title: REVIEW OF CIRCULAR FLEXURE HINGE DESIGN EQUATIONS AND DERIVATION OF EMPIRICAL FORMULATIONS

abstract: Abstract This article presents the comparison of various compliance/stiffness equations of circular flexure hinges with FEA results. The limitation of these equations at different t / R ( R is the radius and t is the neck thickness) ratios are revealed. Based on the limitations of these design equations, a guideline for selecting the most accurate equations for hinge design calculations are presented. In addition to the review and comparisons, general empirical stiffness equations in the x - and y -direction were formulated in this study (with errors less than 3% when compared to FEA results) for a wide range of t / R ratios ( 0.05 ≤ t / R ≤ 0.8 ).


title: Assessment of In vitro Sun Protection Factor of Calendula Officinalis L. (Asteraceae) Essential Oil Formulation

abstract: The present study was undertaken to study the sunscreen activity of herbal formulation. There is no evidence of the sun protection factor (SPF) studies on essential oil of Calendula flowers (Calendula officinalis L., Asteraceae). The study investigates the in vitro SPF by ultraviolet specrtophotometry method of Calendula flower oil in a cream formulation. Calendula oil was isolated by Clavenger's apparatus, compositions were identified by GC-MS and the cream of calendula flower oil was prepared by homogenization method followed by evaluation for physical parameters. The sun protection factor of cream was evaluated by in vitro method employing UV-visible spectrophotometer (Shimazdu-1600). The SPF of Calendula oil in cream formulation exhibited good activity (SPF = 14.84 ± 0.16). Finding of this study suggested that calendula oil cream can be used to protect the skin from UV radiations in form of sunscreen cream and to maintain the natural pigmentation of the skin. Key words: Calendula officinalis , calendula oil, essential oil, sun protection factor.


title: The use of sterilized mosquito nets for hernioplasty: a systematic review

abstract: Introduction ::: Hernia repair is among the most frequent surgeries performed. Surgeons prefer the tension-free mesh repair, but in large parts of the world, commercial meshes are unavailable or unaffordable. Consequently, surgeons have been experimenting with insertion of inexpensive non-commercial meshes, the most common being a non-impregnated, sterilized mosquito net.


title: Inpainting Strategies for Reconstruction of Missing Data in VHR Images

abstract: Missing data in very high spatial resolution (VHR) optical imagery take origin mainly from the acquisition conditions. Their accurate reconstruction represents a great methodological challenge because of the complexity and the ill-posed nature of the problem. In this letter, we present three different solutions, with all based on the inpainting approach, which consists in reconstructing the missing regions in a given image by propagating the spectrogeometrical information retrieved from the remaining parts of the image. They rely on the idea to enrich the patch search process by including local image properties or by isometric transformations or to reformulate it under a multiresolution processing scheme, respectively. Thorough experiments conducted on two different VHR images are reported and discussed.


title: Effects of self-focused rumination on negative thinking and interpersonal problem solving.

abstract: Hypotheses about the effects of self-focused rumination on interpretations of events and interpersonal problem solving were tested in 3 studies with dysphoric and nondysphoric participants. Study 1 supported the hypothesis that dysphoric participants induced to ruminatively self-focus on their feelings and personal characteristics would endorse more negative, biased interpretations of hypothetical situations than dysphoric participants induced to distract themselves from their mood, or nondysphoric participants. Study 2 showed that dysphoric participants who ruminated were more pessimistic about positive events in their future than the other 3 groups. Study 3 showed that dysphoric ruminating participants generated less effective solutions to interpersonal problems than the other 3 groups. In Studies I and 3, dysphoric ruminating participants also offered the most pessimistic explanations for interpersonal problems and hypothetical negative events. In all 3 studies, dysphoric participants who distracted were as optimistic and effective in solving problems as nondysphoric participants.


title: Application of CORDIC in capacitive rotary encoder signal demodulation

abstract: Capacitive rotary encoders are widely used in motor velocity and angular position control, where high-speed and high-precision angle calculation is required. This paper illustrates implementation of arctangent operation, based on the CORDIC (an acronym for COordinate Rotational DIgital Computer) algorithm, in the capacitive rotary encoder signal demodulation in an FPGA to obtain the motor velocity and position. By skipping some unnecessary rotation in CORDIC algorithm, we improve the algorithm's computing accuracy. Experiments show that the residue angle error is almost reduced by half after the CORDIC algorithm is optimized, and is completely meet the precision requirements of the system.


title: SVMTool: A general POS Tagger Generator Based on Support Vector Machines

abstract: This paper presents the SVMTool, a simple, flexible, effective and efficient part–of–speech tagger based on Support Vector Machines. The SVMTool offers a fairly good balance among these properties which make it really practical for current NLP applications. It is very easy to use and easily configurable so as to perfectly fit the needs of a number of different applications. Results are also very competitive, achieving an accuracy of 97.16% for English on the Wall Street Journal corpus. It has been also successfully applied to Spanish exhibiting a similar performance. A first release of the SVMTool Perl prototype is now freely available for public use. A most efficient C++ version is coming very soon.


title: 3D cGAN based cross-modality MR image synthesis for brain tumor segmentation

abstract: Different modalities of magnetic resonance imaging (MRI) can indicate tumor-induced tissue changes from different perspectives, thus benefit brain tumor segmentation when they are considered together. Meanwhile, it is always interesting to examine the diagnosis potential from single modality, considering the cost of acquiring multi-modality images. Clinically, T1-weighted MRI is the most commonly used MR imaging modality, although it may not be the best option for contouring brain tumor. In this paper, we investigate whether synthesizing FLAIR images from T1 could help improve brain tumor segmentation from the single modality of T1. This is achieved by designing a 3D conditional Generative Adversarial Network (cGAN) for FLAIR image synthesis and a local adaptive fusion method to better depict the details of the synthesized FLAIR images. The proposed method can effectively handle the segmentation task of brain tumors that vary in appearance, size and location across samples.


title: Using Social Media to Measure Student Wellbeing: A Large-Scale Study of Emotional Response in Academic Discourse

abstract: Student resilience and emotional wellbeing are essential for both academic and social development. Earlier studies on tracking students’ happiness in academia showed that many of them struggle with mental health issues. For example, a 2015 study at the University of California Berkeley found that 47 % of graduate students suffer from depression, following a 2005 study that showed 10 % had considered suicide. This is the first large-scale study that uses signals from social media to evaluate students’ emotional wellbeing in academia. This work presents fine-grained emotion and opinion analysis of 79,329 tweets produced by students from 44 universities. The goal of this study is to qualitatively evaluate and compare emotions and sentiments emanating from students’ communications across different academic discourse types and across universities in the U.S. We first build novel predictive models to categorize academic discourse types generated by students into personal, social, and general categories. We then apply emotion and sentiment classification models to annotate each tweet with six Ekman’s emotions – joy, fear, sadness, disgust, anger, and surprise and three opinion types – positive, negative, and neutral. We found that emotions and opinions expressed by students vary across discourse types and universities, and correlate with survey-based data on student satisfaction, happiness and stress. Moreover, our results provide novel insights on how students use social media to share academic information, emotions, and opinions that would pertain to students academic performance and emotional well-being.


title: Facilitating Preservice Teachers ' Development of Technological, Pedagogical, and Content Knowledge (TPACK)

abstract: Preparing preservice teachers for ICT integration in the classrooms is a key focus for many teacher education institutes. This paper examines the perceived development of preservice teachers in terms of their technological knowledge, pedagogical knowledge, content knowledge and the synthesis of such knowledge, i.e., the technological, pedagogical, and content knowledge (TPACK). A questionnaire adapted from Schmidt, Baran, Thompson, Mishra, Koehler, and Shin (2009) was validated using factor analyses and the preservice teachers' TPACK perceptions before and after their ICT course were examined. The results reveal statistical significant gains with good effect sizes. Regression analysis further reveals that technological knowledge, pedagogical knowledge and content knowledge are all significant predictors of preservice teachers' TPACK, with pedagogical knowledge having the largest impact. Implications for designing the ICT instruction of preservice teachers are discussed.


title: Talk More Listen Less: Energy-Efficient Neighbor Discovery in Wireless Sensor Networks

abstract: Neighbor discovery is a fundamental service for initialization and managing network dynamics in wireless sensor networks and mobile sensing applications. In this paper, we present a novel design principle named Talk More Listen Less (TMLL) to reduce idle-listening in neighbor discovery protocols by learning the fact that more beacons lead to fewer wakeups. We propose an extended neighbor discovery model for analyzing wakeup schedules in which beacons are not necessarily placed in the wakeup slots. Furthermore, we are the first to consider channel occupancy rate in discovery protocols by introducing a new metric to trade off among duty-cycle, latency and channel occupancy rate. Guided by the TMLL principle, we have designed Nihao, a family of energy-efficient asynchronous neighbor discovery protocols for symmetric and asymmetric cases. We compared Nihao with existing state of the art protocols via analysis and real-world testbed experiments. The result shows that Nihao significantly outperforms the others both in theory and practice.


Ranking Models for Ads at Yelp


title: Traditional Indian fermented foods: a rich source of lactic acid bacteria.

abstract: This review describes the diversity of Indian fermented food and its significance as a potential source of lactic acid bacteria (LAB). Fermented foods consumed in India are categorized based upon their base material. Fermented foods such as dahi, gundruk, sinki, iniziangsang, iromba, fermented rai, kanjika and handua were reported to have significant medicinal properties. Some fermented products such as koozh, dahi and kanjika are consumed unknowingly as, probiotic drinks, by local people. There are very few reports regarding isolation of LAB from Indian fermented foods available in the past; however, due to growing consciousness about potential health benefits of LAB, we now have scores of reports in this field. There is an abundant opportunity available for food microbiologists to explore the Indian fermented foods for the isolation of new LAB strains for their potential role in probiotic research.


title: Controlling risks and fraud in affiliate marketing: A simulation and testing environment

abstract: Affiliate marketing (AM) is a popular and cost-effective marketing model for e-commerce practitioners to drive visitor traffic, to their e-commerce sites. Nevertheless, this popular marketing model is associated with inherent risks and known frauds, which could have a detrimental effect on its popularisation. Many studies have been carried out to explore risks and frauds that are typically associated with cookie-based tracking models. This paper presents a fraud simulation and testing environment of a functional AM network, which can be used by AM practitioners and researchers alike, to test known fraud and risk scenarios through simulation as well as to discover new vulnerabilities. It can further assist in developing and testing effective solutions to mitigate those vulnerabilities, and to proactively control risks associated with AM.


title: Lessons learned from three container-management systems over a decade . BY

abstract: Lessons learned from three container-management systems over a decade.


title: From Adjective Glosses to Attribute Concepts: Learning Different Aspects That an Adjective Can Describe

abstract: Adjectives are one of the major contributors to conveying subjective meaning to sentences. Understanding the semantics of adjectives is essential for understanding natural language sentences. In this paper we propose a novel approach for learning different properties that an adjective can describe, corresponding to the ‘attribute concepts’, which is not currently available in existing linguistic resources. We accomplish this by reading adjective glosses and bootstrapping attribute concepts from a seed of adjectives. We show that we can learn new attribute concepts using adjective glosses of WordNet adjectives with high accuracy as compared with human annotation on a test set.


title: Software for Mathematical Programming

abstract: The NATO Advanced Study Institute on Computational Mathematical Programming was accomplished by several activities of a Software Fair. The most important one was the display of information material on existing optimization software. More than 50 code descriptions were submitted by the authors to describe characteristic features of their programs. In all cases, the codes are well tested and documented, are currently used for solving practical optimization optimization problems, and are available on request either in form of a listing or on a magnetic tape. In most cases, the programming language is FORTRAN.


title: Deeply Supervised Rotation Equivariant Network for Lesion Segmentation in Dermoscopy Images

abstract: Automatic lesion segmentation in dermoscopy images is an essential step for computer-aided diagnosis of melanoma. The dermoscopy images exhibits rotational and reflectional symmetry, however, this geometric property has not been encoded in the state-of-the-art convolutional neural networks based skin lesion segmentation methods. In this paper, we present a deeply supervised rotation equivariant network for skin lesion segmentation by extending the recent group rotation equivariant network~\cite{cohen2016group}. Specifically, we propose the G-upsampling and G-projection operations to adapt the rotation equivariant classification network for our skin lesion segmentation problem. To further increase the performance, we integrate the deep supervision scheme into our proposed rotation equivariant segmentation architecture. The whole framework is equivariant to input transformations, including rotation and reflection, which improves the network efficiency and thus contributes to the segmentation performance. We extensively evaluate our method on the ISIC 2017 skin lesion challenge dataset. The experimental results show that our rotation equivariant networks consistently excel the regular counterparts with the same model complexity under different experimental settings. Our best model achieves 77.23\%(JA) on the test dataset, outperforming the state-of-the-art challenging methods and further demonstrating the effectiveness of our proposed deeply supervised rotation equivariant segmentation network. Our best model also outperforms the state-of-the-art challenging methods, which further demonstrate the effectiveness of our proposed deeply supervised rotation equivariant segmentation network.


title: Deep Learning in the Automotive Industry: Applications and Tools

abstract: Deep Learning refers to a set of machine learning techniques that utilize neural networks with many hidden layers for tasks, such as image classification, speech recognition, language understanding. Deep learning has been proven to be very effective in these domains and is pervasively used by many Internet services. In this paper, we describe different automotive uses cases for deep learning in particular in the domain of computer vision. We surveys the current state-of-the-art in libraries, tools and infrastructures (e. g. GPUs and clouds) for implementing, training and deploying deep neural networks. We particularly focus on convolutional neural networks and computer vision use cases, such as the visual inspection process in manufacturing plants and the analysis of social media data. To train neural networks, curated and labeled datasets are essential. In particular, both the availability and scope of such datasets is typically very limited. A main contribution of this paper is the creation of an automotive dataset, that allows us to learn and automatically recognize different vehicle properties. We describe an end-to-end deep learning application utilizing a mobile app for data collection and process support, and an Amazon-based cloud backend for storage and training. For training we evaluate the use of cloud and on-premises infrastructures (including multiple GPUs) in conjunction with different neural network architectures and frameworks. We assess both the training times as well as the accuracy of the classifier. Finally, we demonstrate the effectiveness of the trained classifier in a real world setting during manufacturing process.


title: Characterizations of Stable Model Semantics for Logic Programs with Arbitrary Constraint Atoms

abstract: This paper studies the stable model semantics of logic programs with (abstract) constraint atoms and their properties. We introduce a succinct abstract representation of these constraint atoms in which a constraint atom is represented compactly. We show two applications. First, under this representation of constraint atoms, we generalize the Gelfond–Lifschitz transformation and apply it to define stable models (also called answer sets) for logic programs with arbitrary constraint atoms. The resulting semantics turns out to coincide with the one defined by Son et al. (2007), which is based on a fixpoint approach. One advantage of our approach is that it can be applied, in a natural way, to define stable models for disjunctive logic programs with constraint atoms, which may appear in the disjunctive head as well as in the body of a rule. As a result, our approach to the stable model semantics for logic programs with constraint atoms generalizes a number of previous approaches. Second, we show that our abstract representation of constraint atoms provides a means to characterize dependencies of atoms in a program with constraint atoms, so that some standard characterizations and properties relying on these dependencies in the past for logic programs with ordinary atoms can be extended to logic programs with constraint atoms.


title: Animal models of atopic dermatitis.

abstract: Atopic dermatitis (AD) is a chronic inflammatory skin disease affecting over 10% of human (sic) children, and it is the most common cause of occupational disability in adults. Atopic dermatitis also is seen commonly in companion animals, especially in dogs, in which its prevalence has been estimated to be about 10% of the canine population. Numerous similarities exist between canine AD and its human counterpart, suggesting the possibility that canine AD could be used as a model for the human disease. Dogs have been used in the past as model for human allergic diseases. The most popular model has been the Basenji–Greyhound, which develops a recurrent, nonseasonal, pruritic dermatitis and asthma. Skin lesions include lichenified plaques and inflammatory papules and nodules. These dogs also display airway hyper-reactivity, with changes in pulmonary mechanics similar to those of symptomatic, human asthma, and they exhibit a variety of immunologic abnormalities analogous to the human disease. Cats also have been reported to develop AD, but our understanding of the condition in this species currently is limited. Certain similarities appear to exist between feline and human AD; however, the feline condition has some peculiar clinical features, and for this reason it will be described separately. Finally, AD recently has been described in laboratory animals, notably the NC/ Nga mouse, which is thought by some authors to be a promising animal model for AD. The purpose of this article is to review the latest information available on AD in companion animals and in mice and describe how these spontaneously arising diseases might serve as models to improve our understanding of human AD.


title: Advanced IOT based combined remote health monitoring, home automation and alarm system

abstract: Nowadays, automation and Internet of Things are changing the world. The day-by-day development of the Internet of Things causes a revolution in modern technology, which makes our life easier and automated. Due to a busy schedule and irregular lifestyle, health hazard is not an age-dependent factor in the recent era. Under these circumstances, Internet of Things has provided a much easier solution for remote real-time health monitoring of patients from the hospital as well as home. Sensors acquire the data of various parameters regarding patients’ health, and the Internet of Things stores that data and displays through the website, which provides access for remote monitoring. Use of Sensor reduces the human error, and the size of the system reduces the occupied space of the room. The unique part of this proposed solution is the alarm generation to provide the prescribed medicine to the patient in time. The other beneficial area of the system is the scheme of sending the notification through email and SMS alert if any of the health parameters crosses the threshold value. Notification scheme will keep the respective authority conscious of the situation. Another significant area of the proposed solution is to create the optimum surroundings as per the requirement of patient's health condition. In this paper, we have discussed the monitoring of heart rate, blood pressure, respiration rate, body temperature, body movement and saline levels.


title: Detection and classification of road signs in natural environments

abstract: An automatic road sign recognition system first locates road signs within images captured by an imaging sensor on-board of a vehicle, and then identifies the detected road signs. This paper presents an automatic neural-network-based road sign recognition system. First, a study of the existing road sign recognition research is presented. In this study, the issues associated with automatic road sign recognition are described, the existing methods developed to tackle the road sign recognition problem are reviewed, and a comparison of the features of these methods is given. Second, the developed road sign recognition system is described. The system is capable of analysing live colour road scene images, detecting multiple road signs within each image, and classifying the type of road signs detected. The system consists of two modules: detection and classification. The detection module segments the input image in the hue-saturation-intensity colour space, and then detects road signs using a Multi-layer Perceptron neural-network. The classification module determines the type of detected road signs using a series of one to one architectural Multi-layer Perceptron neural networks. Two sets of classifiers are trained using the Resillient-Backpropagation and Scaled-Conjugate-Gradient algorithms. The two modules of the system are evaluated individually first. Then the system is tested as a whole. The experimental results demonstrate that the system is capable of achieving an average recognition hit-rate of 95.96% using the scaled-conjugate-gradient trained classifiers.


title: A new golden age for computer architecture

abstract: Innovations like domain-specific hardware, enhanced security, open instruction sets, and agile chip development will lead the way.


title: Real time Google map and Arduino based vehicle tracking system

abstract: A vehicle tracking system is very useful for tracking the movement of a vehicle from any location at any time. In this work, real time Google map and Arduino based vehicle tracking system is implemented with Global Positioning System (GPS) and Global system for mobile communication (GSM) technology. GPS module provides geographic coordinates at regular time intervals. Then the GSM module transmits the location of vehicle to cell phone of owner/user in terms of latitude and longitude. At the same time, location is displayed on LCD. Finally, Google map displays the location and name of the place on cell phone. Thus, owner/user will be able to continuously monitor a moving vehicle using the cell phone. In order to show the feasibility and effectiveness of the system, this work presents experimental result of the vehicle tracking system. The proposed system is user friendly and ensures safety and surveillance at low maintenance cost.


title: Investigating Haze-Relevant Features in a Learning Framework for Image Dehazing

abstract: Haze is one of the major factors that degrade outdoor images. Removing haze from a single image is known to be severely ill-posed, and assumptions made in previous methods do not hold in many situations. In this paper, we systematically investigate different haze-relevant features in a learning framework to identify the best feature combination for image dehazing. We show that the dark-channel feature is the most informative one for this task, which confirms the observation of He et al. [8] from a learning perspective, while other haze-relevant features also contribute significantly in a complementary way. We also find that surprisingly, the synthetic hazy image patches we use for feature investigation serve well as training data for realworld images, which allows us to train specific models for specific applications. Experiment results demonstrate that the proposed algorithm outperforms state-of-the-art methods on both synthetic and real-world datasets.


title: A very fast substring search algorithm

abstract: This article describes a substring search algorithm that is faster than the Boyer-Moore algorithm. This algorithm does not depend on scanning the pattern string in any particular order. Three variations of the algorithm are given that use three different pattern scan orders. These include: (1) a “Quick Search” algorithm; (2) a “Maximal Shift” and (3) an “Optimal Mismatch” algorithm.


title: Intrinsic shape context descriptors for deformable shapes

abstract: In this work, we present intrinsic shape context (ISC) descriptors for 3D shapes. We generalize to surfaces the polar sampling of the image domain used in shape contexts: for this purpose, we chart the surface by shooting geodesic outwards from the point being analyzed; ‘angle’ is treated as tantamount to geodesic shooting direction, and radius as geodesic distance. To deal with orientation ambiguity, we exploit properties of the Fourier transform. Our charting method is intrinsic, i.e., invariant to isometric shape transformations. The resulting descriptor is a meta-descriptor that can be applied to any photometric or geometric property field defined on the shape, in particular, we can leverage recent developments in intrinsic shape analysis and construct ISC based on state-of-the-art dense shape descriptors such as heat kernel signatures. Our experiments demonstrate a notable improvement in shape matching on standard benchmarks.


title: Contraction Sensing With Smart Braid McKibben Muscles

abstract: The inherent compliance of soft fluidic actuators makes them attractive for use in wearable devices and soft robotics. Their flexible nature permits them to be used without traditional rotational or prismatic joints. Without these joints, however, measuring the motion of the actuators is challenging. Actuator-level sensors could improve the performance of continuum robots and robots with compliant or multi-degree-of-freedom joints. We make the reinforcing braid of a pneumatic artificial muscle (PAM or McKibben muscle) "smart" by weaving it from conductive, insulated wires. These wires form a solenoid-like circuit with an inductance that more than doubles over the PAM contraction. The reinforcing and sensing fibers can be used to measure the contraction of a PAM actuator with a simple, linear function of the measured inductance. Whereas other proposed self-sensing techniques rely on the addition of special elastomers or transducers, the technique presented in this work can be implemented without modifications of this kind. We present and experimentally validate two models for Smart Braid sensors based on the long solenoid approximation and the Neumann formula, respectively. We test a McKibben muscle made from a Smart Braid in quasistatic conditions with various end-loads and in dynamic conditions. We also test the performance of the Smart Braid sensor alongside steel.


title: DeLight: Adding Energy Dimension To Deep Neural Networks

abstract: Physical viability, in particular energy efficiency, is a key challenge in realizing the true potential of Deep Neural Networks (DNNs). In this paper, we aim to incorporate the energy dimension as a design parameter in the higher-level hierarchy of DNN training and execution to optimize for the energy resources and constraints. We use energy characterization to bound the network size in accordance to the pertinent physical resources. An automated customization methodology is proposed to adaptively conform the DNN configurations to the underlying hardware characteristics while minimally affecting the inference accuracy. The key to our approach is a new context and resource aware projection of data to a lower-dimensional embedding by which learning the correlation between data samples requires significantly smaller number of neurons. We leverage the performance gain achieved as a result of the data projection to enable the training of different DNN architectures which can be aggregated together to further boost the inference accuracy. Accompanying APIs are provided to facilitate rapid prototyping of an arbitrary DNN application customized to the underlying platform. Proof-of-concept evaluations for deployment of different visual, audio, and smart-sensing benchmarks demonstrate up to 100-fold energy improvement compared to the prior-art DL solutions.


title: On the interaction of social affect and cognition: empathy, compassion and theory of mind

abstract: Empathy, compassion and Theory of Mind (ToM) are central topics in social psychology and neuroscience. While empathy enables the sharing of others’ emotions and may result in empathic distress, a maladaptive form of empathic resonance, or compassion, a feeling of warmth and concern for others, ToM provides cognitive understanding of someone else's thoughts or intentions. These socio-affective and socio-cognitive routes to understanding others are subserved by separable, independent brain networks. Nonetheless they are jointly required in many complex social situations. A process that is critical for both, empathy and ToM, is self-other distinction, which is implemented in different temporoparietal brain regions. Thus, adaptive social behavior is a result of dynamic interplay of socio-affective and socio-cognitive processes.


title: Rethinking Large-Scale Consensus

abstract: In this position paper, we initiate a systematic treatment of reaching consensus in a permissionless network. We prove several simple but hopefully insightful lower bounds that demonstrate exactly why reaching consensus in a permission-less setting is fundamentally more difficult than the classical, permissioned setting. We then present a simplified proof of Nakamoto's blockchain which we recommend for pedagogical purposes. Finally, we survey recent results including how to avoid well-known painpoints in permissionless consensus, and how to apply core ideas behind blockchains to solve consensus in the classical, permissioned setting and meanwhile achieve new properties that are not attained by classical approaches.


title: On strategies for imbalanced text classification using SVM: A comparative study. Decision Support Systems

abstract: Many real-world text classification tasks involve imbalanced training examples. The strategies proposed to address the imbalanced classification (e.g., resampling, instance weighting), however, have not been systematically evaluated in the text domain. In this paper, we conduct a comparative study on the effectiveness of these strategies in the context of imbalanced text classification using Support Vector Machines (SVM) classifier. SVM is the interest in this study for its good classification accuracy reported in many text classification tasks. We propose a taxonomy to organize all proposed strategies following the training and the test phases in text classification tasks. Based on the taxonomy, we survey the methods proposed to address the imbalanced classification. Among them, 10 commonly-used methods were evaluated in our experiments on three benchmark datasets, i.e., Reuters-21578, 20-Newsgroups, and WebKB. Using the area under the Precision-Recall Curve as the performance measure, our experimental results showed that the best decision surface was often learned by the standard SVM, not coupled with any of the proposed strategies. We believe such a negative finding will benefit both researchers and application developers in the area by focusing more on thresholding strategies.


title: A BLE-based data collection system for IoT

abstract: The data collection is an important phase in the internet of things (IoT). The efficient way to collect data from the IoT environment is one of the challenges for the future of IoT. In this paper, we first design a data collection system for IoT. This system is based Bluetooth Low Energy (BLE) technology to forward the collected data from the data collector to the data gateways and proposes the use of smart phones as data collectors. We also propose a first prototype of this system. This prototype was developed with the help of two well-known open technologies, Arduino (Bluno) and Android. Finally, we evaluate the performances of the proposed system through performing a set of experiments on the developed prototype. The experiments show the feasibility and the applicability of the system especially where distance between the data collector and the data gateway is blow 6 meters. They also demonstrate the energy efficiency of the system


title: Improvement of rehabilitation possibilities with the MotionMaker TM

abstract: "MotionMakertrade" is a stationary programmable test and training system for the lower limbs developed at the 'Ecole Polytechnique Federale de Lausanne' with the 'Fondation Suisse pour les Cybertheses'. The system is composed of two robotic orthoses comprising motors and sensors, and a control unit managing the trans-cutaneous electrical muscle stimulation with real-time regulation. The control of the functional electrical stimulation (FES) induced muscle force necessary to mimic natural exercise is ensured by the control unit which receives a continuous input from the position and force sensors mounted on the robot. First results with control subjects showed the feasibility of creating movements by such closed-loop controlled FES induced muscle contractions. To make exercising with the MotionMakertrade safe for clinical trials with spinal cord injured (SCI) volunteers, several original safety features have been introduced. The MotionMakertrade is able to identify and manage the occurrence of spasms. Fatigue can also be detected and overfatigue during exercise prevented


title: From Big Data to Big Knowledge: The art of making Big Data alive

abstract: Nowadays Big Data becomes one of the biggest buzz concepts in IT world especially with the vertiginous development driving the increase of data encouraged by the emergence of high technologies of storage like cloud computing. Big Data can create efficient challenging solutions in health, security, government and more; and usher in a new era of analytics and decisions. Knowledge Management comprises a set of strategies and practices used to identify, create, represent, distribute, and enable creating experience that can constitute a real immaterial capital. However, to bring significant meaning to the perpetual tsunami of data and manage them, Big Data needs Knowledge Management. In the same way, to broaden the scope of its targeted analyzes, Knowledge Management requires Big Data. Thus, there is a complementary relation between these two major concepts. This paper presents a state of art where we try to explore Big Data within the context of Knowledge Management. We discuss the bi-directional relationship linking this two fundamental concepts and their strategic utility in making analytics valuable especially with the combination of their interactions which create an effective Big Knowledge to build experience.


Generating Multi-label Discrete Electronic Health Records using Generative Adversarial Networks.


title: Search Strategies for Subgraph Isomorphism Algorithms

abstract: Searching for subgraph isomorphisms is an essential problem in pattern matching. Most of the algorithms use a branch-and-bound method to sequentially assign pattern nodes to compatible nodes in the target graph. It is well known that the order in which nodes are assigned, a so-called search strategy, influences drastically the size of the search space. In this article we investigate the impact of various search strategies on the efficiency of two algorithms, the first being the Ullmann's algorithm and the second one the recently proposed improvement of Ullmann's algorithm. From the large set of proposed orders we find the most successful ones by thorough testing on a large database of graphs.


title: The value of plants used in traditional medicine for drug discovery.

abstract: In this review we describe and discuss several approaches to selecting higher plants as candidates for drug development with the greatest possibility of success. We emphasize the role of information derived from various systems of traditional medicine (ethnomedicine) and its utility for drug discovery purposes. We have identified 122 compounds of defined structure, obtained from only 94 species of plants, that are used globally as drugs and demonstrate that 80% of these have had an ethnomedical use identical or related to the current use of the active elements of the plant. We identify and discuss advantages and disadvantages of using plants as starting points for drug development, specifically those used in traditional medicine.


title: Modelling Competitive Sports: Bradley-Terry-\'{E}l\H{o} Models for Supervised and On-Line Learning of Paired Competition Outcomes

abstract: Prediction and modelling of competitive sports outcomes has received much recent attention, especially from the Bayesian statistics and machine learning communities. In the real world setting of outcome prediction, the seminal Elő update still remains, after more than 50 years, a valuable baseline which is difficult to improve upon, though in its original form it is a heuristic and not a proper statistical "model". Mathematically, the Elő rating system is very closely related to the Bradley-Terry models, which are usually used in an explanatory fashion rather than in a predictive supervised or on-line learning setting. ::: Exploiting this close link between these two model classes and some newly observed similarities, we propose a new supervised learning framework with close similarities to logistic regression, low-rank matrix completion and neural networks. Building on it, we formulate a class of structured log-odds models, unifying the desirable properties found in the above: supervised probabilistic prediction of scores and wins/draws/losses, batch/epoch and on-line learning, as well as the possibility to incorporate features in the prediction, without having to sacrifice simplicity, parsimony of the Bradley-Terry models, or computational efficiency of Elő's original approach. ::: We validate the structured log-odds modelling approach in synthetic experiments and English Premier League outcomes, where the added expressivity yields the best predictions reported in the state-of-art, close to the quality of contemporary betting odds.


title: Impact of Technostress on End-User Satisfaction and Performance

abstract: Organizational use of information and communications technologies (ICT) is increasingly resulting in negative cognitions in individuals, such as information overload and interruptions. Recent literature has encapsulated these cognitions in the concept of technostress, which is stress caused by an inability to cope with the demands of organizational computer usage. Given the critical role of the user in organizational information processing and accomplishing application-enabled workflows, understanding how these cognitions affect users' satisfaction with ICT and their performance in ICT-mediated tasks is an important step in appropriating benefits from current computing environments. The objective of this paper is to (1) understand the negative effects of technostress on the extent to which end users perceive the applications they use to be satisfactory and can utilize them to improve their performance at work and (2) identify mechanisms that can mitigate these effects. Specifically, we draw from the end-user computing and technostress literature to develop and validate a model that analyzes the effects of factors that create technostress on the individual's satisfaction with, and task performance using, ICT. The model also examines how user involvement in ICT development and support mechanisms for innovation can be used to weaken technostress-creating factors and their outcomes. The results, based on survey data analysis from 233 ICT users from two organizations, show that factors that create technostress reduce the satisfaction of individuals with the ICT they use and the extent to which they can utilize ICT for productivity and innovation in their tasks. Mechanisms that facilitate involvement of users, and encourage them to take risks, learn, explore new ideas, and experiment in the context of ICT use, diminish the factors that create technostress and increase satisfaction with the ICT they use. These mechanisms also have a positive effect on users' appropriation of ICT for productivity and innovation in their tasks. The paper contributes to emerging literature on negative outcomes of ICT use by (1) highlighting the influence of technostress on users' satisfaction and performance (i.e., productivity and innovation in ICT-mediated tasks) with ICT, (2) extending the literature on technostress, which has so far looked largely at the general behavioral and psychological domains, to include the domain of end-user computing, and (3) demonstrating the importance of user involvement and innovation support mechanisms in reducing technostress-creating conditions and their ICT use-related outcomes.


title: Classification of offline gujarati handwritten characters

abstract: Intelligent Character Recognition (ICR) is a specific form of optical character recognition (OCR) dealing mostly with handwritten texts. Due to their specificity, they are usually more adept in interpreting different styles and fonts of handwriting providing eventually higher recognition rates. Factors like language constructs, amount of research on ICR pertaining to the language, etc., essentially determines the amount of success achieved in its character recognition. This research mainly deals with the recognition of Gujarati Handwritten Characters. We have considered 34 consonants and 5 vowels; a total of 39 Gujarati Characters. The structure and lexicons of the language posed a challenge during the initial phase of segmentation; for that we have proposed new algorithm for segmentation. Our segmentation algorithm is able to address these concerns effectively. Different algorithms from different domains have been considered for comparative analysis like Transform Domain (DWT, DCT and DFT), from Spatial Domain; Geometric Method (Gradient feature), Structural method (Freeman chain code) and Statistical method (Zernike Moments). We have also proposed a new Combination of Structural and Statistical methods (Freeman chain code, Hu's invariant moment and center of mass) to extract feature vectors and it results into good amount of accuracy. These extracted feature vectors were further supplied as input into Support Vector Machines and their resulting accuracies were analyzed using 10 fold cross validation. SVM performs well on data sets that have many attributes and can also handle large number of classes.


title: One Shot Learning via Compositions of Meaningful Patches

abstract: The task of discriminating one object from another is almost trivial for a human being. However, this task is computationally taxing for most modern machine learning methods, whereas, we perform this task at ease given very few examples for learning. It has been proposed that the quick grasp of concept may come from the shared knowledge between the new example and examples previously learned. We believe that the key to one-shot learning is the sharing of common parts as each part holds immense amounts of information on how a visual concept is constructed. We propose an unsupervised method for learning a compact dictionary of image patches representing meaningful components of an objects. Using those patches as features, we build a compositional model that outperforms a number of popular algorithms on a one-shot learning task. We demonstrate the effectiveness of this approach on hand-written digits and show that this model generalizes to multiple datasets.


title: Performance, radiation damage, and future plans of the BABAR silicon vertex tracker

abstract: The BABAR silicon vertex tracker (SVT) has been in operation for four years at the PEP-II electron-positron storage ring. During this time the SVT modules have accumulated a radiation dose up to 2 Mrad. We study the degradation in the performance of the SVT due to this accumulated dose which is highly non uniform across the device and also within the individual silicon detectors. To extrapolate the performance of the device to the future we study separately the effect of the irradiation on silicon detectors, front end integrated circuits and on a complete detector module under controlled radiation conditions, using a /sup 60/Co source and a 0.9 GeV e/sup -/ beam. We compare the results to the data from the SVT. In particular we show the dependence of the charge collection efficiency on the radiation dose even when a small stripe of the module is irradiated up to space charge sign inversion. Since the modules that are located in the plane of the beams will suffer significant radiation damage, we will describe our plans for their replacement in 2005 and for the operation of the SVT through the lifetime of the BABAR experiment.


title: Nivolumab versus Docetaxel in Advanced Squamous-Cell Non–Small-Cell Lung Cancer

abstract: BackgroundPatients with advanced squamous-cell non–small-cell lung cancer (NSCLC) who have disease progression during or after first-line chemotherapy have limited treatment options. This randomized, open-label, international, phase 3 study evaluated the efficacy and safety of nivolumab, a fully human IgG4 programmed death 1 (PD-1) immune-checkpoint–inhibitor antibody, as compared with docetaxel in this patient population. MethodsWe randomly assigned 272 patients to receive nivolumab, at a dose of 3 mg per kilogram of body weight every 2 weeks, or docetaxel, at a dose of 75 mg per square meter of body-surface area every 3 weeks. The primary end point was overall survival. ResultsThe median overall survival was 9.2 months (95% confidence interval [CI], 7.3 to 13.3) with nivolumab versus 6.0 months (95% CI, 5.1 to 7.3) with docetaxel. The risk of death was 41% lower with nivolumab than with docetaxel (hazard ratio, 0.59; 95% CI, 0.44 to 0.79; P<0.001). At 1 year, the overall survival rate was 42% (95% CI, 3...


title: Embrace Chattering Students: They May Be Building Community and Interest in Your Class

abstract: When students chatter in class it can be disruptive, but could that chatter also have some redeeming qualities? We asked students to keep track of their social interactions in a particular class. On days when students had more social interactions than usual, they reported a greater sense of belonging, which was, in turn, related to greater class enjoyment (i.e., a within-person effect). Further, students who tended to have more social interactions than others reported a greater sense of belonging, which was, in turn, related to greater class enjoyment (i.e., a between-person effect). These results held when examining daily ratings of social interactions, belonging, and class enjoyment, and when examining overall end-of-semester ratings. Critically, higher average daily feelings of belonging mediated the effect of the number of average daily classroom interactions on students’ end-of-semester class enjoyment and marginally on grades. For educators, promoting peer-to-peer conversation may create a positive ...


title: Speaker diarization using deep neural network embeddings

abstract: Speaker diarization is an important front-end for many speech technologies in the presence of multiple speakers, but current methods that employ i-vector clustering for short segments of speech are potentially too cumbersome and costly for the front-end role. In this work, we propose an alternative approach for learning representations via deep neural networks to remove the i-vector extraction process from the pipeline entirely. The proposed architecture simultaneously learns a fixed-dimensional embedding for acoustic segments of variable length and a scoring function for measuring the likelihood that the segments originated from the same or different speakers. Through tests on the CALLHOME conversational telephone speech corpus, we demonstrate that, in addition to streamlining the diarization architecture, the proposed system matches or exceeds the performance of state-of-the-art baselines. We also show that, though this approach does not respond as well to unsupervised calibration strategies as previous systems, the incorporation of well-founded speaker priors sufficiently mitigates this shortcoming.


title: Solid state transformer in the future smart electrical system

abstract: The concept of the solid state transformer has been investigated extensively in the past decade with the emphasis mainly focused on the circuit topology investigation. With the technology being more and more mature and acceptable, the application issue of solid state transformer in the future smart electrical system needs to be investigated. This paper characterizes and summarizes the main features of the solid state transformer, and correspondingly presents possible application areas of solid state transformer in the future smart electrical system. The future distribution system architecture is proposed and a new wind energy system is presented based on the multifunctional utilization of SST. Simulation results are given to demonstrate the proposal.


title: Spatio-temporal motion tracking with unsynchronized cameras

abstract: We present a new spatio-temporal method for markerless motion capture. We reconstruct the pose and motion of a character from a multi-view video sequence without requiring the cameras to be synchronized and without aligning captured frames in time. By formulating the model-to-image similarity measure as a temporally continuous functional, we are also able to reconstruct motion in much higher temporal detail than was possible with previous synchronized approaches. By purposefully running cameras unsynchronized we can capture even very fast motion at speeds that off-the-shelf but high quality cameras provide.


title: Vigneron and Johnson / A Review and a Conceptual Framework of Prestige A Review and a Conceptual Framework of Prestige-Seeking Consumer Behavior

abstract: This paper discusses the existing consumer knowledge dealing with aspects of prestige, and based on this literature, develops a conceptual framework useful for the analysis of prestige-seeking consumer behavior (PSCB). The purpose of this paper is to combine the concepts of existing research on prestige consumers (rather limited) and studies which examined entirely different aspects of consumer behavior, but coincidentally produce valuable information. By examining all the sources of prestige consumption in a broad and integrated way, we shall provide a new perspective that draws from, rather than substitutes for, previous research. The objectives of this literature review can be summarized as follows: • Contribute to the emerging literature on prestige consumption in examining and defining the key perceived values which form the concept of prestige. • Interpret and expand existing consumer behavior models in order to develop a specific prestige-seeking consumer behavior framework. • Generate a framework to help marketers build and monitor the prestige of brands. • Stimulate further research on prestige-seeking consumer behavior.


Predicting Tags for StackOverflow Questions


title: Efficient digital baseline wander algorithm and its architecture for fast Ethernet

abstract: The paper proposes an efficient digital baseline wander (BLW) algorithm and its hardware architecture for 100BASE-TX Ethernet. The proposed BLW compensator uses four symbols, including the present symbol, and can remove BLW at the channel having large BLW or having a killing packet. The proposed BLW compensator is purely implemented in a digital domain. To verify the performance of the proposed BLW compensator, we simulate a 100BASE-TX DSP submodule using the SPW/spl trade/ tool. The DSP submodule has been modeled by Verilog-HDL and synthesized using the 0.18 /spl mu/m SEC cell library. The measured BER is less than 10/sup -10/ when the transmitted data is received up to 150 m. The implemented DSP submodule operates at 142.7 MHz and consists of 128,528 gates. Since the 1000Base-T receiver uses DSP submodule similar to 100BASE-TX receiver, the proposed architecture can be reused for gigabit Ethernet.


title: Effects of angiotensin II on nitric oxide generation in growing and resting rat aortic endothelial cells.

abstract: OBJECTIVES ::: To assess the effects of angiotensin II (ang II) and its receptors on nitric oxide (NO) production and endothelial NO synthase (eNOS) activity and expression with respect to rat aortic endothelial cell (RAEC) growth. To also assess whether an intact endothelium is required for ang II activity. ::: ::: ::: METHODS ::: RAEC were treated with different doses of ang II, Ca(2+) ionophore A23187, valsartan (an AT(1) receptor inhibitor) or PD-123319 (an AT(2) receptor inhibitor) alone or in combination for 24 h before measuring nitrite levels by Griess reaction as an index of NO production and eNOS activity by L-[3H]-arginine to L-[3H]-citrulline conversion assay. eNOS mRNA and protein expressions were determined by Northern and Western analyses, respectively. The requirement of endothelium for ang II-mediated relaxant/contractile effects was investigated by isometric tension studies. ::: ::: ::: RESULTS ::: NO production and eNOS activity/expression were almost two-fold greater in proliferating RAEC. Ang II or Ca(2+) ionophore A23187 enhanced NO production in proliferating and confluent RAEC without altering the fold-difference in basal NO release. Both valsartan and PD-123319 significantly diminished NO production in RAEC treated with ang II but not Ca(2+) ionophore A23187 while NG-nitro-L-arginine (L-NNA, 10 micromol/l) equally decreased NO generation in response to both stimulators. L-NNA, valsartan and PD-123319 also abolished endothelium-dependent vasorelaxant responses to ACh and Ca(2+) ionophore A23187 in the presence of ang II. Sodium nitroprusside (SNP), a NO donor, increased endothelium-independent vasorelaxant responses that were augmented by valsartan but not L-NNA or PD-123319 in the presence of ang II. ::: ::: ::: CONCLUSIONS ::: Ang II induces vascular NO production through endothelial AT(1) and AT(2)-receptors. This may be beneficial in counterbalancing its vasoconstrictor effect on vascular smooth muscle cells.


title: Consistent and durable data structures for non-volatile byteaddressable memory

abstract: The predicted shift to non-volatile, byte-addressable memory (e.g., Phase Change Memory and Memristor), the growth of "big data", and the subsequent emergence of frameworks such as memcached and NoSQL systems require us to rethink the design of data stores. To derive the maximum performance from these new memory technologies, this paper proposes the use of single-level data stores. For these systems, where no distinction is made between a volatile and a persistent copy of data, we present Consistent and Durable Data Structures (CDDSs) that, on current hardware, allows programmers to safely exploit the low-latency and non-volatile aspects of new memory technologies. CDDSs use versioning to allow atomic updates without requiring logging. The same versioning scheme also enables rollback for failure recovery. When compared to a memory-backed Berkeley DB B-Tree, our prototype-based results show that a CDDS B-Tree can increase put and get throughput by 74% and 138%. When compared to Cassandra, a two-level data store, Tembo, a CDDS B-Tree enabled distributed Key-Value system, increases throughput by up to 250%-286%.


title: Weakly compressible SPH for free surface flows

abstract: We present a weakly compressible form of the Smoothed Particle Hydrodynamics method (SPH) for fluid flow based on the Tait equation. In contrast to commonly employed projection approaches that strictly enforce incompressibility, time-consuming solvers for the Poisson equation are avoided by allowing for small, user-defined density fluctuations. We also discuss an improved surface tension model that is particularly appropriate for single-phase free-surface flows. The proposed model is compared to existing models and experiments illustrate the accuracy of the approach for free surface flows. Combining the proposed methods, volume-preserving low-viscosity liquids can be efficiently simulated using SPH. The approach is appropriate for medium-scale and small-scale phenomena. Effects such as splashing and breaking waves are naturally handled.


title: A Low Cost Flyback CCM Inverter for AC Module Application

abstract: The unfolding-type flyback inverter operating in discontinuous conduction mode (DCM) is popular as a low-cost solution for a photovoltaic (PV) ac module application. This paper aims to improve the efficiency by using a scheme based on continuous conduction mode (CCM) for this application. Design issues, both for the power scheme and the control scheme, are identified and trade-offs investigated. An open-loop control of the secondary current, based on feedback control of the primary current, is proposed in order to bypass the difficulties posed by the moving right half plane zero in the duty cycle to secondary current transfer function. The results presented show an improvement of 8% in California efficiency compared to the benchmark DCM scheme for a 200-W PV module application. The output power quality at rated power level is capable of meeting IEC61727 requirements. The stability of the flyback inverter in CCM has been verified at selected working conditions.


title: Low-Cost Gas Sensors Produced by the Graphite Line-Patterning Technique Applied to Monitoring Banana Ripeness

abstract: A low-cost sensor array system for banana ripeness monitoring is presented. The sensors are constructed by employing a graphite line-patterning technique (LPT) to print interdigitated graphite electrodes on tracing paper and then coating the printed area with a thin film of polyaniline (PANI) by in-situ polymerization as the gas-sensitive layer. The PANI layers were used for the detection of volatile organic compounds (VOCs), including ethylene, emitted during ripening. The influence of the various acid dopants, hydrochloric acid (HCl), methanesulfonic acid (MSA), p-toluenesulfonic acid (TSA) and camphorsulfonic acid (CSA), on the electrical properties of the thin film of PANI adsorbed on the electrodes was also studied. The extent of doping of the films was investigated by UV-Vis absorption spectroscopy and tests showed that the type of dopant plays an important role in the performance of these low-cost sensors. The array of three sensors, without the PANI-HCl sensor, was able to produce a distinct pattern of signals, taken as a signature (fingerprint) that can be used to characterize bananas ripeness.


title: Agglomeration and Elimination of Terms for Dimensionality Reduction

abstract: The vector space model is the usual representation of texts database for computational treatment. However, in such representation synonyms and/or related terms are treated as independent. Furthermore, there are some terms that do not add any information at all to the set of text documents, on the contrary they even might harm the performance of the information retrieval techniques. In an attempt to reduce this problem, some techniques have been proposed in the literature. In this work we present a method to tackle this problem. In order to validate our approach, we carried out a serie of experiments on four databases and we compare the achieved results with other well known techniques. The evaluation results is such that our method obtained in all cases a better or equal performance compared to the other literature techniques.


title: Predicting Symptom Trajectories of Schizophrenia using Mobile Sensing

abstract: Continuously monitoring schizophrenia patients’ psychiatric symptoms is crucial for in-time intervention and treatment adjustment. The Brief Psychiatric Rating Scale (BPRS) is a survey administered by clinicians to evaluate symptom severity in schizophrenia. The CrossCheck symptom prediction system is capable of tracking schizophrenia symptoms based on BPRS using passive sensing from mobile phones. We present results from an ongoing randomized control trial, where passive sensing data, self-reports, and clinician administered 7-item BPRS surveys are collected from 36 outpatients with schizophrenia recently discharged from hospital over a period ranging from 2-12 months. We show that our system can predict a symptom scale score based on a 7-item BPRS within ±1.45 error on average using automatically tracked behavioral features from phones (e.g., mobility, conversation, activity, smartphone usage, the ambient acoustic environment) and user supplied self-reports. Importantly, we show our system is also capable of predicting an individual BPRS score within ±1.59 error purely based on passive sensing from phones without any self-reported information from outpatients. Finally, we discuss how well our predictive system reflects symptoms experienced by patients by reviewing a number of case studies.


title: HIV disease is associated with increased biomarkers of endothelial dysfunction despite viral suppression on long–term antiretroviral therapy in Botswana

abstract: BACKGROUND ::: Untreated HIV infection is associated with increased biomarkers of endothelial dysfunction. However, the predictors and degree of endothelial dysfunction among virally suppressed HIV-infected adults on long-term antiretroviral therapy (ART) have not been well studied in sub-Saharan Africa (SSA). ::: ::: ::: METHODS ::: We enrolled 112 HIV-infected adults with virological suppression on long-term ART and 84 HIV-uninfected controls in Botswana. We measured plasma levels of markers of endothelial injury [soluble vascular adhesion molecule 1 (VCAM-1), intercellular adhesion molecule 1 (ICAM-1) and E-selectin] and plasma levels of biomarkers of inflammation [interleukin 6 (IL-6)] and monocyte activation (sCD163). Baseline traditional cardiovascular disease (CVD) risk factors and bilateral common carotid intima-media thickness (cIMT) were also available for all participants. We assessed whether HIV status (despite virological suppression on ART) was associated with biomarkers of endothelial dysfunction after controlling for traditional CVD risk factors in linear regression models. We additionally assessed the association between IL-6, sCD163 and cIMT with endothelial dysfunction in separate multivariate linear regression models, controlling for cIMT, among virally suppressed HIV-infected participants only. ::: ::: ::: RESULTS ::: In multivariate analysis, HIV infection was significantly associated with increased VCAM-1 (p < 0.01) and ICAM-1 (p = 0.03) but not E-selectin (p = 0.74) levels. Within the HIV-positive group, higher sCD163 levels were associated with decreased ICAM-1 and E-selectin (p < 0.01 and p = 0.01, respectively) but not VCAM-1 (p = 0.13) levels. IL-6 was not associated with any of the biomarkers of endothelial dysfunction. ::: ::: ::: CONCLUSION ::: HIV disease was associated with biomarkers of endothelial dysfunction among virally suppressed adults in Botswana on long-term ART after controlling for traditional CVD risk factors. Future work should explore the clinical impact of persistent endothelial dysfunction following long-term HIV viral suppression on the risk of CVD clinical endpoints among HIV-infected patients in this setting.


title: Decoupled sampling for graphics pipelines

abstract: We propose a generalized approach to decoupling shading from visibility sampling in graphics pipelines, which we call decoupled sampling. Decoupled sampling enables stochastic supersampling of motion and defocus blur at reduced shading cost, as well as controllable or adaptive shading rates which trade off shading quality for performance. It can be thought of as a generalization of multisample antialiasing (MSAA) to support complex and dynamic mappings from visibility to shading samples, as introduced by motion and defocus blur and adaptive shading. It works by defining a many-to-one hash from visibility to shading samples, and using a buffer to memoize shading samples and exploit reuse across visibility samples. Decoupled sampling is inspired by the Reyes rendering architecture, but like traditional graphics pipelines, it shades fragments rather than micropolygon vertices, decoupling shading from the geometry sampling rate. Also unlike Reyes, decoupled sampling only shades fragments after precise computation of visibility, reducing overshading. We present extensions of two modern graphics pipelines to support decoupled sampling: a GPU-style sort-last fragment architecture, and a Larrabee-style sort-middle pipeline. We study the architectural implications of decoupled sampling and blur, and derive end-to-end performance estimates on real applications through an instrumented functional simulator. We demonstrate high-quality motion and defocus blur, as well as variable and adaptive shading rates.


title: Deciding Advantageously Before Knowing the Advantageous Strategy

abstract: Deciding advantageously in a complex situation is thought to require overt reasoning on declarative knowledge, namely, on facts pertaining to premises, options for action, and outcomes of actions that embody the pertinent previous experience. An alternative possibility was investigated: that overt reasoning is preceded by a nonconscious biasing step that uses neural systems other than those that support declarative knowledge. Normal participants and patients with prefrontal damage and decision-making defects performed a gambling task in which behavioral, psychophysiological, and self-account measures were obtained in parallel. Normals began to choose advantageously before they realized which strategy worked best, whereas prefrontal patients continued to choose disadvantageously even after they knew the correct strategy. Moreover, normals began to generate anticipatory skin conductance responses (SCRs) whenever they pondered a choice that turned out to be risky, before they knew explicitly that it was a risky choice, whereas patients never developed anticipatory SCRs, although some eventually realized which choices were risky. The results suggest that, in normal individuals, nonconscious biases guide behavior before conscious knowledge does. Without the help of such biases, overt knowledge may be insufficient to ensure advantageous behavior.


title: Artificial Intelligence (AI) Methods in Optical Networks: A Comprehensive Survey

abstract: Artificial intelligence (AI) is an extensive scientific discipline which enables computer systems to solve problems by emulating complex biological processes such as learning, reasoning and self-correction. This paper presents a comprehensive review of the application of AI techniques for improving performance of optical communication systems and networks. The use of AI-based techniques is first studied in applications related to optical transmission, ranging from the characterization and operation of network components to performance monitoring, mitigation of nonlinearities, and quality of transmission estimation. Then, applications related to optical network control and management are also reviewed, including topics like optical network planning and operation in both transport and access networks. Finally, the paper also presents a summary of opportunities and challenges in optical networking where AI is expected to play a key role in the near future.


Deep learning methods for multimodal hepatic lesion segmentation : Fusing functional and structural medical images Thesis Plan


Forensic Analysis of the Windows Registry


title: An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis

abstract: In this study, a new predictive framework is proposed by integrating an improved grey wolf optimization (IGWO) and kernel extreme learning machine (KELM), termed as IGWO-KELM, for medical diagnosis. The proposed IGWO feature selection approach is used for the purpose of finding the optimal feature subset for medical data. In the proposed approach, genetic algorithm (GA) was firstly adopted to generate the diversified initial positions, and then grey wolf optimization (GWO) was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on KELM. The proposed approach is compared against the original GA and GWO on the two common disease diagnosis problems in terms of a set of performance metrics, including classification accuracy, sensitivity, specificity, precision, -mean, -measure, and the size of selected features. The simulation results have proven the superiority of the proposed method over the other two competitive counterparts.


title: Measuring real-world accuracies and biases in modeling password guessability

abstract: Parameterized password guessability--how many guesses a particular cracking algorithm with particular training data would take to guess a password--has become a common metric of password security. Unlike statistical metrics, it aims to model real-world attackers and to provide per-password strength estimates. We investigate how cracking approaches often used by researchers compare to real-world cracking by professionals, as well as how the choice of approach biases research conclusions. ::: ::: We find that semi-automated cracking by professionals outperforms popular fully automated approaches, but can be approximated by combining multiple such approaches. These approaches are only effective, however, with careful configuration and tuning; in commonly used default configurations, they underestimate the real-world guessability of passwords. We find that analyses of large password sets are often robust to the algorithm used for guessing as long as it is configured effectively. However, cracking algorithms differ systematically in their effectiveness guessing passwords with certain common features (e.g., character substitutions). This has important implications for analyzing the security of specific password characteristics or of individual passwords (e.g., in a password meter or security audit). Our results highlight the danger of relying only on a single cracking algorithm as a measure of password strength and constitute the first scientific evidence that automated guessing can often approximate guessing by professionals.


title: Molecular Basis of the Interaction between Proapoptotic Truncated BID (tBID) Protein and Mitochondrial Carrier Homologue 2 (MTCH2) Protein

abstract: The molecular basis of the interaction between mitochondrial carrier homologue 2 (MTCH2) and truncated BID (tBID) was characterized. These proteins participate in the apoptotic pathway, and the interaction between them may serve as a target for anticancer lead compounds. In response to apoptotic signals, MTCH2 recruits tBID to the mitochondria, where it activates apoptosis. A combination of peptide arrays screening with biochemical and biophysical techniques was used to characterize the mechanism of the interaction between tBID and MTCH2 at the structural and molecular levels. The regions that mediate the interaction between the proteins were identified. The two specific binding sites between the proteins were determined to be tBID residues 59-73 that bind MTCH2 residues 140-161, and tBID residues 111-125 that bind MTCH2 residues 240-290. Peptides derived from tBID residues 111-125 and 59-73 induced cell death in osteosarcoma cells. These peptides may serve as lead compounds for anticancer drugs that act by targeting the tBID-MTCH2 interaction.


title: Dense RGB-D-inertial SLAM with map deformations

abstract: While dense visual SLAM methods are capable of estimating dense reconstructions of the environment, they suffer from a lack of robustness in their tracking step, especially when the optimisation is poorly initialised. Sparse visual SLAM systems have attained high levels of accuracy and robustness through the inclusion of inertial measurements in a tightly-coupled fusion. Inspired by this performance, we propose the first tightly-coupled dense RGB-D-inertial SLAM system. Our system has real-time capability while running on a GPU. It jointly optimises for the camera pose, velocity, IMU biases and gravity direction while building up a globally consistent, fully dense surfel-based 3D reconstruction of the environment. Through a series of experiments on both synthetic and real world datasets, we show that our dense visual-inertial SLAM system is more robust to fast motions and periods of low texture and low geometric variation than a related RGB-D-only SLAM system.


title: Dense and Low-Rank Gaussian CRFs Using Deep Embeddings

abstract: In this work we introduce a structured prediction model that endows the Deep Gaussian Conditional Random Field (G-CRF) with a densely connected graph structure. We keep memory and computational complexity under control by expressing the pairwise interactions as inner products of low-dimensional, learnable embeddings. The G-CRF system matrix is therefore low-rank, allowing us to solve the resulting system in a few milliseconds on the GPU by using conjugate gradient. As in G-CRF, inference is exact, the unary and pairwise terms are jointly trained end-to-end by using analytic expressions for the gradients, while we also develop even faster, Potts-type variants of our embeddings. We show that the learned embeddings capture pixel-to-pixel affinities in a task-specific manner, while our approach achieves state of the art results on three challenging benchmarks, namely semantic segmentation, human part segmentation, and saliency estimation. Our implementation is fully GPU based, built on top of the Caffe library, and is available at https://github.com/siddharthachandra/gcrf-v2.0.


title: A phase shift full bridge based reconfigurable PEV onboard charger with extended ZVS range and zero duty cycle loss

abstract: In this paper, an integrated onboard charger architecture is proposed for plug-in electric vehicle (PEV). In this architecture, the phase shift full bridge (PSFB) converter serves as the main high voltage battery charging topology, and the half bridge LLC resonant converter serves as the low voltage battery charging topology. Under light charging mode, the half-bridge LLC is reconfigured to be paralleled with the PSFB topology, to guarantee zero voltage switching (ZVS) of the lagging-leg MOSFETs. Practical design considerations are presented for both the PSFB and the half bridge LLC converters. Switching frequency and the shifted phase angle provide two degrees of freedom to regulate the output voltage/current of both converters. The proposed architecture maintains low cost and high efficiency in this specific application. A 390V input, 420V/2.4A, 14V/21A outputs converter prototype is designed, simulated, and analyzed to verify the proof of concept.


title: A folded loop antenna with four resonant modes

abstract: A multimode loop antenna with compact volume for mobile applications is presented in this paper. The loop antenna consists of a loop strip with a length of about 0.5λ. The loop strip is meandered to save space and bent to generate three traditional resonant modes, the 0.5-λ, 1-λ and 1.5-λ modes, and an additional higher mode, the 2-λ mode. The additional 2-λ mode is generated by adjusting the distance between the feed point and shorting point of the loop strip. The 1-λ, 1.5-λ and 2-λ modes together form an upper band with a wide bandwidth of 1.71–2.69 GHz to cover the DCS1800, PCS1900, UMTS2100, LTE2300 and LTE2500 systems. By using a matching network at the loop input, the lower band generated by the 0.5-λ mode has a bandwidth of 0.76–1.09 GHz to cover the GSM850 and GSM900 systems.


title: ROSRV: Runtime verification for robots

abstract: We present ROSRV, a runtime verification framework for robotic applications on top of the Robot Operating System (ROS [8]), a widely used open-source framework for robot software development. ROSRV aims to address the safety and security issues of robots by providing a transparent monitoring infrastructure that intercepts and monitors the commands and messages passing through the system. Safety and security properties can be defined in a formal specification language, and are ensured by automatically generated monitors. ROSRV integrates seamlessly with ROS—no change in ROS nor the application code is needed. ROSRV has been applied and evaluated on a commercial robot.


title: Analyzing Twitter Sentiments with Big Data

abstract: Sentimental analysis is the process of defining the opinion of the individual by extracting the data. Sentimental analysis is done by collecting the data from variety of resources like internet and social media platforms like (Face book, twitter) and it stored in different formats. The data extracted from these social media platforms can be used for different purposes like prediction, marketing and sentiment analysis. Politicians use sentimental analysis to know how people feel about themselves and their parties. Twitter is a widely used platform for posting comments through short messages. Every year millions or billions of tweets extracted are subjected to sentiment analysis. But handling such a huge amount of unstructured or structured data is a tedious task to take by the traditional storage system (such as RDBMS, Relational Database System). Therefore., we need a tool that can process and analyze this unrelated data. So; we have utilized big data technology to handle different formats of unstructured and structured data from different sources.


title: Roles and Requirements of Electronic Packaging in 5G

abstract: In this work, we illustrate the roles and requirements of packaging in the implementation of hybrid beamforming-based mmWave massive MIMO architectures for the development of 5G hardware modules and systems.


title: Distilling the Knowledge in a Neural Network

abstract: A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may be too computationally expensive to allow deployment to a large number of users, especially if the individual models are large neural nets. Caruana and his collaborators have shown that it is possible to compress the knowledge in an ensemble into a single model which is much easier to deploy and we develop this approach further using a different compression technique. We achieve some surprising results on MNIST and we show that we can significantly improve the acoustic model of a heavily used commercial system by distilling the knowledge in an ensemble of models into a single model. We also introduce a new type of ensemble composed of one or more full models and many specialist models which learn to distinguish fine-grained classes that the full models confuse. Unlike a mixture of experts, these specialist models can be trained rapidly and in parallel.


title: Exploiting Task-Oriented Resources to Learn Word Embeddings for Clinical Abbreviation Expansion

abstract: In the medical domain, identifying and expanding abbreviations in clinical texts is a vital task for both better human and machine understanding. It is a challenging task because many abbreviations are ambiguous especially for intensive care medicine texts, in which phrase abbreviations are frequently used. Besides the fact that there is no universal dictionary of clinical abbreviations and no universal rules for abbreviation writing, such texts are difficult to acquire, expensive to annotate and even sometimes, confusing to domain experts. This paper proposes a novel and effective approach ‐ exploiting taskoriented resources to learn word embeddings for expanding abbreviations in clinical notes. We achieved 82.27% accuracy, close to expert human performance.


title: EbbRT: a framework for building per-application library operating systems

abstract: General purpose operating systems sacrifice perapplication performance in order to preserve generality. On the other hand, substantial effort is required to customize or construct an operating system to meet the needs of an application. This paper describes the design and implementation of the Elastic Building Block Runtime (EbbRT), a framework for building per-application library operating systems. EbbRT reduces the effort required to construct and maintain library operating systems without hindering the degree of specialization required for high performance. We combine several techniques in order to achieve this, including a distributed OS architecture, a low-overhead component model, a lightweight event-driven runtime, and many language-level primitives. EbbRT is able to simultaneously enable performance specialization, support for a broad range of applications, and ease the burden of systems development. ::: ::: An EbbRT prototype demonstrates the degree of customization made possible by our framework approach. In an evaluation of memcached, EbbRT and is able to attain 2:08× higher throughput than Linux. The node.js runtime, ported to EbbRT, demonstrates the broad applicability and ease of development enabled by our approach.


title: Tomato yellow leaf curl virus (TYLCV-IL): a seed-transmissible geminivirus in tomatoes

abstract: Tomato yellow leaf curl virus (TYLCV) is one of the most well-known tomato-infecting begomoviruses and transmitted by Bemisia tabaci. Seed transmission has previously been reported for some RNA viruses, but TYLCV has not previously been described as a seed-borne virus. In 2013 and 2014, without whitefly-mediated transmission, TYLCV was detected in young tomato plants germinated from fallen fruits produced from TYLCV-infected tomato plants in the previous cultivation season. In addition, TYLCV-Israel (TYLCV-IL) was also detected in seeds and their seedlings of TYLCV-infected tomato plants that were infected by both viruliferous whitefly-mediated transmission and agro-inoculation. The seed infectivity was 20–100%, respectively, and the average transmission rate to seedlings was also 84.62% and 80.77%, respectively. TYLCV-tolerant tomatoes also produced TYLCV-infected seeds, but the amount of viral genome was less than seen in TYLCV-susceptible tomato plants. When tomato plants germinated from TYLCV-infected seeds, non-viruliferous whiteflies and healthy tomato plants were placed in an insect cage together, TYLCV was detected from whiteflies as well as receiver tomato plants six weeks later. Taken together, TYLCV-IL can be transmitted via seeds, and tomato plants germinated from TYLCV-infected seeds can be an inoculum source of TYLCV. This is the first report about TYLCV seed transmission in tomato.


title: Facial Expression Recognition From Image Sequence Based on LBP and Taylor Expansion

abstract: The aim of an automatic video-based facial expression recognition system is to detect and classify human facial expressions from image sequence. An integrated automatic system often involves two components: 1) peak expression frame detection and 2) expression feature extraction. In comparison with the image-based expression recognition system, the video-based recognition system often performs online detection, which prefers low-dimensional feature representation for cost-effectiveness. Moreover, effective feature extraction is needed for classification. Many recent recognition systems often incorporate rich additional subjective information and thus become less efficient for real-time application. In our facial expression recognition system, first, we propose the double local binary pattern (DLBP) to detect the peak expression frame from the video. The proposed DLBP method has a much lower-dimensional size and can successfully reduce detection time. Besides, to handle the illumination variations in LBP, logarithm-laplace (LL) domain is further proposed to get a more robust facial feature for detection. Finally, the Taylor expansion theorem is employed in our system for the first time to extract facial expression feature. We propose the Taylor feature pattern (TFP) based on the LBP and Taylor expansion to obtain an effective facial feature from the Taylor feature map. Experimental results on the JAFFE and Cohn-Kanade data sets show that the proposed TFP method outperforms some state-of-the-art LBP-based feature extraction methods for facial expression feature extraction and can be suited for real-time applications.


title: Comparison of various edge detection filters for ANPR

abstract: Automatic Number Plate Recognition (ANPR) can be identified as a technology which has been developed mainly based on Image Processing methodologies. It is being widely used in identifying vehicles in applications such as red-light enforcement, over speeding, bus lane control, motorway road tolling, border control and access/parking control. However axioms of ANPR rest on Image processing approaches such as binary conversion, morphological functions, filtering and edge detection algorithms. By researching on the solutions which have been suggested through numerous approaches to implement ANPR, this paper will focus on edge detection methodologies. A comparison is made between the edge detection filters Sobel, Canny, Gabor and Log-Gabor, using a variation of preprocessing techniques and using template matching as character recognition method. The algorithms are compared for accuracy as well as processing times. It is observed that as an all-round algorithm, Log-Gabor outperforms others. In terms of processing time, it is second only to Sobel. It has not been used in ANPR and hence has been identified as a suitable candidate for ANPR.


title: A multi-modal floor-space for experiencing material deformation underfoot in virtual reality

abstract: We present a floor-space design that provides the impression of walking on various terrains by rendering graphical, audio and haptic stimuli synchronously with low latency. Currently, interactive floors tend to focus on visual and auditory feedback but have neglected to explore the role of haptics. Our design recreates these three modalities in a direct manner where the sensors and reactive cues are located in the same area. The goal of this project is the creation of a realistic and dynamic area of ground that allows multiple, untethered users to engage in intuitive interaction via locomotion in a virtual or augmented reality environment.


title: Dual-Band EBG Integrated Monopole Antenna Deploying Fractal Geometry for Wearable Applications

abstract: This letter presents the design of a dual-band wearable fractal-based monopole patch antenna integrated with an electromagnetic band-gap (EBG) structure. The prototype covers the GSM-1800 MHz and ISM-2.45 GHz bands. The EBG structure reduces the radiation into the human body over 15 dB. It also reduces the effect of frequency detuning due to the human body. The performance of the antenna under bending, crumpling, and on-body conditions has been studied and presented. Specific absorption rate (SAR) assessment has also been performed to validate the antenna for its usefulness in wearable applications.


title: Social networking addiction, attachment style, and validation of the Italian version of the Bergen Social Media Addiction Scale

abstract: AimResearch into social networking addiction has greatly increased over the last decade. However, the number of validated instruments assessing addiction to social networking sites (SNSs) remains few, and none have been validated in the Italian language. Consequently, this study tested the psychometric properties of the Italian version of the Bergen Social Media Addiction Scale (BSMAS), as well as providing empirical data concerning the relationship between attachment styles and SNS addiction.MethodsA total of 769 participants were recruited to this study. Confirmatory factor analysis (CFA) and multigroup analyses were applied to assess construct validity of the Italian version of the BSMAS. Reliability analyses comprised the average variance extracted, the standard error of measurement, and the factor determinacy coefficient.ResultsIndices obtained from the CFA showed the Italian version of the BSMAS to have an excellent fit of the model to the data, thus confirming the single-factor structure of the ins...


title: The Development and Concurrent Validity of the Procrastination Scale

abstract: The purpose of this study was to develop a self-report measure of procrastination tendencies and to investigate its relationship to a behavioral measure of procrastination and to a self-report measure of general self-efficacy. In a pilot study, the 72-item scale in a 4-point Likert-type response format was developed and administered to 50 college juniors and seniors. A factor analysis of the results yielded two factors which formed the basis for reducing the scale to 35 items with a resulting reliability of .90. The relationship between scores on the 35-item instrument and performance on a self-regulated performance task called the Voluntary Homework System (VHS) yielded a correlation of -.54, and a coefficient of -.47 was observed between the 35-item scale and the General Self-Efficacy Test (GSE; both correlations of p < .001). The correlation between GSE and VHS scores was .29 (p < .05). In a subsequent study of 183 college students, a factor analysis of scores on the 35-item scale yielded a single-fact...


title: An Approach to Model and Predict the Popularity of Online Contents with Explanatory Factors

abstract: In this paper, we propose a methodology to predict the popularity of online contents. More precisely, rather than trying to infer the popularity of a content itself, we infer the likelihood that a content will be popular. Our approach is rooted in survival analysis where predicting the precise lifetime of an individual is very hard and almost impossible but predicting the likelihood of one's survival longer than a threshold or another individual is possible. We position ourselves in the standpoint of an external observer who has to infer the popularity of a content only using publicly observable metrics, such as the lifetime of a thread, the number of comments, and the number of views. Our goal is to infer these observable metrics, using a set of explanatory factors, such as the number of comments and the number of links in the first hours after the content publication, which are observable by the external observer. We use a Cox proportional hazard regression model that divides the distribution function of the observable popularity metric into two components: a) one that can be explained by the given set of explanatory factors (called risk factors) and b) a baseline distribution function that integrates all the factors not taken into account. To validate our proposed approach, we use data sets from two different online discussion forums: dpreview.com, one of the largest online discussion groups providing news and discussion forums about all kinds of digital cameras, and myspace.com, one of the representative online social networking services. On these two data sets we model two different popularity metrics, the lifetime of threads and the number of comments, and show that our approach can predict the lifetime of threads from Dpreview (Myspace) by observing a thread during the first 5~6 days (24 hours, respectively) and the number of comments of Dpreview threads by observing a thread during first 2~3 days.


title: Learning Manipulation Trajectories Using Recurrent Neural Networks

abstract: Robots assisting disabled or elderly people in the performance of activities of daily living need to perform complex manipulation tasks which are highly dependent on the environment and preferences of the user. In addition, these environments and users are not suitable for the collection of massive amounts of training data, as the manipulated objects can be fragile, and the wheelchair-bound users might have difficulty recovering from a failed manipulation task. ::: In this paper, we propose an end-to-end learning mechanism for the type of complex robot arm trajectories used in manipulation tasks for assistive robots. The trajectory is learned using a recurrent neural network that can generate the trajectory in real-time based on the current situation of the end-effector, the objects in the environment and the preferences of the user. The learning data is acquired from a simulation environment where the human can demonstrate the task in a simulation closely modeling his or her own environment. Experiments using two different manipulation tasks show that the robot can learn the manipulation planning as well the ability to recover from failure.


title: Circulating Currents in Stator Coils of Large Turbine Generators and Loss Reduction

abstract: Stator coils transposed by 540deg are widely used as one of the solutions to reduce the circulating current loss in the coil strands of turbine generators. However, the difference of the leakage fluxes in the two coil end parts, which is possibly caused by the coil end length deviation, the winding pitch, and the presence of the connection rings, may induce unbalanced voltage in the stator coil strands, which increase circulating current loss even in the transposition coils. This paper describes the mechanism and the analysis results of the circulating current increase in the coil strands due to the coil end flux difference. Then, some new schemes to reduce the circulating current loss are proposed and discussed.


title: Collaborative factorization for recommender systems

abstract: Recommender system has become an effective tool for information filtering, which usually provides the most useful items to users by a top-k ranking list. Traditional recommendation techniques such as Nearest Neighbors (NN) and Matrix Factorization (MF) have been widely used in real recommender systems. However, neither approaches can well accomplish recommendation task since that: (1) most NN methods leverage the neighbor's behaviors for prediction, which may suffer the severe data sparsity problem; (2) MF methods are less sensitive to sparsity, but neighbors' influences on latent factors are not fully explored, since the latent factors are often used independently. To overcome the above problems, we propose a new framework for recommender systems, called collaborative factorization. It expresses the user as the combination of his own factors and those of the neighbors', called collaborative latent factors, and a ranking loss is then utilized for optimization. The advantage of our approach is that it can both enjoy the merits of NN and MF methods. In this paper, we take the logistic loss in RankNet and the likelihood loss in ListMLE as examples, and the corresponding collaborative factorization methods are called CoF-Net and CoF-MLE. Our experimental results on three benchmark datasets show that they are more effective than several state-of-the-art recommendation methods.


Missing Value Imputation in Multi Attribute Data Set