title: U-Net: Convolutional Networks for Biomedical Image Segmentation

abstract: There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .


title: A 65nm Pure CMOS one-time programmable memory using a two-port antifuse cell implemented in a matrix structure

abstract: A pure CMOS one-time programmable (PCOP) memory using an antifuse is presented. PCOP memory adopts two-port cell architecture implemented in a matrix structure. This architecture achieves optimization of performance both for programming and reading. Furthermore, it solves the write disturb problem and realizes pseudo "1" read test. An 8 Kbit macro is developed utilizing a 65 nm pure CMOS logic technology. The cell area and the macro size are 15.3 mum2 and 0.244 mm2, respectively.


title: Solid waste management practices and review of recovery and recycling operations in Turkey.

abstract: This paper provides a general overview of solid waste data and management practices employed in Turkey during the last decade. Municipal solid waste statistics and management practices including waste recovery and recycling initiatives have been evaluated. Detailed data on solid waste management practices including collection, recovery and disposal, together with the results of cost analyses, have been presented. Based on these evaluations basic cost estimations on collection and sorting of recyclable solid waste in Turkey have been provided. The results indicate that the household solid waste generation in Turkey, per capita, is around 0.6 kg/year, whereas municipal solid waste generation is close to 1 kg/year. The major constituents of municipal solid waste are organic in nature and approximately 1/4 of municipal solid waste is recyclable. Separate collection programmes for recyclable household waste by more than 60 municipalities, continuing in excess of 3 years, demonstrate solid evidence for public acceptance and continuing support from the citizens. Opinion polls indicate that more than 80% of the population in the project regions is ready and willing to participate in separate collection programmes. The analysis of output data of the Material Recovery Facilities shows that, although paper, including cardboard, is the main constituent, the composition of recyclable waste varies strongly by the source or the type of collection point.


title: Efficient Transfer Learning for Neural Network Language Models

abstract: We apply transfer learning techniques to create topically and/or stylistically biased natural language models from small data samples, given generic long short-term memory (LSTM) language models trained on larger data sets. Although LSTM language models are powerful tools with wide-ranging applications, they require enormous amounts of data and time to train. Thus, we build general purpose language models that take advantage of large standing corpora and computational resources proactively, allowing us to build more specialized analytical tools from smaller data sets on demand. We show that it is possible to construct a language model from a small, focused corpus by first training an LSTM language model on a large corpus (e.g., the text from English Wikipedia) and then retraining only the internal transition model parameters on the smaller corpus. We also show that a single general language model can be reused through transfer learning to create many distinct special purpose language models quickly with modest amounts of data.


title: Practical laboratory project in telemedicine: Supervision of electrocardiograms by mobile telephony

abstract: This paper describes a project carried out in the laboratory of Principles of Bioengineering within the Telecommunications Engineering degree (five academic years plus a final thesis). The main object of this project is to set up a portable system for the acquisition, analysis, and transmission by mobile telephony of the electrocardiogram (ECG) of an indeterminate number of patients. The network of patients is managed from a control center (CC), comprising a personal computer (PC) with application software and a mobile telephony communications system. In a real system, each patient would have a patient terminal with a microprocessor-based, ECG-acquisition system with processing and transmission by mobile telephony. Time and technical considerations mean that, in the project, the patient terminal will have to be implemented in a PC. To perform this project, the students must have a wide range of knowledge acquired in this course and in other subjects required for this degree: design of bioamplifiers, information-processing algorithms, mobile-telephony communications, user-interface programming, etc. Having to use these concepts will no doubt serve as a stimulus to the students carrying out the work.


title: ISS-TCA: An Identified Slot Scan-Based Tag Collection Algorithm for Performance Improvement in Active RFID Systems

abstract: The standard tag collection algorithm (TCA) in ISO/IEC 18000-7 has difficulty in collecting data from a massive number of active radio frequency identification (RFID) tags in a timely manner, so it should be improved to allow successful application of active RFID systems to a wide variety of industrial fields. We propose a novel TCA, which is an identified slot scan-based TCA (ISS-TCA), to improve the performance of tag collection (TC) in active RFID systems. In the proposed ISS-TCA, an active RFID reader differentiates identified slots from collided and empty slots via an ISS phase, in which active RFID tags send minimal responses. The reader then avoids collided and empty slots in the following TC phase, in which it collects the actual data from the tags. This reduces the cost of collided and empty slots during TC, resulting in improved TC performance. We carried out simulations to evaluate and compare the performances of the standard TCA and the proposed ISS-TCA in TC. The simulation results with up to 500 tags showed that the proposed ISS-TCA reduced the average time required for TC by 33.2% or 27.3% when the reader collected 50 or 100 bytes of additional data from the tags, respectively. We also experimentally evaluated the performance improvement of TC by the proposed ISS-TCA using an active RFID reader and 60 tags that were implemented for this paper. The experimental results confirmed that the proposed ISS-TCA could improve the TC performance in practice, confirming the simulation results, and showed that it could also decrease the average battery consumption of the tags by 39.3%.


title: Articulated body motion capture by annealed particle filtering

abstract: The main challenge in articulated body motion tracking is the large number of degrees of freedom (around 30) to be recovered. Search algorithms, either deterministic or stochastic, that search such a space without constraint, fall foul of exponential computational complexity. One approach is to introduce constraints: either labelling using markers or colour coding, prior assumptions about motion trajectories or view restrictions. Another is to relax constraints arising from articulation, and track limbs as if their motions were independent. In contrast, we aim for general tracking without special preparation of objects or restrictive assumptions. The principal contribution of the paper is the development of a modified particle filter for search in high dimensional configuration spaces. It uses a continuation principle based on annealing to introduce the influence of narrow peaks in the fitness function, gradually. The new algorithm, termed annealed particle filtering, is shown to be capable of recovering full articulated body motion efficiently.


title: Overview of LTE enhancements for cellular IoT

abstract: The Internet of Things will bring about billions of connected devices. In the cellular space, machine type communications using LTE is under consideration by many operators. LTE, however, was designed to provide broadband high-speed data service with very low latency. Machine type communications, on the other hand, have a different set of requirements, namely low-rate, low-overhead, low-power-consumption, and low-cost. To address these requirements and also to minimize impact from machine traffic to human traffic, 3GPP has been working on numerous LTE features such as power saving, overload control, signaling reduction, complexity reduction, and coverage enhancement. This paper will provide an overview of LTE enhancements for machine type communications. The paper will also present LTE capacity analysis for machine traffic as well as the battery life of the devices.


title: Adaptive sliding mode control of ball and plate systems for its practical application

abstract: In this paper, an adaptive sliding mode controller is proposed, which is robust against external disturbances and parameter uncertainties. The proposed controller is designed to solve the trajectory tracking problem of ball and plate systems. Furthermore, it considers not just theoretical approach but practical approach. The external disturbances should be solved since there are various external disturbances in real systems such as circuit noise, mechanical vibration and so on. Another important issue for implementing the ball and beam system is an unknown parameter problem since the change of the ball weight should be considered. Therefore, an indirect adaptive sliding mode control technique is adopted to overcome both problems. Before the proposed controller is applied to the implemented ball and plate system, its stability analysis is performed by Lyapunov stability theory and some simulation verifies its performance.


title: Asymptotic Properties of Lasso+mLS and Lasso+Ridge in Sparse High-dimensional Linear Regression

abstract: We study the asymptotic properties of Lasso+mLS and Lasso+Ridge under the sparse high-dimensional linear regression model: Lasso selecting predictors and then modified Least Squares (mLS) or Ridge estimating their coefficients. First, we propose a valid inference procedure for parameter estimation based on parametric residual bootstrap after Lasso+mLS and Lasso+Ridge. Second, we derive the asymptotic unbiasedness of Lasso+mLS and Lasso+Ridge. More specifically, we show that their biases decay at an exponential rate and they can achieve the oracle convergence rate of $s/n$ (where $s$ is the number of nonzero regression coefficients and $n$ is the sample size) for mean squared error (MSE). Third, we show that Lasso+mLS and Lasso+Ridge are asymptotically normal. They have an oracle property in the sense that they can select the true predictors with probability converging to 1 and the estimates of nonzero parameters have the same asymptotic normal distribution that they would have if the zero parameters were known in advance. In fact, our analysis is not limited to adopting Lasso in the selection stage, but is applicable to any other model selection criteria with exponentially decay rates of the probability of selecting wrong models.


title: Integrating the Trend of Research Interest for Reviewer Assignment

abstract: Reviewer assignment problem in the research field usually refers to invite experts for comments on the quality of papers, projects, etc. Different factors in conventional approaches are reckoned to choose appropriate reviewers, such as the relevance between reviewer candidates and submissions, the diversity of candidates. However, many studies ignore the temporal changes of reviewer interest and the stability of reviewers' interest trend. Accordingly, in this research, three indispensable aspects are analyzed, including the relevance between reviewer candidates and submissions, the interest trend of candidates as well as the authority of candidates. Next, with extracted aspects, the reviewer assignment is formulated as an integer linear programming problem. Finally, categories of comparative experiments are conducted with two large datasets that are built from WANFANG and ArnetMiner, which shows the availability of the proposed approach in modeling the temporal changes of reviewers' research interest. Also, it demonstrates the effectiveness of the proposed approach for the reviewer assignment problem.


title: Improving Part-of-Speech Tagging for NLP Pipelines

abstract: This paper outlines the results of sentence level linguistics based rules for improving part-of-speech tagging. It is well known that the performance of complex NLP systems is negatively affected if one of the preliminary stages is less than perfect. Errors in the initial stages in the pipeline have a snowballing effect on the pipeline's end performance. We have created a set of linguistics based rules at the sentence level which adjust part-of-speech tags from state-of-the-art taggers. Comparison with state-of-the-art taggers on widely used benchmarks demonstrate significant improvements in tagging accuracy and consequently in the quality and accuracy of NLP systems.


Learning Bilingual Sentiment Word Embeddings for Cross-language Sentiment Classification


title: Software model synthesis using satisfiability solvers

abstract: We introduce a novel approach for synthesis of software models based on identifying deterministic finite state automata. Our approach consists of three important contributions. First, we argue that in order to model software, one should focus mainly on observed executions (positive data), and use the randomly generated failures (negative data) only for testing consistency. We present a new greedy heuristic for this purpose, and show how to integrate it in the state-of-the-art evidence-driven state-merging (EDSM) algorithm. Second, we apply the enhanced EDSM algorithm to iteratively reduce the size of the problem. Yet during each iteration, the evidence is divided over states and hence the effectiveness of this algorithm is decreased. We propose--when EDSM becomes too weak--to tackle the reduced identification problem using satisfiability solvers. Third, in case the amount of positive data is small, we solve the identification problem several times by randomizing the greedy heuristic and combine the solutions using a voting scheme. The interaction between these contributions appeared crucial to solve hard software models synthesis benchmarks. Our implementation, called DFASAT, won the StaMinA competition.


title: How and Why Criteria Defining Moderators and Mediators Differ Between the Baron & Kenny and MacArthur Approaches

abstract: Objective: In recognition of the increasingly important role of moderators and mediators in clinical research, clear definitions are sought of the two terms to avoid inconsistent, ambiguous, and possibly misleading results across clinical research studies. Design: The criteria used to define moderators and mediators proposed by the Baron & Kenny approach, which have been long used in social/behavioral research, are directly compared to the criteria proposed by the recent MacArthur approach, which modified the Baron & Kenny criteria. Results: After clarifying the differences in criteria between approaches, the rationale for the modifications is clarified and the implications for the design and interpretation of future studies considered. Conclusions: Researchers may find modifications introduced in the MacArthur approach more appropriate to their research objectives, particularly if their research might have a direct influence on decision making.


title: Adding Attributes to Role-Based Access Control

abstract: Merging the best features of RBAC and attribute-based systems can provide effective access control for distributed and rapidly changing applications.


title: Towards a Framework for Gamification Design on Crowdsourcing Systems: The G.A.M.E. Approach

abstract: Crowd sourcing systems call a crowd of users to collaborate on solving real-life problems. One key issue for the success of such systems is to guarantee users' participation. A strategy that has been used to promote user participation is the use of game design techniques, since games have successful strategies to grant enjoyable user experience. However, most gamification methods and guidelines are too generic, do not emphasize the collaboration aspects and focus on introducing rewarding elements into the application, instead of designing player-centric applications. Reward-based design is dangerous, specially for collaborative systems, because it may put points gathering as the primary purpose of the application instead of the collaboration goal. This paper presents G.A.M.E., a conceptual framework to guide the design of gamification in crowd sourcing-based systems. The framework provides a flexible step-by-step guideline that combines knowledge from software engineering, collaborative systems, game design and interaction design. To evaluate our proposal, we instantiated G.A.M.E. Into two applications in the domain of public transportation. The influence of gamification in those applications were evaluated through controlled navigation tests in a crowd sourcing usability testing platform. Our findings showed us that gamification improved user interfaces of collaboration activities by 16% on usability and were more trustworthy in 80% the cases.


Inference and Design in Kuba and Zillij Art with Shape Grammars


title: A voltage-scalable & process variation resilient hybrid SRAM architecture for MPEG-4 video processors

abstract: We present a voltage-scalable and process-variation resilient memory architecture, suitable for MPEG-4 video processors such that power dissipation can be traded for graceful degradation in "quality". The key innovation in our proposed work is a hybrid memory array, which is mixture of conventional 6T and 8T SRAM bit-cells. The fundamental premise of our approach lies in the fact that human visual system (HVS) is mostly sensitive to higher order bits of luminance pixels in video data. We implemented a preferential storage policy in which the higher order luma bits are stored in robust 8T bit-cells while the lower order bits are stored in conventional 6T bit-cells. This facilitates aggressive scaling of supply voltage in memory as the important luma bits, stored in 8T bit-cells, remain relatively unaffected by voltage scaling. The not-so-important lower order luma bits, stored in 6T bit-cells, if affected, contribute insignificantly to the overall degradation in output video quality. Simulation results show average power savings of up to 56%, in the hybrid memory array compared to the conventional 6T SRAM array implemented in 65nm CMOS. The area overhead and maximum output quality degradation (PSNR) incurred were 11.5% and 0.56 dB, respectively.


title: Adapting Sequence Models for Sentence Correction

abstract: In a controlled experiment of sequence-to-sequence approaches for the task of sentence correction, we find that character-based models are generally more effective than word-based models and models that encode subword information via convolutions, and that modeling the output data as a series of diffs improves effectiveness over standard approaches. Our strongest sequence-to-sequence model improves over our strongest phrase-based statistical machine translation model, with access to the same data, by 6 M2 (0.5 GLEU) points. Additionally, in the data environment of the standard CoNLL-2014 setup, we demonstrate that modeling (and tuning against) diffs yields similar or better M2 scores with simpler models and/or significantly less data than previous sequence-to-sequence approaches.


title: Joint Latent Dirichlet Allocation for Social Tags

abstract: Social tags, serving as a textual source of simple but useful semantic metadata to reflect the user preference or describe the web objects, has been widely used in many applications. However, social tags have several unique characteristics, i.e., sparseness and data coupling (i.e., non-IIDness), which makes existing text analysis methods such as LDA not directly applicable. In this paper, we propose a new generative algorithm for social tag analysis named joint latent Dirichlet allocation, which models the generation of tags based on both the users and the objects, and thus accounts for the coupling relationships among social tags. The model introduces two latent factors that jointly influence tag generation: the user's latent interest factor and the object's latent topic factor, formulated as user-topic distribution matrix and object-topic distribution matrix, respectively. A Gibbs sampling approach is adopted to simultaneously infer the above two matrices as well as a topic-word distribution matrix. Experimental results on four social tagging datasets have shown that our model is able to capture more reasonable topics and achieves better performance than five state-of-the-art topic models in terms of the widely used point-wise mutual information metric. In addition, we analyze the learnt topics showing that our model recovers more themes from social tags while LDA may lead the topic vanishing problems, and demonstrate its advantages in the social recommendation by evaluating the retrieval results with mean reciprocal rank metric. Finally, we explore the joint procedure of our model in depth to show the non-IID characteristic of social tagging process.


title: Strength-Based Positive Interventions: Further Evidence for Their Potential in Enhancing Well-Being and Alleviating Depression

abstract: The impact of nine strengths-based positive interventions on well-being and depression was examined in an Internet-based randomized placebo-controlled study. The aims of the study were to: (1) replicate findings on the effectiveness of the gratitude visit, three good things, and using character strengths interventions; (2) test variants of interventions (noting three good things for 2 weeks; combining the gratitude visit and three good things interventions; and noting three funny things for a week); and (3) test the effectiveness of the counting kindness, gift of time, and another door opens-interventions in an online setting. A total of 622 adults subjected themselves to one of the nine interventions or to a placebo control exercise (early memories) and thereafter estimated their degrees of happiness and depression at five times (pre- and post-test, 1-, 3-, and 6 months follow-up). Eight of the nine interventions increased happiness; depression was decreased in all groups, including the placebo control group. We conclude that happiness can be enhanced through some “strengths-based” interventions. Possible mechanisms for the effectiveness of the interventions are discussed. Copyright Springer Science+Business Media B.V. 2013


title: Method of integrating public service data on civil aviation based on information grid

abstract: According to the specific needs of aero operation and feature of aero public service data, the article aims to analyze the model of integrating flows and adapter on civil aviation which is on the base of information grid as well as to propose comprehensive data and integrated environmental framework on informative grid, so as to provide public and executive of civil aviation with complete, timing and exact message which can afford technical support.


title: Improved Sentence Modeling using Suffix Bidirectional LSTM

abstract: Recurrent neural networks have become ubiquitous in computing representations of sequential data, especially textual data in natural language processing. In particular, Bidirectional LSTMs are at the heart of several neural models achieving state-of-the-art performance in a wide variety of tasks in NLP. However, BiLSTMs are known to suffer from sequential bias - the contextual representation of a token is heavily influenced by tokens close to it in a sentence. We propose a general and effective improvement to the BiLSTM model which encodes each suffix and prefix of a sequence of tokens in both forward and reverse directions. We call our model Suffix Bidirectional LSTM or SuBiLSTM. This introduces an alternate bias that favors long range dependencies. We apply SuBiLSTMs to several tasks that require sentence modeling. We demonstrate that using SuBiLSTM instead of a BiLSTM in existing models leads to improvements in performance in learning general sentence representations, text classification, textual entailment and paraphrase detection. Using SuBiLSTM we achieve new state-of-the-art results for fine-grained sentiment classification and question classification.


title: Operation in Partitioned Circuits with Scalable Hardware Mechanism

abstract: We propose the Scalable Hardware Mechanism, which enables the operation of a partitioned circuit to prevent the degradation of clock frequency by minimizing its dependence on the usage and the type of FPGA. Our mechanism provides a reduced delay by the collective signal transmission with the partitioned AES code generation circuit and the character string edit distance calculation circuit as partitioned circuits. The collective signal transmission has attained 1.27 times improvement in the speed for the AES code generation circuit and 3.16 times improvement for the character string edit distance calculation circuit compared with the circuit by the conventional method.


title: A cognitive systems engineering perspective on the design of mixed reality systems

abstract: This paper examines usability issues in Mixed Reality (MR) systems from a Cognitive Systems Engineering (CSE) (Hollnagel & Woods 1983; 2005) perspective with the purpose of finding an alternative approach to usability in MR-systems. A qualitative user study has been performed at a Swedish hospital where professionals have tested an MR prototype providing instruction of the use of advanced medical equipment. The results indicate that the participants in this study do not consider the MR system as a traditional computer based manual, but rather as an interactive personal instructor. The fact that users work through the MR system rather than with the MR system raises some fundamental design issues regarding usability and the perspective on usability. This suggests that there may be a need to utilize a different approach for usability concerning MR systems, instead of transferring traditional human-computer usability guidelines to the MR domain.


title: Large-Scale Visual Font Recognition

abstract: This paper addresses the large-scale visual font recogni- tion (VFR) problem, which aims at automatic identification of the typeface, weight, and slope of the text in an image or photo without any knowledge of content. Although vi- sual font recognition has many practical applications, it has largely been neglected by the vision community. To address the VFR problem, we construct a large-scale dataset con- taining 2, 420 font classes, which easily exceeds the scale of most image categorization datasets in computer vision. As font recognition is inherently dynamic and open-ended, i.e., new classes and data for existing categories are constantly added to the database over time, we propose a scalable so- lution based on the nearest class mean classifier (NCM). The core algorithm is built on local feature embedding, lo- cal feature metric learning and max-margin template se- lection, which is naturally amenable to NCM and thus to such open-ended classification problems. The new algo- rithm can generalize to new classes and new data at lit- tle added cost. Extensive experiments demonstrate that our approach is very effective on our synthetic test images, and achieves promising results on real world test images.


title: GreenThumb: superoptimizer construction framework

abstract: Developing an optimizing compiler backend remains a laborious process, especially for nontraditional ISAs that have been appearing recently. Superoptimization sidesteps the need for many code transformations by searching for the most optimal instruction sequence semantically equivalent to the original code fragment. Even though superoptimization discovers the best machine-specific code optimizations, it has yet to become widely-used. We propose GreenThumb, an extensible framework that reduces the cost of constructing superoptimizers and provides a fast search algorithm that can be reused for any ISA, exploiting the unique strengths of enumerative, stochastic, and symbolic (SAT-solver-based) search algorithms. To extend GreenThumb to a new ISA, it is only necessary to implement an emulator for the ISA and provide some ISA-specific search utility functions.


title: Personality and social characteristics of Facebook non-users and frequent users

abstract: Facebook is currently the largest social networking website with an estimated one billion of monthly active users in 2012. While most of the prior research has explored characteristics of Facebook users, less is known about the characteristics of individuals who do not use Facebook. The current study examined personality and social factors that might influence the decision to use Facebook and explored differences between Facebook non-users and frequent users. Online questionnaires examining levels of trust and self-disclosure, number of intimate friendships, peer usage of Facebook and scores on overt and covert narcissism were used for the purpose of the study. The results showed that non-users and frequent users differed on several social and personality characteristics. Facebook non-users had lower tendency to self-disclose, fewer peers participating in the social network and higher covert narcissistic traits. Frequent Facebook users scored higher on overt narcissism and reported more intimate friendships than non-users, indicating that close friendships might actually extend to social networks and contribute to a feeling of closeness and intimacy between friends in both an online and offline context.


title: MEG: Memory and Energy Efficient Garbled Circuit Evaluation on Smartphones

abstract: Garbled circuits are general tools that allow two parties to compute any function without disclosing their respective inputs. Applications of this technique vary from distributed privacy-preserving machine learning tasks to secure outsourced authentication. Unfortunately, the energy cost of garbled circuit evaluation protocols is substantial. This limits the applicability of garbled circuits in scenarios that involve battery-operated devices, such as Internet-of-Things (IoT) devices and smartphones. In this paper, we propose MEG, a Memory- and Energy-efficient Garbled circuit evaluation mechanism. MEG utilizes batch data transmission and multi-threading to reduce memory and energy consumption. We implement MEG on an Android smartphone and compare its performance and energy consumption with state-of-the-art techniques using two garbled circuits of widely different sizes (AES-128 and 256-bit edit distance). Our results show that, compared with “plain” garbled circuit evaluation, MEG decreases memory consumption by more than 90%. When compared with current pipelined garbled circuit evaluation techniques, MEG’s energy usage was 42% lower for AES-128 and 23% lower for EDT-256. Furthermore, our multi-thread implementation of MEG decreased circuit evaluation time by up to 56.7% for AES-128, and by up to 13.5% for EDT-256, compared with state-of-the-art pipelining techniques.


title: Using early view patterns to predict the popularity of youtube videos

abstract: Predicting Web content popularity is an important task for supporting the design and evaluation of a wide range of systems, from targeted advertising to effective search and recommendation services. We here present two simple models for predicting the future popularity of Web content based on historical information given by early popularity measures. Our approach is validated on datasets consisting of videos from the widely used YouTube video-sharing portal. Our experimental results show that, compared to a state-of-the-art baseline model, our proposed models lead to significant decreases in relative squared errors, reaching up to 20% reduction on average, and larger reductions (of up to 71%) for videos that experience a high peak in popularity in their early days followed by a sharp decrease in popularity.


title: SpongeFiles: mitigating data skew in mapreduce using distributed memory

abstract: Data skew is a major problem for data processing platforms like MapReduce. Skew causes worker tasks to spill to disk what they cannot fit in memory, which slows down the task and the overall job. Moreover, performance of other jobs sharing same disk degrades. In many cases, this situation occurs even as the cluster has plenty of spare memory it is just not used evenly. We introduce SpongeFiles, a novel distributed-memory abstraction tailored to data processing environments like MapReduce. A SpongeFile is a logical byte array, comprised of large chunks that can be stored in a variety of locations in the cluster. Spilled data goes to SpongeFiles, which route it to the nearest location with sufficient capacity (local memory, remote memory, local disk, or remote disk as a last resort). By enabling memory-sapped nodes to tap into the spare capacity of their neighbors, SpongeFiles minimize expensive disk spilling, thereby improving performance. In our experiments with Hadoop and Pig, SpongeFiles reduce overall job runtimes by up to 55% and by up to 85% under disk contention.


title: Digital Games in Education: The Design of Games-Based Learning Environments.

abstract: AbstractIn recent years, electronic games have assumed an important place in the lives of children and adolescents. Children acquire digital literacy informally, through play, and neither schools nor other educational institutions take sufficient account of this important aspect. We consider that multimedia design for training and education should combine the most powerful features of interactive multimedia design with the most effective principles of technologically-mediated learning. An examination of the evolution of the design of videogames is a good way to analyze the main contributions and characteristics of games-based learning environments. At the same time, we will discuss the main obstacles and challenges to the use of games for learning.


title: Urban Impervious Surfaces Estimation From Optical and SAR Imagery: A Comprehensive Comparison

abstract: Numerous studies have demonstrated the benefits of fusing optical and synthetic aperture radar (SAR) data for urban impervious surfaces estimation. Given the increasing availability of SAR satellite data, the synergized use of optical and SAR data is increasingly important. However, previous studies have focused primarily on a single scale. This study aims to evaluate the impervious surfaces estimation using different combinations of optical and SAR data, as well as to quantify the scale effects of combining optical and SAR data under various resolutions from 30 to 1050 m. Six combinations of optical and SAR data in three cities, Shenzhen, Mumbai, and Sao Paulo, were compared. Experimental results illustrated the effectiveness of using texture and SAR data in improving impervious surface estimation with producer's accuracy improvement of 5%, 0%, and 4% in Shenzhen, Mumbai, and Sao Paulo at 30 m. The correlation coefficient assessment between the estimated and referenced impervious surfaces at various scales demonstrated that TerraSAR-X data generally outperformed ENVISAT advanced SAR of impervious surface estimation. Estimation accuracy of impervious surfaces in Shenzhen had an improvement of 4% to 5%. Moreover, the texture information of SAR data was observed to provide positive contributions to the impervious surfaces estimation in three study cases at different scales.


title: A Plant-Wide Industrial Process Control Security Problem

abstract: Industrial control systems are a vital part of the critical infrastructure. The potentially large impact of a failure makes them attractive targets for adversaries. Unfortunately, simplistic approaches to intrusion detection using protocol analysis or naive statistical estimation techniques are inadequate in the face of skilled adversaries who can hide their presence with the appearance of legitimate actions.


title: Building pathway clusters from Random Forests classification using class votes

abstract: BackgroundRecent years have seen the development of various pathway-based methods for the analysis of microarray gene expression data. These approaches have the potential to bring biological insights into microarray studies. A variety of methods have been proposed to construct networks using gene expression data. Because individual pathways do not act in isolation, it is important to understand how different pathways coordinate to perform cellular functions. However, there are no published methods describing how to build pathway clusters that are closely related to traits of interest.ResultsWe propose to build pathway clusters from pathway-based classification methods. The proposed methods allow researchers to identify clusters of pathways sharing similar functions. These pathways may or may not share genes. As an illustration, our approach is applied to three human breast cancer microarray data sets. We found that our methods yielded consistent and interpretable results for these three data sets. We further investigated one of the pathway clusters found using PubMatrix. We found that informative genes in the pathway clusters do have more publications with keywords, like estrogen receptor, compared with informative genes in other top pathways. In addition, using the shortest path analysis in GeneGo's MetaCore and Human Protein Reference Database, we were able to identify the links which connect the pathways without shared genes within the pathway cluster.ConclusionOur proposed pathway clustering methods allow bioinformaticians and biologists to investigate how informative genes within pathways are related to each other and understand possible crosstalk between pathways in a cluster. Therefore, building pathway clusters may lead to a better understanding of molecular mechanisms affecting a trait of interest, and help generate further biological hypotheses from gene expression data.


title: House price estimation from visual and textual features

abstract: Most existing automatic house price estimation systems rely only on some textual data like its neighborhood area and the number of rooms. The final price is estimated by a human agent who visits the house and assesses it visually. In this paper, we propose extracting visual features from house photographs and combining them with the house's textual information. The combined features are fed to a fully connected multilayer Neural Network (NN) that estimates the house price as its single output. To train and evaluate our network, we have collected the first houses dataset (to our knowledge) that combines both images and textual attributes. The dataset is composed of 535 sample houses from the state of California, USA. Our experiments showed that adding the visual features increased the R-value by a factor of 3 and decreased the Mean Square Error (MSE) by one order of magnitude compared with textual-only features. Additionally, when trained on the benchmark textual-only features housing dataset, our proposed NN still outperformed the existing model published results.


title: Learning object class detectors from weakly annotated video

abstract: Object detectors are typically trained on a large set of still images annotated by bounding-boxes. This paper introduces an approach for learning object detectors from real-world web videos known only to contain objects of a target class. We propose a fully automatic pipeline that localizes objects in a set of videos of the class and learns a detector for it. The approach extracts candidate spatio-temporal tubes based on motion segmentation and then selects one tube per video jointly over all videos. To compare to the state of the art, we test our detector on still images, i.e., Pascal VOC 2007. We observe that frames extracted from web videos can differ significantly in terms of quality to still images taken by a good camera. Thus, we formulate the learning from videos as a domain adaptation task. We show that training from a combination of weakly annotated videos and fully annotated still images using domain adaptation improves the performance of a detector trained from still images alone.


title: A Measurement Study of a Large-Scale P2P IPTV System

abstract: An emerging Internet application, IPTV, has the potential to flood Internet access and backbone ISPs with massive amounts of new traffic. Although many architectures are possible for IPTV video distribution, several mesh-pull P2P architectures have been successfully deployed on the Internet. In order to gain insights into mesh-pull P2P IPTV systems and the traffic loads they place on ISPs, we have undertaken an in-depth measurement study of one of the most popular IPTV systems, namely, PPLive. We have developed a dedicated PPLive crawler, which enables us to study the global characteristics of the mesh-pull PPLive system. We have also collected extensive packet traces for various different measurement scenarios, including both campus access networks and residential access networks. The measurement results obtained through these platforms bring important insights into P2P IPTV systems. Specifically, our results show the following. 1) P2P IPTV users have the similar viewing behaviors as regular TV users. 2) During its session, a peer exchanges video data dynamically with a large number of peers. 3) A small set of super peers act as video proxy and contribute significantly to video data uploading. 4) Users in the measured P2P IPTV system still suffer from long start-up delays and playback lags, ranging from several seconds to a couple of minutes. Insights obtained in this study will be valuable for the development and deployment of future P2P IPTV systems.


title: Robust and continuous estimation of driver gaze zone by dynamic analysis of multiple face videos

abstract: Analysis of driver's head behavior is an integral part of driver monitoring system. Driver's coarse gaze direction or gaze zone is a very important cue in understanding driver- state. Many existing gaze zone estimators are, however, limited to single camera perspectives, which are vulnerable to occlu- sions of facial features from spatially large head movements away from the frontal pose. Non-frontal glances away from the driving direction, though, are of special interest as interesting events, critical to driver safety, occur during those times. In this paper, we present a distributed camera framework for gaze zone estimation using head pose dynamics to operate robustly and continuously even during large head movements. For experimental evaluations, we collected a dataset from naturalistic on-road driving in urban streets and freeways. A human expert provided the gaze zone ground truth using all vision information including eyes and surround context. Our emphasis is to understand the efficacy of the head pose dynamic information in predicting eye-gaze-based zone ground truth. We conducted several experiments in designing the dynamic features and compared the performance against static head pose based approach. Analyses show that dynamic information significantly improves the results.


title: Lowering the barriers to large-scale mobile crowdsensing

abstract: Mobile crowdsensing is becoming a vital technique for environment monitoring, infrastructure management, and social computing. However, deploying mobile crowdsensing applications in large-scale environments is not a trivial task. It creates a tremendous burden on application developers as well as mobile users. In this paper we try to reveal the barriers hampering the scale-up of mobile crowdsensing applications, and to offer our initial thoughts on the potential solutions to lowering the barriers.


title: Randomized Trial of Verubecestat for Mild‐to‐Moderate Alzheimer's Disease

abstract: BACKGROUND ::: Alzheimer's disease is characterized by the deposition of amyloid-beta (Aβ) plaques in the brain. Aβ is produced from the sequential cleavage of amyloid precursor protein by β-site amyloid precursor protein-cleaving enzyme 1 (BACE-1) followed by γ-secretase. Verubecestat is an oral BACE-1 inhibitor that reduces the Aβ level in the cerebrospinal fluid of patients with Alzheimer's disease. ::: ::: ::: METHODS ::: We conducted a randomized, double-blind, placebo-controlled, 78-week trial to evaluate verubecestat at doses of 12 mg and 40 mg per day, as compared with placebo, in patients who had a clinical diagnosis of mild-to-moderate Alzheimer's disease. The coprimary outcomes were the change from baseline to week 78 in the score on the cognitive subscale of the Alzheimer's Disease Assessment Scale (ADAS-cog; scores range from 0 to 70, with higher scores indicating worse dementia) and in the score on the Alzheimer's Disease Cooperative Study Activities of Daily Living Inventory scale (ADCS-ADL; scores range from 0 to 78, with lower scores indicating worse function). ::: ::: ::: RESULTS ::: A total of 1958 patients underwent randomization; 653 were randomly assigned to receive verubecestat at a dose of 12 mg per day (the 12-mg group), 652 to receive verubecestat at a dose of 40 mg per day (the 40-mg group), and 653 to receive matching placebo. The trial was terminated early for futility 50 months after onset, which was within 5 months before its scheduled completion, and after enrollment of the planned 1958 patients was complete. The estimated mean change from baseline to week 78 in the ADAS-cog score was 7.9 in the 12-mg group, 8.0 in the 40-mg group, and 7.7 in the placebo group (P=0.63 for the comparison between the 12-mg group and the placebo group and P=0.46 for the comparison between the 40-mg group and the placebo group). The estimated mean change from baseline to week 78 in the ADCS-ADL score was -8.4 in the 12-mg group, -8.2 in the 40-mg group, and -8.9 in the placebo group (P=0.49 for the comparison between the 12-mg group and the placebo group and P=0.32 for the comparison between the 40-mg group and the placebo group). Adverse events, including rash, falls and injuries, sleep disturbance, suicidal ideation, weight loss, and hair-color change, were more common in the verubecestat groups than in the placebo group. ::: ::: ::: CONCLUSIONS ::: Verubecestat did not reduce cognitive or functional decline in patients with mild-to-moderate Alzheimer's disease and was associated with treatment-related adverse events. (Funded by Merck; ClinicalTrials.gov number, NCT01739348 .).


title: Quality of Service Support for Fine-Grained Sharing on GPUs

abstract: GPUs have been widely adopted in data centers to provide acceleration services to many applications. Sharing a GPU is increasingly important for better processing throughput and energy efficiency. However, quality of service (QoS) among concurrent applications is minimally supported. Previous efforts are too coarse-grained and not scalable with increasing QoS requirements. We propose QoS mechanisms for a fine-grained form of GPU sharing. Our QoS support can provide control over the progress of kernels on a per cycle basis and the amount of thread-level parallelism of each kernel. Due to accurate resource management, our QoS support has significantly better scalability compared with previous best efforts. Evaluations show that, when the GPU is shared by three kernels, two of which have QoS goals, the proposed techniques achieve QoS goals 43.8% more often than previous techniques and have 20.5% higher throughput.


title: On A Strictly Convex IBM Model 1

abstract: IBM Model 1 is a classical alignment model. Of the first generation word-based SMT models, it was the only such model with a concave objective function. For concave optimization problems like IBM Model 1, we have guarantees on the convergence of optimization algorithms such as Expectation Maximization (EM). However, as was pointed out recently, the objective of IBM Model 1 is not strictly concave and there is quite a bit of alignment quality variance within the optimal solution set. In this work we detail a strictly concave version of IBM Model 1 whose EM algorithm is a simple modification of the original EM algorithm of Model 1 and does not require the tuning of a learning rate or the insertion of an l2 penalty. Moreover, by addressing Model 1’s shortcomings, we achieve AER and F-Measure improvements over the classical Model 1 by over 30%.


title: Lire: lucene image retrieval: an extensible java CBIR library

abstract: LIRe (Lucene Image Retrieval) is a light weight open source Java library for content based image retrieval. It provides common and state of the art global image features and offers means for indexing and retrieval. Due to the fact that it is based on a light weight embedded text search engine, it can be integrated easily in applications without relying on a database server.


title: Anomaly Detection in Computer Networks: A State-of-the-Art Review

abstract: The ever-lasting challenge of detecting and mitigating failures in computer networks has become more essential than ever; especially with the enormous number of smart devices that get connected to all sorts of network everyday. Whether the root cause of a given anomaly is a security breach, a component failure, an environmental factor, or even any combination of these reasons, anomalies need to be detected and mitigated timely and properly. In this paper, we review and evaluate the state-of-the-art studies on the problem of anomaly detection in computer networks. We provide an elaborate description of the anomaly detection problem, and depict the different categorizations of its solutions. We also illustrate some recent state-of-the-art solutions on the network level, and depict current trends in handling malware-induced anomalies in smartphone networks. Additionally, we evaluate the presented solutions and highlight their shortcomings.


title: Scheduling of fog networks with optimized knapsack by symbiotic organisms search

abstract: Internet of things as a concept uses wireless sensor networks that have limitations in power, storage, and delay when processing and sending data to the cloud. Fog computing as an extension of cloud services to the edge of the network reduces latency and traffic, so it is very useful in healthcare, wearables, intelligent transportation systems and smart cities. Scheduling is the NP-hard issues in fog computing. Edge devices due to proximity to sensors and clouds are capable of processing power and are beneficial for resource management algorithms. We present a knapsack-based scheduling optimized by symbiotic organisms search that is simulated in iFogsim as a standard simulator for fog computing. The results show improvements in the energy consumption by 18%, total network usage by 1.17%, execution cost by 15%, and sensor lifetime by 5% in our scheduling method are better than the FCFS (First Come First Served) and knapsack algorithms.


title: Broadband Monopolar Microstrip Patch Antenna With Shorting Vias and Coupled Ring

abstract: A center-fed circular patch antenna with shorting vias and a coupled annular ring is proposed. With a low-profile configuration, the antenna provides a wide bandwidth by merging three resonant modes, including the TM02 mode of the circular patch, the TM01 mode generated by the shorting vias, and the TM02 mode of the coupled annular ring. The antenna operating in these modes would produce an omnidirectional pattern in the horizontal plane similar to that generated by a monopole antenna. A reduced structure is used to simplify the simulation in optimizing the bandwidth of the antenna. Measured results show that the antenna achieves a bandwidth of 27.4% for a profile of 0.029 wavelengths and has a gain of about 6 dBi.


title: Could a Neuroscientist Understand a Microprocessor?

abstract: There is a popular belief in neuroscience that we are primarily data limited, and that producing large, multimodal, and complex datasets will, with the help of advanced data analysis algorithms, lead to fundamental insights into the way the brain processes information. These datasets do not yet exist, and if they did we would have no way of evaluating whether or not the algorithmically-generated insights were sufficient or even correct. To address this, here we take a classical microprocessor as a model organism, and use our ability to perform arbitrary experiments on it to see if popular data analysis methods from neuroscience can elucidate the way it processes information. Microprocessors are among those artificial information processing systems that are both complex and that we understand at all levels, from the overall logical flow, via logical gates, to the dynamics of transistors. We show that the approaches reveal interesting structure in the data but do not meaningfully describe the hierarchy of information processing in the microprocessor. This suggests current analytic approaches in neuroscience may fall short of producing meaningful understanding of neural systems, regardless of the amount of data. Additionally, we argue for scientists using complex non-linear dynamical systems with known ground truth, such as the microprocessor as a validation platform for time-series and structure discovery methods.


title: An Initial Evaluation of the Functions of Human Olfaction

abstract: Although referred to in passing in several places, there have been few attempts to specify the functions of the human olfactory system. This article presents an initial effort at identifying and categorizing these functions, using 3 sources of information as a guide: 1) losses experienced by anosmic participants; 2) olfactory function in other mammals; and 3) capacity, namely, whether the human olfactory system can support the suggested function and whether there is evidence that it does. Three major classes of function were identified, relating to Ingestion (Detection/identification prior to ingestion; Detection of expectancy violations; Appetite regulation; Breast orientation and feeding), Avoiding environmental hazards (Fear related; Disgust related), and Social communication (Reproductive [inbreeding avoidance, fitness detection in prospective mates]; Emotional contagion [fear contagion, stress buffering]). These suggested functions were then examined with respect to 1) issues of ecological validity in human olfactory research; 2) their impact on olfactory loss; and 3) their general and specific implications for the study of human olfaction.


title: A Pervasive Assistive Robot System Including Projection-Camera Technology for Older Adults

abstract: Here we present a projection augmented reality (AR) based assistive robot, which we call the Pervasive Assistive Robot System (PARS). The PARS aims to improve the quality of life by of the elderly and less able-bodied. In particular, the proposed system will support dynamic display and monitoring systems, which will be helpful for older adults who have difficulty moving their limbs and who have a weak memory.We attempted to verify the usefulness of the PARS using various scenarios. We expected that PARSs will be used as assistive robots for people who experience physical discomfort in their daily lives.


title: Stability of matrix factorization for collaborative filtering

abstract: We study the stability vis a vis adversarial noise of matrix factorization algorithm for matrix completion. In particular, our results include: (I) we bound the gap between the solution matrix of the factorization method and the ground truth in terms of root mean square error; (II) we treat the matrix factorization as a subspace fitting problem and analyze the difference between the solution subspace and the ground truth; (III) we analyze the prediction error of individual users based on the subspace stability. We apply these results to the problem of collaborative filtering under manipulator attack, which leads to useful insights and guidelines for collaborative filtering system design.


title: Nonparametric Topic Modeling with Neural Inference

abstract: This work focuses on combining nonparametric topic models with Auto-Encoding Variational Bayes (AEVB). Specifically, we first propose iTM-VAE, where the topics are treated as trainable parameters and the document-specific topic proportions are obtained by a stick-breaking construction. The inference of iTM-VAE is modeled by neural networks such that it can be computed in a simple feed-forward manner. We also describe how to introduce a hyper-prior into iTM-VAE so as to model the uncertainty of the prior parameter. Actually, the hyper-prior technique is quite general and we show that it can be applied to other AEVB based models to alleviate the {\it collapse-to-prior} problem elegantly. Moreover, we also propose HiTM-VAE, where the document-specific topic distributions are generated in a hierarchical manner. HiTM-VAE is even more flexible and can generate topic distributions with better variability. Experimental results on 20News and Reuters RCV1-V2 datasets show that the proposed models outperform the state-of-the-art baselines significantly. The advantages of the hyper-prior technique and the hierarchical model construction are also confirmed by experiments.


title: Effects of 6 Weeks of Balance Training on Chronic Ankle Instability in Athletes: A Randomized Controlled Trial

abstract: The objective of the present study was to determine the effectiveness of a 6-week balance training program on patients with Chronic Ankle Instability (CAI) in relation to the results obtained in Dynamic Balance, subjective feeling of instability and pain using a single-blind randomized controlled trial. 70 athletes were randomly assigned to control or intervention group. The control group performed their usual training, and the intervention group was administered the same usual activity in addition to a balance program. The paired t-test was performed to evaluate the change scores in each group. The t-test for independent samples was performed to evaluate between-group differences in change scores. Significance level was assigned for p-values less than 0.05 for all analyses. There were significant differences between groups in change scores in CAIT and all of the SEBTs reach distances (p<0.001) but not in Pain (p=0.586). The effect sizes were larger for the outcomes measures that showed significant differences. In the within-group change, the experimental groups showed larger effect sizes in CAIT, SEBT posteromedial and SEBT posterolateral, and moderate effect sizes in SEBT anterior. Exercise therapy training based on multi-station balance tasks led to significant improvements in dynamic balance and self-reported sensation of instability in patients with CAI.


title: Microservices validation: Mjolnirr platform case study

abstract: Microservice architecture is a cloud application design pattern that implies that the application is divided into a number of small independent services, each of which is responsible for implementing of a certain feature. The need for continuous integration of developed and/or modified microservices in the existing system requires a comprehensive validation of individual microservices and their co-operation as an ensemble with other microservices. In this paper, we would provide an analysis of existing methods of cloud applications testing and identify features that are specific to the microservice architecture. Based on this analysis, we will try to propose a validation methodology of the microservice systems.


title: Design Procedures for Three-Stage CMOS OTAs With Nested-Miller Compensation

abstract: Design procedures for three-stage CMOS operational transconductance amplifiers employing nested-Miller frequency compensation are presented in this paper. After describing the basic methodology on a Class-A topology, some modifications, to increase swing, slew-rate and current drive capability, are subsequently discussed for a Class-AB solution. The approaches developed are simple as they do not introduce unnecessary circuit constraints and yield accurate results. They are hence suited for a pencil-and-paper design, but can be easily integrated into an analog knowledge-based computer-aided design tool. Experimental prototypes, designed in a 0.35-mum technology by following the proposed procedures, were fabricated and tested. Measurement results were found in close agreement with the target specifications


title: Calibration of piezoelectric actuator-based vision guided cell microinjection system

abstract: In this paper, a new calibration method for piezoelectric actuator-based vision guided cell microinjection system is proposed. This method puts all the parameterspsila uncertainties in a lumped rotational matrix instead of classifying the parameters into extrinsic and intrinsic categories. The difference between the measured and nominal output is assumed to be caused by parameterspsila uncertainties. Parameterspsila uncertainties are identified by collecting 3 pairs of input and output differences. Image-based and actuator-based position controllers are designed based on the calibration results. Experiments are made to demonstrate the usefulness of this calibration method.


title: Using eye-tracking to trace a cognitive process: Gaze behavior during decision making in a natural environment

abstract: The visual behaviour of consumers buying (or searching for) products in a supermarket was measured and used to analyse the stages of their decision process. Traditionally metrics used to trace decision-making processes are difficult to use in natural environments that often contain many options and unstructured information. Unlike previous attempts in this direction (i.e. Russo & Leclerc, 1994), our methodology reveals differences between a decision-making task and a search task. In particular the second (evaluation) stage of a decision task contains more re-dwells than the second stage of a comparable search task. This study addresses the growing concern of taking eye movement research from the laboratory into the ‘real-world’, so findings can be better generalised to natural situations.


title: Image processing based Feature extraction of Bangladeshi banknotes

abstract: Counterfeit currency is a burning question throughout the world. The counterfeiters are becoming harder to track down because of their rapid adoption of and adaptation with highly advanced technology. One of the most effective methods to stop counterfeiting can be the widespread use of counterfeit detection tools/software that are easily available and are efficient in terms of cost, reliability and accuracy. This paper presents a core software system to build a robust automated counterfeit currency detection tool for Bangladeshi bank notes. The software detects fake currency by extracting existing features of banknotes such as micro-printing, optically variable ink (OVI), water-mark, iridescent ink, security thread and ultraviolet lines using OCR (Optical Character recognition), Contour Analysis, Face Recognition, Speeded UP Robust Features (SURF) and Canny Edge & Hough transformation algorithm of OpenCV. The success rate of this software can be measured in terms of accuracy and speed. This paper also focuses on the pros and cons of implementation details that may degrade the performance of image processing based paper currency authentication systems.


title: The Language of Fake News: Opening the Black-Box of Deep Learning Based Detectors

abstract: This work was supported by the National Science Foundation Science and Technology Center Award CCF-123121.


title: Named Entity Recognition: Exploring Features

abstract: We study a comprehensive set of features used in supervised named entity recognition. We explore various combinations of features and compare their impact on recognition performance. We build a conditional random field based system that achieves 91.02% F1-measure on the CoNLL 2003 (Sang and Meulder, 2003) dataset and 81.4% F1-measure on the OntoNotes version 4 (Hovy et al., 2006) CNN dataset, which, to our knowledge, displays the best results in the state of the art for those benchmarks respectively. We demonstrate statistical significance of the boost of performance over the previous top performing system. We also obtained 74.27% F1-measure on NLPBA 2004 (Kim et al., 2004) dataset.


title: PiGraphs: learning interaction snapshots from observations

abstract: We learn a probabilistic model connecting human poses and arrangements of object geometry from real-world observations of interactions collected with commodity RGB-D sensors. This model is encoded as a set of prototypical interaction graphs (PiGraphs), a human-centric representation capturing physical contact and visual attention linkages between 3D geometry and human body parts. We use this encoding of the joint probability distribution over pose and geometry during everyday interactions to generate interaction snapshots, which are static depictions of human poses and relevant objects during human-object interactions. We demonstrate that our model enables a novel human-centric understanding of 3D content and allows for jointly generating 3D scenes and interaction poses given terse high-level specifications, natural language, or reconstructed real-world scene constraints.


title: Achieving lightweight trustworthy traceability

abstract: Despite the fact that traceability is a required element of almost all safety-critical software development processes, the trace data is often incomplete, inaccurate, redundant, conflicting, and outdated. As a result, it is neither trusted nor trustworthy. In this vision paper we propose a philosophical change in the traceability landscape which transforms traceability from a heavy-weight process producing untrusted trace links, to a light-weight results-oriented trustworthy solution. Current traceability practices which retard agility are cast away and replaced with a disciplined, just-in-time approach. The novelty of our solution lies in a clear separation of trusted trace links from untrusted ones, the change in perspective from `living-with' inacurate traces toward rigorous and ongoing debridement of stale links from the trusted pool, and the notion of synthesizing available `project exhaust' as evidence to systematically construct or reconstruct purposed, highly-focused trace links.


title: Applying the PLEX framework in designing for playfulness

abstract: In addition to functionality and usability, interactive products are increasingly expected to provide pleasurable experiences to their users. Playfulness is a part of these experiences. However, playfulness can manifest in many different ways as humans are inherently playful by nature. This poses challenges for designing for playfulness. To tackle this broad field, we have developed the Playful Experiences (PLEX) framework. The two-fold purpose of the PLEX framework is to be a conceptual tool for understanding the playful aspects of user experience (UX), and be a practical tool for designing for such experiences through established user-centered design (UCD) methods. In this paper we present an overview of our work during 2008--2010 on designing for playful experiences. After introducing and summarizing previous studies, we motivate the reasons for designing for playfulness by framing PLEX within the domains of user experience and emotional experience. Then, we briefly discuss the creation and evaluation of the PLEX Cards and its associated techniques as practical design tools based on the PLEX framework, followed by a concrete design case where these tools have been used. We also present the development of the PLEX Design Patterns for actual design solutions for playfulness. Based on this work, we propose the PLEX framework as a powerful tool for understanding playful experiences, and for providing inspiration to design interactive products that elicit playfulness.


title: Fusion of electrocardiogram with unobtrusive biometrics: An efficient individual authentication system

abstract: This paper explores the effectiveness of a novel multibiometric system that is resulted from the fusion of the electrocardiogram (ECG) with an unobtrusive biometric face and another biometric fingerprint which is known to be a least obtrusive for efficient individual authentication. The unimodal systems of the face and the fingerprint biometrics are neither secure nor they can achieve the optimum performance. Using the ECG signal as one of the biometrics offer advantage to a multibiometric system that ECG is inherited to an individual which is confidential, secured and difficult to be forged. It has an inherent feature of vitality signs that ensures a strong protection against spoof attacks to the system. Transformation based score fusion technique is used to measure the performance of the fused system. In particular, the weighted sum of score rule is used where weights are computed using equal error rate (EER) and match score distributions of the unimodal systems. The performance of the proposed multibiometric system is measured using EER and receiver operating characteristic (ROC) curve. The results show the optimum performance of the multibiometric system fusing the ECG signal with the face and fingerprint biometrics which is achieved to an EER of 0.22%, as compared to the unimodal systems that have the EER of 10.80%, 4.52% and 2.12%, respectively for the ECG signal, face and fingerprint biometrics.


title: Advanced Modelling and Simulation Methods for Communication Networks

abstract: This paper contains a brief overview of suitable simulation tools available for education and research on network technologies and protocols. Among the mentioned tools we present advanced methods for network simulations using the OPNET modeler simulation tool. This is one of the most widespread simulation tools for network simulations, appropriate for both for teaching and also for the researching procedures of new internet devices and protocols. The basic package is intended for simulating communication networks and for protocols and devices development. There are also additional specific modules, such as a module assigned to the simulation of wireless networks, an ACE module for application analyzing, 3DNV module for visualizing networks on virtual terrain and the "System in the loop" module for simulating networks with real communication equipment in the loop, in real-time.


title: COMPA: Detecting Compromised Accounts on Social Networks

abstract: As social networking sites have risen in popularity, cyber-criminals started to exploit these sites to spread malware and to carry out scams. Previous work has extensively studied the use of fake (Sybil) accounts that attackers set up to distribute spam messages (mostly messages that contain links to scam pages or drive-by download sites). Fake accounts typically exhibit highly anomalous behavior, and hence, are relatively easy to detect. As a response, attackers have started to compromise and abuse legitimate accounts. Compromising legitimate accounts is very effective, as attackers can leverage the trust relationships that the account owners have established in the past. Moreover, compromised accounts are more difficult to clean up because a social network provider cannot simply delete the correspond-


title: T-Drive: Enhancing Driving Directions with Taxi Drivers' Intelligence

abstract: This paper presents a smart driving direction system leveraging the intelligence of experienced drivers. In this system, GPS-equipped taxis are employed as mobile sensors probing the traffic rhythm of a city and taxi drivers' intelligence in choosing driving directions in the physical world. We propose a time-dependent landmark graph to model the dynamic traffic pattern as well as the intelligence of experienced drivers so as to provide a user with the practically fastest route to a given destination at a given departure time. Then, a Variance-Entropy-Based Clustering approach is devised to estimate the distribution of travel time between two landmarks in different time slots. Based on this graph, we design a two-stage routing algorithm to compute the practically fastest and customized route for end users. We build our system based on a real-world trajectory data set generated by over 33,000 taxis in a period of three months, and evaluate the system by conducting both synthetic experiments and in-the-field evaluations. As a result, 60-70 percent of the routes suggested by our method are faster than the competing methods, and 20 percent of the routes share the same results. On average, 50 percent of our routes are at least 20 percent faster than the competing approaches.


title: A novel high-speed sense-amplifier-based flip-flop

abstract: A new sense-amplifier-based flip-flop is presented. The output latch of the proposed circuit can be considered as an hybrid solution between the standard NAND-based set/reset latch and the NC-/sup 2/MOS approach. The proposed flip-flop provides ratioless design, reduced short-circuit power dissipation, and glitch-free operation. The simulation results, obtained for a 0.25-/spl mu/m technology, show improvements in the clock-to-output delay and the power dissipation with respect to the recently proposed high-speed flip-flops. The new circuit has been successfully employed in a high-speed direct digital frequency synthesizer chip, highlighting the effectiveness of the proposed flip-flop in high-speed standard cell-based applications.


title: The max-min hill-climbing Bayesian network structure learning algorithm

abstract: We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and search-and-score techniques in a principled and effective way. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. In our extensive empirical evaluation MMHC outperforms on average and in terms of various metrics several prototypical and state-of-the-art algorithms, namely the PC, Sparse Candidate, Three Phase Dependency Analysis, Optimal Reinsertion, Greedy Equivalence Search, and Greedy Search. These are the first empirical results simultaneously comparing most of the major Bayesian network algorithms against each other. MMHC offers certain theoretical advantages, specifically over the Sparse Candidate algorithm, corroborated by our experiments. MMHC and detailed results of our study are publicly available at http://www.dsl-lab.org/supplements/mmhc_paper/mmhc_index.html.


title: Knock it off: profiling the online storefronts of counterfeit merchandise

abstract: We describe an automated system for the large-scale monitoring of Web sites that serve as online storefronts for spam-advertised goods. Our system is developed from an extensive crawl of black-market Web sites that deal in illegal pharmaceuticals, replica luxury goods, and counterfeit software. The operational goal of the system is to identify the affiliate programs of online merchants behind these Web sites; the system itself is part of a larger effort to improve the tracking and targeting of these affiliate programs. There are two main challenges in this domain. The first is that appearances can be deceiving: Web pages that render very differently are often linked to the same affiliate program of merchants. The second is the difficulty of acquiring training data: the manual labeling of Web pages, though necessary to some degree, is a laborious and time-consuming process. Our approach in this paper is to extract features that reveal when Web pages linked to the same affiliate program share a similar underlying structure. Using these features, which are mined from a small initial seed of labeled data, we are able to profile the Web sites of forty-four distinct affiliate programs that account, collectively, for hundreds of millions of dollars in illicit e-commerce. Our work also highlights several broad challenges that arise in the large-scale, empirical study of malicious activity on the Web.


title: Intellectual Capital ROI : A causal map of human capital antecedents and consequents

abstract: This report describes the results of a ground‐breaking research study that measured the antecedents and consequents of effective human capital management. The research sample consisted of 76 senior executives from 25 companies in the financial services industry. The results of the study yielded a holistic causal map that integrated constructs from the fields of intellectual capital, knowledge management, human resources, organizational behaviour, information technology and accounting. The integration of both quantitative and qualitative measures in an overall conceptual model yielded several research implications. The resulting structural equation model allows participating organizations and researchers to gauge the effectiveness of an organization’s human capital capabilities. This will allow practitioners and researchers to more efficiently allocate resources with regard to human capital management. The potential outcomes of the study are limitless, since a program of consistent re‐evaluation can lead to the establishment of causal relationships between human capital management and economic and business results.


20 Machine Analysis of Facial Expressions 1. Human Face and Its Expression


title: Enzyme-Free Nucleic Acid Logic Circuits

abstract: Biological organisms perform complex information processing and control tasks using sophisticated biochemical circuits, yet the engineering of such circuits remains ineffective compared with that of electronic circuits. To systematically create complex yet reliable circuits, electrical engineers use digital logic, wherein gates and subcircuits are composed modularly and signal restoration prevents signal degradation. We report the design and experimental implementation of DNA-based digital logic circuits. We demonstrate AND, OR, and NOT gates, signal restoration, amplification, feedback, and cascading. Gate design and circuit construction is modular. The gates use single-stranded nucleic acids as inputs and outputs, and the mechanism relies exclusively on sequence recognition and strand displacement. Biological nucleic acids such as microRNAs can serve as inputs, suggesting applications in biotechnology and bioengineering.


title: The LAMBADA dataset: Word prediction requiring a broad discourse context

abstract: We introduce LAMBADA, a dataset to evaluate the capabilities of computational models for text understanding by means of a word prediction task. LAMBADA is a collection of narrative passages sharing the characteristic that human subjects are able to guess their last word if they are exposed to the whole passage, but not if they only see the last sentence preceding the target word. To succeed on LAMBADA, computational models cannot simply rely on local context, but must be able to keep track of information in the broader discourse. We show that LAMBADA exemplifies a wide range of linguistic phenomena, and that none of several state-of-the-art language models reaches accuracy above 1% on this novel benchmark. We thus propose LAMBADA as a challenging test set, meant to encourage the development of new models capable of genuine understanding of broad context in natural language text.


title: Rhetorical Structure Theory: looking back and moving ahead

abstract: Rhetorical Structure Theory has enjoyed continuous attention since its origins in the 1980s. It has been applied, compared to other approaches, and also criticized in a number of areas in discourse analysis, theoretical linguistics, psycholinguistics, and computational linguistics. In this article, we review some of the discussions about the theory itself, especially addressing issues of the reliability of analyses and psychological validity, together with a discussion of the nature of text relations. We also propose areas for further research. A follow-up article (Taboada and Mann, forthcoming) will discuss applications of the theory in various fields.


title: Intelligent System for Information Security Management: Architecture and Design Issues

abstract: The limitations of each security technology combined with the growth of cyber attacks impact the efficiency of information security management and increase the activities to be performed by network administrators and security staff. Therefore, there is a need for the increase of automated auditing and intelligent reporting mechanisms for the cyber trust. Intelligent systems are emerging computing systems based on intelligent techniques that support continuous monitoring and controlling plant activities. Intelligence improves an individual’s ability to make better decisions. This paper presents a proposed architecture of an Intelligent System for Information Security Management (ISISM). The objective of this system is to improve security management processes such as monitoring, controlling, and decision making with an effect size that is higher than an expert in security by providing mechanisms to enhance the active construction of knowledge about threats, policies, procedures, and risks. We focus on requirements and design issues for the basic components of the intelligent system.


title: IoT based autonomous percipient irrigation system using raspberry Pi

abstract: This paper propounds a design for automatic water supplying system in farmland using raspberry pi 3, Arduino microcontrollers, WiFi module, GSM shield, relay boards and couple of sensors. The components we used in our system ensures overall fecund, scalable and spirited implementation. Depending upon the moisture level of farmland and daylight intensity, the system can detect the appropriate time of water supply in the trees and can also keep track of the water level to prevent water from being accumulated around the roots of the saplings. The analog data received from the sensors are transmitted by Arduino as digital signal via Wifi Module to the Raspberry Pi 3. The system is able to notify the administrator if water shortage arises in the main water supply and an administrator can also communicate with the system by sending SMS (Short message service) of a particular keyword. This system can be applied in farmland as well as small pot plants. Using this system, a very promising outcome is found in sustaining and cherishing the plants in a more scientific way.


title: Particle Filters for State Estimation of Jump Markov Linear Systems

abstract: Jump Markov linear systems (JMLS) are linear systems whose parameters evolve with time according to a finite state Markov chain. In this paper, our aim is to recursively compute optimal state estimates for this class of systems. We present efficient simulation-based algorithms called particle filters to solve the optimal filtering problem as well as the optimal fixed-lag smoothing problem. Our algorithms combine sequential importance sampling, a selection scheme, and Markov chain Monte Carlo methods. They use several variance reduction methods to make the most of the statistical structure of JMLS. Computer simulations are carried out to evaluate the performance of the proposed algorithms. The problems of on-line deconvolution of impulsive processes and of tracking a maneuvering target are considered. It is shown that our algorithms outperform the current methods.


title: Automatic Annotation of Object Instances by Region-Based Recurrent Neural Networks

abstract: In recent years, a wide variety of automatic, semiautomatic and manual approaches to image annotation have been proposed. These prerequisites have been driven by continuous advances of deep learning algorithms that often encounter the problem of insufficient or inappropriate training data, as well as sub-par markings’ accuracy which can have a direct impact on the model’s performance regardless. The main contribution of this paper is the development of a complex annotation framework able to automatically generate high-quality markings. The annotation work-flow aims to be an iterative process allowing automatic labeling of object bounding boxes, while simultaneously predicting the polygon outlining the object instance inside the box. The markings’ format is fully compatible with COCO Detection & Panoptic APIs that provide open-source interfaces for loading, parsing, and visualizing annotations. Following the completion of the research project funding this research, the code will be publicly available.


title: RAPID: Research on Automated Plankton Identification

abstract: Author Posting. © Oceanography Society, 2007. This article is posted here by permission of Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 20, 2 (2007): 172-187.


title: A Multi-Axis Tactile Sensor Array for Touchscreen Applications

abstract: Touchscreens have been prevalent in daily life and ubiquitously applied in consuming electronics, industrial control systems, and other applications as human computer interfaces (HCIs), which offers a convenient way for the human to interact with the smart devices. However, the lack of tactile force feedback from these conventional touchscreens draws limitations on the dexterity and intuitiveness of those devices, which results in multi-level menus, waiting, multi-finger gestures, and so on. To enhance and diversify functions of touchscreens, this paper presents a multi-axis tactile sensor array prototype with a unique layered structure, which is capable of sensing both 3-directional tactile force and location information over an area of 60 mm $\times60$ mm by utilizing only $2\times2$ piezoresistive MEMS force sensors. This paper integrated the sensibility of tangential force and normal force within one system, which sheds light on multi-axis tactile applications and dramatically reduced the number of tactile cells. The sensor array design, fabrication and packaging, and test approaches have been discussed in this paper. Qualitative and quantitative analysis have been conducted to evaluate the performance of the tactile sensor array with tested force range of 0.1 ~ 0.5 N. The results demonstrated the functionality of proposed sensor array, which exhibited promising potential for touchscreen applications. [2017-0176]


title: Robust, precise, and calibration-free shape acquisition with an off-the-shelf camera and projector

abstract: Recently, three-dimensional (3D) shape acquisition systems composed of simple commercial devices have received significant attention from both professional and nonprofessional users. In this work, we propose a flexible projector-camera system that can accurately acquire whole shapes of 3D objects without requiring any special calibrations. The proposed system is based on structured-light and Structure-from-Motion (SfM) techniques that use coded patterns as dense discriminative features instead of the local features popularly used in SfM. To this end, recordings of coded patterns are inserted by moving the camera (to capture image data from multiple camera view points) and projection of the patterns by moving the projector. The proposed system can accurately recover even texture-less objects owing to the advantages of both SfM and structured-light. We demonstrate the benefits of this system by several experiments using real data and compare it to current methods.


title: Discover millions of fake followers in Weibo

abstract: Weibo is the Chinese counterpart of Twitter, which has attracted hundreds of millions of users. Just like other Online Social Networks (hereafter OSNs), Weibo has a large number of fake accounts. They are created to sell their following links to customers, who want to boost their follower counts. These bogus accounts are difficult to identify individually, especially when they are created by sophisticated programs or controlled by human beings directly. This paper proposes a novel fake account detection method that is based on the very purpose of the existence of these accounts: they are created to follow their targets en masse, resulting in high-overlapping between the follower lists of their customers. This paper investigates the top Weibo accounts whose follower lists duplicate or nearly duplicate each other (hereafter called near-duplicates). Discovering near-duplicates is a challenging task. The network is large; the data in its entirety are not available; the pair-wise comparison is very expensive. We developed a sampling-based approach to discover all the near-duplicates of the top accounts, who have at least 50,000 followers. In the experiment, we found 395 near-duplicates, which leads us to 11.90 million fake accounts (4.56 % of total users) who send 741.10 million links (9.50 % of the entire edges). Furthermore, we characterize four typical structures of the spammers, cluster these spammers into 34 groups, and analyze the properties of each group.


title: SkiMap++: Real-Time Mapping and Object Recognition for Robotics

abstract: We introduce SkiMap++, an extension to the recently proposed SkiMap mapping framework for robot navigation [1]. The extension deals with enriching the map with semantic information concerning the presence in the environment of certain objects that may be usefully recognized by the robot, e.g. for the sake of grasping them. More precisely, the map can accommodate information about the spatial locations of certain 3D object features, as determined by matching the visual features extracted from the incoming frames through a random forest learned off-line from a set of object models. Thereby, evidence about the presence of object features is gathered from multiple vantage points alongside with the standard geometric mapping task, so to enable recognizing the objects and estimating their 6 DOF poses. As a result, SkiMap++ can reconstruct the geometry of large scale environments as well as localize some relevant objects therein (Fig.1) in real-time on CPU. As an additional contribution, we present an RGB-D dataset featuring ground-truth camera and object poses, which may be deployed by researchers interested in pursuing SLAM alongside with object recognition, a topic often referred to as Semantic SLAM. 1


Design of Low Voltage Low Power OTA based CCII +


title: Non-Orthogonal Multiple Access Based Integrated Terrestrial-Satellite Networks

abstract: In this paper, we investigate the downlink transmission of a non-orthogonal multiple access (NOMA)-based integrated terrestrial-satellite network, in which the NOMA-based terrestrial networks and the satellite cooperatively provide coverage for ground users while reusing the entire bandwidth. For both terrestrial networks and the satellite network, multi-antennas are equipped and beamforming techniques are utilized to serve multiple users simultaneously. A channel quality-based scheme is proposed to select users for the satellite, and we then formulate the terrestrial user pairing as a max–min problem to maximize the minimum channel correlation between users in one NOMA group. Since the terrestrial networks and the satellite network will cause interference to each other, we first investigate the capacity performance of the terrestrial networks and the satellite networks separately, which can be decomposed into the designing of beamforming vectors and the power allocation schemes. Then, a joint iteration algorithm is proposed to maximize the total system capacity, where we introduce the interference temperature limit for the satellite since the satellite can cause interference to all base station users. Finally, numerical results are provided to evaluate the user paring scheme as well as the total system performance, in comparison with some other proposed algorithms and existing algorithms.


title: Improving the Performance of Distributed TensorFlow with RDMA

abstract: TensorFlow is an open-source software library designed for Deep Learning using dataflow graph computation. Thanks to the flexible architecture of TensorFlow, users can deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. In a distributed TensorFlow work process, it uses gRPC to connect between different nodes. However, when deploying training tasks on high performance computing clusters, the performance of gRPC becomes a bottleneck of distributed TensorFlow system. HPC clusters are usually equipped with Infiniband network, in addition to traditional TCP/IP network. But open-sourced TensorFlow has not taken this advantage. We present a RDMA-capable design of TensorFlow. By porting the Tensor send/receive parts of TensorFlow into RDMA verbs, we finally get nearly 6\(\times \) performance improvements over the original distributed TensorFlow, based on gRPC. The TensorFlow system with RDMA support shows a great scalability among the training scale.


title: Morphological development of the epithelium of the embryonic chick intestine in culture: influence of thyroxine and hydrocortisone.

abstract: A culture system with defined medium has been utilized to elucidate the role of thyroxine (T4) and hydrocortisone (HC) in growth and differentiation of the epithelium of embryonic chick duodenum during the third week of development. Duodenal explants maintained a basically normal morphology for 48 to 72 hours, but during subsequent culture, muscle and mesenchyme deteriorated, previllous ridges shrank, the epithelial surface became pitted, and the tissue lost weight. These degenerative processes were retarded by addition of HC to the culture medium. Previllous ridges of cultured duodenum failed to develop into true villi and ridge growth was subnormal. Epithelial mitotic counts were increased by T4 or HC, as compared to cultured control tissue, but dropped below values in vivo, by 72 hours in culture. Epithelial differentiation proceeded more rapidly in duodenum cultured without hormones than in vivo. Increase in cell height, flattening of the epithelial surface, attainment of uniform staining of the brush border with periodic acid-Schiff, increase in microvillar density, and formation of a terminal web were accelerated by 24 to 48 hours in culture. In the presence of T4, cell height and microvillar growth were stimulated further, reaching the state found at 19 to 21 days in vivo.


title: A Linear Optimal Transportation Framework for Quantifying and Visualizing Variations in Sets of Images

abstract: Transportation-based metrics for comparing images have long been applied to analyze images, especially where one can interpret the pixel intensities (or derived quantities) as a distribution of `mass' that can be transported without strict geometric constraints. Here we describe a new transportation-based framework for analyzing sets of images. More specifically, we describe a new transportation-related distance between pairs of images, which we denote as linear optimal transportation (LOT). The LOT can be used directly on pixel intensities, and is based on a linearized version of the Kantorovich-Wasserstein metric (an optimal transportation distance, as is the earth mover's distance). The new framework is especially well suited for computing all pairwise distances for a large database of images efficiently, and thus it can be used for pattern recognition in sets of images. In addition, the new LOT framework also allows for an isometric linear embedding, greatly facilitating the ability to visualize discriminant information in different classes of images. We demonstrate the application of the framework to several tasks such as discriminating nuclear chromatin patterns in cancer cells, decoding differences in facial expressions, galaxy morphologies, as well as sub cellular protein distributions.


title: Validating the theoretical structure of the Treatment Self-Regulation Questionnaire (TSRQ) across three different health behaviors

abstract: Nearly 40% of mortality in the United States is linked to social and behavioral factors such as smoking, diet and sedentary lifestyle. Autonomous self-regulation of health-related behaviors is thus an important aspect of human behavior to assess. In 1997, the Behavior Change Consortium (BCC) was formed. Within the BCC, seven health behaviors, 18 theoretical models, five intervention settings and 26 mediating variables were studied across diverse populations. One of the measures included across settings and health behaviors was the Treatment Self-Regulation Questionnaire (TSRQ). The purpose of the present study was to examine the validity of the TSRQ across settings and health behaviors (tobacco, diet and exercise). The TSRQ is composed of subscales assessing different forms of motivation: amotivation, external, introjection, identification and integration. Data were obtained from four different sites and a total of 2731 participants completed the TSRQ. Invariance analyses support the validity of the TSRQ acrossallfoursitesandallthreehealthbehaviors. Overall,theinternalconsistencyofeachsubscale was acceptable (most a values >0.73). The present study provides further evidence of the validity of the TSRQ and its usefulness as an assessment tool across various settings and for different health behaviors.