title: Stacked denoising autoencoders for sentiment analysis: a review

abstract: Deep learning has been shown to outperform numerous conventional machine learning algorithms (e.g., support vector machines) in many fields, such as image processing and text analyses. This is due to its outstanding capability to model complex data distributions. However, as networks become deeper, there is an increased risk of overfitting and higher sensitivity to noise. Stacked denoising autoencoders (SDAs) provide an infrastructure to resolve these issues. In the field of sentiment recognition from textual contents, SDAs have been widely used (especially for domain adaptation), and have been consistently refined and improved through defining new alternate topologies as well as different learning algorithms. A wide selection of these approaches are reviewed and compared in this article. The results coming from the reviewed works indicate the promising capability of SDAs to perform sentiment recognition on a multitude of domains and languages. ::: ::: For further resources related to this article, please visit the WIREs website.


title: Differential Evolution With an Individual-Dependent Mechanism

abstract: Differential evolution (DE) is a well-known optimization algorithm that utilizes the difference of positions between individuals to perturb base vectors and thus generate new mutant individuals. However, the difference between the fitness values of individuals, which may be helpful to improve the performance of the algorithm, has not been used to tune parameters and choose mutation strategies. In this paper, we propose a novel variant of DE with an individual-dependent mechanism that includes an individual-dependent parameter (IDP) setting and an individual-dependent mutation (IDM) strategy. In the IDP setting, control parameters are set for individuals according to the differences in their fitness values. In the IDM strategy, four mutation operators with different searching characteristics are assigned to the superior and inferior individuals, respectively, at different stages of the evolution process. The performance of the proposed algorithm is then extensively evaluated on a suite of the 28 latest benchmark functions developed for the 2013 Congress on Evolutionary Computation special session. Experimental results demonstrate the algorithm’s outstanding performance.


title: Using facial images for the diagnosis of genetic syndromes: A survey

abstract: The analysis of facial appearance is significant to an early diagnosis of medical genetic diseases. The fast development of image processing and machine learning techniques facilitates the detection of facial dysmorphic features. This paper is a survey of the recent studies developed for the screening of genetic abnormalities across the facial features obtained from two dimensional and three dimensional images.


Exploration of Data mining techniques in Fraud Detection: Credit Card


title: Printed optics: 3D printing of embedded optical elements for interactive devices

abstract: We present an approach to 3D printing custom optical elements for interactive devices labelled Printed Optics. Printed Optics enable sensing, display, and illumination elements to be directly embedded in the casing or mechanical structure of an interactive device. Using these elements, unique display surfaces, novel illumination techniques, custom optical sensors, and embedded optoelectronic components can be digitally fabricated for rapid, high fidelity, highly customized interactive devices. Printed Optics is part of our long term vision for interactive devices that are 3D printed in their entirety. In this paper we explore the possibilities for this vision afforded by fabrication of custom optical elements using today's 3D printing technology.


title: Calibration-free gaze sensing using saliency maps

abstract: We propose a calibration-free gaze sensing method using visual saliency maps. Our goal is to construct a gaze estimator only using eye images captured from a person watching a video clip. The key is treating saliency maps of the video frames as probability distributions of gaze points. To efficiently identify gaze points from saliency maps, we aggregate saliency maps based on the similarity of eye appearances. We establish mapping between eye images to gaze points by Gaussian process regression. The experimental result shows that the proposed method works well with different people and video clips and achieves 6 degrees of accuracy, which is useful for estimating a person's attention on monitors.


title: Fast simulation of mass-spring systems

abstract: We describe a scheme for time integration of mass-spring systems that makes use of a solver based on block coordinate descent. This scheme provides a fast solution for classical linear (Hookean) springs. We express the widely used implicit Euler method as an energy minimization problem and introduce spring directions as auxiliary unknown variables. The system is globally linear in the node positions, and the non-linear terms involving the directions are strictly local. Because the global linear system does not depend on run-time state, the matrix can be pre-factored, allowing for very fast iterations. Our method converges to the same final result as would be obtained by solving the standard form of implicit Euler using Newton's method. Although the asymptotic convergence of Newton's method is faster than ours, the initial ratio of work to error reduction with our method is much faster than Newton's. For real-time visual applications, where speed and stability are more important than precision, we obtain visually acceptable results at a total cost per timestep that is only a fraction of that required for a single Newton iteration. When higher accuracy is required, our algorithm can be used to compute a good starting point for subsequent Newton's iteration.


title: Real-time position sensorless estimation of position and force of solenoid actuator for haptic devices

abstract: Haptic devices are receiving a lot of attention, especially in robotics and medical field. Position and force of actuators are needed to communicate the haptic information. This paper aims to realize sensorless estimation of position and force of solenoid actuators for haptic devices. The simultaneous estimation of position and force is conducted by applying DC and superposed AC voltage to the solenoid actuator. From the DC signals, the force is estimated. From the AC signals, the position is estimated. Experiments of the estimation were conducted to validate this proposal. The proposal succeeded in realizing high precision position estimation with an accuracy of 50 μm. The simultaneous estimation of the position and force was accurately achieved in real time. This technique is useful, as the application field of the solenoid actuators is enhanced.


title: Japanese and Korean voice search

abstract: This paper describes challenges and solutions for building a successful voice search system as applied to Japanese and Korean at Google. We describe the techniques used to deal with an infinite vocabulary, how modeling completely in the written domain for language model and dictionary can avoid some system complexity, and how we built dictionaries, language and acoustic models in this framework. We show how to deal with the difficulty of scoring results for multiple script languages because of ambiguities. The development of voice search for these languages led to a significant simplification of the original process to build a system for any new language which in in parts became our default process for internationalization of voice search.


How to Make Context More Useful? An Empirical Study on Context-Aware Neural Conversational Models


title: Strategies to Prevent Ventilator-Associated Pneumonia in Neonates

abstract: Ventilator-associated pneumonia (VAP) is one of the leading causes of preventable morbidity and mortality in neonatal intensive care units. This review examines the epidemiology and pathogenesis of VAP in neonates as well as the dilemmas faced by caregivers to diagnose and prevent VAP.


title: Children’s Perceptions of Intimate Partner Violence: Causes, Consequences, and Coping

abstract: Children’s appraisals of conflictual and aggressive parental interactions mediate their effect on children’s adjustment. Previous studies have relied almost exclusively on self-report questionnaires to assess appraisals; consequently we know little about perceptions that occur naturally when children witness interparental aggression. This study employed a semi-structured interview to assess the thoughts and feelings of 34 children (ages 7–12) whose mothers were receiving services at domestic violence agencies, and mothers reported on interparental aggression that took place in the home. Children’s thoughts centered on consequences and efforts to understand why fights occurred. They generally viewed their mother’s partner as responsible for violence, though a significant number viewed both parents as playing a role. Sadness and anger were more common than anxiety, and children often attempted to stop or withdraw from fights or both. When asked why family violence occurs, most focused on perpetrators’ lack of control of anger or personal characteristics, but approximately one-third viewed victims as provoking aggression. These findings support the idea that children actively attempt to understand the causes and consequences of interparental violence and suggest that their perceptions and interpretations are important for understanding the development of beliefs regarding the use of violence in close relationships.


title: Constellation: A science graph network for scalable data and knowledge discovery in extreme-scale scientific collaborations

abstract: Constellation's overarching goal is the federation of information from resources within an extreme-scale scientific collaboration to enable the scalable discovery of data and new knowledge pathways. The resource fabric is comprised of petascale supercomputers and storage systems, users, jobs, datasets and lifecycle artifacts. For an extreme-scale supercomputing center, normal operations can generate hundreds of millions of data products and metadata entries describing the resource fabric. Constellation federates the information extracted from the resources using a custom, transformative science graph network; constructs rich metadata indexes and higher-order derived metadata from the extracted information; and conducts scalable graph analytics to unravel hidden data pathways. Our implementation and deployment for a production, supercomputing facility shows that the graph can scale to more than 750 million vertices, its domain agnostic indexing can answer interesting science queries, and its analytics can aid in structural, topological and temporal analysis to identify usage hotspots.


title: Optimal eye movement strategies in visual search

abstract: Few activities are more important for survival than searching the local area with the eyes to find relevant objects: food, predators, potential mates, oncoming cars. Nonetheless, eye movements recorded during visual search often appear haphazard; it has even been suggested that gaze directions are selected randomly. A study in human subjects tasked to spot a target hidden in a cluttered background now shows that the process is far from random: human eye movements are very near to the mathematically determined optimal strategy. The model developed for this work can also be used to analyse search strategies in other species, and in the refinement of robotic vision systems. To perform visual search, humans, like many mammals, encode a large field of view with retinas having variable spatial resolution, and then use high-speed eye movements to direct the highest-resolution region, the fovea, towards potential target locations1,2. Good search performance is essential for survival, and hence mammals may have evolved efficient strategies for selecting fixation locations. Here we address two questions: what are the optimal eye movement strategies for a foveated visual system faced with the problem of finding a target in a cluttered environment, and do humans employ optimal eye movement strategies during a search? We derive the ideal bayesian observer3,4,5,6 for search tasks in which a target is embedded at an unknown location within a random background that has the spectral characteristics of natural scenes7. Our ideal searcher uses precise knowledge about the statistics of the scenes in which the target is embedded, and about its own visual system, to make eye movements that gain the most information about target location. We find that humans achieve nearly optimal search performance, even though humans integrate information poorly across fixations8,9,10. Analysis of the ideal searcher reveals that there is little benefit from perfect integration across fixations—much more important is efficient processing of information on each fixation. Apparently, evolution has exploited this fact to achieve efficient eye movement strategies with minimal neural resources devoted to memory.


title: Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study

abstract: Recently, methods based on local image features have shown promise for texture and object recognition tasks. This paper presents a large-scale evaluation of an approach that represents images as distributions (signatures or histograms) of features extracted from a sparse set of keypoint locations and learns a Support Vector Machine classifier with kernels based on two effective measures for comparing distributions, the Earth Mover’s Distance and the ÷2 distance. We first evaluate the performance of our approach with different keypoint detectors and descriptors, as well as different kernels and classifiers. We then conduct a comparative evaluation with several state-of-the-art recognition methods on 4 texture and 5 object databases. On most of these databases, our implementation exceeds the best reported results and achieves comparable performance on the rest. Finally, we investigate the influence of background correlations on recognition performance.


title: CNN-based image analysis for malaria diagnosis

abstract: Malaria is a major global health threat. The standard way of diagnosing malaria is by visually examining blood smears for parasite-infected red blood cells under the microscope by qualified technicians. This method is inefficient and the diagnosis depends on the experience and the knowledge of the person doing the examination. Automatic image recognition technologies based on machine learning have been applied to malaria blood smears for diagnosis before. However, the practical performance has not been sufficient so far. This study proposes a new and robust machine learning model based on a convolutional neural network (CNN) to automatically classify single cells in thin blood smears on standard microscope slides as either infected or uninfected. In a ten-fold cross-validation based on 27,578 single cell images, the average accuracy of our new 16-layer CNN model is 97.37%. A transfer learning model only achieves 91.99% on the same images. The CNN model shows superiority over the transfer learning model in all performance indicators such as sensitivity (96.99% vs 89.00%), specificity (97.75% vs 94.98%), precision (97.73% vs 95.12%), F1 score (97.36% vs 90.24%), and Matthews correlation coefficient (94.75% vs 85.25%).


title: Romantic red: Red enhances men’s attraction to women

abstract: In many nonhuman primates, the color red enhances males’ attraction to females. In 5 experiments, the authors demonstrate a parallel effect in humans: Red, relative to other achromatic and chromatic colors, leads men to view women as more attractive and more sexually desirable. Men seem unaware of this red effect, and red does not influence women’s perceptions of the attractiveness of other women, nor men’s perceptions of women’s overall likeability, kindness, or intelligence. The findings have clear practical implications for men and women in the mating game and, perhaps, for fashion consultants, product designers, and marketers. Furthermore, the findings document the value of extending research on signal coloration to humans and of considering color as something of a common language, both within and across species.


title: Realizing Event-Driven SOA

abstract: Event-driven architecture is gaining momentum in research and application areas as it promises enhanced responsiveness and asynchronous communication. The combination of event-driven and service-oriented architectural paradigms and web service technologies provide a viable possibility to achieve these promises. This paper outlines an architectural design and accompanying implementation technologies for its realization as a web services-based event-driven SOA.


title: Automated Theorem Proving in Intuitionistic Propositional Logic by Deep Reinforcement Learning

abstract: The problem-solving in automated theorem proving (ATP) can be interpreted as a search problem where the prover constructs a proof tree step by step. In this paper, we propose a deep reinforcement learning algorithm for proof search in intuitionistic propositional logic. The most significant challenge in the application of deep learning to the ATP is the absence of large, public theorem database. We, however, overcame this issue by applying a novel data augmentation procedure at each iteration of the reinforcement learning. We also improve the efficiency of the algorithm by representing the syntactic structure of formulas by a novel compact graph representation. Using the large volume of augmented data, we train highly accurate graph neural networks that approximate the value function for the set of the syntactic structures of formulas. Our method is also cost-efficient in terms of computational time. We will show that our prover outperforms Coq's $\texttt{tauto}$ tactic, a prover based on human-engineered heuristics. Within the specified time limit, our prover solved 84% of the theorems in a benchmark library, while $\texttt{tauto}$ was able to solve only 52%.


title: The challenges that challenge : Engaging with agile practitioners ’ concerns Journal Item

abstract: ContextThere continues to be concern that research is not addressing the challenges that practice faces. For the benefit of academia and industry, researchers need to be aware of practitioners' challenges and their context so that relevant and applicable research is undertaken. ObjectiveThis paper investigates two research questions: what challenges do agile practitioners face? and, how do practitioner challenges manifest themselves in an organisational setting? It aims to map the practitioner challenge landscape, explore challenge characteristics, compare findings with previous literature and identify implications for research that is relevant to practice. MethodA combination of methods was used: elicitation of practitioner challenges collected using a Challenge Wall at a series of practitioner events; organisational case study using interviews, document analysis and observation; and online survey. Findings were then compared to previous publications. ResultsChallenges collected from the Challenge Wall were grouped under 27 subthemes and seven themes: claims and limitations, organisation, sustainability, culture, teams, scale, and value. Investigating one challenge in the case study uncovered a set of new challenges, which were inter-related. Over 50% of survey respondents experienced challenges highlighted in the case study. ConclusionThe landscape of agile practitioner challenges is complex and intertwined. Some challenges, such as doing agile in a non-agile environment, are multi-dimensional, affect many aspects of practice, and may be experienced simultaneously as business, organisational, social and adaptation problems. Some challenges, such as understanding cultural change or measuring agile value, persist and are hard to address, while others, such as adoption, change focus over time. Some challenges, such as governance and contracts, are under-researched, while others, such as business and IT transformation, have been researched but findings have not had the expected impact. Researchers wishing to address practitioner challenges need to treat them in context rather than in isolation and improve knowledge transfer.


Internet of Things in Precision Agriculture using Wireless Sensor Networks


title: Attachment theory and intergroup bias: evidence that priming the secure base schema attenuates negative reactions to out-groups.

abstract: Five studies examined the effects of priming the secure base schema on intergroup bias. In addition, Studies 1-2 examined the effects of dispositional attachment style, Studies 2-5 examined a mood interpretation, Study 3 examined the mediating role of threat appraisal, and Studies 4-5 examined the effects of secure base priming while inducing a threat to self-esteem or cultural worldview. Secure base priming led to less negative evaluative reactions toward out-groups than positive affect and neutral control conditions. In addition, whereas the effects of secure base priming did not depend on attachment style and were not explained by mood induction, they were mediated by threat appraisal and occurred even when self-esteem or cultural worldview was threatened. The discussion emphasizes the relevance of attachment theory for understanding intergroup attitudes.


title: Noise of a JFET Charge Amplifier for Piezoelectric Sensors

abstract: A comprehensive noise model of a junction gate field-effect transistor (JFET) charge amplifier is presented for applications with piezoelectric sensors. Based on this previously reported amplifier structure, a noise equivalent circuit is derived and discussed. Universal noise gain expressions for each of the twelve considered noise sources are given. By means of an analytical description of the charge amplifier’s noise behavior, the design and optimization of such preamplifiers for various capacitive microelectromechanical sensor systems will be distinctly simplified. Calculations of the noise floor are compared with measurements with a cantilever-type magnetoelectric sensor and with the performance of a so far commonly utilized basic charge amplifier. With the JFET charge amplifier a noise reduction of approximately 22 dB is achieved in the vicinity of the utilized sensor’s resonance frequency of 7.45 kHz. The reported amplifier is the first which has no negative influence on the total noise level of a resonant magnetoelectric sensor system.


title: Virtual network functions orchestration in enterprise WLANs

abstract: Network Function Virtualization (NFV) has recently been proposed as a tool to optimize the deployment of network functions by shifting the processing from dedicated middleboxes to general purpose and inexpensive hardware platforms. In this paper, we propose a novel NFV-based management and orchestration framework for enterprise WLANs. Our framework is compatible with the ETSI NFV architecture and leverages on hybrid nodes combining the forwarding capabilities of a programmable switch with the storage/computational capabilities of a server. We propose an algorithm for Virtual Network Function placement which optimizes the functions deployment according to application level constraints. A proof-of-concept implementation of the proposed framework and a preliminary performance evaluation of selected VNFs are also presented.


title: HIV/AIDS in Ethiopia : an epidemiological synthesis

abstract: The study uncovered a number of major findings which have implications for policy and practice in the country, including: the epidemic may be less severe, less generalized and more heterogeneous than previously believed, with marked regional variations; the diversity of the Human Immunodeficiency Virus (HIV) epidemic seems to be related to sexual behavior patterns; small towns may be HIV hot-spots that have had marginal attention in HIV prevention efforts to date; traditional high-risk groups such as sex workers seem to be reducing some of their risky behaviors. Young populations, especially never-married sexually active females have the greatest risk of HIV infection in the country; discordant couples are also a concern, pointing to a clear need for couple counseling services which are presently non-existent or rudimentary. The lack of recent data and research, especially on high risk groups, makes further conclusions difficult, and highlights the clear need for more research.


INTERACTIVE FOUR-BAR LINKAGE 3 D-SIMULATION USING AUGMENTED REALITY


title: Efficient Deep Learning on Multi-Source Private Data

abstract: Machine learning models benefit from large and diverse datasets. Using such datasets, however, often requires trusting a centralized data aggregator. For sensitive applications like healthcare and finance this is undesirable as it could compromise patient privacy or divulge trade secrets. Recent advances in secure and privacy-preserving computation, including trusted hardware enclaves and differential privacy, offer a way for mutually distrusting parties to efficiently train a machine learning model without revealing the training data. In this work, we introduce Myelin, a deep learning framework which combines these privacy-preservation primitives, and use it to establish a baseline level of performance for fully private machine learning.


title: BYO*: Utilizing 3D Printed Tangible Tools for Interaction on Interactive Surfaces

abstract: Sharing and manipulating information are essential for collaborative work in meeting scenarios. Nowadays, people tend to bring their own devices as a result of increasing mobility possibilities. However, transferring data from one device to another can be cumbersome and tedious if restrictions like different platforms, form factors or environmental limitations apply. In this paper, we present two concepts to enrich interaction on and between devices through 3D printed customized tangibles: 1) Bring your own information, and 2) bring your own tools. For this, we enable interactivity for low-cost and passive tangible 3D printed objects by adding conductive material and make use of touch-enabled surfaces. Our system allows users to easily share digital contents across various devices and to manipulate them with individually designed tools without additional hardware required.


title: Distributed Graph Layout with Spark

abstract: This paper presents a novel way to draw very large graphs, especially those too big to fit the memory of a single computer. This new method takes advantage of the recent progress in distributed computing, notably using the Apache MapReduce library called Spark. Our implementation of a force-directed graph drawing algorithm and the way to compute repulsive forces in MapReduce are exhibited. We demonstrate the horizontal scalability of this algorithm and show layouts computed on a Hadoop cluster with our method.


title: A Machine Learning Approach for Tracking and Predicting Student Performance in Degree Programs

abstract: Accurately predicting students’ future performance based on their ongoing academic records is crucial for effectively carrying out necessary pedagogical interventions to ensure students’ on-time and satisfactory graduation. Although there is a rich literature on predicting student performance when solving problems or studying for courses using data-driven approaches, predicting student performance in completing degrees (e.g., college programs) is much less studied and faces new challenges: 1) Students differ tremendously in terms of backgrounds and selected courses; 2) courses are not equally informative for making accurate predictions; and 3) students’ evolving progress needs to be incorporated into the prediction. In this paper, we develop a novel machine learning method for predicting student performance in degree programs that is able to address these key challenges. The proposed method has two major features. First, a bilayered structure comprising multiple base predictors and a cascade of ensemble predictors is developed for making predictions based on students’ evolving performance states. Second, a data-driven approach based on latent factor models and probabilistic matrix factorization is proposed to discover course relevance, which is important for constructing efficient base predictors. Through extensive simulations on an undergraduate student dataset collected over three years at University of California, Los Angeles, we show that the proposed method achieves superior performance to benchmark approaches.


title: The Nature of Rapport and Its Nonverbal Correlates

abstract: The purpose of this article is to offer a conceptualization of rapport that has utility for identifying the nonverbal correlates associated with rapport. We describe the nature of rapport in terms ...


title: A Hybrid B+-tree as Solution for In-Memory Indexing on CPU-GPU Heterogeneous Computing Platforms

abstract: An in-memory indexing tree is a critical component of many databases. Modern many-core processors, such as GPUs, are offering tremendous amounts of computing power making them an attractive choice for accelerating indexing. However, the memory available to the accelerating co-processor is rather limited and expensive in comparison to the memory available to the CPU. This drawback is a barrier to exploit the computing power of co-processors for arbitrarily large index trees. In this paper, we propose a novel design for a B+-tree based on the heterogeneous computing platform and the hybrid memory architecture found in GPUs. We propose a hybrid CPU-GPU B+-tree, "HB+-tree," which targets high search throughput use cases. Unique to our design is the joint and simultaneous use of computing and memory resources of CPU-GPU systems. Our experiments show that our HB+-tree can perform up to 240 million index queries per second, which is 2.4X higher than our CPU-optimized solution.


title: Antimicrobial substance from a human Lactobacillus strain.

abstract: Lactobacillus sp. strain GG, which was isolated from the feces of a normal person, produced a substance with potent inhibitory activity against a wide range of bacterial species. It inhibited anaerobic bacteria (Clostridium spp., Bacteroides spp., Bifidobacterium spp.), members of the family Enterobacteriaceae, Pseudomonas spp. Staphylococcus spp., and Streptococcus spp., as demonstrated by a microbiological assay; however, it did not inhibit other lactobacilli. The inhibitory activity occurred between pH 3 and 5 and was heat stable. Bactericidal activity against Escherichia coli was demonstrated at a dilution of 1:128. The inhibitory substance was distinct from lactic and acetic acids. It had a low molecular weight (less than 1,000) and was soluble in acetone-water (10:1). Because of these characteristics, the inhibitory material could not be considered a bacteriocin; it most closely resembled a microcin, which has been associated previously with members of the family Enterobacteriaceae.


title: Optimized LTE Cell Planning for Multiple User Density Subareas Using Meta-Heuristic Algorithms

abstract: Base station deployment in cellular networks is one of the most fundamental problems in network design. This paper proposes a novel method for the cell planning problem for the fourth generation 4G-LTE cellular networks using meta heuristic algorithms. In this approach, we aim to satisfy both coverage and cell capacity constraints simultaneously by formulating a practical optimization problem. We start by performing a typical coverage and capacity dimensioning to identify the initial required number of base stations. Afterwards, we implement a Particle Swarm Optimization algorithm or a recently- proposed Grey Wolf Optimizer to find the optimal base station locations that satisfy both problem constraints in the area of interest which can be divided into several subareas with different user densities. Subsequently, an iterative approach is executed to eliminate eventual redundant base stations. We have also performed Monte Carlo simulations to study the performance of the proposed scheme and computed the average number of users in outage. Results show that our proposed approach respects in all cases the desired network quality of services even for large-scale dimension problems.


title: Extraction and analysis of faculty performance of management discipline from student feedback using clustering and association rule mining techniques

abstract: This paper deals with the extraction and analysis of faculty performance of management discipline from student feedback using clustering and association rule mining techniques. The performance of a faculty of any school or an Institute has been found to be dependent on a number of parameters broadly ranging from the individual's qualifications, experience, level of commitment, research activities undertaken to institutional support, financial feasibility, top management's support etc. The parameters which are crucial for assessment of faculty performance range across various verticals, but the paper discusses and covers the performance of faculties based strictly on students feedback only. The other assessors of faculty performance being the Management Body which could be a private body or a Government unit, self and peer faculties of the organization or the University. The parameters act as performance indicators for an individual and group and subsequently can impact on the decision making of the individual and also the stakeholders. The idea proposed in this paper is to perform extraction and analysis of faculty performance using techniques of Data Mining. The rationale behind using Data Mining is to cluster faculty performances on various criteria subject to the certain constraints and also extracting the dependencies amongst the parameters which will help finding meaningful associations between them. These associations in turn help to identify new patterns for decision making. The paper restricts to students feedback from Computer Applications Department across accredited institutes. The analysis depends on many attributes and the paper justifies the usage of mining methodologies rather than following the conventional approach.


title: Low-Power CMOS Rectifier Design for RFID Applications

abstract: We investigate theoretical and practical aspects of the design of far-field RF power extraction systems consisting of antennas, impedance matching networks and rectifiers. Fundamental physical relationships that link the operating bandwidth and range are related to technology dependent quantities like threshold voltage and parasitic capacitances. This allows us to design efficient planar antennas, coupled resonator impedance matching networks and low-power rectifiers in standard CMOS technologies (0.5-mum and 0.18-mum) and accurately predict their performance. Experimental results from a prototype power extraction system that operates around 950 MHz and integrates these components together are presented. Our measured RF power-up threshold (in 0.18-mum, at 1 muW load) was 6 muWplusmn10%, closely matching the predicted value of 5.2 muW.


title: Dynamic control of modern, network-based epidemic models

abstract: In this paper we make the first steps to bridge the gap between classic control theory and modern, network-based epidemic models. In particular, we apply nonlinear model predictive control (NMPC) to a pairwise ODE model which we use to model a susceptible-infectious-susceptible (SIS) epidemic on non-trivial contact structures. While classic control of epidemics concentrates on aspects such as vaccination, quarantine and fast diagnosis, our novel setup allows us to deliver control by altering the contact network within the population. Moreover, the ideal outcome of control is to eradicate the disease while keeping the network well connected. The paper gives a thorough and detailed numerical investigation of the impact and interaction of system and control parameters on the controllability of the system. The analysis reveals, that for certain set parameters it is possible to identify critical control bounds above which the system is controllable. We foresee, that our approach can be extended to even more realistic or simulation-based models with the aim to apply these to real-world situations.


title: 360° view camera based visual assistive technology for contextual scene information

abstract: In this paper, a system to aid the visually impaired by providing contextual information of the surroundings using 360° view camera combined with deep learning is proposed. The system uses a 360° view camera with a mobile device to capture surrounding scene information and provide contextual information to the user in the form of audio. The scene information from the spherical camera feed is classified by identifying objects that contain contextual information of the scene. That is achieved using convolutional neural networks (CNN) for classification by leveraging CNN transfer learning properties using the pre-trained VGG-19 network. There are two challenges related to this paper, a classification and a segmentation challenge. As an initial prototype, we have experimented with general classes such restaurants, coffee shops and street signs. We have achieved a 92.8% classification accuracy in this paper.


title: Quantifying the evolutionary dynamics of language

abstract: As a language evolves, grammatical rules emerge and exceptions die out. Lieberman et al. have calculated the rate at which a language grows more regular, based on 1,200 years of English usage. Of 177 irregular verbs, 79 became regular in the last millennium. And the trend follows a simple rule: a verb's half-life scales as the square root of its frequency. Irregular verbs that are 100 times as rare regularize 10 times faster. The emergence of a rule (such as adding –ed for the past tense) spells death for exceptional forms. The cover graphic makes the point: verb size corresponds to usage frequency, so large verbs stay at the top, and small verbs fall to the bottom. 'Wed', the next irregular verb to go, is on the brink. In a separate study, Pagel et al. looked at changing word meanings. Across the Indo-European languages, words like 'tail' or 'bird' evolve rapidly and are expressed by many unrelated words. Others, like 'two', are expressed by closely related word forms across the whole language family. Data from over 80 modern languages show that the more a word is used, the less it changes. During language evolution, rules emerge and exceptions decline. A quantitative study measures the rate at which a human language becomes more regular over time. Specifically, the regularization of English verbs over the last 1200 years was studied, and it was found that half-life of a verb scales as the square root of its frequency, meaning that irregular verbs that are 100 times as rare regularize ten times faster. Human language is based on grammatical rules1,2,3,4. Cultural evolution allows these rules to change over time5. Rules compete with each other: as new rules rise to prominence, old ones die away. To quantify the dynamics of language evolution, we studied the regularization of English verbs over the past 1,200 years. Although an elaborate system of productive conjugations existed in English’s proto-Germanic ancestor, Modern English uses the dental suffix, ‘-ed’, to signify past tense6. Here we describe the emergence of this linguistic rule amidst the evolutionary decay of its exceptions, known to us as irregular verbs. We have generated a data set of verbs whose conjugations have been evolving for more than a millennium, tracking inflectional changes to 177 Old-English irregular verbs. Of these irregular verbs, 145 remained irregular in Middle English and 98 are still irregular today. We study how the rate of regularization depends on the frequency of word usage. The half-life of an irregular verb scales as the square root of its usage frequency: a verb that is 100 times less frequent regularizes 10 times as fast. Our study provides a quantitative analysis of the regularization process by which ancestral forms gradually yield to an emerging linguistic rule.


title: Automated and on-demand provisioning of virtual machines for database applications

abstract: Utility computing delivers compute and storage resources to applications as an 'on-demand utility', much like electricity, from a distributed collection of computing resources. There is great interest in running database applications on utility resources (e.g., Oracle's Grid initiative) due to reduced infrastructure and management costs, higher resource utilization, and the ability to handle sudden load surges. Virtual Machine (VM) technology offers powerful mechanisms to manage a utility resource infrastructure. However, provisioning VMs for applications to meet system performance goals, e.g., to meet service level agreements (SLAs), is an open problem. We are building two systems at Duke - Shirako and NIMO - that collectively address this problem. Shirako is a toolkit for leasing VMs to an application from a utility resource infrastructure. NIMO learns application performance models using novel techniques based on active learning, and uses these models to guide VM provisioning in Shirako. We will demonstrate: (a) how NIMO learns performance models in an online and automatic fashion using active learning; and (b) how NIMO uses these models to do automated and on-demand provisioning of VMs in Shirako for two classes of database applications - multi-tier web services and computational science workflows.


title: An Updated Model to Determine the Life Remaining of Transformer Insulation

abstract: In this paper, we present an update to the models used to calculate the remaining life of transformer paper insulation. A drawback to the current IEEE method is that it does not take into account the availability of water and oxygen on the life expectancy of Kraft paper insulation. We therefore set out to test an algorithm which does take these into account. For our investigation, we loaded three test transformers and ran them to near their end of life. Kinetic equations were applied to model the fall in the degree of polymerization of paper. Our model showed better agreement with the test results than that given from using the IEEE standard. The IEEE standard gives life expectancy for Kraft paper being aged in minimal oxygen under dry conditions, which is not necessarily representative of an old transformer. Wet paper and using high levels of oxygen can age nearly 40 times faster. The IEEE method needs to take the synergistic effect of water and oxygen on increasing the rate of paper aging into account.


title: A Reward-Modulated Hebbian Learning Rule Can Explain Experimentally Observed Network Reorganization in a Brain Control Task

abstract: It has recently been shown in a brain–computer interface experiment that motor cortical neurons change their tuning properties selectively to compensate for errors induced by displaced decoding parameters. In particular, it was shown that the three-dimensional tuning curves of neurons whose decoding parameters were reassigned changed more than those of neurons whose decoding parameters had not been reassigned. In this article, we propose a simple learning rule that can reproduce this effect. Our learning rule uses Hebbian weight updates driven by a global reward signal and neuronal noise. In contrast to most previously proposed learning rules, this approach does not require extrinsic information to separate noise from signal. The learning rule is able to optimize the performance of a model system within biologically realistic periods of time under high noise levels. Furthermore, when the model parameters are matched to data recorded during the brain–computer interface learning experiments described above, the model produces learning effects strikingly similar to those found in the experiments.


title: Speculations Concerning the First Ultraintelligent Machine

abstract: Publisher Summary An ultra-intelligent machine is a machine that can far surpass all the intellectual activities of any man however clever. The design of machines is one of these intellectual activities; therefore, an ultra-intelligent machine could design even better machines. To design an ultra-intelligent machine one needs to understand more about the human brain or human thought or both. The physical representation of both meaning and recall, in the human brain, can be to some extent understood in terms of a subassembly theory, this being a modification of Hebb's cell assembly theory. The subassembly theory sheds light on the physical embodiment of memory and meaning, and there can be little doubt that both needs embodiment in an ultra-intelligent machine. The subassembly theory leads to reasonable and interesting explanations of a variety of psychological effects.


title: An Abnormal Crowd Behavior Detection Algorithm Based on Fluid Mechanics

abstract: Abnormal crowd behavior detection is an advanced topic researched in fields of computer vision and digital image processing. The problems such as diversity of monitoring scene, different crowd density and mutual occlusion among crowds etc result in a low recognition rate for abnormal crowd behavior detection. In order to solve these problems, this paper combines a streakline model based on fluid dynamics with an abnormal behavior detection method presented by Hassner et al., and proposes a modified algorithm to improve the recognition accuracy of abnormal crowd behavior. Finally, the validity and accuracy of the algorithm are verified via a large amount of challenging real-world surveillance videos.


title: The pros and cons of flocking in the long-range “migration” of mobile robot swarms

abstract: In this paper, we study how flocking affects the accuracy and speed of individuals in long-range ''migration''. Specifically, we extend a behavior that can generate self-organized flocking in a swarm of robots to follow a homing direction sensed through the magnetic field of the Earth and evaluate how the final points reached by the flock are scattered in space and how the speed of the flock is affected. We propose that four factors influence the performance of migration, in the proposed behavior, namely: (1) averaging through heading alignment behavior, (2) disturbances caused by proximal control behavior, (3) noise in sensing the homing direction, and (4) differences in the characteristics of the individuals. Systematic experiments are conducted to evaluate the effects of these factors using both physical and simulated robots. The results show that although flocking reduces the speed of an individual, it increases the accuracy of ''migration'' for flocks that are larger than a certain size.


title: Penalized regression, standard errors, and Bayesian lassos

abstract: Penalized regression methods for simultaneous variable selection and coe-cient estimation, especially those based on the lasso of Tibshirani (1996), have received a great deal of attention in recent years, mostly through frequen- tist models. Properties such as consistency have been studied, and are achieved by difierent lasso variations. Here we look at a fully Bayesian formulation of the problem, which is ∞exible enough to encompass most versions of the lasso that have been previously considered. The advantages of the hierarchical Bayesian for- mulations are many. In addition to the usual ease-of-interpretation of hierarchical models, the Bayesian formulation produces valid standard errors (which can be problematic for the frequentist lasso), and is based on a geometrically ergodic Markov chain. We compare the performance of the Bayesian lassos to their fre- quentist counterparts using simulations, data sets that previous lasso papers have used, and a di-cult modeling problem for predicting the collapse of governments around the world. In terms of prediction mean squared error, the Bayesian lasso performance is similar to and, in some cases, better than, the frequentist lasso.


title: On the Validity of a New SMS Spam Collection

abstract: Mobile phones are becoming the latest target of electronic junk mail. Recent reports clearly indicate that the volume of SMS spam messages are dramatically increasing year by year. Probably, one of the major concerns in academic settings was the scarcity of public SMS spam datasets, that are sorely needed for validation and comparison of different classifiers. To address this issue, we have recently proposed a new SMS Spam Collection that, to the best of our knowledge, is the largest, public and real SMS dataset available for academic studies. However, as it has been created by augmenting a previously existing database built using roughly the same sources, it is sensible to certify that there are no duplicates coming from them. So, in this paper we offer a comprehensive analysis of the new SMS Spam Collection in order to ensure that this does not happen, since it may ease the task of learning SMS spam classifiers and, hence, it could compromise the evaluation of methods. The analysis of results indicate that the procedure followed does not lead to near-duplicates and, consequently, the proposed dataset is reliable to use for evaluating and comparing the performance achieved by different classifiers.


Markov Chain Monte Carlo Convergence Diagnostics : A Comparative Review Author ( s ) :


title: Stability of Topic Modeling via Matrix Factorization

abstract: The problem of the instability of standard topic modeling algorithms is investigated.Three new stability measures for topic models are proposed.Two new ensemble approaches for topic modeling with matrix factorization are proposed.A detailed evaluation of these approaches is performed on 10 text corpora. Topic models can provide us with an insight into the underlying latent structure of a large corpus of documents. A range of methods have been proposed in the literature, including probabilistic topic models and techniques based on matrix factorization. However, in both cases, standard implementations rely on stochastic elements in their initialization phase, which can potentially lead to different results being generated on the same corpus when using the same parameter values. This corresponds to the concept of instability which has previously been studied in the context of k-means clustering. In many applications of topic modeling, this problem of instability is not considered and topic models are treated as being definitive, even though the results may change considerably if the initialization process is altered. In this paper we demonstrate the inherent instability of popular topic modeling approaches, using a number of new measures to assess stability. To address this issue in the context of matrix factorization for topic modeling, we propose the use of ensemble learning strategies. Based on experiments performed on annotated text corpora, we show that a K-Fold ensemble strategy, combining both ensembles and structured initialization, can significantly reduce instability, while simultaneously yielding more accurate topic models.


title: Redundancy optimization problems with fuzzy random lifetimes

abstract: By using fuzzy random lifetimes as basic variables, three types of system performances-expected system lifetime, (/spl alpha/,/spl beta/)-system lifetime and system reliability-evaluated by fuzzy random theory are presented respectively. Some fuzzy random simulations are designed to estimate these system performances. Further, a spectrum of redundancy optimization models are constructed. In order to solve the proposed models, a hybrid intelligent algorithm is designed. Finally, a multistage system is provided for the sake of illustration.


title: A novel approach for embedding communication symbols into physical radar waveforms

abstract: Due to constantly increasing demand from commercial communications, defense applications are losing spectrum while still striving to maintain legacy capabilities, not to mention the need for enhanced performance. Consequently, ongoing research is focused on developing multi-function methods to share spectrum between radar and military communication. One approach is to incorporate information-bearing communication symbols into the emitted radar waveforms. However, varying the radar waveform during a coherent processing interval (CPI) causes range sidelobe modulation (RSM) that results in increased residual clutter in the range-Doppler response, thus leading to reduced target visibility. Here a novel approach is proposed to embed information into radar emissions while preserving constant envelope waveforms with good spectral containment. Information sequences are implemented using continuous phase modulation (CPM) and phase-attached to a polyphase-coded frequency-modulated (PCFM) radar waveform, the implementation of which is also derived from CPM. The resulting communication-embedded radar waveforms therefore maintain high power and spectral efficiency. More importantly, the adjustable parameterization of the proposed approach enables direct control of the degree of RSM by trading off bit error rate (BER) and/or data throughput.


title: Collocation and Thai Word Segmentation

abstract: This paper presents another approach of Thai word segmentation, which is composed of two processes : syllable segmentation and syllable merging. Syllable segmentation is done on the basis of trigram statistics. Syllable merging is done on the basis of collocation between syllables. We argue that many of word segmentation ambiguities can be resolved at the level of syllable segmentation. Since a syllable is a more well-defined unit and more consistent in analysis than a word, this approach is more reliable than other approaches that use a wordsegmented corpus. This approach can perform well at the level of accuracy 81-98% depending on the dictionary used in the segmentation.


title: MASK-BAN: Movement-Aided Authenticated Secret Key Extraction Utilizing Channel Characteristics in Body Area Networks

abstract: Recently, most wireless network security schemes merely based on physical layer characteristics tackle the two fundamental issues-device authentication and secret key extraction separately. It remains an open problem to simultaneously achieve device authentication and fast secret key extraction merely using wireless physical layer characteristics, without the help of advanced hardware or out-of-band channel. In this paper, we answer this open problem in the setting of wireless body area networks (BANs). We propose MASK-BAN, a lightweight fast authenticated secret key extraction scheme for intra-BAN communication. Our scheme neither introduces advanced hardware nor relies on out-of-band channels. To perform device authentication and fast secret key extraction at the same time, we exploit the heterogeneous channel characteristics among the collection of on-body channels during body motion. On one hand, MASK-BAN achieves authentication through multihop stable channels, which greatly reduces the false positive rate as compared to existing work. On the other hand, based on dynamic channels, key extraction between two on-body devices with multihop relay nodes is modeled as a max-flow problem, and a novel collaborative secret key generation algorithm is introduced to maximize the key generation rate. Extensive real-world experiments on low-end commercial-off-the-shelf sensor devices validate MASK-BAN's great authentication capability and high-secret key generation rate.


title: Airport Risk Assessment: Examples, Models and Mitigations

abstract: This paper addresses some of the hazards arising from an airport runway and its associated operations. It describes an approach to risk assessment in this context; some of the tools that may be applied; and some mitigations, both established and innovative.


title: Spontaneous mutation rate is a plastic trait associated with population density across domains of life

abstract: Rates of random, spontaneous mutation can vary plastically, dependent upon the environment. Such plasticity affects evolutionary trajectories and may be adaptive. We recently identified an inverse plastic association between mutation rate and population density at 1 locus in 1 species of bacterium. It is unknown how widespread this association is, whether it varies among organisms, and what molecular mechanisms of mutagenesis or repair are required for this mutation-rate plasticity. Here, we address all 3 questions. We identify a strong negative association between mutation rate and population density across 70 years of published literature, comprising hundreds of mutation rates estimated using phenotypic markers of mutation (fluctuation tests) from all domains of life and viruses. We test this relationship experimentally, determining that there is indeed density-associated mutation-rate plasticity (DAMP) at multiple loci in both eukaryotes and bacteria, with up to 23-fold lower mutation rates at higher population densities. We find that the degree of plasticity varies, even among closely related organisms. Nonetheless, in each domain tested, DAMP requires proteins scavenging the mutagenic oxidised nucleotide 8-oxo-dGTP. This implies that phenotypic markers give a more precise view of mutation rate than previously believed: having accounted for other known factors affecting mutation rate, controlling for population density can reduce variation in mutation-rate estimates by 93%. Widespread DAMP, which we manipulate genetically in disparate organisms, also provides a novel trait to use in the fight against the evolution of antimicrobial resistance. Such a prevalent environmental association and conserved mechanism suggest that mutation has varied plastically with population density since the early origins of life.


title: Performance Modeling and Scalability Optimization of Distributed Deep Learning Systems

abstract: Big deep neural network (DNN) models trained on large amounts of data have recently achieved the best accuracy on hard tasks, such as image and speech recognition. Training these DNNs using a cluster of commodity machines is a promising approach since training is time consuming and compute-intensive. To enable training of extremely large DNNs, models are partitioned across machines. To expedite training on very large data sets, multiple model replicas are trained in parallel on different subsets of the training examples with a global parameter server maintaining shared weights across these replicas. The correct choice for model and data partitioning and overall system provisioning is highly dependent on the DNN and distributed system hardware characteristics. These decisions currently require significant domain expertise and time consuming empirical state space exploration. This paper develops performance models that quantify the impact of these partitioning and provisioning decisions on overall distributed system performance and scalability. Also, we use these performance models to build a scalability optimizer that efficiently determines the optimal system configuration that minimizes DNN training time. We evaluate our performance models and scalability optimizer using a state-of-the-art distributed DNN training framework on two benchmark applications. The results show our performance models estimate DNN training time with high estimation accuracy and our scalability optimizer correctly chooses the best configurations, minimizing the training time of distributed DNNs.


title: BDI Agents: From Theory to Practice

abstract: The study of computational agents capable of rational behaviour has received a great deal of attention in recent years. Theoretical formalizations of such agents and their implementations have proceeded in parallel with little or no connection between them. Tkis paper explores a particular type of rational agent, a BeliefDesire-Intention (BDI) agent. The primary aim of this paper is to integrate (a) the theoretical foundations of BDI agents from both a quantitative decision-theoretic perspective and a symbolic reasoning perspective; (b) the implementations of BDI agents from an ideal theoretical perspective and a more practical perspective; and (c) the building of large-scale applications based on BDI agents. In particular, an air-trafflc management application will be described from both a theoretical and an implementation perspective.


title: Robot Guidance Using Machine Vision Techniques in Industrial Environments: A Comparative Review

abstract: In the factory of the future, most of the operations will be done by autonomous robots that need visual feedback to move around the working space avoiding obstacles, to work collaboratively with humans, to identify and locate the working parts, to complete the information provided by other sensors to improve their positioning accuracy, etc. Different vision techniques, such as photogrammetry, stereo vision, structured light, time of flight and laser triangulation, among others, are widely used for inspection and quality control processes in the industry and now for robot guidance. Choosing which type of vision system to use is highly dependent on the parts that need to be located or measured. Thus, in this paper a comparative review of different machine vision techniques for robot guidance is presented. This work analyzes accuracy, range and weight of the sensors, safety, processing time and environmental influences. Researchers and developers can take it as a background information for their future works.


title: Application of inverse filtering in enhancement of whisper recognition

abstract: The differences between normal speech and whisper, particularly in terms of their acoustic characteristics, are serious problem of ASR (Automatic Speech Recognition) systems. This paper presents the preliminary results of the new way of speech signal pre-processing, which is based on inverse filtering. This method of signal pre-processing improves whisper recognition with ANNs (Artificial Neural Networks). The ANNs showed high capabilities in speech and whisper recognition in matched train/test scenarios, with the average recognition accuracy of 99.8%. However, the recognition scores in mismatched train/test scenarios were highly degraded. Because of their practical significance, the mismatched train/test scenarios were analyzed in detail in this research. Particularly, the speech/whisper scenario is important. This scenario corresponds to real life situation when speaker is in front of ASR system and from speech switches to whisper. The use of inverse filter enhanced whisper recognition by 9.48%, which in this scenario amounts 70.25%.


title: Mitigating Performance Degradation in Congested Sensor Networks

abstract: Data generated in wireless sensor networks may not all be alike: some data may be more important than others and hence may have different delivery requirements. In this paper, we address differentiated data delivery in the presence of congestion in wireless sensor networks. We propose a class of algorithms that enforce differentiated routing based on the congested areas of a network and data priority. The basic protocol, called congestion-aware routing (CAR), discovers the congested zone of the network that exists between high-priority data sources and the data sink and, using simple forwarding rules, dedicates this portion of the network to forwarding primarily high-priority traffic. Since CAR requires some overhead for establishing the high-priority routing zone, it is unsuitable for highly mobile data sources. To accommodate these, we define MAC-enhanced CAR (MCAR), which includes MAC-layer enhancements and a protocol for forming high-priority paths on the fly for each burst of data. MCAR effectively handles the mobility of high-priority data sources, at the expense of degrading the performance of low-priority traffic. We present extensive simulation results for CAR and MCAR, and an implementation of MCAR on a 48-node testbed.


title: Control of a robot with a swedish and a standard wheel

abstract: In this work, a novel design of a wheeled mobile robot (WMR) and an associated motion controller are presented. The kinematic design of the robot combines the advantages of standard wheels and Swedish wheels. The control problems of posture stabilization and posture tracking are solved using a single, singularity-free static state feedback and feedforward linearization controller. The practicability of the robot as well as the controller are demonstrated in real-world experiments.


title: Practical Evasion of a Learning-Based Classifier: A Case Study

abstract: Learning-based classifiers are increasingly used for detection of various forms of malicious data. However, if they are deployed online, an attacker may attempt to evade them by manipulating the data. Examples of such attacks have been previously studied under the assumption that an attacker has full knowledge about the deployed classifier. In practice, such assumptions rarely hold, especially for systems deployed online. A significant amount of information about a deployed classifier system can be obtained from various sources. In this paper, we experimentally investigate the effectiveness of classifier evasion using a real, deployed system, PDFrate, as a test case. We develop a taxonomy for practical evasion strategies and adapt known evasion algorithms to implement specific scenarios in our taxonomy. Our experimental results reveal a substantial drop of PDFrate's classification scores and detection accuracy after it is exposed even to simple attacks. We further study potential defense mechanisms against classifier evasion. Our experiments reveal that the original technique proposed for PDFrate is only effective if the executed attack exactly matches the anticipated one. In the discussion of the findings of our study, we analyze some potential techniques for increasing robustness of learning-based systems against adversarial manipulation of data.


title: Influence of magnet arrangement on performance of flux reversal permanent magnet machine

abstract: This paper provides a comprehensive analysis of performance difference in the flux reversal permanent magnet (FRPM) machines with various magnet arrangements. The influence of different PM arrangements on working airgap flux density harmonics is firstly revealed. The torque variation against rotor pole number for four PM arrangements is then analyzed. Results show that each arrangement exhibits superiority in different ranges of rotor pole number. In addition, the electromagnetic performances of four PM arrangements with associated optimal rotor pole numbers are compared. It indicates that the FRPM machine, in which four PM pieces are mounted on each stator tooth and two adjacent magnets on different stator teeth are of opposite polarities, offers the highest torque density and the highest efficiency, which makes it promising in low-speed high-torque applications.


title: Age differences in the neural systems supporting human allocentric spatial navigation

abstract: Abstract Age-related declines in spatial navigation are well-known in human and non-human species. Studies in non-human species suggest that alteration in hippocampal and other neural circuitry may underlie behavioral deficits associated with aging but little is known about the neural mechanisms of human age-related decline in spatial navigation. The purpose of the present study was to examine age differences in functional brain activation during virtual environment navigation. Voxel-based analysis of activation patterns in young subjects identified activation in the hippocampus and parahippocampal gyrus, retrosplenial cortex, right and left lateral parietal cortex, medial parietal lobe and cerebellum. In comparison to younger subjects, elderly participants showed reduced activation in the hippocampus and parahippocampal gyrus, medial parietal lobe and retrosplenial cortex. Relative to younger participants elderly subjects showed increased activation in anterior cingulate gyrus and medial frontal lobe. These results provide evidence of age specific neural networks supporting spatial navigation and identify a putative neural substrate for age-related differences in spatial memory and navigational skill.


title: Pattern Recognition and Machine Learning

abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.


title: Content-Boosted Collaborative Filtering for Improved Recommendations

abstract: Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid recommender system can overcome these shortcomings. In this paper, we present an elegant and effective framework for combining content and collaboration. Our approach uses a content-based predictor tc enhance existing user data, and then provides personalized suggestions through collaborative filtering. We present experimental results that show how this approach, Content-Boosted Collaborative Filtering, performs better than a pure content-based predictor, pure collaborative filter, and a naive hybrid approach.


title: A 57-to-64 GHz ultra-compact 0.027 mm2 reflection type phase shifter with low insertion loss

abstract: An ultra-compact reflection-type phase shifter (RTPS) with more than 190° continuous phase shift and low insertion loss using 90-nm CMOS process is demonstrated in this paper. Two circuit implementation techniques are employed: LC-type reflection loads with switches and 3-dB quadrature coupler. First, the LC-type reflection loads with switches have been proposed to alleviate the issue of quality factor. Second, the 3-dB quadrature coupler is designed with broadside couple to reduce chip size and enhance couple factor. Measurements show signal losses of 9.3dB±1.5dB including pad loss, loss flatness of ±0.9 dB, and a more than 190° continuously tunable range across 57-64 GHz with 0-mW dc power consumption. To the best of our knowledge, the proposed phase shifter has the smallest die size of 0.027 mm2, among all reported V-band CMOS phase shifter, which is important for a multi-way phased-array system.


title: Combining multiple clusterings using evidence accumulation

abstract: We explore the idea of evidence accumulation (EAC) for combining the results of multiple clusterings. First, a clustering ensemble?a set of object partitions, is produced. Given a data set (n objects or patterns in d dimensions), different ways of producing data partitions are: 1) applying different clustering algorithms and 2) applying the same clustering algorithm with different values of parameters or initializations. Further, combinations of different data representations (feature spaces) and clustering algorithms can also provide a multitude of significantly different data partitionings. We propose a simple framework for extracting a consistent clustering, given the various partitions in a clustering ensemble. According to the EAC concept, each partition is viewed as an independent evidence of data organization, individual data partitions being combined, based on a voting mechanism, to generate a new n \times n similarity matrix between the n patterns. The final data partition of the n patterns is obtained by applying a hierarchical agglomerative clustering algorithm on this matrix. We have developed a theoretical framework for the analysis of the proposed clustering combination strategy and its evaluation, based on the concept of mutual information between data partitions. Stability of the results is evaluated using bootstrapping techniques. A detailed discussion of an evidence accumulation-based clustering algorithm, using a split and merge strategy based on the K-means clustering algorithm, is presented. Experimental results of the proposed method on several synthetic and real data sets are compared with other combination strategies, and with individual clustering results produced by well-known clustering algorithms.


title: Toward an interpersonal life-stress model of depression : The developmental context of stress generation

abstract: The validity of a developmentally based life-stress model of depression was evaluated in 88 clinic-referred youngsters. The model focused on (a) the role of child‐environment transactions, (b) the specificity of stress‐psychopathology relations, and (c) the consideration of both episodic and chronic stress. Semistructured diagnostic and life-stress interviews were administered to youngsters and their parents. As predicted, in the total sample child depression was associated with interpersonal episodic and chronic stress, whereas externalizing disorder was associated with noninterpersonal episodic and chronic stress. However, the pattern of results differed somewhat in boys and girls. Youngsters with comorbid depression and externalizing disorder tended to experience the highest stress levels. Support was obtained for a stress-generation model of depression, wherein children precipitate stressful events and circumstances. In fact, stress that was in part dependent on children’s contribution distinguished best among diagnostic groups, whereas independent stress had little discriminative power. Results suggest that life-stress research may benefit from the application of transactional models of developmental psychopathology, which consider how children participate in the construction of stressful environments.


title: The technical hashtag in Twitter data: A hadoop experience

abstract: The continuously growing wealth of data has radically changed the data science landscape. At the same time, Big Data tools have known important progress in terms of optimising performance and scalability. However, applying them into practical deployment settings is still a challenging task that is highly dependent on the particularities of the data. In this paper, we present our experiences with implementing a Big Data analytics pipeline with the purpose of extracting value from Twitter data. We acquire and process nearly 60 million tweets that capture the recent outbreaks of the Ebola and Zika viruses. Our processing pipeline first extracts useful information from tweets and then applies a topic modelling technique, provided by Mahout, a Hadoop-based machine learning library. We further extend our Twitter analysis with the study of temporal evolution of daily sentiment toward an important topic, as expressed through the social platform. We highlight at each level, the technical challenges originating from the specific nature of Twitter data and the lessons drawn from our work.


title: Ethnicity Identification from Face Images

abstract: Human facial images provide the demographic information, such as ethnicity and gender. Conversely, ethnicity and gender also play an important role in face-related applications. Image-based ethnicity identification problem is addressed in a machine learning framework. The Linear Discriminant Analysis (LDA) based scheme is presented for the two-class (Asian vs. non-Asian) ethnicity classification task. Multiscale analysis is applied to the input facial images. An ensemble framework, which integrates the LDA analysis for the input face images at different scales, is proposed to further improve the classification performance. The product rule is used as the combination strategy in the ensemble. Experimental results based on a face database containing 263 subjects (2,630 face images, with equal balance between the two classes) are promising, indicating that LDA and the proposed ensemble framework have sufficient discriminative power for the ethnicity classification problem. The normalized ethnicity classification scores can be helpful in the facial identity recognition. Useful as a "soft" biometric, face matching scores can be updated based on the output of ethnicity classification module. In other words, ethnicity classifier does not have to be perfect to be useful in practice.© (2004) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.


title: Comparing two classes of end-to-end machine-learning models in lung nodule detection and classification

abstract: End-to-end learning machines enable a direct mapping from the raw input data to the desired outputs, eliminating the need for hand-crafted features. Despite less engineering effort than the hand-crafted counterparts, these learning machines achieve extremely good results for many computer vision and medical image analysis tasks. Two dominant classes of end-to-end learning machines are massive-training artificial neural networks (MTANNs) and convolutional neural networks (CNNs). Although MTANNs have been actively used for a number of medical image analysis tasks over the past two decades, CNNs have recently gained popularity in the field of medical imaging. In this study, we have compared these two successful learning machines both experimentally and theoretically. For that purpose, we considered two well-studied topics in the field of medical image analysis: detection of lung nodules and distinction between benign and malignant lung nodules in computed tomography (CT). For a thorough analysis, we used 2 optimized MTANN architectures and 4 distinct CNN architectures that have different depths. Our experiments demonstrated that the performance of MTANNs was substantially higher than that of CNN when using only limited training data. With a larger training dataset, the performance gap became less evident even though the margin was still significant. Specifically, for nodule detection, MTANNs generated 2.7 false positives per patient at 100% sensitivity, which was significantly (p<0.05) lower than the best performing CNN model with 22.7 false positives per patient at the same level of sensitivity. For nodule classification, MTANNs yielded an area under the receiver-operating-characteristic curve (AUC) of 0.8806 (95% CI: 0.83890.9223), which was significantly (p<0.05) greater than the best performing CNN model with an AUC of 0.7755 (95% CI: 0.71200.8270). Thus, with limited training data, MTANNs would be a suitable end-to-end machine-learning model for detection and classification of focal lesions that do not require high-level semantic features. HighlightsMTANNs yielded higher performance than CNNs for nodule detection and classification.Deep CNN architectures achieved higher performance than shallow architectures for nodule detection.CNN architectures with varying depths performed comparably for nodule classification.MTANNs can achieve desired performance with a smaller training dataset than do the CNNs.MTANNs tend to learn the appearance of lesion parts, whereas CNNs attempt to learn the lesion appearance as a whole.


title: Psychosocial factors are associated with prolonged hospitalization in a population with advanced HIV

abstract: Our aim was to define a subgroup of patients with HIV at risk of adverse outcomes in terms of psychosocial factors in order to improve the targeting of hospital resources. The International Classification of Diseases, 9th Revision (ICD-9) coded discharges of all inpatients with HIV discharged from a tertiary hospital between July 1996 and March 1999 were matched against variables in the HIV/AIDS database. A 'prolonged hospitalization' subgroup was defined as those patients whose cumulative length of stay exceeded 90 days in the 33-month period. There were 2778 non-day stay discharges (n=757 patients) constituting 21,286 bed-days. The prolonged hospitalization group (n=62) accounted for 44.3% of the bed-days. Psychosocial co-diagnoses were associated with prolonged hospitalization in both crude and adjusted logistic analyses. These included psychiatric diagnoses such as mania, psychosis and anxiety, HIV dementia, housing issues and the need for social work interventions. In conclusion, a small group of individuals at risk of adverse outcomes has been defined by markers of psychosocial dysfunction. Increased understanding of this group should enable the development of programmes directed at morbidity and mortality.


title: Community Distribution Outlier Detection in Heterogeneous Information Networks

abstract: Heterogeneous networks are ubiquitous. For example, bibliographic data, social data, medical records, movie data and many more can be modeled as heterogeneous networks. Rich information associated with multi-typed nodes in heterogeneous networks motivates us to propose a new definition of outliers, which is different from those defined for homogeneous networks. In this paper, we propose the novel concept of Community Distribution Outliers (CDOutliers) for heterogeneous information networks, which are defined as objects whose community distribution does not follow any of the popular community distribution patterns.We extract such outliers using a type-aware joint analysis of multiple types of objects. Given community membership matrices for all types of objects, we follow an iterative two-stage approach which performs pattern discovery and outlier detection in a tightly integrated manner. We first propose a novel outlier-aware approach based on joint non-negative matrix factorization to discover popular community distribution patterns for all the object types in a holistic manner, and then detect outliers based on such patterns. Experimental results on both synthetic and real datasets show that the proposed approach is highly effective in discovering interesting community distribution outliers.


title: Return-to-Work Self-Efficacy: Development and Validation of a Scale in Claimants with Musculoskeletal Disorders

abstract: Introduction We report on the development and validation of a 10-item scale assessing self-efficacy within the return-to-work context, the Return-to-Work Self-Efficacy (RTWSE) scale. Methods Lost-time claimants completed a telephone survey 1 month (n = 632) and 6 months (n = 446) after a work-related musculoskeletal injury. Exploratory (Varimax and Promax rotation) and confirmatory factor analyses of self-efficacy items were conducted with two separate subsamples at both time points. Construct validity was examined by comparing scale measurements and theoretically derived constructs, and the phase specificity of RTWSE was studied by examining changes in strength of relationships between the RTWSE Subscales and the other constructs at both time measures. Results Factor analyses supported three underlying factors: (1) Obtaining help from supervisor, (2) Coping with pain (3) Obtaining help from co-workers. Internal consistency (alpha) for the three subscales ranged from 0.66 to 0.93. The total variance explained was 68% at 1-month follow-up and 76% at 6-month follow-up. Confirmatory factor analyses had satisfactory fit indices to confirm the initial model. With regard to construct validity: relationships of RTWSE with depressive symptoms, fear-avoidance, pain, and general health, were generally in the hypothesized direction. However, the hypothesis that less advanced stages of change on the Readiness for RTW scale would be associated with lower RTWSE could not be completely confirmed: on all RTWSE subscales, RTWSE decreased significantly for a subset of participants who started working again. Moreover, only Pain RTWSE was significantly associated with RTW status and duration of work disability. With regard to the phase specificity, the strength of association between RTWSE and other constructs was stronger at 6 months post-injury compared to 1 month post-injury. Conclusions A final 10-item version of the RTWSE has adequate internal consistency and validity to assess the confidence of injured workers to obtain help from supervisor and co-workers and to cope with pain. With regard to phase specificity, stronger associations between RTWSE and other constructs at 6-month follow-up suggest that the association between these psychological constructs consolidates over time after the disruptive event of the injury.


title: Enabling Personalized Search over Encrypted Outsourced Data with Efficiency Improvement

abstract: In cloud computing, searchable encryption scheme over outsourced data is a hot research field. However, most existing works on encrypted search over outsourced cloud data follow the model of “one size fits all” and ignore personalized search intention. Moreover, most of them support only exact keyword search, which greatly affects data usability and user experience. So how to design a searchable encryption scheme that supports personalized search and improves user search experience remains a very challenging task. In this paper, for the first time, we study and solve the problem of personalized multi-keyword ranked search over encrypted data (PRSE) while preserving privacy in cloud computing. With the help of semantic ontology WordNet, we build a user interest model for individual user by analyzing the user’s search history, and adopt a scoring mechanism to express user interest smartly. To address the limitations of the model of “one size fit all” and keyword exact search, we propose two PRSE schemes for different search intentions. Extensive experiments on real-world dataset validate our analysis and show that our proposed solution is very efficient and effective.


title: Resolving motion correspondence for densely moving points

abstract: Studies the motion correspondence problem for which a diversity of qualitative and statistical solutions exist. We concentrate on qualitative modeling, especially in situations where assignment conflicts arise either because multiple features compete for one detected point or because multiple detected points fit a single feature point. We leave out the possibility of point track initiation and termination because that principally conflicts with allowing for temporary point occlusion. We introduce individual, combined, and global motion models and fit existing qualitative solutions in this framework. Additionally, we present a tracking algorithm that satisfies these-possibly constrained-models in a greedy matching sense, including an effective way to handle detection errors and occlusion. The performance evaluation shows that the proposed algorithm outperforms existing greedy matching algorithms. Finally, we describe an extension to the tracker that enables automatic initialization of the point tracks. Several experiments show that the extended algorithm is efficient, hardly sensitive to its few parameters, and qualitatively better than other algorithms, including the presumed optimal statistical multiple hypothesis tracker.


title: Query Optimization Time: The New Bottleneck in Real-time Analytics

abstract: In the recent past, in-memory distributed database management systems have become increasingly popular to manage and query huge amounts of data. For an in-memory distributed database like MemSQL, it is imperative that the analytical queries run fast. A huge proportion of MemSQL's customer workloads have ad-hoc analytical queries that need to finish execution within a second or a few seconds. This leaves us with very little time to perform query optimization for complex queries involving several joins, aggregations, sub-queries etc. Even for queries that are not ad-hoc, a change in data statistics can trigger query re-optimization. Query Optimization, if not done intelligently, could very well be the bottleneck for such complex analytical queries that require real-time response. In this paper, we outline some of the early steps that we have taken to reduce the query optimization time without sacrificing plan quality. We optimized the Enumerator (the optimizer component that determines operator order), which takes up bulk of the optimization time. Generating bushy plans inside the Enumerator can be a bottleneck and so we used heuristics to generate bushy plans via query rewrite. We also implemented new distribution aware greedy heuristics to generate a good starting candidate plan that significantly prunes out states during search space analysis inside the Enumerator. We demonstrate the effectiveness of these techniques over several queries in TPC-H and TPC-DS benchmarks.


title: Prefetching-Based Data Dissemination in Vehicular Cloud Systems

abstract: In the last decade, vehicular ad hoc networks (VANETs) have been widely studied as an effective method for providing wireless communication connectivity in vehicular transportation systems. In particular, vehicular cloud systems (VCSs) have received abundant interest for the ability to offer a variety of vehicle information services. The authors consider the data dissemination problem of providing reliable data delivery services from a cloud data center to vehicles through roadside wireless access points (APs) with local data storage. Due to intermittent wireless connectivity and the limited data storage size of roadside wireless APs, the question of how to use the limited resources of the wireless APs is one of the most pressing issues affecting data dissemination efficiency in VCSs. In this paper, the authors devise a vehicle route-based data prefetching scheme, which maximizes data dissemination success probability in an average sense when the size of local data storage is limited and wireless connectivity is stochastically unknown. The authors propose a greedy algorithm and an online learning algorithm for deterministic and stochastic cases, respectively, to decide how to prefetch a set of data of interest from a data center to roadside wireless APs. Experiment results indicate that the proposed algorithms can achieve efficient data dissemination in a variety of vehicular scenarios.


title: Towards Dropout Training for Convolutional Neural Networks

abstract: Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, dropout is known to work well in fully-connected layers. However, its effect in convolutional and pooling layers is still not clear. This paper demonstrates that max-pooling dropout is equivalent to randomly picking activation based on a multinomial distribution at training time. In light of this insight, we advocate employing our proposed probabilistic weighted pooling, instead of commonly used max-pooling, to act as model averaging at test time. Empirical evidence validates the superiority of probabilistic weighted pooling. We also empirically show that the effect of convolutional dropout is not trivial, despite the dramatically reduced possibility of over-fitting due to the convolutional architecture. Elaborately designing dropout training simultaneously in max-pooling and fully-connected layers, we achieve state-of-the-art performance on MNIST, and very competitive results on CIFAR-10 and CIFAR-100, relative to other approaches without data augmentation. Finally, we compare max-pooling dropout and stochastic pooling, both of which introduce stochasticity based on multinomial distributions at pooling stage.


title: An asset management system based on RFID, WebGIS and SMS

abstract: In this paper, we propose an asset management system using technologies of Radio Frequency Identification (RFID), Web Geographic Information System (WebGIS) and Short Message Service (SMS) for intelligent management of assets. The proposed method tracks and monitors the changes of locations of the assets and shows their statuses on a geographical map, preventing the loss of assets due to unclear reasons. The movement of assets can be automatically identified using the RFID technique. The corresponding data are transferred by the GSM module in real time, enabling the spatial information of assets to be displayed on an electronic geographical map using WebGIS. The proposed method also facilitates automated notification of asset movement and malfunction alarm through SMS. Preliminary experiments show that the proposed method can improve information availability of assets and reduce asset management cost dramatically.


title: An improved multiple minimum support based approach to mine rare association rules

abstract: In this paper we have proposed an improved approach to extract rare association rules. Rare association rules are the association rules containing rare items. Rare items are less frequent items. For extracting rare itemsets, the single minimum support (minsup) based approaches like Apriori approach suffer from “rare item problem” dilemma. At high minsup value, rare itemsets are missed, and at low minsup value, the number of frequent itemsets explodes. To extract rare itemsets, an effort has been made in the literature in which minsup of each item is fixed equal to the percentage of its support. Even though this approach improves the performance over single minsup based approaches, it still suffers from “rare item problem” dilemma. If minsup for the item is fixed by setting the percentage value high, the rare itemsets are missed as the minsup for the rare items becomes close to their support, and if minsup for the item is fixed by setting the percentage value low, the number of frequent itemsets explodes. In this paper, we propose an improved approach in which minsup is fixed for each item based on the notion of “support difference”. The proposed approach assigns appropriate minsup values for frequent as well as rare items based on their item supports and reduces both “rule missing” and “rule explosion” problems. Experimental results on both synthetic and real world datasets show that the proposed approach improves performance over existing approaches by minimizing the explosion of number of frequent itemsets involving frequent items and without missing the frequent itemsets involving rare items.


title: A PULSE-BASED NON-CARRIER 7.5GHz UWB TRANSCEIVER SoC WITH ON-CHIP ADC

abstract: In this paper, a single-chip pulse-based, non-carrier, high throughput, low power ultra wideband (UWB) transceiver system-on-a-chip (SoC) with on-chip analog-to-digital converter (ADC) for high payload wireless video/audio/multimedia streaming applications is presented. To achieve high throughput (>100Mbps) and high simplicity, the UWB SoC adopts single-band (7.5GHz), pulse-based non-carrier architecture, which consists of low-noise amplifier (LNA), correlator, integrator, pulse generator, power switches, timing controller and on-chip ADC. The SoC features BPSK modulation, Gaussian pulsing, and a global switching technique for power reduction. The average power consumption of the transceiver (no ADC) is 6mW only. The SoC is designed in commercial 0.18mum CMOS technology


title: A Delphi-AHP-TOPSIS based benchmarking framework for performance improvement of a cold chain

abstract: This paper aims to develop a benchmarking framework that evaluates the cold chain performance of a company, reveals its strengths and weaknesses and finally identifies and prioritizes potential alternatives for continuous improvement. A Delphi-AHP-TOPSIS based methodology has divided the whole benchmarking into three stages. The first stage is Delphi method, where identification, synthesis and prioritization of key performance factors and sub-factors are done and a novel consistent measurement scale is developed. The second stage is Analytic Hierarchy Process (AHP) based cold chain performance evaluation of a selected company against its competitors, so as to observe cold chain performance of individual factors and sub-factors, as well as overall performance index. And, the third stage is Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) based assessment of possible alternatives for the continuous improvement of the company's cold chain performance. Finally a demonstration of proposed methodology in a retail industry is presented for better understanding. The proposed framework can assist managers to comprehend the present strengths and weaknesses of their cold. They can identify good practices from the market leader and can benchmark them for improving weaknesses keeping in view the current operational conditions and strategies of the company. This framework also facilitates the decision makers to better understand the complex relationships of the relevant cold chain performance factors in decision-making.


title: A SVM-based detection approach for GPS spoofing attacks to UAV

abstract: Today, the necessity of strong cyber security measures is self-evident. The proliferation of cyber attacks is causing increasing damage to companies, governments and individuals and it is not just the number of cyber security attacks that is increasing: the severity of these attacks is on the rise as well. In particular, aviation systems and Remotely Piloted Aircraft Systems are of highest importance in terms of safety and security. In this paper, we introduce a novel approach to the detection of Global Positioning System (GPS) spoofing attack to Unmanned Aerial Vehicle (UAV) based on the analysis of state estimation using the Support Vector Machine (SVM) as a tool for the anomaly detection.


title: Recognizing Stances in Ideological On-Line Debates

abstract: This work explores the utility of sentiment and arguing opinions for classifying stances in ideological debates. In order to capture arguing opinions in ideological stance taking, we construct an arguing lexicon automatically from a manually annotated corpus. We build supervised systems employing sentiment and arguing opinions and their targets as features. Our systems perform substantially better than a distribution-based baseline. Additionally, by employing both types of opinion features, we are able to perform better than a unigram-based system.


title: Single active element PID controllers

abstract: This paper presents a detailed overview of the circuits allowing the realization of PID controllers with single active element. We provide analysis of the circuits using the operational amplifier (OpAmp) and operational transconductance amplifiers (OTA). Based on these analyses, we present an alternative structure using the second generation current conveyer CCII. Compared to the classical solutions, presented controller exhibits improved features such as symmetrical transfers of feedback and reference paths. The paper is completed by the design equations and the example of design.