title: Designing Animated Characters for Children of Different Ages

abstract: Animated characters are commonly used in children's television, movies, and applications. Artists seek to create characters that maximally engage their audiences and tailor these characters carefully. In order to examine the relationship between stylistic elements of animated characters and the target ages of their audiences, we performed a series of qualitative and quantitative studies. By using existing media, we determined that characters created for younger children have larger head height, larger eye height, and rounder eyes than those created for older children. However, we found no systematic differences by age when we had children express preferences for existing characters or create their own characters. These results suggest that current artistic trends do not accurately reflect the character design preferences of children.


title: LANGUAGE-ORIENTED PROGRAMMING VIA DSL STACKING

abstract: According to the paradigm of Language-Oriented Programming, an application for a problem should be implemented in the most appropriate domain-specific language (DSL). This paper introduces DSL stacking, an efficient method for implementing Language-Oriented Programming where DSLs and general-purpose languages are incrementally developed on top of a base language. This is demonstrated with components of a business information system that are implemented in different DSLs for Semantic Web technology in Lisp.


title: Modeling of an electric vehicle charging station for fast DC charging

abstract: The proposed model of an electric vehicle charging station is suitable for the fast DC charging of multiple electric vehicles. The station consists of a single grid-connected inverter with a DC bus where the electric vehicles are connected. The control of the individual electric vehicle charging processes is decentralized, while a separate central control deals with the power transfer from the AC grid to the DC bus. The electric power exchange does not rely on communication links between the station and vehicles, and a smooth transition to vehicle-to-grid mode is also possible. Design guidelines and modeling are explained in an educational way to support implementation in Matlab/Simulink. Simulations are performed in Matlab/Simulink to illustrate the behavior of the station. The results show the feasibility of the model proposed and the capability of the control system for fast DC charging and also vehicle-to-grid.


title: A Stackelberg Game for Cooperative Cognitive Wireless Powered Communication Networks with Multiple Primary Users

abstract: We consider a cooperative cognitive wireless powered communication network consisting of a cognitive base station (CBS), multiples secondary users (SUs) and multiple wireless powered primary users (PUs). A two stage protocol is adopted, in which the CBS transmits information bearing signals and the PUs harvest energy from these RF signals first, and then the PUs transmit information based on the harvested energy. We assume that the CBS prices the energy transferred to the PUs. The interaction between the CBS and the PUs is modeled as a Stackelberg game, in which the CBS acts as a leader to determine the prices and the time allocation, while the PUs act as followers to determine the required transmit power of the CBS. An algorithm is proposed to achieve the Stackelberg equilibrium. It is shown that the proposed algorithm outperforms the equal-time benchmark algorithm in terms of the utility of the CBS, and is inferior to the equal-time benchmark algorithm in terms of the average utility of the PUs.


title: Learning from positive and unlabeled examples with different data distributions

abstract: We study the problem of learning from positive and unlabeled examples. Although several techniques exist for dealing with this problem, they all assume that positive examples in the positive set P and the positive examples in the unlabeled set U are generated from the same distribution. This assumption may be violated in practice. For example, one wants to collect all printer pages from the Web. One can use the printer pages from one site as the set P of positive pages and use product pages from another site as U. One wants to classify the pages in U into printer pages and non-printer pages. Although printer pages from the two sites have many similarities, they can also be quite different because different sites often present similar products in different styles and have different focuses. In such cases, existing methods perform poorly. This paper proposes a novel technique A-EM to deal with the problem. Experiment results with product page classification demonstrate the effectiveness of the proposed technique.


title: elf-organization of multiple spatial and context memories in the hippocampus

abstract: Abstract One obstacle to understanding the exact processes unfolding inside the hippocampus is that it is still difficult to clearly define what the hippocampus actually does, at the system level. Associated for a long time with the formation of episodic and semantic memories, and with their temporary storage, the hippocampus is also regarded as a structure involved in spatial navigation. These two independent perspectives on the hippocampus are not necessarily exclusive: proposals have been put forward to make them fit into the same conceptual frame. We review both approaches and argue that three critical developments need consideration: (a) recordings of neuronal activity in rodents, revealing beautiful spatial codes expressed in entorhinal cortex, upstream of the hippocampus; (b) comparative behavioral results suggesting, in an evolutionary perspective, qualitative similarity of function across homologous structures with a distinct internal organization; (c) quantitative measures of information, shifting the focus from who does what to how much each neuronal population expresses each code. These developments take the hippocampus away from philosophical discussions of all-or-none cause–effect relations, and into the quantitative mainstream of modern neural science.


title: Development of a Low Cost Printable Chipless RFID Humidity Sensor

abstract: A novel low-cost chipless radio frequency identification tag sensor is presented. The tag sensor provides identification data as well as monitors relative humidity (RH) of tagged objects. The tag sensor is made of passive microwave circuit that uses humidity sensitive polymer material for RH sensing. The aim of this paper is to investigate RF sensing properties of moisture absorbing polymer polyvinyl alcohol at microwave frequency. In addition, frequency shifting technique is used to encode data bits for high data capacity. The overall size of the proposed tag sensor is 15 mm × 6.8 mm and has 6 bit data capacity for ID generation and single bit for humidity sensing. Results presented here show ~ 607 MHz frequency deviation for 50% RH increase. The tag sensor has potential to be printed on flexible laminates such as plastic and paper for ultra-low cost item level tagging and ubiquitous sensing.


title: Leadership styles and burnout: is there an association?

abstract: Purpose – The primary purpose of this paper is to investigate the association between leadership styles and burnout among hospitality managers currently working in the industry of Cyprus. Specifically, transformational, transactional and passive/avoidance are to be investigated in order to establish which leadership style is more prone to burnout effects.Design/methodology/approach – Adopting a quantitative approach, the multifactor leadership questionnaire (MLQ 5X‐Short) was utilized to allow research participants to describe their leadership style as they perceive it. In addition, the most widely acknowledged tool for measuring burnout, the Maslach burnout inventory (MBI), was also incorporated in the questionnaire, which was then distributed to 500 hospitality managers in Cyprus. A number of hypotheses, reflecting the primary objective of the study, were postulated.Findings – The findings suggest that transformational leadership has a significant positive association with personal accomplishment and is...


title: The Term Structure with Semi-credible Targeting

abstract: The Federal Reserve sets targets for interest rates which it enforces through direct market intervention. These targets are changed periodically. In this paper, we develop a term structure model in which the short rate is subject to a control which keeps it close to a target which changes from time to time. The probability of target changes is not constant in the model, but changes as a function of observables. The model performs well at explaining the shifts in the yield curve that accompany target changes. Copyright (c) 2003 by the American Finance Association.


title: Validation of the Korean Version of Schedule of Fatigue and Anergia: General Physician Questionnaire

abstract: The Schedule of Fatigue and Anergy/General Physician (SOFA/GP) was developed to screen for prolonged fatigue in the primary care setting. We aimed to evaluate the reliability and validity of the Korean version of the SOFA/GP (SOFA/GP-K), which is adapted from the original English version. We performed translation and back translation, and after conducting a pilot study, we tested the final version of the questionnaire for its reliability and validity in a Korean primary care setting. Two hundred participants that visited a health examination center in a university hospital completed the survey between September and November 2012. A second survey was performed within 2 weeks of the primary survey to test for reliability. We evaluated concurrent validity between the SOFA/GP-K score, the Fatigue Severity Scale (FSS), and Brief Fatigue Index (BFI) scores. The Spearman correlation coefficient between SOFA/GP-K and FSS was 0.71 and 0.61 between SOFA/GP-K and BFI. Internal consistency of SOFA/GP-K was observed (Cronbach's alpha = 0.82) and construct validity was confirmed by factor analysis. The Kappa scores for test-retest reliability for each survey item were between 0.28 and 0.64. The SOFA/GP-K is a valid and reliable questionnaire for screening fatigue in a primary care setting.


title: Design of Sharp Roll-Off Lowpass Filter With Ultra Wide Stopband

abstract: A novel microstrip lowpass filter is proposed to achieve an ultra wide stopband with 12th harmonic suppression and extremely sharp skirt characteristics. The transition band is from 1.26 to 1.37 GHz with -3 and -20 dB, respectively. The operating mechanism of the filter is investigated based on proposed equivalent-circuit model, and the role of each section in creating null points is theoretically discussed. An overall good agreement between measured and simulated results is observed.


title: Antibacterial Potential and Antioxidant Activity of Polyphenols of Sesbania grandiflora

abstract: The antibacterial and antioxidant activity of Sesbanial grandiflora used in traditional pharmacopeia in Burkina Faso and elsewhere was evaluated. Aqueous, methanolic and hydro-acetone extractions were carried out on the leaves, stems, and granules, pods of fruit and roots of the plant. The phytochimic groups were identified by the tests of characterization, then quantified by the tests of proportioning of total phenolics, flavonoides and tanins. Specific compounds to these phytochimic groups were also identified by the analysis in Thin layer chromatography among which the gallic acid, the caffeic acid, Kaempferol, Quercetin, Rutin. An important antioxidant activity of the same extracts was evaluated by the test of reduction of the stable radical, the 2,2-diphenyl-1-picrylhydrazyl (DPPH*) and the test of reduction of iron (FRAP). This activity is related to phenolic compounds contained in the extracts. Extracts also expressed a good antibacterial activity.


title: A Gap Analysis Methodology for Product Lifecycle Management Assessment

abstract: Product lifecycle management (PLM) is a strategic and holistic approach for the management of information, processes, and resources supporting the entire product lifecycle from concept to disposal. Several systems are available to manage data and information during the lifecycle. A PLM system does not have to be considered as a unique solution, but as a set of software supporting different perspectives and activities related to the lifecycle management. A full comprehensive implementation of PLM systems is rare. Since PLM reflects the peculiarities of processes and data structures, implementation differences are evidently observable among companies. The design of methods and tools supporting an assessment of PLM implementation inside a company can enable a correct definition of PLM strategies and goals. Based on these premises, the paper aims to specify a methodology for carrying out a gap analysis of PLM with the scope of discovering existing technological and methodological gaps, and planning actions for improvement. The proposed methodology implements a visual and lean reference model, and an assessment questionnaire for data collection. Lesson learned and feedback from three industrial applications are also described and discussed.


title: Improvements to TZ search motion estimation algorithm for multiview video coding

abstract: This paper, proposes improvements to TZ search motion estimation algorithm with reference to its implementation in JMVC reference software. In TZS, the search patterns that are implemented are 8-point diamond and 8-point square. When these are replaced with hexagonal patterns, there is a large improvement in encoding time. Further, the TZS algorithm is improved by changing the searching threshold for each grid in the search area. Simulation results show that the overall encoding time can be reduced by almost 50% compared to TZS algorithm, while maintaining the same PSNR and bitrate.


title: 'Don't Waste My Time': Use of Time Information Improves Focus

abstract: Maintaining work focus when on a computer is a major challenge, and people often feel that they use their time ineffectively. To improve focus we designed meTime, a real-time awareness application that shows users how they allocate their time across applications. In two real-world deployments involving 118 participants, we examined whether greater awareness of time use improves focus. In our first deployment, we provided awareness information using meTime, to both office workers and students. Exposure to meTime reduced use of social media, email, browsing and total time online. However increased awareness didn't affect time spent in productivity applications. A second educational deployment largely replicated these results and showed that meTime also reduced users' perceptions of their ability to focus effectively. Changed perceptions were associated with higher class grades. We discuss practical and theoretical implications as well as design principles for use of time applications.


title: You-Do, I-Learn: Egocentric Unsupervised Discovery of Objects and their Modes of Interaction Towards Video-Based Guidance

abstract: Discovering task-relevant objects from egocentric video sequences of multiple users, using appearance, position, motion and attention features.Distinguishing different ways in which a task-relevant object has been used.Automatically extracting usage snippets, to be used for video-based guidance.Tested on a variety of daily tasks such as initialising a printer, preparing a coffee and setting up a gym machine. Display Omitted This paper presents an unsupervised approach towards automatically extracting video-based guidance on object usage, from egocentric video and wearable gaze tracking, collected from multiple users while performing tasks. The approach (i)źdiscovers task relevant objects, (ii) builds a model for each, (iii)źdistinguishes different ways in which each discovered object has been used and (iv)źdiscovers the dependencies between object interactions. The work investigates using appearance, position, motion and attention, and presents results using each and a combination of relevant features. Moreover, an online scalable approach is presented and is compared to offline results. The paper proposes a method for selecting a suitable video guide to be displayed to a novice user indicating how to use an object, purely triggered by the user's gaze. The potential assistive mode can also recommend an object to be used next based on the learnt sequence of object interactions. The approach was tested on a variety of daily tasks such as initialising a printer, preparing a coffee and setting up a gym machine.


title: Stochastic MPC With Learning for Driver-Predictive Vehicle Control and its Application to HEV Energy Management

abstract: This paper develops an approach for driver-aware vehicle control based on stochastic model predictive control with learning (SMPCL). The framework combines the on-board learning of a Markov chain that represents the driver behavior, a scenario-based approach for stochastic optimization, and quadratic programming. By using quadratic programming, SMPCL can handle, in general, larger state dimension models than stochastic dynamic programming, and can reconfigure in real-time for accommodating changes in driver behavior. The SMPCL approach is demonstrated in the energy management of a series hybrid electrical vehicle, aimed at improving fuel efficiency while enforcing constraints on battery state of charge and power. The SMPCL controller allocates the power from the battery and the engine to meet the driver power request. A Markov chain that models the power request dynamics is learned in real-time to improve the prediction capabilities of model predictive control (MPC). Because of exploiting the learned pattern of the driver behavior, the proposed approach outperforms conventional model predictive control and shows performance close to MPC with full knowledge of future driver power request in standard and real-world driving cycles.


title: Asynchronous Consensus in Continuous-Time Multi-Agent Systems With Switching Topology and Time-Varying Delays

abstract: The paper studies asynchronous consensus problems of continuous-time multi-agent systems with discontinuous information transmission. The proposed consensus control strategy is implemented based on the state information of each agent's neighbors at some discrete times. The asynchrony means that each agent's update times, at which the agent adjusts its dynamics, are independent of others'. Furthermore, it is assumed that the communication topology among agents is time-dependent and the information transmission is with bounded time-varying delays. If the union of the communication topology across any time interval with some given length contains a spanning tree, the consensus problem is shown to be solvable. The analysis tool developed in this paper is based on nonnegative matrix theory and graph theory. The main contribution of this paper is to provide a valid distributed consensus algorithm that overcomes the difficulties caused by unreliable communication channels, such as intermittent information transmission, switching communication topology, and time-varying communication delays, and therefore has its obvious practical applications. Simulation examples are provided to demonstrate the effectiveness of the theoretical results.


title: A Successive Approximation Recursive Digital Low-Dropout Voltage Regulator With PD Compensation and Sub-LSB Duty Control

abstract: This paper presents a recursive digital low-dropout (RLDO) regulator that improves response time, quiescent power, and load regulation dynamic range over prior digital LDO designs by 1–2 orders of magnitude. The proposed RLDO enables a practical digital replacement to analog LDOs by using an SAR-like binary search algorithm in a coarse loop and a sub-LSB pulse width modulation duty control scheme in a fine loop. A proportional-derivative compensation scheme is employed to ensure stable operation independent of load current, the size of the output decoupling capacitor, and clock frequency. Implemented in 0.0023 mm2 in 65 nm CMOS, the 7-bit RLDO achieves, at a 0.5-V input, a response time of 15.1 ns with a figure of merit of 199.4 ps, along with stable operation across a 20 000 $\times $ dynamic load range.


title: Weighted line graphs for overlapping community discovery

abstract: We propose weighted line graphs for overlapping community discovery where a node in a network can be assigned to more than one community. For undirected connected networks without self-loops, we propose weighted line graphs by: (1) defining weights of a line graph based on the weights in the original network, and (2) removing self-loops in weighted line graphs, while sustaining their properties. By applying some off-the-shelf node partitioning method to the transformed graph, community labels of adjacent links are assigned to each node in the original network. Experiments are conducted over both synthetic and real-world networks, and the results indicate that the proposed approach can improve the quality of discovered overlapping communities.


title: Detecting face region in binary image

abstract: Proposed here is a new face detection technique based on binary image. In this method, a color input image is converted to a gray image and then denoised using low pass filter. The denoised image is transformed with local window standard deviation and then binarized using adaptive thresholding method. The generated binary image has prominent boundary and facial features like eyebrow, eyes, nose and mouth regions which can be easily detected using morphological operations. The binary image is scanned vertically to find the probable region containing the face. Actual face location is found by detecting the location of eye-brow in the probable face region scanning horizontally. The method has been tested on several single shot database images and it is giving good performance of about 93%. It is also fast as compared to existing face detection schemes which require complex processing and involves training databases.


The world through the computer: computer augmented interaction with real world environments


title: Online learning adaptation strategy for DASH clients

abstract: In this work, we propose an online adaptation logic for Dynamic Adaptive Streaming over HTTP (DASH) clients, where each client selects the representation that maximize the long term expected reward. The latter is defined as a combination of the decoded quality, the quality fluctuations and the rebuffering events experienced by the user during the playback. To solve this problem, we cast a Markov Decision Process (MDP) optimization for the selection of the optimal representations. System dynamics required in the MDP model are a priori unknown and are therefore learned through a Reinforcement Learning (RL) technique. The developed learning process exploits a parallel learning technique that improves the learning rate and limits sub-optimal choices, leading to a fast and yet accurate learning process that quickly converges to high and stable rewards. Therefore, the efficiency of our controller is not sacrificed for fast convergence. Simulation results show that our algorithm achieves a higher QoE than existing RL algorithms in the literature as well as heuristic solutions, as it is able to increase average QoE and reduce quality fluctuations.


title: Design & measurement techniques for innovative, high performance, circularly polarized, ultra wideband corrugated horn antenna with septum polarizer for space applications

abstract: This paper discusses design & measurement techniques for innovative, high performance, circularly polarized, ultra wideband corrugated horn antenna with septum polarizer having high gain & extremely low side lobes for space applications. An innovative technique of feeding with septum polarizer has been utilized to make the corrugated horn antenna circularly polarized. Excellent axial ratio of 1.9 dB has been achieved at center frequency which ensures circular polarization as required by space standards of axial ratio<3dB. Beam pattern remains circularly polarized for wide range of values for theta ranging from −70 deg to 70 deg covering the main beam as well as side lobe region. Antenna has high gain of 20dBi allowing the ease to maintain EIRP with sufficient margin for link establishment. Due to proper designing antenna have very low side lobe levels of 55dB as compared to the main lobe. Antenna possesses ultra wideband characteristics showing VSWR<2 for fantastic range of 13GHz. Excellent manufacturing equipments have been utilized to ensure high precision required for space standards. Design meets the ruggedness, strength & reliability, durability, weight, corrosion resistance, temperature variation requirements of space applications. Design, Simulation & Optimization for corrugated horn antenna with septum polarizer has been done on Ansoft HFSS v11.


title: Liquid crystal polymer (LCP) for microwave/millimeter wave multilayer packaging

abstract: This paper presents characterization and analysis of liquid crystal polymer materials for microwave and millimeter multi-layer packaging. Processing techniques have been developed to fabricate interconnects on this new LCP material. The experimental results demonstrate that an interconnect on LCP achieves a measured insertion loss of less than 0.1 dB/mm up to 50 GHz. This material is highly suitable for microwave and millimeter wave packaging.


title: Measurement of Oil Droplet Size Distributions in Food Oil/Water Emulsions by Time Domain Pulsed Field Gradient NMR.

abstract: A new method for measuring oil droplet size distributions by means of a benchtop pulsed field gradient NMR spectrometer operating in the time domain is presented. The continuous water phase is successfully suppressed by gradient pulses in order to measure the dispersed oil phase. Simulations show that for most common oil/water food emulsions the influence of droplet diffusion is negligible due to a rather large droplet size or a high viscosity of the continuous water phase. The merits of the NMR method relative to other methods are discussed in terms of sample preparation, sensitivity to cluster phenomena, and matrix effects. Preliminary results of a short validation study show a good correlation with conventional reference techniques. Copyright 2001 Academic Press.


title: Studying muslim stereotyping through microportrait extraction

abstract: Research from communication science has shown that stereotypical ideas are often reflected in language use. Media coverage of different groups in society influences the perception people have about these groups and even increases distrust and polarization among different groups. Investigating the forms of (especially subtle) stereotyping can raise awareness to journalists and help prevent reinforcing oppositions between groups in society. Conducting large-scale, deep investigations to determine whether we are faced with stereotyping is time-consuming and costly. We propose to tackle this challenges through the means of microportraits: an impression of a target group or individual conveyed in a single text. We introduce the first system implementation for Dutch and show that microportraits allow social scientists to explore various dimensions of stereotyping. We explore the possibilities provided by microportraits by investigating stereotyping of Muslims in the Dutch media. Our (preliminary) results show that microportraits provide more detailed insights into stereotyping compared to more basic models such as word clouds.


title: RNNDROP: A novel dropout for RNNS in ASR

abstract: Recently, recurrent neural networks (RNN) have achieved the state-of-the-art performance in several applications that deal with temporal data, e.g., speech recognition, handwriting recognition and machine translation. While the ability of handling long-term dependency in data is the key for the success of RNN, combating over-fitting in training the models is a critical issue for achieving the cutting-edge performance particularly when the depth and size of the network increase. To that end, there have been some attempts to apply the dropout, a popular regularization scheme for the feed-forward neural networks, to RNNs, but they do not perform as well as other regularization scheme such as weight noise injection. In this paper, we propose rnnDrop, a novel variant of the dropout tailored for RNNs. Unlike the existing methods where dropout is applied only to the non-recurrent connections, the proposed method applies dropout to the recurrent connections as well in such a way that RNNs generalize well. Our experiments show that rnnDrop is a better regularization method than others including weight noise injection. Namely, when deep bidirectional long short-term memory (LSTM) RNNs were trained with rnnDrop as acoustic models for phoneme and speech recognition, they significantly outperformed the current state-of-the-arts; we achieved the phoneme error rate of 16.29% on the TIMIT core test set for phoneme recognition and the word error rate of 5.53% on the Wall Street Journal (WSJ) dataset, dev93, for speech recognition, which are the best reported results on both of the datasets.


title: Rule-Governed Behavior: Teaching a Preliminary Repertoire of Rule-Following to Children With Autism

abstract: Rule-governed behavior is generally considered an integral component of complex verbal repertoires but has rarely been the subject of empirical research. In particular, little or no previous research has attempted to establish rule-governed behavior in individuals who do not already display the repertoire. This study consists of two experiments that evaluated multiple exemplar training procedures for teaching a simple component skill, which may be necessary for developing a repertoire of rule-governed behavior. In both experiments, children with autism were taught to respond to simple rules that specified antecedents and the behaviors that should occur in their presence. In the first study, participants were taught to respond to rules containing “if/then” statements, where the antecedent was specified before the behavior. The second experiment was a replication and extension of the first. It involved a variation on the manner in which rules were presented. Both experiments eventually demonstrated generalization to novel rules for all participants; however variations to the standard procedure were required for several participants. Results suggest that rule-following can be analyzed and taught as generalized operant behavior and implications for future research are discussed.


title: Electromagnetic field analysis of a broadband antenna built for third-generation mobile and UWB communications considering the effects of dielectric housing

abstract: In recent years, ultra-wideband (UWB) technology has drawn considerable attention as a promising option for the development of ubiquitous networks. In comparison with a conventional narrowband antenna system, the development of an electronic device with a built-in broadband antenna is more difficult because antenna performance must be ensured over the entire UWB frequency band. Previously, we proposed and designed an antenna configuration that serves both the 2 GHz band for third-generation (3G) smartphones and 3.1-10.6 GHz band for UWB technology. In this study, we investigated an antenna configuration with a dielectric housing, which operates over both the 2 GHz and 3.1-10.6 GHz band frequencies. The proposed antenna configuration has excellent VSWR characteristics with values less than 2.0 in both the 3G and UWB frequency bands.


title: Understanding the role of community in crowdfunding work

abstract: Crowdfunding provides a new opportunity for entrepre-neurs to launch ventures without having to rely on traditional funding mechanisms, such as banks and angel investing. Despite its rapid growth, we understand little about how crowdfunding users build ad hoc online communities to undertake this new way of performing entrepreneurial work. To better understand this phenomenon, we performed a qualitative study of 47 entrepreneurs who use crowdfunding platforms to raise funds for their projects. We identify community efforts to support crowdfunding work, such as providing mentorship to novices, giving feedback on campaign presentation, and building a repository of example projects to serve as models. We also identify where community efforts and technologies succeed and fail at support-ing the work in order to inform the design of crowdfunding support tools and systems.


title: Generalized Transfer Subspace Learning Through Low-Rank Constraint

abstract: It is expensive to obtain labeled real-world visual data for use in training of supervised algorithms. Therefore, it is valuable to leverage existing databases of labeled data. However, the data in the source databases is often obtained under conditions that differ from those in the new task. Transfer learning provides techniques for transferring learned knowledge from a source domain to a target domain by finding a mapping between them. In this paper, we discuss a method for projecting both source and target data to a generalized subspace where each target sample can be represented by some combination of source samples. By employing a low-rank constraint during this transfer, the structure of source and target domains are preserved. This approach has three benefits. First, good alignment between the domains is ensured through the use of only relevant data in some subspace of the source domain in reconstructing the data in the target domain. Second, the discriminative power of the source domain is naturally passed on to the target domain. Third, noisy information will be filtered out during knowledge transfer. Extensive experiments on synthetic data, and important computer vision problems such as face recognition application and visual domain adaptation for object recognition demonstrate the superiority of the proposed approach over the existing, well-established methods.


title: Redundancy Elimination Might Be Overrated: A Quantitative Study on Real-World Wireless Traffic

abstract: With significant increases in mobile device traffic slated for the foreseeable future, numerous technologies must be embraced to satisfy such demand. Notably, one of the more intriguing approaches has been blending on-device caching and device-to-device (D2D) communications. While various past research has pointed to potentially significant gains (30%+) via redundancy elimination (RE), some skepticism has emerged to whether or not such gains are truly harnessable in practice. The premise of this paper is to explore whether or not significant potential for redundancy elimination exists and whether the rise of video and encryption might blunt said efforts. Critically, we find that absent significant synchronized interests of mobile users, the actual redundancy falls well short of the promising values from the literature. In our paper, we investigate the roots for said shortcomings by exploring RE savings with regards to cache hit characteristics and to what extent client and domain diversity contribute to the realized redundancy savings.


title: Time series prediction with a non-causal neural network

abstract: Neural networks have been widely applied to time series prediction over past few decades. Generally, applications of them restrict to causal models where current values are dependent on past values. In contrast, a non-causal neural network is proposed in this paper to deal with time series prediction by allowing dependence on future values. Both past and future values are used together for training and prediction. In prediction, future values are the expected values of training samples. In addition, weightings of the past and future values are incorporated into the network to improve prediction performance. Experimental results on benchmark and FX time series show that the proposed network is effective.


title: Betrayal trauma theory of dissociative experiences: stroop and directed forgetting findings

abstract: According to betrayal trauma theory, many survivors of childhood sexual abuse learn to compartmentalize (i.e., dissociate) their traumatic experiences from conscious awareness by dividing attention. This theory predicts that those who dissociate extensively should be at an advantage during dual tasks. Using a modified Stroop task in Experiment 1 and a direct forgetting task in Experiment 2, we tried to replicate Freyd and colleagues' finding that high-dissociative people perform better under dual task conditions. However, in Experiment 1 we found that, relative to low-dissociative people, high-dissociative people exhibit a slowing of their Stroop reaction times that is independent of valence and attentional context. In Experiment 2, performance on a directed forgetting task was found to be unrelated to dissociation. Our 2 studies provide no evidence for the betrayal trauma theory, nor do they support other theories assuming that dissociation is the manifestation of an automatic defense mechanism. Language: en


title: Expression of E-cadherin in normal oral mucosa, in oral precancerous lesions and in oral carcinomas

abstract: Objective: The aim of the present study was to assess the expression of E-cad in oral precancerous lesions and conditions and oral carcinomas in comparison with normal mucosa. Materials and Methods: Total of 50 samples were selected for the study and were categorized into five groups and 10 samples in each group as Group I-oral leukoplakia (OL), Group II-oral lichen planus (OLP), Group III-oral submucous fibrosis (OSMF), Group IV-oral squamous cell carcinoma (OSCC) and Group V-normal oral mucosa (NOM) as control group. All the samples were assessed for the expression of E-cad by immunohistochemical study. Results: Upon assessing the expression of E-cad in OL, OSMF, OLP and OSCC, as majority of the samples with OSCC (90%), OL (80%), OLP (70%) and OSMF (60%) showed mild to moderate expression of E-cad staining, which was suggestive of reduction in dysplastic cells on comparison to NOM cells. This difference in expression and variation of E-cad upon comparison with normal mucosa was statistically significant ( P Conclusion: There is significant ( P < 0.001) variation of expression of E-cad with the histopathological dysplasia of the oral precancerous lesions and conditions, and the tumor differentiation of the oral cancers. However, there was no correlation of the degree of loss of expression of E-cad with the degree of dysplasia or the tumor differentiation of oral cancers. We conclude with our study that, there is a variation in the expression of E-cad but its value as a prognostic marker is questionable.


Decision Trees for Predictive Modeling


title: A New Low-Rank Tensor Model for Video Completion

abstract: In this paper, we propose a new low-rank tensor model based on the circulant algebra, namely, twist tensor nuclear norm or t-TNN for short. The twist tensor denotes a 3-way tensor representation to laterally store 2D data slices in order. On one hand, t-TNN convexly relaxes the tensor multi-rank of the twist tensor in the Fourier domain, which allows an efficient computation using FFT. On the other, t-TNN is equal to the nuclear norm of block circulant matricization of the twist tensor in the original domain, which extends the traditional matrix nuclear norm in a block circulant way. We test the t-TNN model on a video completion application that aims to fill missing values and the experiment results validate its effectiveness, especially when dealing with video recorded by a non-stationary panning camera. The block circulant matricization of the twist tensor can be transformed into a circulant block representation with nuclear norm invariance. This representation, after transformation, exploits the horizontal translation relationship between the frames in a video, and endows the t-TNN model with a more powerful ability to reconstruct panning videos than the existing state-of-the-art low-rank models.


Course Concept Extraction in MOOCs via Embedding-Based Graph Propagation


title: STag: A Stable Fiducial Marker System

abstract: In this paper, we propose STag, a fiducial marker system that provides stable pose estimation. The outer square border of the marker is used for detection and pose estimation. This is followed by a novel pose refinement step using the inner circular border. The refined pose is more stable and robust across viewing conditions compared to the state of the art. In addition, the lexicographic generation algorithm is adapted for fiducial markers, and libraries with various sizes are created. This makes the system suitable for applications that require many unique markers, or few unique markers with high occlusion resistance. The edge segment-based detection algorithm is of low complexity, and returns few false candidates. These features are demonstrated with experiments on real images, including comparisons with the state of the art fiducial marker systems.


title: RFID Data Management: Challenges and Opportunities

abstract: Radio-frequency identification (RFID) technology promises to revolutionize the way we track items in supply chain, retail store, and asset management applications. The size and different characteristics of RFID data pose many interesting challenges in the current data management systems. In this paper, we provide a brief overview of RFID technology and highlight a few of the data management challenges that we believe are suitable topics for exploratory research.


title: Multi-dimension semantic dictionary for online intelligence

abstract: The proposed online intelligence (OI) is to provide popular and well-formatted reports for direct decision support based on online data. Completeness, preciseness, conciseness and smartness are proposed as the basic technical indicators to evaluate an OI application. OI requires similar data processing flow and reporting tools with traditional business intelligence (BI). However, OI collects data basically from online other than provided by enterprise application systems. The online data is usually non-structural or semi-structural. Semantics techniques are required by OI to find and process online data into decision-making knowledge automatically in many cases. To this end, a multi-dimension semantic dictionary is proposed to divide online objects' semantics into four semantic aspects, i.e., information semantics, business semantics, application semantics and structural semantics. The four semantic aspects of the semantic dictionary are developed separately and interlinked as a multi-dimension semantic matrix to conduct matchmaking when find and process online data. In this work, the related concepts and implementation framework of the multi-dimension semantic dictionary are discussed and a real application for illegal drug tracing is provided to illustrate how OI make sense and semantics are developed.


title: A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA

abstract: Author(s): Zhang, Xinyu | Advisor(s): Kreutz-Delgado, Ken; Smarr, Larry | Abstract: In recent years deep learning algorithms have shown extremely high performance on machine learning tasks such as image classification and speech recognition. In support of such applications, various FPGA accelerator architectures have been proposed for convolutional neural networks (CNNs) that enable high performance for classification tasks at lower power than CPU and GPU processors. However, to date, there has been little research on the use of FPGA implementations of deconvolutional neural networks (DCNNs). DCNNs, also known as generative CNNs, encode high-dimensional probability distributions and have been widely used for computer vision applications such as scene completion, scene segmentation, image creation, image denoising, and super-resolution imaging. We propose an FPGA architecture for deconvolutional networks built around an accelerator which effectively handles the complex memory access patterns needed to perform strided deconvolutions, and that supports convolution as well. We also develop a three-step design optimization method that systematically exploits statistical analysis, design space exploration and VLSI optimization. To verify our FPGA deconvolutional accelerator design methodology we train DCNNs offline on two representative datasets using the generative adversarial network method (GAN) run on Tensorflow, and then map these DCNNs to an FPGA DCNN-plus-accelerator implementation to perform generative inference on a Xilinx Zynq-7000 FPGA. Our DCNN implementation achieves a peak performance density of 0.012 GOPs/DSP.


Probability and computing: randomized algorithms and probabilistic analysis.


A Survey of High-Frequency Trading Strategies


title: Excitatory and inhibitory neurons express c-Fos in barrel-related columns after exploration of a novel environment

abstract: Recent work has shown that behaviorally meaningful sensory information processing is accompanied by the induction of several transcription factors in the barrel cortex of rodents. It is now generally accepted that stimulus-transcription coupling is an important step in the sequence of events leading to long-term plastic changes in neuronal structure and function. Nevertheless, so far few data are available as to what types of neurons are involved in such a genomic response. Here, we determined the morphological and neurochemical identity of neurons in rat barrel cortex showing a c-Fos-immunoreactive nucleus after exploration of an enriched environment. Double stainings of c-Fos and glial fibrillary acidic protein excluded astrocytes as a possible cell type expressing this transcription factor. By morphological phenotyping with intracellular Lucifer Yellow injections, it was found that a large majority were probably excitatory pyramidal cells, but inhibitory interneurons were also found to contain c-Fos-immunoreactive nuclei. By neurochemical phenotyping of GABAergic interneurons with specific antibodies, a significant induction was found, in a layer-dependent manner, for the populations of glutamic acid decarboxylase-, parvalbumin-, calbindin- and vasoactive intestinal polypeptide-immunoreactive neurons but not for calretinin-immunoreactive cells in experimental compared to control columns. From these data we conclude that thalamic afferents effectively drive cortical excitatory as well as inhibitory intracortical circuits. Thus, the adaptations of receptive field properties of cortical neurons after different manipulations of the sensory periphery are likely to be caused by plastic changes in excitatory and inhibitory networks.


title: A ZVS Pulsewidth Modulation Full-Bridge Converter With a Low-RMS-Current Resonant Auxiliary Circuit

abstract: This paper presents the description and analysis of a phase-shift-modulated full-bridge converter with a novel robust passive low-rms-current resonant auxiliary circuit for zero-voltage switching (ZVS) operation of both the leading and lagging switch legs. Detailed time-domain analysis describes the steady-state behavior of the auxiliary circuit in different operating conditions. An in-depth comparative study between a fully specified baseline converter and the equivalent converter using the proposed resonant auxiliary circuit is presented. For a similar peak auxiliary current to ensure ZVS operation, a minimum of 20% reduction in rms current is obtained, which decreases the conduction losses. Key characteristics and design considerations are also fully discussed. Experimental results from a 750-W prototype confirm the predicted enhancements using the proposed resonant auxiliary circuit.


title: Off-policy learning based on weighted importance sampling with linear computational complexity

abstract: Importance sampling is an essential component of model-free off-policy learning algorithms. Weighted importance sampling (WIS) is generally considered superior to ordinary importance sampling but, when combined with function approximation, it has hitherto required computational complexity that is O(n2) or more in the number of features. In this paper we introduce new off-policy learning algorithms that obtain the benefits of WIS with O(n) computational complexity. Our algorithms maintain for each component of the parameter vector a measure of the extent to which that component has been used in previous examples. This measure is used to determine component-wise step sizes, merging the ideas of stochastic gradient descent and sample averages. We present our main WIS-based algorithm first in an intuitive acausal form (the forward view) and then derive a causal algorithm using eligibility traces that is equivalent but more efficient (the backward view). In three small experiments, our algorithms performed significantly better than prior O(n) algorithms for off-policy policy evaluation. We also show that our adaptive step-size technique can also improve the performance of on-policy algorithms such as TD(λ) and true online TD(λ).


title: Empathy for positive and negative emotions in the gustatory cortex

abstract: Abstract Anterior insula and adjacent frontal operculum (hereafter referred to as IFO) are active during exposure to tastants/odorants (particularly disgusting ones), and during the viewing of disgusted facial expressions. Together with lesion data, the IFO has thus been proposed to be crucial in processing disgust-related stimuli. Here, we examined IFO involvement in the processing of other people’s gustatory emotions more generally by exposing participants to food-related disgusted, pleased and neutral facial expressions during functional magnetic resonance imaging (fMRI). We then exposed participants to pleasant, unpleasant and neutral tastants for the purpose of mapping their gustatory IFO. Finally, we associated participants’ self reported empathy (measured using the Interpersonal Reactivity Index, IRI) with their IFO activation during the witnessing of others’ gustatory emotions. We show that participants’ empathy scores were predictive of their gustatory IFO activation while witnessing both the pleased and disgusted facial expression of others. While the IFO has been implicated in the processing of negative emotions of others and empathy for negative experiences like pain, our finding extends this concept to empathy for intense positive feelings, and provides empirical support for the view that the IFO contributes to empathy by mapping the bodily feelings of others onto the internal bodily states of the observer, in agreement with the putative interoceptive function of the IFO.


title: Rotation-invariant similarity in time series using bag-of-patterns representation

abstract: For more than a decade, time series similarity search has been given a great deal of attention by data mining researchers. As a result, many time series representations and distance measures have been proposed. However, most existing work on time series similarity search relies on shape-based similarity matching. While some of the existing approaches work well for short time series data, they typically fail to produce satisfactory results when the sequence is long. For long sequences, it is more appropriate to consider the similarity based on the higher-level structures. In this work, we present a histogram-based representation for time series data, similar to the "bag of words" approach that is widely accepted by the text mining and information retrieval communities. We performed extensive experiments and show that our approach outperforms the leading existing methods in clustering, classification, and anomaly detection on dozens of real datasets. We further demonstrate that the representation allows rotation-invariant matching in shape datasets.


title: An MDP-based Recommender System

abstract: Typical recommender systems adopt a static view of the recommendation process and treat it as a prediction problem. We argue that it is more appropriate to view the problem of generating recommendations as a sequential optimization problem and, consequently, that Markov decision processes (MDPs) provide a more appropriate model for recommender systems. MDPs introduce two benefits: they take into account the long-term effects of each recommendation and the expected value of each recommendation. To succeed in practice, an MDP-based recommender system must employ a strong initial model, must be solvable quickly, and should not consume too much memory. In this paper, we describe our particular MDP model, its initialization using a predictive model, the solution and update algorithm, and its actual performance on a commercial site. We also describe the particular predictive model we used which outperforms previous models. Our system is one of a small number of commercially deployed recommender systems. As far as we know, it is the first to report experimental analysis conducted on a real commercial site. These results validate the commercial value of recommender systems, and in particular, of our MDP-based approach.


title: A Battery-Less, Implantable Neuro-Electronic Interface for Studying the Mechanisms of Deep Brain Stimulation in Rat Models

abstract: Although deep brain stimulation (DBS) has been a promising alternative for treating several neural disorders, the mechanisms underlying the DBS remain not fully understood. As rat models provide the advantage of recording and stimulating different disease-related regions simultaneously, this paper proposes a battery-less, implantable neuro-electronic interface suitable for studying DBS mechanisms with a freely-moving rat. The neuro-electronic interface mainly consists of a microsystem able to interact with eight different brain regions bi-directionally and simultaneously. To minimize the size of the implant, the microsystem receives power and transmits data through a single coil. In addition, particular attention is paid to the capability of recording neural activities right after each stimulation, so as to acquire information on how stimulations modulate neural activities. The microsystem has been fabricated with the standard 0.18 $\mu$ m CMOS technology. The chip area is 7.74 mm $^2$ , and the microsystem is able to operate with a single supply voltage of 1 V. The wireless interface allows a maximum power of 10 mW to be transmitted together with either uplink or downlink data at a rate of 2 Mbps or 100 kbps, respectively. The input referred noise of recording amplifiers is 1.16 $\mu$ Vrms, and the stimulation voltage is tunable from 1.5 V to 4.5 V with 5-bit resolution. After the electrical functionality of the microsystem is tested, the capability of the microsystem to interface with rat brain is further examined and compared with conventional instruments. All experimental results are presented and discussed in this paper.


title: Sarcoid and the nail: review of the literature

abstract: Summary ::: ::: Nail involvement in sarcoidosis is rare, but its presence is often associated with chronic systemic disease. We have reviewed all 33 reported cases of nail involvement with sarcoidosis, including our own case. Nail changes in sarcoidosis, while rare, can include dystrophy (n = 10, 30%), onycholysis (n = 7, 21%), subungual hyperkeratosis (n = 7, 21%), nail hyperkeratosis (n = 2, 6%) and longitudinal ridging (n = 8, 24%). All reported cases of nail sarcoidosis occurred in the setting of systemic involvement. In almost half of the cases, bony cysts were noted on radiological examination. The presence of nail dystrophy in a patient with sarcoidosis should therefore prompt radiological examination and investigation for systemic involvement.


title: A 1/2.8-inch 24Mpixel CMOS image sensor with 0.9μm unit pixels separated by full-depth deep-trench isolation

abstract: CMOS image sensors (CIS) have attracted much attention for the emerging mobile market, and the demand of high-resolution image sensors in mobile applications continues to increase [1-3]. For this reason, pixel pitch has been reduced down to 1.0μσι for mass production. Nevertheless, CISs are continuously scaling down to meet the strong demand for higher-resolution images. However, when the pixel size is reduced down to the sub-micron regime (possibly smaller than the diffraction limit), it is very important to consider photo sensitivity and crosstalk, which determine signal-to-noise ratio (SNR). To minimize degradation of photo sensitivity, back-side illumination (BSI), which collects light at the back side, is widely used instead of front-side illumination. In addition to BSI technology, deep-trench isolation (DTI) has emerged as a leading candidate to suppress crosstalk since it physically isolates the pixel. Previous work shows that partial-depth DTI can be applied in a 1.12μm-pitch pixel [4]. Furthermore, full-depth DTI has been demonstrated in a 1.12μm pixel with 24% larger full-well capacity (FWC), 30% smaller YSNR10, 2.0dB higher SNR, and especially for lower crosstalk (12.5%) compared with a conventional one [5]. In this work, a 24-Mpixel CIS with 0.9μσι unit pixels that takes advantage of full-depth DTI is demonstrated.


title: Security and Privacy Analysis of Mobile Health Applications: The Alarming State of Practice

abstract: Recent advances in hardware and telecommunications have enabled the development of low cost mobile devices equipped with a variety of sensors. As a result, new functionalities, empowered by emerging mobile platforms, allow millions of applications to take advantage of vast amounts of data. Following this trend, mobile health applications collect users health-related information to help them better comprehend their health status and to promote their overall wellbeing. Nevertheless, health-related information is by nature and by law deemed sensitive and, therefore, its adequate protection is of substantial importance. In this paper we provide an in-depth security and privacy analysis of some of the most popular freeware mobile health applications. We have performed both static and dynamic analysis of selected mobile health applications, along with tailored testing of each application’s functionalities. Long term analyses of the life cycle of the reviewed apps and our general data protection regulation compliance auditing procedure are unique features of the present paper. Our findings reveal that the majority of the analyzed applications do not follow well-known practices and guidelines, not even legal restrictions imposed by contemporary data protection regulations, thus jeopardizing the privacy of millions of users.


title: Video-Based Human Movement Analysis and Its Application to Surveillance Systems

abstract: This paper presents a novel posture classification system that analyzes human movements directly from video sequences. In the system, each sequence of movements is converted into a posture sequence. To better characterize a posture in a sequence, we triangulate it into triangular meshes, from which we extract two features: the skeleton feature and the centroid context feature. The first feature is used as a coarse representation of the subject, while the second is used to derive a finer description. We adopt a depth-first search (dfs) scheme to extract the skeletal features of a posture from the triangulation result. The proposed skeleton feature extraction scheme is more robust and efficient than conventional silhouette-based approaches. The skeletal features extracted in the first stage are used to extract the centroid context feature, which is a finer representation that can characterize the shape of a whole body or body parts. The two descriptors working together make human movement analysis a very efficient and accurate process because they generate a set of key postures from a movement sequence. The ordered key posture sequence is represented by a symbol string. Matching two arbitrary action sequences then becomes a symbol string matching problem. Our experiment results demonstrate that the proposed method is a robust, accurate, and powerful tool for human movement analysis.


title: Accessibility evaluation of top university websites: a comparative study of Kyrgyzstan, Azerbaijan, Kazakhstan and Turkey

abstract: It is important that university websites and services offered through their sites are used effectively, efficiently and satisfactorily by the whole target group of the university, including disabled users. However, universities in many countries are still unable to meet the criteria for web accessibility. This study aimed to test the websites of the top universities in Kyrgyzstan, Kazakhstan, Azerbaijan and Turkey using automated assessment tools. The results showed that university websites are more popular in Turkey, and in Turkish universities developers pay more attention to the performance of websites, followed by websites of Azerbaijani, Kyrgyz and Kazakh universities. The majority of the university websites in the study did not meet the WCAG 2.0 accessibility criteria. Only two Kyrgyz and two Kazakh university websites attained conformance level A, and only three, one Kyrgyz and two Kazakh, achieved accessibility conformance level AAA. Based on the results, it was determined that universities included in the present study need to devote more effort to making their websites more accessible for their users.


title: Data compression issues with pattern matching in historical data

abstract: It is common practice in the process industries to compress plant data before it is archived. However, compression may alter the data in a manner that makes it difficult to extract useful information from it. In this paper we evaluate the effectiveness of a new pattern matching technique for applications involving compressed historical data. We also compare several data compression methods with regard to efficiency, data reconstruction, and suitability for pattern matching applications.


title: Analysis of Deadline Scheduled Real-Time Systems

abstract: A uniform, flexible approach is proposed for analysing the feasibility of deadline scheduled real-time systems. In its most general formulation, the analysis assumes sporadically periodic tasks with arbitrary deadlines, release jitter, and shared resources. System overheads of a tick driven scheduler implementation, and scheduling of soft aperiodic tasks are also accounted for. A procedure for the computation of task worst-case response times is also described for the same model. While this problem has been largely studied in the context of fixed priority systems, we are not aware of other works that have proposed a solution to it when deadline scheduling is assumed. The worst-case response time evaluation is a fundamental tool for analysing {\em end-to-end} timing constraints in distributed systems~\cite{Ti94b}.


title: A 14.5 pW, 31 ppm/°C resistor-less 5 pA current reference employing a self-regulated push-pull voltage reference generator

abstract: This paper presents a gate-leakage-based supply- and temperature-stabilized current reference generator that can output currents as low as 5 pA with minimal power overhead. The output reference current is generated by driving a set of gate-leakage transistors designed to have opposing temperature coefficients with a stabilized voltage reference. Low-power operation is achieved by generating the voltage reference via a novel two-stage, 4T push-pull structure that can operate at a low supply voltage, and driving this reference to the gate-leakage transistors via a low-voltage self-biased amplifier. Designed in a 65 nm CMOS process, the proposed current reference generator is simulated to consume 14.5 pW at a 0.5 V supply voltage. Due to the push-pull structure and complementary gate-leakage transistors, the design achieves a temperature stability of 31 ppm/°C from 0 °C to 100 ° C, and a line sensitivity of 0.94%/V averaged across 500 Monte Carlo samples, thereby enabling an ultra-low-power, area-efficient, and temperature- and supply-stabilized current reference solution at pA-levels.


title: 3-D Tracking of shoes for Virtual Mirror applications

abstract: In this paper, augmented reality techniques are used in order to create a virtual mirror for the real-time visualization of customized sports shoes. Similar to looking into a mirror when trying on new shoes in a shop, we create the same impression but for virtual shoes that the customer can design individually. For that purpose, we replace the real mirror by a large display that shows the mirrored input of a camera capturing the legs and shoes of a person. 3-D tracking of both feet and exchanging the real shoes by computer graphics models gives the impression of actually wearing the virtual shoes. The 3-D motion tracker presented in this paper, exploits mainly silhouette information to achieve robust estimates for both shoes from a single camera view. The use of a hierarchical approach in an image pyramid enables real-time estimation at frame rates of more than 30 frames per second.


title: Transdermal Delivery of Ondansetron Hydrochloride via Bilosomal Systems: In Vitro, Ex Vivo, and In Vivo Characterization Studies

abstract: Ondansetron hydrochloride (OND) is commonly used for management of postoperative and chemotherapeutic-induced nausea and vomiting. It suffers from low bioavailability (60%) and rapid elimination (t1/2; 3-4 h). The current work aimed to develop OND-loaded bilosomes as a promising transdermal delivery system capable of surmount drug limitations. The variables influencing the development of OND-loaded bilosomes and niosomes (18 systems) via the thin film hydration technique were investigated, including surfactant type (Span®60 or Span®80), surfactant/cholesterol molar ratio (7:0, 7:1, or 7:3), and sodium deoxycholate (SDC) concentration (0, 2.5, or 5%, w/v). The systems were characterized for particle size, polydispersity index, zeta potential, drug entrapment efficiency (EE%), and in vitro permeation. Based on factorial analysis (32·21) and calculations of desirability values, six systems were further subjected to ex vivo permeation through excised rat skin, differential scanning calorimetry (DSC), powder x-ray diffraction (PXRD), and transmission electron microscopy. Histopathological and in vivo permeation studies in rats were conducted on the best achieved system (B6) in comparison to drug solution. Higher desirability values were achieved with Span® 60-based bilosomes, surfactant/cholesterol molar ratio of 7:1, and SDC concentration of 2.5% w/v with respect to small vesicle size, polydispersity index and high zeta potential, EE%, and cumulative drug permeation. OND was dispersed in amorphous state as revealed from DSC and PXRD studies. No marked effect was observed in rat skin following application of B6 system while higher ex vivo and in vivo cumulative permeation profiles were revealed. Bilosomal systems were considered as safe and efficient carriers for the transdermal delivery for OND.


title: Transform-Data-by-Example (TDE): Extensible Data Transformation in Excel

abstract: Business analysts and data scientists today increasingly need to clean, standardize and transform diverse data sets, such as name, address, date time, phone number, etc., before they can perform analysis. These ad-hoc transformation problems are typically solved by one-off scripts, which is both difficult and time-consuming. Our observation is that these domain-specific transformation problems have long been solved by developers with code libraries, which are often shared in places like GitHub. We thus develop an extensible data transformation system called Transform-Data-by-Example (TDE) that can leverage rich transformation logic in source code, DLLs, web services and mapping tables, so that end-users only need to provide a few (typically 3) input/output examples, and TDE can synthesize desired programs using relevant transformation logic from these sources. The beta version of TDE was released in Office Store for Excel.


title: Chaotic grasshopper optimization algorithm for global optimization

abstract: Grasshopper optimization algorithm (GOA) is a new meta-heuristic algorithm inspired by the swarming behavior of grasshoppers. The present study introduces chaos theory into the optimization process of GOA so as to accelerate its global convergence speed. The chaotic maps are employed to balance the exploration and exploitation efficiently and the reduction in repulsion/attraction forces between grasshoppers in the optimization process. The proposed chaotic GOA algorithms are benchmarked on thirteen test functions. The results show that the chaotic maps (especially circle map) are able to significantly boost the performance of GOA.


title: Clinical studies on core-carrier obturation: a systematic review and meta-analysis

abstract: BackgroundThis systematic review aimed to evaluate the clinical performance of core-carrier obturation in endodontic treatment.MethodsKeywords of “(core carrier OR Thermafil) OR (cold lateral condensation OR lateral condensation) OR (warm vertical condensation OR vertical condensation) AND (obturation OR root canal filling) AND clinical study” were searched for all obtainable publications up to year 2017 in the databases of PubMed, ScienceDirect, EMBASE, Scopus and Web of Science. The success rate, short-term postoperative pain, overfilling and adaptation of core-carrier obturation from clinical studies were selected. Reviews, laboratory studies, animal studies and irrelevant reports were excluded.Results1349 relevant articles were identified with 149 duplicated articles removed and 1173 irrelevant articles were excluded after screening. The titles and abstracts of the 19 identified articles were screened in the systematic review. The full texts of remaining articles were retrieved with data extracted for meta-analysis on the success rate, postoperative pain, overfilling and adaptation of obturation. The pooled success rate of core-carrier obturation was 83% (95% CI: 69%-91%). The pooled incidence of 1-day and 7-day short-term postoperative pain were 35% (95% CI: 15%-62%) and 6% (95% CI: 1-35%). The pooled proportion of teeth with overfilling and adequate adaptation of the obturation material were 31% (95% CI: 18%-50%) and 85% (95% CI: 75%-91%), respectively.ConclusionsThe success rate of endodontic treatment using core-carrier obturation was 83%. Short-term postoperative pain was not uncommon (24%). Most teeth (85%) had adequate adaptation using core-carrier obturation material, but a considerable amount of teeth (31%) had overfilling.


title: On Flat versus Hierarchical Classification in Large-Scale Taxonomies

abstract: We study in this paper flat and hierarchical classification strategies in the context of large-scale taxonomies. To this end, we first propose a multiclass, hierarchical data dependent bound on the generalization error of classifiers deployed in large-scale taxonomies. This bound provides an explanation to several empirical results reported in the literature, related to the performance of flat and hierarchical classifiers. We then introduce another type of bound targeting the approximation error of a family of classifiers, and derive from it features used in a meta-classifier to decide which nodes to prune (or flatten) in a large-scale taxonomy. We finally illustrate the theoretical developments through several experiments conducted on two widely used taxonomies.


title: MIDA: Multiple Imputation using Denoising Autoencoders

abstract: Missing data is a significant problem impacting all domains. State-of-the-art framework for minimizing missing data bias is multiple imputation, for which the choice of an imputation model remains nontrivial. We propose a multiple imputation model based on overcomplete deep denoising autoencoders. Our proposed model is capable of handling different data types, missingness patterns, missingness proportions and distributions. Evaluation on several real life datasets show our proposed model significantly outperforms current state-of-the-art methods under varying conditions while simultaneously improving end of the line analytics.


title: ActiveSLA: a profit-oriented admission control framework for database-as-a-service providers

abstract: The system overload is a common problem in a Database-as-a-Serice (DaaS) environment because of unpredictable and bursty workloads from various clients. Due to the service delivery nature of DaaS, such system overload usually has direct economic impact on the service provider, who has to pay penalties if the system performance does not meet clients' service level agreements (SLAs). In this paper, we investigate techniques that prevent system overload by using admission control. We propose a profit-oriented admission control framework, called ActiveSLA, for DaaS providers. ActiveSLA is an end-to-end framework that consists of two components. First, a prediction module estimates the probability for a new query to finish the execution before its deadline. Second, based on the predicted probability, a decision module determines whether or not to admit the given query into the database system. The decision is made with the profit optimization objective, where the expected profit is derived from the service level agreements between a service provider and its clients. We present extensive real system experiments with standard database benchmarks, under different traffic patterns, DBMS settings, and SLAs. The results demonstrate that ActiveSLA is able to make admission control decisions that are both more accurate and more profit-effective than several state-of-the-art methods.


title: ArnetMiner: extraction and mining of academic social networks

abstract: This paper addresses several key issues in the ArnetMiner system, which aims at extracting and mining academic social networks. Specifically, the system focuses on: 1) Extracting researcher profiles automatically from the Web; 2) Integrating the publication data into the network from existing digital libraries; 3) Modeling the entire academic network; and 4) Providing search services for the academic network. So far, 448,470 researcher profiles have been extracted using a unified tagging approach. We integrate publications from online Web databases and propose a probabilistic framework to deal with the name ambiguity problem. Furthermore, we propose a unified modeling approach to simultaneously model topical aspects of papers, authors, and publication venues. Search services such as expertise search and people association search have been provided based on the modeling results. In this paper, we describe the architecture and main features of the system. We also present the empirical evaluation of the proposed methods.


title: A Performance Comparison of Container-Based Virtualization Systems for MapReduce Clusters

abstract: Virtualization as a platform for resource-intensive applications, such as MapReduce (MR), has been the subject of many studies in the last years, as it has brought benefits such as better manageability, overall resource utilization, security and scalability. Nevertheless, because of the performance overheads, virtualization has traditionally been avoided in computing environments where performance is a critical factor. In this context, container-based virtualization can be considered a lightweight alternative to the traditional hypervisor-based virtualization systems. In fact, there is a trend towards using containers in MR clusters in order to provide resource sharing and performance isolation (e.g., Mesos and YARN). However, there are still no studies evaluating the performance overhead of the current container-based systems and their ability to provide performance isolation when running MR applications. In this work, we conducted experiments to effectively compare and contrast the current container-based systems (Linux VServer, OpenVZ and Linux Containers (LXC)) in terms of performance and manageability when running on MR clusters. Our results showed that although all container-based systems reach a near-native performance for MapReduce workloads, LXC is the one that offers the best relationship between performance and management capabilities (specially regarding to performance isolation).


title: [Poster] AR for the comprehension of linear perspective in the Renaissance masterpiece the Holy Trinity (Masaccio, 1426)

abstract: This work shows how augmented reality can help the comprehension of the geometric construction of the perspective in the Holy Trinity fresco (Masaccio, 1426) in the church of Santa Maria Novella in Florence, opening new scenarios in the demonstration of the subtle underlying geometric principles.


title: Primary Cavernous Hemangioma of the Glans Penis: Rare Case Report with a Review of the Literature

abstract: Hemangiomas of the urinary tract are rare lesions, and those of the glans penis are even rarer. A 23-year-old man with a hemangioma of the glans penis treated successfully with intralesional sclerotherapy is presented. Other options for the management of this disorder are discussed.


title: Energy based Efficient Resource Scheduling: A Step Towards Green Computing

abstract: Cloud Computing is an evolving area of efficient utilization of computing resources. Data centers accommodating Cloud applications ingest massive quantities of energy, contributing to high functioning expenditures and carbon footprints to the atmosphere. Hence, Green Cloud computing resolutions are required not only to save energy for the environment but also to decrease operating charges. In this paper, we emphasis on the development of energy based resource scheduling framework and present an algorithm that consider the synergy between various data center infrastructures (i.e., software, hardware, etc.), and performance. In specific, this paper proposes (a) architectural principles for energy efficient management of Clouds; (b) energy efficient resource allocation strategies and scheduling algorithm considering Quality of Service (QoS) outlooks. The performance of the proposed algorithm has been evaluated with the existing energy based scheduling algorithms. The experimental results demonstrate that this approach is effective in minimizing the cost and energy consumption of Cloud applications thus moving towards the achievement of Green Clouds.


title: Rumor Detection and Classification for Twitter Data

abstract: With the pervasiveness of online media data as a source of information verifying the validity of this information is becoming even more important yet quite challenging. Rumors spread a large quantity of misinformation on microblogs. In this study we address two common issues within the context of microblog social media. First we detect rumors as a type of misinformation propagation and next we go beyond detection to perform the task of rumor classification. WE explore the problem using a standard data set. We devise novel features and study their impact on the task. We experiment with various levels of preprocessing as a precursor of the classification as well as grouping of features. We achieve and f-measure of over 0.82 in RDC task in mixed rumors data set and 84 percent in a single rumor data set using a two-step classification approach.


Using Tweets to Predict the Stock Market


Design and Implementation of a Smart Home System


title: Locality Sensitive Hashing based deepmatching for optical flow estimation

abstract: DeepMatching (DM) is one of the state-of-art matching algorithms to compute quasi-dense correspondences between images. Recent optical flow methods use DeepMatching to find initial image correspondences and achieves outstanding performance. However, the key building block of DeepMatching, the correlation map computation, is time-consuming. In this paper, we propose a new algorithm, LSHDM, which addresses the problem by employing Locality Sensitive Hashing (LSH) to DeepMatching. The computational complexity is greatly reduced for the correlation map computation step. Experiments show that image matching can be accelerated by our approach in ten times or more compared to DeepMatching, while retaining comparable accuracy for optical flow estimation.


Comparing Supervised vs . Unsupervised Image Segmentation Methods


title: Cognitive radar - enabling techniques for next generation radar systems

abstract: Cognitive radar is a young discipline with the claim to open the door for next generation radar systems providing a higher efficiency, robust operation via intelligent choice of radar actions, and even a high degree of autonomy. The ideas behind are collected in the two most influential textbooks by Haykin [1] and Guerci [2]. Both contain also experimental results while there are not much papers else presenting real world experience. In this paper we summarize the general approach and discuss two examples for application of cognitive radar.


title: Conceptualisation and Development of a Quality of Life Measure for Parents of Children with Autism Spectrum Disorder

abstract: Parents of children with autism spectrum disorder (ASD) tend to experience greater psychological distress than parents of typically developing children or children with other disabilities. Quality of Life (QoL) is increasingly recognised as a critical outcome measure for planning and treatment purposes in ASD. There is a need for ASD-specific QoL measures as generic measures may not capture all relevant aspects of living with ASD. This paper describes the conceptualisation and development of an autism-specific measure of QoL, the Quality of Life in Autism Questionnaire (QoLA) for parents and caregivers of children with ASD, that is suitable to clinical and research settings. Preliminary psychometric properties (reliability and validity) of the measure are also presented. The QoLA has 48 items in two subscales: one comprising QoL items and the second a parent report of how problematic their child's ASD symptoms are. A study involving 39 families suggested the QoLA has excellent internal consistency as well as good known-groups validity between parents of children with ASD and those who were typically developing. The QoLA also showed good convergent validity with other measures of QoL and ASD symptom severity, respectively. The QoLA may be a valuable assessment tool and merits further psychometric evaluation.


title: A Coordinate Descent Primal-Dual Algorithm and Application to Distributed Asynchronous Optimization

abstract: Based on the idea of randomized coordinate descent of $\alpha$ -averaged operators, a randomized primal-dual optimization algorithm is introduced, where a random subset of coordinates is updated at each iteration. The algorithm builds upon a variant of a recent (deterministic) algorithm proposed by Vũ and Condat that includes the well-known Alternating Direction Method of Multipliers as a particular case. The obtained algorithm is used to solve asynchronously a distributed optimization problem. A network of agents, each having a separate cost function containing a differentiable term, seek to find a consensus on the minimum of the aggregate objective. The method yields an algorithm where at each iteration, a random subset of agents wake up, update their local estimates, exchange some data with their neighbors, and go idle. Numerical results demonstrate the attractive performance of the method. The general approach can be naturally adapted to other situations where coordinate descent convex optimization algorithms are used with a random choice of the coordinates.


title: A cloud robot system using the dexterity network and berkeley robotics and automation as a service (Brass)

abstract: In support of Cloud Robotics, Robotics and Automation as a Service (RAaaS) frameworks have the potential to reduce the complexity of software development, simplify software installation and maintenance, and facilitate data sharing for machine learning. In this proof-of-concept paper, we describe Berkeley Robotics and Automation as a Service (Brass), a RAaaS prototype that allows robots to access a remote server that hosts a robust grasp-planning system (Dex-Net 1.0) that maintains data on hundreds of candidate grasps on thousands of 3D object meshes and uses perturbation sampling to estimate and update a stochastic robustness metric for each grasp. Results suggest that such a system can increase grasp reliability over naive locally-computed grasping strategies with network latencies of 30 and 200 msec for servers 500 and 6000 miles away, respectively. We also study how the system can use execution reports from robots in the field to update grasp recommendations over time.


title: Labeled Content of Two Furanocoumarins in Dietary Supplements Correlates with Neither Actual Content nor CYP3A Inhibitory Activity

abstract: Abstract Dietary supplements are a multi-billion dollar business, with yearly profit increases. Allegedly safe, these supplements are marketed to a variety of niches, encompassing claims from immune support to weight loss. Six sports nutrition supplements were acquired that were labeled to contain the furanocoumarin(s) bergamottin and/or 6′,7′-dihydroxybergamottin (DHB), both of which are potent irreversible inhibitors of the prominent drug metabolizing enzyme cytochrome P450 3A (CYP3A). Both furanocoumarins are typically present in grapefruit juice, which has been shown to inhibit intestinal CYP3A, perpetrating an increase in the systemic exposure of certain concomitant ‘victim’ drugs. The acquired supplements were analyzed using ultra-performance liquid chromatography coupled to both a photodiode array (PDA) detector and a triple quadrupole mass spectrometer (MS). Contrary to the product labeling, four of the supplements contained no detectable quantities of either furanocoumarin (LOD 0.060 μg/capsule), while two of the supplements contained minimal amounts (one contained 12.13 (±0.23) μg bergamottin and 65.51 (±0.64) μg DHB per capsule; the other contained 2.705 (±0.069) μg bergamottin per capsule and no detectable quantities of DHB). A CYP3A inhibition bioassay was used to assess whether the actual content of the furanocoumarins correlated with CYP3A inhibitory activity. Despite the low amounts of bergamottin and DHB, CYP3A inhibition by the supplements was greater than could be accounted for by the two furanocoumarins. The additional activity suggests the presence of other potent or highly abundant CYP3A inhibitors.


OBER’S TEST AND MODIFIED OBER’S TEST ARE RELIABLE MEANS OF MEASURING ILIOTIBIAL BAND USING BOTH GONIOMETER AND INCLINOMETER


title: The yeast peroxisomal adenine nucleotide transporter: characterization of two transport modes and involvement in ΔpH formation across peroxisomal membranes

abstract: The yeast peroxisomal adenine nucleotide carrier, Ant1p, was shown to catalyse unidirectional transport in addition to exchange of substrates. In both transport modes, proton movement occurs. Nucleotide hetero-exchange is H+-compensated and electroneutral. Furthermore, microscopic fluorescence imaging of a pH-sensitive green fluorescent protein targeted to peroxisomes shows that Ant1p is involved in the formation of a ΔpH across the peroxisomal membrane, acidic inside.


title: The Berkeley Out-of-Order Machine (BOOM): An Industry-Competitive, Synthesizable, Parameterized RISC-V Processor

abstract: Abstract : BOOM is a synthesizable, parameterized, superscalar out-of-order RISC-V core designed to serve as the prototypical baseline processor for future micro-architectural studies of out-of-order processors. Our goal is to provide a readable, open-source implementation for use in education, research, and industry. BOOM is written in roughly 9,000 lines of the hardware construction language Chisel. We leveraged Berkeleys open-source Rocket-chip SoC generator, allowing us to quickly bring up an entire multi-core processor system (including caches and uncore) by replacing the in-order Rocket core with an out-of-order BOOM core. BOOM supports atomics, IEEE754-2008 floating-point, and page-based virtual memory. We have demonstrated BOOM running Linux, SPEC CINT2006, and CoreMark.


title: BUFFALO: bloom filter forwarding architecture for large organizations

abstract: In enterprise and data center networks, the scalability of the data plane becomes increasingly challenging as forwarding tables and link speeds grow. Simply building switches with larger amounts of faster memory is not appealing, since high-speed memory is both expensive and power hungry. Implementing hash tables in SRAM is not appealing either because it requires significant overprovisioning to ensure that all forwarding table entries fit. Instead, we propose the BUFFALO architecture, which uses a small SRAM to store one Bloom filter of the addresses associated with each outgoing link. We provide a practical switch design leveraging flat addresses and shortest-path routing. BUFFALO gracefully handles false positives without reducing the packet-forwarding rate, while guaranteeing that packets reach their destinations with bounded stretch with high probability. We tune the sizes of Bloom filters to minimize false positives for a given memory size. We also handle routing changes and dynamically adjust Bloom filter sizes using counting Bloom filters in slow memory. Our extensive analysis, simulation, and prototype implementation in kernel-level Click show that BUFFALO significantly reduces memory cost, increases the scalability of the data plane, and improves packet-forwarding performance.


title: Demographic Analysis from Biometric Data: Achievements, Challenges, and New Frontiers

abstract: Biometrics is the technique of automatically recognizing individuals based on their biological or behavioral characteristics. Various biometric traits have been introduced and widely investigated, including fingerprint, iris, face, voice, palmprint, gait and so forth. Apart from identity, biometric data may convey various other personal information, covering affect, age, gender, race, accent, handedness, height, weight, etc. Among these, analysis of demographics (age, gender, and race) has received tremendous attention owing to its wide real-world applications, with significant efforts devoted and great progress achieved. This survey first presents biometric demographic analysis from the standpoint of human perception, then provides a comprehensive overview of state-of-the-art advances in automated estimation from both academia and industry. Despite these advances, a number of challenging issues continue to inhibit its full potential. We second discuss these open problems, and finally provide an outlook into the future of this very active field of research by sharing some promising opportunities.


A Modern Approach to Cyber Security Analysis Using Vulnerability Assessment and Penetration Testing


title: Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks

abstract: In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, considering the noise, varying length and irregular sampling behind sensory data, this kind of sequential data cannot be fed into classiﬁcation and regression models directly. Therefore, previous work focuses on feature extraction/fusion methods requiring expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, which redeﬁne representation learning from raw data, a deep neural network structure named Convolutional Bi-directional Long Short-Term Memory networks (CBLSTM) has been designed here to address raw sensory data. CBLSTM ﬁrstly uses CNN to extract local features that are robust and informative from the sequential input. Then, bi-directional LSTM is introduced to encode temporal information. Long Short-Term Memory networks(LSTMs) are able to capture long-term dependencies and model sequential data, and the bi-directional structure enables the capture of past and future contexts. Stacked, fully-connected layers and the linear regression layer are built on top of bi-directional LSTMs to predict the target value. Here, a real-life tool wear test is introduced, and our proposed CBLSTM is able to predict the actual tool wear based on raw sensory data. The experimental results have shown that our model is able to outperform several state-of-the-art baseline methods.


title: Starting methods for hall-less single phase BLDC motor

abstract: This paper is mainly about the starting methods for single phase brushless DC motor (BLDCM) under Hall-less driving. Because the rotor initial position is unknown when without Hall sensor, it is difficult for the controller to control the motor to rotate in the predefined direction in start-up. Here, the paper develops two different starting methods to realize right start-up under Hall-less driving. In one of the methods, by pulling the rotor to a known position with a special current pulse, the controller can control the direction easily. While in the other method, the controller can make the rotor rotate in the predefined direction just by applying a current pulse to the winding. Both of the methods are simple and feasible.


title: An industrial case study of classifier ensembles for locating software defects

abstract: As the application layer in embedded systems dominates over the hardware, ensuring software quality becomes a real challenge. Software testing is the most time-consuming and costly project phase, specifically in the embedded software domain. Misclassifying a safe code as defective increases the cost of projects, and hence leads to low margins. In this research, we present a defect prediction model based on an ensemble of classifiers. We have collaborated with an industrial partner from the embedded systems domain. We use our generic defect prediction models with data coming from embedded projects. The embedded systems domain is similar to mission critical software so that the goal is to catch as many defects as possible. Therefore, the expectation from a predictor is to get very high probability of detection (pd). On the other hand, most embedded systems in practice are commercial products, and companies would like to lower their costs to remain competitive in their market by keeping their false alarm (pf) rates as low as possible and improving their precision rates. In our experiments, we used data collected from our industry partners as well as publicly available data. Our results reveal that ensemble of classifiers significantly decreases pf down to 15% while increasing precision by 43% and hence, keeping balance rates at 74%. The cost-benefit analysis of the proposed model shows that it is enough to inspect 23% of the code on local datasets to detect around 70% of defects.


title: Traffic sign shape classification evaluation I: SVM using distance to borders

abstract: This paper deals with the detection and classification of traffic signs in outdoor environments. The information provided by traffic signs on roads is very important for the safety of drivers. However, in these situations the illumination conditions can not be predicted, the position and the orientation of signs in the scene are not known and other objects can block the vision of them. For these reasons we have developed an extensive test set which includes all kind of signs. In an artificial vision system, the key to recognize traffic signs is how to detect them and identify their geometric shapes. So, in this work we propose a method that uses a technique based on support vector machines (SVMs) for the classification. The patterns generated by the vectors represent the distances to borders (DtB) of the objects candidate to be traffic signs. Experimental results show the effectiveness of the proposed method.


Policy Search in Reinforcement Learning: A Survey


title: DESIGN OF V-BAND SUBSTRATE INTEGRATED WAVEGUIDE POWER DIVIDER, CIRCULATOR AND COUPLER

abstract: Recently there is growing interest in a new technology, substrate integrated waveguide (SIW), it has been applied successfully to the conception of planar compact components for the microwave and millimeter waves applications. In this study, a V-band substrate integrated waveguide power divider, circulator and coupler are conceived and optimized by Ansoft HFSS code. Thus, through this modeling, design considerations and results are discussed and presented. Attractive features including compact size and planar form make these devices structure easily integrated in planar circuits.


title: An efficient LBP-Based descriptor for facial depth images applied to gender recognition using RGB-D face data

abstract: RGB-D is a powerful source of data providing the aligned depth information which has great potentials in improving the performance of various problems in image understanding, while Local Binary Patterns (LBP) have shown excellent results in representing faces. In this paper, we propose a novel efficient LBP-based descriptor, namely Gradient-LBP (G-LBP), specialized to encode the facial depth information inspired by 3DLBP, yet resolves its inherent drawbacks. The proposed descriptor is applied to gender recognition task and shows its superiority to 3DLBP in all the experimental setups on both Kinect and range scanner databases. Furthermore, a weighted combination scheme of the proposed descriptor for depth images and the state-of-the-art LBPU2 for grayscale images applied in gender recognition is proposed and evaluated. The result reinforces the effectiveness of the proposed descriptor in complementing the source of information from the luminous intensity. All the experiments are carried out on both the high quality 3D range scanner database - Texas 3DFR and images of lower quality obtained from Kinect - EURECOM Kinect Face Dataset to show the consistency of the performance on different sources of RGB-D data.