title: Quantitative Assessment of Enterprise Security System

abstract: In this paper we extend a model-based approach to security management with concepts and methods that provide a possibility for quantitative assessments. For this purpose we introduce security metrics and explain how they are aggregated using the underlying model as a frame. We measure numbers of attack of certain threats and estimate their likelihood of propagation along the dependencies in the underlying model. Using this approach we can identify which threats have the strongest impact on business security ob jectives and how various security controls might differ with regard to their effect in reducing these threats.


title: Using Q Methodology in Quality Improvement Projects

abstract: Q methodology consists of a philosophical framework and procedures to identify subjective viewpoints that may not be well understood, but its use in nursing is still quite limited. We describe how Q methodology can be used in quality improvement projects to better understand local viewpoints that act as facilitators or barriers to the implementation of evidence-based practice. We describe the use of Q methodology to identify nurses' attitudes about the provision of skin-to-skin care after cesarean birth.


title: Parallel Algorithms for Using Lagrangian Markers in Immersed Boundary Method with Adaptive Mesh Refinement in FLASH

abstract: Computational fluid dynamics (CFD) are at the forefront of computational mechanics in requiring large-scale computational resources associated with high performance computing (HPC). Many flows of practical interest also include moving and deforming boundaries. High fidelity computations of fluid-structure interactions (FSI) are amongst the most challenging problems in computational mechanics. Additionally, many FSI applications have different resolution requirements in different parts of the domain and therefore requirement adaptive mesh refinement (AMR) for computational efficiency. FLASH is a well established AMR code with an existing Lagrangian framework which could be augmented and exploited to implement an immersed boundary method for simulating fluid-structure interactions atop an existing infrastructure. This paper describes the augmentations to the Lagrangian framework, and the new parallel algorithms added to the FLASH infrastructure that enabled the implementation of immersed boundary method in FLASH. The paper also presents scaling behavior and performance analysis of the implementations.


title: ROBUST CYLINDER FITTING IN THREE-DIMENSIONAL POINT CLOUD DATA

abstract: This paper investigates the problems of cylinder fitting in laser scanning three-dimensional Point Cloud Data (PCD). Most existing methods require full cylinder data, do not study the presence of outliers, and are not statistically robust. But especially mobile laser scanning often has incomplete data, as street poles for example are only scanned from the road. Moreover, existence of outliers is common. Outliers may occur as random or systematic errors, and may be scattered and/or clustered. In this paper, we present a statistically robust cylinder fitting algorithm for PCD that combines Robust Principal Component Analysis (RPCA) with robust regression. Robust principal components as obtained by RPCA allow estimating cylinder directions more accurately, and an existing efficient circle fitting algorithm following robust regression principles, properly fit cylinder. We demonstrate the performance of the proposed method on artificial and real PCD. Results show that the proposed method provides more accurate and robust results: (i) in the presence of noise and high percentage of outliers, (ii) for incomplete as well as complete data, (iii) for small and large number of points, and (iv) for different sizes of radius. On 1000 simulated quarter cylinders of 1m radius with 10% outliers a PCA based method fit cylinders with a radius of on average 3.63 meter (m); the proposed method on the other hand fit cylinders of on average 1.02 m radius. The algorithm has potential in applications such as fitting cylindrical (e.g., light and traffic) poles, diameter at breast height estimation for trees, and building and bridge information modelling.


title: Structuring E-Commerce Inventory

abstract: Large e-commerce enterprises feature millions of items entered daily by a large variety of sellers. While some sellers provide rich, structured descriptions of their items, a vast majority of them provide unstructured natural language descriptions. In the paper we present a 2 steps method for structuring items into descriptive properties. The first step consists in unsupervised property discovery and extraction. The second step involves supervised property synonym discovery using a maximum entropy based clustering algorithm. We evaluate our method on a year worth of e-commerce data and show that it achieves excellent precision with good recall.


title: A CMOS inverter-based class-AB pseudo differential amplifier for HF applications

abstract: This paper presents a CMOS inverter-based class-AB pseudo differential amplifier for HF applications using new simple rail-to-rail CMFB circuit. The proposed circuit employs two CMOS inverters and the complementary common-mode feedback (CMFB) consisting of current mode common-mode detector and transimpedance amplifiers. The circuit has been designed using 0.18 µm CMOS technology under 1 V supply, and the simulation results shows that the rail to rail output swing is achieved with low common-mode gain (−15 dB). The output swing of the circuit is 0.7 V. The power dissipation of the circuit is 96 µW.


title: Estimating CT Image From MRI Data Using Structured Random Forest and Auto-Context Model

abstract: Computed tomography (CT) imaging is an essential tool in various clinical diagnoses and radiotherapy treatment planning. Since CT image intensities are directly related to positron emission tomography (PET) attenuation coefficients, they are indispensable for attenuation correction (AC) of the PET images. However, due to the relatively high dose of radiation exposure in CT scan, it is advised to limit the acquisition of CT images. In addition, in the new PET and magnetic resonance (MR) imaging scanner, only MR images are available, which are unfortunately not directly applicable to AC. These issues greatly motivate the development of methods for reliable estimate of CT image from its corresponding MR image of the same subject. In this paper, we propose a learning-based method to tackle this challenging problem. Specifically, we first partition a given MR image into a set of patches. Then, for each patch, we use the structured random forest to directly predict a CT patch as a structured output, where a new ensemble model is also used to ensure the robust prediction. Image features are innovatively crafted to achieve multi-level sensitivity, with spatial information integrated through only rigid-body alignment to help avoiding the error-prone inter-subject deformable registration. Moreover, we use an auto-context model to iteratively refine the prediction. Finally, we combine all of the predicted CT patches to obtain the final prediction for the given MR image. We demonstrate the efficacy of our method on two datasets: human brain and prostate images. Experimental results show that our method can accurately predict CT images in various scenarios, even for the images undergoing large shape variation, and also outperforms two state-of-the-art methods.


A Generalized Join Algorithm.


title: A novel coupling factor independent highly efficient resonant based wireless power transfer

abstract: This paper presents a novel design methodology to address range and misalignment intolerance in strongly coupled magnetic resonance wireless power transfer (WPT) systems. A nonlinear resonator circuit is developed to compensate for the effect of coupling factor (k) variation between the transmit and receive coils and thereby maintaining high efficiency of WPT system. Such a method is implemented with an all passive circuit configuration without altering the operating frequency. The method is experimentally verified using a WPT circuit designed at 3.65 MHz. A near-constant wireless power transfer efficiency higher than 75% is achieved over 18 cm of transfer distance, exhibiting significant improvement as compared to a conventional linear WPT circuit operating under the same conditions.


title: Effects of functional groups on the crystallization of ferric (oxyhydr)oxides

abstract: Abstract Effects of four functional groups on the crystallization of ferric (oxyhydr)oxides were investigated in this paper. Hydroxyl ( OH), amino ( NH 2 ), methyl ( CH 3 ) and mercapto ( SH) were used to modify the surfaces. Substrates were immersed into the biomineral solution for 40 h. The biominerals crystallized on the surfaces have been examined by scanning electron microscopy (SEM) and X-ray diffraction (XRD). Atomic force microscope (AFM) was used to observe the morphology of the functional group surfaces, which turned out to be of the same density. Growth of orthorhombic FeOOH was identified on all four functional group surfaces, hematite was also detected on the NH 2 surface. While on the controlled surface of Au, no deposition of iron compounds was found. These results demonstrated different crystallization process of ferric (oxyhydr)oxides induced by functional groups.


title: Histogram-based gravitational optimization algorithm on single MR modality for automatic brain lesion detection and segmentation

abstract: Magnetic resonance imaging (MRI) is a very effective medical imaging technique for the clinical diagnosis and monitoring of neurological disorders. Because of intensity similarities between brain lesions and normal tissues, multispectral MRI modalities are usually applied for brain lesion detection. However, the time and cost restrictions for collecting multi-spectral MRI, and the issue of possible errors from registering multiple MR images necessitate developing an automatic lesion detection approach that can detect lesions using a single anatomical MRI modality. In this paper, an automatic algorithm for brain stroke and tumor lesion detection and segmentation using single-spectral MRI is presented. The proposed algorithm, called histogram-based gravitational optimization algorithm (HGOA), is a novel intensity-based segmentation technique, which applies enhanced gravitational optimization algorithm on histogram analysis results. The mathematical descriptions as well as the convergence criteria of the developed optimization algorithm are presented in detail. Using this algorithm, brain is segmented into different number of regions, which will be labeled as lesion or healthy. Here, the ischemic stroke lesions and tumor lesions are segmented with 91.5% and 88.1% accuracy, respectively.


title: Considering Race a Problem of Transfer Learning

abstract: As biometric applications are fielded to serve large population groups, issues of performance differences between individual sub-groups are becoming increasingly important. In this paper we examine cases where we believe race is one such factor. We look in particular at two forms of problem; facial classification and image synthesis. We take the novel approach of considering race as a boundary for transfer learning in both the task (facial classification) and the domain (synthesis over distinct datasets). We demonstrate a series of techniques to improve transfer learning of facial classification; outperforming similar models trained in the target's own domain. We conduct a study to evaluate the performance drop of Generative Adversarial Networks trained to conduct image synthesis, in this process, we produce a new annotation for the Celeb-A dataset by race. These networks are trained solely on one race and tested on another - demonstrating the subsets of the CelebA to be distinct domains for this task.


title: A flexible unmanned aerial vehicle for precision agriculture

abstract: An unmanned aerial vehicle (“VIPtero”) was assembled and tested with the aim of developing a flexible and powerful tool for site-specific vineyard management. The system comprised a six-rotor aerial platform capable of flying autonomously to a predetermined point in space, and of a pitch and roll compensated multi-spectral camera for vegetation canopy reflectance recording. Before the flight campaign, the camera accuracy was evaluated against high resolution ground-based measurements, made with a field spectrometer. Then, “VIPtero” performed the flight in an experimental vineyard in Central Italy, acquiring 63 multi-spectral images during 10 min of flight completed almost autonomously. Images were analysed and classified vigour maps were produced based on normalized difference vegetation index. The resulting vigour maps showed clearly crop heterogeneity conditions, in good agreement with ground-based observations. The system provided very promising results that encourage its development as a tool for precision agriculture application in small crops.


title: A Parallel Architecture for IISPH Fluids

abstract: We present an architecture for parallel computation of incompressible IISPH simulations on distributed memory systems. We use orthogonal recursive bisection for domain decomposition and present a stable and fast converging load balancing controller. The neighbor search data structure is derived such that it optimally fits into the parallel pipeline. We further show how symmetry aspects of the simulation can be integrated into the architecture. Simultaneous communication and computation are used to minimize parallelization overhead. The seamless integration of these parallel concepts into IISPH results in near linear scaling for large-scale simulations.


title: An Aspect-Oriented Approach for SaaS Application Customization

abstract: Multi-tenancy is one of the most important concepts for any Software as a Service (SaaS) application. Multi-tenant SaaS application serves a large number of tenants with one single application instance. Complex SaaS application that serves significant number of tenants could have a huge number of customizations with complicated relationships, which increases the customization complexity and reduces the customization understandability. Modeling such customizations, validating each tenant's customization, and adapting SaaS applications on the fly based on each tenant's requirements become very complex tasks. To mitigate these challenges, we propose an aspect-oriented approach that makes use of the Orthogonal Variability Model (OVM) and Metagraphs. The OVM is used to provide the tenants with simple and understandable customization model. A Metagraph-based algorithm has been developed to validate tenants' customizations. On the other hand, the aspect-oriented approach offers a high level of runtime adaptability.


title: Lifting the malar fat pad for correction of prominent nasolabial folds.

abstract: The anatomic presence of a discrete malar fat pad has not been widely recognized. In his paper on the "deep plane rhytidectomy," Hamra describes the increased thickness of the subcutaneous fat over the anterior midface. Cadaver and clinical studies confirm the consistent presence of a localized subcutaneous malar fat pad overlying the body of the zygoma and maxilla. Downward displacement of the infraorbital skin and underlying malar fat pad causes an increased prominence of the nasolabial fold. The surgical dissection of the malar fat pad starts from the malar eminence at the plane of the orbicularis oculi muscle and superficial to the origin of the zygomaticus and levator muscles, which are invested by the SMAS. There is an easily dissected plane deep to the fat pad that extends to the nasolabial crease. Surgical correction of the prominent nasolabial fold is enhanced by undermining the malar fat pad and advancing it laterally by traction on the skin flap with additional upward fixation by sutures between the lateral edge of the fat pad and the subcutaneous fascia at the lateral malar eminence.


title: � Human Brain Mapping 24:109–129(2005) � Functional Principal Component Analysis of fMRI Data

abstract: We describe a principal component analysis (PCA) method for functional magnetic resonance imaging (fMRI) data based on functional data analysis, an advanced nonparametric approach. The data delivered by the fMRI scans are viewed as continuous functions of time sampled at the interscan interval and subject to observational noise, and are used accordingly to estimate an image in which smooth functions replace the voxels. The techniques of functional data analysis are used to carry out PCA directly on these functions. We show that functional PCA is more effective than is its ordinary counterpart in recovering the signal of interest, even if limited or no prior knowledge of the form of hemodynamic function or the structure of the experimental design is specified. We discuss the rationale and advantages of the proposed approach relative to other exploratory methods, such as clustering or independent component analysis, as well as the differences from methods based on expanded design matrices. Hum Brain Mapp 24:109 -129, 2005. © 2004 Wiley-Liss, Inc.


title: ECG-based biometrics using recurrent neural networks

abstract: In this paper, we propose the use of recurrent neural networks (RNNs) to develop an effective solution to two problems in electrocardiogram (ECG)-based biometrics: identification/classification and authentication. Different RNN architectures with various parameter settings were evaluated, including traditional, long short-term memory (LSTM), gated recurrent unit (GRU), unidirectional, and bidirectional networks. Unlike many existing methods, the RNN-based method does not require any feature extraction. The method was evaluated using two publicly available datasets: ECG-ID and MIT-BIH Arrhythmia (MITDB). For the identification problem, nearly 100% classification accuracy on the ECG-ID dataset was achieved, and similar results were observed for the MITDB dataset. For the authentication problem, an RNN was trained and the hidden state at the final time step was extracted to make a decision. We evaluated the effect of the training size on the equal error rate (EER), and showed that the EER drops from approximately 3.5% to nearly 0% as we increased the percentage of subjects used for training from approximately 15% to 80%.


title: Chitosan nanoparticles for oral drug and gene delivery

abstract: Chitosan is a widely available, mucoadhesive polymer that is able to increase cellular permeability and improve the bioavailability of orally administered protein drugs. It can also be readily formed into nanoparticles able to entrap drugs or condense plasmid DNA. Studies on the formulation and oral delivery of such chitosan nanoparticles have demonstrated their efficacy in enhancing drug uptake and promoting gene expression. This review summarizes some of these findings and highlights the potential of chitosan as a component of oral delivery systems.


title: Kickstarting Deep Reinforcement Learning

abstract: We present a method for using previously-trained 'teacher' agents to kickstart the training of a new 'student' agent. To this end, we leverage ideas from policy distillation and population based training. Our method places no constraints on the architecture of the teacher or student agents, and it regulates itself to allow the students to surpass their teachers in performance. We show that, on a challenging and computationally-intensive multi-task benchmark (DMLab-30), kickstarted training improves the data efficiency of new agents, making it significantly easier to iterate on their design. We also show that the same kickstarting pipeline can allow a single student agent to leverage multiple 'expert' teachers which specialize on individual tasks. In this setting kickstarting yields surprisingly large gains, with the kickstarted agent matching the performance of an agent trained from scratch in almost 10x fewer steps, and surpassing its final performance by 42 percent. Kickstarting is conceptually simple and can easily be incorporated into reinforcement learning experiments.


title: Automatic table detection and retention from scanned document images via analysis of structural information

abstract: The problem of automatic table detection has always been a great topic of debate in the field of Document Analysis and Recognition (DAR). Digital documents are efficient than their printed counterparts for storage, maintenance and republishing. Being a non-textual object of a document, tables prevent OCR system to digitize a document perfectly and distorts layout and structure of digitized documents. There is no available algorithm or method which solves this problem for all possible types of tables. This paper tackles the problem of table detection and retention by proposing a bi-modular approach based on structural information of tables. This structural information includes bounding lines, row/column separators and space between columns. Through analysis of these properties, our experiments on a dataset of above 600 images consisting of more than 829 tables have detected 90% of the table correctly.


title: Collective Matrix Factorization Hashing for Multimodal Data

abstract: Nearest neighbor search methods based on hashing have attracted considerable attention for effective and efficient large-scale similarity search in computer vision and information retrieval community. In this paper, we study the problems of learning hash functions in the context of multimodal data for cross-view similarity search. We put forward a novel hashing method, which is referred to Collective Matrix Factorization Hashing (CMFH). CMFH learns unified hash codes by collective matrix factorization with latent factor model from different modalities of one instance, which can not only supports cross-view search but also increases the search accuracy by merging multiple view information sources. We also prove that CMFH, a similarity-preserving hashing learning method, has upper and lower boundaries. Extensive experiments verify that CMFH significantly outperforms several state-of-the-art methods on three different datasets.


title: A New Gaze-BCI-Driven Control of an Upper Limb Exoskeleton for Rehabilitation in Real-World Tasks

abstract: This paper proposes a new multimodal architecture for gaze-independent brain-computer interface (BCI)-driven control of a robotic upper limb exoskeleton for stroke rehabilitation to provide active assistance in the execution of reaching tasks in a real setting scenario. At the level of action plan, the patient's intention is decoded by means of an active vision system, through the combination of a Kinect-based vision system, which can online robustly identify and track 3-D objects, and an eye-tracking system for objects selection. At the level of action generation, a BCI is used to control the patient's intention to move his/her own arm, on the basis of brain activity analyzed during motor imagery. The main kinematic parameters of the reaching movement (i.e., speed, acceleration, and jerk) assisted by the robot are modulated by the output of the BCI classifier so that the robot-assisted movement is performed under a continuous control of patient's brain activity. The system was experimentally evaluated in a group of three healthy volunteers and four chronic stroke patients. Experimental results show that all subjects were able to operate the exoskeleton movement by BCI with a classification error rate of 89.4±5.0% in the robot-assisted condition, with no difference of the performance observed in stroke patients compared with healthy subjects. This indicates the high potential of the proposed gaze-BCI-driven robotic assistance for neurorehabilitation of patients with motor impairments after stroke since the earliest phase of recovery.


title: Deception and design: the impact of communication technology on lying behavior

abstract: Social psychology has demonstrated that lying is an important, and frequent, part of everyday social interactions. As communication technologies become more ubiquitous in our daily interactions, an important question for developers is to determine how the design of these technologies affects lying behavior. The present research reports the results of a diary study, in which participants recorded all of their social interactions and lies for seven days. The data reveal that participants lied most on the telephone and least in email, and that lying rates in face-to-face and instant messaging interactions were approximately equal. This pattern of results suggests that the design features of communication technologies (e.g., synchronicity, recordability, and copresence) affect lying behavior in important ways, and that these features must be considered by both designers and users when issues of deception and trust arise. The implications for designing applications that increase, decrease or detect deception are discussed.


AlQuAnS – An Arabic Language Question Answering System


title: Sensorless Current Estimation and Sharing in Multiphase Buck Converters

abstract: Virtually, all of the current-sensing methods for power converters presented to date rely on a priori knowledge of circuit parameters to ensure accurate current measurement. Also, the advent of digital control in low-cost high-volume applications-such as voltage regulator modules (VRMs) for computer microprocessors-has added the challenge and cost of analog-to-digital conversion to this task. This paper introduces a parameter-independent, sensorless current-sharing algorithm for multiphase power converters based on gradient estimation via low-frequency perturbation of the per-phase duty cycles, thus eliminating the need for current sensing. Measures of only the input and output voltages are required, so the analog-to-digital converter (ADC) and communication/interfacing overhead is low. The algorithm is demonstrated through both simulation and experimental implementation using a digitally controlled three-phase buck converter.


Biological Effects of Very Low Frequency ( VLF ) Atmospherics in Humans : A Review


title: Differential Learning as a Key Training Approach to Improve Creative and Tactical Behavior in Soccer

abstract: ABSTRACTPurpose: The aim of this study was to identify the effects of a differential-learning program, embedded in small-sided games, on the creative and tactical behavior of youth soccer players. Forty players from under-13 (U13) and under-15 (U15) were allocated into control and experimental groups and were tested using a randomized pretest to posttest design using small-sided games situations. Method: The experimental group participated in a 5-month differential-learning program embodied in small-sided games situations, while the control group participated in a typical small-sided games training program. In-game creativity was assessed through notational analyses of the creative components, and the players’ positional data were used to compute tactical-derived variables. Results: The findings suggested that differential learning facilitated the development of creative components, mainly concerning attempts (U13, small; U15, small), versatility (U13, moderate; U15, small), and originality (U13, unclear;...


title: Two sites of synaptic integration: relevant for learning?

abstract: Electrophysiological research on the properties of the apical dendrites of cortical deep layer pyramidal cells suggests that it acts, in addition to the soma, as a second site of synaptic integration. Each site integrates input from a subset of synapses and is able to generate regenerative potentials. The sites exchange information in stereotyped ways: Signals from the soma are transmitted to the apical dendrite via actively back-propagating dendritic action potentials. Slow regenerative calcium spikes transmit information from the apical dendrite to the soma. These calcium spikes lead to a strong and prolonged depolarization of the cell generating a burst of action potentials. This paper analyzes how the system learns if these calcium spikes trigger hebbian learning at active synapses. A cell is now described by two main variables the mean activity and the mean potential at the apical dendrite with the first variable defining the input to cells downstream and the latter what the cell learns. A system results where neurons learn to respond to those features that are correlated with activity on the higher layer, this property is similar to maximizing the mutual information between input and higher areas. Furthermore it learns invariances exploiting a spatial smoothness criterion.


title: Analyzing users' sentiment towards popular consumer industries and brands on Twitter

abstract: Social media serves as a unified platform for users to express their thoughts on subjects ranging from their daily lives to their opinion on consumer brands and products. These users wield an enormous influence in shaping the opinions of other consumers and influence brand perception, brand loyalty and brand advocacy. In this paper, we analyze the opinion of 19M Twitter users towards 62 popular industries, encompassing 12,898 enterprise and consumer brands, as well as associated subject matter topics, via sentiment analysis of 330M tweets over a period spanning a month. We find that users tend to be most positive towards manufacturing and most negative towards service industries. In addition, they tend to be more positive or negative when interacting with brands than generally on Twitter. We also find that sentiment towards brands within an industry varies greatly and we demonstrate this using two industries as use cases. In addition, we discover that there is no strong correlation between topic sentiments of different industries, demonstrating that topic sentiments are highly dependent on the context of the industry that they are mentioned in. We demonstrate the value of such an analysis in order to assess the impact of brands on social media. We hope that this initial study will prove valuable for both researchers and companies in understanding users' perception of industries, brands and associated topics and encourage more research in this field.


title: The impact of data integration on the costs and benefits of information systems

abstract: For many organizations, the ability to make coordinated, organization-wide responses to today's business problems is thwarted by the lack of data integration or commonly defined data elements and codes across different information systems. Though many researchers and practitioners have implicitly assumed that data integration always results in net benefits to an organization, this article questions that view. Based on theories of organizational information processing, a model of the impact of data integration is developed that includes gains in organization-wide coordination and organization-wide decision making, as well as losses in local autonomy and flexibility, and changes in system design and implementation costs. The importance of each of these impacts is defended by theoretical arguments and illustrated by case examples. This model suggests that the benefits of data integration will outweigh costs only under certain situations, and probably not for all the data the organization uses. Therefore, MIS researchers and practitioners should consider the need for better conceptualization and methods for implementing "partial integration" in organization.


title: Smart GSM Based Home Automation System

abstract: This research work investigates the potential of Full Home Control, which is the aim of the Home Automation Systems in near future. The analysis and implementation of the home automation technology using Global System for Mobile Communication (GSM) modem to control home appliances such as light, conditional system, and security system via Short Message Service (SMS) text messages is presented in this paper. The proposed research work is focused on the functionality of the GSM protocol, which allows the user to control the target system away from residential using the frequency bandwidths. The concept of serial communication and AT-commands has been applied towards the development of the smart GSM-based home automation system. Homeowners will be able to receive feedback status of any home appliances under control whether switched on or off remotely from their mobile phones. PIC16F887 microcontroller with the integration of GSM provides the smart automated house system with the desired baud rate of 9600 bps. The proposed prototype of GSM based home automation system was implemented and tested with a maximum of four loads and shows the accuracy of greater or equal 98%.


title: Pictory - neural style transfer and editing with coreML

abstract: This work presents advances in the design and implementation of Pictory, an iOS app for artistic neural style transfer and interactive image editing using the CoreML and Metal APIs. Pictory combines the benefits of neural style transfer, e.g., high degree of abstraction on a global scale, with the interactivity of GPU-accelerated state-of-the-art image-based artistic rendering on a local scale. Thereby, the user is empowered to create high-resolution, abstracted renditions in a two-stage approach. First, a photo is transformed using a pre-trained convolutional neural network to obtain an intermediate stylized representation. Second, image-based artistic rendering techniques (e.g., watercolor, oil paint or toon filtering) are used to further stylize the image. Thereby, fine-scale texture noise---introduced by the style transfer---is filtered and interactive means are provided to individually adjust the stylization effects at run-time. Based on qualitative and quantitative user studies, Pictory has been redesigned and optimized to support casual users as well as mobile artists by providing effective, yet easy to understand, tools to facilitate image editing at multiple levels of control.


title: Recognizing the Absence of Opposing Arguments in Persuasive Essays

abstract: In this paper, we introduce an approach for recognizing the absence of opposing arguments in persuasive essays. We model this task as a binary document classification and show that adversative transitions in combination with unigrams and syntactic production rules significantly outperform a challenging heuristic baseline. Our approach yields an accuracy of 75.6% and 84% of human performance in a persuasive essay corpus with various topics.


title: Liquid: Unifying Nearline and Offline Big Data Integration

abstract: With more sophisticated data-parallel processing systems, the new bottleneck in data-intensive companies shifts from the back-end data systems to the data integration stack, which is responsible for the pre-processing of data for back-end applications. The use of back-end data systems with different access latencies and data integration requirements poses new challenges that current data integration stacks based on distributed file systems—proposed a decade ago for batch-oriented processing—cannot address. In this paper, we describe Liquid, a data integration stack that provides low latency data access to support near real-time in addition to batch applications. It supports incremental processing, and is cost-efficient and highly available. Liquid has two layers: a processing layer based on a stateful stream processing model, and a messaging layer with a highly-available publish/subscribe system. We report our experience of a Liquid deployment with backend data systems at LinkedIn, a data-intensive company with over 300 million users.


title: Wideband butler matrix in microstrip-slot technology

abstract: The design of a 4 × 4 Butler Matrix for use in a multi-beam antenna array operating over wide frequency band is presented. In order to achieve wideband characteristics, the matrix uses microstrip-slot technology that employs two microstrip dielectric layers with a common ground plane and a coupling slot. The designed beamforming network is compact in size and does not use crossovers. Its prototype is manufactured. The performance of the designed device is assessed via full-wave electromagnetic simulations and measurements.


title: Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback.

abstract: In this paper, we propose an extractive multi-document summarization (MDS) system using joint optimization and active learning for content selection grounded in user feedback. Our method interactively obtains user feedback to gradually improve the results of a state-of-the-art integer linear programming (ILP) framework for MDS. Our methods complement fully automatic methods in producing high-quality summaries with a minimum number of iterations and feedbacks. We conduct multiple simulation-based experiments and analyze the effect of feedback-based concept selection in the ILP setup in order to maximize the user-desired content in the summary.


title: Web Usability : Principles and Evaluation Methods

abstract: Current Web applications are very complex and highly sophisticated software products, whose usability can greatly determine their success or failure. Defining methods for ensuring usability is one of the current goals of Web engineering research. Also, much attention is currently paid to usability by industry, recognising the importance of adopting methods for usability evaluation before and after application deployment. This chapter introduces principles and evaluation methods to be adopted during the whole application lifecycle for promoting usability. For each evaluation method, the main features, as well as the emerging advantages and drawbacks, are illustrated so as to support the choice of an evaluation plan that best fits the goals to be pursued and the available resources. The design and evaluation of a real application is also described for exemplifying the concepts and methods introduced.


title: Practical Concurrent Unrolled Linked Lists Using Lazy Synchronization

abstract: Linked lists and other list-based sets are one of the most ubiquitous data structures in computer science. They are useful in their own right and are frequently used as building blocks in other data structures. A linked list can be “unrolled” to combine multiple keys in each node; this improves storage density and overall performance. This organization also allows an operation to skip over nodes which cannot contain a key of interest. This paper introduces a new high-performance concurrent unrolled linked list with a lazy synchronization strategy that allows wait-free read operations. Most write operations under this strategy can complete by locking a single node. Experiments show up to a 300% improvement over other concurrent list-based sets.


title: In front of and behind the second screen: viewer and producer perspectives on a companion app

abstract: The growing success of tablets and smartphones has shifted the focus of the interactive TV industry to the introduction of second screen applications. One example is second screen companion apps that offer extra information about a television program, often synchronized with what happens on screen. In this paper, we investigate a second screen companion app, from the perspective of the viewers and producers of such apps. Based on observations and interviews with viewers and producers, and actual usage data of a companion app from Google Analytics, we present several insights and recommendations for how to design companion apps related to ease of use, timing, social interaction, attention and added value.


title: Improving the quality of code snippets in stack overflow

abstract: Question and answer (Q&A) websites like Stack Overflow are one of the important sources of code examples in which developers can ask their questions and leave their answers about programming issues. Since the number of programmers who use these websites are increasing and a large number of questions and answers are being posted there by them, verifying the quality of all the answers and particularly the code snippets in them is impossible. Consequently, some code snippets might be of low quality and/or with faults. To mitigate this issue, we introduce ExRec ( Ex ample Rec ommender), an Eclipse plugin with which programmers can contribute in improving the quality of code snippets in the answers to questions in the Stack Overow website. Using ExRec, a programmer can transfer a code snippet in the answer to a desired question in Stack Overow to her own program, apply her desired modifications to that code snippet, and then contribute the improved code snippet to Stack Overow as an answer to that particular question. Our evaluations of ExRec in a user study with 10 skilled programmers indicate that it is effective in practice and can help in improving the quality of code snippets in Stack Overow.


title: Polite Dialogue Generation Without Parallel Data

abstract: Stylistic dialogue response generation, with valuable applications in personality-based conversational agents, is a challenging task because the response needs to be fluent, contextually-relevant, ...


title: Gene therapy comes of age

abstract: BACKGROUND Nearly five decades ago, visionary scientists hypothesized that genetic modification by exogenous DNA might be an effective treatment for inherited human diseases. This “gene therapy” strategy offered the theoretical advantage that a durable and possibly curative clinical benefit would be achieved by a single treatment. Although the journey from concept to clinical application has been long and tortuous, gene therapy is now bringing new treatment options to multiple fields of medicine. We review critical discoveries leading to the development of successful gene therapies, focusing on direct in vivo administration of viral vectors, adoptive transfer of genetically engineered T cells or hematopoietic stem cells, and emerging genome editing technologies. ADVANCES The development of gene delivery vectors such as replication-defective retro viruses and adeno-associated virus (AAV), coupled with encouraging results in preclinical disease models, led to the initiation of clinical trials in the early 1990s. Unfortunately, these early trials exposed serious therapy-related toxicities, including inflammatory responses to the vectors and malignancies caused by vector-mediated insertional activation of proto-oncogenes. These setbacks fueled more basic research in virology, immunology, cell biology, model development, and target disease, which ultimately led to successful clinical translation of gene therapies in the 2000s. Lentiviral vectors improved efficiency of gene transfer to nondividing cells. In early-phase clinical trials, these safer and more efficient vectors were used for transduction of autologous hematopoietic stem cells, leading to clinical benefit in patients with immunodeficiencies, hemoglobinopathies, and metabolic and storage disorders. T cells engineered to express CD19-specific chimeric antigen receptors were shown to have potent antitumor activity in patients with lymphoid malignancies. In vivo delivery of therapeutic AAV vectors to the retina, liver, and nervous system resulted in clinical improvement in patients with congenital blindness, hemophilia B, and spinal muscular atrophy, respectively. In the United States, Food and Drug Administration (FDA) approvals of the first gene therapy products occurred in 2017, including chimeric antigen receptor (CAR)–T cells to treat B cell malignancies and AAV vectors for in vivo treatment of congenital blindness. Promising clinical trial results in neuromuscular diseases and hemophilia will likely result in additional approvals in the near future. In recent years, genome editing technologies have been developed that are based on engineered or bacterial nucleases. In contrast to viral vectors, which can mediate only gene addition, genome editing approaches offer a precise scalpel for gene addition, gene ablation, and gene “correction.” Genome editing can be performed on cells ex vivo or the editing machinery can be delivered in vivo to effect in situ genome editing. Translation of these technologies to patient care is in its infancy in comparison to viral gene addition therapies, but multiple clinical genome editing trials are expected to open over the next decade. OUTLOOK Building on decades of scientific, clinical, and manufacturing advances, gene therapies have begun to improve the lives of patients with cancer and a variety of inherited genetic diseases. Partnerships with biotechnology and pharmaceutical companies with expertise in manufacturing and scale-up will be required for these therapies to have a broad impact on human disease. Many challenges remain, including understanding and preventing genotoxicity from integrating vectors or off-target genome editing, improving gene transfer or editing efficiency to levels necessary for treatment of many target diseases, preventing immune responses that limit in vivo administration of vectors or genome editing complexes, and overcoming manufacturing and regulatory hurdles. Importantly, a societal consensus must be reached on the ethics of germline genome editing in light of rapid scientific advances that have made this a real, rather than hypothetical, issue. Finally, payers and gene therapy clinicians and companies will need to work together to design and test new payment models to facilitate delivery of expensive but potentially curative therapies to patients in need. The ability of gene therapies to provide durable benefits to human health, exemplified by the scientific advances and clinical successes over the past several years, justifies continued optimism and increasing efforts toward making these therapies part of our standard treatment armamentarium for human disease.


title: SPG-Net: Segmentation Prediction and Guidance Network for Image Inpainting

abstract: In this paper, we focus on image inpainting task, aiming at recovering the missing area of an incomplete image given the context information. Recent development in deep generative models enables an efficient end-to-end framework for image synthesis and inpainting tasks, but existing methods based on generative models don't exploit the segmentation information to constrain the object shapes, which usually lead to blurry results on the boundary. To tackle this problem, we propose to introduce the semantic segmentation information, which disentangles the inter-class difference and intra-class variation for image inpainting. This leads to much clearer recovered boundary between semantically different regions and better texture within semantically consistent segments. Our model factorizes the image inpainting process into segmentation prediction (SP-Net) and segmentation guidance (SG-Net) as two steps, which predict the segmentation labels in the missing area first, and then generate segmentation guided inpainting results. Experiments on multiple public datasets show that our approach outperforms existing methods in optimizing the image inpainting quality, and the interactive segmentation guidance provides possibilities for multi-modal predictions of image inpainting.


title: Design of reactionless 3-DOF and 6-DOF parallel manipulators using parallelepiped mechanisms

abstract: In this paper, the design of reactionless 3-degree-of-freedom (DOF) and 6-DOF parallel manipulators is presented. At first, the design and dynamic balancing of a novel 3-DOF parallel mechanism referred to as the parallelepiped mechanism are addressed. Two types of actuation schemes of the mechanism are considered, and the two corresponding mechanical structures are designed. The balancing equations are derived by imposing that the center of mass of the mechanism is fixed and that the total angular momentum is constant with respect to a fixed point. Optimization is performed to determine the counterweights and counter-rotations based on the balancing conditions. Numerical examples of reactionless 3-DOF parallelepiped mechanisms are given. The dynamic simulation software ADAMS is used to simulate the motion of the mechanisms and to verify that the mechanisms are reactionless at all times and for arbitrary trajectories. Finally, the 3-DOF parallelepiped mechanisms are used as legs to synthesize reactionless 6-DOF parallel manipulators.


title: Optimal resistance of stress grading system in converter-fed rotating machines

abstract: Excessive heat generation and occurrence of partial discharge have been observed in end-turn stress grading (SG) system in form-wound machines under PWM voltage. In this paper, multi-winding stress grading (SG) system is proposed as a method to change resistance of SG per length. Although the maximum field at the edge of stator and CAT are in a trade-off relationship, analytical results suggest that we can suppress field and excessive heat generation at both stator and CAT edges by multi-winding of SG and setting the length of CAT appropriately. This is also experimentally confirmed by measuring potential distribution of model bar-coil and observing partial discharge and temperature rise.


title: An Analysis Of The AskMSR Question-Answering System

abstract: We describe the architecture of the AskMSR question answering system and systematically evaluate contributions of different system components to accuracy. The system differs from most question answering systems in its dependency on data redundancy rather than sophisticated linguistic analyses of either questions or candidate answers. Because a wrong answer is often worse than no answer, we also explore strategies for predicting when the question answering system is likely to give an incorrect answer.


title: X-Band Substrate Integrated Waveguide Amplifier

abstract: Design and realization of a compact X-band single-transistor amplifier with substrate integrated waveguide (SIW)-based input and output matching networks is presented. The overall size of the proposed SIW amplifier is only 1.5lambdag at the center frequency. Using a calibration technique, we extract the S-parameters of the fabricated amplifier with reference to its SIW ports. Measurements show that the amplifier features 10 dB of power gain with less than 2 dB of ripple and more than 10 dB of input and output return losses on the SIW ports in the entire frequency band. Due to an appropriate modeling of the constituent blocks of the amplifier, a good agreement between the simulation and measurement results is observed.


title: A moving-average filter based hybrid ARIMA-ANN model for forecasting time series data

abstract: A suitable combination of linear and nonlinear models provides a more accurate prediction model than an individual linear or nonlinear model for forecasting time series data originating from various applications. The linear autoregressive integrated moving average (ARIMA) and nonlinear artificial neural network (ANN) models are explored in this paper to devise a new hybrid ARIMA-ANN model for the prediction of time series data. Many of the hybrid ARIMA-ANN models which exist in the literature apply an ARIMA model to given time series data, consider the error between the original and the ARIMA-predicted data as a nonlinear component, and model it using an ANN in different ways. Though these models give predictions with higher accuracy than the individual models, there is scope for further improvement in the accuracy if the nature of the given time series is taken into account before applying the models. In the work described in this paper, the nature of volatility was explored using a moving-average filter, and then an ARIMA and an ANN model were suitably applied. Using a simulated data set and experimental data sets such as sunspot data, electricity price data, and stock market data, the proposed hybrid ARIMA-ANN model was applied along with individual ARIMA and ANN models and some existing hybrid ARIMA-ANN models. The results obtained from all of these data sets show that for both one-step-ahead and multistep-ahead forecasts, the proposed hybrid model has higher prediction accuracy.


title: Fairness in an Unfair World: Fair Multiparty Computation from Public Bulletin Boards

abstract: Secure multiparty computation allows mutually distrusting parties to compute a function on their private inputs such that nothing but the function output is revealed. Achieving fairness --- that all parties learn the output or no one does -- is a long studied problem with known impossibility results in the standard model if a majority of parties are dishonest. We present a new model for achieving fairness in MPC against dishonest majority by using public bulletin boards implemented via existing infrastructure such as blockchains or Google's certificate transparency logs. We present both theoretical and practical constructions using either witness encryption or trusted hardware (such as Intel SGX). Unlike previous works that either penalize an aborting party or achieve weaker notions such as $\Delta$-fairness, we achieve complete fairness using existing infrastructure.


title: Internal and external dynamics in language: Evidence from verb regularity in a historical corpus of English

abstract: Human languages are rule governed, but almost invariably these rules have exceptions in the form of irregularities. Since rules in language are efficient and productive, the persistence of irregularity is an anomaly. How does irregularity linger in the face of internal (endogenous) and external (exogenous) pressures to conform to a rule? Here we address this problem by taking a detailed look at simple past tense verbs in the Corpus of Historical American English. The data show that the language is open, with many new verbs entering. At the same time, existing verbs might tend to regularize or irregularize as a consequence of internal dynamics, but overall, the amount of irregularity sustained by the language stays roughly constant over time. Despite continuous vocabulary growth, and presumably, an attendant increase in expressive power, there is no corresponding growth in irregularity. We analyze the set of irregulars, showing they may adhere to a set of minority rules, allowing for increased stability of irregularity over time. These findings contribute to the debate on how language systems become rule governed, and how and why they sustain exceptions to rules, providing insight into the interplay between the emergence and maintenance of rules and exceptions in language.


title: Handwriting development, competency, and intervention

abstract: Failure to attain handwriting competency during the school-age years often has far-reaching negative effects on both academic success and self-esteem. This complex occupational task has many underlying component skills that may interfere with handwriting performance. Fine motor control, bilateral and visual–motor integration, motor planning, in-hand manipulation, proprioception, visual perception, sustained attention, and sensory awareness of the fingers are some of the component skills identified. Poor handwriting may be related to intrinsic factors, which refer to the child's actual handwriting capabilities, or extrinsic factors which are related to environmental or biomechanical components, or both. It is important that handwriting performance be evaluated using a valid, reliable, standardized tool combined with informal classroom observation and teacher consultation. Studies of handwriting remediation suggest that intervention is effective. There is evidence to indicate that handwriting difficulties do not resolve without intervention and affect between 10 and 30% of school-aged children. Despite the widespread use of computers, legible handwriting remains an important life skill that deserves greater attention from educators and health practitioners.


title: Segmentation and Area Measurement for Thyroid Ultrasound Image

abstract: The thyroid measurement and recognition system is very useful in the medical field because the measurement of thyroid is important for the doctor diagnostic and medical analysis. In this paper, we present a simple guide of determine the thyroid lobes in the thyroid ultrasound image using a MATLAB. The image undergoes the contrast enhancement to suppress speckle. The enhancement image is used for further processing of segmentation the thyroid region by local region-based active contour. The thyroid region is segmented into two parts, which are right and left with the active contour method separately. This is accordingly to the thyroid have two lobes; right lobe and left lobe. Thyroid ultrasound image of transverse view is used in this study. Therefore, the measurements only involve the width, depth and area of the thyroid region. The result of thyroid measurement is successfully calculated in pixel unit. The measurement is converted in centimetre (cm) unit. The proposed method is benefited to enhance the image and segmentation the thyroid lobe. It shows that from five samples, different people have different size of thyroid, especially in measurement of the width, depth and area.


title: Traffic Sign Classification Using Deep Inception Based Convolutional Networks

abstract: In this work, we propose a novel deep network for traffic sign classification that achieves outstanding performance on GTSRB surpassing all previous methods. Our deep network consists of spatial transformer layers and a modified version of inception module specifically designed for capturing local and global features together. This features adoption allows our network to classify precisely intraclass samples even under deformations. Use of spatial transformer layer makes this network more robust to deformations such as translation, rotation, scaling of input images. Unlike existing approaches that are developed with hand-crafted features, multiple deep networks with huge parameters and data augmentations, our method addresses the concern of exploding parameters and augmentations. We have achieved the state-of-the-art performance of 99.81\% on GTSRB dataset.


title: The pandemic of physical inactivity: global action for public health

abstract: Physical inactivity is the fourth leading cause of death worldwide. We summarise present global eff orts to counteract this problem and point the way forward to address the pandemic of physical inactivity. Although evidence for the benefi ts of physical activity for health has been available since the 1950s, promotion to improve the health of populations has lagged in relation to the available evidence and has only recently developed an identifi able infrastructure, including eff orts in planning, policy, leadership and advocacy, workforce training and development, and monitoring and surveillance. The reasons for this late start are myriad, multifactorial, and complex. This infrastructure should continue to be formed, intersectoral approaches are essential to advance, and advocacy remains a key pillar. Although there is a need to build global capacity based on the present foundations, a systems approach that focuses on populations and the complex interactions among the correlates of physical inactivity, rather than solely a behavioural science approach focusing on individuals, is the way forward to increase physical activity worldwide.


title: Rare deletions at 16p13.11 predispose to a diverse spectrum of sporadic epilepsy syndromes.

abstract: Deletions at 16p13.11 are associated with schizophrenia, mental retardation, and most recently idiopathic generalized epilepsy. To evaluate the role of 16p13.11 deletions, as well as other structural variation, in epilepsy disorders, we used genome-wide screens to identify copy number variation in 3812 patients with a diverse spectrum of epilepsy syndromes and in 1299 neurologically-normal controls. Large deletions (> 100 kb) at 16p13.11 were observed in 23 patients, whereas no control had a deletion greater than 16 kb. Patients, even those with identically sized 16p13.11 deletions, presented with highly variable epilepsy phenotypes. For a subset of patients with a 16p13.11 deletion, we show a consistent reduction of expression for included genes, suggesting that haploinsufficiency might contribute to pathogenicity. We also investigated another possible mechanism of pathogenicity by using hybridization-based capture and next-generation sequencing of the homologous chromosome for ten 16p13.11-deletion patients to look for unmasked recessive mutations. Follow-up genotyping of suggestive polymorphisms failed to identify any convincing recessive-acting mutations in the homologous interval corresponding to the deletion. The observation that two of the 16p13.11 deletions were larger than 2 Mb in size led us to screen for other large deletions. We found 12 additional genomic regions harboring deletions > 2 Mb in epilepsy patients, and none in controls. Additional evaluation is needed to characterize the role of these exceedingly large, non-locus-specific deletions in epilepsy. Collectively, these data implicate 16p13.11 and possibly other large deletions as risk factors for a wide range of epilepsy disorders, and they appear to point toward haploinsufficiency as a contributor to the pathogenicity of deletions.


title: Automatic test-based assessment of programming: A review

abstract: Systems that automatically assess student programming assignments have been designed and used for over forty years. Systems that objectively test and mark student programming work were developed simultaneously with programming assessment in the computer science curriculum. This article reviews a number of influential automatic assessment systems, including descriptions of the earliest systems, and presents some of the most recent developments. The final sections explore a number of directions automated assessment systems may take, presenting current developments alongside a number of important emerging e-learning specifications.


Word Embedding Techniques for Content-based Recommender Systems: An Empirical Evaluation


title: Design of ZnO microvaristor end corona protection for electrical machines

abstract: For the stator winding of electrical machines, end corona protection arrangements including semi-conductive microvaristor lacquers are analyzed using finite element method (FEM) simulations. Those materials feature nonlinear resistive properties depending on the local electric field. Hence, electroquasistatic FEM simulations considering capacitive and nonlinear resistive material behavior are performed. End corona protection arrangements using semi-conductive materials are compared. The resulting voltage distributions, tangential electric fields and ohmic losses are presented.


title: Beyond PASCAL: A benchmark for 3D object detection in the wild

abstract: 3D object detection and pose estimation methods have become popular in recent years since they can handle ambiguities in 2D images and also provide a richer description for objects compared to 2D object detectors. However, most of the datasets for 3D recognition are limited to a small amount of images per category or are captured in controlled environments. In this paper, we contribute PASCAL3D+ dataset, which is a novel and challenging dataset for 3D object detection and pose estimation. PASCAL3D+ augments 12 rigid categories of the PASCAL VOC 2012 [4] with 3D annotations. Furthermore, more images are added for each category from ImageNet [3]. PASCAL3D+ images exhibit much more variability compared to the existing 3D datasets, and on average there are more than 3,000 object instances per category. We believe this dataset will provide a rich testbed to study 3D detection and pose estimation and will help to significantly push forward research in this area. We provide the results of variations of DPM [6] on our new dataset for object detection and viewpoint estimation in different scenarios, which can be used as baselines for the community. Our benchmark is available online at http://cvgl.stanford.edu/projects/pascal3d


title: Historical Review of Air-Bearing Spacecraft Simulators

abstract: An overview of air-bearing spacecraft simulators is provided. Air bearings have been used for satellite attitude determination and control hardware verie cation and software development for nearly 45 years. It is interesting to consider the history of this technology: how early systems were e rst devised and what diverse capabilities current systems provide. First a survey is given of planar systems that give a payload freedom to translate and spin. Then several classes of rotational air bearings are discussed: those which simulate three-axis satellite attitude dynamics. The subsequent section discusses perhaps the most interesting facilities: those that provide both translational and three-dimensional rotational freedom. Thediverse capabilities each styleofair-bearing testbed provides, themany settings they can be found in, and ways to improve facility performance are described.


title: ECNU: A Combination Method and Multiple Features for Aspect Extraction and Sentiment Polarity Classification

abstract: This paper reports our submissions to the four subtasks of Aspect Based Sentiment Analysis (ABSA) task (i.e., task 4) in SemEval 2014 including aspect term extraction and aspect sentiment polarity classification (Aspect-level tasks), aspect category detection and aspect category sentiment polarity classification (Categorylevel tasks). For aspect term extraction, we present three methods, i.e., noun phrase (NP) extraction, Named Entity Recognition (NER) and a combination of NP and NER method. For aspect sentiment classification, we extracted several features, i.e., topic features, sentiment lexicon features, and adopted a Maximum Entropy classifier. Our submissions rank above average.


title: Playing Tetris decreases drug and other cravings in real world settings.

abstract: Desire, cognitive theory, behavioral research,Elaborated Intrusion theory, addiction, technology, multilevel models


title: Bounded Conditioning: Flexible Inference for Decisions under Scarce Resources

abstract: We introduce a graceful approach to probabilistic inference called bounded conditioning. Bounded conditioning monotonically refines the bounds on posterior probabilities in a belief network with computation, and converges on final probabilities of interest with the allocation of a complete resource fraction. The approach allows a reasoner to exchange arbitrary quantities of computational resource for incremental gains in inference quality. As such, bounded conditioning holds promise as a useful inference technique for reasoning under the general conditions of uncertain and varying reasoning resources. The algorithm solves a probabilistic bounding problem in complex belief networks by breaking the problem into a set of mutually exclusive, tractable subproblems and ordering their solution by the expected effect that each subproblem will have on the final answer. We introduce the algorithm, discuss its characterization, and present its performance on several belief networks, including a complex model for reasoning about problems in intensive-care medicine.


title: Adversarial Information Factorization

abstract: We propose a novel generative model architecture designed to learn representations for images that factor out a single attribute from the rest of the representation. A single object may have many attributes which when altered do not change the identity of the object itself. Consider the human face; the identity of a particular person is independent of whether or not they happen to be wearing glasses. The attribute of wearing glasses can be changed without changing the identity of the person. However, the ability to manipulate and alter image attributes without altering the object identity is not a trivial task. Here, we are interested in learning a representation of the image that separates the identity of an object (such as a human face) from an attribute (such as 'wearing glasses'). We demonstrate the success of our factorization approach by using the learned representation to synthesize the same face with and without a chosen attribute. We refer to this specific synthesis process as image attribute manipulation. We further demonstrate that our model achieves competitive scores, with state of the art, on a facial attribute classification task.


title: A Survey of Network Design Problems and Joint Design Approaches in Wireless Mesh Networks

abstract: Over the last decade, the paradigm of Wireless Mesh Networks (WMNs) has matured to a reasonably commonly understood one, and there has been extensive research on various areas related to WMNs such as design, deployment, protocols, performance, etc. The quantity of research being conducted in the area of wireless mesh design has dramatically increased in the past few years, due to increasing interest in this paradigm as its potential for the "last few miles", and the possibility of significant wireless services in metropolitan area networks. This recent work has focused increasingly on joint design problems, together with studies in designing specific aspects of the WMN such as routing, power control etc. in isolation. While excellent surveys and tutorials pertaining to WMNs exist in literature, the explosive growth of research in the area of specific design issues, and especially joint design, has left them behind. Our objective in this paper is to identify the fundamental WMN design problems of interference modeling, power control, topology control, link scheduling, and routing, and provide brief overviews, together with a survey of the recent research on these topics, with special stress on joint design methods. We believe this paper will fulfill an outstanding need in informing the interested student and researcher in getting familiar with this abundant recent research area, and starting research.


title: BMXNet: An Open-Source Binary Neural Network Implementation Based on MXNet

abstract: Binary Neural Networks (BNNs) can drastically reduce memory size and accesses by applying bit-wise operations instead of standard arithmetic operations. Therefore it could significantly improve the efficiency and lower the energy consumption at runtime, which enables the application of state-of-the-art deep learning models on low power devices. BMXNet is an open-source BNN library based on MXNet, which supports both XNOR-Networks and Quantized Neural Networks. The developed BNN layers can be seamlessly applied with other standard library components and work in both GPU and CPU mode. BMXNet is maintained and developed by the multimedia research group at Hasso Plattner Institute and released under Apache license. Extensive experiments validate the efficiency and effectiveness of our implementation. The BMXNet library, several sample projects, and a collection of pre-trained binary deep models are available for download at https://github.com/hpi-xnor.


title: Sustainable Passenger Transportation: Dynamic Ride-Sharing

abstract: Ride-share systems, which aim to bring together travelers with similar itineraries and time schedules, may provide significant societal and environmental benefits by reducing the number of cars used for personal travel and improving the utilization of available seat capacity. Effective and efficient optimization technology that matches drivers and riders in real-time is one of the necessary components for a successful ride-share system. We formally define dynamic ride-sharing and outline the optimization challenges that arise when developing technology to support ride-sharing. We hope that this paper will encourage more research by the transportation science and logistics community in this exciting, emerging area of public transportation.


title: Intention Oriented Image Captions With Guiding Objects

abstract: Although existing image caption models can produce promising results using recurrent neural networks (RNNs), it is difficult to guarantee that an object we care about is contained in generated descriptions, for example in the case that the object is inconspicuous in the image. Problems become even harder when these objects did not appear in training stage. In this paper, we propose a novel approach for generating image captions with guiding objects (CGO). The CGO constrains the model to involve a human-concerned object when the object is in the image. CGO ensures that the object is in the generated description while maintaining fluency. Instead of generating the sequence from left to right, we start the description with a selected object and generate other parts of the sequence based on this object. To achieve this, we design a novel framework combining two LSTMs in opposite directions. We demonstrate the characteristics of our method on MSCOCO where we generate descriptions for each detected object in the images. With CGO, we can extend the ability of description to the objects being neglected in image caption labels and provide a set of more comprehensive and diverse descriptions for an image. CGO shows advantages when applied to the task of describing novel objects. We show experimental results on both MSCOCO and ImageNet datasets. Evaluations show that our method outperforms the state-of-the-art models in the task with average F1 75.8, leading to better descriptions in terms of both content accuracy and fluency.


title: GDPI: Signature based Deep Packet Inspection using GPUs

abstract: Deep Packet Inspection (DPI) is necessitated for many networked application systems in order to prevent from cyber threats. The signature based Network Intrusion and etection System (NIDS) works on packet inspection and pattern matching mechanisms for the detection of malicious content in network traffic. The rapid growth of high speed networks in data centers demand an efficient high speed packet processing mechanism which is also capable of malicious packets detection. In this paper, we proposed a framework GDPI for efficient packet processing which inspects all incoming packet’s payload with known signature patterns, commonly available is Snort. The ::: framework is developed using enhanced GPU programming techniques, such as asynchronous packet processing using streams, minimizing CPU to GPU latency using pinned memory and zero copy, and memory coalescing with shared memory which reduces read operation from global memory of the GPU. The overall performance of GDPI is tested on heterogeneous NVIDIA GPUs, like Tegra Tk1, GTX 780, and Tesla K40 and observed that the highest throughput is achieved with Tesla K40. The design code of GDPI is made available for research community.


title: Combining Restricted Boltzmann Machine and One Side Perceptron for Malware Detection

abstract: Due to the large increase of malware samples in the last 10 years, the demand of the antimalware industry for an automated classifier has increased. However, this classifier has to satisfy two restrictions in order to be used in real life situations: high detection rate and very low number of false positives. By modifying the perceptron algorithm and combining existing features, we were able to provide a good solution to the problem, called the one side perceptron. Since the power of the perceptron lies in its features, we will focus our study on improving the feature creation algorithm. This paper presents different methods, including simple mathematical operations and the usage of a restricted Boltzmann machine, for creating features designed for an increased detection rate of the one side perceptron. The analysis is carried out using a large dataset of approximately 3 million files.


title: Irony Detection in Twitter: The Role of Affective Content

abstract: Irony has been proven to be pervasive in social media, posing a challenge to sentiment analysis systems. It is a creative linguistic phenomenon where affect-related aspects play a key role. In this work, we address the problem of detecting irony in tweets, casting it as a classification problem. We propose a novel model that explores the use of affective features based on a wide range of lexical resources available for English, reflecting different facets of affect. Classification experiments over different corpora show that affective information helps in distinguishing among ironic and nonironic tweets. Our model outperforms the state of the art in almost all cases.


title: Use of color Doppler ultrasound during ultrasound-guided breast interventional procedures.

abstract: AJR 2014; 203:W747 0361–803X/14/2036–W747 © American Roentgen Ray Society Use of Color Doppler Ultrasound During Ultrasound-Guided Breast Interventional Procedures We read with interest the article by Mahoney et al. [1], “Breast Emergencies: Types, Imaging Features, and Management,” in the April 2014 issue of the AJR. We would like to report two additional tips we routinely use in our practice that might contribute to reduce the risk of per-biopsy hemorrhage and hematoma when doing ultrasound-guided biopsies. First, we routinely use color Doppler ultrasound to visualize and document the location of any vascular structure along the trajectory of the needle (in the vicinity of the lesion or more superficially near the entry site of the needle) to trace a safe path [2]. If a vessel appears to cross the biopsy track, a different approach is usually chosen. In the same context, use of all available settings is known to improve needle visibility. Compounding imaging and angulation of the ultrasound beam might be helpful [3, 4]. Second, identifying the biopsy track with color Doppler ultrasound during the procedure enables the radiologist to specifically compress the bleeding track with the ultrasound probe (Fig. 1). We think this targeted compression between two solid surfaces (probe and chest wall) can aid in controlling bleeding. Romuald Ferre Shaza AlSharif Benoit Mesurolle Cedar Breast Clinic, Royal Victoria Hospital, McGill University Health Center, Montreal, Quebec, Canada


title: Fast Boundary Detection: A Generalization and a New Algorithm

abstract: We study class of fast algorithms that extract object boundaries from digitized images. A set of orthogonal functions related to distinctive image features is presented, which allows efficient extraction of such boundary elements. The properties of these functions are. used to define new criteria for edge detection and a sequential algorithm is presented. Results indicate considerable improvements over, existing techniques, with a very moderate increase of computational cost.


title: Subtle Facial Expression Recognition Using Adaptive Magnification of Discriminative Facial Motion

abstract: Recently, recognizing spontaneous facial expression has gained increasing attention in various emerging applications related to human affect. Spontaneous facial expression may generally have different temporal characteristics across subjects, emotion types, and so on. In this paper, we proposed a facial expression recognition (FER) method which adaptively magnifies a subtle facial motion based on its temporal characteristics. In training stage, we learn the relations between the temporal characteristics of facial motions and their discriminative temporal filtering. The learned model is used to automatically predict the most discriminative temporal filtering that magnifies the subtle facial motion in a test sequence. Experimental result shows that the proposed FER using the adaptive motion magnification performed clearly better than FER using non-adaptive motion magnification as well as FER without motion magnification.


title: Metasurface in microwave region: Theory and applications

abstract: In this paper, recent works of metasurface in microwave region are reviewed. First, ultra-thin metasurface based on phase discontinuity is introduced. Different spatial phase functions are adopted to achieve manipulation of wavefront, including bi-polarization metalens and orbital angular momentum. Second, metasurface based on transformation optics is studied. A four-beam antenna is constructed based on metasurfaces, which is verified by experimental results. Our designs provide a promising approach to miniaturize, planarize and integrate multiple microwave components.


NON-PARAMETRIC DISCRIMINATIVE REWARDS


title: CAPS: Context Aware Personalized POI Sequence Recommender System

abstract: The revolution of World Wide Web (WWW) and smart-phone technologies have been the key-factor behind remarkable success of social networks. With the ease of availability of check-in data, the location-based social networks (LBSN) (e.g., Facebook1, etc.) have been heavily explored in the past decade for Point-of-Interest (POI) recommendation. Though many POI recommenders have been defined, most of them have focused on recommending a single location or an arbitrary list that is not contextually coherent. It has been cumbersome to rely on such systems when one needs a contextually coherent list of locations, that can be used for various day-to-day activities, for e.g., itinerary planning. This paper proposes a model termed as CAPS (Context-Aware Personalized POI Sequence Recommender System) that generates contextually coherent POI sequences relevant to user preferences. To the best of our knowledge, CAPS is the first attempt to formulate the contextual POI sequence modeling by extending Recurrent Neural Network (RNN) and its variants. CAPS extends RNN by incorporating multiple contexts to the hidden layer and by incorporating global context (sequence features) to the hidden layers and the output layer. It extends the variants of RNN (e.g., Long-short term memory (LSTM)) by incorporating multiple contexts and global features in the gate update relations. The major contributions of this paper are: (i) it models the contextual POI sequence problem by incorporating personalized user preferences through multiple constraints (e.g., categorical, social, temporal, etc.), (ii) it extends RNN to incorporate the contexts of individual item and that of the whole sequence. It also extends the gated functionality of variants of RNN to incorporate the multiple contexts, and (iii) it evaluates the proposed models against two real-world data sets.


title: Sales Forecasting in Apparel and Fashion Industry: A Review

abstract: The fashion industry is a very fascinating sector for the sales forecasting. Indeed, the long time-to-market which contrasts with the short life cycle of products, makes the forecasting process very challenging. A suitable forecasting system should also deal with the specificities of the demand: fashion trends, seasonality, influence of many exogenous factors, …. We propose here a review of the different constraints related to the sales forecasting in the fashion industry, the methodologies and techniques existing in the literature to cope with these constraints and finally, the new topics which could be explored in the field of the sales forecasting for fashion products.


title: Federated Identity Management (FIM): Challenges and opportunities

abstract: Federated Identity Management (FIM) is a method that facilitates management of identity processes and policies among the collaborating entities. It also enables secure resource sharing among these entities, but it hasn't been as widely adopted as expected. So, in this paper we have identified factors that are pivotal for a holistic FIM framework or model. These factors include trust management and trust establishment techniques, preservation of user privacy, consistent access rights across Circles of Trust (CoTs), continuous monitoring of collaborating entities and adaptation to unanticipated events. On the basis of these factors, we have presented an extensive comparative analysis on existing FIM frameworks and models that identify current challenges and areas of improvement in this field. We've also analyzed these frameworks and models against a set of attacks to gauge their strengths and weaknesses.


title: A Streaming Accelerator for Deep Convolutional Neural Networks with Image and Feature Decomposition for Resource-limited System Applications

abstract: Deep convolutional neural networks (CNN) are widely used in modern artificial intelligence (AI) and smart vision systems but also limited by computation latency, throughput, and energy efficiency on a resource-limited scenario, such as mobile devices, internet of things (IoT), unmanned aerial vehicles (UAV), and so on. A hardware streaming architecture is proposed to accelerate convolution and pooling computations for state-of-the-art deep CNNs. It is optimized for energy efficiency by maximizing local data reuse to reduce off-chip DRAM data access. In addition, image and feature decomposition techniques are introduced to optimize memory access pattern for an arbitrary size of image and number of features within limited on-chip SRAM capacity. A prototype accelerator was implemented in TSMC 65 nm CMOS technology with 2.3 mm x 0.8 mm core area, which achieves 144 GOPS peak throughput and 0.8 TOPS/W peak energy efficiency.


title: Performances of novel fuzzy logic based indirect vector control for induction motor drive

abstract: This paper presents a novel speed control scheme of an induction motor (IM) using fuzzy logic control. The fuzzy logic controller (FLC) is based on the indirect vector control. The fuzzy logic speed controller is employed in the outer loop. The complete vector control scheme of the IM drive incorporating the FLC is experimentally implemented using a digital signal processor board DS-1102 for the laboratory 1 hp squirrel cage induction motor. The performances of the proposed FLC based IM drive are investigated and compared to those obtained from the conventional proportional integral (PI) controller based drive both theoretically and experimentally at different dynamic operating conditions such as sudden change in command speed, step change in load, etc. The comparative experimental results show that the FLC is more robust and hence found to be a suitable replacement of the conventional PI controller for the high performance industrial drive applications.


title: E-commerce Time Series Forecasting using LSTM Neural Network and Support Vector Regression

abstract: The purpose of this paper is to provide a robust forecasting model to predict phone prices on European markets using Long Short Term Memory neural network (LSTM) and Support Vector Regression (SVR). We propose a comparison study of time series forecasting models for these two techniques. The Long Short Term Memory, due to its architecture, is considered as a perfect solution to problems not solvable by classic Recurrent Neural Networks (RNN). On the other hand, Support Vector machines is a very powerful machine learning method for both classification and regression. After studying and comparing several univariate models, SVR and LSTM neural network appear to be the most accurate ones. In addition, we compared multivariate models for both techniques SVR and LSTM. Considering the multivariate approach, by introducing more variables, we obtain better performance for the prediction. In fact, SVR model is able to predict the next day price with RMSE 35.43 Euros with univariate model. However, using multivariate models, recurrent neural network LSTM gives the most accurate prediction for the next day's price with RMSE of 24.718 Euros.


title: A Comparison of Deep Learning Methods for Semantic Segmentation of Coral Reef Survey Images

abstract: Two major deep learning methods for semantic segmentation, i.e., patch-based convolutional neural network (CNN) approaches and fully convolutional neural network (FCNN) models, are studied in the context of classification of regions in underwater images of coral reef ecosystems into biologically meaningful categories. For the patch-based CNN approaches, we use image data extracted from underwater video accompanied by individual point-wise ground truth annotations. We show that patch-based CNN methods can outperform a previously proposed approach that uses support vector machine (SVM)-based classifiers in conjunction with texture-based features. We compare the results of five different CNN architectures in our formulation of patch-based CNN methods. The Resnet152 CNN architecture is observed to perform the best on our annotated dataset of underwater coral reef images. We also examine and compare the results of four different FCNN models for semantic segmentation of coral reef images. We develop a tool for fast generation of segmentation maps to serve as ground truth segmentations for our FCNN models. The FCNN architecture Deeplab v2 is observed to yield the best results for semantic segmentation of underwater coral reef images.


title: Less-redundant Text Summarization Using Ensemble Clustering Algorithm Based on GA and PSO

abstract: In this paper, a novel text clustering technique is proposed to summarize text documents. The clustering method, so called ‘Ensemble Clustering Method’, combines both genetic algorithms (GA) and particle swarm optimization (PSO) efficiently and automatically to get the best clustering results. The summarization with this clustering method is to effectively avoid the redundancy in the summarized document and to show the good summarizing results, extracting the most significant and non-redundant sentence from clustering sentences of a document. We tested this technique with various text documents in the open benchmark datasets, DUC01 and DUC02. To evaluate the performances, we used F-measure and ROUGE. The experimental results show that the performance capability of our method is about 11% to 24% better than other summarization algorithms.


title: Exploring the Regularity of Sparse Structure in Convolutional Neural Networks

abstract: Sparsity helps reduce the computational complexity of deep neural networks by skipping zeros. Taking advantage of sparsity is listed as a high priority in next generation DNN accelerators such as TPU. The structure of sparsity, i.e., the granularity of pruning, affects the efficiency of hardware accelerator design as well as the prediction accuracy. Coarse-grained pruning creates regular sparsity patterns, making it more amenable for hardware acceleration but more challenging to maintain the same accuracy. In this paper we quantitatively measure the trade-off between sparsity regularity and prediction accuracy, providing insights in how to maintain accuracy while having more a more structured sparsity pattern. Our experimental results show that coarse-grained pruning can achieve a sparsity ratio similar to unstructured pruning without loss of accuracy. Moreover, due to the index saving effect, coarse-grained pruning is able to obtain a better compression ratio than fine-grained sparsity at the same accuracy threshold. Based on the recent sparse convolutional neural network accelerator (SCNN), our experiments further demonstrate that coarse-grained sparsity saves about 2x the memory references compared to fine-grained sparsity. Since memory reference is more than two orders of magnitude more expensive than arithmetic operations, the regularity of sparse structure leads to more efficient hardware design.


title: Air-Gap Convection in Rotating Electrical Machines

abstract: This paper reviews the convective heat transfer within the air gap of both cylindrical and disk geometry rotating electrical machines, including worked examples relevant to fractional horsepower electrical machines. Thermal analysis of electrical machines is important because torque density is limited by maximum temperature. Knowledge of surface convective heat transfer coefficients is necessary for accurate thermal modeling, for example, using lumped parameter models. There exists a wide body of relevant literature, but much of it has traditionally been in other application areas, dominated by mechanical engineers, such as gas turbine design. Particular attention is therefore given to the explanation of the relevant nondimensional parameters and to the presentation of measured convective heat transfer correlations for a wide variety of situations from laminar to turbulent flow at small and large gap sizes for both radial-flux and axial-flux electrical machines.


title: Sinusoidal Versus Square-Wave Current Supply of PM Brushless DC Drives: A Convenience Analysis

abstract: Permanent-magnet (PM) brushless direct current (BLDC) drives are utilized in a variety of applications because of their inherent features and would be even more utilized if their torque performance could be improved. Indeed, when supplied with square-wave currents, the torque–speed characteristic undergoes a drop at high speeds, and a significant torque ripple arises at both low and high speeds due to the commutation phenomena. In this paper, the sinusoidal current supply of the PM BLDC motors is investigated in depth, and an analytical formulation of the torque–speed characteristic, the torque ripple, and the base speed for such a supply are found. Based on the resulting equations, a thorough convenience analysis of the sinusoidal current supply is carried out with respect to the square-wave current in the presence of the commutation phenomena, which is hereafter termed as the real square-wave current supply. Simulation and experimental tests for a study case are given to corroborate the theoretical results. Generation of the sinusoidal current references using Hall sensors is also discussed.


title: Wideband Patch Antenna for HPM Applications

abstract: This paper presents the design, fabrication, and characterization of a compact wideband antenna for high-power microwave applications. The antennas proposed are array of high-power wideband patches with high compactness and less than λ/10 thick. The concept developed can be fed by high-voltage signals (up to 60 kV) in repetitive operation. Two designs are produced at central frequencies of 350 MHz and 1 GHz. Their relative bandwidth is larger than 40% at 350 MHz and 25% at 1 GHz for S11 <; - 10 dB, respectively. The arrays studied produce a gain of more than 14 dB.


title: Semi-supervised kernel matching for domain adaptation

abstract: In this paper, we propose a semi-supervised kernel matching method to address domain adaptation problems where the source distribution substantially differs from the target distribution. Specifically, we learn a prediction function on the labeled source data while mapping the target data points to similar source data points by matching the target kernel matrix to a submatrix of the source kernel matrix based on a Hilbert Schmidt Independence Criterion. We formulate this simultaneous learning and mapping process as a non-convex integer optimization problem and present a local minimization procedure for its relaxed continuous form. Our empirical results show the proposed kernel matching method significantly outperforms alternative methods on the task of across domain sentiment classification.


title: Synthesis of Positron Emission Tomography (PET) Images via Multi-channel Generative Adversarial Networks (GANs)

abstract: Positron emission tomography (PET) image synthesis plays an important role, which can be used to boost the training data for computer aided diagnosis systems. However, existing image synthesis methods have problems in synthesizing the low resolution PET images. To address these limitations, we propose multi-channel generative adversarial networks (M-GAN) based PET image synthesis method. Different to the existing methods which rely on using low-level features, the proposed M-GAN is capable to represent the features in a high-level of semantic based on the adversarial learning concept. In addition, M-GAN enables to take the input from the annotation (label) to synthesize the high uptake regions e.g., tumors and from the computed tomography (CT) images to constrain the appearance consistency and output the synthetic PET images directly. Our results on 50 lung cancer PET-CT studies indicate that our method was much closer to the real PET images when compared with the existing methods.


title: MgO-decorated few-layered graphene as an anode for li-ion batteries.

abstract: Combustion of magnesium in dry ice and a simple subsequent acid treatment step resulted in a MgO-decorated few-layered graphene (FLG) composite that has a specific surface area of 393 m(2)/g and an average pore volume of 0.9 cm(3)/g. As an anode material in Li-ion batteries, the composite exhibited high reversible capacity and excellent cyclic performance in spite of high first-cycle irreversible capacity loss. A reversible capacity as high as 1052 mAh/g was measured during the first cycle. Even at the end of the 60th cycle, more than 83% of the capacity could be retained. Cyclic voltammetry results indicated pseudocapacitance behavior due to electrochemical absorption and desorption of lithium ions onto graphene. An increase in the capacity has been observed during long-term cycling owing to electrochemical exfoliation of graphene sheets. Owing to its good thermal stability and superior cyclic performance with high reversible capacities, MgO-decked FLG can be an excellent alternative to graphite as an anode material in Li-ion batteries, after suitable modifications.


title: Computational cost of Chirp Z-transform and Generalized Goertzel algorithm

abstract: Two natural competitors in the area of narrow-band spectrum analysis, namely the Chirp Z-transform (CZT) and the Generalized Goertzel algorithm (GGA), are taken and compared, with the focus on the computational cost. We present results showing that for real-input data, the GGA is preferable over the CZT in a range of practical situations. This is shown both in theory and in practice.


title: Study of obstacle avoidance navigation robot control based on bland man tracing wall theory

abstract: In the field of robots' obstacle avoidance and navigation, indirect contact sensors such as visual, ultrasonic and infrared detection are widely used. However, the performance of these sensors is always influenced by the severe environment, especially under the dark, dense fog, underwater conditions. The obstacle avoidance robot based on tactile sensor is proposed in this paper to realize the autonomous obstacle avoidance navigation by only using three dimensions force sensor. In addition, the mathematical model and algorithm are optimized to make up the deficiency of tactile sensor. Finally, the feasibility and reliability of this study are verified by the simulation results.