title: EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis

abstract: Single image super-resolution is the task of inferring a high-resolution image from a single low-resolution input. Traditionally, the performance of algorithms for this task is measured using pixel-wise reconstruction measures such as peak signal-to-noise ratio (PSNR) which have been shown to correlate poorly with the human perception of image quality. As a result, algorithms minimizing these metrics tend to produce over-smoothed images that lack high-frequency textures and do not look natural despite yielding high PSNR values. ::: We propose a novel application of automated texture synthesis in combination with a perceptual loss focusing on creating realistic textures rather than optimizing for a pixel-accurate reproduction of ground truth images during training. By using feed-forward fully convolutional neural networks in an adversarial training setting, we achieve a significant boost in image quality at high magnification ratios. Extensive experiments on a number of datasets show the effectiveness of our approach, yielding state-of-the-art results in both quantitative and qualitative benchmarks.


title: Workflow Scheduling in Multi-Tenant Cloud Computing Environments

abstract: Multi-tenancy is one of the key features of cloud computing, which provides scalability and economic benefits to the end-users and service providers by sharing the same cloud platform and its underlying infrastructure with the isolation of shared network and compute resources. However, resource management in the context of multi-tenant cloud computing is becoming one of the most complex task due to the inherent heterogeneity and resource isolation. This paper proposes a novel cloud-based workflow scheduling (CWSA) policy for compute-intensive workflow applications in multi-tenant cloud computing environments, which helps minimize the overall workflow completion time, tardiness, cost of execution of the workflows, and utilize idle resources of cloud effectively. The proposed algorithm is compared with the state-of-the-art algorithms, i.e., First Come First Served (FCFS), EASY Backfilling, and Minimum Completion Time (MCT) scheduling policies to evaluate the performance. Further, a proof-of-concept experiment of real-world scientific workflow applications is performed to demonstrate the scalability of the CWSA, which verifies the effectiveness of the proposed solution. The simulation results show that the proposed scheduling policy improves the workflow performance and outperforms the aforementioned alternative scheduling policies under typical deployment scenarios.


title: Five Major Shifts in 100 Years of Engineering Education

abstract: In this paper, five major shifts in engineering education are identified. During the engineering science revo- lution, curricula moved from hands-on practice to mathemat- ical modeling and scientific analyses. The first shift was initiated by engineering faculty members from Europe; accel- erated during World War II, when physicists contributed mul- tiple engineering breakthroughs; codified in the Grinter report; and kick-started by Sputnik. Did accreditation hinder curricular innovations? Were engineering graduates ready for practice? Spurred by these questions, the Accreditation Board for Engineering and Technology (ABET) required engineering programs to formulate outcomes, systematically assess achievement, and continuously improve student learning. The last three shifts are in progress. Since the engineering science revolution may have marginalized design, a distinctive feature of engineering, faculty members refocused attention on cap- stone and first-year engineering design courses. However, this third shift has not affected the two years in between. Fourth, research on learning and education continues to influence engineering education. Examples include learning outcomes and teaching approaches, such as cooperative learning and inquiry that increase student engagement. In shift five, tech- nologies (e.g., the Internet, intelligent tutors, personal compu- ters, and simulations) have been predicted to transform education for over 50 years; however, broad transformation has not yet been observed. Together, these five shifts characterize changes in engineering education over the past 100 years.


title: Management of inter-dental/inter-implant papilla

abstract: OBJECTIVES ::: The aims of this paper are to review and compare existing techniques for creation of interdental/interimplant papillae, to address factors that may influence its appearance and to present an approach that authors developed that could help clinicians to manage and recreate the interproximal papillae. ::: ::: ::: METHODS ::: Papers related to interdental and interimplant papillae published over the last 30 years were selected and analyzed. ::: ::: ::: RESULTS ::: Thorough treatment planning is essential for maintenance of the height of the interproximal papillae following tooth removal. The key for achieving an esthetically pleasing outcome is the clinicians' ability of properly managing/creating interdental/interimplant papillae. Bone support is the foundation for any soft tissue existence, techniques such as socket augmentation, orthodontic extrusion, guided bone regeneration, onlay graft and distraction osteogenesis are often used for this purpose. Soft tissue grafts as well as esthetic mimic restorations can also be used to enhance the esthetic outcomes. ::: ::: ::: CONCLUSIONS ::: An esthetic triangle is developed to address the foundations that are essential for maintaining/creating papilla. These include adequate bone volume, proper soft tissue thickness as well as esthetic appearing restorations.


title: Apathy in untreated, de novo patients with Parkinson’s disease: validation study of Apathy Evaluation Scale

abstract: Apathy is a behavioural disturbance occurring alone or in concomitance with depression in Parkinson's disease (PD). Here we present a validation study for the self-report version of the Apathy Evaluation Scale (AES-S), carried out in a sample of 60 non-demented, non-depressed untreated, drug-naïve, de novo PD patients; 20 patients of the sample (33.3%) were classified as apathetic according to current clinical criteria. All enrolled patients completed the AES-S and a neurological and cognitive assessment. Mean AES-S score was 34.43. AES-S did not show floor or ceiling effect. Cronbach's alpha was 0.872. Principal component analysis revealed three factors: the first (34.4% of the variance) represented constitutive aspects of the construct of apathy; the second (8.5% of the variance) represented a social dimension; the third factor (7.9% of the variance) represented a dimension related to insight. With respect to clinical criteria for apathy considered as the gold standard, receiver operating characteristics curve analysis showed that a cut-off of 36/37 has the maximum discrimination power. High sensitivity and negative predictive values were obtained with cut-off scores of 33/34 or lower; high specificity and positive predictive values were obtained with cut-off scores of 38/39 or higher. AES-S score correlated with scores on frontal tasks, but not on Beck Depression Inventory, Unified Parkinson's Disease Rating Scale, Hoehn and Yahr scale. The AES-S is a reliable and valid questionnaire for detecting apathy in PD. For screening purposes a 33/34 cut-off score is indicated, but a 38/39 cut-off score is necessary when a high specificity is desired.


title: A SLAM algorithm for indoor mobile robot localization using an Extended Kalman filter and a segment based environment mapping

abstract: The present paper faces the Simultaneous Localization And Mapping (SLAM) problem for a mobile robot in an unknown indoor environment. A set of segments is used to model the robot surrounding environment and segments' starting and ending points are used as SLAM landmarks. A segment based mapping algorithm is proposed and used along with an Extended Kalman filter driven by measurements taken by ultrasonic sensors located on the robot. The proposed SLAM algorithm has been tested in both simulated and real experiments yielding to encouraging estimation and mapping results.


title: FCNN: Fourier Convolutional Neural Networks

abstract: The Fourier domain is used in computer vision and machine learning as image analysis tasks in the Fourier domain are analogous to spatial domain methods but are achieved using different operations. Convolutional Neural Networks (CNNs) use machine learning to achieve state-of-the-art results with respect to many computer vision tasks. One of the main limiting aspects of CNNs is the computational cost of updating a large number of convolution parameters. Further, in the spatial domain, larger images take exponentially longer than smaller image to train on CNNs due to the operations involved in convolution methods. Consequently, CNNs are often not a viable solution for large image computer vision tasks. In this paper a Fourier Convolution Neural Network (FCNN) is proposed whereby training is conducted entirely within the Fourier domain. The advantage offered is that there is a significant speed up in training time without loss of effectiveness. Using the proposed approach larger images can therefore be processed within viable computation time. The FCNN is fully described and evaluated. The evaluation was conducted using the benchmark Cifar10 and MNIST datasets, and a bespoke fundus retina image dataset. The results demonstrate that convolution in the Fourier domain gives a significant speed up without adversely affecting accuracy. For simplicity the proposed FCNN concept is presented in the context of a basic CNN architecture, however, the FCNN concept has the potential to improve the speed of any neural network system involving convolution.


title: Effect of probiotics and synbiotics on blood glucose: a systematic review and meta-analysis of controlled trials

abstract: PURPOSE ::: High fasting blood glucose (FBG) can lead to chronic diseases such as diabetes mellitus, cardiovascular and kidney diseases. Consuming probiotics or synbiotics may improve FBG. A systematic review and meta-analysis of controlled trials was conducted to clarify the effect of probiotic and synbiotic consumption on FBG levels. ::: ::: ::: METHODS ::: PubMed, Scopus, Cochrane Library, and Cumulative Index to Nursing and Allied Health Literature databases were searched for relevant studies based on eligibility criteria. Randomized or non-randomized controlled trials which investigated the efficacy of probiotics or synbiotics on the FBG of adults were included. Studies were excluded if they were review articles and study protocols, or if the supplement dosage was not clearly mentioned. ::: ::: ::: RESULTS ::: A total of fourteen studies (eighteen trials) were included in the analysis. Random-effects meta-analyses were conducted for the mean difference in FBG. Overall reduction in FBG observed from consumption of probiotics and synbiotics was borderline statistically significant (-0.18 mmol/L 95 % CI -0.37, 0.00; p = 0.05). Neither probiotic nor synbiotic subgroup analysis revealed a significant reduction in FBG. The result of subgroup analysis for baseline FBG level ≥7 mmol/L showed a reduction in FBG of 0.68 mmol/L (-1.07, -0.29; ρ < 0.01), while trials with multiple species of probiotics showed a more pronounced reduction of 0.31 mmol/L (-0.58, -0.03; ρ = 0.03) compared to single species trials. ::: ::: ::: CONCLUSION ::: This meta-analysis suggests that probiotic and synbiotic supplementation may be beneficial in lowering FBG in adults with high baseline FBG (≥7 mmol/L) and that multispecies probiotics may have more impact on FBG than single species.


title: Dynamic Resource Allocation in Ad-Hoc Mobile Cloud Computing

abstract: Mobile Cloud Computing (MCC) has draw extensive research attentions due to the increasingly demand on the energy consumption and execution time constraints on Mobile Devices (MDs). MCC has been well investigated in the scenarios when workload is offloaded to a remote cloud or a cloudlet. However, in those scenarios, infrastructure is required to provide connections between MDs and the cloud#x002F;cloudlet. To facilitate MCC for environment when infrastructure is not available, Ad-hoc MCC which allows MDs to share and process workload coordinately is discussed [1]. In this study, the detailed resource allocation problem in Ad-hoc MCC, including how to assign tasks to MDs, how tasks are executed on each MD, and how to arrange task and task result return transmissions so that interference among different MDs can be avoided are carefully studied. Since the problem is NP-hard, a heuristic algorithm is then introduced. As shown in the evaluation section, when no infrastructure is available, compared with the case when an application is executed solely on a single MD, the proposed algorithm can reduce an application's response time significantly.


title: Integration of agent technology into manufacturing enterprise: A review and platform for industry 4.0

abstract: Agent paradigm is a technology recognized as potent tool for the 21st century manufacturing system. Researches are on-going in the applications of agent technology to manufacturing enterprise, production process planning and scheduling, shop floor control, and to reconfigurable manufacturing system as an implementation case study. The article presents the results of a review of published articles on agents' usage at manufacturing enterprise level for reconfigurable manufacturing system between year 2003 and August, 2014 to provide up to date pictures of the technological function of agents and multi agents within the reconfigurable conceptualized manufacturing circles. The paper is viewed from the angle of wide coverage, broad scope of multi-agent techniques and a focus on real world applications. The outcomes include trends of agents and multi-agents usage in manufacturing enterprise resource planning, supply chain managements and the gaps yet to be filled for consolidating future plans on Industry 4.0 in reconfigurable manufacturing system.


Trail Making Test : Normative data for the Latin American Spanish speaking adult population


title: LTE release 14 outlook

abstract: Today's 4G LTE systems bring unprecedented mobile broadband performance to over a billion of users across the globe. Recently, work on a 5G mobile communication system has begun, and next to a new 5G air interface, LTE will be an essential component. The evolution of LTE will therefore strive to meet 5G requirements and to address 5G use cases. In this article, we provide an overview of foreseen key technology areas and components for LTE Release 14, including latency reductions, enhancements for machine-type communication, operation in unlicensed spectrum, massive multi-antenna systems, broadcasting, positioning, and support for intelligent transportation systems.


Manual of Clinical Exercise Testing, Prescription and Rehabilitation.


title: Views of Public Dental Hygienist about Oral Health Hub Center - In the Area Not Implemented

abstract: Korean Ministry of Health and Welfare started to implement oral health hub center to provide oral health preventive program and dental treatment to public, especially dental vulnerable class in 2006. But, there is no applicant area to implement it regardless of national budget arrangement in 2012. This study is aimed to investigate the reason not to be implemented and requirements of implementation. 293 among 1,000 public dental hygienists in the area where have not implemented oral health hub center were surveyed in Korea from April to July in 2012 through convenience sampling. The questionnaire consisted of the reason why oral health hub center have not been implemented, the requirement of implementation, duty area and duty position et al. After removal of insufficient responses, 217 questionnaires were analyzed by t-test and ANOVA using SPSS 20.0. The reason why oral health hub center have not been implemented were deficiency of the priority list as compared with other health program (72.4%), space insufficiency (71.4%), regional budget insufficiency (70.5%), will insufficiency of oral health promotion (70.5%) and manpower insufficiency (62.7%). The first requirement of implementation were space expansion and regional budget expansion, followed by reduction of record-originated and administrative tasks, understanding on oral health program of higher ranking public officials in health center, manpower expansion, reduction of other tasks than oral health program and volunteer source expansion. Budget insufficiency and manpower insufficiency in Metropolis were ranked higher than other area (p


title: Google's Cloud Vision API is Not Robust to Noise

abstract: Google has recently introduced the Cloud Vision API for image analysis. According to the demonstration website, the API "quickly classifies images into thousands of categories, detects individual objects and faces within images, and finds and reads printed words contained within images." It can be also used to "detect different types of inappropriate content from adult to violent content." In this paper, we evaluate the robustness of Google Cloud Vision API to input perturbation. In particular, we show that by adding sufficient noise to the image, the API generates completely different outputs for the noisy image, while a human observer would perceive its original content. We show that the attack is consistently successful, by performing extensive experiments on different image types, including natural images, images containing faces and images with texts. For instance, using images from ImageNet dataset, we found that adding an average of 14.25% impulse noise is enough to deceive the API. Our findings indicate the vulnerability of the API in adversarial environments. For example, an adversary can bypass an image filtering system by adding noise to inappropriate images. We then show that when a noise filter is applied on input images, the API generates mostly the same outputs for restored images as for original images. This observation suggests that cloud vision API can readily benefit from noise filtering, without the need for updating image analysis algorithms.


title: On Cost-Effective Incentive Mechanisms in Microtask Crowdsourcing

abstract: While microtask crowdsourcing provides a new way to solve large volumes of small tasks at a much lower price compared with traditional inhouse solutions, it suffers from quality problems due to the lack of incentives. On the other hand, providing incentives for microtask crowdsourcing is challenging since verifying the quality of submitted solutions is so expensive that it will negate the advantage of microtask crowdsourcing. We study cost-effective incentive mechanisms for microtask crowdsourcing in this paper. In particular, we consider a model with strategic workers, where the primary objective of a worker is to maximize his own utility. Based on this model, we first analyze two basic mechanisms and show their limitations in collecting high-quality solutions with low cost. Then, we propose a cost-effective mechanism that employs quality-aware worker training as a tool to stimulate workers to provide high-quality solutions. We prove theoretically that the proposed mechanism can be designed to obtain high-quality solutions from workers and ensure the budget constraint of the requester at the same time. Beyond its theoretical guarantees, we further demonstrate the effectiveness of our proposed mechanisms through a set of behavioral experiments.


title: Capability, Quality, and Performance of Offshore IS Vendors: A Theoretical Framework and Empirical Investigation

abstract: Information systems (IS) offshoring has become a widespread practice and a strategic sourcing choice for many firms. While much has been written by researchers about the factors that lead to successful offshoring arrangements from the client's viewpoint, the vendor's perspective has been largely scarce. The vendor perspective is equally important as offshore IS vendors need to make important decisions in terms of delivering operational and strategic performance and aligning their resources and processes in order to meet or exceed targeted outcomes. In this article, we propose and test a three-level capability–quality–performance (CQP) theoretical framework to understand vendor outcomes and their antecedents. The first level of the framework represents three vendor capabilities: relationship management, contract management, and information technology management. The second level has three mediating variables representing process quality: partnership, service, and deliverable quality. The third level has three dependent variables representing vendor outcomes: operational performance, strategic performance, and satisfaction. The model was tested with 188 vendor firms from India and China, the two most popular destinations for IS offshoring. Results support the CQP framework; vendor capabilities are significant predictors of intermediate quality measures, which in turn affect vendor outcomes. Implications of the study findings to both theory development and IS offshore vendor strategic decision making are discussed.


title: Designing culturally situated technologies for the home

abstract: As digital technologies proliferate in the home, the Human Computer Interaction (HCI) community has turned its attention from the workplace and productivity tools towards domestic design environments and non-utilitarian activities. In the workplace, applications tend to focus on productivity and efficiency and involve relatively well-understood requirements and methodologies, but in domestic design environments we are faced with the need to support new classes of activities. While usability is still central to the field, HCI is beginning to address considerations such as pleasure, fun, emotional effect, aesthetics, the experience of use, and the social and cultural impacts of new technologies. These considerations are particularly relevant to the home, where technologies are situated or embedded within an ecology that is rich with meaning and nuance.The aim of this workshop is to explore ways of designing domestic technology by incorporating an awareness of cultural context, accrued social meanings, and user experience.


title: View-Dependent Adaptive Cloth Simulation with Buckling Compensation

abstract: This paper describes a method for view-dependent cloth simulation using dynamically adaptive mesh refinement and coarsening. Given a prescribed camera motion, the method adjusts the criteria controlling refinement to account for visibility and apparent size in the camera’s view. Objectionable dynamic artifacts are avoided by anticipative refinement and smoothed coarsening, while locking in extremely coarsened regions is inhibited by modifying the material model to compensate for unresolved sub-element buckling. This approach preserves the appearance of detailed cloth throughout the animation while avoiding the wasted effort of simulating details that would not be discernible to the viewer. The computational savings realized by this method increase as scene complexity grows. The approach produces a $2\times$ speed-up for a single character and more than $4\times$ for a small group as compared to view-independent adaptive simulations, and respectively $5\times$ and $9\times$ speed-ups as compared to non-adaptive simulations.


title: Exploiting a natural network effect for scalable, fine-grained clock synchronization

abstract: Nanosecond-level clock synchronization can be an enabler of a new spectrum of timing- and delay-critical applications in data centers. However, the popular clock synchronization algorithm, NTP, can only achieve millisecond-level accuracy. Current solutions for achieving a synchronization accuracy of 10s-100s of nanoseconds require specially designed hardware throughout the network for combatting random network delays and component noise or to exploit clock synchronization inherent in Ethernet standards for the PHY. ::: ::: In this paper, we present HUYGENS, a software clock synchronization system that uses a synchronization network and leverages three key ideas. First, coded probes identify and reject impure probe data--data captured by probes which suffer queuing delays, random jitter, and NIC timestamp noise. Next, HUYGENS processes the purified data with Support Vector Machines, a widely-used and powerful classifier, to accurately estimate one-way propagation times and achieve clock synchronization to within 100 nanoseconds. Finally, HUYGENS exploits a natural network effect--the idea that a group of pairwise synchronized clocks must be transitively synchronized-- to detect and correct synchronization errors even further. ::: ::: Through evaluation of two hardware testbeds, we quantify the imprecision of existing clock synchronization across server-pairs, and the effect of temperature on clock speeds. We find the discrepancy between clock frequencies is typically 5-10ms/sec, but it can be as much as 30ms/sec. We show that HUYGENS achieves synchronization to within a few 10s of nanoseconds under varying loads, with a negligible overhead upon link bandwidth due to probes. Because HUYGENS is implemented in software running on standard hardware, it can be readily deployed in current data centers.


title: Bayesian Detection of Infrequent Differences in Sets of Time Series with Shared Structure

abstract: We present a hierarchical Bayesian model for sets of related, but different, classes of time series data. Our model performs alignment simultaneously across all classes, while detecting and characterizing class-specific differences. During inference the model produces, for each class, a distribution over a canonical representation of the class. These class-specific canonical representations are automatically aligned to one another — preserving common sub-structures, and highlighting differences. We apply our model to compare and contrast solenoid valve current data, and also, liquid-chromatography-ultraviolet-diode array data from a study of the plant Arabidopsis thaliana.


title: Dual waveband MW/LW focal plane arrays grown by MOVPE on silicon substrates

abstract: The use of silicon substrates has been very successful for producing large area focal plane arrays operating in the MWIR waveband using the MBE growth process. More recently, promising results have been obtained in the LWIR waveband using a MOVPE growth process on a buffered silicon substrate. The MOVPE growth process is also suitable for more complex multi-layer structures and we have now used this technique to produce our first MW/LW dual waveband focal plane arrays. In this paper we show that close to background limited performance can be achieved in both wavebands, however the main challenge with arrays grown on silicon is to obtain low defect counts. These first arrays are promising in this respect and operabilities of 99.4% and 98.2% have been achieved in the MWIR band and LWIR band respectively. The availability of dual waveband arrays allows the correlation of defects in the two wavebands to be compared. In general, we find that the correlation is low and this suggests that defect generation mechanisms which would affect both bands (such as threading dislocations) are currently not the main source of defective devices in MOVPE grown devices on silicon.


title: Mining Cohesive Patterns from Graphs with Feature Vectors

abstract: The increasing availability of network data is creating a great potential for knowledge discovery from graph data. In many applications, feature vectors are given in addition to graph data, where nodes represent entities, edges relationships between entities, and feature vectors associated with the nodes represent properties of entities. Often features and edges contain complementary information. In such scenarios the simultaneous use of both data types promises more meaningful and accurate results. Along these lines, we introduce the novel problem of mining cohesive patterns from graphs with feature vectors, which combines the concepts of dense subgraphs and subspace clusters into a very expressive problem definition. A cohesive pattern is a dense and connected subgraph that has homogeneous values in a large enough feature subspace. We argue that this problem definition is natural in identifying small communities in social networks and functional modules in Protein-Protein interaction networks. We present the algorithm CoPaM (Cohesive Pattern Miner), which exploits various pruning strategies to efficiently find all maximal cohesive patterns. Our theoretical analysis proves the correctness of CoPaM, and our experimental evaluation demonstrates its effectiveness and


title: Adaptive Load Balancing for Parallel IDS on Multi-Core Systems Using Prioritized Flows

abstract: We describe a load balancing system for parallel intrusion detection on multi-core systems using a novel model allowing fine-grained selection of the network traffic to be analyzed. The system receives data from a network and distributes it to multiple IDSs running on individual CPU cores. In contrast to related approaches, we do not assume a static association of flows to IDS processes but adaptively determine the load of each IDS process to allocate network flows for a limited time window. We developed a priority model for the selection of network data and the assignment process. Special emphasis is given to environments with highly dynamic network traffic, where only a fraction of all data can be analyzed due to system constraints. We show that IDSs analyzing packet payload data disproportionately suffer from random packet drops due to overload. Our proposed system ensures loss-free analysis for selected data streams in a specified time interval. Our primary focus lies on the treatment of dynamic network behavior: neither data should be lost unintentionally, nor analysis processes should be needlessly idle. To evaluate the priority model and assignment systems, we implemented a prototype and evaluated it with real network traffic.


title: HICAMP bitmap: space-efficient updatable bitmap index for in-memory databases

abstract: Bitmap represents an efficient indexing structure for querying large amounts of data and is widely deployed in data-warehouse applications. While the size of a bitmap scales linearly with the number of rows in a table, due to its sparseness, it can be greatly reduced via compression based on run-length encoding. However, updating a compressed bitmap is expensive due to the encoding and decoding overheads, in particular, as re-compression can change the compressed sequence length and data layout. Due to this problem, bitmap indices only perform well for read-only workloads. In this paper, we propose a bitmap index structure which is both space-efficient and allows fast updates, by building on top of a smart memory model called HICAMP. As a consequence, our approach enables bitmap indices for workloads that exhibit high update ratios as in OLTP workloads. We also present a new multi-bit bitmap design which addresses the candidate checking problem. In our experiments, the HICAMP bitmap index demonstrates 3~12x reduction in size over B-tree and 8~30x over other commonly used indexing structures such as Red-Black tree, while supporting efficient updates simultaneously.


title: FEATURES-LEVEL FUSION OF FACE AND HANDWRITTEN SIGNATURE IN MULTIMODAL BIOMETRIC IDENTIFICATION SYSTEM

abstract: Biometrics is an emerging tool used to identify humans by their physical and/or behavioral characteristics. This article presents a novel neural network–based approach for features-level fusion in a multimodal biometric identification system by combining both physical human face and behavioral handwritten signature traits. A single biometrics system has the weakness of providing neither 100% identification nor a 0% false accept rate FAR/false reject rate FRR. One solution to this is to combine different biometrics together to get a multimodal biometric identification system. Moreover, a multimodal system is also robust in providing security against spoof attacks. Images of 32 × 32 pixels are used to eliminate bulk storage and processing requirements.


title: Microring Weight Banks

abstract: Microring weight banks could enable novel signal processing approaches in silicon photonics. We analyze factors limiting channel count in microring weight banks, which are central to analog wavelength-division multiplexed processing networks in silicon. We find that microring weight banks require a fundamentally different analysis compared to other wavelength-division multiplexing circuits (e.g., demultiplexers). By introducing a quantitative description of independent weighting, we establish performance tradeoffs between channel count and power penalty. This performance is significantly affected by coherent multiresonator interactions through bus waveguides. We experimentally demonstrate these effects in a fabricated device. Analysis relies on the development of a novel simulation technique combining parametric programming with generalized transmission theory. Experimental measurement fitting of an 8-channel weight bank is presented as an example of another application of the simulator.


title: A Hybrid Approach to Segmentation of Diseased Lung Lobes

abstract: Complete segmentation of diseased lung lobes by automatically identifying fissure surfaces is a nontrivial task, due to incomplete, disrupted, and deformed fissures. In this paper, we present a novel algorithm employing a hybrid two-dimensional/three-dimensional approach for segmenting diseased lung lobes. Our approach models complete fissure surfaces from partial fissures found in individual computed tomography (CT) images. Evaluated using 24 patients' lungs with a variety of different diseases, our algorithm produced root-mean square errors of 2.21 ± 1.21, 2.51 ± 1.36, and 2.38 ± 1.27 mm for segmenting the left oblique fissure (LOF), right oblique fissure (ROF) and right horizontal fissure (RHF), respectively. The average accuracies for segmenting the LOF, ROF, and RHF are 86.59%, 84.80%, and 82.62%, using our ±3-mm percentile measure. These results indicate the feasibility of developing an automatic algorithm for complete segmentation of diseased lung lobes.


title: A survey on Vehicular Cloud Computing and its security

abstract: Vehicular networking has a significant advantages in the today era. It provides desirable features and some specific applications such as efficient traffic management, road safety and infotainment. The vehicle consists of comparatively more communication systems such as on-board computing device, storage and computing power, GPS etc. to provide Intelligent Transportation System (ITS). The new hybrid technology known as Vehicular Cloud Computing (VCC) has great impact on the ITS by using the resources of vehicles such as GPS, storage, internet and computing power for instant decision making and sharing information on the cloud. Moreover, the paper not only present the concept of vehicular cloud but also provide a brief overview on the applications, security issues, threats and security solution for the VCC.


title: Augmented Reality Interactive Storytelling Systems Using Tangible Cubes for Edutainment

abstract: Augmented reality has been used in many areas and it has proved to be useful for storytelling. These systems are adequate for edutainment. In this paper, we present two augmented reality interactive storytelling systems that use tangible cubes. The first shows the story in only one face of the cube and the second presents the story in all visible faces of the cube. The progress of the story can be chosen using two different tangible interfaces. We have tested the systems with the children of the Summer School of the Technical University of Valencia. In these tests we have analysed if the use of a head-mounted display or a typical monitor influence in the experience of the user. Results do not offer statistical significant differences using both visualization systems and confirm that children enjoyed playing with the systems.


title: Wide-band circularly polarized cavity backed crossed dipole antenna

abstract: In this paper, a wide-band circularly polarized (CP) crossed dipole antenna is proposed. The CP crossed dipole antenna is backed by a cavity reflector to achieve wide-band CP radiation with enhanced gain and uni-directional pattern accompanied by high front to back lobe ratio. A comprehensive study on the effect of the vertical walls of the cavity reflector on the CP radiation performance has been performed. It is analyzed that the cavity reflector provides an additional CP mode which is merged with the reference CP mode of crossed dipole to get a bandwidth enhancement in the CP operation. The concentration of electric field vectors on the inner walls of cavity provides an enhanced and stable gain of a CP crossed dipole across the entire CP operating bandwidth as compared to a PEC reflector backed crossed dipole antenna. The concept of wide-band, uni-directional and high gain CP crossed dipole antenna is validated by measurement carried out on the fabricated antenna prototype. Measured results confirm that the cavity backed crossed dipole exhibits a CP operating bandwidth of 22.89% (2.52–3.17 GHz), an average gain of 8 dBic and uni-directional radiation pattern with a front to back lobe ratio > 25 dB.


title: Specification and verification: the Spec# experience

abstract: Spec# is a programming system that puts specifications in the hands of programmers and includes tools that use them. The system includes an object-oriented programming language with specification constructs, a compiler that emits executable code and run-time checks for specifications, a programming methodology that gives rules for structuring programs and for using specifications, and a static program verifier that attempts to mathematically prove the correctness of programs. This paper reflects on the six-year experience of building and using Spec#, the scientific contributions of the project, remaining challenges for tools that seek to establish program correctness, and prospects of incorporating program verification into everyday software engineering. 0. INTRODUCTION: THE SPEC# VISION Software engineering is the process of authoring software that is to fulfill some worldly needs. It is an expensive endeavor that provides difficulties at all levels. At the top level, the gathering of requirements for the software is usually an exploratory and iterative process. Any change in requirements ripples through all parts of the software artifact and is further complicated by having to maintain previous versions of the software. At the level of the program itself, the interaction between program modules requires an understanding of what is expected and what can be assumed by the interacting modules. At the level of each module, one problem is to maintain the consistency of data structures, let alone remember what it means for a particular data structure to be consistent. At the level of individual operations, the algorithmic details can be tricky to get right. Two common themes among all of these difficulties are the problem of having useful and accurate documentation and the problem of making sure that programs adhere to documented behavior and do not misuse features of the programming language. Spec# (pronounced “speck sharp”) is a research project aimed at addressing these two problems. To combat the first problem, Spec# takes the well-known approach of providing contracts, specification constructs to document behavior. To combat the second problem, Spec# adds automatic Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Copyright 2009 Barnett, Fahndrich, Leino, Muller, Schulte, Venter. tool support, which includes an automatic program verifier. The Spec# project set out to explore the programmer experience of using specifications all the time and receiving benefit from them. The tool support is intended not just to help ensure program correctness, but also, importantly, to lure programmers into recording their design decisions in specifications, knowing that the specifications will not just rot as stale comments. In this paper, we describe the Spec# programming system, along with our initial goals of the project, what we have done with it, how it has already had some impact, and how we now, in retrospect, view some of our design decisions. 1. SONGS OF INNOCENCE We started the Spec# project in 2003 as an attempt to build a comprehensive program verification system [4]. Our dream was to build a real system that real programmers can use on real programs to do real verification—a system that “the programming masses” could use in their everyday work. We wanted to explore and push the boundaries of specification and verification technology to get closer to realizing these aspirations. Let us consider in more detail the lay of the land at the time we started the project. Program verification was already several decades old, starting with some formal underpinnings of program semantics and techniques for proving program correctness [13]. Supported by mechanical proof assistants, some early program verifiers were the GYPSY system with the Boyer-Moore prover [0] and the Stanford Pascal Verifier [18]. Later systems, which are still used today, include full-featured proof assistants like PVS [23] and Isabelle/HOL [22]. Another approach that uses verification technology were extended static checkers like ESC/Modula-3 [9] and ESC/Java [12]. These tools have been more closely integrated into existing programming languages and value automation over expressivity or mathematical guarantees like finding all errors in a program. The automation is enabled by a breed of combined decision procedures that today is known as Satisfiability Modulo Theories (SMT) solvers [8]. To make them easier and more cost-effective to use, extended static checkers were intentionally designed to be unsound, that is, they may miss certain errors. Dynamic checking of specifications has always been done by Eiffel [20], which also pioneered the inclusion of specifications in an object-oriented language. The tool suite for the Java Modeling Language (JML) [16] included a facility for dynamic checking of specifications [5]. Eiffel influenced JML, and the strong influence of both of these on Spec# is


title: Deep Features for Text Spotting

abstract: The goal of this work is text spotting in natural images. This is divided into two sequential tasks: detecting words regions in the image, and recognizing the words within these regions. We make the following contributions: first, we develop a Convolutional Neural Network (CNN) classifier that can be used for both tasks. The CNN has a novel architecture that enables efficient feature sharing (by using a number of layers in common) for text detection, character case-sensitive and insensitive classification, and bigram classification. It exceeds the state-of-the-art performance for all of these. Second, we make a number of technical changes over the traditional CNN architectures, including no downsampling for a per-pixel sliding window, and multi-mode learning with a mixture of linear models (maxout). Third, we have a method of automated data mining of Flickr, that generates word and character level annotations. Finally, these components are used together to form an end-to-end, state-of-the-art text spotting system. We evaluate the text-spotting system on two standard benchmarks, the ICDAR Robust Reading data set and the Street View Text data set, and demonstrate improvements over the state-of-the-art on multiple measures.


Deception Detection in News Reports in the Russian Language: Lexics and Discourse


Framework for Next Generation Digital Forensics Models


Clinician Champions and Leaders for Electronic Medical Record Innovations


title: Spoken term detection with Connectionist Temporal Classification: A novel hybrid CTC-DBN decoder

abstract: This paper proposes a novel system for robust keyword detection in continuous speech. Our decoder is composed of a bidirectional Long Short-Term Memory recurrent neural network using a Connectionist Temporal Classification (CTC) output layer, and a Dynamic Bayesian Network (DBN). The CTC network exploits bidirectional context information to reliably identify phonemes, whereas the DBN is able to discriminate between keywords and arbitrary speech while explicitly modeling substitutions, deletions, and insertions in the CTC phoneme output string. Our technique is vocabulary independent and does not require an explicit garbage model. Experiments show that our system architecture prevails over a standard Hidden Markov Model approach.


title: Requirements and design space for interactive public displays

abstract: Digital immersion is moving into public space. Interactive screens and public displays are deployed in urban environments, malls, and shop windows. Inner city areas, airports, train stations and stadiums are experiencing a transformation from traditional to digital displays enabling new forms of multimedia presentation and new user experiences. Imagine a walkway with digital displays that allows a user to immerse herself in her favorite content while moving through public space. In this paper we discuss the fundamentals for creating exciting public displays and multimedia experiences enabling new forms of engagement with digital content. Interaction in public space and with public displays can be categorized in phases, each having specific requirements. Attracting, engaging and motivating the user are central design issues that are addressed in this paper. We provide a comprehensive analysis of the design space explaining mental models and interaction modalities and we conclude a taxonomy for interactive public display from this analysis. Our analysis and the taxonomy are grounded in a large number of research projects, art installations and experience. With our contribution we aim at providing a comprehensive guide for designers and developers of interactive multimedia on public displays.


title: Hypocretin (orexin) deficiency in human narcolepsy

abstract: Alterations in the hypocretin receptor 2 and preprohypocretin genes produce narcolepsy in animal models. Hypocretin was undetectable in seven out of nine people with narcolepsy, indicating abnormal hypocretin transmission.


title: Pathophysiology of depression and mechanisms of treatment

abstract: Major depression is a serious disorder of enormous sociological and clinical relevance. The discovery of antidepressant drugs in the 1950s led to the first biochemical hypothesis of depression, which suggested that an impairment in central monoaminergic function was the major lesion underlying the disorder. Basic research in all fields of neuroscience (including genetics) and the discovery of new antidepressant drugs have revolutionized our understanding of the mechanisms underlying depression and drug action. There is no doubt that the monoaminergic system is one of the cornerstones of these mechanisms, but multiple interactions with other brain systems and the regulation of central nervous system function must also be taken into account In spite of all the progress achieved so far, we must be aware that many open questions remain to be resolved in the future.


title: Type- and Content-Driven Synthesis of SQL Queries from Natural Language

abstract: This paper presents a new technique for automatically synthesizing SQL queries from natural language. Our technique is fully automated, works for any database without requiring additional customization, and does not require users to know the underlying database schema. Our method achieves these goals by combining natural language processing, program synthesis, and automated program repair. Given the user's English description, our technique first uses semantic parsing to generate a query sketch, which is subsequently completed using type-directed program synthesis and assigned a confidence score using database contents. However, since the user's description may not accurately reflect the actual database schema, our approach also performs fault localization and repairs the erroneous part of the sketch. This synthesize-repair loop is repeated until the algorithm infers a query with a sufficiently high confidence score. We have implemented the proposed technique in a tool called Sqlizer and evaluate it on three different databases. Our experiments show that the desired query is ranked within the top 5 candidates in close to 90% of the cases.


title: A novel model for credit card fraud detection using Artificial Immune Systems

abstract: We added Negative Selection, update and dataset analysis to base AIRS algorithm.Antigen affinity is calculated using a novel method.The model uses a cloud computing solution in order to perform the training phase parallel.We used scoring for flagged records which shows how risky a fraud-flagged record might be. The amount of online transactions is growing these days to a large number. A big portion of these transactions contains credit card transactions. The growth of online fraud, on the other hand, is notable, which is generally a result of ease of access to edge technology for everyone. There has been research done on many models and methods for credit card fraud prevention and detection. Artificial Immune Systems is one of them. However, organizations need accuracy along with speed in the fraud detection systems, which is not completely gained yet. In this paper we address credit card fraud detection using Artificial Immune Systems (AIS), and introduce a new model called AIS-based Fraud Detection Model (AFDM). We will use an immune system inspired algorithm (AIRS) and improve it for fraud detection. We increase the accuracy up to 25%, reduce the cost up to 85%, and decrease system response time up to 40% compared to the base algorithm.


title: Phase-Noise Analysis in Rotary Traveling-Wave Oscillators Using Simple Physical Model

abstract: We show that the rotary traveling wave oscillator (RTWO) is well treated as a superposition of multiple standing-wave oscillators (SWOs). Based on the proposed physical interpretation, we derive a phase-noise formula for the SWO, and extend it to the RTWO, which can predict thermally induced phase noise with no more complexity than the well-understood LC voltage-controlled oscillator. Measurement and simulation validate the analysis. The physically based approach and simple resulting expressions make it possible to design the RTWO for a given phase noise without lengthy simulations.


title: Fast Fourier Color Constancy

abstract: We present Fast Fourier Color Constancy (FFCC), a color constancy algorithm which solves illuminant estimation by reducing it to a spatial localization task on a torus. By operating in the frequency domain, FFCC produces lower error rates than the previous state-of-the-art by 13–20% while being 250-3000 times faster. This unconventional approach introduces challenges regarding aliasing, directional statistics, and preconditioning, which we address. By producing a complete posterior distribution over illuminants instead of a single illuminant estimate, FFCC enables better training techniques, an effective temporal smoothing technique, and richer methods for error analysis. Our implementation of FFCC runs at ~700 frames per second on a mobile device, allowing it to be used as an accurate, real-time, temporally-coherent automatic white balance algorithm.


title: A spectral BSSRDF for shading human skin

abstract: We present a novel spectral shading model for human skin. Our model accounts for both subsurface and surface scattering, and uses only four parameters to simulate the interaction of light with human skin. The four parameters control the amount of oil, melanin and hemoglobin in the skin, which makes it possible to match specific skin types. Using these parameters we generate custom wavelength dependent diffusion profiles for a two-layer skin model that account for subsurface scattering within the skin. These diffusion profiles are computed using convolved diffusion multipoles, enabling an accurate and rapid simulation of the subsurface scattering of light within skin. We combine the subsurface scattering simulation with a Torrance-Sparrow BRDF model to simulate the interaction of light with an oily layer at the surface of the skin. Our results demonstrate that this four parameter model makes it possible to simulate the range of natural appearance of human skin including African, Asian, and Caucasian skin types.


title: The use of computational intelligence in intrusion detection systems : A review

abstract: Intrusion detection based upon computational intelligence is currently attracting considerable interest from the research community. Characteristics of computational intelligence (CI) systems, such as adaptation, fault tolerance, high computational speed and error resilience in the face of noisy information, fit the requirements of building a good intrusion detection model. Here we want to provide an overview of the research progress in applying CI methods to the problem of intrusion detection. The scope of this review will encompass core methods of CI, including artificial neural networks, fuzzy systems, evolutionary computation, artificial immune systems, swarm intelligence, and soft computing. The research contributions in each field are systematically summarized and compared, allowing us to clearly define existing research challenges, and to highlight promising new research directions. The findings of this review should provide useful insights into the current IDS literature and be a good source for anyone who is interested in the application of CI approaches to IDSs or related fields.


title: Optimal Workload Allocation in Fog-Cloud Computing Toward Balanced Delay and Power Consumption

abstract: Mobile users typically have high demand on localized and location-based information services. To always retrieve the localized data from the remote cloud, however, tends to be inefficient, which motivates fog computing. The fog computing, also known as edge computing, extends cloud computing by deploying localized computing facilities at the premise of users, which prestores cloud data and distributes to mobile users with fast-rate local connections. As such, fog computing introduces an intermediate fog layer between mobile users and cloud, and complements cloud computing toward low-latency high-rate services to mobile users. In this fundamental framework, it is important to study the interplay and cooperation between the edge (fog) and the core (cloud). In this paper, the tradeoff between power consumption and transmission delay in the fog-cloud computing system is investigated. We formulate a workload allocation problem which suggests the optimal workload allocations between fog and cloud toward the minimal power consumption with the constrained service delay. The problem is then tackled using an approximate approach by decomposing the primal problem into three subproblems of corresponding subsystems, which can be, respectively, solved. Finally, based on simulations and numerical results, we show that by sacrificing modest computation resources to save communication bandwidth and reduce transmission latency, fog computing can significantly improve the performance of cloud computing.


title: ESIM: Edge Similarity for Screen Content Image Quality Assessment

abstract: In this paper, an accurate full-reference image quality assessment (IQA) model developed for assessing screen content images (SCIs), called the edge similarity (ESIM), is proposed. It is inspired by the fact that the human visual system (HVS) is highly sensitive to edges that are often encountered in SCIs; therefore, essential edge features are extracted and exploited for conducting IQA for the SCIs. The key novelty of the proposed ESIM lies in the extraction and use of three salient edge features—i.e., edge contrast , edge width , and edge direction . The first two attributes are simultaneously generated from the input SCI based on a parametric edge model, while the last one is derived directly from the input SCI. The extraction of these three features will be performed for the reference SCI and the distorted SCI, individually. The degree of similarity measured for each above-mentioned edge attribute is then computed independently, followed by combining them together using our proposed edge-width pooling strategy to generate the final ESIM score. To conduct the performance evaluation of our proposed ESIM model, a new and the largest SCI database (denoted as SCID) is established in our work and made to the public for download. Our database contains 1800 distorted SCIs that are generated from 40 reference SCIs. For each SCI, nine distortion types are investigated, and five degradation levels are produced for each distortion type. Extensive simulation results have clearly shown that the proposed ESIM model is more consistent with the perception of the HVS on the evaluation of distorted SCIs than the multiple state-of-the-art IQA methods.


title: Improving the performance of machine learning classifiers for Breast Cancer diagnosis based on feature selection

abstract: This paper proposed a comprehensive algorithm for building machine learning classifiers for Breast Cancer diagnosis based on the suitable combination of feature selection methods that provide high performance over the Area Under receiver operating characteristic Curve (AUC). The new developed method allows both for exploring and ranking search spaces of image-based features, and selecting subsets of optimal features for feeding Machine Learning Classifiers (MLCs). The method was evaluated using six mammography-based datasets (containing calcifications and masses lesions) with different configurations extracted from two public Breast Cancer databases. According to the Wilcoxon Statistical Test, the proposed method demonstrated to provide competitive Breast Cancer classification schemes reducing the number of employed features for each experimental dataset.


title: Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold Simplification

abstract: The latest deep learning approaches perform better than the state-of-the-art signal processing approaches in various image restoration tasks. However, if an image contains many patterns and structures, the performance of these CNNs is still inferior. To address this issue, here we propose a novel feature space deep residual learning algorithm that outperforms the existing residual learning. The main idea is originated from the observation that the performance of a learning algorithm can be improved if the input and/or label manifolds can be made topologically simpler by an analytic mapping to a feature space. Our extensive numerical studies using denoising experiments and NTIRE single-image super-resolution (SISR) competition demonstrate that the proposed feature space residual learning outperforms the existing state-of-the-art approaches. Moreover, our algorithm was ranked high in the NTIRE competition with 5-10 times faster computational time compared to the top ranked teams.


title: Major Cereal Grain Fibers and Psyllium in Relation to Cardiovascular Health

abstract: Numerous studies reveal the cardiovascular benefits of consuming dietary fiber and, especially, cereal fiber. Cereal fiber is associated with cardiovascular risk reduction through multiple mechanisms and consuming a variety of cereal fiber sources offers health benefits specific to the source. Certain cereal fibers have been studied more extensively than others and provide greater support for their incorporation into a healthful diet. β-glucan from oats or barley, or a combination of whole oats and barley, and soluble fiber from psyllium reduces the risk of coronary heart disease; inulin-type fructans added to foods and beverages may modestly decrease serum triacylglycerols; arabinoxylan and resistant starch may improve glycemic control. Individuals with low cereal fiber intake should increase their intake of whole grains in order to receive the benefits of whole grains in addition to fiber. For those adjusting to the texture and palatability of whole grains, turning to added-fiber products rich in β-glucan and psyllium may allow them to reach their fiber goals without increasing caloric intake.


title: Bioinformatics for the synthetic biology of natural products: integrating across the Design–Build–Test cycle

abstract: Covering: 2000 to 2016Progress in synthetic biology is enabled by powerful bioinformatics tools allowing the integration of the design, build and test stages of the biological engineering cycle. In this review we illustrate how this integration can be achieved, with a particular focus on natural products discovery and production. Bioinformatics tools for the DESIGN and BUILD stages include tools for the selection, synthesis, assembly and optimization of parts (enzymes and regulatory elements), devices (pathways) and systems (chassis). TEST tools include those for screening, identification and quantification of metabolites for rapid prototyping. The main advantages and limitations of these tools as well as their interoperability capabilities are highlighted.


title: Using the Surface Reflectance MODIS Terra Product to Estimate Turbidity in Tampa Bay, Florida

abstract: Turbidity is a commonly-used index of the factors that determine light penetration in the water column. Consistent estimation of turbidity is crucial to design environmental and restoration management plans, to predict fate of possible pollutants, and to estimate sedimentary fluxes into the ocean. Traditional methods monitoring fixed geographical locations at fixed intervals may not be representative of the mean water turbidity in estuaries between intervals, and can be expensive and time consuming. Although remote sensing offers a good solution to this limitation, it is still not widely used due in part to required complex processing of imagery. There are satellite-derived products, including the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra surface reflectance daily product (MOD09GQ) Band 1 (620–670 nm) which are now routinely available at 250 m spatial resolution and corrected for atmospheric effect. This study shows this product to be useful to estimate turbidity in Tampa Bay, Florida, after rainfall events (R2 = 0.76, n = 34). Within Tampa Bay, Hillsborough Bay (HB) and Old Tampa Bay (OTB) presented higher turbidity compared to Middle Tampa Bay (MTB) and Lower Tampa Bay (LTB).


title: Underwater Image Enhancement Using an Integrated Colour Model

abstract: In underwater situations, clarity of images are degraded by light absorption and scattering. This causes one colour to dominate the image. In order to improve the perception of underwater images, we proposed an approach based on slide stretching. The objective of this approach is twofold. Firstly, the contrast stretching of RGB algorithm is applied to equalize the colour contrast in images. Secondly, the saturation and intensity stretching of HSI is used to increase the true colour and solve the problem of lighting. Interactive software has been developed for underwater image enhancement. Results of the software are presented in this paper.


title: Design Techniques for a 60-Gb/s 288-mW NRZ Transceiver With Adaptive Equalization and Baud-Rate Clock and Data Recovery in 65-nm CMOS Technology

abstract: Design techniques for a complete 60-Gb/s non-return-to-zero transceiver with adaptive equalization as well as baud-rate clock and data recovery (CDR) are demonstrated. A complete equalization front end with per-path adaptation and per-sampler offset calibration enables 60-Gb/s operation over realistic channels. Current integration in the front end for energy-efficient equalization is combined with integration phase dithering to realize a robust baud-rate CDR. Correlation of the adaptive error sampler output with the phase dithering sequence indicates the direction of phase offset, and the resulting baud-rate CDR saves power and complexity compared to an oversampling CDR by not requiring additional clock phases/deserializers. The proposed 65-nm CMOS transceiver operates at 60 Gb/s with an eye opening of 30% UI and consumes 288 mW while equalizing 21 dB of loss at 30 GHz over a 0.7-m Twinax cable.


title: Speech recognition with deep recurrent neural networks

abstract: Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist Temporal Classification make it possible to train RNNs for sequence labelling problems where the input-output alignment is unknown. The combination of these methods with the Long Short-term Memory RNN architecture has proved particularly fruitful, delivering state-of-the-art results in cursive handwriting recognition. However RNN performance in speech recognition has so far been disappointing, with better results returned by deep feedforward networks. This paper investigates deep recurrent neural networks, which combine the multiple levels of representation that have proved so effective in deep networks with the flexible use of long range context that empowers RNNs. When trained end-to-end with suitable regularisation, we find that deep Long Short-term Memory RNNs achieve a test set error of 17.7% on the TIMIT phoneme recognition benchmark, which to our knowledge is the best recorded score.


title: Learning rotation invariance in deep hierarchies using circular symmetric filters

abstract: Deep hierarchical models for feature learning have emerged as an effective technique for object representation and classification in recent years. Though the features learnt using deep models have shown lot of promise towards achieving invariance to data transformations, this primarily comes at the expense of using much larger training data and model size. In the proposed work we devise a novel technique to incorporate rotation invariance, while training the deep model parameters. The convolution weight parameters in the network architecture are constrained to exhibit circular symmetry resulting in “rotation equivariance” of output feature maps. Rotation invariance is further achieved by max-pooling of the feature maps later in the hierarchy. We also show that by incorporating circular symmetry constraint into the training loss function, rotation invariance can be achieved with-in deep neural network framework with much lesser training data and model parameters. Our experiment results evaluated on rotated MNIST dataset further objectively validate the contribution.


title: Coding, playgrounds and literacy in early childhood education: The development of KIBO robotics and ScratchJr

abstract: This paper describes two programming environments explicitly designed for early childhood education, the screen-based ScratchJr; and the tangible robotic kit KIBO. Both of these tools were explicitly designed to support the learning of concepts and skills of computer science and engineering in a developmentally appropriate way. The design principles are based on the notion of "coding as playground" and "coding as literacy".


title: A survey on vehicular cloud computing

abstract: Vehicular networking has become a significant research area due to its specific features and applications such as standardization, efficient traffic management, road safety and infotainment. Vehicles are expected to carry relatively more communication systems, on board computing facilities, storage and increased sensing power. Hence, several technologies have been deployed to maintain and promote Intelligent Transportation Systems (ITS). Recently, a number of solutions were proposed to address the challenges and issues of vehicular networks. Vehicular Cloud Computing (VCC) is one of the solutions. VCC is a new hybrid technology that has a remarkable impact on traffic management and road safety by instantly using vehicular resources, such as computing, storage and internet for decision making. This paper presents the state-of-the-art survey of vehicular cloud computing. Moreover, we present a taxonomy for vehicular cloud in which special attention has been devoted to the extensive applications, cloud formations, key management, inter cloud communication systems, and broad aspects of privacy and security issues. Through an extensive review of the literature, we design an architecture for VCC, itemize the properties required in vehicular cloud that support this model. We compare this mechanism with normal Cloud Computing (CC) and discuss open research issues and future directions. By reviewing and analyzing literature, we found that VCC is a technologically feasible and economically viable technological shifting paradigm for converging intelligent vehicular networks towards autonomous traffic, vehicle control and perception systems.


title: Experimental Evaluation of Body Channel Response and Digital Modulation Schemes for Intra-body Communications

abstract: Intra-body communications is a short range communication solution suitable for Body Area Networks wherein the human body is used as the transmission medium. Currently, some studies have succeeded to transmit and receive simple information. However, intra-body communications could also provide broadband applications such as video streaming which will need high data rates. In this paper, firstly we investigate the propagation characteristics of the human body as a conductor of radio frequency signals. Afterwards, we evaluate five digital modulation schemes for intra-body communications. In the experiments, several distances between transmitter and receiver, symbol rates and frequency carriers are considered to investigate the optimal modulation scheme and the maximum transmission rate achievable. Constellation and eye diagrams and error vector magnitude are the performance parameters measured to evaluate the modulation accuracy.


title: Multi-User Beamforming and Ground Station Deployment for 5G Direct Air-to-Ground Communication

abstract: On-board of aircraft is one of the last venues without high-speed connectivity, which makes it an important problem to address for both industry and academia. To this end, direct air-to-ground communications (DA2GC), where communication link is provided via direct link between aircraft and ground station, is a promising solution to provide high capacity and low latency backhaul capacity for aircraft. In this paper, we investigate the ground station deployment problem to provide 1.2 Gbps average backhaul capacity for each aircraft. The feasible operation points for the key network parameters: the number of ground stations, antenna array size, transmit power and bandwidth, are calculated. In addition, we propose a multi-user beamforming algorithm for dual-polarized hybrid DA2GC antenna arrays, and derive a tractable expression for the DA2GC cell throughput.


Understanding the HyperLogLog : a Near-Optimal Cardinality Estimation Algorithm


Perception of levels of emotion in prosody


Vision-based Deep Reinforcement Learning


title: A Survey of Automated Text Simplification

abstract: Text simplification modifies syntax and lexicon to ::: improve the understandability of language for an end user. ::: This survey identifies and classifies simplification research within ::: the period 1998-2013. Simplification can be used for many ::: applications, including: Second language learners, preprocessing ::: in pipelines and assistive technology. There are many approaches ::: to the simplification task, including: lexical, syntactic, statistical ::: machine translation and hybrid techniques. This survey also ::: explores the current challenges which this field faces. Text ::: simplification is a non-trivial task which is rapidly growing into ::: its own field. This survey gives an overview of contemporary ::: research whilst taking into account the history that has brought ::: text simplification to its current state.


title: A Systems Theoretic Approach to the Security Threats in Cyber Physical Systems Applied to Stuxnet

abstract: Cyber physical systems (CPSs) are increasingly being adopted in a wide range of industries such as smart power grids. Even though the rapid proliferation of CPSs brings huge benefits to our society, it also provides potential attackers with many new opportunities to affect the physical world such as disrupting the services controlled by CPSs. Stuxnet is an example of such an attack that was designed to interrupt the Iranian nuclear program. In this paper, we show how the vulnerabilities exploited by Stuxnet could have been addressed at the design level. We utilize a system theoretic approach, based on prior research on system safety, that takes both physical and cyber components into account to analyze the threats exploited by Stuxnet. We conclude that such an approach is capable of identifying cyber threats towards CPSs at the design level and provide practical recommendations that CPS designers can utilize to design a more secure CPS.


title: Stimulation of granulopoiesis in patients with malignancy by recombinant human granulocyte-macrophage colony-stimulating factor: assessment of two routes of administration.

abstract: We administered Escherichia coli-derived recombinant human granulocyte-macrophage colony-stimulating factor to 61 patients with malignancy, 36 of whom had normal peripheral blood counts and 25 of whom had peripheral cytopenia due to underlying bone marrow disease, to compare the efficacy of two different routes of administration to stimulate the in vivo granulopoiesis: i.e., continuous i.v. infusion and s.c. injection. Three well-tolerated dose levels were investigated. Application of granulocyte-macrophage colony-stimulating factor resulted in dose-dependent increases in circulating neutrophils, eosinophils, and monocytes and an increase in bone marrow cellularity, irrespective of route of administration. In some patients, mild side effects, including bone pain, dyspnea, flu-like symptoms, and a decrease of platelet counts, were recorded, but they were less pronounced when the hormone was administered subcutaneously.


title: Phenotypes associated with female X chromosome aneuploidy in UK Biobank: an unselected, adult, population-based cohort

abstract: Women with X chromosome aneuploidy such as 45,X (Turner syndrome) or 47,XXX (Triple X syndrome) present with a range of characteristics including differences in stature, an increased risk of cardiovascular disease and premature ovarian insufficiency. Many women with X chromosome aneuploidy undergo lifetime clinical monitoring for possible complications. However, biased ascertainment of cases may mean that the penetrance of phenotypes is overestimated. We aimed to characterise the prevalence and phenotypic consequences of X chromosome aneuploidy in a large population of older adults. We detected 30 women with 45,X, 186 with mosaic 45,X/46,XX and 110 with 47,XXX in 245,203 women from UK Biobank, using SNP array data. The phenotypic features of women with full aneuploidy (whether 45,X or 47,XXX) were similar to those previously reported. Consistent with the recognised Turner syndrome phenotype, those with 45,X were 17.2cm shorter than controls and 53% did not go through menarche. Similarly, the phenotype of women with 47,XXX included increased height (on average 5.3cm taller than controls, P = 1 x 10-18), earlier menopause age (on average 5.12 years earlier than controls, P = 1.2 x 10-14) and a lower fluid intelligence (on average 24% lower than controls, P = 3.7 x 10-8). In contrast, women with 45,X/46,XX mosaicism had a very mild phenotype; were not as short, had a normal reproductive lifespan and birth rate, with no reported cardiovascular complications. This study characterises X chromosome aneuploidy phenotypes in an adult population-based sample of older individuals and suggests that clinical management of women with a 45,X/46,XX mosaic karyotype should be minimal, particularly those identified incidentally.


title: Framework of CANopen Protocol for a Hybrid Electric Vehicle

abstract: This paper discusses the profile framework of CANopen protocol for a hybrid electric vehicle (HEV). Based on the analysis of the CANopen protocol and the HEV system structure, a preliminary version of CANopen profiles for the HEV is firstly drafted out. Then the object dictionary for each HEV module and communication mechanism of the vehicle network is described in detail. Finally the CANopen profile's fault diagnostic modes of HEV based on OBD-II and SAE J1939 are set up. Finally the attempt to implement the profile in slave node of electromotor in HEV based on the DSP of TMS320F2812.


title: Edge and Data Adaptive Equalization of Serial-Link Transceivers

abstract: Limited channel bandwidth introduces inter-symbol interference (ISI) at both data and edge samples. In addition to the ISI at data samples, ISI at the edge samples (edge ISI) increases the bit error rate (BER) by degrading on the eye diagram and increasing the jitter of the clock and data recovery (CDR). This work proposes a forward FIR equalizer and a decision-feedback equalizer (DFE) that compensate for both data and edge samples. To adapt both the data and edge equalizers, a modified LMS adaptation algorithm is introduced to achieve convergence. A transmitter and receiver are implemented in 0.13 mum and 0.18 mum technologies respectively. The edge ISI is improved by 20% and the jitter is improved by 10% in measurement. The link operates over a 120'' FR4 channel with 24 dB attenuation at Nyquist frequency, and the BER is below 10-14 at 3.6 Gb/s.


The Effect of Attention Cueing on Science Text Learning


title: Leveraging Edge Computing through Collaborative Machine Learning

abstract: The Internet of Things (IoT) offers the ability to analyze and predict our surroundings through sensor networks at the network edge. To facilitate this predictive functionality, Edge Computing (EC) applications are developed by considering: power consumption, network lifetime and quality of context inference. Humongous contextual data from sensors provide data scientists better knowledge extraction, albeit coming at the expense of holistic data transfer that threatens the network feasibility and lifetime. To cope with this, collaborative machine learning is applied to EC devices to (i) extract the statistical relationships and (ii) construct regression (predictive) models to maximize communication efficiency. In this paper, we propose a learning methodology that improves the prediction accuracy by quantizing the input space and leveraging the local knowledge of the EC devices.


title: Clustering large datasets using K-means modified inter and intra clustering (KM-I2C) in Hadoop

abstract: Big data has become popular for processing, storing and managing massive volumes of data. The clustering of datasets has become a challenging issue in the field of big data analytics. The K-means algorithm is best suited for finding similarities between entities based on distance measures with small datasets. Existing clustering algorithms require scalable solutions to manage large datasets. This study presents two approaches to the clustering of large datasets using MapReduce. The first approach, K-Means Hadoop MapReduce (KM-HMR), focuses on the MapReduce implementation of standard K-means. The second approach enhances the quality of clusters to produce clusters with maximum intra-cluster and minimum inter-cluster distances for large datasets. The results of the proposed approaches show significant improvements in the efficiency of clustering in terms of execution times. Experiments conducted on standard K-means and proposed solutions show that the KM-I2C approach is both effective and efficient.


title: Gender and Nasal Shape: Measures for Rhinoplasty

abstract: BACKGROUND ::: Gender-specific nasal shapes are recommended for rhinoplasty. This study was conducted to clarify whether there truly are gender-related differences and to determine optimal nasal shapes in a Caucasian population. ::: ::: ::: METHODS ::: The authors created female and male composite photographs of "average" (n = 128 each), "optimal" (n = 16 each), and "most unpleasant" (n = 8 each) noses stratified on the basis of each photographed subject's (n = 311) own evaluation of the attractiveness of her or his nose, using a visual analogue scale. These composites were also assessed by 308 independent judges. ::: ::: ::: RESULTS ::: Optimal female noses showed a horizontally and vertically lower nasion and were concave to straight in profile as compared with optimal male noses, which had a vertically and horizontally higher nasion and a straight profile. A supratip break was not found in any of the composites. At least half of the judges rated average and optimal male composite noses as female. A significant majority mistook the composite of the most unpleasant female noses as male (frontal view, 62.0 percent; lateral view, 72.4 percent; p < 0.001). Optimal and average female and male noses were found to be independently significantly more attractive than the most unpleasant ones (p < 0.001, n = 308 judges). Women and men with a straight or concave profile were significantly (p = 0.017 and p = 0.006, respectively) more satisfied with the appearance of their nose than those with nasal humps. ::: ::: ::: CONCLUSIONS ::: Gender-related differences in nasal shape appear to be subtle, with nasion position being one of the main factors. A nasal hump and a supratip break are not desirable.


title: Modeling Multi-turn Conversation with Deep Utterance Aggregation

abstract: Multi-turn conversation understanding is a major challenge for building intelligent dialogue systems. This work focuses on retrieval-based response matching for multi-turn conversation whose related work simply concatenates the conversation utterances, ignoring the interactions among previous utterances for context modeling. In this paper, we formulate previous utterances into context using a proposed deep utterance aggregation model to form a fine-grained context representation. In detail, a self-matching attention is first introduced to route the vital information in each utterance. Then the model matches a response with each refined utterance and the final matching score is obtained after attentive turns aggregation. Experimental results show our model outperforms the state-of-the-art methods on three multi-turn conversation benchmarks, including a newly introduced e-commerce dialogue corpus.


title: EMNIST: Extending MNIST to handwritten letters

abstract: The MNIST dataset has become a standard benchmark for learning, classification and computer vision systems. Contributing to its widespread adoption are the understandable and intuitive nature of the task, the relatively small size and storage requirements and the accessibility and ease-of-use of the database itself. The MNIST database was derived from a larger dataset known as the NIST Special Database 19 which contains digits, uppercase and lowercase handwritten letters. This paper introduces a variant of the full NIST dataset, which we have called Extended MNIST (EMNIST), which follows the same conversion paradigm used to create the MNIST dataset. The result is a dataset that constitutes a more challenging classification task involving letters and digits, and one that shares the same image structure and parameters as the original MNIST task, allowing for direct compatibility with all existing classifiers and systems. Benchmark results using an online ELM algorithm are presented along with a validation of the conversion process through the comparison of the classification results on NIST digits and the MNIST digits.


title: Concept Cartesianism, Concept Pragmatism, and Frege Cases

abstract: This paper concerns the dialectal role of Frege Cases in the debate between Concept Cartesians and Concept Pragmatists. I take as a starting point Christopher Peacocke’s argument that, unlike Cartesianism, his ‘Fregean’ Pragmatism can account for facts about the rationality and epistemic status of certain judgments. I argue that since this argument presupposes that the rationality of thoughts turn on their content, it is thus question-begging against Cartesians, who claim that issues about rationality turn on the form, not the content, of thoughts. I then consider Jerry Fodor’s argument that ‘modes of presentation’ are not identical with Fregean senses, and argue that explanatory considerations should leads us to reject his ‘syntactic’ treatment of Frege cases. Rejecting the Cartesian treatment of Frege cases, however, is not tantamount to accepting Peacocke’s claim that reasons and rationality are central to the individuation of concepts. For I argue that we can steer a middle course between Fodor’s Cartesianism and Peacocke’s Pragmatism, and adopt a form of Pragmatism that is constrained by Fregean considerations, but at the same time denies that concepts are constitutively tied to reasons and rationality.


title: End-to-End Dialogue with Sentiment Analysis Features

abstract: Psychiatric assistance for suicide prevention does not have a wide enough reach to help the number of victims who commit suicide every year. To help people cope with suicidal thoughts when formal care is unavailable, we propose an artificial intelligence, text-based conversational agent that generates responses similar to those of a counselor. The application will offer a temporary channel for expression that serves as a transition to speaking with a professional psychiatrist. We expand upon existing approaches by utilizing sentiment analysis data, or scores that rank the emotional content of users’ text input, when generating responses. We also train a response generation system based on a dataset of counseling and therapy transcripts. We posit that inclusion of sentiment analysis data provides marginally better responses based on quantitative metrics of quality. We hope our results will advance realistic conversation modeling and promote further research into its humanitarian applications.


title: Event-Driven Headline Generation

abstract: We propose an event-driven model for headline generation. Given an input document, the system identifies a key event chain by extracting a set of structural events that describe them. Then a novel multi-sentence compression algorithm is used to fuse the extracted events, generating a headline for the document. Our model can be viewed as a novel combination of extractive and abstractive headline generation, combining the advantages of both methods using event structures. Standard evaluation shows that our model achieves the best performance compared with previous state-of-the-art systems.


title: Simulation of a laser modulator driven by NRZ pulses

abstract: In an earlier paper we presented a mathematical model of distributed feedback (DFB) and distributed Braggs reflector (DBR) semiconductor lasers that are integrated with an electroabsorption modulator and showed numerical solutions of this model for a sinusoidal modulator drive voltage. In this paper we use the model to demonstrate its behavior for a more realistic nonreturn-to-zero (NRZ) drive voltage of the modulator. In particular, we use the laser-modulator combination to launch the NRZ pulse train into a dispersive fiber and display the resulting pulse distortion by means of eye diagrams. As in the earlier paper, the objective of this study is to explore the interaction between the laser and the modulator in response to a residual reflection at the modulator output that provides unwanted optical feedback between the two devices. We find that frequency modulation of the laser due to optical feedback between the laser and the modulator is the principal cause of pulse distortion if the pulses are launched into dispersive fibers. However, the achievable transmission distance depends surprisingly strongly on the shape of the input pulses. Rectangular pulses with flat tops suffer far less distortion than pulses with more rounded edges.


title: Range correlation and I/Q performance benefits in single-chip silicon Doppler radars for noncontact cardiopulmonary monitoring

abstract: Direct-conversion microwave Doppler-radar transceivers have been fully integrated in 0.25-/spl mu/m silicon CMOS and BiCMOS technologies. These chips, operating at 1.6 and 2.4 GHz, have detected movement due to heartbeat and respiration 50 cm from the subject, which may be useful in infant and adult apnea monitoring. The range-correlation effect on residual phase noise is a critical factor when detecting small phase fluctuations with a high-phase-noise on-chip oscillator. Phase-noise reduction due to range correlation was experimentally evaluated, and the measured residual phase noise was within 5 dB of predicted values on average. In a direct-conversion receiver, the phase relationship between the received signal and the local oscillator has a significant effect on the demodulation sensitivity, and the null points can be avoided with a quadrature (I/Q) receiver. In this paper, measurements that highlight the performance benefits of an I/Q receiver are presented. While the accuracy of the heart rate measured with the single-channel chip ranges from 40% to 100%, depending on positioning, the quadrature chip accuracy is always better than 80%.


title: Runway to Realway: Visual Analysis of Fashion

abstract: Clothing and fashion are an integral part of our everyday lives. In this paper we present an approach to studying fashion both on the runway and in more real-world settings, computationally, and at large scale, using computer vision. Our contributions include collecting a new runway dataset, designing features suitable for capturing outfit appearance, collecting human judgments of outfit similarity, and learning similarity functions on the features to mimic those judgments. We provide both intrinsic and extrinsic evaluations of our learned models to assess performance on outfit similarity prediction as well as season, year, and brand estimation. An example application tracks visual trends as runway fashions filter down to "real way" street fashions.


title: Orthogonal learning particle swarm optimization with variable relocation for dynamic optimization

abstract: Many real-world optimization problems encounter the presence of uncertainties. Dynamic optimization is a class of problems whose fitness functions vary through time. For these problems, evolutionary algorithm is expected to adapt to the changing environments immediately and find the best solution accurately. Besides, most of the environmental changes may not be too drastic in real-world applications, which indicates the evolutionary information in the past may be helpful for finding the optimum solution in the new environment. As a result, effective reuse of historical evolutionary information is necessary, since it leads a faster convergence after a change has occurred. This paper develops an orthogonal learning particle swarm optimization (OLPSO) with the variable relocation strategy (VRS) to solve the dynamic optimization problem (DOP). The proposed OLPSO-VRS algorithm has two advantages as follows. First and foremost, the orthogonal learning strategy guides particles to fly in better directions by constructing a much promising and efficient exemplar, which can achieve a balance between fast convergence and population diversity. Furthermore, the VRS collects historical information in the stable search stage and then reuse such information to guide the particle variable relocation once the search environment has changed. This operation enables the algorithm to quickly shift to the new promising regions in the changing fitness landscape. We evaluated OLPSO-VRS on several dynamic benchmark problems and compared with several state-of-the-art dynamic algorithms. The results show that OLPSO-VRS obtains very competitive results and enjoys a statistically superior performance on most problems.


The Effect of Edmodo Learning Network on Students ’ Perception , Self-Regulated Learning Behaviors and Learning Performance


title: A Survey of Techniques for Internet Topology Discovery

abstract: Capturing an accurate view of the Internet topology is of great interest to the networking research community as it has many uses ranging from the design and evaluation of new protocols and services to the vulnerability analysis of the network's infrastructure. However, the scale of today's Internet coupled with its distributed and heterogeneous nature makes it very challenging to acquire a complete and accurate snapshot of the topology. The purpose of this survey is to examine the main research studies that have been conducted on topics related to Internet topology discovery in the last 15–20 years and present some of the main lessons learned from these past efforts. To this end, we classify these prior studies according to the “resolution” or “level” of the topology; that is, interface-level, router-level, PoP-level and AS-level. For each resolution, we describe the main techniques and tools used for data collection, identify their major limitations and issues, and discuss the key implications that these limitations have on the quality of the collected data. In the process, we present the latest efforts in modeling the Internet's topology at the different levels and report on the role that geographic characteristics play in this context. We present the lessons learned as a checklist that every researcher working on Internet topology discovery-related problems should consult to minimize the risk of repeating some of the same or similar mistakes that have been made in the past and as a result have hampered progress in this important area of Internet research.


title: Low Dimensional Manifold Model for Image Processing

abstract: In this paper, we propose a novel low dimensional manifold model (LDMM) and apply it to some image processing problems. LDMM is based on the fact that the patch manifolds of many natural images have low dimensional structure. Based on this fact, the dimension of the patch manifold is used as a regularization to recover the image. The key step in LDMM is to solve a Laplace--Beltrami equation over a point cloud which is solved by the point integral method. The point integral method enforces the sample point constraints correctly and gives better results than the standard graph Laplacian. Numerical simulations in image denoising, inpainting, and superresolution problems show that LDMM is a powerful method in image processing.


title: Meta-analysis of the heritability of human traits based on fifty years of twin studies

abstract: Despite a century of research on complex traits in humans, the relative importance and specific nature of the influences of genes and environment on human traits remain controversial. We report a meta-analysis of twin correlations and reported variance components for 17,804 traits from 2,748 publications including 14,558,903 partly dependent twin pairs, virtually all published twin studies of complex traits. Estimates of heritability cluster strongly within functional domains, and across all traits the reported heritability is 49%. For a majority (69%) of traits, the observed twin correlations are consistent with a simple and parsimonious model where twin resemblance is solely due to additive genetic variation. The data are inconsistent with substantial influences from shared environment or non-additive genetic variation. This study provides the most comprehensive analysis of the causes of individual differences in human traits thus far and will guide future gene-mapping efforts. All the results can be visualized using the MaTCH webtool.


title: ECOTOURISM AND ECONOMIC INCENTIVES AN EMPIRICAL APPROACH

abstract: Within the new array of ‘green’ products and services, ecotourism claims to combine environmental responsibility with the generation of local economic benefits that will have both a development impact and serve as conservation incentives. Economic incentives are imperative for nature conservation, particularly in remote and ill-monitored regions where a weak presence of the state hinders the use of alternative tools of environmental regulation. In the following, the link between tourism, local benefits and conservation is conceptualised and analysed empirically, using data from the Cuyabeno Wildlife Reserve in the Ecuadorian Amazon region, near the border of Colombia and Peru. Three Cuyabeno indigenous groups have developed different modes of tourism participation, ranging from autonomous operations to pure salary employment. A quantification of local cash flows from tourism allows for a comparative analysis of income structure, spending, and the impacts on local development and on conservation attitudes. It is concluded that in the whole study area, tourism has actually provided significant additional income. Counter to common belief, the mode of participation is less decisive for local income generation than the tourist attraction of the natural site, the degree of tourism specialisation and the level of local organisation. However, as a conservation incentive, the effectiveness of tourism income depends on the incentive structure inherent in the mode of participation, and on the substitution versus complementarity of other productive activities: only if tourism changes labour and land allocation decisions, will it have a local conservation impact. It is discussed under which circumstances the conjectured link between tourism, local incomes and conservation is likely to be effective. This leads to some general lessons for government policies, for the design of integrated conservation and development projects (ICDPs), and to a number of site-specific recommendations for improving incentive structures. © 2000 Elsevier Science B.V. All rights reserved.