title: Segmentation of building facades using procedural shape priors

abstract: In this paper we propose a novel approach to the perceptual interpretation of building facades that combines shape grammars, supervised classification and random walks. Procedural modeling is used to model the geometric and the photometric variation of buildings. This is fused with visual classification techniques (randomized forests) that provide a crude probabilistic interpretation of the observation space in order to measure the appropriateness of a procedural generation with respect to the image. A random exploration of the grammar space is used to optimize the sequence of derivation rules towards a semantico-geometric interpretation of the observations. Experiments conducted on complex architecture facades with ground truth validate the approach.


title: Rotman lens with Ridge Gap Waveguide technology for millimeter wave applications

abstract: In this paper, a Rotman lens with Ridge Gap Waveguide technology (RGW) is proposed as an alternative for beam scanning in millimeter wave applications. To facilitate the design and measurements, a first prototype at 38 GHz will be designed and built; afterwards a final device at 60 GHz will be designed.


Past and Future Use of Wild Relatives in Crop Breeding


RUNNING HEAD : SEXUAL INTEREST IN PREPUBESCENT CHILDREN 1 How common is males ’ self-reported sexual interest in prepubescent children ?


title: A Single-Chip Dual-Band 22–29-GHz/77–81-GHz BiCMOS Transceiver for Automotive Radars

abstract: A clear trend in wireless applications during the last decade has been the push towards higher integration, and multi-mode and multi-band operation, in order to enable low-cost high-functionality consumer devices. As the deployment of silicon-based MMW technology becomes widespread, similar trends may be expected in the MMW space


title: Informant Discrepancies in the Assessment of Childhood Psychopathology: A Critical Review, Theoretical Framework, and Recommendations for Further Study.

abstract: This work was supported, in part, by National Institute of Mental Health Grant MH67540 (awarded to Andres De Los Reyes). This work was also supported by William T. Grant Foundation Grant 98-1872-98, National Institute of Mental Health Grant MH59029, and grants from the Leon Lowenstein Foundation (awarded to Alan E. Kazdin).


title: ORIGAMI - Folded Plate Structures, Architecture

abstract: This research proposes new methods to generate rapidly complex folded plate structures that can be built with cross laminated timber panels. Composition and dimensions of these panels as well as the possibility to mill them by Computer Numerically Controlled machines, show a great potential for surface structures. The aim of this research is to reveal this potential in the domain of folded plate structures. An interdisciplinary team investigates architectural, structural and mathematical aspects of folded plate structures built from cross laminated timber panels. The main concern of the architectural part is the form finding process witch is inspired by Origami, the Japanese art of paper folding. Based on a simple technique, Origami gives birth to an astonishing formal richness and variability. Complex geometries are generated in an economic way and this research aims at transposing these principles to construction with timber panels.


title: Face Detection and Recognition Using Viola-Jones with PCA-LDA and Square Euclidean Distance

abstract: In this paper, an automatic face recognition system is proposed based on appearance-based features that focus on the entire face image rather than local facial features. The first step in face recognition system is face detection. Viola-Jones face detection method that capable of processing images extremely while achieving high detection rates is used. This method has the most impact in the 2000’s and known as the first object detection framework to provide relevant object detection that can run in real time. Feature extraction and dimension reduction method will be applied after face detection. Principal Component Analysis (PCA) method is widely used in pattern recognition. Linear Discriminant Analysis (LDA) method that used to overcome drawback the PCA has been successfully applied to face recognition. It is achieved by projecting the image onto the Eigenface space by PCA after that implementing pure LDA over it. Square Euclidean Distance (SED) is used. The distance between two images is a major concern in pattern recognition. The distance between the vectors of two images leads to image similarity. The proposed method is tested on three databases (MUCT, Face94, and Grimace). Different number of training and testing images are used to evaluate the system performance and it show that increasing the number of training images will increase the recognition rate.


title: Acoustic hole filling for sparse enrollment data using a cohort universal corpus for speaker recognition

abstract: In this study, the problem of sparse enrollment data for in-set versus out-of-set speaker recognition is addressed. The challenge here is that both the training speaker data (5 s) and test material (2 ∼ 6 s) is of limited test duration. The limited enrollment data result in a sparse acoustic model space for the desired speaker model. The focus of this study is on filling these acoustic holes by harvesting neighbor speaker information to leverage overall system performance. Acoustically similar speakers are selected from a separate available corpus via three different methods for speaker similarity measurement. The selected data from these similar acoustic speakers are exploited to fill the lack of phone coverage caused by the original sparse enrollment data. The proposed speaker modeling process mimics the naturally distributed acoustic space for conversational speech. The Gaussian mixture model (GMM) tagging process allows simulated natural conversation speech to be included for in-set speaker modeling, ...


title: Agile for Millennials: A Comparative Study

abstract: Context: Comparative study of software development methodologies in millenials high school students. Objective: This paper compares the performance and satisfaction of both students and teachers in using two development strategies in a K-12 Computer Science teaching practice. Method: This study includes an experiment, administered in a laboratory controlled setting to measure students' performances. Results: Scrum produces less usable software with better working features compared to Waterfall; it is also more appreciated by students. Teachers are more experienced with Waterfall, which also offers more control on the development process. Conclusions: From an educational perspective, the two methods are equivalent. Since we noted an overall positive effect on the learning environment, we recommend that at least one method be taught in practice.


title: Determination of the high temperature elastic properties and diffraction elastic constants of Ni-base superalloys

abstract: Abstract The diffraction elastic constants and phase specific elastic constants of two Ni-base Superalloys, Inconel 718 (as-quenched and aged) and Udimet 720LI (aged) have been determined as a function of temperature by means of in-situ loading using time-of-flight neutron diffraction. Experiments were carried out between room temperature and 750 °C in order to support future in-situ residual stress analyses during ageing treatments. In the case of Udimet 720LI, Rietveld analysis of the diffraction data revealed very similar elastic constants for γ and γ′ suggesting a simplified single-phase model can be used for the analysis of the diffraction data. As one would expect, the Young's moduli fall with increasing temperature for Inconel 718 from 220 GPa at room temperature to 140 GPa at 700 °C with a knee in the curve at around 500 °C. The Udimet 720 is around 15–20 GPa stiffer over the temperature range studied and the individual peaks fall in a similar manner. In addition in all cases the diffraction elastic constants (DECs) of the (311), (220), (200) and (111) reflections varied linearly according to the crystallographic anisotropy factor as expected for all temperatures with the degree of anisotropy increasing with increasing temperature.


title: Emotion expression in human punishment behavior

abstract: Evolutionary theory reveals that punishment is effective in promoting cooperation and maintaining social norms. Although it is accepted that emotions are connected to punishment decisions, there remains substantial debate over why humans use costly punishment. Here we show experimentally that constraints on emotion expression can increase the use of costly punishment. We report data from Ultimatum Games11, where a proposer offers a division of a sum of money and a responder decides whether to accept the split, or reject and leave both players with nothing. Compared to the treatment where expressing emotions directly to proposers is prohibited, rejection of unfair offers is significantly less frequent when responders can convey their feelings to the proposer concurrently with their decisions. These data support the view that costly punishment might itself be used to express negative emotions, and suggest that future studies will benefit by recognizing that human demand for emotion expression can have significant behavioral consequences in social environments including families, courts, companies and markets.


Generic ontology based User Model : GenOUM


title: Simplifying Neural Networks by Soft Weight-Sharing

abstract: One way of simplifying neural networks so they generalize better is to add an extra term to the error function that will penalize complexity. Simple versions of this approach include penalizing the sum of the squares of the weights or penalizing the number of nonzero weights. We propose a more complicated penalty term in which the distribution of weight values is modeled as a mixture of multiple gaussians. A set of weights is simple if the weights have high probability density under the mixture model. This can be achieved by clustering the weights into subsets with the weights in each cluster having very similar values. Since we do not know the appropriate means or variances of the clusters in advance, we allow the parameters of the mixture model to adapt at the same time as the network learns. Simulations on two different problems demonstrate that this complexity term is more effective than previous complexity terms.


title: DMTO: a realistic ontology for standard diabetes mellitus treatment

abstract: BACKGROUND ::: Treatment of type 2 diabetes mellitus (T2DM) is a complex problem. A clinical decision support system (CDSS) based on massive and distributed electronic health record data can facilitate the automation of this process and enhance its accuracy. The most important component of any CDSS is its knowledge base. This knowledge base can be formulated using ontologies. The formal description logic of ontology supports the inference of hidden knowledge. Building a complete, coherent, consistent, interoperable, and sharable ontology is a challenge. ::: ::: ::: RESULTS ::: This paper introduces the first version of the newly constructed Diabetes Mellitus Treatment Ontology (DMTO) as a basis for shared-semantics, domain-specific, standard, machine-readable, and interoperable knowledge relevant to T2DM treatment. It is a comprehensive ontology and provides the highest coverage and the most complete picture of coded knowledge about T2DM patients' current conditions, previous profiles, and T2DM-related aspects, including complications, symptoms, lab tests, interactions, treatment plan (TP) frameworks, and glucose-related diseases and medications. It adheres to the design principles recommended by the Open Biomedical Ontologies Foundry and is based on ontological realism that follows the principles of the Basic Formal Ontology and the Ontology for General Medical Science. DMTO is implemented under Protégé 5.0 in Web Ontology Language (OWL) 2 format and is publicly available through the National Center for Biomedical Ontology's BioPortal at http://bioportal.bioontology.org/ontologies/DMTO . The current version of DMTO includes more than 10,700 classes, 277 relations, 39,425 annotations, 214 semantic rules, and 62,974 axioms. We provide proof of concept for this approach to modeling TPs. ::: ::: ::: CONCLUSION ::: The ontology is able to collect and analyze most features of T2DM as well as customize chronic TPs with the most appropriate drugs, foods, and physical exercises. DMTO is ready to be used as a knowledge base for semantically intelligent and distributed CDSS systems.


title: k-means clustering with outlier removal

abstract: We study the problem of data clustering with outlier detection.We propose a k-means-type algorithm by incorporating an additional cluster into the objective function.The algorithm is able to provide data clustering and outlier detection simultaneously.Outliers are not used in the cluster center calculation.Experiments on synthetic and real data show that the algorithm performs well. Outlier detection is an important data analysis task in its own right and removing the outliers from clusters can improve the clustering accuracy. In this paper, we extend the k-means algorithm to provide data clustering and outlier detection simultaneously by introducing an additional cluster to the k-means algorithm to hold all outliers. We design an iterative procedure to optimize the objective function of the proposed algorithm and establish the convergence of the iterative procedure. Numerical experiments on both synthetic data and real data are provided to demonstrate the effectiveness and efficiency of the proposed algorithm.


title: Generating query substitutions

abstract: We introduce the notion of query substitution, that is, generating a new query to replace a user's original search query. Our technique uses modifications based on typical substitutions web searchers make to their queries. In this way the new query is strongly related to the original query, containing terms closely related to all of the original terms. This contrasts with query expansion through pseudo-relevance feedback, which is costly and can lead to query drift. This also contrasts with query relaxation through boolean or TFIDF retrieval, which reduces the specificity of the query. We define a scale for evaluating query substitution, and show that our method performs well at generating new queries related to the original queries. We build a model for selecting between candidates, by using a number of features relating the query-candidate pair, and by fitting the model to human judgments of relevance of query suggestions. This further improves the quality of the candidates generated. Experiments show that our techniques significantly increase coverage and effectiveness in the setting of sponsored search.


title: of LaborHappiness and Domain Satisfaction: Theory and Evidence

abstract: In the United States happiness, on average, varies positively with socio-economic status; is fairly constant over time; rises to midlife and then declines; and is lower among younger than older birth cohorts. These four patterns of mean happiness can be predicted rather closely from the mean satisfaction people report with each of four domains – finances, family life, work, and health. Even though the domain satisfaction patterns typically differ from each other and from that for happiness, they come together in a way that explains quite well the overall patterns of happiness. The importance of any given domain depends on the happiness relation under study (by socio-economic status, time, age or birth cohort), and no single domain is invariably the key to happiness.


3D video and free viewpoint VideoTechnologies


title: Message ferry route design for sparse ad hoc networks with mobile nodes

abstract: Message ferrying is a networking paradigm where a special node, called a message ferry, facilitates the connectivity in a mobile ad hoc network where the nodes are sparsely deployed. One of the key challenges under this paradigm is the design of ferry routes to achieve certain properties of end-to-end connectivity, such as, delay and message loss among the nodes in the ad hoc network. This is a difficult problem when the nodes in the network move arbitrarily. As we cannot be certain of the location of the nodes, we cannot design a route where the ferry can contact the nodes with certainty. Due to this difficulty, prior work has either considered ferry route design for ad hoc networks where the nodes are stationary, or where the nodes and the ferry move pro-actively in order to meet at certain locations. Such systems either require long-range radio or disrupt nodes' mobility patterns which can be dictated by non-communication tasks. We present a message ferry route design algorithm that we call the Optimized Way-points, or OPWP, that generates a ferry route which assures good performance without requiring any online collaboration between the nodes and the ferry. The OPWP ferry route comprises a set of way-points and waiting times at these way-points, that are chosen carefully based on the node mobility model. Each time that the ferry traverses this route, it contacts each mobile node with a certain minimum probability. The node-ferry contact probability in turn determines the frequency of node-ferry contacts and the properties of end-to-end delay. We show that OPWP consistently outperforms other naive ferry routing approaches.


title: Probabilistic Risk Analysis and Terrorism Risk

abstract: Since the terrorist attacks of September 11, 2001, and the subsequent establishment of the U.S. Department of Homeland Security (DHS), considerable efforts have been made to estimate the risks of terrorism and the cost effectiveness of security policies to reduce these risks. DHS, industry, and the academic risk analysis communities have all invested heavily in the development of tools and approaches that can assist decisionmakers in effectively allocating limited resources across the vast array of potential investments that could mitigate risks from terrorism and other threats to the homeland. Decisionmakers demand models, analyses, and decision support that are useful for this task and based on the state of the art. Since terrorism risk analysis is new, no single method is likely to meet this challenge. In this article we explore a number of existing and potential approaches for terrorism risk analysis, focusing particularly on recent discussions regarding the applicability of probabilistic and decision analytic approaches to bioterrorism risks and the Bioterrorism Risk Assessment methodology used by the DHS and criticized by the National Academies and others.


title: Column-store support for RDF data management: not all swans are white

abstract: This paper reports on the results of an independent evaluation of the techniques presented in the VLDB 2007 paper "Scalable Semantic Web Data Management Using Vertical Partitioning", authored by D. Abadi, A. Marcus, S. R. Madden, and K. Hollenbach [1]. We revisit the proposed benchmark and examine both the data and query space coverage. The benchmark is extended to cover a larger portion of the query space in a canonical way. Repeatability of the experiments is assessed using the code base obtained from the authors. Inspired by the proposed vertically-partitioned storage solution for RDF data and the performance figures using a column-store, we conduct a complementary analysis of state-of-the-art RDF storage solutions. To this end, we employ MonetDB/SQL, a fully-functional open source column-store, and a well-known -- for its performance -- commercial row-store DBMS. We implement two relational RDF storage solutions -- triple-store and vertically-partitioned -- in both systems. This allows us to expand the scope of [1] with the performance characterization along both dimensions -- triple-store vs. vertically-partitioned and row-store vs. column-store -- individually, before analyzing their combined effects. A detailed report of the experimental test-bed, as well as an in-depth analysis of the parameters involved, clarify the scope of the solution originally presented and position the results in a broader context by covering more systems.


title: Distributed privacy-preserving transparency logging

abstract: We present a transparency-enhancing tool in the form of a cryptographic scheme that enables data processors to inform users about the actual data processing that takes place on their personal data. Our proposed solution can handle arbitrary processes while offloading storage and interactions with users to dedicated log servers. On top of strong integrity and confidentiality properties, our scheme takes users' privacy one step further by making it impossible to link multiple log entries for the same user or user identifiers across multiple data processors (for distributed processes). Our proposed solution has several applications, e.g., it can make access to electronic health records transparent to the patients to whom the records relate. Furthermore, we are the first to formalise the required security and privacy properties in this setting in a general manner (not specifically for our scheme) and prove that our scheme fulfils these. Finally, we show that our scheme is applicable in practice, providing performance results for a prototype implementation.


title: Model Predictive Control of a Nonlinear Large-Scale Process Network Used in the Production of Vinyl Acetate

abstract: In this work, we focus on the development and application of two Lyapunov-based model predictive control (LMPC) schemes to a large-scale nonlinear chemical process network used in the production of vinyl acetate. The nonlinear dynamic model of the process consists of 179 state variables and 13 control (manipulated) inputs and features a cooled plug-flow reactor, an eight-stage gas-liquid absorber, and both gas and liquid recycle streams. The two control schemes considered are an LMPC scheme which is formulated with a convectional quadratic cost function and a Lyapunov-based economic model predictive control (LEMPC) scheme which is formulated with an economic (non-quadratic) cost measure. The economic cost measure for the entire process network accounts for the reaction selectivity and the product separation quality. In the LMPC and LEMPC control schemes, five inputs, directly affecting the economic cost, are regulated with LMPC/LEMPC and the remaining eight inputs are computed by proportional-integral controllers. Simulations are carried out to study the economic performance of the closed-loop system under LMPC and under LEMPC formulated with the proposed economic measure. A thorough comparison of the two control schemes is provided.


title: A global name service for a highly mobile internetwork

abstract: Mobile devices dominate the Internet today, however the Internet rooted in its tethered origins continues to provide poor infrastructure support for mobility. Our position is that in order to address this problem, a key challenge that must be addressed is the design of a massively scalable global name service that rapidly resolves identities to network locations under high mobility. Our primary contribution is the design, implementation, and evaluation of auspice, a next-generation global name service that addresses this challenge. A key insight underlying auspice is a demand-aware replica {placement engine} that intelligently replicates name records to provide low lookup latency, low update cost, and high availability. We have implemented a prototype of auspice and compared it against several commercial managed DNS providers as well as state-of-the-art research alternatives, and shown that auspice significantly outperforms both. We demonstrate proof-of-concept that auspice can serve as a complete end-to-end mobility solution as well as enable novel context-based communication primitives that generalize name- or address-based communication in today's Internet.


title: Cloud computing based smart garbage monitoring system

abstract: Healthy environment is imperative to a healthy and happy community. With the age old system of hiring people to regularly check and empty filled dustbins, the process has been prone to human error and neglect. Additionally, due to different frequency of usage of dustbins in different areas, routine checks which are based on time crevices is inefficient because a dustbin might get filled early and may need immediate attention or there might not be any need of a routine check for a long period of time. This makes present system resource expensive and ineffectual, as overflowing, stinking dustbins become more of a problem than a solution. In this paper we present a solution about the SmartBin is a network of dustbins which integrates the idea of IoT with Wireless Sensor Networks. We also put forward the concept of a network of smart garbage bins based on the Stack Based Front End approach of integrating Wireless Sensor Network with the Cloud computing and discuss how Machine Learning techniques like Decision Forest Regression can be applied to the sensor data leveraged by the system to gain useful insights to improve the efficiency of the garbage monitoring.


title: A multi-view context-aware approach to Android malware detection and malicious code localization

abstract: Many existing Machine Learning (ML) based Android malware detection approaches use a variety of features such as security-sensitive APIs, system calls, control-flow structures and information flows in conjunction with ML classifiers to achieve accurate detection. Each of these feature sets provides a unique semantic perspective (or view) of apps’ behaviors with inherent strengths and limitations. Meaning, some views are more amenable to detect certain attacks but may not be suitable to characterize several other attacks. Most of the existing malware detection approaches use only one (or a selected few) of the aforementioned feature sets which prevents them from detecting a vast majority of attacks. Addressing this limitation, we propose MKLDroid, a unified framework that systematically integrates multiple views of apps for performing comprehensive malware detection and malicious code localization. The rationale is that, while a malware app can disguise itself in some views, disguising in every view while maintaining malicious intent will be much harder. MKLDroid uses a graph kernel to capture structural and contextual information from apps’ dependency graphs and identify malice code patterns in each view. Subsequently, it employs Multiple Kernel Learning (MKL) to find a weighted combination of the views which yields the best detection accuracy. Besides multi-view learning, MKLDroid’s unique and salient trait is its ability to locate fine-grained malice code portions in dependency graphs (e.g., methods/classes). Malicious code localization caters several important applications such as supporting human analysts studying malware behaviors, engineering malware signatures, and other counter-measures. Through our large-scale experiments on several datasets (incl. wild apps), we demonstrate that MKLDroid outperforms three state-of-the-art techniques consistently, in terms of accuracy while maintaining comparable efficiency. In our malicious code localization experiments on a dataset of repackaged malware, MKLDroid was able to identify all the malice classes with 94% average recall. Our work opens up two new avenues in malware research: (i) enables the research community to elegantly look at Android malware behaviors in multiple perspectives simultaneously, and (ii) performing precise and scalable malicious code localization.


title: Major Life Event Extraction from Twitter based on Congratulations/Condolences Speech Acts

abstract: Social media websites provide a platform for anyone to describe significant events taking place in their lives in realtime. Currently, the majority of personal news and life events are published in a textual format, motivating information extraction systems that can provide a structured representations of major life events (weddings, graduation, etc. . . ). This paper demonstrates the feasibility of accurately extracting major life events. Our system extracts a fine-grained description of users’ life events based on their published tweets. We are optimistic that our system can help Twitter users more easily grasp information from users they take interest in following and also facilitate many downstream applications, for example realtime friend recommendation.


1 shRNA mediated inhibition of Cdc 42 gene expression in Calu-6 , lung cancer cells Running title : shRNA mediated inhibition of Cdc 42 gene expression


Ray Tracing Implicit Surfaces


title: MicroRNA in TLR signaling and endotoxin tolerance

abstract: Toll-like receptors (TLRs) in innate immune cells are the prime cellular sensors for microbial components. TLR activation leads to the production of proinflammatory mediators and thus TLR signaling must be properly regulated by various mechanisms to maintain homeostasis. TLR4-ligand lipopolysaccharide (LPS)-induced tolerance or cross-tolerance is one such mechanism, and it plays an important role in innate immunity. Tolerance is established and sustained by the activity of the microRNA miR-146a, which is known to target key elements of the myeloid differentiation factor 88 (MyD88) signaling pathway, including IL-1 receptor-associated kinase (IRAK1), IRAK2 and tumor-necrosis factor (TNF) receptor-associated factor 6 (TRAF6). In this review, we comprehensively examine the TLR signaling involved in innate immunity, with special focus on LPS-induced tolerance. The function of TLR ligand-induced microRNAs, including miR-146a, miR-155 and miR-132, in regulating inflammatory mediators, and their impact on the immune system and human diseases, are discussed. Modulation of these microRNAs may affect TLR pathway activation and help to develop therapeutics against inflammatory diseases.


title: Animated Agents for Procedural Training in Virtual Reality: Perception, Cognition, and Motor Control

abstract: This paper describes Steve , an animated agent that helps students learn to perform physical , procedural tasks . The student and Steve cohabit a three - dimensional , simulated mock - up of the student's work environment . Steve can demonstrate how to perform tasks and can also monitor students while they practice tasks , providing assistance when needed . This paper describes Steve's architecture in detail , including perception , cognition , and motor control . The perception module monitors the state of the virtual world , maintains a coherent representation of it , and provides this information to the cognition and motor control modules . The cognition module interprets its perceptual input , chooses appropriate goals , constructs and executes plans to achieve those goals , and sends out motor commands . The motor control module implements these motor commands , controlling Steve's voice , locomotion , gaze , and gestures , allowing Steve to manipulate objects in the virtual world .


title: A 77GHz Antenna-in-Package with Low-Cost Solution for Automotive Radar Applications

abstract: This work proposes a low-cost Antenna in package (AiP) solution implemented on Advanced single sided substrates (aS3 package) for 77-GHz automotive radar applications. The impact of fabrication tolerances is also studied in this work. This work demonstrates firstly the 77-GHz losses of the transition from chip to package among flip-chip ball grid array (FCBGA) package, flip chip chip scale (FCCSP) package, Fan out wafer level chip scale package (Fan-out WLP), and aS3 package. Although Fan-out WLP can minimizes 77-GHz losses of transition from chip to package plus package to PCB, low-cost AiP solutions are very important for 77-GHz automotive radar applications. The proposed AiP solution on aS3 package has low losses of transition from chip to package, and the performance of designed antenna on aS3 package meets the requirements of 77-GHz automotive radar systems. Finally, this work also demonstrates the impact of fabrication inaccuracies on the bandwidth and radiation pattern of AiP on aS3 package. This work provides a cost-effective AiP approach for 77-GHz automotive radar systems.


title: Collaborative filtering with temporal dynamics

abstract: Customer preferences for products are drifting over time. Product perception and popularity are constantly changing as new selection emerges. Similarly, customer inclinations are evolving, leading them to ever redefine their taste. Thus, modeling temporal dynamics should be a key when designing recommender systems or general customer preference models. However, this raises unique challenges. Within the eco-system intersecting multiple products and customers, many different characteristics are shifting simultaneously, while many of them influence each other and often those shifts are delicate and associated with a few data instances. This distinguishes the problem from concept drift explorations, where mostly a single concept is tracked. Classical time-window or instance-decay approaches cannot work, as they lose too much signal when discarding data instances. A more sensitive approach is required, which can make better distinctions between transient effects and long term patterns. The paradigm we offer is creating a model tracking the time changing behavior throughout the life span of the data. This allows us to exploit the relevant components of all data instances, while discarding only what is modeled as being irrelevant. Accordingly, we revamp two leading collaborative filtering recommendation approaches. Evaluation is made on a large movie rating dataset by Netflix. Results are encouraging and better than those previously reported on this dataset.


title: ADMET in silico modelling: towards prediction paradise?

abstract: Following studies in the late 1990s that indicated that poor pharmacokinetics and toxicity were important causes of costly late-stage failures in drug development, it has become widely appreciated that these areas should be considered as early as possible in the drug discovery process. However, in recent years, combinatorial chemistry and high-throughput screening have significantly increased the number of compounds for which early data on absorption, distribution, metabolism, excretion (ADME) and toxicity (T) are needed, which has in turn driven the development of a variety of medium and high-throughput in vitro ADMET screens. Here, we describe how in silico approaches will further increase our ability to predict and model the most relevant pharmacokinetic, metabolic and toxicity endpoints, thereby accelerating the drug discovery process.


title: A compiler-directed data prefetching scheme for chip multiprocessors

abstract: Data prefetching has been widely used in the past as a technique for hiding memory access latencies. However, data prefetching in multi-threaded applications running on chip multiprocessors (CMPs) can be problematic when multiple cores compete for a shared on-chip cache (L2 or L3). In this paper, we (i) quantify the impact of conventional data prefetching on shared caches in CMPs. The experimental data collected using multi-threaded applications indicates that, while data prefetching improves performance in small number of cores, its benefits reduce significantly as the number of cores is increased, that is, it is not scalable; (ii) identify harmful prefetches as one of the main contributors for degraded performance with a large number of cores; and (iii) propose and evaluate a compiler-directed data prefetching scheme for shared on-chip cache based CMPs. The proposed scheme first identifies program phases using static compiler analysis, and then divides the threads into groups within each phase and assigns a customized prefetcher thread (helper thread) to each group of threads. This helps to reduce the total number of prefetches issued, prefetch overheads, and negative interactions on the shared cache space due to data prefetches, and more importantly, makes compiler-directed prefetching a scalable optimization for CMPs. Our experiments with the applications from the SPEC OMP benchmark suite indicate that the proposed scheme improves overall parallel execution latency by 18.3% over the no-prefetch case and 6.4% over the conventional data prefetching scheme (where each core prefetches its data independently), on average, when 12 cores are used. The corresponding average performance improvements with 24 cores are 16.4% (over the no-prefetch case) and 11.7% (over the conventional prefetching case). We also demonstrate that the proposed scheme is robust under a wide range of values of our major simulation parameters, and the improvements it achieves come very close to those that can be achieved using an optimal scheme.


Speech Synthesis Based on Hidden Markov Models and Deep Learning


title: A Synthesis of Reading Interventions and Effects on Reading Comprehension Outcomes for Older Struggling Readers

abstract: This article reports a synthesis of intervention studies conducted between 1994 and 2004 with older students (Grades 6–12) with reading difficulties. Interventions addressing decoding, fluency, vocabulary, and comprehension were included if they measured the effects on reading comprehension. Twenty-nine studies were located and synthesized. Thirteen studies met criteria for a meta-analysis, yielding an effect size (ES) of 0.89 for the weighted average of the difference in comprehension outcomes between treatment and comparison students. Word-level interventions were associated with ES = 0.34 in comprehension outcomes between treatment and comparison students. Implications for comprehension instruction for older struggling readers are described.


title: Simple algorithmic theory of subjective beauty, novelty, surprise, interestingness, attention, curiosity, creativity, art, science, music, jokes (特集 高次機能の学習と創発--脳・ロボット・人間研究における新たな展開)

abstract: In this summary of previous work, I argue that data becomes temporarily interesting by itself to some selfimproving, but computationally limited, subjective observer once he learns to predict or compress the data in a better way, thus making it subjectively more “beautiful.” Curiosity is the desire to create or discover more non-random, non-arbitrary, “truly novel,” regular data that allows for compression progress because its regularity was not yet known. This drive maximizes “interestingness,” the first derivative of subjective beauty or compressibility, that is, the steepness of the learning curve. It motivates exploring infants, pure mathematicians, composers, artists, dancers, comedians, yourself, and recent artificial systems.


title: Triangle mesh-based edge detection and its application to surface segmentation and adaptive surface smoothing

abstract: Triangle meshes are widely used in representing surfaces in computer vision and computer graphics. Although 2D image processing-based edge detection techniques have been popular in many application areas, they are not well developed for surfaces represented by triangle meshes. This paper proposes a robust edge detection algorithm for triangle meshes and its applications to surface segmentation and adaptive surface smoothing. The proposed edge detection technique is based on eigen analysis of the surface normal vector field in a geodesic window. To compute the edge strength of a certain vertex, the neighboring vertices in a specified geodesic distance are involved. Edge information are used further to segment the surfaces with the watershed algorithm and to achieve edge-preserved, adaptive surface smoothing. The proposed algorithm is novel in robustly detecting edges on triangle meshes against noise. The 3D watershed algorithm is an extension from previous work. Experimental results on surfaces reconstructed from multi-view real range images are presented.


title: An Interdisciplinary Perspective on IT Services Management and Service Science

abstract: The increasing importance of information technology (IT) services in the global economy prompts researchers in the field of information systems (IS) to give special attention to the foundations of managerial and technical knowledge in this emerging arena of knowledge. Already we have seen the computer science discipline embrace the challenges of finding new directions in design science toward making services-oriented computing approaches more effective, setting the stage for the development of a new science-service science, management, and engineering (SSME). This paper addresses the issues from the point of view of service science as a fundamental area for IS research. We propose a robust framework for evaluating the research on service science, and the likely outcomes and new directions that we expect to see in the coming decade. We emphasize the multiple roles of producers and consumers of services-oriented technology innovations, as well as value-adding seller intermediaries and systems integrators, and standards organizations, user groups, and regulators as monitors. The analysis is cast in multidisciplinary terms, including computer science and IS, economics and finance, marketing, and operations and supply chain management. Evaluating the accomplishments and opportunities for research related to the SSME perspective through a robust framework enables in-depth assessment in the present, as well as an ongoing evaluation of new knowledge in this area, and the advancement of the related management practice capabilities to improve IT services in organizations.


title: Sentiment/subjectivity analysis survey for languages other than English

abstract: Subjective and sentiment analysis have gained considerable attention recently. Most of the resources and systems built so far are done for English. The need for designing systems for other languages is increasing. This paper surveys different ways used for building systems for subjective and sentiment analysis for languages other than English. There are three different types of systems used for building these systems. The first (and the best) one is the language specific systems. The second type of systems involves reusing or transferring sentiment resources from English to the target language. The third type of methods is based on using language independent methods. The paper presents a separate section devoted to Arabic sentiment analysis.


title: Malware detection vectors and analysis techniques: A brief survey

abstract: Over the last decade, a war has been taking place which involves the computer security community and the black hat hackers. The security community uses every possible routine and strategies in order to cease and also to remove the damage caused by the malicious programs while at the same time the black hat community advances their techniques that can easily bypass the installed security measures. In this paper, a study has been carried out on different classes of Malware and their propagation techniques. We highlight on different detection vectors for malware along with different tools to analyze them. Some challenging issues which need further research are also presented.


title: CNN based music emotion classification

abstract: Music emotion recognition (MER) is usually regarded as a multi-label tagging task, and each segment of music can inspire specific emotion tags. Most researchers extract acoustic features from music and explore the relations between these features and their corresponding emotion tags. Considering the inconsistency of emotions inspired by the same music segment for human beings, seeking for the key acoustic features that really affect on emotions is really a challenging task. In this paper, we propose a novel MER method by using deep convolutional neural network (CNN) on the music spectrograms that contains both the original time and frequency domain information. By the proposed method, no additional effort on extracting specific features required, which is left to the training procedure of the CNN model. Experiments are conducted on the standard CAL500 and CAL500exp dataset. Results show that, for both datasets, the proposed method outperforms state-of-the-art methods.


title: Skin lesion classification with ensemble of squeeze-and-excitation networks and semi-supervised learning

abstract: In this report, we introduce the outline of our system in Task 3: Disease Classification of ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection. We fine-tuned multiple pre-trained neural network models based on Squeeze-and-Excitation Networks (SENet) which achieved state-of-the-art results in the field of image recognition. In addition, we used the mean teachers as a semi-supervised learning framework and introduced some specially designed data augmentation strategies for skin lesion analysis. We confirmed our data augmentation strategy improved classification performance and demonstrated 87.2% in balanced accuracy on the official ISIC2018 validation dataset.


title: Boosting for Text Classification with Semantic Features

abstract: Current text classification systems typically use term stems for representing document content. Semantic Web technologies allow the usage of features on a higher semantic level than single words for text classification purposes. In this paper we propose such an enhancement of the classical document representation through concepts extracted from background knowledge. Boosting, a successful machine learning technique is used for classification. Comparative experimental evaluations in three different settings support our approach through consistent improvement of the results. An analysis of the results shows that this improvement is due to two separate effects.


title: A Web-based Platform for Collection of Human-Chatbot Interactions

abstract: Over recent years, the world has seen multiple uses for conversational agents. Chatbots has been implemented into ecommerce systems, such as Amazon Echo's Alexa [1]. Businesses and organizations like Facebook are also implementing bots into their applications. While a number of amazing chatbot platform exists, there are still difficulties in creating data-driven-systems as they large amount of data is needed for development and training. This paper we describe an advanced platform for evaluating and annotating human-chatbot interactions, its main features and goals, as well as the future plans we have for it.


title: Increasing Student Engagement and Retention Using Mobile Applications: Smartphones, Skype and Texting Technologies

abstract: Mobile technologies are reshaping and reframing the practice of teaching and learning in higher education. This volume critically examines new research on how mobile technologies and m-learning technologies like Skype are being used in higher education to increase learner engagement in an era of increasing globalization and mobility.


title: Co-attending Free-form Regions and Detections with Multi-modal Multiplicative Feature Embedding for Visual Question Answering

abstract: Recently, the Visual Question Answering (VQA) task has gained increasing attention in artificial intelligence. Existing VQA methods mainly adopt the visual attention mechanism to associate the input question with corresponding image regions for effective question answering. The free-form region based and the detection-based visual attention mechanisms are mostly investigated, with the former ones attending free-form image regions and the latter ones attending pre-specified detection-box regions. We argue that the two attention mechanisms are able to provide complementary information and should be effectively integrated to better solve the VQA problem. In this paper, we propose a novel deep neural network for VQA that integrates both attention mechanisms. Our proposed framework effectively fuses features from free-form image regions, detection boxes, and question representations via a multi-modal multiplicative feature embedding scheme to jointly attend question-related free-form image regions and detection boxes for more accurate question answering. The proposed method is extensively evaluated on two publicly available datasets, COCO-QA and VQA, and outperforms state-of-the-art approaches. Source code is available at this https URL


Cell Growth Signal Transduction Is Quantal


title: Influence of lead on the chlorophyll content and on initial steps of its synthesis in greening cucumber seedlings

abstract: Lead uptake by young cucumber ( Couches satires L.) seedlings growing in 10 -4 and 10 -3 M PbCl 2 solution caused δ-aminolevulinic acid synthesis inhibition, reduced the activity of δ-aminolevulinic acid dehydratase and the chlorophyll centent in the cotyledons. Lead mainly accumulated in the roots (ca 93-96% of Pb uptake) and hypocotyls (ca 4-6%), whereas only trace Pb amounts were found in the cotyledons when 10 -3 M solution was used. It is supposed that one of the causes of the reduction of chlorophyll synthesis by lead is its influence on tissue hydration which diminished to about 50 per cent as compared with the control. The direct effect of lead on the examined steps of chlorophyll synthesis cannot, however, be ruled out.


title: Competitive Intelligence Analysis of Augmented Reality Technology Using Patent Information

abstract: Augmented reality has recently achieved a rapid growth through its applications in various industries, including education and entertainment. Despite the growing attraction of augmented reality, trend analyses in this emerging technology have relied on qualitative literature review, failing to provide comprehensive competitive intelligence analysis using objective data. Therefore, tracing industrial competition trends in augmented reality will provide technology experts with a better understanding of evolving competition trends and insights for further technology and sustainable business planning. In this paper, we apply a topic modeling approach to 3595 patents related to augmented reality technology to identify technology subjects and their knowledge stocks, thereby analyzing industrial competitive intelligence in light of technology subject and firm levels. As a result, we were able to obtain some findings from an inventional viewpoint: technological development of augmented reality will soon enter a mature stage, technologies of infrastructural requirements have been a focal subject since 2001, and several software firms and camera manufacturing firms have dominated the recent development of augmented reality.


title: Security and insurance management in networks with heterogeneous agents

abstract: Computer users express a strong desire to prevent attacks and to reduce the losses from computer and information security breaches. However, security compromises are common and widespread and highly damaging. Next to attackers' increased sophistication, a root cause for the harm inflicted is that users often fail to optimally protect their resources or to recover gracefully from a security breach. We argue that users often underestimate the strong mutual dependence between their security strategies and the economic environment (e.g., threat model) in which these choices are made and evaluated. This misunderstanding weakens the effectiveness of users' security investments, and is compounded by heterogeneity within the user population, in some cases further reducing incentives for cooperation and coordination. We study how economic agents invest into security in five different economic environments, that are characteristic of different threat models. We consider generalized models of traditional public goods games (e.g., total effort and weakest link) and two recently proposed games (e.g., weakest target game). Agents may split their contributions between a public good (protection) and a private good (self-insurance). Our analysis centers on how agents respond to incentives when important parameters of the game (i.e., loss probability, loss magnitude, and cost of technology) are heterogeneous in the agent population. We also highlight key differences to the case of homogeneous decision makers. For example, security investments may become substantially more sensitive to the size of the network. We extend our results to discuss important modes of intervention.


Running Head: Virtual Addiction Virtual Addiction: Sometimes New Technology Can Create New Problems


title: {Averaged Least-Mean-Squares: Bias-Variance Trade-offs and Optimal Sampling Distributions}

abstract: We consider the least-squares regression problem and provide a detailed asymptotic analysis of the performance of averaged constant-step-size stochastic gradient descent. In the strongly-convex case, we provide an asymptotic expansion up to explicit exponentially decaying terms. Our analysis leads to new insights into stochastic approximation algorithms: (a) it gives a tighter bound on the allowed step-size; (b) the generalization error may be divided into a variance term which is decaying as O(1/n), independently of the step-size , and a bias term that decays as O(1/ 2 n 2 ); (c) when allowing non-uniform sampling of examples over a dataset, the choice of a good sampling density depends on the trade-off between bias and variance: when the variance term dominates, optimal sampling densities do not lead to much gain, while when the bias term dominates, we can choose larger step-sizes that lead to significant improvements.


title: Non-negative Matrix Factorization on Manifold

abstract: Recently non-negative matrix factorization (NMF) has received a lot of attentions in information retrieval, computer vision and pattern recognition. NMF aims to find two non-negative matrices whose product can well approximate the original matrix. The sizes of these two matrices are usually smaller than the original matrix. This results in a compressed version of the original data matrix. The solution of NMF yields a natural parts-based representation for the data. When NMF is applied for data representation, a major disadvantage is that it fails to consider the geometric structure in the data. In this paper, we develop a graph based approach for parts-based data representation in order to overcome this limitation. We construct an affinity graph to encode the geometrical information and seek a matrix factorization which respects the graph structure. We demonstrate the success of this novel algorithm by applying it on real world problems.


title: Big data for Natural Language Processing

abstract: Requirements in computational power have grown dramatically in recent years. This is also the case in many language processing tasks, due to the overwhelming and ever increasing amount of textual information that must be processed in a reasonable time frame. This scenario has led to a paradigm shift in the computing architectures and large-scale data processing strategies used in the Natural Language Processing field. In this paper we present a new distributed architecture and technology for scaling up text analysis running a complete chain of linguistic processors on several virtual machines. Furthermore, we also describe a series of experiments carried out with the goal of analyzing the scaling capabilities of the language processing pipeline used in this setting. We explore the use of Storm in a new approach for scalable distributed language processing across multiple machines and evaluate its effectiveness and efficiency when processing documents on a medium and large scale. The experiments have shown that there is a big room for improvement regarding language processing performance when adopting parallel architectures, and that we might expect even better results with the use of large clusters with many processing nodes.


title: Basis function construction in hierarchical reinforcement learning

abstract: Much past work on solving Markov decision processes (MDPs) using reinforcement learning (RL) has relied on combining parameter estimation methods with hand-designed function approximation architectures for representing value functions. Recently, there has been growing interest in a broader framework that combines representation discovery and control learning, where value functions are approximated using a linear combination of task-dependent basis functions learned during the course of solving a particular MDP. This paper introduces an approach to automatic basis function construction for hierarchical reinforcement learning (HRL). Our approach generalizes past work on basis construction to multi-level action hierarchies by forming a compressed representation of a semi-Markov decision process (SMDP) at multiple levels of temporal abstraction. The specific approach is based on hierarchical spectral analysis of graphs induced on an SMDP's state space from sample trajectories. We present experimental results on benchmark SMDPs, showing significant speedups when compared to hand-designed approximation architectures.


abstract: Self-adaptive attribute weighting for Naive Bayes classification.Using Artificial Immune Systems (AIS) for attribute weighting.Seamlessly integrating learning objective and AIS affinity function for attribute weighting.Experiments on 42 real-world datasets demonstrating superb performance gain. Naive Bayes (NB) is a popular machine learning tool for classification, due to its simplicity, high computational efficiency, and good classification accuracy, especially for high dimensional data such as texts. In reality, the pronounced advantage of NB is often challenged by the strong conditional independence assumption between attributes, which may deteriorate the classification performance. Accordingly, numerous efforts have been made to improve NB, by using approaches such as structure extension, attribute selection, attribute weighting, instance weighting, local learning and so on. In this paper, we propose a new Artificial Immune System (AIS) based self-adaptive attribute weighting method for Naive Bayes classification. The proposed method, namely AISWNB, uses immunity theory in Artificial Immune Systems to search optimal attribute weight values, where self-adjusted weight values will alleviate the conditional independence assumption and help calculate the conditional probability in an accurate way. One noticeable advantage of AISWNB is that the unique immune system based evolutionary computation process, including initialization, clone, section, and mutation, ensures that AISWNB can adjust itself to the data without explicit specification of functional or distributional forms of the underlying model. As a result, AISWNB can obtain good attribute weight values during the learning process. Experiments and comparisons on 36 machine learning benchmark data sets and six image classification data sets demonstrate that AISWNB significantly outperforms its peers in classification accuracy, class probability estimation, and class ranking performance.


title: A Comparative Study of Exact Algorithms for the Two Dimensional Strip Packing Problem

abstract: In this paper we consider a two dimensional strip packing problem. The problem consists of packing a set of rectangular items in one strip of width W and infinite height. They must be packed without overlapping, parallel to the edge of the strip and we assume that the items are oriented, i.e. they cannot be rotated. To solve this problem, we use three exact methods: a branch and bound method, a dichotomous algorithm and a branch and price method. The three methods were carried out and compared on literature instances.


title: Credit Assignment through Time: Alternatives to Backpropagation

abstract: Learning to recognize or predict sequences using long-term context has many applications. However, practical and theoretical problems are found in training recurrent neural networks to perform tasks in which input/output dependencies span long intervals. Starting from a mathematical analysis of the problem, we consider and compare alternative algorithms and architectures on tasks for which the span of the input/output dependencies can be controlled. Results on the new algorithms show performance qualitatively superior to that obtained with backpropagation.


title: Evaluation of sampling techniques in software fault prediction using metrics and code smells

abstract: The highly imbalanced nature of software fault datasets results in poor performance of machine leaning techniques used for software fault prediction. The objective of this paper is to evaluate sampling techniques and Meta-Cost learning in software fault prediction to alleviate problem of imbalanced data. We evaluate four sampling techniques in metrics as well as code smells based fault prediction on fault data sets of two open source systems ANT and POI. Our results indicate that Resample technique is best for metrics based fault prediction whereas Synthetic Minority Oversampling is best suited for code smells based fault prediction. The results are presented in terms of accuracy measures like G-Mean, F-measure and area under ROC curve. We also evaluate Meta-Cost learning and found that all sampling techniques outperform Meta-Cost learning. Our results also indicate that software metrics are better predictor of software faults than code smells.


title: Differential-Mode/Common-Mode Feedforward Transconductor for Low-Voltage Gm-C filters

abstract: Low-voltage transconductor circuits suitable for Gm-C filter are proposed. The proposed structures were developed from a well-known Nauta's inverter-type class-AB transconductor to improve common-mode rejection without sacrificing extra power consumption. The improvement relies on feed-forwarding both common-mode and differential mode signals, making it power efficient because additionally power has not be wasted only for suppressing common-mode signal. The concept has been confirmed by comparison with simulation on I-V 5th-order Chebyshev lowpass filters constructed from the proposed transconductors in 0.18mum CMOS process. The proposed transconductors offer improvement on common-mode rejection by 50dB with 20% less current consumption while still preserving dynamic range.


title: Barriers and benefits of quality management in the construction industry: An empirical study

abstract: In this paper we discuss the problems of defining quality in the construction industry, examine possible benefits of implementing quality, and look at barriers to quality implementation in construction. We use data collected during interviews with contractors and data from questionnaire surveys. Results show that contractors do understand the potential benefits of quality implementation but that there are also many barriers to implementation. We describe recent developments that might help to overcome the barriers. However, the different actors in construction need to understand that change is a slow and often painful process and that much effort is required to implement quality in construction industry.


title: Proactive failure detection learning generation patterns of large-scale network logs

abstract: With the growth of services in IP networks, network operators are required to perform proactive operation that quickly detects the signs of critical failures and prevents future problems. Network log data, including router syslog, are rich sources for such operations. However, it has become impossible to find genuinely important logs that lead to serious problems due to the large volume and complexity of log data. We propose a log analysis system for proactive detection of failures. Our key observation is that the abnormality of logs depends on not just the keywords in the messages (e.g. ERROR, FAIL), but generation patterns such as burstiness. Our system consists of three functions: (i) extracting log templates automatically and quickly from a massive amount of unstructured log data; (ii) constructing log feature vectors to characterize the generation patterns of logs; and (iii) using a supervised machine learning approach to associate failures with the log data that appeared before them. We validated our system using real log data collected from a large network and determined its effectiveness.


title: Scalable k-NN graph construction for visual descriptors

abstract: The k-NN graph has played a central role in increasingly popular data-driven techniques for various learning and vision tasks; yet, finding an efficient and effective way to construct k-NN graphs remains a challenge, especially for large-scale high-dimensional data. In this paper, we propose a new approach to construct approximate k-NN graphs with emphasis in: efficiency and accuracy. We hierarchically and randomly divide the data points into subsets and build an exact neighborhood graph over each subset, achieving a base approximate neighborhood graph; we then repeat this process for several times to generate multiple neighborhood graphs, which are combined to yield a more accurate approximate neighborhood graph. Furthermore, we propose a neighborhood propagation scheme to further enhance the accuracy. We show both theoretical and empirical accuracy and efficiency of our approach to k-NN graph construction and demonstrate significant speed-up in dealing with large scale visual data.


title: Audio-visual speech recognition in noisy audio environments

abstract: It is a well-known fact that the visual part of speech can improve the resulting recognition rate mainly in noisy conditions. Main goal of this work is to find a set of visual features which would be possible to use in our audio-visual speech recognition systems. Discrete Cosine Transform (DCT) and Active Appearance Model (AAM) based visual features are extracted from visual speech signals, enhanced by a simplified variant of Hierarchical Linear Discriminant Analysis (HiLDA) and normalized across speakers. The visual features are then combined with standard MFCC audio features by the middle fusion method. The results from audio-visual speech recognition are compared with the results from experiments where the log-spectra minimum mean square error and multiband spectral subtraction methods for reducing additive noise in the audio signal are used.


title: Getting Serious about Math: Serious Game Design Framework & an Example of a Math Educational Game

abstract: Serious games have been increasingly interesting to educators recently, but it is difficult to find good games that truly address the needs of young learners, especially in mathematics. Some games are either too simplistic or are not actual games. In the paper, an argument is made for the benefits of game-based learning (GBL) for instruction and assessment given what we know about Millenials and how they learn. The first aim of this discussion is to suggest a framework for designing serious games based on game features in commercial games, opinions of fourth graders and their teachers, literary studies, contemporary learning theories, as well as successful and unsuccessful similar endeavours. The second part of this paper describes a concrete example of a maths game based on the proposed framework that implicitly tests math and collaboration skills. The game is made of three components: the game itself, a social network, and a teacher reporting tool. Despite a growing interest in GBL, some teachers are reluctant to use serious games in school. To increase usage of serious games as resource, it is important to equip teachers with information and address their concerns. The paper concludes with the idea that serious games need to be designed well in order to provide the immersion and collaborative active learning that most learning theories recommend. Further, that they should be games and not just drill and practice. In that way, they can be even more beneficial than books as part of the teacher's repertoire in school. Future research avenues are also discussed.


title: LIFESPAN PSYCHOLOGY: Theory and Application to Intellectual Functioning

abstract: The focus of this review is on theory and research of lifespan (lifespan developmental) psychology. The theoretical analysis integrates evolutionary and ontogenetic perspectives on cultural and human development across several levels of analysis. Specific predictions are advanced dealing with the general architecture of lifespan ontogeny, including its directionality and age-differential allocation of developmental resources into the three major goals of developmental adaptation: growth, maintenance, and regulation of loss. Consistent with this general lifespan architecture, a meta-theory of development is outlined that is based on the orchestrated and adaptive interplay between three processes of behavioral regulation: selection, optimization, and compensation. Finally, these propositions and predictions about the general nature of lifespan development are examined and supported by empirical evidence on the development of cognition and intelligence across the life span.


title: An integrated agent for playing real-time strategy games

abstract: We present a real-time strategy (RTS) game AI agent that integrates multiple specialist components to play a complete game. Based on an analysis of how skilled human players conceptualize RTS gameplay, we partition the problem space into domains of competence seen in expert human play. This partitioning helps us to manage and take advantage of the large amount of sophisticated domain knowledge developed by human players. We present results showing that incorporating expert high-level strategic knowledge allows our agent to consistently defeat established scripted AI players. In addition, this work lays the foundation to incorporate tactics and unit micromanagement techniques developed by both man and machine.


title: AOS: an avionics operating system for multi-level secure real-time environments

abstract: In parallel with advances in the design of real-time systems there is an increasing need for real-time systems that can provide multilevel security. This need is highlighted by the DOD's endorsed move towards integrated avionics to enable real-time avionics and tactical applications to share a common processing platform. A generic Integrated Avionics Platform (IAP) is a heterogeneous distributed system made of a complex network of interconnected systems, each designed to support real-time applications ranging from vehicle management to weapons control. The Avionics Operating System (AOS) meets these evolving needs of multilevel secure real-time avionics systems. The AOS takes advantage of advanced microprocessor features and other innovative techniques to create an efficient yet flexible multilevel secure real-time operating system. >


title: Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials

abstract: Most state-of-the-art techniques for multi-class image segmentation and labeling use conditional random fields defined over pixels or image regions. While region-level models often feature dense pairwise connectivity, pixel-level models are considerably larger and have only permitted sparse graph structures. In this paper, we consider fully connected CRF models defined on the complete set of pixels in an image. The resulting graphs have billions of edges, making traditional inference algorithms impractical. Our main contribution is a highly efficient approximate inference algorithm for fully connected CRF models in which the pairwise edge potentials are defined by a linear combination of Gaussian kernels. Our experiments demonstrate that dense connectivity at the pixel level substantially improves segmentation and labeling accuracy.


title: FLATM: A fuzzy logic approach topic model for medical documents

abstract: One of the challenges for text analysis in medical domains is analyzing large-scale medical documents. As a consequence, finding relevant documents has become more difficult. One of the popular methods to retrieve information based on discovering the themes in the documents is topic modeling. The themes in the documents help to retrieve documents on the same topic with and without a query. In this paper, we present a novel approach to topic modeling using fuzzy clustering. To evaluate our model, we experiment with two text datasets of medical documents. The evaluation metrics carried out through document classification and document modeling show that our model produces better performance than LDA, indicating that fuzzy set theory can improve the performance of topic models in medical domains.


title: Removing vibration blur from a single image based on coded exposure

abstract: In the field of remote sensing, the vibration caused by satellite platform would affect the captured image quality. So the fast image restoration method is proposed in this passage. Combining the vibration detection and coded exposure, this method can remove vibration blur from a single remote sensing image. The vibration is detected by fiber-optic gyroscope, and the PSF is calculated from the vibration data. With coded exposure, the blurred image can be recovered by direct inverse filtering quickly. Experimental results show that the presented algorithm could yield better subjective experience and superior objective evaluation values, and the time consumption significantly reduced.


Language-Independent Twitter Sentiment Analysis


title: Digital Control of High DC Voltage Converter Based on Cockcroft Walton Voltage Multiplier Circuit

abstract: A new method of controlling high DC voltage based on Cockcroft Walton voltage multiplier circuit by using digital controller is presented. The digital controller is developed using complex programmable logic devices (CPLD). The proposed system utilizes a single-phase AC as an input supply. The power switching devices in the controlled bridge are controlled by the multiple-pulse pulse width modulation (PWM) switching technique so as to minimize the low order harmonic present on the AC side of the converter system. A low pass filter is incorporated in the circuit to filter out unwanted harmonics and to give a sinusoidal AC current. A high frequency transformer with 1:1 ratio is incorporated in the design to provide galvanic isolation for better circuit performance and protection. The optimum operation of transformer core in four quadrant of B-H curve is also considered in the proposed converter topology. The laboratory model of the converter is developed and tested. The experimental result is compared with the simulation result.


title: Study of Implementing Automated Attendance System Using Face Recognition Technique

abstract: Authentication is a significant issue in system control in computer based communication. Human face recognition is an important branch of biometric verification and has been widely used in many applications, such as video monitor system, human-computer interaction, and door control system and network security. This paper describes a method for Student's Attendance System which will integrate with the face recognition technology using Personal Component Analysis (PCA) algorithm. The system will record the attendance of the students in class room environment automatically and it will provide the facilities to the faculty to access the information of the students easily by maintaining a log for clock-in and clock-out time.


title: Storage class memory and databases: Opportunities and challenges

abstract: Abstract Storage Class Memory (SCM) is emerging as a viable solution to lift DRAM's scalability limits, both in capacity and energy consumption. Indeed, SCM combines the economic characteristics, non-volatility, and density of traditional storage media with the low latency and byte-addressability of DRAM. In this paper we survey research works on how SCM can be leveraged in databases and explore different solutions ranging from using SCM as disk replacement, to single-level storage architectures, where SCM is used as universal memory (i.e., as memory and storage at the same time), together with the challenges that stem from these opportunities. Finally, we synthesize our findings into recommendations on how to exploit the full potential of SCM in next-generation database architectures.


title: UAVs for smart cities: Opportunities and challenges

abstract: This paper discusses the applications of unmanned aerial vehicles (UAVs) in smart cities, their opportunities and their challenges. UAVs have a wide range of applications in many fields like environmental hazards monitoring, traffic management and pollution monitoring, all of which contributes greatly to the development of any smart city. These opportunities among several others are discussed in this paper. Several challenges and issues such as safety, privacy and ethical uses are of great concern and are also provided in this paper.


title: An ankle-foot orthosis powered by artificial pneumatic muscles

abstract: We developed a pneumatically powered orthosis for the human ankle joint. The orthosis consisted of a carbon fiber shell, hinge joint, and two artificial pneumatic muscles. One artificial pneumatic muscle provided plantar flexion torque and the second one provided dorsiflexion torque. Computer software adjusted air pressure in each artificial muscle independently so that artificial muscle force was proportional to rectified low-pass-filtered electromyography (EMG) amplitude (i.e., proportional myoelectric control). Tibialis anterior EMG activated the artificial dorsiflexor and soleus EMG activated the artificial plantar flexor. We collected joint kinematic and artificial muscle force data as one healthy participant walked on a treadmill with the orthosis. Peak plantar flexor torque provided by the orthosis was 70 Nm, and peak dorsiflexor torque provided by the orthosis was 38 Nm. The orthosis could be useful for basic science studies on human locomotion or possibly for gait rehabilitation after neurological injury.


title: An Active Quasi-Circulator for 77 GHz Automotive FMCW Radar Systems in SiGe Technology

abstract: One of the key functions of transmit and receive modules with a single antenna is the separation of the weak receive signal (RX) from the strong transmit signal (TX). In state-of-the-art FMCW radar MMICs, this function is usually implemented using passive devices, which have relatively large physical dimensions compared to the chip area and exhibit insertion losses. In this letter the design of an active quasi-circulator (QC) is presented with a special emphasis on TX/RX isolation. An analysis is presented to aid in the circuit design, so that the desired isolation requirements can be achieved. The implemented active QC is fabricated in a 200 GHz fT Silicon-Germanium (SiGe) HBT technology. On-wafer measurements show a TX/RX isolation of 16 dB over a bandwidth of 8 GHz. The measured insertion loss for the TX and the RX signal is less than 1 and 1.5 dB, respectively.


DynaMIT: a simulation-based system for traffic prediction


title: Biogas energy production from tropical biomass wastes by anaerobic digestion

abstract: Abstract Anaerobic digestion (AD) is an attractive technology in tropical regions for converting locally abundant biomass wastes into biogas which can be used to produce heat, electricity, and transportation fuels. However, investigations on AD of tropical forestry wastes, such as albizia biomass and food wastes, such as taro, papaya, and sweet potato, are limited. In this study, these tropical biomass wastes were evaluated for biogas production by liquid AD (L-AD) and/or solid-state AD (SS-AD), depending on feedstock characteristics. When albizia leaves and chips were used as feedstocks, L-AD had greater methane yields (161 and 113 L kg −1 VS, respectively) than SS-AD (156.8 and 59.6 L kg −1 VS, respectively), while SS-AD achieved 5-fold higher volumetric methane productivity than L-AD. Mono-digestion and co-digestion of taro skin, taro flesh, papaya, and sweet potato achieved methane yields from 345 to 411 L kg −1 VS, indicating the robustness of AD technology.


title: Context-aware review helpfulness rating prediction

abstract: Online reviews play a vital role in the decision-making process for online users. Helpful reviews are usually buried in a large number of unhelpful reviews, and with the consistently increasing number of reviews, it becomes more and more difficult for online users to find helpful reviews. Therefore most online review websites allow online users to rate the helpfulness of a review and a global helpfulness score is computed for the review based on its available ratings. However, in reality, user-specified helpfulness ratings for reviews are very sparse - a few reviews attract large numbers of helpfulness ratings while most reviews obtain few or even no helpfulness ratings. The available helpfulness ratings are too sparse for online users to assess the helpfulness of reviews. Also the helpfulness of a review is not necessarily equally useful for all users and users with different background may treat the helpfulness of a review very differently. The user idiosyncracy of review helpfulness motivates us to study the problem of review helpfulness rating prediction in this paper. We first identify various types of context information, model them mathematically, and propose a context-aware review helpfulness rating prediction framework CAP. Experimental results demonstrate the effectiveness of the proposed framework and the importance of context awareness in solving the review helpfulness rating prediction problem.


title: Learning Intrinsic Sparse Structures within Long Short-term Memory

abstract: Model compression is significant for the wide adoption of Recurrent Neural Networks (RNNs) in both user devices possessing limited resources and business clusters requiring quick responses to large-scale service requests. This work aims to learn structurally-sparse Long Short-Term Memory (LSTM) by reducing the sizes of basic structures within LSTM units, including input updates, gates, hidden states, cell states and outputs. Independently reducing the sizes of basic structures can result in inconsistent dimensions among them, and consequently, end up with invalid LSTM units. To overcome the problem, we propose Intrinsic Sparse Structures (ISS) in LSTMs. Removing a component of ISS will simultaneously decrease the sizes of all basic structures by one and thereby always maintain the dimension consistency. By learning ISS within LSTM units, the obtained LSTMs remain regular while having much smaller basic structures. Based on group Lasso regularization, our method achieves 10.59x speedup without losing any perplexity of a language modeling of Penn TreeBank dataset. It is also successfully evaluated through a compact model with only 2.69M weights for machine Question Answering of SQuAD dataset. Our approach is successfully extended to non- LSTM RNNs, like Recurrent Highway Networks (RHNs). Our source code is publicly available at this https URL


title: A Review of Routing Protocols in Wireless Body Area Networks

abstract: Recent technological advancements in wireless communication, integrated circuits and Micro-Electro-Mechanical Systems (MEMs) has enabled miniaturized, lowpower, intelligent, invasive/ non-invasive micro and nanotechnology sensor nodes placed in or on the human body for use in monitoring body function and its immediate environment referred to as Body Area Networks (BANs). BANs face many stringent requirements in terms of delay, power, temperature and network lifetime which need to be taken into serious consideration in the design of different protocols. Since routing protocols play an important role in the overall system performance in terms of delay, power consumption, temperature and so on, a thorough study on existing routing protocols in BANs is necessary. Also, the specific challenges of BANs necessitates the design of new routing protocols specifically designed for BANs. This paper provides a survey of existing routing protocols mainly proposed for BANs. These protocols are further classified into five main categories namely, temperature based, crosslayer, cluster based, cost-effective and QoS-based routing, where each protocol is described under its specified category. Also, comparison among routing protocols in each category is given.


title: Effects of Soft Drink Consumption on Nutrition and Health: A Systematic Review and Meta-Analysis

abstract: In a meta-analysis of 88 studies, we examined the association between soft drink consumption and nutrition and health outcomes. We found clear associations of soft drink intake with increased energy intake and body weight. Soft drink intake also was associated with lower intakes of milk, calcium, and other nutrients and with an increased risk of several medical problems (e.g., diabetes).Study design significantly influenced results: larger effect sizes were observed in studies with stronger methods (longitudinal and experimental vs cross-sectional studies). Several other factors also moderated effect sizes (e.g., gender, age, beverage type). Finally, studies funded by the food industry reported significantly smaller effects than did non–industry-funded studies. Recommendations to reduce population soft drink consumption are strongly supported by the available science.


title: Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position

abstract: A neural network model for a mechanism of visual pattern recognition is proposed in this paper. The network is self-organized by “learning without a teacher”, and acquires an ability to recognize stimulus patterns based on the geometrical similarity (Gestalt) of their shapes without affected by their positions. This network is given a nickname “neocognitron”. After completion of self-organization, the network has a structure similar to the hierarchy model of the visual nervous system proposed by Hubel and Wiesel. The network consits of an input layer (photoreceptor array) followed by a cascade connection of a number of modular structures, each of which is composed of two layers of cells connected in a cascade. The first layer of each module consists of “S-cells”, which show characteristics similar to simple cells or lower order hypercomplex cells, and the second layer consists of “C-cells” similar to complex cells or higher order hypercomplex cells. The afferent synapses to each S-cell have plasticity and are modifiable. The network has an ability of unsupervised learning: We do not need any “teacher” during the process of self-organization, and it is only needed to present a set of stimulus patterns repeatedly to the input layer of the network. The network has been simulated on a digital computer. After repetitive presentation of a set of stimulus patterns, each stimulus pattern has become to elicit an output only from one of the C-cell of the last layer, and conversely, this C-cell has become selectively responsive only to that stimulus pattern. That is, none of the C-cells of the last layer responds to more than one stimulus pattern. The response of the C-cells of the last layer is not affected by the pattern's position at all. Neither is it affected by a small change in shape nor in size of the stimulus pattern.


title: Application of SVM in Object Tracking Based on Laplacian Kernel Function

abstract: In the process of object tracking, because of the light, the angle of view as well as the target shape change causes the goal which is easy to lose. In this paper, we use the SVM based on Laplacian Kernel Function. Under the framework of Struck, it is applied to the object tracking, and comparing with other kernel functions, such as Linear Kernel Function, Gauss Kernel Function and so on, which can achieve the object tracking with high accuracy and stability.


title: Identifying Agile Requirements Engineering Patterns in Industry

abstract: Agile Software Development (ASD) is gaining in popularity in today's business world. Industry is adopting agile methodologies both to accelerate value delivery and to enhance the ability to deal with changing requirements. However, ASD has a great impact on how Requirements Engineering (RE) is carried out in agile environments. The integration of Human-Centered Design (HCD) plays an important role due to the focus on user and stakeholder involvement. To this end, we aim to introduce agile RE patterns as main objective of this paper. On the one hand, we will describe our pattern mining process based on empirical research in literature and industry. On the other hand, we will discuss our results and provide two examples of agile RE patterns. In sum, the pattern mining process identifies 41 agile RE patterns. The accumulated knowledge will be shared by means of a web application.


title: Channel Estimation for Massive MIMO Using Gaussian-Mixture Bayesian Learning

abstract: Pilot contamination posts a fundamental limit on the performance of massive multiple-input–multiple-output (MIMO) antenna systems due to failure in accurate channel estimation. To address this problem, we propose estimation of only the channel parameters of the desired links in a target cell, but those of the interference links from adjacent cells. The required estimation is, nonetheless, an underdetermined system. In this paper, we show that if the propagation properties of massive MIMO systems can be exploited, it is possible to obtain an accurate estimate of the channel parameters. Our strategy is inspired by the observation that for a cellular network, the channel from user equipment to a base station is composed of only a few clustered paths in space. With a very large antenna array, signals can be observed under extremely sharp regions in space. As a result, if the signals are observed in the beam domain (using Fourier transform), the channel is approximately sparse, i.e., the channel matrix contains only a small fraction of large components, and other components are close to zero. This observation then enables channel estimation based on sparse Bayesian learning methods, where sparse channel components can be reconstructed using a small number of observations. Results illustrate that compared to conventional estimators, the proposed approach achieves much better performance in terms of the channel estimation accuracy and achievable rates in the presence of pilot contamination.


title: Amplitude Angle Monopulse Estimation for the Four-Channel Hybrid Polarimetric Radar System

abstract: In this letter, the regular amplitude angle monopulse estimation method is proven not suitable for polarimetric radars because it sets up correlation between azimuth angle and elevation angle of the target and can result in low accuracy. To solve this problem, the polarimetric amplitude angle monopulse estimation method is proposed to obtain the correct angle. By changing the channel synthesis method when the radar receives the signals, the proposed method shows a higher accuracy than the regular method through formulation and the experiment results.


title: 5.2 A 118dB-PSRR 0.00067%(−103.5dB) THD+N and 3.1W fully differential class-D audio amplifier with PWM common-mode control

abstract: A high power-supply rejection ratio (PSRR) and high-linearity Class-D audio amplifier (CDA) becomes important as the CDA is directly connected to a battery supply for efficiency considerations in mobile phone applications. Several closed-loop pulse-width-modulation (PWM) CDAs with high PSRR (>90dB) have been published recently. In conventional PWM fully differential CDAs [1,2], these designs are focused on increasing differential loop gain to improve PSRR. However, the PSRR is mainly limited by feedback gain-matching, that is, resistor matching of the feedback network; hence, it is difficult to achieve PSRR >80dB for yield >99%. In [3], a pseudo-differential structure composed of two single-ended opamps is adopted to achieve PSRR >90dB. Nevertheless, it suffers from 3dB noise increase as compared to the fully differential counterpart. Moreover, unity-gain bandwidth (GBW) of single-ended opamps is usually smaller than that of fully differential opamps due to the design trade-off between noise and the location of current-mirror poles; hence, it results in low in-band gain and high total harmonic distortions (THDs) of pseudo-differential CDAs. As for linearity, conventional PWM CDAs suffer from poor linearity due to in-band aliasing tones. In the uniform PWM (UPWM) architecture [4] that achieves THD+N of around −96.5dB, a sinc function, realized by using a sample-and-hold circuit in front of the loop filter, is applied to avoid the signal-dependent intermodulations folding back in-band. However, high switching frequency is required and the power consumption is increased.


title: Large-scale taxonomy induction using entity and word embeddings

abstract: Taxonomies are an important ingredient of knowledge organization, and serve as a backbone for more sophisticated knowledge representations in intelligent systems, such as formal ontologies. However, building taxonomies manually is a costly endeavor, and hence, automatic methods for taxonomy induction are a good alternative to build large-scale taxonomies. In this paper, we propose TIEmb, an approach for automatic unsupervised class subsumption axiom extraction from knowledge bases using entity and text embeddings. We apply the approach on the WebIsA database, a database of subsumption relations extracted from the large portion of the World Wide Web, to extract class hierarchies in the Person and Place domain.


title: Video super-resolution based on spatial-temporal recurrent residual networks

abstract: Abstract In this paper, we propose a new video Super-Resolution (SR) method by jointly modeling intra-frame redundancy and inter-frame motion context in a unified deep network. Different from conventional methods, the proposed Spatial-Temporal Recurrent Residual Network (STR-ResNet) investigates both spatial and temporal residues, which are represented by the difference between a high resolution (HR) frame and its corresponding low resolution (LR) frame and the difference between adjacent HR frames, respectively. This spatial-temporal residual learning model is then utilized to connect the intra-frame and inter-frame redundancies within video sequences in a recurrent convolutional network and to predict HR temporal residues in the penultimate layer as guidance to benefit estimating the spatial residue for video SR. Extensive experiments have demonstrated that the proposed STR-ResNet is able to efficiently reconstruct videos with diversified contents and complex motions, which outperforms the existing video SR approaches and offers new state-of-the-art performances on benchmark datasets.


title: Hospitalization of Rural and Urban Infants During the First Year of Life

abstract: OBJECTIVES ::: To examine hospitalizations and length of stay (LOS) for infants aged <1 year in rural and urban counties, hypothesizing that infants living in rural counties experience significantly different hospital use compared with urban infants. ::: ::: ::: METHODS ::: Birth certificates for infants born in California hospitals between 1993 and 2005 and surviving to discharge were linked to hospital discharge records and death certificates during the first year of life, resulting in a study population of >6.4 million. Hospitalizations, cumulative LOS, readmission rates, and mortality were compared by using univariate and multivariable analysis for infants living in small rural, large rural, small urban, and large urban counties. Odds of hospitalization and cumulative LOS were also examined for common infant diagnoses. ::: ::: ::: RESULTS ::: Infants living in increasingly rural counties experienced decreasing rates of hospitalization and decreasing number of hospitalized days during the first year of life. Infants living in small rural counties experienced 370 hospital days per 1000 infants compared with 474 hospital days per 1000 infants living in large urban counties. In multivariable analysis, infants in large urban counties experienced increased odds of hospitalization (odds ratio: 1.20 [95% confidence interval: 1.06-1.36]) and increased hospitalized days (incidence risk ratio: 1.17 [95% confidence interval: 1.06-1.29]) compared with infants in small rural counties. For most common diagnoses, urban residence was associated with either increased odds of hospitalization or increased cumulative LOS. ::: ::: ::: CONCLUSIONS ::: Infants living in rural California counties experienced decreased hospital utilization, including decreased hospitalization and decreased LOS, compared with infants living in urban counties.


title: Image Quality Assessment Based on Gradient Similarity

abstract: In this paper, we propose a new image quality assessment (IQA) scheme, with emphasis on gradient similarity. Gradients convey important visual information and are crucial to scene understanding. Using such information, structural and contrast changes can be effectively captured. Therefore, we use the gradient similarity to measure the change in contrast and structure in images. Apart from the structural/contrast changes, image quality is also affected by luminance changes, which must be also accounted for complete and more robust IQA. Hence, the proposed scheme considers both luminance and contrast-structural changes to effectively assess image quality. Furthermore, the proposed scheme is designed to follow the masking effect and visibility threshold more closely, i.e., the case when both masked and masking signals are small is more effectively tackled by the proposed scheme. Finally, the effects of the changes in luminance and contrast-structure are integrated via an adaptive method to obtain the overall image quality score. Extensive experiments conducted with six publicly available subject-rated databases (comprising of diverse images and distortion types) have confirmed the effectiveness, robustness, and efficiency of the proposed scheme in comparison with the relevant state-of-the-art schemes.


title: Analysis and modelling of a bidirectional push-pull converter with PWM plus phase-shift control

abstract: This paper presents analysis and modeling for a bidirectional push-pull dc-dc converter. In particular, attention is paid to leakage current reduction with PWM plus phase-shift control. The leakage current in different operating modes has been compared to identify the efficient modes for various operation conditions. The state space averaging method is adopted to derive the small signal and large signal models of the most efficient mode. A 30∼70V/300V prototype was built. Both simulation results and experimental results prove the validity of the model.