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Five years ago our department migrated from a film-based imaging environment to a PACS-based environment. We discovered that our reliance on paper tracking forms for clinical history and dictation information was hindering our practice. Integrating dictation with PACS was one of three key components we needed to free ourselves from the tyranny of paper, the other two being an online worklist and an online patient history. We discuss our evolution to a (mostly) paperless reading room environment, our implementation, general performance, and future development plans, focusing on integrated dictation.

Despite the increasing availability of various forms of digital maps and guides, paper still prevails as the main information medium used by tourists during city visits. The authors describe how recent technologies for digitally augmented paper maps can be used to develop interactive paper maps that provide value-added services for tourists through digital overlays. An initial investigation into the use of these maps to support visitors to the Edinburgh festivals is also presented.

Network immunization strategies have emerged as possible solutions to the challenges of virus propagation. In this paper, an existing interactive model is introduced and then improved in order to better characterize the way a virus spreads in email networks with different topologies. The model is used to demonstrate the effects of a number of key factors, notably nodes’ degree and betweenness. Experiments are then performed to examine how the structure of a network and human dynamics affects virus propagation. The experimental results have revealed that a virus spreads in two distinct phases and shown that the most efficient immunization strategy is the node-betweenness strategy. Moreover, those results have also explained why old virus can survive in networks nowadays from the aspects of human dynamics.

Genomics-based technologies produce large amounts of data. To interpret the results and identify the most important variates related to phenotypes of interest, various multivariate regression and variate selection methods are used. Although inspected for statistical performance, the relevance of multivariate models in interpreting biological data sets often remains elusive. We compare various multivariate regression and variate selection methods applied to a nutrigenomics data set in terms of performance, utility and biological interpretability. The studied data set comprised hepatic transcriptome (10,072 predictor variates) and plasma protein concentrations [2 dependent variates: Leptin (LEP) and Tissue inhibitor of metalloproteinase 1 (TIMP-1)] collected during a high-fat diet study in ApoE3Leiden mice. The multivariate regression methods used were: partial least squares “PLS”; a genetic algorithm-based multiple linear regression, “GA-MLR”; two least-angle shrinkage methods, “LASSO” and “ELASTIC NET”; and a variant of PLS that uses covariance-based variate selection, “CovProc.” Two methods of ranking the genes for Gene Set Enrichment Analysis (GSEA) were also investigated: either by their correlation with the protein data or by the stability of the PLS regression coefficients. The regression methods performed similarly, with CovProc and GA performing the best and worst, respectively (R-squared values based on “double cross-validation” predictions of 0.762 and 0.451 for LEP; and 0.701 and 0.482 for TIMP-1). CovProc, LASSO and ELASTIC NET all produced parsimonious regression models and consistently identified small subsets of variates, with high commonality between the methods. Comparison of the gene ranking approaches found a high degree of agreement, with PLS-based ranking finding fewer significant gene sets. We recommend the use of CovProc for variate selection, in tandem with univariate methods, and the use of correlation-based ranking for GSEA-like pathway analysis methods. Electronic supplementary material The online version of this article (doi:10.1007/s12263-012-0288-4) contains supplementary material, which is available to authorized users.

A new continuous sterilization system was designed, constructed, started up, and qualified for media sterilization for secondary metabolite cultivations, bioconversions, and enzyme production. An existing Honeywell Total Distributed Control 3000-based control system was extended using redundant High performance Process Manager controllers for 98 I/O (input/output) points. This new equipment was retrofitted into an industrial research fermentation pilot plant, designed and constructed in the early 1980s. Design strategies of this new continuous sterilizer system and the expanded control system are described and compared with the literature (including dairy and bio-waste inactivation applications) and the weaknesses of the prior installation for expected effectiveness. In addition, the reasoning behind selection of some of these improved features has been incorporated. Examples of enhancements adopted include sanitary heat exchanger (HEX) design, incorporation of a "flash" cooling HEX, on-line calculation of F(o) and R(o), and use of field I/O modules located near the vessel to permit low-cost addition of new instrumentation. Sterilizer performance also was characterized over the expected range of operating conditions. Differences between design and observed temperature, pressure, and other profiles were quantified and investigated.

Caching, one of the oldest ideas in computer science, often improves performance and sometimes improves availability [1, 3]. Previous uses of caching have focused on data content. It is the presence of a local copy of data that reduces access latency and masks server or network failures. This position paper puts forth the idea that it can sometimes be useful to merely cache knowledge sufficient to recognize valid data. In other words, we do not have a local copy of a data item, but possess a substitute that allows us to verify the content of that item if it is offered to us by an untrusted source. We refer to this concept as caching trust.Mobile computing is a champion application domain for this concept. Wearable and handheld computers are constantly under pressure to be smaller and lighter. However, the potential volume of data that is accessible to such devices over a wireless network keeps growing. Something has to give. In this case, it is the assumption that all data of potential interest can be hoarded on the mobile client [1, 2, 6]. In other words, such clients have to be prepared to cope with cache misses during normal use. If they are able to cache trust, then any untrusted site in the fixed infrastructure can be used to stage data for servicing cache misses --- one does not have to go back to a distant server, nor does one have to compromise security. The following scenario explores this in more detail.

The aim of the KCM (Knowledge Crunching Machine) project was to study how to speed-up Prolog, and more generally Logic Programming, through hardware support at the processor level. An experimental approach was taken, which resulted in the design and implementation of a real system, hardware and software. This short paper outlines the key features of the system as well as the main conclusions which can be drawn from the project.

Inversion of the impacted tooth is a rare condition. The purpose of this article is to review this rare condition in detail on the basis of available literature. In addition, this article also explores the various multidisciplinary approaches varying from space creation to prosthodontic rehabilitation and reimplantation to endodontic treatment for inverted impacted teeth.A very few cases of inverted and impacted third molars have been reported in the literature. We describe few rare cases of inverted impacted teeth which presented to us in the Dept. Of Oral Surgery, K.D. Dental College and Hospital over a period of 2 years approx. we report of four cases presented to us with inverted maxillary third molars (Figs. 1, 2), inverted supernumerary tooth in anterior maxilla (Figs. 3, 4).The first and second case of maxillary third molar and third case, of supernumerary tooth, the patients presented were asymptomatic, hence patient opted for conservative management and continued follow up, in the fourth case, the supernumerary tooth was removed for orthodontic reasons.On the basis of literature available, it has been noticed that related complications particularly depend on justification on whether the tooth follicle is with a pathology or is pathology-free. Pathology-free and asymptomatic cases can be treated in a conservative manner. Possible treatment protocol for impacted inversion is entirely dependent on patient's needs, findings and surgeons skills.

The preceding chapters of this book discuss scientific research results on natural and managed oak forests growing in the highlands of the American Tropics. Chapter authors highlight evolutionary, ecological and socioeconomic aspects of specific oak forests and identify existing gaps in our understanding. Their work and studies published elsewhere forms the basis for this chapter’s overview of the current state of knowledge on ecological patterns and processes which determine the structure and functioning of these magnificent forests. The increasingly important human use and much needed conservation of these biodiverse and threatened habitats are dealt with as well. Cross-cutting trends and issues are discussed and conclusions drawn. Neotropical montane oak forests are a special type of tropical montane forests (Kappelle 2004), in which tall oak trees (Quercus) dominate a 30to 60-m-high forest canopy, often in pure stands or mixed with pines (Pinus) or other predominant trees. Though Neotropical oak forest history, structure, species diversity, species interactions, ecosystem functioning, post-disturbance recovery, and human use may vary considerably over short geographical ranges, regional trends are now becoming evident. Research presented in this book clearly illustrates these large-scale trends. The following sections in this chapter synthesize our knowledge for each of these trends and themes.

International audience; Network Control Systems (NAC) have been used in many industrial processes. They aim to reduce the human factor burden and efficiently handle the complex process and communication of those systems. Supervisory control and data acquisition (SCADA) systems are used in industrial, infrastructure and facility processes (e.g. manufacturing, fabrication, oil and water pipelines, building ventilation, etc.) Like other Internet of Things (IoT) implementations, SCADA systems are vulnerable to cyber-attacks, therefore, a robust anomaly detection is a major requirement. However, having an accurate anomaly detection system is not an easy task, due to the difficulty to differentiate between cyber-attacks and system internal failures (e.g. hardware failures). In this paper, we present a model that detects anomaly events in a water system controlled by SCADA. Six Machine Learning techniques have been used in building and evaluating the model. The model classifies different anomaly events including hardware failures (e.g. sensor failures), sabotage and cyber-attacks (e.g. DoS and Spoofing). Unlike other detection systems, our proposed work helps in accelerating the mitigation process by notifying the operator with additional information when an anomaly occurs. This additional information includes the probability and confidence level of event(s) occurring. The model is trained and tested using a real-world dataset.

[EN] Neighbor discovery represents a first step after the deployment of wireless ad hoc networks, since the nodes that form them are equipped with limited-range radio transceivers, and they typically do not know their neighbors. In this paper two randomized neighbor discovery approaches, called CDH and CDPRR, based on collision detection for static multi-hop wireless ad hoc networks, are presented. Castalia 3.2 simulator has been used to compare our proposed protocols against two protocols chosen from the literature and used as reference: the PRR, and the Hello protocol. For the experiments, we chose five metrics: the neighbor discovery time, the number of discovered neighbors, the energy consumption, the throughput and the number of discovered neighbors versus packets sent ratio. According to the results obtained through simulation, we can conclude that our randomized proposals outperform both Hello and PRR protocols in the presence of collisions regarding all five metrics, for both one-hop and multi-hop scenarios. As novelty compared to the reference protocols, both proposals allow nodes to discover all their neighbors with probability 1, they are based on collision detection and know when to terminate the neighbor discovery process. Furthermore, qualitative comparisons of the existing protocols and the proposals are available in this paper. Moreover, CDPRR presents better results in terms of time, energy consumption and number of discovered neighbors versus packets sent ratio. We found that both proposals achieve to operate under more realistic assumptions. Furthermore, CDH does not need to know the number of nodes in the network., This work has been partially supported by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" within the project under Grant TIN2017-84802-C2-1-P. This work has also been partially supported by European Union through the ERANETMED (Euromediterranean Cooperation through ERANET joint activities and beyond) project ERANETMED3-227 SMARTWATIR.

[EN] Augmented Reality (AR) has become a mainstream technology in the development of solutions for repair and maintenance operations. Although most of the AR solutions are still limited to specific contexts in industry, some consumer electronics companies have started to offer pre-packaged AR solutions as alternative to video-based tutorials (VT) for minor maintenance operations. In this paper, we present a comparative study of the acquired knowledge and user perception achieved with AR and VT solutions in some maintenance tasks of IT equipment. The results indicate that both systems help users to acquire knowledge in various aspects of equipment maintenance. Although no statistically significant differences were found between AR and VT solutions, users scored higher on the AR version in all cases. Moreover, the users explicitly preferred the AR version when evaluating three different usability and satisfaction criteria. For the AR version, a strong and significant correlation was found between the satisfaction and the achieved knowledge. Since the AR solution achieved similar learning results with higher usability scores than the video-based tutorials, these results suggest that AR solutions are the most effective approach to substitute the typical paper-based instructions in consumer electronics., This work has been supported by Spanish MINECO and EU ERDF programs under grant RTI2018-098156-B-C55.

It is more than 4 years since the 2030 agenda for sustainable development was adopted by the United Nations and its member states in September 2015. Several efforts are being made by member countries to contribute towards achieving the 17 Sustainable Development Goals (SDGs). The progress which had been made over time in achieving SDGs can be monitored by measuring a set of quantifiable indicators for each of the goals. It has been seen that geospatial information plays a significant role in measuring some of the targets, hence it is relevant in the implementation of SDGs and monitoring of their progress. Synoptic view and repetitive coverage of the Earth's features and phenomenon by different satellites is a powerful and propitious technological advancement. The paper reviews robustness of Earth Observation data for continuous planning, monitoring, and evaluation of SDGs. The scientific world has made commendable progress by providing geospatial data at various spatial, spectral, radiometric, and temporal resolutions enabling usage of the data for various applications. This paper also reviews the application of big data from earth observation and citizen science data to implement SDGs with a multi-disciplinary approach. It covers literature from various academic landscapes utilizing geospatial data for mapping, monitoring, and evaluating the earth's features and phenomena as it establishes the basis of its utilization for the achievement of the SDGs.

The value of authentication protocols which minimise (or even eliminate) the need for stored state in addressing DoS attacks is well-established — the seminal paper of Aura and Nikander [1] is of particular importance in this context. However, although there is now a substantial literature on this topic, it would seem that many aspects of stateless security protocols remain to be explored. In this paper we consider the design of a novel stateless authentication protocol which has certain implementation advantages. Specifically, neither party needs to maintain significant stored state. The protocol is developed as a series of refinements, at each step eliminating certain undesirable properties arising in previous steps.

The role of prognostics and health management is ever more prevalent with advanced techniques of estimation methods. However, data processing and remaining useful life prediction algorithms are often very different. Some difficulties in accurate prediction can be tackled by redefining raw data parameters into more meaningful and comprehensive health level indicators that will then provide performance information. Proper data processing has a significant importance on remaining useful life predictions, for example, to deal with data limitations or/and multi-regime operating conditions. The framework proposed in this paper considers a similarity-based prognostic algorithm that is fed by the use of data normalisation and filtering methods for operational trajectories of complex systems. This is combined with a data-driven prognostic technique based on feed-forward neural networks with multi-regime normalisation. In particular, the paper takes a close look at how pre-processing methods affect algorithm performance. The work presented herein shows a conceptual prognostic framework that overcomes challenges presented by short-term test datasets and that increases the prediction performance with regards to prognostic metrics. Validerad;2019;Nivå 2;2019-04-12 (johcin)

[EN] In order to be reusable, history-based feature-based parametric CAD models must reliably allow for modifications while maintaining their original design intent. In this paper, we demonstrate that relations that fix the location of geometric entities relative to the reference system produce inflexible profiles that reduce model reusability. We present the results of an experiment where novice students and expert CAD users performed a series of modifications in different versions of the same 2D profile, each defined with an increasingly higher number of fix geometric constraints. Results show that the amount of fix constraints in a 2D profile correlates with the time required to complete reusability tasks, i.e., the higher the number of fix constraints in a 2D profile, the less flexible and adaptable the profile becomes to changes. In addition, a pilot software tool to automatically track this type of constraints was developed and tested. Results suggest that the detection of fix constraint overuse may result in a new metric to assess poor quality models with low reusability. The tool provides immediate feedback for preventing high semantic level quality errors, and assistance to CAD users. Finally, suggestions are introduced on how to convert fix constraints in 2D profiles into a negative metric of 3D model quality., The authors would like to thank Raquel Plumed for her support in the statistical analysis. This work has been partially funded by Grant UJI-A02017-15 (Universitat Jaume I) and DPI201784526-R (MINECO/AEI/FEDER, UE), project CAL-MBE. The authors also wish to thank the editor and reviewers for their valuable comments and suggestions that helped us improve the quality of the paper.

The aim of the sparse analytic hierarchy process (SAHP) problem is to rank a set of alternatives based on their utility/importance; this task is accomplished by asking human decision-makers to compare selected pairs of alternatives and to specify relative preference information, in the form of ratios of utilities. However, such an information is often affected by subjective biases or inconsistencies. Moreover, there is no general consent on the best approach to accomplish this task, and in the literature several techniques have been proposed. Finally, when more than one decision-maker is involved in the process, there is a need to provide adequate methodologies to aggregate the available information. In this view, the contribution of this paper to the SAHP body of knowledge is twofold. From one side, it develops a novel methodology to aggregate sparse data given by multiple sources of information. From another side, the paper undertakes an experimental validation of the most popular techniques to solve the SAHP problem, discussing the strength points and shortcomings of the different methodology with respect to a real case study.

[EN] This paper presents and evaluates a system that provides TV and radio services in mobility using 4G communications. The system has mainly two blocks, one on the cloud and another on the mobile vehicle. On the cloud, a DVB (Digital Video Broadcasting) receiver obtains the TV/radio signal and prepares the contents to be sent through 4G. Specifically, contents are transcoded and packetized using the DASH (Dynamic Adaptive Streaming over HTTP) standard. Vehicles in mobility use their 4G connectivity to receive the flows transmitted by the cloud. The key element of the system is an on-board proxy that manages the received flows and offers them to the final users in the vehicle. The proxy contains a buffer that helps reduce the number of interruptions caused by hand over effects and lack of coverage. The paper presents a comparison between a live transmission using 4G connecting the clients directly with the cloud server and a near real-time transmission based on an on-board proxy. Results prove that the use of the proxy reduces the number of interruptions considerably and, thus, improves the Quality of Experience of users at the expense of slightly increasing the delay., This work is supported by the Centro para el Desarrollo Tecnologico Industrial (CDTI) from the Government of Spain under the project "Plataforma avanzada de conectividad en movilidad" (CDTI IDI-20150126) and the project "Desarrollo de nueva plataforma de entretenimiento multimedia para entornos nauticos" (CDTI TIC-20170102).

This paper identifies certain privacy threats that apply to anonymous credential systems. The focus is on timing attacks that apply even if the system is cryptographically secure. The paper provides some simple heuristics that aim to mitigate the exposure to the threats and identifies directions for further research.

OpenCL has been proposed as a means of accelerating functional computation using FPGA and GPU accelerators. Although it provides ease of programmability and code portability, questions remain about the performance portability and underlying vendor’s compiler capabilities to generate efficient implementations without user-defined, platform specific optimizations. In this work, we systematically evaluate this by formalizing a design space exploration strategy using platform-independent micro-architectural and application-specific optimizations only. The optimizations are then applied across Altera FPGA, NVIDIA GPU and ARM Mali GPU platforms for three computing examples, namely matrix-matrix multiplication, binomial-tree option pricing and 3-dimensional finite difference time domain. Our strategy enables a fair comparison across platforms in terms of throughput and energy efficiency by using the same design effort. Our results indicate that FPGA provides better performance portability in terms of achieved percentage of device’s peak performance (68%) compared to NVIDIA GPU (20%) and also achieves better energy efficiency (up to 1.4\(\times \)) for some of the considered cases without requiring in-depth hardware design expertise.

International audience; This paper reports on a four-year project that aims to raise the abstraction level through the use of model-driven engineering (MDE) techniques in the development of scientific applications relying on high-performance computing. The development and maintenance of high-performance scientific computing software is reputedly a complex task. This complexity results from the frequent evolutions of supercomputers and the tight coupling between software and hardware aspects. Moreover, current parallel programming approaches result in a mixing of concerns within the source code. Our approach relies on the use of MDE and consists in defining domain-specific modeling languages targeting various domain experts involved in the development of HPC applications, allowing each of them to handle their dedicated model in a both user-friendly and hardware-independent way. The different concerns are separated thanks to the use of several models as well as several modeling viewpoints on these models. Depending on the targeted execution platforms, these abstract models are translated into executable implementations by means of model transformations. To make all of these effective, we have developed a tool chain that is also presented in this paper. The approach is assessed through a multi-dimensional validation that focuses on its applicability, its expressiveness and its efficiency. To capitalize on the gained experience, we analyze some lessons learned during this project.

The online shopping market is growing rapidly in the 21st century, leading to a huge amount of duplicate products being sold online. An important component for aggregating online products is duplicate detection, although this is a time consuming process. In this paper, we focus on reducing the amount of possible duplicates that can be used as an input for the Multi-component Similarity Method (MSM), a state-of-the-art duplicate detection solution. To find the candidate pairs, Locality Sensitive Hashing (LSH) is employed. A previously proposed LSH-based algorithm makes use of binary vectors based on the model words in the product titles. This paper proposes several extensions to this, by performing advanced data cleaning and additionally using information from the key-value pairs. Compared to MSM, the MSMP+ method proposed in this paper leads to a minor reduction by \(6\%\) in the \(F_1\)-measure whilst reducing the number of needed computations by \(95\%\).

[EN] Commendable amount of work has been attempted in the field of Sentiment Analysis or Opinion Mining from natural language texts and Twitter texts. One of the main goals in such tasks is to assign polarities (positive or negative) to a piece of text. But, at the same time, one of the important as well as difficult issues is how to assign the degree of positivity or negativity to certain texts. The answer becomes more complex when we perform a similar task on figurative language texts collected from Twitter. Figurative language devices such as irony and sarcasm contain an intentional secondary or extended meaning hidden within the expressions. In this paper we present a novel approach to identify the degree of the sentiment (fine grained in an 11-point scale) for the figurative language texts. We used several semantic features such as sentiment and intensifiers as well as we introduced sentiment abruptness, which measures the variation of sentiment from positive to negative or vice versa. We trained our systems at multiple levels to achieve the maximum cosine similarity of 0.823 and minimum mean square error of 2.170., The work reported in this paper is supported by a grant from the project “CLIA System Phase II” funded by Department of Electronics and Information Technology (DeitY), Ministry of Communications and Information Technology (MCIT), Government of India. The work of the fourth author is also supported by the SomEMBED TIN2015-71147-C2-1-P MINECO research project and by the Generalitat Valenciana under the grant ALMAPATER (PrometeoII/2014/030).

[EN] Modern data-driven spoken language systems (SLS) require manual semantic annotation for training spoken language understanding parsers. Multilingual porting of SLS demands significant manual effort and language resources, as this manual annotation has to be replicated. Crowdsourcing is an accessible and cost-effective alternative to traditional methods of collecting and annotating data. The application of crowdsourcing to simple tasks has been well investigated. However, complex tasks, like cross-language semantic annotation transfer, may generate low judgment agreement and/or poor performance. The most serious issue in cross-language porting is the absence of reference annotations in the target language; thus, crowd quality control and the evaluation of the collected annotations is difficult. In this paper we investigate targeted crowdsourcing for semantic annotation transfer that delegates to crowds a complex task such as segmenting and labeling of concepts taken from a domain ontology; and evaluation using source language annotation. To test the applicability and effectiveness of the crowdsourced annotation transfer we have considered the case of close and distant language pairs: Italian-Spanish and Italian-Greek. The corpora annotated via crowdsourcing are evaluated against source and target language expert annotations. We demonstrate that the two evaluation references (source and target) highly correlate with each other; thus, drastically reduce the need for the target language reference annotations., This research is partially funded by the EU FP7 PortDial Project No. 296170, FP7 SpeDial Project No. 611396, and Spanish contract TIN2014-54288-C4-3-R. The work presented in this paper was carried out while the author was affiliated with Universitat Politecnica de Valencia.

We consider the problem of classifying business process instances based on structural features derived from event logs. The main motivation is to provide machine learning based techniques with quick response times for interactive computer assisted root cause analysis. In particular, we create structural features from process mining such as activity and transition occurrence counts, and ordering of activities to be evaluated as potential features for classification. We show that adding such structural features increases the amount of information thus potentially increasing classification accuracy. However, there is an inherent trade-off as using too many features leads to too long run-times for machine learning classification models. One way to improve the machine learning algorithms' run-time is to only select a small number of features by a feature selection algorithm. However, the run-time required by the feature selection algorithm must also be taken into account. Also, the classification accuracy should not suffer too much from the feature selection. The main contributions of this paper are as follows: First, we propose and compare six different feature selection algorithms by means of an experimental setup comparing their classification accuracy and achievable response times. Second, we discuss the potential use of feature selection results for computer assisted root cause analysis as well as the properties of different types of structural features in the context of feature selection., Comment: Extended version of a paper published in the proceedings of the BPM 2017 workshops

High-Utility Itemset Mining (HUIM) is a relevant data mining task. The goal is to discover recurrent combinations of items characterized by high profit from transactional datasets. HUIM has a wide range of applications among which market basket analysis and service profiling. Based on the observation that items can be clustered into domain-specific categories, a parallel research issue is generalized itemset mining. It entails generating correlations among data items at multiple abstraction levels. The extraction of multiple-level patterns affords new insights into the analyzed data from different viewpoints. This paper aims at discovering a novel pattern that combines the expressiveness of generalized and High-Utility itemsets. According to a user-defined taxonomy items are first aggregated into semantically related categories. Then, a new type of pattern, namely the Generalized High-utility Itemset (GHUI), is extracted. It represents a combinations of items at different granularity levels characterized by high profit (utility). While profitable combinations of item categories provide interesting high-level information, GHUIs at lower abstraction levels represent more specific correlations among profitable items. A single-phase algorithm is proposed to efficiently discover utility itemsets at multiple abstraction levels. The experiments, which were performed on both real and synthetic data, demonstrate the effectiveness and usefulness of the proposed approach.

Concurrent Constraint Programming (CCP) is a declarative model for concurrency where agents interact by telling and asking constraints (pieces of information) in a shared store. Some previous works have developed (approximated) declarative debuggers for CCP languages. However, the task of debugging concurrent programs remains difficult. In this paper we define a dynamic slicer for CCP and we show it to be a useful companion tool for the existing debugging techniques. Our technique starts by considering a partial computation (a trace) that shows the presence of bugs. Often, the quantity of information in such a trace is overwhelming, and the user gets easily lost, since she cannot focus on the sources of the bugs. Our slicer allows for marking part of the state of the computation and assists the user to eliminate most of the redundant information in order to highlight the errors. We show that this technique can be tailored to timed variants of CCP. We also develop a prototypical implementation freely available for making experiments., Comment: Pre-proceedings paper presented at the 26th International Symposium on Logic-Based Program Synthesis and Transformation (LOPSTR 2016), Edinburgh, Scotland UK, 6-8 September 2016 (arXiv:1608.02534)

Lecture Notes in Computer Science, Various security weaknesses have been identified in mobile ad-hoc networks (MANET). The paper focuses on MANET forensics whereby a third party can prove there was attack by collecting and analyzing evidence about it. The paper describes such a MANET forensics analysis method using a Bloom filter.

Image-segmentation techniques based on supervised classification generally perform well on the condition that training and test samples have the same feature distribution. However, if training and test images are acquired with different scanners or scanning parameters, their feature distributions can be very different, which can hurt the performance of such techniques. We propose a feature-space-transformation method to overcome these differences in feature distributions. Our method learns a mapping of the feature values of training voxels to values observed in images from the test scanner. This transformation is learned from unlabeled images of subjects scanned on both the training scanner and the test scanner. We evaluated our method on hippocampus segmentation on 27 images of the Harmonized Hippocampal Protocol (HarP), a heterogeneous dataset consisting of 1.5T and 3T MR images. The results showed that our feature space transformation improved the Dice overlap of segmentations obtained with an SVM classifier from 0.36 to 0.85 when only 10 atlases were used and from 0.79 to 0.85 when around 100 atlases were used.

A side channel attack (SCA) is a serious attack on the implementation of cryptosystems, which can break the secret key using side channel information such as timing, power consumption, etc. Recently, Boneh et al. showed that SSL is vulnerable to SCA if the attacker gets access to the local network of the server. Therefore, public-key infrastructure eventually becomes a target of SCA. In this paper, we investigate the security of RSA cryptosystem using the Chinese remainder theorem (CRT) in the sense of SCA. Novak first proposed a simple power analysis (SPA) against the CRT part using the difference of message modulo p and modulo q. In this paper, we apply Novak’s attack to the other CRT-based cryptosystems, namely Multi-Prime RSA, Multi-Exponent RSA, Rabin cryptosystem, and HIME(R) cryptosystem. Novak-type attack strictly depends on how to implement the CRT. We examine the operations related to CRT of these cryptosystems, and show that an extended Novak-type attack is effective on them. Moreover, we present a novel attack called zero-multiplication attack. The attacker tries to guess the secret prime by producing ciphertexts that cause a multiplication with zero during the decryption, which is easily detected by power analysis. Our experimental result shows that the timing with the zero multiplication is reduced about 10% from the standard one. Finally, we propose countermeasures against these attacks. The proposed countermeasures are based on the ciphertext blinding, but they require no inversion operation. The overhead of the proposed scheme is only about 1–5% of the whole decryption if the bit length of modulus is 1,024.

In Crypto 2000 and 2003, Lenstra-Verheul and Rubin- Silverberg proposed XTR public key system and torus based public key cryptosystem CEILIDH, respectively. The common main idea of XTR and CEILIDH is to shorten the bandwidth of transmission data. Due to the contribution of Granger et al., that is the comparison result of the performance of CEILIDH and XTR, XTR is an excellent alternative to either RSA or ECC in some applications, where computational power and memory capacity are both very limited, such as smart-cards. Among the family of XTR algorithm, Improved XTR Single Exponentiation (XTR-ISE) is the most efficient one, which computes single exponentiation. However, there are few papers investigating the side channel attacks of XTR-ISE, even though the memory constraint devices suffer most from vulnerability to side channel attacks. Chung-Hasan and Page-Stam tried to analyze XTR-ISE with the known simple power analysis, but unfortunately their approach were not practically feasible. Recently, Han et al. proposed new collision attack on it with analysis complexity O(240) when the key size is 160-bit. In this paper we analyze XTR-ISE from other point of view, namely differential power analysis (DPA). One straightforward result is that XTR-ISE can be free from the original DPA. However, a non-trivial result is that an enhancing DPA proposed in this paper threatens XTR-ISE. Furthermore, we show several weak points of the structure of XTR-ISE. From our simulation results, we show the proposed attack requires about 584 times queries to DPA_Oracle to detect the whole 160-bit secret value. This result shows that XTR-ISE is vulnerable to the proposed enhancing DPA.

Service-oriented computing is a promising computing paradigm which facilitates the composition of loosely coupled and adaptable applications. Unfortunately, this new paradigm does not lend itself easily to traditional software engineering methods and principles due to the decentralised nature of software services. The goal of this paper is to identify a set of engineering activities that can be used to develop adaptable service-based applications. Rather than focusing on the entire service-based application development life-cycle, this paper will focus on adaptation-specific processes and activities and map them to an existing high-level service-based application development life-cycle. Existing software engineering literature as well as research results from service engineering research is reviewed for relevant activities. The result is an adaptation framework that can guide software engineers in developing adaptable service-based applications. © 2013 Springer-Verlag London.

To evaluate the feasibility of an iPad-based documented patient briefing for Magnetic Resonance Imaging (MRI) examinations. A standard briefing sheet and questionnaire for a MRI scan was converted from paper form into an iPad application. Twenty patients, who had been referred for an MRI scan, were briefed about the examination in paper form as well as via the iPad application before performing the MRI scan. Time each patient needed for the briefing and the number of questions that came up were documented. Patients’ acceptance of the electronic briefing was assessed using a questionnaire. The mean processing time was 2.36 min (range 0.58 to 09.35 min., standard deviation ±2.05 min) for the paper-based briefing and 4.15 min (range 1.56 to 13.48 min, SD ± 2.36 min) for the app-based briefing. Concerning technical aspects, patients asked two questions during the app-based briefing; no questions arose during the paper-based briefing. Six patients preferred electronic briefing and four patients, the paper-based form. No patient preferred the electronic form with additional multimedial information. Eight participants did not mind which briefing version was used; two participants did not express their preference at all. Our experiences showed that electronic briefing using an iPad is feasible and has the potential to become a user-friendly alternative to the conventional paper-based approach. Owing to the broad range of the results, a follow-up study will seek to determine the influencing factors on processing time and other potential questions.

This paper reports on research, the aim of which has been to investigate the use of virtual manufacturing and data mining techniques to automate the identification of manufacturing process time estimating relationships that form the basis of product and process cost models. Such models provide information that is critical to all stages of the product development process and to ensuring that development of cost-effective product design and production methods. Use of virtual manufacturing models and data mining techniques enables the majority of the activities involved in the cost model development process to be automated. Hence, reducing the time and effort required, reducing the current high level of reliance on expert judgment and enabling higher levels of cost detail to be estimated. The research reported, therefore, focuses on the use of virtual manufacturing to generate datasets which are then analysed using data mining to identify suitable cost models for manufacturing processes. Part I, of this two-part paper, describes the development of a model development methodology that makes use of virtual manufacturing models and data mining techniques and uses case study data to validate this methodology. Part II will then examine in detail the effectiveness of alternative data mining algorithms in terms of their ability to develop relationships that are (1) representative of the real causal relationships that exist and (2) provide a high level of estimating accuracy.

A recent American Medical Association report highlighted failures in communication of abnormal test results as an important but understudied facet of improving safety in ambulatory care.1 Because many outpatient test results are not life-threatening and don’t require verbal communication, health information technology (IT) has potential to reliably transmit result information in the fragmented outpatient setting. Thus, few will disagree that communication of abnormal test results is an obvious context where advantages of health IT will be observed. In this issue of JGIM, Callen et al. report the results of a timely systematic review of 19 studies that documented quantitative evidence of test results not followed up in ambulatory settings.2 They found wide variation in abnormal results lacking follow-up: 7 % to 62 % for laboratory, and 1 % to 36 % for imaging tests. Although evidence of the effectiveness of electronic test management systems was limited, there was a general trend towards improved follow-up in electronic systems. In another article in this issue, El-Kareh et al. discuss the results of a randomized controlled trial that put electronic communication to the test. The authors studied the effectiveness of sending microbiology test result alerts via a secure, internal e-mail system to clinicians when results were finalized post-discharge.3 They found better documented evidence of appropriate follow-up within 3 days in the intervention group (28 % vs. 13 % in controls). Neither group’s laboratory follow-up rate was particularly encouraging. On the bright side, both studies used distinctly different research approaches to reach similar conclusions, i.e., application of information and communication technologies, such as electronic health records (EHRs) with alerting capability, can increase the likelihood of appropriate test result follow-up. In paper-based systems, evaluating evidence of follow-up is itself challenging. On the other hand, both articles remind us that using EHR-based technology by itself does not entirely solve the problem of failure to follow up test results. Callen et al., as well as others, have made a strong case for addressing these failures based on safety implications. Additionally, Stage 2 meaningful EHR use (slated for implementation in 2014) includes laboratory test result reporting criteria. Time is now ripe for novel approaches to understand and improve this complex problem. The use of technology in the complex healthcare system must take into context the social environment where technology is embedded. For example, Callen et al. found lack of clear policies and procedures in relation to test result follow-up. We previously identified ambiguity of responsibility for test result follow-up to be a key factor in failure to follow up abnormal results.4 Several EHRs now use asynchronous alert notifications to transmit results, but providers often receive many other types of notifications in their electronic in-box. We found that primary care providers (PCPs) receive a mean of 57 alerts a day in an integrated delivery system’s EHR, all with new information they need to process and/or act upon.5 Important information about abnormal results might get buried among other alerts. To help understand the complexities involved with electronic communication of test results and facilitate progress in developing multifaceted solutions, a “sociotechnical” approach is needed. In our work, we use an eight-dimension sociotechnical model to study both problems and solutions related to safe and effective EHR implementation and use.6 In the sections below, we illustrate the usefulness of this model by discussing each of its eight dimensions, as applied to issues raised by the two studies. We also take the liberty of making several recommendations that might be useful to reduce failures in test result follow-up in EHR-based systems. Hardware/Software: To maintain superiority over paper, EHRs must be configured to ensure that results are reported to the correct provider in a timely fashion. Thus, all test orders should be placed via a Computer-based Provider Order Entry (CPOE) system. Orders should be transmitted in a coded format to the entity performing the test, and the transmission should occur via a two-way system-to-system interface that can send orders and receive results. Otherwise, results might not make it back into the EHR in a form that allows clinical decision support interventions (e.g., alert for abnormal creatinine will not fire while entering an order for metformin). Clinical Content: Results should be stored as structured/coded data to facilitate reporting and tracking of results. This feature enabled El-Kareh and colleagues to extract results from the EHR, and can facilitate result-tracking functions. Institutions must also define standardized result categories and definitions (e.g., critical, normal, etc.) to facilitate prioritization and reporting. For instance, certain levels of abnormalities can be flagged in the EHR for more immediate action based on urgency. Care should be taken to avoid flagging borderline or clinically insignificant results as urgent. User Interface: A poor user interface can lead providers to miss critical information. EHRs should have result review screens that ensure that all critical information is displayed on one screen (i.e., no scrolling is required) and all columns are sufficiently wide to allow users to see all pertinent information. In addition, users should be able to sort, or filter, results by date, type, patient, or urgency.7 Personnel: Providers should be trained to process their alerts in a timely manner and document follow-up and communication of results to patients in the EHR. Poor documentation is widely prevalent and might be one reason to explain the low follow-up rates reported in El-Kareh et al.’s study. Workflow/Communication: Institutions must avoid partial use of EHRs for test result management (i.e., results or notes, but not both, available electronically8), because this leads to a higher risk of test result follow-up failures. Workflows related to certain high-risk areas (tests ordered by residents, part-time physicians, emergency deparment physicians; send-out tests; and post-discharge results) must be well-defined. This process should include creation of back-up procedures (including use of surrogates) and fail-safe escalation systems to safeguard against results “falling through the cracks”. To what extent this was done, if at all, in the El-Kareh study is unclear, and thus a seemingly straightforward technological intervention might not have reached its full potential. Additionally, practices must create robust processes to send both normal and abnormal test results to patients. In the Veterans’ Health Administration (VA), providers can generate letters though EHR templates, which are then sent to patients through centralized mailing facilities. Many institutions use web-based portals to make results accessible to patients, and some directly notify patients bypassing provider review. Whether the latter approach reduces follow-up failures is unclear.9 Internal Organizational Policies, Procedures, Culture and Environment: Responsibility for test result follow-up is an under-recognized and underemphasized contributory factor in follow-up failures. Responsibility should always be clear, and can be delegated to someone as long as that procedure is clear to both parties. It is unclear how physicians in the El-Kareh study perceived their test result follow-up responsibilities; many that did not answer the survey or follow up appropriately might have attributed this responsibility to someone else (e.g., inpatient physician thought that the PCP was responsible post-discharge). We also recommend that all institutions/clinics should have an annually updated, written policy on all aspects of test result management (e.g., provider notification, patient notification, follow-up responsibilities).10 This document should define processes and procedures for test result communication, including which results are critical and need verbal communication. Institutions should also maintain updated contact information for all providers and patients. Some of the e-mail alerts sent by the investigators might not have reached the study physicians. External Rules and Regulations: In 2009, the VA released a policy directive requiring communication of all test results to patients within 14 calendar days after the test result is available to the ordering practitioner. To the best of our knowledge, there are no other federal or state policies giving guidance on definitions and measurement of timeliness of test result follow-up. Measurement and Monitoring: The VA is now instituting a measurement system for test results follow-up, and we encourage other institutions to do the same. Logs of test result values, alerts, and provider acknowledgment of alert receipt (results review) could be used for this purpose. However, acknowledgment of a test result receipt does not guarantee that the follow-up action has taken place;4 alternative measurement systems should be in place to monitor test result follow-up.