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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.

Abstract writing is a very important since it is the piece of formal writing which should attract the attention of the reader and make him/her read the rest of the paper. Thus, it must be clear, concise, coherent, and powerful enough to attract the reader and provide him/her with the key information about the content of a research paper. All university students have to write an abstract in English in their qualification papers. However, non-native students of English usually have difficulties in writing an abstract in English. For example, the Czech students lack formal writing skills in English since the skill of formal writing is not developed enough in their native language, i.e. Czech. Therefore, the aim of this article is to discuss the use of an off-line scaffolding tool, which can help students develop an abstract in English, as well as to demonstrate how this tool can be incorporated in the whole Course of Practical English taught at the Faculty of Informatics and Management of the University of Hradec Kralove in the Czech Republic. The application was developed for students as support to a face-to-face class instruction, which proved to be insufficient in teaching students how to write an abstract in English. The application reflects the structure and sentence functions of the abstract and provides students with a model abstract, as well as with additional formulations. Overall, the application was designed with the purpose to enhance student’s skill of writing an abstract in English, as well as to lead students to successfully complete the task by making the application user-friendly - easy-to-use, accessible and transferrable as far as the knowledge is concerned. The application aims to enhance students’ autonomous learning by tailoring the content to their specific needs and making it accessible anytime and from anywhere.

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 remarkable increase in the number of authors and publications is observed in almost all fields in last decade. Inherently publication engages process of peer review and is a vital segment in research and development progression. The Reviewer Assignment Problem (RAP) is one of the most important and apparent process that involves the process of assigning a reviewer to paper. RAP has motivated many researchers in the recent decade as the conference organizers and journal editors are demanding for automatic assignment with fair and accurate reviews. Fair and accurate reviews mostly depend on accurate expert assignment to the paper. RAP entails two core phases: matching submitted manuscript and experts, and expert assignment to paper for review. Matching manuscript and expert involves building both paper and expert profiles correctly and then matching them accurately. Similarly assigning an expert to paper accurately involves solving assignment problem by satisfying constraints like a conflict of interest, fulfilling coverage requirements and balancing the load of the expert reviewer. Many researchers have contributed to the topic by proposing solutions for RAP. Each proposed methodology necessitates using an appropriate performance metric for proving its effectiveness. In the case of RAP, we can measure the performance of the whole process; one can measure the performance of the core phases separately as both phases engross different processes and different metric. Testing performance and reducing errors in phases will lead to overall improved results. Both subjective (also known as qualitative) and objective (also known as quantitative) measures have been practiced by the researchers. In this paper, we present trends in using performance measures, their applicability and suitability for RAP. It is observed that ‘Unanimity’ is the most appropriate measure for the overall review process. Use of performance measures- matching score, authority, coverage, and diversity are the most suitable for measuring the performance of phase one- matching paper and reviewer. It is noticed that load balance, fairness and accuracy are suitable for measuring the performance of phase two that assigns a reviewer to paper by satisfying constraints. The study derives the conclusion that hybrid approach with the proper combination of these measures is the best for measuring the performance of process of assigning reviewer to the paper.

In a paper introduction presentation of scientific papers, the presenter needs to understand the contents of a paper, summarizes them by slides and explains them to the audiences, such as participants the paper introduction activity, and discuss the contents with participants. However, if the presenter does not have enough skills for reading the contents deeply or for creating understandable slides, participants are not able to get the importance of the paper precisely and do not have fruitful discussion. For such situation, participants give questions to understand the paper. Therefore, questions regarding to the contents from other participants implicitly suggest problems with the presenter’s understanding of the paper and creating the slides. Therefore, it is valuable for the presenter to reflect on their skills that are used to prepare the paper introduction presentation based on the questions asked by other participants. This research proposes a reflection support environment for promoting the active and sufficient reflection using the questions asked by other participants. Our environment prepares an interface that records the activities involved in preparing the presentation, such as understanding papers and creating slides. Our environment also provides an interface with which the presenter can compare the recorded activities and the participants’ questions. By comparing the questions with their understanding of papers and created slides, the presenter is encouraged to recognize the insufficient points of their reading and presentation methods.

In this expository work, we sketch some applications of annotated logics. Such logics were discovered in the late 1980s and nowadays have become one of the most fertile logics for applications. They constitute a two-sorted logic, and they are paraconsistent and in general paracomplete and non-alethic logics.

Standard computing will be characterized as a functional extension of syntax. Using BICA claim of continuity between human-animal-robot cognitive architectures, we will define strong semantics as the ability of a cognitive architectures to consult cognitive maps, in particular phenomenal content maps (Damasio, 2010). This follows the work on semantic maps, yet the maps we postulate are based on phenomenal markers not syntax and predicative logic. We define extra strong semantic maps (and extra strong computing in general) as those that go beyond human capabilities. The goal of strong semantic computing in autonomous robotics should be to ‘know what is going on’ (Sanz, 2012) before engaging in detailed logical analysis. Such sort of Gestalt computing is needed in advanced autonomous robotics, especially robots functioning in human environments.

This paper presents results of topic modeling and network models of topics using the ICCS corpus, which contains domain-specific (computational science) papers over sixteen years (a total of 5695 papers). We discuss topical structures of ICCS, how these topics evolve over time in response to the topicality of various problems, technologies and methods, and how all these topics relate to one another. This analysis illustrates multidisciplinary research and collaborations among scientific communities, by constructing static and dynamic networks from the topic modeling results and the authors’ keywords. The results of this study give insights about the past and future trends of core discussion topics in computational science. We used the Non-negative Matrix Factorization(NMF) topic modeling algorithm to discover topics and labeled and grouped results hierarchically. We used Gephi to study static networks of topics, and an R library called DyA to analyze the dynamic networks of topics.

The paper introduces a new taxonomy of ill-posed parametric inverse problems, formulated as global optimization ones. It systematizes irremediable problems, which appear quite often in the real life but cannot be solved using the regularization method. The paper also shows a new way of solving irremediable inverse problems by a complex memetic approach including: genetic computation with adaptive accuracy, random sample clustering and a sophisticated local approximation of misfit plateau regions. Finally, we use a benchmark function featuring cross-shaped plateau to discuss some factors that influence the quality of plateau shape approximation.

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.

This research focuses on determining the potential of biogas generation for combustion from the Zungo pig manure (manure mixture of the three sizes of the pig in a proportion of 33.33% for each size) located in the department of the Atlantic-Colombia. The properties of the biogas obtained were evaluated with a gas analyzer and a chromatograph, demonstrating that, under controlled temperature conditions, after the bio digestion of Zungo pig manure there is a methane content greater than 50%, which is acceptable demonstrating that this biogas is flammable. It was also established that there is a correlation between pH and methane content in biogas, so it is required to control it in the process. Esta investigación se enfoca en determinar el potencial de generación de biogás para combustión a partir del estiércol de cerdo Zungo (mezcla de estiércol de los tres tamaños del cerdo en una proporción de 33,33% para cada tamaño) ubicado en el departamento del Atlántico-Colombia. Las propiedades del biogás obtenido se evaluaron con un analizador de gases y un cromatógrafo, demostrando que, en condiciones de temperatura controlada, luego de la biodigestión del estiércol de cerdo Zungo existe un contenido de metano superior al 50%, lo cual es aceptable demostrando que este biogás es inflamable. También se estableció que existe una correlación entre el pH y el contenido de metano en el biogás, por lo que se requiere controlarlo en el proceso.

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.

Aiming to co-channel interference (CCI) for LTE downlink multi-user MIMO-OFDM system, maximizing the successive signal to leakage plus noise ratio (SSLNR) interference cancellation algorithm based on Pre-processing is proposed. The algorithm utilizes SSLNR based on precoding matrix scheme to process emission signal and the known leakages is canceled by dirty paper coding (DPC) algorithm at the transmitter. At the receiver, the received signal are treated by preconditioned matrix, the interference between different users are eliminated in further, the sum-capacity is improved by pre-processing Matrix. Meanwhile, the algorithm is not limited by the number of transmit and receive antennas. The performance improvement is verified by simulation, when BER is 10-4, signal noise ratio (SNR) is improved about 4dB.

Artificial neural network (ANN) can be utilized as a tool for modeling the properties of biodiesel fuel those are related to the fatty acid (FA) composition of a feedstock. The cold flow properties (CFP) define the operability for diesel fuel which are strongly influenced by the FA composition of feedstock. Cloud point (CP), pour point (PP) and cold filter plugging point (CFPP) are used commonly to characterize CFP. Prediction of CFP based on the FA composition of feedstock can reduce the experimental effort to produce a biodiesel suitable for a regional climate. In an attempt for this, 9-6-3 back-propagation ANN architecture was implemented to estimate CP, PP and CFPP of biodiesel samples using nine FA components as input data of 103 biodiesel study collected from literature. To check the accuracy of the model developed, refined canola oil (RCO) and waste frying oil (WFO) were converted to biodiesel then, their CP, PP and CFPP temperatures were determined following the EN and ASTM standards. The CFP estimated by the ANN model were in close agreement with the experimental values. When compared with the experimental data, ANN model predicted the CP, PP and CFPP temperatures within 98%, 94% and 96% accuracy, respectively. The model developed has revealed that CFP of biodiesel were influenced primarily by saturation or unsaturation of FA components with a few exceptions. Since the ANN model can be trained from iterations, it predicted CFP with high accuracy inspite of the presence of nonlinearities, i.e. the average of the mean R2 value for the three CFP was found as 0.96.

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.

We propose the creation of an on-line journal that integrates with a component framework. By means of the framework, simulations referenced in the paper can be run (or re-run) on journal supercomputers at will, allowing verification of the results or deeper analysis of the paper's problem space. The on-line journal would provide standardized job launch and control mechanisms, data formats, and address code portability issues. Through the use of the framework and its components authors can make use of built-in productivity and readability enhancing features (such as automatic parallelism), leverage code published in previous works, and gain stature as their published modules are used by others. We feel that Cactus is an ideal candidate for the on-line journal, therefore we propose the name “The Prickly Pear Archive” for the on-line journal, taking the name from a species of cactus that can be used to make paper.

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.

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 present paper we investigate an approach to intelligent support of the software white box testing process based on evolutionary paradigm. As part of this approach we solve the urgent problem of finding the optimal set of test data that provides maximum coverage of the code when it is used in the testing process. Traditionally, to solve this problem, an approach is used when the genetic algorithm is used to find the most adapted chromosome, which is a set of test data that ensures passage along the most complex (long) path in the code graph. Many data sets that provide maximum code coverage can be found by repeating this procedure multiple times with preliminary zeroing of the code operation weights corresponding to the chromosomes found earlier. Thus, the fitness function of the genetic algorithm has a simple form, but the process of finding all the data sets is quite long and non-optimal. In this paper, we study a different approach, when an additional component is initially included in the fitness function, which is responsible for the greatest possible difference in the paths of the population, along with a component responsible for the complexity of each path. The basic version of the algorithm is examined and it’s more effective modifications have been proposed to achieve the final goal of obtaining the optimal set of test data covering the flow graph in the least expensive way.

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.

The paper analyses the current state of knowledge on approaches for the practical implementation of machine learning based assistance systems for production planning and control. A concept of a procedure model for application-oriented projects in the field of industrial series production is proposed. It focusses on order sequencing and machine allocation in a real time production environment. As part of an application-oriented research project, a use case is referenced. In this paper, a first conceptual approach is presented, using the example of an industrial production of printed circuit boards. In the following steps, practical suitability is checked on the basis of the practical reference, conclusions are drawn and the methodology will be developed further. The aim is a generally valid procedure model for industrial series production.

Agility is the ability of a person to change direction quickly. Measuring the basic and sport-specific physical abilities of athletes is important in sport and recreation. To assess agility, various tests are regularly included in physical abilities test batteries. This paper presents a state-of-the-art approach to measuring human movement in agility tests using infrared optical gates and kinematic sensors. The combination of precise time measurement, position detection and motion measurement enables the acquisition of previously inaccessible kinematic and spatiotemporal variables. This makes the assessment of agility more accurate and provides the athletes and coaches with additional information. With the system presented in this paper, the assessment of the athletes’ agility can be recorded more objectively and with greater accuracy than would be possible with conventional equipment and methods. The athletes’ performance is assessed with a combination of different sensors, all of which are synchronized with our sensor-based system. This combination provides the athletes with new information about their movement, which is not possible with traditional professional timing devices used to measure time in agility tests. The presented sensor system can be used in various sports and test types, including the frequently used T-test. We present summarized results from a pilot study conducted in cooperation with the Slovenian youth volleyball team.

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.

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.

Short-term precise prediction of network traffic can effectively help operators reasonably allocate network resources, improve network service quality and scheduling efficiency. Our model proposed in this paper uses dynamic parameters to adjust the predicted value and refit the predicted sequence for short-term traffic prediction which is called Dynamic Modification Neural Network (DMNN) model. The architecture of our model consists of a prediction module, an adjustment module and a series re-fitting module. The prediction module learns time closeness of the traffic data before the predicted point. The adjustment module takes dynamic period characteristics of data into account and generates adjusted value by linear discrete dynamic parameters. The series re-fitting module refits predicted series from the first two modules with a new hybrid loss function. In this paper, we take different neural networks in the prediction module, such as Long Short-Term Memory (LSTM), Bi-directional Long Short-Term Memory (BiLSTM), Convolutional Long Short-Term Memory (ConvLSTM) and Convolutional Bi-directional Long Short-Term Memory (ConvBiLSTM). We evaluate the performance of the proposed model by using two real-world datasets for short-term traffic flow prediction. Moreover, Experimental results show that the proposed model with dynamic modification has much higher accuracy than other models and decreases the prediction value error and time skew of the inflection points.

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.

Efficient water management of the urban water cycle is one of the current concerns with the increase of (peri) urban areas due to the population growth, economic development, and possibility of water scarcity due to climate change. To face the increase of the water demand it is imperative the creation of digital water solutions to provide a real-time monitoring, decision support system, to manage the water supply network efficiently and optimize the water-energy nexus. This paper presents a Water Wise System – W2S, results from a R&D project supported by an EU and Portuguese Government Grant. The paper provides a preliminary study of an architecture solution to Water Wise System software, focuses on the water challenges, present technology, digital water, IoT and the future of smart cities. The solution aims to support a paradigm shift in the management of water distribution networks, with predictive and analytical convergence supported in Machine Learning, Deep Learning and an integration with SCADA, GIS and EPANET.

Conventionally, majority physicians used manual-based approach to determine heart sound parameters by manually printing heart sound signal waveform on the paper and trace heart sound parameters duration. Towards modern medical diagnostic technologies, this paper presents a modified framework of automated heart sound signal segmentation and parameters identification for deployment in computer aided auscultation. Envelope-based approach with reference information by abrupt changes and peak detection algorithm are proposed for PASCAL Classifying Heart Sounds Challenge database. Noted that the performance of segmentation process was measured by calculating the accuracy and F1-score while the heart sound parameters were identified by computing its mean and standard deviation. The findings yield F1-score and accuracy of proposed approach for segmentation and identification of normal heart sound signal at 95.29% and 91.0%, respectively. Heart sound parameters such as first sound, S1 duration, second sound, S2 duration, systole duration, diastole duration, heart cycle duration and ratio of systole and diastole were also determined. This proposed approach is suitable to be applied on two prominent heart sound signal peaks of S1 and S2, which continuously varies among samples due to the different auscultatory sites.

This paper presents a mobile application developed in conjunction with the City Hall from Viana do Castelo, a city in the north of Portugal. This application targets people with permanent or temporary reduced mobility and intends to show them information about taxi points, parking spaces and reference points in the city with the possibility to navigate to them using an optimized algorithm, based on routes defined for each segment (deaf people, visually impaired people, etc.). The Dijkstra?s algorithm was used to implement the shortestPath given an origin, destination and segment. In this paper we present implementation details as well as final layouts of the prototype. At the current moment, the application is ended and will soon enter in usability tests with the users. 5311-8814-F0ED | Sara Maria da Cruz Maia de Oliveira Paiva N/A

Recently, two types of common sensors, LiDAR and Camera, show significant performance on all tasks in 3D vision. LiDAR provides accurate 3D geometry structure, while camera captures more scene context and semantic information. The fusion of two different sensor becomes a fundamental and common idea to achieve better performance. To give a thorough cognition of the complementary and boosting about two kind of sensors. This paper briefly reviews the fusion and enhancement systems between both two sensors in the field of depth completion, 3D object detection, 2D\3D semantic segmentation and 3D object tracking. Meanwhile, the state of art fusion algorithms is quantitatively demonstrated, in this paper, based on the in KITTI widely-used public dataset. Furthermore, the technical challenge and the future potential of LiDAR and camera fusion are also discussed.

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

In the context of SaaS (Software as a Service) where software has to be up and running 7 days a week and 24 hours a day, keeping the requirements specification and related test cases up to date can be difficult. Managing requirements in this context has additional challenges that need to be taken into account, for instance, re-prioritize requirements continuously and identify/update new dependencies among them. When requirements change, related test cases need to be updated accordingly. We claim that extracting and analyzing the usage of the SaaS can help to maintain requirements and test cases updated and contribute to improve the overall quality of the services provided. This paper presents an extension to REQAnalytics. REQAnalytics is a recommendation system that collects information about the usage of a SaaS and generates recommendations to improve the SaaS provided. The evolution involves extending the analysis performed over the sequences of functionalities (requirements) and refining the data provided for Software Requirements Specification, with the purpose of helping the requirements engineers in the requirement maintenance activities, and to improve the overall quality of the services. Furthermore, the extension presented in this paper is able to generate test cases in a regression testing context.

Aiming at the anti-pendulum problem of bridge crane system, a two-dimensional model of bridge crane system under the action of damping force is established in this paper. Considering the influence of air resistance and friction between trolly and track in complex working environment, a novel air resistance model and friction model are introduced to establish a more accurate two-dimensional bridge crane model with variable rope length under the action of damping force.Based on the fuzzy adaptive PID controller, appropriate fuzzy rules are designed to realize the anti-pendulum positioning control of bridge crane.The simulation results show that compared with the multi-sliding mode controller, the controller designed in this paper has better control effect and strong robustness.

Industry 4.0 (I4.0), Smart Manufacturing, Industrial Internet, Intelligent Manufacturing, and so forth, are different designations for different initiatives, all contributing to the digital transformation of the industrial ecosystem. Besides several reference models/architectures have been developed, a framework to structure and understand the implications and to identify I4.0 action fields is still needed. A harmonized model would allow guiding the developments on how to organizationally prepare for the change. This paper attempts to clarify the implications and action fields of the I4.0, trying to reveal its mostly alleged dimensions and the main planes in which they intervene in the industrial ecosystem. Through the analysis of the most relevant architectures for I4.0, IoT and Cyber-Physical Systems (CPS), and the consequent awareness of which components are missing, the paper proposes a framework for digital factories towards smart cities that stresses the expansion of the I4.0 horizontal integration into human, cities infrastructural, and societal dimensions.

The constant energy requirement was a crucial concern in today’s smart grid era, which needs tremendous attention. New wireless networking technologies should be integrated into the grid to achieve more distributed generation and energy storage. Advancements in wireless sensor networks (WSN) and embedded systems have enabled the implementation of smart grid monitoring and automation systems at low cost. The incorporation of fifth-generation networks (5G) in a smart grid would create novel business models of "edge" and "fog" technology at the utility side, accompanying with smart-control and automation. Nonetheless, WSN’s increased implementation also introduces impediments to safety, particularly relevant to confidentiality, accessibility, and safety management, creating both utilities and customers’ unpredictable expenditure and catastrophe. In this paper, a comprehensive overview of the communication and computing aspects of 5G network infrastructure is provided and discussed how they can be beneficial in promoting advanced smart grid systems in the Kingdom of Saudi Arabia. Besides, this paper discusses the smart grid, current security issues surrounding machine-to-machine information on the smart grid, and solutions available to identify and avoid cyber threats.

With the rapid development of the cloud computing, more and more users choose to store their encrypted data on the cloud server. Searchable encryption (SE) schemes capacitate legal users to retrieve encrypted data in the cloud server and the cloud server does not leak sensitive information about the encrypted data. In this paper, we use searchable symmetric encryption (SSE) to construct our scheme. SSE can be divided into exact keyword searchable encryption and fuzzy keyword searchable encryption. Fuzzy keyword searchable encryption scheme takes into account the minor clerical errors of users. When the query keyword does not match the index stored in the cloud server, the fuzzy keyword searchable encryption schemes can find the closest keyword and return the matching encrypted documents, which improves the availability of the cloud server. In this paper, we propose a multi-keyword fuzzy searchable encryption scheme by using locality sensitive hashing (LSH) and bloom filter which has high search accuracy.

Industry 4.0 is a popular topic in management literature, but it is also an interdisciplinary subject, maintaining a strong engineering connotation. Firm decision-making processes are more and more affected by Industry 4.0 which has introduced numerous tools that benefit and greatly support business activities. In this domain, the focus of this paper is the information made available to stakeholders on Industry 4.0 tools in use through a content analysis of the Annual Reports of the main European innovative firms (from the top ten of the Boston Consulting Group ranking). The analysis seeks to highlight how firms describe and qualify these tools in terms of impact (risk, strategy or environmental), the perspective used in illustrating them (managerial or engineering) as well as the temporal orientation of the information provided (present, past, future or none). Results show that innovative European firms pay attention and use these tools, in different ways and for different purposes. The paper has both theoretical and managerial implications. From a theoretical point of view, it contributes to study and further the literature on Industry 4.0 and its instruments, while from a managerial perspective, it gives a better understanding of how these innovative tools can be communicated to external stakeholders, especially investors.

Cyber physical system (CPS) mainly relies on wireless network to realize information transmission from sensors to controllers and from the controllers to actuators. This paper studies the cyber-physical fusion system that is attacked by data packet timing. The attack and defense process is abstracted as a game process. Based on the two-person zero-sum game model of non-cooperative game, a variable delay situation is designed. In addition, this paper also compares the designed maximum-minimum controller with the linear quadratic Gaussian (LQG) control and carried out the simulation verification. It can be found that the controller designed in this paper can finally achieve stable control under attack.

Companies in highly competitive environments often adopt project management practices to manage their processes and activities in a quest to leverage flexibility. Doing so these companies usually become project based organizations (PBO). This paper aims to design new ways how PBOs respond to change and adapt their project based organization model. This paper adopts an action research methodology and explores the case of a company that faced several environment challenges and so was obliged to change its organizational and information systems processes. From the observed challenges and results, it became obvious that a PBO needs to implement portfolio, program and project management processes and adapt them to volatile business environments. The studied longitudinal case proves that information systems have a crucial role to support the PBO processes as part of an effective change process.

With the rapid development of deep learning and structures of neural networks, parameter settings of trained neural networks become new products from knowledge. Since supervised learning is used in 85% of machine learning to train the neural networks, well-labeled training data becomes new knowledge capital. In order to stimulate the knowledge sharing and protect the Intelligent Property (IP) right and privacy of knowledge capital at the same time, we need a framework to conduct training of neural networks with certain encrypted data and exchange training results with certain rewards. Federated learning provides a framework to train the networks without disclose the privacy and a block-chain based transaction system can organize a group of participants under certain consensus and reward mechanism. Their combination is an effective solution to manage knowledge capital, called knowledge capital bank in this paper. This paper defines the economic meaning of knowledge capital and all behaviors of related participants. Then we propose a block-chain based federated learning algorithm for secured knowledge sharing with IP and privacy protection. Finally, a complete design for a use case of this algorithm called Knowledge capital bank is demonstrated.

This paper investigates possibilities to visually support the collaborative design process of Enterprise Knowledge Graphs for Business Application Systems. Starting with a description of the concept of the Social Network of Business Objects (SoNBO), we show how the SoNBO Visualiser supports the visualisation of a company-specific ontology and the resulting knowledge graph. Our previous research showed that existing visualisation tools from the Semantic Web are unsuited for the human involvement that is required in the design of an ontology for a SoNBO. In order to address the lack of a tool, the SoNBO Visualiser was developed using a Design Science Research approach. This paper aims to provide a methodological as well as a technical contribution: The method provides support for the visualisation of the ontology (on two levels) and facilitates the involvement of domain experts (employees) without technical knowledge in the design process. The complementary technical solution connects heterogeneous source systems in a simple configuration process and allows to visualise the ontology and the resulting knowledge graph.

In recent years, Enterprise Collaboration Systems (ECS) have increasingly been introduced as core components of the digital workplace. Organizations have a range of different goals that they want to achieve when they implement a new collaboration software. In our paper, we argue that traditional controlling indicators with a focus on delivery dates and costs are not suitable to measure the return on investment and the benefit that a user company can achieve from the digital support of work. Conventional indicators provide limited value because they cannot be used to directly attribute the contribution of a digital collaboration project to the achievement of corporate goals. The research described in this paper is part of a long-term university-industry research initiative and the findings contribute to a broader understanding of the metric-based analysis of benefits from ECS use in organizations. We present a method for developing and applying “metrics profiles” to ECS, which makes it possible to analyze the realization of benefits over time. The method was tested and evaluated using non-reactive ECS data from an operational collaboration platform that has been in operation for more than seven years.

In the era of industrial big data, the demand for high-speed data transmission is higher and higher. The traditional Modbus protocol uses fixed data frame length transmission, which can not make full use of link resources, resulting in very low transmission rate. In the early stage, in order to solve the problem of traditional ModBus transmission protocol, an algorithm was proposed to adjust the frame length by calculating the optimal frame length through the frame error rate. However, because the frame length of each data frame is adjusted in this algorithm, the link stability becomes worse and the efficiency is reduced. In order to solve this problem, this paper proposes an improved adaptive frame length adjustment algorithm, which adjusts the frame length according to the retransmission failure rate per unit time, and gives different adjustment schemes according to different situations, which improves the link stability. Finally, this paper takes the servo drive data acquisition system as the carrier, and verifies the effectiveness of the algorithm through experiments.

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.

In the European Union, the silver economy is focused on developing strategies to facilitate the elderly population, mainly in terms of creating services based on innovations and technology. The paper encompasses a detailed overview of the silver economy in Estonia and the introduction of ICT solutions for the elderly population through knowledge management techniques. A knowledge map has been created using the Ontology model in Protege to demonstrate the key knowledge resources for ICT integration in the silver economy. The results and information displayed in the knowledge map have been gathered from two detailed workshops which were further validated through questionnaires and interviews from the professionals in the field. The purpose of this paper is to discuss the different knowledge resources and competencies relevant to the integration of ICT in the silver economy which will serve as a basis for the development of a knowledge management model in the future.

With the fast-emerging trend of deploying Internet-of-things (IoT) in smart homes and cities, next-generation wireless systems (i.e., beyond fifth-generation (5G) of wireless mobile communications) are expected to connect a plurality of devices, including machines, sensors, tablets, cameras, etc. To this end, the radio frequency (RF) spectrum would fail to fulfill the demands of such enormous data-heavy wireless applications, as operating at the RF spectrum would largely suffer from data congestion and interference. Based on the potential of Visible Light Communications (VLC) to solve the problem of spectrum scarcity and satisfy the high data-rate requirements of beyond 5G systems, this paper focuses on utilizing VLC for IoT applications in indoor environments by means of adequately routing data among VLCbased nodes. Operating at the unlicensed optical band in the visible light region utilizes simple Light-Emitting-Diodes (LEDs), which is a health-friendly communication method. VLC has the advantages of providing ultra-high bandwidth, robustness to electromagnetic interference, and inherent physical security. VLC, however, suffers from severe short communication ranges and line-of-sight (LoS) constraints. This paper proposes overcoming such challenges by means of adopting a transmit diversity scheme that transmits messages over several paths. In order to reduce the receiver outage probability, the paper proposes multiple combination schemes at the receiver side, namely selection combining, maximal-ratio combining, and threshold combining. The simulation results assess the effectiveness of the proposed diversity schemes in terms of signal-to-noise-ratio and outage probability and illustrate the suitability of deploying VLC for next-generation indoor networks. The results show that that maximal-ratio combining outperforms selection combining and threshold combining by approximately 10% and 40%, respectively.

This paper explores the possible effect of combining signaling with pacing. The researchers used the Gazepoint eye tracker to track the eye movements of the respondents and OGAMA to analyze and store necessary parameters used for this study. This paper considered 24 respondents divided into two groups – one exposed to a stimulus applied with signaling and the other with a stimulus applied with signaling and pacing. Each stimulus contained a brief history of Poland which was chosen by the researchers to avoid possible bias to the data when the stimulus is a learning material that inherently requires a greater cognitive load to understand. The result of this paper shows that the number of fixation and fixation duration has a positive relationship to the comprehension test result. Further, it also shows that the post test scores, the number of fixation and the fixation duration of the student exposed to stimuli with signaling alone differs significantly to the comprehension test result, the number of fixation, and fixation duration of the student exposed to stimuli with signaling and pacing.