title: Expressive power of recurrent neural networks

abstract: Deep neural networks are surprisingly efficient at solving practical tasks, but the theory behind this phenomenon is only starting to catch up with the practice. Numerous works show that depth is the key to this efficiency. A certain class of deep convolutional networks -- namely those that correspond to the Hierarchical Tucker (HT) tensor decomposition -- has been proven to have exponentially higher expressive power than shallow networks. I.e. a shallow network of exponential width is required to realize the same score function as computed by the deep architecture. In this paper, we prove the expressive power theorem (an exponential lower bound on the width of the equivalent shallow network) for a class of recurrent neural networks -- ones that correspond to the Tensor Train (TT) decomposition. This means that even processing an image patch by patch with an RNN can be exponentially more efficient than a (shallow) convolutional network with one hidden layer. Using theoretical results on the relation between the tensor decompositions we compare expressive powers of the HT- and TT-Networks. We also implement the recurrent TT-Networks and provide numerical evidence of their expressivity.


title: High-performance secure multi-party computation for data mining applications

abstract: Secure multi-party computation (MPC) is a technique well suited for privacy-preserving data mining. Even with the recent progress in two-party computation techniques such as fully homomorphic encryption, general MPC remains relevant as it has shown promising performance metrics in real-world benchmarks. Sharemind is a secure multi-party computation framework designed with real-life efficiency in mind. It has been applied in several practical scenarios, and from these experiments, new requirements have been identified. Firstly, large datasets require more efficient protocols for standard operations such as multiplication and comparison. Secondly, the confidential processing of financial data requires the use of more complex primitives, including a secure division operation. This paper describes new protocols in the Sharemind model for secure multiplication, share conversion, equality, bit shift, bit extraction, and division. All the protocols are implemented and benchmarked, showing that the current approach provides remarkable speed improvements over the previous work. This is verified using real-world benchmarks for both operations and algorithms.


title: A Neuroevolution Approach to General Atari Game Playing

abstract: This paper addresses the challenge of learning to play many different video games with little domain-specific knowledge. Specifically, it introduces a neuroevolution approach to general Atari 2600 game playing. Four neuroevolution algorithms were paired with three different state representations and evaluated on a set of 61 Atari games. The neuroevolution agents represent different points along the spectrum of algorithmic sophistication—including weight evolution on topologically fixed neural networks (conventional neuroevolution), covariance matrix adaptation evolution strategy (CMA–ES), neuroevolution of augmenting topologies (NEAT), and indirect network encoding (HyperNEAT). State representations include an object representation of the game screen, the raw pixels of the game screen, and seeded noise (a comparative baseline). Results indicate that direct-encoding methods work best on compact state representations while indirect-encoding methods (i.e., HyperNEAT) allow scaling to higher dimensional representations (i.e., the raw game screen). Previous approaches based on temporal-difference (TD) learning had trouble dealing with the large state spaces and sparse reward gradients often found in Atari games. Neuroevolution ameliorates these problems and evolved policies achieve state-of-the-art results, even surpassing human high scores on three games. These results suggest that neuroevolution is a promising approach to general video game playing (GVGP).


title: SPIDAL Java: high performance data analytics with Java and MPI on large multicore HPC clusters

abstract: Within the last few years, there have been significant contributions to Java-based big data frameworks and libraries such as Apache Hadoop, Spark, and Storm. While these systems are rich in interoperability and features, developing high performance big data analytic applications is challenging. Also, the study of performance characteristics and high performance optimizations is lacking in the literature for these applications. By contrast, these features are well documented in the High Performance Computing (HPC) domain and some of the techniques have potential performance benefits in the big data domain as well. This paper presents the implementation of a high performance big data analytics library - SPIDAL Java - with a comprehensive discussion on five performance challenges, solutions, and speedup results. SPIDAL Java captures a class of global machine learning applications with significant computation and communication that can serve as a yardstick in studying performance bottlenecks with Java big data analytics. The five challenges present here are the cost of intra-node messaging, inefficient cache utilization, performance costs with threads, overhead of garbage collection, and the costs of heap allocated objects. SPIDAL Java presents its solutions to these and demonstrates significant performance gains and scalability when running on up to 3072 cores in one of the latest Intel Haswell-based multicore clusters.


title: 5V tolerant power clamps for mixed-voltage IC's in 65nm 2.5V salicided CMOS technology

abstract: A cascaded power clamp technique is proposed for mixed-voltage IC's power pins ESD protection using 65nm 2.5V salicided transistors for dual well processes. The effective power-to-ground cascade ESD clamp circuits for mixed 2.5V and 5V powers have been designed to provide low-impedance path between mixed powers and ground lines of IC during the ESD stress. The stacked power clamp also can be used only for 5V power supply without any electrical overstress on transistors. The discussed ESD protection method is easy simulate able and allow having predictable HBM ESD level. The HBM level of this clamp is up to 5kV with allowable layout area.


title: REVIEW RNAi As a Tool for Understanding Germline Development in Caenorhabditis elegans: Uses and Cautions

abstract: Abstract RNA-mediated genetic interference (RNAi) has become a very useful tool for analyzing gene function in development and other processes. RNAi can be used as a complement to traditional genetic studies or as a primary means of determining biological function. However, the efficacy of RNAi depends on a variety of factors that the researcher must take into consideration. This review focuses on germline development in the nematode, Caenorhabditis elegans , and discusses the uses and limitations of RNAi in providing new information about gene function as well as the possible endogenous role RNAi plays in germline physiology.


title: Elastic Refresh: Techniques to Mitigate Refresh Penalties in High Density Memory

abstract: High density memory is becoming more important as many execution streams are consolidated onto single chip many-core processors. DRAM is ubiquitous as a main memory technology, but while DRAM’s per-chip density and frequency continue to scale, the time required to refresh its dynamic cells has grown at an alarming rate. This paper shows how currently-employed methods to schedule refresh operations are ineffective in mitigating the significant performance degradation caused by longer refresh times. Current approaches are deficient– they do not effectively exploit the flexibility of DRAMs to postpone refresh operations. This work proposes dynamically reconfigurable predictive mechanisms that exploit the full dynamic range allowed in the JEDEC DDRx SDRAM specifications. The proposed mechanisms are shown to mitigate much of the penalties seen with dense DRAM devices. We refer to the overall scheme as Elastic Refresh, in that the refresh policy is stretched to fit the currently executing workload, such that the maximum benefit of the DRAM flexibility is realized. We extend the GEMS on SIMICS tool-set to include Elastic Refresh. Simulations show the proposed solution provides a 10% average performance improvement over existing techniques across the entire SPEC CPU suite, and up to a 41%improvement for certain workloads.


title: A Geometrically Exact Model for Externally Loaded Concentric-Tube Continuum Robots

abstract: Continuum robots, which are composed of multiple concentric, precurved elastic tubes, can provide dexterity at diameters equivalent to standard surgical needles. Recent mechanics-based models of these "active cannulas" are able to accurately describe the curve of the robot in free space, given the preformed tube curves and the linear and angular positions of the tube bases. However, in practical applications, where the active cannula must interact with its environment or apply controlled forces, a model that accounts for deformation under external loading is required. In this paper, we apply geometrically exact rod theory to produce a forward kinematic model that accurately describes large deflections due to a general collection of externally applied point and/or distributed wrench loads. This model accommodates arbitrarily many tubes, with each having a general preshaped curve. It also describes the independent torsional deformation of the individual tubes. Experimental results are provided for both point and distributed loads. Average tip error under load was 2.91 mm (1.5%-3% of total robot length), which is similar to the accuracy of existing free-space models.


title: FPGA based implementation of BPSK and QPSK modulators using address reverse accumulators

abstract: Implementation of digital modulators on Field Programmable Gate Array (FPGA) is a research area that has received great attention recently. Most of the research has focused on the implementation of simple digital modulators on FPGAs such as Amplitude Shift Keying (ASK), Frequency Shift Keying (FSK), and Phase Shift Keying (PSK). This paper presented a novel method of implementing Quadrature Phase Shift Keying (QPSK) along with Binary PSK (BPSK) using accumulators with a reverse addressing technique. The implementation of the BPSK modulator required two sinusoidal signals with 180-degree phase shift. The first signal was obtained using Look Up Table(LUT) based on Direct Digital Synthesizer (DDS) technique. The second signal was obtained by using the same LUT but after inverting the most significant bit of the accumulator to get the out of phase signal. For the QPSK modulator, four sinusoidal waves were needed. Using only one LUT, these waves were obtained. The first two wave were generated by using two accumulators working on the rising edge and the falling edge of a perfect twice frequency square wave clock which results in a 90-degree phase shift. The other waves were obtained from the same accumulators after reversing the most significant bit in each one. The implementation of the entire systems was done in the Very high speed integrated circuit Hardware Description Language (VHDL) without the help of Xilinx System Generator or DSP Builder tools as many papers did.


title: Study of fast initial charge loss and it's impact on the programmed states Vt distribution of charge-trapping NAND Flash

abstract: We report for the first time a fast initial charge loss (within 1 sec) in charge-trapping (CT) NAND devices. Using a fast-response pulse I–V system retention transients from μsec to sec are characterized and the correlation with programmed states Vt distribution in various NAND Flash test chips is examined. We clarify that the impacts of fast initial charge loss are: (1) it produces a programmed state Vt offset in the various program-verify (PV) levels, and (2) it broadens the Vt distribution thus threatens the MLC capability. Our findings suggest that both high-K/metal-gate and barrier engineered tunneling barrier approaches should be optimized in order to minimize the initial charge loss. We also propose a “refill” method to suppress this effect, and have successfully demonstrated tight Vt distributions in a BE-SONOS CT NAND test chip.


title: Determinants for Failure and Success of Innovation Projects: The Road to Sustainable Educational Innovation.

abstract: Robert Burns wrote: “The best laid schemes of Mice and Men oft go awry”. This could be considered the motto of most educational innovation. The question that arises is not so much why some innovations fail (although this is very important question), but rather why other innovations succeed? This study investigated the success factors of large-scale educational innovation projects in Dutch higher education. The research team attempted to identify success factors that might be relevant to educational innovation projects. The research design was largely qualitative, with a guided interview as the primary means of data collection, followed by data analysis and a correlation of findings with the success factors identified in the literature review. In order to pursue the research goal, a literature review of success factors was first conducted to identify existing knowledge in this area, followed by a detailed study of the educational innovation projects that have been funded by SURF Education. To obtain a list of potential success factors, existing project documentation and evaluations were reviewed and the project chairs and other important players were interviewed. Reports and evaluations by the projects themselves were reviewed to extract commonalities and differences in the factors that the projects felt were influential in their success of educational innovation. In the next phase of the project experts in the field of project management, project chairs of successful projects and evaluators/raters of projects will be asked to pinpoint factors of importance that were facilitative or detrimental to the outcome of their projects and implementation of the innovations. After completing the interviews all potential success factors will be recorded and clustered using an affinity technique. The clusters will then be labeled and clustered, creating a hierarchy of potential success factors. The project chairs will finally be asked to select the five most important success factors out of the hierarchy, and to rank their importance. This technique – the Experts’ Concept Mapping Method – is based upon Trochim’s concept mapping approach (1989a, 1989b) and was developed and perfected by Stoyanov and Kirschner (2004). Finally, the results will lead to a number of instruments as well as a functional procedure for tendering, selecting and monitoring innovative educational projects. The identification of success factors for educational innovation projects and measuring performance of projects based upon these factors are important as they can aid the development and implementation of innovation projects by explicating and making visible (and thus manageable) those success and failure factors relating to educational innovation projects in higher education. Determinants for Failure and Success of Innovation Projects: The Road to Sustainable Educational Innovation The Dutch Government has invested heavily in stimulating better and more creative use of information and communication technologies (ICT) in all forms of education. The ultimate goal of this investment is to ensure that students and teachers are equipped with the skills and knowledge required for success in the new knowledge-based economy. All stakeholders (i.e., government, industry, educational institutions, society in general) have placed high priority on achieving this goal. However, these highly funded projects have often resulted in either short-lived or local successes or outright failures (see De Bie,


title: Rule-Based Event Processing and Reaction Rules

abstract: Reaction rules and event processing technologies play a key role in making business and IT / Internet infrastructures more agile and active. While event processing is concerned with detecting events from large event clouds or streams in almost real-time, reaction rules are concerned with the invocation of actions in response to events and actionable situations. They state the conditions under which actions must be taken. In the last decades various reaction rule and event processing approaches have been developed, which for the most part have been advanced separately. In this paper we survey reaction rule approaches and rule-based event processing systems and languages.


Multitarget actives from Verbascum and Ajuga species on prevention and treatment of nonmelanoma skin cancer


title: Teaching game AI through Minecraft mods

abstract: One of the issues with teaching artificial intelligence (AI) for games is that many AI algorithms work in theory, but have production consequences in terms of speed or memory when actually used in a game. We report on the benefits and drawbacks of modifying or “modding” the commercial game Minecraft for a course on Game AI. This was done to give students the experience of dealing with a commercial game environment where they would have to worry about production consequences with their algorithms. The course was run as an upper level undergraduate elective during the fall of 2011 and included assignments on dynamic terrain generation, character behavior, and world events.


title: Holiday and school-term sleep patterns of Australian adolescents.

abstract: Abstract The holiday and school-term sleep patterns of 310 Australian senior school students were surveyed in a longitudinal study, along with self-reported sleep quality, mood, daytime functioning, grades and circadian preference. Evidence was found that with the impact of school schedule, students accrued a significant sleep debt, obtaining insufficient sleep for their needs and reporting lowered mood and daytime functioning. Support was found for the hypothesis that trait circadian preference mediates mood, daytime functioning and academic grades through its effect on sleep variables at school time. It was concluded that while the imposition of school schedule negatively impacted on mood and daytime functioning for the sample as a whole, evening-oriented adolescents were the most vulnerable to poorer outcomes. These students obtained poorer quality and less sleep than morning-oriented students. Sleep factors impacted negatively on evening-oriented students’ mood and ability to function at school during the day, which in turn predicted poorer academic achievement.


title: Case-based reasoning is a methodology not a technology

abstract: This paper asks whether case-based reasoning is an artificial intelligence (AI) technology like rule-based reasoning, neural networks or genetic algorithms or whether it is better described as a methodology for problem solving, that may use any appropriate technology. By describing four applications of case-based reasoning (CBR), that variously use: nearest neighbour, induction, fuzzy logic and SQL, the author shows that CBR is a methodology and not a technology. The implications of this are discussed.


title: Autonomous ground vehicle control system for high-speed and safe operation

abstract: This paper describes a new trajectory tracking control system for autonomous ground vehicles (AGV) toward safe and high-speed operation enabled by incorporating vehicle dynamics control (VDC) into the AGV. The control system consists of two levels: an AGV desired yaw rate generator based on a kinematic model, and a yaw rate controller based on the vehicle/tire dynamic models. The separation between AGV trajectory tracking and low-level actuation allows the incorporation of the VDC into the AGV systems. Sliding mode control is utilized to handle the system uncertainties. The performance of the proposed control system is evaluated by using a high-fidelity (experimentally validated) full-vehicle sport utility vehicle (SUV) model (rear-drive and front-steer) provided by CarSimregon a race track. Compared with the results for typically-employed position error based AGV control, significant performance improvement is observed.


title: AUTOMATED EVALUATION OF ESSAYS AND SHORT ANSWERS

abstract: Essay questions designed to measure writing ability, along with open-ended questions ::: requiring short answers, are highly-valued components of effective assessment ::: programs, but the expense and logistics of scoring them reliably often present a barrier ::: to their use. Extensive research and development efforts at Educational Testing ::: Service (ETS) over the past several years (see http://www.ets.org/research/erater.html) ::: in natural language processing have produced two applications with the potential to ::: dramatically reduce the difficulties associated with scoring these types of assessments.


title: Security and Privacy Issues in Wireless Sensor Networks for Healthcare Applications

abstract: The use of wireless sensor networks (WSN) in healthcare applications is growing in a fast pace. Numerous applications such as heart rate monitor, blood pressure monitor and endoscopic capsule are already in use. To address the growing use of sensor technology in this area, a new field known as wireless body area networks (WBAN or simply BAN) has emerged. As most devices and their applications are wireless in nature, security and privacy concerns are among major areas of concern. Due to direct involvement of humans also increases the sensitivity. Whether the data gathered from patients or individuals are obtained with the consent of the person or without it due to the need by the system, misuse or privacy concerns may restrict people from taking advantage of the full benefits from the system. People may not see these devices safe for daily use. There may also possibility of serious social unrest due to the fear that such devices may be used for monitoring and tracking individuals by government agencies or other private organizations. In this paper we discuss these issues and analyze in detail the problems and their possible measures.


title: Feature extraction for outlier detection in highdimensional spaces

abstract: This work addresses the problem of feature extraction for boosting the performance of outlier detectors in high-dimensional spaces. Recent years have observed the prominence of multidimensional data on which traditional detection techniques usually fail to work as expected due to the curse of dimensionality. This paper introduces an efficient feature extraction method which brings nontrivial improvements in detection accuracy when applied on two popular detection techniques. Experiments carried out on real datasets demonstrate the feasibility of feature extraction in outlier detection.


title: Image Inspired Poetry Generation in XiaoIce

abstract: Vision is a common source of inspiration for poetry. The objects and the sentimental imprints that one perceives from an image may lead to various feelings depending on the reader. In this paper, we present a system of poetry generation from images to mimic the process. Given an image, we first extract a few keywords representing objects and sentiments perceived from the image. These keywords are then expanded to related ones based on their associations in human written poems. Finally, verses are generated gradually from the keywords using recurrent neural networks trained on existing poems. Our approach is evaluated by human assessors and compared to other generation baselines. The results show that our method can generate poems that are more artistic than the baseline methods. This is one of the few attempts to generate poetry from images. By deploying our proposed approach, XiaoIce has already generated more than 12 million poems for users since its release in July 2017. A book of its poems has been published by Cheers Publishing, which claimed that the book is the first-ever poetry collection written by an AI in human history.


title: SimFlex: Statistical Sampling of Computer System Simulation

abstract: Timing-accurate full-system multiprocessor simulations can take years because of architecture and application complexity. Statistical sampling makes simulation-based studies feasible by providing ten-thousand-fold reductions in simulation runtime and enabling thousand-way simulation parallelism


title: Closed-Form Position and Orientation Estimation for a Three-Axis Electromagnetic Tracking System

abstract: Electromagnetic tracking systems are a practical and cost-effective alternative to vision-based systems for virtual reality applications, providing a large working range without suffering from the line-of-sight problem. We present closed-form formulas for the optimal least-squares position and orientation estimates of an electromagnetic tracking system. A generic system consisting of three-axis sensor and generator coils—the coils need not be orthogonal—is considered, and a least-squares objective function for tracking is formulated. The optimal solution is then derived in closed form. Experimental validation via numerical simulations and hardware experiments confirm that our least-squares tracking algorithm can run in real time, and that tracking performance is more robust to measurement errors than existing methods.


title: Generating Word Embeddings from an Extreme Learning Machine for Sentiment Analysis and Sequence Labeling Tasks

abstract: Word Embeddings are low-dimensional distributed representations that encompass a set of language modeling and feature learning techniques from Natural Language Processing (NLP). Words or phrases from the vocabulary are mapped to vectors of real numbers in a low-dimensional space. In previous work, we proposed using an Extreme Learning Machine (ELM) for generating word embeddings. In this research, we apply the ELM-based Word Embeddings to the NLP task of Text Categorization, specifically Sentiment Analysis and Sequence Labeling. The ELM-based Word Embeddings utilizes a count-based approach similar to the Global Vectors (GloVe) model, where the word-context matrix is computed then matrix factorization is applied. A comparative study is done with Word2Vec and GloVe, which are the two popular state-of-the-art models. The results show that ELM-based Word Embeddings slightly outperforms the aforementioned two methods in the Sentiment Analysis and Sequence Labeling tasks.In addition, only one hyperparameter is needed using ELM whereas several are utilized for the other methods. ELM-based Word Embeddings are comparable to the state-of-the-art methods: Word2Vec and GloVe models. In addition, the count-based ELM model have word similarities to both the count-based GloVe and the predict-based Word2Vec models, with subtle differences.


title: An autonomous flyer photographer

abstract: In this paper we explore the combination of a latest generation mobile device and a micro quadrotor platform to perform indoor autonomous navigation for the purpose of autonomous photography. We use the Yellowstone tablet from Google's Tango project [1], equipped with onboard, fully integrated sensing platform and with significant computational capability. To the best of our knowledge we are the first to exploit the Google's Tango tablet as source of pose estimate to control the quadrotor's motion. Using the tablet's onboard camera the system is able to detect people and generate a desired pose that the quadrotor will have to reach in order to take a well framed picture of the detected subject. The experimental results and live video demonstrate the capabilities of the autonomous flying robot photographer using the system described throughout this manuscript.


title: Ultracapacitor-Based Auxiliary Energy System for an Electric Vehicle: Implementation and Evaluation

abstract: In the search for better efficiency, an auxiliary energy system (AES) for electric vehicles (EVs) was designed, implemented, and tested. The system, which is composed of an ultracapacitor bank and a buck-boost converter, was installed in an EV, which is powered by a lead-acid battery pack and a 54-kW brushless dc motor. Two control strategies where developed: one based on heuristics and the other based on an optimization model using neural networks. These strategies were translated to algorithms and implemented in a digital signal processor, and their performance was evaluated in urban driving. The results were incorporated to an economic evaluation of the system, which shows that the reduction in costs would only justify the inclusion of this type of system in a lead-acid battery-powered vehicle if the battery life is extended by 50% or more, which is unlikely. The same results were extrapolated to a case in which the lead-acid batteries are replaced by a fuel cell. In this case, the costs of different power support systems were evaluated, such as ultracapacitors and high-specific-power lithium-based batteries. The results showed a significant cost reduction when AES configurations are included in contrast to a system powered by fuel cells only. Also, the cost reduction was higher when using ultracapacitors for this purpose.


title: CubeSat Deployable Ka-Band Mesh Reflector Antenna Development for Earth Science Missions

abstract: CubeSats are positioned to play a key role in Earth Science, wherein multiple copies of the same RADAR instrument are launched in desirable formations, allowing for the measurement of atmospheric processes over a short evolutionary timescale. To achieve this goal, such CubeSats require a high-gain antenna (HGA) that fits in a highly constrained volume. This paper presents a novel mesh deployable Ka-band antenna design that folds in a 1.5 U $(10\times 10 \times 15 \,\text{cm}^{3})$ stowage volume suitable for 6 U $(10\times 20 \times 30 \,\text{cm}^{3})$ class CubeSats. Considering all aspects of the deployable mesh reflector antenna including the feed, detailed simulations and measurements show that 42.6-dBi gain and 52% aperture efficiency is achievable at 35.75 GHz. The mechanical deployment mechanism and associated challenges are also described, as they are critical components of a deployable CubeSat antenna. Both solid and mesh prototype antennas have been developed and measurement results show excellent agreement with simulations.


title: A review of Web-based simulation and supporting tools

abstract: Abstract The area of Web-based simulation (the integration of the Web with the field of simulation) (WBS) has grown since the mid-1990s. The Web itself has evolved rapidly, and current Web-related research areas include Web 2.0, service-oriented architectures and the Semantic Web. This paper gives a review of the area of WBS, exploring the advantages and disadvantages of WBS over classical simulation systems, a classification of different sub- and related-areas of WBS, an exploration of technologies that enable WBS, and the evolution of the Web in terms of its relationship to WBS.


title: Alternating Co-Quantization for Cross-Modal Hashing

abstract: This paper addresses the problem of unsupervised learning of binary hash codes for efficient cross-modal retrieval. Many unimodal hashing studies have proven that both similarity preservation of data and maintenance of quantization quality are essential for improving retrieval performance with binary hash codes. However, most existing cross-modal hashing methods mainly have focused on the former, and the latter still remains almost untouched. We propose a method to minimize the binary quantization errors, which is tailored to cross-modal hashing. Our approach, named Alternating Co-Quantization (ACQ), alternately seeks binary quantizers for each modality space with the help of connections to other modality data so that they give minimal quantization errors while preserving data similarities. ACQ can be coupled with various existing cross-modal dimension reduction methods such as Canonical Correlation Analysis (CCA) and substantially boosts their retrieval performance in the Hamming space. Extensive experiments demonstrate that ACQ can outperform several state-of-the-art methods, even when it is combined with simple CCA.


title: Reversing and Smoothing the Multinomial Naive Bayes Text Classifer

abstract: Lehrstuhl fu¨r Informatik VI, RWTH Aachen – University of TechnologyD-52056 Aachen (Germany)ney@informatik.rwth-aachen.deAbstract. The naive Bayes text classiﬁer has long been a core tech-nique in information retrieval and, more recently, it has emerged as afocus of research itself in machine learning. This paper is concerned withthe naive Bayes text classiﬁer in its multinomial model instantiation.This model and an “equivalent” reversed version proposed here are in-terpreted under the statistical framework of log-linear modelling. Thereversed version provides an alternative way for parameter estimation,which (in a broad sense) is actually the main issue considered. The pa-per is to a large extent devoted to the study of the eﬀects of parametersmoothing and document length normalization. For the purpose of pa-rameter smoothing, we consider a standard smoothing method for textclassiﬁcation and two alternative techniques that are often used in thecontext of statistical language modelling for speech recognition. Empiricalresults are provided comparing these techniques and the eﬀect of lengthnormalization for both the multinomial model and its reversed version.


title: The Impact of Diversity on Optimal Control Policies for Heterogeneous Robot Swarms

abstract: We consider the problem of distributing a large group of heterogeneous robots among a set of tasks that require specialized capabilities in order to be completed. We model the system of heterogeneous robots as a community of species, in which each species (robot type) is defined by the traits (capabilities) that it owns. In order to solve the distribution problem, we develop centralized as well as decentralized methods to efficiently control the heterogeneous swarm of robots. Our methods assume knowledge of the underlying task topology and are based on a continuous model of the system that defines transition rates to and from tasks, for each robot species. Our optimization of the transition rates is fully scalable with respect to the number of robots, number of species, and number of traits. Building on this result, we propose a real-time optimization method that enables an online adaptation of transition rates as a function of the state of the current robot distribution. We also show how the robot distribution can be approximated based on local information only, consequently enabling the development of a decentralized controller. We evaluate our methods by means of microscopic simulations and show how the performance of the latter is well predicted by the macroscopic equations. Importantly, our framework also includes a diversity metric that enables an evaluation of the impact of swarm heterogeneity on performance. The metric defines the notion of minspecies , i.e., the minimum set of species that are required to achieve a given goal. We show that two distinct goal functions lead to two specializations of minspecies, which we term as eigenspecies and coverspecies . Quantitative results show the relation between diversity and performance.


title: Segregated Encoding of Reward-Identity and Stimulus-Reward Associations in Human Orbitofrontal Cortex

abstract: A dominant focus in studies of learning and decision-making is the neural coding of scalar reward value. This emphasis ignores the fact that choices are strongly shaped by a rich representation of potential rewards. Here, using fMRI adaptation, we demonstrate that responses in the human orbitofrontal cortex (OFC) encode a representation of the specific type of food reward predicted by a visual cue. By controlling for value across rewards and by linking each reward with two distinct stimuli, we could test for representations of reward–identity that were independent of associative information. Our results show reward–identity representations in a medial-caudal region of OFC, independent of the associated predictive stimulus. This contrasts with a more rostro-lateral OFC region encoding reward–identity representations tied to the predicate stimulus. This demonstration of adaptation in OFC to reward specific representations opens an avenue for investigation of more complex decision mechanisms that are not immediately accessible in standard analyses, which focus on correlates of average activity.


title: Analysis and synthesis of UHF RFID antennas using the embedded T-match

abstract: The T-match structure is commonly used to match an RFID chip's reactive impedance to a dipole. Models that describe T-match are known, but they are neither sufficiently accurate to model antennas nor to synthesize the antenna geometry. Here, we present the embedded T-match circuit model, which is amenable to accurate analysis and synthesis.


title: Influence Maximization in Near-Linear Time: A Martingale Approach

abstract: Given a social network G and a positive integer k, the influence maximization problem asks for k nodes (in G) whose adoptions of a certain idea or product can trigger the largest expected number of follow-up adoptions by the remaining nodes. This problem has been extensively studied in the literature, and the state-of-the-art technique runs in O((k+l) (n+m) log n e2) expected time and returns a (1-1 e-e)-approximate solution with at least 1 - 1/n l probability. This paper presents an influence maximization algorithm that provides the same worst-case guarantees as the state of the art, but offers significantly improved empirical efficiency. The core of our algorithm is a set of estimation techniques based on martingales, a classic statistical tool. Those techniques not only provide accurate results with small computation overheads, but also enable our algorithm to support a larger class of information diffusion models than existing methods do. We experimentally evaluate our algorithm against the states of the art under several popular diffusion models, using real social networks with up to 1.4 billion edges. Our experimental results show that the proposed algorithm consistently outperforms the states of the art in terms of computation efficiency, and is often orders of magnitude faster.


title: Fatal butane inhalation from gas cartridges: a case report and literature review

abstract: Volatile substances are commonly misused with easy-to-obtain commercial products, such as glue, shoe polish, nail polish remover, butane lighter fluid, gasoline and computer duster spray. This report describes a case of sudden death of a 29-year-old woman after presumably inhaling gas cartridge butane from a plastic bag. Autopsy, pathological and toxicological analyses were performed in order to determine the cause of death. Pulmonary edema was observed pathologically, and the toxicological study revealed 2.1μL/mL of butane from the blood. The causes of death from inhalation of volatile substances have been explained by four mechanisms; cardiac arrhythmia, anoxia, respiratory depression, and vagal inhibition. In this case, the cause of death was determined to be asphyxia from anoxia. Additionally, we have gathered fatal butane inhalation cases with quantitative analyses of butane concentrations, and reviewed other reports describing volatile substance abuse worldwide. Keywords: forensic science, butane, inhalation, volatile substance, autopsy, toxicology.


title: ISO/IEC 27043:2015 — Role and application

abstract: In order for digital evidence from a digital forensic investigation to be admissible, one needs to follow a formalised and ideally standardised process. The authors' previous research and initiative within ISO resulted in a new international standard ISO/IEC 27043:2015, titled “Information technology — Security techniques — Incident investigation principles and processes” as published in March 2015. The standard governs the digital forensic investigation process and covers it from a wide angle, while harmonising existing process models in this field. In this paper, the authors give an analysis of both the standard itself and of related standards so as to enable the reader to understand the ecosystem of standards relating to the digital forensic investigation process and role of ISO/IEC 27043:2015.


title: Constituents of Red Yeast Rice, a Traditional Chinese Food and Medicine

abstract: Detailed analyses were undertaken of the natural constituents of red yeast rice, a traditional Chinese medicine and food known for centuries to improve blood circulation. Preparation of red yeast rice following ancient methods by fermenting the fungal strain Monascus purpureus Went on moist and sterile rice indicated the presence of a group of metabolites belonging to the monacolin family of polyketides, together with fatty acids, and trace elements. The presence of these compounds may explain in part the cholesterol-lowering ability associated with this traditional Chinese food. Keywords: Red yeast rice; traditional Chinese medicine; monacolins; constituents


title: Video Anomaly Search in Crowded Scenes via Spatio-Temporal Motion Context

abstract: Video anomaly detection plays a critical role for intelligent video surveillance. We present an abnormal video event detection system that considers both spatial and temporal contexts. To characterize the video, we first perform the spatio-temporal video segmentation and then propose a new region-based descriptor called “Motion Context,” to describe both motion and appearance information of the spatio-temporal segment. For anomaly measurements, we formulate the abnormal event detection as a matching problem, which is more robust than statistic model-based methods, especially when the training dataset is of limited size. For each testing spatio-temporal segment, we search for its best match in the training dataset, and determine how normal it is using a dynamic threshold. To speed up the search process, compact random projections are also adopted. Experiments on the benchmark dataset and comparisons with the state-of-the-art methods validate the advantages of our algorithm.


title: Circularly-Polarized Reconfigurable Transmitarray in Ka-Band With Beam Scanning and Polarization Switching Capabilities

abstract: The design, realization, and experimental characterization of a 400-element electronically reconfigurable transmitarray operating in the Ka-band is presented. It is based on linearly polarized unit-cells with 180° phase-shifting capability. Several sequential rotation schemes have been compared numerically to generate a circularly polarized beam over a broad frequency band, and a random distribution has been selected to mitigate spurious cross-polarized side-lobes when scanning the main beam. The 2-D electronic beam-steering capabilities of ±60° have been verified experimentally. The prototype, illuminated by a horn antenna as a focal source, exhibits a broadside gain of 20.8 dBi at 29.0 GHz and a 3-dB bandwidth of 14.6% with radiation efficiency of 58%. The axial ratio remains below 2 dB within this bandwidth. Next, a planar substrate integrated waveguide focal source array was designed in order to reduce the focal distance by about 50% and thereby significantly improve the antenna compactness, and similar radiation performance is demonstrated numerically and experimentally. © 1963-2012 IEEE.


title: Discrete Signal Processing on Graphs: Frequency Analysis

abstract: Signals and datasets that arise in physical and engineering applications, as well as social, genetics, biomolecular, and many other domains, are becoming increasingly larger and more complex. In contrast to traditional time and image signals, data in these domains are supported by arbitrary graphs. Signal processing on graphs extends concepts and techniques from traditional signal processing to data indexed by generic graphs. This paper studies the concepts of low and high frequencies on graphs, and low-, high-, and band-pass graph filters. In traditional signal processing, there concepts are easily defined because of a natural frequency ordering that has a physical interpretation. For signals residing on graphs, in general, there is no obvious frequency ordering. We propose a definition of total variation for graph signals that naturally leads to a frequency ordering on graphs and defines low-, high-, and band-pass graph signals and filters. We study the design of graph filters with specified frequency response, and illustrate our approach with applications to sensor malfunction detection and data classification.


title: Diffusion of Innovations in Service Organizations: Systematic Review and Recommendations

abstract: This article summarizes an extensive literature review addressing the question, How can we spread and sustain innovations in health service delivery and organization? It considers both content (defining and measuring the diffusion of innovation in organizations) and process (reviewing the literature in a systematic and reproducible way). This article discusses (1) a parsimonious and evidence-based model for considering the diffusion of innovations in health service organizations, (2) clear knowledge gaps where further research should be focused, and (3) a robust and transferable methodology for systematically reviewing health service policy and management. Both the model and the method should be tested more widely in a range of contexts.


title: Trust mechanisms for cloud computing

abstract: Trust is a critical factor in cloud computing; in present practice it depends largely on perception of reputation, and self assessment by providers of cloud services. We begin this paper with a survey of existing mechanisms for establishing trust, and comment on their limitations. We then address those limitations by proposing more rigorous mechanisms based on evidence, attribute certification, and validation, and conclude by suggesting a framework for integrating various trust mechanisms together to reveal chains of trust in the cloud.


title: Static Analysis of Executables to Detect Malicious Patterns

abstract: Malicious code detection is a crucial component of any defense mechanism. In this paper, we present a unique viewpoint on malicious code detection. We regard malicious code detection as an obfuscation-deobfuscation game between malicious code writers and researchers working on malicious code detection. Malicious code writers attempt to obfuscate the malicious code to subvert the malicious code detectors, such as anti-virus software. We tested the resilience of three commercial virus scanners against code-obfuscation attacks. The results were surprising: the three commercial virus scanners could be subverted by very simple obfuscation transformations! We present an architecture for detecting malicious patterns in executables that is resilient to common obfuscation transformations. Experimental results demonstrate the efficacy of our prototype tool, SAFE (a static analyzer for executables).


title: Whitening Black-Box Neural Networks

abstract: Many deployed learned models are black boxes: given input, returns output. Internal information about the model, such as the architecture, optimisation procedure, or training data, is not disclosed explicitly as it might contain proprietary information or make the system more vulnerable. This work shows that such attributes of neural networks can be exposed from a sequence of queries. This has multiple implications. On the one hand, our work exposes the vulnerability of black-box neural networks to different types of attacks -- we show that the revealed internal information helps generate more effective adversarial examples against the black box model. On the other hand, this technique can be used for better protection of private content from automatic recognition models using adversarial examples. Our paper suggests that it is actually hard to draw a line between white box and black box models.


Re: Human papillomavirus in oral exfoliated cells and risk of head and neck cancer.


title: Packages With Integrated 60-GHz Aperture-Coupled Patch Antennas

abstract: This paper presents balanced-fed and fork-fed aperture-coupled patch antennas and 16-element arrays suitable for broadband millimeter-wave communications. The antennas are realized in a multi-layer organic package structure, to which RF integrated circuits can be integrated. To improve antenna bandwidth and radiation efficiency, an air cavity is used, resulting in a superstrate planar patch-antenna structure. Additionally, resonating apertures are used to further increase the antenna bandwidth. Measured results at 60 GHz for the antennas show good performance in terms of peak gain (about 8 dBi for a single element and 17 dBi for a 16-element array), bandwidth ( >; 10 GHz for 10-dB return loss bandwidths are achievable), and radiation efficiency (80% for single-element from simulation).


Performance Improving of Digital Video Broadcasting-terrestrial ( DVB-T 2 ) Using Alamouti Scheme with Cyclic Delay Diversity


title: Inferring user interests in the Twitter social network

abstract: We propose a novel mechanism to infer topics of interest of individual users in the Twitter social network. We observe that in Twitter, a user generally follows experts on various topics of her interest in order to acquire information on those topics. We use a methodology based on social annotations (proposed earlier by us) to first deduce the topical expertise of popular Twitter users, and then transitively infer the interests of the users who follow them. This methodology is a sharp departure from the traditional techniques of inferring interests of a user from the tweets that she posts or receives. We show that the topics of interest inferred by the proposed methodology are far superior than the topics extracted by state-of-the-art techniques such as using topic models (Labeled LDA) on tweets. Based upon the proposed methodology, we build a system Who Likes What, which can infer the interests of millions of Twitter users. To our knowledge, this is the first system that can infer interests for Twitter users at such scale. Hence, this system would be particularly beneficial in developing personalized recommender services over the Twitter platform.


title: Sequential therapy versus standard triple-drug therapy for Helicobacter pylori eradication: a prospective randomized study

abstract: PURPOSE ::: Eradication rates following standard triple therapy for Helicobacter pylori infection are declining. Recent studies, conducted in a number of countries, have shown that sequential therapy for H. pylori infection yields high cure rates. ::: ::: ::: AIM ::: To compare the efficacy and tolerability of a sequential regimen as a first-line treatment of H. pylori infection with a standard triple treatment regime in Morocco. ::: ::: ::: METHODS ::: A total of 281 naive H. pylori-infected patients, confirmed by histological examination, were assigned randomly to one of two treatment groups: standard triple therapy [omeprazole (20 mg bid) + amoxicillin (1 g bid) + clarithromycin (500 mg bid) for 7 days] or sequential therapy [omeprazole (20 mg bid) + amoxicillin (1 g bid) for 5 days, followed by omeprazole (20 mg bid) + tinidazole (500 mg bid) + clarithromycin (500 mg bid) for an additional 5 days]. H. pylori eradication was checked 4-6 weeks after treatment initiation by using a ¹³C-urea breath test. Compliance and adverse events were assessed. ::: ::: ::: RESULTS ::: The two groups did not differ significantly in gender, age, previous disease history, endoscopic and histological features and smoking. The intention-to-treat and per-protocol eradication rates were 65.9 and 71 % in the standard triple therapy group, and 82.8 and 89.9 % in the sequential therapy group, respectively. The eradication rate was significantly higher in the sequential therapy group than in the standard triple therapy group (p < 0.001), There was no statistically significant difference in compliance (97.5 vs. 96.3 %) and incidence of side-effects (27.5 vs. 27.9 %) between the two groups. ::: ::: ::: CONCLUSIONS ::: Based on our results, we conclude that for eradication of H. pylori infection, the 10-day sequential therapy is more effective than the standard triple therapy and is equally tolerated. These results confirm those of other studies in other countries.


title: Interleaved Boundary Conduction Mode (BCM) Buck Power Factor Correction (PFC) Converter

abstract: An interleaved boundary conduction mode power-factor-correction buck converter that maintains high efficiency across entire load and line range is proposed. The adaptive master-slave interleaving method maintains stable 180° out-of-phase operation during any transient. By interleaving two parallel-connected buck converters, the input current ripple is halved while the ripple frequency is doubled, which leads to a smaller differential mode line filter. The line current harmonic distortion is analyzed to examine the allowable output voltage range while meeting harmonic regulations. The operation and performance of the proposed circuit is verified on a 300 W, universal line experimental prototype with 80 V output. The measured efficiencies remain above 96% down to 20% of full load across the entire universal line range. Even at 10% of full-load condition, the efficiency remains above 94%. The input current harmonics also meet the IEC61000-3-2 (class D) standard.


MEEGA + : An Evolution of a Model for the Evaluation of Educational Games


title: Research on mobile cloud computing: Review, trend and perspectives

abstract: Mobile Cloud Computing (MCC) which combines mobile computing and cloud computing, has become one of the industry buzz words and a major discussion thread in the IT world since 2009. As MCC is still at the early stage of development, it is necessary to grasp a thorough understanding of the technology in order to point out the direction of future research. With the latter aim, this paper presents a review on the background and principle of MCC, characteristics, recent research work, and future research trends. A brief account on the background of MCC: from mobile computing to cloud computing is presented and then followed with a discussion on characteristics and recent research work. It then analyses the features and infrastructure of mobile cloud computing. The rest of the paper analyses the challenges of mobile cloud computing, summary of some research projects related to this area, and points out promising future research directions.


title: Graph Summarization: A Survey

abstract: While advances in computing resources have made processing enormous amounts of data possible, human ability to identify patterns in such data has not scaled accordingly. Thus, efficient computational methods for condensing and simplifying data are becoming vital for extracting actionable insights. In particular, while data summarization techniques have been studied extensively, only recently has summarizing interconnected data, or graphs, become popular. This survey is a structured, comprehensive overview of the state-of-the-art methods for summarizing graph data. We first broach the motivation behind and the challenges of graph summarization. We then categorize summarization approaches by the type of graphs taken as input and further organize each category by core methodology. Finally, we discuss applications of summarization on real-world graphs and conclude by describing some open problems in the field.


title: Deep Model Based Domain Adaptation for Fault Diagnosis

abstract: In recent years, machine learning techniques have been widely used to solve many problems for fault diagnosis. However, in many real-world fault diagnosis applications, the distribution of the source domain data (on which the model is trained) is different from the distribution of the target domain data (where the learned model is actually deployed), which leads to performance degradation. In this paper, we introduce domain adaptation, which can find the solution to this problem by adapting the classifier or the regression model trained in a source domain for use in a different but related target domain. In particular, we proposed a novel deep neural network model with domain adaptation for fault diagnosis. Two main contributions are concluded by comparing to the previous works: first, the proposed model can utilize domain adaptation meanwhile strengthening the representative information of the original data, so that a high classification accuracy in the target domain can be achieved, and second, we proposed several strategies to explore the optimal hyperparameters of the model. Experimental results, on several real-world datasets, demonstrate the effectiveness and the reliability of both the proposed model and the exploring strategies for the parameters.


title: A High-Performance FPGA-Based Implementation of the LZSS Compression Algorithm

abstract: The increasing growth of embedded networking applications has created a demand for high-performance logging systems capable of storing huge amounts of high-bandwidth, typically redundant data. An efficient way of maximizing the logger performance is doing a real-time compression of the logged stream. In this paper we present a flexible high-performance implementation of the LZSS compression algorithm capable of processing up to 50 MB/s on a Virtex-5 FPGA chip. We exploit the independently addressable dual-port block RAMs inside the FPGA chip to achieve an average performance of 2 clock cycles per byte. To make the compressed stream compatible with the ZLib library we encode the LZSS algorithm output using a fixed Huffman table defined by the Deflate specification. We also demonstrate how changing the amount of memory allocated to various internal tables impacts the performance and compression ratio. Finally, we provide a cycle-accurate estimation tool that allows finding a trade-off between FPGA resource utilization, compression ratio and performance for a specific data sample.


title: Challenges and scientific considerations in hypertension management reflected in the 2012 recommendations of the Canadian Hypertension Education Program

abstract: This article provides the scientific rationale and background information for the Canadian Hypertension Education Program’s 2012 recommendations for the management of hypertension. It also summarizes the key new recommendations and the theme for 2012, which is the prevention of hypertension. The full recommendations are available at www.hypertension.ca.


title: Software Debugging, Testing, and Verification

abstract: In commercial software development organizations, increased complexity of products, shortened development cycles, and higher customer expectations of quality have placed a major responsibility on the areas of software debugging, testing, and verification. As this issue of the IBM Systems Journal illustrates, there are exciting improvements in the underlying technology on all three fronts. However, we observe that due to the informal nature of software development as a whole, the prevalent practices in the industry are still immature, even in areas where improved technology exists. In addition, tools that incorporate the more advanced aspects of this technology are not ready for large-scale commercial use. Hence there is reason to hope for significant improvements in this area over the next several years.


title: Representing context in web search with ontological user profiles

abstract: One of the key factors for effective personalization of information access is the user context. We propose a framework which integrates several critical elements that make up the user context, namely, the user's short-term behavior, semantic knowledge from ontologies that provide explicit representations of the domain of interest, and long-term user profiles revealing interests and trends. Our proposed approach involves implicitly building ontological user profiles by assigning interest scores to existing concepts in a domain ontology. These profiles are, therefore, maintained and updated as annotated instances of a reference domain ontology. We propose a spreading activation algorithm for maintaining the interest scores in the user profile based on the user's ongoing behavior. Our experimental results show that the user context can be effectively utilized for Web search personalization. Specifically, re-ranking the search results based on interest scores derived from the semantic evidence in an ontological user profile provides better search results by proficiently bringing results closer to the top when they are most relevant to the user.


title: Identifying Influential Spreaders by Weighted LeaderRank

abstract: Identifying influential spreaders is crucial for understanding and controlling spreading processes on social networks. Via assigning degree-dependent weights onto links associated with the ground node, we proposed a variant to a recent ranking algorithm named LeaderRank (Lu et al., 2011). According to the simulations on the standard SIR model, the weighted LeaderRank performs better than LeaderRank in three aspects: (i) the ability to find out more influential spreaders; (ii) the higher tolerance to noisy data; and (iii) the higher robustness to intentional attacks.


title: A parallel algorithm for big tensor decomposition using randomly compressed cubes (PARACOMP)

abstract: A parallel algorithm for low-rank tensor decomposition that is especially well-suited for big tensors is proposed. The new algorithm is based on parallel processing of a set of randomly compressed, reduced-size `replicas' of the big tensor. Each replica is independently decomposed, and the results are joined via a master linear equation per tensor mode. The approach enables massive parallelism with guaranteed identifiability properties: if the big tensor has low rank and the system parameters are appropriately chosen, then the rank-one factors of the big tensor will be exactly recovered from the analysis of the reduced-size replicas. The proposed algorithm is proven to yield memory / storage and complexity gains of order up to IJ/F for a big tensor of size I × J × K of rank F with F ≤I ≤J ≤K.


Synthesis and fluorescence study of phenylcoumarin / cyanophenylbenzocoumarin-3-carboxylates


title: Quality dimensions in process discovery : the importance of fitness, precision, generalization and simplicity

abstract: Process discovery algorithms typically aim at discovering process models from event logs that best describe the recorded behavior. Often, the quality of a process discovery algorithm is measured by quantifying to what extent the resulting model can reproduce the behavior in the log, i.e. replay fitness. At the same time, there are other measures that compare a model with recorded behavior in terms of the precision of the model and the extent to which the model generalizes the behavior in the log. Furthermore, many measures exist to express the complexity of a model irrespective of the log. In this paper, we first discuss several quality dimensions related to process discovery. We further show that existing process discovery algorithms typically consider at most two out of the four main quality dimensions: replay fitness, precision, generalization and simplicity. Moreover, existing approaches cannot steer the discovery process based on user-defined weights for the four quality dimensions. This paper presents the ETM algorithm which allows the user to seamlessly steer the discovery process based on preferences with respect to the four quality dimensions. We show that all dimensions are important for process discovery. However, it only makes sense to consider precision, generalization and simplicity if the replay fitness is acceptable.


title: Design and implementation of low power FFT/IFFT processor for wireless communication

abstract: Fast Fourier transform (FFT) processing is one of the key procedure in popular orthogonal frequency division multiplexing (OFDM) communication systems. Structured pipeline architectures, low power consumption, high speed and reduced chip area are the main concerns in this VLSI implementation. In this paper, the efficient implementation of FFT/IFFT processor for OFDM applications is presented. The processor can be used in various OFDM-based communication systems, such as Worldwide Interoperability for Microwave access (Wi-Max), digital audio broadcasting (DAB), digital video broadcasting-terrestrial (DVB-T). We adopt single-path delay feedback architecture. To eliminate the read only memories (ROM's) used to store the twiddle factors, this proposed architecture applies a reconfigurable complex multiplier to achieve a ROM-less FFT/IFFT processor and to reduce the truncation error we adopt the fixed width modified booth multiplier. The three processing elements (PE's), delay-line (DL) buffers are used for computing IFFT. Thus we consume the low power, lower hardware cost, high efficiency and reduced chip size.


title: Review: Existing Image Segmentation Techniques

abstract: Image segmentation is the most important part in digital image processing. Segmentation is nothing but a portion of any image and object. In image segmentation, digital image is divided into multiple set of pixels. Image segmentation is generally required to cut out region of interest (ROI) from an image. Currently there are many different algorithms available for image segmentation. Each have their own advantages and purpose. In this paper, different image segmentation algorithms with their prospects are reviewed.


title: Maxillary anterior papilla display during smiling: a clinical study of the interdental smile line.

abstract: The purpose of this research was to quantify the visual display (presence) or lack of display (absence) of interdental papillae during maximum smiling in a patient population aged 10 to 89 years. Four hundred twenty digital single-lens reflex photographs of patients were taken and examined for the visual display of interdental papillae between the maxillary anterior teeth during maximum smiling. Three digital photographs were taken per patient from the frontal, right frontal-lateral, and left frontal-lateral views. The data set of photographs was examined by two examiners for the presence or absence of the visual display of papillae. The visual display of interdental papillae during maximum smiling occurred in 380 of the 420 patients examined in this study, equivalent to a 91% occurrence rate. Eighty-seven percent of all patients categorized as having a low gingival smile line (n = 303) were found to display the interdental papillae upon smiling. Differences were noted for individual age groups according to the decade of life as well as a trend toward decreasing papillary display with increasing age. The importance of interdental papillae display during dynamic smiling should not be left undiagnosed since it is visible in over 91% of older patients and in 87% of patients with a low gingival smile line, representing a common and important esthetic element that needs to be assessed during smile analysis of the patient.


title: Focal Loss for Dense Object Detection

abstract: The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one-stage detectors that are applied over a regular, dense sampling of possible object locations have the potential to be faster and simpler, but have trailed the accuracy of two-stage detectors thus far. In this paper, we investigate why this is the case. We discover that the extreme foreground-background class imbalance encountered during training of dense detectors is the central cause. We propose to address this class imbalance by reshaping the standard cross entropy loss such that it down-weights the loss assigned to well-classified examples. Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training. To evaluate the effectiveness of our loss, we design and train a simple dense detector we call RetinaNet. Our results show that when trained with the focal loss, RetinaNet is able to match the speed of previous one-stage detectors while surpassing the accuracy of all existing state-of-the-art two-stage detectors. Code is at: this https URL


title: CNTK: Microsoft's Open-Source Deep-Learning Toolkit

abstract: This tutorial will introduce the Computational Network Toolkit, or CNTK, Microsoft's cutting-edge open-source deep-learning toolkit for Windows and Linux. CNTK is a powerful computation-graph based deep-learning toolkit for training and evaluating deep neural networks. Microsoft product groups use CNTK, for example to create the Cortana speech models and web ranking. CNTK supports feed-forward, convolutional, and recurrent networks for speech, image, and text workloads, also in combination. Popular network types are supported either natively (convolution) or can be described as a CNTK configuration (LSTM, sequence-to-sequence). CNTK scales to multiple GPU servers and is designed around efficiency. The tutorial will give an overview of CNTK's general architecture and describe the specific methods and algorithms used for automatic differentiation, recurrent-loop inference and execution, memory sharing, on-the-fly randomization of large corpora, and multi-server parallelization. We will then show how typical uses looks like for relevant tasks like image recognition, sequence-to-sequence modeling, and speech recognition.


title: Elucidating Reaction Mechanisms on Quantum Computers

abstract: With rapid recent advances in quantum technology, we are close to the threshold of quantum devices whose computational powers can exceed those of classical supercomputers. Here, we show that a quantum computer can be used to elucidate reaction mechanisms in complex chemical systems, using the open problem of biological nitrogen fixation in nitrogenase as an example. We discuss how quantum computers can augment classical computer simulations used to probe these reaction mechanisms, to significantly increase their accuracy and enable hitherto intractable simulations. Our resource estimates show that, even when taking into account the substantial overhead of quantum error correction, and the need to compile into discrete gate sets, the necessary computations can be performed in reasonable time on small quantum computers. Our results demonstrate that quantum computers will be able to tackle important problems in chemistry without requiring exorbitant resources.


title: A Meta-Analysis of Factors Affecting Trust in Human-Robot Interaction

abstract: Objective: We evaluate and quantify the effects of human, robot, and environmental factors on perceived trust in human-robot interaction (HRI).Background: To date, reviews of trust in HRI have been qualitative or descriptive. Our quantitative review provides a fundamental empirical foundation to advance both theory and practice.Method: Meta-analytic methods were applied to the available literature on trust and HRI. A total of 29 empirical studies were collected, of which 10 met the selection criteria for correlational analysis and 11 for experimental analysis. These studies provided 69 correlational and 47 experimental effect sizes.Results: The overall correlational effect size for trust was r = +0.26, with an experimental effect size of d = +0.71. The effects of human, robot, and environmental characteristics were examined with an especial evaluation of the robot dimensions of performance and attribute-based factors. The robot performance and attributes were the largest contributors to the development ...


CancerLocator: non-invasive cancer diagnosis and tissue-of-origin prediction using methylation profiles of cell-free DNA


title: SWIFT: A Dynamical Model of Saccade Generation During Reading.

abstract: Mathematical models have become an important tool for understanding the control of eye movements during reading. Main goals of the development of the SWIFT model (R. Engbert, A. Longtin, & R. Kliegl, 2002) were to investigate the possibility of spatially distributed processing and to implement a general mechanism for all types of eye movements observed in reading experiments. The authors present an advanced version of SWIFT that integrates properties of the oculomotor system and effects of word recognition to explain many of the experimental phenomena faced in reading research. They propose new procedures for the estimation of model parameters and for the test of the model's performance. They also present a mathematical analysis of the dynamics of the SWIFT model. Finally, within this framework, they present an analysis of the transition from parallel to serial processing.


TECHNIQUES AND TOOLS FOR FORENSIC INVESTIGATION OF E-MAIL


title: High-voltage pulse generators based on capacitor-diode voltage multiplier

abstract: Along with rapid advancement of power semiconductors, voltage multipliers have introduced new series of pulsed power generators. In this paper, current topologies of capacitor-diode voltage multipliers (CDVM) are investigated. Alternative structures for voltage multiplier based on power electronics switches are presented in high voltage pulsed power supplies application. The new topology is able to generate the desired high voltage output without increasing the voltage rating of semiconductor devices as well as capacitors. Finally, a comparative analysis is carried out between different CDVM topologies. Experimental and simulation results are presented to verify the analysis.


title: CU2rCU: Towards the complete rCUDA remote GPU virtualization and sharing solution

abstract: GPUs are being increasingly embraced by the high performance computing and computational communities as an effective way of considerably reducing execution time by accelerating significant parts of their application codes. However, despite their extraordinary computing capabilities, the adoption of GPUs in current HPC clusters may present certain negative side-effects. In particular, to ease job scheduling in these platforms, a GPU is usually attached to every node of the cluster. In addition to increasing acquisition costs this favors that GPUs may frequently remain idle, as applications usually do not fully utilize them. On the other hand, idle GPUs consume non-negligible amounts of energy, which translates into very poor energy efficiency during idle cycles. rCUDA was recently developed as a software solution to address these concerns. Specifically, it is a middleware that allows transparently sharing a reduced number of GPUs among the nodes in a cluster. rCUDA thus increases the GPU-utilization rate, taking care of job scheduling. While the initial prototype versions of rCUDA demonstrated its functionality, they also revealed several concerns related with usability and performance. With respect to usability, in this paper we present a new component of the rCUDA suite that allows an automatic transformation of any CUDA source code, so that it can be effectively accommodated within this technology. In response to performance, we briefly show some interesting results, which will be deeply analyzed in future publications. The net outcome is a new version of rCUDA that allows, for any CUDA-compatible program, to use remote GPUs in a cluster with minimum overhead.


title: Evaluation of CSM-CERES-wheat in simulating wheat yield and its attributes with different sowing environments in Tarai region of Uttarakhand

abstract: Crop Environment Resource Synthesis (CSM-CERES)-Wheat model was used to simulate responses of two wheat varieties with various sowing environments. In this context, during the year 2007-08 and 2008-09, experiments on three sowing dates viz. November 20, December 15, and January 9 and two varieties (PBW-343 and WH-542) with three replications were conducted at the Norman E. Borlaug Crop Research Centre of G.B. Pant University of Agriculture & Technology, Pantnagar (29Â°N, 79.29Â°E with 243.80 m above msl). Soil, plant, management and climatic data were collected from the experimental field. The data of 2007-08 and 2008-09 were used for model calibration and validation, respectively. Results revealed that the for model outputs were in good agreement with their corresponding observed values with 20th November sown crop than other sowings of crop in terms of phenological events, biomass accumulation and grain yields. However, variety PBW-343 showed close proximity between simulated and observed outcomes with all sowing dates. The percent root mean square error (% RMSE) values ranged from 5.9 â€“ 15.6%, 2.2 â€“ 7.6% for days to attain anthesis and physiological maturity, respectively. Moreover, %RMSE and t-value ranged from 5.7â€“12.2% (t= -4.5 to 1.8), 1.6 â€“ 3.3% (t= -4.1 to 4.5) and 1.9 â€“ 5.8% (t= -3.7 to 1.5) for product weight, vegetative weight and product harvest index, respectively. Inspite of that, model fails to simulate maximum leaf area index having % RMSE from 53.2 â€“ 62.9%. These results indicate that CERES-Wheat model can be used as a tool to support decision-making for wheat production in Tarai region of Uttarakhand.


title: The alldifferent Constraint: A Survey

abstract: The constraint of difference is known to the constraint pro- gramming community since Lauriere introduced Alice (11) in 1978. Since then, several solving strategies have been designed for this constraint. In this paper we give both a practical overview and an abstract comparison of these different strategies.


title: Classification of simultaneous multiple partial discharge sources based on probabilistic interpretation using a two-step logistic regression algorithm

abstract: In online condition assessment monitoring of high voltage (HV) insulators, it is often required to identify multiple, simultaneously activated partial discharge (PD) sources that happen in the insulation of the HV apparatus. Phased resolved partial discharge (PRPD) patterns are commonly used to identify PD sources. However, multiple, concurrent PD sources sometimes result in partially overlapped patterns, which make them hard to be identified. In this paper, we develop an accurate, reliable algorithm by constructing a novel two-step logistic regression (LR) model to conduct probabilistic identification of multi-source PDs. To this end, principal component analysis is applied on a database to construct a low dimensional space associated with single-source PDs. Samples of multi-source PDs are then projected onto this space and one-class kernel support vector machine is adopted to distinguish multi-source PDs from single-source ones. Finally, classification is performed by estimating the probability (degree of membership) of each PRPD pattern arising from different multi-source PDs following two rounds of LR modeling. To evaluate the performance of our proposed method, we study a number of multi-source PD models to simulate common defects of Gas-Insulated Switchgear (GIS) in small-scale laboratory test cells with realistic SF6 gas condition. Observations are obtained using fingerprints generated by a novel approach from recorded PRPD patterns. Comprehensive performance evaluation of the proposed algorithm and its advantages are conducted and the development of analytical equations is presented. The results of this paper can be used to design a solid basis for an automated multi-source classification system, which facilitates multi-source PD identification in early stages and safe operation of HV apparatus.


title: Cuckoo search algorithm based satellite image contrast and brightness enhancement using DWT-SVD.

abstract: This paper presents a new contrast enhancement approach which is based on Cuckoo Search (CS) algorithm and DWT-SVD for quality improvement of the low contrast satellite images. The input image is decomposed into the four frequency subbands through Discrete Wavelet Transform (DWT), and CS algorithm used to optimize each subband of DWT and then obtains the singular value matrix of the low-low thresholded subband image and finally, it reconstructs the enhanced image by applying IDWT. The singular value matrix employed intensity information of the particular image, and any modification in the singular values changes the intensity of the given image. The experimental results show superiority of the proposed method performance in terms of PSNR, MSE, Mean and Standard Deviation over conventional and state-of-the-art techniques.


Effect of Motivation by “Instagram” on Adherence to Physical Activity among Female College Students


title: Increased cerebrospinal fluid β-endorphin immunoreactivity in infants with apnea and in siblings of victims of sudden infant death syndrome

abstract: To gain further insight into the possible role of endogenous opioid peptides in the respiratory difficulties associated with the apnea of infancy and other disorders possibly related to apnea, the levels of β-endorphin immunoreactivity were measured in the cerebrospinal fluid (CSF) of five groups of infants: (1) infants with proved apnea, (2) infants with histories of an apparent life-threatening event (ALTE), (3) siblings of victims of the sudden infant death syndrome (SIDS), (4) infants with suspected but unproved apnea, and (5) infants undergoing investigation for other acute illnesses. Twenty-two infants considered at risk for an ALTE (groups 1 to 3) had significantly higher CSF β-endorphin equivalents (88±7 pg/mL) than did the 22 control patients in groups 4 and 5 (31±3 pg/mL). Plasma β-endorphin immunoreactivity, which was also measured in some of the infants, did not correlated with levels in CSF and, in fact, was significantly lower in the groups at risk for an ALTE (50±9 pg/mL; n=14) than in the control subjects (80±6 pg/mL; n=11). These studies indicate that elevated β-endorphin immunoreactivity in CSF may be a marker in infants who have apnea and who may be considered at risk for an ALTE.


title: Large Margin Multi-metric Learning for Face and Kinship Verification in the Wild

abstract: Metric learning has been widely used in face and kinship verification and a number of such algorithms have been proposed over the past decade. However, most existing metric learning methods only learn one Mahalanobis distance metric from a single feature representation for each face image and cannot deal with multiple feature representations directly. In many face verification applications, we have access to extract multiple features for each face image to extract more complementary information, and it is desirable to learn distance metrics from these multiple features so that more discriminative information can be exploited than those learned from individual features. To achieve this, we propose a new large margin multi-metric learning (LM\(^3\)L) method for face and kinship verification in the wild. Our method jointly learns multiple distance metrics under which the correlations of different feature representations of each sample are maximized, and the distance of each positive is less than a low threshold and that of each negative pair is greater than a high threshold, simultaneously. Experimental results show that our method can achieve competitive results compared with the state-of-the-art methods.


title: Review of high power isolated bi-directional DC-DC converters for PHEV/EV DC charging infrastructure

abstract: PHEV/EV DC charging infrastructure attracts more and more attention recently. High power isolated bi-directional DC-DC converters provide galvanic isolation, V2G capability and reduce the cost and footprint of the system. Maintaining high power efficiency in wide vehicle battery pack voltage range is required. Three full bridge based high power bi-directional DC-DC converters are conceptually designed for this application and their advantages and disadvantages are addressed. Experimental test bench is built and efficiency evaluation for bi-directional operation is reported.


title: Neutrophil extracellular traps in cancer progression

abstract: Neutrophils are being increasingly recognized as an important element in tumor progression. They have been shown to exert important effects at nearly every stage of tumor progression with a number of studies demonstrating that their presence is critical to tumor development. Novel aspects of neutrophil biology have recently been elucidated and its contribution to tumorigenesis is only beginning to be appreciated. Neutrophil extracellular traps (NETs) are neutrophil-derived structures composed of DNA decorated with antimicrobial peptides. They have been shown to trap and kill microorganisms, playing a critical role in host defense. However, their contribution to tumor development and metastasis has recently been demonstrated in a number of studies highlighting NETs as a potentially important therapeutic target. Here, studies implicating NETs as facilitators of tumor progression and metastasis are reviewed. In addition, potential mechanisms by which NETs may exert these effects are explored. Finally, the ability to target NETs therapeutically in human neoplastic disease is highlighted.


title: Stochastic Neighbor Embedding

abstract: We describe a probabilistic approach to the task of placing objects, described by high-dimensional vectors or by pairwise dissimilarities, in a low-dimensional space in a way that preserves neighbor identities. A Gaussian is centered on each object in the high-dimensional space and the densities under this Gaussian (or the given dissimilarities) are used to define a probability distribution over all the potential neighbors of the object. The aim of the embedding is to approximate this distribution as well as possible when the same operation is performed on the low-dimensional "images" of the objects. A natural cost function is a sum of Kullback-Leibler divergences, one per object, which leads to a simple gradient for adjusting the positions of the low-dimensional images. Unlike other dimensionality reduction methods, this probabilistic framework makes it easy to represent each object by a mixture of widely separated low-dimensional images. This allows ambiguous objects, like the document count vector for the word "bank", to have versions close to the images of both "river" and "finance" without forcing the images of outdoor concepts to be located close to those of corporate concepts.


title: Adaptive coarse-to-fine quantization for optimizing rate-distortion of progressive mesh compression

abstract: We propose a new connectivity-based progressive compression approach for triangle meshes. The key idea is to adapt the quantization precision to the resolution of each intermediate mesh so as to optimize the rate-distortion trade-off. This adaptation is automatically determined during the encoding process and the overhead is efficiently encoded using geometrical prediction techniques. We also introduce an optimization of the geometry coding by using a bijective discrete rotation. Results show that our approach delivers a better rate-distortion behavior than both connectivity-based and geometry-based compression state of the art methods.


title: IA-32 Execution Layer: a two-phase dynamic translator designed to support IA-32 applications on Itanium-based systems

abstract: IA-32 Execution Layer (IA-32 EL) is a newtechnology that executes IA-32 applications onIntel® Itanium® processor family systems.Currently, support for IA-32 applications onItanium-based platforms is achieved usinghardware circuitry on the Itanium processors.This capability will be enhanced with IA-32EL-software that will ship with Itanium-basedoperating systems and will convert IA-32instructions into Itanium instructions viadynamic translation.In this paper, we describeaspects of the IA-32 Execution Layertechnology, including the general two-phasetranslation architecture and the usage of asingle translator for multiple operatingsystems.The paper provides details of someof the technical challenges such as preciseexception, emulation of FP, MMXTM, and Intel®Streaming SIMD Extension instructions, andmisalignment handling.Finally, the paperpresents some performance results.


title: Guided 3D point cloud filtering

abstract: 3D point cloud has gained significant attention in recent years. However, raw point clouds captured by 3D sensors are unavoidably contaminated with noise resulting in detrimental efforts on the practical applications. Although many widely used point cloud filters such as normal-based bilateral filter, can produce results as expected, they require a higher running time. Therefore, inspired by guided image filter, this paper takes the position information of the point into account to derive the linear model with respect to guidance point cloud and filtered point cloud. Experimental results show that the proposed algorithm, which can successfully remove the undesirable noise while offering better performance in feature-preserving, is significantly superior to several state-of-the-art methods, particularly in terms of efficiency.


title: Predictive approaches to rear axle regenerative braking control in hybrid vehicles

abstract: We consider the problem of rear axle regenerative braking maximization in hybrid vehicles. We focus on cornering maneuvers on low friction surfaces, where excessive braking at the rear axle might induce vehicle instability.


title: Enhanced nanoflow behaviors of polymer melts using dispersed nanoparticles and ultrasonic vibration

abstract: In the micro/nano fabrication of polymer nanostructures, a key factor is the favorable nanoflow behavior of polymer melts. Compared with the fluidic hydrodynamics of simple liquids through micro- or macrochannels, the nanoflow behavior of polymer melts, however, is affected much more by nanoscale effects and surface interactions. Therefore, achieving a favorable nanoflow of polymer melts in nanochannels is the key to fabricate high quality polymer nanoproducts. In this paper, the improved nanoflow behaviors of polystyrene melts in ordered porous alumina templates with the addition of nanoparticles and ultrasonic vibration were reported for the first time. Compared with bulk polystyrene (PS), the nanoflow rate of PS melts was enhanced when nanoparticles, such as surface-modified nano-silica (nano-SiO2) or β-cyclodextrin (β-CD), were added in a dispersed phase into a polystyrene matrix due to the decrease of the melts’ viscosity caused by interactions between nanoparticles and PS segments. The enhancement action of β-CD was observed to be more significant than that of nano-SiO2 based on the adsorption and the supramolecular self-assembly interactions between PS segments and β-CD. The enhanced nanoflow rate has shown to be more pronounced under ultrasonic vibration than those of the static condition and the low frequency vibration attributed to the synergetic effects of mechanical vibration and ultrasonic oscillation. The nanoflow rate of polymer melts increases with the gradual increase of vibration frequency. The optimal nanoflow behavior can be obtained by simultaneously adding β-CD as dispersed phase into PS matrix and applying ultrasonic vibration in one nanoflow system. These new findings will help the preparation of polymer-based functional nanocomposites, ultrasonic vibration-assisted nanofluidics, and micro/nano injection moldingetc.


title: Running time analysis of multiobjective evolutionary algorithms on pseudo-Boolean functions

abstract: This paper presents a rigorous running time analysis of evolutionary algorithms on pseudo-Boolean multiobjective optimization problems. We propose and analyze different population-based algorithms, the simple evolutionary multiobjective optimizer (SEMO), and two improved versions, fair evolutionary multiobjective optimizer (FEMO) and greedy evolutionary multiobjective optimizer (GEMO). The analysis is carried out on two biobjective model problems, leading ones trailing zeroes (LOTZ) and count ones count zeroes (COCZ), as well as on the scalable m-objective versions mLOTZ and mCOCZ. Results on the running time of the different population-based algorithms and for an alternative approach, a multistart (1+1)-EA based on the /spl epsi/-constraint method, are derived. The comparison reveals that for many problems, the simple algorithm SEMO is as efficient as this (1+1)-EA. For some problems, the improved variants FEMO and GEMO are provably better. For the analysis, we propose and apply two general tools, an upper bound technique based on a decision space partition and a randomized graph search algorithm, which facilitate the analysis considerably.


title: Ambiguity modeling in latent spaces

abstract: We are interested in the situation where we have two or more representations of an underlying phenomenon. In particular we are interested in the scenario where the representation are complementary. This implies that a single individual representation is not sufficient to fully discriminate a specific instance of the underlying phenomenon, it also means that each representation is an ambiguous representation of the other complementary spaces. In this paper we present a latent variable model capable of consolidating multiple complementary representations. Our method extends canonical correlation analysis by introducing additional latent spaces that are specific to the different representations, thereby explaining the full variance of the observations. These additional spaces, explaining representation specific variance, separately model the variance in a representation ambiguous to the other. We develop a spectral algorithm for fast computation of the embeddings and a probabilistic model (based on Gaussian processes) for validation and inference. The proposed model has several potential application areas, we demonstrate its use for multi-modal regression on a benchmark human pose estimation data set.


title: Coevolution of Birdsong Grammar without Imitation

abstract: The mating song of the male Bengalese finch can be described by a finite-state grammar and has the feature that more complex songs are preferred by females [1]-[3]. These facts suggest that complex song grammars may have evolved via sexual selection. How, then, do the female birds gauge a song’s complexity? Assuming that they can measure the complexity of a song while communicating with a male, but without making a model of the song, we studied the evolution of song grammars. In our simulation, it was demonstrated that song grammars became more complex through communication between coevolving males and females. Furthermore, when singing and listening were subject to fluctuations, peculiar features were observed in communication and evolution.


title: Accelerating financial applications on the GPU

abstract: The QuantLib library is a popular library used for many areas of computational finance. In this work, the parallel processing power of the GPU is used to accelerate QuantLib financial applications. Black-Scholes, Monte-Carlo, Bonds, and Repo code paths in QuantLib are accelerated using hand-written CUDA and OpenCL codes specifically targeted for the GPU. Additionally, HMPP and OpenACC versions of the applications were created to drive the automatic generation of GPU code from sequential code. The results demonstrate a significant speedup for each code using each parallelization method. We were also able to increase the speedup of HMPP-generated code with auto-tuning.


A Guide to Design MIMO Controllers for Architectures


title: Alopecia syphilitica with detection of Treponema pallidum in the hair follicle

abstract: Alopecia is one of the clinical manifestations of secondary syphilis. It is uncommon for hair loss to be the sole or predominant manifestation, as hair loss is the chief clinical and histologic differential diagnosis of alopecia areata. The main difference between these two entities is the detection of Treponema pallidum in syphilis. We present the case of a 24-year-old Hispanic man, human immunodeficiency virus seropositive in treatment, with tiny patches of non-cicatricial alopecia in the parieto-occipital regions of his scalp. The patient denied previous history of genital or other skin lesions. A biopsy from an alopecic patch was performed which showed an inflammatory non-scarring alopecia with a discrete lymphocytic type inflammatory infiltrate localized in the peribulbar region. There was lymphocyte exocytosis into the matrix, associated with vacuolar degeneration, and scattered apoptotic cells were observed. Plasma cells were scattered. Immunohistochemical studies showed the presence of T. pallidum limited to the peribulbar region and penetrating into the follicle matrix. To the authors' knowledge, this is the first time that spirochetes have been shown in the hair follicle in alopecia syphilitica, suggesting that the spirochetes may be pathogenetic and responsible for the alopecia.


title: Photogrammetry Assisted Measurement of Interstory Drift for Rapid Post-disaster Building Damage Reconnaissance

abstract: Timely and accurate evaluation of damage sustained by buildings after seismic events such as earthquakes or blasts is critical to determine the buildings’ safety and suitability for their future occupancy. Time used in conducting the evaluations substantially affects the duration for which the potentially damaged buildings remain unusable. The elapsed time may lead to direct economic losses to both public and private owners, and society at large. The presented research investigates the application of close-range photogrammetry surveying techniques and Augmented Reality (AR) visualization to design a semi-automated method for rapidly measuring structural damage induced in tall buildings by seismic events such as earthquakes or explosions. Close-range photogrammetry algorithms were designed to extract spatial information from photographic image data, and geometrically measure the horizontal drift (also called interstory drift) sustained at key floors along the edge of a damaged building. The measured drift can then be used to compute damage indices that correlate the drift to the building’s structural integrity and safety. In this research, the measurement accuracy of the calculated drift using photogrammetry principles is particularly studied. The experimental results achieved an acceptable and usable accuracy level of 5 mm using a consumer grade digital SLR camera, demonstrating the potential of photogrammetry assisted rapid measurement of earthquake-induced building damage. It is expected that in the future, as advances in optical-sensing and positioning technologies continue to make high-end cameras and measurement systems (GPS, compass) more affordable, higher accuracy will be practically achievable for further improvement in measurements using the proposed method.