title: A hybrid fuzzy evaluation method for safety assessment of food-waste feed based on entropy and the analytic hierarchy process methods

abstract: The product safety of food-waste feed is the key factor limiting the development of its industrial chain. In this paper, we construct a method based on data from the testing of food-waste feed with comprehensive evaluation of its product safety by integrating fuzzy mathematics effectively, i.e., the entropy method (EM), and the model of the analytic hierarchy (AHP) process. Furthermore, a hierarchical three-level evaluation-index system including biological-safety and chemical-safety considerations is first established via data analysis, data surveys and expert experiential investigation as well, with an actual case in China being fully applied. In addition, we apply the EM and AHP process to calculate the weights of the individual evaluation indices. Finally, through the dimensionless treatment of test data from samples, we determine the degree of membership of each test value relative to the different levels of safety using a trapezoidal membership function. By adopting the developed three-level model of fuzzy mathematics for comprehensive evaluation, we derive the safety grades of tested samples. The comprehensive evaluation method developed in this paper can effectively overcome the shortcomings of traditional single-factor evaluation and offer the qualitative and quantitative advantages of expert survey and basic data research as well. As a result, it is considerably applicable for the product-safety analysis and production control of animal feed generated from food waste.


title: VOWL 2: User-Oriented Visualization of Ontologies

abstract: Ontologies become increasingly important as a means to structure and organize information. This requires methods and tools that enable not only ontology experts but also other user groups to work with ontologies and related data. We have developed VOWL, a comprehensive and well-specified visual language for the user-oriented representation of ontologies, and conducted a comparative study on an initial version of VOWL. Based upon results from that study, as well as an extensive review of other ontology visualizations, we have reworked many parts of VOWL. In this paper, we present the new version VOWL 2 and describe how the initial definitions were used to systematically redefine the visual notation. Besides the novelties of the visual language, which is based on a well-defined set of graphical primitives and an abstract color scheme, we briefly describe two implementations of VOWL 2. To gather some insight into the user experience with the new version of VOWL, we have conducted a qualitative user study. We report on the study and its results, which confirmed that not only the general ideas of VOWL but also most of our enhancements for VOWL 2 can be well understood by casual ontology users.


title: Semantic 3D Reconstruction with Continuous Regularization and Ray Potentials Using a Visibility Consistency Constraint

abstract: We propose an approach for dense semantic 3D reconstruction which uses a data term that is defined as potentials over viewing rays, combined with continuous surface area penalization. Our formulation is a convex relaxation which we augment with a crucial non-convex constraint that ensures exact handling of visibility. To tackle the non-convex minimization problem, we propose a majorize-minimize type strategy which converges to a critical point. We demonstrate the benefits of using the non-convex constraint experimentally. For the geometry-only case, we set a new state of the art on two datasets of the commonly used Middlebury multi-view stereo benchmark. Moreover, our general-purpose formulation directly reconstructs thin objects, which are usually treated with specialized algorithms. A qualitative evaluation on the dense semantic 3D reconstruction task shows that we improve significantly over previous methods.


title: The evolution and future of logistics and supply chain management

abstract: Purpose – This paper traces the evolution of logistics and supply chain management (SCM) and projects the state of the field in the near term with the attendant challenges for those who must plan and control logistics/supply chain operations.Design/methodology/approach – Key events that created business logistics are noted, the views of the field leaders are identified and assessed, and research outcomes are compared as a basis for how logistics/SCM is viewed today.Findings – The scope of the field has broadened bringing new challenges for researchers and managers, but the broader scope was envisioned from its formation. Improvements in information technology and the just‐in‐time philosophy are the principal drivers for realizing the potential of boundary‐spanning channel management.Research limitations/implications – The conclusions and projections are based on the author's interpretation of the events surrounding logistics and the supply chain as they have occurred over the last 45 years.Practical impli...


title: Dual-Polarized Dual-Band Microstrip Antenna With Similar-Shaped Radiation Pattern

abstract: A dual-polarized dual-band (DPDB) microstrip antenna with two orthogonal feeding ports and similar-shaped radiation pattern is studied. The working mechanism of the dual-band operation and the similar-shaped radiation pattern is analyzed based on the S-parameters and the electric field distributions, respectively. A prototype shows that the measured impedance bandwidths for ${\text{VSWR}} are 150 MHz (810–960 MHz, 17%) and 550 MHz (1690–2240 MHz, 28%) with mutual couplings of lower than $- {32}$ and $- {26}\;{\text{dB}}$ at the lower and higher frequency bands, respectively. The antenna covers the GSM850/GSM900/GSM1800/GSM1900/UMTS bands for 2G/3G base station applications. Within the two frequency bands, the performance of similar-shaped radiation pattern, low cross-polarization, and stable gain is achieved.


title: A quantum decoding algorithm of the simplex code

abstract: The paper is devoted to the quantum implementation of the decoding of the (classical) simplex code of length n. The implementation attempts at trading off time complexity with circuit complexity of decoding. We suggest a quantum decoding algorithm that operates on a circuit of size O(log n) and has time complexity O(log n). It also requires an additional circuit of size O(n) needed to gain bitwise access to the input vector. The best known classical parallel algorithm for this problem requires circuit size O(n log n) and time O(log n).


title: The Use and Alpha-Lipoic Acid (ALA), Gamma Linolenic Acid (GLA) and Rehabilitation in the Treatment of Back Pain: Effect on Health-Related Quality of Life

abstract: The aim of this trial was to evaluate the effects of α-lipoic acid (ALA) and γ-linolenic acid (GLA) and the beneficial effect of physical exercise on positive sensory symptoms and neuropathic pain in patients with compressive radiculopathy syndrome from disc-nerve root conflict. Often these painful syndromes after the acute event, tend to recurrence becoming subacute or chronic syndromes that become for the period of interest disabiling is an event very important in these cases proper prevention, based on a maintenance drug therapy and the strengthening exercises of paravertebral muscles, flexibility exercises on the spine and when needed on the reduction of body weight. In this Observational Cohort, two-arm trial, 203 patients were enrolled and divided into two groups, the first, “ALA and GLA group”, (n = 101) received oral dose of 600 mg of α-lipoic acid (ALA) and 360 mg of γ-linolenic acid (GLA) and a rehabilitation program for six weeks, the second (n = 102) treated with only rehabilitation program. P...


title: Fulfilling the Needs of eSports Consumers: A Uses and Gratifications Perspective

abstract: eSports services are situated between cooperation distinctive for many hedonic activities and competition relevant to the creation of hedonic behavior. This raises the challenge for eSports providers to offer services that fulfill consumers' needs. Against this background, we apply Uses and Gratifications theory (Rayburn and Palmgreen, 1984) and investigate which competitive and hedonic need gratifications drive continuous use of eSports. We conduct ten in-depth expert interviews and a multiple regression analysis based on survey data collected from 360 eSports players. With competition, challenge, and escapism both competitive and hedonic need gratifications drive continuous eSports use.


title: Type III Secretion: More Systems Than You Think

abstract: The type III secretion (T3S) pathway allows bacteria to inject effector proteins into the cytosol of target animal or plant cells. T3S systems evolved into seven families that were distributed among Gram-negative bacteria by horizontal gene transfer. There are probably a few hundred effectors interfering with control and signaling in eukaryotic cells and offering a wealth of new tools to cell biologists.


title: Micro-synchrophasors for distribution systems

abstract: This paper describes a research project to develop a network of high-precision phasor measurement units, termed micro-synchrophasors or μPMUs, and explore the applications of μPMU data for electric power distribution systems.


title: Identifying prosumer's energy sharing behaviours for forming optimal prosumer-communities

abstract: Smart Grid (SG) achieves bidirectional energy and information flow between the energy user and the utility grid, allowing energy users not only to consume energy, but also to generate the energy and share with the utility grid or with other energy consumers. This type of energy user who consumes energy and who also can generate the energy is called the “prosumer”. The sustainability of the SG energy sharing process heavily depends on its participating prosumers, making prosumer participation and management schemes crucial within the energy sharing approaches. However in literature, there is very little attention on prosumer management schemes. The contribution of this article is twofold. First, this article introduces a novel concept of participating and managing the prosumers in the SG energy sharing process in the form of autonomous, intelligent goal-oriented virtual communities. Here, the community of prosumers can collectively increase the amount of power to be auctioned or bought offering higher bargaining power, thereby settling for a higher price per kilowatt in long-term. According to the literature, this research is the first of this type introducing a community approach for prosumer management. The initial step to build an effective prosumer-community is the identification of those prosumers who would be suitable to make efficient prosumer communities. This leads the necessity of identifying parameters that influence the energy sharing behaviours of prosumers. The second contribution of this article is that, this comprehensively analyzes the different parameters influencing the prosumer's energy sharing behaviours and thus presents multi-agent architecture for optimal prosumer-community formation.


title: Weather classification with deep convolutional neural networks

abstract: In this paper, we study weather classification from images using Convolutional Neural Networks (CNNs). Our approach outperforms the state of the art by a huge margin in the weather classification task. Our approach achieves 82.2% normalized classification accuracy instead of 53.1% for the state of the art (i.e., 54.8% relative improvement). We also studied the behavior of all the layers of the Convolutional Neural Networks, we adopted, and interesting findings are discussed.


Linguistic camouflage in girls with autism spectrum disorder


A Corpus of Annotated Revisions for Studying Argumentative Writing


title: 3D NAND Flash Based on Planar Cells

abstract: In this article, the transition from 2D NAND to 3D NAND is first addressed, and the various 3D NAND architectures are compared. The article carries out a comparison of 3D NAND architectures that are based on a “punch-and-plug” process—with gate-all-around (GAA) cell devices—against architectures that are based on planar cell devices. The differences and similarities between the two classes of architectures are highlighted. The differences between architectures using floating-gate (FG) and charge-trap (CT) devices are also considered. Although the current production of 3D NAND is based on GAA cell devices, it is suggested that architectures with planar cell devices could also be viable for mass production.


title: Multi-emotion extraction from text based on linguistic analysis

abstract: Emotions as one of the important elements of human nature are a part of everyday communications of people. We can distinguish person's emotions from some outcome behaviors such as speech, facial expression, body movements and gestures. Another outcome behavior that reflects the inner states of the person is his/her grammar and written method. Because nowadays, people are more likely to use textual tools to make connection, emotion extraction from the text has attracted a lot of attention. This paper provides a framework for the extraction of emotions in the text. By considering that a text may contain more than one emotion that only one of them is text dominant emotion, our proposed method, has modeled emotion extraction problem as a multi-label classification problem by removing the fixed boundaries of emotions, and recognizes all the existing emotions in the sentence and also dominant emotion. The proposed method extracts emotions by using structural and semantic information in the sentence, linguistic information and machine learning techniques. The experiments have been done on multi-label dataset contains 629 sentences with eight emotional categories. Based on the results, our proposed method, compared with used multi-label learning methods (BR, RAKEL, MLANN) have shown a better performance.


title: Keshif: Rapid and Expressive Tabular Data Exploration for Novices

abstract: General purpose graphical interfaces for data exploration are typically based on manual visualization and interaction specifications. While designing manual specification can be very expressive, it demands high efforts to make effective decisions, therefore reducing exploratory speed. Instead, principled automated designs can increase exploratory speed, decrease learning efforts, help avoid ineffective decisions, and therefore better support data analytics novices. Towards these goals, we present Keshif, a new systematic design for tabular data exploration. To summarize a given dataset, Keshif aggregates records by value within attribute summaries, and visualizes aggregate characteristics using a consistent design based on data types. To reveal data distribution details, Keshif features three complementary linked selections: highlighting, filtering, and comparison. Keshif further increases expressiveness through aggregate metrics, absolute/part-of scale modes, calculated attributes, and saved selections, all working in synchrony. Its automated design approach also simplifies authoring of dashboards composed of summaries and individual records from raw data using fluid interaction. We show examples selected from $160+ $ datasets from diverse domains. Our study with novices shows that after exploring raw data for 15 minutes, our participants reached close to 30 data insights on average, comparable to other studies with skilled users using more complex tools.


title: Multi-microphone acoustic echo cancellation using multi-channel warped linear prediction of common acoustical poles

abstract: A computationally cheap extension from single-microphone acoustic echo cancellation (AEC) to multi-microphone AEC is presented for the case of a single loudspeaker. It employs the idea of common-acoustical-pole and zero modeling of room transfer functions (RTFs). The RTF models used for multi-microphone AEC share a fixed common denominator polynomial, which is calculated off-line by means of a multi-channel warped linear prediction. By using the common denominator polynomial as a prefilter, only the numerator polynomial has to be estimated recursively for each microphone, hence adapting to changes in the RTFs. This approach allows to decrease the number of numerator coefficients by one order of magnitude for each microphone compared with all-zero modeling. In a first configuration, the prefiltering is done on the adaptive filter signal, hence achieving a pole-zero model of the RTF in the AEC. In a second configuration, the (inverse) prefiltering is done on the loudspeaker signal, hence achieving a dereverberation effect, in addition to AEC, on the microphone signals.


title: Facial expression transfer method based on frequency analysis

abstract: We propose a novel expression transfer method based on an analysis of the frequency of multi-expression facial images. We locate the facial features automatically and describe the shape deformations between a neutral expression and non-neutral expressions. The subtle expression changes are important visual clues to distinguish different expressions. These changes are more salient in the frequency domain than in the image domain. We extract the subtle local expression deformations for the source subject, coded in the wavelet decomposition. This information about expressions is transferred to a target subject. The resulting synthesized image preserves both the facial appearance of the target subject and the expression details of the source subject. This method is extended to dynamic expression transfer to allow a more precise interpretation of facial expressions. Experiments on Japanese Female Facial Expression (JAFFE), the extended Cohn-Kanade (CK+) and PIE facial expression databases show the superiority of our method over the state-of-the-art methods. The subtle expression changes are salient in the frequency domain.A local expression deformation transfer method based on frequency analysis.Dynamic expression synthesis for the source subject using expression manifold.A unified framework automatic dynamic expression transfer method.


LIDAR and vision-based pedestrian detection system


title: The CHEMDNER corpus of chemicals and drugs and its annotation principles

abstract: The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of different approaches that detect chemicals in documents. We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines. Each of the chemical entity mentions was manually labeled according to its structure-associated chemical entity mention (SACEM) class: abbreviation, family, formula, identifier, multiple, systematic and trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study between annotators, obtaining a percentage agreement of 91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts) we provide not only the Gold Standard manual annotations, but also mentions automatically detected by the 26 teams that participated in the BioCreative IV CHEMDNER chemical mention recognition task. In addition, we release the CHEMDNER silver standard corpus of automatically extracted mentions from 17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus in the BioC format has been generated as well. We propose a standard for required minimum information about entity annotations for the construction of domain specific corpora on chemical and drug entities. The CHEMDNER corpus and annotation guidelines are available at: http://www.biocreative.org/resources/biocreative-iv/chemdner-corpus/


title: A Novel Bayesian Framework for Discriminative Feature Extraction in Brain-Computer Interfaces

abstract: As there has been a paradigm shift in the learning load from a human subject to a computer, machine learning has been considered as a useful tool for Brain-Computer Interfaces (BCIs). In this paper, we propose a novel Bayesian framework for discriminative feature extraction for motor imagery classification in an EEG-based BCI in which the class-discriminative frequency bands and the corresponding spatial filters are optimized by means of the probabilistic and information-theoretic approaches. In our framework, the problem of simultaneous spatiospectral filter optimization is formulated as the estimation of an unknown posterior probability density function (pdf) that represents the probability that a single-trial EEG of predefined mental tasks can be discriminated in a state. In order to estimate the posterior pdf, we propose a particle-based approximation method by extending a factored-sampling technique with a diffusion process. An information-theoretic observation model is also devised to measure discriminative power of features between classes. From the viewpoint of classifier design, the proposed method naturally allows us to construct a spectrally weighted label decision rule by linearly combining the outputs from multiple classifiers. We demonstrate the feasibility and effectiveness of the proposed method by analyzing the results and its success on three public databases.


title: C to O-O Translation: Beyond the Easy Stuff

abstract: Can we reuse some of the huge code-base developed in C to take advantage of modern programming language features such as type safety, object-orientation, and contracts? This paper presents a source-to-source translation of C code into Eiffel, a modern object-oriented programming language, and the supporting tool C2Eif. The translation is completely automatic and supports the entire C language (ANSI, as well as many GNU C Compiler extensions, through CIL) as used in practice, including its usage of native system libraries and inlined assembly code. Our experiments show that C2Eif can handle C applications and libraries of significant size (such as vim and libgsl), as well as challenging benchmarks such as the GCC torture tests. The produced Eiffel code is functionally equivalent to the original C code, and takes advantage of some of Eiffel's features to produce safe and easy-to-debug translations.


Common Statistical Errors and how to Avoid them.


title: Hybrid Soft Computing for Image Segmentation

abstract: This book proposes soft computing techniques for segmenting real-life images in applications such as image processing, image mining, video surveillance, and intelligent transportation systems. The book suggests hybrids deriving from three main approaches: fuzzy systems, primarily used for handling real-life problems that involve uncertainty; artificial neural networks, usually applied for machine cognition, learning, and recognition; and evolutionary computation, mainly used for search, exploration, efficient exploitation of contextual information, and optimization. The contributed chapters discuss both the strengths and the weaknesses of the approaches, and the book will be valuable for researchers and graduate students in the domains of image processing and computational intelligence.


title: Holographic design of hexagonal photonic crystals of irregular columns with large full band gap

abstract: Abstract The shape and size of the dielectric columns or particles (“atoms”) of photonic crystals (PhCs) formed by holographic lithography are determined by the isointensity surfaces of the interference field; consequently the PhCs’ photonic band gap (PBG) properties are closely related to their fabrication design. Here we have proposed a new structure of two-dimensional (2-D) hexagonal lattice with irregular columns, which can yield a 2-D complete relative band gap of 24.0% in case of the dielectric columns of e = 13.6 in air, about 27% increase compared with that of the same lattice with regular triangular columns. This band gap size is among the largest for all the possible 2-D PhCs reported until now. The relationship between band gap properties of resultant structure and the specific fabrication conditions such as structure design and the choice of optimum intensity threshold and filling ratio are systematically discussed. The optical design for making this structure by two exposures is explained. This work may demonstrate the unique feature and advantages of photonic crystals made by holographic method and provide a guideline for their design and experimental fabrication.


title: A Multi-scale Multiple Instance Video Description Network

abstract: Generating natural language descriptions for in-the-wild videos is a challenging task. Most state-of-the-art methods for solving this problem borrow existing deep convolutional neural network (CNN) architectures (AlexNet, GoogLeNet) to extract a visual representation of the input video. However, these deep CNN architectures are designed for single-label centered-positioned object classification. While they generate strong semantic features, they have no inherent structure allowing them to detect multiple objects of different sizes and locations in the frame. Our paper tries to solve this problem by integrating the base CNN into several fully convolutional neural networks (FCNs) to form a multi-scale network that handles multiple receptive field sizes in the original image. FCNs, previously applied to image segmentation, can generate class heat-maps efficiently compared to sliding window mechanisms, and can easily handle multiple scales. To further handle the ambiguity over multiple objects and locations, we incorporate the Multiple Instance Learning mechanism (MIL) to consider objects in different positions and at different scales simultaneously. We integrate our multi-scale multi-instance architecture with a sequence-to-sequence recurrent neural network to generate sentence descriptions based on the visual representation. Ours is the first end-to-end trainable architecture that is capable of multi-scale region processing. Evaluation on a Youtube video dataset shows the advantage of our approach compared to the original single-scale whole frame CNN model. Our flexible and efficient architecture can potentially be extended to support other video processing tasks.


title: A Delegation Framework for Task-Role Based Access Control in WFMS *

abstract: Access control is important for protecting information integrity in workflow management system (WfMS). Compared to conventional access control technology such as discretionary, mandatory, and role-based access control models, task-role-based access control (TRBAC) model, an access control model based on both tasks and roles, meets more requirements for modern enterprise environments. However, few discussions on delegation mechanisms for TRBAC are made. In this paper, a framework considering temporal constraints to improve delegation and help automatic delegation in TRBAC is presented. In the framework, the methodology for delegations requested from both users and WfMS is discussed. The constraints for delegatee selection such as delegation loop and separation of duty (SOD) are addressed. With the framework, a sequence of algorithms for delegation and revocation of tasks are constructed gradually. Finally, a comparison is made between our approach and the representative related works.


title: Real-time event-driven spiking neural network object recognition on the SpiNNaker platform

abstract: This paper presents a real-time spiking neural network adaptation of the HMAX object recognition model on an event-driven platform. Visual input is provided by a spiking silicon retina, while the SpiNNaker system is used as a computational hardware platform for implementation. We show the implementation of a simple Leaky Integrate-and-Fire (LIF) neuron model on SpiNNaker to create an event driven network, where a neuron only updates when it receives an interrupt indicating that a new input spike has been received. The model output consists of view tuned neurons which respond selectively to a particular view of an object. The network can be used to discriminate between objects, or between the same object at different views. On a 26 class character recognition task, the correct class is always assigned the highest probability (69.42% on average).


title: Corporate Values on Strategic Planning Process: A Research about the Universities in Turkey☆

abstract: Abstract Values are the organization-specific features determining the way of thinking, behaviors and responses to the events. Corporate values are the criteria which determine the boundaries on actions and decisions. At the same time, corporate values represent the quality of products and services, company performance, organizational behavior and corporate identity. This study is about whether the managers use the concept of strategic value during the strategic management process. Therefore, websites of the universities related with Higher Education Council have been analyzed. The relationship between public universities and the private universities in terms of the corporate values has been taken into consideration. As a result of the analysis of the websites and through the method of content analysis, the study has been carried out by reaching to the fact that 39% of private universities and 79% of public universities have corporate values.


title: Piccolo: an ultra-lightweight blockcipher

abstract: We propose a new 64-bit blockcipher Piccolo supporting 80 and 128-bit keys. Adopting several novel design and implementation techniques, Piccolo achieves both high security and notably compact implementation in hardware. We show that Piccolo offers a sufficient security level against known analyses including recent related-key differential attacks and meet-in-the-middle attacks. In our smallest implementation, the hardware requirements for the 80 and the 128-bit key mode are only 683 and 758 gate equivalents, respectively. Moreover, Piccolo requires only 60 additional gate equivalents to support the decryption function due to its involution structure. Furthermore, its efficiency on the energy consumption which is evaluated by energy per bit is also remarkable. Thus, Piccolo is one of the competitive ultra-lightweight blockciphers which are suitable for extremely constrained environments such as RFID tags and sensor nodes.


title: Actionable and Political Text Classification using Word Embeddings and LSTM

abstract: In this work, we apply word embeddings and neural networks with Long Short-Term Memory (LSTM) to text classification problems, where the classification criteria are decided by the context of the application. We examine two applications in particular. The first is that of Actionability, where we build models to classify social media messages from customers of service providers as Actionable or Non-Actionable. We build models for over 30 different languages for actionability, and most of the models achieve accuracy around 85%, with some reaching over 90% accuracy. We also show that using LSTM neural networks with word embeddings vastly outperform traditional techniques. Second, we explore classification of messages with respect to political leaning, where social media messages are classified as Democratic or Republican. The model is able to classify messages with a high accuracy of 87.57%. As part of our experiments, we vary different hyperparameters of the neural networks, and report the effect of such variation on the accuracy. These actionability models have been deployed to production and help company agents provide customer support by prioritizing which messages to respond to. The model for political leaning has been opened and made available for wider use.


title: Decarbonizing the Global Energy System: Implications for Energy Technology and Security

abstract: Since the Intergovernmental Panel on Climate Change (IPCC) was formed in 1988, it has engaged a substantial proportion of those individuals with relevant scientific expertise in the process of forming reasonable judgments about the effects of aggregate human activity on the composition of the earth’s atmosphere and about the resulting implications for global climate. It is now widely agreed that in concert with other so-called “greenhouse gases,” carbon dioxide (CO2) released from the burning of fossil fuels for energy is causing the earth’s climate to change. Over the last century, the concentration of CO2 in the atmosphere increased from about 300 to 375 parts per million by volume (ppmv), and global average surface temperature increased by 0.4 to 0.8 C. In the absence of policies designed to substantially reduce global emissions, scenarios developed by the IPCC indicate that CO2 concentrations will reach 550 to 1000 ppmv in 2100 and that global average surface temperature will increase by an additional 1.5 to 6 C (IPCC 2001a).


title: Handwritten Digit Recognition with a Back-Propagation Network

abstract: We present an application of back-propagation networks to handwritten digit recognition. Minimal preprocessing of the data was required, but architecture of the network was highly constrained and specifically designed for the task. The input of the network consists of normalized images of isolated digits. The method has 1% error rate and about a 9% reject rate on zipcode digits provided by the U.S. Postal Service.


title: Towards segment-based recognition of argumentation structure in short texts

abstract: Despite recent advances in discourse parsing and causality detection, the automatic recognition of argumentation structure of authentic texts is still a very challenging task. To approach this problem, we collected a small corpus of German microtexts in a text generation experiment, resulting in texts that are authentic but of controlled linguistic and rhetoric complexity. We show that trained annotators can determine the argumentation structure on these microtexts reliably. We experiment with different machine learning approaches for automatic argumentation structure recognition on various levels of granularity of the scheme. Given the complex nature of such a discourse understanding tasks, the first results presented here are promising, but invite for further investigation.


title: Substrate integrated waveguide six-port broadband front-end circuit for millimeter-wave radio and radar systems

abstract: A novel multi-purpose integrated planar six-port front-end circuit combining both substrate integrated waveguide (SIW) technology and integrated loads is presented and demonstrated. The use of SIW technology allows a very compact circuit and very low radiation loss at millimeter frequencies. An integrated load is used to simplify the fabrication process and also reduce dimensions and cost. To validate the proposed concept, an integrated broadband six-port front-end circuit prototype was fabricated and measured. Simulation and measurement results show that the proposed six-port circuit can easily operate at 24 GHz for radar systems and also over 23–29 GHz for broadband millimetre-wave radio services.


title: Toward SWSs Discovery: Mapping from WSDL to OWL-S Based on Ontology Search and Standardization Engine

abstract: Semantic Web Services (SWSs) represent the most recent and revolutionary technology developed for machine-to-machine interaction on the web 3.0. As for the conventional web services, the problem of discovering and selecting the most suitable web service represents a challenge for SWSs to be widely used. In this paper, we propose a mapping algorithm that facilitates the redefinition of the conventional web services annotations (i.e., WSDL) using semantic annotations (i.e., OWL-S). This algorithm will be a part of a new discovery mechanism that relies on the semantic annotations of the web services to perform its task. The “local ontology repository” and “ontology search and standardization engine” are the backbone of this algorithm. Both of them target to define any data type in the system using a standard ontology-based concept. The originality of the proposed mapping algorithm is its applicability and consideration of the standardization problem. The proposed algorithm is implemented and its components are validated using some test collections and real examples. An experimental test of the proposed techniques is reported, showing the impact of the proposed algorithm in decreasing the time and the effort of the mapping process. Moreover, the experimental results promises that the proposed algorithm will have a positive impact on the discovery process as a whole.


title: Multi-Scale EEG Brain Dynamics During Sustained Attention Tasks

abstract: We present a novel experimental paradigm and data analysis methodology for studying brain dynamics during sustained-attention tasks. 256-channel EEG data were recorded while subjects participated in hour-long simulated driving sessions. Every few seconds, the vehicle drifted away from the center of the left lane, and subjects were instructed to steer back to the lane center. The error of each drifting event was measured by the maximum absolute distance from the vehicle's position at deviation onset. EEG data were analyzed using independent component analysis and time-frequency analysis. An independent component with equivalent dipole sources located bilaterally in lateral occipital cortex exhibited multi-scale brain dynamics. Tonic (~20s) alpha-band power increased in high-error compared to low-error epochs, while phasic (~1s) alpha power was suppressed briefly after deviation onset, then increased strongly just before response offset. Other components also exhibited distinct tonic and/or phasic activity patterns relating to deviation onsets or response onsets.


title: Neural network CAPTCHA crackers

abstract: This paper describes several experiments using deep neural networks to break character-based image CAPTCHAs. The goal of our research was to see if one could develop a single neural network capable of breaking all character-based, image CAPTCHAs. Our main deep neural net uses convolutional neural network layers followed by a dense layer, and a recurrent recurrent neural network layer instead of the conventional method of CAPTCHA breaking based on segmenting and recognizing individual letters. Our experiments with these networks were conducted using a synthetically generated dataset of CAPTCHAs which is independently useful for future research. We trained on both fixed-and variable-length CAPTCHAs and our main neural net configuration was able to achieve accuracy levels of 99.8% and 81%, respectively.


title: Analyzing temporal role based access control models

abstract: Today, Role Based Access Control (RBAC) is the de facto model used for advanced access control, and is widely deployed in diverse enterprises of all sizes. Several extensions to the authorization as well as the administrative models for RBAC have been adopted in recent years. In this paper, we consider the temporal extension of RBAC (TRBAC), and develop safety analysis techniques for it. Safety analysis is essential for understanding the implications of security policies both at the stage of specification and modification. Towards this end, in this paper, we first define an administrative model for TRBAC. Our strategy for performing safety analysis is to appropriately decompose the TRBAC analysis problem into multiple subproblems similar to RBAC. Along with making the analysis simpler, this enables us to leverage and adapt existing analysis techniques developed for traditional RBAC. We have adapted and experimented with employing two state of the art analysis approaches developed for RBAC as well as tools developed for software testing. Our results show that our approach is both feasible and flexible.


title: Genetic and Physiological Characterization of Brettanomyces bruxellensis Strains Isolated from Wines

abstract: Brettanomyces bruxellensis isolates, primarily from wine, were selected for characterization by geographic diversity, vintage year of isolation, and variety of wine. A total of 47 isolates were characterized genetically by sequencing a portion of the 26S rDNA gene. Physiological differences were tested in a subset of 35 isolates. The majority of the strains characterized by the 26S rDNA sequence were grouped into one of six clusters. Some similarities of physiology were noted, but many traits were highly variable and did not cluster into the same groupings identified by the DNA analysis. Statistical analysis found 4-ethylphenol and 4-ethylguaiacol production to be the most discriminant characters.


title: A Mutual Authentication Framework for Wireless Medical Sensor Networks

abstract: Wireless medical sensor networks (WMSN) comprise of distributed sensors, which can sense human physiological signs and monitor the health condition of the patient. It is observed that providing privacy to the patient's data is an important issue and can be challenging. The information passing is done via the public channel in WMSN. Thus, the patient, sensitive information can be obtained by eavesdropping or by unauthorized use of handheld devices which the health professionals use in monitoring the patient. Therefore, there is an essential need of restricting the unauthorized access to the patient's medical information. Hence, the efficient authentication scheme for the healthcare applications is needed to preserve the privacy of the patients' vital signs. To ensure secure and authorized communication in WMSN, we design a symmetric key based authentication protocol for WMSN environment. The proposed protocol uses only computationally efficient operations to achieve lightweight attribute. We analyze the security of the proposed protocol. We use a formal security proof algorithm to show the scheme security against known attacks. We also use the Automated Validation of Internet Security Protocols and Applications (AVISPA) simulator to show protocol secure against man-in-the-middle attack and replay attack. Additionally, we adopt an informal analysis to discuss the key attributes of the proposed scheme. From the formal proof of security, we can see that an attacker has a negligible probability of breaking the protocol security. AVISPA simulator also demonstrates the proposed scheme security against active attacks, namely, man-in-the-middle attack and replay attack. Additionally, through the comparison of computational efficiency and security attributes with several recent results, proposed scheme seems to be battered.


title: MATHEMATICAL MODELLING OF DRUG RELEASE FROM HYDROXYPROPYLMETHYLCELLULOSE MATRICES : EFFECT OF TEMPERATURE

abstract: Abstract A number of mathematical models have been used to describe the release of promethazine hydrochloride from matrices containing hydroxypropylmethylcellulose. Relationships, such as predicted by the Korsmeyer equation ( M t / M ∞ = kt n ), were considered inappropriate since the introduction of a lag period was essential to describe accurately the quantity of drug released. An equation ( M t / M ∞ = k ( t − l ) n + k ′( t − l ) 2 n ) incorporating a lag period ( l ), kinetic constants ( k and k ′) for diffusion and erosion controlled release and a diffusional component ( n ) produced the best fit of the data as evaluated by information criteria and unweighted sums of squares. The kinetic constants were not normally additive, k ′ becoming increasingly negative with increase in temperature. Values of n were in the range 0.563–0.764 indicating that release was controlled by both diffusion and erosion. Increasing the temperature increased the root time release rate constants from the matrices but its effect on the overall contribution to mechanisms controlling release was difficult to interpret.


title: Epidemiology of surgically managed pelvic organ prolapse and urinary incontinence.

abstract: Objective To determine the incidence of surgically managed pelvic organ prolapse and urinary incontinence in a population-based cohort, and to describe their clinical characteristics. Methods Our retrospective cohort study included all patients undergoing surgical treatment for prolapse and incontinence during 1995; all were members of Kaiser Permanente Northwest, which included 149,554 women age 20 or older. A standardized data-collection form was used to review all inpatient and outpatient charts of the 395 women identified. Variables examined included age, ethnicity, height, weight, vaginal parity, smoking history, medical history, and surgical history, including the preoperative evaluation, procedure performed, and details of all prior procedures. Analysis included calculation of age-specific and cumulative incidences and determination of the number of primary operations compared with repeat operations performed for prolapse or incontinence. Results The age-specific incidence increased with advancing age. The lifetime risk of undergoing a single operation for prolapse or incontinence by age 80 was 11.1%. Most patients were older, postmenopausal, parous, and overweight. Nearly half were current or former smokers and one-fifth had chronic lung disease. Reoperation was common (29.2% of cases), and the time intervals between repeat procedures decreased with each successive repair. Conclusion Pelvic floor dysfunction is a major health issue for older women, as shown by the 11.1% lifetime risk of undergoing a single operation for pelvic organ prolapse and urinary incontinence, as well as the large proportion of reoperations. Our results warrant further epidemiologic research in order to determine the etiology, natural history, and long-term treatment outcomes of these conditions.


title: Recent trends in implementation of Internet of Things — A review

abstract: As of 2016 due to a convergence of multiple technologies ranging from wireless communication to the internet and from embedded systems to micro-electro mechanical systems (MEMS) the prospect of “internet” of things has evolved. This means that the traditional fields of embedded systems, control systems, automation and others contribute to enable the internet of things. The Internet of Things (IoT) is a network of intelligently connected devices communicating together. With the help of such an interconnected smart global network one can make lives much easier and safer. It provides us the ability to sense and control objects remotely across pre-existing network infrastructures creating new opportunities for a more direct integration of the physical world into a cloud based system. This results in improved accuracy, efficiency and economic benefit for today's society.


title: Object tracking based on evidential dynamic occupancy grids in urban environments

abstract: Occupancy grid mapping approaches, especially those that additionally estimate the dynamics, enable a robust and consistent modeling of the local environment in a cell-level representation. But a scene understanding of surrounding traffic participants requires a generalized object-level representation. This work presents an object tracking approach based on dynamic occupancy grids. The association of occupied grid cells with existing object tracks is solved individually on the cell-level without clustering or forming object hypotheses. New object tracks are extracted using a clustering strategy and a velocity variance analysis of neighboring occupied cells to reduce false positives. In order to improve the estimates of the position and size, an object boundary extraction is presented that takes the surrounding free space of the selected box representation into account. Experimental results with real sensor data show the effectiveness of the proposed object tracking approach in challenging urban scenarios with dense traffic.


title: Chinese Acquisition of the Spratly Archipelago and Its Implications for the Future

abstract: In this study, I first examine the place of the Spratly Islands dispute at the heart of interstate relations between China and its regional neighbors around the South China Sea. In doing so, I trace the varied historical claims and periodic disputes over the archipelago, along with the current state of affairs, which is increasingly dominated by the need for more and cheaper sources of oil and natural gas. This background lays the groundwork for the core of this study, which is a simulation analysis looking at the potential ramifications of a more aggressive Chinese position in the South China Sea. Using computer simulation techniques, I compare the desirability of two scenarios related to Chinese policy in the area. The findings suggest that a more aggressive Chinese policy on the Spratlys would be beneficial in some ways, especially in the areas of energy production and imports over the following forty years. At the same time, there are several potential drawbacks as well, including increased environmental degradation and renewed energy supply problems in the second half of the century. I conclude by examining the implications of both conflictual and peaceful Chinese approaches to policy in the South China Sea, in light of China's obvious ascension as the leading state in East and Southeast Asia, as well as its rapidly improving global position.


title: CorMet: A Computational, Corpus-Based Conventional Metaphor Extraction System

abstract: CorMet is a corpus-based system for discovering metaphorical mappings between concepts. It does this by finding systematic variations in domain-specific selectional preferences, which are inferred from large, dynamically mined Internet corpora.Metaphors transfer structure from a source domain to a target domain, making some concepts in the target domain metaphorically equivalent to concepts in the source domain. The verbs that select for a concept in the source domain tend to select for its metaphorical equivalent in the target domain. This regularity, detectable with a shallow linguistic analysis, is used to find the metaphorical interconcept mappings, which can then be used to infer the existence of higher-level conventional metaphors.Most other computational metaphor systems use small, hand-coded semantic knowledge bases and work on a few examples. Although CorMet's only knowledge base is WordNet (Fellbaum 1998) it can find the mappings constituting many conventional metaphors and in some cases recognize sentences instantiating those mappings. CorMet is tested on its ability to find a subset of the Master Metaphor List (Lakoff, Espenson, and Schwartz 1991).


title: A Survey on the Generalized Problematic Internet Use in Chinese College Students and its Relations to Stressful Life Events and Coping Style

abstract: Problematic internet use (PIU) during the college life becomes a significant public health concern with a variety of negative outcomes. The aim of the present study was to examine the incidence of generalized PIU (GPIU) and its relationship to stressful life events and coping style among Chinese college students. Six hundred and fifty-four college students recruited from Guangzhou completed the self-reported measures of the Chinese version of GPIU scale, Chinese College student Stress Questionnaire (CCSQ), and Coping Style Questionnaire (CSQ). Analysis of Structural Equation Model was used to explore the interactive effects of stressful life events and coping style on GPIU. Of the sample, 13.6% was classified as generalized problematic internet users. The prevalence of GPIU did not vary with gender, grade or major. The frequency of stressful events over the past 6 months in the GPIU group was significantly higher than that in the non-PIU group. Compared to the non-PIU group, the generalized problematic internet users used the more avoidant coping styles, and the less problem-solving style. Significant correlations were found between stressful life events, avoidant coping style and GPIU at 0.01 levels. The standardized path coefficients from stressful life events to avoidant coping style (β = 0.35, p < 0.01), and from avoidant coping style to GPIU (β = 0.33, p < 0.01) were significant. The current findings imply that stressful life events are involved in the pre-existing mechanisms of GPIU. Stressful life event contribution to GPIU was mainly mediated through avoidant coping style (e.g. self-blame, fantasy, withdrawal or rationalization). Future prevention programs must focus on developing individual confrontative coping skills to reduce stress and GPIU among college students.


title: Assessment of Bronchodilator Responsiveness Following Methacholine-Induced Bronchoconstriction in Children With Asthma

abstract: Purpose ::: The aim of this study was to investigate bronchodilator responsiveness (BDR) following methacholine-induced bronchoconstriction and to determine differences in BDR according to clinical parameters in children with asthma.


title: Structure of Fibonacci cubes: a survey

abstract: The Fibonacci cube Γn is the subgraph of the n-cube induced by the binary strings that contain no two consecutive 1s. These graphs are applicable as interconnection networks and in theoretical chemistry, and lead to the Fibonacci dimension of a graph. In this paper a survey on Fibonacci cubes is given with an emphasis on their structure, including representations, recursive construction, hamiltonicity, degree sequence and other enumeration results. Their median nature that leads to a fast recognition algorithm is discussed. The Fibonacci dimension of a graph, studies of graph invariants on Fibonacci cubes, and related classes of graphs are also presented. Along the way some new short proofs are given.


title: Learning Invariants using Decision Trees

abstract: The problem of inferring an inductive invariant for verifying program safety can be formulated in terms of binary classification. This is a standard problem in machine learning: given a sample of good and bad points, one is asked to find a classifier that generalizes from the sample and separates the two sets. Here, the good points are the reachable states of the program, and the bad points are those that reach a safety property violation. Thus, a learned classifier is a candidate invariant. In this paper, we propose a new algorithm that uses decision trees to learn candidate invariants in the form of arbitrary Boolean combinations of numerical inequalities. We have used our algorithm to verify C programs taken from the literature. The algorithm is able to infer safe invariants for a range of challenging benchmarks and compares favorably to other ML-based invariant inference techniques. In particular, it scales well to large sample sets.


title: Classification of benign and malignant tumors in histopathology images

abstract: Breast cancer leads the list of cancer that act on women worldwide. It starts when cells in the breast begin to build up beyond control. These cells normally create a tumour that can usually be seen on an x-ray or felt as a lump. Analysing and grading the tumour will take up much of a pathologist time. Pathologists have been largely diagnosing disease the same way for the past years, by manually reviewing images under a microscope. Thus, to help the pathologists improve accuracy and significantly change the way breast cancer been diagnosed, this paper presents an automated classification program. BreakHis dataset was used which build of 7909 breast tumor images gathered from 82 patients. This system is developed in order to categorize the cancer cells into two classes of cancer which are benign and malignant. The classification system compared different types of feature extractors using k-nearest neighbours classifier to efficiently observe the performance of the classification system. An extensive set of experiments showed that the overall accuracy rates range from 83% to 86%.


title: A survey on annotation tools for the biomedical literature

abstract: New approaches to biomedical text mining crucially depend on the existence of comprehensive annotated corpora. Such corpora, commonly called gold standards, are important for learning patterns or models during the training phase, for evaluating and comparing the performance of algorithms and also for better understanding the information sought for by means of examples. Gold standards depend on human understanding and manual annotation of natural language text. This process is very time-consuming and expensive because it requires high intellectual effort from domain experts. Accordingly, the lack of gold standards is considered as one of the main bottlenecks for developing novel text mining methods. This situation led the development of tools that support humans in annotating texts. Such tools should be intuitive to use, should support a range of different input formats, should include visualization of annotated texts and should generate an easy-to-parse output format. Today, a range of tools which implement some of these functionalities are available. In this survey, we present a comprehensive survey of tools for supporting annotation of biomedical texts. Altogether, we considered almost 30 tools, 13 of which were selected for an in-depth comparison. The comparison was performed using predefined criteria and was accompanied by hands-on experiences whenever possible. Our survey shows that current tools can support many of the tasks in biomedical text annotation in a satisfying manner, but also that no tool can be considered as a true comprehensive solution.


title: Role of Combined 68Ga-DOTATOC and 18F-FDG Positron Emission Tomography/Computed Tomography in the Diagnostic Workup of Pancreas Neuroendocrine Tumors: Implications for Managing Surgical Decisions

abstract: OBJECTIVES ::: Ga-DOTATOC (Ga) positron emission tomography (PET)/computed tomography (CT) is recommended in the workup of pancreas neuroendocrine tumors (PanNETs); evidence suggests that F-FDG (F) PET/CT can also provide prognostic information. Aims of this study were to assess the role of combined Ga- and F-PET/CT in the evaluation of grade (G) 1-2 PanNETs and to test the correlation between F-PET/CT positivity and tumor grade. ::: ::: ::: METHODS ::: Preoperative Ga- and F-PET/CT of 35 patients with surgically resected G1-2 PanNETs were evaluated. For grading, the 2010 World Health Organization Classification was used; an ancillary analysis with Ki67 cutoffs at 5% to 20% was conducted. Correlation between F-PET/CT positivity (SUVmax > 3.5) and grade was assessed. ::: ::: ::: RESULTS ::: Of 35 PanNETs, 28.6% and 71.4% were G1 and G2 as per World Health Organization. Ga-PET/CT showed high sensitivity (94.3%) in detecting G1-2 PanNETs. F-PET/CT was positive in 20% and 76% G1 and G2 tumors (P = 0.002). F-PET/CT identified G2 PanNETs with high positive predictive value (PPV, 90.5%). F-PET/CT correlated with tumor grade also in the ancillary analysis (P = 0.009). ::: ::: ::: CONCLUSIONS ::: The high sensitivity of Ga-PET/CT in NET detection is known. The high PPV of F-PET/CT in the identification of G2 forms suggests its potential role in PanNETs prognostication and risk stratification.


title: Inferring human activities from GPS tracks

abstract: The collection of huge amount of tracking data made possible by the widespread use of GPS devices, enabled the analysis of such data for several applications domains, ranging from traffic management to advertisement and social studies. However, the raw positioning data, as it is detected by GPS devices, lacks of semantic information since this data does not natively provide any additional contextual information like the places that people visited or the activities performed. Traditionally, this information is collected by hand filled questionnaire where a limited number of users are asked to annotate their tracks with the activities they have done. With the purpose of getting large amount of semantically rich trajectories, we propose an algorithm for automatically annotating raw trajectories with the activities performed by the users. To do this, we analyse the stops points trying to infer the Point Of Interest (POI) the user has visited. Based on the category of the POI and a probability measure based on the gravity law, we infer the activity performed. We experimented and evaluated the method in a real case study of car trajectories, manually annotated by users with their activities. Experimental results are encouraging and will drive our future works.


title: Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information

abstract: Falls are dangerous for the aged population as they can adversely affect health. Therefore, many fall detection systems have been developed. However, prevalent methods only use accelerometers to isolate falls from activities of daily living (ADL). This makes it difficult to distinguish real falls from certain fall-like activities such as sitting down quickly and jumping, resulting in many false positives. Body orientation is also used as a means of detecting falls, but it is not very useful when the ending position is not horizontal, e.g. falls happen on stairs. In this paper we present a novel fall detection system using both accelerometers and gyroscopes. We divide human activities into two categories: static postures and dynamic transitions. By using two tri-axial accelerometers at separate body locations, our system can recognize four kinds of static postures: standing, bending, sitting, and lying. Motions between these static postures are considered as dynamic transitions. Linear acceleration and angular velocity are measured to determine whether motion transitions are intentional. If the transition before a lying posture is not intentional, a fall event is detected. Our algorithm, coupled with accelerometers and gyroscopes, reduces both false positives and false negatives, while improving fall detection accuracy. In addition, our solution features low computational cost and real-time response.


title: A Fast Ellipse Detector Using Projective Invariant Pruning

abstract: Detecting elliptical objects from an image is a central task in robot navigation and industrial diagnosis, where the detection time is always a critical issue. Existing methods are hardly applicable to these real-time scenarios of limited hardware resource due to the huge number of fragment candidates (edges or arcs) for fitting ellipse equations. In this paper, we present a fast algorithm detecting ellipses with high accuracy. The algorithm leverages a newly developed projective invariant to significantly prune the undesired candidates and to pick out elliptical ones. The invariant is able to reflect the intrinsic geometry of a planar curve, giving the value of −1 on any three collinear points and +1 for any six points on an ellipse. Thus, we apply the pruning and picking by simply comparing these binary values. Moreover, the calculation of the invariant only involves the determinant of a $3\times 3$ matrix. Extensive experiments on three challenging data sets with 648 images demonstrate that our detector runs 20%–50% faster than the state-of-the-art algorithms with the comparable or higher precision.


title: Electronic word-of-mouth in hospitality and tourism management

abstract: Abstract Interpersonal influence and word-of-mouth (WOM) are ranked the most important information source when a consumer is making a purchase decision. These influences are especially important in the hospitality and tourism industry, whose intangible products are difficult to evaluate prior to their consumption. When WOM becomes digital, the large-scale, anonymous, ephemeral nature of the Internet induces new ways of capturing, analyzing, interpreting, and managing the influence that one consumer may have on another. This paper describes online interpersonal influence, or eWOM, as a potentially cost-effective means for marketing hospitality and tourism, and discusses some of the nascent technological and ethical issues facing marketers as they seek to harness emerging eWOM technologies.


title: The calculation of quantum radar scattering characteristic for the 3D circular cone target

abstract: With the development of quantum radar technology, the study of quantum radar scattering characteristic has gradually become a new academic and engineering hotspot. Quantum radar cross section (QRCS) is an important parameter of quantum radar scattering characteristic which describes the visibility of the target illuminated by a handful of photons. However, the current study is still limited to 2D objects. We first developed a new calculation method that can solve the simulation of quantum radar scattering for 3D circular cone targets. Then, the method has been verified the for a typical 2D circular plate. Finally, we presented the QRCS results of a 3D circular cone and compared them with the classical radar cross section.


title: COLAP: A predictive framework for service function chain placement in a multi-cloud environment

abstract: Network function virtualization (NFV) over multi-cloud promises network service providers amazing flexibility in service deployment and optimizing cost. Telecommunications applications are, however, sensitive to performance indicators, especially latency, which tend to get degraded by both the virtualization and the multiple cloud requirement for widely distributed coverage. In this work we propose an efficient framework that uses the novel concept of random cloud selection combined with a support vector regression based predictive model for cost optimized latency aware placement (COLAP) of service function chains. Extensive empirical analysis has been carried out with training datasets generated using a queuing-theoretic model. The results show good generalization performance of the predictive algorithm. The proposed framework can place thousands of virtual network functions in less than a minute and has high acceptance ratio.


title: Persuasive robotic assistant for health self-management of older adults: Design and evaluation of social behaviors

abstract: Daily health self-management, such as the harmonization of food, exercise and medication, is a major problem for a large group of older adults with obesity or diabetics. Computer-based personal assistance can help to behave healthy by persuading and guiding older adults. For effective persuasion, the assistant should express social behaviors (e.g., turn taking, emotional expressions) to be trustworthy and show empathy. From the motivational interviewing method and synthetic assistants' literature, we derived a set of social behaviors, and implemented a subset in a physical character, a virtual character and a text interface. The first behavior type concerns conversing with high-level dialogue (semantics, intentions), which could be implemented in all 3 assistants. The other behavior types could only be implemented in the characters: showing natural cues (e.g., gaze, posture), expressing emotions (e.g., compassionate face), and accommodating social conversations (e.g., turn taking). In an experiment, 24 older adults (45-65) interacted with the text interface and one of the characters, conform a ''one-week diabetics scenario''. They experienced the virtual and physical character as more empathic and trustworthy than the text-based assistant, and expressed more conversational behavior with the characters. However, it seems that the preference of interacting with the character or the text interface was influenced by the conscientiousness of the participant; more conscientious people liked the text interface better. Older adults responded more negative to the characters that lacked the social behaviors than to the text interface. Some differences between the virtual and physical character probably occurred due to the specific constraints of the physical character.


title: Circular data matrix fiducial system and robust image processing for a wearable vision-inertial self-tracker

abstract: A wearable low-power hybrid vision-inertial tracker has been demonstrated based on a flexible sensor fusion core architecture, which allows easy reconfiguration by plugging-in different kinds of sensors. A particular prototype implementation consists of one inertial measurement unit and one out-ward-looking wide-angle smart camera, with a built-in DSP to run all required image-processing tasks. The smart camera operates on newly designed 2D bar-coded fiducials printed on a standard black-and-white printer. The fiducial design allows having thousands of different codes, thus enabling uninterrupted tracking throughout a large building or even a campus at very reasonable cost. The system operates in various real-world lighting conditions without user intervention due to homomorphic image processing algorithms for extracting fiducials in the presence of very non-uniform lighting.


title: Predicting system performance for multi-tenant database workloads

abstract: Database consolidation is gaining wide acceptance as a means to reduce the cost and complexity of managing database systems. However, this new trend poses many interesting challenges for understanding and predicting system performance. The consolidated databases in multi-tenant settings share resources and compete with each other for these resources. In this work we present an experimental study to highlight how these interactions can be fairly complex. We argue that individual database staging or workload profiling is not an adequate approach to understanding the performance of the consolidated system. Our initial investigations suggest that machine learning approaches that use monitored data to model the system can work well for important tasks.


title: DRAM errors in the wild: a large-scale field study

abstract: Errors in dynamic random access memory (DRAM) are a common form of hardware failure in modern compute clusters. Failures are costly both in terms of hardware replacement costs and service disruption. While a large body of work exists on DRAM in laboratory conditions, little has been reported on real DRAM failures in large production clusters. In this paper, we analyze measurements of memory errors in a large fleet of commodity servers over a period of 2.5 years. The collected data covers multiple vendors, DRAM capacities and technologies, and comprises many millions of DIMM days. The goal of this paper is to answer questions such as the following: How common are memory errors in practice? What are their statistical properties? How are they affected by external factors, such as temperature and utilization, and by chip-specific factors, such as chip density, memory technology and DIMM age? We find that DRAM error behavior in the field differs in many key aspects from commonly held assumptions. For example, we observe DRAM error rates that are orders of magnitude higher than previously reported, with 25,000 to 70,000 errors per billion device hours per Mbit and more than 8% of DIMMs affected by errors per year. We provide strong evidence that memory errors are dominated by hard errors, rather than soft errors, which previous work suspects to be the dominant error mode. We find that temperature, known to strongly impact DIMM error rates in lab conditions, has a surprisingly small effect on error behavior in the field, when taking all other factors into account. Finally, unlike commonly feared, we don't observe any indication that newer generations of DIMMs have worse error behavior.


title: Development of a 4DoFs exoskeleton robot for passive arm movement assistance

abstract: Proper functioning of the upper limbs is important to manage essential activities of daily living. To provide assistance and rehabilitation to individuals with upper limb dysfunction due to conditions such as stroke or spinal cord injuries, we have developed a 4DoFs exoskeleton robot (ExoRob). The ExoRob was designed to be worn on the lateral side of the upper arm, to conform to a naturalistic range of movement for the shoulder and elbow joints. This paper focuses on the modelling, design, development, and control of the ExoRob. Experiments were carried out with healthy human subjects where trajectories tracking in the form of passive rehabilitation exercises were performed. Further experiments were carried out with the master exoskeleton arm (mExoArm, an upper-limb prototype exoskeleton arm) where subjects operate the mExoArm (like a joystick) to maneuver the ExoRob to provide passive rehabilitation. Experimental results show that the ExoRob can effectively perform passive rehabilitation exercises.


title: Current Perceptions of Marital Rape Some Good and Not-So-Good News

abstract: The current study extends previous research on marital rape and acceptance of general rape myths by comparing the perceptions of undergraduate college students (n = 85) to those of college alumni/ae (n = 44) who graduated from the same university three decades earlier. Participants read a hypothetical rape scenario that depicted the perpetrator as either the victim's husband or neighbor and completed three measures of different aspects of rape myth acceptance. Results indicated that although participants reported fairly low levels of support for different aspects of rape myths, certain rape myths were more strongly endorsed than were others. Furthermore, rape myth acceptance was stronger for marital rape than for acquaintance rape and for undergraduates than for college alumni/ae. Practical and theoretical implications of this study are discussed.


title: Constraint-based Task Specification and Estimation for Sensor-Based Robot Systems in the Presence of Geometric Uncertainty

abstract: This paper introduces a systematic constraint-based approach to specify complex tasks of general sensor-based robot systems consisting of rigid links and joints. The approach integrates both instantaneous task specification and estimation of geometric uncertainty in a unified framework. Major components are the use of feature coordinates, defined with respect to object and feature frames, which facilitate the task specification, and the introduction of uncertainty coordinates to model geometric uncertainty. While the focus of the paper is on task specification, an existing velocity- based control scheme is reformulated in terms of these feature and uncertainty coordinates. This control scheme compensates for the effect of time varying uncertainty coordinates. Constraint weighting results in an invariant robot behavior in case of conflicting constraints with heterogeneous units. The approach applies to a large variety of robot systems (mobile robots, multiple robot systems, dynamic human-robot interaction, etc.), various sensor systems, and different robot tasks. Ample simulation and experimental results are presented.


title: State of the Art: Automated Black-Box Web Application Vulnerability Testing

abstract: Black-box web application vulnerability scanners are automated tools that probe web applications for security vulnerabilities. In order to assess the current state of the art, we obtained access to eight leading tools and carried out a study of: (i) the class of vulnerabilities tested by these scanners, (ii) their effectiveness against target vulnerabilities, and (iii) the relevance of the target vulnerabilities to vulnerabilities found in the wild. To conduct our study we used a custom web application vulnerable to known and projected vulnerabilities, and previous versions of widely used web applications containing known vulnerabilities. Our results show the promise and effectiveness of automated tools, as a group, and also some limitations. In particular, "stored" forms of Cross Site Scripting (XSS) and SQL Injection (SQLI) vulnerabilities are not currently found by many tools. Because our goal is to assess the potential of future research, not to evaluate specific vendors, we do not report comparative data or make any recommendations about purchase of specific tools.


title: FA-Stack: A Fast Array-Based Stack with Wait-Free Progress Guarantee

abstract: The prevalence of multicore processors necessitates the design of efficient concurrent data structures. Shared concurrent stacks are widely used as inter-thread communication structures in parallel applications. Wait-free stacks can ensure that each thread completes operations on them in a finite number of steps. This characteristic is valuable for parallel applications and operating systems, especially in real-time environments. Unfortunately, because wait-free algorithms are typically hard to design and considered inefficient, practical wait-free stacks are rare. In this paper, we present a practical, fast array-based concurrent stack with wait-free progress guarantee, named FA-Stack. A series of optimizations are proposed to bound the number of steps required to complete every push and pop operation. In addition, FA-Stack adopts a time-stamped scheme to reclaim memory. We use linearizability , a correctness condition for concurrent data structures, to prove that FA-Stack is a wait-free linearizable stack with respect to the Last in First Out (LIFO) semantics. Our evaluation with representative benchmarks shows that FA-Stack is an efficient wait-free stack. For example, compared to Sim-Stack (a state-of-the-art wait-free stack), FA-Stack improves the throughput of halfhalf benchmark by up to 2.4 $\times$ .


title: Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution

abstract: Current machine learning systems operate, almost exclusively, in a statistical, or model-free mode, which entails severe theoretical limits on their power and performance. Such systems cannot reason about interventions and retrospection and, therefore, cannot serve as the basis for strong AI. To achieve human level intelligence, learning machines need the guidance of a model of reality, similar to the ones used in causal inference tasks. To demonstrate the essential role of such models, I will present a summary of seven tasks which are beyond reach of current machine learning systems and which have been accomplished using the tools of causal modeling.


title: A strain-sensing based scheme for indoor localization: Analysis, algorithm, and demonstration

abstract: Abstract In this work, a strain-based positioning problem is analyzed for indoor localization applications. This inverse mapping problem for estimating force-applied location by a few known strain data is formulated and analyzed for a square plate structure. Finite element (FE) simulations are performed to obtain the correlation between the force-applied location and the output index, which is a combination of measurable strain-gauge signals. Due to nonlinear coupling between major axes, the effective zone, which guarantees localization with sufficient accuracy is poor and must be enhanced. A novel iterative scheme is then proposed and implemented, aiming to improve the resolution and the effective zone. By such an effort, the spatial resolution is improved, and the effective zone is enhanced based on the simulation results. Meanwhile, a prototype of localization system is also constructed as the first step toward experimental demonstrations. The test results agree with the theoretical prediction qualitatively and the effective zone indeed can be further improved from 39% to 57% by the iterative algorithm based on a ±5% spatial uncertainty. The lessons and conclusion learned from this work serve as the basis for the 2D smart-floor tile designs currently underway for smart building applications.


title: Global changes in diets and the consequences for land requirements for food

abstract: Provision of food is a prerequisite for the functioning of human society. Cropland where food and feed are grown is the central, limiting resource for food production. The amount of cropland needed depends on population numbers, average food consumption patterns, and output per unit of land. Around the globe, these factors show large differences. We use data from the Food and Agriculture Organization to consistently assess subcontinental dynamics of how much land was needed to supply the prevailing diets during a span of 46 y, from 1961 to 2007. We find that, in most regions, diets became richer while the land needed to feed one person decreased. A decomposition approach is used to quantify the contributions of the main drivers of cropland requirements for food: changes in population, agricultural technology, and diet. We compare the impact of these drivers for different subcontinents and find that potential land savings through yield increases were offset by a combination of population growth and dietary change. The dynamics of the three factors were the largest in developing regions and emerging economies. The results indicate an inverse relationship between the two main drivers behind increased land requirements for food: with socioeconomic development, population growth decreases and, at the same time, diets become richer. In many regions, dietary change may override population growth as major driver behind land requirements for food in the near future.


title: Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN)

abstract: Simultaneous modeling of the spatio-temporal variation patterns of brain functional network from 4D fMRI data has been an important yet challenging problem for the field of cognitive neuroscience and medical image analysis. Inspired by the recent success in applying deep learning for functional brain decoding and encoding, in this work we propose a spatio-temporal convolutional neural network (ST-CNN)to jointly learn the spatial and temporal patterns of targeted network from the training data and perform automatic, pin-pointing functional network identification. The proposed ST-CNN is evaluated by the task of identifying the Default Mode Network (DMN) from fMRI data. Results show that while the framework is only trained on one fMRI dataset,it has the sufficient generalizability to identify the DMN from different populations of data as well as different cognitive tasks. Further investigation into the results show that the superior performance of ST-CNN is driven by the jointly-learning scheme, which capture the intrinsic relationship between the spatial and temporal characteristic of DMN and ensures the accurate identification.


title: A 10-bit 205-MS/s 1.0- $\hbox{mm}^{2}$ 90-nm CMOS Pipeline ADC for Flat Panel Display Applications

abstract: This paper describes a 10-bit 205-MS/s pipeline analog-to-digital converter (ADC) for flat panel display applications with the techniques to alleviate the design limitations in the deep-submicron CMOS process. The switched source follower combined with a resistor-switch ladder eliminates the sampling switches and achieves high linearity for a large single-ended input signal. Multistage amplifiers adopting the complementary common-source topology increase the output swing range with lower transconductance variation and reduce the power consumption. The supply voltage for the analog blocks is provided by the low drop-out regulator for a high power-supply rejection ratio (PSRR) under the noisy operation environment. The pipeline stages of the ADC are optimized in the aspect of power consumption through the iterated calculation of the sampling capacitance and transconductance. The ADC occupies an active area of 1.0 mm2 in a 90-nm CMOS process and achieves a 53-dB PSRR for a 100-MHz noise tone with the regulator and a 55.2-dB signal-to-noise-and-distortion ratio for a 30-MHz 1.0-VPP single-ended input at 205 MS/s. The ADC core dissipates 40 mW from a 1.0-V nonregulated supply voltage.


title: Baicalein improves behavioral dysfunction induced by Alzheimer’s disease in rats

abstract: BACKGROUND ::: Alzheimer's disease (AD) is considered to be a neurodegenerative disorder that is characterized by increased oxidative stress. Medicinal plants, with their antioxidant properties, have been used to cure several human diseases. The aim of the current study was to explore the protective and therapeutic effect of baicalein on AD-induced rats. ::: ::: ::: MATERIALS AND METHODS ::: Swiss Wistar rats were used in the study. The rats were divided into five groups. Group I: normal control group treated with water; Group II: disease control treated with AlCl3 to induce the mimicking AD for 4 successive weeks (SW); Group III: normal control group treated with baicalein (5 mg/kg) for 2 SW followed by combination of baicalein and AlCl3 for 4 SW; Group IV: normal control group treated with baicalein (10 mg/kg) for 2 SW followed by combination of baicalein and AlCl3 for 4 SW; Group V: normal control group treated with rivastigmine (0.3 mg/kg) for 2 SW followed by combination of rivastigmine and AlCl3 for 4 SW. Moreover, the therapeutic groups are as follows: Group VI: AD disease control treated with AlCl3 for 4 SW and serving as the therapeutic positive group; Group VII: AD disease control + baicalein (5 mg/kg) for 12 SW; Group VIII: AD disease control + baicalein (10 mg/kg) for 12 SW; Group IX: AD disease control + rivastigmine (0.3 mg/kg) for 12 SW. Behavioral test, T-maze, and rotarod test were also performed before and after the treatment. At the end of the experimental study, all the rats were sacrificed and their brains were removed and divided into two portions. The first portion was homogenated for estimating the level of acetylcholinesterase (AchE) and acetylcholine (Ach). Another portion was used for histopathological evaluation. ::: ::: ::: RESULTS ::: The current investigation showed that baicalein significantly reduced the duration of revolving on the rotarod, cage activity, and T-maze activity in a dose-dependent manner compared with the AD control group rats. It also altered the AchE and Ach levels in the brain homogenates. The histopathology study also provides strength to the protective effect of baicalein. ::: ::: ::: CONCLUSION ::: The current study showed that baicalein significantly (P<0.05) improved the biochemical and histopathological condition of AD in rats.


title: From inflammation to sickness and depression: when the immune system subjugates the brain

abstract: In response to a peripheral infection, innate immune cells produce pro-inflammatory cytokines that act on the brain to cause sickness behaviour. When activation of the peripheral immune system continues unabated, such as during systemic infections, cancer or autoimmune diseases, the ensuing immune signalling to the brain can lead to an exacerbation of sickness and the development of symptoms of depression in vulnerable individuals. These phenomena might account for the increased prevalence of clinical depression in physically ill people. Inflammation is therefore an important biological event that might increase the risk of major depressive episodes, much like the more traditional psychosocial factors.


title: Musketeer: all for one, one for all in data processing systems

abstract: Many systems for the parallel processing of big data are available today. Yet, few users can tell by intuition which system, or combination of systems, is "best" for a given workflow. Porting workflows between systems is tedious. Hence, users become "locked in", despite faster or more efficient systems being available. This is a direct consequence of the tight coupling between user-facing front-ends that express workflows (e.g., Hive, SparkSQL, Lindi, GraphLINQ) and the back-end execution engines that run them (e.g., MapReduce, Spark, PowerGraph, Naiad). We argue that the ways that workflows are defined should be decoupled from the manner in which they are executed. To explore this idea, we have built Musketeer, a workflow manager which can dynamically map front-end workflow descriptions to a broad range of back-end execution engines. Our prototype maps workflows expressed in four high-level query languages to seven different popular data processing systems. Musketeer speeds up realistic workflows by up to 9x by targeting different execution engines, without requiring any manual effort. Its automatically generated back-end code comes within 5%--30% of the performance of hand-optimized implementations.


title: Automatic Generation of String Signatures for Malware Detection

abstract: Scanning files for signatures is a proven technology, but exponential growth in unique malware programs has caused an explosion in signature database sizes. One solution to this problem is to use string signatures , each of which is a contiguous byte sequence that potentially can match many variants of a malware family. However, it is not clear how to automatically generate these string signatures with a sufficiently low false positive rate. Hancock is the first string signature generation system that takes on this challenge on a large scale. ::: ::: To minimize the false positive rate, Hancock features a scalable model that estimates the occurrence probability of arbitrary byte sequences in goodware programs, a set of library code identification techniques, and diversity-based heuristics that ensure the contexts in which a signature is embedded in containing malware files are similar to one another. With these techniques combined, Hancock is able to automatically generate string signatures with a false positive rate below 0.1%.


title: Number of people required for usability evaluation: the 10±2 rule

abstract: Introduction Usability evaluation is essential to make sure that software products newly released are easy to use, efficient, and effective to reach goals, and satisfactory to users. For example, when a software company wants to develop and sell a new product, the company needs to evaluate usability of the new product before launching it at a market to avoid the possibility that the new product may contain usability problems, which span from cosmetic problems to severe functional problems. Three widely used methods for usability evaluation are Think Aloud (TA), Heuristic Evaluation (HE) and Cognitive Walkthrough (CW). TA method is commonly employed with a lab-based user testing, while there are variants of TA methods, including thinking out aloud at user's workplace instead of at labs. What we discuss here is the TA method that is combined with a lab-based user testing, in which test users use products while simultaneously and continuously thinking out aloud, and experimenters record users' behaviors and verbal protocols in the laboratory. HE is a usability inspection method, in which a small number of evaluators find usability problems in a user interface design by examining an interface and judging its compliance with well-known usability principles, called heuristics. CW is a theory-based method, in which evaluators evaluate every step necessary to perform a scenario-based task, and look for usability problems that would interfere with learning by exploration. These three methods have their own advantages and disadvantages. For instance, TA method provides good qualitative data from a small number of test users, but laboratory environment may influence test user's behaviors. HE is a cheap, fast and easy-to-use method, while it often finds too specific and low-priority usability problems, including even not real problems. CW helps find mismatches between users' and designers' conceptualization of a task, but it needs extensive knowledge of cognitive psychology and technical details to apply. However, even though these advantages and disadvantages show overall characteristics of three major usability evaluation methods, we cannot compare them quantitatively and see their efficiency clearly. Because one of reasons why so-called discounted methods, such as HE and CW, were developed is to save costs of usability evaluation, cost-related criteria for comparing usability evaluation are meaningful to usability practitioners as well as usability researchers. One of the most disputable issues related to cost of usability evaluation is sample size. That is, how many users or evaluators are needed to achieve a targeted usability evaluation performance, for example, 80% of overall discovery rate? The sample size of usability evaluation is known to depend on an estimate of problem discovery rate across participants. The overall discovery rate is a common quantitative measure that is used to show the effectiveness of a specific usability evaluation method in most of usability evaluation studies. It is also called overall detection rate or thoroughness measure, which is the ratio of 'the sum of unique usability problems detected by all experiment participants' against 'the number of usability problems that exist in the evaluated systems', ranging between 0 and 1. The overall discovery rates were reported more than any other criterion measure in the usability evaluation experiments and also a key component for projecting required sample size for usability evaluation study. Thus, how many test users or evaluators participate in the usability evaluation is a critical issue, considering its cost-effectiveness.


title: Ensemble based Distributed K-Modes Clustering

abstract: Clustering has been recognized as the unsupervised classification of data items into groups. Due to the explosion in the number of autonomous data sources, there is an emergent need for effective approaches in distributed clustering. The distributed clustering algorithm is used to cluster the distributed datasets without gathering all the data in a single site. The K-Means is a popular clustering method owing to its simplicity and speed in clustering large datasets. But it fails to handle directly the datasets with categorical attributes which are generally occurred in real life datasets. Huang proposed the K-Modes clustering algorithm by introducing a new dissimilarity measure to cluster categorical data. This algorithm replaces means of clusters with a frequency based method which updates modes in the clustering process to minimize the cost function. Most of the distributed clustering algorithms found in the literature seek to cluster numerical data. In this paper, a novel Ensemble based Distributed K-Modes clustering algorithm is proposed, which is well suited to handle categorical data sets as well as to perform distributed clustering process in an asynchronous manner. The performance of the proposed algorithm is compared with the existing distributed K-Means clustering algorithms, and K-Modes based Centralized Clustering algorithm. The experiments are carried out for various datasets of UCI machine learning data repository.


title: An extensor mechanism for an anatomical robotic hand

abstract: The human finger possesses a structure called the extensor mechanism, a web-like collection of tendinuous material that lies on the dorsal side of each finger and connects the controlling muscles to the bones of the finger. In past robotic hand designs, extensor mechanisms have generally not been employed due in part to their complexity and a lack of understanding of their utility. This paper presents our first design and analysis effort of an artificial extensor mechanism. The goal of our analysis is to provide an understanding of the extensor mechanism's functionality so that we can extract the crucial features that need to be mimicked to construct an anatomical robotic hand. With the inclusion of an extensor mechanism, we believe that all possible human finger postures can be achieved using four cable driven actuators, we identified that this extensor mechanism gives independent control of the metacarpo-phalangeal (MCP) joint and acts not only as an extensor but also as a flexor, abductor, adductor, or rotator depending on the finger's posture.


title: Dual Diagnosis in Substance Abuse

abstract: Addresses often overlooked ideas such as, just because a patient only has seizures while on drugs does not mean he/she does not have epilepsy. Considers primarily however, second, often underlying, conditions accompanying substance abuse that are of a psychiatric nature. The chapters discuss genetic


title: Accelerating Fluid Registration Algorithm on Multi-FPGA Platforms

abstract: In the clinical applications, medical image registrations on the images taken from different times and/or through different modalities are needed in order to have an objective clinical assessment of the patient. Viscous fluid registration is a powerful PDE-based method that can register large deformations in the imaging process. This paper presents our implementation of the fluid registration algorithm on a multi-FPGA platform Convey HC-1. We obtain a 35X speedup versus single-threaded software on a CPU. The implementation is achieved using a high-level synthesis (HLS) tool, with additional source-code level optimizations including fixed-point conversion, tiling, prefetching, data-reuse, and streaming across modules using a ghost zone (time-tiling) approach. The experience of this case study also identifies further automation steps needed by existing HLS software.


title: SERVICE QUALITY, CORPORATE IMAGE AND CUSTOMER'S SATISFACTION TOWARDS CUSTOMERS PERCEPTION: AN EXPLORATORY STUDY ON TELECOM CUSTOMERS IN BANGLADESH

abstract: The purpose of this paper is to find out the determinants that are significantly influencing telecom customer’s perception in Bangladesh. The study surveyed 450 telecom customers in Bangladesh from Dhaka city to determine the key influential factors that significantly influence on their perception. The data analyses were conducted by exploratory factor analysis, confirmatory factor analysis and structural equation modeling to test the hypothesis. The results of the statistical analysis reflected that most of the telecom customers are highly concerned about service quality followed by corporate image. Few studies have explored over the years under this market regarding this research issue. Moreover, this is a relatively new issue that remains largely undiscovered by researchers under underdeveloped country’s telecom operator’s perspective. It is hoped that the findings may assist the mobile phone operators in production of their services and


title: Autonomous navigation for BigDog

abstract: BigDog is a four legged robot with exceptional rough-terrain mobility. In this paper, we equip BigDog with a laser scanner, stereo vision system, and perception and navigation algorithms. Using these sensors and algorithms, BigDog performs autonomous navigation to goal positions in unstructured forest environments. The robot perceives obstacles, such as trees, boulders, and ground features, and steers to avoid them on its way to the goal. We describe the hardware and software implementation of the navigation system and summarize performance. During field tests in unstructured wooded terrain, BigDog reached its goal position 23 of 26 runs and traveled over 130 meters at a time without operator involvement.