CSI Neural Network: Using Side-channels to Recover Your Artificial Neural Network Information

Machine learning has become mainstream across industries. Numerous examples proved the validity of it for security applications. In this work, we investigate how to reverse engineer a neural network by using only power side-channel information. To this end, we consider a multilayer perceptron as the machine learning architecture of choice and assume a non-invasive and eavesdropping attacker capable of measuring only passive side-channel leakages like power consumption, electromagnetic radiation, and reaction time. We conduct all experiments on real data and common neural net architectures in order to properly assess the applicability and extendability of those attacks. Practical results are shown on an ARM CORTEX-M3 microcontroller. Our experiments show that the side-channel attacker is capable of obtaining the following information: the activation functions used in the architecture, the number of layers and neurons in the layers, the number of output classes, and weights in the neural network. Thus, the attacker can effectively reverse engineer the network using sidechannel information. Next, we show that once the attacker has the knowledge about the neural network architecture, he/she could also recover the inputs to the network with only a single-shot measurement. Finally, we discuss several mitigations one could use to thwart such attacks.


Parking Guidance System Based on ZigBee and Geomagnetic Sensor Technology

Concerning the phenomenon that common parking service could not satisfy the increasing demand of the private vehicle owners, an intelligent parking guidance system based on Zig Bee network and geomagnetic sensors was designed. Real-time vehicle position and related traffic information were collected by geomagnetic sensors around parking lots and updated to center server via Zig Bee network. On the other hand, out-door Liquid Crystal Display screens controlled by center server can display information of available parking places. In this paper, guidance strategy was divided into four levels, which could provide clear and effective information to drivers. The experimental results prove that the distance detection accuracy of geomagnetic sensors was within 0.4m, and the lowest package loss rate of the wireless network in the range of 150m is 0%. This system can provide solution for better parking service in intelligent cities.


Normalized ( Pointwise ) Mutual Information in Collocation Extraction

In this paper, we discuss the related information theoretical association measures of mutual information and pointwise mutual information, in the context of collocation extraction. We introduce normalized variants of these measures in order to make them more easily interpretable and at the same time less sensitive to occurrence frequency. We also provide a small empirical study to give more insight into the behaviour of these new measures in a collocation extraction setup.


Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs

Most analysis of web search relevance and performance takes a single query as the unit of search engine interaction. When studies attempt to group queries together by task or session, a timeout is typically used to identify the boundary. However, users query search engines in order to accomplish tasks at a variety of granularities, issuing multiple queries as they attempt to accomplish tasks. In this work we study real sessions manually labeled into hierarchical tasks, and show that timeouts, whatever their length, are of limited utility in identifying task boundaries, achieving a maximum precision of only 70%. We report on properties of this search task hierarchy, as seen in a random sample of user interactions from a major web search engine's log, annotated by human editors, learning that 17% of tasks are interleaved, and 20% are hierarchically organized. No previous work has analyzed or addressed automatic identification of interleaved and hierarchically organized search tasks. We propose and evaluate a method for the automated segmentation of users' query streams into hierarchical units. Our classifiers can improve on timeout segmentation, as well as other previously published approaches, bringing the accuracy up to 92% for identifying fine-grained task boundaries, and 89-97% for identifying pairs of queries from the same task when tasks are interleaved hierarchically. This is the first work to identify, measure and automatically segment sequences of user queries into their hierarchical structure. The ability to perform this kind of segmentation paves the way for evaluating search engines in terms of user task completion.


Query segmentation revisited

We address the problem of query segmentation: given a keyword query, the task is to group the keywords into phrases, if possible. Previous approaches to the problem achieve reasonable segmentation performance but are tested only against a small corpus of manually segmented queries. In addition, many of the previous approaches are fairly intricate as they use expensive features and are difficult to be reimplemented.
 The main contribution of this paper is a new method for query segmentation that is easy to implement, fast, and that comes with a segmentation accuracy comparable to current state-of-the-art techniques. Our method uses only raw web n-gram frequencies and Wikipedia titles that are stored in a hash table. At the same time, we introduce a new evaluation corpus for query segmentation. With about 50,000 human-annotated queries, it is two orders of magnitude larger than the corpus being used up to now.


Exploratory search: from finding to understanding

Research tools critical for exploratory search success involve the creation of new interfaces that move the process beyond predictable fact retrieval.


Delphi method: techniques and applications

Cross-impact analysis is a method for revising estimated probabilities of future events in terms of estimated interactions among those events. This Report presents an elementary cross-impact model where the cross-impacts are formulated as relative probabilities. Conditions are derived for the consistency of the matrix of relative probabilities of n events. An extension also provides a necessary condition for the vector of absolute probabilities to be consistent with the relative probability matrix. An averaging technique is formulated for resolving inconsistencies in the matrix, and a nearest-point computation derived for resolving inconsistencies between the set of absolute probabilities and the matrix. Although elementary, the present model clarifies some of the conceptual problems associated with cross-impact analysis, and supplies a relatively sound basis for revising probability estimates in the limited case where interactions can be approximated by relative probabilities.


Phishing e-mail detection by using deep learning algorithms

Phishing e-mails are considered as spam e-mails, which aim to collect sensitive personal information about the users via network. Since the main purpose of this behavior is mostly to harm users financially, it is vital to detect these phishing or spam e-mails immediately to prevent unauthorized access to users' vital information. To detect phishing e-mails, using a quicker and robust classification method is important. Considering the billions of e-mails on the Internet, this classification process is supposed to be done in a limited time to analyze the results. In this work, we present some of the early results on the classification of spam email using deep learning and machine methods. We utilize word2vec to represent emails instead of using the popular keyword or other rule-based methods. Vector representations are then fed into a neural network to create a learning model. We have tested our method on an open dataset and found over 96% accuracy levels with the deep learning classification methods in comparison to the standard machine learning algorithms.


Bundle Adjustment in the Large

We present the design and implementation of a new inexact Newton type algorithm for solving large-scale bundle adjustment problems with tens of thousands of images. We explore the use of Conjugate Gradients for calculating the Newton step and its performance as a function of some simple and computationally efficient preconditioners. We show that the common Schur complement trick is not limited to factorization-based methods and that it can be interpreted as a form of preconditioning. Using photos from a street-side dataset and several community photo collections, we generate a variety of bundle adjustment problems and use them to evaluate the performance of six different bundle adjustment algorithms. Our experiments show that truncated Newton methods, when paired with relatively simple preconditioners, offer state of the art performance for large-scale bundle adjustment. The code, test problems and detailed performance data are available at http://grail.cs.washington.edu/projects/bal.


Building Caring Healthcare Systems in the Internet of Things

The nature of healthcare and the computational and physical technologies and constraints present a number of challenges to systems designers and implementers. In spite of the challenges, there is a significant market for systems and products to support caregivers in their tasks as the number of people needing assistance grows substantially. In this paper, we present a structured approach for describing Internet of Things (IoT) for healthcare systems. We illustrate the approach for three use cases and discuss relevant quality issues that arise, in particular, the need to consider caring as a requirement.


Mining Online Reviews for Predicting Sales Performance: A Case Study in the Movie Domain

Posting reviews online has become an increasingly popular way for people to express opinions and sentiments toward the products bought or services received. Analyzing the large volume of online reviews available would produce useful actionable knowledge that could be of economic values to vendors and other interested parties. In this paper, we conduct a case study in the movie domain, and tackle the problem of mining reviews for predicting product sales performance. Our analysis shows that both the sentiments expressed in the reviews and the quality of the reviews have a significant impact on the future sales performance of products in question. For the sentiment factor, we propose Sentiment PLSA (S-PLSA), in which a review is considered as a document generated by a number of hidden sentiment factors, in order to capture the complex nature of sentiments. Training an S-PLSA model enables us to obtain a succinct summary of the sentiment information embedded in the reviews. Based on S-PLSFA, we propose ARSA, an Autoregressive Sentiment-Aware model for sales prediction. We then seek to further improve the accuracy of prediction by considering the quality factor, with a focus on predicting the quality of a review in the absence of user-supplied indicators, and present ARSQA, an Autoregressive Sentiment and Quality Aware model, to utilize sentiments and quality for predicting product sales performance. Extensive experiments conducted on a large movie data set confirm the effectiveness of the proposed approach.


Entity Retrieval via Entity Factoid Hierarchy

We propose that entity queries are generated via a two-step process: users first select entity facts that can distinguish target entities from the others; and then choose words to describe each selected fact. Based on this query generation paradigm, we propose a new entity representation model named as entity factoid hierarchy. An entity factoid hierarchy is a tree structure composed of factoid nodes. A factoid node describes one or more facts about the entity in different information granularities. The entity factoid hierarchy is constructed via a factor graph model, and the inference on the factor graph is achieved by a modified variant of Multiple-try Metropolis algorithm. Entity retrieval is performed by decomposing entity queries and computing the query likelihood on the entity factoid hierarchy. Using an array of benchmark datasets, we demonstrate that our proposed framework significantly improves the retrieval performance over existing models.


A Foray into Conficker's Logic and Rendezvous Points

We present an in depth static analysis of the Conficker worm, primarily through the exploration of the client-side binary logic. In this paper, we summarize various aspects of the inner workings of binary variants A and B,1 which were the first in a chain of recent revisions aimed to keep this epidemic resistant to ongoing eradication attempts. These first two variants have combined to produce a multi-million node population of infected hosts, whose true main purpose has yet to be fully understood. We further validate aspects of our analysis through in-situ network analyses, and discuss some attribution links about its origins.


Information Security Threats Classification Pyramid

Threat classification is extremely important for organizations, as it is an important step towards implementation of information security. Most of the existing threat classifications listed threats in static ways without linking threats to information system areas. The aim of this paper is to design a methodology that can classify deliberate threats in a dynamic way to represent each threat in different areas of the information system. This technique is based on the following factors: the attacker's prior knowledge (i. e. the knowledge hold by the source of the threat) about the system, loss of security information and the criticality of the area that might be affected by that threat.


Collaborative privacy management: mobile privacy beyond your own devices

As the development of mobile devices and applications, mobile privacy has become a very important issue. Current researches on mobile privacy mainly focus on potential leakages on a particular device. However, leakage of sensitive data on a mobile device not only violates the privacy of the phone (or data) owner, but also violates the privacy of many other people whose information are contained in the data directly or indirectly (they are called data involvers). To address such problems, we introduce a collaborative privacy management framework, which aims to provide fine-grained data privacy protection for both data owners and data involvers in a distributed manner. Based on individual privacy policies specified by each user, a collaborative privacy policy is generated and enforced on different devices automatically. As a proof-of-concept prototype, we implement the proposed framework on Android and demonstrate its applicability with two case studies.


Adding nesting structure to words

We propose the model of nested words for representation of data with both a linear ordering and a hierarchically nested matching of items. Examples of data with such dual linear-hierarchical structure include executions of structured programs, annotated linguistic data, and HTML/XML documents. Nested words generalize both words and ordered trees, and allow both word and tree operations. We define nested word automata—finite-state acceptors for nested words, and show that the resulting class of regular languages of nested words has all the appealing theoretical properties that the classical regular word languages enjoys: deterministic nested word automata are as expressive as their nondeterministic counterparts; the class is closed under union, intersection, complementation, concatenation, Kleene-*, prefixes, and language homomorphisms; membership, emptiness, language inclusion, and language equivalence are all decidable; and definability in monadic second order logic corresponds exactly to finite-state recognizability. We also consider regular languages of infinite nested words and show that the closure properties, MSO-characterization, and decidability of decision problems carry over.
 The linear encodings of nested words give the class of visibly pushdown languages of words, and this class lies between balanced languages and deterministic context-free languages. We argue that for algorithmic verification of structured programs, instead of viewing the program as a context-free language over words, one should view it as a regular language of nested words (or equivalently, a visibly pushdown language), and this would allow model checking of many properties (such as stack inspection, pre-post conditions) that are not expressible in existing specification logics.
 We also study the relationship between ordered trees and nested words, and the corresponding automata: while the analysis complexity of nested word automata is the same as that of classical tree automata, they combine both bottom-up and top-down traversals, and enjoy expressiveness and succinctness benefits over tree automata.


Smart Refrigerator Based on Internet of Things (IoT): An Approach to Efficient Food Management

This paper focuses on the technology of the smart refrigerator connected through the Internet of Things (IoT) platform. It deals with the issue of food wastage and efficient food distribution through a network of connected refrigerators within a defined neighborhood. This paper defines the three-layer architecture: front-end layer, gateway layer and the back-end layer that can allow the implementation of the system. This low-cost network can be created through a microcontroller and wireless sensors at the front-end architecture. The process was discussed to determine: the stock item below a defined quantity or threshold limit, determining the product expiration date and eventually determining the purchase or exchange of food items across the network. It also includes the proof of concept technology by allowing connection of an ultrasonic sensor to an IoT platform that can allow the exchange of information on the positioning of the food items inside the refrigerator. This eventually can allow for a large-scale commercial implementation of the project to deal with the current issue of food distribution.


1.3 μm, 56-Gbit/s EML module target to 400GbE

We developed 1.3 μm, EML module built in EML driver IC for both 50 and 56 Gbit/s. 40-km SMF transmission with clear eye openings under 56 Gbit/s operation were demonstrated for the first time.


Hybrid digital and analog beamforming design for large-scale MIMO systems

Large-scale multiple-input multiple-output (MIMO) systems enable high spectral efficiency by employing large antenna arrays at both the transmitter and the receiver of a wireless communication link. In traditional MIMO systems, full digital beamforming is done at the baseband; one distinct radio-frequency (RF) chain is required for each antenna, which for large-scale MIMO systems can be prohibitive from either cost or power consumption point of view. This paper considers a two-stage hybrid beamforming structure to reduce the number of RF chains for large-scale MIMO systems. The overall beamforming matrix consists of analog RF beamforming implemented using phase shifters and baseband digital beamforming of much smaller dimension. This paper considers precoder and receiver design for maximizing the spectral efficiency when the hybrid structure is used at both the transmitter and the receiver. On the theoretical front, bounds on the minimum number of transmit and receive RF chains that are required to realize the theoretical capacity of the large-scale MIMO system are presented. It is shown that the hybrid structure can achieve the same performance as the fully-digital beamforming scheme if the number of RF chains at each end is greater than or equal to twice the number of data streams. On the practical design front, this paper proposes a heuristic hybrid beamforming design strategy for the critical case where the number of RF chains is equal to the number of data streams, and shows that the performance of the proposed hybrid beamforming design can achieve spectral efficiency close to that of the fully-digital solution.


Design and implementation of embedded Web server based on arm and Linux

This paper achieves the design of an embedded Web server, which takes ARM920T-S3c2410s chip as its core and Linux as its operating system. This is because Linux can be reduced and transplanted. The method used to transplant Web server Boa on the embedded Linux platform is also discussed in detail, and through CGI technology functions of dynamic Web page is successfully realized. Relevant experiments show that after the Web server is embedded into the network video monitoring system, dynamic page interaction can be achieved between the Web server and the embedded system via the browser in the Windows environment.


Machine Learning Strategies for Time Series Forecasting

The increasing availability of large amounts of historical data and the need of performing accurate forecasting of future behavior in several scientific and applied domains demands the definition of robust and efficient techniques able to infer from observations the stochastic dependency between past and future. The forecasting domain has been influenced, from the 1960s on, by linear statistical methods such as ARIMA models. More recently, machine learning models have drawn attention and have established themselves as serious contenders to classical statistical models in the forecasting community. This chapter presents an overview of machine learning techniques in time series forecasting by focusing on three aspects: the formalization of one-step forecasting problems as supervised learning tasks, the discussion of local learning techniques as an effective tool for dealing with temporal data and the role of the forecasting strategy when we move from one-step to multiple-step forecasting.


A qualitative study on factors that influence women’s choice of delivery in health facilities in Addis Ababa, Ethiopia

BACKGROUND
Facility based delivery for mothers is one of the proven interventions to reduce maternal and neonatal morbidity and mortality. This study identified women's reasons for seeking to give birth in a health facility and captured their perceptions of the quality of care they received during their most recent birth, in a population with high utilization of facility based deliveries.

METHODS
This qualitative study was conducted in eight health centers in Addis Ababa. Women bringing their index child for first vaccinations were invited to participate in an in-depth interview about their last delivery. Sixteen in-depth interviews were conducted. Interviews were conducted by trained researchers using a semi-structured interview guide. The data were transcribed verbatim in Amharic and translated into English. A thematic analysis was conducted to answer specific study questions.

RESULTS
All research participants expressed a preference for facility based delivery because of their awareness of obstetric complications, and related perceptions that facility-birth is safer for the mother and child. Dimensions of quality of care and the cost of services were identified as influencing decisions about whether to seek care in the public or private sector. Media campaigns, information from social networks and women's experiences with healthcare providers and facilities influenced care-seeking decisions.

CONCLUSIONS
The universal preference for facility-based birth by women in this study indicates that, in Addis Ababa, facility based delivery has become a preferred norm. Sources of information for decision-making and the dimensions of quality prioritized by women should be taken into account to develop interventions to promote facility-based births in other settings.


Compressive Sensing Image Sensors-Hardware Implementation

The compressive sensing (CS) paradigm uses simultaneous sensing and compression to provide an efficient image acquisition technique. The main advantages of the CS method include high resolution imaging using low resolution sensor arrays and faster image acquisition. Since the imaging philosophy in CS imagers is different from conventional imaging systems, new physical structures have been developed for cameras that use the CS technique. In this paper, a review of different hardware implementations of CS encoding in optical and electrical domains is presented. Considering the recent advances in CMOS (complementary metal-oxide-semiconductor) technologies and the feasibility of performing on-chip signal processing, important practical issues in the implementation of CS in CMOS sensors are emphasized. In addition, the CS coding for video capture is discussed.


Partitioning Well-Clustered Graphs with k-Means and Heat Kernel

We study a suitable class of well-clustered graphs that admit good k-way partitions and present the first almost-linear time algorithm for with almost-optimal approximation guarantees partitioning such graphs. A good k-way partition is a partition of the vertices of a graph into disjoint clusters (subsets) {Si}i=1, such that each cluster is better connected on the inside than towards the outside. This problem is a key building block in algorithm design, and has wide applications in community detection and network analysis. Key to our result is a theorem on the multi-cut and eigenvector structure of the graph Laplacians of these well-clustered graphs. Based on this theorem, we give the first rigorous guarantees on the approximation ratios of the widely used k-means clustering algorithms. We also give an almost-linear time algorithm based on heat kernel embeddings and approximate nearest neighbor data structures.


Approximate Computing for Low Power and Security in the Internet of Things

To save resources for Internet of Things (IoT) devices, a proposed approach segments operands and corresponding basic arithmetic operations that can be carried out by approximate function units for almost all applications. The approach also increases the security of IoT devices by hiding information for IP watermarking, digital fingerprinting, and lightweight encryption.


Speaker identification on the SCOTUS corpus

This paper reports the results of our experiments on speaker identification in the SCOTUS corpus, which includes oral arguments from the Supreme Court of the United States. Our main findings are as follows: 1) a combination of Gaussian mixture models and monophone HMM models attains near-100% textindependent identification accuracy on utterances that are longer than one second; 2) the sampling rate of 11025 Hz achieves the best performance (higher sampling rates are harmful); and a sampling rate as low as 2000 Hz still achieves more than 90% accuracy; 3) a distance score based on likelihood numbers was used to measure the variability of phones among speakers; we found that the most variable phone is the phone UH (as in good), and the velar nasal NG is more variable than the other two nasal sounds M and N; 4.) our models achieved “perfect” forced alignment on very long speech segments (40 minutes). These findings and their significance are discussed.


Diffusion-on-Manifold Aggregation of Local Features for Shape-based 3D Model Retrieval

Aggregating a set of local features has become one of the most common approaches for representing a multi-media data such as 2D image and 3D model. The success of Bag-of-Features (BF) aggregation [2] prompted several extensions to BF, that are, VLAD [12], Fisher Vector (FV) coding [22] and Super Vector (SV) coding [34]. They all learn small number of codewords, or representative local features, by clustering a set of large number of local features. The set of local features extracted from a media data (e.g., an image) is encoded by considering distribution of features around the codewords; BF uses frequency, VLAD and FV uses displacement vector, and SV uses a combination of both. In doing so, these encoding algorithms assume linearity of feature space about a codeword. Consequently, even if the set of features form a non-linear manifold, its non-linearity would be ignored, potentially degrading quality of aggregated features. In this paper, we propose a novel feature aggregation algorithm called Diffusion-on-Manifold (DM) that tries to take into account, via diffusion distance, structure of non-linear manifold formed by the set of local features. In view of 3D shape retrieval, we also propose a local 3D shape feature defined for oriented point set. Experiments using shape-based 3D model retrieval scenario show that the DM aggregation results in better retrieval accuracy than the existing aggregation algorithms we've compared against, that are, VLAD, FV, and SV, etc..


Fractional-Slot Concentrated-Windings Synchronous Permanent Magnet Machines: Opportunities and Challenges

Fractional-slot concentrated-winding (FSCW) synchronous permanent magnet (PM) machines have been gaining interest over the last few years. This is mainly due to the several advantages that this type of windings provides. These include high-power density, high efficiency, short end turns, high slot fill factor particularly when coupled with segmented stator structures, low cogging torque, flux-weakening capability, and fault tolerance. This paper is going to provide a thorough analysis of FSCW synchronous PM machines in terms of opportunities and challenges. This paper will cover the theory and design of FSCW synchronous PM machines, achieving high-power density, flux-weakening capability, comparison of single- versus double-layer windings, fault-tolerance rotor losses, parasitic effects, comparison of interior versus surface PM machines, and various types of machines. This paper will also provide a summary of the commercial applications that involve FSCW synchronous PM machines.


Comparison between SPM and IPM motor drives for EV application

Electric Vehicles make use of permanent magnet synchronous traction motors for their high torque density and efficiency. A comparison between interior permanent magnet (IPM) and surface mounted permanent magnet (SPM) motors is carried out, in terms of performance at given inverter ratings. A simplified analytical approach is proposed and its results are confirmed by actual motor design, validated through FEA. The main result of the proposed analysis is to put in evidence the different behavior of IPM and SPM motors in terms of overload capability: as expected, the overload current results in a power overload of the IPM motor, while in the SPM motor the maximum power is upper limited irrespectively of the current overload.


A new structure of universal motor using soft magnetic composites

The universal or ac commutator motor, widely used in hand tools and domestic appliances generally uses a two-pole stator with a concentrated winding and an armature with interlocked coils elements. The copper volume and the axial length of the end windings of such conventional structures are then usually very important. In this paper, the authors present a new universal motor structure based on an efficient use of the isotropic magnetic properties of the soft magnetic composites and on the concentrated winding technique. The stator core presents a claw-pole structure and the armature has a concentrated winding with several coils wound around the same tooth. With this new ac commutator motor structure, a reduction of the total volume by a ratio equal to 200% is obtained when compared to a classical universal motor structure with nearly identical performance.


Comparison of synchronous PM machine types for wide constant-power speed range operation

This paper presents a detailed comparison of the high-speed operating characteristics of four synchronous PM machines for applications that require wide speed ranges of constant-power operation. These machines include surface PM machines with both distributed and fractional-slot concentrated windings, and two interior PM machine with distributed windings. These two versions of the interior PM machine include one with and a tight constraint on the machine's back-emf voltage at top speed and one without this constraint The target application is an automotive direct-drive starter/alternator requiring a very wide 10:1 constant power speed ratio (CPSR). Detailed comparisons of the performance characteristics of the machines are presented that include important issues such as the back-emf voltage at top speed, machine mass and cost, and eddy current losses in the magnets. Analytical results are verified using finite element analysis (FEA). Guidelines are developed to help drive designers decide which type of machine is most suitable for high-CPSR applications. Tradeoffs associated with choosing each of these machines are presented.


Electrical machine topologies and technologies for electric, hybrid, and fuel cell vehicles

This paper overviews various electrical machine technologies, including brushless permanent magnet, induction, switched reluctance, and brushless reluctance machines, with particular emphasis on the concept of hybrid electrical machine topologies, and compares their relative merits, in terms of torque and power density, operating speed range, over load capability, and efficiency, for application in electric, hybrid and fuel cell vehicles. electric vehicles.


Spacer-First Damascene-Gate FinFET Architecture Featuring Stringer-Free Integration

This letter presents a new Damascene-gate FinFET process that inherently suppresses stringers, resulting from gate and spacers patterning. The so-called spacer-first integration scheme relies on the engineering of a hydrogen silsesquioxane layer by electron beam lithography followed by two selective compartmentalized development steps to successively release the Damascene-gate cavity and the source/drain (S/D) contact regions. In contrast to the existing gate-first and gate-last integration approaches, the resulting FinFET process does not impose any restriction or interdependency on the sizing of the fins, gate, spacers, and S/D regions. A complete morphological and electrical validation is proposed in the particular case of wrap-around self-aligned metallic Schottky S/D contacts.


Thickness Reduction and Performance Enhancement of Steerable Square Loop Antenna Using Hybrid High Impedance Surface

In order to reduce the thickness of a steerable beam square loop antenna, the effects of combining it with various periodic high impedance surfaces (HISs) are investigated. When a via-less HIS is used, the radiation pattern has high side lobes, which are shown to be due to surface waves propagating in the HIS lattice. Using a HIS with vias between the plates and ground removes the surface waves, but the beam is distorted due to strong coupling between the HIS's vias and the antenna element. Consequently a hybrid HIS is designed. This uses a via-less lattice beneath the loop, with vias at the edge of the HIS to suppress surface wave propagation. Consequently, a square loop antenna with four feeds on a hybrid HIS substrate is proposed for beam steering applications. This antenna has a low-profile with a total thickness of 4.69 mm for a test frequency band of 4.3 GHz to 5.0 GHz. It exhibits a gain of 8.65 dB at the test frequency (4.7 GHz). Compared to the earlier reported steerable square loop antenna, the new antenna achieves a 61% reduction in substrate thickness, a bandwidth enhancement by 150 MHz and an increase in gain by 1.95 dB.


X iv :1 31 1. 25 61 v2 [ cs .C V ] 1 2 N ov 2 01 3 Performing edge detection by difference of Gaussians using q-Gaussian kernels Lucas Assirati, Núbia R. da Silva, 2 Lilian Berton, Alneu de A. Lopes, and Odemir M. Bruno 2 Scientific Computing Group, São Carlos Institute of Physics, University of São Paulo (USP), cx 369 13560-970 São Carlos, São Paulo, Brazil www.scg.ifsc.usp.br Institute of Mathematics and Computer Science, University of São Paulo (USP), Avenida Trabalhador são-carlense, 40


Cyber Defense Capability Model: A Foundation Taxonomy

Cyber attacks have significantly increased over the last few years, where the attackers are highly skilled, more organized and supported by other powerful actors to devise attacks towards specific targets. To aid the development of a strategic plan to defend against emerging attacks, we present a high-level taxonomy along with a cyber defense model to address the interaction and relationships between taxonomy elements. A cyber-kinetic reference model which is used widely by U.S Air Force is adopted as a baseline for the model and taxonomy development. Asset, Cyber Capability, and Preparation Process are the three high-level elements that are presented for the cyber defense capability model. The Cyber Capability, as the focal point of the study, uses three classifiers to characterize the strategic cyber defense mechanisms, which are classified by active, passive and collaborative defense. To achieve a proper cyber defense strategy, the key actors, assets and associated preparation procedure are identified. Finally, the proposed taxonomy is extensible so that additional dimensions or classifications can be added to future needs.


Practical Privacy-Preserving Medical Diagnosis Using Homomorphic Encryption

The use of remote services offered by cloud providers have been popular in the last lustrum. Services allow users to store remote files, or to analyze data for several purposes, like health-care or message analysis. However, when personal data are sent to the Cloud, users may lose privacy on the data-content, and on the other side cloud providers may use those data for their own businesses. In this paper, we present our solution to analyze users health-data directly into the Cloud while preserving users privacy. Our solution makes use of homomorphic encryption to protect users data during the analysis. In particular, we developed a mobile application that offloads users data into the Cloud, and a homomorphic encryption algorithm that processes those data without leaking any information to the Cloud provider. Performed empirical tests show that our HE algorithm is able to evaluate users data in reasonable time proving the feasibility of this emerging way of private-data evaluation.


Explaining consumer satisfaction of services: The role of innovativeness and emotion in an electronic mediated environment

a r t i c l e i n f o The services section has started to dominate economic activity in many industrialized economies since the last decade. The growth of services in Electronic Mediated Environment (EME) has changed the manner in which firms and consumers interact. Drawing on such theoretical foundations as trust, risk perceptions, trying, emotion, and satisfaction, this study proposes an integrative research model to investigate the relationships among these constructs in the EME context. We collected data from 415 consumers and found that consumer innovativeness and emotion are significant antecedents of trust, risk perception of service, perceived benefits, and service quality. These important factors will influence consumer satisfaction in EME. This empirical investigation breaks a new ground for future studies to understand and gauge the influence of innovativeness and emotion in emerging IT artifacts such as EME services. The service sector has become quantitatively the most essential sector in the world economy over the last decade. It now leads economic activities in a wide variety of industrialized economies. According to CIA World Factbook [14], the service industry accounted for 76.8% and 73.1% of the U.S. and European Union gross domestic product in 2011, respectively. Concurrently, there are more service companies on the list of Future 500 companies than before. Given the precipitously growing rate of the service section, technologically empowered advancements of information technology (IT) and the ever developing Internet are constantly galvanizing today's organizations to more effectively and efficiently coordinate with their constituents via the value-added electronic services (e-services) conduit. Furthermore, the mushrooming growth of e-commerce reflects the increasingly paramount role of IT in e-services while IT spurs the influx of e-service economy across the Internet. It is apparent that Information Systems (IS) plays a crucial role in EME because contemporary organizations have to leverage IT to improve business performance and assist the communication and service delivery amid various value-chain constituents. Consumers play a focal role in the paradigmatic change from the exchange of goods towards a service-centered model of exchange as found in both IS and marketing literature [73]. In this study, the conceptual definition of services in EME is based on Dai and Salam [19] and thereafter refers to the " services being facilitated by IT where the consumer interacts with a proper user interface including web site, mobile phone, social media, virtual world environment, tablets, etc., so as to gain a consumption …


ITALICA at PAN 2013: An Ensemble Learning Approach to Author Profiling Notebook for PAN at CLEF 2013

This notebook discusses the approach to the Author Profiling task developed by the Italica group for PAN 2013. This system implements two different sets of classifiers which are combined later in order to build a final classifier that takes into account the decisions of the previous ones. The initial classifiers are focused on vector space representations of the documents as a bag of words and n-grams of POS tags and also on a set of stylistic features of the texts. The final classifier consists of a stacking schema that combines the other ones. This approach has obtained better results for the Spanish dataset than for the English dataset, probably due to the use of more detailed POS tagset in the former.


An extension of the GQM+Strategies approach with formal causal reasoning

Context: Successful organizations need to manage and synchronize their strategic objectives with daily operations and activities. In general, achieving that requires a continuous process of organizational alignment. GQM + Strategies is an approach that helps software organizations with documenting and aligning organizational goals and strategies, and developing measurement programs. Objective: In this paper, the GQM + Strategies approach is further evolved and extended to include capabilities to evaluate the relationships of organizational goals and strategies through causal analysis. Method: We used an analytical paradigm to develop a formal causal model over the GQM + Strategies structure. In addition, an empirical pre-experimental study was designed to test practitioners’ abilities to provide necessary input for the formal causal model. Results: A developed formal causal model over the GQM + Strategies structure allows the use of causal reasoning for the purpose of analyzing dependencies among chosen sets of goals. We illustrate this by showing how to analyze the impact of risky goals on other goals in the grid. The results of the empirical study showed that the practitioners had no difficulties providing their predictions, i.e. inputs into the causal model. Conclusion: The proposed solution extends the existing GQM + Strategies knowledge base by further elaborating and clarifying the process of creating grids by introducing causality theory. The use of causality theory allows experts to quantify their knowledge and beliefs regarding the effectiveness of organizational strategies. As a part of future work, formal causal models and causal reasoning can be implemented as a supporting software tool for the GQM + Strategies approach. © 2017 Elsevier B.V. All rights reserved.


Recommender System Based on Product Taxonomy in E-Commerce Sites

This article describes a novel and fast recommender system for websites based on product taxonomy and user click patterns. The proposed system consists of the following four steps. First, a product-preference matrix for each customer is estimated through a linear combination of the click, basket placement, and purchase statuses. Second, the preference matrix of the genre and that of the specific type are constructed by computing the ratios of the number of clicks, basket placements, and purchases of a product to the totals. Third, cluster analysis is performed using the genre preference matrix, and a neighborhood formation process is conducted using the specific-type preference matrix. Finally, data are generated for prediction, in which a customer’s preference for specific types is greater than a given threshold value. Using these data sets, computational burden and processing time are greatly reduced. The effectiveness of the proposed approach was assessed by applying the F1 metric to an experimental e-commerce website. The proposed method outperformed conventional methods.


A novel approach for efficient supergraph query processing on graph databases

In recent years, large amount of data modeled by graphs, namely graph data, have been collected in various domains. Efficiently processing queries on graph databases has attracted a lot of research attentions. Supergraph query is a kind of new and important queries in practice. A supergraph query, q, on a graph database D is to retrieve all graphs in D such that q is a supergraph of them. Because the number of graphs in databases is large and subgraph isomorphism testing is NP-complete, efficiently processing such queries is a big challenge. This paper first proposes an optimal compact method for organizing graph databases. Common subgraphs of the graphs in a database are stored only once in the compact organization of the database, in order to reduce the overall cost of subgraph isomorphism testings from stored graphs to queries during query processing. Then, an exact algorithm and an approximate algorithm for generating significant feature set with optimal order are proposed to construct indices on graph databases. The optimal order on the feature set is to reduce the number of subgraph isomorphism testings during query processing. Based on the compact organization of graph databases, a novel algorithm of testing subgraph isomorphisms from multiple graphs to one graph is presented. Finally, based on all these techniques, a query processing method is proposed. Analytical and experimental results show that the proposed algorithms outper-form the existing similar algorithms by one to two orders of magnitude.


Eye images increase generosity , but not for long : the limited effect of a false cue

⁎ Corresponding author. E-mail address: asparks@uoguelph.ca (A. Sparks). 1090-5138/$ – see front matter © 2013 Elsevier Inc. Al http://dx.doi.org/10.1016/j.evolhumbehav.2013.05.001 Article history: Initial receipt 5 November 2012 Final revision received 3 May 2013


A New Approach of Digital Forensic Model for Digital Forensic Investigation

The research introduces a structured and consistent approach for digital forensic investigation. Digital forensic science provides tools, techniques and scientifically proven methods that can be used to acquire and analyze digital evidence. The digital forensic investigation must be retrieved to obtain the evidence that will be accepted in the court. This research focuses on a structured and consistent approach to digital forensic investigation. This research aims at identifying activities that facilitate and improves digital forensic investigation process. Existing digital forensic framework will be reviewed and then the analysis will be compiled. The result from the evaluation will produce a new model to improve the whole investigation process.


Real-time object tracking via CamShift-based robust framework

In recent years, lots of object tracking methods have been presented for better tracking accuracies. However, few of them can be applied to the real-time applications due to high computational cost. Aiming at achieving better realtime tracking performance, we propose an adaptive robust framework for object tracking based on the CamShift approach, which is notable for its simplicity and high processing efficiency. An adaptive local search method is presented to search for the best object candidate to avoid that the CamShift tracker gets confused by the surrounding background and erroneously incorporates it into the object region. A Kalman filter is also incorporated into our framework for prediction of the object's movement, so as to reduce the search effort and possible tracking failure caused by fast object motion. The experimental results demonstrate that the proposed tracking framework is robust and computationally effective.


2.4 GHz inkjet-printed RF energy harvester on bulk cardboard substrate

An experimental investigation on the inkjet-printed power harvester for 2.4GHz and review of RF characterization of substrate and printed conductors are presented in this paper. A one stage discrete rectifier based on a voltage doubler structure and a planar monopole antenna are fabricated on cardboard using inkjet printing. The performance of the whole system is examined by measuring the output voltage of the RF power harvester. By the utilization of the proposed idea, the fabrication of low-cost environmentally-friendly battery-less wireless modules is conceivable.


DCnet: A new data center network architecture

The migration of computation and storage to cloud data centers has imposed new communication requirements and a need for fundamental changes in infrastructure. The cloud paradigm means that significant network communication will occur between servers in data centers. Furthermore, the migration of virtual machines and containers across physical servers means that in addition to traditional requirements of efficiency, scalability, and easy manageability, future data center networks must also support efficient mobility. To solve the problem, the paper proposes a radical redesign for data center networks, using a new architectural approach that we call DCnet. The DCnet approach changes the addressing and routing schemes at layers 2 and 3 completely. The new addressing mechanism allows an organization to assign addresses that span multiple data centers and permits VM mobility across data centers. Despite radical changes in addressing and routing, the DCnet architecture retains basics from Ethernet and IP, allowing existing hardware building blocks to be reused. The paper uses IPv6 in examples, but the new addressing model and address mobility approach can be applied to IPv4. By assigning each addressable entity a unique ID, and using the ID as the host suffix in an IP address, DCnet allows legacy operating systems and applications to be used without changes. The paper reports a simulation study that uses mininet and SDN to demonstrate the feasibility of the DCnet approach.


Software engineering for security: a roadmap

doesn't need to be secure? Almost every softwarecontrolled system faces threats from potential adversaries, from Internet-aware client applications running on PCs, to complex telecommunications and power systems accessible over the Internet, to commodity software with copy protection mechanisms. Software engineers must be cognizant of these threats and engineer systems with credible defenses, while still delivering value to customers. In this paper, we present our perspectives on the research issues that arise in the interactions between software engineering and security.


A novel soil measuring wireless sensor network

Emerging technologies have made low-power and low-cost wireless sensor networks feasible. This paper presents a hierarchical wireless sensor network for measuring soil parameters such as temperature and humidity. Specifically, we designed sensor nodes that are placed completely underground and are used to collect soil measurements. These nodes use their radios to deliver the collected measurements to one of multiple relay nodes located above ground. In turn, relay nodes that are capable of long-range communications forward the data collected from the network's sensor nodes to a base node, which is connected to a workstation. The proposed hierarchical wireless sensor network uses a probabilistic communication protocol to achieve a very low duty cycle and hence a long life-time for soil monitoring applications.


Improved FIFO Scheduling Algorithm Based on Fuzzy Clustering in Cloud Computing

In cloud computing, some large tasks may occupy too many resources and some small tasks may wait for a long time based on First-In-First-Out (FIFO) scheduling algorithm. To reduce tasks’ waiting time, we propose a task scheduling algorithm based on fuzzy clustering algorithms. We construct a task model, resource model, and analyze tasks’ preference, then classify resources with fuzzy clustering algorithms. Based on the parameters of cloud tasks, the algorithm will calculate resource expectation and assign tasks to different resource clusters, so the complexity of resource selection will be decreased. As a result, the algorithm will reduce tasks’ waiting time and improve the resource utilization. The experiment results show that the proposed algorithm shortens the execution time of tasks and increases the resource utilization.


A Fast Approach for Semantic Similar Short Texts Retrieval

Retrieving semantic similar short texts is a crucial issue to many applications, e.g., web search, ads matching, questionanswer system, and so forth. Most of the traditional methods concentrate on how to improve the precision of the similarity measurement, while current real applications need to efficiently explore the top similar short texts semantically related to the query one. We address the efficiency issue in this paper by investigating the similarity strategies and incorporating them into the FAST framework (efficient FrAmework for semantic similar Short Texts retrieval). We conduct comprehensive performance evaluation on real-life data which shows that our proposed method outperforms the state-ofthe-art techniques.


2013-01-1544 Simplified Extended Kalman Filter Observer for SOC Estimation of Commercial Power-Oriented LFP Lithium Battery Cells

The lithium iron phosphate (LFP) cell chemistry is finding wide acceptance for energy storage on-board hybrid electric vehicles (HEVs) and electric vehicles (EVs), due to its high intrinsic safety, fast charging, and long cycle life. However, three main challenges need to be addressed for the accurate estimation of their state of charge (SOC) at runtime:  Long voltage relaxation time to reach its open circuit voltage (OCV) after a current pulse  Time-, temperatureand SOC-dependent hysteresis  Very flat OCV-SOC curve for most of the SOC range In view of these problems, traditional SOC estimation techniques such as coulomb counting with error correction using the SOC-OCV correlation curve are not suitable for this chemistry. This work addressed these challenges with a novel combination of the extended Kalman filter (EKF) algorithm, a two-RC-block equivalent circuit and the traditional coulomb counting method. The simplified implementation of the EKF algorithm offers a computationally efficient option for runtime SOC evaluation on-board vehicles. The SOC estimation was validated with experimental data of a current profile contaminated with pseudo-random noise and with an offset in the initial condition. The model rapidly converged to within 4% of the true SOC even with imposed errors of 40% to initial SOC, 24% to current measurement and 6% to voltage measurement. INTRODUCTION The LFP olivine has emerged as one of the favored cathode materials for lithium ion batteries, especially for use as a rechargeable energy storage device (RESS) on-board HEVs and EVs, thanks to its high intrinsic safety [1], capacity for fast charging, and long cycle life [2]. Recent research and development advancements in this cell technology, especially the commercial launch of high-power LFP cells, have led to these cells matching the performance of the latest supercapacitors over short time periods (up to 30 seconds). A metric of great importance for a rechargeable lithium battery pack is the accurate runtime evaluation of its SOC, which is defined as the percentage of the completely extractable charge capacity remaining in the battery. It is akin to the fuel gauge of a conventional vehicle. The SOC indicates the amount of electrical energy remaining in the battery pack that is available to do work. An accurate runtime estimate of the SOC is important for the battery application designers and the battery users. However, the charge capacity of a battery depends upon a number of factors, including average current, discharge time, voltage cut-off limit, electrolyte temperature, aging, and battery storage time [3]. Armed with the confidence that the battery SOC would be determined accurately, the designer is able to efficiently use available battery capacity and reduce over-engineering; enabling the use of smaller and lighter batteries. With an accurate indication of the battery SOC, the user ensures that the battery is not over-charged or under-discharged; and suffers less range anxiety. Overall, the battery lasts longer and provides better performance. An accurate SOC is also a very important input for the battery management system. Over the years, many techniques have been proposed for estimation of the battery SOC, and they generally depend upon the battery chemistry and the final application [4-9]. The most reliable test for establishing the SOC of a battery is to charge or discharge it completely, thus physically reaching 100% or 0% SOC. This test is often adopted for an EV or a PHEV that is charged completely every evening, and allows the onboard SOC estimation algorithm to gain valuable feedback to recalibrate itself. For an HEV, which is never charged from the grid, ampere-hour counting remains the most popular technique. This technique (also called the bookkeeping system or coulomb counting) uses discrete integration of measured current over time as a direct indicator of SOC. Since this integration includes errors in current measurement and battery losses, the result needs to be periodically corrected. The OCV vs. SOC correlation curve is often used to provide points for recalibration. Other techniques such as direct measurement of the cell physical properties, including impedance or internal resistance, are not practical for LFP cells during runtime. The coulomb counting technique with correction (after rest periods) using an OCV-SOC correlation curve is not practical for cells exhibiting hysteresis since the battery cell takes a long time to reach a steady-state OCV after a current pulse. The problem is aggravated for the LFP batteries that also have a very flat OCV-SOC correlation curve. Current SOCestimation models are unable to take care of all of these complications. A more robust algorithm is needed to estimate the instantaneous total charge available for work inside an LFP cell. The EKF technique, an adaptive estimator, has emerged as one of the practical solutions to enhance the accuracy of SOC determination, but is complicated and needs heavy computing resources on-board the vehicle [6-8]. This paper presents a novel, simplified implementation of the extended Kalman filter technique that overcomes the practical challenges involved in runtime evaluation of the SOC of commercial high-power LFP cells. Its formulation demands a lower level of resources compared to traditional EKF implementations. CHALLENGES IN RUNTIME ESTIMATION OF SOC OF LFP CELLS Voltage relaxation time Figure 1: One pulse of the pulse discharge test (sign convention from Figure 1). The OCV-SOC correlation curve is often used to correct the current integral errors during runtime. This is usually done when the vehicle has been at rest (with its battery neither charging nor discharging) for a sufficiently long duration (3060 minutes), and when its battery voltage at the terminals is assumed to approximate the value of the OCV. This assumption is valid for most battery chemistries. The authors attempted to validate this assumption for the LFP cells using pulse discharge and charge tests. Under this test, the cell was first completely charged, rested for two hours, and then subjected to ten discharge pulses at 1C rate interspersed by one-hour rest phases until the cell was completely discharged. Subsequently, the cell was charged using ten charge pulses at 1C rate interspersed by one-hour rest phases until the cell was completely charged. The cell was then allowed to rest for 13 hours. A schematic of one pulse of the discharge test is shown in Figure 1. Figure 2 presents the cell current and terminal voltage measurements during the complete pulse discharge and charge experimental test. Figure 2: Input current and voltage response for a pulse charge and discharge test (inset in figure 3). Figure 3: Inset from figure 2 to highlight the long voltage stabilization time. The authors’ primary interest in the experiment was to validate the assumption that the voltage of the LFP cell relaxes to approximately reach its OCV (as shown in Figure 1) after a long rest of one hour. However, it was observed that after the complete pulse discharge and charge test (Figure 2), when the SOC had reached 100%, the voltage did not relax to its OCV even after 13 hours (Figure 3). Voltage relaxation in 2 hours Voltage relaxation in 13 hours


OpenStreetMap: User-Generated Street Maps

The OpenStreetMap project is a knowledge collective that provides user-generated street maps. OSM follows the peer production model that created Wikipedia; its aim is to create a set of map data that's free to use, editable, and licensed under new copyright schemes. A considerable number of contributors edit the world map collaboratively using the OSM technical infrastructure, and a core group, estimated at approximately 40 volunteers, dedicate their time to creating and improving OSM's infrastructure, including maintaining the server, writing the core software that handles the transactions with the server, and creating cartographical outputs. There's also a growing community of software developers who develop software tools to make OSM data available for further use across different application domains, software platforms, and hardware devices. The OSM project's hub is the main OSM Web site.


Neural Networks: A Comprehensive Foundation

El profesor Haykin ha escrito un libro muy interesante, que ya lleva su segunda edición, lo cual es extrafio dentro de la gran cantidad de libros que se producen en la actualidad. Es fácil encontrar en la literatura contemporánea la constante referencia que se hace a este libro; sin embargo, el libro no es lo que el título indica, ya que no es un libro ortodoxo de las teorías y técnicas que son conocidas como redes neuronales (también conocidas como sistemas conexionistas o sistemas neurocomputacionales), tampoco es un libro que presente los fundamentos o bases teóricas de las redes neuronales. neuronales, así como los grandes modelos de esta área de investigación, como son los perceptrones y las redes RBFN (Radial-Basis Function Networks). La presentación de estos cuatro capítulos es excelente.


Hypertrophy of labia minora: experience with 163 reductions.

OBJECTIVE
Our purpose was to describe the surgical procedure, its results, and its complications and to determine whether patients are satisfied with surgical reduction of labia minora in cases of hypertrophy.

STUDY DESIGN
The records of 163 patients who underwent reduction of the labia minora during a 9-year period were reviewed. The ages of the patients ranged from 12 to 67 years (median, 26). Motives for requesting surgery were aesthetic concerns in 87% of the cases, discomfort in clothing in 64%, discomfort with exercise in 26%, and entry dyspareunia in 43%. Anatomic results were assessed 1 month postoperatively. Patient satisfaction was assessed by means of a mailed questionnaire.

RESULTS
No surgery-related significant complications were noticed. Anatomic results were satisfactory for 151 patients (93%). Ninety-eight completed questionnaires were returned. Eighty-one patients (83%) found that the results after surgery were satisfactory. Eighty-seven (89%) were satisfied with the aesthetic result, and 91 (93%) approved the functional outcome. Four patients (4%) would not undergo the same procedure again.

CONCLUSION
Labia minora reduction is a simple surgical procedure associated with a high degree of patient satisfaction.


Cue-based feeding in the NICU: using the infant's communication as a guide.

Although studies have shown cue-based feeding can lead to earlier achievement of full oral feeding, the successful implementation of cue-based feeding has been constrained by the volume-driven culture, which has existed for many years in the NIC U. This culture was built on the notion that a "better" nurse is one who could "get more in," and infants who are "poor feeders" are ones who "can't take enough." The infant who feeds faster is often viewed as more skilled in this task-oriented approach. The feeding relationship and the infant's communication about the experience of feeding may not be nurtured. This article will explain the central role of the preterm infant's communication in successful cue-based feeding. When the infant is perceived as having meaningful behavior (i.e., communicative intent), the focus changes from a volume-driven to a co-regulated approach, through which the infant guides the caregiver. This is cue-based feeding.


Connective tissue graft to correct peri-implant soft tissue margin: A clinical report.

This clinical report describes the use of a subepithelial connective tissue graft to recontour a soft tissue margin discrepancy for a single-implant crown in the anterior maxilla. This procedure demonstrates that the use of soft tissue grafts to correct an esthetic deficiency may be a feasible approach to establish new and stable peri-implant soft tissue contours. The patient presented was followed for 18 months.


Flexible Electronics and Displays: High-Resolution, Roll-to-Roll, Projection Lithography and Photoablation Processing Technologies for High-Throughput Production

Flexible electronics are increasingly being used in a number of applications which benefit from their low profile, light weight, and favorable dielectric properties. However, despite these advantages, the range of practical, high-volume, applications for flexible electronics will remain limited in the future unless a number of challenges related to lithographic patterning on flexible substrates are successfully addressed. The most critical of these pertain to system parameters that affect the cost and performance of flexible circuits, including the resolution, panel size, process throughput, substrate distortion, material handling, and yield. We present a new class of roll-to-roll lithography systems, developed in recent years, that were designed to address the challenges in each of these critical areas. These systems provides high-resolution projection imaging over very large exposure areas, on flexible substrate materials. Additionally, they achieve high-precision alignment by means of image scaling to compensate for substrate distortion due to processing; and they also performs high-throughput photoablation, patterning millions of pixels simultaneously, by means of projection imaging. This technology is attractive for current and emerging applications, such as flexible circuit boards and flexible chip carriers, as well as for potential future applications such as such as flexible displays and macroelectronic systems.


Concept Detection on Medical Images using Deep Residual Learning Network

Medical images are often used in clinical diagnosis. However, interpreting the insights gained from them is often a time-consuming task even for experts. For this reason, there is a need for methods that can automatically approximate the mapping from medical images to condensed textual descriptions. For identifying the presence of relevant biomedical concepts in medical images for the ImageCLEF 2017 Caption concept detection subtask we propose the use of a pretrained residual deep neural network. Specifically, a 50-layered resNet was used and retrained on the medical images. The proposed method achieved F1 Score 0.1583 on the test data.


Fair and balanced?: bias in bug-fix datasets

Software engineering researchers have long been interested in where and why bugs occur in code, and in predicting where they might turn up next. Historical bug-occurence data has been key to this research. Bug tracking systems, and code version histories, record when, how and by whom bugs were fixed; from these sources, datasets that relate file changes to bug fixes can be extracted. These historical datasets can be used to test hypotheses concerning processes of bug introduction, and also to build statistical bug prediction models. Unfortunately, processes and humans are imperfect, and only a fraction of bug fixes are actually labelled in source code version histories, and thus become available for study in the extracted datasets. The question naturally arises, are the bug fixes recorded in these historical datasets a fair representation of the full population of bug fixes? In this paper, we investigate historical data from several software projects, and find strong evidence of systematic bias. We then investigate the potential effects of "unfair, imbalanced" datasets on the performance of prediction techniques. We draw the lesson that bias is a critical problem that threatens both the effectiveness of processes that rely on biased datasets to build prediction models and the generalizability of hypotheses tested on biased data.


Angels and Devils of Digital Social Norm Enforcement: A Theory about Aggressive versus Civilized Online Comments

We develop a theory that explains when commenters choose to be aggressive versus civilized in social media depending on their personal social norm context. In particular, we enrich traditional social norm theory by introducing the concept of moral legitimacy. This concept suggests that justifications, particularly those that put social norm violators outside of moral boundaries, cause aggression. Using the diversity of 45,982 comments of a real-world online firestorm, our results confirm that social norm contexts matter strongly for online behavior. The developed theory challenges existing speculations about online aggression and helps to develop strategies to encourage enlightened, civilized discourse on the Internet.


A Sensing Chair Using Pressure Distribution Sensors

One challenge in multimodal interface research is the lack of robust subsystems that support multimodal interactions. By focusing on a chair—an object that is involved in virtually all human–computer interactions, the sensing chair project enables an ordinary office chair to become aware of its occupant’s actions and needs. Surface-mounted pressure distribution sensors are placed over the seatpan and backrest of the chair for real time capturing of contact information between the chair and its occupant. Given the similarity between a pressure distribution map and a grayscale image, pattern recognition techniques commonly used in computer and robot vision, such as principal components analysis, have been successfully applied to solving the problem of sitting posture classification. The current static posture classification system operates in real time with an overall classification accuracy of 96% and 79% for familiar (people it had felt before) and unfamiliar users, respectively. Future work is aimed at a dynamic posture tracking system that continuously tracks not only steady-state (static) but transitional (dynamic) sitting postures. Results reported here form important stepping stones toward an intelligent chair that can find applications in many areas including multimodal interfaces, intelligent environment, and safety of automobile operations.


A Geometric Framework for Rectangular Shape Detection

Rectangular shape detection has a wide range of applications, such as license plate detection, vehicle detection, and building detection. In this paper, we propose a geometric framework for rectangular shape detection based on the channel-scale space of RGB images. The framework consists of algorithms developed to address three issues of a candidate shape (i.e., a connected component of edge points), including: 1) outliers; 2) open shape; and 3) fragmentation. Furthermore, we propose an interestness measure for rectangular shapes by integrating imbalanced points (one type of interest points). Our experimental study shows the promise of the proposed framework.


DESC: enabling secure data exchange based on smart contracts

Dear editor, As data are one of the most important factors of today’s intelligent systems but are relatively scarce, people try exchanging data with other organizations or public marketplaces. However, data exchange is now lack of a secure execution mechanism to automatically and fairly protect the rights which are seriously concerned by data owners. For instance, data owners cannot limit the identity of a data transferee who is banned to take the data by any way. Moreover, data transferees are also afraid that their applications for exchange may be unfairly treated. For instance, data owners could dynamically change data price when multiple transferees submit their applications at the same time. A data marketplace can take over the role of a trusted third party during data exchange, but single point failure is always a serious problem in a large-scale distributed system. Furthermore, these marketplaces usually use contracts based on natural languages which may be maliciously misinterpreted and cannot be automatically executed. Numerous researches have been presented to provide digital rights management (DRM) [1]. For instance, Zhu et al. [2] presented a privacypreserving video subscription scheme with the limitation of expire date. However, rights of data owners are more complex than rights of digital content owners. Decentralized and trustworthy enforcement for access control policies is a long-term issue. Han et al. [3] proposed an optimized mechanism which is a trusted and decentralized access control framework for the client/server architecture. These studies still need third party authorities which are used to execute or supervise the rights control, leading to a few trust and security issues. Existing studies [4] on attribute-based access control have been applied to many fields, such as e-commerce and Internet of Things. This article introduces attribute-based access control and proposes a secure framework for data exchange based on smart contracts (DESC) to enable automatic and fair access control. Featuring with decentralized, fair, transparent, immutable and traceable advantages, blockchain 2.0 [5] provides a generalized framework for implementing decentralized compute resources named smart contracts which define rights and policies in mathematical and programming forms. Once the predefined smart contract is triggered by a transaction, it can automatically execute the specific contractual clauses. To find out which data rights are seriously concerned by data owners, we investigate the DRM mechanisms and several data exchange platforms: GBDEX, national engineering lab for big data distribution and exchange technologies. Then,


Multi-style paper pop-up designs from 3D models

Paper pop-ups are interesting three-dimensional books that fascinate people of all ages. The design and construction of these pop-up books however are done manually and require a lot of time and effort. This has led to computer-assisted or automated tools for designing paper pop-ups. This paper proposes an approach for automatically converting a 3D model into a multi-style paper pop-up. Previous automated approaches have only focused on single-style pop-ups, where each is made of a single type of pop-up mechanisms. In our work, we combine multiple styles in a pop-up, which is more representative of actual artist’s creations. Our method abstracts a 3D model using suitable primitive shapes that both facilitate the formation of the considered pop-up mechanisms and closely approximate the input model. Each shape is then abstracted using a set of 2D patches that combine to form a valid pop-up. We define geometric conditions that ensure the validity of the combined pop-up structures. In addition, our method also employs an image-based approach for producing the patches to preserve the textures, finer details and important contours of the input model. Finally, our system produces a printable design layout and decides an assembly order for the construction instructions. The feasibility of our results is verified by constructing the actual paper pop-ups from the designs generated by our system.


Server virtualization architecture and implementation

Virtual machine technology, or virtualization, is gaining momentum in the information technology community. While virtual machines are not a new concept, recent advances in hardware and software technology have brought virtualization to the forefront of IT management. Stability, cost savings, and manageability are among the reasons for the recent rise of virtualization. Virtual machine solutions can be classified by hardware, software, and operating system/containers. From its inception on the mainframe to distributed servers on x86, the virtual machine has matured and will play an increasing role in systems management.


Personality-based Adaptation for Teamwork in Game Agents

This paper presents a novel learning framework to provide computer game agents the ability to adapt to the player as well as other game agents. Our technique generally involves a personality adaptation module encapsulated in a reinforcement learning framework. Unlike previous work in which adaptation normally involves a decision process on every single action the agent takes, we introduce a two-level process whereby adaptation only takes place on an abstracted actions set which we coin as agent personality. With the personality defined, each agent will then take actions according to the restrictions imposed in its personality. In doing so, adaptation takes place in appropriately defined intervals in the game, without disrupting or slowing down the game constantly with intensive decision-making computations, hence improving enjoyment for the player. Moreover, by decoupling adaptation from action selection, we have a modular adaptive system that can be used with existing action planning methods. With an actual typical game scenario that we have created, it is shown that a team of agents using our framework to adapt towards the player are able to perform better than a team with scripted behavior. Consequently, we also show the team performs even better when adapted to-


A vision-based method for weeds identification through the Bayesian decision theory

One of the objectives of precision agriculture is to minimize the volume of herbicides that are applied to the fields through the use of site-specific weed management systems. This paper outlines an automatic computer vision-based approach for the detection and differential spraying of weeds in corn crops. The method is designed for post-emergence herbicide applications where weeds and corn plants display similar spectral signatures and the weeds appear irregularly distributed within the crop’s field. The proposed strategy involves two processes: image segmentation and decision making. Image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based measuring relationships between crop and weeds. The decision making determines the cells to be sprayed based on the computation of a posterior probability under a Bayesian framework. The a priori probability in this framework is computed taking into account the dynamic of the physical system (tractor) where the method is embedded. The main contributions of this paper are: (1) the combination of the image segmentation and decision making processes and (2) the decision making itself which exploits a previous knowledge which is mapped as the a priori probability. The performance of the method is illustrated by comparative analysis against some existing strategies. 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.


Text detection and recognition using enhanced MSER detection and a novel OCR technique

Detection and recognition of text from any natural scene image is challenging but essential extensively for extracting information from the image. In this paper, we propose an accurate and effective algorithm for detecting enhanced Maximally Stable Extremal Regions (MSERs) as main character candidates and these character candidates are filtered by stroke width variation for removing regions where the stroke width exhibits too much variation. For the detection of text regions, firstly some preprocessing is applied to the natural image and then after detecting MSERs, an intersection of canny edge and MSER region is produced to locate regions that are even more likely to belong to text. Finally, the selected text region is taken as an input of a novel Optical Character Recognition (OCR) technique to make the text editable and usable. The evaluation results substantiates 77.47% of the f-measure on the ICDAR 2011 dataset which is better than the previous performance 76.22%.


European option pricing with transaction costs and trading restrictions

In this paper, using the utility based option pricing approach, pioneered by Hodges and Neuberger, we propose a new model in a continuous-time market with proportional transaction costs and several trading restrictions. According to this model, we study the problem of option pricing and prove that the value function of our model is a unique constrained viscosity solution of a HJB equation.


Strategies Critical Paths to Improving Employee Commitment

This article addresses how key leader communication strategies can increase worker loyalty. Leader communication has long been shown to be a critical factor in superior worker motivation and performance (Levering, 1988; Robbins, 2001), and has great potential to aid organizations in their quest for committed employees. Organizations’ need for committed employees is now acute due to such recent shifts in the business environment as the current economic slowdown (which places a premium on increasing worker productivity), skilled worker shortages, increasing ethnic and cultural diversity, rapid technological innovations, and growing organizational reliance on “knowledge workers” to achieve competitive advantage. Research clearly shows that leader communication practices play an integral role in developing and sustaining the employee commitment demanded by the preceding scenarios (Goleman, 1998; Goleman, 2000; Reina & Reina, 1999). However, leaders are often faced with a plethora of options and communication techniques that are not directly linked with strategic goals. To make sense of these various communication tactics, leaders need a systemic method which links practice to results. This article presents such a model, which can also be used to diagnostically assess training needs. In essence, the model is a set of strategy-based “best practices” that guide leaders to more effectively transmit and foster organizational trust; which, in turn, will significantly enhance worker loyalty and organizational outcomes. To best explain this model and its relevance, this study first examines the link between leader communication and worker loyalty. Next, selected, well-documented leader communication skills and practices are discussed, followed by a section that presents the Motivating Language Model and a resultant “best practices” checklist for managers. Finally, our study concludes with suggested future directions for both the research and applications of leader communications that promote worker loyalty.


Data-driven Automated Induction of Prerequisite Structure Graphs

With the growing popularity of MOOCs and sharp trend of digitalizing education, there is a huge amount of free digital educational material on the web along with the activity logs of large number of participating students. However, this data is largely unstructured and there is hardly any information about the relationship between material from different sources. We propose a generic algorithm to use educational material and student activity data from heterogeneous sources to create a Prerequisite Structure Graph (PSG). A PSG is a directed acyclic graph, where the nodes are educational units and the edges specify the pairwise ordering of the units in effective teaching by instructors or for effective learning by students. We propose an unsupervised approach utilizing both text content and student data, which outperforms to supervised methods (utilizing only text content) on the task of estimating a PSG.


Im2Text: Describing Images Using 1 Million Captioned Photographs

We develop and demonstrate automatic image description methods using a large captioned photo collection. One contribution is our technique for the automatic collection of this new dataset – performing a huge number of Flickr queries and then filtering the noisy results down to 1 million images with associated visually relevant captions. Such a collection allows us to approach the extremely challenging problem of description generation using relatively simple non-parametric methods and produces surprisingly effective results. We also develop methods incorporating many state of the art, but fairly noisy, estimates of image content to produce even more pleasing results. Finally we introduce a new objective performance measure for image captioning.


Real-time Accurate Object Detection using Multiple Resolutions

We propose a multi-resolution framework inspired by human visual search for general object detection. Different resolutions are represented using a coarse-to-fine feature hierarchy. During detection, the lower resolution features are initially used to reject the majority of negative windows at relatively low cost, leaving a relatively small number of windows to be processed in higher resolutions. This enables the use of computationally more expensive higher resolution features to achieve high detection accuracy. We applied this framework on Histograms of Oriented Gradient (HOG) features for object detection. Our multi-resolution detector produced better performance for pedestrian detection than state-of-the-art methods (Dalal and Triggs, 2005), and was faster during both training and testing. Testing our method on motorbikes and cars from the VOC database revealed similar improvements in both speed and accuracy, suggesting that our approach is suitable for realtime general object detection applications.


Scaling Optoelectronic-VLSI Circuits into the 21 st Century : A Technology Roadmap

Technologies now exist for implementing dense surface-normal optical interconnections for silicon CMOS VLSI using hybrid integration techniques. The critical factors in determining the performance of the resulting photonic chip are the yield on the transceiver device arrays, the sensitivity and power dissipation of the receiver and transmitter circuits, and the total optical power budget available. The use of GaAs–AlGaAs multiple-quantum-well p-i-n diodes for on-chip detection and modulation is one effective means of implementing the optoelectronic transceivers. We discuss a potential roadmap for the scaling of this hybrid optoelectronic VLSI technology as CMOS linewidths shrink and the characteristics of the hybrid optoelectronic tranceiver technology improve. An important general conclusion is that, unlike electrical interconnects, such dense optical interconnections directly to an electronic circuit will likely be able to scale in capacity to match the improved performance of future CMOS technology.


An automatic tool for the analysis of natural language requirements

Using automatic tools for the quality analysis of Natural Language (NL) requirements is recognized as a key factor for achieving software quality. Unfortunately few tools and techniques for the NL requirements analysis are currently available. This paper presents a methodology and a tool (called QuARS Quality Analyzer for Requirement Specifications) for analyzing NL requirements in a systematic and automatic way. QuARS allows requirements engineers to perform an initial parsing of the requirements in order to automatically detect potential linguistic defects that could cause interpretation problems at subsequent stages in developing the software. This tool is also able to partially support the consistency and completeness analysis by clustering the requirements according to specific topics.


Control of lower limb exoskeleton for elderly assistance on basic mobility tasks

This paper presents the modelling, simulation and control of a lower limbs exoskeleton devices whose aim is to assist elderly people during standing-up and walking tasks. A humanoid and actuated exoskeleton frames were modelled in Solid Works and assembled in Visual Nastran 4D virtual environment. Control of exoskeletons actuators by means of PID and fuzzy controllers, together with a finite state machine, was designed. Simulations of the humanoid wearing an exoskeleton to perform the mentioned tasks, using controllers with set and adaptive orientation references as input, were performed. It is assumed that the humanoid, representing an old person, is only capable to provide 70% of the required torque, therefore the exoskeleton must supply 30% to complete the motions. With the aid of exoskeleton devices, the elderly will be able to perform motions of a physically able people. Optimization of fuzzy controller parameters for walking motion was done using SDA. The exoskeleton succeeded on assisting standing-up and walking motions but research is still needed to improve the performance and adaptability of the system.


HD Maps: Fine-Grained Road Segmentation by Parsing Ground and Aerial Images

In this paper we present an approach to enhance existing maps with fine grained segmentation categories such as parking spots and sidewalk, as well as the number and location of road lanes. Towards this goal, we propose an efficient approach that is able to estimate these fine grained categories by doing joint inference over both, monocular aerial imagery, as well as ground images taken from a stereo camera pair mounted on top of a car. Important to this is reasoning about the alignment between the two types of imagery, as even when the measurements are taken with sophisticated GPS+IMU systems, this alignment is not sufficiently accurate. We demonstrate the effectiveness of our approach on a new dataset which enhances KITTI [8] with aerial images taken with a camera mounted on an airplane and flying around the city of Karlsruhe, Germany.


Metric localization using Google Street View

Accurate metrical localization is one of the central challenges in mobile robotics. Many existing methods aim at localizing after building a map with the robot. In this paper, we present a novel approach that instead uses geo-tagged panoramas from the Google Street View as a source of global positioning. We model the problem of localization as a non-linear least squares estimation in two phases. The first estimates the 3D position of tracked feature points from short monocular camera sequences. The second computes the rigid body transformation between the Street View panoramas and the estimated points. The only input of this approach is a stream of monocular camera images and odometry estimates. We quantified the accuracy of the method by running the approach on a robotic platform in a parking lot by using visual fiducials as ground truth. Additionally, we applied the approach in the context of personal localization in a real urban scenario by using data from a Google Tango tablet.


Autonomous Robot Navigation in Highly Populated Pedestrian Zones

In the past, there has been a tremendous progress in the area of autonomous robot navigation and a large variety of robots have been developed who demonstrated robust navigation capabilities indoors, in non-urban outdoor environments, or on roads and relatively few approaches focus on navigation in urban environments such as city centers. Urban areas, however, introduce numerous challenges for autonomous robots as they are rather unstructured and dynamic. In this paper, we present a navigation system for mobile robots designed to operate in crowded city environments and pedestrian zones. We describe the different components of this system including a SLAM module for dealing with huge maps of city centers, a planning component for inferring feasible paths taking also into account the traversability and type of terrain, a module for accurate localization in dynamic environments, and means for calibrating and monitoring the platform. Our navigation system has been implemented and tested in several large-scale field tests, in which a real robot autonomously navigated over several kilometers in a complex urban environment. This also included a public demonstration, during which the robot autonomously traveled along a more than three kilometer long route through the city center of Freiburg, Germany.


Distance Transforms of Sampled Functions

We describe linear-time algorithms for solving a class of problems that involve transforming a cost function on a grid using spatial information. These problems can be viewed as a generalization of classical distance transforms of binary images, where the binary image is replaced by an arbitrary function on a grid. Alternatively they can be viewed in terms of the minimum convolution of two functions, which is an important operation in grayscale morphology. A consequence of our techniques is a simple and fast method for computing the Euclidean distance transform of a binary image. Our algorithms are also applicable to Viterbi decoding, belief propagation, and optimal control. ACM Classification: F.2.1, I.4 AMS Classification: 68T45, 68W40


The ODD protocol : a review and first update

The ‘ODD’ (Overview, Design concepts, and Details) protocol was published in 2006 to standardize the published descriptions of individual-based and agent-based models (ABMs). The primary objectives of ODD are to make model descriptions more understandable and complete, thereby making ABMs less subject to criticism for being irreproducible. We have systematically evaluated existing uses of the ODD protocol and identified, as expected, parts of ODD needing improvement and clarification. Accordingly, we revise the definition of ODD to clarify aspects of the original version and thereby facilitate future standardization of ABM descriptions. We discuss frequently raised critiques in ODD but also two emerging, and unanticipated, benefits: ODD improves the rigorous formulation of models and helps make the theoretical foundations of large models more visible. Although the protocol was designed for ABMs, it can help with documenting any large, complex model, alleviating some general objections against such models.


Predicting inter-thread cache contention on a chip multi-processor architecture

This paper studies the impact of L2 cache sharing on threads that simultaneously share the cache, on a chip multi-processor (CMP) architecture. Cache sharing impacts threads nonuniformly, where some threads may be slowed down significantly, while others are not. This may cause severe performance problems such as sub-optimal throughput, cache thrashing, and thread starvation for threads that fail to occupy sufficient cache space to make good progress. Unfortunately, there is no existing model that allows extensive investigation of the impact of cache sharing. To allow such a study, we propose three performance models that predict the impact of cache sharing on co-scheduled threads. The input to our models is the isolated L2 cache stack distance or circular sequence profile of each thread, which can be easily obtained on-line or off-line. The output of the models is the number of extra L2 cache misses for each thread due to cache sharing. The models differ by their complexity and prediction accuracy. We validate the models against a cycle-accurate simulation that implements a dual-core CMP architecture, on fourteen pairs of mostly SPEC benchmarks. The most accurate model, the inductive probability model, achieves an average error of only 3.9%. Finally, to demonstrate the usefulness and practicality of the model, a case study that details the relationship between an application's temporal reuse behavior and its cache sharing impact is presented.


Accounting Information System Risk Assessment Algorithm Based on Analytic Hierarchy Process

So far, there is little research on accounting information system risk assessment in our country. In order to provide the basis to meet the security needs of the accounting information system and reduce the risk of accounting information system security, reduce financial loses and improve the work efficiency, a model of enterprise accounting information system risk assessment method based on Analytic Hierarchy Process is proposed. The analytic hierarchy process model is applied to one corporate accounting information system for risk assessment. It can be concluded that the proposed method get better result of risk assessment, and have strong operability and effectiveness of risk assessment in the accounting information system for enterprise.


Scalable pseudo-random noise radar

This paper describes the implementation of a pseudo-random noise radar system on a scalable sensor platform that consists of several multi-purpose 61- and 122-GHz transceivers in a Silicon-Germanium BiCMOS technology. The multi-band transceivers are equipped with a frequency multiplier to generate the 61- and 122-GHz carrier signals from a single 30.5-GHz LO signal and can be cascaded to build a MIMO radar system with 2 different frequency bands. The implemented scalable sensor system is suitable for various radar and communication applications by using the same transceiver chips with integrated binary phase-shift keying modulators and I/Q receiver. A suitable pseudo-random binary sequence was used in a radar measurement to demonstrate the applicability of the developed chips.