper, we present an experimentation study to compare ODQA candidate solutions in the context of troubleshooting documents. We mainly focus on a well known open-source framework which is called Haystack. This framework comprises two key components which are the Retriever and the Reader. The Haystack Framework comes with several Retriever-Reader combinations and the choice of the best one is still unanswered till now. In this paper, we conduct an experimentation study to compare different Retriever-Reader combinations. Our aim is to come up with the best combination of components in regard to the speed and the processing power within the context of troubleshooting queries.

There are more than 80 million students in the Arab world. Students take tests and examinations as an intrinsic part of their educational journey to assess the quality of learning and understanding of the examined material. Exams require an immense amount of resources to be conducted, but even a bigger amount of resources to be scored. Scoring of exams and papers takes up enormous time and effort from teachers all over the Arab world. This time and effort can be better utilized in other teaching activities that would elevate the quality of learning in the region. For that reason, the work presented in this paper investigates multiple supervised learning approaches for the domain of Arabic short answer scoring. Moreover, due to the scarcity of Arabic datasets, and to our knowledge, there is only one publicly available dataset for this specific task(AR-ASAG). Thus, we introduce a new publicly available dataset AraScore-Dataset. The model proposed is evaluated on 3 different datasets: 1-AraScore-Dataset 2-AR-ASAG 3-Two translated answer sets taken from the Hewlett Foundation SAS dataset. Based on the investigation, the most effective approach for Arabic automated short answer scoring is proposed, and the performance of using our dataset is compared to other publicly available datasets. The aim is to scale efficient and unbiased content scoring applications across different Arabic educational domains. The results showed that our newly developed response-based system and AraScore-Dataset have achieved state-of-the-art performance on Arabic Automated Short Answer Scoring.

The purpose of current paper is to create a smart and effective tool for telemedicine to early detect and diagnose COVID-19 disease and therefore help to manage Pandemic Crisis (MCPC) in Sultanate of Oman, as a tool for future pandemic containment. In this paper, we used tools to create robust models in real-time to support Telemedicine, it is Machine Learning (ML), Deep Learning (DL), Convolutional Neural Networks using Tensorflow (CNN-TF), and CNN Deployment. These models will assist telemedicine, 1) developing Automated Medical Immediate Diagnosis service (AMID). 2) Analysis of Chest X-rays image (CXRs). 3) Simplifying Classification of confirmed cases according to its severity. 4) Overcoming the lack of experience, by improving the performance of medical diagnostics and providing recommendations to the medical staff. The results show that the best Regression among the five Regression models is Random Forest Regression. while the best classification among the eight classification models and Recurrent Neural Network using Tensorflow (RNNTF) is Random Forest classification, and the best Clustering model among two Clustering models is K-Means++. Furthermore, CNN-TF model was able to discriminate between those with positive cases Covid-19 and those with negative cases.

Predicting sports results is a well-known problem, and it applies to the more predictable individual sports as well as to the less predictable team sports – e.g., football. In the vast amount of information and parameters that are associated with team sports, there are few solutions related to the comprehensive approach of knowledge discovery in databases (KDD). In this paper, we propose a new approach to the whole process of discovering knowledge from data to predict the result of Bundesliga football matches. The main goal of the paper is to improve the prediction of football match results using heterogeneous ensembles of classifiers. To achieve the goal, it is necessary to prepare a training set and to develop a learning model – in this case, we analyze whether the use of limited information allows for the correct prediction of the results of football matches. The use of the developed model based on heterogeneous ensembles in the prediction of Bundesliga match results allows for improved prediction compared to classical ensembles models and single classifiers. Additionally, the possibilities arising from the use of heterogeneous ensembles of classifiers allow to distinguish some of the analyzed cases. The proposed approach was tested on real-world data, the results of Bundesliga football matches analyzed from the 2010/2011 season to the 2019/2020 season. Experiments were performed on methods selected based on an in-depth literature review. Conducted experiments, supported by the proper statistical analysis, confirm the usefulness of the proposed approach in predicting Bundesliga football results.

Over time, our lives turn to digitization and technology and its multiple applications and uses. The smart city, its technologies and the services provided during it have become a way of life. The idea of smart cities relied on different mechanisms to provide reliable services. One of the important technologies is the blockchain that has proven to be extremely reliable and has a high security level technology. This paper presents a mechanism to explain the application of blockchain technology in smart contracts, how to increase reliability, data security, and many positive benefits as part of the multiple services provided by the smart city environment. This paper also provides important details about this technology and its impact on the overall administrative system of any service provided by smart governments. The paper dealt with an example of how to manage the real estate rental file electronically to explain the advantages of blockchain technology through which we overcome existing problems in this type of contract and service.

The paper considers the possibility of constructing a specialized computing system oriented at the transmission of data through information channels, that are determined taking into account the semantics of the selected data. In the process of computations the data is connected with semantic characteristics that describe the channel of computations, which can be considered as a method of semantic data enrichment. The system of information channels as a whole can be considered as an information graph describing the structuring of the processed data. The information graph supports the data model in the form of a network, the framework of which are objects and the relationships between them. The paper proposes language tools for determining the information graph and interpretation tools that provide practical computations. The set of information channels that make up the information graph can be considered as a low-level tool for data enrichment. The paper studies the possibility of determining tools of higher level. An applicative type language is proposed for defining information graphs, the syntax and semantics of the language are specified. The proposed language can be considered as an intermediate level tool for defining semantics. A procedure is proposed for compiling the language into a low-level construct, preserving the semantics of the language. The supporting system for the proposed computing system includes a low-level language interpreter, as well as an intermediate-level language compiler into a low-level language. The supporting system is implemented in an applicative programming environment. Some elements of the supporting system were tested when developing applied information systems in the field of jurisprudence.

In this paper, we propose an OCR system that recognizes the contents of manual writing in handouts. Since the target handouts contain a mixture of Japanese and mathematical formulas, we thought that one type of OCR systems alone would not provide sufficient accuracy. Therefore, in this proposed system, we will consider combining two types of OCR systems. The OCRs to be combined are Tesseract and Mathpix, which are good at recognizing Japanese and mathematical formulas, respectively. When the two OCRs are combined, the final plausible result must be selected, but we wondered if the OCR score could be used as an indicator of selection. The OCR score is a self-evaluation value that is output together with the recognition result. However, there is no guarantee that the OCR score and the actual recognition result show the same tendency. Therefore, in this paper, after classifying the written contents into three categories, words, sentences, and mathematical formulas, we verified whether the recognition results of each OCR can be appropriately selected based on the OCR score. At this point, the result is that further improvement is needed to make the selection based on the OCR score alone. However, if it becomes possible to classify handwritten contents with the proposal system by additional learning etc., it will be possible to analyze the handwritten contents efficiently, and it is expected that it will be possible to improve the handouts and provide feedback to the lesson plan.

Aiming at the requirements of effective assessment and accurate quantification of vehicle target apparent damage degree in war, natural disasters and other environments, this paper presents a damage assessment technique based on deep learning regression calculation. First, the image containing vehicle target is preprocessed by scale adjustment, segmentation and graying. Then, extracting and fusing the high-dimensional features of the preprocessed image through the deep convolution neural network. At last, obtaining the evaluation value of vehicle target damage degree through the fusion feature calculation of full connection regression network. In this paper, the automobile target is taken as the experimental object, and completing the relevant data collection, training and testing. The experimental results show the accuracy and effectiveness of this method for vehicle target apparent damage degree evaluation.

Skill mismatch is worldwide issue as it can be effect job satisfaction, wages and multiple other factors. Analyzing selft-reported skill mismatch is highly studied in prior work. In this paper we study evidence of skill mismatch between three categories: Professionals, Students and Faculty member. We collect data from a survey. In this paper we use the Chi-Test to find evidence of skill mismatch between Professionals, Faculty members and students. We suppose that the three proportions are equals which means they agree on the importance of skills. If the hypothesis in not true we use the Marascuillo procedure to make comparisons between all pairs of groups Results of our paper shows that categories agree on the importance of skills categories especially programming languages; However, we find evidence of a mismatch between skills types especially Databases and Audit and risk analysis. We also find agreement between groups for some skills such as C++, as participants agree that this skill is not highly used in the cybersecurity job market.

This paper explores the role of enterprise platforms in the context of pervasive enterprise-wide processes of digital transformation. The paper empirically illustrates and theorizes on how digital transformation processes are challenged by inertia rooted in existing historical layers of technical and social arrangements. Empirically, we report from a longitudinal case study of a global oil and gas company following their various attempts at digitally transforming their document management practices and platforms. After prolonged efforts to facilitate transformation with an enterprise platform they eventually succeed with digital transformation after nearly six years. In making sense of this, we build on path theory and assemblage theory and theorize digital transformation as a path dependent process, where new paths can be created through stabilizing new assemblages and de-destabilizing others. The paper contributes by theorizing the role of inertia in digital transformation processes and the role of enterprise platforms. We conclude by giving insights on how practitioners can manage path dependency in digital transformation processes.

The users of cloud span to several types of tasks for various purposes, such as users who need to accomplish tasks that utilize cloud based on as Infrastructure as a Service. These tasks are usually of high latency. There are some tasks that require immediate response. That is, ultra-low latency tasks like IoT device requirements. It is not feasible to always depend on a far cloud datacenter. Unlike traditional cloud computing, it is possible to place edge and fog nodes can be placed close to the IoT devices provides noticeable reduction in latency. The emerging fog computing technology is characterized by ultra-low latency response, which benefits several time-sensitive services and applications. Nodes in fog are deployed in less centralized. In a fog layer, the computing equipment dedicates parts of its limited resources to process IoT application tasks. Therefore, efficient utilization of computing resources is essential and requires an optimal and intelligent strategy for task scheduling. This paper focuses on scheduling IoT tasks in a fog-based environment with the aim to minimizing energy, cost, and service delay. Towards this end, a deep reinforcement learning based algorithm named Clipped Double Deep Q-learning using target networks and experience replay techniques is proposed. To ensure there is no lag in using resources optimally, a parallel queueing is utilized. One of the important factors in cloud (and fog) computing research is to address the problem of long waiting time of the task in the virtual machine queue. This paper proposes a dual queue method.

In recent years, the Internet of Things (IoT) technology has led to the emergence of multiple smart applications in different vital sectors including healthcare, education, agriculture, energy management, etc. IoT aims to interconnect several intelligent devices over the Internet such as sensors, monitoring systems, and smart appliances to control, store, exchange, and analyze collected data. The main issue in IoT environments is that they can present potential vulnerabilities to be illegally accessed by malicious users, which threatens the safety and privacy of gathered data. To face this problem, several recent works have been conducted using microservices-based architecture to minimize the security threats and attacks related to IoT data. By employing microservices, these works offer extensible, reusable, and reconfigurable security features. In this paper, we aim to provide a survey about microservices-based approaches for securing IoT applications. This survey will help practitioners understand ongoing challenges and explore new and promising research opportunities in the IoT security field. To the best of our knowledge, this paper constitutes the first survey that investigates the use of microservices technology for securing IoT applications.

Natural Language Processing (NLP) has witnessed considerable advances in textual understanding through statistical and rule-based techniques, which have now yielded to developments in the field of neural networks and deep learning. The paper surveys research relevant to the topic of Reading Comprehension and Question Answering (QA) implementations. The initial focus of the paper is on Attention and Transformer models. A brief description of the architectures is presented highlighting the essence of the ‘Attention is all you Need’ paper wherein the authors of Bidirectional Encoding Transformer (BERT) have elucidated the significant departure from the recurrence concept of Recurrent Neural Networks (RNN). Subsequently the trends in Open Domain Question Answering (ODQA) which mark the progression from the passage- based question answering is presented. Of particular interest is Haystack which is an end-to-end open-source framework for Question Answering & Neural search. This field seems a promising avenue for a more intelligent form of ‘search’. In a nutshell, the paper weaves through RNNs, Long Short-Term Memory (LSTM), and the currently trending Attention based Transformer models in NLP. Finally, we dwell on more contemporary pieces of research such as ODQA, Multi-Hop QA, evaluation using Adversarial Networks.

Imbalanced classification represents a challenge for supervised learning, as an unequal distribution of classes in the training data set is mainly connected to poor predictive performance for the minority class. However, usually the minority class is the most relevant one, from a practical perspective. But due to the imbalancement of the training data, the classification errors for the minority class are higher, as the classifiers are usually biased to predict the majority class. In this paper we investigate the use of autoencoders for improving the predictive performance for imbalanced binary classification problems. As an application domain we consider breast cancer detection, that is an imbalanced classification problem of great interest in the medical domain. According to the World Health Organisation, breast cancer represents the primary cause of cancer mortality in women. Nowadays there is an increasing interest in applying conventional machine learning and more recently deep learning techniques in the breast cancer detection field by helping medical experts in the early detection of the disease. One of the paper’s goal is to investigate the ability of deep autoencoders to learn patterns within the classes of benign and malignant instances. Secondly, we propose and compare two autoencoders-based classification models for breast cancer detection. The performances of the proposed models were empirically assessed on data sets previously used in the breast cancer detection literature. The results show that our best model compares favourably with the results of most of the classifiers used for comparison and that it is able to handle well the data imbalancement.

In this new era of the internet world, long-distance effective communication depends on the choice of IP and routing protocols. Various decisions are considered to find the optimal routing protocol for network deployment. The routing protocol which gives the best performance on some objective parameters (convergence, jitter, delay, bandwidth utilization, throughput, etc.) have opted for deployment. Also, according to the network and traffic demands, these routing protocols decide the performance of real-time applications. As IPv4 addresses are exhausting, moving to IPv6 is expected and challenging. This next-generation internet protocol brings new modified routing protocols which are enhanced versions of the existing ones. In this paper, the IS-ISv6 routing protocol is considered as it is scalable, more flexible towards IPv6 addresses, robust, support larger areas and can give better performance than other existing routing protocols. The IPv6 network is configured to measure the performance of real-time applications-voice and video using IS-ISv6 routing protocol and the network is simulated on riverbed modeler academic edition 17.5. The main objective of this paper is to find the performance of IS-ISv6 for routing the traffic. For both, the applications, the traffic sent and received are present to calculate the maximum throughput of the network. The results are also present for end-to- end delay and packet delay variations for voice and video applications. The simulation results for IPv6 traffic dropped and jitter in voice are also obtained in this paper. This research paper finds that the IS-ISv6 routing protocol enhances the throughput of video applications in the IPv6 network. However, the average performance for voice application is achieved with less end-to-end delay and packet delay variations. Thus IS-ISv6 routing protocol can be used efficiently for high-performance real-time application networks.

Sentiment Analysis deals with analysing the reviews stated by its consumers for any product. If such analysis is performed at a deeper level, it enables us to identify the consumer’s sentiment towards each feature of the product. The sentiment expressed may not be same towards each feature. The analysis of this sort is called Aspect Based Sentiment Analysis (ABSA) and it has been sub-divided into four subtasks. In this paper, the detailed study of the approaches used for the first subtask of ABSA, i.e. Aspect Term Extraction (ATE) is presented. This paper discusses how ATE can be performed for the reviews in a rich morphological language, like Hindi. The models proposed for ATE of Hindi Reviews are Conditional Random Field (CRF) and Bidirectional Long-Short-Term-Memory (Bi-LSTM) models with novel architecture. The CRF based approach with novel feature, ‘Cluster-id’ improved F-measure from 41.07% to 42.71%. However, with 5-fold cross-validation, the CRF model attained an F-measure of 44.54%. By using our proposed Bi-LSTM based model with PoS vector, the F-measure obtained is 44.49%.