We describe a prototype ontology-driven information system (ODIS) that exploits what we call Portion of Reality (POR) representations. The system takes both sensor data and natural language text as inputs and composes on this basis logically structured POR assertions. The goal of our prototype is to represent both natural language and sensor data within a single framework that is able to support both axiomatic reasoning and computation. In addition, the framework should be capable of discovering and (...) representing new kinds of situations and thematic roles, (e.g., roles such as agent, patient and instrument), based on new compositions of existing representations. We applied our prototype in an intelligence analysis use case to test the hypothesis that a framework of this sort can produce usefully structured information from combined natural language and sensor data inputs. We further tested our hypothesis by adding an enhanced US Air Force ontology framework to our ODIS in order to (1) process a collection of sensor data, intel reports, and mission plans; (2) build composite POR representations from these data; and (3) machine analyze the fused results to infer mission threats. (shrink)

This article presents a formal theory of robot perception as a form of abduction. The theory pins down the process whereby low‐level sensor data is transformed into a symbolic representation of the external world, drawing together aspects such as incompleteness, top‐down information flow, active perception, attention, and sensor fusion in a unifying framework. In addition, a number of themes are identified that are common to both the engineer concerned with developing a rigorous theory of perception, such as (...) the one on offer here, and the philosopher of mind who is exercised by questions relating to mental representation and intentionality. (shrink)

Tradition wireless sensor networks transmit data by single or multiple hops. However, some sensor nodes close to a static base station forward data more frequently than others, which results in the problem of energy holes and makes networks fragile. One promising solution is to use a mobile node as a mobile sink, which is especially useful in energy-constrained networks. In these applications, the tour planning of MS is a key to guarantee the network performance. In this (...) paper, a novel strategy is proposed to reduce the latency of mobile data gathering in a WSN while the routing strategies and tour planning of MS are jointly optimized. First, the issue of network coverage is discussed before the appropriate number of clusters being calculated. A dynamic clustering scheme is then developed where a virtual cluster center is defined as the MS sojourn for data collection. Afterwards, a tour planning of MS based on prediction is proposed subject to minimizing the traveling distance to collect data. The proposed method is simulated in a MATLAB platform to show the overall performance of the developed system. Furthermore, the physical tests on a test rig are also carried out where a small WSN based on an unmanned aerial vehicle is developed in our laboratory. The test results validate the feasibility and effectiveness of the method proposed. (shrink)

Agitation is one of the most common behavioural and psychological symptoms in people living with dementia. This behaviour can cause tremendous stress and anxiety on family caregivers and healthcare providers. Direct observation of PLwD is the traditional way to measure episodes of agitation. However, this method is subjective, bias-prone and timeconsuming. Importantly, it does not predict the onset of the agitation. Therefore, there is a need to develop a continuous monitoring system that can detect and/or predict the onset of agitation. (...) In this study, a multi-modal sensor platform with video cameras, motion and door sensors, wristbands and pressure mats were set up in a hospital-based dementia behavioural care unit to develop a predictive system to identify the onset of agitation. The research team faced several barriers in the development and initiation of the study, namely addressing concerns about the study ethics, logistics and costs of study activities, device design for PLwD and limitations of its use in the hospital. In this paper, the strategies and methodologies that were implemented to address these challenges are discussed for consideration by future researchers who will conduct similar studies in a hospital setting. (shrink)

Reliable data transfer and energy efficiency are the essential considerations for network performance in resource-constrained underwater environments. One of the efficient approaches for data routing in underwater wireless sensor networks (UWSNs) is clustering, in which the data packets are transferred from sensor nodes to the cluster head (CH). Data packets are then forwarded to a sink node in a single or multiple hops manners, which can possibly increase energy depletion of the CH as compared (...) to other nodes. While several mechanisms have been proposed for cluster formation and CH selection to ensure efficient delivery of data packets, less attention has been given to massive data communication processes with sink node. As such, failure in communicating nodes would lead to a significant network void-holes problem. Considering the limited energy resources of nodes in UWSNs along with the heavy load of CHs in the routing process, this paper proposes a void-holes aware and reliable data forwarding strategy (VHARD-FS) in a proactive mode to control data packets delivery from CH nodes to the sink in UWSNs. In the proposed strategy, each CH node is aware of its neighbor’s performance ranking index to conduct a reliable packet transmission to the sink via the most energy-efficient route. Extensive simulation results indicate that the VHARD-FS outperforms existing routing approaches while comparing energy efficiency and network throughput. This study helps to effectively alleviate the resource limitations associated with UWSNs by extending network life and increasing service availability even in a harsh underwater environment. (shrink)

Sensor-based data are becoming increasingly widespread in social, behavioral and organizational sciences. Far from providing a neutral window on 'reality', sensor-based big-data are highly complex, constructed data sources. Nevertheless, a more systematic approach to the validation of sensors as a method of data collection is lacking, as their use and conceptualization have been spread out across different strands of social-, behavioral- and computer science literature. Further debunking the myth of raw data, the present (...) article argues that, in order to validate sensor-based data, researchers need to take into account the mutual interdependence between types of sensors available on the market, the conceptual (construct) choices made in the research process, and the contextual cues. Sensor-based data in research are usually combined with additional quantitative and qualitative data sources. However, the incompatibility between the highly granular nature of sensor data and the static, a-temporal character of traditional quantitative and qualitative data has not been sufficiently emphasized as a key limiting factor of sensor-based research. It is likely that the failure to consider the basic quality criteria of social science measurement indicators more explicitly may lead to the production of insignificant results, despite the availability of high volume and high-resolution data. The paper concludes with recommendations for designing and conducting mixed methods studies using sensors. (shrink)

In the traditional case, the uncertainty of the ambient temperature measured by the experiential distributed sensor is considered. In this paper, a model based on the moving least square method in the fusion algorithm is proposed to study the optimal monitoring point of the sensor in the greenhouse and determine the most suitable installation position of the sensor in the greenhouse to improve the control effect of the temperature control device of the system. MATLAB simulation software is (...) used to simulate each working condition of the greenhouse. Temperature data measured at 15 locations in the greenhouse were used to evaluate all possible combinations of monitoring locations and to estimate the optimal location for indoor temperature sensors. Compared with the traditional method, the error is reduced to 0.373, and the data are more accurate. (shrink)

This paper describes the investigations performed to better understand two-stage rotor speed matching in a contrarotating fan. In addition, this study develops a comprehensive measuring and communication system for a contrarotating fan using ZigBee network. The investigation method is based on three-dimensional RANS simulations; the RANS equations are solved by the numerical method in conjunction with a SST turbulence model. A wireless measurement system using big data method is first designed, and then a comparison is done with experimental measurements (...) to outline the capacity of the numerical method. The results show that when contrarotating fan worked under designed speed, performance of two-stages rotors could not be matched as the designed working condition was deviated. Rotor 1 had huge influences on flow rate characteristics of a contrarotating fan. Rotor 2 was influenced by flow rates significantly. Under large flow rate condition, the power capability of rotor 2 became very weak; under working small flow rate condition, overloading would take place to class II motor. In order to solve the performance mismatch between two stages of CRF under nondesigned working conditions, under small flow rate condition, the priority shall be given to increase of the speed of rotor 1, while the speed of rotor 2 shall be reduced appropriately; under large flow rate condition, the speed of rotor 1 shall be reduced and the speed of rotor 2 shall be increased at the same time. (shrink)

Timers and sensors are common devices that make our daily life safer, more convenient, and more efficient. In a cellular context, they arguably play an even more crucial role as they ensure the survival of cells in the presence of various extrinsic and intrinsic stresses. Biological timers and sensors generate distinct signaling profiles, enabling them to produce different types of cellular responses. Recent data suggest that they can work together to guarantee correct timing and responsiveness. By exploring examples of (...) cellular stress signaling from mitosis, DNA damage, and hypoxia, the authors discuss the common architecture of timer‐sensor integration, and how its added features contribute to the generation of desired signaling profiles when dealing with stresses of variable duration and strength. The authors propose timer‐sensor integration as a widespread mechanism with profound biological implications and therapeutic potential. -/- . (shrink)

Smart cities build on the idea of collecting data about the city in order for city administration to be operated more efficiently. Within a research project gathering an interdisciplinary team of researchers – engineers, designers, gender scholars and human geographers – we have been working together using participatory design approaches to explore how paying attention to the diversity of human needs may contribute to making urban spaces comfortable and safe for more people. The project team has deployed sensors collecting (...) data on air quality, sound and mobility in a smart city testbed in Norrköping, Sweden. While these sensors are meant to capture an accurate ‘map’ of the street and what is going on along it, our interdisciplinary conversations around the sensors have revealed the heterogeneity both of smart city planning and spatial formulations of the city. The discussions have given rise to questions regarding the work that goes into constructing the sensor box itself, as well as the work of deploying it, and how these influence the ‘map’ that the sensors produce. In this paper, we draw on Lefebvre to explore how the sensors themselves produce smart spaces. We analyze how the box depends on perceived space to function (e.g. requiring electricity), and simultaneously it produces conceptualizations of space that are influenced by the materiality of the box itself (e.g. sensors being affected by heat and noise). Further, we explore how the (in)visibility of sensor technology influences lived space. (shrink)

Background: Smart sensors have been developed as diagnostic tools for rehabilitation to cover an increasing number of geriatric patients. They promise to enable an objective assessment of complex movement patterns. -/- Objective: This research aimed to identify and analyze the conflicting ethical values associated with smart sensors in geriatric rehabilitation and provide ethical guidance on the best use of smart sensors to all stakeholders, including technology developers, health professionals, patients, and health authorities. -/- Methods: On the basis of a systematic (...) literature search of the scientific databases PubMed and ScienceDirect, we conducted a qualitative document analysis to identify evidence-based practical implications of ethical relevance. We included 33 articles in the analysis. The practical implications were extracted inductively. Finally, we carried out an ethical analysis based on the 4 principles of biomedical ethics: autonomy, beneficence, nonmaleficence, and justice. The results are reported in categories based on these 4 principles. -/- Results: We identified 8 conflicting aims for using smart sensors. Gains in autonomy come at the cost of patient privacy. Smart sensors at home increase the independence of patients but may reduce social interactions. Independent measurements performed by patients may result in lower diagnostic accuracy. Although smart sensors could provide cost-effective and high-quality diagnostics for most patients, minorities could end up with suboptimal treatment owing to their underrepresentation in training data and studies. This could lead to algorithmic biases that would not be recognized by medical professionals when treating patients. -/- Conclusions: The application of smart sensors has the potential to improve the rehabilitation of geriatric patients in several ways. It is important that patients do not have to choose between autonomy and privacy and are well informed about the insights that can be gained from the data. Smart sensors should support and not replace interactions with medical professionals. Patients and medical professionals should be educated about the correct application and the limitations of smart sensors. Smart sensors should include an adequate representation of minorities in their training data and should be covered by health insurance to guarantee fair access. (shrink)

Intelligent sensors should be seamlessly, securely, and trustworthy interconnected to enable automated high-level smart applications. Semantic metadata can provide contextual information to support the accessibility of these features, making it easier for machines and humans to process the sensory data and achieve interoperability. The unique overview of sensor ontologies according to the semantic needs of the layers of IoT solutions can serve a guideline of engineers and researchers interested in the development of intelligent sensor-based solutions. The explored (...) trends show that ontologies will play an even more essential role in interlinked IoT systems as interoperability and the generation of controlled linkable data sources should be based on semantically enriched sensory data. (shrink)

Wearable sensors are an integral part of the new telemedicine concept supporting the idea that Information Technologies will improve the quality and efficiency of healthcare. The use of sensors in diagnosis, treatment and monitoring of patients not only potentially changes medical practice but also one’s relationship with one’s body and mind, as well as the role and responsibilities of patients and healthcare professionals. In this paper, we focus on knowledge assessment of the online communities of Fitbit and the Quantified Self (...) movement. Through their online forums, we investigate how users’ knowledge claims, shared experiences and imaginations about wearable sensors interrogate or confirm the narratives through which they are introduced to the publics. Citizen initiatives like the Quantified Self movement claim the right to ‘own’ the sensor generated data. But how these data can be used through traditional healthcare systems is an open question. More importantly, wearable sensors trigger a social function that is transformative of the current idea of care and healthcare, focused on sharing, socialising and collectively reflecting about individual problems. Whether this is aligned with current policy making about healthcare, whose central narrative is focused on efficiency and productivity, is to be seen. (shrink)

The use of sensors has had an enormous increment in the last years, becoming a valuable tool in many different areas. In this kind of scenario, the quality of data becomes an extremely important issue; however, not much attention has been paid to this specific topic, with only a few existing works that focus on it. In this paper, we present a proposal for managing data streams from sensors that are installed in patients’ homes in order to monitor (...) their health. It focuses on processing the sensors’ data streams, taking into account data quality. In order to achieve this, a data quality model for this kind of data streams and an architecture for the monitoring system are proposed. Moreover, our work introduces a mechanism for avoiding false alarms generated by data quality problems. (shrink)

Event cameras which transmit per-pixel intensity changes have emerged as a promising candidate in applications such as consumer electronics, industrial automation, and autonomous vehicles, owing to their efficiency and robustness. To maintain these inherent advantages, the trade-off between efficiency and accuracy stands as a priority in event-based algorithms. Thanks to the preponderance of deep learning techniques and the compatibility between bio-inspired spiking neural networks and event-based sensors, data-driven approaches have become a hot spot, which along with the dedicated hardware (...) and datasets constitute an emerging field named event-based data-driven technology. Focusing on data-driven technology in event-based vision, this paper first explicates the operating principle, advantages, and intrinsic nature of event cameras, as well as background knowledge in event-based vision, presenting an overview of this research field. Then, we explain why event-based data-driven technology becomes a research focus, including reasons for the rise of event-based vision and the superiority of data-driven approaches over other event-based algorithms. Current status and future trends of event-based data-driven technology are presented successively in terms of hardware, datasets, and algorithms, providing guidance for future research. Generally, this paper reveals the great prospects of event-based data-driven technology and presents a comprehensive overview of this field, aiming at a more efficient and bio-inspired visual system to extract visual features from the external environment. (shrink)

Today’s bodies are akin to ‘walking sensor platforms’. Bodies either host, or are the subjects of, an array of sensing devices that act to convert bodily movements, actions and dynamics into circulative data. This article proposes the notions of ‘disembodied exhaust’ and ‘embodied exhaustion’ to conceptualise processes of bodily sensorisation and datafication. As the material body interfaces with networked sensor technologies and sensing infrastructures, it emits disembodied exhaust: gaseous flows of personal information that establish a representational (...) class='Hi'>data-proxy. It is this networked actant that progressively structures how embodied subjects experience their daily lives. The significance of this symbiont medium in determining the outcome of interplays between networked individuals and audiences necessitates that it is carefully contrived. The article explores the nature and function of the data-proxy, and its impact on social relations. Drawing on examples that depict individuals engaging with their data-proxies, the article suggests that managing a virtual presence is analogous to a work relation, demanding diligence and investment. But it also shows how the data-proxy operates as a mode of affect that challenges conventional distinctions made between organic and inorganic bodies, agency and actancy, mortality and immortality, presence and absence. (shrink)

Affordable light detection and ranging sensors are becoming available for tasks such as simultaneous localization and mapping (SLAM) in robotics and autonomous driving; however, these sensors offer less quality data of lower resolution that hinders the performance of registration methods. The deep learning based approaches seem to be sensitive to these data flaws. Specifically, a state-of-the-art deep learning-based approach failed to produce meaningful results after several attempts to carry out transfer learning over a dataset collected indoors with one (...) such affordable sensors. The paper introduces a hybrid approach combining two well-established registration techniques, the iterative closest point algorithm and the normal distributions transform that achieves good performance on the SLAM task over the same dataset. (shrink)

The safety of the transmission lines maintains the stable and efficient operation of the smart grid. Therefore, it is very important and highly desirable to diagnose the health status of transmission lines by developing an efficient prediction model in the grid sensor network. However, the traditional methods have limitations caused by the characteristics of high dimensions, multimodality, nonlinearity, and heterogeneity of the data collected by sensors. In this paper, a novel model called LPR-MLP is proposed to predict the (...) health status of the power grid sensor network. The LPR-MLP model consists of two parts: local binary pattern, principal component analysis, and ReliefF are used to process image data and meteorological and mechanical data and the multilayer perceptron method is then applied to build the prediction model. The results obtained from extensive experiments on the real-world data collected from the online system of China Southern Power Grid demonstrate that this new LPR-MLP model can achieve higher prediction accuracy and precision of 86.31% and 85.3%, compared with four traditional methods. (shrink)

This edited collection brings together a series of interdisciplinary contributions in the field of Information Technology Law. The topics addressed in this book cover a wide range of theoretical and practical legal issues that have been created by cutting-edge Internet technologies, primarily Big Data, the Internet of Things, and Cloud computing. Consideration is also given to more recent technological breakthroughs that are now used to assist, and - at times - substitute for, human work, such as automation, robots, sensors, (...) and algorithms. The chapters presented in this edition address these issues from the perspective of different legal backgrounds. The first part of the book discusses some of the shortcomings that have prompted legislators to carry out reforms with regard to privacy, data protection, and data security. Notably, some of the complexities and salient points with regard to the new European General Data Protection Regulation (EU GDPR) and the new amendments to the Japan's Personal Information Protection Act (PIPA) have been scrutinized. The second part looks at the vital role of Internet intermediaries (or brokers) for the proper functioning of the globalized electronic market and innovation technologies in general. The third part examines an electronic approach to evidence with an evaluation of how these technologies affect civil and criminal investigations. The authors also explore issues that have emerged in e-commerce, such as Bitcoin and its blockchain network effects. The book aims to explain, systemize and solve some of the lingering legal questions created by the disruptive technological change that characterizes the early twenty-first century. (shrink)

Demodulating harmful nodes and diminishing the energy waste in sensor nodes can prolong the lifespan of wireless sensor networks. In this study, a genetic algorithm and an adaptive neuro fuzzy inference system were used to diminish the energy waste of sensors. Weighted trust evaluation was applied to search for harmful nodes in the network to prolong the lifespan of WSNs. A low-energy adaptive clustering hierarchy method was used to analyze the results. It was discovered that searching for harmful (...) nodes with GA-ANFIS using weighted trust evaluation significantly increased the lifespan of WSNs. For evaluation of the proposed method we used the mean of energy of all sensors against of the round, data packets received in base station, minimum energy versus rounds and number of alive sensors versus rounds. Also, in this paper we compared the proposed method results with LEACH, LEACH-DT, Random, SIF and GA-Fuzzy methods. As results the proposed method has high life time than other methods. A representation of the overall system was implemented using MATLAB software. (shrink)

Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the (...) specific properties of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns and generate new information, or simply store and further process large amounts of sensor data is then reviewed, and examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence (AI) aimed at coping with the big data deluge in the near future. Keywords: big data; non-dimensionality; applied data science; paradigm shift; artificial intelligence; principle of parsimony (Occam’s razor) . (shrink)

The trend to implement the monitoring system with a wireless sensor network has been becoming urgent due to guaranteed flight safety and the passengers comfortability in travel. In this paper, a new dynamic routing algorithm is proposed to prolong the lifetime of the monitoring system with a distributed network based on the K-coverage method, and filter algorithm to be used for data fusion. Finally, the simulation results validate the effectiveness of the proposed approach.

With agents relying more and more on information from central servers rather than their own sensors, knowledge becomes property not of a specific agent but of the data that the agents can access. The article proposes a dynamic logic of data-informed knowledge that describes an interplay between three modalities and one relation capturing the properties of this form of knowledge. The main technical results are the undefinability of two dynamic operators through each other, a sound and complete axiomatisation, (...) and a model checking algorithm. (shrink)

Included in the acute response of lowlanders exposed to reduced oxygen availability is an elevated red blood cell count due to increased erythropoietin (Epo) synthesis. According to current thinking, hypoxia is “sensed” by hydroxylases that permit Hypoxia Inducible Factor 1α (HIF‐1α) to complex with HIF‐1β to form a transcriptional activator (HIF‐1) that drives expression of hypoxia‐sensitive genes (such as EPO) under hypoxic conditions. In altitude‐adapted Andean natives, the Epo hypoxic response may be blunted; however, our data indicate that the (...) DNA sequences of the genes encoding Epo (including the 3′ regulatory region) and HIF‐1α appear to be conserved. Hence, adaptive changes in the Andean hypoxic response are not a consequence of changes in the primary sequence of these proteins or of known transcriptional regulatory domains of EPO. These results suggest that the altered erthropoietic response in Andean natives reflects adaptations in hypoxia sensing, rather than hypoxia response, mechanisms. BioEssays 25:515–519, 2003. © 2003 Wiley Periodicals, Inc. (shrink)

The design of collaborative robotics, such as driver-assisted operations, engineer a potential automation of decision-making predicated on unobtrusive data gathering of human users. This form of ‘somatic surveillance’ increasingly relies on behavioural biometrics and sensory algorithms to verify the physiology of bodies in cabin interiors. Such processes secure cyber-physical space, but also register user capabilities for control that yield data as insured risk. In this technical re-formation of human–machine interactions for control and communication ‘a dissonance of attribution’ :7684, (...) 2019. https://doi.org/10.1073/pnas.1805770115) is created between perceptions of phenomena, materials and decision-making. This reconfigures relations not only between humans and machines, objects and subjects, but possibly disrupts attributive functions in the social system of Law. What it requires is shifting a legal accountability for action from a sovereignty of the human to a new materialist account based on a ‘cognitive assemblage’ between physiological data, computation and algorithmic sensing. This paper investigates the function of law as a guidance system to acknowledge this account of sensory and algorithmic computation as autonomous ‘sensing agents’ that may be accountable in situations of risk. This assemblage of robotic computation and sensory determination requires a clearer legal differentiation across the current static terminologies of person, property, liability and rights that maintain strict separations of object from subject. To neglect this, we argue, law will solely impute attributions of error to humans despite evidence of operation via mutual control. (shrink)

The system reachability set is calculated by covering all possible behaviours of the system through a finite number of simulation steps to ensure that the system trajectory stays within a set safety region. In this paper, the theory of the game method is applied to the design of the controller, a very small controller is designed, and good control results are obtained by simulation. The system gradually shows a divergent trend and cannot achieve stable control. A multihop channel reservation Medium (...) Access Control protocol based on a parallel mechanism is proposed. The multihop channel reservation mechanism is proposed based on periodic node sleep, and the node uses the reservation frame to make the reservation of the channel and transmits the data according to the reservation information; the parallel mechanism is used to make the reservation information and data transmission simultaneously. (shrink)

One of the main goals of modern philosophy was to achieve an in-depth insight into the foundations of empirical knowledge. The problem was expected to be resolved by the analysis of experience. However, the road to a plausible account of experience was at the very beginning obstructed by turning the analysis into a search for clear and distinctive elements of experience and by sticking to purely intellectual intuition as means of this analysis. Moreover, clear and distinctive elements of experience were (...) thought of as the basis of cognitive certainty. Both psychology and philosophy, at least until the nineteen-thirties, were deeply influenced by this essentially rationalistic conception of sensor experience. It is gestalt psychology and phenomenology that should be merited for overcoming that ill-conceived model. Only by taking into account the immediate sensor relation between the human subject and the environment, it is possible to show the kind of unity which is the prerequisite of human intellect. (shrink)

In a world surrounded by smart objects from sensors to automated medical devices, the ubiquity of ‘smart’ seems matched only by its lack of clarity. In this article, we use our discussions with expert stakeholders working in areas of implantable medical devices such as cochlear implants, implantable cardiac defibrillators, deep brain stimulators and in vivo biosensors to interrogate the difference facets of smart in ‘implantable smart technologies’, considering also whether regulation needs to respond to the autonomy that such artefacts carry (...) within them. We discover that when smart technology is deconstructed it is a slippery and multi-layered concept. A device’s ability to sense and transmit data and automate medicine can be associated with the ‘sting’ of autonomy being disassociated from human control as well as affecting individual, group, and social environments. (shrink)

Health research increasingly relies on organized collections of health data and biological samples. There are many types of sample and data collections that are used for health research, though these are collected for many purposes, not all of which are health-related. These collections exist under different jurisdictional and regulatory arrangements and include: 1) Population biobanks, cohort studies, and genome databases 2) Clinical and public health data 3) Direct-to-consumer genetic testing 4) Social media 5) Fitness trackers, health apps, (...) and biometric data sensors Population biobanks, cohort studies, and genome databases Clinical and public health data Direct-to-consumer genetic testing Social media Fitness trackers, health apps, and biometric data sensors Ethical, legal, and social challenges of such collections are well recognized, but there has been limited attention to the broader societal implications of the existence of these collections. Although health research conducted using these collections is broadly recognized as beneficent, secondary uses of these data and samples may be controversial. We examine both documented and hypothetical scenarios of secondary uses of health data and samples. In particular, we focus on the use of health data for purposes of: Forensic investigations Civil lawsuits Identification of victims of mass casualty events Denial of entry for border security and immigration Making health resource rationing decisions Facilitating human rights abuses in autocratic regimes Forensic investigations Civil lawsuits Identification of victims of mass casualty events Denial of entry for border security and immigration Making health resource rationing decisions Facilitating human rights abuses in autocratic regimes Current safeguards relating to the use of health data and samples include research ethics oversight and privacy laws. These safeguards have a strong focus on informed consent and anonymization, which are aimed at the protection of the individual research subject. They are not intended to address broader societal implications of health data and sample collections. As such, existing arrangements are insufficient to protect against subversion of health databases for non-sanctioned secondary uses, or to provide guidance for reasonable but controversial secondary uses. We are concerned that existing debate in the scholarly literature and beyond has not sufficiently recognized the secondary data uses we outline in this paper. Our main purpose, therefore, is to raise awareness of the potential for unforeseen and unintended consequences, in particular negative consequences, of the increased availability and development of health data collections for research, by providing a comprehensive review of documented and hypothetical non-health research uses of such data. (shrink)

This paper describes an AFOSR-supported basic research program that focuses on developing a new framework for combining hard with soft data in order to improve space situational awareness. The goal is to provide, in an automatic and near real-time fashion, a ranking of possible threats to blue assets (assets trying to be protected) from red assets (assets with hostile intentions). The approach is based on Conceptual Spaces models, which combine features from traditional associative and symbolic cognitive models. While Conceptual (...) Spaces are revolutionary, they lack an underlying mathematical framework. Several such frameworks have attempted to represent Conceptual Spaces, but by far the most robust is the model developed by Holender. His model utilizes integer linear programming in order to obtain an overall similarity value between observations and concepts that support the formation of hypotheses. This paper will describe a method for building Conceptual Spaces models for threats that utilizes ontologies as a means to provide a clear semantic foundation for this inferencing process; in particular threat ontologies and space domain ontologies are developed and employed in this approach. A space situational awareness use-case is presented involving a kinetic kill scenario and results are shown to assess the performance of this fusion-based inferencing framework. (shrink)

In a world surrounded by smart objects from sensors to automated medical devices, the ubiquity of ‘smart’ seems matched only by its lack of clarity. In this article, we use our discussions with expert stakeholders working in areas of implantable medical devices such as cochlear implants, implantable cardiac defibrillators, deep brain stimulators and in vivo biosensors to interrogate the difference facets of smart in ‘implantable smart technologies’, considering also whether regulation needs to respond to the autonomy that such artefacts carry (...) within them. We discover that when smart technology is deconstructed it is a slippery and multi-layered concept. A device’s ability to sense and transmit data and automate medicine can be associated with the ‘sting’ of autonomy being disassociated from human control as well as affecting individual, group, and social environments. (shrink)

According to the theory of contextual integrity (CI), privacy norms prescribe information flows with reference to five parameters — sender, recipient, subject, information type, and transmission principle. Because privacy is grasped contextually (e.g., health, education, civic life, etc.), the values of these parameters range over contextually meaningful ontologies — of information types (or topics) and actors (subjects, senders, and recipients), in contextually defined capacities. As an alternative to predominant approaches to privacy, which were ineffective against novel information practices enabled by (...) IT, CI was able both to pinpoint sources of disruption and provide grounds for either accepting or rejecting them. Mounting challenges from a burgeoning array of networked, sensor-enabled devices (IoT) and data-ravenous machine learning systems, similar in form though magnified in scope, call for renewed attention to theory. This Article introduces the metaphor of a data (food) chain to capture the nature of these challenges. With motion up the chain, where data of higher order is inferred from lower-order data, the crucial question is whether privacy norms governing lower-order data are sufficient for the inferred higher-order data. While CI has a response to this question, a greater challenge comes from data primitives, such as digital impulses of mouse clicks, motion detectors, and bare GPS coordinates, because they appear to have no meaning. Absent a semantics, they escape CI’s privacy norms entirely. (shrink)

Recently, big data analytics have gained significant attention in healthcare industry due to generation of massive quantities of data in various forms such as electronic health records, sensors, medical imaging, and pharmaceutical details. However, the data gathered from various sources are intrinsically uncertain owing to noise, incompleteness, and inconsistency. The analysis of such huge data necessitates advanced analytical techniques using machine learning and computational intelligence for effective decision making. To handle data uncertainty in healthcare sector, (...) this article presents a novel metaheuristic rough set-based feature selection with rule-based medical data classification technique on MapReduce framework. The proposed MRSFS-RMDC technique designs a butterfly optimization algorithm for minimal rough set selection. In addition, Hadoop MapReduce is applied to process massive quantity of data. Moreover, a rule-based classification approach named Repeated Incremental Pruning for Error Reduction is used with the inclusion of a set of conditional rules. The RIPPER will scale in a linear way with the number of training records utilized and is suitable to build models with data uncertainty. The proposed MRSFS-RMDC technique is validated using benchmark dataset and the results are inspected under varying aspects. The experimental results highlighted the supremacy of the MRSFS-RMDC technique over the recent state of art methods in terms of different performance measures. The proposed methodology has achieved a higher F-score of 96.49%. (shrink)

In a world surrounded by smart objects from sensors to automated medical devices, the ubiquity of ‘smart’ seems matched only by its lack of clarity. In this article, we use our discussions with expert stakeholders working in areas of implantable medical devices such as cochlear implants, implantable cardiac defibrillators, deep brain stimulators and in vivo biosensors to interrogate the difference facets of smart in ‘implantable smart technologies’, considering also whether regulation needs to respond to the autonomy that such artefacts carry (...) within them. We discover that when smart technology is deconstructed it is a slippery and multi-layered concept. A device’s ability to sense and transmit data and automate medicine can be associated with the ‘sting’ of autonomy being disassociated from human control as well as affecting individual, group, and social environments. (shrink)

Internet of thing building sensors can capture several types of building operations, performances, and conditions and send them to a central dashboard to analyze data to support decision-making. Traditionally, laptops and cell phones are the majority of Internet-connected devices. IoT tracking allows customers to close the distance between devices and enterprises by collecting and analyzing various IoT data through connected devices, customers, and applications on the network. There is a lack of requirements for IoT edge applications security and (...) approval. There are no best practices regarding operations focused on IoT incidents. IoT elements are not covered by audit and logging requirements. In this article, a big data analytics-based customer operation system analyzes the operation. With the exponential rise in data usage, the explosive development in the IoT devices reflects the ideal overlap of big data growth with IoT. Big data analytics continuously evolving network raises trivial questions about the performance, distribution of data, analysis, and protection of data collection. IoT modifies almost all the construction industry characteristics. Human-centered artificial intelligence is described as systems that always improve because of human input while also delivering an effective experience between the human and the robotic. The IoT is the key factor that ensures greater building performance. It was the first evolution of technology in a long time to turn genuine inventions into an industry that depended heavily on paper and manual processes. The benefits of the IoT in construction are now quite obviously much heavier than those of current manual processes. As a result, more construction companies explore and incorporate IoT strategies to address their productivity challenges, increasing efficiencies and profits. The simulation analysis shows that the proposed BDA-CO model enhances the trust score of 98.5%, accuracy detection ratio of 93.4%, probability ratio of 97.6%, and security ratio of 98.7% and reduces the false negative ratio of 21.3%, response time of 10.5%, delay rate of 19.9%, and packet loss ratio of 15.4% when compared to other existing techniques. (shrink)

IoT connects devices, humans, places, and even abstract items like events. Driven by smart sensors, powerful embedded microelectronics, high-speed connectivity and the standards of the internet, IoT is on the brink of disrupting today’s value chains. Big Data, characterized by high volume, high velocity and a high variety of formats, is a result of and also a driving force for IoT. The datafication of business presents completely new opportunities and risks. To hedge the technical risks posed by the interaction (...) between “everything”, IoT requires comprehensive modelling tools. Furthermore, new IT platforms and architectures are necessary to process and store the unprecedented flow of structured and unstructured, repetitive and non-repetitive data in real-time. In the end, only powerful analytic tools are able to extract “sense” from the exponentially growing amount of data and, as a consequence, data science becomes a strategic asset. The era of IoT relies heavily on standards for technologies which guarantee the interoperability of everything. This paper outlines some fundamental standardization activities. Big Data approaches for real-time processing are outlined and tools for analytics are addressed. As consequence, IoT is a evolutionary process whose success in penetrating all dimensions of life heavily depends on close cooperation between standardization organizations, open source communities and IT experts. (shrink)

One of the important issues in mobile robots is finding the position of robots in space. This is normally achieved by using a sensor to locate the position of the robot. However, relying on more than one sensor and then using multisenor data fusion algorithms tends to be more reliable than just using a reading from a single sensor. If these sensors provide inconsistent data, catastrophic fusion may occur, and thus the estimated position of the (...) robot obtained will be less accurate than if an individual sensor is used. This article uses an approach that relies on combining modified Bayesian fusion algorithm with Kalman filtering to estimate the position of a mobile robot. Two case studies are presented to prove the efficiency of the proposed approach in estimating the position of a mobile robot. Both scenarios show that combining fusion with filtering provides an accurate estimate of the location of the robot by handling the problem of uncertainty and inconsistency of the data provided by the sensors. (shrink)

Aquaculture plays a significant role in both economic development and food production. Maintaining an ecological environment with good water quality is essential to ensure the production efficiency and quality of aquaculture. Effective management of water quality can prevent abnormal conditions and contribute significantly to food security. Detecting anomalies in the aquaculture environment is crucial to ensure that the environment is maintained correctly to meet healthy and proper requirements for fish farming. This article focuses on the use of deep learning techniques (...) to detect anomalies in water quality data in the aquaculture environment. Four deep learning anomaly detection techniques, including Autoencoder, Variational Autoencoder, Long-Short Term Memory Autoencoder, and Spectral-Residual Convolutional Neural Network, were analysed using multiple real-world sensor datasets collected from IoT aquaculture systems. Extensive experiments were conducted for temperature, dissolved oxygen, and pH parameters, and the evaluation analysis revealed that the Long-Short Term Memory Autoencoder anomaly detection method showed promising results in detecting anomalies for the temperature and oxygen datasets, while the Spectral-Residual Convolutional Neural Network demonstrated the best performance on the pH datasets. (shrink)

In recent years, universities have been urged to restructure and re-evaluate their ability to trace and monitor their students as the “smart campus” is being built upon datafication, while networked apps and sensors serve as the means through which its constituents are connected and governed. This paper advances a dialectical and communication-centered approach to the Internet of Things campus ecosystem and provides an empirical investigation into the tensions experienced by students and the ways that these students envision alternative practices that (...) support their digital engagement. Drawing upon student focus group interviews in a large American research and innovation intensive university, dialectical tensions identified include convenience–annoyance, integration–independence, and safety–insecurity, brought upon by students’ ongoing and prospective negotiations with Internet of Things. Furthermore, in a bid to understand students’ alternative data imaginaries, this project examined students’ preferred Internet of Things-related communication practices with campus digital application platforms, analog and older forms of digital media, as well as in-person interactions with traditional authorities within classroom and group settings. Finally, this contribution presents a discussion of the findings for theory and praxis, particularly for smart campus innovation and social data governance, in terms of potential growing challenges involving complexifying student privacy concerns, data normalization and coercion, and tertiary digital divides and inequalities. (shrink)

Remote sensing image simulation is a very effective method to verify the feasibility of sensor devices for ground observation. The key to remote sensing image application is that simultaneous interpreting of remote sensing images can make use of the different characteristics of different data, eliminate the redundancy and contradiction between different sensors, and improve the timeliness and reliability of remote sensing information extraction. The hotspots and difficulties in this direction are based on remote sensing image simulation of 3D (...) scenes on the ground. Therefore, constructing the 3D scene model on the ground rapidly and accurately is the focus of current research. Because different scenes have different radiation characteristics, therefore, when using MATLAB to write a program generated by 3D scenes, 3D scenes must be saved as different text files according to different scene types, and then extension program of the scene is written to solve the defect that the calculation efficiency is not ideal due to the huge amount of data. This paper uses POV ray photon reverse tracking software to simulate the imaging process of remote sensing sensors, coordinate transformation is used to convert a triangle text file to POV ray readable information and input the RGB value of the base color based on the colorimetry principle, and the final 3D scene is visualized. This paper analyzes the thermal radiation characteristics of the scene and proves the rationality of the scene simulation. The experimental results show that introducing the chroma in the visualization of the scene model makes the whole scene have not only fidelity, but also radiation characteristics in shape and color. This is indispensable in existing 3D modeling and visualization studies. Compared with the complex radiation transmission method, using the multiple angle two-dimensional image generated by POV rays to analyze the radiation characteristics of the scene, the result is intuitive and easy to understand. (shrink)

This study reveals that increases in the global population command an augmented demand for products and services that calls for more effective ways of using existing natural resources and materials. The recent development of information and communication technologies, which had a great impact on many areas, also had a damaging effect on the environment and human health. Therefore, societies are moving toward a greener future by reducing the consumption of nonrenewable materials, raw materials, and resources while at the same time (...) decreasing energy pollution and consumption. Since information technology is considered a tool for solving ecological difficulties, the green Internet of things is playing a vital role in creating a sustainable home. Extensive data analysis is required to obtain a valuable overview of the large and diverse data generated by the G-IoT. The gathered information will facilitate forecasting, decision-making, and other activities related to smart urban services and then contribute to the incessant development of G-IoT technology. Therefore, even if sustainable and smart cities become an actuality, the G-IoT approach and the knowledge gained through big data analysis will make cities more sustainable, safer, and smarter. The goal of this article is to combine innovation in technological development with the main focus on resource sharing in creating cities that improve the quality of life while reducing pollution and realizing more efficient use of the raw materials. In the practice of big data science, it is always of interest to provide the best description of the data under consideration. Recent studies have pointed out the applicability of the statistical distributions in modeling data in applied sciences. In this article, we introduce a new family of statistical models to provide the best description of the life span of the wireless sensors network’s data. Based on the proposed approach, a special submodel called new exponent power-Weibull distribution is studied in detail. The applicability of the proposed model is shown by analyzing the life span of the wireless sensors network’s data. (shrink)

Nowadays, the development of big data is getting faster and faster, and the related research on motion sensing recognition and complex systems under the background of big data is gradually being valued. At present, there are relatively few related researches on vertical Baduanjin in the academic circles; research in this direction can make further breakthroughs in motion sensor recognition. In order to carry out related action recognition research on the lifting action of vertical Baduanjin, this paper uses (...) sensor technology to collect the motion video image of vertical Baduanjin based on the background of big data and uses action recognition technology and related algorithms to obtain the action. Recognize the video image to obtain the data, get the acceleration, angular velocity, and EMG data, and count the end time and duration according to the change of the action. According to the data table and graph change trend compiled at the end of the experiment, we can see the following: after the data is preprocessed, the acceleration signal change range is limited to [−1, 1], and the acceleration change has a clear directionality; and, after 15 lifts of the detected object, its angular velocity in X-axis direction is basically negative. However, when the ninth lift is performed, the angular velocity of the movement in X-axis direction is 36.09, the largest of all angular velocities. When performing the 15th lifting action, the angular velocity of this action in Z-axis direction is −26.05, which is the smallest of all angular velocities. The longest duration of the left muscle discharge during the lifting action of the subject is 15.24 for the tibial anterior muscle and 8.91 for the external oblique muscle with the shortest duration. The longest discharge duration of the right muscle is also the tibial anterior muscle with 12.15, and the shortest duration is the erector spinae with 8.79. (shrink)

Head stabilization is fundamental for balance during locomotion but can be impaired in elderly or diseased populations. Previous studies have identified several parameters of head stability with possible diagnostic value in a laboratory setting. Recently, the ecological validity of measures obtained in such controlled contexts has been called into question. The aim of this study was to investigate the ecological validity of previously described parameters of head stabilization in a real-world setting. Ten healthy subjects participated in the study. Head and (...) trunk movements of each subject were recorded with inertial measurement units (IMUs) for a period of at least ten hours. Periods of locomotion were extracted from the measurements and predominant frequencies, root mean squares (RMSs) and bout lengths were estimated. As parameters of head stabilization, attenuation coefficients (ACs), harmonic ratios (HRs), coherences and phase differences were computed. Predominant frequencies were distributed tightly around 2 Hz and ACs, HRs and coherences exhibited the highest values in this frequency range. All head stability parameters exhibited characteristics consistent with previous reports, although higher variances were observed. These results suggest that head stabilization is tuned to the 2 Hz fundamental frequency of locomotion and that previously described measures of head stability could generalize to a real-world setting. This is the first study to address the ecological validity of these measures, highlighting the potential use of head stability parameters as diagnostic tools or outcome measures for clinical trials. The low cost and ease of use of the IMU technology used in this study could additionally be of benefit for a clinical application. (shrink)