Query: Efficient and secure data storage operations for mobile cloud computing

Relevant passages:
Provably secure ciphertext policy ABE <=SEP=> In ciphertext policy attribute-based encryption (CP-ABE), every secret key is associated with a set of attributes, and every ciphertext is associated with an access structure on attributes. Decryption is enabled if and only if the user's attribute set satisfies the ciphertext access structure. This provides fine-grained access control on shared data in many practical settings, e.g., secure database and IP multicast.
 In this paper, we study CP-ABE schemes in which access structures are AND gates on positive and negative attributes. Our basic scheme is proven to be chosen plaintext (CPA) secure under the decisional bilinear Diffie-Hellman (DBDH) assumption. We then apply the Canetti-Halevi-Katz technique to obtain a chosen ciphertext (CCA) secure extension using one-time signatures. The security proof is a reduction to the DBDH assumption and the strong existential unforgeability of the signature primitive.
 In addition, we introduce hierarchical attributes to optimize our basic scheme - reducing both ciphertext size and encryption/decryption time while maintaining CPA security. We conclude with a discussion of practical applications of CP-ABE.

Practical Techniques for Searches on Encrypted Data <=SEP=> It is desirable to store data on data storage servers such as mail servers and file servers in encrypted form to reduce security and privacy risks. But this usually implies that on e has to sacrifice functionality for security. For example, if a client wishes to retrieve only documents containing certai n words, it was not previously known how to let the data storage server perform the search and answer the query without loss of data confidentiality. In this paper, we describe our cryptographic schemes for the problem of searching on encrypted data and provide proofs of security for the resulting crypto systems. Ou r techniques have a number of crucial advantages. They are provably secure : they provideprovable secrecy for encryption, in the sense that the untrusted server cannot learn anything about the plaintext when only given the ciphertext; they providequery isolationfor searches, meaning that the untrusted server cannot learn anything more about the plaintext than the search result; they provide controlled searching, so that the untrusted server cannot search for an arbitrary word without the user’s authorization; they also supporthidden queries , so that the user may ask the untrusted server to search for a secret word without revealing the word to the server. The algorithms we present are simple, fast (for a document of length n, the encryption and search algorithms only need O(n) stream cipher and block cipher operations), and introduce almost no space and communication overhead, and hence are practical to use today. We gratefully acknowledge support for this research from se veral US government agencies. This research was suported in part by t he Defense Advanced Research Projects Agency under DARPA contract N66 01-9928913 (under supervision of the Space and Naval Warfare Syst ems Center San Diego), by the National Science foundation under grant F D99-79852, and by the United States Postal Service under grant USPS 1025 90-98-C3513. Views and conclusions contained in this document are t hose of the authors and do not necessarily represent the official opinio n or policies, either expressed or implied of the US government or any of its agencies, DARPA, NSF, USPS.

Computer security technology planning study <=SEP=> Approved for pubJic reJease; distribution unri mited. When U.S. Government drawings, specifications or other data are used for any purpose other than a definitely related government procurement operation, the government thereby incurs no responsibility nor any obligation whatsoever; and the fact that the government may have formulated, fui·nished, or in any way sup­ plied the said drawings, specifications, or other data is not to be regarded by implication or otherwise as in any manner licensing the holder or any other person or conveying any rights or permission to manufacture, use, or sell any patented invention that may in any way be related thereto.

Attribute-based encryption for fine-grained access control of encrypted data <=SEP=> As more sensitive data is shared and stored by third-party sites on the Internet, there will be a need to encrypt data stored at these sites. One drawback of encrypting data, is that it can be selectively shared only at a coarse-grained level (i.e., giving another party your private key). We develop a new cryptosystem for fine-grained sharing of encrypted data that we call Key-Policy Attribute-Based Encryption (KP-ABE). In our cryptosystem, ciphertexts are labeled with sets of attributes and private keys are associated with access structures that control which ciphertexts a user is able to decrypt. We demonstrate the applicability of our construction to sharing of audit-log information and broadcast encryption. Our construction supports delegation of private keys which subsumesHierarchical Identity-Based Encryption (HIBE).

Plutus: Scalable Secure File Sharing on Untrusted Storage <=SEP=> Plutus is a cryptographic storage system that enables secure file sharing without placing much trust on the file servers. In particular, it makes novel use of cryptographic primitives to protect and share files. Plutus features highly scalable key management while allowing individual users to retain direct control over who gets access to their files. We explain the mechanisms in Plutus to reduce the number of cryptographic keys exchanged between users by using filegroups, distinguish file read and write access, handle user revocation efficiently, and allow an untrusted server to authorize file writes. We have built a prototype of Plutus on OpenAFS. Measurements of this prototype show that Plutus achieves strong security with overhead comparable to systems that encrypt all network traffic.

Diaphragmatic Eventration in Sisters with Asparagine Synthetase Deficiency: A Novel Homozygous ASNS Mutation and Expanded Phenotype. <=SEP=> BACKGROUND
Asparagine Synthetase Deficiency (ASNSD; OMIM #615574) is a newly described rare autosomal recessive neurometabolic disorder, characterised by congenital microcephaly, severe psychomotor delay, encephalopathy and progressive cerebral atrophy. To date, seven families and seven missense mutations in the ASNSD disease causing gene, ASNS, have been published.


METHODS
We report two further affected infant sisters from a consanguineous Indian family, who in addition to the previously described features had diaphragmatic eventration. Both girls died within the first 6 months of life. Whole exome sequencing (WES) was performed for both sisters to identify the pathogenic mutation. The clinical and biochemical parameters of our patient are compared to previous reports.


RESULTS
WES demonstrated a homozygous novel missense ASNS mutation, c.1019G > A, resulting in substitution of the highly conserved arginine residue by histidine (R340H).


CONCLUSION
This report expands the phenotypic and mutation spectrum of ASNSD, which should be considered in neonates with congenital microcephaly, seizures and profound neurodevelopmental delay. The presence of diaphragmatic eventration suggests extracranial involvement of the central nervous system in a disorder that was previously thought to exclusively affect the brain. Like all previously reported patients, these cases were diagnosed with WES, highlighting the clinical utility of next generation sequencing in the diagnosis of rare, difficult to recognise disorders.

Challenges in developing opinion mining tools for social media <=SEP=> While much work has recently focused on the analysis of social media in order to get a feel for what people think about current topics of interest, there are, however, still many challenges to be faced. Text mining systems originally designed for more regular kinds of texts such as news articles may need to be adapted to deal with facebook posts, tweets etc. In this paper, we discuss a variety of issues related to opinion mining from social media, and the challenges they impose on a Natural Language Processing (NLP) system, along with two example applications we have developed in very different domains. In contrast with the majority of opinion mining work which uses machine learning techniques, we have developed a modular rule-based approach which performs shallow linguistic analysis and builds on a number of linguistic subcomponents to generate the final opinion polarity and score.

Background Prior-Based Salient Object Detection via Deep Reconstruction Residual <=SEP=> Detection of salient objects from images is gaining increasing research interest in recent years as it can substantially facilitate a wide range of content-based multimedia applications. Based on the assumption that foreground salient regions are distinctive within a certain context, most conventional approaches rely on a number of hand-designed features and their distinctiveness is measured using local or global contrast. Although these approaches have been shown to be effective in dealing with simple images, their limited capability may cause difficulties when dealing with more complicated images. This paper proposes a novel framework for saliency detection by first modeling the background and then separating salient objects from the background. We develop stacked denoising autoencoders with deep learning architectures to model the background where latent patterns are explored and more powerful representations of data are learned in an unsupervised and bottom-up manner. Afterward, we formulate the separation of salient objects from the background as a problem of measuring reconstruction residuals of deep autoencoders. Comprehensive evaluations of three benchmark datasets and comparisons with nine state-of-the-art algorithms demonstrate the superiority of this paper.

p-Cycle-based node failure protection for survivable virtual network embedding <=SEP=> Network Virtualization offers to Physical Infrastructure Provider (PIP) the possibility to smoothly roll out multiple isolated networks on top of his infrastructure. A major challenge in this respect is the embedding problem which deals with the mapping of Virtual Networks (VNs) resources on PIP network. In literature, a number of research proposals have been focused on providing heuristic approaches to solve this NP-hard problem while most often assuming that PIP infrastructure is operational at all time. In virtualization environment a single physical node failure can result in single/multi logical link failure(s) as it effects all VNs with a mapping that spans over. Setup a dedicated backup for each VN embedding, i.e., no sharing, is inefficient in terms of resource usage. To address these concerns, we propose in this work an approach for VN mapping with combined physical node and multiple logical links protection mechanism (VNM-CNLP) that: (a) uses a large scale optimization technique, developed in our previous work, to calculate in a first stage a cost-efficient VN mapping while minimizing the effects of a single node failure in VN layer, and (b) proposes in a second stage link p-Cycle based protection techniques that minimize the backup resources while providing a full VN protection scheme against a single physical node failure and a multiple logical links failure. Our simulations show a clear advantage of VNM-CNLP approach over benchmark in terms of VN backup cost and resources utilization.

DAISY Filter Flow: A Generalized Discrete Approach to Dense Correspondences <=SEP=> Establishing dense correspondences reliably between a pair of images is an important vision task with many applications. Though significant advance has been made towards estimating dense stereo and optical flow fields for two images adjacent in viewpoint or in time, building reliable dense correspondence fields for two general images still remains largely unsolved. For instance, two given images sharing some content exhibit dramatic photometric and geometric variations, or they depict different 3D scenes of similar scene characteristics. Fundamental challenges to such an image or scene alignment task are often multifold, which render many existing techniques fall short of producing dense correspondences robustly and efficiently. This paper presents a novel approach called DAISY filter flow (DFF) to address this challenging task. Inspired by the recent PatchMatch Filter technique, we leverage and extend a few established methods: DAISY descriptors, filter-based efficient flow inference, and the PatchMatch fast search. Coupling and optimizing these modules seamlessly with image segments as the bridge, the proposed DFF approach enables efficiently performing dense descriptor-based correspondence field estimation in a generalized high-dimensional label space, which is augmented by scales and rotations. Experiments on a variety of challenging scenes show that our DFF approach estimates spatially coherent yet discontinuity-preserving image alignment results both robustly and efficiently.

Making TCP/IP Viable for Wireless Sensor Networks <=SEP=> The TCP/IP protocol suite, which has proven itself highly successful in wired networks, is often claimed to be unsuited for wireless micro-sensor networks. In this work, we question this conventional wisdom and present a number of mechanisms that are intended to enable the use of TCP/IP for wireless sensor networks: spatial IP address assignment, shared context header compression, application overlay routing, and distributed TCP caching (DTC). Sensor networks based on TCP/IP have the advantage of being able to directly communicate with an infrastructure consisting either of a wired IP network or of IP-based wireless technology such as GPRS. We have implemented parts of our mechanisms both in a simulator environment and on actual sensor nodes. Our preliminary results are promising.

Introducing the Occupational Balance Questionnaire (OBQ). <=SEP=> OBJECTIVE
The concept of occupational balance is frequently used in occupational therapy but the fact that it has been defined and measured differently is a limitation. This article introduces the Occupational Balance Questionnaire (OBQ), which focuses on satisfaction with the amount and variation of occupations. It consists of 13 items measured on six-step ordinal scales. It has shown good content validity in a sample of 21 occupational therapists but other psychometric properties have not been investigated. The aim was to investigate the OBQ regarding internal consistency, test-retest reliability, and floor/ceiling effects.


METHODS
The OBQ was administered twice to a sample selected through convenience sampling. Internal consistency was investigated by Cronbach's alpha and test-retest reliability analysed with Spearman's Rho correlation for the total score and weighted kappa on each item. Potential floor/ceiling effects were explored by checking for the percentage of participants who scored lowest and highest.


RESULTS
The results demonstrated that the OBQ has good internal consistency (Cronbach's alpha 0.936) and sufficient test-retest reliability (Spearman's Rho for the total score was 0.926) and, thus, seems stable over time. No floor or ceiling effect was detected.


CONCLUSIONS
The OBQ therefore showed promising reliability, although further instrument development studies to examine its construct validity are required.

Design and implementation of a single-phase buck-boost inverter <=SEP=> In order to simplify the configuration of photovoltaic (PV) grid-connection system, this paper proposes to adopt a buck-boost dc-dc converter, and then develops a single-phase inverter via the connection with an H-bridge unfolding circuit with line-commutated. Depending on the conditions of dc input-voltage and ac output-voltage, the proposed circuit can work functionally as either a step-down or step-up inverter. It is suitable for the applications with wide voltage-variation range. Since only one switch is operated with high-frequency, the switching loss can be significantly reduced to improve the efficiency. Finally, a laboratory prototype with 110 Vrms / 60 Hz output voltage is then simulated and implemented accordingly to verify the feasibility of the proposed inverter.

Chi: A Scalable and Programmable Control Plane for Distributed Stream Processing Systems <=SEP=> Stream-processing workloads and modern shared cluster environments exhibit high variability and unpredictability. Combined with the large parameter space and the diverse set of user SLOs, this makes modern streaming systems very challenging to statically configure and tune. To address these issues, in this paper we investigate a novel control-plane design, Chi, which supports continuous monitoring and feedback, and enables dynamic re-configuration. Chi leverages the key insight of embedding control-plane messages in the data-plane channels to achieve a low-latency and flexible control plane for stream-processing systems. Chi introduces a new reactive programming model and design mechanisms to asynchronously execute control policies, thus avoiding global synchronization. We show how this allows us to easily implement a wide spectrum of control policies targeting different use cases observed in production. Large-scale experiments using production workloads from a popular cloud provider demonstrate the flexibility and efficiency of our approach. PVLDB Reference Format: Luo Mai, Kai Zeng, Rahul Potharaju, Le Xu, Steve Suh, Shivaram Venkataraman, Paolo Costa, Terry Kim, Saravanan Muthukrishnan, Vamsi Kuppa, Sudheer Dhulipalla, Sriram Rao. Chi: A Scalable and Programmable Control Plane for Distributed Stream Processing Systems. PVLDB, 11 (10): xxxx-yyyy, 2018. DOI: https://doi.org/10.14778/3231751.3231765

Using Dynamic Programming for Solving Variational Problems in Vision <=SEP=> Variational approaches have been proposed for solving many inverse problems in early vision, such as in the computation of optical flow, shape from shading, and energy-minimizing active contour models. In general however, variational approaches do not guarantee global optimality of the solution, require estimates of higher order derivatives of the discrete data, and do not allow direct and natural enforcement of constraints. In this paper we discuss dynamic programming as a novel approach to solving variational problems in vision. Dynamic programming ensures global optimality of the solution, it is numerically stable, and it allows for hard constraints to be enforced on the behavior of the solution within a natural and straightforward structure. As a specific example of the efficacy of the proposed approach, application of dynamic programming to the energy-minimizing active contours is described. The optimization problem is set up as a discrete multistage decision process and is solved by a " time-delayed " discrete dynamic programming algorithm. A parallel procedure is discussed that can result in savings in computational costs.

VisTrails: visualization meets data management <=SEP=> Scientists are now faced with an incredible volume of data to analyze. To successfully analyze and validate various hypothesis, it is necessary to pose several queries, correlate disparate data, and create insightful visualizations of both the simulated processes and observed phenomena. Often, insight comes from comparing the results of multiple visualizations. Unfortunately, today this process is far from interactive and contains many error-prone and time-consuming tasks. As a result, the generation and maintenance of visualizations is a major bottleneck in the scientific process, hindering both the ability to mine scientific data and the actual use of the data. The VisTrails system represents our initial attempt to improve the scientific discovery process and reduce the time to insight. In VisTrails, we address the problem of visualization from a data management perspective: VisTrails manages the data and metadata of a visualization product. In this demonstration, we show the power and flexibility of our system by presenting actual scenarios in which scientific visualization is used and showing how our system improves usability, enables reproducibility, and greatly reduces the time required to create scientific visualizations.

Short Tamil sentence similarity calculation using knowledge-based and corpus-based similarity measures <=SEP=> Sentence similarity calculation plays an important role in text processing-related research. Many unsupervised techniques such as knowledge-based techniques, corpus-based techniques, string similarity based techniques, and graph alignment techniques are available to measure sentence similarity. However, none of these techniques have been experimented with Tamil. In this paper, we present the first-ever system to measure semantic similarity for Tamil short phrases using a hybrid approach that makes use of knowledge-based and corpus-based techniques. We tested this system with 2000 general sentence pairs and 100 mathematical sentence pairs. For the dataset of 2000 sentence pairs, this approach achieved a Mean Squared Error of 0.195 and a Pearson Correlation factor of 0.815. For the 100 mathematical sentence pairs, this approach achieved an 85% of accuracy.

The twisted string actuation system: Modeling and control <=SEP=> This paper describes a novel actuation system for very compact and light-weight robotic devices, like artificial hands. The actuation concept presented here allows the implementation of powerful tendon-based driving systems, using as actuators small-size DC motors characterized by high speed and low torque. After the presentation of the basic concept of this novel actuation system, the constitutive equations of the system are given, validated by means of laboratory tests. Moreover, the problem of tracking a desired actuation force profile is taken into account, considering as load a mass-spring-damper system. A control algorithm based on a second-order sliding manifold has been firstly evaluated by means of simulations, and then validated by experiments. This output-feedback controller has been chosen to guarantee a high level of robustness against disturbances, parameter variations and uncertainties while maintaining a low computational burden.

An Empirical Evaluation of Resources for the Identification of Diseases and Adverse Effects in Biomedical Literature <=SEP=> The mentions of human health perturbations such as the diseases and adverse effects denote a special entity class in the biomedical literature. They help in understanding the underlying risk factors and develop a preventive rationale. The recognition of these named entities in texts through dictionary-based approaches relies on the availability of appropriate terminological resources. Although few resources are publicly available, not all are suitable for the text mining needs. Therefore, this work provides an overview of the well known resources with respect to human diseases and adverse effects such as the MeSH, MedDRA, ICD-10, SNOMED CT, and UMLS. Individual dictionaries are generated from these resources and their performance in recognizing the named entities is evaluated over a manually annotated corpus. In addition, the steps for curating the dictionaries, rule-based acronym disambiguation and their impact on the dictionary performance is discussed. The results show that the MedDRA and UMLS achieve the best recall. Besides this, MedDRA provides an additional benefit of achieving a higher precision. The combination of search results of all the dictionaries achieve a considerably high recall. The corpus is available on http://www.scai.fraunhofer.de/disease-ae-corpus.html

The iSLIP scheduling algorithm for input-queued switches <=SEP=> h increasing number of high performance internetworking protocol routers, LAN and asynchronous transfer mode (ATM) switches use a switched backplane based on a crossbar switch. Most often, these systems use input queues to hold packets waiting to traverse the switching fabric. It is well known that if simple first in fh-st out (FIFO) input queues are used to hold packets then, even under benign conditions, head-of-line (HOL) blocking limits the achievable bandwidth to approximately 58.6% of the maximum. HOL blocking can be overcome by the use of virtual output queueing, which is described in this paper. A scheduling algorithm is used to configure the crossbar switch, deciding the order in which packets will be served. Recent results have shown that with a suitable scheduling algorithm, 100% throughput can he achieved. In this paper, we present a scheduling algorithm called iSLIP. An iterative, round-robin algorithm, iSLIP can achieve 100% throughput for uniform traffic, yet is simple to implement in hardware. Iterative and noniterative versions of the algorithms are presented, along with modified versions for prioritized traffic. Simulation results are presented to indicate the performance of iSLIP under benign and bursty traffic conditions. Prototype and commercial implementations of SLIP exist in systems with aggregate bandwidths ranging from 50 to SO0 Gb/s. When the traffic is nonuniform, SLIP quickly adapts to a fair scheduling policy that is guaranteed never to starve an input queue. Finally, we describe the implementation complexity of SLIP. Based on a two-dimensional (2-D) array of priority encoders, single-chip schedulers have been built supporting up to 32 ports, and making approximately 100 million scheduling decisions per second. I~l&x TermsATM switch, crossbar switch, input-queueing, IP router, scheduling.

Reliable detection and skew correction method of license plate for PTZ camera-based license plate recognition system <=SEP=> The skewness of license plates affects negatively all processing steps of license plate recognition including character segmentation and character classification. The skewness of the license plate in the captured image may appear severely under PTZ camera environments since license plate appears to have various postures in the captured image due to panning and tilting positions of PTZ cameras. Thus far, most of previous works on this subject have mainly resolved skewness problem due to rotation in plane, but not rotation in depth. In this paper, by utilizing planar projective transformation (planar homography), we propose a reliable skew correction method of license plates which handles also the skewness due to rotation in depth. For more reliable deskewing process, the license plate needs to be detected more reliably so that this paper also proposes a reliable license plate detection method. The effectiveness of the proposed skew correction method as well as the proposed license plate detection method is verified though experiments.

Rapid Graph Layout Using Space Filling Curves <=SEP=> Network data frequently arises in a wide variety of fields, and node-link diagrams are a very natural and intuitive representation of such data. In order for a node-link diagram to be effective, the nodes must be arranged well on the screen. While many graph layout algorithms exist for this purpose, they often have limitations such as high computational complexity or node colocation. This paper proposes a new approach to graph layout through the use of space filling curves which is very fast and guarantees that there will be no nodes that are colocated. The resulting layout is also aesthetic and satisfies several criteria for graph layout effectiveness.

Context-Free Transductions with Neural Stacks <=SEP=> This paper analyzes the behavior of stackaugmented recurrent neural network (RNN) models. Due to the architectural similarity between stack RNNs and pushdown transducers, we train stack RNN models on a number of tasks, including string reversal, contextfree language modelling, and cumulative XOR evaluation. Examining the behavior of our networks, we show that stack-augmented RNNs can discover intuitive stack-based strategies for solving our tasks. However, stack RNNs are more difficult to train than classical architectures such as LSTMs. Rather than employ stack-based strategies, more complex networks often find approximate solutions by using the stack as unstructured memory.

Radio resource allocation and power control scheme to mitigate interference in device-to-device communications underlaying LTE-A uplink cellular networks <=SEP=> The integration of Device-to-Device (D2D) communication into a cellular network improves system spectral efficiency, increases capacity, reduces power consumption and also reduces traffic to the evolved NodeB (eNB). However, D2D communication generates interference to the conventional cellular network which deteriorates the system performance. In this paper, we propose a radio resource allocation and power control scheme to mitigate the interference using cell sectorization scheme while reusing uplink cellular resources by the D2D pairs. By simulations, we show that our proposed scheme improves the overall system performance compared with the existing methods.

Humanlike Driving: Empirical Decision-Making System for Autonomous Vehicles <=SEP=> The autonomous vehicle, as an emerging and rapidly growing field, has received extensive attention for its futuristic driving experiences. Although the fast developing depth sensors and machine learning methods have given a huge boost to self-driving research, existing autonomous driving vehicles do meet with several avoidable accidents during their road testings. The major cause is the misunderstanding between self-driving systems and human drivers. To solve this problem, we propose a humanlike driving system in this paper to give autonomous vehicles the ability to make decisions like a human. In our method, a convolutional neural network model is used to detect, recognize, and abstract the information in the input road scene, which is captured by the on-board sensors. And then a decision-making system calculates the specific commands to control the vehicles based on the abstractions. The biggest advantage of our work is that we implement a decision-making system which can well adapt to real-life road conditions, in which a massive number of human drivers exist. In addition, we build our perception system with only the depth information, rather than the unstable RGB data. The experimental results give a good demonstration of the efficiency and robustness of the proposed method.

A descent modified <=SEP=> In this paper, we propose a modified Polak–Ribière–Polyak (PRP) conjugate gradient method. An attractive property of the proposed method is that the direction generated by the method is always a descent direction for the objective function. This property is independent of the line search used. Moreover, if exact line search is used, the method reduces to the ordinary PRP method. Under appropriate conditions, we show that the modified PRP method with Armijo-type line search is globally convergent. We also present extensive preliminary numerical experiments to show the efficiency of the proposed method.

Robot Learning from Demonstration: A Task-level Planning Approach: <=SEP=> In this paper, we deal with the problem of task level learning and planning for robotic applications that involve object manipulation. In dynamic environments, preprogramming robots for execution of complex tasks such as for example setting a dinner table is of little use, since the same order of subtasks may not be conceivable due to the changed state of the world in the execution phase. In our approach, we aim to learn the goal of the task, and use a task planner to reach the goal given different initial states of the world. However, for some tasks, there are underlying constraints that must be fulfilled, and knowing just the final goal is not sufficient. We propose two techniques for constraint identification. In the first case, the teacher can directly instruct the system about the underlying constraints. In the second case, the constraints are identified by the robot itself through the merging of multiple observations. The constraints are then considered in the planning phase, allowing the task to be executed without violating any of them. We evaluate our work on a real robot performing pick-and-place tasks with several objects of various size.

Does Life Satisfaction influence the intention (We-Intention) to use Facebook? <=SEP=> Several studies have investigated a variety of factors affecting use of social networking sites (SNSs), but the investigation of these factors is still under development. In this study, we aim to contribute to the literature by extending and testing an existent conceptual model in a new context, including Life Satisfaction as an independent variable to explain the intention (We-Intention) to use Facebook. The previous models has Subjective Norm, Group Norms, Social Identity, Purposive Value, Self-Discovery, Maintaining Interpersonal Interconnectivity, Social Enhancement, Entertainment Value and Social Presence as the predictor variables. An online survey with Brazilians (n = 1111) was conducted. Our structural equation modeling reveals that Life Satisfaction influence on We-Intention is mediated by Subjective Norm, Group Norm, Social Identity, Entertainment Value, and Maintaining Interpersonal Interconnectivity (R-squared value is 0.36). Our findings, while consistent with existing literature in terms of theories, reveal different arrangements among factors influencing Brazilian consumers’ behavior. 2015 Elsevier Ltd. All rights reserved.

NailTactors: eyes-free spatial output using a nail-mounted tactor array <=SEP=> This paper investigates the feasibility of using a nail-mounted array of tactors, NailTactors, as an eyes-free output device. By rim-attached eccentric-rotating-mass (ERM) vibrators to artificial nails, miniature high-resolution tactile displays were realized as an eyes-free output device. To understand how to deliver rich signals to users for valid signal perception, three user studies were conducted. The results suggest that users can not only recognized absolute and relative directional cues, but also recognized numerical characters in EdgeWrite format with an overall 89% recognition rate. Experiments also identified the optimal placement of ERM actuators for maximizing information transfer.

Automatic medical image annotation in ImageCLEF 2007: Overview, results, and discussion <=SEP=> In this paper, the automatic medical annotation task of the 2007 CLEF cross-language image retrieval campaign (ImageCLEF) is described. The paper focusses on the images used, the task setup, and the results obtained in the evaluation campaign. Since 2005, the medical automatic image annotation task exists in ImageCLEF with increasing complexity to evaluate the performance of state-of-the-art methods for completely automatic annotation of medical images based on visual properties. The paper also describes the evolution of the task from its origin in 2005 to 2007. The 2007 task, comprising 11,000 fully annotated training images and 1,000 test images to be annotated, is a realistic task with a large number of possible classes at different levels of detail. Detailed analysis of the methods across participating groups is presented with respect to the (i) image representation, (ii) classification method, and (iii) use of the class hierarchy. The results show that methods which build on local image descriptors and discriminative models are able to provide good predictions of the image classes, mostly by using techniques that were originally developed in the machine learning and computer vision domain for object recognition in non-medical images.