title: Face off: Implications of visual cues on initiating friendship on Facebook

abstract: This research investigates how moderating factors and theoretically relevant contextual variables affect impression formation and the willingness to initiate virtual friendship. An experiment examined both main and interaction effects for visual cues, profile owner's gender, and evaluator's gender; a 2 (stimulus gender: male and female)x3 (visual conditions: attractive, unattractive, and no-photo)x2 (evaluator's gender: male and female) between subjects model analysis of variance (ANOVA) was employed. A three-way interaction between gender and appearance was revealed. The results indicated that both male and female subjects were more willing to initiate friendships with opposite-sex profile owners with attractive photos. Subjects also displayed comparatively higher willingness to make friends with profile owners who did not include visual cues than with those who revealed an unattractive photo. The hyperpersonal model was supported and extended to address gender attributes.


PLANTING DENSITY TRIAL WITH âARBEQUINAâ OLIVE CULTIVAR IN CATALONIA (SPAIN)


title: Hydra: A Block-Mapped Parallel Flash Memory Solid-State Disk Architecture

abstract: Flash memory solid-state disks (SSDs) are replacing hard disk drives (HDDs) in mobile computing systems because of their lower power consumption, faster random access, and greater shock resistance. We describe Hydra, a high-performance flash memory SSD architecture that translates the parallelism inherent in multiple flash memory chips into improved performance, by means of both bus-level and chip-level interleaving. Hydra has a prioritized structure of memory controllers, consisting of a single high-priority foreground unit, to deal with read requests, and multiple background units, all capable of autonomous execution of sequences of high-level flash memory operations. Hydra also employs an aggressive write buffering mechanism based on block mapping to ensure that multiple flash memory chips are used effectively, and also to expedite the processing of write requests. Performance evaluation of an FPGA implementation of the Hydra SSD architecture shows that its performance is more than 80 percent better than the best of the comparable HDDs and SSDs that we considered.


title: Low turn-off switching energy 1200 V IGBT module

abstract: A new 5th generation IGBT module with low turn-off energy has been developed. The module utilizes IGBT chips optimized for high frequency industrial power supplies in applications such as X-ray, MRI (magnetic resonance imaging), and induction heating. This technology is designed to provide a simplified cost effective alternative to parallel discrete MOSFETs in these applications.


title: Quantitative analysis of small-plastic debris on beaches in the Hawaiian Archipelago.

abstract: Small-plastic beach debris from nine coastal locations throughout the Hawaiian Archipelago was analyzed. At each beach, replicate 20 l samples of sediment were collected, sieved for debris between 1 and 15 mm in size, sorted by type, counted and weighed. Small-plastic debris occurred on all of the beaches, but the greatest quantity was found at three of the most remote beaches on Midway Atoll and Moloka'i. Of the debris analyzed, 72% by weight was plastic. A total of 19100 pieces of plastic were collected from the nine beaches, 11% of which was pre-production plastic pellets. This study documents for the first time the presence of small-plastic debris on Hawaiian beaches and corroborates estimates of the abundance of plastics in the marine environment in the North Pacific.


title: Attribute-based functional encryption on lattices (Extended Abstract)

abstract: We introduce a broad lattice manipulation technique for expressive cryptography, and use it to realize functional encryption for access structures from post-quantum hardness assumptions. ::: ::: Specifically, we build an efficient key-policy attribute-based encryption scheme, and prove its security in the selective sense from learning-with-errors intractability in the standard model.


title: Optimal Auction for Edge Computing Resource Management in Mobile Blockchain Networks: A Deep Learning Approach

abstract: Blockchain has recently been applied in many applications such as bitcoin, smart grid, and Internet of Things (IoT) as a public ledger of transactions. However, the use of blockchain in mobile environments is still limited because the mining process consumes too much computing and energy resources on mobile devices. Edge computing offered by the Edge Computing Service Provider can be adopted as a viable solution for offloading the mining tasks from the mobile devices, i.e., miners, in the mobile blockchain environment. However, a mechanism needs to be designed for edge resource allocation to maximize the revenue for the Edge Computing Service Provider and to ensure incentive compatibility and individual rationality is still open. In this paper, we develop an optimal auction based on deep learning for the edge resource allocation. Specifically, we construct a multi-layer neural network architecture based on an analytical solution of the optimal auction. The neural networks first perform monotone transformations of the miners' bids. Then, they calculate allocation and conditional payment rules for the miners. We use valuations of the miners as the data training to adjust parameters of the neural networks so as to optimize the loss function which is the expected, negated revenue of the Edge Computing Service Provider. We show the experimental results to confirm the benefits of using the deep learning for deriving the optimal auction for mobile blockchain with high revenue


title: A framework of traveling companion discovery on trajectory data streams

abstract: The advance of mobile technologies leads to huge volumes of spatio-temporal data collected in the form of trajectory data streams. In this study, we investigate the problem of discovering object groups that travel together (i.e., traveling companions) from trajectory data streams. Such technique has broad applications in the areas of scientific study, transportation management, and military surveillance. To discover traveling companions, the monitoring system should cluster the objects of each snapshot and intersect the clustering results to retrieve moving-together objects. Since both clustering and intersection steps involve high computational overhead, the key issue of companion discovery is to improve the efficiency of algorithms. We propose the models of closed companion candidates and smart intersection to accelerate data processing. A data structure termed traveling buddy is designed to facilitate scalable and flexible companion discovery from trajectory streams. The traveling buddies are microgroups of objects that are tightly bound together. By only storing the object relationships rather than their spatial coordinates, the buddies can be dynamically maintained along the trajectory stream with low cost. Based on traveling buddies, the system can discover companions without accessing the object details. In addition, we extend the proposed framework to discover companions on more complicated scenarios with spatial and temporal constraints, such as on the road network and battlefield. The proposed methods are evaluated with extensive experiments on both real and synthetic datasets. Experimental results show that our proposed buddy-based approach is an order of magnitude faster than the baselines and achieves higher accuracy in companion discovery.


title: On Fault Representativeness of Software Fault Injection

abstract: The injection of software faults in software components to assess the impact of these faults on other components or on the system as a whole, allowing the evaluation of fault tolerance, is relatively new compared to decades of research on hardware fault injection. This paper presents an extensive experimental study (more than 3.8 million individual experiments in three real systems) to evaluate the representativeness of faults injected by a state-of-the-art approach (G-SWFIT). Results show that a significant share (up to 72 percent) of injected faults cannot be considered representative of residual software faults as they are consistently detected by regression tests, and that the representativeness of injected faults is affected by the fault location within the system, resulting in different distributions of representative/nonrepresentative faults across files and functions. Therefore, we propose a new approach to refine the faultload by removing faults that are not representative of residual software faults. This filtering is essential to assure meaningful results and to reduce the cost (in terms of number of faults) of software fault injection campaigns in complex software. The proposed approach is based on classification algorithms, is fully automatic, and can be used for improving fault representativeness of existing software fault injection approaches.


Lattice : A Scalable Layer-Agnostic Packet Classification Framework


title: A 6-bit, 0.2 V to 0.9 V Highly Digital Flash ADC With Comparator Redundancy

abstract: A 6-bit highly digital flash ADC is implemented in a 0.18 mum CMOS process. The ADC operates in the subthreshold regime down to 200 mV and employs comparator redundancy and reconfigurability to improve linearity. The low-voltage sampling switch employs voltage boosting, stacking and feedback to reduce leakage. Common-mode rejection is implemented digitally via an IIR filter. The minimum FOM of the ADC is 125 fJ/conversion-step at a 0.4 V supply, where it achieves an ENOB of 5.05 at 400 kS/s. The clocked comparators' switching thresholds are adjusted through a combination of device sizing and stacking. A quadratic relationship between the amount of device stacking and the strength of an input network in the subthreshold regime is derived, demonstrating an advantage of stacking over device width scaling to adjust comparator thresholds.


title: COPLINK: Visualization and Collaboration for Law Enforcement

abstract: Funded by the National Institute of Justice and the National Science Foundation, the University of Arizona's Artificial Intelligence Lab has teamed with the Tucson Police Department (TPD) and the Phoenix Police Department (PPD) to develop the COPLINK application. The COPLINK project aims to develop knowledge management systems technologies and methodology that are appropriate for capturing, analyzing, visualizing, and sharing law enforcement related information in social and organizational contexts. The basis of such research is grounded in information retrieval, computational linguistics, information visualization, artificial intelligence, multimedia systems, multi-agent systems, and telecommunications. We also study the organizational, social, cultural and methodological impacts and changes organizations must implement to maximize and leverage on a law enforcement agency's investments in information and knowledge management. The academic foundation for such research is based on social informatics, decision theory, communication theory, cognitive psychology, and managerial and organizational research (Hauck & Chen, 1999). The focus of this paper is on the visualization and collaboration components of COPLINK. For more information on the COPLINK project, refer to: (Chen et al., 2002a and Hauck et al., 2002).


title: Creative Help: A Story Writing Assistant

abstract: We present Creative Help, an application that helps writers by generating suggestions for the next sentence in a story as it being written. Users can modify or delete suggestions according to their own vision of the unfolding narrative. The application tracks users’ changes to suggestions in order to measure their perceived helpfulness to the story, with fewer edits indicating more helpful suggestions. We demonstrate how the edit distance between a suggestion and its resulting modification can be used to comparatively evaluate different models for generating suggestions. We describe a generation model that uses case-based reasoning to find relevant suggestions from a large corpus of stories. The application shows that this model generates suggestions that are more helpful than randomly selected suggestions at a level of marginal statistical significance. By giving users control over the generated content, Creative Help provides a new opportunity in open-domain interactive storytelling.


title: The Evolution of MEMS Displays

abstract: Due to the advancement of microoptoelectromechanical systems and microelectromechanical systems (MEMS) technologies, novel display architectures have emerged. One of the most successful and well-known examples is the Digital Micromirror Device from Texas Instruments, a 2-D array of bistable MEMS mirrors, which function as spatial light modulators for the projection display. This concept of employing an array of modulators is also seen in the grating light valve and the interferometric modulator display, where the modulation mechanism is based on optical diffraction and interference, respectively. Along with this trend comes the laser scanning display, which requires a single scanning device with a large scan angle and a high scan frequency. A special example in this category is the retinal scanning display, which is a head-up wearable module that laser-scans the image directly onto the retina. MEMS technologies are also found in other display-related research, such as stereoscopic (3-D) displays and plastic thin-film displays.


title: Tagger: Deep Unsupervised Perceptual Grouping

abstract: We present a framework for efficient perceptual inference that explicitly reasons about the segmentation of its inputs and features. Rather than being trained for any specific segmentation, our framework learns the grouping process in an unsupervised manner or alongside any supervised task. By enriching the representations of a neural network, we enable it to group the representations of different objects in an iterative manner. By allowing the system to amortize the iterative inference of the groupings, we achieve very fast convergence. In contrast to many other recently proposed methods for addressing multi-object scenes, our system does not assume the inputs to be images and can therefore directly handle other modalities. For multi-digit classification of very cluttered images that require texture segmentation, our method offers improved classification performance over convolutional networks despite being fully connected. Furthermore, we observe that our system greatly improves on the semi-supervised result of a baseline Ladder network on our dataset, indicating that segmentation can also improve sample efficiency.


title: Comparing Containers versus Virtual Machines for Achieving High Availability

abstract: In recent decades, virtualization as an abstraction from physical hardware has become a popular solution to resource isolation and server consolidation. With the surge in adoption of virtualization technologies, ensuring High Availability (HA) for applications hosted in virtualized environments emerges as an important problem and has garnered substantial attention. In this paper, we present a brief comparison of virtualization technologies from a HA perspective. The state-of-the-art HA solutions in two mainstream types of virtualized platforms (i.e., hypervisor-based platform and container-based platform) are respectively investigated in terms of limitations and features such as live migration, failure detection, and checkpoint/ restore. One of our key findings is that, compared with hypervisor-based platforms, HA features in container-based platforms are far from enough. From a HA perspective, extensions on top of container technologies are required.


title: A Broadband Dual-Element Folded Dipole Antenna With a Reflector

abstract: A wideband dual-element planar folded dipole antenna is proposed in this letter. It is composed of a pair of asymmetric coplanar strip folded dipoles. The dual-element folded dipole antenna is fed by a microstrip feedline. With suitable design of the asymmetric coplanar strip folded dipole and the microstrip feedline, a 10-dB return-loss operating bandwidth of 107% (1.23 ~ 4.07 GHz) is achieved. By adding a metal plate, a wideband unidirectional antenna can be obtained with a measured impedance bandwidth of 113% (1.15 ~ 4.07 GHz) with better than 10 dB return loss. Its fabricated prototype is also measured with stable unidirectional radiation patterns in working band 1.13 ~ 3.3 GHz with a gain in + z-direction higher than 8.2 dBi and a front-to-back ratio better than 11.5 dB.


Automatic Acquisition of Linguistic Patterns for Conceptual Modeling


title: On-Line Fault Detection Techniques for Technical Systems: A Survey

abstract: On-line fault detection and isolation techniques have been developed for automated processes during the last few years. These methods include numerical methods, artificial intelligence methods or combinations of the two methodologies. This paper includes a reference to recent research work on numerical methods, an extensive presentation of artificial intelligence methods used for the fault detection process in technical systems and relevant survey material. Special reference is made to the on-line expert systems development where specific resent research work is illustrated.


title: Mitophagy: a balance regulator of NLRP3 inflammasome activation

abstract: Abstract The NLRP3 inflammasome is activated by a variety of external or host-derived stimuli and its activation initiates an inflammatory response through caspase-1 activation, resulting in inflammatory cytokine IL-1β maturation and secretion. The NLRP3 inflammasome activation is a kind of innate immune response, most likely mediated by myeloid cells acting as a host defense mechanism. However, if this activation is not properly regulated, excessive inflammation induced by overactivated NLRP3 inflammasome can be detrimental to the host, causing tissue damage and organ dysfunction, eventually causing several diseases. Previous studies have suggested that mitochondrial damage may be a cause of NLRP3 inflammasome activation and autophagy, which is a conserved self-degradation process that negatively regulates NLRP3 inflammasome activation. Recently, mitochondria-selective autophagy, termed mitophagy, has emerged as a central player for maintaining mitochondrial homeostasis through the elimination of damaged mitochondria, leading to the prevention of hyperinflammation triggered by NLRP3 inflammasome activation. In this review, we will first focus on the molecular mechanisms of NLRP3 inflammasome activation and NLRP3 inflammasome-related diseases. We will then discuss autophagy, especially mitophagy, as a negative regulator of NLPP3 inflammasome activation by examining recent advances in research. [BMB Reports 2016; 49(10): 529-535].


title: Leveraging Machine Learning to Improve Unwanted Resource Filtering

abstract: Advertisements simultaneously provide both economic support for most free web content and one of the largest annoyances to end users. Furthermore, the modern advertisement ecosystem is rife with tracking methods which violate user privacy. A natural reaction is for users to install ad blockers which prevent advertisers from tracking users or displaying ads. Traditional ad blocking software relies upon hand-crafted filter expressions to generate large, unwieldy regular expressions matched against resources being included within web pages. This process requires a large amount of human overhead and is susceptible to inferior filter generation. We propose an alternate approach which leverages machine learning to bootstrap a superior classifier for ad blocking with less human intervention. We show that our classifier can simultaneously maintain an accuracy similar to the hand-crafted filters while also blocking new ads which would otherwise necessitate further human intervention in the form of additional handmade filter rules.


title: The blood brain barrier and neuropsychiatric lupus: new perspectives in light of advances in understanding the neuroimmune interface.

abstract: Abstract Experts have previously postulated a linkage between lupus associated vascular pathology and abnormal brain barriers in the immunopathogenesis of neuropsychiatric lupus. Nevertheless, there are some discrepancies between the experimental evidence, or its interpretation, and the working hypotheses prevalent in this field; specifically, that a primary contributor to neuropsychiatric disease in lupus is permeabilization of the blood brain barrier. In this commonly held view, any contribution of the other known brain barriers, including the blood-cerebrospinal fluid and meningeal barriers, is mostly excluded from the discussion. In this review we will shed light on some of the blood brain barrier hypotheses and try to trace their roots. In addition, we will suggest new research directions to allow for confirmation of alternative interpretations of the experimental evidence linking the pathology of intra-cerebral vasculature to the pathogenesis of neuropsychiatric lupus.


title: Design, Implementation, and Operation of a Mobile Honeypot

abstract: Mobile nodes, in particular smartphones are one of the most relevant devices in the current Internet in terms of quantity and economic impact. There is the common believe that those devices are of special interest for attackers due to their limited resources and the serious data they store. On the other hand, the mobile regime is a very lively network environment, which misses the (limited) ground truth we have in commonly connected Internet nodes. In this paper we argue for a simple long-term measurement infrastructure that allows for (1) the analysis of unsolicited traffic to and from mobile devices and (2) fair comparison with wired Internet access. We introduce the design and implementation of a mobile honeypot, which is deployed on standard hardware for more than 1.5 years. Two independent groups developed the same concept for the system. We also present preliminary measurement results.


title: Surgical tool detection in cataract surgery videos through multi-image fusion inside a convolutional neural network

abstract: The automatic detection of surgical tools in surgery videos is a promising solution for surgical workflow analysis. It paves the way to various applications, including surgical workflow optimization, surgical skill evaluation and real-time warning generation. A solution based on convolutional neural networks (CNNs) is proposed in this paper. Unlike existing solutions, the proposed CNN does not analyze images independently. it analyzes sequences of consecutive images. Features extracted from each image by the CNN are fused inside the network using the optical flow. For improved performance, this multi-image fusion strategy is also applied while training the CNN. The proposed framework was evaluated in a dataset of 30 cataract surgery videos (6 hours of videos). Ten tool categories were defined by surgeons. The proposed system was able to detect each of these categories with a high area under the ROC curve (0.953 ≤ A z ≤ 0.987). The proposed detector, based on multi-image fusion, was significantly more sensitive and specific than a similar system analyzing images independently (p = 2.98 × 10−6 and p = 2.07 × 10−3, respectively).


title: Relationship between time management skills and anxiety and academic motivation of nursing students in Tehran

abstract: Time management skills are essential for nursing students' success, and development of clinical competence. The purpose of this study was to determine the relationship between time management skills and anxiety and academic motivation of nursing students in Tehran medical sciences universities in 2015.This cross-sectional study was carried out on 441 nursing students in three medical universities in Tehran. Random stratified sampling was done to select the samples. Data were collected using demographic Questionnaire, Time Management Questionnaire (TMQ), Spielberger State-Trait Anxiety Inventory (STAI) and Academic Motivation Scale (AMS), which was completed t by self-report. Data were analyzed using SPSS 18 software with descriptive and analytical statistics such as ANOVA, independent t-test, Regression and Pearson Correlation Coefficient.Most participants had a moderate level of time Management skills (49%), State Anxiety (58%), Trait Anxiety (60%) and Academic Motivation (58%). The results also showed a statistically significant negative correlation between the students' TMQ scores and the state anxiety (r= -0.282, p< 0.001) and trait anxiety scores (r= -0.325, p<0.001). Moreover, there was a statistically significant positive correlation between the students' TMQ scores and AMS scores (r= 0.279, p< 0.001).Regarding the findings, it seems that it is necessary to plan for improving time management skills in order to enhance academic motivation and reduce anxiety rates among nursing students.


title: Modelling biological processes using workflow and Petri Net models

abstract: Motivation: Biological processes can be considered at many levels of detail, ranging from atomic mechanism to general processes such as cell division, cell adhesion or cell invasion. The experimental study of protein function and gene regulation typically provides information at many levels. The representation of hierarchical process knowledge in biology is therefore a major challenge for bioinformatics. To represent high-level processes in the context of their component functions, we have developed a graphical knowledge model for biological processes that supports methods for qualitative reasoning. Results: We assessed eleven diverse models that were developed in the fields of software engineering, business, and biology, to evaluate their suitability for representing and simulating biological processes. Based on this assessment, we combined the best aspects of two models: Workflow/Petri Net and a biological concept model. The Workflow model can represent nesting and ordering of processes, the structural components that participate in the processes, and the roles that they play. It also maps to Petri Nets, which allow verification of formal properties and qualitative simulation. The biological concept model, TAMBIS, provides a framework for describing biological entities that can be mapped to the workflow model. We tested our model by representing malaria parasites invading host erythrocytes, and composed queries, in five general classes, to discover relationships among processes and structural components. We used reachability analysis to answer queries about the dynamic aspects of the model. Availability: The model is available at http://smi.stanford. edu/projects/helix/pubs/process-model/


title: Deep high dynamic range imaging of dynamic scenes

abstract: Producing a high dynamic range (HDR) image from a set of images with different exposures is a challenging process for dynamic scenes. A category of existing techniques first register the input images to a reference image and then merge the aligned images into an HDR image. However, the artifacts of the registration usually appear as ghosting and tearing in the final HDR images. In this paper, we propose a learning-based approach to address this problem for dynamic scenes. We use a convolutional neural network (CNN) as our learning model and present and compare three different system architectures to model the HDR merge process. Furthermore, we create a large dataset of input LDR images and their corresponding ground truth HDR images to train our system. We demonstrate the performance of our system by producing high-quality HDR images from a set of three LDR images. Experimental results show that our method consistently produces better results than several state-of-the-art approaches on challenging scenes.


title: A channel model for power line communication in the smart grid

abstract: Although Power Line Communication (PLC) is not a new technology, its use to support communication with low rate on low voltage (LV) distribution networks is still the focus of ongoing research. In this paper, we propose a PLC channel modeling method based on the bottom-up approach for LV PLC in a narrow, low frequency band between 9 kHz and 490 kHz. We employ the model to derive the transfer function of a typical LV PLC network, which is comprised of two common cable types (copper cables and aluminum conductor steel reinforced). We then investigate the multipath effect of the LV PLC in the studied low frequency bandwidth using numerical computations. Our simulation results based on the proposed channel model show an acceptable performance between neighboring nodes, in terms of bit error rate versus signal to noise ratio, which enables communication required for smart grid applications. Furthermore, we show that data transmission beyond one-hop communication in LV PLC networks will have to rely on upper layer protocols.


title: Impact of E-Banking on Traditional Banking Services

abstract: Internet banking is changing the banking industry, having the major effects on banking relationships. Banking is now no longer confined to the branches were one has to approach the branch in person, to withdraw cash or deposit a cheque or request a statement of accounts. In true Internet banking, any inquiry or transaction is processed online without any reference to the branch (anywhere banking) at any time. Providing Internet banking is increasingly becoming a "need to have" than a "nice to have" service. The net banking, thus, now is more of a norm rather than an exception in many developed countries due to the fact that it is the cheapest way of providing banking services. This research paper will introduce you to e-banking, giving the meaning, functions, types, advantages and limitations of e-banking. It will also show the impact of e-banking on traditional services and finally the result documentation.


title: Consumer Trust in an Internet Store: A Cross-Cultural Validation

abstract: Many have speculated that trust plays a critical role in stimulating consumer purchases over the Internet. Most of the speculations have rallied around U.S. consumers purchasing from U.S.–based online merchants. The global nature of the Internet raises questions about the robustness of trust effects across cultures. Culture may also affect the antecedents of consumer trust; that is, consumers in different cultures might have differing expectations of what makes a web merchant trustworthy. Here we report on a cross-cultural validation of an Internet consumer trust model. The model examined both antecedents and consequences of consumer trust in a Web merchant. The results provide tentative support for the generalizability of the model.


title: Designing game-based learning environments for elementary science education: A narrative-centered learning perspective

abstract: Game-based learning environments hold significant promise for STEM education, yet they are enormously complex. Crystal Island: Uncharted Discovery, is a game-based learning environment designed for upper elementary science education that has been under development in our laboratory for the past four years. This article discusses curricular and narrative interaction design requirements, presents the design of the Crystal Island learning environment, and describes its evolution through a series of pilots and field tests. Additionally, a classroom integration study was conducted to initiate a shift towards ecological validity. Results indicated that Crystal Island produced significant learning gains on both science content and problem-solving measures. Importantly, gains were consistent for gender across studies. This finding is key in light of past studies that revealed disproportionate participation by boys within game-based learning environments.


title: Architectural core salvaging in a multi-core processor for hard-error tolerance

abstract: The incidence of hard errors in CPUs is a challenge for future multicore designs due to increasing total core area. Even if the location and nature of hard errors are known a priori, either at manufacture-time or in the field, cores with such errors must be disabled in the absence of hard-error tolerance. While caches, with their regular and repetitive structures, are easily covered against hard errors by providing spare arrays or spare lines, structures within a core are neither as regular nor as repetitive. Previous work has proposed microarchitectural core salvaging to exploit structural redundancy within a core and maintain functionality in the presence of hard errors. Unfortunately microarchitectural salvaging introduces complexity and may provide only limited coverage of core area against hard errors due to a lack of natural redundancy in the core. This paper makes a case for architectural core salvaging. We observe that even if some individual cores cannot execute certain operations, a CPU die can be instruction-set-architecture (ISA) compliant, that is execute all of the instructions required by its ISA, by exploiting natural cross-core redundancy. We propose using hardware to migrate offending threads to another core that can execute the operation. Architectural core salvaging can cover a large core area against faults, and be implemented by leveraging known techniques that minimize changes to the microarchitecture. We show it is possible to optimize architectural core salvaging such that the performance on a faulty die approaches that of a fault-free die--assuring significantly better performance than core disabling for many workloads and no worse performance than core disabling for the remainder.


Towards a Gamification Framework for Software Process Improvement Initiatives: Construction and Validation.


title: The Vivaldi Aerial

abstract: The Vivaldi Aerial is a new member of the class of aperiodic continuously scaled antenna structures and, as such, it has theoretically unlimited instantaneous frequency bandwidth. This aerial has significant gain and linear polarisation and can be made to conform to a constant gain vs. frequency performance. One such design has been made with approximately 10 dBI gain and ?20 dB sidelobe level over an instantaneous frequency bandwidth extending from below 2 GHz to above 40 GHz.


title: Semiotics and the social analysis of material things

abstract: Abstract This article discusses certain aspects of Peircean semiotics as they can contribute to the social analysis of material artifacts. It focuses on the concepts of iconicity and indexicality, paying particular attention to their roles in mediating contingency and causality, and to their relation with possible actions. Because iconicity and indexicality themselves ‘assert nothing,’ their various social roles turn on their mediation by ‘Thirdness’. This circumstance requires an account of semiotic ideologies and their practical embodiment in representational economies. The article concludes with a call for a richer concept of the multiple possible modes of ‘objectification’ in social life.


title: Ford Campus vision and lidar data set

abstract: In this paper we describe a data set collected by an autonomous ground vehicle testbed, based upon a modified Ford F-250 pickup truck. The vehicle is outfitted with a professional (Applanix POS-LV) and consumer (Xsens MTi-G) inertial measurement unit, a Velodyne three-dimensional lidar scanner, two push-broom forward-looking Riegl lidars, and a Point Grey Ladybug3 omnidirectional camera system. Here we present the time-registered data from these sensors mounted on the vehicle, collected while driving the vehicle around the Ford Research Campus and downtown Dearborn, MI, during November-December 2009. The vehicle path trajectory in these data sets contains several large- and small-scale loop closures, which should be useful for testing various state-of-the-art computer vision and simultaneous localization and mapping algorithms.


title: Deformation embedding for point-based elastoplastic simulation

abstract: We present a straightforward, easy-to-implement, point-based approach for animating elastoplastic materials. The core idea of our approach is the introduction of embedded space—the least-squares best fit of the material's rest state into three dimensions. Nearest-neighbor queries in the embedded space efficiently update particle neighborhoods to account for plastic flow. These queries are simpler and more efficient than remeshing strategies employed in mesh-based finite element methods. We also introduce a new estimate for the volume of a particle, allowing particle masses to vary spatially and temporally with fixed density. Our approach can handle simultaneous extreme elastic and plastic deformations. We demonstrate our approach on a variety of examples that exhibit a wide range of material behaviors.


title: Overview of PI-Based Solutions for the Control of DC Buses of a Single-Phase H-Bridge Multilevel Active Rectifier

abstract: The H-bridge based multilevel active rectifier is an attractive topology that allows feeding of multiple dc loads. However the balancing of the dc buses often causes instability problems. The main control problem is to manage n+1 state variables (one current plus n dc voltages) with only n switching functions. In this paper several PI-based solutions are reviewed through simulations and experimental tests.


title: Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning

abstract: Multi-task learning (MTL) with neural networks leverages commonalities in tasks to improve performance, but often suffers from task interference which reduces the benefits of transfer. To address this issue we introduce the routing network paradigm, a novel neural network and training algorithm. A routing network is a kind of self-organizing neural network consisting of two components: a router and a set of one or more function blocks. A function block may be any neural network - for example a fully-connected or a convolutional layer. Given an input the router makes a routing decision, choosing a function block to apply and passing the output back to the router recursively, terminating when a fixed recursion depth is reached. In this way the routing network dynamically composes different function blocks for each input. We employ a collaborative multi-agent reinforcement learning (MARL) approach to jointly train the router and function blocks. We evaluate our model against cross-stitch networks and shared-layer baselines on multi-task settings of the MNIST, mini-imagenet, and CIFAR-100 datasets. Our experiments demonstrate a significant improvement in accuracy, with sharper convergence. In addition, routing networks have nearly constant per-task training cost while cross-stitch networks scale linearly with the number of tasks. On CIFAR-100 (20 tasks) we obtain cross-stitch performance levels with an 85% reduction in training time.


title: An intervention to preschool children for reducing screen time: a randomized controlled trial

abstract: BACKGROUND ::: Screen time, defined as time spent watching television, DVDs, or videos or playing computer or video games, has been related to serious health consequences in children, such as impaired language acquisition, violent behaviour, tobacco smoking and obesity. Our aim was to determine if a simple intervention aimed at preschool-aged children, applied at the health maintenance visits, in the primary care setting, would be effective in reducing screen time. ::: ::: ::: METHODS ::: We used a two group randomized controlled trial design. Two- to 6-year-old children and their parents were randomly assigned to receive an intervention to reduce their screen time, BMI and parental report of aggressive behaviour. At the end of the intervention we made home visits at 2, 6 and 9 months and the parents completed questionnaire. ::: ::: ::: RESULTS ::: Parents in the intervention group reported less screen time and less aggressive behaviour than those in the control group but there were no differences in BMI z scores. ::: ::: ::: CONCLUSIONS ::: This study shows that a preschool-based intervention can lead to reductions in young children's television/video viewing.


title: Intermediate elemental image reconstruction for refocused three-dimensional images in integral imaging by convolution with δ-function sequences

abstract: Abstract We propose a novel approach for intermediate elemental image reconstruction in integral imaging. To reconstruct intermediate elemental images, we introduce a null elemental image whose pixels are all zero. In the proposed method a number of null elemental images are inserted into a given elemental image array. The elemental image array with null elemental images is convolved with the δ-function sequence. The convolution result shows that the proposed method provides an efficient structure to expand an elemental image array. The resulting elemental image array from the proposed method can supply three-dimensional information for an object at a specific depth. In addition, the proposed method provides adjustable parameters, which can be utilized in design of integral imaging systems. The feasibility of the proposed method has been confirmed through preliminary experiments and theoretical analysis.


Divorce in matrilineal customary law marriage in Malawi: a comparative analysis with the patrilineal customary law marriage in South Africa


title: Introduction to This is Watson

abstract: In 2007, IBM Research took on the grand challenge of building a computer system that could compete with champions at the game of Jeopardy!™. In 2011, the open-domain question-answering (QA) system, dubbed Watson, beat the two highest ranked players in a nationally televised two-game Jeopardy! match. This paper provides a brief history of the events and ideas that positioned our team to take on the Jeopardy! challenge, build Watson, IBM Watson™, and ultimately triumph. It describes both the nature of the QA challenge represented by Jeopardy! and our overarching technical approach. The main body of this paper provides a narrative of the DeepQA processing pipeline to introduce the articles in this special issue and put them in context of the overall system. Finally, this paper summarizes our main results, describing how the system, as a holistic combination of many diverse algorithmic techniques, performed at champion levels, and it briefly discusses the team's future research plans.


title: Text mining in the biocuration workflow: applications for literature curation at WormBase, dictyBase and TAIR

abstract: WormBase, dictyBase and The Arabidopsis Information Resource (TAIR) are model organism databases containing information about Caenorhabditis elegans and other nematodes, the social amoeba Dictyostelium discoideum and related Dictyostelids and the flowering plant Arabidopsis thaliana, respectively. Each database curates multiple data types from the primary research literature. In this article, we describe the curation workflow at WormBase, with particular emphasis on our use of text-mining tools (BioCreative 2012, Workshop Track II). We then describe the application of a specific component of that workflow, Textpresso for Cellular Component Curation (CCC), to Gene Ontology (GO) curation at dictyBase and TAIR (BioCreative 2012, Workshop Track III). We find that, with organism-specific modifications, Textpresso can be used by dictyBase and TAIR to annotate gene productions to GO's Cellular Component (CC) ontology.


title: Estimation of the Botanical Composition of Clover-Grass Leys from RGB Images Using Data Simulation and Fully Convolutional Neural Networks

abstract: Optimal fertilization of clover-grass fields relies on knowledge of the clover and grass fractions. This study shows how knowledge can be obtained by analyzing images collected in fields automatically. A fully convolutional neural network was trained to create a pixel-wise classification of clover, grass, and weeds in red, green, and blue (RGB) images of clover-grass mixtures. The estimated clover fractions of the dry matter from the images were found to be highly correlated with the real clover fractions of the dry matter, making this a cheap and non-destructive way of monitoring clover-grass fields. The network was trained solely on simulated top-down images of clover-grass fields. This enables the network to distinguish clover, grass, and weed pixels in real images. The use of simulated images for training reduces the manual labor to a few hours, as compared to more than 3000 h when all the real images are annotated for training. The network was tested on images with varied clover/grass ratios and achieved an overall pixel classification accuracy of 83.4%, while estimating the dry matter clover fraction with a standard deviation of 7.8%.


The Reformation of the Landscape


title: Embedded systems security: Threats, vulnerabilities, and attack taxonomy

abstract: Embedded systems are the driving force for technological development in many domains such as automotive, healthcare, and industrial control in the emerging post-PC era. As more and more computational and networked devices are integrated into all aspects of our lives in a pervasive and “invisible” way, security becomes critical for the dependability of all smart or intelligent systems built upon these embedded systems. In this paper, we conduct a systematic review of the existing threats and vulnerabilities in embedded systems based on public available data. Moreover, based on the information, we derive an attack taxonomy for embedded systems. We envision that the findings in this paper provide a valuable insight of the threat landscape facing embedded systems. The knowledge can be used for a better understanding and the identification of security risks in system analysis and design.


title: USARSim: a robot simulator for research and education

abstract: This paper presents USARSim, an open source high fidelity robot simulator that can be used both for research and education. USARSim offers many characteristics that differentiate it from most existing simulators. Most notably, it constitutes the simulation engine used to run the virtual robots competition within the Robocup initiative. We describe its general architecture, describe examples of utilization, and provide a comprehensive overview for those interested in robot simulations for education, research and competitions.


title: The amyloid hypothesis of Alzheimer's disease: progress and problems on the road to therapeutics.

abstract: It has been more than 10 years since it was first proposed that the neurodegeneration in Alzheimer's disease (AD) may be caused by deposition of amyloid beta-peptide (Abeta) in plaques in brain tissue. According to the amyloid hypothesis, accumulation of Abeta in the brain is the primary influence driving AD pathogenesis. The rest of the disease process, including formation of neurofibrillary tangles containing tau protein, is proposed to result from an imbalance between Abeta production and Abeta clearance.


title: Diatom cultivation and biotechnologically relevant products. Part I: Cultivation at various scales

abstract: Biotechnological applications of diatoms are still in development. Further development at the industrial scale will depend on optimisation of the culture process with the aim of reducing costs. Because of the photoautotrophic status of the majority of diatoms, microalgal cultures suffer from the limitation of light diffusion, which requires the development of suitable photobioreactors. Thus, genetically engineered microalgae that may be cultivated in heterotrophic conditions present a new opportunity. Other limiting factors, such as nutrients (phosphate, nitrogen, silicon), pH, temperature, bioturbation and many more must be taken into account. Most of the time, metabolic stress conditions lead to an overproduction of the products of interest, with a decrease in biomass production as a consequence. Outdoor cultures in open ponds are usually devoted to aquaculture for the feeding of shrimps and bivalve molluscs (commercial production), while closed axenic indoor/outdoor photobioreactors are used for biotechnological compounds of homogeneous composition (still at the laboratory scale). In addition to the optimum culture conditions that have to be taken into account for photobioreactor design, the localisation of produced metabolites (intra- or extracellular) may also be taken into account when choosing the design. Microalgal cell immobilisation may be a suitable technique for application to benthic diatoms, which are usually sensitive to bioturbation and/or metabolites which may be overexpressed.


title: Sanskrit Machine Translation Systems: A Comparative Analysis

abstract: Machine Translation is area of research since six decades. It is gaining popularity since last decade due to better computational facilities available at personal computer systems. This paper presents different Machine Translation system where Sanskrit is involved as source, target or key support language. Researchers employ various techniques like Rule based, Corpus based, Direct for machine translation. The main aim to focus on Sanskrit in Machine Translation in this paper is to uncover the language suitability, its morphology and employ appropriate MT techniques.


title: Dynamic video anomaly detection and localization using sparse denoising autoencoders

abstract: The emergence of novel techniques for automatic anomaly detection in surveillance videos has significantly reduced the burden of manual processing of large, continuous video streams. However, existing anomaly detection systems suffer from a high false-positive rate and also, are not real-time, which makes them practically redundant. Furthermore, their predefined feature selection techniques limit their application to specific cases. To overcome these shortcomings, a dynamic anomaly detection and localization system is proposed, which uses deep learning to automatically learn relevant features. In this technique, each video is represented as a group of cubic patches for identifying local and global anomalies. A unique sparse denoising autoencoder architecture is used, that significantly reduced the computation time and the number of false positives in frame-level anomaly detection by more than 2.5%. Experimental analysis on two benchmark data sets - UMN dataset and UCSD Pedestrian dataset, show that our algorithm outperforms the state-of-the-art models in terms of false positive rate, while also showing a significant reduction in computation time.


abstract: Drought Spatial Object Prediction Approach using Artificial Neural Network ::: The concept of object identification and modeling has fueled a lengthy scientific effort to convert remotely sensed images into geographic phenomena. The objective of this article was to develop a new concept for characterizing and identifying drought spatial objects from satellite images for improved drought prediction and mitigation using a back propagation artificial neural network (ANN). To characterize drought as a spatial object, 11 attributes from multi-sensors and resolutions (such as Standardized Deviation of Normalized Difference Vegetation Index [SDNDVI], Digital Elevation Model [DEM], Soil Water Holding Capacity, Ecological Regions, Land Cover, Standard Precipitation Index [SPI], and oceanic indices were used.


title: Trust based mechanism for Securing IoT Routing Protocol RPL against Wormhole &Grayhole Attacks

abstract: Internet of Things is attracting a lot of interest in the modern world and has become a part of daily life leading to a large scale of distribution of Low power and Lossy Networks (LLN). For such networks constrained by low power and storage, IETF has proposed RPL an open standard routing protocol. However RPL protocol is exposed to a number of attacks which may degrade the performance and resources of the network leading to incorrect output. In this paper, to address Wormhole and Grayhole attack we propose a light weight Trust based mechanism. The proposed method uses direct trust which is computed based on node properties and Indirect Trust which is based on opinion of the neighboring nodes. The proposed method is energy friendly and does not impose excessive overhead on network traffic.


title: Mockup Builder: Direct 3D Modeling On and Above the Surface in a Continuous Interaction Space

abstract: Our work introduces a semi-immersive environment for conceptual design where virtual mockups are obtained from gestures we aim to get closer to the way people conceive, create and manipulate three-dimensional shapes. We present on-and-above-the-surface interaction techniques following Guiard's asymmetric bimanual model to take advantage of the continuous interaction space for creating and editing 3D models in a stereoscopic environment. To allow for more expressive interactions, our approach continuously combines hand and finger tracking in the space above the table with multi-touch on its surface. This combination brings forth an alternative design environment where users can seamlessly switch between interacting on the surface or in the space above it depending on the task. Our approach integrates continuous space usage with bimanual interaction to provide an expressive set of 3D modeling operations. Preliminary trials with our experimental setup show this as a very promising avenue for further work.


title: Energy-efficient data centers

abstract: Energy consumption of the Information and Communication Technology (ICT) sector has grown exponentially in recent years. A major component of the today’s ICT is constituted by the data centers which have experienced an unprecedented growth in their size and population, recently. The Internet giants like Google, IBM and Microsoft house large data centers for cloud computing and application hosting. Many studies, on energy consumption of data centers, point out to the need to evolve strategies for energy efficiency. Due to large-scale carbon dioxide (\(\mathrm{CO}_2\)) emissions, in the process of electricity production, the ICT facilities are indirectly responsible for considerable amounts of green house gas emissions. Heat generated by these densely populated data centers needs large cooling units to keep temperatures within the operational range. These cooling units, obviously, escalate the total energy consumption and have their own carbon footprint. In this survey, we discuss various aspects of the energy efficiency in data centers with the added emphasis on its motivation for data centers. In addition, we discuss various research ideas, industry adopted techniques and the issues that need our immediate attention in the context of energy efficiency in data centers.


title: MultiGranCNN: An Architecture for General Matching of Text Chunks on Multiple Levels of Granularity

abstract: We present MultiGranCNN, a general deep learning architecture for matching text chunks. MultiGranCNN supports multigranular comparability of representations: shorter sequences in one chunk can be directly compared to longer sequences in the other chunk. MultiGranCNN also contains a flexible and modularized match feature component that is easily adaptable to different types of chunk matching. We demonstrate stateof-the-art performance of MultiGranCNN on clause coherence and paraphrase identification tasks.


title: SAMI: Service-Based Arbitrated Multi-Tier Infrastructure for Mobile Cloud Computing

abstract: Mobile Cloud Computing (MCC) is the state-of-the-art mobile computing technology aims to alleviate resource poverty of mobile devices. Recently, several approaches and techniques have been proposed to augment mobile devices by leveraging cloud computing. However, long-WAN latency and trust are still two major issues in MCC that hinder its vision. In this paper, we analyze MCC and discuss its issues. We leverage Service Oriented Architecture (SOA) to propose an arbitrated multi-tier infrastructure model named SAMI for MCC. Our architecture consists of three major layers, namely SOA, arbitrator, and infrastructure. The main strength of this architecture is in its multi-tier infrastructure layer which leverages infrastructures from three main sources of Clouds, Mobile Network Operators (MNOs), and MNOs' authorized dealers. On top of the infrastructure layer, an arbitrator layer is designed to classify Services and allocate them the suitable resources based on several metrics such as resource requirement, latency and security. Utilizing SAMI facilitate development and deployment of service-based platform-neutral mobile applications.


title: An efficient automated design to generate UML diagram from Natural Language Specifications

abstract: The foremost problem that arises in the Software Development Cycle is during the Requirements specification and analysis. Errors that are encountered during the first phase of the cycle migrate to other phases too which in turn results in the most costly process than the original specified process. The reason is that the specifications of software requirements are termed in the Nature Language Format. One can easily transform the requirements specified into computer model using UML. To minimize the errors that arise in the existing system, we have proposed a new technique that enhances the generation of UML models through Natural Language requirements, which can easily provide automatic assistance to the developers. The main aim of our paper is to focus on the production of Activity Diagram and Sequence Diagram through Natural Language Specifications. Standard POS tagger and parser analyze the input i.e., requirements in English language given by the users and extract phrases, activities, etc. from the text specifies. The technique is beneficial as it reduces the gap between informal natural language and the formal modeling language. The input is the requirements laid down by the users in English language. Some stages like pre-processing, part of speech (POs), tagging, parsing, phrase identification and designing of UML diagrams occur along with the input. The application and its framework is developed in Java and it is tested on by implementing on a few technical documents


title: Spontaneous analogising in engineering design: a comparative analysis of experts and novices.

abstract: Abstract Although analogical reasoning is claimed to play a central role in creative cognition and the development of expertise, few studies have explored the nature and prevalence of spontaneous analogising in design contexts. We report an experimental comparison of analogy use by expert and novice design engineers. Concurrent think-aloud protocols were analysed to derive measures of the rate of schema-driven analogising (i.e., the recognition-primed application of abstract experiential knowledge that could afford a design solution to a familiar problem type), and case-driven analogising (i.e., the invocation of a concrete prior design problem whose solution elements could be mapped onto the current problem). Results supported our prediction that expert designers would demonstrate more schema-driven than case-driven analogising, whilst novices would show the reverse pattern of analogising. We discuss the implications of these results for theories of design cognition and expert design practice.


title: Crime data mining: a general framework and some examples

abstract: A major challenge facing all law-enforcement and intelligence-gathering organizations is accurately and efficiently analyzing the growing volumes of crime data. Detecting cybercrime can likewise be difficult because busy network traffic and frequent online transactions generate large amounts of data, only a small portion of which relates to illegal activities. Data mining is a powerful tool that enables criminal investigators who may lack extensive training as data analysts to explore large databases quickly and efficiently. We present a general framework for crime data mining that draws on experience gained with the Coplink project, which researchers at the University of Arizona have been conducting in collaboration with the Tucson and Phoenix police departments since 1997.


title: FlexTiles: A Flexible, Stretchable, Formable, Pressure-Sensitive, Tactile Input Sensor

abstract: In the FlexTiles demonstration, we present a flexible, stretchable, pressure-sensitive, tactile input sensor consisting of three layers of fabric. We demonstrate the implementation of FlexTiles for covering large areas, 3D objects, and deformable underlying shapes. In order to measure these large areas with high framerate, we demonstrate a simple measurement implementation. Finally, we outline the benefits of our system compared to other tactile sensing techniques.


title: Automated Windows event log forensics

abstract: This paper proposes methods to automate recovery and analysis of Windows NT5 (XP and 2003) event logs for computer forensics. Requirements are formulated and methods are evaluated with respect to motivation and process models. A new, freely available tool is presented that, based on these requirements, automates the repair of a common type of corruption often observed in data carved NT5 event logs. This tool automates repair of multiple event logs in a single step without user intervention. The tool was initially developed to meet immediate needs of computer forensic engagements. Automating recovery, repair, and correlation of multiple logs make these methods more feasible for consideration in both a wider range of cases and earlier phases of cases, and hopefully, in turn, standard procedures. The tool was developed to fill a gap between capabilities of certain other freely available tools that may recover and correlate large volumes of log events, and consequently permit correlation with various other kinds of Windows artifacts. The methods are examined in the context of an example digital forensic service request intended to illustrate the kinds of civil cases that motivated this work.


title: The performance measurement revolution 205 The performance measurement revolution : why now and what next ?

abstract: Asks why business performance measurement has become so topical, so recently. Argues that there are seven main reasons: the changing nature of work; increasing competition; specific improvement initiatives; national and international quality awards; changing organisational roles; changing external demands; and the power of information technology. Evidence to support this assertion is drawn from the academic and practitioner literatures, interviews and discussions with people specialising in the field and a broad review of the current state‐of‐the‐art in business performance measurement. Presents a framework onto which current research in business performance measurement can be mapped and identifies areas which require further work.


title: Filter design of PWM AC chopper on soft starting application 3 phase induction motors

abstract: The use of PWM AC Chopper for soft starter applications to feed 3 phase induction motors causes current and voltage ripple. Higher ripple means higher harmonic distortion in the system. To minimize the ripple, a filter consisting of an inductor and a capacitor, connected in series to the load was designed and analyzed. A prototype of AC chopper soft starter was also built to feed 3 phase 1 hp induction motor for experimental verifications. The analysis includes varying the switching frequency from 1 kHz to 5 kHz, the inductor from 0.01 to 0.05 H, and the capacitor from 2.2 to 10 micro Farad. The best ripple voltage was obtained as 0.16 V of 220 V normal voltage.


title: Improved radial heat sink for led lamp cooling

abstract: Abstract This paper presents a numerical study concerning an improved heat sink for a light emitting diodes (LED) lamp operating under natural convection conditions. Basic geometry of the heat sink is of cylindrical nature, to be obtained from cutting an aluminum extruded bar comprising a cylindrical central core and a number of uniformly distributed radial fins. Minimum diameter of the central core is fixed and the parameters to be explored are the number of fins, their thickness, length (radial dimension) and height. Although not included in the numerical simulations, the thermal resistance due to the use of a thin thermal interface material (TIM) layer between the LED lamp back and the heat sink is taken into account in the analysis. The main objective of the heat sink is to cool the LED lamp so that the lamp maximum temperature at the contact region with the heat sink is maintained below the critical temperature given by the manufacturer. This is a crucial aspect in what concerns the expected lifetime of the LED lamp and should be achieved at the expenses of as low as possible aluminum mass. Taking these criteria in mind, a design procedure is proposed and followed in the search for the improved heat sink to cool a particular LED lamp. Results obtained with the commercial code ANSYS-CFX clearly show the relative importance of the different governing parameters on the heat sink performance and allow the choice of the better solution within the frame of dimensional constrains. Although the present results concern a particular LED lamp, the proposed methodology can be extended to other types of heat sinks for general light and/or electronic components.


title: Using Genetic Algorithm Optimization SVM to Construction of Investment Model

abstract: At the mo ment, S upport Ve ctor Machine ( SVM) has been widely u sed i n t he study of stock investment related topics. Stock investment can be further divided into three s trategies such as: buy, sell and hold. Using data concerning China Steel Corporation, this article adopts genetic algorithm for the search of the best SVM parameter and the selection of the best SVM prediction variable, then it will be compared with Logistic Regression for the classification prediction capability of stock investment. From the classification prediction result and the result of AUC of the models presented in this article, it can be seen that the SVM after adjustment of input variables and parameters will have classification prediction capability relatively superior to that of the other three models.


title: Highly linear 1.6 GHz 3.3V RF power amplifier using floating body technique in triple-well 130 nm CMOS technology

abstract: Power amplifiers (PAs) in CMOS technology have drawn considerable interest for meeting 4th generation (4 G) wireless standards due to their low cost and high level of integration. At increasing power levels, (around 1 W), it becomes challenging to meet the linearity constraints. In this paper, we investigate a differential common source class-AB RF PA employing floating body and compare it with a body-source shorted version. We use triple-well NMOS RF devices at 1.6 GHz in 130nm CMOS technology. Linearity is improved for the floating body based amplifier in terms of AM/PM and 3rd order intermodulation (IMD 3 ) suppression by 1.8° and 1.5 dBc at P 1dB as compared to a body-source shorted amplifier. The linearity is evident over all output power (P out ) levels. The linear gain is reduced by 0.42 dB due to increased coupling between drain and source terminals while the PAE is increased by 1.6% at P 1dB for floating body based amplifier.


title: Microbial diversity in the floral nectar of seven Epipactis (Orchidaceae) species

abstract: Floral nectar of animal-pollinated plants is commonly infested with microorganisms, yet little is known about the microorganisms inhabiting the floral nectar of orchids. In this study, we investigated microbial communities occurring in the floral nectar of seven Epipactis (Orchidaceae) species. Culturable bacteria and yeasts were isolated and identified by partially sequencing the small subunit (SSU) ribosomal RNA (rRNA) gene and the D1/D2 domains of the large subunit (LSU) rRNA gene, respectively. Using three different culture media, we found that bacteria were common inhabitants of the floral nectar of Epipactis. The most widely distributed bacterial operational taxonomic units (OTUs) in nectar of Epipactis were representatives of the family of Enterobacteriaceae, with an unspecified Enterobacteriaceae bacterium as the most common. In contrast to previous studies investigating microbial communities in floral nectar, very few yeast species (mainly of the genus Cryptococcus) were observed, and most of them occurred in very low densities. Total OTU richness (i.e., the number of bacterial and yeast OTUs per orchid species) varied between 4 and 20. Cluster analysis revealed that microbial communities of allogamous species differed from those of autogamous and facultatively autogamous species. This study extends previous efforts to identify microbial communities in floral nectar and indicates that the floral nectar of the orchids investigated mainly contained bacterial communities with moderate phylogenetic diversity.


title: Photographic style transfer

abstract: Image style transfer has attracted much attention in recent years. However, results produced by existing works still have lots of distortions. This paper investigates the CNN-based artistic style transfer work specifically and finds out the key reasons for distortion coming from twofold: the loss of spatial structures of content image during content-preserving process and unexpected geometric matching introduced by style transformation process. To tackle this problem, this paper proposes a novel approach consisting of a dual-stream deep convolution network as the loss network and edge-preserving filters as the style fusion model. Our key contribution is the introduction of an additional similarity loss function that constrains both the detail reconstruction and style transfer procedures. The qualitative evaluation shows that our approach successfully suppresses the distortions as well as obtains faithful stylized results compared to state-of-the-art methods.


title: Genetic Algorithm for Solving a Dynamic Vehicle Routing Problem with Time Windows

abstract: The Vehicle Routing Problem (VRP) introduced by Dantzing and Ranser (1959) is a prominent combinatorial optimization problem. Over the last several decades, many variants of the multi-constrained vehicle routing problem have been studied and a class of problems known as rich vehicle routing problem (RVRPs), has been formed. This work is about solving a variant of RVRP with dynamically changing orders and time windows constraints. In the real world application, during the working day, new orders often occur dynamically and need to be integrated into the routes planing. A Genetic Algorithm (GA) with a simple heuristic is proposed to solve the dynamic vehicle routing problem with time windows. The performance is tested on Solomon's benchmark with different percentage of the orders revealed to the algorithm during operation time.


title: Learning Explanatory Rules from Noisy Data

abstract: Artificial Neural Networks are powerful function approximators capable of modelling solutions to a wide variety of problems, both supervised and unsupervised. As their size and expressivity increases, so too does the variance of the model, yielding a nearly ubiquitous overfitting problem. Although mitigated by a variety of model regularisation methods, the common cure is to seek large amounts of training data---which is not necessarily easily obtained---that sufficiently approximates the data distribution of the domain we wish to test on. In contrast, logic programming methods such as Inductive Logic Programming offer an extremely data-efficient process by which models can be trained to reason on symbolic domains. However, these methods are unable to deal with the variety of domains neural networks can be applied to: they are not robust to noise in or mislabelling of inputs, and perhaps more importantly, cannot be applied to non-symbolic domains where the data is ambiguous, such as operating on raw pixels. In this paper, we propose a Differentiable Inductive Logic framework, which can not only solve tasks which traditional ILP systems are suited for, but shows a robustness to noise and error in the training data which ILP cannot cope with. Furthermore, as it is trained by backpropagation against a likelihood objective, it can be hybridised by connecting it with neural networks over ambiguous data in order to be applied to domains which ILP cannot address, while providing data efficiency and generalisation beyond what neural networks on their own can achieve.


title: Vitamin E and Alzheimer disease: the basis for additional clinical trials

abstract: Many lines of evidence suggest that oxidative stress is important in the pathogenesis of Alzheimer disease. In particular, β-amyloid, which is found abundantly in the brains of Alzheimer disease patients, is toxic in neuronal cell cultures through a mechanism involving free radicals. Vitamin E prevents the oxidative damage induced by β-amyloid in cell culture and delays memory deficits in animal models. A placebo-controlled, clinical trial of vitamin E in patients with moderately advanced Alzheimer disease was conducted by the Alzheimer's Disease Cooperative Study. Subjects in the vitamin E group were treated with 2000 IU (1342 α-tocopherol equivalents) vitamin E/d. The results indicated that vitamin E may slow functional deterioration leading to nursing home placement. A new clinical trial is planned that will examine whether vitamin E can delay or prevent a clinical diagnosis of Alzheimer disease in elderly persons with mild cognitive impairment.


title: The development of the SGI-16: a shortened sensory gating deficit and distractibility questionnaire for adults with ADHD

abstract: The Sensory Gating Inventory (SGI) is a questionnaire composed of 36 items designed to investigate abnormal perception related to the inability to control sensitivity to sensory stimuli frequently reported in adult with ADHD. This questionnaire can be considered too lengthy to be taken by people with ADHD, and a shortened version is needed. One hundred and sixty-three adults with ADHD responded to the SGI-36. An item reduction process took into account both the results of statistical analyses and the expertise of a steering committee. Construct validity, reliability, and external validity were tested for a short version (16 items). The structure of the SGI-16 was confirmed by principal components factor analysis. Cronbach’s alpha coefficients ranged from 0.78 to 0.89. The SGI-16 dimension scores were highly correlated with their respective SGI-36 dimension scores. The SGI-16 seems to be both appropriate and useful for use in clinical practice to investigate perceptual abnormalities in adults with ADHD.


title: Look and Think Twice: Capturing Top-Down Visual Attention with Feedback Convolutional Neural Networks

abstract: While feedforward deep convolutional neural networks (CNNs) have been a great success in computer vision, it is important to note that the human visual cortex generally contains more feedback than feedforward connections. In this paper, we will briefly introduce the background of feedbacks in the human visual cortex, which motivates us to develop a computational feedback mechanism in deep neural networks. In addition to the feedforward inference in traditional neural networks, a feedback loop is introduced to infer the activation status of hidden layer neurons according to the "goal" of the network, e.g., high-level semantic labels. We analogize this mechanism as "Look and Think Twice." The feedback networks help better visualize and understand how deep neural networks work, and capture visual attention on expected objects, even in images with cluttered background and multiple objects. Experiments on ImageNet dataset demonstrate its effectiveness in solving tasks such as image classification and object localization.


title: Tracking the Evolution of the Internet of Things Concept Across Different Application Domains

abstract: Both the idea and technology for connecting sensors and actuators to a network to remotely monitor and control physical systems have been known for many years and developed accordingly. However, a little more than a decade ago the concept of the Internet of Things (IoT) was coined and used to integrate such approaches into a common framework. Technology has been constantly evolving and so has the concept of the Internet of Things, incorporating new terminology appropriate to technological advances and different application domains. This paper presents the changes that the IoT has undertaken since its conception and research on how technological advances have shaped it and fostered the arising of derived names suitable to specific domains. A two-step literature review through major publishers and indexing databases was conducted; first by searching for proposals on the Internet of Things concept and analyzing them to find similarities, differences, and technological features that allow us to create a timeline showing its development; in the second step the most mentioned names given to the IoT for specific domains, as well as closely related concepts were identified and briefly analyzed. The study confirms the claim that a consensus on the IoT definition has not yet been reached, as enabling technology keeps evolving and new application domains are being proposed. However, recent changes have been relatively moderated, and its variations on application domains are clearly differentiated, with data and data technologies playing an important role in the IoT landscape.


Improved PCR-Walking for Large-Scale Isolation of Plant T-DNA Borders


title: Can you Find a Face in a HEVC Bitstream?

abstract: Finding faces in images is one of the most important tasks in computer vision, with applications in biometrics, surveillance, human-computer interaction, and other areas. In our earlier work, we demonstrated that it is possible to tell whether or not an image contains a face by only examining the HEVC syntax, without fully reconstructing the image. In the present work we move further in this direction by showing how to localize faces in HEVC-coded images, without full reconstruction. We also demonstrate the benefits that such approach can have in privacy-friendly face localization.


title: Information and digital literacies; a review of concepts

abstract: The concepts of ‘information literacy’ and ‘digital literacy’ are described, and reviewed, by way of a literature survey and analysis. Related concepts, including computer literacy, library literacy, network literacy, Internet literacy and hyper‐literacy are also discussed, and their relationships elucidated. After a general introduction, the paper begins with the basic concept of ‘literacy’, which is then expanded to include newer forms of literacy, more suitable for complex information environments. Some of these, for example library, media and computer literacies, are based largely on specific skills, but have some extension beyond them. They lead togeneral concepts, such as information literacy and digital literacy which are based on knowledge, perceptions and attitudes, though reliant on the simpler skills‐based literacies


title: Buried umbilicus with inflammation due to retained rubber foreign body after liposuction: A case report

abstract: INTRODUCTION ::: Liposuction is one of the most widely performed aesthetic surgeries. It is mainly used for the correction of deep and superficial fat accumulations and remodeling of the body contour. ::: ::: ::: CASE PRESENTATION ::: We present a rare case of unusual rubber foreign body found within the periumbilical area during the reconstruction of buried umbilicus after liposuction.A 61-year-old female had undergone a liposuction surgery 10 years ago. Last year, she experienced signs of inflammation around the periumbilical area. The patient was treated with antibiotics and daily dressing at a local clinic. However, her symptoms did not improve with treatment.After admission, we decided to reconstruct the umbilicus and explore the previous operative site. During reconstruction of umbilicus, we finally discovered a rubber foreign body in the periumbilical area and buried umbilicus. ::: ::: ::: CONCLUSION ::: Retained surgical foreign body can clinically manifest as acute reaction, such as an inflammatory response, infection, or abscess within days or weeks after the operation. Patients may complain of pain and discomfort, even months or years after the procedure.Our patient had suffered from delayed inflammation due to retained surgical foreign body after liposuction surgery. We emphasize the need for excellent communication within the surgical team to prevent the incidence of retained surgical bodies.


Single-View Hair Reconstruction using Convolutional Neural Networks


title: A cytokine-mediated link between innate immunity, inflammation, and cancer.

abstract: It has been established that cancer can be promoted and/or exacerbated by inflammation and infections. Indeed, chronic inflammation orchestrates a tumor-supporting microenvironment that is an indispensable participant in the neoplastic process. The mechanisms that link infection, innate immunity, inflammation, and cancer are being unraveled at a fast pace. Important components in this linkage are the cytokines produced by activated innate immune cells that stimulate tumor growth and progression. In addition, soluble mediators produced by cancer cells recruit and activate inflammatory cells, which further stimulate tumor progression. However, inflammatory cells also produce cytokines that can limit tumor growth. Here we provide an overview of the current understanding of the role of inflammation-induced cytokines in tumor initiation, promotion, and progression.


title: Semantic Annotation of Data Processing Pipelines in Scientific Publications

abstract: Data processing pipelines are a core object of interest for data scientist and practitioners operating in a variety of data-related application domains. To effectively capitalise on the experience gained in the creation and adoption of such pipelines, the need arises for mechanisms able to capture knowledge about datasets of interest, data processing methods designed to achieve a given goal, and the performance achieved when applying such methods to the considered datasets. However, due to its distributed and often unstructured nature, this knowledge is not easily accessible. In this paper, we use (scientific) publications as source of knowledge about Data Processing Pipelines. We describe a method designed to classify sentences according to the nature of the contained information (i.e. scientific objective, dataset, method, software, result), and to extract relevant named entities. The extracted information is then semantically annotated and published as linked data in open knowledge repositories according to the DMS ontology for data processing metadata. To demonstrate the effectiveness and performance of our approach, we present the results of a quantitative and qualitative analysis performed on four different conference series.


title: A Review of the Diagnosis and Treatment of Ochratoxin A Inhalational Exposure Associated with Human Illness and Kidney Disease including Focal Segmental Glomerulosclerosis

abstract: Ochratoxin A (OTA) exposure via ingestion and inhalation has been described in the literature to cause kidney disease in both animals and humans. This paper reviews Ochratoxin A and its relationship to human health and kidney disease with a focus on a possible association with focal segmental glomerulosclerosis (FSGS) in humans. Prevention and treatment strategies for OTA-induced illness are also discussed, including cholestyramine, a bile-acid-binding resin used as a sequestrant to reduce the enterohepatic recirculation of OTA.


title: Knowledge Discovery from Legal Documents Dataset using Text Mining Techniques

abstract: few decades have witnessed exponential increase in the use of IT which has resulted into large amount of data being generated, stored and searched. Data may be highly structured stored as records of a DBMS, or may be totally unstructured like blog posts or plain text documents. With the abundance of information being available as text documents, the issue of retrieval of knowledge from such unstructured dataset is posing new challenges to the research community. Legal document analysis is one domain which generates and uses text information in semi structured as well as unstructured form. The process of legal reasoning and decision making is heavily dependent on information stored in text documents. Text Mining (TM) is defined as the process of extracting useful information from text data. Legal text documents are stored using natural languages. For efficient analysis of such documents, text mining, a specialized branch of machine learning can be suitably used. Text mining - which "mines text", is heavily associated with natural language processing and Information Retrieval. TM techniques can be used for extracting relevant knowledge from stored legal documents. The extracted knowledge is used to simplify the preparation of case base, facilitate in decision making and legal reasoning or for automatic identification of legal arguments. Research in the fields of information extraction, natural language processing, artificial intelligence and expert system has augmented text mining process for enhancing the knowledge discovery process in this domain. This paper proposes a study which is aimed at grouping of legal documents based on the contents without taking any external input using unsupervised text mining techniques.


title: Blockmania: from Block DAGs to Consensus

abstract: Blockmania is a byzantine consensus protocol. Nodes emit blocks forming a directed acyclic graph (block DAG) that is subsequently interpreted by each node separately to ensure consensus with safety, liveness and finality. The resulting system has communication complexity $O(N^2)$ even in the worse case, and very low constant factors --- as compared to $O(N^4)$ for PBFT; it is leaderless; and network operations do not depend on the composition of the quorum or node stake. This makes Blockmania very efficient (leading to over 400K transactions per second on a wide area network), and ideal for dynamic membership and flexible and non-interrupted proof-of-stake protocols. A X-Blockmania variant, has $O(N)$ communication cost but also higher latency $O(\log N)$.


title: Extractive Adversarial Networks: High-Recall Explanations for Identifying Personal Attacks in Social Media Posts

abstract: We introduce an adversarial method for producing high-recall explanations of neural text classifier decisions. Building on an existing architecture for extractive explanations via hard attention, we add an adversarial layer which scans the residual of the attention for remaining predictive signal. Motivated by the important domain of detecting personal attacks in social media comments, we additionally demonstrate the importance of manually setting a semantically appropriate `default' behavior for the model by explicitly manipulating its bias term. We develop a validation set of human-annotated personal attacks to evaluate the impact of these changes.


title: The Effect of Music on Human Brain; Frequency Domain and Time Series Analysis Using Electroencephalogram

abstract: The aim of this paper is to investigate the effect of music stimuli on human brain using electroencephalogram (EEG). The study comprises of two experiments, a short term and a long term experiment referred to as experiment 1 and experiment 2, respectively. Two types of music stimuli; favorite music (preferred music of the subjects) and relaxing music (composed of alpha binaural beats) are used in experiment 1. Experiment 2 is conducted using relaxing music. Assessment of soothing effects of the music on human brain is done by analyzing different features; absolute power in alpha band, approximate entropy, sample entropy, and frontal asymmetry using EEG recordings. The ANOVA measures for the extracted features indicated no significant change in experiment 1. In experiment 2, the features are evaluated for the music listening group and control group separately. From ANOVA results, no significant change is observed in the control group after both conditions ( $1^{\mathrm {st}}$ week and $2^{\mathrm {nd}}$ week) with respect to the baseline. On the other hand, significant change was observed in the music listening group, for all features investigated; (a) absolute alpha power: condition (baseline and $1^{\mathrm {st}}$ week) $F=4.59$ , $P , condition (baseline and $2^{\mathrm {nd}}$ week) $F=18.87$ , $P (b) approximate entropy: condition (baseline and $2^{\mathrm {nd}}$ week) $F=30.62$ , $P (c) sample entropy: condition (baseline and $1^{\mathrm {st}}$ week) $F=4.75$ , $P , condition (baseline and $2^{\mathrm {nd}}$ week) $F=38.37$ , $P . For inter-hemispheric alpha asymmetry index measured from the frontal region of the brain; no significant change is observed in both experiments. Hence, the main contribution of this study is to investigate the EEG dynamics under different music stimuli. The results indicate that relaxing music has better soothing effects as compared to the favorite music. It is also observed that the effect of relaxation is significant when the relaxing music is listened for a longer period of time (2 weeks).


title: Pangr: A Behavior-Based Automatic Vulnerability Detection and Exploitation Framework

abstract: Nowadays, with the size and complexity of software increasing rapidly, vulnerabilities are becoming diversified and hard to identify. It is unpractical to detect and exploit vulnerabilities by manual construction. Therefore, an efficient automatic method of detecting and exploiting software vulnerability is in critical demand. This paper implements Pangr, an entire system for automatic vulnerability detection, exploitation, and patching. Pangr builds a complete vulnerability model based on its triggering behavior to identify vulnerabilities and generate exp or exploit schemes. According to the type and feature of the vulnerability, Pangr can generate the specific patch for the software. In the experiment, we tested 20 vulnerable programs on 32-bit Linux machine. Pangr detected 16 vulnerabilities, generated 10 exp, and patched 14 programs.


title: 2T2M memristor based TCAM cell for low power applications

abstract: This paper presents a 2-transistors-memristors (2T2M) bitcell for content-addressable memory design suitable for low-power applications. It uses memristors to store data and MOS transistors as access devices. The low power proposed design splits the search lines to search logic 1 and logic 0 separately to reduce the search power consumption. Different word sizes of the proposed bitcells with and without low power structure are simulated, including full parasitics using BPTM, 45nm CMOS technology node to evaluate and compare different performance parameters. The proposed low power design reduces the active power dissipation of about 30% compared to the design with conventional search approach.


title: A hybrid approach to automatic text summarization

abstract: Automatic text summarization is to compress an original document into an abridged version by extracting almost all of the essential concepts with text mining techniques. This research focuses on developing a hybrid automatic text summarization approach, KCS, to enhancing the quality of summaries. KCS employs the K-mixture probabilistic model to establish term weights in a statistical sense, and further identifies the term relationships to derive the connective strength (CS) of nouns. Sentences are ranked and extracted based on their CS values. We conduct two experiments to justify the proposed approach. The quality of extracted summary is examined by its capability of increasing text classification accuracy. The results show that our proposed approach, KCS, performs best among all approaches considered. It implies that KCS can extract more representative sentences from the document and its feasibility in text summarization applications is thus justified.


title: Reinforcement learning for dynamic environment: a classification of dynamic environments and a detection method of environmental changes

abstract: Engineers and researchers are paying more attention to reinforcement learning (RL) as a key technique for realizing computational intelligence such as adaptive and autonomous decentralized systems. In general, it is not easy to put RL into practical use. In prior research our approach mainly dealt with the problem of designing state and action spaces and we have proposed an adaptive co-construction method of state and action spaces. However, it is more difficult to design state and action spaces in dynamic environments than in static ones. Therefore, it is even more effective to use an adaptive co-construction method of state and action spaces in dynamic environments. In this paper, our approach mainly deals with a problem of adaptation in dynamic environments. First, we classify tasks of dynamic environments and propose a detection method of environmental changes to adapt to dynamic environments. Next, we conducted computational experiments using a so-called "path planning problem" with a slowly changing environment where the aging of the system is assumed. The performances of a conventional RL method and the proposed detection method were confirmed.


title: Toward intelligent machine-to-machine communications in smart grid

abstract: The advanced metering infrastructure of the smart grid presents the biggest growth potential in the machine-to-machine market today. Spurred by recent advances in M2M technologies, SG smart meters are expected not to require human intervention in characterizing power requirements and energy distribution. However, there are many challenges in the design of the SG communications network whereby the electrical appliances and smart meters are able to exchange information pertaining to varying power requirements. Furthermore, different types of M2M gateways are required at different points (e.g., at home, in the building, at the neighborhood, and so forth) of the SG communication network. This article surveys a number of existing communication technologies that can be adopted for M2M communication in SG. Among these, the most reliable technology to facilitate M2M communication in the SG home area network is pointed out, and its shortcoming is also noted. Furthermore, a possible solution to deal with this shortcoming to improve SG communications scalability is also presented.


title: Social anxiety disorder: Questions and answers for the DSM-V.

abstract: Background: This review evaluates the DSM-IV criteria of social anxiety disorder (SAD), with a focus on the generalized specifier and alternative specifiers, the considerable overlap between the DSM-IV diagnostic criteria for SAD and avoidant personality disorder, and developmental issues. Method: A literature review was conducted, using the validators provided by the DSM-V Spectrum Study Group. This review presents a number of options and preliminary recommendations to be considered for DSM-V. Results/Conclusions: Little supporting evidence was found for the current specifier, generalized SAD. Rather, the symptoms of individuals with SAD appear to fall along a continuum of severity based on the number of fears. Available evidence suggested the utility of a specifier indicating a ‘‘predominantly performance’’ variety of SAD. A specifier based on ‘‘fear of showing anxiety symptoms’’ (e.g., blushing) was considered. However, a tendency to show anxiety symptoms is a core fear in SAD, similar to acting or appearing in a certain way. More research is needed before considering subtyping SAD based on core fears. SAD was found to be a valid diagnosis in children and adolescents. Selective mutism could be considered in part as a young child’s avoidance response to social fears. Pervasive test anxiety may belong not only to SAD, but also to generalized anxiety disorder. The data are equivocal regarding whether to consider avoidant personality disorder simply a severe form of SAD. Secondary data analyses, field trials, and validity tests are needed to investigate the recommendations and options. Depression and Anxiety 27:168–189, 2010. r 2010 Wiley-Liss, Inc.


A Diverse Dataset for Pedestrian Detection


title: Attributions of Trust in Decision Support Technologies: A Study of Recommendation Agents for E-Commerce

abstract: As organizations increasingly utilize Web-based technologies to support customers better, trust in decision support technologies has emerged as an important issue in online environments. In this study, we identify six reasons users trust (or do not trust) a technology in the early stages of its use by extending the theories of trust formation in interpersonal and organizational contexts to that of decision support technologies. We study the particular context of decision support technologies for e-commerce: online recommendation agents (RAs), which facilitate users' decision making by providing advice on what to buy based on user-specified needs and preferences. ::: ::: A laboratory experiment is conducted using a multimethod approach to collect data. Both quantitative data about participants' trust in RAs and written protocols that explain the reasons for their levels of trust are collected. A content analysis of the written protocols identifies both positive and negative trust attributions that are then mapped to six trust reasons. A structural equation modeling analysis is employed to test the causal strengths of the trust reasons in explaining participants' trust in RAs. The results reveal that in the early stages of trust formation, four positive reasons (i.e., knowledge-based, interactive, calculative, and dispositional) are associated with higher trust in RAs and two negative reasons (i.e., calculative and interactive) are associated with lower trust in RAs. The results also demonstrate some distinctive features of trust formation with respect to decision support technologies. We discuss the research and practical implications of the findings and describe opportunities for future research.