title: Composite Decoupling Control of Gimbal Servo System in Double-Gimbaled Variable Speed CMG Via Disturbance Observer

abstract: In order to realize the decoupling and disturbance rejection control of the inner and outer gimbal system under two working conditions of a double-gimbaled variable speed control moment gyroscope (DGVSCMG), this paper presents a composite controller based on the disturbance observer and state feedback control. We first developed a dynamic model of a double-gimbal servo system, and analyzed the system characteristics of coupling and mismatched disturbance in CMG and flywheel working modes. Then, we propose a novel decoupling method through regarding the coupling term as systems lumped disturbance. The system disturbance was estimated by the designed disturbance observer and was rejected via the proposed composite controller combining a state feedback method. Finally, we proved the overall system stability by using a Lyapunov approach and derived an estimated upper bound of the state variables to guide controller parameters selection. We concluded from simulation and experimental results that the proposed method was effective in decoupling and disturbance compensation control of the DGVSCMG double-gimbal servo system, and enhanced system disturbance rejection and speed servo performance.


title: A Dual-Band Highly Miniaturized Patch Antenna

abstract: A highly miniaturized dual-band patch antenna is proposed for small form factor devices. The antenna is designed to cover two wireless local area network bands at 2.4 and 5.2 GHz. The antenna is miniaturized using a shorting post and a novel defected ground structure (DGS). The patch size is 8.8 × 10 mm2, which means 74% miniaturization ratio is achieved at 2.4 GHz. The dual-band property is achieved by etching a U-slot in the ground as a part of the proposed DGS. The antenna is fabricated on Rogers RO4350 board, and its radiation characteristics are measured. The measured results have good agreement with simulated ones.


title: Research on deep neural network's hidden layers in phoneme recognition

abstract: In spite of the great success of the deep neural network (DNN) in speech processing, it is still unclear what kind of underlying mechanisms are involved in this achievement. This preliminary study attempts to find an answer by investigating the functions of DNN's hidden layers in representing speech articulations. Two sets of experiments are performed on the hidden layers in speech recognition. The layer removing experiment is conducted on the English TIMIT database, and the layer replacing experiment is to substitute a layer in an English DNN by the corresponding layer in a Japanese DNN. It is found that the different layers seem to be responsible for different phoneme groups according to the place of articulation. The lower layers are responsible for the back vowels, and the higher layers are responsible for the front vowels. The second layer (i.e. the first hidden layer) of the seven-layer network has major responsibility for more than half of the consonants with the constriction located in the front of the vocal tract, while the other consonants rely on the middle and higher layers. The layer replacing experiment demonstrated that the above relation was language independent. It is necessary to design elaborate studies to discover more details in the future.


title: Multiple documents summarization based on evolutionary optimization algorithm

abstract: This paper proposes an optimization-based model for generic document summarization. The model generates a summary by extracting salient sentences from documents. This approach uses the sentence-to-document collection, the summary-to-document collection and the sentence-to-sentence relations to select salient sentences from given document collection and reduce redundancy in the summary. To solve the optimization problem has been created an improved differential evolution algorithm. The algorithm can adjust crossover rate adaptively according to the fitness of individuals. We implemented the proposed model on multi-document summarization task. Experiments have been performed on DUC2002 and DUC2004 data sets. The experimental results provide strong evidence that the proposed optimization-based approach is a viable method for document summarization.


title: Monte Carlo Methods on Advanced Computer Architectures

abstract: Monte Carlo methods describe a wide class of computational methods that utilize random numbers to perform a statistical simulation of a physical problem, which itself need not be a stochastic process (1). For example, Monte Carlo can be used to evaluate definite integrals, which are not stochastic processes, or may be used to simulate the transport of electrons in a space vehicle, which is a stochastic process. The name Monte Carlo came about during the Manhattan Project to describe the new mathematical methods being developed which had some similarity to the games of chance played in the casinos of Monte Carlo. Particle transport Monte Carlo is just one application of Monte Carlo methods, and will be the subject of this review paper. Other applications of Monte Carlo, such as reliability studies, classical queueing theory, molecular structure, the study of phase transitions, or quantum chromodynamics calculations for basic research in particle physics, are not included in this review. The reference by Kalos (1) is an introduction to general Monte Carlo methods and references to other applications of Monte Carlo can be found in this excellent book. For the remainder of this paper, the term Monte Carlo will be synonymous to particle transport Monte Carlo, unless otherwise noted.


title: Ellipse Detection for Visual Cyclists Analysis “In the Wild”

abstract: Autonomous driving safety is becoming a paramount issue due to the emergence of many autonomous vehicle prototypes. The safety measures ensure that autonomous vehicles are safe to operate among pedestrians, cyclists and conventional vehicles. While safety measures for pedestrians have been widely studied in literature, little attention has been paid to safety measures for cyclists. Visual cyclists analysis is a challenging problem due to the complex structure and dynamic nature of the cyclists. The dynamic model used for cyclists analysis heavily relies on the wheels. In this paper, we investigate the problem of ellipse detection for visual cyclists analysis in the wild. Our first contribution is the introduction of a new challenging annotated dataset for bicycle wheels, collected in real-world urban environment. Our second contribution is a method that combines reliable arcs selection and grouping strategies for ellipse detection. The reliable selection and grouping mechanism leads to robust ellipse detections when combined with the standard least square ellipse fitting approach. Our experiments clearly demonstrate that our method provides improved results, both in terms of accuracy and robustness in challenging urban environment settings.


title: Cloud Computing - The Business Perspective

abstract: If cloud computing (CC) is to achieve its potential, there needs to be a clear understanding of the various issues involved, both from the perspectives of the providers and the consumers of the technology. There is an equally urgent need for understanding the business-related issues surrounding CC. We interviewed several industry executives who are either involved as developers or are evaluating CC as an enterprise user. We identify the strengths, weaknesses, opportunities and threats for the industry. We also identify the various issues that will affect the different stakeholders of CC. We issue a set of recommendations for the practitioners who will provide and manage this technology. For IS researchers, we outline the different areas of research that need attention so that we are in a position to advise the industry in the years to come. Finally, we outline some of the key issues facing governmental agencies who will be involved in the regulation of cloud computing.


title: Classifying photographic and photorealistic computer graphic images using natural image statistics. ADVENT

abstract: As computer graphics (CG) is getting more photorealistic, for the purpose of image authentication, it becomes increasingly important to construct a detector for classifying photographic images (PIM) and photorealistic computer graphics (PRCG). To this end, we propose that photographic images contain natural-imaging quality (NIQ) and natural-scene quality (NSQ). NIQ is due to the imaging process, while NSQ is due to the subtle physical light transport in a real-world scene. We explicitly model NSQ of photographic images using natural image statistics (NIS). NIS has been used as an image prior in applications such as image compression and denoising. However, NIS has not been comprehensively and systematically employed for classifying PIM and PRCG. In this work, we study three types of NIS with different statistical order, i.e., NIS derived from the power spectrum, wavelet transform and local patch of images. The experiment shows that the classification is in line with the statistical order of the NIS. The local patch NIS achieves a classification accuracy of 83% which outperforms the features derived from modeling the characteristics of computer graphics.


title: Experimental Study of Friction in a Pneumatic Actuator at Constant Velocity

abstract: This paper describes an experimental method of determining sliding friction forces in a pneumatic actuator. Several empirical and semi-empirical friction models are evaluated using measured friction force data. A repeatability study is also performed to qualitatively assess friction randomness and a change in friction regimes.


title: Soft tissue cephalometric analysis: diagnosis and treatment planning of dentofacial deformity.

abstract: Abstract This article will present a new soft tissue cephalometric analysis tool. This analysis may be used by the orthodontist and surgeon as an aid in diagnosis and treatment planning. The analysis is a radiographic instrument that was developed directly from the philosophy expressed in Arnett and Bergman “Facial keys to orthodontic diagnosis and treatment planning, Parts I and II” (Am J Orthop Dentofacial Orthod 1993; 103:299-312 and 395-411). The novelty of this approach, as with the “Facial Keys” articles, is an emphasis on soft tissue facial measurement. (Am J Orthod Dentofacial Orthop 1999;116:239-53)


title: Quorum sensing in bacteria.

abstract: Quorum sensing is the regulation of gene expression in response to fluctuations in cell-population density. Quorum sensing bacteria produce and release chemical signal molecules called autoinducers that increase in concentration as a function of cell density. The detection of a minimal threshold stimulatory concentration of an autoinducer leads to an alteration in gene expression. Gram-positive and Gram-negative bacteria use quorum sensing communication circuits to regulate a diverse array of physiological activities. These processes include symbiosis, virulence, competence, conjugation, antibiotic production, motility, sporulation, and biofilm formation. In general, Gram-negative bacteria use acylated homoserine lactones as autoinducers, and Gram-positive bacteria use processed oligo-peptides to communicate. Recent advances in the field indicate that cell-cell communication via autoinducers occurs both within and between bacterial species. Furthermore, there is mounting data suggesting that bacterial autoinducers elicit specific responses from host organisms. Although the nature of the chemical signals, the signal relay mechanisms, and the target genes controlled by bacterial quorum sensing systems differ, in every case the ability to communicate with one another allows bacteria to coordinate the gene expression, and therefore the behavior, of the entire community. Presumably, this process bestows upon bacteria some of the qualities of higher organisms. The evolution of quorum sensing systems in bacteria could, therefore, have been one of the early steps in the development of multicellularity.


title: A Salience-based Quality Metric for Visualization

abstract: Salience detection is a principle mechanism to facilitate visual attention. A good visualization guides the observer's attention to the relevant aspects of the representation. Hence, the distribution of salience over a visualization image is an essential measure of the quality of the visualization. We describe a method for computing such a metric for a visualization image in the context of a given dataset. We show how this technique can be used to analyze a visualization's salience, improve an existing visualization, and choose the best representation from a set of alternatives. The usefulness of this proposed metric is illustrated using examples from information visualization, volume visualization and flow visualization.


title: Using The Integrated Capability And Maturity Model In The Development Process Of Software Systems

abstract: In this paper is presented the CMMI model - a complex model that integrates aspects related to features such as capacity and maturity. It provides a modern approach to software development and improvement processes that support organizations in increasing their own performance. CMMI models can be used to guide the improvement of a project, a division, or a whole organization. Application of the CMMI model in software engineering and organizational development is a process improvement approach that provides organizations with the essential elements for streamlining processes. In addition, the paper describes briefly aspects related to the architecture, components and main features of the CMMI.


title: DC Arc flash calculations for solar farms

abstract: The NFPA 70E standard - 2012 edition and the CSA Z462-12 standard describe the calculations required to determine the approximated incident energy in a DC arc flash. These calculations assume that the power source for the DC system is linear. This paper describes the calculations required to estimate the incident energy in a DC arc flash for solar farms, where the power source is highly nonlinear. The equations required for the calculation are derived from fundamentals and compared to the equations provided by the above standards. The incident energy levels found in a typical solar farm are presented in an example, where the results obtained by the application of the standards and the proposed method are compared. Recommendations to include in the standards the provisions for the calculation of DC arc flash in solar farms are given in this paper.


title: A low complexity embedded image coding algorithm using Hierarchical Listless DTT

abstract: Listless SPECK (LSPECK) is a low complexity image coding algorithm compared to SPECK. The problem of LSPECK is that, it encode each insignificant subband by a zero. Therefore, these block based coders codes as many zeros as the number of insignificant subbands. This gives rise to many zeros at the encoder output on early bit plane passes. By looking at the statistics of transformed images, the number of significant coefficients at some of the higher bitplanes are likely to be very few. We propose a variant of LSPECK algorithm, called as Improved LSPECK (ILSPECK), that code a single zero to several insignificant subbands. This reduces the length of the output bit string as well as encoding/decoding time. Further, ILSPECK algorithm is coupled with discrete tchebichef transform (DTT). The propose new coder called as Hierarchical Listless DTT (HLDTT), preserves most of the properties of wavelet coders. Extensive simulations on various kind of images shows the effictiveness of our coder.


title: Optimization of different welding processes using statistical and numerical approaches - A reference guide

abstract: Welding input parameters play a very significant role in determining the quality of a weld joint. The joint quality can be defined in terms of properties such as weld-bead geometry, mechanical properties, and distortion. Generally, all welding processes are used with the aim of obtaining a welded joint with the desired weld-bead parameters, excellent mechanical properties with minimum distortion. Nowadays, application of design of experiment (DoE), evolutionary algorithms and computational network are widely used to develop a mathematical relationship between the welding process input parameters and the output variables of the weld joint in order to determine the welding input parameters that lead to the desired weld quality. A comprehensive literature review of the application of these methods in the area of welding has been introduced herein. This review was classified according to the output features of the weld, i.e. bead geometry and mechanical properties of the welds.


Comparison between the Conventional Methods and PSO Based MPPT Algorithm for Photovoltaic Systems


DESIGN OF BROADBAND CIRCULARLY POLARIZED SQUARE SLOT ANTENNA WITH A COMPACT SIZE


Consumer learning and 3-D ecommerce: The effects of sequential exposure of a virtual experience relative to indirect and direct product experience on product knowledge, brand attitude and purchase intention


title: Secure Routing Based on Social Similarity in Opportunistic Networks

abstract: The lack of pre-existing infrastructure or dynamic topology makes it impossible to establish end-to-end connections in opportunistic networks (OppNets). Instead, a store-and-forward strategy can be employed. However, such loosely knit routing paths depend heavily on the cooperation among participating nodes. Selfish or malicious behaviors of nodes impact greatly on the network performance. In this paper, we design and validate a dynamic trust management model for secure routing optimization. We propose the concept of incorporating social trust into the routing decision process and design a trust routing based on social similarity (TRSS) scheme. TRSS is based on the observation that nodes move around and contact each other according to their common interests or social similarities. A node sharing more social features in social history record with the destination is more likely to travel close to the latter in the near future and should be chosen as the next-hop forwarder. Furthermore, social trust can be established based on an observed node’s trustworthiness and its encounter history. Based on direct and recommended trust, those untrustworthy nodes will be detected and purged from the trusted list. Since only trusted nodes’ packets will be forwarded, the selfish nodes have the incentives to behave well again. Simulation evaluation demonstrates that TRSS is very effective in detecting selfish or even malicious nodes and achieving better performance.


title: Dynamic Self-Protection and Tamperproofing for Android Apps Using Native Code

abstract: With over one billion sold devices, representing 80% market share, Android remains the most popular platform for mobile devices. Application piracy on this platform is a major concern and a cause of significant losses: about 97% of the top 100 paid apps were found to be hacked in terms of repackaging or the distribution of clones. Therefore new and stronger methods aiming to increase the burden on reverse engineering and modification of proprietary mobile software are required. In this paper, we propose an application of the Android native code component to implement strong software self-protection for apps. Within this scope, we present three dynamic obfuscation techniques, namely dynamic code loading, dynamic re-encryption, and tamper proofing. We provide a practical evaluation of this approach, assessing both the cost and efficiency of its achieved protection level. Our results indicate that with the proposed methods one can reach significant complication of the reverse-engineering process, while being affordable in terms of execution time and application size.


title: Digital refocusing with incoherent holography

abstract: Light field cameras allow us to digitally refocus a photograph after the time of capture. However, recording a light field requires either a significant loss in spatial resolution [11, 21, 10] or a large number of images to be captured [12]. In this paper, we propose incoherent holography for digital refocusing without loss of spatial resolution from only 3 captured images. The main idea is to capture 2D coherent holograms of the scene instead of the 4D light fields. The key properties of coherent light propagation are that the coherent spread function (hologram of a single point source) encodes scene depths and has a broadband spatial frequency response. These properties enable digital refocusing with 2D coherent holograms, which can be captured on sensors without loss of spatial resolution. Incoherent holography does not require illuminating the scene with high power coherent laser, making it possible to acquire holograms even for passively illuminated scenes. We provide an in-depth performance comparison between light field and incoherent holographic cameras in terms of the signal-to-noise-ratio (SNR). We show that given the same sensing resources, an incoherent holography camera outperforms light field cameras in most real world settings. We demonstrate a prototype incoherent holography camera capable of performing digital refocusing from only 3 acquired images. We show results on a variety of scenes that verify the accuracy of our theoretical analysis.


Linoleic acid metabolic pathway allows for an efficient increase of intramuscular fat content in pigs


Exploiting Cognitive Computing and Frame Semantic Features for Biomedical Document Clustering


title: Psychological testing and assessment in the 21st century.

abstract: As spin-offs of the current revolution in the cognitive and neurosciences, clinical neuropsychologists in the 21st century will be using biological tests of intelligence and cognition that record individual differences in brain functions at the neuromolecular, neurophysiologic, and neurochemical levels. Assessment of patients will focus more on better use of still intact functions, as well as rehabilitating or bypassing impaired functions, than on diagnosis, as is the focus today. Better developed successors to today's scales for assessing personal competency and adaptive behavior, as well as overall quality of life, also will be in wide use in clinical settings. With more normal individuals, use of new generations of paper-and-pencil inventories, as well as biological measures for assessing differences in interests, attitudes, personality styles, and predispositions, is predicted.


title: Weighted Generalized Mean Pooling for Deep Image Retrieval

abstract: Spatial pooling over convolutional activations (e.g., max pooling or sum pooling) has been shown to be successful in learning deep representations for image retrieval. However, most pooling techniques assume that every activation is equally important, and as a result they suffer from the presence of uninformative image regions that play a negative role as regards matching or lead to the confusion of particular visual instances. To address this issue, we propose a trainable building block that steers pooling to local information important to the task at hand. The method formulates pooling as a weighted generalized mean (wGeM), in which weights are learned on activations, reflecting the discriminative power of each activation in image matching. Embedding wGeM in a deep network improves image representation and boosts retrieval performance on standard benchmarks. wGeM does not require any bounding box annotations, but instead learns the latent probabilities of activations from scratch. It even goes beyond objectness, and learns to look at important visual details rather than the whole region of the object of interest.


title: Deep Image Clustering Using Convolutional Autoencoder Embedding with Inception-Like Block

abstract: Image clustering is one of the challenging tasks in machine learning, and has been extensively used in various applications. Recently, various deep clustering methods has been proposed. These methods take a two-stage approach, feature learning and clustering, sequentially or jointly. We observe that these works usually focus on the combination of reconstruction loss and clustering loss, relatively little work has focused on improving the learning representation of the neural network for clustering. In this paper, we propose a deep convolutional embedded clustering algorithm with inception-like block (DCECI). Specifically, an inception-like block with different type of convolution filters are introduced in the symmetric deep convolutional network to preserve the local structure of convolution layers. We simultaneously minimize the reconstruction loss of the convolutional autoencoders with inception-like block and the clustering loss. Experimental results on multiple image datasets exhibit the promising performance of our proposed algorithm compared with other competitive methods.


title: Emergence of Scenario-Appropriate Collaborative Behaviors for Teams of Robotic Bodyguards

abstract: We are considering the problem of controlling a team of robotic bodyguards protecting a VIP from physical assault in the presence of neutral and/or adversarial bystanders in a variety of scenarios. This problem is challenging due to the large number of active entities with different agendas and dynamic movement patterns, the need of cooperation between the robots as well as the requirement to take into consideration criteria such as social norms in addition to the main goal of VIP safety. We show how a multi-agent reinforcement learning approach can evolve behavior policies that outperform hand-engineered approaches. Furthermore, we propose a novel multi-agent reinforcement learning algorithm inspired by universal value function approximators that can learn policies that exhibit appropriate, distinct behavior in environments with different requirements.


title: Study on GSP algorithm based on Hadoop

abstract: According to the traditional sequential pattern mining algorithm with high complexity and not suitable for massive data, this paper puts forward Ha-GSP, which is a sequential pattern mining algorithm based on cloud computing platform Hadoop. On the basis of GSP algorithm, this paper researchs on implementation of GSP algorithm on Hadoop platform, and uses MapReduce model to solve the problem of massive data mining, and then finds frequent sequences. The experimental results show that: improved algorithm with less time, high efficiency.


UNHACKABLE DRONES : THE CHALLENGES OF SECURELY INTEGRATING UNMANNED AIRCRAFT INTO THE NATIONAL AIRSPACE


title: A Survey on Array Storage, Query Languages, and Systems

abstract: Since scientific investigation is one of the most important providers of massive amounts of ordered data, there is a renewed interest in array data processing in the context of Big Data. To the best of our knowledge, a unified resource that summarizes and analyzes array processing research over its long existence is currently missing. In this survey, we provide a guide for past, present, and future research in array processing. The survey is organized along three main topics. Array storage discusses all the aspects related to array partitioning into chunks. The identification of a reduced set of array operators to form the foundation for an array query language is analyzed across multiple such proposals. Lastly, we survey real systems for array processing. The result is a thorough survey on array data storage and processing that should be consulted by anyone interested in this research topic, independent of experience level. The survey is not complete though. We greatly appreciate pointers towards any work we might have forgotten to mention.


title: The Expectation Maximization Algorithm A short tutorial

abstract: Revision history 2009-01-09 Corrected grammar in the paragraph which precedes Equation (17). Changed datestamp format in the revision history. 2008-07-05 Corrected caption for Figure (2). Added conditioning on θn for l in convergence discussion in Section (3.2). Changed email contact info to reduce spam. 2006-10-14 Added explanation and disambiguating parentheses in the development leading to Equation (14). Minor corrections. 2006-06-28 Added Figure (1). Corrected typo above Equation (5). Minor corrections. Added hyperlinks. 2005-08-26 Minor corrections. 2004-07-18 Initial revision.


title: McKibben artificial muscles: pneumatic actuators with biomechanical intelligence

abstract: Reports on the design of a biorobotic actuator. Biological requirements are developed from published reports in the muscle physiology literature whose parameters are extracted and applied in the form of the Hill muscle model. Data from several vertebrate species (rat, frog, cat, and human) are used to evaluate the performance of a McKibben pneumatic actuator. The experimental results show the force-length properties of the actuator are muscle-like, but the force-velocity properties are not. The design of a hydraulic damper with fixed orifices, placed in parallel with the McKibben actuator, is proposed to improve the force-velocity performance. Simulation results of this practical design indicate a significant improvement.


title: A Class-E2 wireless power transfer system with different efficiency curves

abstract: Class-E inverters and Class-E rectifiers are combined to compose isolated Class-E2 DC/DC converters to transfer power through loosely coupled inductive coils. A design method for this converter is discussed to achieve ZVS (zero-voltage switching) and ZDS (zero-derivative switching). The converter is operated in a fix-frequency and open-loop mode. Load characteristics for this converter are analyzed in graphs including the relations among output voltage, output current and efficiency. High impedance design fitting for light load and load insensitive design fitting for heavy load are introduced to get different efficiency curves, which are suitable for different load probability distribution in different applications.


title: Classification and clustering with continuous time Bayesian network models

abstract: Classification and clustering of streaming data are relevant in finance, computer science, and engineering while they are becoming increasingly important in medicine and biology. Streaming data are analyzed with algorithms and models capable to represent dynamics, sequences and time. Dynamic Bayesian networks and hidden Markov models are commonly used to analyze streaming data. However, they are concerned with evenly spaced time series data and thus suffer from several limitations. Indeed, it is not clear how timestamps should be discretized even if some approaches to mitigate this problem have been recently made available. In this paper we describe the class of continuous time Bayesian networks classifiers and develop algorithms for their parametric and structural learning to solve classification and clustering of multivariate discrete state continuous time trajectories. Numerical experiments on synthetic and real world data are used to compare the performance of continuous time Bayesian network models to that achieved by dynamic Bayesian networks. In particular, post-stroke rehabilitation data is used for the classification task while urban traffic data from continuous time loop is used for the clusteirng task. The achieved results confirm the effectiveness of the proposed approaches.


title: Fusion of color histogram and LBP-based features for texture image retrieval and classification

abstract: Abstract The Local Binary Pattern (LBP) operator and its variants play an important role as the image feature extractor in the textural image retrieval and classification. The LBP-based operator extracts the textural information of an image by considering the neighboring pixel values. A single or join histogram can be derived from the LBP code which can be used as an image feature descriptor in some applications. However, the LBP-based feature is not a good candidate in capturing the color information of an image, making it is less suitable for measuring the similarity of color images with rich color information. This work overcomes this problem by adding an additional color feature, namely Color Information Feature (CIF), along with the LBP-based feature in the image retrieval and classification systems. The CIF and LBP-based feature adequately represent the color and texture features. As documented in the experimental result, the hybrid CIF and LBP-based feature presents a promising result and outperforms the existing methods over several image databases. Thus, it can be a very competitive candidate in retrieval and classification application.


Is Integration of Uncertainty Management and the Last Planner System a Good Idea?


title: Large-Scale Semantic 3D Reconstruction: An Adaptive Multi-resolution Model for Multi-class Volumetric Labeling

abstract: We propose an adaptive multi-resolution formulation of semantic 3D reconstruction. Given a set of images of a scene, semantic 3D reconstruction aims to densely reconstruct both the 3D shape of the scene and a segmentation into semantic object classes. Jointly reasoning about shape and class allows one to take into account class-specific shape priors (e.g., building walls should be smooth and vertical, and vice versa smooth, vertical surfaces are likely to be building walls), leading to improved reconstruction results. So far, semantic 3D reconstruction methods have been limited to small scenes and low resolution, because of their large memory footprint and computational cost. To scale them up to large scenes, we propose a hierarchical scheme which refines the reconstruction only in regions that are likely to contain a surface, exploiting the fact that both high spatial resolution and high numerical precision are only required in those regions. Our scheme amounts to solving a sequence of convex optimizations while progressively removing constraints, in such a way that the energy, in each iteration, is the tightest possible approximation of the underlying energy at full resolution. In our experiments the method saves up to 98% memory and 95% computation time, without any loss of accuracy.


title: DeNet: Scalable Real-Time Object Detection with Directed Sparse Sampling

abstract: We define the object detection from imagery problem as estimating a very large but extremely sparse bounding box dependent probability distribution. Subsequently we identify a sparse distribution estimation scheme, Directed Sparse Sampling, and employ it in a single end-to-end CNN based detection model. This methodology extends and formalizes previous state-of-the-art detection models with an additional emphasis on high evaluation rates and reduced manual engineering. We introduce two novelties, a corner based region-of-interest estimator and a deconvolution based CNN model. The resulting model is scene adaptive, does not require manually defined reference bounding boxes and produces highly competitive results on MSCOCO, Pascal VOC 2007 and Pascal VOC 2012 with real-time evaluation rates. Further analysis suggests our model performs particularly well when finegrained object localization is desirable. We argue that this advantage stems from the significantly larger set of available regions-of-interest relative to other methods. Source-code is available from: https://github.com/lachlants/denet


title: Comparison of Background Subtraction Methods for a Multimedia Learning Space.

abstract: A press-formed article has a quenched area and a required strength distribution. The quenched area has a hardness distribution exhibiting a hardness transition which corresponds to the required strength distribution. The press-formed article may include one end portion, the other end portion, and a central area disposed between the one end portion and the other end portion. The quenched area is extended from the one end portion and to the other end portion. The hardness distribution of the quenched area exhibits a condition that hardness is the highest in the central area in such a manner that hardness is gradually decreased from the central area to the one end portion and to the other end portion.


title: Unmanned Aerial Vehicle Path Following: A Survey and Analysis of Algorithms for Fixed-Wing Unmanned Aerial Vehicless

abstract: Unmanned aerial vehicles (UAVs) are mainly used by military and government organizations, but with low-cost sensors, electronics, and airframes there is significant interest in using low-cost UAVs among aircraft hobbyists, academic researchers, and industries. Applications such as mapping, search and rescue, patrol, and surveillance require the UAV to autonomously follow a predefined path at a prescribed height. The most commonly used paths are straight lines and circular orbits. Path-following algorithms ensure that the UAV will follow a predefined path in three or two dimensions at constant height. A basic requirement for these path-following algorithms is that they must be accurate and robust to wind disturbances.


title: Gestural communication over video stream: supporting multimodal interaction for remote collaborative physical tasks

abstract: We present a system integrating gesture and live video to support collaboration on physical tasks. The architecture combines network IP cameras, desktop PCs, and tablet PCs to allow a remote helper to draw on a video feed of a workspace as he/she provides task instructions. A gesture recognition component enables the system both to normalize freehand drawings to facilitate communication with remote partners and to use pen-based input as a camera control device. Results of a preliminary user study suggest that our gesture over video communication system enhances task performance over traditional video-only systems. Implications for the design of multimodal systems to support collaborative physical tasks are also discussed.


title: Papillary regeneration: anatomical aspects and treatment approaches

abstract: Introduction and objective: This paper aims to report a literature review on the anatomy and morphology of the interproximal papilla and present the options of both surgical and nonsurgical treatment for the recovery of interdental papilla. Literature review: The loss of the interdental papilla because of the interproximal bone loss accounts for aesthetic, phonetic and functional problems of patients with periodontal disease. The interproximal tissue reconstruction has been reported in literature through both surgical procedures with the use of subepithelial connective tissue graft, restorative and orthodontic treatment. Conclusion: The etiology of gingival black space is multifactorial, therefore, it is important to diagnose properly the etiological factor to establish an appropriate treatment planing. However, the treatment approaches are not predictable and further studies are necessary to recommend the clinical practices available to date.


title: Intergenerational transmission of subthreshold autistic traits in the general population

abstract: Background Autistic disorder (AD) is a disabling oligogenic condition characterized by severe social impairment. Subthreshold autistic social impairments are known to aggregate in the family members of autistic probands; therefore, we conducted this study to examine the intergenerational transmission of such traits in the general population. Methods The Social Responsiveness Scale (SRS), a quantitative measure of autistic traits, was completed on 285 pairs of twins (by maternal report) and on their parents (by spouse report). Results Correlation for social impairment or competence between parents and their children and between spouses was on the order of .4. In families in which both parents scored in the upper quartile for social impairment on the SRS, mean SRS score of offspring was significantly elevated (effect size 1.5). Estimated assortative mating explained approximately 30% of the variation in parent SRS scores. Conclusions Children from families in which both parents manifest subthreshold autistic traits exhibit a substantial shift in the distribution of their scores for impairment in reciprocal social behavior, toward the pathological end. As has been previously demonstrated in children, heritable subthreshold autistic impairments are measurable in adults and appear continuously distributed in the general population.


title: Novel differential drive steering system with energy saving and normal tire using spur gear for an omni-directional mobile robot

abstract: Holonomic omnidirectional mobile robots are useful because of their high level of mobility in narrow or crowded areas, and omnidirectional robots equipped with normal tires are desired for their ability to surmount difference in level as well as their vibration suppression and ride comfort. A caster-drive mechanism using normal tires has been developed to realize a holonomic omnidiredctional robot, but some problems has remain. Here we describe effective systems to control the caster-drive wheels of an omnidirectional mobile robot. We propose a Differential-Drive Steering System (DDSS) using differential gearing to improve the operation ratio of motors. The DDSS generates driving and steering torque effectively from two motors. Simulation and experimental results show that the proposed system is effective for holonomic omnidirectional mobile robots.


title: HDFI: Hardware-Assisted Data-Flow Isolation

abstract: Memory corruption vulnerabilities are the root cause of many modern attacks. Existing defense mechanisms are inadequate; in general, the software-based approaches are not efficient and the hardware-based approaches are not flexible. In this paper, we present hardware-assisted data-flow isolation, or, HDFI, a new fine-grained data isolation mechanism that is broadly applicable and very efficient. HDFI enforces isolation at the machine word granularity by virtually extending each memory unit with an additional tag that is defined by dataflow. This capability allows HDFI to enforce a variety of security models such as the Biba Integrity Model and the Bell -- LaPadula Model. We implemented HDFI by extending the RISC-V instruction set architecture (ISA) and instantiating it on the Xilinx Zynq ZC706 evaluation board. We ran several benchmarks including the SPEC CINT 2000 benchmark suite. Evaluation results show that the performance overhead caused by our modification to the hardware is low (< 2%). We also developed or ported several security mechanisms to leverage HDFI, including stack protection, standard library enhancement, virtual function table protection, code pointer protection, kernel data protection, and information leak prevention. Our results show that HDFI is easy to use, imposes low performance overhead, and allows us to create more elegant and more secure solutions.


title: WHY SUMMARIES OF RESEARCH ON PSYCHOLOGICAL THEORIES ARE OFTEN UNINTERPRETABLE

abstract: Null hypothesis testing of correlational predictions from weak substantive theories in soft psychology is subject to the influence of ten obfuscating factors whose effects are usually (1) sizeable, (2) opposed, (3) variable, and (4) unknown. The net epistemic effect of these ten obfuscating influences is that the usual research literature review is well-nigh uninterpretable. Major changes in graduate education, conduct of research, and editorial policy are proposed.


Variational Adversarial Deep Domain Adaptation for Health Care Time Series Analysis


Toward Natural Language Generation by Humans


DTC-SVM Scheme for Induction Motors Fed with a Three-level Inverter


title: Metaheuristic optimization frameworks: a survey and benchmarking

abstract: This paper performs an unprecedented comparative study of Metaheuristic optimization frameworks. As criteria for comparison a set of 271 features grouped in 30 characteristics and 6 areas has been selected. These features include the different metaheuristic techniques covered, mechanisms for solution encoding, constraint handling, neighborhood specification, hybridization, parallel and distributed computation, software engineering best practices, documentation and user interface, etc. A metric has been defined for each feature so that the scores obtained by a framework are averaged within each group of features, leading to a final average score for each framework. Out of 33 frameworks ten have been selected from the literature using well-defined filtering criteria, and the results of the comparison are analyzed with the aim of identifying improvement areas and gaps in specific frameworks and the whole set. Generally speaking, a significant lack of support has been found for hyper-heuristics, and parallel and distributed computing capabilities. It is also desirable to have a wider implementation of some Software Engineering best practices. Finally, a wider support for some metaheuristics and hybridization capabilities is needed.


title: Pseudo-Random Number Generators for Massively Parallel Discrete-Event Simulation

abstract: A significant problem faced by scientific investigation of complex modern systems is that credible simulation studies of such systems on single computers can frequently not be finished in a feasible time. Discrete-event simulation of dynamic stochastic systems, allowing multiple replications in parallel (MRIP) to speed up simulation time, has become one of the most popular paradigms of investigation in many areas of science and engineering. One of the general problems related with distributed simulation is the need of parallel generation of multiple sequences of pseudo-random numbers across cooperating processors, with the number of known, good parallel generators being very limited. This report assesses currently known techniques proposed for generation of pseudo-random numbers in processing systems, particularly the statistical properties of multiple sequences of numbers generated in parallel, and the speed of generation of these parallel streams and also the pseudo-random numbers themselves. Parallel implementations of the MRG32k3a and DX-120-2 generators are found to be the most suitable of those tested.


title: Smartphone-based Home Robotics

abstract: Humanoid robotics is a promising field because the strong human preference to interact with anthropomorphic interfaces. Despite this, humanoid robots are far from reaching main stream adoption and the features available in such robots seem to lag that of the latest smartphones. A fragmented robot ecosystem and low incentives to developers do not help to foster the creation of Robot-Apps either. In contrast, smartphones enjoy high adoption rates and a vibrant app ecosystem (4M apps published). Given this, it seems logical to apply the mobile SW and HW development model to humanoid robots. One way is to use a smartphone to power the robot. Smartphones have been embedded in toys and drones before. However, they have never been used as the main compute unit in a humanoid embodiment. Here, we introduce a novel robot architecture based on smartphones that demonstrates x3 cost reduction and that is compatible with iOS/Android.


title: Capturing Meaning in Product Reviews with Character-Level Generative Text Models.

abstract: We present a character-level recurrent neural network that generates relevant and coherent text given auxiliary information such as a sentiment or topic. Using a simple input replication strategy, we preserve the signal of auxiliary input across wider sequence intervals than can feasibly be trained by backpropagation through time. Our main results center on a large corpus of 1.5 million beer reviews from BeerAdvocate. In generative mode, our network produces reviews on command, tailored to a star rating or item category. The generative model can also run in reverse, performing classification with surprising accuracy. Performance of the reverse model provides a straightforward way to determine what the generative model knows without relying too heavily on subjective analysis. Given a review, the model can accurately determine the corresponding rating and infer the beer's category (IPA, Stout, etc.). We exploit this capability, tracking perceived sentiment and class membership as each character in a review is processed. Quantitative and qualitative empirical evaluations demonstrate that the model captures meaning and learns nonlinear dynamics in text, such as the effect of negation on sentiment, despite possessing no a priori notion of words. Because the model operates at the character level, it handles misspellings, slang, and large vocabularies without any machinery explicitly dedicated to the purpose.


title: A Survey of Botnet and Botnet Detection

abstract: Among the various forms of malware, botnets are emerging as the most serious threat against cyber-security as they provide a distributed platform for several illegal activities such as launching distributed denial of service attacks against critical targets, malware dissemination, phishing, and click fraud. The defining characteristic of botnets is the use of command and control channels through which they can be updated and directed. Recently, botnet detection has been an interesting research topic related to cyber-threat and cyber-crime prevention. This paper is a survey of botnet and botnet detection. The survey clarifies botnet phenomenon and discusses botnet detection techniques. This survey classifies botnet detection techniques into four classes: signature-based, anomaly-based, DNS-based, and mining-base. It summarizes botnet detection techniques in each class and provides a brief comparison of botnet detection techniques.


title: Measuring Calorie and Nutrition From Food Image

abstract: As people across the globe are becoming more interested in watching their weight, eating more healthy, and avoiding obesity, a system that can measure calories and nutrition in every day meals can be very useful. In this paper, we propose a food calorie and nutrition measurement system that can help patients and dietitians to measure and manage daily food intake. Our system is built on food image processing and uses nutritional fact tables. Recently, there has been an increase in the usage of personal mobile technology such as smartphones or tablets, which users carry with them practically all the time. Via a special calibration technique, our system uses the built-in camera of such mobile devices and records a photo of the food before and after eating it to measure the consumption of calorie and nutrient components. Our results show that the accuracy of our system is acceptable and it will greatly improve and facilitate current manual calorie measurement techniques.


title: Mining, indexing, and search approaches to entity and graph information retrieval for chemoinformatics

abstract: In this work, we show how to build a domain specific search engine that enables both entity and 2D graph searches for chemical molecules. First of all, documents are collected from the Web, and then preprocessed using document classification and segmentation. We apply Support Vector Machines for classification and propose a novel method of text segmentation. Then chemical entities in the documents are tagged and indexed to provide fast searches. Simultaneously, chemical structure information are collected, processed, and indexed for fast graph searches. ::: We show how chemical entity searches can improve the relevance of returned documents by avoiding those ambiguous terms. Our search engine first extracts chemical entities from text, performs novel indexing suitable for chemical names and formulae, and supports different query models that a scientist may require. We propose a model of hierarchical conditional random fields for entity tagging that considers long-term dependencies at the sentence level. Then to support efficient and effective entity searches, we propose two feature selection methods for entity index building. One is to select frequent and discriminative subsequences from all the candidate features for chemical formula indexing. The other is to first discover subterms of chemical names with corresponding probabilities using a proposed independent frequent subsequence mining algorithm, and then segment a chemical name hierarchically into a tree structure based on discovered independent frequent subsequences. A unsupervised hierarchical text segmentation (HTS) method is proposed for this. Then subterms on the HTS tree can be indexed. Finally, query models with corresponding ranking functions are introduced for chemical entity searches. Experiments show that our approaches to chemical entity tagging, indexing, and search perform well. ::: In addition to text searches, massive amounts of structured data in Cheminformatics and Bioinformatics raise an issue of effective and efficient structure searches. Graphs are used to model structures for a long time. A typical type of graph search is a subgraph query that retrieves all graphs containing the query graph. To support efficient search, graph features such as subgraphs are extracted from graphs for graph indexing. One of the key issues is, given the set of all the possible subgraphs of the graph set, which subset of features is the optimal to achieve the highest precision for a given query set? To answer this question, first, we introduce a graph search process for subgraph queries. Then, we propose several novel feature selection criteria, Max-Precision, Max-Irredundant-Information, and Max-Information-Min-Redundancy, based on pointwise mutual information. We also propose a greedy feature search algorithm to find a near optimal feature set based on Max-Information-Min-Redundancy. Finally we show empirically that our proposed methods achieve a higher precision than previous methods. ::: Besides subgraph queries, users often execute similarity graph queries to search for graphs similar to the query graph. Previous methods usually use the maximum common edge subgraph (MCEG) to measure the similarity of two graphs. However, MCEG isomorphism is NP-hard and prohibitively slow to scan all graphs in the data set, so that it is not feasible for online searches to use MCEG in the fly to measure the similarity of retrieved graphs. We propose a novel approach to this issue from a different view by ranking search results using a weighted linear graph kernel that can avoid real time MCEG isomorphism tests, but achieve a reasonably high quality of search results. First, subgraphs features are extracted from graphs and indexed. Second, for a given graph query, graphs containing its subgraph features are retrieved and similarity scores are computed based on the indexed subgraph features. Finally, graphs are sorted and returned based on similarity scores. Subgraph weights used by the graph kernel are learned offline from a training set generated using MCEG isomorphism. We show empirically that the proposed methods of learning to rank for similarity graph queries can achieve a reasonably high normalized discounted cumulative gain in comparison with the "gold standard" method based on MCEG isomorphism. (Abstract shortened by UMI.)


title: Social Network Analysis of Alice in Wonderland

abstract: We present a network analysis of a literary text, Alice in Wonderland. We build novel types of networks in which links between characters are different types of social events. We show that analyzing networks based on these social events gives us insight into the roles of characters in the story. Also, static network analysis has limitations which be- come apparent from our analysis. We propose the use of dynamic network analysis to over- come these limitations. tify these limitations, few have done so with a strict and specific rubric for categorizing interactions. In this paper, we annotate Lewis Carroll's Alice in Wonderland using a well-defined annotation scheme which we have previously developed on newswire text Agarwal et al. (2010). It is well suited to deal with the aforementioned limitations. We show that using different types of networks can be useful by al- lowing us to provide a model for determining point- of-view. We also show that social networks allow characters to be categorized into roles based on how they function in the text, but that this approach is limited when using static social networks. We then build and visualize dynamic networks and show that static networks can distort the importance of char- acters. By using dynamic networks, we can build a fuller picture of how each character works in a liter- ary text. Our paper uses an annotation scheme that is well- defined and has been used in previous computational models that extract social events from news articles (Agarwal and Rambow, 2010). This computational model may be adapted to extract these events from literary texts. However, the focus of this paper is not to adapt the previously proposed computational model to a new domain or genre, but to first demon- strate the usefulness of this annotation scheme for the analysis of literary texts, and the social networks derived from it. All results reported in this paper are based on hand annotation of the text. Further- more, we are investigating a single text, so that we do cannot draw conclusions about the usefulness of our methods for validating theories of literature. We summarize the contributions of this paper: • We manually extract a social network from Al-


title: Self-regulation, motivation, and math achievement in middle school: Variations across grade level and math context

abstract: Abstract The current study examined grade level, achievement group, and math-course-type differences in student self-regulation and motivation in a sample of 880 suburban middle-school students. Analysis of variance was utilized to assess group differences in student self-regulation and motivation, and linear regression analysis was used to identify variables that best predicted students' use of regulatory strategies. A key finding was that although seventh graders exhibited a more maladaptive self-regulation and motivation profile than sixth graders, achievement groups in seventh grade (high, moderate, low) were more clearly differentiated across both self-regulation and motivation than achievement groups in sixth grade. The pattern of achievement group differences also varied across math course type, as self-regulation and motivation processes more consistently differentiated achievement groups in advanced classes than regular math courses. Finally, task interest was shown to be the primary motivational predictor of students' use of regulatory strategies during math learning. The study highlights the importance of identifying shifting student motivation and self-regulation during the early middle school years and the potential role that context may have on these processes.


title: An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering

abstract: Assignment methods are at the heart of many algorithms for unsupervised learning and clustering -- in particular, the well-known K-means and Expectation-Maximizatian (EM) algorithms. In this work, we study several different methods of assignment, including the "hard" assignments used by K-means and the "soft" assignments used by EM. While it is known that K-means minimizes the distortion on the data and EM maximizes the likelihood, little is known about the systematic differences of behavior between the two algorithms. Here we shed light on these differences via an information-theoretic analysis. The cornerstone of our results is a simple decomposition of the expected distortion, showing that K-means (and its extension for inferring general parametric densities from unlabeled sample data) must implicitly manage a trade-off between how similar the data assigned to each cluster are, and how the data are balanced among the clusters. How well the data are balanced is measured by the entropy of the partition defined by the hard assignments. In addition to letting us predict and verify systematic differences between K-means and EM on specific examples, the decomposition allows us to give a rather general argument showing that K-means will consistently find densities with less "overlap" than EM. We also study a third natural assignment method that we call posterior assignment, that is close in spirit to the soft assignments of EM, but leads to a surprisingly different algorithm.


title: MYO Armband for physiotherapy healthcare: A case study using gesture recognition application

abstract: As there is a need for innovative and new medical technologies in the healthcare, we identified Thalmic's “MYO Armband”, which is used for gaming systems and controlling applications in mobiles and computers. We can exploit this development in the field of medicine and healthcare to improve public health care system. So, we spotted “MYO diagnostics”, a computer-based application developed by Thalmic labs to understand Electromyography (EMG) lines (graphs), bits of vector data, and electrical signals of our complicated biology inside our arm. The human gestures will allow to gather huge amount of data and series of EMG lines which can be analysed to detect medical abnormalities and hand movements. This application has powerful algorithms which are translated into commands to recognise human hand gestures. The effect of doctors experience on user satisfaction metrics in using MYO armband can be measured in terms of effectiveness, efficiency and satisfaction which are based on the metrics-task completion, error counts, task times and satisfaction scores. In this paper, we considered only satisfaction metrics using a widely used System Usability Scale (SUS) questionnaire model to study the usability on the twenty-four medical students of the Brighton and Sussex Medical School. This helps in providing guidelines about the use of MYO armband for physiotherapy analysis by the doctors and patients. Another questionnaire with a focus on ergonomic (human factors) issues related to the use of the device such as social acceptability, ease of use and ease of learning, comfort and stress, attempted to discover characteristics of hand gestures using MYO. The results of this study can be used in a way to support the development of interactive physiotherapy analysis by individuals using MYO and hand gesture applications at their home for self-examination. Also, the relationship and correlation between the signals received will lead to a better understanding of the whole myocardium system and assist doctors in early diagnosis.


title: P-FatTree: A multi-channel datacenter network topology

abstract: The bandwidth and latency requirements of next-generation datacenter networks stress the limits of CMOS manufacturing. A key trend in their design will be a move from single-channel links and switches to multi-channel links and switches. Today's network topologies erase this distinction, providing the illusion of a unified network fabric. In this work we propose P-FatTree, which is a FatTree topology designed specifically for the future multi-channel reality. P-FatTree requires fewer switch chips and as a result has lower cost, power consumption, and latency than existing approaches. Furthermore, by embracing the parallel nature of the network itself, it enables compelling new ways to better manage and deliver application traffic.


title: Healthy #fondue #dinner: analysis and inference of food and drink consumption patterns on instagram

abstract: Social media generate large-scale data to study food and drink consumption in everyday life. Using Instagram posts in Switzerland over five years, our goal is two-fold. First, we extract key food & drink consumption patterns, through the lenses of a data-driven dictionary of popular items extracted from hashtags, and of a food categorization system used by the Swiss Federal government for national statistics purposes. Patterns related to spatial and temporal distributions of food & drink consumption, demographics, and eating events are extracted and compared to official statistics. Second, using the insights from this analysis, we define two eating event classification tasks, including a two-class task (healthy vs. unhealthy) and a six-class task (the three main meals break-fast/lunch/dinner/plus brunch/coffee/tea). Both tasks use hash-tags as labels for supervised learning. We study how content (hashtags and food categories), context (time and location), and social features (likes) can discriminate these eating events. A random forest and a combination of content and context features can classify healthy vs. unhealthy eating posts with 85.8% accuracy, and the six daily eating occasions with 61.7% accuracy.


title: Health, functioning, and disability in older adults—present status and future implications

abstract: Ageing is a dynamic process, and trends in the health status of older adults aged at least 60 years vary over time because of several factors. We examined reported trends in morbidity and mortality in older adults during the past two decades to identify patterns of ageing across the world. We showed some evidence for compression of morbidity (ie, a reduced amount of time spent in worse health), in four types of studies: 1) of good quality based on assessment criteria scores; 2) those in which a disability-related or impairment-related measure of morbidity was used; 3) longitudinal studies; or 4) studies undertaken in the USA and other high-income countries. Many studies, however, reported contrasting evidence (ie, for an expansion of morbidity), but with different methods, these measures are not directly comparable. Expansion of morbidity was more common when trends in chronic disease prevalence were studied. Our secondary analysis of data from longitudinal ageing surveys presents similar results. However, patterns of limitations in functioning vary substantially between countries and within countries over time, with no discernible explanation. Data from low-income countries are very sparse, and efforts to obtain information about the health of older adults in less-developed regions of the world are urgently needed. We especially need studies that focus on refining measurements of health, functioning, and disability in older people, with a core set of domains of functioning, that investigate the effects of these evolving patterns on the health-care system and their economic implications.


title: Cytokine production and helper T cell subsets in Vogt-Koyanagi-Harada’s disease

abstract: Purpose. To investigate helper T (Th) cell subsets and cytokine production in patients with Vogt-Koyanagi-Harada’s (VKH) disease. Methods. Nine patients in the acute stage of VKH disease and 9 healthy controls were studied. Interferon-gamma (IFN-?), interleukin-2 (IL-2) and IL-4 mRNA from peripheral blood mononuclear cells (PBMC) was detected by reverse transcriptase-polymerase chain reaction. Cytokine levels in plasma, cerebrospinal fluid (CSF) and PBMC culture supernatant were measured by enzyme-linked immunosorbent assay (ELISA). The proportions of each cytokine-producing CD4+ or CD8+ cells in PBMC cultured with or without anti-CD3 antibody stimulation were analyzed by flow cytometry. Results. We observed a significant increase of cytokine mRNA positive cases of VKH patients in comparison with controls only in IFN-? mRNA detection from PBMC. ELISA detected IFN-? in CSF from only 1 patient and no cytokine in plasma. The levels of IFN-? and IL-2 were significantly higher in the stimulated cell culture su...


title: Automatic analysis of call-center conversations

abstract: We describe a system for automating call-center analysis and monitoring. Our system integrates transcription of incoming calls with analysis of their content; for the analysis, we introduce a novel method of estimating the domain-specific importance of conversation fragments, based on divergence of corpus statistics. Combining this method with Information Retrieval approaches, we provide knowledge-mining tools both for the call-center agents and for administrators of the center.


title: Experiment of indoor position presumption based on RSSI of Bluetooth LE beacon

abstract: Real-time indoor location estimation needs multiple signals from different sources. To ensure accuracy of estimation, adopting Bluetooth Low Energy (LE) beacon devices as signal sources is a reasonable solution. We have installed a number of Bluetooth Low Energy (LE) beacon devices in and around a room and observed beacons by portable devices with RSSI to achieve fundamental knowledge of beacons and feasibility of location estimation. By applying an estimation method, we have achieved over 95% of correct estimation rate and complete separation that a device is in a room or not.


title: Reprint of: pyrolysis technologies for municipal solid waste: A review

abstract: Pyrolysis has been examined as an attractive alternative to incineration for municipal solid waste (MSW) disposal that allows energy and resource recovery; however, it has seldom been applied independently with the output of pyrolysis products as end products. This review addresses the state-of-the-art of MSW pyrolysis in regards to its technologies and reactors, products and environmental impacts. In this review, first, the influence of important operating parameters such as final temperature, heating rate (HR) and residence time in the reaction zone on the pyrolysis behaviours and products is reviewed; then the pyrolysis technologies and reactors adopted in literatures and scale-up plants are evaluated. Third, the yields and main properties of the pyrolytic products from individual MSW components, refuse-derived fuel (RDF) made from MSW, and MSW are summarised. In the fourth section, in addition to emissions from pyrolysis processes, such as HCl, SO2 and NH3, contaminants in the products, including PCDD/F and heavy metals, are also reviewed, and available measures for improving the environmental impacts of pyrolysis are surveyed. It can be concluded that the single pyrolysis process is an effective waste-to-energy convertor but is not a guaranteed clean solution for MSW disposal. Based on this information, the prospects of applying pyrolysis technologies to dealing with MSW are evaluated and suggested.


Effect of Increasing Age on Lung Functions of Sedentary Persons


title: Quality-of-experience driven adaptive HTTP media delivery

abstract: This paper presents a Quality of Experience (QoE) driven approach for multi-user resource optimization in Dynamic Adaptive Streaming over HTTP (DASH) over next generation wireless networks. Our objective is to enhance the user experience in adaptive HTTP streaming by jointly considering the characteristics of the media content and the available wireless resources in the operator network. Specifically, we propose a proactive QoE-based approach for rewriting the client HTTP requests at a proxy in the mobile network. The advantage of the proposed approach is its applicability for over-the-top (OTT) streaming as it requires no adaptation of the media content. We compare our proposed scheme to both reactive QoE-optimized and to standard-DASH HTTP streaming. Our contributions are: 1) We first show that standard OTT DASH leads to unsatisfactory performance since the content agnostic resource allocation by the LTE scheduler is far from optimal, and we can achieve a clear QoE improvement when considering the content characteristics. 2) We additionally show that proactively rewriting the client requests gives control of the video content adaptation to the network operator which has better information than the client on the load and radio conditions in the cell. This results in additional gains in user perceived video quality. 3) A standard unmodified DASH client remains unaware of the proposed rewriting of the HTTP requests and can decode and play the redirected media segments.


title: Robust and sparse fuzzy K-means clustering

abstract: The partition-based clustering algorithms, like K-Means and fuzzy K-Means, are most widely and successfully used in data mining in the past decades. In this paper, we present a robust and sparse fuzzy K-Means clustering algorithm, an extension to the standard fuzzy K-Means algorithm by incorporating a robust function, rather than the square data fitting term, to handle outliers. More importantly, combined with the concept of sparseness, the new algorithm further introduces a penalty term to make the object-clusters membership of each sample have suitable sparseness. Experimental results on benchmark datasets demonstrate that the proposed algorithm not only can ensure the robustness of such soft clustering algorithm in real world applications, but also can avoid the performance degradation by considering the membership sparsity.


title: Towards Audio to Scene Image Synthesis using Generative Adversarial Network

abstract: Humans can imagine a scene from a sound. We want machines to do so by using conditional generative adversarial networks (GANs). By applying the techniques including spectral norm, projection discriminator and auxiliary classifier, compared with naive conditional GAN, the model can generate images with better quality in terms of both subjective and objective evaluations. Almost three-fourth of people agree that our model have the ability to generate images related to sounds. By inputting different volumes of the same sound, our model output different scales of changes based on the volumes, showing that our model truly knows the relationship between sounds and images to some extent.


title: Automated deep-neural-network surveillance of cranial images for acute neurologic events

abstract: Rapid diagnosis and treatment of acute neurological illnesses such as stroke, hemorrhage, and hydrocephalus are critical to achieving positive outcomes and preserving neurologic function—‘time is brain’1–5. Although these disorders are often recognizable by their symptoms, the critical means of their diagnosis is rapid imaging6–10. Computer-aided surveillance of acute neurologic events in cranial imaging has the potential to triage radiology workflow, thus decreasing time to treatment and improving outcomes. Substantial clinical work has focused on computer-assisted diagnosis (CAD), whereas technical work in volumetric image analysis has focused primarily on segmentation. 3D convolutional neural networks (3D-CNNs) have primarily been used for supervised classification on 3D modeling and light detection and ranging (LiDAR) data11–15. Here, we demonstrate a 3D-CNN architecture that performs weakly supervised classification to screen head CT images for acute neurologic events. Features were automatically learned from a clinical radiology dataset comprising 37,236 head CTs and were annotated with a semisupervised natural-language processing (NLP) framework16. We demonstrate the effectiveness of our approach to triage radiology workflow and accelerate the time to diagnosis from minutes to seconds through a randomized, double-blinded, prospective trial in a simulated clinical environment. A deep-learning algorithm is developed to provide rapid and accurate diagnosis of clinical 3D head CT-scan images to triage and prioritize urgent neurological events, thus potentially accelerating time to diagnosis and care in clinical settings.


title: MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform

abstract: A multiple sequence alignment program, MAFFT, has been developed. The CPU time is drastically reduced as compared with existing methods. MAFFT includes two novel techniques. (i) Homo logous regions are rapidly identified by the fast Fourier transform (FFT), in which an amino acid sequence is converted to a sequence composed of volume and polarity values of each amino acid residue. (ii) We propose a simplified scoring system that performs well for reducing CPU time and increasing the accuracy of alignments even for sequences having large insertions or extensions as well as distantly related sequences of similar length. Two different heuristics, the progressive method (FFT-NS-2) and the iterative refinement method (FFT-NS-i), are implemented in MAFFT. The performances of FFT-NS-2 and FFT-NS-i were compared with other methods by computer simulations and benchmark tests; the CPU time of FFT-NS-2 is drastically reduced as compared with CLUSTALW with comparable accuracy. FFT-NS-i is over 100 times faster than T-COFFEE, when the number of input sequences exceeds 60, without sacrificing the accuracy.


title: Pure-pursuit reactive path tracking for nonholonomic mobile robots with a 2D laser scanner

abstract: Due to its simplicity and efficiency, the pure-pursuit path tracking method has been widely employed for planned navigation of nonholonomic ground vehicles. In this paper, we investigate the application of this technique for reactive tracking of paths that are implicitly defined by perceived environmental features. Goal points are obtained through an efficient interpretation of range data from an onboard 2D laser scanner to follow persons, corridors, and walls. Moreover, this formulation allows that a robotic mission can be composed of a combination of different types of path segments. These techniques have been successfully tested in the tracked mobile robot Auriga-α in an indoor environment.


DeepDTA: deep drug–target binding affinity prediction


title: Exploring the link between reading fiction and empathy: Ruling out individual differences and examining outcomes

abstract: Readers of fiction tend to have better abilities of empathy and theory of mind (Mar et al., 2006). We present a study designed to replicate this finding, rule out one possible explanation, and extend the assessment of social outcomes. In order to rule out the role of personality, we first identified Openness as the most consistent correlate. This trait was then statistically controlled for, along with two other important individual differences: the tendency to be drawn into stories and gender. Even after accounting for these variables, fiction exposure still predicted performance on an empathy task. Extending these results, we also found that exposure to fiction was positively correlated with social support. Exposure to nonfiction, in contrast, was associated with loneliness, and negatively related to social support.


title: Game Development for Computer Science Education

abstract: Educators have long used digital games as platforms for teaching. Games tend to have several qualities that aren't typically found in homework: they often situate problems within a compelling alternate reality that unfolds through intriguing narrative, they often draw more upon a player's intrinsic motivations than extrinsic ones, they can facilitate deliberate low intensity practice, and they often emphasize a spirit of play instead of work. At ITiCSE 2016, this working group convened to survey the landscape of existing digital games that have been used to teach and learn computer science concepts. Our group discovered that these games lacked explicitly defined learning goals and even less evaluation of whether or not the games achieved these goals. As part of this process, we identified and played over 120 games that have been released or described in literature as means for learning computer science concepts. In our report, we classified how these games support the learning objectives outlined in the ACM/IEEE Computer Science Curricula 2013. While we found more games than we expected, few games explicitly stated their learning goals and even fewer were evaluated for their capacity to meet these goals. Most of the games we surveyed fell into two categories: short-lived proof-of-concept projects built by academics or closed-source games built by professional developers. Gathering adequate learning data is challenging in either situation. Our original intent for the second year of our working group was to prepare a comprehensive framework for collecting and analyzing learning data from computer science learning games. Upon further discussion, however, we decided that a better next step is to validate the design and development guidelines that we put forth in our final report for ITiCSE 2016. We extend this working group to a second year---with a mission to collaboratively develop a game with clearly defined learning objectives and define a methodology for evaluating its capacity to meet its goals.


title: Lapses in alertness: coherence of fluctuations in performance and EEG spectrum.

abstract: Abstract Thirteen subjects detected noise burst targets presented in a white noise background at a mean rate 10/min. Within each session, local error rate, defined as the fraction of targets detected in a 33 sec moving window, fluctuated widely. Mean coherence between slow mean variations in EEG power and in local error rate computed for each EEG frequency and performance cycle length, and was shown by a Monte Carlo procedure to be significant for many EEG frequencies and performance cycle lengths, particularly in 4 well-defined EEG frequency bands, near 3,10,13, and 19 Hz, and at higher frequencies in two cycle length ranges, one longer than 4 min and the other near 90 sec/cycle. The coherence phase contained a prominent phase reversal near 6 Hz. Sorting individual spectra by local error rate confirmed the close relation between performance and EEG power and its relative within-subject stability. These results show that attempts to maintain alertness in an auditory detection task result in concurrent minute and multi-minute scale fluctuations in performance and the EEG power spectrum.


title: Digitizing, Securing and Sharing Vehicles Life-cycle over a Consortium Blockchain: Lessons Learned

abstract: Nowadays, vehicles odometer fraud is becoming a growing problem internationally, and is costing European consumers between 5.6 to 9.6 billion euros per year. This is partly due to the lack of unified vehicles life-cycle management, and to the fact that vehicles data are currently spread across multiple stakeholders that do not trust each other or collaborate together. In this paper, we propose a Blockchain- backed Vehicles Data and Processes Ledger framework to streamline the management of vehicles life-cycle and data history, and hence to provide more transparency and collaborations between the involved stakeholders. The architecture and lessons learned from the first implementation phase are discussed, followed by future research challenges.


title: Co-labeling: A New Multi-view Learning Approach for Ambiguous Problems

abstract: We propose a multi-view learning approach called co-labeling which is applicable for several machine learning problems where the labels of training samples are uncertain, including semi-supervised learning (SSL), multi-instance learning (MIL) and max-margin clustering (MMC). Particularly, we first unify those problems into a general ambiguous problem in which we simultaneously learn a robust classifier as well as find the optimal training labels from a finite label candidate set. To effectively utilize multiple views of data, we then develop our co-labeling approach for the general multi-view ambiguous problem. In our work, classifiers trained on different views can teach each other by iteratively passing the predictions of training samples from one classifier to the others. The predictions from one classifier are considered as label candidates for the other classifiers. To train a classifier with a label candidate set for each view, we adopt the Multiple Kernel Learning (MKL) technique by constructing the base kernel through associating the input kernel calculated from input features with one label candidate. Compared with the traditional co-training method which was specifically designed for SSL, the advantages of our co-labeling are two-fold: 1) it can be applied to other ambiguous problems such as MIL and MMC, 2) it is more robust by using the MKL method to integrate multiple labeling candidates obtained from different iterations and biases. Promising results on several real-world multi-view data sets clearly demonstrate the effectiveness of our proposed co-labeling for both MIL and SSL.


title: Cloud Computing Business Value and Human Determinants: An Empirical Investigation

abstract: There are high expectations that cloud computing (CC) can generate sizeable business value (BV) for firms, which includes important benefits, associated with costs reduction, agility enhancement and innovation facilitation and support. However, limited empirical research has been conducted in this direction, in order to investigate to what extent these high expectations are fulfilled, as well as the level of business value that firms realize from CC, and the magnitudes of the specific benefits that firms really obtain from it, as well as their determinants. Our paper contributes to filling this research gap. It investigates the effect of a set of human factors, which concern firm's general human capital, ICT specific human capital, as well the co-operation and relationship between the personnel of the ICT unit and the personnel of the business units, on the BV generated by CC, having as theoretical foundation the 'resource-based view' of the firm theory. For this purpose, we have used data collected through a survey from 115 firms from technologically developed sectors of the Greek economy, from which a CC BV regression model has been estimated. It has been concluded that neither firm's general human capital (quantified by the share of firm's employees having tertiary-level formal education), nor its ICT human capital (quantified by the share of ICT employees), per se impact the BV generated by CC. On the contrary, the relationship and co-operation between the ICT unit personnel and the business units' personnel have a positive effect on the BV from CC usage. Our findings indicate that it is not the mere acquisition of human resources (highly educated business and ICT employees), but their utilization for the development of capability for productive and effective co-operation and relationship among the ICT and business personnel, that can lead to higher levels of BV from CC usage.


title: Coalescing Minds : Brain Uploading-Related Group Mind Scenarios

abstract: We present a hypothetical process of mind coalescence, where artificial connections are created between two or more brains. This might simply allow for an improved form of communication. At the other extreme, it might merge the minds into one in a process that can be thought of as a reverse split-brain operation. We propose that one way mind coalescence might happen is via an exocortex, a prosthetic extension of the biological brain which integrates with the brain as seamlessly as parts of the biological brain integrate with each other. An exocortex may also prove to be the easiest route for mind uploading, as a person's personality gradually moves away from the aging biological brain onto the exocortex. Memories might also be copied and shared even without minds being permanently merged. Over time, the borders of personal identity may become loose or even unnecessary.


title: Fast High-Accuracy Part-of-Speech Tagging by Independent Classifiers

abstract: Part-of-speech (POS) taggers can be quite accurate, but for practical use, accuracy often has to be sacrificed for speed. For example, the maintainers of the Stanford tagger (Toutanova et al., 2003; Manning, 2011) recommend tagging with a model whose per tag error rate is 17% higher, relatively, than their most accurate model, to gain a factor of 10 or more in speed. In this paper, we treat POS tagging as a single-token independent multiclass classification task. We show that by using a rich feature set we can obtain high tagging accuracy within this framework, and by employing some novel feature-weight-combination and hypothesis-pruning techniques we can also get very fast tagging with this model. A prototype tagger implemented in Perl is tested and found to be at least 8 times faster than any publicly available tagger reported to have comparable accuracy on the standard Penn Treebank Wall Street Journal test set.


title: A Computer-Aided Decision Support System for Detection and Localization of Cutaneous Vasculature in Dermoscopy Images Via Deep Feature Learning

abstract: Vascular structures of skin are important biomarkers in diagnosis and assessment of cutaneous conditions. Presence and distribution of lesional vessels are associated with specific abnormalities. Therefore, detection and localization of cutaneous vessels provide critical information towards diagnosis and stage status of diseases. However, cutaneous vessels are highly variable in shape, size, color and architecture, which complicate the detection task. Considering the large variability of these structures, conventional vessel detection techniques lack the generalizability to detect different vessel types and require separate algorithms to be designed for each type. Furthermore, such techniques are highly dependent on precise hand-crafted features which are time-consuming and computationally inefficient. As a solution, we propose a data-driven feature learning framework based on stacked sparse auto-encoders (SSAE) for comprehensive detection of cutaneous vessels. Each training image is divided into small patches of either containing or non-containing vasculature. A multilayer SSAE is designed to learn hidden features of the data in hierarchical layers in an unsupervised manner. The high-level learned features are subsequently fed into a classifier which categorizes each patch into absence or presence of vasculature and localizes vessels within the lesion. Over a test set of 3095 patches derived from 200 images, the proposed framework demonstrated superior performance of 95.4% detection accuracy over a variety of vessel patterns; outperforming other techniques by achieving the highest positive predictive value of 94.7%. The proposed Computer-Aided Diagnosis (CAD) framework can serve as a decision support system assisting dermatologists for more accurate diagnosis, especially in teledermatology applications in remote areas.


title: Delay-Dependent $\hbox{H}_{\infty }$ Filter Design for Discrete-Time Fuzzy Systems With Time-Varying Delays

abstract: This paper investigates delay-dependent Hinfin filter design problems for discrete-time fuzzy systems with time-varying delays. First, a novel delay-dependent piecewise Lyapunov-Krasovskii functional (DDPLKF) is proposed in which both the upper bound of delays and the delay interval are considered. Based on this DDPLKF, the delay-dependent stability criteria for discrete-time systems with constant or time-varying delays are obtained, respectively. Then, delay-dependent full-order and reduced-order Hinfin filter design approaches are proposed. The filter parameters can be obtained by solving a set of linear matrix inequalities (LMIs). Simulation examples are also given to illustrate the performance of the proposed approaches. It is shown that our approaches are less conservative and that the corresponding Hinfin filters can achieve better performance than the existing approaches.


title: Field evaluation of mulberry accessions for susceptibility to foliar diseases in Uasin-Gishu district, Kenya

abstract: The incidence and severity of foliar disease pathogens in five mulberry accessions (Embu, Thika, Thailand, Kanva-2 and S41) grown in Kenya were evaluated under field conditions in Eldoret. The plants were rated for disease incidence and severity in two mulberry growth cycles over a period of 10 months (May 2009 to February 2010). A high incidence of 58.3% bacterial leaf blight (Xanthomonas campestris pv. mori) disease prevalence was recorded on Embu accession, 36.6% incidence of fungal leaf blight (Alternaria alternata) on Thika and 26.0% for S41 accessions. Black leaf spot (Pseudocercospora mori) disease was high (48.93%) on the Thailand accession, while brown leaf spot (Myrothecium roridum) was recorded with a low (3.61%) incidence on the Kanva-2. Susceptibility of accessions to diseases was highest in Embu, followed by Thailand, Thika and S41 that were moderately susceptible, while Kanva-2 accession was resistant. Our results suggest that Kanva-2 accession can be utilized in future disease resistance breeding programmes to confer disease resistant trait in mulberry varieties.


title: Analysis and Design of Voltage-Controlled Oscillator Based Analog-to-Digital Converter

abstract: A voltage-controlled oscillator (VCO) based analog-to-digital converter (ADC) is a time-based architecture with a first-order noise-shaping property, which can be implemented using a VCO and digital circuits. This paper analyzes the performance of VCO-based ADCs in the presence of nonidealities such as jitter, nonlinearity, mismatch, and the metastability of D flip-flops. Based on this analysis, design criteria for determining parameters for VCO-based ADCs are described. In addition, a digital calibration technique to enhance the spurious-free dynamic range degraded by the nonlinearity is also introduced. To verify the theoretical analysis, a prototype chip is implemented in a 0.13-?m CMOS process. With a 500-MHz sampling frequency, the prototype achieves a signal-to-noise ratio ranging from 71.8 to 21.3 dB for an input bandwidth of 100 kHz-247 MHz, while dissipating 12.6 mW and occupying an area of 0.078 mm2.


title: Reverse Engineering of Biological Complexity

abstract: Advanced technologies and biology have extremely different physical implementations, but they are far more alike in systems-level organization than is widely appreciated. Convergent evolution in both domains produces modular architectures that are composed of elaborate hierarchies of protocols and layers of feedback regulation, are driven by demand for robustness to uncertain environments, and use often imprecise components. This complexity may be largely hidden in idealized laboratory settings and in normal operation, becoming conspicuous only when contributing to rare cascading failures. These puzzling and paradoxical features are neither accidental nor artificial, but derive from a deep and necessary interplay between complexity and robustness, modularity, feedback, and fragility. This review describes insights from engineering theory and practice that can shed some light on biological complexity.


title: Design and simulation of 13.56 MHz RFID tag in ink-jet printing technology

abstract: This paper presents design, modeling and simulation cost-effective RFID (Radio Frequency Identification) tag using ink-jet printing technology. Passive 13.56 MHz RFID tag is based on an inductive-capacitive resonant circuit. Designed RFID tag is part of the ID (Identification) card, which has standard ISO ID1 size (85.60 mm × 53.98 mm). It consists of rectangular spiral planar inductor (antenna) and interdigital capacitor. The modeling and optimization of RFID tag are discussed. At the first step, inductor and capacitor were designed using in-house developed simulation tools AntInd and INDICON, respectively, in order to provide proper electrical characteristics of tag. After that, the designed geometrical structures are modeled and simulated using CST Microwave Studio software. A radiation pattern at operational frequency of 13.56 MHz simulated in CST of the RFID tag insulated in free spaces is presented.


title: Esthetic Considerations in Interdental Papilla: Remediation and Regeneration

abstract: This article reviews the etiology and treatment of open gingival embrasures or black triangles. An open gingival embrasure or black triangle occurs as a result of a deficiency of papilla beneath the contact point. The treatment of open embrasures may require restorative, orthodontic and periodontal considerations depending on the underlying etiology. The authors reviewed a total of 42 articles including review of literature, radiographic, cross-sectional, and retrospective studies in Ovid search engine using the terms "open gingival embrasure,""interdental papilla," and "black triangle." The studies provided information regarding etiology, diagnosis, and treatment of open embrasures. There are several risk factors leading to the development of open gingival embrasures. These factors include aging, periodontal disease, loss of height of the alveolar bone relative to the interproximal contact, length of embrasure area, root angulations, interproximal contact position, and triangular-shaped crowns. Treatment of open embrasures requires an interdisciplinary approach of orthodontic, periodontic, and restorative treatment. CLINICAL SIGNIFICANCE Open gingival embrasures are complex esthetic and functional problems. An interdisciplinary team approach with the general dentist, orthodontist, and periodontist is critical. Management of open embrasures requires careful evaluation of the underlying etiology.


title: Attribute-based encryption for fine-grained access control of encrypted data

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


title: CRUST: cryptographic remote untrusted storage without public keys

abstract: This paper presents CRUST, a stackable file system layer designed to provide secure file sharing over remote untrusted storage systems. CRUST is intended to be layered over insecure network file systems without changing the existing systems. In our approach, data at rest is kept encrypted, and data integrity and access control are provided by cryptographic means. Our design completely avoids public-key cryptography operations and uses more efficient symmetric-key alternatives to achieve improved performance. As a generic and self-contained system, CRUST includes its own in-band key distribution mechanism and does not rely on any special capabilities of the server or the clients. We have implemented CRUST as a Linux file system and shown that it performs comparably with typical underlying file systems, while providing significantly stronger security.