title: Digital Transformation Strategies

abstract: In recent years, firms in almost all industries have conducted a number of initiatives to explore new digital technologies and to exploit their benefits. This frequently involves transformations of key business operations and affects products and processes, as well as organizational structures and management concepts. Companies need to establish management practices to govern these complex transformations. An important approach is to formulate a digital transformation strategy that serves as a central concept to integrate the entire coordination, prioritization, and implementation of digital transformations within a firm. The exploitation and integration of digital technologies often affect large parts of companies and even go beyond their borders, by impacting products, business processes, sales channels, and supply chains. Potential benefits of digitization are manifold and include increases in sales or productivity, innovations in value creation, as well as novel forms of interaction with customers, among others. As a result, entire business models can be reshaped or replaced (Downes and Nunes 2013). Owing to this wide scope and the far-reaching consequences, digital transformation strategies seek to coordinate and prioritize the many independent threads of digital transformation. To account for their company-spanning characteristics, digital transformation strategies cut across other business strategies and should be aligned with them (Fig. 1). While there are various concepts of IT strategies (Teubner 2013), these mostly define the current and the future operational activities, the necessary application systems and infrastructures, and the adequate organizational and financial framework for providing IT to carry out business operations within a company. Hence, IT strategies usually focus on the management of the IT infrastructure within a firm, with rather limited impact on driving innovations in business development. To some degree, this restricts the product-centric and customer-centric opportunities that arise from new digital technologies, which often cross firms’ borders. Further, IT strategies present systemcentric road maps to the future uses of technologies in a firm, but they do not necessarily account for the transformation of products, processes, and structural aspects that go along with the integration of technologies. Digital transformation strategies take on a different perspective and pursue different goals. Coming from a business-centric perspective, these strategies focus on the transformation of products, processes, and organizational aspects owing to new technologies. Their scope is more broadly designed and explicitly includes digital activities at the interface with or fully on the side of customers, such as Accepted after one revision by Prof. Dr. Sinz.


title: Breakdown, obsolescence and reuse: HCI and the art of repair

abstract: This paper describes an integrated program of theoretical, ethnographic, and building work meant to explore post-humanist alternatives to questions around HCI creativity and design. We review recent theories in the humanities, social sciences, and HCI that argue for different ways of framing the relationship between human agents and the object world around them. We then describe a program of ethnographic work with artists who feature found and broken technologies as central methods and topics of work. Finally, we describe an installation and self-study project of our own, 'Scale,' that extends these lines of analysis through collaborative acts of building with broken and discarded technologies. We argue that such integrated programs of work offer one useful model for leveraging the theoretical, ethnographic and material dimensions of HCI work; and that the distinct 'propensities' of found and broken objects can challenge and extend HCI notions of creativity and design itself.


title: How Does the Valence of Online Consumer Reviews Matter in Consumer Decision Making? Differences between Search Goods and Experience Goods

abstract: Existing empirical studies have drawn inconsistent conclusions about the effect of electronic word-of-mouth valence on consumer decision making. Based on attribution theory and prospect theory, this study attempts to explain this discrepancy through exploring how product type moderates the impact of online consumer reviews valence. Our results from a 2 (Positive reviews vs. Negative reviews) 2 (Search goods vs. Experience goods) experiment design show that the effect of online consumer reviews valence is asymmetrically moderated by product type: The effect of positive reviews is greater for search goods than that for experience goods, whereas the effects of negative reviews have no significant difference between these two types of goods; And the impact difference between negative reviews and positive reviews is greater for experience goods than for search goods. Our study not only confirms the moderating role of product type, but also further explores how product type moderates the effect of reviews valence. We also provide implications for e-marketers.


title: The Inductor as a Smart Sensor for Material Identification in Domestic Induction Cooking

abstract: In induction home appliances, the characterization of the physical properties of the vessel allows for better control strategies and new cooking functionalities. Current modelling techniques calculate a simple ${R}$ – ${L}$ equivalent circuit that models the inductor impedance taking into account all the physical coupling parameters, but these techniques do not allow to directly extract the physical parameters, such as vessel material. In this paper, a new methodology for online detection of the pot material is presented. On this approach, the inductor included in the hob simultaneously serves as heating element and sensor. First, harmonic impedances are calculated by spectral estimation from the currents and voltages waveforms recorded at the inductor. Then, a feature set, including four harmonic impedances and the power factor, is processed by machine learning algorithms for the identification of the pot material. The material identification system is implemented onto an embedded processor similar to those included in current induction hobs; thus, smart sensing features are incorporated onto a domestic cooker without using additional sensors, at no additional cost.


title: Theory of the effectivity of human problem solving

abstract: Abstract The question of how humans solve problem has been addressed extensively. However, the direct study of the effectiveness of this process seems to be overlooked. In this paper, we address the issue of the effectiveness of human problem solving: we analyze where this effectiveness comes from and what cognitive mechanisms or heuristics are involved. Our results are based on the optimal probabilistic problem solving strategy that appeared in Solomonoff paper on general problem solving system. We provide arguments that a certain set of cognitive mechanisms or heuristics drive human problem solving in the similar manner as the optimal Solomonoff strategy. Specifically, we argue that there is a concrete mathematical background for the effectiveness of human problem solving, and we show how it is connected with several well established components of human cognition. The results presented in this paper can serve both cognitive psychology in better understanding of human problem solving processes as well as artificial intelligence in designing more human-like agents.


title: Interpretation of emotionally ambiguous faces in older adults

abstract: Research suggests that there is an age-related decline in the processing of negative emotional information, which may contribute to the reported decline in emotional problems in older people. We used a signal detection approach to investigate the effect of normal aging on the interpretation of ambiguous emotional facial expressions. High-functioning older and younger adults indicated which emotion they perceived when presented with morphed faces containing a 60% to 40% blend of two emotions (mixtures of happy, sad, or angry faces). They also completed measures of mood, perceptual ability, and cognitive functioning. Older and younger adults did not differ significantly in their ability to discriminate between positive and negative emotions. Response-bias measures indicated that older adults were significantly less likely than younger adults to report the presence of anger in angry-–happy face blends. Results are discussed in relation to other research into age-related effects on emotion processing.


title: Resource Management with Deep Reinforcement Learning

abstract: Resource management problems in systems and networking often manifest as difficult online decision making tasks where appropriate solutions depend on understanding the workload and environment. Inspired by recent advances in deep reinforcement learning for AI problems, we consider building systems that learn to manage resources directly from experience. We present DeepRM, an example solution that translates the problem of packing tasks with multiple resource demands into a learning problem. Our initial results show that DeepRM performs comparably to state-of-the-art heuristics, adapts to different conditions, converges quickly, and learns strategies that are sensible in hindsight.


title: AENet: Learning Deep Audio Features for Video Analysis

abstract: We propose a new deep network for audio event recognition, called AENet. In contrast to speech, sounds coming from audio events may be produced by a wide variety of sources. Furthermore, distinguishing them often requires analyzing an extended time period due to the lack of clear subword units that are present in speech. In order to incorporate this long-time frequency structure of audio events, we introduce a convolutional neural network (CNN) operating on a large temporal input. In contrast to previous works, this allows us to train an audio event detection system end to end. The combination of our network architecture and a novel data augmentation outperforms previous methods for audio event detection by 16%. Furthermore, we perform transfer learning and show that our model learned generic audio features, similar to the way CNNs learn generic features on vision tasks. In video analysis, combining visual features and traditional audio features, such as mel frequency cepstral coefficients, typically only leads to marginal improvements. Instead, combining visual features with our AENet features, which can be computed efficiently on a GPU, leads to significant performance improvements on action recognition and video highlight detection. In video highlight detection, our audio features improve the performance by more than 8% over visual features alone.


title: Unveiling turbo codes: some results on parallel concatenated coding schemes

abstract: A parallel concatenated coding scheme consists of two simple constituent systematic encoders linked by an interleaver. The input bits to the first encoder are scrambled by the interleaver before entering the second encoder. The codeword of the parallel concatenated code consists of the input bits to the first encoder followed by the parity check bits of both encoders. This construction can be generalized to any number of constituent codes. Parallel concatenated schemes employing two convolutional codes as constituent codes, in connection with an iterative decoding algorithm of complexity comparable to that of the constituent codes, have been previously shown to yield remarkable coding gains close to theoretical limits. They have been named, and are known as, "turbo codes". We propose a method to evaluate an upper bound to the bit error probability of a parallel concatenated coding scheme averaged over all interleavers of a given length. The analytical bounding technique is then used to shed some light on some crucial questions, which have been floating around in the communications community since the proposal of turbo codes.


title: File Size Distributions and Caching for Offloading

abstract: We consider order statistics of products of random variables to understand the relationship between traffic offloading and storage space consumption in an edge caching network. Given a statistical model for the popularities and sizes of data objects in a caching network, we derive the order statistics describing the probability density function of the size of the object that accounts for, e.g., the largest amount of data traffic. We further study the asymptotic behavior of these distributions, and give analytical results for both the general case and the asymptotic case. The derived results can be used to estimate an upper bound on the cache size needed when one is interested in offloading a certain fraction of all traffic for file catalogues with changing content but known distributions of popularities and object sizes.


Paper Number Development of a Hydro-Mechanical Hydraulic Hybrid Drive Train with Independent Wheel Torque Control for an Urban Passenger Vehicle


title: An introduction to the Rasch measurement model: An example using the Hospital Anxiety and Depression Scale (HADS)

abstract: Objectives To demonstrate the use of Rasch analysis by assessing the appropriateness of utilizing the Hospital Anxiety and Depression Scale (HADS) total score (HADS-14) as a measure of psychological distress. ::: ::: Design Cross-sectional, using Rasch analysis. ::: ::: Methods The HADS was administered to 296 patients attending an out-patient musculoskeletal rehabilitation program. Rasch analysis was conducted using RUMM2020 software to assess the overall fit of the model, the response scale used, individual item fit, differential item functioning (DIF) and person separation. ::: ::: Results Rasch analysis supported the viability of the HADS-14 as a measure of psychological distress. It showed good person separation, little disordering of the thresholds and no evidence of DIF. One anxiety item (item 11) showed some misfit to the model. The residuals patterned into the two subscales (anxiety and depression), but the person estimate derived from these two subscales was not statistically different to that derived from all items taken together, supporting the assumption of unidimensionality. A cut-point of 12 on the HADS-14 identified all cases that were classified as both anxious and depressed on the original individual HADS subscales. ::: ::: Conclusions The results of Rasch analysis support the use of the HADS-14 as a global measure of psychological distress. The study demonstrates the usefulness of Rasch analysis in assessing the psychometric properties of a scale and suggests that further use of this technique to assess the HADS-14 in other clinical groups is warranted.


title: Sketching with projective 2D strokes

abstract: Freehand sketching has long had appeal as an artistic medium for conceptual design because of its immediacy in capturing and communicating design intent and visual experience. We present a sketching paradigm that supports the early stages of design by preserving the fluidity of traditional freehand drawings. In addition, it attempts to fill the gap between 2D drawing programs, which have fixed views, and 3D modeling programs that allow arbitrary views. We implement our application as a two-dimensional drawing program that utilizes a projective representation of points — i.e. points that lie on the surface of a unit sphere centered at the viewpoint. This representation facilitates the production of novel re-projections generated from an initial perspective sketch and gives the user the impression of being immersed in the drawing or space. We describe a method for aligning a sketch drawn outside the system using its vanishing points, allowing the integration of computer sketching and freehand sketching on paper in an iterative manner. The user interface provides a virtual camera, projective grids to guide in the construction of proportionate scenes, and the ability to underlay sketches with other drawings or photographic panoramas.


title: Network Analysis of Recurring YouTube Spam Campaigns

abstract: As the popularity of content sharing websites such as YouTube and Flickr has increased, they have become targets for spam, phishing and the distribution of malware. On YouTube, the facility for users to post comments can be used by spam campaigns to direct unsuspecting users to bogus e-commerce websites. In this paper, we demonstrate how such campaigns can be tracked over time using network motif profiling, i.e. by tracking counts of indicative network motifs. By considering all motifs of up to five nodes, we identify discriminating motifs that reveal two distinctly different spam campaign strategies. One of these strategies uses a small number of spam user accounts to comment on a large number of videos, whereas a larger number of accounts is used with the other. We present an evaluation that uses motif profiling to track two active campaigns matching these strategies, and identify some of the associated user accounts.


Learning to Pinpoint Singing Voice from Weakly Labeled Examples


title: Sales Taxes and Internet Commerce

abstract: We estimate the sensitivity of Internet retail purchasing to sales taxes using data from the eBay marketplace. Our first approach exploits the fact that seller locations are revealed only after buyers have expressed interest in an item by clicking on its listing. We use millions of location "surprises" to estimate price elasticities with respect to the effective sales tax. We then use aggregated data to estimate cross-state substitution parameters, and substitution between offline and online purchases, relying on the variation in state and local sales taxes, and on changes in these rates over time. We find substantial sensitivity to sales taxes. Using our item-level approach, we find a price elasticity of around -2 for interested buyers. Using our aggregate approach, we find that a one percentage point increase in a state's sales tax increases online purchases by state residents by just under two percent, but decreases their online purchases from home-state retailers by 3-4 percent.


title: 3D object classification with point convolution network

abstract: Recognizing objects from the point cloud captured by modern 3D sensors is an important task for robots operating autonomously in real-world environments. However, the existing well-performing approaches typically suffer from a trade-off between resolution of representation and computational efficiency. In this paper, raw point cloud normals are fed into the Point Convolution Network (PCN) without any other representation converts. The point cloud set disordered and unstructured problems are tackled by Kd-tree-based local permutation and spatial commutative pooling strategies proposed in this paper. Experiments on ModelNet illustrate that our method has two orders of magnitude less floating point computation in each non-linear mapping layer while it contributes to significant classification accuracy improvement. Compared to some of the state-of-the-art methods using the 3D volumetric image convolution, the PCN method also yields comparable classification accuracy.


title: A Planar UWB Antenna With a Broadband Feeding Structure

abstract: A planar antenna with a broadband feeding structure is presented and analyzed for ultrawideband applications. The proposed antenna consists of a suspended radiator fed by an n-shape microstrip feed. Study shows that this antenna achieves an impedance bandwidth from 3.1-5.1 GHz (48%) for a reflection of coefficient of iotaS11iota < -10 dB, and an average gain of 7.7 dBi. Stable boresight radiation patterns are achieved across the entire operating frequency band, by suppressing the high order mode resonances. This design exhibits good mechanical tolerance and manufacturability.


title: Folding Rigid Origami With Closure Constraints

abstract: Rigid origami is a class of origami whose entire surface remains rigid during folding except at crease lines. Rigid origami finds applications in manufacturing and packaging, such as map folding and solar panel packing. Advances in material science and robotics engineering also enable the realization of self-folding rigid origami and have fueled the interests in computational origami, in particular the issues of foldability, i.e., finding folding steps from a flat sheet of crease patterns to desired folded state. For example, recent computational methods allow rapid simulation of folding process of certain rigid origamis. However, these methods can fail even when the input crease pattern is extremely simple. This paper attempts to address this problem by modeling rigid origami as a kinematic system with closure constraints and solve the foldability problem through a randomized method. Our experimental results show that the proposed method successfully fold several types of rigid origamis that the existing methods fail to fold.Copyright © 2014 by ASME


Assessing Soft- and Hardware Bottlenecks in PC-based Packet Forwarding Systems


title: Prediction of driver intended path at intersections

abstract: The complexity of situations occurring at intersections is demanding on the cognitive abilities of drivers. Advanced Driver Assistance Systems (ADAS) are intended to assist particularly in those situations. However, for adequate system reaction strategies it is essential to develop situation assessment. Especially the driver's intention has to be estimated. So, the criticality can be inferred and efficient intervention strategies can take action. In this paper, we present a prediction framework based on Hidden Markov Models (HMMs) and analyze its performance using a large database of real driving data. Our focus is on the variation of the model parameters and the choice of the dataset for learning. The direction of travel while approaching a 4-way intersection is to be estimated. A solid prediction is accomplished with high prediction rates above 90% and mean prediction times up to 7 seconds before entering the intersection area.


title: Cognitive load and science text comprehension: Effects of drawing and mentally imagining text content

abstract: One hundred and eleven 10th graders read an expository science text on the dipole character of water molecules (ca. 1600 words). Reading instruction was varied according to a 2x2 experimental design with factors 'drawing pictures of text content on paper' (yes, no) and 'mentally imagining text content while reading' (yes, no). The results indicate that drawing pictures, mediated through increased cognitive load, decreased text comprehension and, thus, learning (d=-0.37), whereas mental imagery, although decreasing cognitive load, increased comprehension only when students did not have to draw pictures simultaneously (d=0.72). No evidence was found that the effects were moderated by domain-specific prior knowledge, verbal ability, or spatial ability. The results are in line with cognitive theories of multimedia learning, self-regulated learning, and mental imagery as well as conceptions of science learning that focus on promoting mental model construction by actively visualizing the content to be learned. Constructing mental images seems to reduce cognitive load and to increase comprehension and learning outcome when the mental visualization processes are not disturbed by externally drawing pictures on paper, whereas drawing pictures seems to increase cognitive load resulting in reduced comprehension and learning outcome.


title: Fuzzy and neural control of an induction motor

abstract: This paper presents some design approaches to hybrid control systems combining conventional control techniques with fuzzy logic and neural networks. Such a mixed implementation leads to a more effective control design with improved system performance and robustness. While conventional control allows different design objectives such as steady state and transient characteristics of the closed loop system to be specified, fuzzy logic and neural networks are integrated to overcome the problems with uncertainties in the plant parameters and structure encountered in the classical model-based design. Induction motors are characterised by complex, highly non-linear and time-varying dynamics and inaccessibility of some states and outputs for measurements, and hence can be considered as a challenging engineering problem. The advent of vector control techniques has partially solved induction motor control problems, because they are sensitive to drive parameter variations and performance may deteriorate if conventional controllers are used. Fuzzy logic and neural network-based controllers are considered as potential candidates for such an application. Three control approaches are developed and applied to adjust the speed of the drive system. The first control design combines the variable structure theory with the fuzzy logic concept. In the second approach neural networks are used in an internal model control structure. Finally, a fuzzy state feedback controller is developed based on the pole placement technique. A simulation study of these methods is presented. The effectiveness of these controllers is demonstrated for different operating conditions of the drive system.


title: Ethernet/IP-industrial protocol

abstract: DeviceNet and ControlNet are two well known industrial networks based on the CIP protocol (CIP = Control and Information Protocol). Both networks have been developed by Rockwell Automation, but are now owned and maintained by the two manufacturers organizations ODVA (Open DeviceNet Vendors Association) and ControlNet International. ODVA and ControlNet International have introduced the newest member of this family-Ethernet/IP ("IP" stands for "Industrial Protocol"). The paper describes the techniques and mechanisms that are used to implement a fully consistent set of services and data objects on a TCP/UDP/IP based Ethernet network.


title: A Low-Cost Field-Programmable Pin-Constrained Digital Microfluidic Biochip

abstract: This paper introduces a field-programmable pin-constrained digital microfluidic biochip (FPPC-DMFB), which offers general-purpose assay execution at a lower cost than general-purpose direct addressing DMFBs and highly optimized application-specific pin-constrained DMFBs. One of the key cost drivers for DMFBs is the number of printed circuit board (PCB) layers, onto which the device is mounted. We demonstrate a scalable single-layer PCB wiring scheme for several FPPC-DMFB variations, for PCB technology with orthogonal routing capacity of at least three; for PCB technology with orthogonal capacity of two, more PCB layers are required, but the FPPC-DMFB retains its cost advantage. These results offer new insights on the relationship between PCB layer count, pin count, and cost. Additionally, to reduce the execution time of assays on the FPPC-DMFB, we present efficient algorithms for droplet routing, with and without contamination removal via wash droplets.


Hydrothermal Alteration Associated with Beryllium Deposits at Spar Mountain , Utah GEOLOGICAL SURVEY PROFESSIONAL


title: Reliability investigations on the programming currents of 28nm metal e-Fuse

abstract: The reliability performance of 28nm metal e-Fuse programmed with different current is investigated. High temperature stress (HTS) or temperature cycling (TC) may cause the shift of metal-e-fuse element resistance and shape. In this paper, we find that 28nm metal e-Fuse programming with low current reliability performance is more stable than metal e-Fuse programming with high current; Resistance shift was only observed on fuses programmed in the over-programmed mode. In addition, the SEM profile of metal e-Fuse programming with low current is obviously better than high current SEM profile.


title: Sharpness Enhancement and Super-Resolution of Around-View Monitor Images

abstract: In the wide-angle (WA) images embedded in an around-view monitor system, the subject(s) in the peripheral region is normally small and has little information. Furthermore, since the outer region suffers from the non-uniform blur phenomenon and artifact caused by the inherent optical characteristic of WA lenses, its visual quality tends to deteriorate. According to our experiments, conventional image enhancement techniques rarely improve the degraded visual quality of the outer region of WA images. In order to solve the above-mentioned problem, this paper proposes a joint sharpness enhancement (SE) and super-resolution (SR) algorithm which can improve the sharpness and resolution of WA images together. The proposed SE algorithm improves the sharpness of the deteriorated WA images by exploiting self-similarity. Also, the proposed SR algorithm generates super-resolved images by using high-resolution information which is classified according to the extended local binary pattern-based classifier and learned on a pattern basis. Experimental results show that the proposed scheme effectively improves the sharpness and resolution of the input deteriorated WA images. Even in terms of quantitative metrics such as just noticeable blur, structural similarity, and peak signal-to-noise ratio. Finally, the proposed scheme guarantees real-time processing such that it achieves 720p video at 29 Hz on a low cost GPU platform.


title: Security Challenges for the Public Cloud

abstract: Cloud computing represents today's most exciting computing paradigm shift in information technology. However, security and privacy are perceived as primary obstacles to its wide adoption. Here, the authors outline several critical security challenges and motivate further investigation of security solutions for a trustworthy public cloud environment.


title: Face Recognition Using LDA Based Algorithms

abstract: Low-dimensional feature representation with enhanced discriminatory power is of paramount importance to face recognition (FR) systems. Most of traditional linear discriminant analysis (LDA)-based methods suffer from the disadvantage that their optimality criteria are not directly related to the classification ability of the obtained feature representation. Moreover, their classification accuracy is affected by the "small sample size" (SSS) problem which is often encountered in FR tasks. In this paper, we propose a new algorithm that deals with both of the shortcomings in an efficient and cost effective manner. The proposed method is compared, in terms of classification accuracy, to other commonly used FR methods on two face databases. Results indicate that the performance of the proposed method is overall superior to those of traditional FR approaches, such as the eigenfaces, fisherfaces, and D-LDA methods.


title: The Effect of Visual Appearance on the Performance of Continuous Sliders and Visual Analogue Scales

abstract: Sliders and Visual Analogue Scales (VASs) are input mechanisms which allow users to specify a value within a predefined range. At a minimum, sliders and VASs typically consist of a line with the extreme values labeled. Additional decorations such as labels and tick marks can be added to give information about the gradations along the scale and allow for more precise and repeatable selections. There is a rich history of research about the effect of labelling in discrete scales (i.e., Likert scales), however the effect of decorations on continuous scales has not been rigorously explored. In this paper we perform a 2,000 user, 250,000 trial online experiment to study the effects of slider appearance, and find that decorations along the slider considerably bias the distribution of responses received. Using two separate experimental tasks, the trade-offs between bias, accuracy, and speed-of-use are explored and design recommendations for optimal slider implementations are proposed.


title: Analyzing the Contributor Activity of a Volunteered Geographic Information Project - The Case of OpenStreetMap

abstract: The OpenStreetMap (OSM) project, founded in 2004, has gathered an exceptional amount of interest in recent years and counts as one of the most impressive sources of Volunteered Geographic Information (VGI) on the Internet. In total, more than half a million members had registered for the project by the end of 2011. However, while this number of contributors seems impressive, questions remain about the individual contributions that have been made by the project members. This research article contains several studies regarding the contributions by the community of the project. The results show that only 38% (192,000) of the registered members carried out at least one edit in the OSM database and that only 5% (24,000) of all members actively contributed to the project in a more productive way. The majority of the members are located in Europe (72%) and each member has an activity area whose size may range from one soccer field up to more than 50 km2. In addition to several more analyses conducted for this article, predictions will be made about how this newly acquired knowledge can be used for future research.


MIRROR NEURONS: OUR CURRENT UNDERSTANDING


title: Survey on Various Gesture Recognition Technologies and Techniques

abstract: Gestures considered as the most natural expressive way for communications between human and computers in virtual system. Hand gesture is a method of non-verbal communication for human beings for its freer expressions much more other than body parts. Hand gesture recognition has greater importance in designing an efficient human computer interaction system. Using gestures as a natural interface benefits as a motivation for analyzing, modeling, simulation, and recognition of gestures. In this paper a survey on various recent gesture recognition approaches is provided with particular emphasis on hand gestures. A review of static hand posture methods are explained with different tools and algorithms applied on gesture recognition system, including connectionist models, hidden Markov model, and fuzzy clustering. Challenges and future research directions are also highlighted.


title: Data Hiding using Advanced LSB with RSA Algorithm

abstract: In this paper, RSA algorithm is used to encrypt the secret message and advanced LSB technique is used to hide the encrypted message. Firstly, the message is encrypted and then encrypted message is being divided in two parts. First part of the encrypted message is xored with odd position and second part with even position of LSB+1. After that the xored encrypted message is being hidden on LSB position.


title: Systematic design methodology for development and flight testing of a variable pitch quadrotor biplane VTOL UAV for payload delivery

abstract: This paper discusses the conceptual design and proof-of-concept flight demonstration of a novel variable pitch quadrotor biplane Unmanned Aerial Vehicle concept for payload delivery. The proposed design combines vertical takeoff and landing (VTOL), precise hover capabilities of a quadrotor helicopter and high range, endurance and high forward cruise speed characteristics of a fixed wing aircraft. The proposed UAV is designed for a mission requirement of carrying and delivering 6 kg payload to a destination at 16 km from the point of origin. First, the design of proprotors is carried out using a physics based modified Blade Element Momentum Theory (BEMT) analysis, which is validated using experimental data generated for the purpose. Proprotors have conflicting requirement for optimal hover and forward flight performance. Next, the biplane wings are designed using simple lifting line theory. The airframe design is followed by power plant selection and transmission design. Finally, weight estimation is carried out to complete the design process. The proprotor design with 24 deg preset angle and -24 deg twist is designed based on 70% weightage to forward flight and 30% weightage to hovering flight conditions. The operating RPM of the proprotors is reduced from 3200 during hover to 2000 during forward flight to ensure optimal performance during cruise flight. The estimated power consumption during forward flight mode is 64% less than that required for hover, establishing the benefit of this hybrid concept. A proof-of-concept scaled prototype is fabricated using commercial-off-the-shelf parts. A PID controller is developed and implemented on the PixHawk board to enable stable hovering flight and attitude tracking.


title: Routing and scheduling in Multi-AGV systems based on dynamic banker algorithm

abstract: In this paper we propose modified Banker's algorithm for routing and scheduling in multi-AGV systems. A mission path, determined by string algebra is executed in a way that some unsafe states are allowed in order to achieve better utilization of vehicles. Moreover, a method for splitting a mission path is proposed so that number of resources (arcs) needed for mission execution is gradually reduced which provides less restrictions on availability vector, thus allowing more traveling space for other vehicles. Results shown at the end of the paper demonstrate strength of the proposed method compared with standard Banker's algorithm.


title: Botulinum Toxin for the Treatment of Excessive Gingival Display: A Systematic Review

abstract: Background To date, no standardized minimally invasive approach for the treatment of excessive gingival display exists. ::: ::: Objectives This systematic review aims to assess the evidence in the literature regarding the role of botulinum toxin injection in the management of gummy smile. ::: ::: Methods All publications through December 2014 and pertaining to the subject were electronically searched in PubMed, Embase, Scopus, and Web of Science, and the bibliographies of retrieved articles were manually screened. ::: ::: Results Out of 33 articles, 29 were discarded based on exclusion criteria. Although all 4 selected articles were in line with a role for botulinum toxin injection in the treatment of gummy smiles and the importance of targeting the levator labii superioris alaeque nasi muscle, studies differed in the type and the dose of toxin administered and the technique adopted. ::: ::: Conclusions Injection with botulinum toxin is a novel, safe, and cosmetically effective treatment for gummy smile when performed by experienced practitioners. However, further randomized controlled trials are warranted. ::: ::: Level of Evidence 4![Graphic][1] Therapeutic ::: ::: [10.1093/asj/sjv126][2] ::: ::: [1]: /embed/inline-graphic-1.gif ::: [2]: /lookup/doi/10.1093/asj/sjv126


title: New Methods for Automatic Reading of VLP's (Vehicle License Plates)

abstract: ABSTRACT In a former work, we built a VLP (Vehicle License Plates)reader using computer vision. To test other methods, wedecided to implement a whole new system from scratch. Inthis communication, we summarize the methods that wehave tested and the results we have obtained. Platelocation is based on mathematical morphology andcharacter recognition is implemented using Hausdorffdistance. Results are comparable to those obtained withother methods. KEY WORDS Computer Vision, Morphologic Analysis, TextRecognition, Hausdorff Distance. 1. Introduction This communication describes the development of anautomatic VLP recognizer. Such a system has manypractical applications: security and accounting systems inparking facilities, traffic monitoring, stolen car detectionand other law enforcement applications...To develop this system we cleared our minds and decidedto use methods that were new for us. Our purpose was totest these methods and to compare them to other methodswe had used previously in the same problem [1]. Our nextproject is to merge both systems to get an improved one.The structure of this work is as follows:a) Recognition Algorithms: this part is the core of thepaper and explains the different computer visionmethods. We used mathematical morphology forlocating the plate. We also used image projectionsfor segmenting the caracters (this is a widely usedmethod of O.C.R. technology, but we had neverused it for plate images). Finally, we tested theHausdorff Distance for character recognition(Haussdorf Distance is a well behaved method forcomparing bilevel images).b) Practical Results: here, we see the global resultsachieved by our system. We also compare thissystem to our previous system.c) Future Lines.d) Conclusions.There are other publications that describe VLPrecognizing. For example [2] uses syntactic patternrecognition, [3] also uses morphology for location andneural networks for recognition (they do not publish theirresults), in [4] they use a bunch of heuristical methods(they get good partial results but they do not publish theglobal ones).


title: A Simple and Practical Approach to Unit Testing: The JML and JUnit Way

abstract: Writing unit test code is labor-intensive, hence it is often not done as an integral part of programming. However, unit testing is a practical approach to increasing the correctness and quality of software; for example, the Extreme Programming approach relies on frequent unit testing. In this paper we present a new approach that makes writing unit tests easier. It uses a formal specification language's runtime assertion checker to decide whether methods are working correctly, thus automating the writing of unit test oracles. These oracles can be easily combined with hand-written test data. Instead of writing testing code, the programmer writes formal specifications (e.g., pre- and postconditions). This makes the programmer's task easier, because specifications are more concise and abstract than the equivalent test code, and hence more readable and maintainable. Furthermore, by using specifications in testing, specification errors are quickly discovered, so the specifications are more likely to provide useful documentation and inputs to other tools. We have implemented this idea using the Java Modeling Language (JML) and the JUnit testing framework, but the approach could be easily implemented with other combinations of formal specification languages and unit test tools.


title: Horizon Detection Using Machine Learning Techniques

abstract: Detecting a horizon in an image is an important part of many image related applications such as detecting ships on the horizon, flight control, and port security. Most of the existing solutions for the problem only use image processing methods to identify a horizon line in an image. This results in good accuracy for many cases and is fast in computation. However, for some images with difficult environmental conditions like a foggy or cloudy sky these image processing methods are inherently inaccurate in identifying the correct horizon. This paper investigates how to detect the horizon line in a set of images using a machine learning approach. The performance of the SVM, J48, and Na?ve Bayes classifiers, used for the problem, has been compared. Accuracy of 90-99% in identifying horizon was achieved on image data set of 20 images.


title: Software engineering for big data projects: Domains, methodologies and gaps

abstract: Context: Big data has become the new buzzword in the information and communication technology industry. Researchers and major corporations are looking into big data applications to extract the maximum value from the data available to them. However, developing and maintaining stable and scalable big data applications is still a distant milestone. Objective: To look at existing research on how software engineering concepts, namely the phases of the software development project life cycle (SDPLC), can help build better big data application projects. Method: A literature survey was performed. A manual search covered papers returned by search engines resulting in approximately 2,000 papers being searched and 170 papers selected for review. Results: The search results helped in identifying data rich application projects that have the potential to utilize big data successfully. The review helped in exploring SDPLC phases in the context of big data applications and performing a gap analysis of the phases that have yet to see detailed research efforts but deserve attention.


title: Spectral distribution of a sampled 1st-order lowpass filtered white noise

abstract: In the letter, the spectrum of a sampled 1st-order lowpass filtered white noise is calculated, and theoretical results are compared with measurements made on a laboratory model. In this context, the effect of undersampling appears clearly for noise bandwidths exceeding the sampling rate. This analysis contributes to the study of noise in switched capacitor networks.


title: Transparent operation of Kronecker product based full dimension MIMO to exploit 2D antenna array

abstract: A concept of full-dimension MIMO (FD-MIMO) adopting 2-dimensional (2D) antenna array has been proposed with intent to cope with practical challenges of massive MIMO systems. In this paper, we investigate an FD-MIMO operation that can be transparently applied with current 3GPP LTE specifications, and propose a novel method that facilitates an accurate estimation of channel quality indicator (CQI) at base station (BS). We first introduce a feedback scheme in which horizontal and vertical dimension channel information are separately reported. To further improve accuracy of modulation and coding scheme (MCS) selection to maximize system throughput, we propose an enhanced feedback scheme that is helpful to more accurate CQI estimation at the BS. In the evaluation results, we show performance improvement of the proposed scheme over the conventional MIMO scheme.


title: Reasoning about Human Participation in Self-Adaptive Systems

abstract: Self-adaptive systems overcome many of the limitations of human supervision in complex software-intensive systems by endowing them with the ability to automatically adapt their structure and behavior in the presence of runtime changes. However, adaptation in some classes of systems (e.g., safety-critical) can benefit by receiving information from humans (e.g., acting as sophisticated sensors, decision-makers), or by involving them as system-level effectors to execute adaptations (e.g., when automation is not possible, or as a fallback mechanism). However, human participants are influenced by factors external to the system (e.g., training level, fatigue) that affect the likelihood of success when they perform a task, its duration, or even if they are willing to perform it in the first place. Without careful consideration of these factors, it is unclear how to decide when to involve humans in adaptation, and in which way. In this paper, we investigate how the explicit modeling of human participants can provide a better insight into the trade-offs of involving humans in adaptation. We contribute a formal framework to reason about human involvement in self-adaptation, focusing on the role of human participants as actors (i.e., effectors) during the execution stage of adaptation. The approach consists of: (i) a language to express adaptation models that capture factors affecting human behavior and its interactions with the system, and (ii) a formalization of these adaptation models as stochastic multiplayer games (SMGs) that can be used to analyze human-system-environment interactions. We illustrate our approach in an adaptive industrial middleware used to monitor and manage sensor networks in renewable energy production plants.


Web-CASRE: A Web-Based Tool for Software Reliability Measurement


title: Substrate integrated waveguide with corrugated wall

abstract: In this work we propose a new SIW structure, called the corrugated SIW (CSIW), which does not require conducting vias to achieve TE10 type boundary conditions at the side walls. Instead, the vias are replaced by quarter wavelength microstrip stubs arranged in a corrugated pattern on the edges of the waveguide. This, along with series interdigitated capacitors, results in a waveguide section comprising two separate conductors, which facilitates shunt connection of active components such as Gunn diodes.


title: An empirical study on detecting fake reviews using machine learning techniques

abstract: Reputation systems in E-commerce (EC) play a substantial role that allows various parties to achieve mutual benefits by establishing relationships. The reputation systems aim at helping consumers in deciding whether to negotiate with a given party. Many factors negatively influence the sight of the customers and the vendors in terms of the reputation system. For instance, lack of honesty or effort in providing the feedback reviews, by which users might create phantom feedback from fake reviews to support their reputation. Moreover, the opinions obtained from users can be classified into positive or negative which can be used by a consumer to select a product. In this paper, we study online movie reviews using Sentiment Analysis (SA) methods in order to detect fake reviews. Text classification and SA methods are applied on a real conducted dataset of movie reviews. Specifically, we compare four supervised machine learning algorithms: Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN-IBK), and Decision Tree (DT-J48) for sentiment classification of reviews in two different situations without stopwords and with stopwords methods are employed. The measured results show that for both methods the SVM algorithm outperforms other algorithms, and it reaches the highest accuracy not only in text classification but also to detect fake reviews.


title: A Fast Density and Grid Based Clustering Method for Data With Arbitrary Shapes and Noise

abstract: This paper presents a density- and grid- based (DGB) clustering method for categorizing data with arbitrary shapes and noise. As most of the conventional clustering approaches work only with round-shaped clusters, other methods are needed to be explored to proceed classification of clusters with arbitrary shapes. Clustering approach by fast search and find of density peaks and density-based spatial clustering of applications with noise, and so many other methods are reported to be capable of completing this task but are limited by their computation time of mutual distances between points or patterns. Without the calculation of mutual distances, this paper presents an alternative method to fulfill clustering of data with any shape and noise even faster and with more efficiency. It was successfully verified in clustering industrial data (e.g., DNA microarray data) and several benchmark datasets with different kinds of noise. It turned out that the proposed DGB clustering method is more efficient and faster in clustering datasets with any shape than the conventional methods.


title: ‘Maintaining balance and harmony’: Javanese perceptions of health and cardiovascular disease

abstract: UNLABELLED ::: Community intervention programmes to reduce cardiovascular disease (CVD) risk factors within urban communities in developing countries are rare. One possible explanation is the difficulty of designing an intervention that corresponds to the local context and culture. ::: ::: ::: OBJECTIVES ::: To understand people's perceptions of health and CVD, and how people prevent CVD in an urban setting in Yogyakarta, Indonesia. ::: ::: ::: METHODS ::: A qualitative study was performed through focus group discussions and individual research interviews. Participants were selected purposively in terms of socio-economic status (SES), lay people, community leaders and government officers. Data were analysed by using content analysis. ::: ::: ::: RESULTS ::: SEVEN CATEGORIES WERE IDENTIFIED: (1) heart disease is dangerous, (2) the cause of heart disease, (3) men have no time for health, (4) women are caretakers for health, (5) different information-seeking patterns, (6) the role of community leaders and (7) patterns of lay people's action. Each category consists of sub-categories according to the SES of participants. The main theme that emerged was one of balance and harmony, indicating the necessity of assuring a balance between 'good' and 'bad' habits. ::: ::: ::: CONCLUSIONS ::: The basic concepts of balance and harmony, which differ between low and high SES groups, must be understood when tailoring community interventions to reduce CVD risk factors.


title: Very thin flexible coupled inductors for PV module integrated GaN converter

abstract: As the PV energy penetrates into the urban area, shortcomings of the conventional centralized PV system architectures become more pronounced under the influence of urban environment with shading, soiling and orientation mismatch issues. To overcome these limitations new PV system architectures were proposed with more granular power processing by means of distributed maximum power point tracking (DMPPT). This is achieved by adjoining a MPPT unit to each PV module in the PV array. This paper presents a step further in the distributed power tracking approach, with a concept of very low profile power converter integrated directly into a low cost flexible PV module. Additional problems arise in this case, specifically in magnetics design, due to the requirements for very low profile flexible construction and limited thermal headroom. Overcoming these limitations presents a challenge, but can lead to a cost effective, reliable solution for PV systems with improved integration level and power density.


title: Examples are not enough, learn to criticize! Criticism for interpretability

abstract: Example-based explanations are widely used in the effort to improve the interpretability of highly complex distributions. However, prototypes alone are rarely sufficient to represent the gist of the complexity. In order for users to construct better mental models and understand complex data distributions, we also need {\em criticism} to explain what are \textit{not} captured by prototypes. Motivated by the Bayesian model criticism framework, we develop \texttt{MMD-critic} which efficiently learns prototypes and criticism, designed to aid human interpretability. A human subject pilot study shows that the \texttt{MMD-critic} selects prototypes and criticism that are useful to facilitate human understanding and reasoning. We also evaluate the prototypes selected by \texttt{MMD-critic} via a nearest prototype classifier, showing competitive performance compared to baselines.


title: Spare parts inventory control: a literature review

abstract: Spare parts inventory are needed for maintenance and repair of final products, vehicles, industrial machines and equipments, frequently requiring high investments and significantly affecting customer satisfaction. Inventory management is complex due to the large number of different items and low demands. This article presents a literature review on single location spare parts inventory control, embracing both demand forecasting techniques and inventory control decisions on the different life cycle stages. Overall, the literature review identified the following research opportunities on inventory management: criteria to decide to stock or not an item, how much to order in the first and the last batch, demand forecasting and inventory control models integration and case studies on real applications.


title: Unsupervised Transfer Learning for Spoken Language Understanding in Intelligent Agents

abstract: User interaction with voice-powered agents generates large amounts of unlabeled utterances. In this paper, we explore techniques to efficiently transfer the knowledge from these unlabeled utterances to improve model performance on Spoken Language Understanding (SLU) tasks. We use Embeddings from Language Model (ELMo) to take advantage of unlabeled data by learning contextualized word representations. Additionally, we propose ELMo-Light (ELMoL), a faster and simpler unsupervised pre-training method for SLU. Our findings suggest unsupervised pre-training on a large corpora of unlabeled utterances leads to significantly better SLU performance compared to training from scratch and it can even outperform conventional supervised transfer. Additionally, we show that the gains from unsupervised transfer techniques can be further improved by supervised transfer. The improvements are more pronounced in low resource settings and when using only 1000 labeled in-domain samples, our techniques match the performance of training from scratch on 10-15x more labeled in-domain data.


title: Universal Decision-Based Black-Box Perturbations: Breaking Security-Through-Obscurity Defenses

abstract: We study the problem of finding a universal (image-agnostic) perturbation to fool machine learning (ML) classifiers (e.g., neural nets, decision tress) in the hard-label black-box setting. Recent work in adversarial ML in the white-box setting (model parameters are known) has shown that many state-of-the-art image classifiers are vulnerable to universal adversarial perturbations: a fixed human-imperceptible perturbation that, when added to any image, causes it to be misclassified with high probability Kurakin et al. [2016], Szegedy et al. [2013], Chen et al. [2017a], Carlini and Wagner [2017]. This paper considers a more practical and challenging problem of finding such universal perturbations in an obscure (or black-box) setting. More specifically, we use zeroth order optimization algorithms to find such a universal adversarial perturbation when no model information is revealed-except that the attacker can make queries to probe the classifier. We further relax the assumption that the output of a query is continuous valued confidence scores for all the classes and consider the case where the output is a hard-label decision. Surprisingly, we found that even in these extremely obscure regimes, state-of-the-art ML classifiers can be fooled with a very high probability just by adding a single human-imperceptible image perturbation to any natural image. The surprising existence of universal perturbations in a hard-label black-box setting raises serious security concerns with the existence of a universal noise vector that adversaries can possibly exploit to break a classifier on most natural images.


title: Training Neural Speech Recognition Systems with Synthetic Speech Augmentation

abstract: Building an accurate automatic speech recognition (ASR) system requires a large dataset that contains many hours of labeled speech samples produced by a diverse set of speakers. The lack of such open free datasets is one of the main issues preventing advancements in ASR research. To address this problem, we propose to augment a natural speech dataset with synthetic speech. We train very large end-to-end neural speech recognition models using the LibriSpeech dataset augmented with synthetic speech. These new models achieve state of the art Word Error Rate (WER) for character-level based models without an external language model.


title: Trade area analysis using user generated mobile location data

abstract: In this paper, we illustrate how User Generated Mobile Location Data (UGMLD) like Foursquare check-ins can be used in Trade Area Analysis (TAA) by introducing a new framework and corresponding analytic methods. Three key processes were created: identifying the activity center of a mobile user, profiling users based on their location history, and modeling users' preference probability. Extensions to traditional TAA are introduced, including customer-centric distance decay analysis and check-in sequence analysis. Adopting the rich content and context of UGMLD, these methods introduce new dimensions to modeling and delineating trade areas. Analyzing customers' visits to a business in the context of their daily life sheds new light on the nature and performance of the venue. This work has important business implications in the field of mobile computing.


title: Synthesis of Transportation Applications of Mobile LIDAR

abstract: A thorough review of available literature was conducted to inform of advancements in mobile LIDAR technology, techniques, and current and emerging applications in transportation. The literature review touches briefly on the basics of LIDAR technology followed by a more in depth description of current mobile LIDAR trends, including system components and software. An overview of existing quality control procedures used to verify the accuracy of the collected data is presented. A collection of case studies provides a clear description of the advantages of mobile LIDAR, including an increase in safety and efficiency. The final sections of the review identify current challenges the industry is facing, the guidelines that currently exist, and what else is needed to streamline the adoption of mobile LIDAR by transportation agencies. Unfortunately, many of these guidelines do not cover the specific challenges and concerns of mobile LIDAR use as many have been developed for airborne LIDAR acquisition and processing. From this review, there is a lot of discussion on “what” is being done in practice, but not a lot on “how” and “how well” it is being done. A willingness to share information going forward will be important for the successful use of mobile LIDAR.


A Cost-effectiveness Comparison of Liquor Carbonis Distillate Solution and Calcipotriol Cream in the Treatment of Moderate Chronic Plaque Psoriasis


title: Declarative Machine Learning - A Classification of Basic Properties and Types

abstract: Declarative machine learning (ML) aims at the high-level specification of ML tasks or algorithms, and automatic generation of optimized execution plans from these specifications. The fundamental goal is to simplify the usage and/or development of ML algorithms, which is especially important in the context of large-scale computations. However, ML systems at different abstraction levels have emerged over time and accordingly there has been a controversy about the meaning of this general definition of declarative ML. Specification alternatives range from ML algorithms expressed in domain-specific languages (DSLs) with optimization for performance, to ML task (learning problem) specifications with optimization for performance and accuracy. We argue that these different types of declarative ML complement each other as they address different users (data scientists and end users). This paper makes an attempt to create a taxonomy for declarative ML, including a definition of essential basic properties and types of declarative ML. Along the way, we provide insights into implications of these properties. We also use this taxonomy to classify existing systems. Finally, we draw conclusions on defining appropriate benchmarks and specification languages for declarative ML.


title: Automatically Constructing a Normalisation Dictionary for Microblogs

abstract: Microblog normalisation methods often utilise complex models and struggle to differentiate between correctly-spelled unknown words and lexical variants of known words. In this paper, we propose a method for constructing a dictionary of lexical variants of known words that facilitates lexical normalisation via simple string substitution (e.g. tomorrow for tmrw). We use context information to generate possible variant and normalisation pairs and then rank these by string similarity. Highly-ranked pairs are selected to populate the dictionary. We show that a dictionary-based approach achieves state-of-the-art performance for both F-score and word error rate on a standard dataset. Compared with other methods, this approach offers a fast, lightweight and easy-to-use solution, and is thus suitable for high-volume microblog pre-processing.


title: A Collaborative Intrusion Detection Mechanism Against False Data Injection Attack in Advanced Metering Infrastructure

abstract: Smart meters are inherent components in advanced metering infrastructure (AMI) in the smart power grid. They are serving as the crucial interfaces through which the cyber, physical, and social domains of the smart grid can interact with each other. Due to the complicated interactions, smart meters may face a large variety of threats. In this paper, we exploit the colored Petri net to describe the information flows among units in a smart meter. Then, we propose a threat model for smart meters. Considering the constrained computation and storage resources of a smart meter, we present a collaborative intrusion detection mechanism against false data injection attack. The proposed scheme can work regardless of changes in a smart meter’s software. Numerical results demonstrate the low cost and effectiveness of our proposed intrusion detection mechanism.


title: Multispectral Filter Arrays: Recent Advances and Practical Implementation

abstract: Thanks to some technical progress in interferencefilter design based on different technologies, we can finally successfully implement the concept of multispectral filter array-based sensors. This article provides the relevant state-of-the-art for multispectral imaging systems and presents the characteristics of the elements of our multispectral sensor as a case study. The spectral characteristics are based on two different spatial arrangements that distribute eight different bandpass filters in the visible and near-infrared area of the spectrum. We demonstrate that the system is viable and evaluate its performance through sensor spectral simulation.


title: Soft Actuators for Small-Scale Robotics

abstract: This review comprises a detailed survey of ongoing methodologies for soft actuators, highlighting approaches suitable for nanometer- to centimeter-scale robotic applications. Soft robots present a special design challenge in that their actuation and sensing mechanisms are often highly integrated with the robot body and overall functionality. When less than a centimeter, they belong to an even more special subcategory of robots or devices, in that they often lack on-board power, sensing, computation, and control. Soft, active materials are particularly well suited for this task, with a wide range of stimulants and a number of impressive examples, demonstrating large deformations, high motion complexities, and varied multifunctionality. Recent research includes both the development of new materials and composites, as well as novel implementations leveraging the unique properties of soft materials.


title: Context Representation and Fusion: Advancements and Opportunities

abstract: The acceptance and usability of context-aware systems have given them the edge of wide use in various domains and has also attracted the attention of researchers in the area of context-aware computing. Making user context information available to such systems is the center of attention. However, there is very little emphasis given to the process of context representation and context fusion which are integral parts of context-aware systems. Context representation and fusion facilitate in recognizing the dependency/relationship of one data source on another to extract a better understanding of user context. The problem is more critical when data is emerging from heterogeneous sources of diverse nature like sensors, user profiles, and social interactions and also at different timestamps. Both the processes of context representation and fusion are followed in one way or another; however, they are not discussed explicitly for the realization of context-aware systems. In other words most of the context-aware systems underestimate the importance context representation and fusion. This research has explicitly focused on the importance of both the processes of context representation and fusion and has streamlined their existence in the overall architecture of context-aware systems’ design and development. Various applications of context representation and fusion in context-aware systems are also highlighted in this research. A detailed review on both the processes is provided in this research with their applications. Future research directions (challenges) are also highlighted which needs proper attention for the purpose of achieving the goal of realizing context-aware systems.


title: Machine Learning on Sequential Data Using a Recurrent Weighted Average

abstract: Abstract Recurrent Neural Networks (RNN) are a type of statistical model designed to handle sequential data. The model reads a sequence one symbol at a time. Each symbol is processed based on information collected from the previous symbols. With existing RNN architectures, each symbol is processed using only information from the previous processing step. To overcome this limitation, we propose a new kind of RNN model that computes a recurrent weighted average (RWA) over every past processing step. Because the RWA can be computed as a running average, the computational overhead scales like that of any other RNN architecture. The approach essentially reformulates the attention mechanism into a stand-alone model. The performance of the RWA model is assessed on the variable copy problem, the adding problem, classification of artificial grammar, classification of sequences by length, and classification of the MNIST images (where the pixels are read sequentially one at a time). On almost every task, the RWA model is found to fit the data significantly faster than a standard LSTM model.


title: Understanding and Supporting Cross-Device Web Search for Exploratory Tasks with Mobile Touch Interactions

abstract: Mobile devices enable people to look for information at the moment when their information needs are triggered. While experiencing complex information needs that require multiple search sessions, users may utilize desktop computers to fulfill information needs started on mobile devices. Under the context of mobile-to-desktop web search, this article analyzes users’ behavioral patterns and compares them to the patterns in desktop-to-desktop web search. Then, we examine several approaches of using Mobile Touch Interactions (MTIs) to infer relevant content so that such content can be used for supporting subsequent search queries on desktop computers. The experimental data used in this article was collected through a user study involving 24 participants and six properly designed cross-device web search tasks. Our experimental results show that (1) users’ mobile-to-desktop search behaviors do significantly differ from desktop-to-desktop search behaviors in terms of information exploration, sense-making and repeated behaviors. (2) MTIs can be employed to predict the relevance of click-through documents, but applying document-level relevant content based on the predicted relevance does not improve search performance. (3) MTIs can also be used to identify the relevant text chunks at a fine-grained subdocument level. Such relevant information can achieve better search performance than the document-level relevant content. In addition, such subdocument relevant information can be combined with document-level relevance to further improve the search performance. However, the effectiveness of these methods relies on the sufficiency of click-through documents. (4) MTIs can also be obtained from the Search Engine Results Pages (SERPs). The subdocument feedbacks inferred from this set of MTIs even outperform the MTI-based subdocument feedback from the click-through documents.


title: Within-class Covariance Normalization for SVM-based Speaker Recognition

abstract: This paper extends the within-class covariance normalization (WCCN) technique described in [1, 2] for training generalized linear kernels. We describe a practical procedure for applying WCCN to an SVM-based speaker recognition system where the input feature vectors reside in a high-dimensional space. Our approach involves using principal component analysis (PCA) to split the original feature space into two subspaces: a low-dimensional “PCA space” and a high-dimensional “PCA-complement space.” After performing WCCN in the PCA space, we concatenate the resulting feature vectors with a weighted version of their PCAcomplements. When applied to a state-of-the-art MLLR-SVM speaker recognition system, this approach achieves improvements of up to 22% in EER and 28% in minimum decision cost function (DCF) over our previous baseline. We also achieve substantial improvements over an MLLR-SVM system that performs WCCN in the PCA space but discards the PCA-complement.


Cloning Cryptographic RFID


title: A Planar Switchable 3-D-Coverage Phased Array Antenna and Its User Effects for 28-GHz Mobile Terminal Applications

abstract: This paper introduces a planar switchable 3-D-coverage phased array for 28-GHz mobile terminal applications. In order to realize 3-D-coverage beam scan with a simple planar array, chassis surface waves are efficiently excited and controlled by three identical slot subarrays. Three subarrays switch their beams to three distinct regions. Each subarray works as a phased array to steer the beam within each region. Large coverage efficiency is achieved. (e.g., 80% of the space sphere has the realized gain of over 8 dBi.) The proposed antenna covers a bandwidth of over 2 GHz in the band of 28 GHz. User effects on the switchable array are also studied in both data mode and talk mode (voice) at 28 GHz. In talk mode, good directivity and beam switching can be realized by placing the switchable array at the top of the chassis (close to the index finger). And the user shadowing can be significantly reduced by placing it at the bottom of the chassis (close to the palm). In data mode, the switchable array, mounted at the top, achieves less body loss and larger coverage than at the bottom. The proposed antenna is fabricated and measured. The array at the top in talk mode is measured with a real human. The measurements align well with simulations.


title: Alpha Activity in EEG and Intelligence

abstract: Intelligence of a human being in general is considered as to its variations in the ability to learn, to function in society, and to behave according to contemporary social expectations Intelligence of a human being is associated with brain the brain is considered as the most complex biological existent structure. Electroencephalograph (EEG) is an instrument used for recording the electrical activity of the brain. EEG is the variation of the electrical fields in the cortex or on the surface of scalp caused by the physiological activities of the brain. EEG is currently the most widely adopted method for assessing brain activities. Detecting the changes of these waves is critical for understanding of brain function. In clinical applications, spontaneous EEG signals can be divided into several rhythms according to their frequency. They are δ rhythm (0.1-4Hz), θ rhythm (4-8Hz), α rhythm (8-13), β and rhythm (13-30Hz). The EEG signals have close relationships with the cerebral diseases, mental status and human qualities like intelligence. As a consequence it is very useful to analyze process and classify the EEG signal on the basis of frequency bands and then extract their underlying features and so as to correlate with normal and abnormal functioning of brain, sleep, mental status and also with intelligence. In this paper we propose to conduct pointed literature survey of alpha activity and intelligence correlation. We propose to conduct test on subjects by computer, EEG interface. We propose to conclude from practical experimentation, whether there is a correlation between alpha activity power and intelligence.


title: Limits to growth in the new economy: exploring the 'get big fast' strategy in

abstract: Many e-businesses have pursued a ‘get big fast’ (GBF) strategy, pricing low and marketing heavily to build their user base, in the belief that there were significant sources of increasing returns favoring early entrants and large players. Until early 2000 the capital markets rewarded the GBF strategy, but since then market values have collapsed and scores of new-economy firms have failed. The rise and fall of the dot coms is not merely a case of a speculative bubble. Many firms stumbled when they grew so rapidly that they were unable to fulfill orders or provide quality service. GBF proponents focus on the positive feedbacks that create increasing returns and favor aggressive firms, but have not paid adequate attention to the negative feedbacks that can limit growth, e.g., service quality erosion. The faster a firm grows, the stronger these negative feedbacks may be. We address these issues with a formal dynamic model of competition among online and click-and-mortar companies in business-to-consumer e-commerce. The model endogenously generates demand, market share, service quality, employee skill and retention, content creation, market valuation, and other key variables. The model is calibrated to the online book market and Amazon.com as a test case. We explore growth strategies for e-commerce firms and their sustainability under different scenarios for customer, competitor, and capital market behavior. Copyright  2003 John Wiley & Sons, Ltd.


title: Time Based Measurement of the Impedance of the Skin-Electrode Interface for Dry Electrode ECG Recording

abstract: This paper reports the measurement of the properties of dry or pasteless conductive electrodes to be used for long-term recording of the human electrocardiogram (ECG). Knowledge of these properties is essential for the correct design of the input stage of associated recording amplifiers. Measurements were made on three commercially available conductive carbon based electrodes at pressures of 5 mmHg and 20 mmHg, located on the lower abdomen of the body on three subjects having different skin types. Parameter values were fitted to a two-time-constant based model of the electrode using data measured over a period of 10s. Values of resistance, ranging from 40kOmega to 1590kOmega and of capacitance ranging from 0.05 microF to 38 microF were obtained for the components, while the values of the time-constants varied from 0.07 s to 3.9s.


title: PixelCNN Models with Auxiliary Variables for Natural Image Modeling

abstract: We study probabilistic models of natural images and extend the autoregressive family of PixelCNN architectures by incorporating auxiliary variables. Subsequently, we describe two new generative image models that exploit different image transformations as auxiliary variables: a quantized grayscale view of the image or a multi-resolution image pyramid. The proposed models tackle two known shortcomings of existing PixelCNN models: 1) their tendency to focus on low-level image details, while largely ignoring high-level image information, such as object shapes, and 2) their computationally costly procedure for image sampling. We experimentally demonstrate benefits of the proposed models, in particular showing that they produce much more realistically looking image samples than previous state-of-the-art probabilistic models.


abstract: Contemporary educational theory holds that one of the pivotal causes of inadequate school performance is the inability of schools to adequately staff classrooms with qualified teachers. Contemporary theory also holds that these staffing problems are primarily due to shortages of teachers, which, in turn, are primarily due to recent increases in teacher retirements and student enrollments. This analysis investigates the possibility that there are other factors that might have an impact on teacher turnover levels, and, in turn, the staffing problems of schools, factors rooted in the organizational characteristics and conditions of schools. The data utilized in this investigation are from the Schools and Staffing Survey and its supplement, the Teacher Followup Survey, a large, comprehensive, nationally representative survey of teachers and schools conducted by the National Center for Education Statistics. The results of this analysis show that, net of teacher effects, there are significant effects of school characteristics and organizational conditions on teacher turnover which have largely been overlooked by previous research. For example, the data show that while high-poverty public schools have moderately higher rates, contrary to conventional wisdom, neither larger schools, nor public schools in large school districts, nor urban public schools have especially high rates of teacher turnover. In contrast, small private schools stand out for their high rates of turnover. Moreover, the data show, again contrary to popular wisdom, that the amount of turnover accounted for by retirement is relatively minor, especially when compared to that resulting from two related causes – teacher job dissatisfaction and teachers pursuing other jobs. The data show that, in particular, low salaries, inadequate support from the school administration, student discipline problems, and limited faculty input into school decision-making all contribute to higher rates of turnover, after controlling for the characteristics of both teachers and schools. The results of this investigation suggest that school staffing problems are neither synonymous with, nor primarily due to, teacher shortages in the conventional sense of a deficit in the supply of teachers. Rather, this study suggests that school staffing problems are primarily due to excess demand resulting from a "revolving door" – where large numbers of teachers depart their jobs for reasons other than retirement. This study also suggests that popular education initiatives, such as teacher recruitment programs, will not solve the staffing problems of such schools if they do not also address the organizational sources of low teacher retention. Disciplines Educational Administration and Supervision | Education Economics | Social and Philosophical Foundations of Education | Teacher Education and Professional Development Comments View on the CPRE website. This report is available at ScholarlyCommons: http://repository.upenn.edu/cpre_researchreports/12 ctp Center for the Study of Teaching and Policy U N I V E R S I T Y O F W A S H I N G T O N Teacher Turnover, Teacher Shortages, and the Organization of Schools by Richard M. Ingersoll University of Pennsylvania


title: A Supply Chain Network Game Theoretic Framework for Time-Based Competition with Transportation Costs and Product Differentiation

abstract: In this paper, we developed a supply chain network game theory model with differentiated products and transportation costs in the case of time-based oligopolistic competition. The firms are profit-maximizers and have, as their strategic variables, the product shipments and the delivery times to the consumers at the demand markets with the delivery times including both the production times and the transportation times. The demand price functions are functions of the demands for the products at the different demand markets as well as their delivery times. The governing Nash equilibrium conditions are formulated as alternative variational inequalities. An algorithm is proposed, which yields closed form expressions, at each iteration, for the product shipments, the delivery times, as well as the associated Lagrange multipliers. Supply chain network numerical examples are given to illustrate the modeling and the computational approach.


title: Towards Efficient Vehicle Classification in Intelligent Transportation Systems

abstract: The classification of vehicles is an important task in Intelligent Transportation Systems (ITS) for applications such as analyzing traffic, checking for fraud, tracking targets, and other security applications. In the recent years, automated systems to recognize makes and models of oncoming vehicles are gaining attention, utilizing existing infrastructure of traffic cameras. To this end, we present an unexplored approach for vehicle make and model recognition (VMMR) and demonstrate its highly accurate and real-time performance, using a recently published real-world dataset. The encouraging results of our approach pave the way towards efficient large-scale and distributed vehicular surveillance in ITS.


title: A low THD analog Class D Amplifier based on self-oscillating modulation with complete feedback network

abstract: We propose a novel analog Class D Amplifier (CDA) based on self-oscillating modulation where the input of the feedback network is taken at the output of the lowpass LC filter (‘complete feedback’) as opposed to the prevalent (Pulse Width Modulation CDA) approach where the feedback input is taken at the output of the output stage of the CDA (‘incomplete feedback’). The complete feedback approach substantially suppresses the non-linearity of the inductor of the Lowpass filter as this filter now constitutes part of the feedback loop. The proposed approach is highly hardware efficient over reported prevalent complete feedback CDAs. When the proposed complete feedback CDA is compared against the prevalent incomplete feedback approach, the proposed CDA exhibited highly suppressed THD (∼40dB relative suppression in most cases). The THD of the proposed CDA is ≤0.012% for the complete modulation index range and pertinent input signal range. Means to further reduce the THD is also suggested.


title: Color indexing

abstract: Computer vision is moving into a new era in which the aim is to develop visual skills for robots that allow them to interact with a dynamic, unconstrained environment. To achieve this aim, new kinds of vision algorithms need to be developed which run in real time and subserve the robot's goals. Two fundamental goals are determining the identity of an object with a known location, and determining the location of a known object. Color can be successfully used for both tasks. ::: This dissertation demonstrates that color histograms of multicolored objects provide a robust, efficient cue for indexing into a large database of models. It shows that color histograms are stable object representations in the presence of occlusion and over change in view, and that they can differentiate among a large number of objects. For solving the identification problem, it introduces a technique called Histogram Intersection, which matches model and image histograms and a fast incremental version of Histogram Intersection which allows real-time indexing into a large database of stored models. It demonstrates techniques for dealing with crowded scenes and with models with similar color signatures. For solving the location problem it introduces an algorithm called Histogram Backprojection which performs this task efficiently in crowded scenes.


title: Visual Analysis of Network Flow Data with Timelines and Event Plots

abstract: This paper describes Isis, a system that uses progressive multiples of timelines and event plots to support the iterative investigation of intrusions by experienced analysts using network flow data. The visual representations have been designed to make temporal relationships apparent, allow visual classification of events with dynamic brushing, and enable users to organize their visualizations to reveal traffic structure and patterns by reordering rows. Isis combines visual affordances with SQL to provide a flexible tool for investigation. We present an annotated case study using anonymized data of a real intrusion that demonstrates the features of Isis.


title: A heuristic approach to effective and efficient clustering on uncertain objects

abstract: We study the problem of clustering uncertain objects whose locations are uncertain and described by probability density functions (pdf). We analyze existing pruning algorithms and experimentally show that there exists a new bottleneck in the performance due to the overhead of pruning candidate clusters for assignment of each uncertain object in each iteration. In this article, we will show that by considering squared Euclidean distance, UK-means (without pruning techniques) is reduced to K-means and performs much faster than pruning algorithms, however, with some discrepancies in the clustering results due to using different distance functions. Thus, we propose Approximate UK-means to heuristically identify objects of boundary cases and re-assign them to better clusters. Three models for the representation of cluster representative (certain model, uncertain model and heuristic model) are proposed to calculate expected squared Euclidean distance between objects and cluster representatives in this paper. Our experimental results show that on average the execution time of Approximate UK-means is only 25% more than K-means and our approach reduces the discrepancies of K-means' clustering results by up to 70%.


title: Enhanced Transmission Power Control Mechanism based on RSSI for MANET

abstract: Today’s power consumption is the critical issue in MANET. Since, we have limited battery power to operate nodes. Due to the lack of power (Energy), the nodes “die out” from the network. Recently, power control in mobile ad hoc networks has been the focus of extensive research. Energy of nodes is consumed according to transmission power required by the radios. In this paper, we develop a mechanism, Enhanced Transmission Power Control Mechanism (ETPCM) that minimizes the required transmission power consumption of radio during packet transmission. In this work, the transmission power is dynamically set according to the distance and the distance can be calculated by using a parameter Receiving Signal Strength Indicator (RSSI) between these nodes. IEEE 802.11b is a standard that specifies the physical layer and media access control designed for low-rate wireless local area networks (WLAN), it is used as radio at PHY layer (PHY802.11b) and protocol at MAC layer (MAC802.11), and it is generally used for MANET.


title: An Analysis of Probabilistic Methods for Top-N Recommendation in Collaborative Filtering

abstract: In this work we perform an analysis of probabilistic approaches to recommendation upon a different validation perspective, which focuses on accuracy metrics such as recall and precision of the recommendation list. Traditionally, state-of-art approches to recommendations consider the recommendation process from a "missing value prediction" perspective. This approach simplifies the model validation phase that is based on the minimization of standard error metrics such as RMSE. However, recent studies have pointed several limitations of this approach, showing that a lower RMSE does not necessarily imply improvements in terms of specific recommendations. We demonstrate that the underlying probabilistic framework offers several advantages over traditional methods, in terms of flexibility in the generation of the recommendation list and consequently in the accuracy of recommendation.


title: Sentilo: Frame-Based Sentiment Analysis

abstract: Sentilo is an unsupervised, domain-independent system that performs sentiment analysis by hybridizing natural language processing techniques and semantic Web technologies. Given a sentence expressing an opinion, Sentilo recognizes its holder, detects the topics and subtopics that it targets, links them to relevant situations and events referred to by it and evaluates the sentiment expressed on each topic/subtopic. Sentilo relies on a novel lexical resource, which enables a proper propagation of sentiment scores from topics to subtopics, and on a formal model expressing the semantics of opinion sentences. Sentilo provides its output as a RDF graph, and whenever possible it resolves holders’ and topics’ identity on Linked Data.


title: Fast and Accurate Calibration of a Kinect Sensor

abstract: The article describes a new algorithm for calibrating a Kinect sensor that achieves high accuracy using only 6 to 10 image-disparity pairs of a planar checkerboard pattern. The method estimates the projection parameters for both color and depth cameras, the relative pose between them, and the function that converts kinect disparity units (kdu) into metric depth. We build on the recent work of Herrera et. al [8] that uses a large number of input frames and multiple iterative minimization steps for obtaining very accurate calibration results. We propose several modifications to this estimation pipeline that dramatically improve stability, usability, and runtime. The modifications consist in: (i) initializing the relative pose using a new minimal, optimal solution for registering 3D planes across different reference frames, (ii) including a metric constraint during the iterative refinement to avoid a drift in the disparity to depth conversion, and (iii) estimating the parameters of the depth distortion model in an open-loop post-processing step. Comparative experiments show that our pipeline can achieve a calibration accuracy similar to [8] while using less than 1/6 of the input frames and running in 1/30 of the time.


title: Mining User Check-In Behavior with a Random Walk for Urban Point-of-Interest Recommendations

abstract: In recent years, research into the mining of user check-in behavior for point-of-interest (POI) recommendations has attracted a lot of attention. Existing studies on this topic mainly treat such recommendations in a traditional manner—that is, they treat POIs as items and check-ins as ratings. However, users usually visit a place for reasons other than to simply say that they have visited. In this article, we propose an approach referred to as Urban POI-Walk (UPOI-Walk), which takes into account a user's social-triggered intentions (SI), preference-triggered intentions (PreI), and popularity-triggered intentions (PopI), to estimate the probability of a user checking-in to a POI. The core idea of UPOI-Walk involves building a HITS-based random walk on the normalized check-in network, thus supporting the prediction of POI properties related to each user's preferences. To achieve this goal, we define several user--POI graphs to capture the key properties of the check-in behavior motivated by user intentions. In our UPOI-Walk approach, we propose a new kind of random walk model—Dynamic HITS-based Random Walk—which comprehensively considers the relevance between POIs and users from different aspects. On the basis of similitude, we make an online recommendation as to the POI the user intends to visit. To the best of our knowledge, this is the first work on urban POI recommendations that considers user check-in behavior motivated by SI, PreI, and PopI in location-based social network data. Through comprehensive experimental evaluations on two real datasets, the proposed UPOI-Walk is shown to deliver excellent performance.


title: Design and Construction of a Big Data Analytics Framework for Health Applications

abstract: We propose to establish a framework for supporting Big Data Analytics (BDA) on real healthcare big data. To test the analytic framework, we used UVic WestGrid (4412 cores computer cluster) to analyze the emulation of 10 billion healthcare records that represented the main hospital system and its reporting via its data warehouse stored at Vancouver Island Health Authority (VIHA). The study showed that the build of the BDA platform requires changes to the configurations to the MapReduce component of Hadoop (HDFS) and to the indexing of HBASE. The ingestion and replication of the data over a large volume iteratively offers a method for data migration of large volumes of real healthcare data via HDFS and to query in that some distributed filing system. Furthermore, the query performance was very satisfied via Apache Phoenix layer that is run in parallel across all nodes on HBASE. The study has demonstrated that the proposed BDA process and configuration met patient data security and performance requirements of healthcare BDA.


title: Metals, toxicity and oxidative stress

abstract: Metal-induced toxicity and carcinogenicity, with an emphasis on the generation and role of reactive oxygen and nitrogen species, is reviewed. Metal-mediated formation of free radicals causes various modifications to DNA bases, enhanced lipid peroxidation, and altered calcium and sulfhydryl homeostasis. Lipid peroxides, formed by the attack of radicals on polyunsaturated fatty acid residues of phospholipids, can further react with redox metals finally producing mutagenic and carcinogenic malondialdehyde, 4-hydroxynonenal and other exocyclic DNA adducts (etheno and/or propano adducts). Whilst iron (Fe), copper (Cu), chromium (Cr), vanadium (V) and cobalt (Co) undergo redox-cycling reactions, for a second group of metals, mercury (Hg), cadmium (Cd) and nickel (Ni), the primary route for their toxicity is depletion of glutathione and bonding to sulfhydryl groups of proteins. Arsenic (As) is thought to bind directly to critical thiols, however, other mechanisms, involving formation of hydrogen peroxide under physiological conditions, have been proposed. The unifying factor in determining toxicity and carcinogenicity for all these metals is the generation of reactive oxygen and nitrogen species. Common mechanisms involving the Fenton reaction, generation of the superoxide radical and the hydroxyl radical appear to be involved for iron, copper, chromium, vanadium and cobalt primarily associated with mitochondria, microsomes and peroxisomes. However, a recent discovery that the upper limit of "free pools" of copper is far less than a single atom per cell casts serious doubt on the in vivo role of copper in Fenton-like generation of free radicals. Nitric oxide (NO) seems to be involved in arsenite-induced DNA damage and pyrimidine excision inhibition. Various studies have confirmed that metals activate signalling pathways and the carcinogenic effect of metals has been related to activation of mainly redox-sensitive transcription factors, involving NF-kappaB, AP-1 and p53. Antioxidants (both enzymatic and non-enzymatic) provide protection against deleterious metal-mediated free radical attacks. Vitamin E and melatonin can prevent the majority of metal-mediated (iron, copper, cadmium) damage both in vitro systems and in metal-loaded animals. Toxicity studies involving chromium have shown that the protective effect of vitamin E against lipid peroxidation may be associated rather with the level of non-enzymatic antioxidants than the activity of enzymatic antioxidants. However, a very recent epidemiological study has shown that a daily intake of vitamin E of more than 400 IU increases the risk of death and should be avoided. While previous studies have proposed a deleterious pro-oxidant effect of vitamin C (ascorbate) in the presence of iron (or copper), recent results have shown that even in the presence of redox-active iron (or copper) and hydrogen peroxide, ascorbate acts as an antioxidant that prevents lipid peroxidation and does not promote protein oxidation in humans in vitro. Experimental results have also shown a link between vanadium and oxidative stress in the etiology of diabetes. The impact of zinc (Zn) on the immune system, the ability of zinc to act as an antioxidant in order to reduce oxidative stress and the neuroprotective and neurodegenerative role of zinc (and copper) in the etiology of Alzheimer's disease is also discussed. This review summarizes recent findings in the metal-induced formation of free radicals and the role of oxidative stress in the carcinogenicity and toxicity of metals.