title: Industrial Internet: A Survey on the Enabling Technologies, Applications, and Challenges

abstract: This paper provides an overview of the Industrial Internet with the emphasis on the architecture, enabling technologies, applications, and existing challenges. The Industrial Internet is enabled by recent rising sensing, communication, cloud computing, and big data analytic technologies, and has been receiving much attention in the industrial section due to its potential for smarter and more efficient industrial productions. With the merge of intelligent devices, intelligent systems, and intelligent decisioning with the latest information technologies, the Industrial Internet will enhance the productivity, reduce cost and wastes through the entire industrial economy. This paper starts by investigating the brief history of the Industrial Internet. We then present the 5C architecture that is widely adopted to characterize the Industrial Internet systems. Then, we investigate the enabling technologies of each layer that cover from industrial networking, industrial intelligent sensing, cloud computing, big data, smart control, and security management. This provides the foundations for those who are interested in understanding the essence and key enablers of the Industrial Internet. Moreover, we discuss the application domains that are gradually transformed by the Industrial Internet technologies, including energy, health care, manufacturing, public section, and transportation. Finally, we present the current technological challenges in developing Industrial Internet systems to illustrate open research questions that need to be addressed to fully realize the potential of future Industrial Internet systems.


title: Encoding natural movement as an agent-based system : an investigation into human pedestrian behaviour in the built environment

abstract: Gibson's ecological theory of perception has received considerable attention within psychology literature, as well as in computer vision and robotics. However, few have applied Gibson's approach to agent-based models of human movement, because the ecological theory requires that individuals have a vision-based mental model of the world, and for large numbers of agents this becomes extremely expensive computationally. Thus, within current pedestrian models, path evaluation is based on calibration from observed data or on sophisticated but deterministic route-choice mechanisms; there is little open-ended behavioural modelling of human-movement patterns. One solution which allows individuals rapid concurrent access to the visual information within an environment is an 'exosomatic visual architecture', where the connections between mutually visible locations within a configuration are prestored in a lookup table. Here we demonstrate that, with the aid of an exosomatic visual architecture, it is possible to develop behavioural models in which movement rules originating from Gibson's principle of affordance are utilised. We apply large numbers of agents programmed with these rules to a built-environment example and show that, by varying parameters such as destination selection, field of view, and steps taken between decision points, it is possible to generate aggregate movement levels very similar to those found in an actual building context.


title: Elastic network functions: opportunities and challenges

abstract: Network function virtualization (NFV) and software defined networking (SDN) are key technology enablers for cost reductions and new business models in networking. The possibility to automatically and dynamically scale network services at run time is one of the main claims of NFV. Elastic NFV could be similar to what elastic cloud services provide for compute, with pay-per-use cost models for customers. However, control of resources for elastic services is far from trivial. We show how current NFV and SDN architectures could support elastic resource services for network functions (NFs). We reveal that the current NFV architecture does not allow recursive resource orchestration, therefore preventing resource scaling requests from being handled by a resource orchestrator overseeing the entire domain where an NF is executed. We introduce a logical centralization of joint compute and network resource orchestration as a UNIFY framework, which enables direct control of elastic resources for the NFs. We show opportunities and challenges associated with such an architecture.


title: Accurate triangulations of deformed, intersecting surfaces

abstract: A quadtree algorithm is developed to triangulate deformed, intersecting parametric surfaces. The biggest problem with adaptive sampling is to guarantee that the triangulation is accurate within a given tolerance. A new method guarantees the accuracy of the triangulation, given a "Lipschitz" condition on the surface definition. The method constructs a hierarchical set of bounding volumes for the surface, useful for ray tracing and solid modeling operations. The task of adaptively sampling a surface is broken into two parts: a subdivision mechanism for recursively subdividing a surface, and a set of subdivision criteria for controlling the subdivision process.An adaptive sampling technique is said to be robust if it accurately represents the surface being sampled. A new type of quadtree, called a restricted quadtree, is more robust than the traditional unrestricted quadtree at adaptive sampling of parametric surfaces. Each sub-region in the quadtree is half the width of the previous region. The restricted quadtree requires that adjacent regions be the same width within a factor of two, while the traditional quadtree makes no restriction on neighbor width. Restricted surface quadtrees are effective at recursively sampling a parametric surface. Quadtree samples are concentrated in regions of high curvature, and along intersection boundaries, using several subdivision criteria. Silhouette subdivision improves the accuracy of the silhouette boundary when a viewing transformation is available at sampling time. The adaptive sampling method is more robust than uniform sampling, and can be more efficient at rendering deformed, intersecting parametric surfaces.


title: BenchNN: On the broad potential application scope of hardware neural network accelerators

abstract: Recent technology trends have indicated that, although device sizes will continue to scale as they have in the past, supply voltage scaling has ended. As a result, future chips can no longer rely on simply increasing the operational core count to improve performance without surpassing a reasonable power budget. Alternatively, allocating die area towards accelerators targeting an application, or an application domain, appears quite promising, and this paper makes an argument for a neural network hardware accelerator. After being hyped in the 1990s, then fading away for almost two decades, there is a surge of interest in hardware neural networks because of their energy and fault-tolerance properties. At the same time, the emergence of high-performance applications like Recognition, Mining, and Synthesis (RMS) suggest that the potential application scope of a hardware neural network accelerator would be broad. In this paper, we want to highlight that a hardware neural network accelerator is indeed compatible with many of the emerging high-performance workloads, currently accepted as benchmarks for high-performance micro-architectures. For that purpose, we develop and evaluate software neural network implementations of 5 (out of 12) RMS applications from the PARSEC Benchmark Suite. Our results show that neural network implementations can achieve competitive results, with respect to application-specific quality metrics, on these 5 RMS applications.


title: The Influence of Religious Participation on Sexual Activity in a Randomized Effectiveness Trial for Minority Youth

abstract: ABSTRACT This study examined the influence of religious participation on the outcomes of a comprehensive sex education program for minority youth in Miami, FL. Data collection occurred at pretest and at 3-month and 6-month follow-up. A sample of teenagers was randomly selected from high schools, with a large majority of minority youth, and was assigned into treatment (n = 549) and control (n = 424) groups. Results indicated religious participation had some influence on predicting teen sexual activity at pretest but did not contribute to teen responses at 3- or 6-month follow-up. Gender and use of alcohol also significantly predicted responses from teens at pretest and at 3-month and 6-month follow-up. Future research is needed comparing the influence of religious participation on sexual activity with youth in sex education programs from high schools in multiple regions of the country. Discussion of the findings, as well as implications for social service policy and practice, conclude the article.


title: On smartphone camera based fingerphoto authentication

abstract: Authenticating fingerphoto images captured using a smartphone camera, provide a good alternate solution in place of traditional pin or pattern based approaches. There are multiple challenges associated with fingerphoto authentication such as background variations, environmental illumination, estimating finger position, and camera resolution. In this research, we propose a novel ScatNet feature based fingerphoto matching approach. Effective fingerphoto segmentation and enhancement are performed to aid the matching process and to attenuate the effect of capture variations. Further, we propose and create a publicly available smartphone fingerphoto database having three different subsets addressing the challenges of environmental illumination and background, along with their corresponding live scan fingerprints. Experimental results show improved performance across multiple challenges present in the database.


title: DCAN: Deep contour‐aware networks for object instance segmentation from histology images

abstract: In histopathological image analysis, the morphology of histological structures, such as glands and nuclei, has been routinely adopted by pathologists to assess the malignancy degree of adenocarcinomas. Accurate detection and segmentation of these objects of interest from histology images is an essential prerequisite to obtain reliable morphological statistics for quantitative diagnosis. While manual annotation is error-prone, time-consuming and operator-dependant, automated detection and segmentation of objects of interest from histology images can be very challenging due to the large appearance variation, existence of strong mimics, and serious degeneration of histological structures. In order to meet these challenges, we propose a novel deep contour-aware network (DCAN) under a unified multi-task learning framework for more accurate detection and segmentation. In the proposed network, multi-level contextual features are explored based on an end-to-end fully convolutional network (FCN) to deal with the large appearance variation. We further propose to employ an auxiliary supervision mechanism to overcome the problem of vanishing gradients when training such a deep network. More importantly, our network can not only output accurate probability maps of histological objects, but also depict clear contours simultaneously for separating clustered object instances, which further boosts the segmentation performance. Our method ranked the first in two histological object segmentation challenges, including 2015 MICCAI Gland Segmentation Challenge and 2015 MICCAI Nuclei Segmentation Challenge. Extensive experiments on these two challenging datasets demonstrate the superior performance of our method, surpassing all the other methods by a significant margin.


title: Vermeer: direct interaction with a 360° viewable 3D display

abstract: We present Vermeer, a novel interactive 360° viewable 3D display. Like prior systems in this area, Vermeer provides viewpoint-corrected, stereoscopic 3D graphics to simultaneous users, 360° around the display, without the need for eyewear or other user instrumentation. Our goal is to over-come an issue inherent in these prior systems which - typically due to moving parts - restrict interactions to outside the display volume. Our system leverages a known optical illusion to demonstrate, for the first time, how users can reach into and directly touch 3D objects inside the display volume. Vermeer is intended to be a new enabling technology for interaction, and we therefore describe our hardware implementation in full, focusing on the challenges of combining this optical configuration with an existing approach for creating a 360° viewable 3D display. Initially we demonstrate direct involume interaction by sensing user input with a Kinect camera placed above the display. However, by exploiting the properties of the optical configuration, we also demonstrate novel prototypes for fully integrated input sensing alongside simultaneous display. We conclude by discussing limitations, implications for interaction, and ideas for future work.


title: Role-Based Access Control Models

abstract: Security administration of large systems is complex, but it can be simplified by a role-based access control approach. This article explains why RBAC is receiving renewed attention as a method of security administration and review, describes a framework of four reference models developed to better understand RBAC and categorizes different implementations, and discusses the use of RBAC to manage itself.


title: PSO based localization of multiple mobile robots emplying LEGO EV3

abstract: The problem of robotic path planning is one of the most noteworthy task in mobile robots. Control the path of robot to reach at the exact target without effecting by environmental conditions is the most difficult task to obtain. Multi-Robotic path planning is a challenging computational problem seduce in robotics because of its complexity and high cost by the number of robots. Path planning of multi robots using particle swarm optimization algorithm somehow reduces complexity. This work introduces a new algorithm of path planning using PSO techniques and displays a model of path planning of robots in a square and obstacle free environment. These robots are assemble with two LEGO MINDSTORMS EV3 kits and are customized with its own software. The Bluetooth protocol is used to establish communication between two robots. Each robot is equipped with a GYRO and ULTRASONIC sensors for moving and detecting each other. This research also shows its successfully achieved hardware results using LEGO Mindstorms EV3 kits.


title: Design and development of an open antropomorphic robotic hand development system

abstract: The present work shows the design and development of a dexterous anthropomorphic robotic hand that meets the requirements of size and similar movement range of a human hand. The main purpose of this project is to support students and researchers to improve their development times. This work gives a close description of how the mechanical system was conceived, how the problems were solved and the processing capacity of the electronic system and its general characteristics.


title: Foreign Investment in a Least Developed Country: The Nepalese Experience

abstract: This paper aims to contribute to the literature on the developmental role of foreign direct investment through an examination of the Nepalese experience during the period 1988- 2001. Despite significant liberalisation of the foreign investment regime and the introduction of attractive investment incentives, Nepal’s achievements during this period, both in terms of the volume of FDI and its developmental impact, failed to mach the national expectations. Nepal obviously has intrinsic disadvantages arising from its geography and other typographical characteristics in attracting FDI. However, comparative international experience suggests that her lackluster achievements as a host to foreign investors cannot be explained in terms of these factors alone. Policies that underpin the overall investment climate also seem to matter. Mere liberalisation of the investment regime and introducing financial incentives are not a substitute for an all-encompassing effort to improve the investment climate.


title: Lisbon Emoji and Emoticon Database (LEED): Norms for emoji and emoticons in seven evaluative dimensions

abstract: The use of emoticons and emoji is increasingly popular across a variety of new platforms of online communication. They have also become popular as stimulus materials in scientific research. However, the assumption that emoji/emoticon users’ interpretations always correspond to the developers’/researchers’ intended meanings might be misleading. This article presents subjective norms of emoji and emoticons provided by everyday users. The Lisbon Emoji and Emoticon Database (LEED) comprises 238 stimuli: 85 emoticons and 153 emoji (collected from iOS, Android, Facebook, and Emojipedia). The sample included 505 Portuguese participants recruited online. Each participant evaluated a random subset of 20 stimuli for seven dimensions: aesthetic appeal, familiarity, visual complexity, concreteness, valence, arousal, and meaningfulness. Participants were additionally asked to attribute a meaning to each stimulus. The norms obtained include quantitative descriptive results (means, standard deviations, and confidence intervals) and a meaning analysis for each stimulus. We also examined the correlations between the dimensions and tested for differences between emoticons and emoji, as well as between the two major operating systems—Android and iOS. The LEED constitutes a readily available normative database (available at www.osf.io/nua4x) with potential applications to different research domains.


title: A survey and analysis of ontology-based software tools for semantic interoperability in IoT and WoT landscapes

abstract: The current Internet of Things (IoT) ecosystem consists of non-interoperable products and services. The Web of Things (WoT) advances the IoT by allowing consumers to interact with the IoT ecosystem through the open and standard web technologies. But the Web alone does not solve the interoperability issues. It is widely acknowledged that Semantic Web Technologies hold the potential of achieving data and platform interoperability in both the IoT and WoT landscapes. In this context, the paper attempts to review and analyze the current state of ontology-based software tools for semantic interoperability.


title: A Multilevel NER Framework for Automatic Clinical Name Entity Recognition

abstract: In this paper, we propose a novel multilevel NER framework, for addressing the challenges of clinical name entity recognition, based on different machine learning and text mining algorithms. The proposed framework, with multiple levels, allows models for increasingly complex NER tasks to be built. The experimental evaluation on two different publicly available datasets, corresponding to different application contexts - the CLEF 2016 challenge shared task 1A for nursing handover context, and the BIONLP/NLPBPA 2004 challenge shared task on GENIA corpus for recognizing entities in microbiology, has validated the proposed framework.


title: Real-time Optimization and Adaptation of the Crosswind Flight of Tethered Wings for Airborne Wind Energy

abstract: Airborne wind energy systems aim to generate renewable energy by means of the aerodynamic lift produced using a wing tethered to the ground and controlled to fly crosswind paths. The problem of maximizing the average power developed by the generator, in the presence of limited information on wind speed and direction, is considered. At constant tether speed operation, the power is related to the traction force generated by the wing. First, a study of the traction force is presented for a general path parametrization. In particular, the sensitivity of the traction force on the path parameters is analyzed. Then, the results of this analysis are exploited to design an algorithm to maximize the force, hence the power, in real-time. The algorithm uses only the measured traction force on the tether and the wing’s position, and it is able to adapt the system’s operation to maximize the average force with uncertain and time-varying wind. The influence of inaccurate sensor readings and turbulent wind are also discussed. The presented algorithm is not dependent on a specific hardware setup and can act as an extension of existing control structures. Both numerical simulations and experimental results are presented to highlight the effectiveness of the approach.


title: Axial-flux-machine with an ironless fractional slot winding

abstract: In this paper, a novel 42 slot, 20 pole ironless winding for use in an axial-flux machine is presented. The proposed design improves the utilization of the available air gap area and is based on the implementation of the winding as a printed circuit board (PCB). Besides, it is compared with a conventional 21 slot, 20 pole ironless axial-flux winding regarding the criteria: torque generation and induced voltage at constant speed. The comparison is based on analytical calculated results, which were obtained by using the winding function theory and an exponential function approach for the magnetic flux density produced by the permanent magnets. The analytical description of the MMF is derived from the geometry whereas the torque and the induced EMF are calculated by using complex Fourier series. Furthermore, a special case of Parseval's theorem is used to get the torque and voltage from the Fourier coefficients in a very simple way. The analytical calculated results are compared to those obtained by FEM.


title: A Bluetooth signal strength based indoor localization method

abstract: Indoor location awareness is intended for many applications like patient monitoring and store navigation. Short range wireless protocols are usually preferred when other ways of location sensing methods such as Global Positioning Systems or Inertial Measurement Units may not be feasible to use indoors. Devices using Bluetooth protocol have the advantage of being low cost and also being widely available. In this paper an affordable indoor localization system is proposed where location evaluation is performed by measuring unknown positioned Bluetooth transmitters' signal levels at some known base stations. A localization method is proposed which uses calculated values of a defined error function to estimate the positions of unknown transmitters. The error function is based on a modified Root-Mean-Square-Error (RMSE) metric. The error function is calculated using Received Signal Strength Indicator (RSSI) measurements at each point under consideration. The error function is calculated at discrete points in the area of interest and the points with low calculated error values indicates locations where transmitters may be located with a high probability. The validity of the decision method is verified with a real-world experiment.


Substitution and Refutation Revisited


title: Natural language querying in SAP-ERP platform

abstract: With the omnipresence of mobile devices coupled with recent advances in automatic speech recognition capabilities, there has been a growing demand for natural language query (NLQ) interface to retrieve information from the knowledge bases. Business users particularly find this useful as NLQ interface enables them to ask questions without the knowledge of the query language or the data schema. In this paper, we apply an existing research technology called ``ATHENA: An Ontology-Driven System for Natural Language Querying over Relational Data Stores'' in the industry domain of SAP-ERP systems. The goal is to enable users to query SAP-ERP data using natural language. We present the challenges and their solutions of such a technology transfer. We present the effectiveness of the natural language query interface on a set of questions given by a set of SAP practitioners.


title: Potentials and Challenges of C-RAN Supporting Multi-RATs Toward 5G Mobile Networks

abstract: This paper presents an overview of the cloud radio access network (C-RAN), which is a key enabler for future mobile networks in order to meet the explosive capacity demand of mobile traffic, and reduce the capital and operating expenditure burden faced by operators. We start by reviewing the requirements of future mobile networks, called 5G, followed by a discussion on emerging network concepts for 5G network architecture. Then, an overview of C-RAN and related works are presented. As a significant scenario of a 5G system, the ultra dense network deployment based on C-RAN is discussed with focuses on flexible backhauling, automated network organization, and advanced mobility management. Another import feature of a 5G system is the long-term coexistence of multiple radio access technologies (multi-RATs). Therefore, we present some directions and preliminary thoughts for future C-RAN-supporting Multi-RATs, including joint resource allocation, mobility management, as well as traffic steering and service mapping.


title: Design Optimization of a Fluxgate Current Sensor With Low Interference

abstract: A new fluxgate current sensor is presented in this paper. The sensor consists of an additional magnetic toroidal core and an additional winding, aside from the elements in a traditional sensor. In addition, the exciting winding is wound along the circumference of the toroidal core to decouple the exciting magnetic field and the feedback magnetic field in order to reduce internal interference. With the additional core and the additional winding, the proposed current sensor can extend its bandwidth to medium- and high-frequency ac measurement. Another purpose of introducing the additional core is to concentrate on the exciting magnetic field and to reduce interference from external disturbances. A prototype of the proposed sensor was designed and tested. Test results show that the sensor is less affected by hysteresis effects and has a measurement error (precision) within 0.4% of the full scale, a 40-kHz small-signal bandwidth, remarkable sensitivity, and linearity in the measurement range up to 25 A.


title: Multiple-path testing for cross site scripting using genetic algorithms

abstract: Use path coverage as a test coverage criteria.Formalize the problem of test data generation as a search problem.Focus on cross site scripting, in short XSS, type of vulnerability.Propose an approach that uses genetic algorithms (GA) along with a database of XSS vulnerability patterns to generate multiple-path coverage for a script under test (SUT) against XSS vulnerability.Design genetic algorithms to allow the generation of multiple test data, in one run, to cover multiple vulnerable paths. Web applications suffer from different security vulnerabilities that could be exploited by hackers to cause harm in a variety of ways. A number of approaches have been proposed to test for such vulnerabilities. However, some gaps are still to be addressed. In this paper, we address one of such gaps: the problem of automatically generating test data (i.e., possible attacks) to test for cross site scripting (XSS) type of vulnerability. The objective is to generate a set of test data to exercise candidate security-vulnerable paths in a given script. The desirable set of test data must be effective in the sense that it uncovers whether any path can indeed be exploited to launch an attack. We designed a genetic algorithm-based test data generator that uses a database of XSS attack patterns to generate possible attacks and assess whether the attack is successful. We considered different types of XSS vulnerability: stored, reflected and DOM based. We empirically validated our test data generator using case studies of Web applications developed using PHP and MySQL. Empirical results show that our test data generator is effective in generating, in one run, multiple test data to cover multiple target paths.


title: Magiclock: Scalable Detection of Potential Deadlocks in Large-Scale Multithreaded Programs

abstract: We present Magiclock, a novel potential deadlock detection technique by analyzing execution traces (containing no deadlock occurrence) of large-scale multithreaded programs. Magiclock iteratively eliminates removable lock dependencies before potential deadlock localization. It divides lock dependencies into thread specific partitions, consolidates equivalent lock dependencies, and searches over the set of lock dependency chains without the need to examine any duplicated permutations of the same lock dependency chains. We validate Magiclock through a suite of real-world, large-scale multithreaded programs. The experimental results show that Magiclock is significantly more scalable and efficient than existing dynamic detectors in analyzing and detecting potential deadlocks in execution traces of large-scale multithreaded programs.


title: Review Typology: The Basic Types of Reviews for Synthesizing Evidence for the Purpose of Knowledge Translation.

abstract: With advances in medical practice and fields of research, reviews occupy a key position for summarizing existing knowledge. Due to the differences and overlap in terminology, the full potential for reviews may be lost due to confusion of indistinct approaches. The main objective of this study was to provide a descriptive outline of each of the common review types with their characteristics and examples in a health care system. Ascoping search was conducted using the keywords associated with the literature review typology. The SALSA(Search, Appraisal, Synthesis and Analysis) analytical framework was used to identify and distinguish each type of review. Nine common types of reviews and associated methodologies were evaluated against the already established SALSA framework. Their description, strengths and weaknesses are presented. The results provided a basic idea of different types of reviews based on the intended level of knowledge synthesis by which researchers can identify the appropriate type of review based on their intended audience.


title: A Primal-Dual Method for Training Recurrent Neural Networks Constrained by the Echo-State Property

abstract: We present an architecture of a recurrent neural network (RNN) with a fully-connected deep neural network (DNN) as its feature extractor. The RNN is equipped with both causal temporal prediction and non-causal look-ahead, via auto-regression (AR) and moving-average (MA), respectively. The focus of this paper is a primal-dual training method that formulates the learning of the RNN as a formal optimization problem with an inequality constraint that provides a sufficient condition for the stability of the network dynamics. Experimental results demonstrate the effectiveness of this new method, which achieves 18.86% phone recognition error on the TIMIT benchmark for the core test set. The result approaches the best result of 17.7%, which was obtained by using RNN with long short-term memory (LSTM). The results also show that the proposed primal-dual training method produces lower recognition errors than the popular RNN methods developed earlier based on the carefully tuned threshold parameter that heuristically prevents the gradient from exploding.


title: Meander-line polarizer for arbitrary rotation of linear polarization

abstract: A versatile meander-line polarizer is described and demonstrated for arbitrary rotation of linear polarization. This polarizer consists of eight-layer meander-line grids with rigid foam spacers between any two layers. It is shown that the field polarization rotation angle through this meander-line polarizer is twice the angle between the incident polarization and the polarizer grating lines. Hence, any polarization rotation can be accomplished by a simple mechanical rotation of this low-mass polarizer instead of rotating or modifying the much heavier antenna and its complex feed components.<<ETX>>


title: Multilingual deep neural network based acoustic modeling for rapid language adaptation

abstract: This paper presents a study on multilingual deep neural network (DNN) based acoustic modeling and its application to new languages. We investigate the effect of phone merging on multilingual DNN in context of rapid language adaptation. Moreover, the combination of multilingual DNNs with Kullback--Leibler divergence based acoustic modeling (KL-HMM) is explored. Using ten different languages from the Globalphone database, our studies reveal that crosslingual acoustic model transfer through multilingual DNNs is superior to unsupervised RBM pre-training and greedy layer-wise supervised training. We also found that KL-HMM based decoding consistently outperforms conventional hybrid decoding, especially in low-resource scenarios. Furthermore, the experiments indicate that multilingual DNN training equally benefits from simple phoneset concatenation and manually derived universal phonesets.


title: Map-based navigation in mobile robots: : II. A review of map-learning and path-planning strategies

abstract: For a robot, an animal, and even for man, to be able to use an internal representation of the spatial layout of its environment to position itself is a very complex task, which raises numerous issues of perception, categorization and motor control that must all be solved in an integrated manner to promote survival. This point is illustrated here, within the framework of a review of localization strategies in mobile robots. The allothetic and idiothetic sensors that may be used by these robots to build internal representations of their environment, and the maps in which these representations may be instantiated, are first described. Then map-based navigation systems are categorized according to a three-level hierarchy of localization strategies, which respectively call upon direct position inference, single-hypothesis tracking, and multiple-hypothesis tracking. The advantages and drawbacks of these strategies, notably with respect to the limitations of the sensors on which they rely, are discussed throughout the text.


title: Wavelet based real-time smoke detection in video

abstract: A method for smoke detection in video is proposed. It is assumed the camera monitoring the scene is stationary. Since the smoke is semi-transparent, edges of image frames start loosing their sharpness and this leads to a decrease in the high frequency content of the image. To determine the smoke in the field of view of the camera, the background of the scene is estimated and decrease of high frequency energy of the scene is monitored using the spatial wavelet transforms of the current and the background images. Edges of the scene are especially important because they produce local extrema in the wavelet domain. A decrease in values of local extrema is also an indicator of smoke. In addition, scene becomes grayish when there is smoke and this leads to a decrease in chrominance values of pixels. Periodic behavior in smoke boundaries and convexity of smoke regions are also analyzed. All of these clues are combined to reach a final decision.


title: A 5.9-GHz Fully Integrated GaN Frontend Design With Physics-Based RF Compact Model

abstract: This paper presents the design of a fully integrated high-efficiency and high-power RF frontend for the IEEE 802.11p standard in GaN HEMT technology. An embedded transmitter/receiver (Tx/Rx) switching scheme and a dual-bias power amplifier linearization technique are used to improve Tx efficiency and linearity. An accurate physics-based nonlinear large-signal device model is developed and used for the design, providing insight into the impact of the behavioral nuances of the GaN HEMTs on RF circuit performance. The fully integrated RF frontend is fabricated in 0.25- $\mu{\hbox{m}}$ GaN-on-SiC technology and occupies only 2 mm $\times$ 1.2 mm. The Tx branch achieves 48.5% drain efficiency at 33.9 dBm, $P{\rm sat}$ with 28-V supply. With orthogonal frequency-division multiplexing modulated signals, it achieves 30% average efficiency at 27.8-dBm output power while meeting the $-$ 25-dB error vector magnitude limit without predistortion. The Rx branch achieves $+$ 22-dBm output third-order intercept point with 3.7-dB noise figure at 12-V supply. The fully integrated high-efficiency and linear RF frontend designed with physics-based RF GaN compact models is demonstrated for the first time for future device-to-device applications.


title: Points-to analysis for JavaScript

abstract: JavaScript is widely used by web developers and the complexity of JavaScript programs has increased over the last year. Therefore, the need for program analysis for JavaScript is evident. Points-to analysis for JavaScript is to determine the set of objects to which a reference variable or an object property may point. Points-to analysis for JavaScript is a basis for further program analyses for JavaScript. It has a wide range of applications in code optimization and software engineering tools. However, points-to analysis for JavaScript has not yet been developed. JavaScript has dynamic features such as the runtime modification of objects through addition of properties or updating of methods. We propose a points-to analysis for JavaScript which precisely handles the dynamic features of JavaScript. Our work is the first attempt to analyze the points-to behavior of JavaScript. We evaluate the analysis on a set of JavaScript programs. We also apply the analysis to a code optimization technique to show that the analysis can be practically useful.


title: A Printed Dipole Antenna for Ultra High Frequency (UHF) Radio Frequency Identification (RFID) Handheld Reader

abstract: We propose a new printed dipole antenna for ultra high frequency (UHF) radio frequency identification (RFID) systems in North America. The antenna consists of a microstrip-to-coplanar stripline transition, a meandered driven dipole, a closely-coupled parasitic element, and a folded finite-size ground plane. The antenna geometry, design concept, simulated and measured results are carefully discussed throughout the paper. The experimental results demonstrate that the proposed antenna features a compact size, wide impedance bandwidth, moderate gain, and excellent front-to-back ratio. This antenna is well suitable for the applications in RFID handheld readers.


title: Electropolymerization of Pyrrole and N-Methylpyrrole on Mild Steel Surface

abstract: Electrochemical polymerization of various ratios of pyrrole and N-methylpyrrole monomers were performed in aqueous toluene-4-sulphinic acid sodium salt (T4SNa) electrolyte, using galvanostatic method. The pH of electrolyte was adjusted by p-toluene sulphonic acid (PTSA). In order to prevent corrosion of mild steel substrates during coating deposition, all samples were pretreated in 0.5 M oxalic acid solution, employing galvanostatic method. This would passivate the steel substrate and facilitate the coating process as well. Corrosion resistance of coated substrates was investigated in 1 M NaCl solution using Tafel polarization technique. In addition, by using scanning electron microscopy (SEM), morphological characterization of coatings produced, was investigated. The ratio of 1 to 1 (pyrrole/N-methylpyrrole) could play an important role on corrosion properties of the resulting coated mild steel. Ali Ashrafi, Mohammad Ali Golozar, and Shadpour Mallakpour


Distributed Packet Inspection By Using PIL-2 Language


title: PAW: a hybrid wheeled-leg robot

abstract: This paper discusses current wheeled mobility work on a hybrid wheeled-leg robot called PAW. In addition to providing design details, controllers are proposed for inclined turning and sprawled braking which take advantage of the hybrid nature of the platform and improve stability. Power consumption values for a number of its basic behaviours are given, as is the range of the robot


title: HTTP: a new framework for bus travel time prediction based on historical trajectories

abstract: In this paper, we develop a new bus travel time prediction framework, called Historical Trajectory based Travel/Arrival Time Prediction (HTTP) for real-time prediction of travel time over future segments (and thus the arrival time at stops) of an on-going bus journey. The basic idea behind HTTP is to use a collection of historical trajectories "similar" to the current bus trajectory to predict the future segments. Specifically, the HTTP framework (1) samples a set of similar trajectories as the basis for travel time estimation instead of relying on only one historical trajectory best matching the on-going bus journey; and (2) explores different prediction schemes, namely, passed segments, temporal features, and hybrid methods, to identify the sample set of similar trajectories. We conduct a comprehensive empirical experimentation using real bus trajectory data collected from Taipei City, Taiwan to validate our ideas and to evaluate the proposed schemes. Experimental result shows that the proposed prediction schemes significantly outperforms the state-of-the-art and baseline techniques.


title: Human skin detection and face recognition using fuzzy logic and eigenface

abstract: Human skin detection and face recognition is a very active area for research in the field of image processing. It is used to prevent many crimes and helps to provide public security. Due to the high complexity in techniques such as real time processing and image contents analysis/understanding, a well-developed product is not available until now in this field. In this work, human presence is detected from an image by extracting the skin region (if present). Since human skin can be found in a varied colour, fuzzy approach is used to extract the skin region. After that individual faces are detected. Eigenface is used to recognize different faces. The experimental results show the effectiveness of the proposed technique.


title: Opportunistic relaying in wireless body area networks: Coexistence performance

abstract: In this paper, a cooperative two-hop communication scheme, together with opportunistic relaying (OR), is applied within a mobile wireless body area network (WBAN). Its effectiveness in interference mitigation is investigated in a scenario where there are multiple closely-located networks. Due to a typical WBAN's nature, no coordination is used among different WBANs. A suitable time-division-multiple-access (TDMA) scheme is adopted as both an intra-network and also an internetwork access scheme. Extensive on-body and off-body channel gain measurements are employed to gauge performance, which are overlaid to simulate a realistic WBAN working environment. It is found that opportunistic relaying is able to improve the signal-to-interference-plus-noise ratio (SINR) performance at an outage probability of 10% by an average of 5 dB, and it is also shown that it can reduce level crossing rate (LCR) significantly at low SINRs. Furthermore, this scheme is more efficient when on-body channels fade more rapidly.


title: Diverse Region-Based CNN for Hyperspectral Image Classification

abstract: Convolutional neural network (CNN) is of great interest in machine learning and has demonstrated excellent performance in hyperspectral image classification. In this paper, we propose a classification framework, called diverse region-based CNN, which can encode semantic context-aware representation to obtain promising features. With merging a diverse set of discriminative appearance factors, the resulting CNN-based representation exhibits spatial-spectral context sensitivity that is essential for accurate pixel classification. The proposed method exploiting diverse region-based inputs to learn contextual interactional features is expected to have more discriminative power. The joint representation containing rich spectral and spatial information is then fed to a fully connected network and the label of each pixel vector is predicted by a softmax layer. Experimental results with widely used hyperspectral image data sets demonstrate that the proposed method can surpass any other conventional deep learning-based classifiers and other state-of-the-art classifiers.


title: Erythrocyte membrane-camouflaged polymeric nanoparticles as a biomimetic delivery platform

abstract: Efforts to extend nanoparticle residence time in vivo have inspired many strategies in particle surface modifications to bypass macrophage uptake and systemic clearance. Here we report a top-down biomimetic approach in particle functionalization by coating biodegradable polymeric nanoparticles with natural erythrocyte membranes, including both membrane lipids and associated membrane proteins for long-circulating cargo delivery. The structure, size and surface zeta potential, and protein contents of the erythrocyte membrane-coated nanoparticles were verified using transmission electron microscopy, dynamic light scattering, and gel electrophoresis, respectively. Mice injections with fluorophore-loaded nanoparticles revealed superior circulation half-life by the erythrocyte-mimicking nanoparticles as compared to control particles coated with the state-of-the-art synthetic stealth materials. Biodistribution study revealed significant particle retention in the blood 72 h following the particle injection. The translocation of natural cellular membranes, their associated proteins, and the corresponding functionalities to the surface of synthetic particles represents a unique approach in nanoparticle functionalization.


title: Accurate Activity Recognition in a Home Setting

abstract: A sensor system capable of automatically recognizing activities would allow many potential ubiquitous applications. In this paper, we present an easy to install sensor network and an accurate but inexpensive annotation method. A recorded dataset consisting of 28 days of sensor data and its annotation is described and made available to the community. Through a number of experiments we show how the hidden Markov model and conditional random fields perform in recognizing activities. We achieve a timeslice accuracy of 95.6% and a class accuracy of 79.4%.


title: Enabling preemptive multiprogramming on GPUs

abstract: GPUs are being increasingly adopted as compute accelerators in many domains, spanning environments from mobile systems to cloud computing. These systems are usually running multiple applications, from one or several users. However GPUs do not provide the support for resource sharing traditionally expected in these scenarios. Thus, such systems are unable to provide key multiprogrammed workload requirements, such as responsiveness, fairness or quality of service. In this paper, we propose a set of hardware extensions that allow GPUs to efficiently support multiprogrammed GPU workloads. We argue for preemptive multitasking and design two preemption mechanisms that can be used to implement GPU scheduling policies. We extend the architecture to allow concurrent execution of GPU kernels from different user processes and implement a scheduling policy that dynamically distributes the GPU cores among concurrently running kernels, according to their priorities. We extend the NVIDIA GK110 (Kepler) like GPU architecture with our proposals and evaluate them on a set of multiprogrammed workloads with up to eight concurrent processes. Our proposals improve execution time of high-priority processes by 15.6x, the average application turnaround time between 1.5x to 2x, and system fairness up to 3.4x


title: DevoFlow: scaling flow management for high-performance networks

abstract: OpenFlow is a great concept, but its original design imposes excessive overheads. It can simplify network and traffic management in enterprise and data center environments, because it enables flow-level control over Ethernet switching and provides global visibility of the flows in the network. However, such fine-grained control and visibility comes with costs: the switch-implementation costs of involving the switch's control-plane too often and the distributed-system costs of involving the OpenFlow controller too frequently, both on flow setups and especially for statistics-gathering. ::: In this paper, we analyze these overheads, and show that OpenFlow's current design cannot meet the needs of high-performance networks. We design and evaluate DevoFlow, a modification of the OpenFlow model which gently breaks the coupling between control and global visibility, in a way that maintains a useful amount of visibility without imposing unnecessary costs. We evaluate DevoFlow through simulations, and find that it can load-balance data center traffic as well as fine-grained solutions, without as much overhead: DevoFlow uses 10--53 times fewer flow table entries at an average switch, and uses 10--42 times fewer control messages.


title: Unpacking Spear Phishing Susceptibility

abstract: We report the results of a field experiment where we sent to over 1200 university students an email or a Facebook message with a link to (non-existing) party pictures from a non-existing person, and later asked them about the reasons for their link clicking behavior. We registered a significant difference in clicking rates: 20% of email versus 42.5% of Facebook recipients clicked. The most frequently reported reason for clicking was curiosity (34%), followed by the explanations that the message fit recipient’s expectations (27%). Moreover, 16% thought that they might know the sender. These results show that people’s decisional heuristics are relatively easy to misuse in a targeted attack, making defense especially challenging.


Abstractive Text Summarization with Quasi-Recurrent Neural Networks


title: Predicting Depression Levels Using Social Media Posts

abstract: The use of Social Network Sites (SNS) is increasing nowadays especially by the younger generations. The availability of SNS allows users to express their interests, feelings and share daily routine. Many researchers prove that using user-generated content (UGC) in a correct way may help determine people's mental health levels. Mining the UGC could help to predict the mental health levels and depression. Depression is a serious medical illness, which interferes most with the ability to work, study, eat, sleep and having fun. However, from the user profile in SNS, we can collect all the information that relates to person's mood, and negativism. In this research, our aim is to investigate how SNS user's posts can help classify users according to mental health levels. We propose a system that uses SNS as a source of data and screening tool to classify the user using artificial intelligence according to the UGC on SNS. We created a model that classify the UGC using two different classifiers: Support Vector Machine (SVM), and Naive Bayes.


title: Body Sensor Network for Mobile Health Monitoring, a Diagnosis and Anticipating System

abstract: A system capable of mobile heath monitoring was designed and implemented from the first principles. The system is capable of measuring vital physiological parameters, interpret the measured signals, and provide a sense of monitoring and biofeedback to the user. In the case, a medical emergency is detected notifications are sent to a medical team for the analysis. The system was designed through the investigation of various biosignal extraction methods. An optical pulse oximeter sensor was designed along with the required software algorithms. The photoplethysmographic signals were extracted and used to calculate heart rate, saturation of oxygen, and pulse transits time. The pulse transit time was utilized in the estimation of continuous blood pressure measurement. The pulse oximetry implementation applies reflectance-based sensing on the user’s fingertip and palm. Skin temperature is also measurable through the use of a digital temperature sensor. The system is capable of providing feedback to the user by means of a smartphone application receiving data from the device through Bluetooth. The measurement of the proposed physiological parameters were successfully measured with the appropriate degree of accuracy required by medical standards. The system is capable of health monitoring through accurate measurements, providing the user with feedback and successfully identifying medical stress resulting in the sending of a notification to a medical doctor.


title: Hybrid driver IC for real-time TFT non-uniformity compensation of ultra high-definition AMOLED display

abstract: An UHD AMOLED display driver IC, enabling real-time TFT non-uniformity compensation, is presented with a hybrid driving scheme. The proposed hybrid driving scheme drives a mobile UHD (3840×1920) AMOLED panel, whose scan time is 7.7μs at a scan frequency of 60Hz, through the load of 30kohm resistance and 30pF capacitance. A proposed accurate current sensor embedded in the column driver and a back-end compensation scheme reduce maximum current error between emulated TFTs within 0.94 LSB (37nA) of 8-bit gray scales. Since the TFT variation is externally compensated, a simple 3T1C pixel circuit is employed in each pixel.


title: A Study of Meta Learning for Regression

abstract: In regression applications, there is no single algorithm which performs well with all data since the performance of an algorithm depends on the dataset used. In practice, different algorithms / approaches are tried, and the best one is selected in each application. It is meaningful to ask whether there is a different way instead of running such tedious experiments. In meta learning studies, one investigates clues for the performance of an algorithm over a dataset using several features of the dataset. In this context, it is important to estimate which dataset features (meta features) are most significant for the performance of the algorithm. In the literature, meta learning studies mostly specialize to classification problems. In this study, meta regression problems are comprehensively studied on 3 big dataset collections (totally 181 datasets). New and existent meta features (about 300) are used. The relationships between the datasets and the algorithms are investigated. Several relations are found between meta features and related performances. The created meta datasets are made available to interested researchers.


title: Automated News Generation for TV Program Ratings

abstract: Automated journalism, automatically generating stories based on algorithms, has received considerable critical attention in diverse fields. However, automated journalism has not addressed the TV industry in much detail. This research aims to create a system to automatically generate news about TV ratings. The framework will involve undergoing the processes of data gathering, identifying important events by predefined algorithms, generating a story in narrative format, and publishing the output. The algorithm that determines the structure of the stories is defined by analyzing existing news about TV ratings that reflects key variables. Although the output of the research is limited to one type of news template, further attempts could expand to various formats.


title: Feasibility of Using a Smartwatch to Intensively Monitor Patients With Chronic Obstructive Pulmonary Disease: Prospective Cohort Study

abstract: BACKGROUND ::: Acute exacerbations of chronic obstructive pulmonary disease (COPD) are associated with accelerated decline in lung function, diminished quality of life, and higher mortality. Proactively monitoring patients for early signs of an exacerbation and treating them early could prevent these outcomes. The emergence of affordable wearable technology allows for nearly continuous monitoring of heart rate and physical activity as well as recording of audio which can detect features such as coughing. These signals may be able to be used with predictive analytics to detect early exacerbations. Prior to full development, however, it is important to determine the feasibility of using wearable devices such as smartwatches to intensively monitor patients with COPD. ::: ::: ::: OBJECTIVE ::: We conducted a feasibility study to determine if patients with COPD would wear and maintain a smartwatch consistently and whether they would reliably collect and transmit sensor data. ::: ::: ::: METHODS ::: Patients with COPD were recruited from 3 hospitals and were provided with a smartwatch that recorded audio, heart rate, and accelerations. They were asked to wear and charge it daily for 90 days. They were also asked to complete a daily symptom diary. At the end of the study period, participants were asked what would motivate them to regularly use a wearable for monitoring of their COPD. ::: ::: ::: RESULTS ::: Of 28 patients enrolled, 16 participants completed the full 90 days. The average age of participants was 68.5 years, and 36% (10/28) were women. Survey, heart rate, and activity data were available for an average of 64.5, 65.1, and 60.2 days respectively. Technical issues caused heart rate and activity data to be unavailable for approximately 13 and 17 days, respectively. Feedback provided by participants indicated that they wanted to actively engage with the smartwatch and receive feedback about their activity, heart rate, and how to better manage their COPD. ::: ::: ::: CONCLUSIONS ::: Some patients with COPD will wear and maintain smartwatches that passively monitor audio, heart rate, and physical activity, and wearables were able to reliably capture near-continuous patient data. Further work is necessary to increase acceptability and improve the patient experience.


title: Influence of a Perspective Cockpit Traffic Display Format on Pilot Avoidance Maneuvers

abstract: Abstract : Laboratory experiments have examined perspective projections of Cockpit Displays of Traffic Information (CDTI) on CRTs as a means of presenting vertical separation information to airline pilots. Ten airline pilots served as subjects in an experiment comparing the perspective projection with plan-view projections of the same air traffic situations. Comparison of the pilots' avoidance maneuvers made when using the perspective display with those made while using more the conventional plan-view display has shown that pilots maneuvered somewhat earlier with perspective displays. When they did maneuver with the perspective display, they maneuvered more frequently in the vertical dimension. Thus, a previously observed bias to maneuver horizontally was probably not based on the procedural reasons that were given by the pilots. That bias thus probably reflected the poorer presentation of vertical separation on previously used plan-view traffic displays. Tabulation of the outcomes of the avoidance decisions made by pilots using both perspective and plan-view displays showed fewer unsuccessful maneuvers and fewer maneuvers producing spacing violations with the perspective format.


title: EFFECT OF TRUST ON CUSTOMER ACCEPTANCE OF INTERNET BANKING

abstract: Abstract Two beliefs, ease of use and usefulness, have been considered to be fundamental in determining the acceptance of various IS in the past decades. These beliefs may not, however, fully explain the users’ behavior in an emerging environment such as Internet banking. In this study, we introduce trust as another belief that has an impact on the acceptance of Internet banking. We collected 845 cases on the Web to survey users’ behavior towards Internet banks. The results of statistical analyses using structural equation modeling indicate that trust has a significant impact on the acceptance of Internet banking.


title: The ContikiMAC Radio Duty Cycling Protocol

abstract: Low-power wireless devices must keep their radio ::: transceivers off as much as possible to reach a low power ::: consumption, but must wake up often enough to be able to ::: receive communication from their neighbors. This report ::: describes the ContikiMAC radio duty cycling mechanism, ::: the default radio duty cycling mechanism in Contiki 2.5, ::: which uses a power efficient wake-up mechanism with ::: a set of timing constraints to allow device to keep their ::: transceivers off. With ContikiMAC, nodes can participate ::: in network communication yet keep their radios turned ::: off for roughly 99% of the time. This report describes the ::: ContikiMAC mechanism, measures the energy consumption ::: of individual ContikiMAC operations, and evaluates ::: the efficiency of the fast sleep and phase-lock optimizations.


title: Multi-criteria IaaS Service Selection Based on QoS History

abstract: The growing number of cloud services have made service selection a challenging decision-making problem by providing wide ranging choices for cloud service consumers. This necessitates the use of formal decision making methodologies to assist a decision maker in selecting the service that best fulfils the user's requirements. In this paper, we present a cloud service selection methodology that utilizes QoS history over different time periods, performs Multi-Criteria Decision Analysis to rank all cloud services in each time period in accordance with user preferences before aggregating the results to determine the overall service rank of all the available services for cloud service selection.


title: Designing for a billion users: a case study of facebook

abstract: Facebook is the world's largest social network, connecting over 800 million users worldwide. The type of phenomenal growth experienced by Facebook in a short time is rare for any technology company. As the Facebook user base approaches the 1 billion mark, a number of exciting opportunities await the world of social networking and the future of the web. We present a case study of what it is like to design for a billion users at Facebook from the perspective of designers, engineers, managers, user experience researchers, and other stakeholders at the company. Our case study illustrates various complexities and tradeoffs in design through a Human-Computer Interaction (HCI) lens and highlights implications for tackling the challenges through research and practice.


title: To Generate the Ontology from Java Source Code

abstract: Software development teams design new components and code by employing new developers for every new project. If the company archives the completed code and components, they can be reused with no further testing unlike the open source code and components. Program File components can be extracted from the Application files and folders using API’s. The proposed framework extracts the metadata from the source code using QDox code generators and stores it in the OWL using Jena framework automatically. The source code will be stored in the HDFS repository. Code stored in the repository can be reused for software development. By Archiving all the project files in to one ontology will enable the developers to reuse the code efficiently.


Local Learning to Improve Bag of Visual Words Model for Facial Expression Recognition


title: DFS: A file system for virtualized flash storage

abstract: This paper presents the design, implementation and evaluation of Direct File System (DFS) for virtualized flash storage. Instead of using traditional layers of abstraction, our layers of abstraction are designed for directly accessing flash memory devices. DFS has two main novel features. First, it lays out its files directly in a very large virtual storage address space provided by FusionIO's virtual flash storage layer. Second, it leverages the virtual flash storage layer to perform block allocations and atomic updates. As a result, DFS performs better and it is much simpler than a traditional Unix file system with similar functionalities. Our microbenchmark results show that DFS can deliver 94,000 I/O operations per second (IOPS) for direct reads and 71,000 IOPS for direct writes with the virtualized flash storage layer on FusionIO's ioDrive. For direct access performance, DFS is consistently better than ext3 on the same platform, sometimes by 20%. For buffered access performance, DFS is also consistently better than ext3, and sometimes by over 149%. Our application benchmarks show that DFS outperforms ext3 by 7% to 250% while requiring less CPU power.


title: VRCEMIG: a novel approach to power substation control

abstract: In this work, we propose a Virtual Reality based solution to provide a more natural and intuitive environment for controlling electrical operation centers. The research is being carried out with the collaboration of one electric company called Cemig. The novelty of this approach is the ability operators will have to manage the electric system and its electric components by being immersed within a 3D world, reflecting the very true arrangement found in the real electrical substation. Besides, the solution has been designed in a way to provide the operator with all supervisory data in the same virtual environment. We have conducted experiments with the electric company operators Mental efforts to understand the reality of the field have been reduced, according to Cemig's employees. They also claim that a unique environment with all data integrated is very important for taking engineering decisions.


title: Dynamic subcarrier allocation for single carrier - FDMA systems

abstract: In this paper we propose an enhanced greedy dynamic subcarrier allocation algorithm for Single Carrier Frequency Division Multiple Access (SC-FDMA) systems. This so called mean-enhanced greedy algorithm allocates subcarriers in a greedy fashion, based on the information of the users' subcarriers mean gains. We show through simulation results that the proposed algorithm outperforms the conventional greedy algorithm. It also outperforms the benchmark Hungarian algorithms in terms of Bit Error Rate (BER) with a lower computational complexity. Furthermore, the proposed algorithm is generic and can be easily extended for Orthogonal Frequency Division Multiple Access (OFDMA). We compare the performance of SCFDMA and OFDMA, and point out that for a high number of users, there is no frequency diversity gain of SC-FDMA over OFDMA as both systems demonstrate benefit from multiuser diversity.


title: Modeling and Propagating CNNs in a Tree Structure for Visual Tracking

abstract: We present an online visual tracking algorithm by managing multiple target appearance models in a tree structure. The proposed algorithm employs Convolutional Neural Networks (CNNs) to represent target appearances, where multiple CNNs collaborate to estimate target states and determine the desirable paths for online model updates in the tree. By maintaining multiple CNNs in diverse branches of tree structure, it is convenient to deal with multi-modality in target appearances and preserve model reliability through smooth updates along tree paths. Since multiple CNNs share all parameters in convolutional layers, it takes advantage of multiple models with little extra cost by saving memory space and avoiding redundant network evaluations. The final target state is estimated by sampling target candidates around the state in the previous frame and identifying the best sample in terms of a weighted average score from a set of active CNNs. Our algorithm illustrates outstanding performance compared to the state-of-the-art techniques in challenging datasets such as online tracking benchmark and visual object tracking challenge.


title: Universal Multimode Background Subtraction

abstract: In this paper, we present a complete change detection system named multimode background subtraction. The universal nature of system allows it to robustly handle multitude of challenges associated with video change detection, such as illumination changes, dynamic background, camera jitter, and moving camera. The system comprises multiple innovative mechanisms in background modeling, model update, pixel classification, and the use of multiple color spaces. The system first creates multiple background models of the scene followed by an initial foreground/background probability estimation for each pixel. Next, the image pixels are merged together to form mega-pixels, which are used to spatially denoise the initial probability estimates to generate binary masks for both RGB and YCbCr color spaces. The masks generated after processing these input images are then combined to separate foreground pixels from the background. Comprehensive evaluation of the proposed approach on publicly available test sequences from the CDnet and the ESI data sets shows superiority in the performance of our system over other state-of-the-art algorithms.


title: Negotiating flexible agreements by combining distributive and integrative negotiation

abstract: This paper presents an approach to automated negotiation between agents which attempts to combine the advantages of a co-operative value adding approach, with the reality that negotiating agents are also competing. We use the concept of a trusted mediator to facilitate openness regarding what one values, without disadvantaging oneself by revealing sensitive information such as a reserve price to the other party. Social science and management literature deals with negotiation between people, and so can be both more complex, and less well defined than automated negotiation between software agents. We take inspiration from the social science literature and develop a computational framework to support negotiating software agents. The framework includes recognition that agents are self interested, and therefore will manipulate the system to their advantage if possible. We include mechanisms to discourage this kind of manipulation in the form of a transaction cost associated with making only small concessions, and a bias in dividing the pie which is the gain from trade which favours the agent who is most ``honest'' in making offers.


title: Toward Large-Scale Vulnerability Discovery using Machine Learning

abstract: With sustained growth of software complexity, finding security vulnerabilities in operating systems has become an important necessity. Nowadays, OS are shipped with thousands of binary executables. Unfortunately, methodologies and tools for an OS scale program testing within a limited time budget are still missing. In this paper we present an approach that uses lightweight static and dynamic features to predict if a test case is likely to contain a software vulnerability using machine learning techniques. To show the effectiveness of our approach, we set up a large experiment to detect easily exploitable memory corruptions using 1039 Debian programs obtained from its bug tracker, collected 138,308 unique execution traces and statically explored 76,083 different subsequences of function calls. We managed to predict with reasonable accuracy which programs contained dangerous memory corruptions. We also developed and implemented VDiscover, a tool that uses state-of-the-art Machine Learning techniques to predict vulnerabilities in test cases. Such tool will be released as open-source to encourage the research of vulnerability discovery at a large scale, together with VDiscovery, a public dataset that collects raw analyzed data.


title: A universal noise removal algorithm with an impulse detector

abstract: We introduce a local image statistic for identifying noise pixels in images corrupted with impulse noise of random values. The statistical values quantify how different in intensity the particular pixels are from their most similar neighbors. We continue to demonstrate how this statistic may be incorporated into a filter designed to remove additive Gaussian noise. The result is a new filter capable of reducing both Gaussian and impulse noises from noisy images effectively, which performs remarkably well, both in terms of quantitative measures of signal restoration and qualitative judgements of image quality. Our approach is extended to automatically remove any mix of Gaussian and impulse noise.


title: Vritthi - a theoretical framework for IT recruitment based on machine learning techniques applied over Twitter, LinkedIn, SPOJ and GitHub profiles

abstract: In this model, we propose an innovative recruitment system using social networking websites like Twitter and LinkedIn along with code repository hosting website GitHub and competitive coding platforms like SPOJ. It is aimed to develop advanced search engines to automatically sort the job-seekers based on job offer requirements using various data mining and machine learning techniques. Vritthi allows job-seekers to quantify their job preparedness and offer a list of specific areas for them to focus on. We propose the formulation of VPQF (Vritthi Professional Quotient) that involves the use of K-means algorithm to classify users into appropriate clusters and provide them with appropriate suggestions for improvement. Using classic data mining techniques like filtration, classification, clustering, profiling as well as string matching & user profiling, this tool will enable recruiters to effectively select candidates who fit their organization in a hassle-free automated manner.


title: Fuzziness based semi-supervised learning approach for intrusion detection system

abstract: Countering cyber threats, especially attack detection, is a challenging area of research in the field of information assurance. Intruders use polymorphic mechanisms to masquerade the attack payload and evade the detection techniques. Many supervised and unsupervised learning approaches from the field of machine learning and pattern recognition have been used to increase the efficacy of intrusion detection systems (IDSs). Supervised learning approaches use only labeled samples to train a classifier, but obtaining sufficient labeled samples is cumbersome, and requires the efforts of domain experts. However, unlabeled samples can easily be obtained in many real world problems. Compared to supervised learning approaches, semi-supervised learning (SSL) addresses this issue by considering large amount of unlabeled samples together with the labeled samples to build a better classifier. This paper proposes a novel fuzziness based semi-supervised learning approach by utilizing unlabeled samples assisted with supervised learning algorithm to improve the classifier's performance for the IDSs. A single hidden layer feed-forward neural network (SLFN) is trained to output a fuzzy membership vector, and the sample categorization (low, mid, and high fuzziness categories) on unlabeled samples is performed using the fuzzy quantity. The classifier is retrained after incorporating each category separately into the original training set. The experimental results using this technique of intrusion detection on the NSL-KDD dataset show that unlabeled samples belonging to low and high fuzziness groups make major contributions to improve the classifier's performance compared to existing classifiers e.g., naive bayes, support vector machine, random forests, etc.


title: Sensor networks for emergency response: challenges and opportunities

abstract: Sensor networks, a new class of devices has the potential to revolutionize the capture, processing, and communication of critical data for use by first responders. CodeBlue integrates sensor nodes and other wireless devices into a disaster response setting and provides facilities for ad hoc network formation, resource naming and discovery, security, and in-network aggregation of sensor-produced data. We designed CodeBlue for rapidly changing, critical care environments. To test it, we developed two wireless vital sign monitors and a PDA-based triage application for first responders. Additionally, we developed MoteTrack, a robust radio frequency (RF)-based localization system, which lets rescuers determine their location within a building and track patients. Although much of our work on CodeBlue is preliminary, our initial experience with medical care sensor networks raised many exciting opportunities and challenges.


title: Defection Detection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models

abstract: Abstract This article provides a descriptive analysis of how methodological factors contribute to the accuracy of customer churn predictive models. The study is based on a tournament in which both academics and practitioners downloaded data from a publicly available Web site, estimated a model, and made predictions on two validation databases. The results suggest several important findings. First, methods do matter. The differences observed in predictive accuracy across submissions could change the profitability of a churn management campaign by hundreds of thousands of dollars. Second, models have staying power. They suffer very little decrease in performance if they are used to predict churn for a database compiled three months after the calibration data. Third, researchers use a variety of modeling “approaches,” characterized by variables such as estimation technique, variable selection procedure, number of variables included, and time allocated to steps in the model-building process. The authors find ...


title: A RSSI-based and calibrated centralized localization technique for wireless sensor networks

abstract: This paper presents a multi-hop localization technique for WSNs exploiting acquired received signal strength indications. The proposed system aims at providing an effective solution for the self localization of nodes in static/semi-static wireless sensor networks without requiring previous deployment information.


title: Judding The Autonomy of Deed for Progressive Political Economy

abstract: The rise of neo-liberalism with a post-modern cultural logic that accompanies it not only outdates social projects based on general codes, but also hampers the essence of liberalism. At first sight, destruction of general codes seems to enlarge individual rights and freedoms. Nevertheless, there occurs an implicit discrepancy between economic and political domains. Destruction of general codes by definition increases individual rights because emancipation of every individual idiosyncrasy as a means of identity positioning quadruples the options for individual choices. However, enlargement of rights take place with a clandestine shrivel of individual freedom because it has become almost impossible to achieve self-realizing under omnipresence of dominative communication. Therefore, along with the environment that covers individual, the ultimate meaning of individual’s and society’s positioning within the system passes through judgment of the deed by interrogating whether individual expressions have been shaped by external factors no matter whether be it determined on the basis of ethical or non-ethical; or whether individual’s initiatives are outcome of his/her free will whether be it determined on the basis of ethical or non-ethical.


title: Recent advances in planar optics: from plasmonic to dielectric metasurfaces

abstract: This article reviews recent progress leading to the realization of planar optical components made of a single layer of phase shifting nanostructures. After introducing the principles of planar optics and discussing earlier works on subwavelength diffractive optics, we introduce a classification of metasurfaces based on their different phase mechanisms and profiles and a comparison between plasmonic and dielectric metasurfaces. We place particular emphasis on the recent developments on electric and magnetic field control of light with dielectric nanostructures and highlight the physical mechanisms and designs required for efficient all-dielectric metasurfaces. Practical devices of general interest such as metalenses, beam deflectors, holograms, and polarizing interfaces are discussed, including high-performance metalenses at visible wavelengths. Successful strategies to achieve achromatic response at selected wavelengths and near unity transmission/reflection efficiency are discussed. Dielectric metasurfaces and dispersion management at interfaces open up technology opportunities for applications including wavefront control, lightweight imaging systems, displays, electronic consumer products, and conformable and wearable optics.


title: Predictors of Problematic Smartphone Use: An Examination of the Integrative Pathways Model and the Role of Age, Gender, Impulsiveness, Excessive Reassurance Seeking, Extraversion, and Depression

abstract: Background: The progression of mobile phone technology has led to the development of multi-functional smartphones providing access to features such as social media, e-mail, and videos alongside the basic functions of a mobile phone. Increasing amounts of research has explored the potential addictive nature of smartphones to develop a theoretical framework that describes personality factors related to problematic use. The present study examined the Integrative Pathways Model and the effect of age, gender, impulsiveness, excessive reassurance seeking, extraversion, and depression on problematic smartphone use. Method: A total of 147 smartphone users (mean age = 30.96, SD = 12.97, 69.4% female) completed an online survey comprising of measures of problematic smartphone use, excessive reassurance seeking, extraversion, depression, and impulsiveness. Results: Age, impulsiveness, excessive reassurance seeking, and depression were all significantly related to problematic smartphone use, however extraversion was not significantly related. Furthermore, age and impulsiveness were significant independent predictors of problematic smartphone use. No gender differences were found. Conclusions: The findings presented several factors that predict problematic smartphone use, implications and suggestions for future research are discussed.


Generating Reasonable and Diversiﬁed Story Ending Using Sequence to Sequence Model with Adversarial Training


title: BlockQNN: Efficient Block-wise Neural Network Architecture Generation

abstract: Convolutional neural networks have gained a remarkable success in computer vision. However, most usable network architectures are hand-crafted and usually require expertise and elaborate design. In this paper, we provide a block-wise network generation pipeline called BlockQNN which automatically builds high-performance networks using the Q-Learning paradigm with epsilon-greedy exploration strategy. The optimal network block is constructed by the learning agent which is trained to choose component layers sequentially. We stack the block to construct the whole auto-generated network. To accelerate the generation process, we also propose a distributed asynchronous framework and an early stop strategy. The block-wise generation brings unique advantages: (1) it yields state-of-the-art results in comparison to the hand-crafted networks on image classification, particularly, the best network generated by BlockQNN achieves 2.35% top-1 error rate on CIFAR-10. (2) it offers tremendous reduction of the search space in designing networks, spending only 3 days with 32 GPUs. A faster version can yield a comparable result with only 1 GPU in 20 hours. (3) it has strong generalizability in that the network built on CIFAR also performs well on the larger-scale dataset. The best network achieves very competitive accuracy of 82.0% top-1 and 96.0% top-5 on ImageNet.


title: Outsourcing Tasks Online: Matching Supply and Demand on Peer-to-Peer Internet Platforms

abstract: We study a central economic problem for peer-to-peer online marketplaces: how to create successful matches when demand and supply are highly variable. To do this, we develop a parsimonious model of a frictional matching market for services, which lets us derive the elasticity of labor demand and supply, the split of surplus between buyers and sellers, and the efficiency with which requests and offers for services are successfully matched. We estimate the model using data from TaskRabbit, a rapidly expanding platform for domestic tasks, and report three main findings. First, supply is highly elastic: in periods when demand doubles, sellers work almost twice as hard, prices hardly increase and the probability of requested tasks being matched only slightly falls. Second, we estimate average gains from each trade to be $37. Because of the matching frictions and search costs needed to find potential matches, the ex-ante gains are more modest, but are maximized by the elastic labor supply: if the number of hours worked were held constant, there would be 15 percent fewer matches in equilibrium. Third, we find that platform success varies greatly across cities. The cities which grow fast in the number of users are also those where the market fundamentals promote efficient matching of buyers and sellers. This heterogeneity in matching efficiency is not attributable to scale economies, but is instead related to two measures of market thickness: geographic density (buyers and sellers living close together), and level of task standardization (buyers requesting homogeneous tasks). ∗We would like to thank Jon Levin, Susan Athey, and Liran Einav for their mentorship and guidance. We are also grateful to Tim Bresnahan, Pascaline Dupas, Brad Larsen, Andrey Fradkin, Michael Dinerstein, Akshaya Jha, and many other seminar participants at Stanford University for their invaluable comments. We appreciate TaskRabbit for providing essential data access. Support for this research was provided through the B.F. Haley and E.S. Shaw Fellowship for Economics through the Stanford Institute for Economic Policy Research. †Department of Economics, Stanford University. Email: zcullen@stanford.edu, chiaraf@stanford.edu.


title: Differential speed steering control for four-wheel independent driving electric vehicle

abstract: The increasing prospect of electric drive vehicles has led many different control schemes to apply on the driving system. Differential speed steering which do not use traditional steering mechanism is studied on a four-wheel-driving vehicle. By comparison with the traditional Ackerman-steered vehicle, differential-steered method shown its advantage on the simple structure. However, since the steering performance is coupling with the velocities of all wheels, the strategy of differential speed control is a technological challenge. With the analysis of kinematic model of the vehicle, the relation between the turning behavior and the wheel parameters are investigated. A steering speed control is proposed to get a steady turning performance. The control strategy is implemented in a simulation to verify its rationality.


title: Policy and progress in Moroccan agriculture: A retrospective and perspective

abstract: Since 1985, Morocco's government has reformed the country's agriculture especially with respect to domestic agricultural markets. Drawing on available sources and our experience, we argue three related points. First, the impacts of reforms undertaken have, by and large, been positive. This reflects much more the dismal state of agricultural policy in 1985 rather than a positive rating of the reform process or the current policy environment. Second, further reforms, especially with respect to trade policy, are desirable. Third, future reforms involve important policy tradeoffs, especially with respect to price stability for strategic agricultural commodities (bread wheat, sugar and oilseeds).


title: Integrated wavelength-selective optical MEMS switching using ring resonator filters

abstract: An integrated optical microelectromechanical system (MEMS) switch that provides wavelength selectivity is described. The switching mechanism is based on moving a MEMS actuated optically absorbing membrane into the evanescent field of a high-index-contrast optical ring resonator. By controlling the loss, and thus, the cavity quality factor, the resonant wavelength is switched between the drop and through ports.


Using the Bitcoin Transaction Graph to Predict the Price of Bitcoin


title: A new algorithm for surface deformation monitoring based on small baseline differential SAR interferograms

abstract: We present a new differential synthetic aperture radar (SAR) interferometry algorithm for monitoring the temporal evolution of surface deformations. The presented technique is based on an appropriate combination of differential interferograms produced by data pairs characterized by a small orbital separation (baseline) in order to limit the spatial decorrelation phenomena. The application of the singular value decomposition method allows us to easily "link" independent SAR acquisition datasets, separated by large baselines, thus increasing the observation temporal sampling rate. The availability of both spatial and temporal information in the processed data is used to identify and filter out atmospheric phase artifacts. We present results obtained on the data acquired from 1992 to 2000 by the European Remote Sensing satellites and relative to the Campi Flegrei caldera and to the city of Naples, Italy, that demonstrate the capability of the proposed approach to follow the dynamics of the detected deformations.


title: Convolutional Networks for Shape from Light Field

abstract: Convolutional Neural Networks (CNNs) have recently been successfully applied to various Computer Vision (CV) applications. In this paper we utilize CNNs to predict depth information for given Light Field (LF) data. The proposed method learns an end-to-end mapping between the 4D light field and a representation of the corresponding 4D depth field in terms of 2D hyperplane orientations. The obtained prediction is then further refined in a post processing step by applying a higher-order regularization. Existing LF datasets are not sufficient for the purpose of the training scheme tackled in this paper. This is mainly due to the fact that the ground truth depth of existing datasets is inaccurate and/or the datasets are limited to a small number of LFs. This made it necessary to generate a new synthetic LF dataset, which is based on the raytracing software POV-Ray. This new dataset provides floating point accurate ground truth depth fields, and due to a random scene generator the dataset can be scaled as required.


title: Semantically Enhanced Collaborative Filtering on the Web

abstract: Item-based Collaborative Filtering (CF) algorithms have been designed to deal with the scalability problems associated with traditional user-based CF approaches without sacrificing recommendation or prediction accuracy. Item-based algorithms avoid the bottleneck in computing user-user correlations by first considering the relationships among items and performing similarity computations in a reduced space. Because the computation of item similarities is independent of the methods used for generating predictions, multiple knowledge sources, including structured semantic information about items, can be brought to bear in determining similarities among items. The integration of semantic similarities for items with rating- or usage-based similarities allows the system to make inferences based on the underlying reasons for which a user may or may not be interested in a particular item. Furthermore, in cases where little or no rating (or usage) information is available (such as in the case of newly added items, or in very sparse data sets), the system can still use the semantic similarities to provide reasonable recommendations for users. In this paper, we introduce an approach for semantically enhanced collaborative filtering in which structured semantic knowledge about items, extracted automatically from the Web based on domain-specific reference ontologies, is used in conjunction with user-item mappings to create a combined similarity measure and generate predictions. Our experimental results demonstrate that the integrated approach yields significant advantages both in terms of improving accuracy, as well as in dealing with very sparse data sets or new items.


title: Cross-cultural Differences in Mental Health, Quality of Life, Empathy, and Burnout between US and Brazilian Medical Students

abstract: Objective ::: This study aimed to compare mental health, quality of life, empathy, and burnout in medical students from a medical institution in the USA and another one in Brazil.


title: Multitask coactive learning

abstract: In this paper we investigate the use of coactive learning in a multitask setting. In coactive learning, an expert presents the learner with a problem and the learner returns a candidate solution. The expert then improves on the solution if necessary and presents the improved solution to the learner. The goal for the learner is to learn to produce solutions which cannot be further improved by the expert while minimizing the average expert effort. In this paper, we consider the setting where there are multiple experts (tasks), and in each iteration one expert presents a problem to the learner. While the experts are expected to have different solution preferences, they are also assumed to share similarities, which should enable generalization across experts. We analyze several algorithms for this setting and derive bounds on the average expert effort during learning. Our main contribution is the balanced Perceptron algorithm, which is the first coactive learning algorithm that is both able to generalize across experts when possible, while also guaranteeing convergence to optimal solutions for individual experts. Our experiments in three domains confirm that this algorithm is effective in the multitask setting, compared to natural baselines.


title: Seatbelt detection based on cascade Adaboost classifier

abstract: Vehicle safety is increasingly becoming a concern. Whether the driver is wearing a seatbelt and whether the vehicle is speeding out or not become important indicators of the vehicle safety. However, manually searching, detecting, recording and other work will spend a lot of manpower and time inefficiently. This paper proposes a cascade Adaboost classifier based seatbelt detection system to detect the vehicle windows, to complete Canny edge detection on gradient map of vehicle window images, and to perform the probabilistic Hough transform to extract the straight-lines of seatbelts. The system achieves the goal of seatbelt detection intelligently.


title: Persistent Infection byHelicobacter pyloriDown‐Modulates Virus‐Specific CD8+Cytotoxic T Cell Response and Prolongs Viral Infection

abstract: To determine whether Helicobacter pylori infection affects clearance of a concomitant viral infection and cytotoxic T lymphocyte (CTL) and cytokine response to that infection, H. pylori-infected BALB/c mice were challenged with a recombinant vaccinia virus expressing human immunodeficiency virus type 1 gp160. Two H. pylori strains, a colonizing clinical isolate (KS612) and an established standard noncolonizing strain (NCTC11637), were compared. Clearance of recombinant vaccinia virus was reduced in KS612-infected mice compared with NCTC11637-infected and control mice. As a potential mechanism, in contrast to control or NCTC11637-infected mice, the H. pylori clinical isolate KS612 diminished gp160-specific and vaccinia virus-specific CTL activity, even in the presence of exogenous interleukin-2. Furthermore, KS612-infected mice had reduced Th1 cytokine responses to gp120 in vitro compared with control or NCTC11637-infected mice. These results have implications for possible effects of prevalent H. pylori infection on other human diseases.


title: Confused or not Confused?: Disentangling Brain Activity from EEG Data Using Bidirectional LSTM Recurrent Neural Networks

abstract: Brain fog, also known as confusion, is one of the main reasons of the low performance in the learning process or any kind of daily task that involves and requires thinking. Detecting confusion in human's mind in real time is a challenging and important task which can be applied to online education, driver fatigue detection and so on. In this paper, we applied Bidirectional LSTM Recurrent Neural Networks to classify students' confusions. The results show that Bidirectional LSTM model achieves the state-of-the-art performance compared with other machine learning approaches, and shows strong robustness as evaluated by cross-validation. We can predict whether or not a student is confused in the accuracy of 73.3%. Furthermore, we find the most important feature to detecting the brain confusion is gamma 1 wave of EEG signal. Our results suggest that machine learning is a potentially powerful tool to model and understand brain activities.


title: A distributed algorithm for 2D shape duplication with smart pebble robots

abstract: We present our digital fabrication technique for manufacturing active objects in 2D from a collection of smart particles. Given a passive model of the object to be formed, we envision submerging this original in a vat of smart particles, executing the new shape duplication algorithm described in this paper, and then brushing aside any extra modules to reveal both the original object and an exact copy, side-by-side. Extensions to the duplication algorithm can be used to create a magnified version of the original or multiple copies of the model object. Our novel duplication algorithm uses a distributed approach to identify the geometric specification of the object being duplicated and then forms the duplicate from spare modules in the vicinity of the original. This paper details the duplication algorithm and the features that make it robust to (1) an imperfect packing of the modules around the original object; (2) missing communication links between neighboring modules; and (3) missing modules in the vicinity of the duplicate object(s). We show that the algorithm requires O(1) storage space per module and that the algorithm exchanges O(n) messages per module. Finally, we present experimental results from 60 hardware trials and 150 simulations. These experiments demonstrate the algorithm working correctly and reliably despite broken communication links and missing modules.


Adaptive Detection of Human Skin in Color Images


title: Dengue disease mapping in Malaysia based on stochastic SIR models in human populations

abstract: Relative risk estimation is one of the most important issues in the study of geographical distributions of disease occurrence or disease mapping. For the case of dengue, there are only a few studies that use statistical methods to estimate the relative risk for disease mapping. Therefore this research will introduce an alternative method to estimate the relative risk of dengue occurrence based initially on discrete-time, discrete-space stochastic SIR models (Susceptible-Infective-Removed) in human populations for dengue disease transmission, to overcome the drawbacks of relative risk estimation in disease mapping using a classical method based on standardized morbidity ratio (SMR), and the earliest example of Bayesian mapping which involves a Poisson-Gamma model. The estimation of relative risk is applied to dengue data in Malaysia which will then be displayed in a map to represent the high and low risk areas of dengue occurrence.