title: Misco: a MapReduce framework for mobile systems

abstract: The proliferation of increasingly powerful, ubiquitous mobile devices has created a new and powerful sensing and computational environment. Software development and application deployment in such distributed mobile settings is especially challenging due to issues of failures, concurrency, and lack of easy programming models. We present a framework which provides a powerful software abstraction that hides many of such complexities from the application developer. We design and implement a mobile MapReduce framework targeted at any device which supports Python and network connectivity. We have implemented our system on a testbed of Nokia N95 8GB smartphones and demonstrated the feasibility and performance of our approach.


title: Imaging and laboratory testing in acute abdominal pain.

abstract: When discussing which laboratory tests or imaging to order in the setting of acute abdominal pain, it is practical to organize information by disease process (eg, acute appendicitis, cholecystitis). Because studies on the accuracy of diagnostic tests are of necessity related to the presence or absence of specific diagnoses, and because clinicians frequently look to tests to help them rule in or rule out specific conditions, this article is organized by region of pain and common abdominal diagnoses. It focuses on the contributions that laboratory testing and imaging make in the emergency management of abdominal complaints.


title: Group topic model: organizing topics into groups

abstract: Latent Dirichlet allocation defines hidden topics to capture latent semantics in text documents. However, it assumes that all the documents are represented by the same topics, resulting in the "forced topic" problem. To solve this problem, we developed a group latent Dirichlet allocation (GLDA). GLDA uses two kinds of topics: local topics and global topics. The highly related local topics are organized into groups to describe the local semantics, whereas the global topics are shared by all the documents to describe the background semantics. GLDA uses variational inference algorithms for both offline and online data. We evaluated the proposed model for topic modeling and document clustering. Our experimental results indicated that GLDA can achieve a competitive performance when compared with state-of-the-art approaches.


title: Intelligent monitoring of business processes using case-based reasoning

abstract: The work in this thesis presents an approach towards the effective monitoring of business processes using Case-Based Reasoning (CBR). The rationale behind this research was that business processes constitute a fundamental concept of the modern world and there is a constantly emerging need for their efficient control. They can be efficiently represented but not necessarily monitored and diagnosed effectively via an appropriate platform. ::: ::: Motivated by the above observation this research pursued to which extent there can be efficient monitoring, diagnosis and explanation of the workflows. Workflows and their effective representation in terms of CBR were investigated as well as how similarity measures among them could be established appropriately. The monitoring results and their following explanation to users were questioned as well as which should be an appropriate software architecture to allow monitoring of workflow executions. ::: ::: Throughout the progress of this research, several sets of experiments have been conducted using existing enterprise systems which are coordinated via a predefined workflow business process. Past data produced over several years have been used for the needs of the conducted experiments. Based on those the necessary knowledge repositories were built and used afterwards in order to evaluate the suggesting approach towards the effective monitoring and diagnosis of business processes. ::: ::: The produced results show to which extent a business process can be monitored and diagnosed effectively. The results also provide hints on possible changes that would maximize the accuracy of the actual monitoring, diagnosis and explanation. Moreover the presented approach can be generalised and expanded further to enterprise systems that have as common characteristics a possible workflow representation and the presence of uncertainty. ::: ::: Further work motivated by this thesis could investigate how the knowledge acquisition can be transferred over workflow systems and be of benefit to large-scale multidimensional enterprises. Additionally the temporal uncertainty could be investigated further, in an attempt to address it while reasoning. Finally the provenance of cases and their solutions could be explored further, identifying correlations with the process of reasoning.


A Multilevel Algorithm for Force-Directed Graph-Drawing


title: Beamwidth Control of Omnidirectional Antenna Using Conformal Frequency Selective Surface of Different Curvatures

abstract: Development and implementation of different curvatures of a conformal frequency selective surface (FSS) for achieving beam diversity in a dual-band monopole antenna are demonstrated. The FSS based on a flexible dielectric substrate (polymer) is reflective at 3.5 GHz. The curved FSS when incorporated in the antenna gives different beamwidths (3 dB) between 68°–130° for concave shape and 144°–210° for convex shape at 3.5 GHz. In both the cases, omnidirectional radiation from the antenna is maintained at 5.8 GHz. Later, a corner reflector based on the FSS instead of a metal, operating at 5.8 GHz, is integrated in the antenna along with the convex shaped curved FSS. The new antenna system exhibits a beamwidth of 190° with a peak gain of 6.8 dBi at 3.5 GHz and the beamwidth of 58° with a peak gain of 8.3 dBi at 5.8 GHz. The proposed method is useful for achieving beam diversity in multiband antennas. Simulation results from ANSYS HFSS and experimental measurements of the fabricated prototypes are provided.


title: Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference

abstract: We propose a simple and general variant of the standard reparameterized gradient estimator for the variational evidence lower bound. Specifically, we remove a part of the total derivative with respect to the variational parameters that corresponds to the score function. Removing this term produces an unbiased gradient estimator whose variance approaches zero as the approximate posterior approaches the exact posterior. We analyze the behavior of this gradient estimator theoretically and empirically, and generalize it to more complex variational distributions such as mixtures and importance-weighted posteriors.


title: Aspect-Based Sentiment Analysis Using Convolutional Neural Network and Bidirectional Long Short-Term Memory

abstract: In order to improve performance of previous aspect-based sentiment analysis (ABSA) on restaurant reviews in Indonesian language, this paper adapts the research achieving the highest F1 at SemEval 2016. We use feedforward neural network with one-vs-all strategy for aspect category classification (Slot 1), Conditional Random Field (CRF) for opinion target expression extraction (Slot 2), and Convolutional Neural Network (CNN) for sentiment polarity classification (Slot 3). Aside from lexical features we also use additional features learned from neural networks. We train our model on 992 sentences and evaluate them on 382 sentences. Higher performances are achieved for Slot 1 (F1 0.870) and Slot 3 (F1 0.764) but lower on Slot 2 (F1 0.787).


title: Theory of edge detection

abstract: A theory of edge detection is presented. The analysis proceeds in two parts. (1) Intensity changes, which occur in a natural image over a wide range of scales, are detected separately at different scales. An appropriate filter for this purpose at a given scale is found to be the second derivative of a Gaussian, and it is shown that, provided some simple conditions are satisfied, these primary filters need not be orientation-dependent. Thus, intensity changes at a given scale are best detected by finding the zero values of ∇ 2 G(x, y) * I(x, y) for image I, where G(x, y) is a two-dimensional Gaussian distribution and ∇ 2 is the Laplacian. The intensity changes thus discovered in each of the channels are then represented by oriented primitives called zero-crossing segments, and evidence is given that this representation is complete. (2) Intensity changes in images arise from surface discontinuities or from reflectance or illumination boundaries, and these all have the property that they are spatially localized. Because of this, the zero-crossing segments from the different channels are not independent, and rules are deduced for combining them into a description of the image. This description is called the raw primal sketch. The theory explains several basic psychophysical findings, and the operation of forming oriented zero-crossing segments from the output of centre-surround ∇ 2 G filters acting on the image forms the basis for a physiological model of simple cells (see Marr & Ullman 1979).


title: Deep learning for sentiment analysis

abstract: Copyright © 2016 John Wiley & Sons, Ltd. Research and industry are becoming more and more interested in finding automatically the polarised opinion of the general public regarding a specific subject. The advent of social networks has opened the possibility of having access to massive blogs, recommendations, and reviews. The challenge is to extract the polarity from these data, which is a task of opinion mining or sentiment analysis. The specific difficulties inherent in this task include issues related to subjective interpretation and linguistic phenomena that affect the polarity of words. Recently, deep learning has become a popular method of addressing this task. However, different approaches have been proposed in the literature. This article provides an overview of deep learning for sentiment analysis in order to place these approaches in context.


Rheumatoid arthritis: HDAC and HDACi: pathogenetic and mechanistic insights


title: Interactive holographic application using augmented reality EduCard and 3D holographic pyramid for interactive and immersive learning

abstract: The advancement of augmented reality (AR) and holographic display technologies have a great potential to support and improve teaching and learning process, because the 3D images give new perspectives to the students to understand certain topic easily and intuitively. In this paper, an Interactive Holographic Display is introduced, which is aimed to ease the teachers to deliver the knowledge to the students as well as to provide self-learning for the students. This proposed solution is implemented using the AR tracking technique and fused with 3D holographic pyramid display. This makes the virtual objects can display in thin air like a real object and makes the holographic effect more realistic and interactive, as the user can interact with the virtual objects using an image target. This paper explains how the system is physically realized in term of hardware configuration and software design.


title: Review of Energy Storage System Technologies in Microgrid Applications: Issues and Challenges

abstract: A microgrid (MG) is a local entity that consists of distributed energy resources (DERs) to achieve local power reliability and sustainable energy utilization. The MG concept or renewable energy technologies integrated with energy storage systems (ESS) have gained increasing interest and popularity because it can store energy at off-peak hours and supply energy at peak hours. However, existing ESS technology faces challenges in storing energy due to various issues, such as charging/discharging, safety, reliability, size, cost, life cycle, and overall management. Thus, an advanced ESS is required with regard to capacity, protection, control interface, energy management, and characteristics to enhance the performance of ESS in MG applications. This paper comprehensively reviews the types of ESS technologies, ESS structures along with their configurations, classifications, features, energy conversion, and evaluation process. Moreover, details on the advantages and disadvantages of ESS in MG applications have been analyzed based on the process of energy formations, material selection, power transfer mechanism, capacity, efficiency, and cycle period. Existing reviews critically demonstrate the current technologies for ESS in MG applications. However, the optimum management of ESSs for efficient MG operation remains a challenge in modern power system networks. This review also highlights the key factors, issues, and challenges with possible recommendations for the further development of ESS in future MG applications. All the highlighted insights of this review significantly contribute to the increasing effort toward the development of a cost-effective and efficient ESS model with a prolonged life cycle for sustainable MG implementation.


title: Unilateral gestational macromastia--a rare disorder.

abstract: Macromastia is the massive enlargement of the breast, unilateral or bilateral, disproportionate to the growth of the rest of the body. It is called gravid macromastia or gigantomastia of pregnancy when it occurs during pregnancy. It may or may not regress following parturition. Gestational macromastia is exceptionally rare. We report a 28-year-old female with gigantomastia of the left breast. She presented at four months post-partum with painful massive enlargement of the left breast since the third month of pregnancy. The overlying skin was stretched out and showed multiple ulcers with foul smelling discharge. The nipple and areola were unremarkable. Simple mastectomy was done, as fine needle aspiration cytology was suggestive of phylloides tumour. The breast specimen measured 30 x 30 x 9 cm and was replaced totally by grey-white tissue involving all the resection margins. No normal breast tissue or fat was identified. Histopathology showed periductal as well as diffuse fibrosis, adenosis and lactational changes. No features of phylloides tumour or carcinoma were present and it was diagnosed as unilateral gestational macromastia.


title: Detecting Deceptive Behavior via Integration of Discriminative Features From Multiple Modalities

abstract: Deception detection has received an increasing amount of attention in recent years, due to the significant growth of digital media, as well as increased ethical and security concerns. Earlier approaches to deception detection were mainly focused on law enforcement applications and relied on polygraph tests, which had proved to falsely accuse the innocent and free the guilty in multiple cases. In this paper, we explore a multimodal deception detection approach that relies on a novel data set of 149 multimodal recordings, and integrates multiple physiological, linguistic, and thermal features. We test the system on different domains, to measure its effectiveness and determine its limitations. We also perform feature analysis using a decision tree model, to gain insights into the features that are most effective in detecting deceit. Our experimental results indicate that our multimodal approach is a promising step toward creating a feasible, non-invasive, and fully automated deception detection system.


title: Effect of Lateral Vibrations during Directional solidification on Mechanical Properties of Al-18%wt Si Alloys

abstract: Abstract Experimental Investigation on the effect of mold vibration on the mechanical properties of Al-18%WtSi have been carried out using Taguchi Technique, as the molten metal at 750°C is poured in to the insulated mold which is subjected to lateral vibrations, directional solidification takes place due to the presence of chill at the bottom which gives rise to a functionally graded alloy. Due to the excitation, silicon present in the molten metal precipitates along its length. The castings are prepared with varying chill materials and chill volumes without vibrations and with a vibrating frequency of 50Hz. ANOVA was applied to optimize the process parameters. Hardness and Tensile test for the samples was carried out. Improvements in the properties can be attributed to the grain refinement of the casting prepared under vibrations.


title: Coresets in dynamic geometric data streams

abstract: A dynamic geometric data stream consists of a sequence of m insert/delete operations of points from the discrete space 1,…,Δd [26]. We develop streaming (1 + e)-approximation algorithms for k-median, k-means, MaxCut, maximum weighted matching (MaxWM), maximum travelling salesperson (MaxTSP), maximum spanning tree (MaxST), and average distance over dynamic geometric data streams. Our algorithms maintain a small weighted set of points(a coreset) that approximates with probability 2/3 the current point set with respect to the considered problem during the m insert/delete operations of the data stream. They use poly (e-1, log m, log Δ) space and update time per insert/delete operation for constant k and dimension dHaving a coreset one only needs a fast approximation algorithm for the weighted problem to compute a solution quickly. In fact, even an exponential algorithm is sometimes feasible as its running time may still be polynomial in n. For example one can compute in poly(log n, exp(O((1+log (1⁄e)⁄e)d-1))) time a solution to k-median and k-means [21] where n is the size of the current point set and k and d are constants. Finding an implicit solution to MaxCut can be done in poly(log n, exp((1⁄e)O(1))) time. For MaxST and average distance we require poly(log n, e-1) time and for MaxWM we require O(n3) time to do this.


title: Supercritical Fluid Extraction of Plant Flavors and Fragrances

abstract: Supercritical fluid extraction (SFE) of plant material with solvents like CO2, propane, butane, or ethylene is a topic of growing interest. SFE allows the processing of plant material at low temperatures, hence limiting thermal degradation, and avoids the use of toxic solvents. Although today SFE is mainly used for decaffeination of coffee and tea as well as production of hop extracts on a large scale, there is also a growing interest in this extraction method for other industrial applications operating at different scales. In this review we update the literature data on SFE technology, with particular reference to flavors and fragrance, by comparing traditional extraction techniques of some industrial medicinal and aromatic crops with SFE. Moreover, we describe the biological activity of SFE extracts by describing their insecticidal, acaricidal, antimycotic, antimicrobial, cytotoxic and antioxidant properties. Finally, we discuss the process modelling, mass-transfer mechanisms, kinetics parameters and thermodynamic by giving an overview of SFE potential in the flavors and fragrances arena.


title: Assessing human mirror activity with EEG mu rhythm: A meta-analysis.

abstract: A fundamental issue in cognitive neuroscience is how the brain encodes others’ actions and intentions. In recent years, a potential advance in our knowledge on this issue is the discovery of mirror neurons in the motor cortex of the nonhuman primate. These neurons fire to both execution and observation of specific types of actions. Researchers use this evidence to fuel investigations of a human mirror system, suggesting a common neural code for perceptual and motor processes. Among the methods used for inferring mirror system activity in humans are changes in a particular frequency band in the electroencephalogram (EEG) called the mu rhythm. Mu frequency appears to decrease in amplitude (reflecting cortical activity) during both action execution and action observation. The current meta-analysis reviewed 85 studies (1,707 participants) of mu that infer human mirror system activity. Results demonstrated significant effect sizes for mu during execution (Cohen’s d = 0.46, N = 701) as well as observation of action (Cohen’s d = 0.31, N = 1,508), confirming a mirroring property in the EEG. A number of moderators were examined to determine the specificity of these effects. We frame these meta-analytic findings within the current discussion about the development and functions of a human mirror system, and conclude that changes in EEG mu activity provide a valid means for the study of human neural mirroring. Suggestions for improving the experimental and methodological approaches in using mu to study the human mirror system are offered.


title: Investigating the Association Between University Teachers’ Professional Autonomy and Their Innovation Performance

abstract: The purpose of this paper is to investigate both - theoretical and practical aspects in association between foreign language teachers’ professional autonomy and their innovation performance under the current educational circumstances. The findings demonstrate that the development of innovative university teachers is possible in the framework of the autonomy-oriented approach that gets the teachers to be involved in intensive analysis and project activity, based on an analysis and a review of teachers’ personal learning and professional experience in a wide range of educational context. Keywords: teacher’s development, professional autonomy, creativity, innovation performance; autonomy-oriented approach, foreign language teaching. DOI: https://doi.org/10.15823/p.2019.133.7


title: Dual Grid Array Antennas in a Thin-Profile Package for Flip-Chip Interconnection to Highly Integrated 60-GHz Radios

abstract: We examine the current development of highly integrated 60-GHz radios with an interest in antenna-circuit interfaces. We design and analyze grid array antennas with special attention to the differential feeding and the patterned ground plane. More importantly, we integrate two grid array antennas in a package; propose the way of assembling it to the system printed circuit board; and demonstrate a total solution of low cost and thin profile to highly integrated 60-GHz radios. We show that the package in low temperature cofired ceramic (LTCC) technology measures only 13×13×0.575 mm3 ; can carry a 60-GHz radio die of current and future sizes with flip-chip bonding; and achieves good antenna performance in the 60-GHz band with maximum gain of 13.5 and 14.5 dBi for the single-ended and differential antennas, respectively.


title: Implementation and control of the Velvet Fingers: A dexterous gripper with active surfaces

abstract: The design of grasping and manipulation systems is one of the most investigated topics in recent robotic and automation engineering. It is a process that has to take into account many development possibilities and to face different trade offs, as that between application possibilities and design complexity. In this work we present the design of a novel end-effector that merges the essential mechanics and control simplicity of underactuated devices, together with the high levels of manipulability usually featured in dexterous robotic hands. To obtain this enhancement, the proposed gripper considers the possibility offered by active surfaces, i.e. engineered contact surfaces able to simulate different levels of friction and to apply tangential thrust to the contacted object. The actual dexterity enhancement is evaluated by an analytical manipulability analysis and some examples of in hand manipulations and grasps are taken into account. A mechanical solution is presented, which implements the proposed idea through the adoption of one DoF active surfaces mounted on the fingers. The proposed solution presents a manipulability index one order of magnitude higher than common grippers.


title: On the Cache Behavior of SPLASH-2 Benchmarks on ARM and ALPHA Processors in Gem5 Full System Simulator

abstract: Today cache size and hierarchy level of caches play an important role in improving computer performance. By using full system simulations of gem5, the variation in memory bandwidth, system bus throughput, L1 and L2 cache size misses are measured by running SPLASH-2 Benchmarks on ARM and ALPHA Processors. In this work we calculate cache misses, memory bandwidth and system bus throughput by running SPLASH2 benchmarks on gem5 Full System Mode. Our results show that L1 cache misses decrease as L1 cache size is varied from 16KB to 64KB. L1 cache misses are independent of L2 cache size after the program data resides in L2 cache. The memory bandwidth and system bus throughput decreases as L1 and L2 cache size increases.


title: Twitter is Faster: Personalized Time-Aware Video Recommendation from Twitter to YouTube

abstract: Traditional personalized video recommendation methods focus on utilizing user profile or user history behaviors to model user interests, which follows a static strategy and fails to capture the swift shift of the short-term interests of users. According to our cross-platform data analysis, the information emergence and propagation is faster in social textual stream-based platforms than that in multimedia sharing platforms at micro user level. Inspired by this, we propose a dynamic user modeling strategy to tackle personalized video recommendation issues in the multimedia sharing platform YouTube, by transferring knowledge from the social textual stream-based platform Twitter. In particular, the cross-platform video recommendation strategy is divided into two steps. (1) Real-time hot topic detection: the hot topics that users are currently following are extracted from users' tweets, which are utilized to obtain the related videos in YouTube. (2) Time-aware video recommendation: for the target user in YouTube, the obtained videos are ranked by considering the user profile in YouTube, time factor, and quality factor to generate the final recommendation list. In this way, the short-term (hot topics) and long-term (user profile) interests of users are jointly considered. Carefully designed experiments have demonstrated the advantages of the proposed method.


title: A 40-to-56 Gb/s PAM-4 Receiver With Ten-Tap Direct Decision-Feedback Equalization in 16-nm FinFET

abstract: A 40–56 Gb/s PAM-4 receiver with ten-tap decision-feedback equalization (DFE) targeting chip-to-module and board-to-board cable interconnects is designed in 16-nm FinFET. The design implements direct feedback of the first post-cursor (h1) DFE tap to reduce the number of slicers. The h1 feedback signals are directly tapped from the master latch output of the StrongArm-based slicers. A CMOS amplifier with delayed pre-charge release is used to boost and pre-condition the h1 feedback signals before being applied to current-mode logic tap cell for optimum DFE summer settling time. The receiver achieves less than 1E-12 PRBS31 bit error rate (BER) over a channel with 10-dB loss at 14-GHz consuming 230 mW. Fully adapted by off-chip software, the receiver performance demonstrates the effectiveness of direct h1 loop and the need for higher DFE taps to achieve a required BER over channels with reflections. Receiver performance over higher loss channels up to 23 dB and/or under emulated cross-talk noise injection cases are also presented.


title: Introduction to the CoNLL-2002 Shared Task: Language-Independent Named Entity Recognition

abstract: We describe the CoNLL-2002 shared task: language-independent named entity recognition. We give background information on the data sets and the evaluation method, present a general overview of the systems that have taken part in the task and discuss their performance.


title: Feedback control of FSH secretion in the male rat.

abstract: Site of feedback control of FSH secretion in the male rat was studied by measuring changes in serum LH, FSH and hypothalamic LH-RH by radioimmunoassay in rats after castration and after 500 rad X-irradiation to the testis. The rise in serum LH and FSH in castrated animals was associated with a significant fall in hypothalamic LH-RH 16 and 24 days after castration. Serum FSH rose significantly after X-irradiation without a significant change in serum LH or hypothalamic LH-RH content up to 30 days after irradiation. When pituitary halves from X-irradiated animals were incubated in vitro in the presence or absence of synthetic LH-RH, there was a significant rise in FSH (but not LH) released in the incubation medium in the absence of added LH-RH. The response of the pituitaries to LH-RH was, however, not different between control and irradiated rats. It is concluded that the testicular FSH-inhibitory substance acts predominantly at the pituitary gland on the LH-RH independent release of FSH.


title: Design of permutation-based sparse code multiple access system

abstract: We consider the problem of designing a sparse code multiple access (SCMA) codebook that is based on permutation. Conventional SCMA is a multidimensional codebook-based non-orthogonal spreading technique, in which each layer of incoming bits are directly mapped to multidimensional codewords which have nonzero entries on the same resources. In this paper, unlike the traditional SCMA scheme, the data bits to be transmitted determine the nonzero entry locations based on the permutation. The proposed SCMA codebooks are converted to a multiuser code-division multiple-access (CDMA) detection system before applying an iterative decoder. The complexity of the proposed iterative decoder is lower in comparison to exponential message passing algorithm (MPA) and outperforms in terms of bit-error-rate (BER).


title: A ±36-A Integrated Current-Sensing System With a 0.3% Gain Error and a 400- $\mu \text{A}$ Offset From −55 °C to +85 °C

abstract: This paper presents an integrated shunt-based current-sensing system (CSS) capable of handling ±36-A currents, the highest ever reported. It also achieves a 0.3% gain error and a 400- $\mu \text{A}$ offset, which is significantly better than the state-of-the-art systems. The heart of the system is a robust 260- $\mu \Omega $ shunt resistor made from the lead frame of a standard HVQFN plastic package. The resulting voltage drop is then digitized by a precision $\Delta \Sigma $ ADC and a bandgap reference (BGR). At the expense of current handling capability, a ±5-A version of the CSS uses a 10- $\text{m}\Omega $ on-chip metal shunt to achieve just a 4- $\mu \text{A}$ offset. Both designs are realized in a standard 0.13- $\mu \text{m}$ CMOS process and draw 13 $\mu \text{A}$ from a 1.5-V supply. Compensation of the spread and nonlinear temperature dependency of the shunt resistor $R_{\mathrm {shunt}}$ is accomplished by the use of a fixed polynomial master curve and a single room temperature calibration. This procedure also effectively compensates for the residual spread and nonlinearity of the ADC and the BGR.


title: Medical diagnosis system using fuzzy logic toolbox

abstract: Medical diagnosis is basically a pattern classification phenomena: based on some input provided by a patient, an expert gives a conclusion on the basis of its knowledge, which is normally stored in a binary form, and finally the result is calculated i.e. either the patient suffering from a certain disease or not. There are a number of properties in fuzzy set theory has a number of facilities that make it suitable for medical diagnosis. The results and findings from the study had shown that the technique of fuzzy logic can contribute a reliable result in order to notify the disease. Human subject has been used to test the system. In this paper we have proposed a system for medical diagnosis which is a generated using Fuzzy Logic Toolbox in MATLAB. Specifically, it focuses on medical diagnosis.


title: Collision-Free Accumulators and Fail-Stop Signature Schemes Without Trees

abstract: One-way accumulators, introduced by Benaloh and de Mare, can be used to accumulate a large number of values into a single one, which can then be used to authenticate every input value without the need to transmit the others. However, the one-way property does is not sufficient for all applications. ::: ::: In this paper, we generalize the definition of accumulators and define and construct a collision-free subtype. As an application, we construct a fail-stop signature scheme in which many one-time public keys are accumulated into one short public key. In contrast to previous constructions with tree authentication, the length of both this public key and the signatures can be independent of the number of messages that can be signed.


title: An accelerated gradient method for trace norm minimization

abstract: We consider the minimization of a smooth loss function regularized by the trace norm of the matrix variable. Such formulation finds applications in many machine learning tasks including multi-task learning, matrix classification, and matrix completion. The standard semidefinite programming formulation for this problem is computationally expensive. In addition, due to the non-smooth nature of the trace norm, the optimal first-order black-box method for solving such class of problems converges as O(1/√k), where k is the iteration counter. In this paper, we exploit the special structure of the trace norm, based on which we propose an extended gradient algorithm that converges as O(1/k). We further propose an accelerated gradient algorithm, which achieves the optimal convergence rate of O(1/k2) for smooth problems. Experiments on multi-task learning problems demonstrate the efficiency of the proposed algorithms.


title: One-day wave forecasts using buoy data and artificial neural networks

abstract: The forecasting of wave heights is an essential prerequisite for planning, operation, and maintenance works associated with offshore engineering, navigation, and other activities. The requisite wave height information is currently derived from numerical models which use predicted wind information based on wind-wave relationships. These models sometimes cannot be applied easily at remote locations or on small geographic scales where forcing functions (wind-fields and open ocean boundary conditions) may be unavailable. This paper attempts to forecast waves based on observed wave data only, using the data-driven artificial neural network (ANN) approach. Wave heights with varying lead times of 6 to 24 hours are predicted at five different NDBC buoy locations around the US, viz. two near Alaska (in Prince William Sound), two off the northeast coast (in the Gulf of Maine) and one in the northern Gulf in Mexico (south of Galveston, Texas). Three-layered feed-forward back-propagation networks were used along with the conjugate gradient algorithm. The results show that ANN models perform extremely well at all five locations for the 6 to 12 hour predictions and moderately well for 18 to 24 hours predictions. Online models were also developed using these trained networks and run daily for a period of two months (April-May 2005) to forecast the next day's wave heights. The results of these models also follow the same level of accuracy achieved in testing these networks as mentioned earlier. When large grid-scale modeling is not possible, buoys that measure wave heights provide the requisite wave information. The present work can be viewed as an attempt to enhance the value of buoy data by providing a forecast. This modeling approach will provide a useful tool for forecasting or supplement wave data especially for locations where buoys have been recently installed or where established wave models are difficult to use due to area limitations.


title: Online recurrent extreme learning machine and its application to time-series prediction

abstract: Online sequential extreme learning machine (OS-ELM) is an online learning algorithm training single-hidden layer feedforward neural networks (SLFNs), which can learn data one-by-one or chunk-by-chunk with fixed or varying data size. Due to its characteristics of online sequential learning, OS-ELM is popularly used to solve time-series prediction problem, such as stock forecast, weather forecast, passenger count forecast, etc. OS-ELM, however, has two fatal drawbacks: Its input weights cannot be adjusted and it cannot be applied to learn recurrent neural network (RNN). Therefore we propose a modified version of OS-ELM, called online recurrent extreme learning machine (OR-ELM), which is able to adjust input weights and can be applied to learn RNN, by applying ELM-auto-encoder and a normalization method called layer normalization (LN). Proposed method is used to solve a time-series prediction problem on NewYork City passenger count dataset, and the results show that R-ELM outperforms OS-ELM and other online-sequential learning algorithms such as hierarchical temporal memory (HTM) and online long short-term memory (online LSTM).


title: Scaffold Library for Tissue Engineering: A Geometric Evaluation

abstract: Tissue engineering scaffold is a biological substitute that aims to restore, to maintain, or to improve tissue functions. Currently available manufacturing technology, that is, additive manufacturing is essentially applied to fabricate the scaffold according to the predefined computer aided design (CAD) model. To develop scaffold CAD libraries, the polyhedrons could be used in the scaffold libraries development. In this present study, one hundred and nineteen polyhedron models were evaluated according to the established criteria. The proposed criteria included considerations on geometry, manufacturing feasibility, and mechanical strength of these polyhedrons. CAD and finite element (FE) method were employed as tools in evaluation. The result of evaluation revealed that the close-cellular scaffold included truncated octahedron, rhombicuboctahedron, and rhombitruncated cuboctahedron. In addition, the suitable polyhedrons for using as open-cellular scaffold libraries included hexahedron, truncated octahedron, truncated hexahedron, cuboctahedron, rhombicuboctahedron, and rhombitruncated cuboctahedron. However, not all pore size to beam thickness ratios (PO : BT) were good for making the open-cellular scaffold. The PO : BT ratio of each library, generating the enclosed pore inside the scaffold, was excluded to avoid the impossibility of material removal after the fabrication. The close-cellular libraries presented the constant porosity which is irrespective to the different pore sizes. The relationship between PO : BT ratio and porosity of open-cellular scaffold libraries was displayed in the form of Logistic Power function. The possibility of merging two different types of libraries to produce the composite structure was geometrically evaluated in terms of the intersection index and was mechanically evaluated by means of FE analysis to observe the stress level. The couples of polyhedrons presenting low intersection index and high stress level were excluded. Good couples for producing the reinforced scaffold were hexahedron-truncated hexahedron and cuboctahedron-rhombitruncated cuboctahedron.


Advantages of ACT-R over Prolog for Natural Language Analysis


title: Personal Iris Recognition Using Neural Network

abstract: Iris recognition is one of important biometric recognition approach in a human identification is becoming very active topic in research and practical application. Iris recognition system consists of localization of the iris region and generation of data set of iris images followed by iris pattern recognition. In this paper, a fast algorithm is proposed for the localization of the inner and outer boundaries of the iris region. Located iris is extracted from an eye image, and, after normalization and enhancement, it is represented by a data set. Using this data set a Neural Network (NN) is used for the classification of iris patterns. The adaptive learning strategy is applied for training of the NN. The results of simulations illustrate the effectiveness of the neural system in personal identification.


title: A Novel Economic Sharing Model in a Federation of Selfish Cloud Providers

abstract: This paper presents a novel economic model to regulate capacity sharing in a federation of hybrid cloud providers (CPs). The proposed work models the interactions among the CPs as a repeated game among selfish players that aim at maximizing their profit by selling their unused capacity in the spot market but are uncertain of future workload fluctuations. The proposed work first establishes that the uncertainty in future revenue can act as a participation incentive to sharing in the repeated game. We, then, demonstrate how an efficient sharing strategy can be obtained via solving a simple dynamic programming problem. The obtained strategy is a simple update rule that depends only on the current workloads and a single variable summarizing past interactions. In contrast to existing approaches, the model incorporates historical and expected future revenue as part of the virtual machine (VM) sharing decision. Moreover, these decisions are not enforced neither by a centralized broker nor by predefined agreements. Rather, the proposed model employs a simple grim trigger strategy where a CP is threatened by the elimination of future VM hosting by other CPs. Simulation results demonstrate the performance of the proposed model in terms of the increased profit and the reduction in the variance in the spot market VM availability and prices.


title: Manifold bootstrapping for SVBRDF capture

abstract: Manifold bootstrapping is a new method for data-driven modeling of real-world, spatially-varying reflectance, based on the idea that reflectance over a given material sample forms a low-dimensional manifold. It provides a high-resolution result in both the spatial and angular domains by decomposing reflectance measurement into two lower-dimensional phases. The first acquires representatives of high angular dimension but sampled sparsely over the surface, while the second acquires keys of low angular dimension but sampled densely over the surface. ::: We develop a hand-held, high-speed BRDF capturing device for phase one measurements. A condenser-based optical setup collects a dense hemisphere of rays emanating from a single point on the target sample as it is manually scanned over it, yielding 10 BRDF point measurements per second. Lighting directions from 6 LEDs are applied at each measurement; these are amplified to a full 4D BRDF using the general (NDF-tabulated) microfacet model. The second phase captures N=20-200 images of the entire sample from a fixed view and lit by a varying area source. We show that the resulting N-dimensional keys capture much of the distance information in the original BRDF space, so that they effectively discriminate among representatives, though they lack sufficient angular detail to reconstruct the SVBRDF by themselves. At each surface position, a local linear combination of a small number of neighboring representatives is computed to match each key, yielding a high-resolution SVBRDF. A quick capture session (10-20 minutes) on simple devices yields results showing sharp and anisotropic specularity and rich spatial detail.


title: Colorgorical: Creating discriminable and preferable color palettes for information visualization

abstract: We present an evaluation of Colorgorical, a web-based tool for creating discriminable and aesthetically preferable categorical color palettes. Colorgorical uses iterative semi-random sampling to pick colors from CIELAB space based on user-defined discriminability and preference importances. Colors are selected by assigning each a weighted sum score that applies the user-defined importances to Perceptual Distance, Name Difference, Name Uniqueness, and Pair Preference scoring functions, which compare a potential sample to already-picked palette colors. After, a color is added to the palette by randomly sampling from the highest scoring palettes. Users can also specify hue ranges or build off their own starting palettes. This procedure differs from previous approaches that do not allow customization (e.g., pre-made ColorBrewer palettes) or do not consider visualization design constraints (e.g., Adobe Color and ACE). In a Palette Score Evaluation, we verified that each scoring function measured different color information. Experiment 1 demonstrated that slider manipulation generates palettes that are consistent with the expected balance of discriminability and aesthetic preference for 3-, 5-, and 8-color palettes, and also shows that the number of colors may change the effectiveness of pair-based discriminability and preference scores. For instance, if the Pair Preference slider were upweighted, users would judge the palettes as more preferable on average. Experiment 2 compared Colorgorical palettes to benchmark palettes (ColorBrewer, Microsoft, Tableau, Random). Colorgorical palettes are as discriminable and are at least as preferable or more preferable than the alternative palette sets. In sum, Colorgorical allows users to make customized color palettes that are, on average, as effective as current industry standards by balancing the importance of discriminability and aesthetic preference.


title: Energy-Efficient Power Control: A Look at 5G Wireless Technologies

abstract: This paper develops power control algorithms for energy efficiency (EE) maximization (measured in bit/Joule) in wireless networks. Unlike previous related works, minimum-rate constraints are imposed and the signal-to-interference-plus-noise ratio takes a more general expression, which allows one to encompass some of the most promising 5G candidate technologies. Both network-centric and user-centric EE maximizations are considered. In the network-centric scenario, the maximization of the global EE and the minimum EE of the network is performed. Unlike previous contributions, we develop centralized algorithms that are guaranteed to converge, with affordable computational complexity, to a Karush–Kuhn–Tucker point of the considered non-convex optimization problems. Moreover, closed-form feasibility conditions are derived. In the user-centric scenario, game theory is used to study the equilibria of the network and to derive convergent power control algorithms, which can be implemented in a fully decentralized fashion. Both scenarios above are studied under the assumption that single or multiple resource blocks are employed for data transmission. Numerical results assess the performance of the proposed solutions, analyzing the impact of minimum-rate constraints, and comparing the network-centric and user-centric approaches.


title: Cash Flows: Another Approach to Ratio Analysis

abstract: Listed here are the steps required to tap the pension market for a new and profitable consulting business. CPA firms frequently field questions from their small business clients about qualified retirement plans. Just as frequently, after getting that professional guidance the clients turn to a non-CPA firm to administer those plans. That's a major opportunity loss. With the right kind of planning, such business could be retained and made into a profitable specialty of the firm. Many CPA firms are in a unique position to provide administration services for qualified plans to their clients. After all, they are familiar with clients' financial history and goals. If they fail to capture this business, they may lose yet another opportunity: to expand the relationship by providing personal financial planning services to business owners. This article provides practical guidance for CPA firms considering beginning or expanding retirement plan administration practice. Before a firm decides to undertake such a highly technical practice, its managing partners should determine the potential market and the scope of services that could be offered. The assessment should include a profile of clients that are most likely to require the services and a marketing study to determine other potential clients and the CPA firms that already are providing the services. In assessing the competition, the firm's partners should not dwell on competitors' style or services with a mind to emulating them. Firms starting up in this specialty usually discover their range of services is best selected by determining their own business goals and the needs of targeted clients. If the partners decide to add this practice area, they should examine all plan administration services and determine which they believe will complement the firm's other services. Then they must assess the size and experience of the firm's staff and the gap between their current abilities and the resources the firm will need to undertake the work--including the time, effort and money it is willing to expend to develop those resources. A FULL-SERVICE PRACTICE One option, and it's the most expensive one, is to offer all plan administration services for all types of qualified retirement programs in what amounts to a "turnkey" service--from design consulting through plan-termination services. Partners should be aware, however, that the range of qualified retirement programs and service levels is extensive, requiring a significant amount of resources. Turnkey administration services include * Design-feasibility analyses. This is typically the initial phase for services in the employee benefit practice. It requires some expertise in all types of qualified retirement plan programs. The staff must be able to illustrate the cost of a proposed program (in terms of cash flow, executive and nonexecutive benefits and projected tax savings) and other advantages and disadvantages of a recommended benefit program. * Implementation. This service involves providing the client with the many documents it needs to begin the plan, which includes plan descriptions, administrative manuals, employee notices, Internal Revenue Service requests for a determination letter, salary reduction agreements, beneficiary designations, survivor annuity waiver forms, loan policies, etc. In addition, hybrid plans may require the assistance of an attorney in drafting some documents. The firm also must consider how it will assist clients in adopting programs that it recommends. * Annual administration. The type of qualified plan will dictate the annual services the practice will need to develop internally. Annual services can include recordkeeping; contribution calculations; Financial Accounting Standards Board financial reporting calculations; summary annual reports; allocation of contributions; minimum funding calculations; top-heavy calculations; trust accounting; discrimination testing; benefit certificates; reporting and disclosure to the IRS, Department of Labor and participants; etc. …


title: On stigma and its consequences: evidence from a longitudinal study of men with dual diagnoses of mental illness and substance abuse.

abstract: Numerous studies have demonstrated a strong connection between the experience of stigma and the well-being of the stigmatized. But in the area of mental illness there has been controversy surrounding the magnitude and duration ofthe effects of labeling and stigma. One of the arguments that has been used to downplay the importance of these factors is the substantial body of evidence suggesting that labeling leads to positive effects through mental health treatment. However, as Rosenfield (1997) points out, labeling can simultaneously induce both positive consequences through treatment and negative consequences through stigma. In this study we test whether stigma has enduring effects on well-being by interviewing 84 men with dual diagnoses of mental disorder and substance abuse at two points in time-at entry into treatment, when they were addicted to drugs and had many psychiatric symptoms and then again after a year of treatment, when they were far less symptomatic and largely drug- and alcohol-free. We found a relatively strong and enduring effect of stigma on well-being. This finding indicates that stigma continues to complicate the lives of the stigmatized even as treatment improves their symptoms and functioning. It follows that if health professionals want to maximize the well-being of the people they treat, they must address stigma as a separate and important factor in its own right.


title: Foundations for engineering biology

abstract: Engineered biological systems have been used to manipulate information, construct materials, process chemicals, produce energy, provide food, and help maintain or enhance human health and our environment. Unfortunately, our ability to quickly and reliably engineer biological systems that behave as expected remains quite limited. Foundational technologies that make routine the engineering of biology are needed. Vibrant, open research communities and strategic leadership are necessary to ensure that the development and application of biological technologies remains overwhelmingly constructive.


title: Applications of Hidden Markov Chains in Image Analysis

abstract: In image analysis, two-dimensional Markov models, i.e. Markov field models, have been applied for segmentation purposes, but except for the area of text recognition, the application of hidden Markov chains has been rare. Through four very different examples, this paper demonstrates the applicability also for hidden Markov chains in image analysis, and shows that the problems of image analysis often may have one- dimensional characteristics even though the images are two-dimensional.


title: Resurrecting the (C)CAPM: A Cross-Sectional Test When Risk Premia Are Time-Varying

abstract: This paper explores the ability of theoretically based asset pricing models such as the CAPM and the consumption CAPM-referred to jointly as the (C)CAPM - to explain the cross-section of average stock returns. Unlike many previous empirical tests of the (C)CAPM, we specify the pricing kernel as a conditional linear factor model, as would be expected if risk premia vary over time. Central to our approach is the use of a conditioning variable which proxies for fluctuations in the log consumption-aggregate wealth ratio and is likely to be important for summarizing conditional expectations of excess returns. We demonstrate that such conditional factor models are able to explain a substantial fraction of the cross-sectional variation in portfolio returns. These models perform much better than unconditional (C)CAPM specifications, and about as well as the three-factor Fama-French model on portfolios sorted by size and book-to-market ratios. This specification of the linear conditional consumption CAPM, using aggregate consumption data, is able to account for the difference in returns between low book-to-market and high book-to-market firms and exhibits little evidence of residual size or book-to-market effects.


title: Towards ontology-based OLAP: datalog-based reasoning over multidimensional ontologies

abstract: Understandability, reuse, and maintainability of analytical queries belong to the key challenges of Data Warehousing, especially in settings where a large number of business analysts work together and need to share knowledge. To tackle these challenges we propose Ontology-based OLAP where an ontology acts as superimposed conceptual layer between business analysts and multidimensional data. In Ontology-based OLAP, dimensions and facts are enriched by concept definitions capturing the semantics of relevant business terms used to define measures and to formulate analytical queries. Using traditional ontology languages, it is, however, very difficult to capture the hierarchical and multidimensional conceptualizations of business analysts. In this paper, we propose hierarchical and multidimensional ontologies to better capture these structural specificities. We define and implement the abstract structure and semantics of multidimensional ontologies as rules and constraints in Datalog with negation and represent multidimensional ontologies as Datalog facts. In addition to reasoning over multidimensional ontologies (open-world) we discuss their grounding in Data Warehouses (closed-world) as the fundament of Ontology-based OLAP.


title: Label-aware Double Transfer Learning for Cross-Specialty Medical Named Entity Recognition

abstract: We study the problem of named entity recognition (NER) from electronic medical records, which is one of the most fundamental and critical problems for medical text mining. Medical records which are written by clinicians from different specialties usually contain quite different terminologies and writing styles. The difference of specialties and the cost of human annotation makes it particularly difficult to train a universal medical NER system. In this paper, we propose a label-aware double transfer learning framework (La-DTL) for cross-specialty NER, so that a medical NER system designed for one specialty could be conveniently applied to another one with minimal annotation efforts. The transferability is guaranteed by two components: (i) we propose label-aware MMD for feature representation transfer, and (ii) we perform parameter transfer with a theoretical upper bound which is also label aware. We conduct extensive experiments on 12 cross-specialty NER tasks. The experimental results demonstrate that La-DTL provides consistent accuracy improvement over strong baselines. Besides, the promising experimental results on non-medical NER scenarios indicate that La-DTL is potential to be seamlessly adapted to a wide range of NER tasks.


title: Eye-Tracking Analysis of User Behavior and Performance in Web Search on Large and Small Screens

abstract: In recent years, searching the web on mobile devices has become enormously popular. Because mobile devices have relatively small screens and show fewer search results, search behavior with mobile devices may be different from that with desktops or laptops. Therefore, examining these differences may suggest better, more efficient designs for mobile search engines. In this experiment, we use eye tracking to explore user behavior and performance. We analyze web searches with 2 task types on 2 differently sized screens: one for a desktop and the other for a mobile device. In addition, we examine the relationships between search performance and several search behaviors to allow further investigation of the differences engendered by the screens. We found that users have more difficulty extracting information from search results pages on the smaller screens, although they exhibit less eye movement as a result of an infrequent use of the scroll function. However, in terms of search performance, our findings suggest that there is no significant difference between the 2 screens in time spent on search results pages and the accuracy of finding answers. This suggests several possible ideas for the presentation design of search results pages on small devices.


title: Action recognition in RGB-D egocentric videos

abstract: In this paper, we investigate the problem of action recognition in RGB-D egocentric videos. These self-generated and embodied videos provide richer semantic cues than the conventional videos captured from the third-person view for action recognition. Moreover, they contain both appearance information and 3D structure of the scenes from the RGB modality and depth modality respectively. Motivated by these advantages, we first collect a video-based RGB-D egocentric dataset (THU-READ) with diverse types of daily-life actions. Then we evaluate several approaches including hand-crafted features and deep learning methods on THU-READ. To improve the performance, we further develop a tri-stream convolutional network (TCNet) method, which learns to exploit the fuse with both the RGB and depth modalities for action recognition. Experimental results show that our model achieves competitive performance with state-of-the-art methods.


title: An Examination of Organizational and Team Commitment in a Self-Directed Team Environment

abstract: A model hypothesizing differential relationships among predictor variables and individual commitment to the organization and work team was tested. Data from 485 members of sewing teams supported the existence of differential relationships between predictors and organizational and team commitment. In particular, intersender conflict and satisfaction with coworkers were more strongly related to team commitment than to organizational commitment. Resource-related conflict and satisfaction with supervision were more strongly related to organizational commitment than to team commitment. Perceived task interdependence was strongly related to both commitment loci. Contrary to prediction, the relationships between perceived task interdependence and the 2 commitment loci were not significantly different. Relationships with antecedent variables help explain how differential levels of commitment to the 2 foci may be formed. Indirect effects of exogenous variables are reported. Research indicates that commitment in the workplace is a multidimensional phenomenon, and the focus of commitment (i.e., to whom or what an employee is committed) is an important dimension in assessing worker attachment (Becker, 1992). Current emphasis on work teams and participative management systems suggests that two important foci of commitment are an employee's work group or team and the employing organization (Becker, 1992; Hackman, 1987; Reichers, 1985). Organizational commitment has been linked to extrarole behavior (Gregersen, 1993; Morrison, 1994), job performance (Brett, Cron, & Slocum, 1995; Mathieu & Zajac, 1990), and lower turnover (Bishop, Scott, & Casino, 1997; Mathieu & Zajac, 1990). Team commitment has been linked to extrarole behavior (Becket & Billings, 1993; Hackman, 1987) and team performance (Bishop et al., 1997; Hackman, 1987; Scott & Townsend, 1994). Furthermore, individuals may experience high levels of commitment to one of these foci and not the other, or to both, or to neither (Becket & Billings, 1993). However, the question of which antecedents may be related to different levels of commitment to these foci has not been explored. The purpose of this research was to empirically examine a model hypothesizing differential relationships between certain work characteristics and the commitment individuals have to the organization and their work team (see Figure 1). This study extends research on work teams and employee commitment in three


title: Visual technology, youth interventions and participation: Two cases from the Netherlands

abstract: In this chapter, we explore the impact of visual technology on youth participation. Our research addresses the benefits of the use of visual technology in education and the implications for contemporary adult educators. We empirically ground our research by looking at two specific projects, which use participatory video as an intervention method with youth in order to strengthen participation. In the first part of this chapter, we sketch the characteristics of multimedia with the fast development of new media technologies and the vast impact of media in contemporary culture. In the second part we discuss the impact of technological convergence on youth culture. We will argue that youth participation, defined as active citizens' involvement, is a prerequisite for learning and sustainable change. In the third part we discuss in more detail participatory practices that are embedded and implemented in everyday life in more detail. We take a close look at two cases from the Netherlands, exploring the conditions of participatory video for strengthening youth participation. We focus on the added value of visual media, building on the work by Pat Thompson (2008) who developed criteria that can be used for assessing the value of visuals in research. We also discuss the role that adult professionals-as filmmakers, facilitators and mediators-play in these projects, using the theoretical work of John Dewey (1938) and Paolo Freire (1970,2005).


title: Training Compact Neural Networks with Binary Weights and Low Precision Activations

abstract: In this paper, we propose to train a network with binary weights and low-bitwidth activations, designed especially for mobile devices with limited power consumption. Most previous works on quantizing CNNs uncritically assume the same architecture, though with reduced precision. However, we take the view that for best performance it is possible (and even likely) that a different architecture may be better suited to dealing with low precision weights and activations. ::: Specifically, we propose a "network expansion" strategy in which we aggregate a set of homogeneous low-precision branches to implicitly reconstruct the full-precision intermediate feature maps. Moreover, we also propose a group-wise feature approximation strategy which is very flexible and highly accurate. Experiments on ImageNet classification tasks demonstrate the superior performance of the proposed model, named Group-Net, over various popular architectures. In particular, with binary weights and activations, we outperform the previous best binary neural network in terms of accuracy as well as saving more than 5 times computational complexity on ImageNet with ResNet-18 and ResNet-50.


title: Effective Character-augmented Word Embedding for Machine Reading Comprehension

abstract: Machine reading comprehension is a task to model relationship between passage and query. In terms of deep learning framework, most of state-of-the-art models simply concatenate word and character level representations, which has been shown suboptimal for the concerned task. In this paper, we empirically explore different integration strategies of word and character embeddings and propose a character-augmented reader which attends character-level representation to augment word embedding with a short list to improve word representations, especially for rare words. Experimental results show that the proposed approach helps the baseline model significantly outperform state-of-the-art baselines on various public benchmarks.


title: Pricing and Repurchasing for Big Data Processing in Multi-Clouds

abstract: Processing streaming big data becomes critical as new diver Internet of Thing applications begin to emerge. The existing cloud pricing strategy is unfriendly for processing streaming big data with varying loads. Multiple cloud environments are a potential solution with an efficient pay-on-demand pricing strategy for processing streaming big data. In this paper, we propose an intermediary framework with multiple cloud environments to provide streaming big data computing service with lower cost per load, in which a cloud service intermediary rents the cloud service from multiple cloud providers and provides streaming processing service to the users with multiple service interfaces. In this framework, we also propose a pricing strategy to maximize the revenue of the multiple cloud intermediaries. With extensive simulations, our pricing strategy brings higher revenue than other pricing methods.


title: Site-Specific Hydrolysis Reaction C-Terminal of Methionine in Met-His during Metal-Catalyzed Oxidation of IgG-1.

abstract: The metal-catalyzed oxidation by [Fe(II)(EDTA)](2-)/H2O2 of IgG-1 leads to the site-specific hydrolysis of peptide bonds in the Fc region. The major hydrolytic cleavage occurs between Met428 and His429, consistent with a mechanism reported for the site-specific hydrolysis of parathyroid hormone (1-34) between Met8 and His9 (Mozziconacci, O.; et al. Mol. Pharmaceutics 2013, 10 (2), 739-755). In IgG-1, to a lesser extent, we also observe hydrolysis reactions between Met252 and Ile253. After 2 h of oxidation (at pH 5.8, 37 °C) approximately 5% of the protein is cleaved between Met428 and His429. For comparison, after 2 h of oxidation, the amount of tryptic peptides containing a Met sulfoxide residue represents less than 0.1% of the protein. The effect of this site-specific hydrolysis on the conformational stability and aggregation propensity of the antibody was also examined. No noticeable differences in structural integrity and conformational stability were observed between control and oxidized IgG-1 samples as measured by circular dichroism (CD), fluorescence spectroscopy, and static light scattering (SLS). Small amounts of soluble and insoluble aggregates (3-6%) were, however, observed in the oxidized samples by UV-visible absorbance spectroscopy and size exclusion chromatography (SEC). Over the course of metal-catalyzed oxidation, increasing amounts of fragments were also observed by SEC. An increase in the concentration of subvisible particles was detected by microflow imaging (MFI).


title: High Accuracy VLC Indoor Positioning System With Differential Detection

abstract: Recently, several positioning systems based on visible light communications (VLC) have been proposed, and most of them are based on received signal strength (RSS) because of its simplicity and high accuracy. In order to improve the accuracy further, a differential detection-based positioning algorithm is proposed. It can reduce positioning instability caused by light intensity fluctuation and the area outside the light-emitting diode (LED) cell can also be positioned. Experimental results show that the proposed method can improve the positioning accuracy from 10.0 to 4.0 cm and reduce the standard deviation from 9.0 to 2.5 cm. Meanwhile, the positioning accuracy outside the cell can reach 10.0 cm or less.


title: Trust Your Heart: Assessing Cooperation and Trust with Biosignals in Computer-Mediated Interactions

abstract: This study examines heartrate sharing in the context of a trust-building game. Through quantitative and qualitative analyses, we find that "elevated" (versus "normal") heartrate of an exchange partner is associated with negative mood attributions and reduced cooperation in a social dilemma game. To investigate how specific our findings are to heartrate (as opposed to some other "elevated" signal collected from the body), we replicate our initial experiment with an unfamiliar biosignal, "skin reflectivity". We find that both heartrate and the unfamiliar biosignal are associated with negative mood attributions, but we observe a decrease in cooperative behavior only with elevated heartrate. Qualitative results indicate that individuals may learn an association between our unfamiliar biosignal and the cooperative, trusting behavior of their partner. Our findings highlight the role prior beliefs can play in shaping interpretations of a biosignal, while suggesting that designers can, perhaps inadvertently, train users to associate signals with social meanings. We discuss implications for how wearable sensors can mediate social interactions.


title: Privacy-Preserving Multi-Class Support Vector Machine for Outsourcing the Data Classification in Cloud

abstract: Emerging cloud computing infrastructure replaces traditional outsourcing techniques and provides flexible services to clients at different locations via Internet. This leads to the requirement for data classification to be performed by potentially untrusted servers in the cloud. Within this context, classifier built by the server can be utilized by clients in order to classify their own data samples over the cloud. In this paper, we study a privacy-preserving (PP) data classification technique where the server is unable to learn any knowledge about clients' input data samples while the server side classifier is also kept secret from the clients during the classification process. More specifically, to the best of our knowledge, we propose the first known client-server data classification protocol using support vector machine. The proposed protocol performs PP classification for both two-class and multi-class problems. The protocol exploits properties of Pailler homomorphic encryption and secure two-party computation. At the core of our protocol lies an efficient, novel protocol for securely obtaining the sign of Pailler encrypted numbers.


title: Finding application errors and security flaws using PQL: a program query language

abstract: A number of effective error detection tools have been built in recent years to check if a program conforms to certain design rules. An important class of design rules deals with sequences of events asso-ciated with a set of related objects. This paper presents a language called PQL (Program Query Language) that allows programmers to express such questions easily in an application-specific context. A query looks like a code excerpt corresponding to the shortest amount of code that would violate a design rule. Details of the tar-get application's precise implementation are abstracted away. The programmer may also specify actions to perform when a match is found, such as recording relevant information or even correcting an erroneous execution on the fly.We have developed both static and dynamic techniques to find solutions to PQL queries. Our static analyzer finds all potential matches conservatively using a context-sensitive, flow-insensitive, inclusion-based pointer alias analysis. Static results are also use-ful in reducing the number of instrumentation points for dynamic analysis. Our dynamic analyzer instruments the source program to catch all violations precisely as the program runs and to optionally perform user-specified actions.We have implemented the techniques described in this paper and found 206 errors in 6 large real-world open-source Java applica-tions containing a total of nearly 60,000 classes. These errors are important security flaws, resource leaks, and violations of consis-tency invariants. The combination of static and dynamic analysis proves effective at addressing a wide range of debugging and pro-gram comprehension queries. We have found that dynamic analysis is especially suitable for preventing errors such as security vulner-abilities at runtime.


title: Network Hardware-Accelerated Consensus

abstract: Consensus protocols are the foundation for building many fault-tolerant distributed systems and services. This paper posits that there are significant performance benefits to be gained by offering consensus as a network service (CAANS). CAANS leverages recent advances in commodity networking hardware design and programmability to implement consensus protocol logic in network devices. CAANS provides a complete Paxos protocol, is a drop-in replacement for software-based implementations of Paxos, makes no restrictions on network topologies, and is implemented in a higher-level, data-plane programming language, allowing for portability across a range of target devices. At the same time, CAANS significantly increases throughput and reduces latency for consensus operations. Consensus logic executing in hardware can transmit consensus messages at line speed, with latency only slightly higher than simply forwarding packets.


title: Dynamics of Entanglement in Three Coupled Harmonic Oscillator System with Arbitrary Time-Dependent Frequency and Coupling Constants

abstract: The dynamics of mixedness and entanglement is examined by solving the time-dependent Schr\"{o}dinger equation for three coupled harmonic oscillator system with arbitrary time-dependent frequency and coupling constants parameters. We assume that part of oscillators is inaccessible and remaining oscillators accessible. We compute the dynamics of entanglement between inaccessible and accessible oscillators. In order to show the dynamics pictorially we introduce three quenched models. In the quenched models both mixedness and entanglement exhibit oscillatory behavior in time with multi-frequencies. It is shown that the mixedness for the case of one inaccessible oscillator is larger than that for the case of two inaccessible oscillators in the most time interval. Contrary to the mixedness entanglement for the case of one inaccessible oscillator is smaller than that for the case of two inaccessible oscillators in the most time interval.


title: Is There “One” DLPFC in Cognitive Action Control? Evidence for Heterogeneity From Co-Activation-Based Parcellation

abstract: The dorsolateral prefrontal cortex (DLPFC) has consistently been implicated in cognitive control of motor behavior. There is, however, considerable variability in the exact location and extension of these activations across functional magnetic resonance imaging (fMRI) experiments. This poses the question of whether this variability reflects sampling error and spatial uncertainty in fMRI experiments or structural and functional heterogeneity of this region. This study shows that the right DLPFC as observed in 4 different experiments tapping executive action control may be subdivided into 2 distinct subregions-an anterior-ventral and a posterior-dorsal one - based on their whole-brain co-activation patterns across neuroimaging studies. Investigation of task-dependent and task-independent connectivity revealed both clusters to be involved in distinct neural networks. The posterior subregion showed increased connectivity with bilateral intraparietal sulci, whereas the anterior subregion showed increased connectivity with the anterior cingulate cortex. Functional characterization with quantitative forward and reverse inferences revealed the anterior network to be more strongly associated with attention and action inhibition processes, whereas the posterior network was more strongly related to action execution and working memory. The present data provide evidence that cognitive action control in the right DLPFC may rely on differentiable neural networks and cognitive functions.


title: A vision for personalized service level agreements in the cloud

abstract: Public Clouds today provide a variety of services for data analysis such as Amazon Elastic MapReduce and Google BigQuery. Each service comes with a pricing model and service level agreement (SLA). Today's pricing models and SLAs are described at the level of compute resources (instance-hours or gigabytes processed). They are also different from one service to the next. Both conditions make it difficult for users to select a service, pick a configuration, and predict the actual analysis cost. To address this challenge, we propose a new abstraction, called a Personalized Service Level Agreement, where users are presented with what they can do with their data in terms of query capabilities, guaranteed query performance and fixed hourly prices.


title: A configurable method for multi-style license plate recognition

abstract: Despite the success of license plate recognition (LPR) methods in the past decades, few of them can process multi-style license plates (LPs), especially LPs from different nations, effectively. In this paper, we propose a new method for multi-style LP recognition by representing the styles with quantitative parameters, i.e., plate rotation angle, plate line number, character type and format. In the recognition procedure these four parameters are managed by relevant algorithms, i.e., plate rotation, plate line segmentation, character recognition and format matching algorithm, respectively. To recognize special style LPs, users can configure the method by defining corresponding parameter values, which will be processed by the relevant algorithms. In addition, the probabilities of the occurrence of every LP style are calculated based on the previous LPR results, which will result in a faster and more precise recognition. Various LP images were used to test the proposed method and the results proved its effectiveness.


title: Asian language resources: the state-of-the-art

abstract: This special issue of Language Resources and Evaluation, entitled "New Frontiers in Asian Language Resources", complements the earlier special double issue on Asian Language Processing: State of the Art Resources and Processing (Huang et al. 2006) by presenting eight papers describing specific Asian language resources. As Bird and Simons (2003) explain, research on language resources must deal with how the resources can be acquired and documented as well as how the resources can be accessed and used. Among the eight papers in this issue, the first four papers focus on resources, while the latter four target specific application tasks and describe resource building in the contexts of these applications. In the early days of corpus building, a "large scale" corpus might consist of one million words. Kilgarriff and Grenfenstette's (2003) survey of the historical developments in corpus construction shows that the size of English corpora has increased roughly tenfold every decade since the 1960s, when the one million word Brown Corpus was developed. In the 1980s, the COBUILD project built an eight million word corpus, and the British National Corpus (BNC), completed in 1994, includes 100 million words. This trend continues with LDC's Gigaword Corpus, published in 2003, which contains nearly two billion words. A central question for


title: A Particle Swarm Optimization Approach for Optimal Design of PID Controller for Temperature Control in HVAC

abstract: Particle swarm optimization (PSO) is a novel evolutionary algorithm which has a better convergence rate and computation precision compared with other evolutionary algorithms. From the perspective of optimization, the self-tuning of PID controller parameters is to find the best global optimum value in the solution space of Kp, Ki, Kd. In this paper an optimal design of PID controller based on particle swarm optimization approach for temperature control in HVAC is presented. The mathematical model of a HVAC system had been approximate near the operating point for the PSO algorithm to adjust PID parameters for the minimum integrated absolute error (IAE) condition. The results show that the adjustment of PID parameters has converted into the optimal point and the good control response based on the optimal values by the PSO technique.


title: When Professionals Become Mothers, Warmth Doesn't Cut the Ice

abstract: Working moms risk being reduced to one of two subtypes: homemakers—viewed as warm but incompetent, or female professionals—characterized as competent but cold. The current study ( N= 122 college students) presents four important findings. First, when working women become mothers, they trade perceived competence for perceived warmth. Second, working men don't make this trade; when they become fathers, they gain perceived warmth and maintain perceived competence. Third, people report less interest in hiring, promoting, and educating working moms relative to working dads and childless employees. Finally, competence ratings predict interest in hiring, promoting, and educating workers. Thus, working moms' gain in perceived warmth does not help them, but their loss in perceived competence does hurt them.


title: From Institutions to Code: Towards Automated Generation of Smart Contracts

abstract: Blockchain technology has emerged as a solution to consistency problems in peer to peer networks. By now, it has matured as a solution to a range of use cases in which it can effectively provide the notion of third party trust without the need for a trusted (physical) third party, which makes it an attractive coordination mechanism for distributed systems. To promote the wide adoption of this technology, we yet lack mechanisms that make the specification and interpretation of smart contracts accessible to a broader audience. In this work, we propose a modeling approach that supports the semi-automated translation of human-readable contract representations into computational equivalents in order to enable the codification of laws into verifiable and enforceable computational structures that reside within a public blockchain. We identify smart contract components that correspond to real world institutions, and propose a mapping that we operationalize using a domain-specific language in order to support the contract modeling process. We explore this capability based on selected examples and plot out directions for future research on smart contracts.


title: On Identifying Disaster-Related Tweets: Matching-Based or Learning-Based?

abstract: Social media such as tweets are emerging as platforms contributing to situational awareness during disasters. Information shared on Twitter by both affected population (e.g., requesting assistance, warning) and those outside the impact zone (e.g., providing assistance) would help first responders, decision makers, and the public to understand the situation first-hand. Effective use of such information requires timely selection and analysis of tweets that are relevant to a particular disaster. Even though abundant tweets are promising as a data source, it is challenging to automatically identify relevant messages since tweet are short and unstructured, resulting to unsatisfactory classification performance of conventional learning-based approaches. Thus, we propose a simple yet effective algorithm to identify relevant messages based on matching keywords and hashtags, and provide a comparison between matching-based and learning-based approaches. To evaluate the two approaches, we put them into a framework specifically proposed for analyzing disaster-related tweets. Analysis results on eleven datasets with various disaster types show that our technique provides relevant tweets of higher quality and more interpretable results of sentiment analysis tasks when compared to learning approach.


title: Design considerations of 10kw wireless charger for EV

abstract: This paper presents the design method of a 10kW wireless charger for electric vehicle (EV). The measured efficiency (DC-DC) is 94%. The designed wireless charger includes high frequency inverter, primary compensation network and coil, secondary compensation network and coil, and high frequency rectifier. Design procedures for each stage are elaborated. Simulation and experimental results validate the design methods.


title: Feature Clustering for Anomaly Detection Using Improved Fuzzy Membership Function

abstract: Earlier research focus towards anomaly detection has been towards using classifiers such as kNN, SVM and using existing distance measures to perform classification. Traditionally IDSs (Intrusion detection systems) have been developed by applying machine learning techniques and adopted single learning mechanism. This is later extended by developing Intrusion Detection Systems by adopting multiple learning mechanisms. Such systems have addressed better detection rates compared to single learning Intrusion Detection Systems. Dimensionality is one more serious concern which affects the performance of classification algorithms. Approaches such as "Feature selection" have been studied and adopted which selects a subset features from the feature set. However, the feature extraction approach for dimensionality reduction has proved to be better compared to feature selection and achieved better classification and detection rates. In this research, we address "Feature extraction" using "Evolutionary feature clustering" by proposing a "Novel fuzzy membership function" which addresses Dimensionality Reduction (DR). The idea is to transform the initial connection representation so that its equivalent representation has reduced noise affect and achieves better classification or detection rates. Experimental results on KDD datasets with 19 and 41 attributes, prove that the proposed approach has improved detection rates for R2L and U2R attack classes when compared to CANN, CLAPP, and SVM approaches. CANN approach recorded lower detection rates w.r.t U2R and R2L attacks. This failure is addressed in our earlier studies through proposing, CLAPP which proved comparatively better accuracy rates to CANN. The fuzzy membership function proposed in this paper, recorded better classification and detection rates in experiments conducted


title: POLSAR Image Classification via Wishart-AE Model or Wishart-CAE Model

abstract: Neural network such as an autoencoder (AE) and a convolutional autoencoder (CAE) have been successfully applied in image feature extraction. For the statistical distribution of polarimetric synthetic aperture radar (POLSAR) data, we combine the Wishart distance measurement into the training process of the AE and the CAE. In this paper, a new type of AE and CAE is specially defined, which we name them Wishart-AE (WAE) and Wishart-CAE (WCAE). Furthermore, we connect the WAE or the WCAE with a softmax classifier to compose a classification model for the purpose of POLSAR image classification. Compared with AE and CAE models, WAE and WCAE models can achieve higher classification accuracy because they could obtain the classification features, which are more suitable for POLSAR data. What is more, the WCAE model utilizes the local spatial information of a POLSAR image when compared with the WAE model. A convolutional natural network (CNN), which also makes use of the spatial information, has been widely applied in image classification, but our WCAE model is time-saving than the CNN model. Given the above, our methods not only improve the classification performance but also save the experimental time. Experimental results on four POLSAR datasets also demonstrate that our proposed methods are significantly effective.


Compact image stabilization system using camera posture information


title: Topology-independent 3D garment fitting for virtual clothing

abstract: ]Using computer-aided design system to design an elegant 3D garment for a virtual human is often tedious and labor-intensive. Moreover, the garment is usually designed for a reference human model and generally not fitted to other individuals, which largely reduces the reusability of existing 3D garments. In this paper, we introduce proxy mesh to fit 3D garment to another human model whose topology or shape is different from the garment's reference human model. Firstly, a proxy mesh is generated for the reference human model and the specified human model respectively. Secondly, the garment is parameterized based on the proxy mesh of the reference model and an independent dataset is obtained. Thirdly, the dataset is decoded to the proxy mesh of the other human model and a roughly fitted garment is gained. Lastly, local shape constrains are enforced to the fitted garment and garment-body penetrations are resolved to get a well fitted garment. Our approach is efficient, simple to implement and is potential to be applied to existing applications such as virtual try-on and virtual clothing design.


title: Linking transformational leadership to nurses' extra-role performance: the mediating role of self-efficacy and work engagement

abstract: salanova m., lorente l., chambel m.j. & martinez i.m. (2011) Linking transformational leadership to nurses’ extra-role performance: the mediating role of self-efficacy and work engagement. Journal of Advanced Nursing 67(9), 2256–2266. ::: ::: Abstract ::: Aims. This paper is a report of a social cognitive theory-guided study about the link between supervisors’ transformational leadership and staff nurses’ extra-role performance as mediated by nurse self-efficacy and work engagement. ::: ::: Background. Past research has acknowledged the positive influence that transformational leaders have on employee (extra-role) performance. However, less is known about the psychological mechanisms that may explain the links between transformational leaders and extra-role performance, which encompasses behaviours that are not considered formal job requirements, but which facilitate the smooth functioning of the organization as a social system. ::: ::: Methods. Seventeen supervisors evaluated nurses’ extra-role performance, the data generating a sample consisting of 280 dyads. The nurses worked in different health services in a large Portuguese hospital and the participation rate was 76·9% for nurses and 100% for supervisors. Data were collected during 2009. A theory-driven model of the relationships between transformation leadership, self-efficacy, work engagement and nurses’ extra-role performance was tested using Structural Equation Modelling. ::: ::: Results. Data analysis revealed a full mediation model in which transformational leadership explained extra-role performance through self-efficacy and work engagement. A direct relationship between transformational leadership and work engagement was also found. ::: ::: Conclusion. Nurses’ supervisors with a transformational leadership style enhance different ‘extra-role’ performance in nurses and this increases hospital efficacy. They do so by establishing a sense of self-efficacy but also by amplifying their levels of engagement in the workplace.


title: Model-Based Security Engineering of Distributed Information Systems Using UMLsec

abstract: Given the explosive growth of digitally stored information in modern enterprises, distributed information systems together with search engines are increasingly used in companies. By enabling the user to search all relevant information sources with one single query, however, crucial risks concerning information security arise. In order to make these applications secure, it is not sufficient to penetrate-and- patch past system development, but security analysis has to be an integral part of the system design process for such distributed information systems. This work presents the experiences and results of the security analysis of a search engine in the intranet of a German car manufacturer, by making use of an approach to Model-based Security Engineering that is based on the UML extension UMLsec. The focus lies on the application's single-sign-on-mechanism, which was analyzed using the UMLsec method and tools. Main results of the paper include a field report on the employment of the UMLsec method in an industrial context as well as indications on its benefits and limitations.


title: Decentralized Execution of Smart Contracts: Agent Model Perspective and Its Implications

abstract: Smart contracts are one of the most important applications of the blockchain. Most existing smart contract systems assume that for executing contract over a network of decentralized nodes, the outcome in accordance with the majority can be trusted. However, we observe that users involved with a smart contract may strategically take actions to manipulate execution of the contract for purpose to increase their own benefits. We propose an agent model, as the underpinning mechanism for contract execution over a network of decentralized nodes and public ledger, to address this problem and discuss the possibility of preventing users from manipulating smart contract execution by applying principles of game theory and agent based analysis.


Classification Techniques used in Speech Recognition Applications : A Review


title: Taxonomy for classifying engineering decision problems and support systems

abstract: The design of even the simplest product requires thousands of decisions. Yet few of these decisions are supported with methods on paper or on computers. Is this because engineering design decisions do not need support or is it because techniques have yet to be developed that are usable on a wide basis ? In considering this question a wide range of decision problem characteristics need to be addressed. In engineering design some decisions are made by individuals, others by teams-some are about the product and others about the processes that support the product - some are based on complete, consistent, quantitative data and others on sparse, conflicting, qualitative discussions. To address the reasons why so little support is used and the characteristics of potentially useful decision support tools, a taxonomy of decision characteristics is proposed. This taxonomy is used to classify current techniques and to define the requirements for an ideal engineering design decision support system.


title: Non-transitive Hashing with Latent Similarity Components

abstract: Approximating the semantic similarity between entities in the learned Hamming space is the key for supervised hashing techniques. The semantic similarities between entities are often non-transitive since they could share different latent similarity components. For example, in social networks, we connect with people for various reasons, such as sharing common interests, working in the same company, being alumni and so on. Obviously, these social connections are non-transitive if people are connected due to different reasons. However, existing supervised hashing methods treat the pairwise similarity relationships in a simple and unified way and project data into a single Hamming space, while neglecting that the non-transitive property cannot be ade- quately captured by a single Hamming space. In this paper, we propose a non-transitive hashing method, namely Multi-Component Hashing (MuCH), to identify the latent similarity components to cope with the non-transitive similarity relationships. MuCH generates multiple hash tables with each hash table corresponding to a similarity component, and preserves the non-transitive similarities in different hash table respectively. Moreover, we propose a similarity measure, called Multi-Component Similarity, aggregating Hamming similarities in multiple hash tables to capture the non-transitive property of semantic similarity. We conduct extensive experiments on one synthetic dataset and two public real-world datasets (i.e. DBLP and NUS-WIDE). The results clearly demonstrate that the proposed MuCH method significantly outperforms the state-of-art hashing methods especially on search efficiency.


title: Generation of Affective Accompaniment in Accordance With Emotion Flow

abstract: The emotion expressed by a music piece varies as the music unrolls in time. To create such dynamic expression, we develop an algorithm that automatically generates the accompaniment for a melody according to the emotion flow specified by a user. The emotion flow is given in the form of arousal and valence curves, each as a function of time. The affective accompaniment is composed of chord progression and accompaniment pattern. The chord progression, which controls the valence of the composed music, is generated by dynamic programming using the input melody and valence data as constraints. A mathematical model is developed to describe the temporal relationship between valence and chord progression. The accompaniment pattern, which controls the arousal of the composed music, is determined according to the quantized arousal values. The performance of the system is evaluated by subjective tests. The cross-correlation coefficient between the input arousal (valence) and the perceived arousal (valence) of the composed music is 0.85 (0.52). It rises to 0.92 for arousal and 0.88 for valence if only musician subjects in the test are considered. Overall, the proposed system is capable of generating subjectively appropriate accompaniments conforming to the user specification.