title: Quantifying smartphone vulnerabilities

abstract: Nowadays, smartphones are an integral part of our lives. They provide a wide variety of functionality through their applications (apps), whether it's the latest news, social connectivity, market updates or picture editing and many more similar things. People rely on their smartphones for all these small tasks. These applications often have sensitive data/information about the users. As a result, smartphones are hot target for the hackers these days. Due to this, the number of vulnerabilities in smartphones is on a rise too. As soon as a new version of a smartphone operating system is launched, hackers look to exploit that and new vulnerabilities are discovered. In this paper we analyze the various versions of Google's Android and Apple's iOS operating systems and the vulnerabilities present in them.


1 BLUEBERRY MATURITY STAGE DETECTION BASED ON SPECTRAL-SPATIAL DETECTION OF HYPERSPECTRAL IMAGE USING SELECTED BANDS


title: New clock-gating techniques for low-power flip-flops

abstract: Two novel low power flip-flops are presented in the paper. The proposed flip-flops use new gating techniques that reduce power dissipation deactivating the clock signal. The presented circuits overcome the clock duty-cycle limitation of previously reported gated flip-flops. Circuit simulations with the inclusion of parasitics show that sensible power dissipation reduction is possible if the input signal has reduced switching activity. A 16-bit counter is presented as a simple low power application.


title: On analyzing of fingerprint direct-access strategies

abstract: This paper analyzed several aspect of publicly available fingerprint direct-access strategies over three public data sets, including their advantages and drawbacks, related works, and accuracy performance comparison on the graph of error rate and penetration rate. On recent research, smaller penetration rate at certain error rate (smaller area under curve) could be obtained by certain strategy. Penetration rate equal to 5% could be produced at error rate equal to 4%, 10%, and 1% on FVC2000 DB2A, FVC2000 DB3A, and FVC2002 DB1A, respectively. Through this analysis, hopely it could be used as a foundation for better future developments over new strategies or future improvements over existing strategies in area of fingerprint identification, especially to obtain more accurate result with smaller area under curve and achieved with faster search time.


title: Kinematic Model of a New Staircase Climbing Wheelchair and its Experimental Validation

abstract: This paper describes the mechanical devices, the movements and the associated kinematic models of a novel wheelchair prototype capable of climbing staircases. The key feature of the mechanical design is the use of two decoupled mechanisms in each axle, one used to negotiate steps, and the other to position the axle with respect to the chair to accommodate the overall slope. This decoupling makes possible many different climbing strategies, the overall mechanism being extraordinarily versatile from a control point of view. Kinematic models have been developed for the different mechanical configurations that appear during all the ascend/descend processes. These models are required to control the actuators of the wheelchair in such a way that its centre of mass is able to follow arbitrary spatial trajectories. This is very important as one has to design very smooth spatial trajectories, keeping a near null inclination of the seat all the time in order to guarantee the comfort of the passenger, usually a handicapped or injured person. A real prototype is presented, and experimental results are reported that show the efficiency of the mechanism and the accuracy of the kinematic models developed.


title: Efficient Testing of Recovery Code Using Fault Injection

abstract: A critical part of developing a reliable software system is testing its recovery code. This code is traditionally difficult to test in the lab, and, in the field, it rarely gets to run; yet, when it does run, it must execute flawlessly in order to recover the system from failure. In this article, we present a library-level fault injection engine that enables the productive use of fault injection for software testing. We describe automated techniques for reliably identifying errors that applications may encounter when interacting with their environment, for automatically identifying high-value injection targets in program binaries, and for producing efficient injection test scenarios. We present a framework for writing precise triggers that inject desired faults, in the form of error return codes and corresponding side effects, at the boundary between applications and libraries. These techniques are embodied in LFI, a new fault injection engine we are distributing http://lfi.epfl.ch. This article includes a report of our initial experience using LFI. Most notably, LFI found 12 serious, previously unreported bugs in the MySQL database server, Git version control system, BIND name server, Pidgin IM client, and PBFT replication system with no developer assistance and no access to source code. LFI also increased recovery-code coverage from virtually zero up to 60p entirely automatically without requiring new tests or human involvement.


title: Classifying paintings by artistic genre: An analysis of features & classifiers

abstract: This paper describes an approach to automatically classify digital pictures of paintings by artistic genre. While the task of artistic classification is often entrusted to human experts, recent advances in machine learning and multimedia feature extraction has made this task easier to automate. Automatic classification is useful for organizing large digital collections, for automatic artistic recommendation, and even for mobile capture and identification by consumers. Our evaluation uses variableresolution painting data gathered across Internet sources rather than solely using professional high-resolution data. Consequently, we believe this solution better addresses the task of classifying consumer-quality digital captures than other existing approaches. We include a comparison to existing feature extraction and classification methods as well as an analysis of our own approach across classifiers and feature vectors.


title: A neural model of rule generation in inductive reasoning

abstract: Inductive reasoning is a fundamental and complex aspect of human intelligence. In particular, how do subjects, given a set of particular examples, generate general descriptions of the rules governing that set? We present a biologically plausible method for accomplishing this task and implement it in a spiking neuron model. We demonstrate the success of this model by applying it to the problem domain of Raven’s Progressive Matrices, a widely used tool in the field of intelligence testing. The model is able to generate the rules necessary to correctly solve Raven’s items, as well as recreate many of the experimental effects observed in human subjects.


title: Good features to track

abstract: No feature-based vision system can work unless good features can be identified and tracked from frame to frame. Although tracking itself is by and large a solved problem, selecting features that can be tracked well and correspond to physical points in the world is still hard. We propose a feature selection criterion that is optimal by construction because it is based on how the tracker works, and a feature monitoring method that can detect occlusions, disocclusions, and features that do not correspond to points in the world. These methods are based on a new tracking algorithm that extends previous Newton-Raphson style search methods to work under affine image transformations. We test performance with several simulations and experiments. >


title: LEARNING TO DETECT PHISHING URLS

abstract: Phishing attacks have been on the rise and performing certain actions such as mouse hovering, clicking, etc. on malicious URLs may cause unsuspecting Internet users to fall victims of identity theft or other scams. In this paper, we study the anatomy of phishing URLs that are created with the specific intent of impersonating a trusted third party to trick users into divulging personal data. Unlike previous work in this area, we only use a number of publicly available features on URL alone; in addition, we compare performance of different machine learning techniques and evaluate the efficacy of real-time application of our method. Applying it on real-world data sets, we demonstrate that the proposed approach is highly effective in detecting phishing URLs with an error rate of 0.3%, false positive rate of 0.2% and false negative rate of about 0.5%, thereby improving previous results on the important problem of phishing detection.


title: Multi-connectivity functional architectures in 5G

abstract: Future mobile networks need to fulfill stringent requirements on data rates, reliability, and availability. In order to satisfy these requirements, heterogeneous radio access technologies and deployments need to be used. To make efficient use of these technologies, multi-connectivity has been proposed to connect to multiple different technologies simultaneously. In this paper, we discuss different options to connect to multiple radio access points. Each of these options is further detailed, novel functionality required for multi-connectivity is introduced, and expected benefits are explained.


title: On pixel count based crowd density estimation for visual surveillance

abstract: Surveillance systems for public security are going beyond the conventional CCTV. A new generation of systems relies on image processing and computer vision techniques, deliver more ready-to-use information, and provide assistance for early detection of unusual events. Crowd density is a useful source of information because unusual crowdedness is often related to unusual events. Previous works on crowd density estimation either ignore perspective distortion or perform the correction based on incorrect formulation. Also there is no investigation on whether the geometric correction derived for the ground plane can be applied to human objects standing upright to the plane. This paper derives the relation for geometric correction for the ground plane and proves formally that it can be directly applied to all the foreground pixels. We also propose a very efficient implementation because it is important for a real-time application. Finally a time-adaptive criterion for unusual crowdedness detection is described.


title: Computer Game Mods, Modders, Modding, and the Mod Scene

abstract: Computer games have increasingly been the focus of user-led innovations in the form of game mods. This paper examines how different kinds of socio-technical affordances serve to organize the actions of the people who develop and share their game mods. The affordances examined include customization and tailoring mechanisms, software and content copyright licenses, game software infrastructure and development tools, career contingencies and organizational practices of mod teams, and social worlds intersecting the mod scene. Numerous examples will be used to ground this review and highlight how such affordances can organize, facilitate or constrain what can be done. Overall, this study helps to provide a deeper understanding of how a web of associated affordances collectively serve to govern what mods get made, how modding practices emerge and flourish, and how modders and the game industry serve each others' interests, though not always in equivocal terms.


title: Sideslip Angle Estimation of an Electric Ground Vehicle via Finite-Frequency $\mathcal {H}_{\infty}$ Approach

abstract: The lateral stability is critically important especially when the vehicle is steered at a high longitudinal speed. Loss of lateral stability would lead to severe accidents. Therefore, the study of lateral stability has been a hot topic for decades. In order to monitor the lateral stability or improve the stability by using the feedback control, the sideslip angle is an important index. However, the sideslip angle is not measurable by using an affordable physical sensor. An alternative approach is to estimate the sideslip angle with the measurements of relatively cheap sensors. In this paper, we investigate the problem of sideslip angle observer design for an electric ground vehicle (EGV). The EGV is equipped with an advanced navigation system. The lateral velocity, the longitudinal velocity, and the yaw rate are available. Thus, the sideslip angle which is defined as the ratio of lateral velocity and longitudinal velocity is also available. Meanwhile, the hand-wheel steering angle is also measurable in this application. The main work is to estimate the sideslip angle with the measurements of yaw rate based on the vehicle lateral dynamics. The dynamic model is first established and the parameters are identified with experimental data. Since the dynamic model is nonlinear, in order to facilitate the system analysis and observer design, the nonlinear model is transformed to a linear-parameter-varying (LPV) system. An observer is proposed based on the LPV form. By defining the estimation error, a compact system which contains the estimation error and the original dynamics is obtained. Considering the frequency of the front-wheel steering angle, the finite-frequency $\mathcal {H}_{\infty}$ performance of the compact system is exploited. An optimal observer design method is then developed. For the EGV, the observer is designed according to the developed method. The performance of the designed observer is illustrated with experimental test data.


title: Towards Detecting Rumours in Social Media

abstract: The spread of false rumours during emergencies can jeopardise the well-being of citizens as they are monitoring the stream of news from social media to stay abreast of the latest updates. In this paper, we describe the methodology we have developed within the PHEME project for the collection and sampling of conversational threads, as well as the tool we have developed to facilitate the annotation of these threads so as to identify rumourous ones. We describe the annotation task conducted on threads collected during the 2014 Ferguson unrest and we present and analyse our findings. Our results show that we can collect effectively social media rumours and identify multiple rumours associated with a range of stories that would have been hard to identify by relying on existing techniques that need manual input of rumour-specific keywords.


title: Image domain dual material decomposition for dual‐energy CT using butterfly network

abstract: Dual-energy CT (DECT) has been increasingly used in imaging applications because of its capability for material differentiation. However, material decomposition suffers from magnified noise from two CT images of independent scans, leading to severe degradation of image quality. Existing algorithms achieve suboptimal decomposition performance since they fail to accurately depict the mapping relationship between DECT and the basis material images. Inspired by the impressive potential of CNN, we developed a new Butterfly network to perform the image domain dual material decomposition due to its strong approximation ability to the mapping functions in DECT. The Butterfly network is derived from the image domain DECT decomposition model by exploring the geometric relationship between mapping functions of data model and network components. The network is designed as the double-entry double-out crossover architecture based on the decomposition formulation. It enters a pair of dual-energy images as inputs and defines the ground true decomposed images as each label. The crossover architecture, which plays an important role in material decomposition, is designed to implement the information exchange between the two material generation pathways in the network. Network components exhibit different sensitivity to basis materials in the visualization. By analyzing their sensitivity to different basis materials, we determined the roles of network components in material decomposition. This visualization evaluation reveals what the network can learn and verifies the rationality of network design. The qualitative and quantitative evaluations in material decomposition of patient data indicate that the proposed network outperforms its counterpart.


title: Modality in Tae’ language: a grammatical-lexical view

abstract: Abstract: There are many ways to find modality m languages Modality of language can be expressed through grammatical or lexical feature However, modality in Tae' language specifically. can be described through both these features This research aims to express the modality in lac' language based on grammatical-lexical point of view This is a descriptive qualitative research using library research methods. Library research aims to get of completed and detail data. Further, this research analyzes eighteen discourses of Tae" language consisting of folklore, fable, messages, and Tae' specific food The result shows that there are some features marking modality in Tae' language that expressed through grammatical and lexical feature Grammatical modality of Tae" established through affixation process, i.e. prefix la-, and suffixes -ri. ra functioning as verbs and particles Further, modality m Tae" is also established by lexical feature using the words me/o. parotht, musu. beta, wo 'ding, hang ami. JW. iade, k . ohm, tae, lanma. saha. lake, andknnuui functioning ax auxiliary verbs, particles, negations, and conjunctions Semantically. these forms mark epistemit and deontic modality in Tae" language Lpistemic and deontic modality in Tae" describe as declarative, subjunctive, dubitativc. imperative, dcsiderative and volition, interrogative, and possibility form


title: Robust Confidence Intervals for Effect Sizes: A Comparative Study of Cohen's d and Cliff's Delta Under Non-normality and Heterogeneous Variances

abstract: A battery exposed to heating from incident sunlight is cooled by rejecting heat to air drawn through a chimney by the heating of at least portions of the chimney by the incident sunlight. The battery is placed within the chimney so that the air flow in the chimney passes around the battery, enabling heat to be transferred directly from the battery to the air. Air flow in the chimney is generated by sunlight incident on the exterior of the chimney, causing heating of air within at least a portion of the chimney to create a natural convective air flow within the chimney, with relatively cool outside ambient air being drawn in at one end of the chimney and heated air being exhausted out the other end of the chimney.


title: Recognizing Disguised Faces: Human and Machine Evaluation

abstract: Face verification, though an easy task for humans, is a long-standing open research area. This is largely due to the challenging covariates, such as disguise and aging, which make it very hard to accurately verify the identity of a person. This paper investigates human and machine performance for recognizing/verifying disguised faces. Performance is also evaluated under familiarity and match/mismatch with the ethnicity of observers. The findings of this study are used to develop an automated algorithm to verify the faces presented under disguise variations. We use automatically localized feature descriptors which can identify disguised face patches and account for this information to achieve improved matching accuracy. The performance of the proposed algorithm is evaluated on the IIIT-Delhi Disguise database that contains images pertaining to 75 subjects with different kinds of disguise variations. The experiments suggest that the proposed algorithm can outperform a popular commercial system and evaluates them against humans in matching disguised face images.


title: Ontology Matching Based on hypernym, hyponym, holonym, and meronym Sets in WordNet

abstract: Considerable research in the field of ontology matching has been performed where information sharing and reuse becomes necessary in ontology development. Measurement of lexical similarity in ontology matching is performed using synset, defined in WordNet. In this paper, we defined a Super Word Set, which is an aggregate set that includes hypernym, hyponym, holonym, and meronym sets in WordNet. The Super Word Set Similarity is calculated by the rate of words of concept name and synset’s words inclusion in the Super Word Set. In order to measure of Super Word Set Similarity, we first extracted Matched Concepts(MC), Matched Properties(MP) and Property Unmatched Concepts(PUC) from the result of ontology matching. We compared these against two ontology matching tools – COMA++ and LOM. The Super Word Set Similarity shows an average improvement of 12% over COMA++ and 19% over LOM.


title: Low-Power, High-Data Rate 915 MHz Transceiver with Fully Passive Wake-Up Receiver for Biomedical Implants

abstract: A 915 MHz transceiver has been developed for implantable medical devices. The transceiver offers exceptionally low power consumption while providing a high data rate using injection-locked oscillator. The transceiver utilizes Frequency- Shift-Keying (FSK) modulation at a data rate of 8 Mbit/s to provide wireless link between target implantable device and a central base-station unit operating in the North American Industrial, Scientific and Medical (ISM) frequency band. The circuit features a fully passive wake-up receiver (WuRx) enabling energy harvesting from Radio Frequency (RF) input. The amplitude of the FSK RF signal is controlled low for short distance transmission to further reduce the energy consumption. The transceiver, implemented in 0.13 u⊤m CMOS process, is built on new FSK modulation scheme and based on frequency to amplitude conversion. The WuRx achieves a sensitivity of -53 dBm while the main receiver shows -78 dBm sensitivity. Thanks to the simplified hardware, the receiver dissipates only 640 u⊤W while the transmitter consumes 1.4 mW from 1.2 V supply voltage.


title: A Spiking Neural Network System for Robust Sequence Recognition

abstract: This paper proposes a biologically plausible network architecture with spiking neurons for sequence recognition. This architecture is a unified and consistent system with functional parts of sensory encoding, learning, and decoding. This is the first systematic model attempting to reveal the neural mechanisms considering both the upstream and the downstream neurons together. The whole system is a consistent temporal framework, where the precise timing of spikes is employed for information processing and cognitive computing. Experimental results show that the system is competent to perform the sequence recognition, being robust to noisy sensory inputs and invariant to changes in the intervals between input stimuli within a certain range. The classification ability of the temporal learning rule used in the system is investigated through two benchmark tasks that outperform the other two widely used learning rules for classification. The results also demonstrate the computational power of spiking neurons over perceptrons for processing spatiotemporal patterns. In summary, the system provides a general way with spiking neurons to encode external stimuli into spatiotemporal spikes, to learn the encoded spike patterns with temporal learning rules, and to decode the sequence order with downstream neurons. The system structure would be beneficial for developments in both hardware and software.


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title: Single Active Switch Power Electronics for Kilowatt Scale Capacitive Power Transfer

abstract: The development of capacitive power transfer (CPT) as a competitive wireless/contactless power transfer solution over short distances is proving viable in both consumer and industrial electronic products/systems. The CPT is usually applied in low-power applications, due to small coupling capacitance. Recent research has increased the coupling capacitance from the pF to the nF scale, enabling extension of CPT to kilowatt power level applications. This paper addresses the need of efficient power electronics suitable for CPT at higher power levels, while remaining cost effective. Therefore, to reduce the cost and losses single-switch–single-diode topologies are investigated. Four single active switch CPT topologies based on the canonical Cuk, SEPIC, Zeta, and Buck-boost converters are proposed and investigated. Performance tradeoffs within the context of a CPT system are presented and corroborated with experimental results. A prototype single active switch converter demonstrates 1-kW power transfer at a frequency of 200 kHz with >90% efficiency.


title: Simultaneous Feature Learning and Hash Coding with Deep Neural Networks

abstract: Similarity-preserving hashing is a widely-used method for nearest neighbour search in large-scale image retrieval tasks. For most existing hashing methods, an image is first encoded as a vector of hand-engineering visual features, followed by another separate projection or quantization step that generates binary codes. However, such visual feature vectors may not be optimally compatible with the coding process, thus producing sub-optimal hashing codes. In this paper, we propose a deep architecture for supervised hashing, in which images are mapped into binary codes via carefully designed deep neural networks. The pipeline of the proposed deep architecture consists of three building blocks: 1) a sub-network with a stack of convolution layers to produce the effective intermediate image features; 2) a divide-and-encode module to divide the intermediate image features into multiple branches, each encoded into one hash bit; and 3) a triplet ranking loss designed to characterize that one image is more similar to the second image than to the third one. Extensive evaluations on several benchmark image datasets show that the proposed simultaneous feature learning and hash coding pipeline brings substantial improvements over other state-of-the-art supervised or unsupervised hashing methods.


title: Enhancing purchase intention through social media brand community: the roles of social presence, interactivity, and peer motivation

abstract: By integrating the perspectives of social presence, interactivity, and peer motivation, this study developed a theoretical model to examine the factors affecting members' purchase intention in the context of social media brand community. Data collected from members of a fan page brand community on Facebook in Taiwan was used to test the model. The results also show that peer extrinsic motivation and peer intrinsic motivation have positive influences on purchase intention. The results also reveal that human-message interaction exerts significant influence on peer extrinsic motivation and peer intrinsic motivation, while human-human interaction has a positive effect on human-message interaction. Finally, the results report that awareness impacts human-message interaction significantly, whereas awareness, affective social presence, and cognitive social presence influence human-human interaction significantly.


title: Word Sense Disambiguation Using Label Propagation Based Semi-Supervised Learning

abstract: Shortage of manually sense-tagged data is an obstacle to supervised word sense disambiguation methods. In this paper we investigate a label propagation based semi-supervised learning algorithm for WSD, which combines labeled and unlabeled data in learning process to fully realize a global consistency assumption: similar examples should have similar labels. Our experimental results on benchmark corpora indicate that it consistently outperforms SVM when only very few labeled examples are available, and its performance is also better than monolingual bootstrapping, and comparable to bilingual bootstrapping.


title: Sum Product Networks for Activity Recognition

abstract: This paper addresses detection and localization of human activities in videos. We focus on activities that may have variable spatiotemporal arrangements of parts, and numbers of actors. Such activities are represented by a sum-product network (SPN). A product node in SPN represents a particular arrangement of parts, and a sum node represents alternative arrangements. The sums and products are hierarchically organized, and grounded onto space-time windows covering the video. The windows provide evidence about the activity classes based on the Counting Grid (CG) model of visual words. This evidence is propagated bottom-up and top-down to parse the SPN graph for the explanation of the video. The node connectivity and model parameters of SPN and CG are jointly learned under two settings, weakly supervised, and supervised. For evaluation, we use our new Volleyball dataset, along with the benchmark datasets VIRAT, UT-Interactions, KTH, and TRECVID MED 2011. Our video classification and activity localization are superior to those of the state of the art on these datasets.


title: Cloud Computing: An Overview

abstract: In order to support the maximum number of user and elastic service with the minimum resource, the Internet service provider invented the cloud computing. within a few years, emerging cloud computing has became the hottest technology. From the publication of core papers by Google since 2003 to the commercialization of Amazon EC2 in 2006, and to the service offering of AT&T Synaptic Hosting, the cloud computing has been evolved from internal IT system to public service, from cost-saving tools to revenue generator, and from ISP to telecom. This paper introduces the concept, history, pros and cons of cloud computing as well as the value chain and standardization effort.


title: Beyond Left-to-Right: Multiple Decomposition Structures for SMT

abstract: Standard phrase-based translation models do not explicitly model context dependence between translation units. As a result, they rely on large phrase pairs and target language models to recover contextual e ects in translation. In this work, we explore n-gram models over Minimal Translation Units (MTUs) to explicitly capture contextual dependencies across phrase boundaries in the channel model. As there is no single best direction in which contextual information should flow, we explore multiple decomposition structures as well as dynamic bidirectional decomposition. The resulting models are evaluated in an intrinsic task of lexical selection for MT as well as a full MT system, through n-best reranking. These experiments demonstrate that additional contextual modeling does indeed benefit a phrase-based system and that the direction of conditioning is important. Integrating multiple conditioning orders provides consistent benefit, and the most important directions di er by language pair.


title: Characteristics of network traffic flow anomalies

abstract: One of the primary tasks of network administrators is monitoring routers and switches for anomalous traffic behavior such as outages, configuration changes, flash crowds and abuse. Recognizing and identifying anomalous behavior is often based on ad hoc methods developed from years of experience in managing networks. A variety of commercial and open source tools have been developed to assist in this process, however these require policies and/or or thresholds to be defined by the user in order to trigger alerts. The better the description of the anomalous behavior, the more effective these tools become. In this extended abstract we describe a project focused on precise characterization of anomalous network traffic behavior. The first step in our project is to gather passive measurements of network traffic at the IP flow level. IP flow level data as defined in [1] is a unidirectional series of IP packets of a given protocol traveling between a source and a destination IP/port pair within a certain period of time. While flow level data is certainly not as precise as passive measurements of packet level data, we demonstrate that it is sufficient for exposing many different types of aberrant network traffic behavior in close to real time. It also has the benefit of generating much smaller data sets than packet level measurements which can become a significant issue in large, heavily used networks. We use the FlowScan [2] open source software to gather and analyze network flow data. FlowScan takes Netflow [3] feeds from Cisco or other Lightweight Flow Accounting Protocol (LFAP) enabled routers, processes the data and then it in an efficient data structure. FlowScan also has a graphical interface which is currently the principal means for anomaly identification by network managers. FlowScan is currently deployed at the border router at the University of Wisconsin Madison as well as over 100 other sites nation wide.


title: Active Throughput Estimation Using RTT of Differing ICMP Packet Sizes

abstract: Network measurements always play a vital role for good network management purposes and are essential for quality of service (QoS) requirements. Packet delay derivations are critical measurement parameters in diagnosing simple network performance measures. The traditional ping tool is popular for target host connectivity testing and average delay measurements. However, ping is a poor indicator of round trip time (RTT) measurements at the network layer. In this paper, an active round trip time (RTT) packet delay measurements using Internet control message protocol (ICMP) data packets of varying sizes is proposed. It was observed that in the proposed RTT measurement technique, peak throughput values were obtained compared to the ping measurements. The proposed approach eliminates the need for special purpose cooperation and/or software applications at the destination in the measured network path when using the ICMP Echo Request/ Reply technique.


title: ScaleGraph: A high-performance library for billion-scale graph analytics

abstract: Recently, large-scale graph analytics has become a very popular topic owing to the emergence of gigantic graphs whose number of vertices and edges is in millions, billions or even trillions. Many graph analytics libraries and frameworks have been proposed with various computational models and programming languages to deal with such graphs. X10 programming language is a PGAS language that aims at both software performance and programmer's productivity. We introduce ScaleGraph library developed using X10 programming to illustrate the use of X10 for large-scale graph analytics. ScaleGraph library provides XPregel framework that is inspired by Google's Pregel computation model, serving as a building block for implementing graph kernels. We also optimized X10 runtime in some parts such as collective communication and memory management. We evaluated the performance and scalability of ScaleGraph libraries. The result shows that most graph kernels have good performance and scalability. ScaleGraph library is 9.4 times faster than Giraph in the experiment of PageRank with 16 machine nodes. To the best of our knowledge, ScaleGraph is the first X10-based library to address performance, scalability and productivity issues in dealing with large-scale graph analytics.


title: Large size, lightweight, Luneburg Lenses for multi-beam antenna applications

abstract: Using newly developed meta-materials, multilayer Luneburg Lenses were fabricated with diameters 0.6m and 1.0m. The meta-materials used are broad-band, have low losses, have controllable permittivity and average density of 40kg/m3. This allows for a construction of large diameter broadband Luneburg Lenses with multilayer structure and low weight, the 0.6m and 1.0m Lenses weighing less then 5kg and 21kg respectively. The measurement results of the light weight, large size, broadband Luneburg Lenses are presented.


title: Narratives of satisfying and unsatisfying experiences of current mobile augmented reality applications

abstract: Over the last few years, mobile applications demonstrating Augmented Reality (AR) - such as Layar, Junaio and Google Goggles - have been introduced to consumers. We conducted an online survey to explore the user experience (UX) of early stage mobile AR applications available in the market in spring 2011, covering both location-based AR browsers and image recognition AR applications for object-based interaction. We identify various types of experiences such applications have evoked by qualitatively analyzing 84 users' narratives of their most satisfying and unsatisfying experiences. The results highlight, for example, experiences of awareness of surroundings, empowerment, positive surprise, amazement and fascination from the novelty value, as well as some examples of immersion and social connectivity. The analysis indicates that the applications have not yet reached their potential in evoking a multifaceted user experience that is characteristic especially to AR. This work helps in understanding the experiential design potential in mobile AR and points out UX issues to further focus on in design.


title: Derivative Delay Embedding: Online Modeling of Streaming Time Series

abstract: The staggering amount of streaming time series coming from the real world calls for more efficient and effective online modeling solution. For time series modeling, most existing works make some unrealistic assumptions such as the input data is of fixed length or well aligned, which requires extra effort on segmentation or normalization of the raw streaming data. Although some literature claim their approaches to be invariant to data length and misalignment, they are too time-consuming to model a streaming time series in an online manner. We propose a novel and more practical online modeling and classification scheme, DDE-MGM, which does not make any assumptions on the time series while maintaining high efficiency and state-of-the-art performance. The derivative delay embedding (DDE) is developed to incrementally transform time series to the embedding space, where the intrinsic characteristics of data is preserved as recursive patterns regardless of the stream length and misalignment. Then, a non-parametric Markov geographic model (MGM) is proposed to both model and classify the pattern in an online manner. Experimental results demonstrate the effectiveness and superior classification accuracy of the proposed DDE-MGM in an online setting as compared to the state-of-the-art.


title: Adaptive deconvolutional networks for mid and high level feature learning

abstract: We present a hierarchical model that learns image decompositions via alternating layers of convolutional sparse coding and max pooling. When trained on natural images, the layers of our model capture image information in a variety of forms: low-level edges, mid-level edge junctions, high-level object parts and complete objects. To build our model we rely on a novel inference scheme that ensures each layer reconstructs the input, rather than just the output of the layer directly beneath, as is common with existing hierarchical approaches. This makes it possible to learn multiple layers of representation and we show models with 4 layers, trained on images from the Caltech-101 and 256 datasets. When combined with a standard classifier, features extracted from these models outperform SIFT, as well as representations from other feature learning methods.


title: Human Daily Activity Recognition With Sparse Representation Using Wearable Sensors

abstract: Human daily activity recognition using mobile personal sensing technology plays a central role in the field of pervasive healthcare. One major challenge lies in the inherent complexity of human body movements and the variety of styles when people perform a certain activity. To tackle this problem, in this paper, we present a novel human activity recognition framework based on recently developed compressed sensing and sparse representation theory using wearable inertial sensors. Our approach represents human activity signals as a sparse linear combination of activity signals from all activity classes in the training set. The class membership of the activity signal is determined by solving a l1 minimization problem. We experimentally validate the effectiveness of our sparse representation-based approach by recognizing nine most common human daily activities performed by 14 subjects. Our approach achieves a maximum recognition rate of 96.1%, which beats conventional methods based on nearest neighbor, naive Bayes, and support vector machine by as much as 6.7%. Furthermore, we demonstrate that by using random projection, the task of looking for “optimal features” to achieve the best activity recognition performance is less important within our framework.


title: Meerkat and Periscope: I Stream, You Stream, Apps Stream for Live Streams

abstract: We conducted a mixed methods study of the use of the Meerkat and Periscope apps for live streaming video and audio broadcasts from a mobile device. We crowdsourced a task to describe the content, setting, and other characteristics of 767 live streams. We also interviewed 20 frequent streamers to explore their motivations and experiences. Together, the data provide a snapshot of early live streaming use practices. We found a diverse range of activities broadcast, which interviewees said were used to build their personal brand. They described live streaming as providing an authentic, unedited view into their lives. They liked how the interaction with viewers shaped the content of their stream. We found some evidence for multiple live streams from the same event, which represent an opportunity for multiple perspectives on events of shared public interest.


title: Recurrence techniques for the calculation of bessel functions

abstract: where Fv(x) may be either J,(x) or Y,(x). If one is given Y,(x) and Yv+?(x) then Eq. (1) may be used to generate the functions Yv+,(x). For ? >> (x/2) the function Y,(x) increases extremely rapidly with increasing order, i.e., Y,(x) -(2,u/x) and the functions Y,+n(x) calculated from Eq. (1) yield good accuracy for large n. However, if one is given JA(x) and J,+? (x), Eq. (1) gives poor accuracy for Jv+n(x), since for ? >> (x/2), J,(x) -(2l/x)->. On the other hand, if one is given Jv+n(x) and Jv+n+l(x), where n >> (x/2), then one may again recur without loss of accuracy but this time in the direction of decreasing order. We shall first treat the problem of using the recurrence technique in the calculation of the regular Bessel function Jv+n(x). Thus, let us find Jv+n(x), for 0 < v < 1 and n < N.


title: Generalized Solutions for Quantum Mechanical Oscillator on Kähler Conifold

abstract: We study the possible generalized boundary conditions and the corresponding solutions for the quantum mechanical oscillator model on Kahler conifold. We perform it by self-adjoint extension of the initial domain of the effective radial Hamiltonian. Remarkable effect of this generalized boundary condition is that at certain boundary condition the orbital angular momentum degeneracy is restored! We also recover the known spectrum in our formulation, which of course corresponds to some other boundary condition.


title: Towards Practical Privacy-Preserving Analytics for IoT and Cloud Based Healthcare Systems

abstract: Modern healthcare systems now rely on advanced computing methods and technologies, such as Internet of Things (IoT) devices and clouds, to collect and analyze personal health data at an unprecedented scale and depth. Patients, doctors, healthcare providers, and researchers depend on analytical models derived from such data sources to remotely monitor patients, early-diagnose diseases, and find personalized treatments and medications. However, without appropriate privacy protection, conducting data analytics becomes a source of a privacy nightmare. In this article, we present the research challenges in developing practical privacy-preserving analytics in healthcare information systems. The study is based on kHealth-a personalized digital healthcare information system that is being developed and tested for disease monitoring. We analyze the data and analytic requirements for the involved parties, identify the privacy assets, analyze existing privacy substrates, and discuss the potential tradeoff among privacy, efficiency, and model quality.


title: 2006), “Advertising and Portfolio Choice

abstract: This paper examines the role that advertising plays in the mutual fund industry and whether advertising affects investors’ fund and portfolio choices. Content analysis shows that only a small fraction of fund advertising is directly informative about characteristics relevant for rational investors, such as fund fees. Higher quantities of advertising do not signal ex ante higher unobservable fund manager ability, because funds that advertise more are not associated with higher post-advertising excess returns. Fund advertising is shown to affect investors’choices, although it provides little information. These results do not seem to be driven by the endogeneity of advertising, and are robust to a series of robustness checks. Finally, advertising is found to steer people towards portfolios with higher fees and more risk, through higher exposure to equities, more active management, more “hot” sectors, and more home bias. This evidence has implications for welfare analysis, asset pricing and public policy, and may serve as a starting point for broader analysis of marketing and persuasion efforts in financial markets


title: Brand Experience: What is it? How is it Measured? Does it Affect Loyalty?

abstract: Abstract Brand experience is conceptualized as sensations, feelings, cognitions, and behavioral responses evoked by brand-related stimuli that are part of a brand's design and identity, packaging, communications, and environments. The authors distinguish several experience dimensions and construct a brand experience scale that includes four dimensions: sensory, affective, intellectual, and behavioral. In six studies, the authors show that the scale is reliable, valid, and distinct from other brand measures, including brand evaluations, brand involvement, brand attachment, customer delight, and brand personality. Moreover, brand experience affects consumer satisfaction and loyalty directly and indirectly through brand personality associations.


title: Developmental Learning of Sensorimotor Models for Control in Robotics

abstract: Robotics is evolving today toward applications in which robots enter into everyday life and thus need to be able to interact with non-engineers; robots that assist people with disabilities in their homes are an example [5]. For such applications, robots must be able to manipulate and use objects in the human environment that cannot be known and modelled beforehand by engineers. For reasons of safety and energy efficiency, the use of soft materials and soft actuators is flourishing in these applications [12]; difficult control problems arise, however, because the dynamics of these soft materials are difficult to model analytically. For these reasons, a crucial goal has become the development of learning methods that allow robots to acquire models of the dynamics of their own bodies and their interactions with new objects.


title: Multiple distance sensors based smart stick for visually impaired people

abstract: In this paper, we present a novel low-cost yet durable and accurate smart stick to assist visually impaired people while they walk in indoor/outdoor unstructured environments. There is a large group of people who have difficulties in their daily routine work due to losing their eyesight. Walking with confidence is one of them which may have different challenges in different environments/countries. We have considered Indian context where outdoor environments are often clustered and noisy. Keeping these challenges in mind, a new smart stick is developed which is capable of detecting obstacles of any height in front or slightly sideways of the person. The stick gives a fair idea about the distance and the location of obstacles through vibration in hand and audio in the ear of the person. The wireless connection has been set up using Bluetooth between the earphone and the stick. Different frequencies of the generated vibration and different tracks of the audio alert the person about the distance of the obstacle. Real-time experiments have been conducted in different environments by different people to observe the accuracy of the stick and results are quite encouraging.


title: Automatic Modulation Recognition Using Deep Learning Architectures

abstract: In this paper, we present an automatic modulation recognition framework for the detection of radio signals in a communication system. The framework considers both a deep convolutional neural network (CNN) and a long short term memory network. Further, we propose a pre-processing signal representation that combines the in-phase, quadrature and fourth-order statistics of the modulated signals. The presented data representation allows our CNN and LSTM models to achieve 8% improvements on our testing dataset. We compare the recognition accuracy of the proposed recognition methods with existing methods under various SNR values. Experimental results show that our methods perform better than the existing methods.


title: Exposure to opposing views on social media can increase political polarization

abstract: There is mounting concern that social media sites contribute to political polarization by creating “echo chambers” that insulate people from opposing views about current events. We surveyed a large sample of Democrats and Republicans who visit Twitter at least three times each week about a range of social policy issues. One week later, we randomly assigned respondents to a treatment condition in which they were offered financial incentives to follow a Twitter bot for 1 month that exposed them to messages from those with opposing political ideologies (e.g., elected officials, opinion leaders, media organizations, and nonprofit groups). Respondents were resurveyed at the end of the month to measure the effect of this treatment, and at regular intervals throughout the study period to monitor treatment compliance. We find that Republicans who followed a liberal Twitter bot became substantially more conservative posttreatment. Democrats exhibited slight increases in liberal attitudes after following a conservative Twitter bot, although these effects are not statistically significant. Notwithstanding important limitations of our study, these findings have significant implications for the interdisciplinary literature on political polarization and the emerging field of computational social science.


title: A Machine Learning-Based Approximation of Strong Branching

abstract: We present in this paper a new generic approach to variable branching in branch and bound for mixed-integer linear problems. Our approach consists in imitating the decisions taken by a good branching strategy, namely strong branching, with a fast approximation. This approximated function is created by a machine learning technique from a set of observed branching decisions taken by strong branching. The philosophy of the approach is similar to reliability branching. However, our approach can catch more complex aspects of observed previous branchings to take a branching decision. The experiments performed on randomly generated and MIPLIB problems show promising results.


title: Measurement-driven probability hypothesis density filter for multi-target tracking in passive radar

abstract: A measurement-driven probability hypothesis density (MD-PHD) filter based on track initiation is proposed in this paper to handle the incapability of PHD filter when dealing with unknown newborn intensity. Measurements are distinguished into two parts by using gating technology. The existing measurements are used to update intensity while the suspicious measurements are utilized to explore new target via track initiation. The sequential Monte Carlo (SMC) implementation of the proposed filter is derived and applied to passive scenario. The simulation results demonstrate that the proposed method not only tracks existing target accurately but also initiates new target duly without any prior knowledge of newborn intensity.


title: Method of measuring Doppler shift of moving targets using FMCW maritime radar

abstract: During the operation of an FMCW (frequency modulated continuous wave) maritime radar, the system transmits a continuous radio energy with a frequency modulated by a triangular or sawtooth-shaped signal. As a result, the frequency of the transmitted signal varies gradually with time. When the signal is reflected by an object, the received waveform will build up a delayed replica of the transmitted waveform, with the time delay as a measure of the target range. If the target is moving, the radar system will register a Doppler shift within the received signal. Compared to the frequency of the emitted signal, the received signal will show a higher frequency when the target is approaching and a lower frequency when the target is moving away from the radar location. Thus, the total Doppler shift may result from the superposition of both source and observer motions. Specifically, the amount of Doppler shift is directly proportional to the radial speed of the target. The Doppler shift can be determined after performing the range Fourier transform (range FFT) first. For a target of interest, we can repeat the range FFT until we have enough data to perform the second level of FFT. The result of this second FFT is a two dimensional complex valued matrix, whose spectral peak corresponds to the Doppler shift of the moving target. This method is known as Doppler FFT. Some results of the measurement of target speed using this method are presented in this paper.


title: Clickbait Convolutional Neural Network

abstract: With the development of online advertisements, clickbait spread wider and wider. Clickbait dissatisfies users because the article content does not match their expectation. Thus, clickbait detection has attracted more and more attention recently. Traditional clickbait-detection methods rely on heavy feature engineering and fail to distinguish clickbait from normal headlines precisely because of the limited information in headlines. A convolutional neural network is useful for clickbait detection, since it utilizes pretrained Word2Vec to understand the headlines semantically, and employs different kernels to find various characteristics of the headlines. However, different types of articles tend to use different ways to draw users’ attention, and a pretrained Word2Vec model cannot distinguish these different ways. To address this issue, we propose a clickbait convolutional neural network (CBCNN) to consider not only the overall characteristics but also specific characteristics from different article types. Our experimental results show that our method outperforms traditional clickbait-detection algorithms and the TextCNN model in terms of precision, recall and accuracy.


title: Assessing a mobile-based deep learning model for plant disease surveillance

abstract: Convolutional neural network models (CNNs) have made major advances in computer vision tasks in the last five years. Given the challenge in collecting real world datasets, most studies report performance metrics based on available research datasets. In scenarios where CNNs are to be deployed on images or videos from mobile devices, models are presented with new challenges due to lighting, angle, and camera specifications, which are not accounted for in research datasets. It is essential for assessment to also be conducted on real world datasets if such models are to be reliably integrated with products and services in society. Plant disease datasets can be used to test CNNs in real time and gain insight into real world performance. We train a CNN object detection model to identify foliar symptoms of diseases (or lack thereof) in cassava (Manihot esculenta Crantz). We then deploy the model on a mobile app and test its performance on mobile images and video of 720 diseased leaflets in an agricultural field in Tanzania. Within each disease category we test two levels of severity of symptoms - mild and pronounced, to assess the model performance for early detection of symptoms. In both severities we see a decrease in the F-1 score for real world images and video. The F-1 score dropped by 32% for pronounced symptoms in real world images (the closest data to the training data) due to a drop in model recall. If the potential of smartphone CNNs are to be realized our data suggest it is crucial to consider tuning precision and recall performance in order to achieve the desired performance in real world settings. In addition, the varied performance related to different input data (image or video) is an important consideration for the design of CNNs in real world applications.


title: Wideband, low loss and compact 3D LTCC balun with asymmetric structure for millimeter wave applications

abstract: A 3D LTCC (low temperature co-fired ceramic) millimeter wave balun using asymmetric structure was investigated in this paper. The proposed balun consists of embedded multilayer microstrip and CPS (coplanar strip) lines. It was designed at 40GHz. The measured insertion loss of the back-to-back balanced transition is -1.14dB, thus the estimated insertion loss of each device is -0.57dB including the CPS line loss. The 10dB return loss bandwidth of the unbalanced back-to-back transition covers the frequency range of 17.3/spl sim/46.6GHz (91.7%). The area occupied by this balun is 0.42 /spl times/ 0.066/spl lambda//sub 0/ (2.1 /spl times/ 0.33mm/sup 2/). The high performances have been achieved using the low loss and relatively high dielectric constant of LTCC (/spl epsiv//sub r/=5.4, tan/spl delta/=0.0015 at 35GHz) and a 3D stacked configuration. This balun can be used as a transition of microstrip-to-CPS and vice-versa and insures also an impedance transformation from 50 to 110 Ohm for an easy integration with a high input impedance antenna. This is the first reported 40 GHz wideband 3D LTCC balun using asymmetric structure to balance the output amplitude and phase difference.


title: HF MIMO NVIS Measurements with Co-located Dipoles for Future Tactical Communications

abstract: Multiple antennas in transceivers can increase system spectral efficiency, reduce transmit power, enable robustness to interference, and increase overall reliability through multiple-input multiple-output processing (MIMO). Consequently, high frequency (HF) networks, which feature extreme spectrum scarcity and unreliability, are prime for MIMO exploitation. Unfortunately, the desired antenna spacing for MIMO is proportional to the wavelength (tens of meters at HF). One promising approach is to utilize two antennas in a single antenna footprint through cross-polarization. This configuration has not yet been fully validated for MIMO at HF. In this paper, we demonstrate MIMO capabilities in near vertical incidence sky-wave (NVIS) propagation through a measurement campaign. This paper shows that MIMO is a game changer for HF NVIS with up to 2.27 times higher data rate, up to 9 times less transmit power, and more than 3 times fewer link failures. This paper also provides critical channel metrics for baseband designers of future MIMO HF protocols as demonstrated in our companion paper.


title: Rich Image Captioning in the Wild

abstract: We present an image caption system that addresses new challenges of automatically describing images in the wild. The challenges include generating high quality caption with respect to human judgments, out-of-domain data handling, and low latency required in many applications. Built on top of a state-of-the-art framework, we developed a deep vision model that detects a broad range of visual concepts, an entity recognition model that identifies celebrities and landmarks, and a confidence model for the caption output. Experimental results show that our caption engine outperforms previous state-of-the-art systems significantly on both in-domain dataset (i.e. MS COCO) and out-of-domain datasets. We also make the system publicly accessible as a part of the Microsoft Cognitive Services.


title: Identifying Liver Cancer and Its Relations with Diseases, Drugs, and Genes: A Literature-Based Approach

abstract: In biomedicine, scientific literature is a valuable source for knowledge discovery. Mining knowledge from textual data has become an ever important task as the volume of scientific literature is growing unprecedentedly. In this paper, we propose a framework for examining a certain disease based on existing information provided by scientific literature. Disease-related entities that include diseases, drugs, and genes are systematically extracted and analyzed using a three-level network-based approach. A paper-entity network and an entity co-occurrence network (macro-level) are explored and used to construct six entity specific networks (meso-level). Important diseases, drugs, and genes as well as salient entity relations (micro-level) are identified from these networks. Results obtained from the literature-based literature mining can serve to assist clinical applications.


title: Supervised Learning of Complete Morphological Paradigms

abstract: We describe a supervised approach to predicting the set of all inflected forms of a lexical item. Our system automatically acquires the orthographic transformation rules of morphological paradigms from labeled examples, and then learns the contexts in which those transformations apply using a discriminative sequence model. Because our approach is completely data-driven and the model is trained on examples extracted from Wiktionary, our method can extend to new languages without change. Our end-to-end system is able to predict complete paradigms with 86.1% accuracy and individual inflected forms with 94.9% accuracy, averaged across three languages and two parts of speech.


title: Detecting overlapping communities in social networks by game theory and structural equivalence concept

abstract: Most complex networks demonstrate a significant property 'community structure', meaning that the network nodes are often joined together in tightly knit groups or communities, while there are only looser connections between them. Detecting these groups is of great importance and has immediate applications, especially in the popular online social networks like Facebook and Twitter. Many of these networks are divided into overlapping communities, i.e. communities with nodes belonging to more than one community simultaneously. Unfortunately most of the works cannot detect such communities. In this paper, we consider the formation of communities in social networks as an iterative game in a multiagent environment, in which, each node is regarded as an agent trying to be in the communities with members structurally equivalent to her. Remarkable results on the real world and benchmark graphs show efficiency of our approach in detecting overlapping communities compared to the other similar methods.


title: Mindfulness and Health Behaviors: Is Paying Attention Good for You?

abstract: Abstract Objective: The investigators examined relations between mindfulness and health behaviors in college students, as well as the role of stress in mediating these effects. Participants: Participants were 553 undergraduates (385 females; mean age = 18.8 years, SD = 2.1) recruited from a university in the northeastern United States. Methods: Participants completed questionnaires assessing mindfulness, perceived health, health behaviors, health-related activity restriction, and stress. Data were collected from September 2007 through December 2007. Results: Overall perceived health and health-related activity restriction, as well as some health behaviors (eg, binge eating, sleep quality, and physical activity) were related to the Five-Factor Mindfulness Questionnaire and were partially mediated by stress. Conclusions: These results suggest that mindfulness is related to decreased stress, which in turn contributes to increased positive health perceptions and health behaviors. The findings support the util...


title: Holistic Security Requirements Engineering for Socio-Technical Systems

abstract: Security has been a growing concern for large organizations, especially financial and gov- ernmental institutions, as security breaches in the systems they depend have repeatedly resulted in losses of billions per year, and this cost is on the rise. A primary reason for these breaches is the “socio-technical” nature of today’s systems that consist of an amal- gam of social and human actors, processes, technology and infrastructure. We refer to such systems as Socio-Technical Systems (STSs). Finding secure solutions for STSs is a difficult and error-prone task because of their heterogeneity and complexity. The thesis proposes a holistic security requirements analysis framework which catego- rizes system security concerns into three layers, including a social layer (social actors and business processes), a software layer (software applications that support the social layer) and an infrastructure layer (physical infrastructure, hardware, and devices). Within each layer, security requirements are elicited, and security mechanisms are designed to satisfy the security requirements. In particular, a cross-layer support link is defined to capture how security mechanisms deployed at one layer influence security requirements of the next layer down, allowing us to systematically and iteratively analyze security for all three layers and eventually produce holistic security solutions for the systems. To ensure the quality of the analysis of our approach and to promote practical adoption of the three-layer approach, the thesis includes two additional components. Firstly, we propose a holistic attack analysis, which takes an attacker’s perspective to explore realistic attacks that can happen to a system and thus contributes to the identification of critical security requirements. This approach consists of an attack strategy identification method which analyzes attacker’s alternative malicious intentions, and an attack strategy operationalization method which analyzes realistic attack actions that can be performed by attackers. Secondly, the thesis proposes a systematic approach for selecting and applying security patterns, which describe proven security solutions to known security problems. As such, analysts with little security knowledge can efficiently leverage reusable security knowledge to operationalize security requirements in terms of security mechanisms. This approach also allows us to systematically analyze and enforce the impact of deployed security mechanisms on system functional specifications. We have developed a prototype tool, which implements the formalized analysis methods of our three-layer framework and enables the semi-automatic application of our proposal. With the help of the tool, we apply our framework to two large-scale case studies so as to validate the efficacy of our approach.


title: Geolocation of data in the cloud

abstract: We introduce and analyze a general framework for authentically binding data to a location while providing strong assurances against cloud storage providers that (either accidentally or maliciously) attempt to re-locate cloud data. We then evaluate a preliminary solution in this framework that combines constraint-based host geolocation with proofs of data possession, called constraint-based data geolocation (CBDG). We evaluate CBDG using a combination of experiments with PlanetLab and real cloud storage services, demonstrating that we can bind fetched data to the location originally hosting it with high precision. We geolocate data hosted on the majority of our PlanetLab targets to regions no larger than 118,000 km^2, and we geolocate data hosted on Amazon S3 to an area no larger than 12,000 km^2, sufficiently small to identify the state or service region.


title: TriRank: Review-aware Explainable Recommendation by Modeling Aspects

abstract: Most existing collaborative filtering techniques have focused on modeling the binary relation of users to items by extracting from user ratings. Aside from users' ratings, their affiliated reviews often provide the rationale for their ratings and identify what aspects of the item they cared most about. We explore the rich evidence source of aspects in user reviews to improve top-N recommendation. By extracting aspects (i.e., the specific properties of items) from textual reviews, we enrich the user--item binary relation to a user--item--aspect ternary relation. We model the ternary relation as a heterogeneous tripartite graph, casting the recommendation task as one of vertex ranking. We devise a generic algorithm for ranking on tripartite graphs -- TriRank -- and specialize it for personalized recommendation. Experiments on two public review datasets show that it consistently outperforms state-of-the-art methods. Most importantly, TriRank endows the recommender system with a higher degree of explainability and transparency by modeling aspects in reviews. It allows users to interact with the system through their aspect preferences, assisting users in making informed decisions.


title: Reader-Aware Multi-Document Summarization via Sparse Coding

abstract: We propose a new MDS paradigm called reader-aware multi-document summarization (RA-MDS). Specifically, a set of reader comments associated with the news reports are also collected. The generated summaries from the reports for the event should be salient according to not only the reports but also the reader comments. To tackle this RAMDS problem, we propose a sparse-coding-based method that is able to calculate the salience of the text units by jointly considering news reports and reader comments. Another reader-aware characteristic of our framework is to improve linguistic quality via entity rewriting. The rewriting consideration is jointly assessed together with other summarization requirements under a unified optimization model. To support the generation of compressive summaries via optimization, we explore a finer syntactic unit, namely, noun/verb phrase. In this work, we also generate a data set for conducting RA-MDS. Extensive experiments on this data set and some classical data sets demonstrate the effectiveness of our proposed approach.


title: The shared wireless infostation model: a new ad hoc networking paradigm (or where there is a whale, there is a way)

abstract: In wireless ad hoc networks, capacity can be traded for delay. This tradeoff has been the subject of a number of studies, mainly concentrating on the two extremes: either minimizing the delay or maximizing the capacity. However, in between these extremes, there are schemes that allow instantiations of various degrees of this tradeoff. Infostations, which offer geographically intermittent coverage at high speeds, are one such an example. Indeed, through the use of the Infostation networking paradigm, the capacity of a mobile network can be increased at the expense of delay. We propose to further extend the Infostation concept by integrating it with the ad hoc networking technology. We refer to this networking model as the Shared Wireless Infostation Model (SWIM). SWIM allows additional improvement in the capacity-delay tradeoff through a moderate increase in the storage requirements. To demonstrate how SWIM can be applied to solve a practical problem, we use the example of a biological information acquisition system - radio-tagged whales - as nodes in an ad hoc network. We derive an analytical formula for the distribution of end-to-end delays and calculate the storage requirements. We further extend SWIM by allowing multi-tiered operation; which in our biological information acquisition system could be realized through seabirds acting as mobile data collection nodes.


www.cosc.brocku.ca Ant Colony Optimization for Job Shop Scheduling Problem


Error Minimization in 3-Dimensional Model Reconstruction Using Sparse Bundle Adjustment and the Levenberg-Marquardt Algorithm on Stereo Camera Pairs


title: Finding Event, Temporal and Causal Structure in Text: A Machine Learning Approach

abstract: Humans often describe their experiences through the event, temporal and causal structures they perceive. These structures are often expressed in textual forms, for example in timelines, where text is summarized by aligning events with the times at which they occurred. These same sorts of temporal-causal structures are also useful for a variety of computational tasks, like summarization and question answering. However, to reason over such structures they must first be extracted from their textual representations and organized into a machine readable form. ::: This work demonstrates that various important parts of the event, temporal and causal structure of a text can be extracted automatically using machine learning methods. Events, which serve as the basic anchors of temporal and causal relations, can be extracted with F-measures in the 70s and 80s using a word-chunking approach. Temporal relations between adjacent events in some common syntactic constructions can be identified with almost 90% accuracy using pair-wise classification. Causal relations are much more difficult, but initial work suggests that even this task may become tractable to machine learning methods. ::: Analyses of the various tasks lead to several conclusions about how best to approach the automatic extraction of temporal-causal structure. Tasks with little linguistic motivation had low agreement between humans and low machine learning model performance. Tasks with clear annotation guidelines based on known linguistic constructions had much higher inter-annotator agreement and much better model performance. Thus, future progress will depend on careful task selection guided by linguistic knowledge about how event, temporal and causal relations are expressed in text.


title: Estrogen and Progesterone Receptor Expression in Neuroendocrine and Related Neurons of the Pubertal Female Monkey Hypothalamus

abstract: Expression of hypothalamic estrogen receptors (ER) and progesterone receptors (PR) is barely evident in prepubertal monkeys but is prominent in adults. To investigate whether adult patterns of ER and PR expression are established in mid-pubertal female cynomolgus monkeys, we labeled neuroendocrine (NEU) neurons by microinjection of retrograde tracer into the median eminence, and then identified ER and PR by specific immunostaining in separate sets of hypothalamic sections. ER and PR appeared in the cytoplasm and nuclei of cells identified exclusively as neurons, and retrograde tracer remained clearly visible in the cytoplasm of NEU neurons after immunostaining. Numbers of NEU and related neurons expressing ER or PR were quantified in principal hypothalamic regions. In the supraoptic nucleus, almost all neurons analyzed (n = 580) contained ER (94%) with many also NEU (73% ER + NEU), while lesser amounts of the neurons examined (n = 214) expressed PR (75%) and were NEU (53% PR + NEU). In the paraventricular nucleus, most of the neurons analyzed (n = 302) contained ER (90% ER; 54% ER + NEU), but few of the neurons studied (n = 269) contained PR (34% PR; 19% PR + NEU). In the periventricular zone, nearly all neurons examined (n = 795) contained ER (95% ER; 48% ER + NEU), but fewer of those studied (n = 298) exhibited PR (79% PR; 47% PR + NEU). In the arcuate-periventricular zone, all neurons examined (n = 542) contained ER (100%) but few were NEU (4% ER + NEU), while nearly all neurons studied (n = 418) contained PR (95%), some of which were NEU (21% PR + NEU). Neurons expressing ER were also prevalent in areas without NEU labeling, including the diagonal band of Broca, medial preoptic area, and mammillary bodies, but were less common in the septum and dorsomedial hypothalamus. Likewise, neuronal PR expression was seen frequently in the mammillary bodies, but occurred less often in the diagonal band of Broca, medial preoptic area, and dorsomedial hypothalamus. Neurons in the suprachiasmatic nucleus and lateral hypothalamic area lacked retrograde labeling. These results identify the principal sites and subsets of NEU and related neurons which express ER and PR in the mid-pubertal female monkey hypothalamus. They appear to correlate well with known populations of steroid-sensitive NEU neurons present in these areas in adults. The data also suggest that functional patterns of ER and PR expression arise upon reactivation of the hypothalamic-pituitary-gonadal axis at puberty. The degrees of receptor expression and of nuclear translocation most likely reflect peripubertal changes in the levels of gonadal steroids. Taken together, these results provide important insights into the mechanisms and development of neuroendocrine control during the pubertal period in primates.


title: Perceptual visual quality metrics: A survey

abstract: Visual quality evaluation has numerous uses in practice, and also plays a central role in shaping many visual processing algorithms and systems, as well as their implementation, optimization and testing. In this paper, we give a systematic, comprehensive and up-to-date review of perceptual visual quality metrics (PVQMs) to predict picture quality according to human perception. Several frequently used computational modules (building blocks of PVQMs) are discussed. These include signal decomposition, just-noticeable distortion, visual attention, and common feature and artifact detection. Afterwards, different types of existing PVQMs are presented, and further discussion is given toward feature pooling, viewing condition, computer-generated signal and visual attention. Six often-used image metrics(namely SSIM, VSNR, IFC, VIF, MSVD and PSNR) are also compared with seven public image databases (totally 3832 test images). We highlight the most significant research work for each topic and provide the links to the extensive relevant literature.


title: Towards flattenable mesh surfaces

abstract: In many industries, products are constructed by assembled surface patches in @?^3, where each patch is expected to have an isometric map to a corresponding region in @?^2. The widely investigated developable surfaces in differential geometry show this property. However, the method to model a piecewise-linear surface with this characteristic is still under research. To distinguish from the continuous developable surface, we name them as flattenable mesh surfaces since a polygonal mesh has the isometric mapping property if it can be flattened into a two-dimensional sheet without stretching. In this paper, a novel flattenable mesh surface (Flattenable Laplacian mesh) is introduced and the relevant modelling tool is formulated. Moreover, for a given triangular mesh which is almost flattenable, a local perturbation approach is developed to improve its flattenability. The interference between the meshes under process and their nearby objects has been prevented in this local flattenable perturbation. Both the computations of Flattenable Laplacian meshes and the flattenable perturbation are based on the constrained optimization technology.


title: BER Analysis of MIMO-OFDM System using Alamouti STBC and MRC Diversity Scheme over Rayleigh Multipath Channel

abstract: This paper represents a bit error rate performance analysis of multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system with Alamouti Space Time Block Code (STBC) and Maximal Ratio Combining (MRC) diversity scheme over Rayleigh fading channel. Recently, Alamouti STBC has gained much attention as an effective transmit diversity scheme to provide reliable transmission with high peak data rates to increase the capacity of wireless communication system. In this paper, the analysis of Alamouti STBC is used in MIMO-OFDM system to assure transmit diversity and the receive diversity is resolved with MRC diversity technique. For a fixed number of transmit antennas, the performance of Alamouti STBC is analyzed in terms of probability of bit error and diversity gain for a Rayleigh fading channel. At the receiving end, the signals received from multiple paths are weighted and summed in accordance with MRC scheme which provides maximum performance improvement by maximizing the SNR of the MIMO-OFDM system. The simulated results depict that the proposed MIMO-OFDM system concatenated with Alamouti STBC and MRC outperforms conventional SISO-OFDM, MISO-OFDM with Alamouti STBC and SIMO- OFDM with MRC technique in a scattering environment.


title: A Comparative Analysis of Traditional Software Engineering and Agile Software Development

abstract: Over the last decade (or two) the pendulum of developer mindshare has swung decidedly towards agile software development from a more traditional engineering approach to software development. To ascertain the essential differences and any possible incompatibilities between these two software development paradigms this research investigates a number of traditional and agile methodologies, methods, and techniques. The essential differences between traditional software engineering and agile software development are found not to be (as one may first suspect from a cursory consideration) related to iteration length or project management, but rather more related to other attributes like the variety of models employed, the purpose of the models, and the approach to modeling. In the end though the two approaches are not seen to be incompatible, leading to the future possibility of an Agile Software Engineering (ASE).


title: Medical Concept Embedding with Time-Aware Attention

abstract: Embeddings of medical concepts such as medication, procedure and diagnosis codes in Electronic Medical Records (EMRs) are central to healthcare analytics. Previous work on medical concept embedding takes medical concepts and EMRs as words and documents respectively. Nevertheless, such models miss out the temporal nature of EMR data. On the one hand, two consecutive medical concepts do not indicate they are temporally close, but the correlations between them can be revealed by the time gap. On the other hand, the temporal scopes of medical concepts often vary greatly (e.g., \textit{common cold} and \textit{diabetes}). In this paper, we propose to incorporate the temporal information to embed medical codes. Based on the Continuous Bag-of-Words model, we employ the attention mechanism to learn a "soft" time-aware context window for each medical concept. Experiments on public and proprietary datasets through clustering and nearest neighbour search tasks demonstrate the effectiveness of our model, showing that it outperforms five state-of-the-art baselines.


title: Sentiment analysis on social campaign “Swachh Bharat Abhiyan” using unigram method

abstract: Sentiment analysis is the field of natural language processing to analyze opinionated data, for the purpose of decision making. An opinion is a statement about a subject which expresses the sentiments as well as the emotions of the opinion makers on the topic. In this paper, we develop a sentiment analysis tool namely SENTI-METER. This tool estimates the success rate of social campaigns based on the algorithms we developed that analyze the sentiment of word as well as blog. Social campaigns have a huge impact on the mindset of people. One such campaign was launched in India on October 2, 2014, named Swachh Bharat Abhiyan (SBA). Our tool computes an elaborated analysis of Swachh Bharat Abhiyan, which examines the success rate of this social campaign. Here, we performed the location-wise analysis of the campaign and predict the degree of polarity of tweets along with the monthly and weekly analysis of the tweets. The experiments were conducted in five phases namely extraction and preprocessing of tweets, tokenization, sentiment evaluation of a line, sentiment evaluation of a blog (document) and analysis. Our tool is also capable of handling transliterated words. Unbiased tweets were extracted from Twitter related to this specific campaign, and on comparing with manual tagging we were able to achieve 84.47 % accuracy using unigram machine learning approach. This approach helps the government to implement the social campaigns effectively for the betterment of the society.


title: Assistive grasping with an augmented reality user interface

abstract: Assisting impaired individuals with robotic devices is an emerging and potentially transformative technology. This paper describes the design of an assistive robotic grasping system that allows impaired individuals to interact with the system in a human-in-the-loop manner, including the use of a novel cranio-facial electromyography input device. The system uses an augmented reality interface that allows users to plan grasps online that match their task-oriented intents. The system uses grasp quality measurements that generate more robust grasps by considering the local geometry of the object and the effect of uncertainty during grasp acquisition. This interface is validated by testing with real users, both healthy and impaired. This work forms the foundation for a flexible, fully featured human-in-the-loop system that allows users to grasp known and unknown objects in cluttered spaces using novel, practical human–robot interaction paradigms that have the potential to bring human-in-the-loop assistive devi...


title: FID: A Faster Image Distribution System for Docker Platform

abstract: Docker has been widely adopted in enterprise-level container environment. As an important part of Docker-based container ecosystem, Docker Registry provides the service of storing, distributing and managing Docker images, which is crucial to run Docker containers. In large-scale container platforms, deploying applications is prone to overburdening Docker Registry with flooding network traffic, and this situation may even cause failures of image services. In this paper, we present a new P2P-based large-scale image distribution system called FID (Faster Image Distribution), which is able to accelerate the speed of distributing Docker images by taking full advantage of the bandwidth of not only Docker Registry but also other nodes in the cluster. We implemented and validated FID on the enterprise-level cluster. The experiment results show that, compared to the native image distribution method, FID reduces at least 97% of network traffic for Docker Registry. Furthermore, it reduces 83.50% of distribution time on average when distributing images among 200 nodes, and particularly, reduces up to 91.35% of distribution time for the 500M image of Hadoop.


title: Relevance of World Health Organization surgical safety checklist to trauma and orthopaedic surgery.

abstract: Surgery like civil aviation is a risk-prone occupation. Civil Aviation has reported a death rate of less than 1 in a million exposures. On the other hand, surgery has a reported mortality rate of 100 per million surgeries. The National Reporting and Learning System (NRLS) database in England reported 152,017 'incidents' occurred during 4.2 million surgeries in 2008. Trauma and orthopaedic surgery accounted for 32.4 percent of these 'incidents'. Wrong-site surgery occurred in a total of 133 patients, with an incidence of 31.6 per million surgeries. A system to implement and maintain safe surgical practice is mandatory to prevent these 'incidents'. The factors identified in the genesis of these incidents are errors in decision making, lack of communication, leadership and teamwork. These human errors can easily be prevented using a formal structured communication, like the checklists. In 2008, the WHO published a set of guidelines to ensure the safety of surgical patients. In 2009, the checklist was modified with an intention to reduce major surgical complications and was proved to be effective. Wrong level spinal surgery needs special emphasis. There may be an increasing role for checklists in Trauma and Orthopaedic surgical practice to improve its safety profile by being procedure-specific.


title: Tunnel boring machine positioning automation in tunnel construction

abstract: Purpose Tunnel construction using a tunnel boring machine (TBM) entails precise machine positioning and guidance in the underground space. In contrast to traditional laser-based machine guidance solutions, the proposed research aims to develop an automation alternative to facilitate TBMguidance and as-built tunnel alignment survey during tunnelling operations. Method A fully automated system is proposed, in which a robotic total station is employed to automate the continuous process of TBM -tracking and positioning in the 3D underground working space. ZigBeebased wireless sensor networks are applied for wireless data communication inside the tunnel. A camera is mounted on the telescope of the total station to capture online operational videos. Real-time survey data are thus acquired, processed and displayed on a tablet PC on the fly, resulting in: (i) TBM’s precise coordinates in the underground space; (ii) three-axis body rotations of the TBM; (iii) tunnelling chainage progress; and (iv) line and grade deviations of the tunnel alignment. Results & Discussion For proof-of-concept, a prototype TBM-positioning automation system has been developed in-house for laboratory testing. The accuracy testing was conducted by the automation system and a specialist surveyor independently. The differences between the two sets of surveying results were less than 2mm, which sufficiently validated the high accuracy of the automation solution. In April 2012, the prototype will be field tested on a 2.4 m diameter and 1,040 m long drainage tunnel project in Edmonton, Canada.


title: Role of antioxidants and trace elements in health and immunity of transition dairy cows.

abstract: A number of antioxidants and trace minerals have important roles in immune function and may affect health in transition dairy cows. Vitamin E and beta-carotene are important cellular antioxidants. Selenium (Se) is involved in the antioxidant system via its role in the enzyme glutathione peroxidase. Inadequate dietary vitamin E or Se decreases neutrophil function during the perpariturient period. Supplementation of vitamin E and/or Se has reduced the incidence of mastitis and retained placenta, and reduced duration of clinical symptoms of mastitis in some experiments. Research has indicated that beta-carotene supplementation may enhance immunity and reduce the incidence of retained placenta and metritis in dairy cows. Marginal copper deficiency resulted in reduced neutrophil killing and decreased interferon production by mononuclear cells. Copper supplementation of a diet marginal in copper reduced the peak clinical response during experimental Escherichia coli mastitis. Limited research indicated that chromium supplementation during the transition period may increase immunity and reduce the incidence of retained placenta.


title: Leveraging Linux Containers to Achieve High Availability for Cloud Services

abstract: In this work, we present a novel approach that leverages Linux containers to achieve High Availabilitya#x007E; (HA) for cloud applications. A middleware that is comprised of a set of HA agents is defined to compensate the limitations of Linux containers in achieving HA. In our approach we start modeling at the application level, considering the dependencies among application components. We generate the proper scheduling scheme and then deploy the application across containers in the cloud. For each container that hosts critical component(s), we continuously monitor its status and checkpoint its full state, and then react to its failure by restarting locally or failing over to another host where we resume the computing from the most recent state. By using this strategy, all components hosted in a container are preserved without intrusively imposing modification on the application side. Finally, the feasibility of our approach is verified by building a proof-of-concept prototype and a case study of a video streaming application.


title: Embedding Word Similarity with Neural Machine Translation

abstract: Neural language models learn word representations, or embeddings, that capture rich linguistic and conceptual information. Here we investigate the embeddings learned by neural machine translation models, a recently-developed class of neural language model. We show that embeddings from translation models outperform those learned by monolingual models at tasks that require knowledge of both conceptual similarity and lexical-syntactic role. We further show that these effects hold when translating from both English to French and English to German, and argue that the desirable properties of translation embeddings should emerge largely independently of the source and target languages. Finally, we apply a new method for training neural translation models with very large vocabularies, and show that this vocabulary expansion algorithm results in minimal degradation of embedding quality. Our embedding spaces can be queried in an online demo and downloaded from our web page. Overall, our analyses indicate that translation-based embeddings should be used in applications that require concepts to be organised according to similarity and/or lexical function, while monolingual embeddings are better suited to modelling (nonspecific) inter-word relatedness.


title: Automatic acquisition of class disjointness

abstract: Although it is widely acknowledged that adding class disjointness to ontologies enables a wide range of interesting applications, this type of axiom is rarely used on today's Semantic Web. This is due to the enormous skill and effort required to make the necessary modeling decisions. Automatically generating disjointness axioms could lower the barrier of entry and lead to a wider spread adoption. Different methods have been proposed for this automatic generation. These include supervised, top-down approaches which base their results on heterogeneous types of evidence and unsupervised, bottom-up approaches which rely solely on the instance data available for the ontology. However, current literature is missing a thorough comparison of these approaches. In this article, we provide this comparison by presenting two fundamentally different state-of-the-art approaches and evaluating their relative ability to enrich a well-known, multi-purpose ontology with class disjointness. To do so, we introduce a high-quality gold standard for class disjointness. We describe the creation of this standard in detail and provide a thorough analysis. Finally, we also present improvements to both approaches, based in part on discoveries made during our analysis and evaluation.


title: Data Mining in Sports: A Systematic Review

abstract: Data mining technique has attracted attention in the information industry and society as a whole, because of the big amount of data and the imminent need to transform that data into useful information and knowledge. Recently conducted studies with successfully demarcated results using this technique, to estimate several parameters in a variety of domains. However, the effective use of data in some areas is still developing, as is the case of sports, which has shown moderate growth. In this context, the objective of this article is to present a systematic review of the literature about research involving sports data mining. As systematic searches were made out in five databases, resulting in 21 articles that answered a question that grounded this article.


title: Cognitive, collaborative, conceptual and creative - Four characteristics of the next generation of knowledge-based CAD systems: A study in biologically inspired design

abstract: We envision that the next generation of knowledge-based CAD systems will be characterized by four features: they will be based on cognitive accounts of design, and they will support collaborative design, conceptual design, and creative design. In this paper, we first analyze these four dimensions of CAD. We then report on a study in the design, development and deployment of a knowledge-based CAD system for supporting biologically inspired design that illustrates these four characteristics. This system, called DANE for Design by Analogy to Nature Engine, provides access to functional models of biological systems. Initial results from in situ deployment of DANE in a senior-level interdisciplinary class on biologically inspired design indicates its usefulness in helping designers conceptualize design of complex systems, thus promising enough to motivate continued work on knowledge-based CAD for biologically inspired design. More importantly from our perspective, DANE illustrates how cognitive studies of design can inform the development of CAD systems for collaborative, conceptual, and creative design, help assess their use in practice, and provide new insights into human interaction with knowledge-based CAD systems.


title: Examining project history narratives: an analytic approach

abstract: Scientific interest in human beings’ ability and propensity to construe reality through narrative constructions has increased since the 1970s. Although narrative processes have been addressed in the organizational literature, little research attention has yet been given to the role and function of narratives in organizational efforts to develop, implement, and apply information technology. An analytic approach drawn from Mishler (1986b) for the analysis of project history narratives found in research interviews is described. Three project history narratives collected during a field study of systems development are analyzed using this approach. Differences in sensemaking and interpretation revealed in the analysis of each informant’s story and comparison of the analysis of multiple stories are discussed. Insights that narrative analysis may provide into the social cognitive worlds of participants in IS development and its applications in IS research are then considered.