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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.

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.

Adessowiki is a collaborative platform for scientific programming and document writing. It is a wiki environment that carries simultaneously documentation, programming code and results of its execution without any software con- figuration such as compilers, libraries and special tools at the client side. This combination of a collaborative wiki environment, central server and execution of code at rendering time enables the use of Adessowiki as an executable paper platform, since it fulfills the need to disseminate, validate, and archive research data.

Unimodal biometric systems are susceptible to a variety of problems such as noisy data, intra-class variations, limited degrees of freedom, non-universality, spoof attacks and unacceptable error rates. Some of these limitations can be addressed by deploy multimodal biometric systems that integrates the evidence presented by multiple sources of information The proposed system provides effective fusion scheme that combines information presented by the multiple domain experts based on the Rank level fusion integration method, thereby increasing the efficiency of the system which is not possible by the unimodal biometric system. The proposed multimodal biometric system has a number of unique qualities, including principal component analysis and fisher's linear discriminate methods for individual matchers authentication. The novel rank level fusion method is used in order to consolidate the results obtained from different biometric matchers.

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.

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.

Reproducibility of experiments is considered as one of the main principles of the scientific method. Recent developments in data and computation intensive science, i.e. e-Science, and state of the art in Cloud computing provide the necessary components to preserve data sets and re-run code and software that create research data. The Executable Paper (EP) concept uses state of the art technology to include data sets, code, and software in the electronic publication such that readers can validate the presented results. In this paper we present how to advance current state of the art to preserve, data sets, code, and software that create research data, the basic components of an execution platform to preserve long term compatibility of EP, and we identify a number of issues and challenges in the realization of EP. © 2011 Published by Elsevier Ltd.

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.

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 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.

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.

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%.

In dynamic spectrum access (DSA) a primary user shares the spectrum with one or more secondary users, with or without the primary user knowing the existence of the secondary users and offering assistance to the secondary users. When the primary user is inactive in the channel, the secondary users use the spectrum for data communication. When the primary user becomes active, the secondary users back off to return the spectrum back to the primary user. It is therefore that an arrival of the primary user traffic interrupts the ongoing communication among the secondary users. The arrivals of the primary user traffic are, in general, random, and thus secondary users cannot predict the arrivals accurately. However, when the primary user engages in non-interactive communications, it has a degree of flexibility in distributing its transmissions of data in the time domain. The paper studies the impact of the arrival distribution of primary user traffic on secondary users in terms of the secondary user network performance. Based on a state conservation scheme in DSA, the paper studies how the change of the arrival distribution of primary user traffic may affect the average network throughput, packet delay, and network power consumption of the secondary user network.

Blockchain has its focus around sharing, distribution and encryption. Particularly, the newer blockchain implementations revolve around the implementation of smart-contracts, second-layer systems and permissioned blockchains. This type of potential has generated a lot of attention towards blockchain, thus making it a great candidate technology within healthcare. In this paper, the role of permissioned and permissionless blockchain and its possible implementations will be discussed and compared as well as the process in which a blockchain network agrees whether a transaction is valid or not, maintaining consistency in ledger synchronization, or in other words, Consensus Algorithms. Several should be considered as viable and many can be used by both permissioned and permissionless blockchain frameworks. As chosen as part of this implementation Practical Byzantine Fault Tolerance (Pbft) is presented and described. This paper presents a solution, as part of the Intelligence Decision Support Systems for Intensive Medicine (ICDS4IM) project, which objective is to increase veracity and value to data from vital sensors and monitors by assuring its immutability and oversee; and also privacy and accountability for inadequate data management.

Security of data has become a necessity in today’s world. Data as all know is the raw form and information can be extracted from it. Thus, data needs to be secured. This concern becomes even more significant when share information and it mostly constitute of images. As already know that a picture speaks a thousand words, there is no reason not to believe that picture carry the most information. Furthermore, they can be easily manipulated. This paper presents two schemes to secure such information in the form of images. Under the concept of (k, n) - Visual Cryptography, authors present these two schemes to secretly share images, that can be practically used to secure the information sent. The (3, 3) - enhanced visual cryptographic scheme, enhances security of the currently existing (3, 3) - visual cryptographic scheme by utilizing the concept of keys to secure information. Authors also introduce a new scheme, (2, 3) - visual cryptographic scheme. The (2, 3) - visual cryptographic scheme also utilizes the concepts of keys but reduces the bandwidth requirement or increases reliability. This provides us two modes of operation that are more practical than existing scheme. The paper shall discuss the scheme with a schematic analysis and an analysis of its implementation both subjectively and objectively. Using several parameters of security, this paper presents our findings as our results. The schemes discussed are found to be more secure than existing visual cryptographic scheme and are more reliable and imperceptible.

In today’s world of internet, a massive amount of data is getting generated every day and content-based classification of images is becoming an essential aspect for efficient retrieval of images and have attracted application in several fields and one of such field is sports. Sport is an integral part of everybody’s daily life and it is very important to play the sport with the right posture and environment otherwise it can lead to medical issues. This paper presents a robust framework for classifying the sport images based on the environment and related surroundings. In this paper, our approach is based on the use of the Inception V3 for the extraction of features and Neural Networks for the classification of various sport categories. Six categories rugby, tennis, cricket, basketball, volleyball, and badminton have been used for analysis and classification. To validate the effectiveness of the framework and Neural Networks, comparisons have been done with other classifiers like Random Forest, K-Nearest Neighbors (KNN) and Support Vector Machine (SVM). Our framework has successfully achieved an average accuracy of 96.64 % over six categories which demonstrate the effectiveness of the framework and can be used for the detection and classification of various sport activities in an efficient manner.

Software security has become an essential component of software development process. It is necessary for an organisation to maintain software security in order to ensure integrity, authenticity and availability of the software product. To ensure software security, one of the major task is to identify vulnerabilities present in the source code before the software is being deployed. Detecting vulnerabilities in early phases of software development cycle, makes the process of fixing those vulnerabilities much easier for software developers. The vulnerability detection can be done either at the production phase, this means when the software is still being developed by statically auditing the source code, or dynamically at run time. In this study, vulnerability detection was done through Static code analysis process. Static code analysis can be done either manually or through automated tools. This paper focuses on using automated source code scanning tools for vulnerabilities detection in a software. Automated static Code Analysis tools audits the entire source code for its quality and identify any potential security vulnerability, if present. Unlike dynamic source code analysis that evaluates the source code behaviour during code execution, which is done quite late in the software development life cycle, Static Code Analysis leads to detection of security vulnerabilities in a source code in early stages of software development process, when the software is still in production phase because it does not require code to be in execution state. This paper firstly explains the importance of incorporating static code analysis in software development life cycle process so as to facilitate early detection of vulnerabilities in software product, and then present a comparative study of various static code analysis tools available for vulnerability detection in C/C++ and JAVA source code. The comparative study of three C/C++ static code analysis tools (flawfinder, RATS and CPPCheck) and two JAVA static code analysis tools (spotbugs and PMD) is done using Juliet (version1.3) test suite and APACHE tomcat dataset respectively, on the basis of category of vulnerability detected by each of the selected tool and the likelihood of false positive reported by each tool. Results showed that Flawfinder detected maximum categories of vulnerabilities and RATS and CPPCheck were almost similar in types of vulnerabilities detected. Also, it was observed that CPPCheck reported maximum number of false positives as compared to other two tools. Java static code analysis tools Spot bugs was able to detect more number of vulnerabilities than PMD.

Nowadays, standard intake of healthy food is necessary for keeping a balanced diet to avoid obesity in the human body. In this paper, we present a novel system based on machine learning that automatically performs accurate classification of food images and estimates food attributes. This paper proposes a deep learning model consisting of a convolutional neural network that classifies food into specific categories in the training part of the prototype system. The main purpose of the proposed method is to improve the accuracy of the pre-training model. The paper designs a prototype system based on the client server model. The client sends an image detection request and processes it on the server side. The prototype system is designed with three main software components, including a pre-trained CNN model training module for classification purposes, a text data training module for attribute estimation models, and a server-side module. We experimented with a variety of food categories, each containing thousands of images, and through machine learning training to achieve higher classification accuracy.

This paper presents the use of a class of Deep Neural Networks for recognizing indoor home scenes so as to aid Intelligent Assistive Systems (IAS) in performing indoor services to assist elderly or infirm people. Identifying exact indoor location is important so that objects associated with particular tasks can be located speedily and efficiently irrespective of position or orientation. In this way, IAS developed for providing services may become more efficient in accomplishing designated tasks satisfactorily. There are many Convolutional Neural Networks (CNNs) which have been developed for outdoor scene classification and, also, for interior (not necessarily indoor home) scene classification. However, to date, there are no CNNs which are trained, validated and tested on indoor home scene datasets as there appears to be an absence of sufficiently large databases of home scenes. Nonetheless, it is important to train systems which are meant to operate within home environments with the correct relevant data. To counteract this problem, it is proposed that a different type of network is used, which is not very deep (i.e., a network which does not have too many layers) but which can attain sufficiently high classification accuracy using smaller training datasets. A type of neural network likely to help achieve this is a Capsule Neural Network (CapsNet). In this paper, 20,000 indoor home scenes were used for training the CapsNet, and 5000 images were used for testing it. The validation accuracy achieved is 71% and testing accuracy achieved is 70%.

Cloud computing environment offers many services which have attracted the criminals or oppugners to commit cyber-crimes in a more sophisticated manner. Most of the crimes conducted by the criminals are data tampering, spam assaults, distributed denial of service attack and many others. There is a deficiency of reports which relates crime and the usage of cloud computing services. This paper discusses about the threat model STRIDE and the ill-use of the cloud services for many mischievous purpose by the oppugners around the world. This paper employs STRIDE threat modelling approach as a reference to relate the attacks to threats. Some of the malicious activities committed using cloud are distribution of considerable amount of spams, using the cloud service provider’s reputation to cheat the firewalls, distributed denial of service, virus dissemination, credit card fraud and arraying the botnet command and control servers. This paper also discusses on a few real websites which have been affected by threats in the recent years. Most of the criminals get away because of lack of evidence. Evidence collection and analysis in the digital world and especially in cloud environment is a great challenge. This paper also touches on a few issues of forensics in cloud computing environment.

Crime has been and still is one of the major threats and issues for every government and society around the world. Nowadays, a new generation of (cyber)criminals exploits modern and state-of-the-art technology and tools, especially social media channels, in order to achieve their malicious purposes. Thus, Law Enforcement Agencies (LEAs), analysts and security practitioners are facing the need to engage new methods and tools which can support them in the fight and prevention of crime by minimizing the prediction and response time. This paper focuses on two specific functionalities of a crime prediction and fighting software framework which assists LEAs to adopt and utilize emerging technologies such as Data mining and Big Data tools, Semantic Analysis, Visual Intelligence and more in the context of their everyday operations. In this light, the authors of this paper present an innovative Semantic Engine with a person fusion tool as well as an ontology visualization tool as parts of a discussed framework. Both of these tools are presented in detail in separate sections so that the readers can understand better their architecture and their integration into this common framework alongside with other tools. These two tools use state-of-the-art components and software libraries in order to offer robust and future proof solutions for LEAs. Also, they can be integrated and expanded in future research and commercial projects both as independent tools as well as parts of other integrated frameworks.

Healthcare industries generate enormous amount of data, so called big data that accommodates hidden knowledge or pattern for decision making. The huge volume of data is used to make decision which is more accurate than intuition. Exploratory Data Analysis (EDA) detects mistakes, finds appropriate data, checks assumptions and determines the correlation among the explanatory variables. In the context, EDA is considered as analysing data that excludes inferences and statistical modelling. Analytics is an essential technique for any profession as it forecast the future and hidden pattern. Data analytics is considered as a cost effective technology in the recent past and it plays an essential role in healthcare which includes new research findings, emergency situations and outbreaks of disease. The use of analytics in healthcare improves care by facilitating preventive care and EDA is a vital step while analysing data. In this paper, the risk factors that causes heart disease is considered and predicted using K-means algorithm and the analysis is carried out using a publicly available data for heart disease. The dataset holds 209 records with 8 attributes such as age, chest pain type, blood pressure, blood glucose level, ECG in rest, heart rate and four types of chest pain. To predict the heart disease, K-means clustering algorithm is used along with data analytics and visualization tool. The paper discusses the pre-processing methods, classifier performances and evaluation metrics. In the result section, the visualized data shows that the prediction is accurate.