title: Application Effect Analysis of Image Fusion Methods for Extraction of Shoreline in Coastal Zone Using Landsat ETM

abstract: Extraction of shoreline incoastal zone is important for coast protection and management. This paper presents extractingthe shoreline with fusion images, which are obtained using various image fusion methods such as IHStransform, Brovey Transform, Multiplicative, Principle Component, Wavelet Resolution Merge. Artificial constructions (e.g. coastalembankments), islands, lakes, tidal mudflats and estuaries have been selected as evaluation objects, shorelines of which are extracted and analyzed. The result indicates that shoreline extraction effect by the Principle Component method is bestamong other methods.


title: SDN Northbound REST API with Efficient Caches

abstract: In this paper, we present a REST Chart based approach to design and implement RESTful northbound API in SDN. In our approach, the RESTful API is modeled with Petri Net, and REST constraints, such as hypermedia driven, are enforced naturally. The RESTful structure of the designed northbound API can be checked almost automatically based on the described REST Chart model. The proposed approach has been applied to design and implement the northbound API of SDN in data center networks with OpenStack Neutron. To improve the protocol efficiency, we developed a structured caching mechanism for data network applications with SDN, that reduces the response time overheads by more than 65%, while it fully maintains the desired flexibility and scalability of the RESTful northbound API.


title: Color Constancy with Derivative Colors

abstract: Information about the illuminant color is well contained in both achromatic regions and the specular components of highlight regions. In this paper, we propose a novel way to achieve color constancy by exploiting such clues. The key to our approach lies in the use of suitably extracted derivative colors, which are able to compute the illuminant color robustly with kernel density estimation. While extracting derivative colors from achromatic regions to approximate the illuminant color well is basically straightforward, the success of our extraction in highlight regions is attributed to the different rates of variation of the diffuse and specular magnitudes in the dichromatic reflection model. The proposed approach requires no training phase and is simple to implement. More significantly, it performs quite satisfactorily under inter-database parameter settings. Our experiments on three standard databases demonstrate its effectiveness and fine performance in comparison to state-of-the-art methods.


ARCHITECTURE-BASED DEPENDABILITY ANALYSIS APPROACHES


title: Implementation and effectiveness of organizational information security measures

abstract: Purpose – The purpose of this paper is to study the implementation of organizational information security measures and assess the effectiveness of such measures.Design/methodology/approach – A survey was designed and data were collected from information security managers in a selection of Norwegian organizations.Findings – Technical‐administrative security measures such as security policies, procedures and methods are the most commonly implemented organizational information security measures in a sample of Norwegian organizations. Awareness‐creating activities are applied by the organizations to a considerably lesser extent, but are at the same time these are assessed as being more effective organizational measures than technical‐administrative ones. Consequently, the study shows an inverse relationship between the implementation of organizational information security measures and assessed effectiveness of the organizational information security measures.Originality/value – Provides insight into the non‐t...


title: Comparison of losses in different topologies of step-up/step-down inverters

abstract: Standard Voltage Source Inverters (VSI) operates with a DC voltage source greater than the peak voltage of the AC source and Current Source Inverters (CSI) with a DC voltage source lower than the peak voltage of the AC source. This paper shows how several topologies of reversible AC/DC buck-boost inverters which do not suffer from such constraints can be compared on the basis of semiconductor and inductor losses. The main quantities in these converters, such as average current, RMS current, etc. are derived from an analytical analysis and they are combined to semiconductor loss models to derive losses and efficiency of each topology for different working conditions.


title: Two dimmensional model of a permanent magnet spur gear-A mathematical method used to model a parallel magnetised magnetic spur gear

abstract: This paper extends an analysis method developed for a radial magnetized spur gear. The extension describes how a parallel magnetized spur gear can be modeled analytically. The analytical method for a parallel magnetized gear is verified with the finite element method, which showed good agreements. The results with the parallel magnetized are also compared with results from the previous developed analytical method for radial magnetized gear, and the parallel magnetized version turned out to give a better performance. A test model was also built to verify the theoretical calculations.


title: A robot system design for low-cost multi-robot manipulation

abstract: Multi-robot manipulation allows for scalable environmental interaction, which is critical for multi-robot systems to have an impact on our world. A successful manipulation model requires cost-effective robots, robust hardware, and proper system feedback and control. This paper details key sensing and manipulator capabilities of the r-one robot. The r-one robot is an advanced, open source, low-cost platform for multi-robot manipulation and sensing that meets all of these requirements. The parts cost is around $250 per robot. The r-one has a rich sensor suite, including a flexible IR communication/localization/obstacle detection system, high-precision quadrature encoders, gyroscope, accelerometer, integrated bump sensor, and light sensors. Two years of working with these robots inspired the development of an external manipulator that gives the robots the ability to interact with their environment. This paper presents an overview of the r-one, the r-one manipulator, and basic manipulation experiments to illustrate the efficacy our design. The advanced design, low cost, and small size can support university research with large populations of robots and multi-robot curriculum in computer science, electrical engineering, and mechanical engineering. We conclude with remarks on the future implementation of the manipulators and expected work to follow.


title: Overview of 5G Security Challenges and Solutions

abstract: 5G networks will use novel technological concepts to meet the requirements of broadband access everywhere, high user and device mobility, and connectivity of massive number of devices (e.g., the Internet of Things) in an ultra-reliable and affordable way. Software defined networking and network functions virtualization leveraging the advances in cloud computing such as mobile edge computing are the most sought out technologies to meet these requirements. However, securely using these technologies and providing user privacy in future wireless networks are the new concerns. Therefore, this article provides an overview of the security challenges in clouds, software defined networking, and network functions virtualization, and the challenges of user privacy. Henceforth, this article presents solutions to these challenges and future directions for secure 5G systems.


title: Score Look-Alike Audiences

abstract: Look-alike models, which are efficient tools for finding similar users from a smaller user set, are quickly revolutionizing the online programmatic advertising industry. The datasets in these contexts exhibit extremely sparse feature spaces on a massive scale, so traditionally, the state-of-the-art look-alike models have used pairwise similarities to construct these similar user sets. One of the key challenges of the similarity-based models is that they do not provide a way to measure the potential value of the users to an advertiser, which is crucial in an advertising context. We propose methods to score users within the expanded audience in a way which relates directly to the business metric that the advertiser wants to optimize. We present three scoring models and show that, through empirical evaluation using real-world, large-scale data, by incorporating the potential value of a user to an advertiser into our scoring model, we can significantly improve the performance of the look-alike models over methods which only use pairwise similarities of users.


title: A Predictive Collision Avoidance Model for Pedestrian Simulation

abstract: We present a new local method for collision avoidance that is based on collision prediction. In our model, each pedestrian predicts possible future collisions with other pedestrians and then makes an efficient move to avoid them. Experiments show that the new approach leads to considerably shorter and less curved paths, ensuring smooth avoidance behaviour and visually compelling simulations. The method reproduces emergent behaviour like lane formation that have been observed in real crowds. The technique is easy to implement and is fast, allowing the simulation in real time of crowds of thousands of pedestrians.


title: Discriminative log-linear grammars with latent variables

abstract: We demonstrate that log-linear grammars with latent variables can be practically trained using discriminative methods. Central to efficient discriminative training is a hierarchical pruning procedure which allows feature expectations to be efficiently approximated in a gradient-based procedure. We compare L1 and L2 regularization and show that L1 regularization is superior, requiring fewer iterations to converge, and yielding sparser solutions. On full-scale treebank parsing experiments, the discriminative latent models outperform both the comparable generative latent models as well as the discriminative non-latent baselines.


title: On Graphical Models via Univariate Exponential Family Distributions

abstract: Undirected graphical models, or Markov networks, are a popular class of statistical models, used in a wide variety of applications. Popular instances of this class include Gaussian graphical models and Ising models. In many settings, however, it might not be clear which subclass of graphical models to use, particularly for non-Gaussian and non-categorical data. In this paper, we consider a general sub-class of graphical models where the node-wise conditional distributions arise from exponential families. This allows us to derive multivariate graphical model distributions from univariate exponential family distributions, such as the Poisson, negative binomial, and exponential distributions. Our key contributions include a class of M-estimators to fit these graphical model distributions; and rigorous statistical analysis showing that these M-estimators recover the true graphical model structure exactly, with high probability. We provide examples of genomic and proteomic networks learned via instances of our class of graphical models derived from Poisson and exponential distributions.


title: Intrinsic Plagiarism Detection

abstract: Current research in the field of automatic plagiarism detection for text documents focuses on algorithms that compare plagiarized documents against potential original documents. Though these approaches perform well in identifying copied or even modified passages, they assume a closed world: a reference collection must be given against which a plagiarized document can be compared. ::: ::: This raises the question whether plagiarized passages within a document can be detected automatically if no reference is given, e. g. if the plagiarized passages stem from a book that is not available in digital form. We call this problem class intrinsic plagiarism detection. The paper is devoted to this problem class; it shows that it is possible to identify potentially plagiarized passages by analyzing a single document with respect to variations in writing style. ::: ::: Our contributions are fourfold: (i) a taxonomy of plagiarism delicts along with detection methods, (ii) new features for the quantification of style aspects, (iii) a publicly available plagiarism corpus for benchmark comparisons, and (iv) promising results in non-trivial plagiarism detection settings: in our experiments we achieved recall values of 85% with a precision of 75% and better.


title: Autonomous braking system via deep reinforcement learning

abstract: In this paper, we propose a new autonomous braking system based on deep reinforcement learning. The proposed autonomous braking system automatically decides whether to apply the brake at each time step when confronting the risk of collision using the information on the obstacle obtained by the sensors. The problem of designing brake control is formulated as searching for the optimal policy in Markov decision process (MDP) model where the state is given by the relative position of the obstacle and the vehicle's speed, and the action space is defined as whether brake is stepped or not. The policy used for brake control is learned through computer simulations using the deep reinforcement learning method called deep Q-network (DQN). In order to derive desirable braking policy, we propose the reward function which balances the damage imposed to the obstacle in case of accident and the reward achieved when the vehicle runs out of risk as soon as possible. DQN is trained for the scenario where a vehicle is encountered with a pedestrian crossing the urban road. Experiments show that the control agent exhibits desirable control behavior and avoids collision without any mistake in various uncertain environments.


title: Statistical machine translation enhancements through linguistic levels: A survey

abstract: Machine translation can be considered a highly interdisciplinary and multidisciplinary field because it is approached from the point of view of human translators, engineers, computer scientists, mathematicians, and linguists. One of the most popular approaches is the Statistical Machine Translation ( smt ) approach, which tries to cover translation in a holistic manner by learning from parallel corpus aligned at the sentence level. However, with this basic approach, there are some issues at each written linguistic level (i.e., orthographic, morphological, lexical, syntactic and semantic) that remain unsolved. Research in smt has continuously been focused on solving the different linguistic levels challenges. This article represents a survey of how the smt has been enhanced to perform translation correctly at all linguistic levels.


title: Design of a wideband transition from double-ridge waveguide to microstrip line

abstract: The microwave characteristics of double-ridge waveguide-microstrip line transition are analyzed and simulated by full-wave analysis software. A couple of double-ridge waveguide-microstrip line transitions have been finished. The experimental test data is in well agreement with simulation result in the range of 6.5~18GHz. The testing result indicates that the waveguide-microstrip line transition has good performance in the whole frequency range and fits the requirement of engineering applications.


title: Lateral thinking about power devices (LDMOS)

abstract: BiCMOS Power technology LDMOS are reviewed with respect to category and structure definition and briefly as to how the structures relate to figure of merit performance. Stepped gate oxide devices are introduced making use of popular dual gate technologies and exhibit improved R/sub sp/ vs. BV performance of up to 30% at low V/sub gs/ without sacrifice of BV. Production use of thick copper plated bussing up to 25 /spl mu/m thick with R/sub sh/=0.8 m/spl Omega//sq is revealed for power, enabling up to 40% efficiency improvement on LDMOS power transistors.


title: Autonomous vehicles testing methods review

abstract: Driving test is critical to the deployment of autonomous vehicles. It is necessary to review the related works since the methodologies summaries are rare, which will help to set up an integrated method for autonomous driving test in different development stages, and help to provide a reliable, quick, safe, low cost and reproducible method and accelerate the development of autonomous vehicle. In this paper, we review the related autonomous driving test works, including autonomous vehicle functional verification, vehicle integrated testing, system validation in different architectures. This review work will be helpful for autonomous vehicle development.


title: Web-scale user modeling for targeting

abstract: We present the experiences from building a web-scale user modeling platform for optimizing display advertising targeting at Yahoo!. The platform described in this paper allows for per-campaign maximization of conversions representing purchase activities or transactions. Conversions directly translate to advertiser's revenue, and thus provide the most relevant metrics of return on advertising investment. We focus on two major challenges: how to efficiently process histories of billions of users on a daily basis, and how to build per-campaign conversion models given the extremely low conversion rates (compared to click rates in a traditional setting). We first present mechanisms for building web-scale user profiles in a daily incremental fashion. Second, we show how to reduce the latency through in-memory processing of billions of user records. Finally, we discuss a technique for scaling the number of handled campaigns/models by introducing an efficient labeling technique that allows for sharing negative training examples across multiple campaigns.


title: Collaborative ordinal regression

abstract: Ordinal regression has become an effective way of learning user preferences, but most research focuses on single regression problems. In this paper we introduce collaborative ordinal regression, where multiple ordinal regression tasks are handled simultaneously. Rather than modeling each task individually, we explore the dependency between ranking functions through a hierarchical Bayesian model and assign a common Gaussian Process (GP) prior to all individual functions. Empirical studies show that our collaborative model outperforms the individual counterpart in preference learning applications.


title: Network Unfolding Map by Edge Dynamics Modeling

abstract: The emergence of collective dynamics in neural networks is a mechanism of the animal and human brain for information processing. In this paper, we develop a computational technique using distributed processing elements in a complex network, which are called particles, to solve semisupervised learning problems. Three actions govern the particles’ dynamics: generation, walking, and absorption. Labeled vertices generate new particles that compete against rival particles for edge domination. Active particles randomly walk in the network until they are absorbed by either a rival vertex or an edge currently dominated by rival particles. The result from the model evolution consists of sets of edges arranged by the label dominance. Each set tends to form a connected subnetwork to represent a data class. Although the intrinsic dynamics of the model is a stochastic one, we prove that there exists a deterministic version with largely reduced computational complexity; specifically, with linear growth. Furthermore, the edge domination process corresponds to an unfolding map in such way that edges “stretch” and “shrink” according to the vertex-edge dynamics. Consequently, the unfolding effect summarizes the relevant relationships between vertices and the uncovered data classes. The proposed model captures important details of connectivity patterns over the vertex-edge dynamics evolution, in contrast to the previous approaches, which focused on only vertex or only edge dynamics. Computer simulations reveal that the new model can identify nonlinear features in both real and artificial data, including boundaries between distinct classes and overlapping structures of data.


title: Extracting Parallel Sentences from Comparable Corpora using Document Level Alignment

abstract: The quality of a statistical machine translation (SMT) system is heavily dependent upon the amount of parallel sentences used in training. In recent years, there have been several approaches developed for obtaining parallel sentences from non-parallel, or comparable data, such as news articles published within the same time period (Munteanu and Marcu, 2005), or web pages with a similar structure (Resnik and Smith, 2003). One resource not yet thoroughly explored is Wikipedia, an online encyclopedia containing linked articles in many languages. We advance the state of the art in parallel sentence extraction by modeling the document level alignment, motivated by the observation that parallel sentence pairs are often found in close proximity. We also include features which make use of the additional annotation given by Wikipedia, and features using an automatically induced lexicon model. Results for both accuracy in sentence extraction and downstream improvement in an SMT system are presented.


title: Towards Monetary Incentives in Social Q&A Services

abstract: Community-based question answering (CQA) services are facing key challenges to motivate domain experts to provide timely answers. Recently, CQA services are exploring new incentive models to engage experts and celebrities by allowing them to set a price on their answers. In this paper, we perform a data-driven analysis on two emerging payment-based CQA systems: Fenda (China) and Whale (US). By analyzing a large dataset of 220K questions (worth 1 million USD collectively), we examine how monetary incentives affect different players in the system. We find that, while monetary incentive enables quick answers from experts, it also drives certain users to aggressively game the system for profits. In addition, in this supplier-driven marketplace, users need to proactively adjust their price to make profits. Famous people are unwilling to lower their price, which in turn hurts their income and engagement over time. Finally, we discuss the key implications to future CQA design.


title: Person Recognition Based on Micro-Doppler and Thermal Infrared Camera Fusion for Firefighting

abstract: This paper examines the recognition of real persons, mirrored persons and other objects using thermal infrared (TIR) images and radar micro-Doppler (µ-D). Mirrored persons lead to confusion of firefighters, when only a TIR camera is used. However, mirrored persons exhibit the µ- D of the mirroring objects, hence radar can resolve this ambiguity. In this paper, multiple sensor fusion architectures are investigated for this classification task. The first approach uses an attention stage, where bounding boxes of candidates for real/mirrored persons are determined in TIR images. These bounding boxes are associated to the radar targets and subsequently classified. A joint classification of the radar µ- D and TIR image at measurement level is compared to a separate classification with subsequent combination (object level). Furthermore, a classification of the complete scene is proposed, omitting the TIR attention stage and data association. Experiments with real measurements are used for an evaluation of the presented approaches.


title: Support collars: a preliminary survey of their benefits and problems

abstract: Many support collars are prescribed despite the lack of controlled evidence as to their benefits, or the problems they may produce. We report a preliminary survey of 50 rheumatology, medical and ear, nose and throat outpatients prescribed a support collar during a two-month period. Of these, 27 (54%) completed a structured telephone interview two to four months later. Instructions received by patients about collar use varied widely, including advice to wear it continuously, intermittently, or only by day or night. Of the patients, 21 out of 26 (81 %) reported at least moderate relief of pain; four out of 14 (24%) reduction in paraesthesia; and five out of nine (57%) lessening of dizziness. Eighteen (67%) said that the collar caused significant discomfort, 14 (25%) had difficulty keeping their collar clean, and seven (26%) were unable to fit the collar themselves. Despite these problems compliance was good, with only three patients (11 %) of those interviewed abandoning their collar because of lack of bene...


title: Highly Efficient and Multipaction-Free P-Band GaN High-Power Amplifiers for Space Applications

abstract: In this paper, the authors report upon the development of multipaction-free P-band (UHF) GaN high-power amplifiers (HPAs) with target RF output power values of 140 W and power-added efficiency beyond 70%. Initially, two different 80-W class single-ended power modules were designed, manufactured, and tested using GaN devices from two different manufacturers. Load–pull techniques were used in both designs to achieve the best tradeoff in terms of RF output power, efficiency, and stability. Secondly, two identical power modules have been combined in a balanced architecture in order to obtain the required level of RF output power. Multipaction analyses and tests have been carried out to guarantee reliable operation in space. The HPAs have been characterized over temperature from $-{\hbox{15}}\ ^{\circ}{\hbox{C}}$ to $+{\hbox{55}}\ ^{\circ}{\hbox{C}}$ in pulsed and constant-wave conditions, showing negligible drifts over temperature and multipaction-free operation. RF output power in excess of 180 W at 70% drain efficiency is also demonstrated.


title: Controller Design of an Electric Power Steering System

abstract: This brief presents a new method of analyzing stability and design of a controller for an electric power steering (EPS) system. The most important task when designing a steering system is ensuring that the driver is pleased with how the steering feels. The way that the steering feels is dependent upon the assist torque map. The assist torque map is a one-to-one map between sensed driver torque and assist motor torque that varies with the cars speed. However, an assist torque map cannot be applied alone as an EPS controller because of high level of steering assist gain and nonlinearity of the torque map. Both elements of torque map can result in instability, which leads to vibration or divergence of the steering system. Therefore, an EPS system always needs to be designed with a stabilizing compensator and an assist torque map. The objective of designing a compensator is to stabilize the system with robustness and attenuate any unpleasant vibration. This brief presents a mechanical model of the EPS system and demonstrates a method to identify the model parameter. Based on the EPS model, stability of the system with an approximate linear torque map and nonlinear torque map is analyzed. Furthermore, criteria for designing the stabilizing compensator are suggested. Lead-lag compensators with different parameters are applied with the torque map in simulations, and vehicle experiments are performed to verify the theoretical analysis.


title: Oblivious Neural Network Predictions via MiniONN Transformations

abstract: Machine learning models hosted in a cloud service are increasingly popular but risk privacy: clients sending prediction requests to the service need to disclose potentially sensitive information. In this paper, we explore the problem of privacy-preserving predictions: after each prediction, the server learns nothing about clients' input and clients learn nothing about the model. We present MiniONN, the first approach for transforming an existing neural network to an oblivious neural network supporting privacy-preserving predictions with reasonable efficiency. Unlike prior work, MiniONN requires no change to how models are trained. To this end, we design oblivious protocols for commonly used operations in neural network prediction models. We show that MiniONN outperforms existing work in terms of response latency and message sizes. We demonstrate the wide applicability of MiniONN by transforming several typical neural network models trained from standard datasets.


title: Data-Raspberry data for 'Wearable multimodal skin sensing for the diabetic foot'

abstract: This data is supplied in support of the the article "Wearable multimodal skin sensing for the diabetic foot" by Coates, Chipperfield and Clough in the open access journal 'electronics - Raspberry Pi Special edition'.A data set is provided for each volunteer referenced as 001, 009 and 1001 are enclosed. Biomentric data is supplied for each volunteer along with the data set used in the article. The title of each graph presented in the article identifies the data set used together with the chosen data filter type (low pass) and filter cut off frequency. TA 6 pole Butterwoth filter was used. Data was analysed in Python Spyder 2.3.5.2.Data is taken in time series with a descriptor of the data stream collected in row 5 of the respective column. The unis for each data stream is in row 6. All data ws taken at 20Hz.


title: Contributing to success in an introductory computer science course: a study of twelve factors

abstract: This study was conducted to determine factors that promote success in an introductory college computer science course. The model included twelve possible predictive factors including math background, attribution for success/failure (luck, effort, difficulty of task, and ability), domain specific self-efficacy, encouragement, comfort level in the course, work style preference, previous programming experience, previous non-programming computer experience, and gender. Subjects included 105 students enrolled in a CS1 introductory computer science course at a midwestern university. The study revealed three predictive factors in the following order of importance: comfort level, math, and attribution to luck for success/failure. Comfort level and math background were found to have a positive influence on success, whereas attribution to luck had a negative influence. The study also revealed by considering the different types of previous computer experiences (including formal programming class, self-initiated programming, internet use, game playing, and productivity software use) that both a formal class in programming and game playing were predictive of success. Formal training had a positive influence and games a negative influence on class grade.


title: Learning What to Remember: Long-term Episodic Memory Networks for Learning from Streaming Data

abstract: Current generation of memory-augmented neural networks has limited scalability as they cannot efficiently process data that are too large to fit in the external memory storage. One example of this is lifelong learning scenario where the model receives unlimited length of data stream as an input which contains vast majority of uninformative entries. We tackle this problem by proposing a memory network fit for long-term lifelong learning scenario, which we refer to as Long-term Episodic Memory Networks (LEMN), that features a RNN-based retention agent that learns to replace less important memory entries based on the retention probability generated on each entry that is learned to identify data instances of generic importance relative to other memory entries, as well as its historical importance. Such learning of retention agent allows our long-term episodic memory network to retain memory entries of generic importance for a given task. We validate our model on a path-finding task as well as synthetic and real question answering tasks, on which our model achieves significant improvements over the memory augmented networks with rule-based memory scheduling as well as an RL-based baseline that does not consider relative or historical importance of the memory.


Large-Scale Hierarchical Topic Models


title: Biclustering Methods: Biological Relevance and Application in Gene Expression Analysis

abstract: DNA microarray technologies are used extensively to profile the expression levels of thousands of genes under various conditions, yielding extremely large data-matrices. Thus, analyzing this information and extracting biologically relevant knowledge becomes a considerable challenge. A classical approach for tackling this challenge is to use clustering (also known as one-way clustering) methods where genes (or respectively samples) are grouped together based on the similarity of their expression profiles across the set of all samples (or respectively genes). An alternative approach is to develop biclustering methods to identify local patterns in the data. These methods extract subgroups of genes that are co-expressed across only a subset of samples and may feature important biological or medical implications. In this study we evaluate 13 biclustering and 2 clustering (k-means and hierarchical) methods. We use several approaches to compare their performance on two real gene expression data sets. For this purpose we apply four evaluation measures in our analysis: (1) we examine how well the considered (bi)clustering methods differentiate various sample types; (2) we evaluate how well the groups of genes discovered by the (bi)clustering methods are annotated with similar Gene Ontology categories; (3) we evaluate the capability of the methods to differentiate genes that are known to be specific to the particular sample types we study and (4) we compare the running time of the algorithms. In the end, we conclude that as long as the samples are well defined and annotated, the contamination of the samples is limited, and the samples are well replicated, biclustering methods such as Plaid and SAMBA are useful for discovering relevant subsets of genes and samples.


title: Blockchain inefficiency in the Bitcoin peers network

abstract: We investigate Bitcoin network observing transactions broadcasted into the network during a week from 04/05/2016 and then monitoring their inclusion into the blockchain during the following seven months.We unveil that 42% of the transactions are still not included in the Blockchain after 1 h from their appearance and 20% of the transactions are still not included in the Blockchain after 30 days, therefore revealing a great inefficiency in the Bitcoin system. However, we observe that most of these “forgotten” transactions have low values and in terms of transferred value the system is less inefficient with 93% of the transactions value being included into the Blockchain within 3 h and 98.8% within a day. The fact that a sizeable fraction of transactions is not processed timely casts serious doubts on the usability of the Bitcoin Blockchain for reliable time-stamping purposes. It also calls for a debate about the right systems of incentives which a peer-to-peer unintermediated system should introduce to promote efficient transaction recording


title: Planar Broadband Tag Antenna Mounted on the Metallic Material for UHF RFID System

abstract: A novel broadband design of planar tag antenna mounted on the metallic objects for the UHF radio frequency identification (RFID) system is proposed. We derive a new resonant mode that is excited with frequency shifting to enhance the operating bandwidth for broadband operation by employing the shorting pins posted at the corner of the proposed tag antenna. The obtained impedance bandwidth across the operating band can reach about 112 MHz (12.5%) for the UHF band, which is from 850 to 962 MHz. Also, with unidirectional pattern, the measured reading distance is about 5.2 m as the proposed tag antenna mounted on the metallic object.


title: A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessment

abstract: Abstract In most practical situations, the compression or transmission of images and videos creates distortions that will eventually be perceived by a human observer. Vice versa, image and video restoration techniques, such as inpainting or denoising, aim to enhance the quality of experience of human viewers. Correctly assessing the similarity between an image and an undistorted reference image as subjectively experienced by a human viewer can thus lead to significant improvements in any transmission, compression, or restoration system. This paper introduces the Haar wavelet-based perceptual similarity index (HaarPSI), a novel and computationally inexpensive similarity measure for full reference image quality assessment. The HaarPSI utilizes the coefficients obtained from a Haar wavelet decomposition to assess local similarities between two images, as well as the relative importance of image areas. The consistency of the HaarPSI with the human quality of experience was validated on four large benchmark databases containing thousands of differently distorted images. On these databases, the HaarPSI achieves higher correlations with human opinion scores than state-of-the-art full reference similarity measures like the structural similarity index (SSIM), the feature similarity index (FSIM), and the visual saliency-based index (VSI). Along with the simple computational structure and the short execution time, these experimental results suggest a high applicability of the HaarPSI in real world tasks.


title: Efficient Sparse Coding in Early Sensory Processing: Lessons from Signal Recovery

abstract: Sensory representations are not only sparse, but often overcomplete: coding units significantly outnumber the input units. For models of neural coding this overcompleteness poses a computational challenge for shaping the signal processing channels as well as for using the large and sparse representations in an efficient way. We argue that higher level overcompleteness becomes computationally tractable by imposing sparsity on synaptic activity and we also show that such structural sparsity can be facilitated by statistics based decomposition of the stimuli into typical and atypical parts prior to sparse coding. Typical parts represent large-scale correlations, thus they can be significantly compressed. Atypical parts, on the other hand, represent local features and are the subjects of actual sparse coding. When applied on natural images, our decomposition based sparse coding model can efficiently form overcomplete codes and both center-surround and oriented filters are obtained similar to those observed in the retina and the primary visual cortex, respectively. Therefore we hypothesize that the proposed computational architecture can be seen as a coherent functional model of the first stages of sensory coding in early vision.


title: Cryptocurrency portfolio management with deep reinforcement learning

abstract: Portfolio management is the decision-making process of allocating an amount of fund into different financial investment products. Cryptocurrencies are electronic and decentralized alternatives to government-issued money, with Bitcoin as the best-known example of a cryptocurrency. This paper presents a model-less convolutional neural network with historic prices of a set of financial assets as its input, outputting portfolio weights of the set. The network is trained with 0.7 years' price data from a cryptocurrency exchange. The training is done in a reinforcement manner, maximizing the accumulative return, which is regarded as the reward function of the network. Backtest trading experiments with trading period of 30 minutes is conducted in the same market, achieving 10-fold returns in 1.8 months' periods. Some recently published portfolio selection strategies are also used to perform the same back-tests, whose results are compared with the neural network. The network is not limited to cryptocurrency, but can be applied to any other financial markets.


title: Seating arrangements that promote positive academic and behavioural outcomes: a review of empirical research

abstract: Seating arrangements are important classroom setting events because they have the potential to help prevent problem behaviours that decrease student attention and diminish available instructional time. The purpose of this synthesis of empirical literature is to determine which arrangements of desks best facilitate positive academic and behavioural outcomes for primary through secondary high school students with a range of characteristics. Eight studies that investigated at least two of three common arrangements (i.e., rows, groups or semi-circles) were considered. Results indicate that teachers should let the nature of the task dictate seating arrangements. Evidence supports the idea that students display higher levels of appropriate behaviour during individual tasks when they are seated in rows, with disruptive students benefiting the most.


Detecting and Classifying Emotion in Popular Music


title: Macrophage colony-stimulating factor-, granulocyte-macrophage colony-stimulating factor-, or IL-3-dependent survival of macrophages, but not proliferation, requires the expression of p21(Waf1) through the phosphatidylinositol 3-kinase/Akt pathway.

abstract: Mouse bone marrow-derived macrophages proliferate in the presence of macrophage colony-stimulating factor (M-CSF), granulocyte-macrophage colony-stimulating factor, or IL-3, but undergo apoptosis in their absence. Inhibition of extracellular signal-regulated kinases (ERK)-1/2 blocks growth factor-dependent proliferation but not survival, indicating that the two processes require independent signaling pathways. Although M-CSF induces the activation of other kinase pathways, such as c-Jun N-terminal kinase, p38, and phosphatidylinositol 3-kinase (PI-3K), these pathways are not required for proliferation. However, PI-3K is the only one necessary for the induction of survival, as demonstrated using the inhibitors LY294002 and Wortmannin. Growth factors also activate Akt kinase and a transient expression of the cdk inhibitor p21(Waf1), which inhibits apoptosis but is not required for proliferation. PI-3K inhibitors also block growth factor-dependent expression of p21(Waf1) and the activation of Akt. Moreover, the survival induced by cyclosporin A or decorin is also dependent on the PI-3K/Akt kinases and p21(Waf1). These findings demonstrate that the induction of p21(Waf1) through the PI-3K/Akt pathway is a general survival response of macrophages. Our results show that growth factors in macrophages use two pathways: one for proliferation, mediated by ERK, and the other for survival, which requires the PI-3K/Akt kinases and p21(Waf1).


title: Efficient Similarity Search over Encrypted Data

abstract: In recent years, due to the appealing features of cloud computing, large amount of data have been stored in the cloud. Although cloud based services offer many advantages, privacy and security of the sensitive data is a big concern. To mitigate the concerns, it is desirable to outsource sensitive data in encrypted form. Encrypted storage protects the data against illegal access, but it complicates some basic, yet important functionality such as the search on the data. To achieve search over encrypted data without compromising the privacy, considerable amount of searchable encryption schemes have been proposed in the literature. However, almost all of them handle exact query matching but not similarity matching, a crucial requirement for real world applications. Although some sophisticated secure multi-party computation based cryptographic techniques are available for similarity tests, they are computationally intensive and do not scale for large data sources. In this paper, we propose an efficient scheme for similarity search over encrypted data. To do so, we utilize a state-of-the-art algorithm for fast near neighbor search in high dimensional spaces called locality sensitive hashing. To ensure the confidentiality of the sensitive data, we provide a rigorous security definition and prove the security of the proposed scheme under the provided definition. In addition, we provide a real world application of the proposed scheme and verify the theoretical results with empirical observations on a real dataset.


title: Aperture coupled Dielectric Resonator Antenna design with flat reflector

abstract: A single element circular slot aperture coupled Cylindrical Dielectric Resonator Antenna (CDRA) with a flat reflector is designed and presented. The measured bandwidth of the antenna is about 1.8 % (5.32 - 5.52) while the simulated bandwidth is 1.11% (5.32 - 5.44 GHz). A comprehensive parametric study has been conducted to realize the effects of slot size, slot position and reflector parameters to increase the directivity of the designed antenna. The experimental results show that the antenna covers 5 GHz band, which satisfies the basic requirement of the WLAN application IEEE 802.11a.


title: SCPKI: A Smart Contract-based PKI and Identity System

abstract: The Public Key Infrastructure (PKI) in use today on the Internet to secure communications has several drawbacks arising from its centralised and non-transparent design. In the past there has been instances of certificate authorities publishing rogue certificates for targeted attacks, and this has been difficult to immediately detect as certificate authorities are not transparent about the certificates they issue. Furthermore, the centralised selection of trusted certificate authorities by operating system and browser vendors means that it is not practical to untrust certificate authorities that have issued rogue certificates, as this would disrupt the TLS process for many other hosts. SCPKI is an alternative PKI system based on a decentralised and transparent design using a web-of-trust model and a smart contract on the Ethereum blockchain, to make it easily possible for rogue certificates to be detected when they are published. The web-of-trust model is designed such that an entity or authority in the system can verify (or vouch for) fine-grained attributes of another entity's identity (such as company name or domain name), as an alternative to the centralised certificate authority identity verification model.


title: Millisecond-timescale, genetically targeted optical control of neural activity

abstract: Temporally precise, noninvasive control of activity in well-defined neuronal populations is a long-sought goal of systems neuroscience. We adapted for this purpose the naturally occurring algal protein Channelrhodopsin-2, a rapidly gated light-sensitive cation channel, by using lentiviral gene delivery in combination with high-speed optical switching to photostimulate mammalian neurons. We demonstrate reliable, millisecond-timescale control of neuronal spiking, as well as control of excitatory and inhibitory synaptic transmission. This technology allows the use of light to alter neural processing at the level of single spikes and synaptic events, yielding a widely applicable tool for neuroscientists and biomedical engineers.


title: A Total Variation based approach for robust consensus in distributed networks

abstract: Consider a connected network of agents endowed with local cost functions representing private objectives. Agents seek to find an agreement on some minimizer of the aggregate cost, by means of repeated communications between neighbors. This paper investigates the case where some agents are unreliable in the sense that they permanently inject some false value in the network. We introduce a new relaxation of the initial optimization problem. We show that the relaxed problem is equivalent to the initial one under some regularity conditions which are characterized. We propose two iterative distributed algorithms for finding minimizers of the relaxed problem. When all agents are reliable, these algorithms converge to the sought consensus provided that the above regularity conditions are satisfied. In the presence of misbehaving agents, we show in simple scenario that our algorithms converge to a solution which remains in the vicinity of the sought consensus. Unlike standard distributed algorithms, our approach turns out to be unsensitive to large perturbations. Numerical experiments complete our theoretical results.


title: The artificiality of natural user interfaces

abstract: Toward user-defined gestural interfaces.


title: Data-driven grasping with partial sensor data

abstract: To grasp a novel object, we can index it into a database of known 3D models and use precomputed grasp data for those models to suggest a new grasp. We refer to this idea as data-driven grasping, and we have previously introduced the Columbia Grasp Database for this purpose. In this paper we demonstrate a data-driven grasp planner that requires only partial 3D data of an object in order to grasp it. To achieve this, we introduce a new shape descriptor for partial 3D range data, along with an alignment method that can rigidly register partial 3D models to models that are globally similar but not identical. Our method uses SIFT features of depth images, and encapsulates “nearby” views of an object in a compact shape descriptor.


title: UMLsec: Extending UML for Secure Systems Development

abstract: Developing secure-critical systems is difficult and there are many well-known examples of security weaknesses exploitedin practice. Thus a sound methodology supporting secure systems development is urgently needed.Our aim is to aid the difficult task of developing security-critical systems in an approach basedon the notation of the Unified Modeling Language. We present the extension UMLsec of UML that allows to express securityrelevant information within the diagrams in a system specification. UMLsec is defined in form of a UML profile using the standard UML extension mechanisms. In particular, the associatedc onstraints give criteria to evaluate the security aspects of a system design, by referring to a formal semantics of a simplifiedfragm ent of UML. We demonstrate the concepts with examples.


title: The fabrication of transmission line on the glass substrate

abstract: Glass substrate has advantages of lower cost and lower RF loss compared with high resistivity Si substrate, but there is a challenge within process fabrication by the conventional CMOS fabrication system because of wafer transparency. Temporary bonding and de-bonding technology was used within process development. Transmission line was designed and fabricated on the glass substrate after process optimization.


title: Advances in Reversed Nested Miller Compensation

abstract: The use of two frequency compensation schemes for three-stage operational transconductance amplifiers, namely the reversed nested Miller compensation with nulling resistor (RN-MCNR) and reversed active feedback frequency compensation (RAFFC), is presented in this paper. The techniques are based on the basic RNMC and show an inherent advantage over traditional compensation strategies, especially for heavy capacitive loads. Moreover, they are implemented without entailing extra transistors, thus saving circuit complexity and power consumption. A well-defined design procedure, introducing phase margin as main design parameter, is also developed for each solution. To verify the effectiveness of the techniques, two amplifiers have been fabricated in a standard 0.5-mum CMOS process. Experimental measurements are found in good agreement with theoretical analysis and show an improvement in small-signal and large-signal amplifier performances. Finally, an analytical comparison with the nonreversed counterparts topologies, which shows the superiority of the proposed solutions, is also included.


title: Label embedding trees for large multiclass task

abstract: Multi-class classification becomes challenging at test time when the number of classes is very large and testing against every possible class can become computationally infeasible. This problem can be alleviated by imposing (or learning) a structure over the set of classes. We propose an algorithm for learning a tree-structure of classifiers which, by optimizing the overall tree loss, provides superior accuracy to existing tree labeling methods. We also propose a method that learns to embed labels in a low dimensional space that is faster than non-embedding approaches and has superior accuracy to existing embedding approaches. Finally we combine the two ideas resulting in the label embedding tree that outperforms alternative methods including One-vs-Rest while being orders of magnitude faster.


title: Efficient Action Detection in Untrimmed Videos via Multi-task Learning

abstract: This paper studies the joint learning of action recognition and temporal localization in long, untrimmed videos. We employ a multi-task learning framework that performs the three highly related steps of action proposal, action recognition, and action localization refinement in parallel instead of the standard sequential pipeline that performs the steps in order. We develop a novel temporal actionness regression module that estimates what proportion of a clip contains action. We use it for temporal localization but it could have other applications like video retrieval, surveillance, summarization, etc. We also introduce random shear augmentation during training to simulate viewpoint change. We evaluate our framework on three popular video benchmarks. Results demonstrate that our joint model is efficient in terms of storage and computation in that we do not need to compute and cache dense trajectory features, and that it is several times faster than its sequential ConvNets counterpart. Yet, despite being more efficient, it outperforms stateof-the-art methods with respect to accuracy.


title: Interactions with a moody robot

abstract: This paper reports on the results of a long-term experiment in which a social robot's facial expressions were changed to reflect different moods. While the facial changes in each condition were not extremely different, they still altered how people interacted with the robot. On days when many visitors were present, average interactions with the robot were longer when the robot displayed either a "happy" or a "sad" expression instead of a neutral face, but the opposite was true for low-visitor days. The implications of these findings for human-robot social interaction are discussed.


title: Learning Distributed Representations from Reviews for Collaborative Filtering

abstract: Recent work has shown that collaborative filter-based recommender systems can be improved by incorporating side information, such as natural language reviews, as a way of regularizing the derived product representations. Motivated by the success of this approach, we introduce two different models of reviews and study their effect on collaborative filtering performance. While the previous state-of-the-art approach is based on a latent Dirichlet allocation (LDA) model of reviews, the models we explore are neural network based: a bag-of-words product-of-experts model and a recurrent neural network. We demonstrate that the increased flexibility offered by the product-of-experts model allowed it to achieve state-of-the-art performance on the Amazon review dataset, outperforming the LDA-based approach. However, interestingly, the greater modeling power offered by the recurrent neural network appears to undermine the model's ability to act as a regularizer of the product representations.


title: Analysis and Design of Binary Message-Passing Decoders

abstract: Binary message passing decoders for low-density parity-check codes are studied by using extrinsic information transfer charts. The channel delivers hard or soft decisions and the variable node decoder performs all computations in the log-likelihood ratio (L-value) domain. A hard decision results in the Gallager B algorithm and examples show that increasing the channel output alphabet to two bits gains more than 1.0 dB in signal to noise ratio when using optimized codes. Finally, it is shown that errors on cycles consisting only of degree two and three variable nodes cannot be corrected and a necessary and sufficient condition for the existence of a cycle-free subgraph is derived.


title: A Survey of Trust and Reputation Systems for Online Service Provision

abstract: Trust and reputation systems represent a significant trend in decision support for Internet mediated service provision. The basic idea is to let parties rate each other, for example after the completion of a transaction, and use the aggregated ratings about a given party to derive a trust or reputation score, which can assist other parties in deciding whether or not to transact with that party in the future. A natural side effect is that it also provides an incentive for good behaviour, and therefore tends to have a positive effect on market quality. Reputation systems can be called collaborative sanctioning systems to reflect their collaborative nature, and are related to collaborative filtering systems. Reputation systems are already being used in successful commercial online applications. There is also a rapidly growing literature around trust and reputation systems, but unfortunately this activity is not very coherent. The purpose of this article is to give an overview of existing and proposed systems that can be used to derive measures of trust and reputation for Internet transactions, to analyse the current trends and developments in this area, and to propose a research agenda for trust and reputation systems.


title: Quality Inspection of Remote Radio Unit (RRU) Power Port Using IBVS

abstract: For the quality inspection task of RRU, it is necessary to insert the testing probes into its power and network ports. In this paper, the problem of alignment of robot's end effector with the power port of remote radio unit (RRU) in 4 degrees of freedom (DoF) has been solved. An image based visual servo (IBVS) controller has been designed to perform the alignment task by using the visual features of the power port in image plane. Decoupled features have been selected which eliminate the need to use image jacobian during the control design. This not only reduces the computational cost but also removes the hassle of dealing with image jacobian singularities during the visual servo. The findings have been validated both by performing simulations and designing an experiment which uses an industrial manipulator.


title: Bimodal network architectures for automatic generation of image annotation from text

abstract: Medical image analysis practitioners have embraced big data methodologies. This has created a need for large annotated datasets. The source of big data is typically large image collections and clinical reports recorded for these images. In many cases, however, building algorithms aimed at segmentation and detection of disease requires a training dataset with markings of the areas of interest on the image that match with the described anomalies. This process of annotation is expensive and needs the involvement of clinicians. In this work we propose two separate deep neural network architectures for automatic marking of a region of interest (ROI) on the image best representing a finding location, given a textual report or a set of keywords. One architecture consists of LSTM and CNN components and is trained end to end with images, matching text, and markings of ROIs for those images. The output layer estimates the coordinates of the vertices of a polygonal region. The second architecture uses a network pre-trained on a large dataset of the same image types for learning feature representations of the findings of interest. We show that for a variety of findings from chest X-ray images, both proposed architectures learn to estimate the ROI, as validated by clinical annotations. There is a clear advantage obtained from the architecture with pre-trained imaging network. The centroids of the ROIs marked by this network were on average at a distance equivalent to 5.1% of the image width from the centroids of the ground truth ROIs.


title: BRDF Acquisition with Basis Illumination

abstract: Realistic descriptions of surface reflectance have long been a topic of interest in both computer vision and computer graphics research. In this paper, we describe a novel and fast approach for the acquisition of bidirectional reflectance distribution functions (BRDFs). We develop a novel theory for directly measuring BRDFs in a basis representation by projecting incident light as a sequence of basis functions from a spherical zone of directions. We derive an orthonormal basis over spherical zones that is ideally suited for this task. BRDF values outside the zonal directions are extrapolated by re-projecting the zonal measurements into a spherical harmonics basis, or by fitting analytical reflection models to the data. We verify this approach with a compact optical setup that requires no moving parts and only a small number of image measurements. Using this approach, a BRDF can be measured in just a few minutes.


title: Using deep learning to detect price change indications in financial markets

abstract: Forecasting financial time-series has long been among the most challenging problems in financial market analysis. In order to recognize the correct circumstances to enter or exit the markets investors usually employ statistical models (or even simple qualitative methods). However, the inherently noisy and stochastic nature of markets severely limits the forecasting accuracy of the used models. The introduction of electronic trading and the availability of large amounts of data allow for developing novel machine learning techniques that address some of the difficulties faced by the aforementioned methods. In this work we propose a deep learning methodology, based on recurrent neural networks, that can be used for predicting future price movements from large-scale high-frequency time-series data on Limit Order Books. The proposed method is evaluated using a large-scale dataset of limit order book events.


title: Inferring Biological Networks by Sparse Identification of Nonlinear Dynamics

abstract: Inferring the structure and dynamics of network models is critical to understanding the functionality and control of complex systems, such as metabolic and regulatory biological networks. The increasing quality and quantity of experimental data enable statistical approaches based on information theory for model selection and goodness-of-fit metrics. We propose an alternative method to infer networked nonlinear dynamical systems by using sparsity-promoting $\ell_1$ optimization to select a subset of nonlinear interactions representing dynamics on a fully connected network. Our method generalizes the sparse identification of nonlinear dynamics (SINDy) algorithm to dynamical systems with rational function nonlinearities, such as biological networks. We show that dynamical systems with rational nonlinearities may be cast in an implicit form, where the equations may be identified in the null-space of a library of mixed nonlinearities including the state and derivative terms; this approach applies more generally to implicit dynamical systems beyond those containing rational nonlinearities. This method, implicit-SINDy, succeeds in inferring three canonical biological models: Michaelis-Menten enzyme kinetics, the regulatory network for competence in bacteria, and the metabolic network for yeast glycolysis.


title: Understanding latent sector errors and how to protect against them

abstract: Latent sector errors (LSEs) refer to the situation where particular sectors on a drive become inaccessible. LSEs are a critical factor in data reliability, since a single LSE can lead to data loss when encountered during RAID reconstruction after a disk failure. LSEs happen at a significant rate in the field [1], and are expected to grow more frequent with new drive technologies and increasing drive capacities. While two approaches, data scrubbing and intra-disk redundancy, have been proposed to reduce data loss due to LSEs, none of these approaches has been evaluated on real field data. ::: ::: This paper makes two contributions. We provide an extended statistical analysis of latent sector errors in the field, specifically from the view point of how to protect against LSEs. In addition to providing interesting insights into LSEs, we hope the results (including parameters for models we fit to the data) will help researchers and practitioners without access to data in driving their simulations or analysis of LSEs. Our second contribution is an evaluation of five different scrubbing policies and five different intra-disk redundancy schemes and their potential in protecting against LSEs. Our study includes schemes and policies that have been suggested before, but have never been evaluated on field data, as well as new policies that we propose based on our analysis of LSEs in the field.


title: Edge computing for interactive media and video streaming

abstract: Video streaming and computer games are among the most popular and highest bandwidth consuming media in the Internet. Video contents consume around 70% of the total bandwidth usage in the Internet today. Advancements in media generation tools, high processing power, and high speed connectivity have enabled generation of live, interactive, multi-view media generation. Cognitive assisted and online multi-player gaming have unlocked new horizons for gaming experience. However, such interactive gaming, and multi-view and 360-degree view videos, are currently limited by delay intolerance and excessive bandwidth usage. Edge computing is the name of a set of new technologies, such as cloudlets, micro data centers, fog, and mobile edge computing. It aims to provide storage and computational resources near to user at the network edge, to minimize latency and response time. Edge computing is foreseen as a significant enabler of Internet of Every Thing (IoET) era by extending the cloud services and resources at the end of the network to deliver very low latency and real-time communication. It can provide significant services to video and gaming applications and enable new stream of interactive multimedia era. In this paper, we highlight some of the potentials and prospects of edge computing for interactive media, and present some preliminary works in the area. We shed light on how edge computing can be used to tackle various challenges faced by todays interactive media application. We also present the benefits of using edge computing to save cost, bandwidth, and energy in multimedia applications, video streaming, and transcoding.


Dry etching process for GaAs and InP based devices


title: Portable smart non-invasive hemoglobin measurement system

abstract: Analysis of blood substances such as hemoglobin, glucose, protein, cholesterol, etc. are important parameters for health condition monitoring. Traditionally, blood sample is taken using a needle. Some chemical process such as enzyme reaction is required before the final value for concentration is read. Reagents involved in such processes are expensive for both the hospital and patient. Non-invasive measurement is much more attractive by its evident advantages such as real time monitoring and infection free operation. The Hemoglobin (Hb) in human blood is measured and analyzed using Advanced RISC (Reduced Instruction Set Computing) machine processor non-invasively. This investigation helps in ruling out the diseases like anemia (a low hemoglobin level) and polycythemia vera (a high hemoglobin level). At present, the procedures used to measure the Hb concentration in blood are invasive. For this purpose blood is taken and analyzed. The hindrance of this method is the delay between the blood collection and its analysis. The portable smart non-invasive measurement system, discussed in this paper, gives a detailed idea about the hemoglobin concentration in the blood.


title: Evaluating probabilistic queries over imprecise data

abstract: Many applications employ sensors for monitoring entities such as temperature and wind speed. A centralized database tracks these entities to enable query processing. Due to continuous changes in these values and limited resources (e.g., network bandwidth and battery power), it is often infeasible to store the exact values at all times. A similar situation exists for moving object environments that track the constantly changing locations of objects. In this environment, it is possible for database queries to produce incorrect or invalid results based upon old data. However, if the degree of error (or uncertainty) between the actual value and the database value is controlled, one can place more confidence in the answers to queries. More generally, query answers can be augmented with probabilistic estimates of the validity of the answers. In this paper we study probabilistic query evaluation based upon uncertain data. A classification of queries is made based upon the nature of the result set. For each class, we develop algorithms for computing probabilistic answers. We address the important issue of measuring the quality of the answers to these queries, and provide algorithms for efficiently pulling data from relevant sensors or moving objects in order to improve the quality of the executing queries. Extensive experiments are performed to examine the effectiveness of several data update policies.


title: Not All Neural Embeddings are Born Equal

abstract: Neural language models learn word representations that capture rich linguistic and conceptual information. Here we investigate the embeddings learned by neural machine translation models. We show that translation-based embeddings outperform those learned by cutting-edge monolingual models at single-language tasks requiring knowledge of conceptual similarity and/or syntactic role. The findings suggest that, while monolingual models learn information about how concepts are related, neural-translation models better capture their true ontological status.


title: OCR of historical printings of Latin texts: problems, prospects, progress

abstract: This paper deals with the application of OCR methods to historical printings of Latin texts. Whereas the problem of recognizing historical printings of modern languages has been the subject of the IMPACT program, Latin has not yet been given any serious consideration despite the fact that it dominated literature production in Europe up to the 17th century. Using finite state tools and methods developed during the IMPACT program we show that efficent batch-oriented post-correction can work for Latin as well, and that a lexicon of historical Latin spelling variants can be constructed to aid in the correction phase. Initial experiments for the OCR engines Tesseract and OCRopus show that some training on historical fonts and the application of lexical resources raise character accuracies beyond those of Finereader and that accuracies above 90% may be expected even for 16th century material.


title: A complete procedure for the design and optimization of arbitrarily shaped integrated lens antennas

abstract: This paper proposes a new design methodology of arbitrarily shaped integrated lens antennas (ILAs). First, we describe the design principles and numerical techniques of the optimization iterative loop. The starting lens shape, deduced from a general synthesis method based on geometrical optics principles, is optimized so that the radiation pattern of the ILA complies with an arbitrary amplitude-shaped template. The optimization procedure is local and is based on a multidimensional conjugate gradient method. Then, the capabilities and potentialities of this approach are demonstrated numerically using two examples. In the first one, an ILA radiating a secant-squared beam in one principal plane and a flat-top beam in the orthogonal plane is designed for indoor communications in V-band. In the second example (Gaussian/flat-top beam), we show for the first time that the joint optimization of the feed together with the lens shape is a very promising design methodology. Finally, our design tools are validated experimentally in Q-band through the synthesis and optimization of a peculiar small shaped ILA (28 mm/spl times/28 mm/spl times/15 mm) fed by an aperture-coupled microstrip patch antenna.


title: Social Influence Modulates the Neural Computation of Value

abstract: Social influence—individuals’ tendency to conform to the beliefs and attitudes of others—has interested psychologists for decades. However, it has traditionally been difficult to distinguish true modification of attitudes from mere public compliance with social norms; this study addressed this challenge using functional neuroimaging. Participants rated the attractiveness of faces and subsequently learned how their peers ostensibly rated each face. Participants were then scanned using functional MRI while they rated each face a second time. The second ratings were influenced by social norms: Participants changed their ratings to conform to those of their peers. This social influence was accompanied by modulated engagement of two brain regions associated with coding subjective value—the nucleus accumbens and orbitofrontal cortex—a finding suggesting that exposure to social norms affected participants’ neural representations of value assigned to stimuli. These findings document the utility of neuroimaging to...


title: A DC-100 GHz Bandwidth and 20.5 dB Gain Limiting Amplifier in 0.25µm InP DHBT Technology

abstract: A DC-100 GHz limiting amplifier is designed and fabricated in a 0.25μm InP DHBT technology. The amplifier is designed in two stages using a modified Cherry-Hooper architecture proceeding an emitter-follower at each stage. Consuming 145 mW of power from a -2.5 V supply, it achieves 20.5 dB differential S21 gain with less than 1 dB gain ripple and better than 20 dB and 15 dB input and output return losses, respectively. The group delay of S21 is 9 psec with a variation less than ±5.5 psec around that. Time domain large signal measurements verifies a single-ended output swing of 260 mV.


title: A Practical Approach for Extracting Tree Models in Forest Environments Based on Equirectangular Projections of Terrestrial Laser Scans

abstract: Extracting 3D tree models based on terrestrial laser scanning (TLS) point clouds is a challenging task as trees are complex objects. Current TLS devices acquire high-density data that allow a detailed reconstruction of the tree topology. However, in dense forests a fully automatic reconstruction of trees is often limited by occlusion, wind influences and co-registration issues. In this paper, a semi-automatic method for extracting branching and stem structure based on equirectangular projections (range and intensity maps) is presented. The digitization of branches and stems is based on 2D maps, which enables simple navigation and raster processing. The modeling is performed for each viewpoint individually instead of using a registered point cloud. Previously reconstructed 2D-skeletons are transformed between the maps. Therefore, wind influences, orientation imperfections of scans and data gaps can be overcome. The method is applied to a TLS dataset acquired in a forest in Germany. In total 34 scans were carried out within a managed forest to measure approximately 90 spruce trees with minimal occlusions. The results demonstrate the feasibility of the presented approach to extract tree models with a high completeness and correctness and provide an excellent input for further modeling applications.


title: Breast cancer diagnosis using Artificial Neural Network models

abstract: Breast cancer is the second leading cause of cancer deaths worldwide and occurrs in one out of eight women. In this paper we develop a system for diagnosis, prognosis and prediction of breast cancer using Artificial Neural Network (ANN) models. This will assist the doctors in diagnosis of the disease. We implement four models of neural networks namely Back Propagation Algorithm, Radial Basis Function Networks, Learning vector Quantization and Competitive Learning Network Experimental results show that Learning Vector Quantization shows the best performance in the testing data set This is followed in order by CL, MLP and RBFN The high accuracy of the LVQ against the other models indicates its better ability for solving the classificatory problem of Breast Cancer diagnosis.


title: A secure message authentication scheme for VANET using ECDSA

abstract: Mobile Ad Hoc Networks (MANETs) have several features, such as dynamic topologies, limited bandwidth usage, limited energy, etc... Vehicular ad hoc networks (VANETs) are a special case of mobile ad hoc networks. VANET communication can be formed between vehicles with vehicle to vehicle (V2V) communication or between Vehicle-to-Road Side Units (V2R). VANETs provides a promising network scenario for facilitating safety on roads, managing traffic, and infotainment dissemination for both passengers and drivers. The communication between V2V and V2R is the most critical part, so a proper security and privacy methods have to be maintained. Messages are critical and important like a warning message, so that the message must be authenticated which guarantee's the message integrity. Elliptic Curve Digital Signature Algorithm (ECDSA) provides an efficient message authentication scheme. A combination of P2P, VANET and ECDSA can make the scenario more efficient and perform better in terms of time delay in message delivery.


title: Evaluating the state of the art in coreference resolution for electronic medical records

abstract: Background The fifth i2b2/VA Workshop on Natural Language Processing Challenges for Clinical Records conducted a systematic review on resolution of noun phrase coreference in medical records. Informatics for Integrating Biology and the Bedside (i2b2) and the Veterans Affair (VA) Consortium for Healthcare Informatics Research (CHIR) partnered to organize the coreference challenge. They provided the research community with two corpora of medical records for the development and evaluation of the coreference resolution systems. These corpora contained various record types (ie, discharge summaries, pathology reports) from multiple institutions. ::: ::: Methods The coreference challenge provided the community with two annotated ground truth corpora and evaluated systems on coreference resolution in two ways: first, it evaluated systems for their ability to identify mentions of concepts and to link together those mentions. Second, it evaluated the ability of the systems to link together ground truth mentions that refer to the same entity. Twenty teams representing 29 organizations and nine countries participated in the coreference challenge. ::: ::: Results The teams' system submissions showed that machine-learning and rule-based approaches worked best when augmented with external knowledge sources and coreference clues extracted from document structure. The systems performed better in coreference resolution when provided with ground truth mentions. Overall, the systems struggled in solving coreference resolution for cases that required domain knowledge.


title: An Islanding Detection Method for Inverter-Based Distributed Generators Based on the Reactive Power Disturbance

abstract: In this paper, an islanding detection method for inverter-based distributed generators (DGs) is presented, which is based on perturbing reactive power output. Two sets of disturbances are configured in this method, which have different amplitudes and duration time. The first set of reactive power disturbance (FSORPD) is periodic with small amplitudes to break the reactive power balance during islanding, whereas the magnitude of the second set of reactive power disturbance (SSORPD) is sufficient to force the frequency to deviate outside its threshold limits. Considering all the possible frequency variation characteristics with the FSORPD after islanding, three criterions are designed for switching the disturbance from the FSORPD to the SSORPD. Since DGs located at different positions have the same frequency variation characteristics, the SSORPDs can be added on different DGs at the same time without the need of communication. Therefore, synchronization of the SSORPDs can be guaranteed for the system with multiple DGs and the method can detect islanding with a zero nondetection zone property. Moreover, the method can be applied to the DG either operating at unity power factor or supplying reactive power as well for its local load. According to the antiislanding test system recommended in IEEE Std.929-2000 and IEEE Std.1547-2003, the effectiveness of the method has been validated with several case studies in the power systems computer-aided design/Electro magnetic transient in DC system environment.


title: Improving late life depression and cognitive control through the use of therapeutic video game technology: A proof‐of‐concept randomized trial

abstract: Background ::: Existing treatments for depression are known to have only modest effects, are insufficiently targeted, and are inconsistently utilized, particularly in older adults. Indeed, older adults with impaired cognitive control networks tend to demonstrate poor response to a majority of existing depression interventions. Cognitive control interventions delivered using entertainment software have the potential to not only target the underlying cerebral dysfunction associated with depression, but to do so in a manner that is engaging and engenders adherence to treatment protocol. ::: ::: Methods ::: In this proof-of-concept trial (Clinicaltrials.gov #: NCT02229188), individuals with late life depression (LLD) (22; 60+ years old) were randomized to either problem solving therapy (PST, n = 10) or a neurobiologically inspired digital platform designed to enhance cognitive control faculties (Project: EVO™, n = 12). Given the overlapping functional neuroanatomy of mood disturbances and executive dysfunction, we explored the impact of an intervention targeting cognitive control abilities, functional disability, and mood in older adults suffering from LLD, and how those outcomes compare to a therapeutic gold standard. ::: ::: Results ::: EVO participants demonstrated similar improvements in mood and self-reported function after 4 weeks of treatment to PST participants. The EVO participants also showed generalization to untrained measures of working memory and attention, as well as negativity bias, a finding not evident in the PST condition. Individuals assigned to EVO demonstrated 100% adherence. ::: ::: Conclusions ::: This study provides preliminary findings that this therapeutic video game targeting cognitive control deficits may be an efficacious LLD intervention. Future research is needed to confirm these findings.


title: BIOLOGICAL PRETREATMENT OF LIGNOCELLULOSES WITH WHITE-ROT FUNGI AND ITS APPLICATIONS: A REVIEW

abstract: Lignocellulosic carbohydrates, i.e. cellulose and hemicellulose, have abundant potential as feedstock for production of biofuels and chemicals. However, these carbohydrates are generally infiltrated by lignin. Breakdown of the lignin barrier will alter lignocelluloses structures and make the carbohydrates accessible for more efficient bioconversion. White-rot fungi produce ligninolytic enzymes (lignin peroxidase, manganese peroxidase, and laccase) and efficiently mineralise lignin into CO2 and H2O. Biological pretreatment of lignocelluloses using white-rot fungi has been used for decades for ruminant feed, enzymatic hydrolysis, and biopulping. Application of white-rot fungi capabilities can offer environmentally friendly processes for utilising lignocelluloses over physical or chemical pretreatment. This paper reviews white-rot fungi, ligninolytic enzymes, the effect of biological pretreatment on biomass characteristics, and factors affecting biological pretreatment. Application of biological pretreatment for enzymatic hydrolysis, biofuels (bioethanol, biogas and pyrolysis), biopulping, biobleaching, animal feed, and enzymes production are also discussed.


title: Optimization of 6Slots-7Poles & 12Slots-14Poles flux-switching permanent magnet machines for plug-in HEV

abstract: Plug-in hybrid electric vehicle (PHEV) is independent of the internal combustion engine but acts as the auxiliary unit mostly on the electric drive system. Main while, the Flux-switching permanent magnet machine (FSPMM) has been studied in terms of structure with its operation principle been analyzed. To achieve a higher torque with a corresponding high power density and lower torque ripple, FSPMM with 6Slots-7Poles and 12Slots-14Poles, according investigations, are optimized based on their objective functions. Moreover, it analyzes several typical performance curves, such as cogging torque, flux linkage and back-EMF. Finally, the torque and corresponding power, efficiency and rotor mechanical strength analysis of optimized designed are obtained. The results indicate that FSPMM is a viable candidate for PHEV and also has good mechanical robustness with strong flux-weakening ability.


Training medical professionals on the concepts of essential drugs and rational drug use.


title: The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans.

abstract: PURPOSE ::: The development of computer-aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography (CT) scans. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process. ::: ::: ::: METHODS ::: Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories ("nodule > or =3 mm," "nodule <3 mm," and "non-nodule > or =3 mm"). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus. ::: ::: ::: RESULTS ::: The Database contains 7371 lesions marked "nodule" by at least one radiologist. 2669 of these lesions were marked "nodule > or =3 mm" by at least one radiologist, of which 928 (34.7%) received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings. ::: ::: ::: CONCLUSIONS ::: The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.


title: A comparison of the effects of problem‐based learning and lecturing on the development of students' critical thinking

abstract: Background Educational approaches are thought to have facilitative or hindering effects on students' critical thinking development. The aim of this study was to compare the effects of problem-based learning (PBL) and lecturing approaches on the development of students' critical thinking. ::: ::: Methods All 79 Year 1 undergraduate nursing students at a university in Hong Kong were randomly assigned to 1 of 2 parallel courses delivered by either PBL (n = 40) or lecturing (n = 39) over 1 academic year. The primary outcome measure was students' critical thinking disposition as measured by the California Critical Thinking Disposition Inventory (CCTDI). Individual interviews were also conducted to elicit the students' perceptions of their learning experience. Data were collected at 4 timepoints spanning 3 years. ::: ::: Results The overall CCTDI and subscale scores for the PBL group were not significantly different from those of the lecture group at the first timepoint (pretest). Compared with lecture students, PBL students showed significantly greater improvement in overall CCTDI (P = 0.0048), Truthseeking (P = 0.0008), Analyticity (P = 0.0368) and Critical Thinking Self-confidence (P = 0.0342) subscale scores from the first to the second timepoints; in overall CCTDI (P = 0.0083), Truthseeking (P = 0.0090) and Analyticity (P = 0.0354) subscale scores from the first to the third timepoints; and in Truthseeking (P = 0.0173) and Systematicity (P = 0.0440) subscale scores from the first to the fourth timepoints. ::: ::: Conclusions There were significant differences in the development of students' critical thinking dispositions between those who undertook the PBL and lecture courses, respectively.


title: Solar PV-Based Scalable DC Microgrid for Rural Electrification in Developing Regions

abstract: In this paper, we detail the design, analysis, and implementation of a highly distributed off-grid solar photovoltaic dc microgrid architecture suitable for rural electrification in developing countries. The proposed architecture is superior in comparison with existing architectures for rural electrification because of its 1) generation and storage scalability, 2) higher distribution efficiency (because of distributed generation and distributed storage for lower line losses), 3) ability to provide power for larger communal loads without the requirement for large, dedicated generation by extracting the benefit of usage diversity, and 4) localized control by using the hysteresis-based voltage droop method, thus eliminating the need for a central controller. The proposed microgrid architecture consists of several nanogrids capable of the self-sustained generation, storage, and bidirectional flow of power within the microgrid. Bidirectional power flow and distributed voltage droop control are implemented through the duty cycle control of a modified flyback converter. A detailed analysis in terms of power flow, loss, and system efficiency was conducted by using the Newton–Raphson method modified for dc power flow at varying distribution voltages, conductor sizes, and schemes of interconnection among the contributing nanogrids. A scaled-down version of the proposed architecture with various power sharing scenarios was also implemented on hardware, and yielded satisfactory results.


title: Motion-based grasp selection: Improving traditional control strategies of myoelectric hand prosthesis

abstract: This paper introduces a novel prosthetic hand control architecture using inertial information for grasp prediction in order to reduce the cognitive burden of amputees. A pair of inertial measurement sensors (IMUs) are fitted on the wrist and bicep to record arm trajectory when reaching to grasp an object. Each object class can be associated with different methods for grasping and manipulation. An observation experiment was conducted to find the most common grasping methods for generic object classes: Very Small (VS), Small (S), and Medium (M). A Cup (CP) class was also examined to find differences in grasping habits for pick and place, and drinking applications. The resulting grasps were used to test the discriminatory ability of inertial motion features in the upper limb for VS, S and CP object classes. Subject experiments demonstrated an average classification success rate of 90.8%, 69.2% and 88.1% for VS, S and CP classes respectively when using a k-nearest neighbors algorithm with a Euclidean distance metric. The results suggest that inertial motion features have great potential to predict the grasp pattern during reach, and to the authors' knowledge, is the first IMU-based control strategy to utilize natural motion that is aimed at hand prosthesis control.


title: Augmented Reality-Based Indoor Navigation Using Google Glass as a Wearable Head-Mounted Display

abstract: This research comprehensively illustrates the design, implementation and evaluation of a novel marker less environment tracking technology for an augmented reality based indoor navigation application, adapted to efficiently operate on a proprietary head-mounted display. Although the display device used, Google Glass, had certain pitfalls such as short battery life, slow processing speed, and lower quality visual display but the tracking technology was able to complement these limitations by rendering a very efficient, precise, and intuitive navigation experience. The performance assessments, conducted on the basis of efficiency and accuracy, substantiated the utility of the device for everyday navigation scenarios, whereas a later conducted subjective evaluation of handheld and wearable devices also corroborated the wearable as the preferred device for indoor navigation.


title: Current strategies for the treatment of intracranial atherosclerotic internal carotid artery stenosis

abstract: We evaluate the current literature regarding treatment of intracranial atherosclerotic disease, with particular emphasis on emerging medical, surgical, and endovascular trials. A MEDLINE and PubMed search of the literature related to intracranial atherosclerotic disease was performed. Articles which discussed medical, surgical, and endovascular therapies were reviewed. Currently, best medical management of intracranial atherosclerotic stenosis consists of aspirin therapy. Despite this, a significant percentage of patients with high-grade stenoses experience recurrent stroke soon after their initial event. Although previous randomized trials demonstrated no benefit to performing intracranial–extracranial bypass surgery in patients with intracranial atherosclerotic stenosis, current studies seem to suggest a benefit in a particular subset of these patients, specifically those with hemodynamic compromise. Endovascular therapy for intracranial atherosclerotic stenosis is in its infancy but is beginning to show technical promise. Nonetheless, real concerns exist regarding rates of in-stent restenosis, and large-scale follow-up studies must be completed before the true efficacy of endovascular therapies can be evaluated. Symptomatic intracranial atherosclerotic stenosis is associated with considerable morbidity and mortality. However, in a subgroup of patients, specifically those with high-grade stenoses and hemodynamic compromise, the risk of morbidity and mortality is significantly higher. It is this group of patients that would most benefit from more aggressive interventions, as best medical therapy still results in a significant rate of recurrent strokes. The emergence of improved surgical and endovascular techniques will be of particular importance in treating these high-risk patients.


title: Optimal Brain Surgeon: Extensions and performance comparisons

abstract: We extend Optimal Brain Surgeon (OBS) - a second-order method for pruning networks - to allow for general error measures, and explore a reduced computational and storage implementation via a dominant eigenspace decomposition. Simulations on nonlinear, noisy pattern classification problems reveal that OBS does lead to improved generalization, and performs favorably in comparison with Optimal Brain Damage (OBD). We find that the required retraining steps in OBD may lead to inferior generalization, a result that can be interpreted as due to injecting noise back into the system. A common technique is to stop training of a large network at the minimum validation error. We found that the test error could be reduced even further by means of OBS (but not OBD) pruning. Our results justify the t → 0 approximation used in OBS and indicate why retraining in a highly pruned network may lead to inferior performance.


title: Automatic colorization of grayscale images using multiple images on the web

abstract: Colorization is the process of adding color to monochrome images and video. It is used to increase the visual appeal of images such as old black and white photos, classic movies, and scientific visualizations. Since colorizing grayscale images involves assigning three-dimensional (RGB) pixel values to an image whose elements are characterized by one feature (luminance) only, the colorization problem does not have a unique solution. Hence, human interaction is typically required in the colorization process. Although existing colorization methods attempt to minimize the amount of user intervention, they require users to manually sellect a similar image to the target image or input a set of color seeds for different regions of the target image. In this paper, we present an entirely automatic colorization method using multiple images collected from the Web. The method generates various and natural colorized images from an input monochrome image by using the information of the scene structure.