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EU-funded project FRANCIS focuses on involving citizens in the development of so-called Frugal Innovations, i.e. simple, affordable and sustainable solutions. It does so by organising open-innovations challenges that are managed through an IT-platform and supported by various recruitment and ideation formats. Usually, Citizen Science only involves citizens for minor tasks such as data collection. FRANCIS jump ahead in science is that the project invites them to propose and work on new solutions that have a real chance to enter the market. Before starting the challenges, the team of FRANCIS already made some assumptions about how to encourage and support participants in the best possible way based on existing publications and first findings from primary research. Now that the first challenge is running, the team had the opportunity to check whether they have proven to be true so far. This paper presents the interim lessons learned and details the way forward [ABSTRACT FROM AUTHOR]

Collecting and structuring data on which to perform specific data analyses is a long and complicated process. It requires substantial advanced planning of what data and what data characteristics will be needed for the study. It is important to specify in what format they should be collected. On the other side, there are huge amounts of data stored in a variety of applications. This data is clean and consistent. In many cases, it can be used for new analyses, different from the original purposes for which the information was collected. This is particularly true for data in healthcare. Medical information accumulated in multiple systems can be used for further research and analysis. This paper presents a methodology, appropriate data models, and relevant software decisions that support the reuse and explorations of medical data for specific treatment assessments based on data routinely collected from clinical systems. Some initial results are presented and discussed. [ABSTRACT FROM AUTHOR]

Automatic detection and prediction of falls specifically in elderly population who live alone can prevent untoward incidents by taking action at appropriate time. Validating these automated systems requires a labeled dataset. The available datasets are mostly based on inertial features. A health-related concern may also be a reason behind a fall. Considering physiological features along with inertial features can increase the accuracy of the system. In this paper, a labeled multiclass dataset is presented based on physiological and Inertial features of the body (PIF v2). Two trials are performed by each participant. The participants are young as well as elderly performing activities and falls in real-time using the prototype containing various sensors designed for collecting inertial as well as physiological features. Two trials are performed by each participant. 27 subjects (15 males and 12 females) between the age 10 years to 75 years participated in data collection. A detailed comparison between PIF v1 where the data were collected individually using each sensor and PIF v2 is given along with statistical analysis of PIF v2. The statistical analysis of various features with a t-test shows that the different activities and falls can be differentiated using inertial as well as physiological features. [ABSTRACT FROM AUTHOR]

In today's dynamic business environment, achieving and sustaining operational efficiency is paramount for organizations. To remain competitive and meet evolving customer demands, companies strive to optimize their operations and enhance efficiency. Over time, various indicators have been developed to assess efficiency across multiple operational dimensions, aligning with technological advancements and industrial practices. One such indicator is Overall Equipment Effectiveness (OEE), which evaluates equipment efficiency based on availability, performance, and quality. However, with the advent of the digital industry, the management of data for efficiency calculations has become increasingly complex. In this context, this paper proposes a digitized, real-time OEE calculation system that leverages advanced technologies to continuously monitor OEE, enable real-time decision-making, and drive performance improvements. Harnessing the potential of digitalization, the system facilitates seamless data collection, analysis, and reporting while automating manual processes. By focusing on OEE as a comprehensive efficiency indicator, the proposed system aims to enhance organizational performance and establish a foundation for calculating other efficiency measures. [ABSTRACT FROM AUTHOR]

The implementation of intelligent techniques produces good results in automating fault finding and predicting future outcomes. These approaches have been on the increase in the past years, especially so to detect faults within Compressed Air Systems (CASs). With the use of intelligent techniques, one could minimise the manual and time-consuming aspect of CAS maintenance, improve the environmental impact of the system, while minimising downtime. This paper proposes a general framework for the implementation of intelligent analysis techniques within a real-world system. Such an approach has been implemented on the demand-side of a CAS. In literature, no open datasets are available for use by artificial intelligence models. Hence, as part of this research, a fault generating and monitoring system has been connected to an existing production machine in a manufacturing site to collect the required data. Two classification machine learning methods were implemented and compared across a number of performance metrics. Both the general framework, and its implementation, provide a stepping stone in integrating smart systems with real-time intelligent data analytics for the demand-side of a CAS. These systems would provide a sustainable CAS operation through the effective detection of anomalies and their timely repair. [ABSTRACT FROM AUTHOR]

This paper provides an overview of crowdsensing and some of its applications. Crowdsensing is a part of the collecting data situations also; it's built on a data system on multiple customer interactions. Moreover, writing the general information of the smart cities can be used to boost to received number frequency to send messages. This work mentioned the Crowdsensing layers that describe Mobile crowdsensing. The article focuses on crowdsensing layers, developed an application in Coalition Game using crowdsensing in terms of GPS. In addition, this paper discussed the Mobile crowdsensing system and how important the cloud is in serving the wireless network, the Internet of things (IoT), and data collection. Furthermore, this research also has developed a smart crowdsensing parking system that will help by reducing the time-wasting users. [ABSTRACT FROM AUTHOR]

This paper describes the concept of a novel research planned to be carried out in Polish accounting firms providing accounting services to micro and small enterprises. The accounting firms consist of around 36,000 active entities providing services to as many as about 2 million entrepreneurs. The principal objective of this research is to determine the extent of robotic process automation in Polish accounting firms, as well as to identify the benefits of and obstacles to its implementation. The research is to be structured into 6 tasks, while the methods to be applied include a literature review, interviews with the owners and accountants working in accounting firms, survey methods/pen-and-paper personal interview and computer-assisted web interview, as well as raw data collection and statistical analysis. The results of a preliminary pilot study in two accounting firms are also presented in the paper. [ABSTRACT FROM AUTHOR]

Production planning poses major challenges due to high product diversity, high fluctuations in demand and strong dynamic fluctuations in delivery quantities and times. This makes automation of production planning increasingly necessary to enable optimized sequence planning based on current production data. Production control systems (PRS) offer the possibility of automatic corrective actions in the event of deviations from the specified target dates. Production, PRS and data acquisition are considered as a single control loop and are the subject of various development and research topics. However, automatic interventions in planning and control can lead to serious disruptions if there are errors in the software of the PRS. Therefore, testing and validation of PRS is a crucial factor in development, but is hardly possible in real production environments due to the availability of physical manufacturing facilities and the associated risks. Therefore, in the scope of the paper, a concept for a software framework is proposed to provide a test environment for a PRS. A virtual production environment, which can be combined with the PRS and parameterized in form of a discrete-event simulation, offers the possibility to test the PRS. It is also possible to couple the simulation with a reference calculation to show the behavior of the production environment. The entire framework is designed to enable the identification of critical machines and times as well as, in further steps, the detection of software errors in the PRS. [ABSTRACT FROM AUTHOR]

Escalation in the real world applications using Internet of Things (IoT) now demands low cost and minimum power consuming devices having sensing, communicating, and computing capabilities. Low-rate Wireless Personal Area Network (LR-WPAN) is an emerging solution which can provide these capabilities with low data rate and less complexity. The longevity and connectivity of the network is a critical issue during the data collection in LR-WPAN. An Energy aware Load Balanced Clustering (ELBC) approach is proposed in this paper that enhances the network life time by minimizing the energy consumption. Clustering is done using the Golden Eagle Optimization (GEO) and by taking connectivity, Average energy of cluster, balancing factor and cohesion as parameters for optimal cluster head selection. Mobile Sink based data collection strategy using Gravitational Search Algorithm (GSA) is used to collect data from clusters. The simulation is done in network simulator-3 and the result shows that, the performance of ELBC-GEO is better in terms of network lifetime, average energy of nodes, and data collection delay. [ABSTRACT FROM AUTHOR]

Energy minimization is one of the significant tasks in the WSN which can be achieved through clustering process. Clustering in the WSN is an efficient method to increase the lifespan of the network. In the proposed work the main concentration is given to the network lifetime which is very necessary for the less node failures and high packet deliveries. In this paper the hierarchical structuring is done using clustering approach based on efficient data aggregation process. The aggregation process is defined into two phases. The first phase defines the arrangement of the nodes in the cluster using uniform distribution and second phase defines the selection of the cluster head and aggregation of the data using linear programming. The main objective is to minimize the energy consumption and increase of the robustness of the sensor network. Eventually the performance of the network will be evaluated in terms of low energy consumption, high packet deliveries and low congestions in the WSN. [ABSTRACT FROM AUTHOR]

The general concept and implementation of a practice-oriented social network data storage and analysis system are discussed in this paper. The need for such a concept comes from the fact that at the moment little attention is paid to the internal structure of such systems. The proposed system organizes the process of collecting data, filling the database from third-party sources and analyzing the collected data. The main problem of gathering fragmented data from different sources is also addressed. Due to the variety of possible information formats, approaches for designing efficient data warehouse for further social network user's data analysis are discussed in this paper. Considered approach for design and implementation of the data collection subsystem can be used for storing user's data with the current trends in the development of social networks. The problem of data confidentiality in such systems is also discussed. An example is provided for the implementation of the main parts of the system. [ABSTRACT FROM AUTHOR]

Abstract The Internet of Things environment consists of a large amount of devices deployed in the terrain that operate to accomplish different tasks. There are different applications operating in such an environment, which requires various types of devices to sense and communicate. To achieve network efficiency, it is required to have collaboration among these devices. In IoT environment, the services must be accessed at any time, from any where and at any place. In this view, there is a need to have the location of these devices available at all times. Also, the location information must be available for all the devices at any time in order to achieve real time response. This paper proposes a device localization scheme in a dynamic scenario, where minimum communication based location estimation is performed using the two layer IoT framework, namely, sensing layer and IoT layer. The location estimation and sharing of the estimated location information by sensor and IoT devices are performed, irrespective of their physical characteristics. The novelty of this paper may be noted by the fact that the proposed algorithm uses a real test bed with unmanned aerial vehicles for data collection and data dissemination among different entities in the network. [ABSTRACT FROM AUTHOR]

Urban bicycling data collection with mobile digital devices has become ubiquitous in cities across the globe in the last decade. However, planners, developers, data and city scientists often struggle to process, avoid biases, clean datasets, and select relevant data points, when processing, analyzing and modelling vast troves of data, when engineering applications for urban mobility. This paper aims to shed light on these data sources and bases using a descriptive and comparative analysis of the social layers embodied into this data. These ultimately and inadvertently might result in algorithmic analysis and predictive simulations with inadequate sensitivity and specificity outputs for desired requirements. Actors and social groups have vested and specific interests in these bicycling databases, which in turn shape data bases biases or disclosure policies. Therefore, their data will reflect mostly the way they interact with bicycles, cyclists and cities. How pervasive, available for academic research, and which uses are currently made with bicycling bulk data collections in the case of Berlin? Sixteen social groups that operate or hold digital bicycling data systems in that city are investigated. Results show that a clear distinction between the data produced by public and private social groups, as different uses and disclosure polices for it are identified. These contrasts significantly reduce the range of models and applications that can derive from this data. In the development of diagnostic and prognostic systems, public owned are more suitable, whereas private is for commercial application. Digitization of urban bicycling data appeared to produce different data quality and quantity. The brief description and grouping of a specific sociotechnical ensemble dynamic, such as the one conducted here, is particularly useful for the early stages of research, data collection planning and modelling, and in the design of requirements for technology development. [ABSTRACT FROM AUTHOR]

For WSNs deployed in remote tactical edge networks without available infrastructure, using UAVs to collect sensors data can greatly extend sensor networks life. Due to constraints such as energy consumption and communication delay, rapid path planning is necessary for UAVs flight. This paper mainly proposes a new path planning algorithm based on spiral decomposition, which is named as Spiral Path Planning (SPP) algorithm. Besides, the paper improves the existing FPPWR algorithm. Simulation proves SPP is more suitable for a UAV to collect data from sensors nodes evenly distributed in a circle area. Compared with FPPWR, the path SPP plans is shorter, and runtime is less when the number of sensor nodes is small. [ABSTRACT FROM AUTHOR]

Recently, multirotor Unmanned Aerial Vehicles(UAVs) or drones have become increasingly popular for a vast variety of civil applications. Efficient traffic data collection and extraction of various flow parameters are some of the futuristic applications of this technology. However, such applications still need to be streamlined and thoroughly explored for varying traffic and infrastructural conditions. In this paper, the focus is on the authentication of the application of small multirotor UAVs for traffic data collection and subsequent analysis of traffic streams at urban roundabouts. This paper presents an analytical methodology to evaluate the performance of roundabouts by extracting various parameters and performance indicators. The performance evaluation methodology is based on: (i) determining traffic volume via OD matrices for each leg, and (ii) analyzing drivers’ behavior via gap-acceptance analysis. The overall analytical process is principally based on the authors’ previously proposed automated UAV video-processing framework for the extraction of vehicle trajectories. The extracted trajectories are further employed to extract useful traffic information. The experimental data to analyse roundabout traffic flow conditions was obtained in the city of Sint-Truiden (Belgium). The results reflect the value of flexibility and bird-eye view provided by UAV videos; thereby depicting the overall applicability of the UAV-based traffic analysis system. With the significant increase in the usage of UAVs expected in the coming years, such studies could become a useful resource for practitioners as well as future researchers. The future research will mainly focus on further extensions of the UAV-based traffic applications. [ABSTRACT FROM AUTHOR]

Recently, Big Data analytics has been one of the most emerging topics in the business field. Data is collected, processed and analyzed to gain useful insight for their organization. Big Data analytics has the potential to improve the quality of life and help to achieve Sustainable Development Goals (SDG). To ensure that SDG goals are achieved, we must utilize existing data to meet those targets and ensure accountability. However, data quality is often left out when dealing with data. Any types of errors presented in the dataset should be properly addressed to ensure the analysis provided is accurate and truthful. In this paper, we have addressed the concept of data quality diagnosis to identify the outlier presented in the dataset. The cause of the outlier is further discussed to identify potential improvements that can be done to the dataset. In addition, recommendations to improve the quality of data and data collection systems are provided. [ABSTRACT FROM AUTHOR]

The focus of this paper is upon developing a comparative methodology that establishes if there are any statistical commonalities in divergent groups. The methodology developed employees a three tiered data collection approach that integrates a range of methods such as questionnaires, interviews and Q matrices; along with combining multiple methodologies, the main two being Delphi Convergence Approach and Q Methodology. Furthermore this paper will discuss the use of the IBM SPSS Software as a program that offers flexible analysis of the data collected, weather this is for individual factors or the data as a whole. The methodology discussed was created to analyze if minority groups, (Case Study: LGBT+, disabilities, ethnic and religious minorities within Durham University), have shared experiences. The individuals with in these groups are vastly different which is what lead to the need to develop a flexible methodology which can simultaneously evaluate the commonalities between multiple varying groups. This multi-tiered methodology yielded statistically sound results showing the areas of strong correlation and disagreements between the different groups thus proposing a method for investigating potentially massive data through analyzing samples of representatives of key profiles. [ABSTRACT FROM AUTHOR]

This paper presents the results of a study on research methodology applied within the field of Value and Value Creation in construction industry. The purpose of the paper is to give an overview of approaches that are used, and map which research philosophies and methods are most common within this field. The research is based on an extensive literature review. Relevant research contributions from 1980 to 2016 were studied. The publications have been categorized based on their data collection method (literature review, interviews, surveys etc.), and which philosophical approach (positivism, interpretivism, realism and idealism) that is applied. Research on value has been conducted in many different contexts such as marketing, psychology, manufacturing and construction. However, this research is limited to publications that are directly associated with buildings, construction projects and construction industry. The paper also presents the development of research methods on value and value creation. The results, besides providing a knowledge status, give an overview that can be helpful for fellow researchers to improve the research quality, and see if there is a need to look into the topic from alternative perspectives. [ABSTRACT FROM AUTHOR]

Check: I accept the terms and conditions and privacy policy statements associated with this technological artefact! The informed consent process is becoming more of a challenge with the emergence of Internet of Things (IoT) as data may be collected without the digital health citizen being aware. It is argued in this paper that the first phase for universal usability of IoT within the smart health domain is to ensure that digital health citizens (i.e. user of technology) are fully aware of what they are consenting to when they register an account with such technological artefacts. This point is further reinforced by the proposed ‘Privacy by Design’ requirements associated with the forthcoming General Data Protection Regulation (GDPR). This paper proposes some practical approaches which should be considered when designing and developing IoT for data collection and data sharing within the health domain. [ABSTRACT FROM AUTHOR]

Social Media provides organizations ability to survey feelings towards the contents and events associated to them in real time. Moreover, the first demarche of the sentiment analysis is the pre-processing of data collected from Social Media. Most of existing research works that deals with social media analysis based on extracting new features related to sentiment. This paper presents the usage of Twitter in a number of proposed subjects, which is the largest social networking website where Twitter data is in increasing at higher rates every day that considers it as Big Data Source. Then, describing in detail the way in which Big data technology, such as, InfoSphere BigInsights enables processing of this data, which are primarily collected from social networks by Apache Flume and stored in Hadoop storage. In addition, we have investigated a Big Data platform for collecting social media data based on Apache Flume and analyzing this data using InfoSphere BigInsights. Moreover, our paper integrates the visualization of these analysis results using BigSheets. To that end, evaluation through analysis of results confirms that the proposed Big Data platform produces better results in terms of social media analysis. [ABSTRACT FROM AUTHOR]

The recent standard CMMN presents an approach to deal with situations, referred to as cases, that call for more flexibility than that offered by traditional control-flow based approaches. The information (documents and data) needed by a case is collected in a hierarchical structure called case file. The advancement of a case is based on the execution of the tasks shown in the case process model; the determination of the tasks to be carried out takes place through human decisions or events (e.g., those related to changes in the case file). However, the standard does not explain how the actual assignments of tasks to the participants in the case are generated. This paper tries to fill the gap by presenting a more detailed treatment of assignments, and by proposing an extension to the CMMN notation. This extension enables the features of assignments to be inferred from the case process model; moreover, it allows for the representation of the dataflow between tasks. There are two major benefits: one is to show the types of the inputs of the assignments, and the other is the possibility of determining the assignees of tasks as a function of the input entities. The notation is illustrated with the help of an example that concerns the handling of the papers submitted to a conference. [ABSTRACT FROM AUTHOR]

Cognitive neuroscience techniques have become a great complement to traditional methods in marketing research. However, many difficulties have arisen, such as the collection of reliable data, processing and proper analysis. The paper presents good practices of collecting neurophysiological data for the evaluation of media messages in social campaigns. For this purpose, the most common difficulties during signal recording for devices such as EEG, GSR, HR, Eye Tracker were identified. The problems that arose were assigned to three main groups that could be affected, i.e.the examing person, the examing enviroment and the examing equipment. In the final stage, solutions were proposed to reduce or eliminate them. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is rapidly growing as a topic of research and conversation. IoT includes monitoring applications such as environment, healthcare, and military, requiring real-time gathering and data delivery. In dealing with time-sensitive applications, time synchronization is essential to collaborative wireless sensor network (WSN) nodes' data collection and routing information sharing. This paper proposes an ad hoc routing and data collection protocol with a time synchronization function. A hybrid packet-based time synchronization is introduced, wherein reference time is sourced from the sink node (centralized) and sourced from the adjacent nodes (distributed). The time synchronization process is integrated into the routing and data transmissions in the network as it lessens overheads and transmission exchanges, which would be beneficial for large-scale WSN. Finally, we evaluate the protocol's performance using a realistic low-cost WSN testbed. Our proposed protocol has achieved an averaged time offset of less than 100 milliseconds for a 15-minute test duration in multiple topology scenarios. [ABSTRACT FROM AUTHOR]

The deep sea has a large strategic depth, in which target detection is a crucial link, that is, how to detect the target submarine. For this reason, since this century, countries represented by the United States have innovated ideas, innovated underwater detection technology, and enhanced new underwater detection capabilities. In this paper, by consulting more than 100 literatures, the data acquisition technology in underwater acoustic detection in recent years has been investigated in detail, passive detection and active detection have been comprehensively reviewed, and the collaborative detection technology has also been summarized. In order to provide reference for related research. [ABSTRACT FROM AUTHOR]

Inspired by the victory of Apache's Hadoop this paper suggests a new reduce task scheduler. Hadoop is an open source implementation of Google's MapReduce framework. Programs which are written in this functional style are automatically executed and parallelized on a large cluster of commodity machines. The details how to partition the input data, setting up the program's for execution across a set of machines, handling failures of machine and managing the necessary inter-device communication is taken care by runtime system. In the current versions of Hadoop, the map tasks are scheduled with respect to the locality of their inputs in order to shrink network traffic and improve performance. On the other hand, the reduce tasks are scheduled without taking into consideration data locality leading to ruin the performance at requesting nodes. In this paper, we use data locality that is natural with reduce tasks. To accomplish the same, we schedule them on nodes that will result in least amount data- local traffic. Experimental results signify an 11-80 percent decrease in the number of bytes shuffled in a Hadoop cluster . [ABSTRACT FROM AUTHOR]

This paper presents a novel large scale data management model for future smart cities. The system is exploiting the emerging cloud computing services availability over the globe by processing the collected data at the edge. To handle the large scale requirements in smart city, we introduce a Mobile Edge Computing (MEC) framework in order to increase the reliability of the deployed applications. MEC is a promising framework to reduce the cloud core utilization as well as to provide applications that require low latency to mobile end users. As near as possible to the location of the end user, MEC relates to the mobile network applications and data stream acceleration through caching and/or compressing of relevant data at the edge of the mobile network. Although MEC is not yet deployed in real-life systems, recent studies discussed some technical details and related concepts. This paper studies the definition of MEC and similar concepts with respect to typical application scenarios, like in smart cities, and provides scope and limitations that may be encountered when implementing and deploying MEC. Then, a MEC framework is used to explore the data management infrastructure for upcoming smart cities. [ABSTRACT FROM AUTHOR]

Land Ports of Entry (LPOEs) are facilities that control access into the U.S., as well as departures of persons and freight. While traffic simulation modeling techniques are not typically used to evaluate LPOE operational processes, they should be considered more often, because LPOEs have a significant impact on surrounding traffic conditions and vice versa. This paper proposes a methodology for developing microscopic traffic simulation models of LPOEs along the U.S.-Mexico border. The methodology consists of seven steps: (i) collect data; (ii) develop traffic or roadway network; (iii) model inspection and toll booths; (iv) develop traffic management strategies; (v) set up traffic demand; (vi) calibrate model; and (vii) validate model. The paper also presents a case study in which this methodology was followed to develop the Ysleta – Zaragoza LPOE traffic simulation model, which can be controlled from a web-based interface to help LPOE personnel without previous transportation modeling experience. [ABSTRACT FROM AUTHOR]

This paper investigates the effectiveness of a hybrid genetic and fuzzy set algorithm for the recognition of gait patterns in falls risk patients. In a previous work, we have shown the usefulness of fuzzy set techniques for gait pattern identification. In this paper, we apply a genetic algorithm in conjunction with fuzzy logic rules to better select the optimal combination of pathological gait features for improved gait diagnostic capability. Gait features were calculated using minimum foot clearance data collected during continuous walking on a treadmill for 20 older adults. The subjects are composed of two groups, 10 individuals with normal gait, and 10 with a history of falls. Fuzzy rules were extracted from the gait dataset using subtractive clustering. The genetic algorithm was introduced in order to select the optimum combination of gait features. Using cross validation test data, the results indicated that the generalization performance, in terms of accuracy, for the hybrid system was 97.5%, compared to89.3% that was obtained using only the fuzzy system. The generalization performance of the gait classifier was also analyzed by determining the areas under the receiver operating characteristic plot. We observed that an improved gait classification performance became evident when the fuzzy system classifier used a small number of features that were selected by the genetic algorithm. [ABSTRACT FROM AUTHOR]

This paper reports on our ongoing efforts to collect speech data in under-resourced or endangered languages of Africa. Data collection is carried out using an improved version of the Android application Aikuma developed by Steven Bird and colleagues 1. Features were added to the app in order to facilitate the collection of parallel speech data in line with the requirements of the French-German ANR/DFG BULB (Breaking the Unwritten Language Barrier) project. The resulting app, called Lig-Aikuma, runs on various mobile phones and tablets and proposes a range of different speech collection modes (recording, respeaking, translation and elicitation). Lig-Aikuma's improved features include a smart generation and handling of speaker metadata as well as respeaking and parallel audio data mapping. It was used for field data collections in Congo-Brazzaville resulting in a total of over 80 hours of speech. Design issues of the mobile app as well as the use of Lig-Aikuma during two recording campaigns, are further described in this paper. [ABSTRACT FROM AUTHOR]

Real-time data stream processing technologies play an important role in enabling time-critical decision making in many applications. This paper aims at evaluating the performance of platforms that capable of processing streaming data. Candidate technologies include Storm, Samza, and Spark Streaming. To form the recommendation, a prototype pipeline is designed and implemented in each of the platform using data collected from sensors used in monitoring heavy-haul railway systems. Through the testing and evaluation of each candidate platform, using both quantitative and qualitative metrics, the paper describes the findings. [ABSTRACT FROM AUTHOR]

This paper presents a passive hydrostatic stabilization system design for a box-shaped autonomous underwater vehicle. In order to ensure the quality of data collected, underwater inspection mission require a high stability AUV, especially in roll and pitch direction. However, conventional AUV adopt torpedo-shaped body, which has limited passive restoring moment in roll and pitch direction. Therefore, this paper presents design of a box-shaped autonomous underwater vehicle which is highly stable in roll and pitch direction. The passive stability of the designed autonomous underwater vehicle is improved by putting the heavy components at the bottom part of the vehicle whereas light component at the top part of the vehicle. The designed vehicle weights 68.8 kilogram in air with 0.6 kilogram positive buoyant. It has centre of buoyancy exactly above the centre of gravity with 7.39 centimeter metacentric height. By calculation, the vehicle will have 35.68 newton meter restoring moment at 45 degree. By pool test, result shows that the vehicle is able to retain its original vertical axis as vertical without steady state error. [ABSTRACT FROM AUTHOR]

In several papers, we have been stressing the importance of curation in chance discovery. In order to build user-firendly guidance, which we call curation, in using KeyGraph, Hatanaka analyzed participant's bahaviour during data analysis using KeyGraph. The result is not the central aim of this paper. It will be reported in the other paper. For the study, he collected various types of data, for instance eye-moving, speaking data etc. In fact, the data were collected for the analysis of user's behaviour. However, if we consider the data market applicaiton, this type of data can be applied to such data market. For the analysis from various perspective, we have collected several types of data. In this paper, we review those data as data types in data market which will become important when we use the data. [ABSTRACT FROM AUTHOR]

Business Intelligence (BI) has been an object of study for many researchers around the world. From collecting, treating and storing data, to systems solutions, database administration, and analysis techniques applied to various fields such as retail, call centers, financial institutions, and telecommunication. This paper aims to present a small sample of what is possible to achieve by analyzing text data from academic papers by using the software R-project. The methodology consisted of analyzing a sample of the top 35 most relevant papers regarding Business Intelligence, obtained through an academic search engine and offered the results of this text mining study. [ABSTRACT FROM AUTHOR]

A digitalized Smart Factory can be considered as a data island. Moreover, engineers have focused on the development of new technologies and techniques not only for transforming information to data but also to achieve efficient data utilization to further optimize manufacturing processes. However, the Zero-Defect Manufacturing concept has emerged, where the main goal is production optimization. The cornerstone in achieving the factories of the future is to further optimize the design of new assets so as they comply with the unique requirements of the customers. Therefore, this paper proposes the conceptualization, design, and initial development of a platform for the utilization of data derived from industrial environments for the optimization of the equipment design. The main aspects of the proposed framework are the data acquisition, data processing and the simulation. The applicability of the proposed framework has been tested in a laboratory-based machine shop utilizing data from a real-life industrial scenario. [ABSTRACT FROM AUTHOR]

Monitoring activities detect changes in the environment and can be used for several purpose. To develop new advanced services for smart environments, data gathered during the monitoring need to be stored, processed and correlated to different pieces of information that characterize or influence the environment itself. In this paper we propose a Cloud storage solution able to store huge amount of heterogeneous data, and provide them in a uniform way. To this aim, we adopt an hyrid architecture that couple Document and Object oriented strategies, in order to optimize data storage, querying and retrieval. In this paper, we present the architecture design and discuss some implementation details in the development of the architecture within a specific use case. [ABSTRACT FROM AUTHOR]

It's a big challenge to deal with security in a city. Technology advancements are influencing our life, cities are evolving, and modern cities are referring more and more to digital technologies. Currently, a huge amount of standalone independent-systems operate in the city, their goal is to satisfy some business activities, e.g. banking, customs, hospitals, etc. Data collected by these systems represents, if integrated, a key element in any decision making process. This paper presents a, working, smart collaborative platform to integrate multiple systems to serve the surveillance activities in a city or country. It consists of a collaborative surveillance system, called CityPro. The architecture that we propose is a future vision to protect people and monitor public infrastructures, such as bridges, roads, buildings, etc.; it is designed to deal with and/or prevent abnormal activities like terrorist attacks. CityPro is expected to operate in live-mode by using (intended to use) city adapted IT-infrastructures. At the end of this paper, a typical case study is given, and challenges and future works are also discussed. [ABSTRACT FROM AUTHOR]

Modern survey data collection systems must balance cost and quality while supporting multiple response modes (paper, internet, telephone and personal interview) and addressing unpredictable respondent behavior. The next generation of survey systems utilizes adaptive methods to address these issues, but this may affect system behavior and introduces new issues. The paper discusses the development of a system model to analyze system behavior, determine the level of complexity present, define the conditions under which complex behaviors occur and explore approaches to manage complexity. The model, built in NetLogo, uses an agent representation for control, response mode management and the respondent. It not only represents control logic, particularly survey strategies, but also realistic stochastic respondent demographics and external influences which are independent of the system. The paper frames the problem as a potential complex adaptive system, discusses the approach and modeling of the system and reviews analysis of the model to date and its impacts on system design. Preliminary results indicate that basic system behavior is complicated (according to the Cynefin framework) but external influences can introduce unpredictable behaviors that make the system complex, requiring careful management in order to achieve system objectives. [ABSTRACT FROM AUTHOR]

In this paper, we study the complementary characteristics of stationary and mobile devices for traffic data collection. Since stationary devices continuously collect traffic data at fixed locations in a network, they can give insight of the traffic at particular locations over a longer period of time. Mobile devices have wider range and are able to collect traffic data over a larger geographic region. Thus, we argue that both types of technology should be considered to obtain high-quality information about vehicle movements. We present a traffic simulation model, which we use to study the share of successfully identified vehicles when considering both stationary and mobile technologies with varying identification rate. The results of our study, where we focus on freight transport in southern Sweden, confirms that it is possible to identify the majority of vehicles, even when the identification rate is low, and that the share of identified vehicles can be increased by using both stationary and mobile measurement devices. [ABSTRACT FROM AUTHOR]

The ubiquity of smart mobile devices facilitates data collection in the healthcare domain. Two of the concepts, which can be applied in this context, are mobile crowdsensing (MCS) and ecological momentary assessment (EMA). TrackYourTinnitus (TYT) is an advanced mobile healthcare platform that combines both concepts enabling the monitoring and evaluation of the users' individual variability of tinnitus symptoms. This paper describes the underlying data set and structure of the TYT mobile platform and highlights selected issues whose investigation provides advanced insights into the users of this mobile platform as well as their data. [ABSTRACT FROM AUTHOR]

Industrial Internet of Things (IIoT) automation should be based on a framework that guarantees flexible and energy efficient monitoring and control, without the need for frequent human intervention. The ability to analyse and process machine faults in real time is vital, however it poses many technical difficulties and challenges, mainly for industrial application environments. In our paper, we propose a novel, energy efficient, IoT and Cloud based decentralised framework for real time machine condition monitoring (MCM) and fault prediction, where computational demanding tasks are distributed across fog nodes and decision fusion rules are set and controlled by the Cloud. In particular, data acquisition phase is done by sensors distributed across machines, feature extraction and health condition classification is done by fog nodes, after receiving data and instructions as processed by the Cloud node. Our framework is based on collaboration and information flow among IoT, Fog and Cloud layers. To this purpose, we formulate a global consensus cross layer optimisation problem, concerning industrial healthy status monitoring, and we solve it in a distributed manner by applying asynchronous altering direction method of multipliers (ADMM) algorithm. [ABSTRACT FROM AUTHOR]

Throughout decades, many concepts to renovate cities have surfaced such as digital city, green city, smart cities and so forth, so that citizens can reach urban services and exercise their activities easily with respect to the environment's sustainability, by limiting energy waste, optimization of urban infrastructure costs, automation of many routines and efficient allocation of human resources. In effect, decision makers varying from government administrations to private sector have log started using information technology asa mean of to evolve cities, and maintain a consistent correlation between the population culture and the environment they live in, moreover, the solutions they came up with where specific to many problems but unique to each one, as efficient as it may seem to be. Moreover, the technology sector was long ago mature enough to turn inanimate objects into smart objects that interact with each other, and send data. As a result, these solutions make it possible to extract a great amount of data, which is used to turn future predictions more accurate. Nevertheless, the technical solutions are not designed to cover data from multiple sectors, and if so, they lack the possibility to be extended with new sector layers without going through a whole development processes that can turn the old system obsolete. Therefore, as a solution in this paper, to cover new aspects and facilitate addition of new layers of data types, we brought out an abstract layer to cover not only natural events (temperature, humidity, water level, noise) that occurs in the urban field of a city, but also, urban events for example: train arrivals, accidents (traffic, security infringements), population concentration variation, etc, we called it complex events.Furthermore, we also present the architecture of a system that is scalable and supports the variety of complex events, how it collects and broadcast it to external actors. [ABSTRACT FROM AUTHOR]

Abstract: Wireless sensors networks (WSNs) are traditionally composed of large number of low-cost and tiny homogenous sensors nodes connected through a wireless network that gather data to be treated locally or relayed to the sink node through multi-hop wireless transmission. Moreover, such issues are very critical due servers resources constraints like efficient energy, stock limitation and lifetime of network. Several solutions were proposed to minimize the traffic into network. Clustering algorithms have been widely used to reduce energy consumption. In this context, the key point in such topology is to select a cluster. One of solutions is to select a cluster alternately. However, this choice does not consider the energy as important criteria in actual papers. In order to limit energy consumption, our new method is proposed in this paper to optimization Low Energy Adaptive Clustering Hierarchy (O- LEACH) to improve existing LEACH and LEACH-C by selecting cluster according to the residual energy of nodes dynamically. The simulation results show that proposed algorithm achieve longer stability by comparison to original LEACH and LEACH-C. [Copyright &y& Elsevier]

Abstract: The system engineering literature acknowledges that lean principles foster the achievement of higher program performance. However in the literature it is not clear how exactly the six lean principles affect the performance of the systems engineering programs. This paper addresses this gap by discussing about the core benefits derived from the implementation of each lean principle. Main contribution of this paper is the proposal –through a deep literature investigation refined through a series of focus groups with the Lean in Program Management Community of Practice (MIT-PMI-INCOSE) – of a list of metrics to be considered in order to measure the performance affected by the introduction of a list of 43 Lean Enablers. Only when measuring the current situation of the systems engineering program in fact, it is possible to direct improvement efforts. Further steps of authors’ research are direct to understand how the collected metrics could practically and effectively support the lean journey within system engineering programs. [Copyright &y& Elsevier]

Abstract: Negotiation and collaboration issues in large organizations, with centralized control, across the globe, are becoming more complex with each passing day. The Acknowledged System of Systems (SoS) is a new approach that addresses some of these issues in a systematic, efficient manner. This paper proposes a hierarchical architectural framework to support Acknowledged SoS architecting and analysis for a Department of Defense (DoD) Acquisition process. A major challenge of the successful planning and evolution of an Acknowledged SoS is the lack of understanding of the impact of presence or absence of a system and its interface with another constituent system on the overall architecture. The agent based model (ABM) structure developed here provides Acknowledged SoS manager that has both ability to address the managerial issues as well as a decision making tool for SoS architecting. This paper offers a complete integration of the techniques used to represent SoS architectures. The work illustrates the modeling approach through a domain setting. [Copyright &y& Elsevier]

Abstract: This paper describes a long-term filed data collection system from a person living house required for designing a life pattern sensor. The life pattern sensor is an activity recognition sensor both for measuring electric power consumption and detecting use of electronic devices by utilizing high-frequency electrical current waveform as a signature of each device. A prototype of the life pattern sensor has been developed and evaluated in five homes. The results show the life pattern sensor measured power consumption within 1% error and identified uses of devices with over 95% accuracy. The capability of the sensor is enough for realizing home energy management system. The long-term data collection system is constructed so as to collect annotated field data for sophisticating the sensor in practical level. In this paper, the overview of the data collecting system and discussion about the following questions based on the field data: [1.] Can current waveform be in stable at any conditions in a house? [2.] Can sequence of electronic devices describe residents’ real life pattern? [3.] Can residents manage energy efficiently by knowing their own life pattern? [Copyright &y& Elsevier]

Abstract: The Internet of Things (IoT) describes the interconnection of objects (or Things) for various purposes including identification, communication, sensing, and data collection. “Things” in this context range from traditional computing devices like Personal Computers (PC) to general household objects embedded with capabilities for sensing and/or communication through the use of technologies such as Radio Frequency Identification (RFID). This conceptual paper, from a philosophical viewpoint, introduces an initial set of guiding principles - also referred to in the paper as commandments - that can be applied by all the stakeholders involved in the IoT during its introduction, deployment and thereafter. [Copyright &y& Elsevier]

Abstract: In their search for satisfactory solutions to complex combinatorial problems, metaheuristics methods are expected to intelligently explore the solution space. Various forms of memory have been used to achieve this goal and improve the performance of metaheuristics, which warranted the development of the Adaptive Memory Programming (AMP) framework [1]. This paper follows this framework by integrating Machine Learning (ML) concepts into metaheuristics as a way to guide metaheuristics while searching for solutions. The target metaheuristic method is Meta-heuristic for Randomized Priority Search (Meta-RaPS). Similar to most metaheuristics, Meta-RaPS consists of construction and improvement phases. Randomness coupled with a greedy heuristic are typically employed in the construction phase. While a local search heuristic is used in the improvement phase. This paper proposes adding a new learning phase, in which a ML method will be integrated. An Inductive Decision Tree (IDT) will be incorporated into the learning phase in an effort to learn from the information collected during the construction and improvement phases. The proposed approach will be demonstrated using instances for the Capacitated Vehicle Routing Problem (CVRP). [Copyright &y& Elsevier]

Abstract: Freight modeling frameworks are frequently based on passenger modeling approaches. Comprehensive and specific freight modeling frameworks are required to improve the evaluation of freight related policies. In this paper, a new agent-based freight modeling framework is presented which is able to incorporate important elements in modern freight markets: agent interactions, product differentiation and economies of scope/scale. These elements are combined in a market dynamic environment assuming agent rationality and that agents acquire information while interacting with each other. The conceptual presentation of this framework is the focus of this paper and the data required for its implementation is also discussed. [Copyright &y& Elsevier]

Abstract: One of the challenges of systems engineering research is that the expertise and the application happen “in the field”. The field can be an industrial company or a government agency. Researchers need methods to research in the field; methods to try-out ideas, collect data, analyze, and evaluate. In this paper, we present a simple research model to help researchers to shape their research. We provide and discuss a number of elementary research methods. The model and the elementary research methods are based on our systems engineering research experiences in the past five years. We conclude the paper with some validation challenges and pitfalls. [Copyright &y& Elsevier]

Abstract: This paper compares two fundamentally different systems engineering approaches found in two separate NASA systems engineering manuals. These manuals and their corresponding approaches exist as a collection of information that have been gathered, analyzed, and structured in order to help guide systems engineers and systems engineering in the development and management of complex launch systems. As a result, both manuals demonstrate in-depth insight into systems engineering as a discipline and its evolution through decades of practice, research, and collective thought. However, in order to fully understand the insight these manuals can provide, this paper presents a clear description, discussion, and distinction between the 1995 NASA manual with its focus on the product aspects within systems engineering and the 2007 NASA manual with its focus on the process aspects. By allowing this separation, this paper abstracts the characteristics, merits, and issues surrounding these two systems engineering approaches and leverages them to critically examine NASA''s formal systems engineering culture. [Copyright &y& Elsevier]