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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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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

Remote support tools offer great savings potential for maintenance work and simplify communication between on-site workers and remote experts in the back-office. Despite the predicted benefits and the existence of an increasing number of commercial augmented reality (AR) remote support tools, the technology still struggles with various challenges, mainly caused by technical limitations, safety concerns or industry-specific constraints. In this paper, we describe the implementation of an open source AR remote support application with a strong industry focus. The software was developed based on the feedback of four industry partners with different needs in the field of maintenance work. Therefore, we set the focus on configurability to provide the right tools for each use case. The developed remote support tool was then compared with traditional support measures in a user study. This paper presents the lessons learned from the expert discussions with our company partners and the results of the user study.

International audience; In this paper we give an overview of both hardware and software architectures of real time systems used in devices for various purposes-from lab bench contraptions to cars. The goal of the paper is to reveal separate classes of such architectures as well as to define preconditions for choosing a particular architecture.

In recent years, urban population growth and the diversity of vehicles have increased. Location prediction in VANETs is extremely necessary for consumer applications such as routing, network management, knowledge dissemination protocols, and road cognition, among others. This could increase the performance of VANETs. In this paper a Kalman filter is used to predict the vehicle’s future location. We conducted experiments exploitation each vehicle quality traces and model-driven traces. We quantitatively compare the prediction performance of a Kalman filter and neural network-based methods. This paper proposes a location prediction algorithm for nonlinear vehicular movement using an Extended Kalman filter (EKF). Evaluation of the ESCL-VNET algorithm with EKF assess the given better results.

Project teams (PTs) are critical resources in the success of projects in the Information and Communication Technology (ICT) sector, as projects in this sector are highly dependent on human resources. Numerous researchers have written about the key role played by the PTs. However, PTs have not received commensurate recognition from their project managers (PMs) when it comes to the addressing of their needs and interests. ICT PTs have become forgotten stakeholders. In an effort to address the neglect of project teams by PMs, a model was developed by the authors of this paper to assist ICT PMs in treating PTs as key stakeholders. The purpose of this paper is to present the results of the refinement and evaluation process to which this model was subjected, using a two-phase Delphi methodology, using experienced experts comprising five PMs, twenty PT members as well as five academics. The data collected from the experts using interviews was analysed by means of statistical and qualitative methods. The results show that the experts have welcomed the use of the model in the ICT sector, acknowledging its suitability for a real-life project environment. Future studies should include the validation of the model in a real-life project environment.

Logistics activities were included in the wave of changes brought with the advent of the fourth industrial revolution. Several applications can be recognized for several activities, aiming all at efficiently optimizing processes and consequently production and volumes. Among them, collaborative robots involved for instance in picking, palletizing or assembly operations are quickly spreading, as also demonstrated by the increase in the number of publications available in literature. In response, this paper presents the results of a bibliometric analysis carried out on 64 scientific papers which deal with this topic within the logistics field. Analysis were made through two different software applications, namely Microsoft Excel™ and VOSviewer. Results are quite optimistic as they delineate great opportunities for collaborative robots to establish their position in the industrial context.

The Industrial Internet of Things (IoT) has an unique opportunity to have a greater impact on the manufacturing sector. Monitoring the health of the expensive equipments in the factory is critical for the business and the opportunities where IoT can be truly transformational. Most often the industries operates on a primitive way of monitoring these equipments on a Statistical Process Control (SPC) Limits. The major flaw in this monitoring system is unable to detect drifts within the static limit and upon triggering of the limits, it is usually too late for the team in the manufacturing floor to take preventive actions before the system goes down. In this paper, we developed a generic model for detecting drifts and identifying potential outliers. The model uses a double linear regression method to identify both aggressive and progressive drift, as well as adjusted boxplot method to detect outliers in both symmetric and skewed distributions. Unlike conventional drift detection approaches, this model has low computational complexity and can be applied to both batch and stream data. This paper will also introduce the infrastructure and architecture on enabling near real-time analytics using the IoT platform and streaming cluster, which reduces the data latency available for analysis to 10 minutes. Enabling real-time monitoring allows the end users to react to the alarms in a timely manner. This system has proven that it is able to provide early detection before the impact was observed as compared to the existing system. Manufacturing operation team could establish a new business process to respond to the early drift alarms by using quality shift left approach.

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

With the rapid development of modern communication technology, more and more attention has been paid to the security of communication. In recent years, the physical layer security technology provides a new way to solve the problem of secure transmission in wireless communication. Physical layer security has become a hot research topic, but there is no special physical layer security simulation platform to provide great help for technical research. In view of this situation, this paper develops a physical layer security simulation platform based on the cross platform development framework QT, which provides visual demonstration and performance evaluation functions for specific technologies. The platform is developed with C plus plus language, and different simulation scenarios are customized for different technical schemes to simulate the simulation process. Starting from the architecture of the simulation platform, this paper presents the simulation process and evaluation results of several selected physical layer security directions, and describes the simulation platform in detail.

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

Iris and face are two most popular biometric traits. Unfortunately, their performance can be degraded when these systems encountered with non-ideal images. The primary motivation of this paper is to analyze the usability of a new feature rich biometric trait known as periocular region (nearby region around the eye) for two different non-ideal scenarios 1) Matching of images with different pose variation and 2) Matching of different side of periocular images (Left eye to Right eye & Right eye to Left eye). In this paper, seven different off-the-shelf deep learning based Convolutional Neural Networks (CNN) using transfer learning approach are implemented on UBIPr database. Experimental results demonstrated that for images with pose variation VGG 19 obtained maximum recognition accuracy while for matching of two different side of periocular region Res Net 18 outperform others. As an additional contribution, a novel approach for the use of mirror images to improve the matching accuracy of two different side of periocular region is also presented.

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

Climatic chamber are enclosures where the ambient conditions, i.e. the temperature and humidity, are finely controlled. This one can play multiple roles such as the cultivation of plants (phytotron), the breeding of insects or habitat for exotic animals. The availability on the market of a wide variety of equipment makes it difficult to share settings and operating recipes. In this paper, we propose a versatile and automated modular climatic enclosure system that can be adapted according to the use cases and available material. In this paper, we use IoT device virtual representation, data validation by means AI algorithm, the semantic description of material, and ontology locally deployed from the cloud allowing to automate the local installation with container technology.

Nowadays, we are in the era of advanced technologies where tremendous amount of data is produced by multiple sources such as sensors, devices, social media, user experiences, etc. Furthermore, this raw data has a low value, and major part is not really useful or important for business. One way to give an added value to this stored data is to extract useful knowledge from it, for the ending-system or the end-users by a process commonly called knowledge Discovery in Database (KDD). Smart Farming uses a large amount of connected technologies producing also a huge amount of data in order to maximize productions by reducing: human efforts, environment impact and wasting natural resources. In this paper, We develop a new data analytic architecture dedicated to Precision Livestock Farming (PLF) to improve in particular the livestock animals production, animals’ welfare, and farming processes. We present a new data processing architecture for a knowledge-base management system (KBMS) allowing to ease decision support and monitoring operations that can help farmers and stakeholders to better exploit data and have a long-term view of the evolution of the knowledge it contains. Our main contribution in the present paper is a new architecture specifically developed for the precision livestock farming integrating a periodical data reevaluation which address the problematic of data conservation and the decrease in data value over time.

Nowadays tremendous amounts of heterogeneous information known as Big Data have completely changed the modern scientific landscape. On the one hand, Big Data provides experts with massive opportunities for conducting research in almost every filed of human endeavor, developing novel technologies and disseminating scientific knowledge. On the other hand, a strong need for creating new techniques to handle Big Data has emerged. This paper is devoted to analytical search indexes, which allow researchers to obtain the information of interest from Big Data. Two types of analytical search indexes, namely parametric and semantic, are considered in the paper. Parametric search index will allow researchers to find information about a technology or material with specific physical parameters which values lie in the given interval, as opposed to the substring search, that enables researchers to find only a particular parameter value. The idea behind using semantic search index in this paper is based on the notion of the technology life cycle, which will allow identifying the current state of a particular technology. Existing models of life cycle have been analyzed and a new model has been suggested. The ontology of physical parameters and the life cycle ontology have been developed. The next step has been the development of the algorithm, which uses the corresponding ontologies to split, filter and mark texts and saves the results to the database for further use as search indexes. The Chinese language has been chosen as a hieroglyphic language for conducting this research.

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

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

With the rapid development of Unmanned Aerial Vehicle (UAV), stealth warship and stealth tank, the mission of radar has become more difficult. The modern radar system must detect and track target through strong clutter from many aspect, such as ground and sea. Radar detection for low slow small （LSS）targets has gradually become a new focus. LSS target has a small Radar Cross Section (RCS) because of its small size, making the SNR of the echo signal very low. At the same time, the Doppler frequency of slow target is very low, making it difficult for conventional signal processing methods to separate the Doppler information from the clutter spectrum. Thus, it does make sense to study how to detect the LSS target in strong clutter efficiently. In this paper, the distribution characteristics of radar clutter and coherent accumulation algorithm for LSS target detection in complex electromagnetic environment are studied. On the base of that, a double-threshold target detection method based on coherent accumulation is proposed. To check out the efficiency of the detection method, an experiment has been conducted, and the data collected was used to test the detection algorithm, and the result has been shown in this paper.

This paper focuses on the development of smart grid in the trends of the energy Internet, analyze the development situation of smart grid and studies the requirements for the construction of major advanced smart grid demonstration areas in China. Under this background, this paper proposes medium and long term China smart grid development strategies, key tasks, technical routes and implementation plans. And planning practices in typical regions are carried out to support the implementation of major national strategic decision-making deployments.

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

Assisting table tennis coaching using modern technologies is one of the most trending researches in the sports field. In this paper, we present a methodology to identify and recognize the wrong strokes executed by players to improve the training experience by the usage of an IR depth camera. The proposed system focuses mainly on the errors in table tennis player’s strokes and evaluating them efficiently and based on the analysis and classification of the data obtained from an IR depth camera using multiple algorithms. This paper is a continuation of our previous work [10], focusing more on identifying common wrong strokes in table tennis by utilizing IR depth camera classification algorithms. The classification of the mistakes that took place while playing can be classified based on each player dependently or independently for all players.

With in a social network, users interact with each other by sharing news, picture, videos, political content or product promotion content. This makes social networks information diffusion(or marketing) platform. To realize the extent of the adoption of such ideas, it becomes important to study the dynamics of adoption underlying within the social network. However, to analyze social networks as information diffusion/marketing platform, many challenges have to be met. In this paper, we address one of the challenge, namely Influence Maximization in Social Networks. Our main purpose is to select k influential users which can gain maximum spread while cost of selecting k users is minimum. In this paper, a) We have proposed an degree heuristic algorithm under Independent Cascade Model (ICM) to extract top k influential users efficiently. b) A modification in ICM is proposed which derives propagation probability based on similarity metrics. The proposed work is implemented and evaluated on two network data- sets, academic collaboration is taken from the online archival database arXiv.org. The obtained results prove that degree heuristic algorithm is very efficient and has influence spread far better than many centrality based heuristics and close to benchmark greedy algorithm. On the other hand, modification in ICM has a significant stable increase in the influence achieved for implemented algorithms compared to influence achieved by the same algorithms on classic ICM.

Visual tracking is an art of tracking a moving object over video frames using non-stationary cameras, for which feature descriptors of the target are computed and applied to a motion model. In this paper, SURF is used to compute the feature descriptors and Mean-Shift algorithm as a motion model. Mean-shift algorithm is fundamentally a logical approach to track the object on an image frame where the appearance is described by histograms. Mean-Shift tracking can directly be applied to SURF features but there is a big constraint of availability of an adequate number of feature keypoints for a given object. To get over this limitation, the re-projection technique is implemented in this paper for tracking an object in any video recorded from a mobile or stationary camera. Re-projection, online updates the histogram of object template for every frame by superimposing the feature keypoints from upcoming frames to the first frame. The object can be tracked using SURF descriptors without using any secondary information (color, texture, optical flow, gradient, etc.) regarding the object, which makes it computationally inexpensive to be used in real-time systems. Also, the SURF feature is calculated only for the target object that is to be tracked, this further reduces computational complexity. The simulation results prove the effectiveness of the work.

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

Analyzing sensor data from a production environment is quite challenging because of the high-dimensional nature of the data. In addition, the generated data is in the form of time-series, where the sequence of registrations may be of utmost significance. One of the main goals of the paper is to determine if the given time-series of feature combinations is normal or rare. This goal could successfully be achieved by combining multiple machine learning models. In this paper, a sliding window based ensemble method is proposed to detect outliers in a streaming fashion. The proposed method uses a combination of clustering algorithms to construct subgroups (clusters) representing different data structures. These structures are later used in a one-class classification algorithm to identfy the outliers. Thus, if a pattern does not belong to any of the common structures or clusters, it is an outlier. Further, based on the rare pattern classification, machine failures could be predicted in advance.

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