title: Partial updates to accelerate GPU-based interactive hydraulic erosion

abstract: We present a method to speedup the simulation step of a water and erosion simulation. Our system is inspired by physics, but nevertheless, achieves interactive frame-rates. We use the DirectX 11 API to accelerate the simulation on the GPU by using DirectCompute. Our acceleration technique eliminates the calculations for those dispatch groups that do not require their physics system to be updated. Using indirect dispatch calls and other sophisticated DirectX API functions, we completely eliminate the need to map any data to the CPU memory, thus preventing synchronization points between CPU and GPU. We investigate the additional cost for checking which areas need to be updated and for supporting the proposed system. The results show that for typical use-cases (∼20% water) our acceleration algorithm provides an increase in speed by about a factor of 3. We obtain performance improvements for up to 95% water coverage.


title: Preliminary experiments on English-Amharic statistical machine translation.

abstract: This paper discusses the preliminary experiment conducted to translate from English to Amharic using the Statistical Machine Translation (EASMT) approach. The experiment on the EASMT system is being conducted on training corpus of both languages based on expressions that are found in parallel documents. The experiment involves collecting of a total of 632 Parliamentary corpora of which 115 have been used in the experiment. The corpus coverage is 15 years from Aug 21, 1995 to July 16, 2010. Each document contains data, which are translations of each other. The experiment has been conducted using 18,432 English-Amharic sentence pairs extracted from these corpora in order to measure the accuracy of the translation system. Accordingly, the baseline phrase-based BLEU score result is 35.32%. A 0.34% increase in BLEU has been achieved by applying morpheme segmentation to the tokens of the Amharic output result and the reference of the baseline system. The increase is 0.92% when compared with the same segmented reference between the baseline and the segmented system.


Fast Collocation-Based Bayesian HMM Word Alignment


title: Modified Vector Field Histogram with a Neural Network Learning Model for Mobile Robot Path Planning and Obstacle Avoidance

abstract: In this work, a Modified Vector Field Histogram (MVFH) has been developed to improve path planning and obstacle avoidance for a wheeled driven mobile robot. It permits the detection of unknown obstacle to avoid collisions by simultaneously a steering the mobile robot toward the target; a regular grid map representation for a work space environment is caurried out. A Neural Network (NN) model is used to learn many critical situations of environment during robot navigation among obstacles using MVFH. Also, digital filter has been utilized for improving the robustness of obstacle avoidance trajectory of mobile robot. The proposed MVFH-NN has been implemented and tested by using MobotSim program simulation and MATLAB . The developed algorithm showed good navigation properties and can be used in complex real world (maze-like environment), it also shows good ability to overcome limitations of the traditional VFH algorithm (like wide candidate valley, narrow hallway and target distance limitation).


title: An ultracapacitor integrated power conditioner for intermittency smoothing and improving power quality of distribution grid

abstract: Penetration of various types of distributed energy resources (DERs) like solar, wind and PHEVs onto the distribution grid is on the rise. There is a corresponding increase in power quality problems and intermittencies on the distribution grid. In order to reduce the intermittencies and improve the power quality of the distribution grid an Ultracapacitor (UCAP) integrated power conditioner is proposed in this paper. UCAP integration gives the power conditioner active power capability which is useful in tackling the grid intermittencies and in improving the voltage sag and swell compensation. UCAPs have low energy density, high power density and fast charge/discharge rates which are all ideal characteristics for meeting high power low energy events like grid intermittencies, sags/swells. In this paper UCAP is integrated into dc-link of the power conditioner through a bi-directional dc-dc converter which helps in providing a stiff dc-link voltage. The integration helps in providing active/reactive power support, intermittency smoothing and sag/swell compensation. Design and control of both the dc-ac inverters and the dc-dc converter are discussed. The simulation model of the overall system is developed and compared to the experimental hardware setup.


title: Past review, current progress, and challenges ahead on the cocktail party problem

abstract: The cocktail party problem, i.e., tracing and recognizing the speech of a specific speaker when multiple speakers talk simultaneously, is one of the critical problems yet to be solved to enable the wide application of automatic speech recognition (ASR) systems. In this overview paper, we review the techniques proposed in the last two decades in attacking this problem. We focus our discussions on the speech separation problem given its central role in the cocktail party environment, and describe the conventional single-channel techniques such as computational auditory scene analysis (CASA), non-negative matrix factorization (NMF) and generative models, the conventional multi-channel techniques such as beamforming and multi-channel blind source separation, and the newly developed deep learning-based techniques, such as deep clustering (DPCL), the deep attractor network (DANet), and permutation invariant training (PIT). We also present techniques developed to improve ASR accuracy and speaker identification in the cocktail party environment. We argue effectively exploiting information in the microphone array, the acoustic training set, and the language itself using a more powerful model. Better optimization objective and techniques will be the approach to solving the cocktail party problem.


title: Relationship between Mobile Phone Addiction and the Incidence of Poor and Short Sleep among Korean Adolescents: a Longitudinal Study of the Korean Children & Youth Panel Survey

abstract: Three of ten teenagers in Korea are addicted to mobile phones. The aim of this study was to examine the relationship between mobile phone addiction and the incidence of poor sleep quality and short sleep duration in adolescents. We used longitudinal data from the Korean Children & Youth Panel Survey conducted by the National Youth Policy Institute in Korea (2011-2013). A total of 1,125 students at baseline were included in this study after excluding those who already had poor sleep quality or short sleep duration in the previous year. A generalized estimating equation was used to analyze the data. High mobile phone addiction (mobile phone addiction score > 20) increased the risk of poor sleep quality but not short sleep duration. We suggest that consistent monitoring and effective intervention programs are required to prevent mobile phone addiction and improve adolescents' sleep quality.


title: Effects of pectoral fins' spanwise flexibility on forward thrust generation

abstract: Pectoral fins of manta rays have been a great source of inspiration for propulsive mechanism of underwater vehicles in the last decade. In the existing literature, effects of the chordwise flexibility of these fins on the forward thrust generation has been widely studied, while the study on the spanwise flexibility is considerably lacking. In this paper, we aim to investigate the effects of the fins' spanwise flexibility (quantified by bending stiffness) on the forward thrust generation. An experiment was conducted in a water channel to measure the forward thrust generated by 16 fins under free stream condition (0.5 m/s). The fins' bending stiffness ranged from 0.018 N/mm to 0.499 N/mm. All the fins had the same span length and surface area in order to preserve the hydrodynamic integrity of the experiment. The experimental results showed that for the range of the spanwise flexibility considered in this study, the forward thrust generation increases by decreasing the fins' spanwise flexibility until a certain bending stiffness value. After that the rate of the change in the thrust generation becomes very minor, indicating a plateau region.


Adding Heuristics to Conflict-Based Search for Multi-Agent Path Finding.


title: SIW textile antennas as a novel technology for UWB RFID tags

abstract: A wearable ultra-wideband (UWB) textile antenna based on substrate integrated waveguide technology is presented for application in the next generation radio-frequency identification (RFID) systems operating in the low-duty cycle restricted [3.4–4.8] GHz UWB band. The design of a UWB antenna for application in smart textiles is very challenging. For end users, the antenna should be unobtrusively integrated into smart textile and comfortable to wear. From a system point of view, good impedance matching and radiation characteristics over a very large bandwidth are of major importance, even under harsh operating conditions. Our approach consists of applying copper tube eyelets to implement a cavity-backed slot antenna in textile materials. A prototype was realized and measured. The measured return loss characteristic and radiation performance after bending and deploying the antenna on different human body parts prove that our antenna is extremely suited for on-body use. A −10 dB-bandwidth of 1.33 GHz, or 33 %, was realized, in which the radiation pattern remains quasi invariable.


title: Sending money home: are remittances always beneficial to those who stay behind?

abstract: This paper examines the importance of remittances from international migrants to those who stay behind. The paper looks in particular at the Zimbabwean case, and argues that while money sent from the 'other side' has a beneficial effect on close kin, remittances can also undermine the purchasing power of those households without migrating members. This is in part a result of asset price inflation, and in part due to the inflationary effects of parallel currency markets. The situation for those excluded from benefiting from foreign currency inputs is aggravated by chronic scarcity in the availability of consumables. The paper argues that further research is required to understand the costs, as well as the benefits, of money sent home by migrants, in terms of assessing the class and social agency of different groups of remittance senders and receivers. The paper suggests that one non-economic, but significant effect, of remittance-underwritten parallel markets might be an undermining of inclusive governance and democratic state accountability in the long-run. © 2003 John Wiley & Sons, Ltd.


title: Twitter Sentiment Analysis: A Review

abstract: The basic knowledge required to do sentiment analysis of Twitter is discussed in this review paper. Sentiment Analysis can be viewed as field of text mining, natural language processing. Thus we can study sentiment analysis in various aspects. This paper presents levels of sentiment analysis, approaches to do sentiment analysis, methodologies for doing it, and features to be extracted from text and the applications. Twitter is a microblogging service to which if sentiment analysis done one has to follow explicit path. Thus this paper puts overview about tweets extraction, their preprocessing and their sentiment analysis. N text mining, Sentiment Analysis and Opinion Mining consists study of sentiments, attitudes, reactions, evaluation of the content of the text. Many times while analyzing peoples' opinions, sentiments, evaluations, attitudes, reactions towards entities, such as services, products, organizations, individuals , events, topics, issues and their attributes Sentiment Analysis is also called as Opinion Mining (1).


Multiobjective optimization by decomposition with Pareto-adaptive weight vectors


title: Distributed Fault-Tolerant Topology Control in Cooperative Wireless Ad Hoc Networks

abstract: Current researches on topology control with cooperative communication (CC) in wireless ad hoc networks have focused on network connectivity, path energy-efficiency and node transmission power reduction. However, fault-tolerance related issues have not been adequately addressed. In this paper, we propose a CC-based scheme to achieve more efficient fault-tolerant topology control. We first define k- connectivity under the CC model and then design a distributed scheme for building a t- spanner with k -connectivity of an arbitrary communication network. Simulation results confirm that the proposed scheme can tolerate node failures as well as exploit the advantage of CC to achieve path energy-efficiency and lower power consumption of the network.


The Validity of Presence as a Reliable Human Performance Metric in Immersive Environments


title: Forces acting on a biped robot. Center of pressure-zero moment point

abstract: In the area of biped robot research, much progress has been made in the past few years. However, some difficulties remain to be dealt with, particularly about the implementation of fast and dynamic walking gaits, in other words anthropomorphic gaits, especially on uneven terrain. In this perspective, both concepts of center of pressure (CoP) and zero moment point (ZMP) are obviously useful. In this paper, the two concepts are strictly defined, the CoP with respect to ground-feet contact forces, the ZMP with respect to gravity plus inertia forces. Then, the coincidence of CoP and ZMP is proven, and related control aspects are examined. Finally, a virtual CoP-ZMP is defined, allowing us to extend the concept when walking on uneven terrain. This paper is a theoretical study. Experimental results are presented in a companion paper, analyzing the evolution of the ground contact forces obtained from a human walker wearing robot feet as shoes.


title: Adaptive protocols for information dissemination in wireless sensor networks

abstract: In this paper, we present a family of adaptive protocols, called SPIN (Sensor Protocols for Information via Negotiation), that efficiently disseminates information among sensors in an energy-constrained wireless sensor network. Nodes running a SPIN communication protocol name their data using high-level data descriptors, called meta-data. They use meta-data negotiations to eliminate the transmission of redundant data throughout the network. In addition, SPIN nodes can base their communication decisions both upon application-specific knowledge of the data and upon knowledge of the resources that are available to them. This allows the sensors to efficiently distribute data given a limited energy supply. We simulate and analyze the performance of two specific SPIN protocols, comparing them to other possible approaches and a theoretically optimal protocol. We find that the SPIN protocols can deliver 60% more data for a given amount of energy than conventional approaches. We also find that, in terms of dissemination rate and energy usage, the SPlN protocols perform close to the theoretical optimum.


title: Ontology Model-Based Static Analysis on Java Programs

abstract: Typical enterprise and military software systems consist of millions of lines of code with complicated dependence on diverse library abstractions. Manually debugging these codes imposes developers overwhelming workload and difficulties. To address software quality concerns efficiently, this paper proposes an ontology-based static analysis approach to automatically detect bugs in the source code of Java programs. First, we elaborate bug list collected, classify bugs into different categories, and translate bug patterns into SWRL (semantic Web rule language) rules using an ontology tool, Protege. An ontology model of Java program is created according to Java program specification using Protege as well. Both SWRL rules and the program ontology model are exported in OWL (Web ontology language) format. Second, Java source code under analysis is parsed into the abstract syntax tree (AST), which is automatically mapped to the individuals of the program ontology model. SWRL bridge takes in the exported OWL file (representing the SWRL rules model and program ontology model) and the individuals created for the Java code, conduits to Jess (a rule engine), and obtains inference results indicating any bugs. We perform experiments to compare bug detection capability with well-known FindBugs tool. A prototype of bug detector tool is developed to show the validity of the proposed static analysis approach.


title: HIVE-COTE: The Hierarchical Vote Collective of Transformation-Based Ensembles for Time Series Classification

abstract: There have been many new algorithms proposed over the last five years for solving time series classification (TSC) problems. A recent experimental comparison of the leading TSC algorithms has demonstrated that one approach is significantly more accurate than all others over 85 datasets. That approach, the Flat Collective of Transformation-based Ensembles (Flat-COTE), achieves superior accuracy through combining predictions of 35 individual classifiers built on four representations of the data into a flat hierarchy. Outside of TSC, deep learning approaches such as convolutional neural networks (CNN) have seen a recent surge in popularity and are now state of the art in many fields. An obvious question is whether CNNs could be equally transformative in the field of TSC. To test this, we implement a common CNN structure and compare performance to Flat-COTE and a recently proposed time series-specific CNN implementation. We find that Flat-COTE is significantly more accurate than both deep learning approaches on 85 datasets. These results are impressive, but Flat-COTE is not without deficiencies. We improve the collective by adding new components and proposing a modular hierarchical structure with a probabilistic voting scheme that allows us to encapsulate the classifiers built on each transformation. We add two new modules representing dictionary and interval-based classifiers, and significantly improve upon the existing frequency domain classifiers with a novel spectral ensemble. The resulting classifier, the Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is significantly more accurate than Flat-COTE and represents a new state of the art for TSC. HIVE-COTE captures more sources of possible discriminatory features in time series and has a more modular, intuitive structure.


title: Revising the Redundancy Principle in Multimedia Learning

abstract: College students viewed a short multimedia PowerPoint presentation consisting of 16 narrated slides explaining lightning formation (Experiment 1) or 8 narrated slides explaining how a car's braking system works (Experiment 2). Each slide appeared for approximately 8-10 s and contained a diagram along with 1-2 sentences of narration spoken in a female voice. For some students (the redundant group), each slide also contained 2-3 printed words that were identical to the words in the narration, conveyed the main event described in the narration, and were placed next to the corresponding portion of the diagram. For other students (the nonredundant group), no on-screen text was presented. Results showed that the group whose presentation included short redundant phrases within the diagram outperformed the nonredundant group on a subsequent test of retention (d = 0.47 and 0.70, respectively) but not on transfer. Results are explained by R. E. Mayer's (2001, 2005a) cognitive theory of multimedia learning, in which the redundant text served to guide the learner's attention without priming extraneous processing.


title: Modeling social interestingness in conversational stories

abstract: Telling stories about our daily lives is one of the most ubiquitous, consequential and seamless ways in which we socialize. Current narrative generation methods mostly require specification of a priori knowledge or comprehensive domain models, which are not generalizable across contexts. Hence, such approaches do not lend themselves well to new and unpredictable domains of observation and interaction, in which social stories usually occur. In this paper, we describe a methodology for categorizing event descriptions as being socially interesting. The event sequences are drawn from crowd-sourced Plot Graphs. The models include low-level natural language and higher-level features. The results from classification and regression tasks look promising overall, indicating that general metrics of social interestingness of stories could be modeled for sociable agents.


title: Women in the information technology profession: a literature review, synthesis and research agenda

abstract: Gender differences in IT careers appear to be affecting the competitiveness of companies globally. It is posited that given the current labor shortage in the IT industry, it has become more important than ever to reduce sources of leakage in the IT career paths of women. A model of barriers faced by women in the field of information technology is presented. Three distinct career stages of career choices, persistence and advancement are analyzed. At each stage, the effects of social and structural factors which may act as barriers are identified and discussed. Social factors include social expectations, work–family conflict and informal networks, while the structural factors are occupational culture, lack of role models and mentors, demographic composition and institutional structures. A proposed research agenda is offered. It is suggested that these social and structural factors as well as their interactions will result in turnover of women in IT.


title: DLM: Decentralized Linearized Alternating Direction Method of Multipliers

abstract: This paper develops the Decentralized Linearized Alternating Direction Method of Multipliers (DLM) that minimizes a sum of local cost functions in a multiagent network. The algorithm mimics operation of the decentralized alternating direction method of multipliers (DADMM) except that it linearizes the optimization objective at each iteration. This results in iterations that, instead of successive minimizations, implement steps whose cost is akin to the much lower cost of the gradient descent step used in the distributed gradient method (DGM). The algorithm is proven to converge to the optimal solution when the local cost functions have Lipschitz continuous gradients. Its rate of convergence is shown to be linear if the local cost functions are further assumed to be strongly convex. Numerical experiments in least squares and logistic regression problems show that the number of iterations to achieve equivalent optimality gaps are similar for DLM and ADMM and both much smaller than those of DGM. In that sense, DLM combines the rapid convergence of ADMM with the low computational burden of DGM.


title: Performance of different hollow fiber membranes for seawater desalination using membrane distillation

abstract: AbstractMembrane distillation requires a highly porous hydrophobic membrane with low surface energy. In this paper, we compare the direct contact membrane distillation (DCMD) performances of four different types of in-house fabricated hollow fiber membranes and two different commercially available hollow fiber membranes. Hollow fiber membranes are fabricated using wet-jet phase inversion technique and the polymeric matrices used for the fabrication are polyvinylidine fluoride (PVDF) and polyvinyl chloride (PVC). Commercial hollow fiber membrane materials are made of polytetrafluoroethylene (PTFE) and polypropylene (PP). PVDF hollow fibers showed a superior performance among all the hollow fibers tested in the DCMD process and gave a water vapor flux of 31 kg m−2h−1 at a feed and coolant inlet temperatures of 80 and 20°C, respectively. Under the same conditions, the water vapor flux observed for PP, PTFE, and PVC hollow fiber membranes are 13, 11, and 6 kg m−2h−1, respectively, with 99.99% salt rejection o...


title: Potential Friend Recommendation in Online Social Network

abstract: As the wide popularization of online social networks, online users are not content only with keeping online friendship with social friends in real life any more. They hope the system designers can help them exploring new friends with common interest. However, the large amount of online users and their diverse and dynamic interests possess great challenges to support such a novel feature in online social networks. In this paper, by leveraging interest-based features, we design a general friend recommendation framework, which can characterize user interest in two dimensions: context (location, time) and content, as well as combining domain knowledge to improve recommending quality. We also design a potential friend recommender system in a real online social network of biology field to show the effectiveness of our proposed framework.


title: A survey on trust in Internet of Things

abstract: Internet of Things (IoT) brings a new paradigm of ‘talking’ sensors or objects with the help of the Internet. Important data from objects are sent for mining or processing purpose into critical information. Internet makes the data vulnerable to the threat of attacks, so some secure ways are needed to secure the data communications process. Restriction access only to certain trustable objects can also be an alternative solution for mitigating attacks. In this paper we made a literature survey on trust in the area of IoT. Furthermore, some research challenges that can be developed with the topic of trust in the IoT area also described.


title: A Note on Two Classical Shock Filters and Their Asymptotics

abstract: We establish in 2D, the PDE associated with a classical debluring filter, the Kramer operator and compare it with another classical shock filter.


How Lufthansa Capitalized on Big Data for Business Model Renovation


title: The grand challenge of computer Go: Monte Carlo tree search and extensions

abstract: The ancient oriental game of Go has long been considered a grand challenge for artificial intelligence. For decades, computer Go has defied the classical methods in game tree search that worked so successfully for chess and checkers. However, recent play in computer Go has been transformed by a new paradigm for tree search based on Monte-Carlo methods. Programs based on Monte-Carlo tree search now play at human-master levels and are beginning to challenge top professional players. In this paper, we describe the leading algorithms for Monte-Carlo tree search and explain how they have advanced the state of the art in computer Go.


title: Why Do I Retweet It? An Information Propagation Model for Microblogs

abstract: Microblogging platforms are Web 2.0 services that represent a suitable environment for studying how information is propagated in social networks and how users can become influential. In this work we analyse the impact of the network features and of the users' behaviour on the information diffusion. Our analysis highlights a strong relation between the level of visibility of a message in the flow of information seen by a user and the probability that the user further disseminates the message. In addition, we also highlight the existence of other latent factors that impact on the dissemination probability, correlated with the properties of the user that generated the message. Considering these results we define an information propagation model that generates information cascades (i.e. flows of messages propagated from user to user) whose statistical properties match empirical observations.


title: A Satellite-LTE Network with Delay-Tolerant Capabilities: Design and Performance Evaluation

abstract: In this paper, a delay tolerant approach for traffic management in LTE over satellite link is presented. Due to the long propagation delays and frequent disruptions, mobile satellite communications pose many challenges, especially to the transport layer when the TCP protocol is used. We design and evaluate the performance of a transport protocol solution based on the DTN architecture in the satellite-LTE system. Our proposal, named Tiny Bundle Layer, acts as an overlay on top of different versions of TCP in order to overcome the satellite link impairments.


title: OPENDPI: A Toolkit for Developing Document-Centered Environments

abstract: Documents are ubiquitous in modern desktop environments, yet these environments are based on the notion of application rather than document. As a result, editing a document often requires juggling with several applications to edit its different parts. This paper presents OpenDPI, an experimental user-interface toolkit designed to create document-centered environments, therefore getting rid of the concept of application. OpenDPI relies on the DPI (Document, Presentation, Instrument) model: documents are visualized through one or more presentations, and manipulated with interaction instruments. The implementation is based on a component model that cleanly separates documents from their presentations and from the instruments that edit them. OpenDPI supports advanced visualization and interaction techniques such as magic lenses and bimanual interaction. Document sharing is also supported with single display groupware as well as remote shared editing. The paper describes the component model and illustrates the use of the toolkit through concrete examples, including multiple views and concurrent interaction.


title: Should TED Talks Be Teaching Us Something

abstract: The nonprofit phenomenon “TED,” the brand name for the concepts of Technology Education and Design, was born in 1984. It launched into pop culture stardom in 2006 when the organization’s curators began offering short, free, unrestricted, and educational video segments.1 Known as “TED Talks,” these informational segments are designed to be no longer than 18 minutes in length and provide succinct, targeted enlightenment on various topics or ideas that are deemed “worth spreading.”1 TED Talks, often delivered in sophisticated studios with trendy backdrops, follow a format that focuses learners on the presenter and limited, extremely purposeful visual aids. Topics range from global warming to running to the developing world. Popular TED Talks, such as Sir Ken Robinson’s “Schools Kill Creatively” or Dan Gilbert’s “Why Are We Happy?” can easily garner well over a million views.2 ::: ::: TED Talks are a curious phenomenon for educators to observe. They are in many ways the antithesis of traditional lectures, which are typically 60-120 minutes in length and delivered in cavernous halls by faculty members engaged in everyday academic lives. Perhaps the formality of the lecture is the biggest superficial difference in comparison to casual TED Talks (Table 1). However, TED Talks are not as unstructured as they may appear. Presenters are well coached and instructed to follow a specific presentation formula, which maximizes storyboarding and highlights passion for the subject. While learning is not formally assessed, TED Talks do seem to accomplish their goals of spreading ideas while sparking curiosity within the learner. The fact that some presentations have been viewed more than 16 million times points to the effectiveness of the platform in at least reaching learners and stimulating a desire to click, listen, and learn. Moreover, the TED Talks website is the fourth most popular technology website and the single most popular conference and events website in the world.3 ::: ::: ::: ::: Table 1. ::: ::: Comparison of Essential Aspects of “TED Talks” versus Traditional Academic Lectures1 ::: ::: ::: ::: The TED phenomenon may have both direct and subliminal messages for academia. Perhaps an initial question to ponder is whether the TED phenomenon is a logical grassroots educational evolution or a reaction to the digital generation and their preference for learning that occurs “wherever, whenever.”4 The diverse cross-section of TED devotees ranging in background and age would seem to provide evidence that the platform does not solely appeal to younger generations of learners. Instead, it suggests that adult learners are either more drawn to digital learning than they think they are or than they are likely to admit. The perceived efficacy of TED once again calls into question the continued reliance of academia on the lecture as the primary currency of learning. TED Talks do not convey large chunks of information but rather present grander ideas. Would TED-like educational modules or blocks of 18-20 minutes be more likely to pique student curiosity across a variety of pharmacy topics, maintain attention span, and improve retention? Many faculty members who are recognized as outstanding teachers or lecturers might confess that they already teach through a TED-like lens. Collaterally, TED Talks or TED-formatted learning experiences might be ideal springboards for incorporation into inverted or flipped classroom environments where information is gathered and learned at home, while ideas are analyzed, debated, and assimilated within the classroom. ::: ::: Unarguably, TED Talks have given scientists and other researchers a real-time, mass media driven opportunity to disseminate their research, ideas, and theories that might otherwise have gone unnoticed.4 Similar platforms or approaches may be able to provide opportunities for the academy to further transmit research to the general public. ::: ::: The TED approach to idea dissemination is not without its critics. Several authors have criticized TED for flattening or dumbing down ideas so they fit into a preconceived, convenient format that is primarily designed to entertain.2,5,6 Consequently, the oversimplified ideas and concepts may provoke little effort from the learner to analyze data, theory, or controversy. Some may argue that TED gives learners a false sense of simplicity of the real world and reinforces a convenient approach to learning that one rarely encounters in everyday life. TED learners may not be able to conceptualize the academic discourse, footwork, and failures that drive the development of theories, concepts, and new ideas. TED talks are frequently cited as too often presenting overly enthusiastic and positive “infomercialized” bits of data that lack concrete critical assessment.6 ::: ::: The debate regarding the exact academic role of TED will likely continue for some time as the platform continues to grow and evolve. In the interim, the academy could learn from the TED phenomenon. While TED has limitations and is subject to valid criticisms, educators could use the talks as a component of an overall course sequence that helps students connect with material or spark student interest in particular topics that are then explored in greater detail within the curriculum. The TED platform could also be used as an active-learning strategy, where students are either asked to critique an existing TED Talk and/or design their own TED Talk regarding a particular topic of interest or relevance. TED presenters are often selected not only for their expertise on a given topic but also for their ability to effectively and succinctly communicate information to learners. In fact, students familiar with the TED Talk paradigm may hold lecturers to a higher standard given the effectiveness of the platform. In turn, faculty members might be able to improve their own delivery skills by observing TED Talks and adopting techniques used by presenters. Administrators charged with curricular oversight and development might familiarize themselves with the platform and its potential implications for future delivery of information in all settings including higher education. Given the vast reach and impact of the platform to date, it would be shortsighted for both individual educators and the academy to ignore what TED is teaching us.


title: Improving Trajectory Modelling for DNN-based Speech Synthesis by using Stacked Bottleneck Features and Minimum Generation Error Training

abstract: We propose two novel techniques---stacking bottleneck features and minimum generation error (MGE) training criterion---to improve the performance of deep neural network (DNN)-based speech synthesis. The techniques address the related issues of frame-by-frame independence and ignorance of the relationship between static and dynamic features, within current typical DNN-based synthesis frameworks. Stacking bottleneck features, which are an acoustically informed linguistic representation, provides an efficient way to include more detailed linguistic context at the input. The MGE training criterion minimises overall output trajectory error across an utterance, rather than minimising the error per frame independently, and thus takes into account the interaction between static and dynamic features. The two techniques can be easily combined to further improve performance. We present both objective and subjective results that demonstrate the effectiveness of the proposed techniques. The subjective results show that combining the two techniques leads to significantly more natural synthetic speech than from conventional DNN or long short-term memory recurrent neural network systems.


title: From word embeddings to document similarities for improved information retrieval in software engineering

abstract: The application of information retrieval techniques to search tasks in software engineering is made difficult by the lexical gap between search queries, usually expressed in natural language (e.g. English), and retrieved documents, usually expressed in code (e.g. programming languages). This is often the case in bug and feature location, community question answering, or more generally the communication between technical personnel and non-technical stake holders in a software project. In this paper, we propose bridging the lexical gap by projecting natural language statements and code snippets as meaning vectors in a shared representation space. In the proposed architecture, word embeddings are first trained on API documents, tutorials, and reference documents, and then aggregated in order to estimate semantic similarities between documents. Empirical evaluations show that the learned vector space embeddings lead to improvements in a previously explored bug localization task and a newly defined task of linking API documents to computer programming questions.


title: Localized classification

abstract: The main problem with localized discriminant techniques is the curse of dimensionality, which seems to restrict their use to the case of few variables. However, if localization is combined with a reduction of dimension the initial number of variables is less restricted. In particular it is shown that localization yields powerful classifiers even in higher dimensions if localization is combined with locally adaptive selection of predictors. A robust localized logistic regression (LLR) method is developed for which all tuning parameters are chosen data-adaptively. In an extended simulation study we evaluate the potential of the proposed procedure for various types of data and compare it to other classification procedures. In addition we demonstrate that automatic choice of localization, predictor selection and penalty parameters based on cross validation is working well. Finally the method is applied to real data sets and its real world performance is compared to alternative procedures.


title: Short Chemical Ischemia Triggers Phosphorylation of eIF2α and Death of SH-SY5Y Cells but not Proteasome Stress and Heat Shock Protein Response in both SH-SY5Y and T98G Cells

abstract: Both translation arrest and proteasome stress associated with accumulation of ubiquitin-conjugated protein aggregates were considered as a cause of delayed neuronal death after transient global brain ischemia; however, exact mechanisms as well as possible relationships are not fully understood. The aim of this study was to compare the effect of chemical ischemia and proteasome stress on cellular stress responses and viability of neuroblastoma SH-SY5Y and glioblastoma T98G cells. Chemical ischemia was induced by transient treatment of the cells with sodium azide in combination with 2-deoxyglucose. Proteasome stress was induced by treatment of the cells with bortezomib. Treatment of SH-SY5Y cells with sodium azide/2-deoxyglucose for 15 min was associated with cell death observed 24 h after treatment, while glioblastoma T98G cells were resistant to the same treatment. Treatment of both SH-SY5Y and T98G cells with bortezomib was associated with cell death, accumulation of ubiquitin-conjugated proteins, and increased expression of Hsp70. These typical cellular responses to proteasome stress, observed also after transient global brain ischemia, were not observed after chemical ischemia. Finally, chemical ischemia, but not proteasome stress, was in SH-SY5Y cells associated with increased phosphorylation of eIF2α, another typical cellular response triggered after transient global brain ischemia. Our results showed that short chemical ischemia of SH-SY5Y cells is not sufficient to induce both proteasome stress associated with accumulation of ubiquitin-conjugated proteins and stress response at the level of heat shock proteins despite induction of cell death and eIF2α phosphorylation.


title: MoFIS: a mobile user interface for semi-automatic extraction of food product ingredient lists

abstract: The availability of food ingredient information in digital form is a major factor in modern information systems related to diet management and health issues. Although ingredient information is printed on food product labels, corresponding digital data is rarely available for the public. In this demo, we present the Mobile Food Information Scanner (MoFIS), a mobile user interface designed to enable users to semi-automatically extract ingredient lists from food product packaging.


Using Hashtags as Labels for Supervised Learning of Emotions in Twitter Messages


title: 3-D Pose Estimation of Articulated Instruments in Robotic Minimally Invasive Surgery

abstract: Estimating the 3-D pose of instruments is an important part of robotic minimally invasive surgery for automation of basic procedures as well as providing safety features, such as virtual fixtures. Image-based methods of 3-D pose estimation provide a non-invasive low cost solution compared with methods that incorporate external tracking systems. In this paper, we extend our recent work in estimating rigid 3-D pose with silhouette and optical flow-based features to incorporate the articulated degrees-of-freedom (DOFs) of robotic instruments within a gradient-based optimization framework. Validation of the technique is provided with a calibrated ex-vivo study from the da Vinci Research Kit (DVRK) robotic system, where we perform quantitative analysis on the errors each DOF of our tracker. Additionally, we perform several detailed comparisons with recently published techniques that combine visual methods with kinematic data acquired from the joint encoders. Our experiments demonstrate that our method is competitively accurate while relying solely on image data.


title: Absent nasal bone at 11-14 weeks of gestation and chromosomal defects.

abstract: OBJECTIVE ::: To examine the association between absence of the nasal bone at the 11-14-week ultrasound scan and chromosomal defects. ::: ::: ::: METHODS ::: Ultrasound examination was carried out in 3829 fetuses at 11-14 weeks' gestation immediately before fetal karyotyping. At the scan the fetal crown-rump length (CRL) and nuchal translucency (NT) thickness were measured and the fetal profile was examined for the presence or absence of the nasal bone. Maternal characteristics including ethnic origin were also recorded. ::: ::: ::: RESULTS ::: The fetal profile was successfully examined in 3788 (98.9%) cases. In 3358/3788 cases the fetal karyotype was normal and in 430 it was abnormal. In the chromosomally normal group the incidence of absent nasal bone was related firstly to the ethnic origin of the mother (2.8% for Caucasians, 10.4% for Afro-Caribbeans and 6.8% for Asians), secondly to fetal CRL (4.6% for CRL of 45-54 mm, 3.9% for CRL of 55-64 mm, 1.5% for CRL of 65-74 mm and 1.0% for CRL of 75-84 mm) and thirdly, to NT thickness, (1.8% for NT < 2.5 mm, 3.4% for NT 2.5-3.4 mm, 5.0% for NT 3.5-4.4 mm and 11.8% for NT > or = 4.5 mm. In the chromosomally abnormal group the nasal bone was absent in 161/242 (66.9%) with trisomy 21, in 48/84 (57.1%) with trisomy 18, in 7/22 (31.8%) with trisomy 13, in 3/34 (8.8%) with Turner syndrome and in 4/48 (8.3%) with other defects. ::: ::: ::: CONCLUSION ::: At the 11-14-week scan the incidence of absent nasal bone is related to the presence or absence of chromosomal defects, CRL, NT thickness and ethnic origin.


TWO-WAY MODIFIED WILKINSON POWER DIVIDER FOR UWB APPLICATIONS USING TWO SECTIONS OF UNEQUAL ELECTRICAL LENGTHS


title: A summary of recent developments in satellite antennas at MDA

abstract: This document presents a summary of the latest developments in satellite antennas at MDA. It covers high-performance multibeam antennas used in geostationary missions, low-cost dual-band gimballed Ka-band antennas used in non-geostationary constellations and reconfigurable antennas.


title: Colorimetric Detection of Warfare Gases by Polydiacetylenes Toward Equipment-Free Detection

abstract: Rationally designed polydiacetylene (PDA) molecules have been developed for rapid, selective, sensitive, and convenient colorimetric detection of organophosphate (OP) nerve agents, a mass destruction weapon. Oxime (OX) functionality was incorporated into diacetylene molecules to utilize its strong affinity toward organophosphates. The diacetylene molecules having an OX functional group (OX-PDA) were self-assembled to form PDA liposomes in an aqueous solution. Upon exposure to organophosphate nerve agent simulants, OX at the OX-PDA liposome surface interacts with nerve agent simulants, which results in intraliposomal repulsive stress due to steric repulsion between OP-occupied OX units at the liposome surface as well as interliposomal aggregation induced by increased hydrophobicity of the liposome surface via OP-OX complex formation. The resulting intra- and interliposomal stress causes disturbance of the conjugated backbone of OX-PDA, producing color change as a label-free and sensitive sensory signal. The effects of molecular structure on selectivity and sensitivity of OX-PDA liposome solution, OX-PDA liposome-embedded agarose gels, and OX-PDA liposome-coated cellulose acetate membranes were systematically investigated. The optimized OX-PDA liposome in the solid state showed selective and rapid optical transition upon exposure down to 160 ppb of diisopropylfluorophosphate (DFP), a nerve agent simulant. The results provide an insightful molecular design principle of PDA-based colorimetric sensor and suggest portable sensory patches for rapid, selective, sensitive, and convenient colorimetric detection of organophosphate nerve agents.


title: Technology to reduce social isolation and loneliness

abstract: Large numbers of individuals, many of them senior citizens, live in social isolation. This typically leads to loneliness, depression, and vulnerability, and subsequently to other negative health consequences. We report on research focused on understanding the communication needs of people in environments associated with social isolation and loneliness, and how technology facilitates social connection. Our work consists of successive iterations of field studies and technology prototype design, deployment, and analysis. Particular attention is paid to seniors in retirement communities and in long-term care settings (nursing homes). We present design implications for technology to enable seniors' social connections, the "InTouch" prototype that satisfies most of the implications, and a report on one older adult's experience of InTouch.


title: Nonverbal Channel Use in Communication of Emotion: How May Depend on Why

abstract: This study investigated the hypothesis that different emotions are most effectively conveyed through specific, nonverbal channels of communication: body, face, and touch. Experiment 1 assessed the production of emotion displays. Participants generated nonverbal displays of 11 emotions, with and without channel restrictions. For both actual production and stated preferences, participants favored the body for embarrassment, guilt, pride, and shame; the face for anger, disgust, fear, happiness, and sadness; and touch for love and sympathy. When restricted to a single channel, participants were most confident about their communication when production was limited to the emotion’s preferred channel. Experiment 2 examined the reception or identification of emotion displays. Participants viewed videos of emotions communicated in unrestricted and restricted conditions and identified the communicated emotions. Emotion identification in restricted conditions was most accurate when participants viewed emotions displayed via the emotion’s preferred channel. This study provides converging evidence that some emotions are communicated predominantly through different nonverbal channels. Further analysis of these channel-emotion correspondences suggests that the social function of an emotion predicts its primary channel: The body channel promotes social-status emotions, the face channel supports survival emotions, and touch supports intimate emotions.


title: Smart simulation using collaborative formal and simulation engines

abstract: We present Ketchum, a tool that was developed to improve the productivity of simulation-based functional verification by providing two capabilities: (1) automatic test generation and (2) unreachability analysis. Given a set of "interesting" signals in the design under test (DUT), automatic test generation creates input stimuli that drive the DUT through as many different combinations (called coverage states) of these signals as possible to thoroughly exercise the DUT. Unreachability analysis identifies as many unreachable coverage states as possible.Ketchum differs from the previous published results for several reasons. First, Ketchum provides 10x higher capacity than previous published results. The higher capacity is achieved by carefully orchestrating simulation and multiple formal methods including symbolic simulation, SAT-based BMC, symbolic fixpoint computation and automatic abstraction. Second, Ketchum performs not only automatic test generation but also unreachability analysis, which enables the test generation effort to be focused on coverage states that are not unreachable. Third, the backbone of Ketchum is an off-the-shelf commercial simulator. It enables Ketchum to reach deep states of the design quickly and supports simulation monitors through the standard API of the simulator during test generation.We applied Ketchum to several industrial designs, including the picoJava microprocessor from SUN and the DW8051 microcontroller from Synopsys and obtained very promising results. The experiments show that Ketchum can (1) handle design blocks containing more than 4500 latches and 170K gates, (2) reach up to 6x more coverage states than random simulation and (3) identify a majority of the unreachable coverage states.


title: Deep Learning for Textual Entailment Recognition

abstract: In this paper we propose a novel two-step procedure to recognize textual entailment. Firstly, we build a joint Restricted Boltzmann Machines (RBM) layer to learn the joint representation of the text-hypothesis pairs. Then the reconstruction error is calculated by comparing the original representation with reconstructed representation derived from the joint layer for each pair to recognize textual entailment. The joint RBM training data is automatically generated from a large news corpus. Experiment results show the contribution of the idea to the performance on textual entailment.


title: Simulation of DC- DC Boost Converter by Coupled Inductor Using PSIM

abstract: The Conventional Boost Converters is not possible to obtain the high voltage gain. In this paper a DC- DC boost converter is proposed for achieving high voltage gain and to reduce the harmonic content in the output side. The circuit diagram of the proposed converter is consists of coupled inductor. Therefore the voltage stress on the active switch is reduced due to the presence of the inductor and the output voltage is high in the proposed converter and the response of various factors are analyzed using the simulation software PSIM.


title: Augmented reality in medical education?

abstract: Learning in the medical domain is to a large extent workplace learning and involves mastery of complex skills that require performance up to professional standards in the work environment. Since training in this real-life context is not always possible for reasons of safety, costs, or didactics, alternative ways are needed to achieve clinical excellence. Educational technology and more specifically augmented reality (AR) has the potential to offer a highly realistic situated learning experience supportive of complex medical learning and transfer. AR is a technology that adds virtual content to the physical real world, thereby augmenting the perception of reality. Three examples of dedicated AR learning environments for the medical domain are described. Five types of research questions are identified that may guide empirical research into the effects of these learning environments. Up to now, empirical research mainly appears to focus on the development, usability and initial implementation of AR for learning. Limited review results reflect the motivational value of AR, its potential for training psychomotor skills and the capacity to visualize the invisible, possibly leading to enhanced conceptual understanding of complex causality.


title: Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?

abstract: Abstract We conduct large-scale studies on ‘human attention’ in Visual Question Answering (VQA) to understand where humans choose to look to answer questions about images. We design and test multiple game-inspired novel attention-annotation interfaces that require the subject to sharpen regions of a blurred image to answer a question. Thus, we introduce the VQA-HAT (Human ATtention) dataset. We evaluate attention maps generated by state-of-the-art VQA models against human attention both qualitatively (via visualizations) and quantitatively (via rank-order correlation). Our experiments show that current attention models in VQA do not seem to be looking at the same regions as humans. Finally, we train VQA models with explicit attention supervision, and find that it improves VQA performance.


title: From gaming to training: A review of studies on fidelity, immersion, presence, and buy-in and their effects on transfer in pc-based simulations and games,” November 2005. [Online]. Available: http://www.darwars.com/downloads/ DARWARS%2520Paper%252012205.p

abstract: The volatile global security environment has changed warfighting. Battles in the arena of terrorism demand mature judgment, decision-making, and collaboration of warfighters who are often young, and often “part-time” (Reserve or National Guard) soldiers. Traditional methods of training are strained by logistical challenges, geographic distribution of personnel, and limited resources that preclude frequent field training. This situation is changing the way we train warfighters. Reaching high levels of skill requires novel approaches to training (DoD, 2004) to ensure that training resources are allocated in a cost-effective manner that optimizes skill acquisition and retention (Ricci, Salas, & Cannon-Bowers, 2002). There are two traditional approaches to training in the military. Classroom education provides valuable declarative knowledge to warfighters. Such education is often supplemented today by web-based distance learning courses, for which the Department of Defense has defined the current industry format standards (Advanced Distributed Learning, 2004). However, warfighters also need practice applying the complex skills they study, and practicing them to proficiency. This training is typically delivered in field exercises that expose warfighters to battle environments and stressors, and give them experience interacting in battle environments. However, exercises are expensive, logistically cumbersome, and often dangerous. Computer-based alternatives to live training have become more common in recent years. These alternatives are simulators, computer-based training systems, and video games. Simulators are systems that emulate visual stimuli and physical controls from the operational environment (Bonk & Dennen, 2005), common examples being flight and driving simulators. Computer-based training systems, sometimes referred to as “lightweight simulations,” are web- or PC-based systems designed to provide individual instruction on specific mission skills. These systems represent the physical and behavioral characteristics of military systems with very high fidelity. PC flight simulators such as AirBook


title: Error estimation for second-order PDEs in non-variational form

abstract: Second-order partial differential equations in non-divergence form are considered. Equations of this kind typically arise as subproblems for the solution of Hamilton-Jacobi-Bellman equations in the context of stochastic optimal control, or as the linearization of fully nonlinear second-order PDEs. The non-divergence form in these problems is natural. If the coefficients of the diffusion matrix are not differentiable, the problem can not be transformed into the more convenient variational form. ::: We investigate tailored non-conforming finite element approximations of second-order PDEs in non-divergence form, utilizing finite element Hessian recovery strategies to approximate second derivatives in the equation. We study both approximations with continuous and discontinuous trial functions. Of particular interest are a priori and a posteriori error estimates as well as adaptive finite element methods. In numerical experiments our method is compared with other approaches known from the literature.


title: Dependency Parsing as Head Selection

abstract: Conventional graph-based dependency parsers guarantee a tree structure both during training and inference. Instead, we formalize dependency parsing as the problem of independently selecting the head of each word in a sentence. Our model which we call \textsc{DeNSe} (as shorthand for {\bf De}pendency {\bf N}eural {\bf Se}lection) produces a distribution over possible heads for each word using features obtained from a bidirectional recurrent neural network. Without enforcing structural constraints during training, \textsc{DeNSe} generates (at inference time) trees for the overwhelming majority of sentences, while non-tree outputs can be adjusted with a maximum spanning tree algorithm. We evaluate \textsc{DeNSe} on four languages (English, Chinese, Czech, and German) with varying degrees of non-projectivity. Despite the simplicity of the approach, our parsers are on par with the state of the art.


title: The sources of position errors of omni-directional mobile robot with Mecanum wheel

abstract: The use of conventional kinematic equations for Mecanum wheel based omni-directional mobile robot suffers from position errors mainly because of slips on the wheels. To overcome the problem, we first identify the sources that originate position errors and then introduce the wheel parameter adjustment to reduce the position errors of the robot system. The experimental results show that the presented wheel parameter adjustment indeed reduces position errors and provides a good calibration mechanism.


title: What Every Programmer Should Know About Memory

abstract: As CPU cores become both faster and more numerous, the limiting factor for most programs is now, and will be for some time, memory access. Hardware designers have come up with ever more sophisticated memory handling and acceleration techniques‐such as CPU caches‐but these cannot work optimally without some help from the programmer. Unfortunately, neither the structure nor the cost of using the memory subsystem of a computer or the caches on CPUs is well understood by most programmers. This paper explains the structure of memory subsystems in use on modern commodity hardware, illustrating why CPU caches were developed, how they work, and what programs should do to achieve optimal performance by utilizing them.


title: Interpolation of orientation distribution functions in diffusion weighted imaging using multi-tensor model

abstract: BACKGROUND ::: Diffusion weighted imaging (DWI) is a non-invasive method for investigating the brain white matter structure and can be used to evaluate fiber bundles. However, due to practical constraints, DWI data acquired in clinics are low resolution. ::: ::: ::: NEW METHOD ::: This paper proposes a method for interpolation of orientation distribution functions (ODFs). To this end, fuzzy clustering is applied to segment ODFs based on the principal diffusion directions (PDDs). Next, a cluster is modeled by a tensor so that an ODF is represented by a mixture of tensors. For interpolation, each tensor is rotated separately. ::: ::: ::: RESULTS ::: The method is applied on the synthetic and real DWI data of control and epileptic subjects. Both experiments illustrate capability of the method in increasing spatial resolution of the data in the ODF field properly. The real dataset show that the method is capable of reliable identification of differences between temporal lobe epilepsy (TLE) patients and normal subjects. ::: ::: ::: COMPARISON WITH EXISTING METHODS ::: The method is compared to existing methods. Comparison studies show that the proposed method generates smaller angular errors relative to the existing methods. Another advantage of the method is that it does not require an iterative algorithm to find the tensors. ::: ::: ::: CONCLUSIONS ::: The proposed method is appropriate for increasing resolution in the ODF field and can be applied to clinical data to improve evaluation of white matter fibers in the brain.


title: A note on Platt’s probabilistic outputs for support vector machines

abstract: Platt's probabilistic outputs for Support Vector Machines (Platt, J. in Smola, A., et al. (eds.) Advances in large margin classifiers. Cambridge, 2000) has been popular for applications that require posterior class probabilities. In this note, we propose an improved algorithm that theoretically converges and avoids numerical difficulties. A simple and ready-to-use pseudo code is included.


title: Cross-domain transfer for reinforcement learning

abstract: A typical goal for transfer learning algorithms is to utilize knowledge gained in a source task to learn a target task faster. Recently introduced transfer methods in reinforcement learning settings have shown considerable promise, but they typically transfer between pairs of very similar tasks. This work introduces Rule Transfer, a transfer algorithm that first learns rules to summarize a source task policy and then leverages those rules to learn faster in a target task. This paper demonstrates that Rule Transfer can effectively speed up learning in Keepaway, a benchmark RL problem in the robot soccer domain, based on experience from source tasks in the gridworld domain. We empirically show, through the use of three distinct transfer metrics, that Rule Transfer is effective across these domains.


title: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

abstract: A new paradigm, Random Sample Consensus (RANSAC), for fitting a model to experimental data is introduced. RANSAC is capable of interpreting/smoothing data containing a significant percentage of gross errors, and is thus ideally suited for applications in automated image analysis where interpretation is based on the data provided by error-prone feature detectors. A major portion of this paper describes the application of RANSAC to the Location Determination Problem (LDP): Given an image depicting a set of landmarks with known locations, determine that point in space from which the image was obtained. In response to a RANSAC requirement, new results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form. These results provide the basis for an automatic system that can solve the LDP under difficult viewing


title: Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia

abstract: Using whole-exome sequencing, the authors identified 244,246 coding-sequence and splice-site ultra-rare variants (URVs) and found that gene-disruptive and putatively protein-damaging URVs were significantly more abundant in schizophrenia cases than in controls. The excess of protein-compromising URVs was concentrated in brain-specific genes, particularly in neuronally expressed genes whose proteins are located at the synapse.


title: Decision Tree Based Predictive Models for Breast Cancer Survivability on Imbalanced Data

abstract: Based on imbalanced data, the predictive models for 5-year survivability of breast cancer using decision tree are proposed. After data preprocessing from SEER breast cancer datasets, it is obviously that the category of data distribution is imbalanced. Under-sampling is taken to make up the disadvantage of the performance of models caused by the imbalanced data. The performance of the models is evaluated by AUC under ROC curve, accuracy, specificity and sensitivity with 10-fold stratified cross-validation. The performance of models is best while the distribution of data is approximately equal. Bagging algorithm is used to build an integration decision tree model for predicting breast cancer survivability.


title: A thin electroadhesive inchworm climbing robot driven by an electrostatic film actuator for inspection in a narrow gap

abstract: To inspect in a narrow gap of a critical construction or a large machine, such as a generator in a power plant, a thin inchworm climbing robot is proposed. It achieves the unique feature of low height by employing electrostatic adhesion and an electrostatic film actuator, both of which are composed of flexible printed circuit films. A prototype was developed in this research, which has only 2.4 mm of thickness and weighs 21.7 g. The experiments demonstrated that it can climb up a slope of 80°, and carry a 0.5 N payload on a horizontal surface with applied voltage of 600 V.


title: New compact S-band antenna for Nanosatellite TeleMetry and TeleCommand applications - EyeSat program

abstract: This paper presents a new compact single-feed circularly polarized S-band antenna for Nanosatellite TT&C applications. The antenna is composed of two crossed dipoles printed on both sides of a substrate, center-fed by a 50Ω coaxial cable. It provides a unidirectional pattern thanks to a reflector placed below the antenna. In order to reduce the size of the structure, this reflector is composed of an Artificial Magnetic Conductor (AMC). The bandwidth of the antenna is 17% (1.98 – 2.35 GHz) and covers the 2.025–2.110GHz TeleCommand band and the 2.2–2.29 GHz TeleMetry band. The size of the entire structure including the radome and the satellite interface is 0.58λmin of diameter and 0.081λmin of height where λmin is the wavelength at the lowest frequency. This new antenna concept will be used in the EYESAT CNES Program in 2017.


title: Card-660: Cambridge Rare Word Dataset - a Reliable Benchmark for Infrequent Word Representation Models

abstract: Rare word representation has recently enjoyed a surge of interest, owing to the crucial role that effective handling of infrequent words can play in accurate semantic understanding. However, there is a paucity of reliable benchmarks for evaluation and comparison of these techniques. We show in this paper that the only existing benchmark (the Stanford Rare Word dataset) suffers from low-confidence annotations and limited vocabulary; hence, it does not constitute a solid comparison framework. In order to fill this evaluation gap, we propose CAmbridge Rare word Dataset (Card-660), an expert-annotated word similarity dataset which provides a highly reliable, yet challenging, benchmark for rare word representation techniques. Through a set of experiments we show that even the best mainstream word embeddings, with millions of words in their vocabularies, are unable to achieve performances higher than 0.43 (Pearson correlation) on the dataset, compared to a human-level upperbound of 0.90. We release the dataset and the annotation materials at this https URL.


title: GAP-8: A RISC-V SoC for AI at the Edge of the IoT

abstract: Current ultra-low power smart sensing edge devices, operating for years on small batteries, are limited to low-bandwidth sensors, such as temperature or pressure. Enabling the next generation of edge devices to process data from richer sensors such as image, video, audio, or multi-axial motion/vibration has huge application potential. However, edge processing of data-rich sensors poses the extreme challenge of squeezing the computational requirements of advanced, machine-Iearning-based near-sensor data analysis algorithms (such as Convolutional Neural Networks) within the mW-range power envelope of always-ON battery-powered IoT end-nodes. To address this challenge, we propose GAP-8: a multi-GOPS fully programmable RISC-V IoT-edge computing engine, featuring a 8-core cluster with CNN accelerator, coupled with an ultra-low power MCU with 30 μW state-retentive sleep power. GAP-8 delivers up to 10 GMAC/s for CNN inference (90 MHz, 1.0V) at the energy efficiency of 600 GMAC/s/W within a worst-case power envelope of75 mW.


title: PsyHeal: An Approach to Remote Mental Health Monitoring System

abstract: Numerous research initiatives to develop and provide cloud-based healthcare solutions have been undertaken by academicians and industry alike. It holds major significance especially when dealing with mental health issues. Inputs taken from a patient undergoing treatment, when collected and analyzed over a time period can help the doctor in making appropriate deductions and inferences. The aim of this paper is to demonstrate the potential of using third party APIs' in creating a remote health assessment system pertaining to mental illness. In this paper, a proof-of-concept cloud based psychological assessment/ monitoring service named PsyHeal is proposed. It is a web app which interfaces with a handful of third party API's for its implementation. We first discuss the primary system design and limitations of the same. We then elaborate on some enhancements which have been added to the primary system in order to overcome the identified flaws and thus explain the final design of this web app. Future scope of the application is discussed in order to build upon the aforementioned application.


title: Informed consent with suicidal patients: Rethinking risks in (and out of) treatment.

abstract: Informed consent is uniformly accepted as essential to the treatment process. However, the relevant literature has not discussed issues of risk specific to suicidal patients, nor has such information routinely been included in the informed consent process. The implications of including suicide-specific risk information in the informed consent process is discussed and examples provided. (PsycINFO Database Record (c) 2010 APA, all rights reserved). Language: en


title: A Tabu Search Method Guided By Shifting Bottleneck for the Job Shop Scheduling Problem

abstract: Abstract A computationally effective heuristic method for solving the minimum makespan problem of job shop scheduling is presented. The proposed local search method is based on a tabu search technique and on the shifting bottleneck procedure used to generate the initial solution and to refine the next-current solutions. Computational experiments on a standard set of problem instances show that, in several cases, our approach, in a reasonable amount of computer time, yields better results than the other heuristic procedures discussed in the literature.


title: A Refreshable Braille Cell Based on Pneumatic Microbubble Actuators

abstract: A refreshable Braille cell as a tactile display prototype has been developed based on a 2 x 3 pneumatic microbubble actuator array and an array of commercial valves. The microbubble actuator acting as a Braille dot consists of a parylene corrugated diaphragm and an overcoated elastomer layer. Polyurethane (PU) and polydimethylsiloxane (PDMS) elastomer-based Braille cells were fabricated and compared in terms of their displacement-pressure characteristics, force-displacement characteristics, dynamic response, and hysteresis of elastic deformation. Both PU- and PDMS-based pneumatic Braille cells demonstrate satisfactory properties against the static and vibrational tactile display requirements. The displacement of the PU-based Braille dot is 0.56 mm at a 0.2-Hz operating frequency, and the generated force is 66 mN at a 100-kPa applied pressure. The strong hysteresis observed in microbubble actuators made with PU elastomer is resolved by the use of PDMS elastomer. The refreshable Braille cell was also designed to meet the criteria of lightness and compactness to permit portable operation. The design is scalable with respect to the number of tactile actuators while maintaining fabrication simplicity.


title: Transitive Reduction of Citation Networks

abstract: In many complex networks the vertices are ordered in time, and edges represent causal connections. We propose methods of analysing such directed acyclic graphs taking into account the constraints of causality and highlighting the causal structure. We illustrate our approach using citation networks formed from academic papers, patents, and US Supreme Court verdicts. We show how transitive reduction reveals fundamental differences in the citation practices of different areas, how it highlights particularly interesting work, and how it can correct for the effect that the age of a document has on its citation count. Finally, we transitively reduce null models of citation networks with similar degree distributions and show the difference in degree distributions after transitive reduction to illustrate the lack of causal structure in such models.


title: Irregular Bipolar Fuzzy Graphs

abstract: In this paper, we define irregular bipolar fuzzy graphs and its various classifications. Size of regular bipolar fuzzy graphs is derived. The relation between highly and neighbourly irregular bipolar fuzzy graphs are established. Some basic theorems related to the stated graphs have also been presented.


title: Image-Based Pothole Detection System for ITS Service and Road Management System

abstract: Potholes can generate damage such as flat tire and wheel damage, impact and damage of lower vehicle, vehicle collision, and major accidents. Thus, accurately and quickly detecting potholes is one of the important tasks for determining proper strategies in ITS (Intelligent Transportation System) service and road management system. Several efforts have been made for developing a technology which can automatically detect and recognize potholes. In this study, a pothole detection method based on two-dimensional (2D) images is proposed for improving the existing method and designing a pothole detection system to be applied to ITS service and road management system. For experiments, 2D road images that were collected by a survey vehicle in Korea were used and the performance of the proposed method was compared with that of the existing method for several conditions such as road, recording, and brightness. The results are promising, and the information extracted using the proposed method can be used, not only in determining the preliminary maintenance for a road management system and in taking immediate action for their repair and maintenance, but also in providing alert information of potholes to drivers as one of ITS services.


title: Dynamic Computation Offloading for Mobile-Edge Computing with Energy Harvesting Devices

abstract: Mobile-edge computing (MEC) is an emerging paradigm to meet the ever-increasing computation demands from mobile applications. By offloading the computationally intensive workloads to the MEC server, the quality of computation experience, e.g., the execution latency, could be greatly improved. Nevertheless, as the on-device battery capacities are limited, computation would be interrupted when the battery energy runs out. To provide satisfactory computation performance as well as achieving green computing, it is of significant importance to seek renewable energy sources to power mobile devices via energy harvesting (EH) technologies. In this paper, we will investigate a green MEC system with EH devices and develop an effective computation offloading strategy. The execution cost , which addresses both the execution latency and task failure, is adopted as the performance metric. A low-complexity online algorithm is proposed, namely, the Lyapunov optimization-based dynamic computation offloading algorithm, which jointly decides the offloading decision, the CPU-cycle frequencies for mobile execution, and the transmit power for computation offloading. A unique advantage of this algorithm is that the decisions depend only on the current system state without requiring distribution information of the computation task request, wireless channel, and EH processes. The implementation of the algorithm only requires to solve a deterministic problem in each time slot, for which the optimal solution can be obtained either in closed form or by bisection search. Moreover, the proposed algorithm is shown to be asymptotically optimal via rigorous analysis. Sample simulation results shall be presented to corroborate the theoretical analysis as well as validate the effectiveness of the proposed algorithm.


title: Critical Survey of the Freely Available Arabic Corpora

abstract: The availability of corpora is a major factor in building natural language processing applications. However, the costs of acquiring corpora can prevent some researchers from going further in their endeavours. The ease of access to freely available corpora is urgent needed in the NLP research community especially for language such as Arabic. Currently, there is not easy was to access to a comprehensive and updated list of freely available Arabic corpora. We present in this paper, the results of a recent survey conducted to identify the list of the freely available Arabic corpora and language resources. Our preliminary results showed an initial list of 66 sources. We presents our findings in the various categories studied and we provided the direct links to get the data when possible.


title: Guidance for human navigation using a vibro-tactile belt interface and robot-like motion planning

abstract: We present a navigation guidance system that guides a human to a goal point with a tactile belt interface and a stationary laser scanner. We make use of ROS local navigation planner to find an obstacle-free path by modeling the human as a non-holonomic robot. Linear and angular velocities to keep the ’robot’ on the path are dynamically calculated, which are then converted to vibrations and applied by the tactile belt. We define directional and rotational vibration patterns and evaluate which ones are suitable for guiding humans. Continuous patterns for representing directions had the least average angular error with 8.4◦, whereas rotational patterns were recognized with near-perfect accuracy. All patterns had a reaction time slightly more than 1 seconds. The person is tracked in laser scans by fitting an ellipse to the torso. Average tracking error is found to be 5cm in position and 14◦ in orientation in our experiments with 23 people. Best tracking results was achieved when the person is 2.5m away and is facing the sensor or the opposite way. The human control system as a whole is successfully demonstrated in a navigation guidance scenario.


title: A requirement engineering model for big data software

abstract: Most prevailing software engineering methodologies assume that software systems are developed from scratch to capture business data and subsequently generate reports. Nowadays, massive data may exist even before software systems are developed. These data may also be freely available on Internet or may present in silos in organizations. The advancement in artificial intelligence and computing power has also prompted the need for big data analytics to unleash more business values to support evidence-based decisions. Some business values are less evident than others, especially when data are analyzed in silos. These values could be potentially unleashed and augmented from the insights discovered by data scientists through data mining process. Data mining may involve overlaying and merging data from different sources to extract data patterns. Ideally, these values should be eventually incorporated into the information systems to be. To realize this, we propose that software engineers ought to elicit software requirements together with data scientists. However, in the traditional software engineering process, such collaboration and business values are usually neglected. In this paper, we present a new requirement engineering model that allows software engineers and data scientists to discover these values hand in hand as part of software requirement process. We also demonstrate how the proposed requirement model captures and expresses business values that unleashed through big data analytics using an adapted use case diagram.


title: 14.5 Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks

abstract: Eyeriss is an accelerator for state-of-the-art deep convolutional neural networks (CNNs). It optimizes for the energy efficiency of the entire system, including the accelerator chip and off-chip DRAM, for various CNN shapes by reconfiguring the architecture. CNNs are widely used in modern AI systems but also bring challenges on throughput and energy efficiency to the underlying hardware. This is because its computation requires a large amount of data, creating significant data movement from on-chip and off-chip that is more energy-consuming than computation. Minimizing data movement energy cost for any CNN shape, therefore, is the key to high throughput and energy efficiency. Eyeriss achieves these goals by using a proposed processing dataflow, called row stationary (RS), on a spatial architecture with 168 processing elements. RS dataflow reconfigures the computation mapping of a given shape, which optimizes energy efficiency by maximally reusing data locally to reduce expensive data movement, such as DRAM accesses. Compression and data gating are also applied to further improve energy efficiency. Eyeriss processes the convolutional layers at 35 frames/s and 0.0029 DRAM access/multiply and accumulation (MAC) for AlexNet at 278 mW (batch size $N = 4$ ), and 0.7 frames/s and 0.0035 DRAM access/MAC for VGG-16 at 236 mW ( $N = 3$ ).


title: A socio-marketing analysis of the concept of cute and its consumer culture implications

abstract: Cute has become the favored language of (the predominantly female) popular consumer culture. This paper examines the roots of “cute” and its evolution with reference to its relevance to marketers. We follow the cultural appropriation of Japan’s “Kawaii” by the Western “cute”, and introduce a social, marketing-oriented description and analysis of the concept. We present its socio-cultural, experiential, symbolic, and ideological relevance to consumption in general, and to consumer culture theory in particular. This examination steers clear of scientific generalizations prevalent in consumer behavior research and aims, instead, to illuminate the cultural dimensions of the consumption cycle, and allow a better understanding of what it is like to form social attachment and loyalty in the context of cute consumption and consumer culture theory. Especially interesting questions arise regarding the trans-social acculturating elements of cute, as well as the apparent mainstreaming (cultural appropriation) of the ...


title: EFFICIENT VEHICLE TRACKING AND CLASSIFICATION FOR AN AUTOMATED TRAFFIC SURVEILLANCE SYSTEM

abstract: As digital cameras and powerful computers have become wide-spread, the number of applications using vision techniques has increased significantly. One such application that has received significant attention from the computer vision community is traffic surveillance. We propose a new traffic surveillance system that works without prior, explicit camera calibration, and has the ability to perform surveillance tasks in real time. Camera intrinsic parameters and its position with respect to the ground plane were derived using geometric primitives common to any traffic scene. We use optical flow and knowledge of camera parameters to detect the pose of a vehicle in the 3D world. This information is used in a model-based vehicle detection and classification technique employed by our traffic surveillance application. The object (vehicle) classification uses two new techniques − color contour based matching and gradient based matching. Our experiments on several real traffic video sequences demonstrate good results for our foreground object detection, tracking, vehicle detection and vehicle speed estimation approaches.


title: Remyelination in the CNS: from biology to therapy

abstract: Remyelination involves reinvesting demyelinated axons with new myelin sheaths. In stark contrast to the situation that follows loss of neurons or axonal damage, remyelination in the CNS can be a highly effective regenerative process. It is mediated by a population of precursor cells called oligodendrocyte precursor cells (OPCs), which are widely distributed throughout the adult CNS. However, despite its efficiency in experimental models and in some clinical diseases, remyelination is often inadequate in demyelinating diseases such as multiple sclerosis (MS), the most common demyelinating disease and a cause of neurological disability in young adults. The failure of remyelination has profound consequences for the health of axons, the progressive and irreversible loss of which accounts for the progressive nature of these diseases. The mechanisms of remyelination therefore provide critical clues for regeneration biologists that help them to determine why remyelination fails in MS and in other demyelinating diseases and how it might be enhanced therapeutically.


title: The relationship between workflow graphs and free-choice workflow nets

abstract: Workflow graphs represent the main control-flow constructs of industrial process modeling languages such as BPMN, EPC and UML activity diagrams, whereas free-choice workflow nets are a well understood class of Petri nets that possesses many efficient analysis techniques. In this paper, we provide new results on the translation between workflow graphs and free-choice workflow nets.We distinguish workflow graphs with and without inclusive Or-logic. For workflow graphs without inclusive logic, we show that workflow graphs and free-choice workflow nets are essentially the same thing. More precisely, each workflow graph and each free-choice workflow net can be brought into an equivalent normal form such that the normal forms are, in some sense, isomorphic. This result gives rise to a translation from arbitrary free-choice workflow nets to workflow graphs.For workflow graphs with inclusive logic, we provide various techniques to replace inclusive Or-joins by subgraphs without inclusive logic, thus giving rise to translations from workflow graphs to free-choice nets. Additionally, we characterize the applicability of these replacements. Finally, we also display a simple workflow graph with an inclusive Or-join, which, in some sense, cannot be replaced. This shows a limitation of translating inclusive logic into free-choice nets and illustrates also a difficulty of translating inclusive logic into general Petri nets.


title: Dense Dilated Network for Few Shot Action Recognition

abstract: Recently, video action recognition has been widely studied. Training deep neural networks requires a large amount of well-labeled videos. On the other hand, videos in the same class share high-level semantic similarity. In this paper, we introduce a novel neural network architecture to simultaneously capture local and long-term spatial temporal information. The dilated dense network is proposed with the blocks being composed of densely-connected dilated convolutions layers. The proposed framework is capable of fusing each layer's outputs to learn high-level representations, and the representations are robust even with only few training snippets. The aggregations of dilated dense blocks are also explored. We conduct extensive experiments on UCF101 and demonstrate the effectiveness of our proposed method, especially with few training examples.


title: Quantitative comparisons of the state-ofthe-art data center architectures

abstract: Data centers are experiencing a remarkable growth in the number of interconnected servers. Being one of the foremost data center design concerns, network infrastructure plays a pivotal role in the initial capital investment and ascertaining the performance parameters for the data center. Legacy data center network (DCN) infrastructure lacks the inherent capability to meet the data centers growth trend and aggregate bandwidth demands. Deployment of even the highest-end enterprise network equipment only delivers around 50% of the aggregate bandwidth at the edge of network. The vital challenges faced by the legacy DCN architecture trigger the need for new DCN architectures, to accommodate the growing demands of the ‘cloud computing’ paradigm. We have implemented and simulated the state of the art DCN models in this paper, namely: (a) legacy DCN architecture, (b) switch-based, and (c) hybrid models, and compared their effectiveness by monitoring the network: (a) throughput and (b) average packet delay. The presented analysis may be perceived as a background benchmarking study for the further research on the simulation and implementation of the DCN-customized topologies and customized addressing protocols in the large-scale data centers. We have performed extensive simulations under various network traffic patterns to ascertain the strengths and inadequacies of the different DCN architectures. Moreover, we provide a firm foundation for further research and enhancement in DCN architectures. Copyright ？ 2012 John Wiley & Sons, Ltd.


title: A robust flash image shadow detection method and seamless recovery of shadow regions

abstract: In low or moderate ambient light conditions, flash image is sharp and less noisy as compared to no-flash image; however, it contains unwanted sharp shadows at silhouettes. In our previous work, we proposed a new flash image shadow detection method which utilizes a pair of flash/no-flash images to detect sharp shadows at silhouettes; however, the method often leads to false shadow detection due its inability to effectively discriminate dark background areas. In this paper, we present an enhanced shadow detection algorithm which shows significant improvement in detection results. A seamless cloning method is used to fill detected shadow regions using no-flash image information. A quantitative as well as qualitative comparison of the proposed shadow detection method on our flash/no-flash image dataset shows that it outperforms current state-of-the-art shadow detection algorithms, both in terms of accuracy as well as speed. The qualitative results of proposed shadow recovery technique show that it generates good quality resultant flash images without sharp shadows.


title: An Approach To Automatic Text Summarization Using Simplified Lesk Algorithm And Wordnet

abstract: Text Summarization is a way to produce a text, which contains the significant portion of information of the original text(s). Different methodologies are developed till now depending upon several parameters to find the summary as the position, format and type of the sentences in an input text, formats of different words, frequency of a particular word in a text etc. But according to different languages and input sources, these parameters are varied. As result the performance of the algorithm is greatly affected. The proposed approach summarizes a text without depending upon those parameters. Here, the relevance of the sentences within the text is derived by Simplified Lesk algorithm and WordNet, an online dictionary. This approach is not only independent of the format of the text and position of a sentence in a text, as the sentences are arranged at first according to their relevance before the summarization process, the percentage of summarization can be varied according to needs. The proposed approach gives around 80% accurate results on 50% summarization of the original text with respect to the manually summarized result, performed on 50 different types and lengths of texts. We have achieved satisfactory results even upto 25% summarization of the original text.


title: Deep Kinematic Pose Regression

abstract: Learning articulated object pose is inherently difficult because the pose is high dimensional but has many structural constraints. Most existing work do not model such constraints and does not guarantee the geometric validity of their pose estimation, therefore requiring a post-processing to recover the correct geometry if desired, which is cumbersome and sub-optimal. In this work, we propose to directly embed a kinematic object model into the deep neutral network learning for general articulated object pose estimation. The kinematic function is defined on the appropriately parameterized object motion variables. It is differentiable and can be used in the gradient descent based optimization in network training. The prior knowledge on the object geometric model is fully exploited and the structure is guaranteed to be valid. We show convincing experiment results on a toy example and the 3D human pose estimation problem. For the latter we achieve state-of-the-art result on Human3.6M dataset.


title: Generating Descriptions with Grounded and Co-referenced People

abstract: Learning how to generate descriptions of images or videos received major interest both in the Computer Vision and Natural Language Processing communities. While a few works have proposed to learn a grounding during the generation process in an unsupervised way (via an attention mechanism), it remains unclear how good the quality of the grounding is and whether it benefits the description quality. In this work we propose a movie description model which learns to generate description and jointly ground (localize) the mentioned characters as well as do visual co-reference resolution between pairs of consecutive sentences/clips. We also propose to use weak localization supervision through character mentions provided in movie descriptions to learn the character grounding. At training time, we first learn how to localize characters by relating their visual appearance to mentions in the descriptions via a semi-supervised approach. We then provide this (noisy) supervision into our description model which greatly improves its performance. Our proposed description model improves over prior work w.r.t. generated description quality and additionally provides grounding and local co-reference resolution. We evaluate it on the MPII Movie Description dataset using automatic and human evaluation measures and using our newly collected grounding and co-reference data for characters.


title: Learning from Comparisons and Choices

abstract: When tracking user-specific online activities, each user's preference is revealed in the form of choices and comparisons. For example, a user's purchase history is a record of her choices, i.e. which item was chosen among a subset of offerings. A user's preferences can be observed either explicitly as in movie ratings or implicitly as in viewing times of news articles. Given such individualized ordinal data in the form of comparisons and choices, we address the problem of collaboratively learning representations of the users and the items. The learned features can be used to predict a user's preference of an unseen item to be used in recommendation systems. This also allows one to compute similarities among users and items to be used for categorization and search. Motivated by the empirical successes of the MultiNomial Logit (MNL) model in marketing and transportation, and also more recent successes in word embedding and crowdsourced image embedding, we pose this problem as learning the MNL model parameters that best explain the data. We propose a convex relaxation for learning the MNL model, and show that it is minimax optimal up to a logarithmic factor by comparing its performance to a fundamental lower bound. This characterizes the minimax sample complexity of the problem, and proves that the proposed estimator cannot be improved upon other than by a logarithmic factor. Further, the analysis identifies how the accuracy depends on the topology of sampling via the spectrum of the sampling graph. This provides a guideline for designing surveys when one can choose which items are to be compared. This is accompanied by numerical simulations on synthetic and real data sets, confirming our theoretical predictions.