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In society, there is widespread and growing acceptance of the value of cooperation between industry and universities being collaborative Research and Development (R&D) domain and industrial policy the fuel of knowledge-based economies. In this paper, different domains of cooperation between the University and Industry as well as their generated benefits for each partner are identified. These benefits have been assigned to each domain of cooperation, showing a large number of benefits associated to collaborative R&D. Leading us to make a statement - collaborative R&D is the key cooperation domain for sustainable long-term university-industry partnerships. Suggestions are given about how to continue this research study.

Success management can bring valuable contributions to improve project results and stakeholders’ relationships. However, the success management process must be explicitly integrated into project management approaches, guides and methodologies (such as PMBOK, PRINCE2 or PM2), which is not currently happening. The purpose of this position paper is to discuss the need and importance for this integration and to present a first proposal on how this can be operationalized. To support the position, the methodology of the European Union PM2 was used. The result of the integration demonstrates that it is possible to advance management and raise the level of project success.

The subsector of pulp and paper presented in 2013 an electric power consumption of 9.3% of electricity consumption in the industrial sector in Brazil. It experienced a significant growth of production in the last 20 years, exceeding 160%, and their consumption of electricity increased by 130%, reaching the level of 19,594GWh. Due to its characteristics, with a known structure and production processes, and its importance in the country's industrial sector, this subsector was selected for study in this paper, to evaluate the behavior of its long-term annual electricity demand, at the horizon of 20 years ahead, in energy efficiency scenarios through bottom-up approach. This approach is data intensive and requires data with good quality; it consists of a hierarchical structure that enables to check how specific actions can affect the results at each level and its effect on higher levels.

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.

Research collaboration is very important in the modern scientific world, because it provides a wide range of opportunities, from the knowledge transfer between departments, institutions or even countries, to experimental research, where scientists also share materials and equipment. Because of that the field of building and analyzing collaboration networks grows in popularity. In this work we propose the methodology of building a hybrid Scientific similarity network based on the features obtained from two leading Scientific platforms – ResearchGate and Scopus. Experimental evaluation demonstrates good quality of the proposed approach and ability of the hybrid network to generate interconnections not distinguishable when using a single network.

Small area femtocell increases the energy efficiency of the cellular network as the distance between base station and user is very less. Mobile femtocell causes the spectrum reuse to be used at very large extent making spectral efficiency suffer. This paper discusses recently researched techniques that have been used to increase the spectral efficiency in mobile femtocell network. But these techniques also come with their own limitations. Considering this, data aggregation and compression technique can be consider as one of the best contender to increase the spectral efficiency in cellular network which is also been explained in this paper.

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.

In this work we describe the design of our simulation tool for solid state lasers. The software is based on modules to simplify several complex processes. Primarily, the adaptation of laser physics (e.g. thermal lensing, optical wave) with the appropriate simulation technique. Another aspect is to provide the possibility of exchanging the applied calculation methods or even to change the representation of the computational domain on demand.

Image acquisition has great influence on the performance of any computer vision application. Different methods can be utilized to acquire the digital image of a paper, whilst scanning scheme is among the most attractive methods. This attractiveness is because of the fewer types of potential deformations and the low cost of the scanning devices, e.g. flatbed scanners. However, paper is commonly placed imperfectly on the scanner. This slight rotation is not usually based on a pivot around the paper's geometrical center (the well known regular rotation) but instead it is based on a pivot placed at the corner of the paper. Thus, the result is a digital image that is deformed with an “irregular rotation”. The characteristic of this deformation phenomenon is currently unknown to computer vision scientists. In this paper we provide an extensive investigation of this deformation. In addition, a new set of equations that sway and measure the transformation is proposed. Our investigation leads to the conclusion that the “irregular rotation” phenomenon produces a shear transformation. Furthermore, the experimental results confirm the theoretical findings.

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.

The requirements of public administration institutions are increasing and projects becoming progressively challenging. Managing a project is a complex activity, in particular when it involves many people working over long periods of time and many different stakeholders. This increasing complexity requires management practices and tools that assure an efficient use of resources. In this context, a Project Management Office (PMO) can be of great value. In this position paper we discuss several scenarios for PMO implementation in the public administration sector, as a promoter of project success and a key ingredient for a better resources usage., info:eu-repo/semantics/publishedVersion

What is the nature of knowledge representation needed for human-level artificial intelligence? This position paper contends that to achieve human-level AI, a system architecture for human-level knowledge representation would benefit from a neuro-symbolic approach combining deep neural networks with a ‘natural language of thought’ and would be greatly handicapped if relying only on formal logic systems.

Patients with advanced-stage cancer often have a high symptom burden and reduced functional status, implying that the patients themselves should be involved in development and testing of interactive assessment tools. This paper reports on an assessment of use of a pain tool, which led to changes in both medium and program in order to adapt to the patients’ needs and abilities. Our study shows how a change in medium for pain assessment from paper via laptop to iPad affects the interaction with the tool on important aspects of use. We also show how changes of medium affect the readability of the output for health care workers. We achieved better results with an iPad-based pain assessment tool developed through user-centered design compared to both a paper-based and conventional laptop-based tool.

Wireless Sensor Networks (WSNs) have been mainly utilized in military applications due to its certainty and efficiency. Nowadays, it is considered to be the first-hand in many civilian applications such as leakage detection. Various technical issues, like power consumption, radio propagation models and sensors need to be considered for different types of applications. WSN has emerged lately as an effective solution for fluid leakage. This paper will discuss four different solutions for leaks in pipelines using wireless sensor networks (WSNs). The WSN-based solutions are magnetic induction based, continuous pressure monitoring, underground to above ground radio propagation and wireless signal networks (WSiN).

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.

Developing applications for IoT and Cloud is di ffi cult for a number of reasons; even without considering the inherent complexity of distributed computing, there are several competing platforms, programming languages and communication protocols. It can be argued that this is holding back the industry as a whole: Applications are di ffi cult to write, deploy and manage. In this position paper we present Calvin , a hybrid framework combining ideas from the Actor model and Flow Based Computing. We show that by dividing applications into four well-defined aspects – describe, connect, deploy , and manage – we get an intuitive method for application development, and a flexible, distributed platform for deploying and managing applications. Additionally, we keep Calvin language and platform agnostic by only prescribing a lightweight runtime API, with a limited number of specified communication protocols – with no requirements on the carrier – for communicating with runtimes, between runtimes, and passing data between components.

Biomass is considered as a clean source of renewable energy, utilization of biomass may reduce the environmental pollutions. This paper presents a review of the chemistry and process of depolymerization of biomass; which results in production of oil and gaseous fuels from such important renewable energy source. Also, thermal hydro-cracking of lignin enhances coal liquefaction by producing intermediates which then react further with coal producing lower molecular weight material, which more desirable. Enhancement of coal conversion due to the addition of lignin is found to be a function of time. It is dependent upon temperature of reaction and the type of lignin used. By increasing the ratio of lignin to coal will result in deploymerizing larger portions of coal. This indicates that it can be used as a catalyst in liquefaction of higher molecular weight of hydrocarbon solid fuels such as coal and heavy petroleum fuels.

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.

Anaerobic digestion is a biological process in which the organic material is converted by microorganisms to methane and carbon dioxide (biogas) in the absence of oxygen. This process is interesting but the control on industrial scale spontaneous biological reactions requires good knowledge of the phenomena involved. The search for appropriate models to be use in control theory is now a high priority to optimize fermentation processes and solve important problems, such as the development of renewable energy from biodegradable organic waste. The aim of this study is modeling of biokinetics of anaerobic digestion on several aspects such as microbial activity, substrate degradation and methane production. For this, we developed a mathematical model based on mass balances on biomass, the organic substrate and biogas. This model is then simulate on Matlab using the experimental data from the literature and comparison between other models and our experimental results. The sensitivity of the model to the process parameters was study by varying the initial concentration of the biomass and the dose of the organic substrate.

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.

Social Networks are powerful social media for sharing information about various issues and can be used to raise awareness and collect pointers about associated risk factors and preventive measures in chronical disease like diabetes. Since the olden times, knowledge in medicine was established through recording and analysing human experiences. This paper presents the results of text mining techniques of more than five hundred thousands of texts retrieved from social networks, blogs, forums, and also research papers from MEDLINE database to discovering new knowledge related to diabetes disease covering symptoms and treatments. The text mining approach consists of two tasks, descriptive and predictive. The descriptive task was to identify explicit references to the diabetes diseases diagnosis and treatments, whereas the predictive task focused on the prediction of the diabetes disease status when the evidence was not explicitly asserted. The findings are then compared to the standard diabetes diagnosis and treatments and only those which are not listed in the standards are retained as hypothesis for further validation by clinicians and medical researchers in the domain of diabetic disease.

HYSOL technology allows the hybridization of Concentrated Solar Power (CSP) technology with biomethane for the efficient generation of clean and sustainable electricity in a firm and dispatchable manner. Biomass from horticultural residues and drought-tolerant crops is a promising feedstock for biomethane production in areas with high solar radiation. This paper investigates the biomass potential of these two resources in Spain by considering the volume of horticultural residues generated, availability of suitable land for dedicated prickly pear plantations and its methane generation capacity. Experimental biomethane yields have also been determined for different substrates mixtures of overripe tomatoes and prickly pear cladodes. Potential biomethane and electricity production are then calculated, as well as the number of conventional CSP and HYSOL plants that could be supplied with this energy. Results show that a significant number of CSP plant could be methane-supplied by the studied resources: up to 45 conventional CSP or 4 HYSOL plants.

Since the start of the Coronavirus disease 2019 (COVID-19) governments and health authorities across the world have find it very difficult in controlling infections. Digital technologies such as artificial intelligence (AI), big data, cloud computing, blockchain and 5G have effectively improved the efficiency of efforts in epidemic monitoring, virus tracking, prevention, control and treatment. Surveillance to halt COVID-19 has raised privacy concerns, as many governments are willing to overlook privacy implications to save lives. The purpose of this paper is to conduct a focused Systematic Literature Review (SLR), to explore the potential benefits and implications of using digital technologies such as AI, big data and cloud to track COVID-19 amongst people in different societies. The aim is to highlight the risks of security and privacy to personal data when using technology to track COVID-19 in societies and identify ways to govern these risks. The paper uses the SLR approach to examine 40 articles published during 2020, ultimately down selecting to the most relevant 24 studies. In this SLR approach we adopted the following steps; formulated the problem, searched the literature, gathered information from studies, evaluated the quality of studies, analysed and integrated the outcomes of studies while concluding by interpreting the evidence and presenting the results. Papers were classified into different categories such as technology use, impact on society and governance. The study highlighted the challenge for government to balance the need of what is good for public health versus individual privacy and freedoms. The findings revealed that although the use of technology help governments and health agencies reduce the spread of the COVID-19 virus, government surveillance to halt has sparked privacy concerns. We suggest some requirements for government policy to be ethical and capable of commanding the trust of the public and present some research questions for future research.

Smart cities provide smart services that rely on flexible infrastructure that responds to challenges posed by the rapid growth of urbanization. Nonetheless, the smart city concept is still emerging and not mainstreamed worldwide due to technological, economic, and governance obstacles. This paper reveals the essence of smart cities. It demonstrated the requirements for implementations and discussed four smart applications considering the required input data and produced services. We proposed and illustrate smart city main pillars: transition entities (Governmental Entities, Private Sector Investors, and Ad-Hoc Research Units), list of policies standards and protocols (Holistic, Flexible, Applicable, Sustainable, Integration, Highly Obliged ), the components that should govern by regulations. The paper also demonstrated the proposed smart city architecture layers, including the infrastructure layer, Application Layer, Services Layer, the big data clouds, and the end-users. Moreover, it provides a pure exploration of expected challenges and responsibilities for real breakthroughs in smart city implementations.

This paper attempts to explore more precise and intelligent assisted methodological references for the diagnosis and treatment of diabetes in Traditional Chinese Medicine (TCM) by using medical record data from TCM diabetes, and to construct a TCM diabetes assisted diagnosis and treatment framework of “association mining-quantifying calculation-intelligent assisted diagnosis and treatment” what can be described as cluster analyzing and match mining for symptom and drug by association mining, as quantitatively calculating and evaluating for the condition of diabetes; as disease diagnosis, drug recommendation and decision making by intelligent assisted diagnosis and treatment. This paper synthetically employs the technical methodology of association rules, topic clustering, emotion recognition and content recommendation, to realize the knowledge mining, quantify the evaluation and assist decision-making of TCM diabetes, and to propose a reference for the process of consultation and treatment. Overall, the study contributes to the academic and practical value in diabetes by elaborating a new technical methodology and providing a brief empirical test for doctors and researchers.

The issue of economic evaluation of investment projects is a field focusing on socio-economic impact assessment of projects implemented within the public sector. This paper is focused on socio-economic evaluation of public investment projects concerning railway infrastructure. It builds on previous research aimed at evaluating the impacts of measures implemented to increase safety and reliability of railway transport. The subject matter of the paper is the evaluation of socio-economic impacts of partial occurrences that arise in the field of railway infrastructure. From a methodological point of view, the evaluation is based on a detailed survey of occurrences incurring in the 2011–2018 period. Using the results of previous research, occurrences incurring on the railway infrastructure relevant to the evaluation were identified. Furthermore, unit impacts of occurrences in the form of health impacts, train delays and overall costs were identified. By combining the above-mentioned input information and outputs of a detailed analysis of the Occurrence Database, the average values of the socio-economic impacts of relevant occurrences were determined. The results of the research carried out shall be further used as an input for the evaluation of the benefits associated with increasing the safety and reliability of the railway infrastructure.

In this paper, an approach to isolated sign language recognition with data provided by a depth camera is presented. In the introduced method, sequences of depth maps of dynamic sign language gestures are divided into smaller regions (cells). Then, statistical information is used to describe the cells. Since gesture executions have different lengths, the dynamic time warping (DTW) technique with the nearest neighbor (NN) rule is often employed for their comparison. However, due to time-consuming computations, the DTW limits the usability of the classifier. Therefore, in this paper, a selection of representative depth maps using statistics for cells is proposed. It is shown that such gesture representation can be successfully employed for isolated sign language recognition with the NN classifier using the city block distance. Furthermore, the NN rule with the DTW and the introduced statistics for cells provides superior gesture recognition performance.

The precise diagnostics of different diseases is very important for its treatment. It is particularly important to differentiate the disease on the early stages when the pathological alterations have not yet caused great harm to the whole organism, since this allows using greater number of therapies and increase the recovery probability. One of the methods of early diagnostics is based on examination of human biological liquids (blood, tears, cervical mucus, urine, etc,). A small drop of liquid is drawn on an object-plate and dried out slowly, thus a thin dry film (facies) remains. In the process of fluid crystallization there appear characteristic patterns (markers) in the facies. Each marker is a highly definite sign of some pathology even at an early stage of a disease development. When mass population health examination is carried out, it is necessary to analyze a large number of images This is the reason the problem of algorithm and software development for automated processing of images is rather urgent nowadays. Mathematical models of facies images are needed to formalize the tasks of image processing and synthesis of algorithms. The facies has a round shape, as it is obtained from a drop of liquid. Facies images have a radial-ring structure due to the peculiarities of the drying and crystallization of the contained fractions. These facies features should be taken into account in their imaging models. In this paper, autoregressive models of circular images are used to represent facies images. These models use grids defined on concentric circles or Archimedes’ spirals. The radial-ring structure of facies image correlations is naturally taken into account in these models. The paper contains examples of model identification, that is, estimation of the values ​​of its parameters at which the correlation properties of the model are closest to the characteristics of a given facies. It is shown that the classification of the facies type (corresponding to the general condition of the patient) is possible according to these estimates.

Our daily activities depend on complex software systems that must guarantee certain performance. Several approaches have been devised in the last decade to validate software systems against performance requirements. However, software designers still encounter problems in the interpretation of performance analysis results (e.g., mean values, probability distribution functions) and in the definition of design alternatives (e.g., to split a software component in two and redeploy one of them) aimed at fulfilling performance requirements. This paper describes a general model-based performance engineering framework to support designers in dealing with such problems aimed at enhancing the system. The framework relies on a formalization of the knowledge needed in order to characterize performance flaws and provide alternative system design. Such knowledge can be instantiated based on the techniques devised for interpreting performance analysis results and providing feedback to designers. Three techniques are considered in this paper for instantiating the framework and the main challenges to face during such process are pointed out and discussed.

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.

In order to support fast development cycles and deploying software components in productive environments, there are three crucial trends in data science. These are agile process models, development of many technologies and increasing usage of cloud platforms. Therefore, effective architectures are needed to support this trend in data science context. This paper explores and evaluates first approaches for, why and how microservice architecture style can support fast development cycles for data science workflows. Microservices are becoming a popular architectural style for designing modern applications due to several advantages like scalability, reliability and maintainability. First, this paper points out the research gap on why microservices could be a suitable way to design data science workflows. Second, it defines relevant research questions for future research that addresses challenges of the microservice architectural style in the data science context. An essential prerequisite for this architecture style is to identify the “right context” of a microservice for data science workflows.

With the continuing development in integrating electronics into textiles in the field of smart textiles and wearables, new innovations for monitoring the human environment and new possibilities for human-machine interaction are constantly emerging. Depending on the type of sensor technology used, monitoring human motion and its environment allows applications in various fields, like occupational safety, protection and rehabilitation in the form of protective work clothing. In this paper the development of a sensor shirt is described, which records the movement and position of the upper body by several inertial sensors and transmits the sensor values via Wi-Fi. Based on the developed prototype, this paper shows the typical problems in the development of intelligent clothing and presents possible concepts which can be implemented already in the design phase. First measurements of the prototype show the potential of the shirt as well as possible future applications in the field of occupational safety and human-machine interaction.

This paper addresses the concept of Industry 4.0 from the perspective of the molds industry, a key industry in today’s industrial panorama. With its constant modernization, several technologies have been introduced, in particular regarding machining equipment. With each brand and model requiring different (proprietary) interfaces and communication protocols, this technological diversity renders the automatic interconnection with production management software extremely challenging. In this paper a methodology to build monitoring solutions for machining devices is defined, based on the main equipment and operations used by molds industry companies. For a standardized approach, OPC UA is used for high-level communication between the various systems. As a key result of this paper, and given the variety of monitoring systems and communication protocols, the developed approach combines various different machine interfaces on a single system, in order to cover a relevant subset of machining equipment currently in use by the molds industry. This kind of all-in-one approach will give production managers access to the information needed for a continuous monitoring and improvement of the entire production process.

The aim of the paper is to examine the issue of automated decision-making, i.e. decisions made by artificial intelligence. The use of automated decision-making is on the rise not only within private sectors, but also within the public sector. In today’s world, people increasingly expect speedy decision-making, in particular in areas of public administration where to the so-called mass decisions are made pertaining to, i.a., taxation, social benefits, fees, permits, licences. Despite the fact that artificial intelligence has long been used in many areas of public administration in Poland, the automated, algorithmic decision-making process has not been regulated at the national level. The existent regulations pertain only to the serving and signing of documents. In Poland, there is a severe lack of comprehensive rules regulating automated-decision making, and at the same time, safeguarding administrative due process. The paper is based on analysis of normative acts and practice, supported by the study of secondary sources.

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.

Consumer behavior, by definition, is concerning with various actions adopted by a person to acquire, use and dispose of consumer goods and services, including the decision-making process that precedes and determines these actions. In real practice, such behavior can be altered with the introduction of various internet-based financing services. Especially for college students, who have relatively weak knowledge of personal finance and insufficient vigilance of excessive consumption, their consumption behavior can be easily manipulated by internet-based financing service providing firms. In this paper, the objective is to determine the dominant factors adopted by internet-based financing service providers to modify the consumption behavior of college students. From 571 college students’ questionnaires, the collected data are subjected to factor and logistic regression analysis to verify what factors are likely to stimulate the willingness of college students to actively employing the internet-based financing services to fulfill their advanced consumption demands. The obtained result indicates the service and convenience factors are likely to influence and alter their consumption behavior, and the corresponding recommendations on how to refine and regulate such market are proposed at the end of this paper.

The development of the Internet of Things over the last decade has led to large amounts of data being generated at the network edge. This highlights the importance of local data processing and reasoning. Machine learning is most commonly used to automate tasks and perform complex data processing and reasoning. Collecting such data in a centralized location has become increasingly problematic in recent years due to network bandwidth and data privacy concerns. The easy-to-change behavior of edge infrastructure enabled by software-defined networking (SDN) allows IoT data to be gathered on edge servers and gateways, where federated learning (FL) can be performed: creating a centralized model without uploading data to the cloud. In this paper, we analyze the use of edge computing and federated learning, a decentralized machine learning methodology that increases the amount and variety of data used to train deep learning models. To the best of our knowledge, this paper reports the first use of federated learning to help the Microgrid Energy Management System (EMS) predict load and obtain promising results. Simulations were performed using TensorFlow Federated with data from a modified version of the Dataport site.

Block symmetric cryptographic algorithms are the essential and inalienable element of any information security system. The main component of block symmetric cryptographic algorithms is the S-box, which largely determines the speed and cryptographic quality of the entire cryptographic transformation. The rapid development of cryptanalysis methods, including the development of quantum computers, requires a more careful research of all possible representations of nonlinear elements of block symmetric cryptographic algorithms. In view of the fact that in addition to the mathematical apparatus of Boolean functions used today to estimate the cryptographic quality of S-boxes, a cryptanalyst can apply the mathematical apparatus of functions of many-valued logic, which makes relevant the task of researching and comparing the cryptographic quality of S-boxes of modern cryptographic algorithms, represented by functions of many-valued logic. This paper presents a research and comparative analysis of nonlinear transformations of the cryptographic algorithms AES, Kalyna, BelT, and Kuznechik when represented by functions of many-valued logic. It was found that only the nonlinear transformation of the BelT cryptographic algorithm is characterized by growth when represented by many-valued logic functions, while the Kalyna cryptographic algorithm demonstrates the greatest decrease in nonlinearity when represented by 16-functions among the cryptographic algorithms researched. The results obtained in the paper indicate a significant unused reserve of cryptographic quality, which can be used in the design of new cryptographic algorithms and their structural elements.

Machine learning algorithms have been used to predict student performance in academic institutions over the last decade. The developed prediction models classified students as either those who were likely to receive a distinctive or those who were likely to be at-risk or withdraw from the class. Although, the early prediction aid in the targeting of educational interventions and achieved a more efficient allocation of educational resources. All proposed solutions fell within the scope of predictions that result in active or proactive actions to support universities and learners. On the other hand, they fail to comprehend the various forms of education systems and whether it appropriate for the twenty-first century and future generations. The paper classifies education into five types based on the design mode, the scope of production, and the interaction between learners and educational systems (Push, Pull, coupling, Integrated, and Sustainable). The paper proposes a sustainable education paradigm that maximizes the knowledge and skill matrix accumulated for the desired program. The proposed theory implementation phases are modelled and demonstrated using 21st-century technologies, such as personalized and coaching education based on the learner’s learning style and remediation actions for strong learners with innovative competencies. The study emphasized various aspects of sustainable education systems that are required for smart city transition. The limitations and proposed solutions for dealing with anticipated issues were demonstrated, and the benefits of sustainable education are based on the proposed maximization theory rather than the current block-based learning of outcomes.

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.