title: Adaptive bidding for display advertising

abstract: Motivated by the emergence of auction-based marketplaces for display ads such as the Right Media Exchange, we study the design of a bidding agent that implements a display advertising campaign by bidding in such a marketplace. The bidding agent must acquire a given number of impressions with a given target spend, when the highest external bid in the marketplace is drawn from an unknown distribution P. The quantity and spend constraints arise from the fact that display ads are usually sold on a CPM basis. We consider both the full information setting, where the winning price in each auction is announced publicly, and the partially observable setting where only the winner obtains information about the distribution; these differ in the penalty incurred by the agent while attempting to learn the distribution. We provide algorithms for both settings, and prove performance guarantees using bounds on uniform closeness from statistics, and techniques from online learning. We experimentally evaluate these algorithms: both algorithms perform very well with respect to both target quantity and spend; further, our algorithm for the partially observable case performs nearly as well as that for the fully observable setting despite the higher penalty incurred during learning.


title: Inverse dynamics control of floating-base robots with external constraints: A unified view

abstract: Inverse dynamics controllers and operational space controllers have proved to be very efficient for compliant control of fully actuated robots such as fixed base manipulators. However legged robots such as humanoids are inherently different as they are underactuated and subject to switching external contact constraints. Recently several methods have been proposed to create inverse dynamics controllers and operational space controllers for these robots. In an attempt to compare these different approaches, we develop a general framework for inverse dynamics control and show that these methods lead to very similar controllers. We are then able to greatly simplify recent whole-body controllers based on operational space approaches using kinematic projections, bringing them closer to efficient practical implementations. We also generalize these controllers such that they can be optimal under an arbitrary quadratic cost in the commands.


Measuring the Insecurity of Mobile Deep Links of Android


title: Geographical Topic Discovery and Comparison

abstract: This paper studies the problem of discovering and comparing geographical topics from GPS-associated documents. GPS-associated documents become popular with the pervasiveness of location-acquisition technologies. For example, in Flickr, the geo-tagged photos are associated with tags and GPS locations. In Twitter, the locations of the tweets can be identified by the GPS locations from smart phones. Many interesting concepts, including cultures, scenes, and product sales, correspond to specialized geographical distributions. In this paper, we are interested in two questions: (1) how to discover different topics of interests that are coherent in geographical regions? (2) how to compare several topics across different geographical locations? To answer these questions, this paper proposes and compares three ways of modeling geographical topics: location-driven model, text-driven model, and a novel joint model called LGTA (Latent Geographical Topic Analysis) that combines location and text. To make a fair comparison, we collect several representative datasets from Flickr website including Landscape, Activity, Manhattan, National park, Festival, Car, and Food. The results show that the first two methods work in some datasets but fail in others. LGTA works well in all these datasets at not only finding regions of interests but also providing effective comparisons of the topics across different locations. The results confirm our hypothesis that the geographical distributions can help modeling topics, while topics provide important cues to group different geographical regions.


title: A New Model of Information Content for Semantic Similarity in WordNet

abstract: Information Content (IC) is an important dimension of assessing the semantic similarity between two terms or word senses in word knowledge. The conventional method of obtaining IC of word senses is to combine knowledge of their hierarchical structure from an ontology like WordNet with actual usage in text as derived from a large corpus. In this paper, a new model of IC is presented, which relies on hierarchical structure alone. The model considers not only the hyponyms of each word sense but also its depth in the structure. The IC value is easier to calculate based on our model, and when used as the basis of a similarity approach it yields judgments that correlate more closely with human assessments than others, which using IC value obtained only considering the hyponyms and IC value got by employing corpus analysis.


title: Occlusion for fixed prosthodontics: a historical perspective of the gnathological influence.

abstract: This article addresses the historical perspective of the gnathological influence upon the concepts of occlusion for fixed prosthodontics. A critical assessment and subsequent scientific validation of occlusal theories require an understanding of their evolution in the formative years and the subsequent development of effective models for clinical practice. While gnathological concepts offer a structured methodology for prosthodontic procedures, further research is needed to corroborate current occlusal treatment approaches. This review focuses on the "classic" fixed prosthodontic literature and the currently available scientific literature involving fixed prosthodontic dentate occlusion and gnathology. A MEDLINE search was performed to identify English-language peer-reviewed publications spanning the last 56 years, along with an extensive hand search for years prior. Electronic searches of the literature were performed in MEDLINE using the key words: case series, clinical trials, cohort studies, fixed partial denture occlusion, dental occlusion, dental occlusion research, centric relation, incisal guidance, maximal intercuspation, occlusal vertical dimension, and occlusion, in various combinations to obtain potential references for review. A total of 10,382 English-language nonduplicate titles were obtained for 1950-2006 for the key words "dental occlusion." Other key word searches produced smaller numbers of articles, many of which were duplicates due to multiple searches and were subsequently eliminated. Manual hand searching of the MEDLINE reference list and other journals of interest was performed to identify any articles missed in the original search. Articles were included for review if they contained emerging occlusal theories, new technologies, or occlusal studies that included multiple subjects in contrast to case reports.


title: A systematic review of the survival and complication rates of all-ceramic and metal-ceramic reconstructions after an observation period of at least 3 years. Part I: Single crowns.

abstract: OBJECTIVES ::: The objective of this systematic review was to assess the 5-year survival rates of all-ceramic single crowns and to compare it with the survival rates of metal-ceramic crowns and to describe the incidence of biological and technical complications. ::: ::: ::: METHODS ::: An electronic Medline and Dental Global Publication Research System search complemented by manual searching was conducted to identify prospective and retrospective cohort studies on all-ceramic and metal-ceramic crowns with a mean follow-up time of at least 3 years. Patients had to have been examined clinically at the follow-up visit. Assessment of the identified studies and data abstraction was performed independently by two reviewers. Annual failure rates were analyzed using standard and random-effects Poisson regression models to obtain summary estimates of survival proportions. ::: ::: ::: RESULTS ::: The search provided 3473 titles and 177 abstracts. Full-text analysis was performed for 86 articles, resulting in 34 studies that met the inclusion criteria. In meta-analysis, the 5-year survival of all-ceramic crowns was estimated at 93.3% (95% confidence interval (CI): 91.1%-95%) and 95.6% (95% CI: 92.4%-97.5%) for metal-ceramic crowns. All-ceramic crowns were also analyzed according to the material utilized. Densely sintered alumina (Procera technique) crowns showed the highest 5-year survival rate of 96.4%, followed by reinforced glass-ceramic crowns (Empress technique) and InCeram-crowns with survival rates of 95.4% and 94.5%, respectively. A significantly lower survival rate of 87.5% was calculated for glass-ceramic crowns after 5 years. All-ceramic crowns were also grouped and analyzed regarding the position in the mouth. All four types of all-ceramic crowns showed lower survival rates when seated on posterior teeth. Moreover, for glass-ceramic crowns (84.4%) and InCeram-crowns (90.4%), this difference reached statistical significance (P = 0.009, P = 0.028). ::: ::: ::: CONCLUSION ::: Based on the present systematic review, all-ceramic crowns, when used for anterior teeth, showed survival rates at 5 years comparable to those seen for metal-ceramic crowns. When used for posterior teeth, the survival rates at 5 years of densely sintered alumina crowns (94.9%) and reinforced glass-ceramic crowns (93.7%) were similar to those obtained for metal-ceramic crowns. Furthermore, lower survival rates of 90.4% and 84.4% can be expected for InCeram crowns and glass-ceramic crowns when utilized for premolars and molars.


title: Motion sickness susceptibility

abstract: Motion sickness can be caused by a variety of motion environments (e.g., cars, boats, planes, tilting trains, funfair rides, space, virtual reality) and given a sufficiently provocative motion stimulus almost anyone with a functioning vestibular system can be made motion sick. Current hypotheses of the 'Why?' of motion sickness are still under investigation, the two most important being 'toxin detector' and the 'vestibular–cardiovascular reflex'. By contrast, the 'How?' of motion sickness is better understood in terms of mechanisms (e.g., 'sensory conflict' or similar) and stimulus properties (e.g., acceleration, frequency, duration, visual–vestibular time-lag). Factors governing motion sickness susceptibility may be divided broadly into two groups: (i) those related to the stimulus (motion type and provocative property of stimulus); and (ii) those related to the individual person (habituation or sensitisation, individual differences, protective behaviours, administration of anti-motion sickness drugs). The aim of this paper is to review some of the more important factors governing motion sickness susceptibility, with an emphasis on the personal rather than physical stimulus factors.


title: A novel escapable social interaction test reveals that social behavior and mPFC activation during an escapable social encounter are altered by post‐weaning social isolation and are dependent on the aggressiveness of the stimulus rat

abstract: Abstract Post-weaning social isolation (PSI) has been shown to increase aggressive behavior and alter medial prefrontal cortex (mPFC) function in social species such as rats. Here we developed a novel escapable social interaction test (ESIT) allowing for the quantification of escape and social behaviors in addition to mPFC activation in response to an aggressive or nonaggressive stimulus rat. Male rats were exposed to 3 weeks of PSI (ISO) or group (GRP) housing, and exposed to 3 trials, with either no trial, all trials, or the last trial only with a stimulus rat. Analysis of social behaviors indicated that ISO rats spent less time in the escape chamber and more time engaged in social interaction, aggressive grooming, and boxing than did GRP rats. Interestingly, during the third trial all rats engaged in more of the quantified social behaviors and spent less time escaping in response to aggressive but not nonaggressive stimulus rats. Rats exposed to nonaggressive stimulus rats on the third trial had greater c-fos and ARC immunoreactivity in the mPFC than those exposed to an aggressive stimulus rat. Conversely, a social encounter produced an increase in large PSD-95 punctae in the mPFC independently of trial number, but only in ISO rats exposed to an aggressive stimulus rat. The results presented here demonstrate that PSI increases interaction time and aggressive behaviors during escapable social interaction, and that the aggressiveness of the stimulus rat in a social encounter is an important component of behavioral and neural outcomes for both isolation and group-reared rats.


title: Comparing and experimenting machine learning techniques for code smell detection

abstract: Several code smell detection tools have been developed providing different results, because smells can be subjectively interpreted, and hence detected, in different ways. In this paper, we perform the largest experiment of applying machine learning algorithms to code smells to the best of our knowledge. We experiment 16 different machine-learning algorithms on four code smells (Data Class, Large Class, Feature Envy, Long Method) and 74 software systems, with 1986 manually validated code smell samples. We found that all algorithms achieved high performances in the cross-validation data set, yet the highest performances were obtained by J48 and Random Forest, while the worst performance were achieved by support vector machines. However, the lower prevalence of code smells, i.e., imbalanced data, in the entire data set caused varying performances that need to be addressed in the future studies. We conclude that the application of machine learning to the detection of these code smells can provide high accuracy (>96 %), and only a hundred training examples are needed to reach at least 95 % accuracy.


title: A model free automatic tuning method for a restricted structured controller by using Simultaneous Perturbation Stochastic Approximation (SPSA)

abstract: A model free auto tuning algorithm is developed by using simultaneous perturbation stochastic approximation (SPSA). For such a method, plant models are not required. A set of closed loop experiments are conducted to generate data for an online optimization procedure. The optimum of the parameters of the restricted structured controllers will be found via SPSA algorithm. Compared to the conventional gradient approximation methods, SPSA only needs the small number of measurement of the cost function. It will be beneficial to application with high dimensional parameters. In the paper, a cost function is formulated to directly reflect the control performances widely used in industry, like overshoot, settling time and integral of absolute error. Therefore, the proposed auto tuning method will naturally lead to the desired closed loop performance. A case study of auto tuning of spool position control in a twin spool two stage valve is conducted. Both simulation and experimental study in TI C2000 target demonstrate effectiveness of the algorithm.


title: Removal of Ocular Artifacts in the EEG through Wavelet Transform without using an EOG Reference Channel

abstract: This paper presents a statistical method for removing ocular artifacts in the electroencephalogram (EEG) records. Artifacts in EEG signals are caused by various factors, like line interference, EOG (electro-oculogram) and ECG (electrocardiogram). The removal of ocular artifact from scalp EEGs is of considerable importance for both the automated and visual analysis of underlying brainwave activity. These noise sources increase the difficulty in analyzing the EEG and obtaining clinical information. For this reason, it is necessary to design a procedure to decrease such artifacts in EEG records. In this paper, a statistical method for removing ocular artifacts from EEG recordings through wavelet transform without using an EOG reference channel is proposed.


title: Source distinguishability under corrupted training

abstract: We study a new variant of the source identification game with training data in which part of the training data is corrupted by an adversary. In such a scenario, the defender wants to decide whether a test sequence ξn has been drawn from the same source which generated a training sequence tN, part of which has been corrupted by the adversary. By adopting a game theoretical formulation, we derive the unique rationalizable equilibrium of the game in the asymptotic setup. Moreover, by mimicking Stein’s lemma, we derive the best achievable performance for the defender, permitting us to analyze the ultimate distinguishability of the two sources.We conclude the paper by comparing the performance of the test with corrupted training to the simpler case in which the adversary can not modify the training sequence, and by deriving the percentage of samples that the adversary needs to modify to make source identification impossible.


title: Acquiring evidence-based medicine and research skills in the undergraduate medical curriculum: three different didactical formats compared

abstract: Medical schools have recently witnessed a call for authentic research activities that equip students with the skills required for evidence-based medicine (EBM) and research. Because it is not always possible to make such activities available as a part of the curriculum, evaluating the effectiveness of the various choices of traditional and authentic EBM and research skills courses is essential. This study’s purpose was to evaluate students’ perceived EBM and research skill acquisition in three different courses in a Dutch medical school. Self-reported surveys were conducted among 163 Dutch medical undergraduates who participated in an undergraduate research project, a basic EBM skills elective, or a traditional lecture-based skills course. MANCOVA was employed to test for group differences in perceived skill acquisition. Students who finished their research project perceived themselves as more experienced in writing and information retrieval skills than students who participated in the lecture-based course or basic skills elective. Students in the lecture-based course identified themselves as being the most experienced in critical judgment. No group differences were found for overall gains. Authentic research activities may have benefits over traditional lecture-based courses in the undergraduate medical curriculum, especially in terms of equipping students with writing and information retrieval skills.


title: Robust stock trading using fuzzy decision trees

abstract: Stock market analysis has traditionally been proven to be difficult due to the large amount of noise present in the data. Different approaches have been proposed to predict stock prices including the use of computational intelligence and data mining techniques. Many of these methods operate on closing stock prices or on known technical indicators. Limited studies have shown that Japanese candlestick analysis serve as rich information sources for the market. In this paper decision trees based on the ID3 algorithm are used to derive short-term trading decisions from candlesticks. To handle the large amount of uncertainty in the data, both inputs and output classifications are fuzzified using well-defined membership functions. Testing results of the derived decision trees show significant gains compared to ideal mid and long-term trading simulations both in frictionless and realistic markets.


title: SW-Store: a vertically partitioned DBMS for Semantic Web data management

abstract: Efficient management of RDF data is an important prerequisite for realizing the Semantic Web vision. Performance and scalability issues are becoming increasingly pressing as Semantic Web technology is applied to real-world applications. In this paper, we examine the reasons why current data management solutions for RDF data scale poorly, and explore the fundamental scalability limitations of these approaches. We review the state of the art for improving performance of RDF databases and consider a recent suggestion, "property tables". We then discuss practically and empirically why this solution has undesirable features. As an improvement, we propose an alternative solution: vertically partitioning the RDF data. We compare the performance of vertical partitioning with prior art on queries generated by a Web-based RDF browser over a large-scale (more than 50 million triples) catalog of library data. Our results show that a vertically partitioned schema achieves similar performance to the property table technique while being much simpler to design. Further, if a column-oriented DBMS (a database architected specially for the vertically partitioned case) is used instead of a row-oriented DBMS, another order of magnitude performance improvement is observed, with query times dropping from minutes to several seconds. Encouraged by these results, we describe the architecture of SW-Store, a new DBMS we are actively building that implements these techniques to achieve high performance RDF data management.


title: Perceptions of beauty in Renaissance art

abstract: Summary ::: The Renaissance was a cultural revolution that spread from Florence, in 1400, throughout Italy and into the rest of Europe. Its impetus was the philosophy of Humanism, which strove to resurrect and emulate the literature and art of the ancient Greeks and Romans. Artists had previously been limited to formulaic religious iconography. They now began to reproduce descriptions of classical paintings and copy the antique statues that were being newly rediscovered. ::: ::: The Renaissance artist's perception of beauty was therefore determined by his philosophical environment, his visual experience (the ‘period eye’), the demands of his patrons and by attempts to enhance his professional status in society to equal that of poets and architects. The image of Venus portrayed by Botticelli as the idealization of beauty in Renaissance Florence is significantly different from the Venus portrayed by the German artist, Lucas Cranach. The northern European Venus is much less voluptuous than her Italian counterpart but is still inspired by humanist principals and retains considerable sexuality. Raphael's paintings epitomise the idealization of female beauty of this period but, by his own admission they were rarely based on real models. Often the same facial type was repeated in many different paintings. Indeed Renaissance portrait artists tended to avoid realistic interpretation, emphasizing instead the positive attributes of their subjects, both physical and political. Thus, Bronzino's Portrait of a Young Man not only depicts his subject's idealized appearance but also his scholarship, background and potential. ::: ::: The depiction of beauty in Renaissance art is shown to be more complex than a mere photograph-like representation of sexuality or of a person's physical appearance. Instead, Renaissance art created physically perfect images resulting from scholarly expectation, the artist's ambitions and his developing skills.


title: Security implementation in cloud computing using user behaviour profiling and decoy technology

abstract: Cloud is the cluster of geographically connected network bearing information. Cloud Computing make feasible for multiple users to, share common computing resources, and to access and store their personal and business information. In cloud computing We stored data on server side as well on client side. Application like Dropbox provide service in such a way that data on server and on client machine are synchronous. So implementing security become very necessary on client side. Traditional algorithm failed once key is compromised. User behavior profiling and decoy technology provide an alternate way to secure data on server which is more efficient and secure. There are many algorithm on user behavior profiling and decoy technology but no one address the problem of efficiently delivering the decoy file in such a way the intruder not able to recognize the difference between the genuine and decoy file, once the anonymous behavior of the user identified. We proposed a system in which we going to use the two technology together i.e user behavior profiling and decoy technology and we also proposed the mechanism by which we generate the decoy file in such a way it matches the content of the genuine file up to the accuracy of 98 percent.


Using Imiquimod-Induced Psoriasis-Like Skin as a Model to Measure the Skin Penetration of Anti-Psoriatic Drugs


title: Crosslinking of high‐density polyethylene in the presence of organic peroxides

abstract: The crosslinking efficiency of various commercially available organic peroxides (dicumyl peroxide, O,O-t-butyl O-2-ethylhexylperoxycarbonate, t-butyl peroxybenzoate, t-butyl 3,5,5-trimethylperoxyhexanoate, and t-butyl 2-ethylperoxyhexanoate) was tested on high-density polyethylene (HDPE) in its molten state. The variations of the concentrations of the peroxides versus the crosslinking extent were plotted for these peroxides, and the values were compared. Dicumyl peroxide was found to be the best crosslinking agent for HDPE. The efficiency of the HDPE crosslinking with each peroxy derivative was analyzed on the basis of the behavior of the radicals generated from it. © 2004 Wiley Periodicals, Inc. J Appl Polym Sci 93: 75–81, 2004


Sequence Adversarial Training and Minimum Bayes Risk Decoding for End-to-end Neural Conversation Models


title: Real-time iris tracking with a smart camera

abstract: This paper presents a real-time iris detection procedure for gray intensity images. Typical ::: applications for iris detection utilize template and feature based methods. These methods are ::: generally time and memory intensive and not applicable for all practical real-time embedded ::: realizations. Here, we propose a method that utilizes a simple algorithm that is time-efficient with ::: high detection and low error rates that is implemented in a smart camera. The system used for this ::: research involves a National Instruments smart camera with LabVIEW Real-Time Module. First, the ::: images are analyzed to determine the region of interest (face). The iris location is determined by ::: applying a convolution-based algorithm on the edge image and then using the Hough Transform. ::: The edge-based less complex and less computationally expensive algorithm results in an efficient ::: analysis method. The extracted iris location information is stored in the camera's image buffer, and ::: used to model one specific eye pattern. The location of the iris thus determined is used as a reference ::: to reduce the search region for the iris in the subsequent images. The iris detection algorithm has ::: been applied at different frame rates. The results demonstrate the speed of this algorithm allows the ::: tracking of the iris when the eyes or the subject is moving in front of the camera at reasonable ::: speeds and with limited occlusions.


title: Interactive Visual Steering - Rapid Visual Prototyping of a Common Rail Injection System

abstract: Interactive steering with visualization has been a common goal of the visualization research community for twenty years, but it is rarely ever realized in practice. In this paper we describe a successful realization of a tightly coupled steering loop, integrating new simulation technology and interactive visual analysis in a prototyping environment for automotive industry system design. Due to increasing pressure on car manufacturers to meet new emission regulations, to improve efficiency, and to reduce noise, both simulation and visualization are pushed to their limits. Automotive system components, such as the powertrain system or the injection system have an increasing number of parameters, and new design approaches are required. It is no longer possible to optimize such a system solely based on experience or forward optimization. By coupling interactive visualization with the simulation back-end (computational steering), it is now possible to quickly prototype a new system, starting from a non-optimized initial prototype and the corresponding simulation model. The prototyping continues through the refinement of the simulation model, of the simulation parameters and through trial-and-error attempts to an optimized solution. The ability to early see the first results from a multidimensional simulation space - thousands of simulations are run for a multidimensional variety of input parameters - and to quickly go back into the simulation and request more runs in particular parameter regions of interest significantly improves the prototyping process and provides a deeper understanding of the system behavior. The excellent results which we achieved for the common rail injection system strongly suggest that our approach has a great potential of being generalized to other, similar scenarios.


title: A Note on Auto-tuning GEMM for GPUs

abstract: The development of high performance dense linear algebra (DLA) critically depends on highly optimized BLAS, and especially on the matrix multiplication routine (GEMM). This is especially true for Graphics Processing Units (GPUs), as evidenced by recently published results on DLA for GPUs that rely on highly optimized GEMM. However, the current best GEMM performance, e.g. of up to 375 GFlop/s in single precision and of up to 75 GFlop/s in double precision arithmetic on NVIDIA's GTX 280, is difficult to achieve. The development involves extensive GPU knowledge and even backward engineering to understand some undocumented insides about the architecture that have been of key importance in the development. In this paper, we describe some GPU GEMM auto-tuning optimization techniques that allow us to keep up with changing hardware by rapidly reusing, rather than reinventing, the existing ideas. Auto-tuning, as we show in this paper, is a very practical solution where in addition to getting an easy portability, we can often get substantial speedups even on current GPUs (e.g. up to 27% in certain cases for both single and double precision GEMMs on the GTX 280).


title: Face, Age and Gender Recognition using Local Descriptors

abstract: This thesis focuses on the area of face processing and aims at designing a reliable framework to facilitate face, age, and gender recognition. A Bag-of-Words framework has been optimized for the task of face recognition by evaluating different feature descriptors and different bag-of-words configurations. More specifically, we choose a compact set of features (e.g., descriptors, window locations, window sizes, dictionary sizes, etc.) in order to produce the highest possible rate of accuracy. Experiments on a challenging dataset shows that our framework achieves a better level of accuracy when compared to other popular approaches such as dimension reduction techniques, edge detection operators, and texture and shape feature extractors. The second contribution of this thesis is the proposition of a general framework for age and gender classification. Although the vast majority of the existing solutions focus on a single visual descriptor that often only encodes a certain characteristic of the image regions, this thesis aims at integrating multiple feature types. For this purpose, feature selection is employed to obtain more accurate and robust facial descriptors. Once descriptors have been computed, a compact set of features is chosen, which facilitates facial image processing for age and gender analysis. In addition to this, a new color descriptor (CLR-LBP) is proposed and the results obtained is shown to be comparable to those of other pre-existing color descriptors. The experimental results indicates that our age and gender framework outperforms other proposed methods when examined on two challenging databases, where face objects are present with different expressions and levels of illumination. This achievement demonstrates the effectiveness of our proposed solution and allows us to achieve a higher accuracy over the existing state-of-the-art methods.


title: Ternary CAM Power and Delay Model: Extensions and Uses

abstract: Applications in computer networks often require high throughput access to large data structures for lookup and classification. While advanced algorithms exist to speed these search primitives on network processors and even custom application-specific integrated circuits (ASICs), achieving tight bounds on worst case performance with standard memories often requires a very careful analysis of all possible access patterns. An alternative, and often times more simple solution, is possible if a ternary CAM (TCAM) is used to perform a fully parallel search across the entire data set. Unfortunately, this parallelism means that large portions of the chip are switching during each cycle, causing large amounts of power to be consumed. While researchers at all levels of design (from algorithms to circuits) have begun to explore new ways of managing the power consumption, quantifying design alternatives is difficult due to a lack of available models. In this paper, we examine the structure of a modern TCAM and present a simple, yet accurate, power and delay model. We present techniques to estimate the dynamic power consumption and leakage power of a TCAM structure and validate the model using a combination of industrial TCAM datasheets and prior published works. Such a model is a critical first step in bridging the intellectual divide between circuit-level and algorithm-level optimizations. To demonstrate the utility of our model, we present an extensive analysis of the model by varying various architectural parameters and describe how our model can be easily extended to handle several circuit optimizations in the TCAM structure. In addition, we present a comparative study of SRAM and TCAM energy consumption to directly quantify the many design options which will be very useful for network designers to explore various power management schemes.


Strategies to stimulate Ghana’s economic transformation and diversification


DEVELOPMENT OF HIGH-PERFORMANCE, HIGH-VOLUME CONSUMER MEMS GYROSCOPES


title: Histograms of Optical Flow Orientation and Magnitude and Entropy to Detect Anomalous Events in Videos

abstract: This paper presents an approach for detecting anomalous events in videos with crowds. The main goal is to recognize patterns that might lead to an anomalous event. An anomalous event might be characterized by the deviation from the normal or usual, but not necessarily in an undesirable manner, e.g., an anomalous event might just be different from normal but not a suspicious event from the surveillance point of view. One of the main challenges of detecting such events is the difficulty to create models due to their unpredictability and their dependency on the context of the scene. Based on these challenges, we present a model that uses general concepts, such as orientation, velocity, and entropy to capture anomalies. Using such a type of information, we can define models for different cases and environments. Assuming images captured from a single static camera, we propose a novel spatiotemporal feature descriptor, called histograms of optical flow orientation and magnitude and entropy , based on optical flow information. To determine the normality or abnormality of an event, the proposed model is composed of training and test steps. In the training, we learn the normal patterns. Then, during test, events are described and if they differ significantly from the normal patterns learned, they are considered as anomalous. The experimental results demonstrate that our model can handle different situations and is able to recognize anomalous events with success. We use the well-known UCSD and Subway data sets and introduce a new data set, namely, Badminton.


title: Influence of social media on tourism – especially among students of the University of Debrecen

abstract: Derive from the characteristic, decisions connected with travelling have high risk for the travellers therefore they try to collect more detailed information and thoroughly map decision alternatives in order to decrease uncertainty. Wide spread of the Internet and rapid technological evolution have revolutionized all industries in the World especially tourism. Platform of tourism increasingly get to the Internet nowadays which is vitally important because tourism is an informationbased and information-intensive industry. Thanks to development of the internet tourists have an opportunity to access such information and purchasing opportunities which were available with the help of intermediaries earlier. Providing wide range of possibilities, Web 2.0 fundamentally changed the way of touristsâ€™ information search behaviour and travelling decision making. This article collects some of the most significant new applications (social networking sites, blogs) in tourism â€“ examine them from the two sides of tourism (demand, supply) â€“ which principally based on active participation of users. Furthermore an offline questionnaire was made in order to survey the social media usage of the student (University of Debrecen, Centre for Agricultural and Applied Economic Sciences) during their leisure travel planning process. Although findings of the study reveal that vast majority of students use social networking sites every day, they donâ€™t really use these platforms during their trip planning process. Among students, friends and relatives are the most important and the most trustworthy source of information due to characteristics of sample.


title: Beyond spatial pooling: Fine-grained representation learning in multiple domains

abstract: Object recognition systems have shown great progress over recent years. However, creating object representations that are robust to changes in viewpoint while capturing local visual details continues to be a challenge. In particular, recent convolutional architectures employ spatial pooling to achieve scale and shift invariances, but they are still sensitive to out-of-plane rotations. In this paper, we formulate a probabilistic framework for analyzing the performance of pooling. This framework suggests two directions for improvement. First, we apply multiple scales of filters coupled with different pooling granularities, and second we make use of color as an additional pooling domain, thereby reducing the sensitivity to spatial deformations. We evaluate our algorithm on the object instance recognition task using two independent publicly available RGB-D datasets, and demonstrate significant improvements over the current state-of-the-art. In addition, we present a new dataset for industrial objects to further validate the effectiveness of our approach versus other state-of-the-art approaches for object recognition using RGB-D data.


title: Facial Augmentation With Structural Fat Grafting

abstract: Fat grafting through a blunt cannula has been used by plastic surgeons for altering facial contours for 100 years. Autologous tissue is completely biocompatible and is usually the safest choice for altering facial volume or contours. Fat grafts can be placed in such a fashion that they are long lasting, completely integrated, and natural appearing. Only in the past 20 years have advances in techniques and instrumentation allowed us to obtain predictable results that make fat grafting a viable option for soft tissue augmentation. Our understanding of aging and methods of rejuvenation have developed also. We now approach rejuvenation and adjustment of facial proportion with a better understanding of the need for the restoration or adjustment of facial volume.


title: Mobile commerce integrated with RFID technology in a container depot

abstract: In this paper, we present the findings of a case study on the development of a radio frequency identification (RFID) prototype system that is integrated with mobile commerce (m-commerce) in a container depot. A system architecture capable of integrating mobile commerce and RFID applications is proposed. The system architecture is examined and explained in the context of the case study. The aims of the system are to (i) keep track of the locations of stackers and containers, (ii) provide greater visibility of the operations data, and (iii) improve the control processes. The case study illustrates the benefits and advantages of using an RFID system, particularly its support of m-commerce activities in the container depot, and describes some of the most important problems and issues. Finally, several research issues and directions of RFID applications in container depots are presented and discussed.


title: Frequency Domain Acoustic Echo Suppression Based on Soft Decision

abstract: In this letter, we propose a novel acoustic echo suppression (AES) technique based on soft decision in a frequency domain. The proposed approach provides an efficient and unified framework for such procedures as AES gain computation, AES gain modification using soft decision, and estimation of relevant parameters based on the same statistical model assumption of the near-end and far-end signal instead of the conventional strategies requiring the additional residual echo suppression (RES) step. Performances of the proposed AES algorithm are evaluated by objective and subjective tests under various environments, and better results compared with the conventional AES method are obtained.


title: Feature Selection Using Artificial Neural Networks

abstract: Machine learning is useful for building robust learning models, and it is based on a set of features that identify a state of an object. Unfortunately, some data sets may contain a large number of features making, in some cases, the learning process time consuming and the generalization capability of machine learning poor. To make a data set easy to learn and understand, it is typically recommended to remove the most irrelevant features from the set. However, choosing what data should be kept or eliminated may be performed by complex selection algorithms, and optimal feature selection may require an exhaustive search of all possible subsets of features which is computationally expensive. This paper proposes a simple method to perform feature selection using artificial neural networks. It is shown experimentally that genetic algorithms in combination with artificial neural networks can easily be used to extract those features that are required to produce a desired result. Experimental results show that very few hidden neurons are required for feature selection as artificial neural networks are only used to assess the quality of an individual, which is a chosen subset of features.


title: Minimal Loss Hashing for Compact Binary Codes

abstract: We propose a method for learning similarity-preserving hash functions that map high-dimensional data onto binary codes. The formulation is based on structured prediction with latent variables and a hinge-like loss function. It is efficient to train for large datasets, scales well to large code lengths, and outperforms state-of-the-art methods.


title: Versatile iPad forensic acquisition using the Apple Camera Connection Kit

abstract: The Apple iPad is a popular tablet device presented by Apple in early 2010. The idiosyncracies of this new portable device and the kind of data it may store open new opportunities in the field of computer forensics. Given that its design, both internal and external, is very similar to the iPhone, the current easiest way to obtain a forensic image is to install an SSH server and some tools, dump its internal storage and transfer it to a remote host via wireless networking. This approach may require up to 20 hours. In this paper, we present a novel approach that takes advantage of an undocumented feature so that it is possible to use a cheap iPad accessory, the Camera Connection Kit, to image the disk to an external hard drive attached via USB connection, greatly reducing the required time.


title: Supervised Rank Aggregation for Predicting Influence in Networks

abstract: Much work in Social Network Analysis has focused on the identification of the most important actors in a social network. This has resulted in several measures of influence and authority. While most of such sociometrics (e.g., PageRank) are driven by intuitions based on an actors location in a network, asking for the "most influential" actors in itself is an ill-posed question, unless it is put in context with a specific measurable task. Constructing a predictive task of interest in a given domain provides a mechanism to quantitatively compare different measures of influence. Furthermore, when we know what type of actionable insight to gather, we need not rely on a single network centrality measure. A combination of measures is more likely to capture various aspects of the social network that are predictive and beneficial for the task. Towards this end, we propose an approach to supervised rank aggregation, driven by techniques from Social Choice Theory. We illustrate the effectiveness of this method through experiments on Twitter and citation networks.


title: Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec

abstract: Since the invention of word2vec, the skip-gram model has significantly advanced the research of network embedding, such as the recent emergence of the DeepWalk, LINE, PTE, and node2vec approaches. In this work, we show that all of the aforementioned models with negative sampling can be unified into the matrix factorization framework with closed forms. Our analysis and proofs reveal that: (1) DeepWalk empirically produces a low-rank transformation of a network's normalized Laplacian matrix; (2) LINE, in theory, is a special case of DeepWalk when the size of vertices' context is set to one; (3) As an extension of LINE, PTE can be viewed as the joint factorization of multiple networks» Laplacians; (4) node2vec is factorizing a matrix related to the stationary distribution and transition probability tensor of a 2nd-order random walk. We further provide the theoretical connections between skip-gram based network embedding algorithms and the theory of graph Laplacian. Finally, we present the NetMF method as well as its approximation algorithm for computing network embedding. Our method offers significant improvements over DeepWalk and LINE for conventional network mining tasks. This work lays the theoretical foundation for skip-gram based network embedding methods, leading to a better understanding of latent network representation learning.


title: Composable Deep Reinforcement Learning for Robotic Manipulation

abstract: Model-free deep reinforcement learning has been shown to exhibit good performance in domains ranging from video games to simulated robotic manipulation and locomotion. However, model-free methods are known to perform poorly when the interaction time with the environment is limited, as is the case for most real-world robotic tasks. In this paper, we study how maximum entropy policies trained using soft Q-learning can be applied to real-world robotic manipulation. The application of this method to real-world manipulation is facilitated by two important features of soft Q-learning. First, soft Q-learning can learn multimodal exploration strategies by learning policies represented by expressive energy-based models. Second, we show that policies learned with soft Q-learning can be composed to create new policies, and that the optimality of the resulting policy can be bounded in terms of the divergence between the composed policies. This compositionality provides an especially valuable tool for real-world manipulation, where constructing new policies by composing existing skills can provide a large gain in efficiency over training from scratch. Our experimental evaluation demonstrates that soft Q-learning is substantially more sample efficient than prior model-free deep reinforcement learning methods, and that compositionality can be performed for both simulated and real-world tasks.


title: A Panorama of Artificial and Computational Intelligence in Games

abstract: This paper attempts to give a high-level overview of the field of artificial and computational intelligence (AI/CI) in games, with particular reference to how the different core research areas within this field inform and interact with each other, both actually and potentially. We identify ten main research areas within this field: NPC behavior learning, search and planning, player modeling, games as AI benchmarks, procedural content generation, computational narrative, believable agents, AI-assisted game design, general game artificial intelligence and AI in commercial games. We view and analyze the areas from three key perspectives: 1) the dominant AI method(s) used under each area; 2) the relation of each area with respect to the end (human) user; and 3) the placement of each area within a human–computer (player-game) interaction perspective. In addition, for each of these areas we consider how it could inform or interact with each of the other areas; in those cases where we find that meaningful interaction either exists or is possible, we describe the character of that interaction and provide references to published studies, if any. We believe that this paper improves understanding of the current nature of the game AI/CI research field and the interdependences between its core areas by providing a unifying overview. We also believe that the discussion of potential interactions between research areas provides a pointer to many interesting future research projects and unexplored subfields.


title: Temporal frequency probing for 5D transient analysis of global light transport

abstract: We analyze light propagation in an unknown scene using projectors and cameras that operate at transient timescales. In this new photography regime, the projector emits a spatio-temporal 3D signal and the camera receives a transformed version of it, determined by the set of all light transport paths through the scene and the time delays they induce. The underlying 3D-to-3D transformation encodes scene geometry and global transport in great detail, but individual transport components (e.g., direct reflections, inter-reflections, caustics, etc.) are coupled nontrivially in both space and time. To overcome this complexity, we observe that transient light transport is always separable in the temporal frequency domain. This makes it possible to analyze transient transport one temporal frequency at a time by trivially adapting techniques from conventional projector-to-camera transport. We use this idea in a prototype that offers three never-seen-before abilities: (1) acquiring time-of-flight depth images that are robust to general indirect transport, such as interreflections and caustics; (2) distinguishing between direct views of objects and their mirror reflection; and (3) using a photonic mixer device to capture sharp, evolving wavefronts of "light-in-flight".


title: Artificial intelligence in radiology

abstract: Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced. In this Opinion article, Hosny et al. discuss the application of artificial intelligence to image-based tasks in the field of radiology and consider the advantages and challenges of its clinical implementation.


title: Function Assistant: A Tool for NL Querying of APIs

abstract: In this paper, we describe Function Assistant, a lightweight Python-based toolkit for querying and exploring source code repositories using natural language. The toolkit is designed to help end-users of a target API quickly find information about functions through high-level natural language queries and descriptions. For a given text query and background API, the tool finds candidate functions by performing a translation from the text to known representations in the API using the semantic parsing approach of Richardson and Kuhn (2017). Translations are automatically learned from example text-code pairs in example APIs. The toolkit includes features for building translation pipelines and query engines for arbitrary source code projects. To explore this last feature, we perform new experiments on 27 well-known Python projects hosted on Github.


title: LDSScanner: Exploratory Analysis of Low-Dimensional Structures in High-Dimensional Datasets

abstract: Many approaches for analyzing a high-dimensional dataset assume that the dataset contains specific structures, e.g., clusters in linear subspaces or non-linear manifolds. This yields a trial-and-error process to verify the appropriate model and parameters. This paper contributes an exploratory interface that supports visual identification of low-dimensional structures in a high-dimensional dataset, and facilitates the optimized selection of data models and configurations. Our key idea is to abstract a set of global and local feature descriptors from the neighborhood graph-based representation of the latent low-dimensional structure, such as pairwise geodesic distance (GD) among points and pairwise local tangent space divergence (LTSD) among pointwise local tangent spaces (LTS). We propose a new LTSD-GD view, which is constructed by mapping LTSD and GD to the $x$ axis and $y$ axis using 1D multidimensional scaling, respectively. Unlike traditional dimensionality reduction methods that preserve various kinds of distances among points, the LTSD-GD view presents the distribution of pointwise LTS ( $x$ axis) and the variation of LTS in structures (the combination of $x$ axis and $y$ axis). We design and implement a suite of visual tools for navigating and reasoning about intrinsic structures of a high-dimensional dataset. Three case studies verify the effectiveness of our approach.


title: Policy Networks with Two-Stage Training for Dialogue Systems

abstract: In this paper, we propose to use deep policy networks which are trained with an advantage actor-critic method for statistically optimised dialogue systems. First, we show that, on summary state and action spaces, deep Reinforcement Learning (RL) outperforms Gaussian Processes methods. Summary state and action spaces lead to good performance but require pre-engineering effort, RL knowledge, and domain expertise. In order to remove the need to define such summary spaces, we show that deep RL can also be trained efficiently on the original state and action spaces. Dialogue systems based on partially observable Markov decision processes are known to require many dialogues to train, which makes them unappealing for practical deployment. We show that a deep RL method based on an actor-critic architecture can exploit a small amount of data very efficiently. Indeed, with only a few hundred dialogues collected with a handcrafted policy, the actor-critic deep learner is considerably bootstrapped from a combination of supervised and batch RL. In addition, convergence to an optimal policy is significantly sped up compared to other deep RL methods initialized on the data with batch RL. All experiments are performed on a restaurant domain derived from the Dialogue State Tracking Challenge 2 (DSTC2) dataset.


title: ADMIT: anomaly-based data mining for intrusions

abstract: Security of computer systems is essential to their acceptance and utility. Computer security analysts use intrusion detection systems to assist them in maintaining computer system security. This paper deals with the problem of differentiating between masqueraders and the true user of a computer terminal. Prior efficient solutions are less suited to real time application, often requiring all training data to be labeled, and do not inherently provide an intuitive idea of what the data model means. Our system, called ADMIT, relaxes these constraints, by creating user profiles using semi-incremental techniques. It is a real-time intrusion detection system with host-based data collection and processing. Our method also suggests ideas for dealing with concept drift and affords a detection rate as high as 80.3% and a false positive rate as low as 15.3%.


title: A Novel Classification Framework for Evaluating Individual and Aggregate Diversity in Top-N Recommendations

abstract: The primary goal of a recommender system is to generate high quality user-centred recommendations. However, the traditional evaluation methods and metrics were developed before researchers understood all the factors that increase user satisfaction. This study is an introduction to a novel user and item classification framework. It is proposed that this framework should be used during user-centred evaluation of recommender systems and the need for this framework is justified through experiments. User profiles are constructed and matched against other users’ profiles to formulate neighbourhoods and generate top-N recommendations. The recommendations are evaluated to measure the success of the process. In conjunction with the framework, a new diversity metric is presented and explained. The accuracy, coverage, and diversity of top-N recommendations is illustrated and discussed for groups of users. It is found that in contradiction to common assumptions, not all users suffer as expected from the data sparsity problem. In fact, the group of users that receive the most accurate recommendations do not belong to the least sparse area of the dataset.


Differentially Constrained Mobile Robot Motion Planning in State Lattices


title: Heaviside image edge sharpening

abstract: In this paper, we propose an automatic and efficient method to enhance edge sharpness of images. Starting from an image with blur edges, we improve the edges using transformed Heaviside functions for better visualization. In addition, we provide an efficient method to directly compute the scaling and shifting factors of the transformed Heaviside functions, so that blur edges can be improved accurately. Experimental results show that the proposed method is fast and can get sharper image edges than some recent state-of-the-art edge enhancement methods. We also apply the edge sharpening method to image super-resolution and obtained promising results.


title: Visual SLAM: Why filter?

abstract: While the most accurate solution to off-line structure from motion (SFM) problems is undoubtedly to extract as much correspondence information as possible and perform batch optimisation, sequential methods suitable for live video streams must approximate this to fit within fixed computational bounds. Two quite different approaches to real-time SFM - also called visual SLAM (simultaneous localisation and mapping) - have proven successful, but they sparsify the problem in different ways. Filtering methods marginalise out past poses and summarise the information gained over time with a probability distribution. Keyframe methods retain the optimisation approach of global bundle adjustment, but computationally must select only a small number of past frames to process. In this paper we perform a rigorous analysis of the relative advantages of filtering and sparse bundle adjustment for sequential visual SLAM. In a series of Monte Carlo experiments we investigate the accuracy and cost of visual SLAM. We measure accuracy in terms of entropy reduction as well as root mean square error (RMSE), and analyse the efficiency of bundle adjustment versus filtering using combined cost/accuracy measures. In our analysis, we consider both SLAM using a stereo rig and monocular SLAM as well as various different scenes and motion patterns. For all these scenarios, we conclude that keyframe bundle adjustment outperforms filtering, since it gives the most accuracy per unit of computing time.


title: Non-Photorealistic Rendering and the Science of Art

abstract: I argue that Non-Photorealistic Rendering (NPR) research will play a key role in the scientific understanding of visual art and illustration. NPR can contribute to scientific understanding of two kinds of problems: how do artists create imagery, and how do observers respond to artistic imagery? I sketch out some of the open problems, how NPR can help, and what some possible theories might look like. Additionally, I discuss the thorny problem of how to evaluate NPR research and theories.


title: Joint Modeling of Users' Interests and Mobility Patterns for Point-of-Interest Recommendation

abstract: Point-of-Interest (POI) recommendation has become an important means to help people discover interesting places, especially when users travel out of town. However, extreme sparsity of user-POI matrix creates a severe challenge. To cope with this challenge, we propose a unified probabilistic generative model, Topic-Region Model (TRM), to simultaneously discover the semantic, temporal and spatial patterns of users' check-in activities, and to model their joint effect on users' decision-making for POIs. We conduct extensive experiments to evaluate the performance of our TRM on two real large-scale datasets, and the experimental results clearly demonstrate that TRM outperforms the state-of-art methods.


title: The wisdom of minority: discovering and targeting the right group of workers for crowdsourcing

abstract: Worker reliability is a longstanding issue in crowdsourcing, and the automatic discovery of high quality workers is an important practical problem. Most previous work on this problem mainly focuses on estimating the quality of each individual worker jointly with the true answer of each task. However, in practice, for some tasks, worker quality could be associated with some explicit characteristics of the worker, such as education level, major and age. So the following question arises: how do we automatically discover related worker attributes for a given task, and further utilize the findings to improve data quality? In this paper, we propose a general crowd targeting framework that can automatically discover, for a given task, if any group of workers based on their attributes have higher quality on average; and target such groups, if they exist, for future work on the same task. Our crowd targeting framework is complementary to traditional worker quality estimation approaches. Furthermore, an advantage of our framework is that it is more budget efficient because we are able to target potentially good workers before they actually do the task. Experiments on real datasets show that the accuracy of final prediction can be improved significantly for the same budget (or even less budget in some cases). Our framework can be applied to many real word tasks and can be easily integrated in current crowdsourcing platforms.


An Encoder-decoder Approach to Predicting Causal Relations in Stories


title: Enhanced Naive Bayes Classifier for real-time sentiment analysis with SparkR

abstract: Correct and fast sentiment analysis of continuously generated data such as Twitter message is very important for providing real-time customized service to the users. While Naive Bayes Classifier(NBC) is the most popular classifier employed for sentiment analysis, the existing studies on it have been based on single server environment. Consequently, they are not adequate for handling real-time stream data. In this paper, thus, we propose a scheme adopting the Laplace Smoothing technique with Binarized NBC for enhancing the accuracy, and employing SparkR for speed-up via distributed and parallel processing. Computer simulation with Sentiment140 reveals that the proposed approach consistently allows higher accuracy than the existing schemes. It also identifies that the SparkR environment allows faster training than R.


Personalized POI Recommendation on Location-Based Social Networks Disseration Prospectus


title: Deep sketch feature for cross-domain image retrieval

abstract: Deep learning has been proven to be very effective for various image recognition tasks, e.g., image classification, semantic segmentation, image retrieval, shape classification, etc. However, existing works on deep learning for image recognition mainly focus on either natural image data or binary shape data. In this paper, we show that deep convolutional neural networks (DCNN) is also suitable for cross-domain image recognition, i.e., using sketch as query to retrieve natural images in a large dataset. To solve this kind of cross-domain problem, we propose to train CNN jointly using image data and sketch data in a novel way. The learned deep feature is effective for cross-domain image retrieval - using simple Euclidean distance on the learned feature can significantly outperform the previous state-of-the-arts. In addition, we find that pre-training and a feasible data-argumentation for DCNN can largely surpass human-level performance in the standard sketch classification benchmark.


title: FANDA: A Novel Approach to Perform Follow-up Query Analysis

abstract: Recent work on Natural Language Interfaces to Databases (NLIDB) has attracted considerable attention. NLIDB allow users to search databases using natural language instead of SQL-like query languages. While saving the users from having to learn query languages, multi-turn interaction with NLIDB usually involves multiple queries where contextual information is vital to understand the users’ query intents. In this paper, we address a typical contextual understanding problem, termed as follow-up query analysis. In spite of its ubiquity, follow-up query analysis has not been well studied due to two primary obstacles: the multifarious nature of follow-up query scenarios and the lack of high-quality datasets. Our work summarizes typical follow-up query scenarios and provides a new FollowUp dataset with 1000 query triples on 120 tables. Moreover, we propose a novel approach FANDA, which takes into account the structures of queries and employs a ranking model with weakly supervised max-margin learning. The experimental results on FollowUp demonstrate the superiority of FANDA over multiple baselines across multiple metrics.


title: A Gaussian Mixture MRF for Model-Based Iterative Reconstruction With Applications to Low-Dose X-Ray CT

abstract: Markov random fields (MRFs) have been widely used as prior models in various inverse problems such as tomographic reconstruction. While MRFs provide a simple and often effective way to model the spatial dependencies in images, they suffer from the fact that parameter estimation is difficult. In practice, this means that MRFs typically have very simple structure that cannot completely capture the subtle characteristics of complex images. In this paper, we present a novel Gaussian mixture Markov random field model (GM-MRF) that can be used as a very expressive prior model for inverse problems such as denoising and reconstruction. The GM-MRF forms a global image model by merging together individual Gaussian-mixture models (GMMs) for image patches. In addition, we present a novel analytical framework for computing MAP estimates using the GM-MRF prior model through the construction of surrogate functions that result in a sequence of quadratic optimizations. We also introduce a simple but effective method to adjust the GM-MRF so as to control the sharpness in low- and high-contrast regions of the reconstruction separately. We demonstrate the value of the model with experiments including image denoising and low-dose CT reconstruction.


title: Approximation of Points on Low-Dimensional Manifolds Via Random Linear Projections

abstract: This paper considers the approximate reconstruction of points x ∈RD which are close to a given compact d-dimensional submanifold M of RD using a small number of linear measurements of x. In particular, it is shown that a number of measurements of x, which is independent of the extrinsic dimension D suffices for a highly accurate reconstruction of x. Furthermore, it is also proved that all vectors x which are sufficiently close to M can be reconstructed with uniform approximation guarantees when the number of linear measurements of x depends logarithmically on D. Finally, the proofs of these facts are constructive: a practical algorithm for manifold-based signal recovery is presented in the process of proving the two main results mentioned above.


Sketching Interfaces: Toward More Human Interface Design


title: Adaptive reservoir computing through evolution and learning

abstract: The development of real-world, fully autonomous agents would require mechanisms that would offer generalization capabilities from experience, suitable for a large range of machine learning tasks, like those from the areas of supervised and reinforcement learning. Such capacities could be offered by parametric function approximators that could either model the environment or the agent's policy. To promote autonomy, these structures should be adapted to the problem at hand with no or little human expert input. Towards this goal, we propose an adaptive function approximator method for developing appropriate neural networks in the form of reservoir computing systems through evolution and learning. Our neuro-evolution of augmenting reservoirs approach comprises of several ideas, successful on their own, in an effort to develop an algorithm that could handle a large range of problems, more efficiently. In particular, we use the neuro-evolution of augmented topologies algorithm as a meta-search method for the adaptation of echo state networks for handling problems to be encountered by autonomous entities. We test our approach on several test-beds from the realms of time series prediction and reinforcement learning. We compare our methodology against similar state-of-the-art algorithms with promising results.


title: Class Specific 3D Object Shape Priors Using Surface Normals

abstract: Dense 3D reconstruction of real world objects containing textureless, reflective and specular parts is a challenging task. Using general smoothness priors such as surface area regularization can lead to defects in the form of disconnected parts or unwanted indentations. We argue that this problem can be solved by exploiting the object class specific local surface orientations, e.g. a car is always close to horizontal in the roof area. Therefore, we formulate an object class specific shape prior in the form of spatially varying anisotropic smoothness terms. The parameters of the shape prior are extracted from training data. We detail how our shape prior formulation directly fits into recently proposed volumetric multi-label reconstruction approaches. This allows a segmentation between the object and its supporting ground. In our experimental evaluation we show reconstructions using our trained shape prior on several challenging datasets.


title: Neural Network Classification of White Blood Cell using Microscopic Images

abstract: With the technological advances in medical field, the need for faster and more accurate analysis tools becomes essential for better patients’ diagnosis. In this work, the image recognition problem of white blood cells (WBC) is investigated. Five types of white blood cells are classified using a feed forward back propagation neural network. After segmentation of blood cells that are obtained from microscopic images, the most 16 significant features of these cells are fed as inputs to the neural network. Half of the 100 of the WBC sub-images that are found after segmentation are used to train the neural network, while the other half is used for test. The results found are promising with classification accuracy being 96%.


title: Classification of ECG signals based on 1D convolution neural network

abstract: Recently, with the obvious increasing number of cardiovascular disease, the automatic classification research of Electrocardiogram signals (ECG) has been playing a significantly important part in the clinical diagnosis of cardiovascular disease. In this paper, a 1D convolution neural network (CNN) based method is proposed to classify ECG signals. The proposed CNN model consists of five layers in addition to the input layer and the output layer, i.e., two convolution layers, two down sampling layers and one full connection layer, extracting the effective features from the original data and classifying the features automatically. This model realizes the classification of 5 typical kinds of arrhythmia signals, i.e., normal, left bundle branch block, right bundle branch block, atrial premature contraction and ventricular premature contraction. The experimental results on the public MIT-BIH arrhythmia database show that the proposed method achieves a promising classification accuracy of 97.5%, significantly outperforming several typical ECG classification methods.


title: Transforming growth factor beta-induced failure of resistance to infection with blood-stage Plasmodium chabaudi in mice.

abstract: The role of transforming growth factor beta (TGF-beta) in infection with Plasmodium chabaudi was investigated with resistant and susceptible mouse models. C57BL/10 mice produced gamma interferon (IFN-gamma) and nitric oxide (NO) shortly after infection and cleared the parasite spontaneously. In contrast, BALB/c mice showed a transient enhancement of TGF-beta production, followed by a relative lack of IFN-gamma and NO production, and succumbed to the infection. However, there was no correlation between levels of serum TGF-beta and splenic TGF-beta mRNA in both mouse strains before and after infection. Administration of recombinant TGF-beta (rTGF-beta) rendered resistant mice susceptible because of suppression of subsequent production of IFN-gamma and NO. Administration of anti-TGF-beta antibody to the infected BALB/c mice resulted in remarkable increases in serum IFN-gamma and NO, and the mice resisted the infection. Splenic CD4(+) T and CD11b+ cells of C57BL/10 mice were significantly activated after infection, but this was completely abrogated by administration of rTGF-beta. These results suggested that, in the P. chabaudi-susceptible but not resistant mice, production of TGF-beta was promoted, and subsequent failure of IFN-gamma- and NO-dependent resistance to the parasite was induced. This study is the first to indicate that TGF-beta production was the key event in failure of resistance to mouse malaria.


title: High boost converter using voltage multiplier

abstract: With the increasing demand for renewable energy, distributed power included in fuel cells have been studied and developed as a future energy source. For this system, a power conversion circuit is necessary to interface the generated power to the utility. In many cases, a high step-up DC/DC converter is needed to boost low input voltage to high voltage output. Conventional methods using cascade DC/DC converters cause extra complexity and higher cost. The conventional topologies to get high output voltage use flyback DC/DC converters. They have the leakage components that cause stress and loss of energy that results in low efficiency. This paper presents a high boost converter with a voltage multiplier and a coupled inductor. The secondary voltage of the coupled inductor is rectified using a voltage multiplier. High boost voltage is obtained with low duty cycle. Theoretical analysis and experimental results verify the proposed solutions using a 300 W prototype.


title: Labeled Homomorphic Encryption - Scalable and Privacy-Preserving Processing of Outsourced Data.

abstract: In privacy-preserving processing of outsourced data a Cloud server stores data provided by one or multiple data providers and then is asked to compute several functions over it. We propose an efficient methodology that solves this problem with the guarantee that a honest-but-curious Cloud learns no information about the data and the receiver learns nothing more than the results. Our main contribution is the proposal and efficient instantiation of a new cryptographic primitive called Labeled Homomorphic Encryption (labHE). The fundamental insight underlying this new primitive is that homomorphic computation can be significantly accelerated whenever the program that is being computed over the encrypted data is known to the decrypter and is not secret—previous approaches to homomorphic encryption do not allow for such a trade-off. Our realization and implementation of labHE targets computations that can be described by degree-two multivariate polynomials. As an application, we consider privacy preserving Genetic Association Studies (GAS), which require computing risk estimates from features in the human genome. Our approach allows performing GAS efficiently, non interactively and without compromising neither the privacy of patients nor potential intellectual property of test laboratories.


title: Solid modeling of polyhedral objects by Layered Depth-Normal Images on the GPU

abstract: We introduce a novel solid modeling framework taking advantage of the architecture of parallel computing on modern graphics hardware. Solid models in this framework are represented by an extension of the ray representation - Layered Depth-Normal Images (LDNI), which inherits the good properties of Boolean simplicity, localization and domain decoupling. The defect of ray representation in computational intensity has been overcome by the newly developed parallel algorithms running on the graphics hardware equipped with Graphics Processing Unit (GPU). The LDNI for a solid model whose boundary is represented by a closed polygonal mesh can be generated efficiently with the help of hardware accelerated sampling. The parallel algorithm for computing Boolean operations on two LDNI solids runs well on modern graphics hardware. A parallel algorithm is also introduced in this paper to convert LDNI solids to sharp-feature preserved polygonal mesh surfaces, which can be used in downstream applications (e.g., finite element analysis). Different from those GPU-based techniques for rendering CSG-tree of solid models Hable and Rossignac (2007, 2005) [1,2], we compute and store the shape of objects in solid modeling completely on graphics hardware. This greatly eliminates the communication bottleneck between the graphics memory and the main memory.


title: Big Data Deep Learning: Challenges and Perspectives

abstract: Deep learning is currently an extremely active research area in machine learning and pattern recognition society. It has gained huge successes in a broad area of applications such as speech recognition, computer vision, and natural language processing. With the sheer size of data available today, big data brings big opportunities and transformative potential for various sectors; on the other hand, it also presents unprecedented challenges to harnessing data and information. As the data keeps getting bigger, deep learning is coming to play a key role in providing big data predictive analytics solutions. In this paper, we provide a brief overview of deep learning, and highlight current research efforts and the challenges to big data, as well as the future trends.


Predicting Quitting in Students Playing a Learning Game


title: Consumer reviews and the creation of booking transaction value: Lessons from the hotel industry

abstract: Abstract In recent years, much has been said about online consumer-generated feedback. Concern typically emerges regarding consumer decision-making as well as the preservation of an organization's image. Additionally, a company's financial performance can be affected by customer online ratings. The present study explores the impact of a hotel's rating and number of reviews on the value generated through online transactions. Through collaboration with consulting company Travel Click, the research team gathered a sample of 178 hotels representing various companies and brands within the United States. Research results demonstrate that TripAdvisor ratings as well as the number of reviews had positive relationship with the average size of each online booking transaction. The paper concludes with theoretical and practical implications.


title: A 1.8V 74mW UHF RFID reader receiver with 18.5dBm IIP3 and −77dBm sensitivity in 0.18µm CMOS

abstract: A UHF RFID reader receiver is implemented in 0.18µm CMOS. The direct-conversion receiver consists of an LNA, passive mixers, baseband PGAs and LPFs. As high as 18.5dBm measured IIP3 of the RF front-end is achieved by using passive mixers driven by 25% duty cycle square wave LO. The receiver has a sensitivity of −77dBm in the normal mode and −87dBm in the LBT mode. The total power dissipation in the normal mode is 74mW from 1.8V power supply.


title: Quantitative Comparison of Xen and KVM

abstract: initial results from and quantitative analysis of two leading open source hypervisors, Xen and KVM. This study focuses on the overall performance, performance isolation, and scalability of virtual machines running on these hypervisors. Our comparison was carried out using a benchmark suite that we developed to make the results easily repeatable. Our goals are to understand how the different architectural decisions taken by different hypervisor developers affect the resulting hypervisors, to help hypervisor developers realize areas of improvement for their hypervisors, and to help users make informed decisions about their choice of hypervisor.


title: Prediction and ranking algorithms for event-based network data

abstract: Event-based network data consists of sets of events over time, each of which may involve multiple entities. Examples include email traffic, telephone calls, and research publications (interpreted as co-authorship events). Traditional network analysis techniques, such as social network models, often aggregate the relational information from each event into a single static network. In contrast, in this paper we focus on the temporal nature of such data. In particular, we look at the problems of temporal link prediction and node ranking, and describe new methods that illustrate opportunities for data mining and machine learning techniques in this context. Experimental results are discussed for a large set of co-authorship events measured over multiple years, and a large corporate email data set spanning 21 months.


title: LAVEA: latency-aware video analytics on edge computing platform

abstract: Along the trend pushing computation from the network core to the edge where the most of data are generated, edge computing has shown its potential in reducing response time, lowering bandwidth usage, improving energy efficiency and so on. At the same time, low-latency video analytics is becoming more and more important for applications in public safety, counter-terrorism, self-driving cars, VR/AR, etc. As those tasks are either computation intensive or bandwidth hungry, edge computing fits in well here with its ability to flexibly utilize computation and bandwidth from and between each layer. In this paper, we present LAVEA, a system built on top of an edge computing platform, which offloads computation between clients and edge nodes, collaborates nearby edge nodes, to provide low-latency video analytics at places closer to the users. We have utilized an edge-first design and formulated an optimization problem for offloading task selection and prioritized offloading requests received at the edge node to minimize the response time. In case of a saturating workload on the front edge node, we have designed and compared various task placement schemes that are tailed for inter-edge collaboration. We have implemented and evaluated our system. Our results reveal that the client-edge configuration has a speedup ranging from 1.3x to 4x (1.2x to 1.7x) against running in local (client-cloud configuration). The proposed shortest scheduling latency first scheme outputs the best overall task placement performance for inter-edge collaboration.


title: Correlates and Predictors of Health-Related Quality of Life among Patients with COPD: An Integrative Review

abstract: Background: Health-related quality of life is an important outcome variable with COPD patients. Impaired health-related quality of life among COPD patients is closely related to various factors. There are many factors that predict the HRQoL and the correlation with other important variables. Predictors of health-related quality of life were debatable in the literature. Aim: This review aimed to describe the correlation with HRQoL and to explore the predictors of HRQoL among patients with COPD. Methods: An integrative review method was conducted. A search protocol was conducted using EBSCO, Pubmed, CINAHL, Ovid, ProQuest, Cochrane, and Google Scholar databases up to October 1, 2019. Results: The review results out of 30 articles that discuss the predictors of HRQoL and 22 articles that discuss the HRQoL correlations with other important variables. The HRQoL predictors are classified according to sociodemographic factors and clinical factors, and also, the correlation and prediction of HRQoL with anxiety, fatigue, and uncertainty. The results of this review showed controversies in predictors between the studies. The most retrieved predictors were age, educational level, comorbidities, socioeconomic status, FEV1% predicted, GOLD stages, and the number of hospitalizations. The variables of anxiety and fatigue were strongly correlated and predicted HRQoL. While uncertainty was not reported as a significant HRQoL predictor. Conclusion: This review provides support about reliable predictors than unreliable one although some predictors still need further research in different races and cultures. Future research is needed to explore whether uncertainty can predict HRQoL among patients with COPD.


title: THE COPA MODEL: A Comprehensive Framework Designed to Promote Quality Care and Competence for Patient Safety

abstract: Abstract Patient safety and quality care are issues of major concern for nursing and all health care professions. Initiatives driven by these concerns have been undertaken during the past decade by organizations and agencies at the local, state, and national levels. One comprehensive framework used by many schools and agencies is Lenburg's Competency Outcomes and Performance Assessment Model (COPA). This article explores the basic concepts and related principles that are fundamental in refocusing the curriculum to promote practice-based competence. The framework emphasizes eight core practice competencies; competency-based outcomes; practice-driven learning; and competency performance examinations. The article also highlights Klein's doctoral research, which compares the effects on teaching and learning in a sample of diverse nursing programs, some of which use and some that do not use the model. Key findings are summarized with recommendations for further study. The COPA Model also is briefly compared to...


title: High-Resolution Remote Sensing Data Classification over Urban Areas Using Random Forest Ensemble and Fully Connected Conditional Random Field

abstract: As an intermediate step between raw remote sensing data and digital maps, remote sensing data classification has been a challenging and long-standing problem in the remote sensing research community. In this work, an automated and effective supervised classification framework is presented for classifying high-resolution remote sensing data. Specifically, the presented method proceeds in three main stages: feature extraction, classification, and classified result refinement. In the feature extraction stage, both multispectral images and 3D geometry data are used, which utilizes the complementary information from multisource data. In the classification stage, to tackle the problems associated with too many training samples and take full advantage of the information in the large-scale dataset, a random forest (RF) ensemble learning strategy is proposed by combining several RF classifiers together. Finally, an improved fully connected conditional random field (FCCRF) graph model is employed to derive the contextual information to refine the classification results. Experiments on the ISPRS Semantic Labeling Contest dataset show that the presented 3-stage method achieves 86.9% overall accuracy, which is a new state-of-the-art non-CNN (convolutional neural networks)-based classification method.


title: A New Image Steganography Algorithm Based on MLSB Method with Random Pixels Selection

abstract: In recent years, the rapid growth of information technology and digital communication has become very important to secure information transmission between the sender and receiver. Therefore, steganography introduces strongly to hide information and to communicate a secret data in an appropriate multimedia carrier, e.g., image, audio and video files. In this paper, a new algorithm for image steganography has been proposed to hide a large amount of secret data presented by secret color image. This algorithm is based on different size image segmentations (DSIS) and modified least significant bits (MLSB), where the DSIS algorithm has been applied to embed a secret image randomly instead of sequentially; this approach has been applied before embedding process. The number of bit to be replaced at each byte is non uniform, it bases on byte characteristics by constructing an effective hypothesis. The simulation results justify that the proposed approach is employed efficiently and satisfied high imperceptible with high payload capacity reached to four bits per byte.


title: Brain activity related to working memory for temporal order and object information

abstract: Abstract Maintaining items in an appropriate sequence is important for many daily activities; however, remarkably little is known about the neural basis of human temporal working memory. Prior work suggests that the prefrontal cortex (PFC) and medial temporal lobe (MTL), including the hippocampus, play a role in representing information about temporal order. The involvement of these areas in successful temporal working memory, however, is less clear. Additionally, it is unknown whether regions in the PFC and MTL support temporal working memory across different timescales, or at coarse or fine levels of temporal detail. To address these questions, participants were scanned while completing 3 working memory task conditions (Group, Position and Item) that were matched in terms of difficulty and the number of items to be actively maintained. Group and Position trials probed temporal working memory processes, requiring the maintenance of hierarchically organized coarse and fine temporal information, respectively. To isolate activation related to temporal working memory, Group and Position trials were contrasted against Item trials, which required detailed working memory maintenance of visual objects. Results revealed that working memory encoding and maintenance of temporal information relative to visual information was associated with increased activation in dorsolateral PFC (DLPFC), and perirhinal cortex (PRC). In contrast, maintenance of visual details relative to temporal information was characterized by greater activation of parahippocampal cortex (PHC), medial and anterior PFC, and retrosplenial cortex. In the hippocampus, a dissociation along the longitudinal axis was observed such that the anterior hippocampus was more active for working memory encoding and maintenance of visual detail information relative to temporal information, whereas the posterior hippocampus displayed the opposite effect. Posterior parietal cortex was the only region to show sensitivity to temporal working memory across timescales, and was particularly involved in the encoding and maintenance of fine temporal information relative to maintenance of temporal information at more coarse timescales. Collectively, these results highlight the involvement of PFC and MTL in temporal working memory processes, and suggest a dissociation in the type of working memory information represented along the longitudinal axis of the hippocampus.


title: Deep learning based FACS Action Unit occurrence and intensity estimation

abstract: Ground truth annotation of the occurrence and intensity of FACS Action Unit (AU) activation requires great amount of attention. The efforts towards achieving a common platform for AU evaluation have been addressed in the FG 2015 Facial Expression Recognition and Analysis challenge (FERA 2015). Participants are invited to estimate AU occurrence and intensity on a common benchmark dataset. Conventional approaches towards achieving automated methods are to train multiclass classifiers or to use regression models. In this paper, we propose a novel application of a deep convolutional neural network (CNN) to recognize AUs as part of FERA 2015 challenge. The 7 layer network is composed of 3 convolutional layers and a max-pooling layer. The final fully connected layers provide the classification output. For the selected tasks of the challenge, we have trained two different networks for the two different datasets, where one focuses on the AU occurrences and the other on both occurrences and intensities of the AUs. The occurrence and intensity of AU activation are estimated using specific neuron activations of the output layer. This way, we are able to create a single network architecture that could simultaneously be trained to produce binary and continuous classification output.


title: A Semantic Web Primer

abstract: The development of the Semantic Web, with machine-readable content, has the potential to revolutionize the World Wide Web and its uses. A Semantic Web Primer provides an introduction and guide to this continuously evolving field, describing its key ideas, languages, and technologies. Suitable for use as a textbook or for independent study by professionals, it concentrates on undergraduate-level fundamental concepts and techniques that will enable readers to proceed with building applications on their own and includes exercises, project descriptions, and annotated references to relevant online materials.The third edition of this widely used text has been thoroughly updated, with significant new material that reflects a rapidly developing field. Treatment of the different languages (OWL2, rules) expands the coverage of RDF and OWL, defining the data model independently of XML and including coverage of N3/Turtle and RDFa. A chapter is devoted to OWL2, the new W3C standard. This edition also features additional coverage of the query language SPARQL, the rule language RIF and the possibility of interaction between rules and ontology languages and applications. The chapter on Semantic Web applications reflects the rapid developments of the past few years. A new chapter offers ideas for term projects. Additional material, including updates on the technological trends and research directions, can be found at http://www.semanticwebprimer.org.


title: Helminths of the Tsushima Leopard Cat (Felis bengalensis euptilura)

abstract: Three Tsushima leopard cats (Felis bengalensis euptilura) in Japan were collected as road kills. Three species of trematodes (Pharyngostomum cordatum, Paragonimus sp. and Dicrocoeliidae gn. sp.), one species of cestode (Spirometra erinacei) and nine species of nematodes (Arthrostoma hunanensis, Uncinaria felidis, Uncinaria sp., Ancylostoma tubaeforme, Molineus springsmithi, Toxocara cati, Capillaria aerophila, Capillaria feliscati, and Capillaria sp.) were found. Among these helminths, Arthrostoma hunanensis, Uncinaria spp., and Molineus springsmithi have not been reported previously in Japanese domestic cats.


title: Hybrid envelope tracking for efficiency enhancement in concurrent dual-band PAs

abstract: A hybrid envelope tracking scheme is proposed for concurrent dual-band power amplifiers (PAs). Ideal power supply sweeping measurement shows this scheme can improve the efficiency of dual-band PAs effectively. The feasibility of this scheme is validated using baseband and analog co-simulation with real large signal circuit models. © 2012 Wiley Periodicals, Inc. Microwave Opt Technol Lett 54:662–664, 2012; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.26667


title: Prediction of emerging technologies based on analysis of the US patent citation network

abstract: The network of patents connected by citations is an evolving graph, which provides a representation of the innovation process. A patent citing another implies that the cited patent reflects a piece of previously existing knowledge that the citing patent builds upon. A methodology presented here (1) identifies actual clusters of patents: i.e., tech- nological branches, and (2) gives predictions about the temporal changes of the structure of the clusters. A predictor, called the citation vector, is defined for characterizing techno- logical development to show how a patent cited by other patents belongs to various industrial fields. The clustering technique adopted is able to detect the new emerging recombinations, and predicts emerging new technology clusters. The predictive ability of our new method is illustrated on the example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of patents is determined based on citation data up to 1991, which