Rights management to enable a true Internet of Things

2016 IEEE Conference on Intelligence and Security Informatics (ISI).In this paper, we differentiate between a true ‘Internet of things’ and its component parts. We argue that the determining aspect of the ‘Internet of Things’ (IoT) is the accessibility of ‘things’ on the global Internet, as opposed to a simple interconnection of networked ‘things’. We observe that most reported applications of the ‘Internet of Things’ would be more accurately described as ‘Intranets of Things’. In large part, this is because the owners and operators of AIDC (Automatic identification and data capture) systems and sensor networks that in the main make up the IoT have understandable concerns about the security of their assets and therefore will limit access to that which serves their own purposes. In the wider field of the Internet ‘in the large’, the open mining of the Web for information has become the mainstay of many genres of research, allowing the assembly of huge corpora, enabling analytical techniques that can reveal far more information than previous limited studies. It is argued that part of the expected dividend for the IoT is to enable use on a similar scale of sensor and AIDC data, and that the results will be availability of information fusion on a huge scale, which will allow significant new knowledge to be generated. We give an example of how in one project, the RFID from Farm to Fork traceability project, this prospect has been validated to an extent on the basis that data owners voluntarily made their data available on the Web for specific purposes. Extrapolating to a more general case, we suggest that there are two services that need to be provided in order for the generalized information mining that occurs on the Internet-at-large to occur in the Internet of Things. The first is a means of cataloguing available data, which is already being addressed by services such as HyperCAT. The second is an automatic rights management service (IoT-RM), which would manage the rights and permissions and allow data owners to determine in advance to whom their data should be released, for what purposes, subject to which restrictions (such as, for instance, anonymisation) and whether any remuneration should be involved. We make some concrete proposals about the form that such an IoT-RM should take

Terminološka načela in oblikoslovnoskladenjske terminološke variacije

The article deals with terminological principles and terminological variability, with an emphasis on morphosyntactic terminological variations. Examples of such variations were empirically observed in a specialized corpus of texts on computer and information sciences. Based on the examples retrieved, the article presents a typology for describing morphosyntactic terminological variations in Slovenian. It also highlights the logical and conceptual relations established through such terminological variations.Članek obravnava tematiko terminoloških načel in terminološke variabilnosti, s poudarkom na oblikoslovno-skladenjskih terminoloških variacijah. Primeri tovrstnih terminoloških variacij so bili empirično opazovani v specializiranem korpusu besedil s področja računalništva in informatike. Na podlagi odkritih primerov članek ponuja tipologijo za opis oblikoslovno-skladenjskih variacij v slovenščini ter obenem ponazarja logične in pojmovne odnose, ki se vzpostavljajo s takšnimi variacijami

Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks

In this paper we propose and investigate a novel nonlinear unit, called $L_p$ unit, for deep neural networks. The proposed $L_p$ unit receives signals from several projections of a subset of units in the layer below and computes a normalized $L_p$ norm. We notice two interesting interpretations of the $L_p$ unit. First, the proposed unit can be understood as a generalization of a number of conventional pooling operators such as average, root-mean-square and max pooling widely used in, for instance, convolutional neural networks (CNN), HMAX models and neocognitrons. Furthermore, the $L_p$ unit is, to a certain degree, similar to the recently proposed maxout unit (Goodfellow et al., 2013) which achieved the state-of-the-art object recognition results on a number of benchmark datasets. Secondly, we provide a geometrical interpretation of the activation function based on which we argue that the $L_p$ unit is more efficient at representing complex, nonlinear separating boundaries. Each $L_p$ unit defines a superelliptic boundary, with its exact shape defined by the order $p$. We claim that this makes it possible to model arbitrarily shaped, curved boundaries more efficiently by combining a few $L_p$ units of different orders. This insight justifies the need for learning different orders for each unit in the model. We empirically evaluate the proposed $L_p$ units on a number of datasets and show that multilayer perceptrons (MLP) consisting of the $L_p$ units achieve the state-of-the-art results on a number of benchmark datasets. Furthermore, we evaluate the proposed $L_p$ unit on the recently proposed deep recurrent neural networks (RNN).Comment: ECML/PKDD 201

RFID Data Loggers in Fish Supply Chain Traceability

Radio frequency identification (RFID) is an innovative and well-recognized technology that supports all kinds of traceability systems in many areas. It becomes very important in the food industry where the electronic systems are used to capture the data in the supply chain. Additionally, RFID data loggers with sensors are available to perform a cold chain optimization for perishable foods. This paper presents the temperature monitoring solution at the box level in the fish supply chain as part of the traceability system implemented with RFID technology. RFID data loggers are placed inside the box to measure the temperature of the product and on the box for measuring ambient temperature. The results show that the system is very helpful during the phases of storage and transportation of fish to provide the quality control. The sensor data is available immediately at the delivery to be checked on the mobile RFID reader and afterwards stored in the traceability systems database to be presented on a web to stakeholders and private consumers

Improvement of Traceability Processes in the Farmed Fish Supply Chain

In the project "RFID from Farm to Fork" an implementation of RFID technologies are used along the food supply chain to be deployed in SMEs: from the farm to the consumer. As part of the project, two pilot deployments are being undertaken in the farmed fish business. The purpose of this paper is to show how the business process of the farmed fish supply chain can benefit from a novel system architecture that uses Radiofrequency Identification (RFID) and Wireless Sensor Networks (WSN) to improve the processes of fish traceability. In order to show the technological evaluation, both a definition of each company´s business processes and how to upgrade them to the new technologies are presented. Keywords: Traceability, farmed fish, supply chain, aquaculture, RFID, WSN.NSFC – National Natural Science Foundation of China K. C. Wong Education Foundation (Hong Kong) Springer-Verla

Traceability of Goods by Radio Systems: Proposals, Techniques, and Applications

none4Iñigo Cuiñas;Robert Newman;Mira Trebar;Luca CatarinucciIñigo, Cuiñas; Robert, Newman; Mira, Trebar; Catarinucci, Luc

Nutrient-enriched formula versus standard formula milk for preterm infants

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: To compare the effects of feeding with nutrient-enriched formula versus standard formula on growth and development of preterm infants

Detection of cold chain breaks using partial least squares-class modelling based on biogenic amine profiles in tuna

The maintenance of the cold chain is essential to ensure foodstuff conformity and safety. However, gaps in the cold chain may be expected so designing analytical methods capable to detect cold chain breaks is a worthwhile issue. In this paper, the possibility of using the amount of nine biogenic amines (BAs) determined in Thunnus albacares by HPLC-FLD for detecting cold chain breaks is approached. Tuna is stored at 3 different temperature conditions for 8 storage periods. The evolution of the content of BAs is analyzed through parallel factor analysis (PARAFAC), in such a way that storage temperature, BAs and storage time profiles are estimated. PARAFAC has made it possible to observe two spoilage routes with different relative evolution of BAs. In addition, it has enabled to estimate the storage time, by considering the three storage temperatures, with errors of 0.5 and 1.0 days in fitting and in prediction, respectively. Furthermore, a class-modelling technique based on partial least squares is sequentially applied to decide, from the amount of BAs, if there has been a cold chain break. Firstly, samples stored at 25 °C are statistically discriminated from those kept at 4 °C and −18 °C; next, frozen samples are distinguished from those refrigerated. In the first case, the probabilities of false non-compliance and false compliance are almost zero, whereas in the second one, both probabilities are 10%. Globally, the results of this work have pointed out the feasibility of using the amount of BAs together with PLS-CM to decide if the cold chain has been maintained or not.Agencia Estatal de Investigación of Spanish Ministerio de Economía, Industria y Competitividad, Gobierno de España [project CTQ2017-88894-R] and Consejería de Educación de la Junta de Castilla y León [project BU012P17] both co-financed with European Regional Development Fun

Exploring synergetic effects of dimensionality reduction and resampling tools on hyperspectral imagery data classification

The present paper addresses the problem of the classification of hyperspectral images with multiple imbalanced classes and very high dimensionality. Class imbalance is handled by resampling the data set, whereas PCA and a supervised filter are applied to reduce the number of spectral bands. This is a preliminary study that pursues to investigate the benefits of combining several techniques to tackle the imbalance and the high dimensionality problems, and also to evaluate the order of application that leads to the best classification performance. Experimental results demonstrate the significance of using together these two preprocessing tools to improve the performance of hyperspectral imagery classification. Although it seems that the most effective order corresponds to first a resampling strategy and then a feature (or extraction) selection algorithm, this is a question that still needs a much more thorough investigation in the futureThis work has partially been supported by the Spanish Ministry of Education and Science under grants CSD2007–00018, AYA2008–05965–0596 and TIN2009–14205, the Fundació Caixa Castelló–Bancaixa under grant P1–1B2009–04, and the Generalitat Valenciana under grant PROMETEO/2010/02

Development and evaluation on a wireless multi-gas-sensors system for improving traceability and transparency of table grape cold chain

There is increasing requirement to improve traceability and transparency of table grapes cold chain. Key traceability indicators including temperature, humidity and gas microenvironments (e.g., CO2, O2, and SO2) based on table grape cold chain management need to be monitored and controlled. This paper presents a Wireless Multi-Gas-Sensors System (WGS2) as an effective real-time cold chain monitoring system, which consists of three units: (1) the WMN which applies the 433 MHz as the radio frequency to increase the transmission performance and forms a wireless sensor network; (2) the WAN which serves as the intermediary to connect the users and the sensor nodes to keep the sensor data without delay by the GPRS remote transmission module; (3) the signal processing unit which contains embedded software to drive the hardware to normal operation and shelf life prediction for table grapes. Then the study evaluates the WGS2 in a cold chain scenario and analyses the monitoring data. The results show that the WGS2 is effective in monitoring quality, and improving transparency and traceability of table grape cold chains. Its deploy ability and efficiency in implantation can enable the establishment of a more efficient, transparent and traceable table grape supply chain.N/