title: Planar inductor with quasi-distributed gap core and busbar based planar windings

abstract: Inductor design is investigated for battery charger used in plug-in hybrid vehicles. The inductor is designed to be implemented at the input and output parts of prototype charger (operation at 250 kHz) under development. Circuit simulations revealed inductor resistances at DC, 60 Hz and 500 kHz are the most dominant factors determining inductor efficiency. To reduce DC resistance, high density winding structure based on planar busbar stacking is proposed. To reduce AC resistance, quasi-distributed gap core configuration is investigated. A conventional concentrated gap configuration with E-E core pair and a custom-assembled quasi-distributed gap core set are compared. Each prototype was fabricated and characterized with impedance analyzer. Our new prototype made by a combination of busbar winding and quasi-distributed gap configuration demonstrates the best performance at DC and 500 kHz, and thus, the best structure for the application herein.


title: Visual Relationship Detection with Language Priors

abstract: Visual relationships capture a wide variety of interactions between pairs of objects in images (e.g. “man riding bicycle” and “man pushing bicycle”). Consequently, the set of possible relationships is extremely large and it is difficult to obtain sufficient training examples for all possible relationships. Because of this limitation, previous work on visual relationship detection has concentrated on predicting only a handful of relationships. Though most relationships are infrequent, their objects (e.g. “man” and “bicycle”) and predicates (e.g. “riding” and “pushing”) independently occur more frequently. We propose a model that uses this insight to train visual models for objects and predicates individually and later combines them together to predict multiple relationships per image. We improve on prior work by leveraging language priors from semantic word embeddings to finetune the likelihood of a predicted relationship. Our model can scale to predict thousands of types of relationships from a few examples. Additionally, we localize the objects in the predicted relationships as bounding boxes in the image. We further demonstrate that understanding relationships can improve content based image retrieval.


Synergy of IoT and AI in Modern Society: The Robotics and Automation Case


title: Threshold selection in gene co-expression networks using spectral graph theory techniques

abstract: BackgroundGene co-expression networks are often constructed by computing some measure of similarity between expression levels of gene transcripts and subsequently applying a high-pass filter to remove all but the most likely biologically-significant relationships. The selection of this expression threshold necessarily has a significant effect on any conclusions derived from the resulting network. Many approaches have been taken to choose an appropriate threshold, among them computing levels of statistical significance, accepting only the top one percent of relationships, and selecting an arbitrary expression cutoff.ResultsWe apply spectral graph theory methods to develop a systematic method for threshold selection. Eigenvalues and eigenvectors are computed for a transformation of the adjacency matrix of the network constructed at various threshold values. From these, we use a basic spectral clustering method to examine the set of gene-gene relationships and select a threshold dependent upon the community structure of the data. This approach is applied to two well-studied microarray data sets from Homo sapiens and Saccharomyces cerevisiae.ConclusionThis method presents a systematic, data-based alternative to using more artificial cutoff values and results in a more conservative approach to threshold selection than some other popular techniques such as retaining only statistically-significant relationships or setting a cutoff to include a percentage of the highest correlations.


title: MEC: Network Optimized Multi-stage Erasure Coding for Scalable Storage Systems

abstract: In scalable storage systems, there are two kinds of methods for data redundancy: mirroring and parity. Each has its pros and cons. Mirroring creates a large amount of redundancy data, resulting in less usable space. Write performance degrades proportionally to the redundancy level due to an increase in communication. Parity-based methods partition data into multiple pieces, add parity information, and distribute the pieces of data and parity information. Parity-based methods are not often used with memory class media that are faster than the network, because distributing the data across servers results in low read performance. This research aims to establish an efficient data protection method that can be applied to fast, memory class media. We propose a new parity-based method called Multi-stage Erasure Coding (MEC), which creates two different erasure codes: one at the data transmission source server, and the other at the destination server. We show that our method reduces the space required to achieve redundancy while achieving high performance by making the amount of write communication independent of the redundancy level. We built a prototype program using MEC on a commodity cluster server. We show that compared with conventional parity-based methods with redundancy level 2, read I/O throughput is over one order of magnitude higher thanks to local reads and that write I/O throughput is almost the same due to network bottleneck.


PC based diagnostic system for the condition monitoring of rotating machines


title: Processed meat: the real villain?

abstract: Meat is a food rich in protein, minerals such as iron and zinc as well as a variety of vitamins, in particular B vitamins. However, the content of cholesterol and saturated fat is higher than in some other food groups. Processed meat is defined as products usually made of red meat that are cured, salted or smoked (e.g. ham or bacon) in order to improve the durability of the food and/or to improve colour and taste, and often contain a high amount of minced fatty tissue (e.g. sausages). Hence, high consumption of processed foods may lead to an increased intake of saturated fats, cholesterol, salt, nitrite, haem iron, polycyclic aromatic hydrocarbons, and, depending upon the chosen food preparation method, also heterocyclic amines. Several large cohort studies have shown that a high consumption of processed (red) meat is related to increased overall and cause-specific mortality. A meta-analysis of nine cohort studies observed a higher mortality among high consumers of processed red meat (relative risk (RR) = 1·23; 95 % CI 1·17, 1·28, top v. bottom consumption category), but not unprocessed red meat (RR = 1·10; 95 % CI 0·98, 1·22). Similar associations were reported in a second meta-analysis. All studies argue that plausible mechanisms are available linking processed meat consumption and risk of chronic diseases such as CVD, diabetes mellitus or some types of cancer. However, the results of meta-analyses do show some degree of heterogeneity between studies, and it has to be taken into account that individuals with low red or processed meat consumption tend to have a healthier lifestyle in general. Hence, substantial residual confounding cannot be excluded. Information from other types of studies in man is needed to support a causal role of processed meat in the aetiology of chronic diseases, e.g. studies using the Mendelian randomisation approach.


Systematic Synthesis of Elliptic Curve Cryptography Implementations


Using Class Frequency for Improving Centroid-based Text Classification


title: Restoring highly corrupted images by impulse noise using radial basis functions interpolation

abstract: Preserving details in restoring images highly corrupted by impulse noise remains a challenging problem. We proposed an algorithm based on radial basis functions (RBF) interpolation which estimates the intensities of corrupted pixels by their neighbors. In this algorithm, first intensity values of noisy pixels in the corrupted image are estimated using RBFs. Next, the image is smoothed. The proposed algorithm can effectively remove the highly dense impulse noise. Experimental results show the superiority of the proposed algorithm in comparison to the recent similar methods both in noise suppression and detail preservation. Extensive simulations show better results in measure of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), especially when the image is corrupted by very highly dense impulse noise.


title: An adaptive threshold based algorithm for optic disc and cup segmentation in fundus images

abstract: This paper presents an image processing technique for segmentation of optic disc and cup based on adaptive thresholding using features from the image. The proposed algorithm uses the features obtained from the image, such as mean and standard deviation, to remove information from the red and green channel of a fundus image and obtain an image which contains only the optic nerve head region in both the channels. The optic disc is segmented from the red channel and optic cup from the green channel respectively. The threshold is determined from the smoothed histogram of the preprocessed image. The results of the proposed algorithm are compared with the images that are marked by doctors. The accuracy of the algorithm is good and is computationally very fast. The proposed method can be used for screening purpose.


title: An Investigation of Personality Traits in Relation to Intention to Withdraw From College

abstract: We examined the Big Five(De Raad, 2000) personality traits of Agreeableness, Conscientiousness, Emotional Stability, Extraversion, and Openness, plus the narrow personality traits of Aggression, Career-Decidedness, Optimism, Self-Directed Learning, Sense of Identity, Tough-Mindedness, and Work Drive in relation to intention to withdraw from college. Among 233 university freshmen, all of the traits except Tough-Mindedness and Openness were significantly related to withdrawal intention, with three traits—Sense of Identity, Emotional Stability, and Work Drive—accounting for 22% of the variance in intention to withdraw. Theoretical and practical implications were discussed.


title: Single-inductor dual-output (SIDO) DC-DC converters for minimized cross regulation and high efficiency in SoC supplying systems

abstract: A compact size and high efficient single-inductor dual-output (SIDO) DC-DC converter is proposed. Generally speaking, it is important to implement DC-DC converters with minimized components and small footprint area. However, large external compensated resistors and capacitors are required to stabilize DC-DC converters. The proposed SIDO DC-DC converter not only provides dual output sources but also has minimized cross regulation without using any external compensated components. Importantly, our proposed mode-switch technique corrects the mistakes in the previous design [1], which didn't mention the unstable condition. Besides, owing to dynamically adjusting DC current level like CCM operation, the SIDO DC-DC converter achieves high conversion efficiency. Experimental results show a high efficiency from 85% at light load condition to 94% at heavy load condition.


title: 177Lu-PSMA Radioligand Therapy for Prostate Cancer

abstract: Lu-177-prostate-specific membrane antigen (PSMA) radioligand therapy (RLT) using inhibitors of PSMA is a novel therapeutic option in patients withmetastatic castration-resistant prostate cancer. The current literature suggests that this therapy is well tolerated and effective. On the basis of clinical need and current evidence, the therapy is being implemented in a growing number of centers worldwide. Here, we review important aspects of Lu-177-PSMA RLT, including patient stratification, the therapy protocol, concomitant medication, and follow-up, to inform medical staff involved in the RLT and care of patients with metastatic castration-resistant prostate cancer.


title: Fuzzy Logic in Control: Truck Backer-Upper Problem Revisited

abstract: The truck backer-upper problem, considered an acknowledged benchmark in nonlinear system identification, is an excellent test-bed for fuzzy control systems. A fuzzy controller, formulated on the basis of human understanding of the process or identified from measured control actions, can be regarded as an emulator of human operator. Controller design, however, may become difficult, especially if the number of state variables is large. In the paper, a supervisory control system is proposed that reduces the complexity of the control problem and enhances control. This is demonstrated with backing simulations in comparison with other fuzzy control techniques.


title: Ovulatory shifts in human female ornamentation: Near ovulation, women dress to impress

abstract: Humans differ from many other primates in the apparent absence of obvious advertisements of fertility within the ovulatory cycle. However, recent studies demonstrate increases in women's sexual motivation near ovulation, raising the question of whether human ovulation could be marked by observable changes in overt behavior. Using a sample of 30 partnered women photographed at high and low fertility cycle phases, we show that readily-observable behaviors – self-grooming and ornamentation through attractive choice of dress – increase during the fertile phase of the ovulatory cycle. At above-chance levels, 42 judges selected photographs of women in their fertile (59.5%) rather than luteal phase (40.5%) as “trying to look more attractive.” Moreover, the closer women were to ovulation when photographed in the fertile window, the more frequently their fertile photograph was chosen. Although an emerging literature indicates a variety of changes in women across the cycle, the ornamentation effect is striking in both its magnitude and its status as an overt behavioral difference that can be easily observed by others. It may help explain the previously documented finding that men's mate retention efforts increase as their partners approach ovulation.


title: Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover

abstract: Abstract Reliable representations of global urban extent remain limited, hindering scientific progress across a range of disciplines that study functionality of sustainable cities. We present an efficient and low-cost machine-learning approach for pixel-based image classification of built-up areas at a large geographic scale using Landsat data. Our methodology combines nighttime-lights data and Landsat 8 and overcomes the lack of extensive ground-reference data. We demonstrate the effectiveness of our methodology, which is implemented in Google Earth Engine, through the development of accurate 30 m resolution maps that characterize built-up land cover in three geographically diverse countries: India, Mexico, and the US. Our approach highlights the usefulness of data fusion techniques for studying the built environment and is a first step towards the creation of an accurate global-scale map of urban land cover over time.


title: Future directions for GaN in 5G and satellite communications

abstract: GaN will play a strong role in advanced RF and microwave applications including 5G and satellite communications. The specifications of these systems will push next-gen GaN devices towards mm-wave operation. The challenges and opportunities for commercial deployment of GaN are identified and a variety of circuit designs are presented. A 5G high-linearity power amplifier MMIC in 0.20um GaN with Pout=36dBm at 51.1% PAE and a Sat-com Ku-band mixer in 0.25um GaN with conversion loss < 10.5dB and IIP3=36.4dBm are demonstrated.


title: The Pathogenesis of Coronary Artery Disease and the Acute Coronary Syndromes

abstract: IN the 19th century there were two major hypotheses to explain the pathogenesis of atherosclerosis: the "incrustation" hypothesis and the "lipid" hypothesis. The incrustation hypothesis of von Rokitansky,1 proposed in 1852 and modified by Duguid,2 suggested that intimal thickening resulted from fibrin deposition, with subsequent organization by fibroblasts and secondary lipid accumulation. The lipid hypothesis, proposed by Virchow3 in 1856, suggested that lipid in the arterial wall represented a transduction of blood lipid, which subsequently formed complexes with acid mucopolysaccharides; lipid accumulated in arterial walls because mechanisms of lipid deposition predominated over those of removal. The two hypotheses are now . . .


title: Design of a 3.24μW, 39nV/√Hz chopper amplifier with 5.5Hz noise corner frequency for invasive neural signal acquisition

abstract: This paper proposed the design of a low noise chopper amplifier with low total harmonic distortion (THD) for neural signal acquisition. A Gm-C integrator based DC-servo-loop (DSL) with complementary input and elaborate bias scheme was integrated in the proposed work to improve the linearity and noise performance, which is dominated by DSL of the chopper amplifier in low frequency band. The proposed amplifier was fabricated in TSMC 0.18um CMOS process, occupying an area of 0.2mm2, featuring a power consumption of 3.24μW/channel under 1.8V supply voltage. The THD for a 5mV pp input is lower than −60dB. An input-referred thermal noise PSD of 39 nV/√Hz is measured. The measured input-referred noise is 0.65μV rms in the 0.3Hz-200Hz frequency band and 2.14μV rms in 200Hz-5kHz, respectively. In addition, the high-pass corner frequency can be precisely configured and linearly adjusted with the external bias current from 0.35Hz to 54.5Hz.


Investigating Different Syntactic Context Types and Context Representations for Learning Word Embeddings


title: Modified DBpedia entities expansion for tagging automatically NER dataset

abstract: Developing NER system using machine learning approach needs a big dataset which is costly if the dataset labeling is done manually. The previous works proposed methods in tagging automatically the Indonesian NER dataset using Wikipedia articles as the source of the dataset and DBpedia as the reference of the entity type. However, the quality of the resulting dataset was still inadequate. A method named DBpedia Entities Expansion (DEE) had introduced several rules to expand named entities in DBpedia in order to improve recall, but it had not managed to remove noise that makes precision decline, especially for person names. The objective of this research is to propose the modification to DEE method with the main focus to remove invalid names from the list of person names in the Expanded DBpedia. We call this modification as Modified DEE (M-DEE). The evaluation shows that M-DEE can improve the precision for person names around 3% compared to the original DEE. By adding gazetteers for place and organization names into the Expanded DBpedia created by M-DEE, the margin about 10% of the overall F1-score for all types was achieved.


title: Toll-Like Receptor Signaling Pathways

abstract: Toll-like receptors (TLRs) play crucial roles in the innate immune system by recognizing pathogen-associated molecular patterns derived from various microbes. TLRs signal through the recruitment of specific adaptor molecules, leading to activation of the transcription factors NF-κB and IRFs, which dictate the outcome of innate immune responses. During the past decade, the precise mechanisms underlying TLR signaling have been clarified by various approaches involving genetic, biochemical, structural, cell biological and bioinformatics studies. TLR signaling appears to be divergent and to play important roles in many aspects of the innate immune responses to given pathogens. In this review, we describe recent progress in our understanding of TLR signaling regulation and its contributions to host defense.


title: A model-based approach to security flaw detection of network protocol implementations

abstract: A lot of efforts have been devoted to the analysis of network protocol specification for reliability and security properties using formal techniques. However, faults can also be introduced during system implementation; it is indispensable to detect protocol implementation flaws, yet due to the black-box nature of protocol implementation and the unavailability of protocol specification most of the approaches resort to random or manual testing. In this paper we propose a model-based approach for security flaw detection of protocol implementation with a high fault coverage, measurability, and automation. Our approach first synthesizes an abstract behavioral model from a protocol implementation and then uses it to guide the testing process for detecting security and reliability flaws. For protocol specification synthesis we reduce the problem a trace minimization with a finite state machine model and an efficient algorithm is presented for state space reduction. Our method is implemented and applied to real network protocols. Guided by the synthesized model our testing tool reveals a number of unknown reliability and security issues by automatically crashing the implementations of the Microsoft MSN instant messaging (MSNIM) protocol. Analytical comparison between our model-based and prevalent syntax-based flaw detection schemes is also provided with the support of experimental results.


title: Computer Vision Approach for Low Cost, High Precision Measurement of Grapevine Trunk Diameter in Outdoor Conditions

abstract: Trunk diameter is a variable of agricultural interest, used mainly in the prediction of fruit trees production. It is correlated with leaf area and biomass of trees, and consequently gives a good estimate of the potential production of the plants. This work presents a low cost, high precision method for the measurement of trunk diameter of grapevines based on Computer Vision techniques. Several methods based on Computer Vision and other techniques are introduced in the literature. These methods present different advantages for crop management: they are amenable to be operated by unknowledgeable personnel, with lower operational costs; they result in lower stress levels to knowledgeable personnel, avoiding the deterioration of the measurement quality over time; and they make the measurement process amenable to be embedded in larger autonomous systems, allowing more measurements to be taken with equivalent costs. To date, all existing autonomous methods are either of low precision, or have a prohibitive cost for massive agricultural adoption, leaving the manual Vernier caliper or tape measure as the only choice in most situations. In this work we present a semi-autonomous measurement method that is susceptible to be fully automated, cost effective for mass adoption, and its precision is competitive (with slight improvements) over the caliper manual method.


title: Fast LIDAR-based road detection using fully convolutional neural networks

abstract: In this work, a deep learning approach has been developed to carry out road detection using only LIDAR data. Starting from an unstructured point cloud, top-view images encoding several basic statistics such as mean elevation and density are generated. By considering a top-view representation, road detection is reduced to a single-scale problem that can be addressed with a simple and fast fully convolutional neural network (FCN). The FCN is specifically designed for the task of pixel-wise semantic segmentation by combining a large receptive field with high-resolution feature maps. The proposed system achieved excellent performance and it is among the top-performing algorithms on the KITTI road benchmark. Its fast inference makes it particularly suitable for real-time applications.


title: Ultra-Wideband Vialess Microstrip Line-to-Stripline Transition in Multilayer LCP Substrate for $E$ - and $W$ -Band Applications

abstract: In this letter, we present an ultra-wideband microstrip line (MSL)-to-stripline (SL) transition without using vias or coupled slot in multilayer liquid crystal polymer (LCP) substrate at millimeter-wave frequencies. The proposed transition is achieved by using an asymmetric parallel SL to connect MSL and SLs signal lines in different layers, and its full-wave simulation predicts an insertion loss of less than 1.1 dB at <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>- and <inline-formula> <tex-math notation="LaTeX">$W$ </tex-math></inline-formula>-bands. To demonstrate this design experimentally, an MSL-to-SL-to-MSL transition was fabricated and characterized. The measured insertion loss of this back-to-back transition is less than 2 dB with a return loss higher than 13 dB from 60 to 110 GHz, showing good agreement with the simulated data.


title: Smart Kitchen: A User Centric Cooking Support System

abstract: Several cooking support systems have been studied to give users instructions based on the recipes stepby-step, using multi-media contents. These systems usually disturb users’ cooking process forcing them to provide information to the system in order to give them beneficial information. In this sense, these systems are considered to be ”system centric”. We propose a system called ”Smart Kitchen” considered to be in ”user centric”, in which a user can cook normally without being concerned about the system. Smart Kitchen can understand cooking processes, in other words, what the user is doing. In this paper, we discuss the design of the Smart Kitchen system and explain three essential modules of tracking food, recognizing food material, and recognizing cooking action.


title: Petri-net-based hypertext: document structure with browsing semantics

abstract: We present a formal definition of the Trellis model of hypertext and describe an authoring and browsing prototype called αTrellis that is based on the model. The Trellis model not only represents the relationships that tie individual pieces of information together into a document (i.e., the adjacencies), but specifies the browsing semantics to be associated with the hypertext as well (i.e., the manner in which the information is to be visited and presented). The model is based on Petri nets, and is a generalization of existing directed graph-based forms of hypertext. The Petri net basis permits more powerful specification of what is to be displayed when a hypertext is browsed and permits application of previously developed Petri net analysis techniques to verify properties of the hypertext. A number of useful hypertext constructs, easily described in the Trellis model, are presented. These include the synchronization of simultaneous traversals of separate paths through a hypertext, the incorporation of access controls into a hypertext (i.e., specifying nodes that can be proven to be accessible only to certain classes of browsers), and construction of multiple specialized (tailored) versions from a single hypertext.


title: Strategic management of stakeholders: theory and practice

abstract: This article explores how top management teams can increase the robustness of their strategies by attending to important concepts emerging from the stakeholder literature. Analysis of three themes emanating from this literature leads to the development of a method composed of three techniques which elaborates how stakeholder management concepts can be applied in practice. The research, which spanned a 15 year time period, was based on working with 16 top management teams while they were developing their strategies, so that the method – which developed incrementally in response to the teams’ reactions to its utility – was grounded in both theory and practice.


title: Coexistence of digital terrestrial television and next generation cellular networks in the 700 MHz band

abstract: With the spectrum liberation obtained by the deployment of digital terrestrial television and the analog TV switch-off, new bands are being assigned to IMT LTE. In the first cellular deployments in the digital dividend at the 800 MHz band, problems emerged due to the interference cellular networks can cause to DTT signals. Possible solutions imply either an inefficient use of the spectrum (increasing the guard band and reducing the number of DTT channels) or a high cost (using anti-LTE filters for DTT receivers). The new spectrum allocated to mobile communications is the 700 MHz band, also known as the second digital dividend. In this new IMT band, the LTE uplink is placed in the lower part of the band. Hence, the ITU-R invited several studies to be performed and reported the results to WRC-15. In this article, we analyze the coexistence problem in the 700 MHz band and evaluate the interference of LTE signals to DTT services. Several coexistence scenarios have been considered, and laboratory tests have been performed to measure interference protection ratios.


title: Cuckoo Search via Levy Flights

abstract: In this paper, we intend to formulate a new metaheuristic algorithm, called Cuckoo Search (CS), for solving optimization problems. This algorithm is based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Levy flight behaviour of some birds and fruit flies. We validate the proposed algorithm against test functions and then compare its performance with those of genetic algorithms and particle swarm optimization. Finally, we discuss the implication of the results and suggestion for further research.


title: The Knowledge Repository: Organizational Memory Information Systems

abstract: An organizational memory information system (OMIS) is a tangible conceptualization of the nebulous concept of knowledge. It combines the attributes of culture, history, business process, human memory, and fact into an integrated business system. Such an integrated system can facilitate a major step in the capture of the knowledge assets.


title: Caching in information centric networking: A survey

abstract: Internet usage has drastically shifted from host-centric end-to-end communication to receiver-driven content retrieval. In order to adapt to this change, a handful of innovative information/content centric networking (ICN) architectures have recently been proposed. One common and important feature of these architectures is to leverage built-in network caches to improve the transmission efficiency of content dissemination. Compared with traditional Web Caching and CDN Caching, ICN Cache takes on several new characteristics: cache is transparent to applications, cache is ubiquitous, and content to be cached is more ine-grained. These distinguished features pose new challenges to ICN caching technologies. This paper presents a comprehensive survey of state-of-art techniques aiming to address these issues, with particular focus on reducing cache redundancy and improving the availability of cached content. As a new research area, this paper also points out several interesting yet challenging research directions in this subject.


title: Deep learning evaluation using deep linguistic processing

abstract: We discuss problems with the standard approaches to evaluation for tasks like visual question answering, and argue that artificial data can be used to address these as a complement to current practice. We demonstrate that with the help of existing 'deep' linguistic processing technology we are able to create challenging abstract datasets, which enable us to investigate the language understanding abilities of multimodal deep learning models in detail, as compared to a single performance value on a static and monolithic dataset.


title: SkyLiTE: End-to-End Design of Low-Altitude UAV Networks for Providing LTE Connectivity

abstract: Un-manned aerial vehicle (UAVs) have the potential to change the landscape of wide-area wireless connectivity by bringing them to areas where connectivity was sparing or non-existent (e.g. rural areas) or has been compromised due to disasters. While Google's Project Loon and Facebook's Project Aquila are examples of high-altitude, long-endurance UAV-based connectivity efforts in this direction, the telecom operators (e.g. AT&T and Verizon) have been exploring low-altitude UAV-based LTE solutions for on-demand deployments. Understandably, these projects are in their early stages and face formidable challenges in their realization and deployment. The goal of this document is to expose the reader to both the challenges as well as the potential offered by these unconventional connectivity solutions. We aim to explore the end-to-end design of such UAV-based connectivity networks particularly in the context of low-altitude UAV networks providing LTE connectivity. Specifically, we aim to highlight the challenges that span across multiple layers (access, core network, and backhaul) in an inter-twined manner as well as the richness and complexity of the design space itself. To help interested readers navigate this complex design space towards a solution, we also articulate the overview of one such end-to-end design, namely SkyLiTE-- a self-organizing network of low-altitude UAVs that provide optimized LTE connectivity in a desired region.


title: Nighttime Foreground Pedestrian Detection Based on Three-Dimensional Voxel Surface Model

abstract: Pedestrian detection is among the most frequently-used preprocessing tasks in many surveillance application fields, from low-level people counting to high-level scene understanding. Even though many approaches perform well in the daytime with sufficient illumination, pedestrian detection at night is still a critical and challenging problem for video surveillance systems. To respond to this need, in this paper, we provide an affordable solution with a near-infrared stereo network camera, as well as a novel three-dimensional foreground pedestrian detection model. Specifically, instead of using an expensive thermal camera, we build a near-infrared stereo vision system with two calibrated network cameras and near-infrared lamps. The core of the system is a novel voxel surface model, which is able to estimate the dynamic changes of three-dimensional geometric information of the surveillance scene and to segment and locate foreground pedestrians in real time. A free update policy for unknown points is designed for model updating, and the extracted shadow of the pedestrian is adopted to remove foreground false alarms. To evaluate the performance of the proposed model, the system is deployed in several nighttime surveillance scenes. Experimental results demonstrate that our method is capable of nighttime pedestrian segmentation and detection in real time under heavy occlusion. In addition, the qualitative and quantitative comparison results show that our work outperforms classical background subtraction approaches and a recent RGB-D method, as well as achieving comparable performance with the state-of-the-art deep learning pedestrian detection method even with a much lower hardware cost.


title: Deep Learning for Infrared Thermal Image Based Machine Health Monitoring

abstract: The condition of a machine can automatically be identified by creating and classifying features that summarize characteristics of measured signals. Currently, experts, in their respective fields, devise these features based on their knowledge. Hence, the performance and usefulness depends on the expert's knowledge of the underlying physics or statistics. Furthermore, if new and additional conditions should be detectable, experts have to implement new feature extraction methods. To mitigate the drawbacks of feature engineering, a method from the subfield of feature learning, i.e., deep learning (DL), more specifically convolutional neural networks (NNs), is researched in this paper. The objective of this paper is to investigate if and how DL can be applied to infrared thermal (IRT) video to automatically determine the condition of the machine. By applying this method on IRT data in two use cases, i.e., machine-fault detection and oil-level prediction, we show that the proposed system is able to detect many conditions in rotating machinery very accurately (i.e., 95 and 91.67% accuracy for the respective use cases), without requiring any detailed knowledge about the underlying physics, and thus having the potential to significantly simplify condition monitoring using complex sensor data. Furthermore, we show that by using the trained NNs, important regions in the IRT images can be identified related to specific conditions, which can potentially lead to new physical insights.


title: A study of large-scale ethnicity estimation with gender and age variations

abstract: In this paper we study large-scale ethnicity estimation under variations of age and gender. The biologically-inspired features are applied to ethnicity classification for the first time. Through a large number of experiments on a large database with more than 21,000 face images, we systematically study the effect of gender and age variations on ethnicity estimation. Our finding is that ethnicity classification can have high accuracy in most cases, but an interesting phenomenon is observed that the ethnic classification accuracies could be reduced by 6∼8% in average when female faces are used for training while males for testing. The study results provide a guide for face processing on a multi-ethnic database, e.g., image collection from the Internet, and may inspire further psychological studies on ethnic grouping with gender and age variations. We also apply the methods to the whole MORPH-II database with more than 55,000 face images for ethnicity classification of five races. It is the first time that ethnicity estimation is performed on so large a database.


title: Hands as a Controller: User Preferences for Hand Specific On-Skin Gestures

abstract: Hand-specific on-skin (HSoS) gestures are a trending interaction modality yet there is a gap in the field regarding users' preferences about these gestures. Thus, we conducted a user-elicitation study collecting 957 gestures from 19 participants for 26 commands. Results indicate that (1) users use one hand as a reference object, (2) load different meanings to different parts of the hand, (3) give importance to hand-properties rather than the skin properties and (4) hands can turn into self-interfaces. Moreover, according to users' subjective evaluations, (5) exclusive gestures are less tiring than the intuitive ones. We present users' subjective evaluations regarding these and present a 33-element taxonomy to categorize them. Furthermore, we present two user-defined gesture sets; the intuitive set including users' first choices and natural-feeling gestures, and the exclusive set which includes more creative gestures indigenous to this modality. Our findings can inspire and guide designers and developers of HSoS.


Development and Analysis of a Novel Isosceles Trapezoidal Dielectric Resonator Antenna For Wireless Communication


title: Modular Brain Networks

abstract: The development of new technologies for mapping structural and functional brain connectivity has led to the creation of comprehensive network maps of neuronal circuits and systems. The architecture of these brain networks can be examined and analyzed with a large variety of graph theory tools. Methods for detecting modules, or network communities, are of particular interest because they uncover major building blocks or subnetworks that are particularly densely connected, often corresponding to specialized functional components. A large number of methods for community detection have become available and are now widely applied in network neuroscience. This article first surveys a number of these methods, with an emphasis on their advantages and shortcomings; then it summarizes major findings on the existence of modules in both structural and functional brain networks and briefly considers their potential functional roles in brain evolution, wiring minimization, and the emergence of functional specialization...


title: Design of 64-bit low power parallel prefix VLSI adder for high speed arithmetic circuits

abstract: The addition of two binary numbers is the basic and most often used arithmetic operation on microprocessors, digital signal processors and data processing application specific integrated circuits. Parallel prefix adder is a general technique for speeding up binary addition. This method implements logic functions which determine whether groups of bits will generate or propagate a carry. The proposed 64-bit adder is designed using four different types prefix cell operators, even-dot cells, odd-dot cells, even-semi-dot cells and odd-semi-dot cells; it offers robust adder solutions typically used for low power and high-performance design application needs. The comparison can be made with various input ranges of Parallel Prefix adders in terms power, number of transistor, number of nodes. Tanner EDA tool was used for simulating the parallel prefix adder designs in the 250nm technologies.


title: How We Transmit Memories to Other Brains: Constructing Shared Neural Representations Via Communication

abstract: Humans are able to mentally construct an episode when listening to another person's recollection, even though they themselves did not experience the events. However, it is unknown how strongly the neural patterns elicited by mental construction resemble those found in the brain of the individual who experienced the original events. Using fMRI and a verbal communication task, we traced how neural patterns associated with viewing specific scenes in a movie are encoded, recalled, and then transferred to a group of naïve listeners. By comparing neural patterns across the 3 conditions, we report, for the first time, that event-specific neural patterns observed in the default mode network are shared across the encoding, recall, and construction of the same real-life episode. This study uncovers the intimate correspondences between memory encoding and event construction, and highlights the essential role our common language plays in the process of transmitting one's memories to other brains.


title: Knowledge Graph Embedding by Dynamic Translation

abstract: Knowledge graph embedding aims at representing entities and relations in a knowledge graph as dense, low-dimensional and real-valued vectors. It can efficiently measure semantic correlations of entities and relations in knowledge graphs, and improve the performance of knowledge acquisition, fusion and inference. Among various embedding models appeared in recent years, the translation-based models such as TransE, TransH, TransR and TranSparse achieve state-of-the-art performance. However, the translation principle applied in these models is too strict and can not deal with complex entities and relations very well. In this paper, by introducing parameter vectors into the translation principle which treats each relation as a translation from the head entity to the tail entity, we propose a novel dynamic translation principle which supports flexible translation between the embeddings of entities and relations. We use this principle to improve the TransE, TransR and TranSparse models respectively and build new models named TransE-DT, TransR-DT and TranSparse-DT correspondingly. Experimental results show that our dynamic translation principle achieves great improvement in both the link prediction task and the triple classification task.


title: Resting brains never rest: computational insights into potential cognitive architectures

abstract: Resting-state networks (RSNs), which have become a main focus in neuroimaging research, can be best simulated by large-scale cortical models in which networks teeter on the edge of instability. In this state, the functional networks are in a low firing stable state while they are continuously pulled towards multiple other configurations. Small extrinsic perturbations can shape task-related network dynamics, whereas perturbations from intrinsic noise generate excursions reflecting the range of available functional networks. This is particularly advantageous for the efficiency and speed of network mobilization. Thus, the resting state reflects the dynamical capabilities of the brain, which emphasizes the vital interplay of time and space. In this article, we propose a new theoretical framework for RSNs that can serve as a fertile ground for empirical testing.


title: A trio-interaction theory for Madden–Julian oscillation

abstract: The Madden–Julian oscillation (MJO) is the dominant mode of tropical atmospheric intraseasonal variability and a primary source of predictability for global sub-seasonal prediction. Understanding the origin and perpetuation of the MJO has eluded scientists for decades. The present paper starts with a brief review of progresses in theoretical studies of the MJO and a discussion of the essential MJO characteristics that a theory should explain. A general theoretical model framework is then described in an attempt to integrate the major existing theoretical models: the frictionally coupled Kelvin–Rossby wave, the moisture mode, the frictionally coupled dynamic moisture mode, the MJO skeleton, and the gravity wave interference, which are shown to be special cases of the general MJO model. The last part of the present paper focuses on a special form of trio-interaction theory in terms of the general model with a simplified Betts–Miller (B-M) cumulus parameterization scheme. This trio-interaction theory extends the Matsuno–Gill theory by incorporating a trio-interaction among convection, moisture, and wave-boundary layer (BL) dynamics. The model is shown to produce robust large-scale characteristics of the observed MJO, including the coupled Kelvin–Rossby wave structure, slow eastward propagation (~5 m/s) over warm pool, the planetary (zonal) scale circulation, the BL low-pressure and moisture convergence preceding major convection, and amplification/decay over warm/cold sea surface temperature (SST) regions. The BL moisture convergence feedback plays a central role in coupling equatorial Kelvin and Rossby waves with convective heating, selecting a preferred eastward propagation, and generating instability. The moisture feedback can enhance Rossby wave component, thereby substantially slowing down eastward propagation. With the trio-interaction theory, a number of fundamental issues of MJO dynamics are addressed: why the MJO possesses a mixed Kelvin–Rossby wave structure and how the Kelvin and Rossby waves, which propagate in opposite directions, could couple together with convection and select eastward propagation; what makes the MJO move eastward slowly in the eastern hemisphere, resulting in the 30–60-day periodicity; why MJO amplifies over the warm pool ocean and decays rapidly across the dateline. Limitation and ramifications of the model results to general circulation modeling of MJO are discussed.


title: Design of a new, light and portable mechanism for knee CPM machine with a user-friendly interface

abstract: After a knee joint surgery, due to severe pain and immobility of the patient, the tissue around the knee become harder and knee stiffness will occur, which may causes many problems such as scar tissue swelling, bleeding, and fibrosis. A CPM (Continuous Passive Motion) machine is an apparatus that is being used to patient recovery, retrieving moving abilities of the knee, and reducing tissue swelling, after the knee joint surgery. This device prevents frozen joint syndrome (adhesive capsulitis), joint stiffness, and articular cartilage destruction by stimulating joint tissues, and flowing synovial fluid and blood around the knee joint. In this study, a new, light, and portable CPM machine with an appropriate interface, is designed and manufactured. The knee joint can be rotated from the range of −15° to 120° with a pace of 0.1 degree/sec to 1 degree/sec by this machine. One of the most important advantages of this new machine is its own user-friendly interface. This apparatus is controlled via an Android-based application; therefore, the users can use this machine easily via their own smartphones without the necessity to an extra controlling device. Besides, because of its apt size, this machine is a portable device. Smooth movement without any vibration and adjusting capability for different anatomies are other merits of this new CPM machine.


title: Fighting Detection Based on Optical Flow Context Histogram

abstract: This paper proposes a new feature, optical flow context histogram (OFCH) for detecting abnormal events, especially the fighting violence events from a live camera stream. The optical flow context histogram is a log-polar histogram system which combines the histogram of orientation and magnitude of optical flow together. The human action is represented by using the histogram sequence of orientation and magnitude of optical flow. PCA is adopted to reduce the dimension of the human action representation. Several machine learning methods, including random forest, support vector machine and Bayesnet are employed for sequence classification. The experiments were carried out on the video clips downloaded from the Internet. The results show that the proposed methods work well when using a fixed surveillance camera.


title: Phantom limb pain: a case of maladaptive CNS plasticity?

abstract: Phantom limb pain frequently occurs following limb amputation or deafferentiation. Emerging evidence suggests that in addition to peripheral changes, plasticity in the CNS has an important role in the pathophysiological mechanisms of phantom pain, highlighting potential targets for therapeutic interventions.


title: A distributed trust model

abstract: The widespread use of the Internet signals the need for a better understanding of trust as a basis for secure on-line interaction. In the face of increasing uncertainty and risk, users must be allowed to reason flectively about the trustworthiness of on-line entities. In this paper, we outline the shortcomings of current security approaches for managing trust and propose a model for trust, based on distributed recommendations.


title: Customers Behavior Modeling by Semi-Supervised Learning in Customer Relationship Management

abstract: Leveraging the power of increasing amounts of data to analyze customer base for attracting and retaining the most valuable customers is a major problem facing companies in this information age. Data mining technologies extract hidden information and knowledge from large data stored in databases or data warehouses, thereby supporting the corporate decision making process. CRM uses data mining (one of the elements of CRM) techniques to interact with customers. This study investigates the use of a technique, semi-supervised learning, for the management and analysis of customer-related data warehouse and information. The idea of semi-supervised learning is to learn not only from the labeled training data, but to exploit also the structural information in additionally available unlabeled data. The proposed semi-supervised method is a model by means of a feed-forward neural network trained by a back propagation algorithm (multi-layer perceptron) in order to predict the category of an unknown customer (potential customers). In addition, this technique can be used with Rapid Miner tools for both labeled and unlabeled data.


title: Iris recognition using shape-guided approach and game theory

abstract: Most state-of-the-art iris recognition algorithms claim to perform with a very high recognition accuracy in a strictly controlled environment. However, their recognition accuracies significantly decrease when the acquired images are affected by different noise factors including motion blur, camera diffusion, head movement, gaze direction, camera angle, reflections, contrast, luminosity, eyelid and eyelash occlusions, and problems due to contraction and dilation. The novelty of this research effort is that we propose to apply a variational model to localize the iris region belonging to given shape space using active contour method, a geometric shape prior, and the Mumford–Shah functional. This variational model is robust against noise, poor localization and weak iris/sclera boundaries. Furthermore, we apply the Modified Contribution-Selection Algorithm (MCSA) for iris feature ranking based on the Multi-Perturbation Shapley Analysis (MSA), a framework which relies on cooperative game theory to estimate the effectiveness of the features iteratively and select them accordingly, using either forward selection or backward elimination approaches. The verification and identification performance of the proposed scheme is validated using the ICE 2005, the UBIRIS Version 1, the CASIA Version 3 Interval, and WVU Nonideal datasets.


title: Distributed Secondary Level Control for Energy Storage Management in DC Microgrids

abstract: DC microgrids have been known to be a promising solution for improving renewable energy integration with electrical grid and enhancing the system’s overall energy efficiency. A key component of this microgrid is the energy storage system, which besides smoothing the intermittent behavior of renewable sources, also allows intentional islanding and the execution of optimization routines to improve the microgrid performance. Assuming that storage systems in commercial and residential buildings will mostly be composed of multiple storage units, an energy storage management system, which provides charge/discharge monitoring and state-of-charge (SOC) equalization, is needed to prevent overcharging the units or their uneven use, which can lead to faster deterioration of battery banks. This paper proposes an energy storage management system based on distributed secondary level control, which promotes charge/discharge control and provides SOC equalization simultaneously. The SOC imbalance compensation alters the energy storage unit virtual droop resistance according to the difference between the unit SOC and the microgrid average SOC, thus the compensation intensity is dependent on the imbalance level being suitable to be employed in dc bus signaling controlled microgrids.


title: Extended Proxy-Assisted Approach: Achieving Revocable Fine-Grained Encryption of Cloud Data

abstract: Attribute-based encryption has the potential to be deployed in a cloud computing environment to provide scalable and fine-grained data sharing. However, user revocation within ABE deployment remains a challenging issue to overcome, particularly when there is a large number of users. In this work, we introduce an extended proxy-assisted approach, which weakens the trust required of the cloud server. Based on an all-or-nothing principle, our approach is designed to discourage a cloud server from colluding with a third party to hinder the user revocation functionality. We demonstrate the utility of our approach by presenting a construction of the proposed approach, designed to provide efficient cloud data sharing and user revocation. A prototype was then implemented to demonstrate the practicality of our proposed construction.


Data mining for preterm birth prediction


Uncertainty in Deep Learning


title: Accelerated Image Processing on FPGAs 1

abstract: The Cameron project has developed a language called single assignment C (SA-C), and a compiler for mapping image-based applications written in SA-C to field programmable gate arrays (FPGAs). The paper tests this technology by implementing several applications in SA-C and compiling them to an Annapolis Microsystems (AMS) WildStar board with a Xilinx XV2000E FPGA. The performance of these applications on the FPGA is compared to the performance of the same applications written in assembly code or C for an 800 MHz Pentium III. (Although no comparison across processors is perfect, these chips were the first of their respective classes fabricated at 0.18 microns, and are therefore of comparable ages.) We find that applications written in SA-C and compiled to FPGAs are between 8 and 800 times faster than the equivalent program run on the Pentium III.


title: The Trouble of Thinking Activation and Application of Stereotypic Beliefs

abstract: Two studies investigated the effects of cognitive busyness on the activation and application of stereotypes. In Experiment 1, not-busy subjects who were exposed to an Asian target showed evidence of stereotype activation, but busy subjects (who rehearsed an 8-digit number during their exposure) did not. In Experiment 2, cognitive busyness once again inhibited the activation of stereotypes about Asians. However, when stereotype activation was allowed to occur, busy subjects (who performed a visual search task during their exposure) were more likely to apply these activated stereotypes than were not-busy subjects. Together, these findings suggest that cognitive busyness may decrease the likelihood that a particular stereotype will be activated but increase the likelihood that an activated stereotype will be applied.


title: Algorithms and Architecture for Real-time Recommendations at News UK

abstract: Recommendation systems are recognised as being hugely important in industry, and the area is now well understood. At News UK, there is a requirement to be able to quickly generate recommendations for users on news items as they are published. However, little has been published about systems that can generate recommendations in response to changes in recommendable items and user behaviour in a very short space of time. In this paper we describe a new algorithm for updating collaborative filtering models incrementally, and demonstrate its effectiveness on clickstream data from The Times. We also describe the architecture that allows recommendations to be generated on the fly, and how we have made each component scalable. The system is currently being used in production at News UK.


title: Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex

abstract: Pyramidal neurons represent the majority of excitatory neurons in the neocortex. Each pyramidal neuron receives input from thousands of excitatory synapses that are segregated onto dendritic branches. The dendrites themselves are segregated into apical, basal, and proximal integration zones, which have different properties. It is a mystery how pyramidal neurons integrate the input from thousands of synapses, what role the different dendrites play in this integration, and what kind of network behavior this enables in cortical tissue. It has been previously proposed that non-linear properties of dendrites enable cortical neurons to recognize multiple independent patterns. In this paper we extend this idea in multiple ways. First we show that a neuron with several thousand synapses segregated on active dendrites can recognize hundreds of independent patterns of cellular activity even in the presence of large amounts of noise and pattern variation. We then propose a neuron model where patterns detected on proximal dendrites lead to action potentials, defining the classic receptive field of the neuron, and patterns detected on basal and apical dendrites act as predictions by slightly depolarizing the neuron without generating an action potential. By this mechanism, a neuron can predict its activation in hundreds of independent contexts. We then present a network model based on neurons with these properties that learns time-based sequences. The network relies on fast local inhibition to preferentially activate neurons that are slightly depolarized. Through simulation we show that the network scales well and operates robustly over a wide range of parameters as long as the network uses a sparse distributed code of cellular activations. We contrast the properties of the new network model with several other neural network models to illustrate the relative capabilities of each. We conclude that pyramidal neurons with thousands of synapses, active dendrites, and multiple integration zones create a robust and powerful sequence memory. Given the prevalence and similarity of excitatory neurons throughout the neocortex and the importance of sequence memory in inference and behavior, we propose that this form of sequence memory may be a universal property of neocortical tissue.


title: Opponent Modeling in Scrabble

abstract: Computers have already eclipsed the level of human play in competitive Scrabble, but there remains room for improvement. In particular, there is much to be gained by incorporating information about the opponent's tiles into the decision-making process. In this work, we quantify the value of knowing what letters the opponent has. We use observations from previous plays to predict what tiles our opponent may hold and then use this information to guide our play. Our model of the oppoent, based on Bayes' theorem, sacrifices accuracy for simplicity and ease of computation. But even with this simplified model, we show significant improvement in play over an existing Scrabble program. These empirical results suggest that this simple approximation may serve as a suitable substitute for the intractable partially observable Markov decision process. Although this work focuses on computer-vs-computer Scrabble play, the tools developed can be of great use in training humans to play against other humans.


title: Cognitive theories of early gender development

abstract: How individuals develop a “gendered” self and gender-typed differentiation has intrigued researchers of human behavior for as long as social development has been a field of empirical study. Initially, observations of clear gender typing in children as young as 5 years old led researchers to examine how socialization processes in the home might contribute to this early acquisition of gender-linked behavior (e.g., Sears, Maccoby, & Levin, 1957). In 1966, several important theories of gender development, including Kohlberg’s cognitive– developmental approach and Mischel’s social learning approach, were outlined in Maccoby’s book on the development of sex differences. In the mid-1970s, a groundbreaking book by Maccoby and Jacklin (1974) broadened the discussion about the etiology of sex differences and gender typing and questioned the significance of socialization processes, thereby invigorating the field and generating considerable controversy. In the 1980s, Huston (1983, 1985) called for researchers to develop a more integrative, comprehensive approach to the study of gender development. This integration was to include consideration of the many facets of gender that needed investigation, such as concepts, identity, preferences, and behavior, as well as a wide variety of factors that influence the development of these facets, including biology, cognitions, and social influences. This idea resonated with gender researchers and has been a driving force in the field ever since, leading to an expansion of theoretical approaches and the inclusion of multiple measures in empirical studies. Recently, Bussey and Bandura (1999) presented a social– cognitive theory (SCT) of gender development and differentiation. This theory represents a particular view of how gender-related behavior is acquired and maintained, based on contemporary social learning theory (Bandura, 1986). Because the theory has now integrated cognitive constructs and makes reference to biological underpinnings and sociostructural factors, Bussey and Bandura described their theory as comprehensive and integrative, consistent with Huston’s (1983) proposal, and implied that other theories are invalid and/or no longer necessary. In particular, they were highly critical of previous cognitive approaches to early gender development and proposed that children’s cognitive structures, namely gender identity and stereotype knowledge, play a minor role relative to social forces. One goal of the present article is to rebut these claims. A second goal is to present a contemporary account of the role of cognitive structures in early gender development. We begin with a brief history of the debate between cognitive and social learning theories of gender development. We then examine the contributions and limitations of Bussey and Bandura’s (1999) recent account of gender development. Next, we review the current status of what have historically been the two major cognitive accounts of gender development: cognitive– developmental theory and gender schema theory. We then evaluate the empirical evidence relevant to the major controversies surrounding these views. Given the centrality of early gender development to the cognitive perspective, we then introduce the latest evidence on how infants and toddlers develop the abilities to discriminate the sexes and learn the attributes correlated with sex. Finally, we provide an overview of the essence of cognitive approaches and point to directions for future research.


title: Analyzing Sentiments Expressed on Twitter by UK Energy Company Consumers

abstract: Automatic sentiment analysis provides an effective way to gauge public opinion on any topic of interest. However, most sentiment analysis tools require a general sentiment lexicon to automatically classify sentiments or opinion in a text. One of the challenges presented by using a general sentiment lexicon is that it is insensitive to the domain since the scores assigned to the words are fixed. As a result, while one general sentiment lexicon might perform well in one domain, the same lexicon might perform poorly in another domain. Most sentiment lexica will need to be adjusted to suit the specific domain to which it is applied. In this paper, we present results of sentiment analysis expressed on Twitter by UK energy consumers. We optimised the accuracy of the sentiment analysis results by combining functions from two sentiment lexica. We used the first lexicon to extract the sentiment-bearing terms and negative sentiments since it performed well in detecting these. We then used a second lexicon to classify the rest of the data. Experimental results show that this method improved the accuracy of the results compared to the common practice of using only one lexicon.


title: Smoking as a risk factor for autoimmune liver disease: what we can learn from primary biliary cirrhosis.

abstract: Primary biliary cirrhosis (PBC) is a cholestatic liver disease characterised by the immune-mediated destruction of biliary epithelial cells in small intrahepatic bile ducts. The disease is characterised by circulating anti-mitochondrial antibodies (AMA) as well as disease specific anti-nuclear antibodies (ANA), cholestatic liver biochemistry, and characteristic histology. The disease primarily affects middle-aged females, and its incidence is apparently increasing worldwide. Epidemiological studies have indicated several risk factors for the development of PBC, with family history of PBC, recurrent urinary tract infection, and smoking being the most widely cited. Smoking has been implicated as a risk factor in several autoimmune diseases, including the liver, by complex mechanisms involving the endocrine and immunological systems to name a few. Studies of smoking in liver disease have also shown that smoking may progress the disease towards fibrosis and subsequent cirrhosis. This review will examine the literature surrounding smoking as a risk factor for PBC, as well as a potential factor in the progression of fibrosis in PBC patients.


title: FoodCam: A Real-Time Mobile Food Recognition System Employing Fisher Vector

abstract: In the demo, we demonstrate a mobile food recognition system with Fisher Vector and liner one-vs-rest SVMs which enables us to record our food habits easily. In the experiments with 100 kinds of food categories, we have achieved the 79.2% classification rate for the top 5 category candidates when the ground-truth bounding boxes are given. The prototype system is open to the public as an Android-based smartphone application.


title: An eye gaze model for dyadic interaction in an immerse virtual environment: Practice and experience

abstract: This paper describes a behavioural model used to simulate realistic eye-gaze behaviour and body animations for avatars representing participants in a shared immersive virtual environment (IVE). The model was used in a study designed to explore the impact of avatar realism on the perceived quality of communication within a negotiation scenario. Our eye-gaze model was based on data and studies carried out on the behaviour of eye-gaze during face-to-face communication. The technical features of the model are reported here. Information about the motivation behind the study, experimental procedures and a full analysis of the results obtained are given in [17].


title: Job Scheduling without Prior Information in Big Data Processing Systems

abstract: Job scheduling plays an important role in improving the overall system performance in big data processing frameworks. Simple job scheduling policies, such as Fair and FIFO scheduling, do not consider job sizes and may degrade the performance when jobs of varying sizes arrive. More elaborate job scheduling policies make the convenient assumption that jobs are recurring, and complete information about their sizes is available from their prior runs. In this paper, we design and implement an efficient and practical job scheduler for big data processing systems to achieve better performance even without prior information about job sizes. The superior performance of our job scheduler originates from the design of multiple level priority queues, where jobs are demoted to lower priority queues if the amount of service consumed so far reaches a certain threshold. In this case, jobs in need of a small amount of service can finish in the topmost several levels of queues, while jobs that need a large amount of service to complete are moved to lower priority queues to avoid head-of-line blocking. Our new job scheduler can effectively mimic the shortest job first scheduling policy without knowing the job sizes in advance. To demonstrate its performance, we have implemented our new job scheduler in YARN, a popular resource manager used by Hadoop/Spark, and validated its performance with both experiments on real datasets and large-scale trace-driven simulations. Our experimental and simulation results have strongly confirmed the effectiveness of our design: our new job scheduler can reduce the average job response time of the Fair scheduler by up to 45%.


title: A case-based reasoning framework for workflow model management

abstract: In order to support efficient workflow design, recent commercial workflow systems are providing templates of common business processes. These templates, called cases, can be modified individually or collectively into a new workflow to meet the business specification. However, little research has been done on how to manage workflow models, including issues such as model storage, model retrieval, model reuse and assembly. In this paper, we propose a novel framework to support workflow modeling and design by adapting workflow cases from a repository of process models. Our approach to workflow model management is based on a structured workflow lifecycle and leverages recent advances in model management and case-based reasoning techniques. Our contributions include a conceptual model of workflow cases, a similarity flooding algorithm for workflow case retrieval, and a domain-independent AI planning approach to workflow case composition. We illustrate the workflow model management framework with a prototype system called Case-Oriented Design Assistant for Workflow Modeling (CODAW).


title: Automatic Discovery of Action Taxonomies from Multiple Views

abstract: We present a new method for segmenting actions into primitives and classifying them into a hierarchy of action classes. Our scheme learns action classes in an unsupervised manner using examples recorded by multiple cameras. Segmentation and clustering of action classes is based on a recently proposed motion descriptor which can be extracted efficiently from reconstructed volume sequences. Because our representation is independent of viewpoint, it results in segmentation and classification methods which are surprisingly efficient and robust. Our new method can be used as the first step in a semi-supervised action recognition system that will automatically break down training examples of people performing sequences of actions into primitive actions that can be discriminatingly classified and assembled into high-level recognizers.


title: Thinking about intentions

abstract: In this fMRI study, we investigated the convergence of underlying neural networks in thinking about a scenario involving one's own intentional action and its consequences and setting up and holding in mind an intention to act. A factorial design was employed comprising two factors: i. Causality (intentional or physical events) and ii. Prospective Memory (present or absent). In each condition, subjects answered questions about various hypothetical scenarios, which related either to the link between the subject's own intentions and consequential actions (Intentional Causality) or to the link between a natural, physical event and its consequences (Physical Causality). A prospective memory task was embedded in half the blocks. In this task, subjects were required to keep in mind an intention (to press a key on seeing a red stimulus background) whilst carrying out the ongoing Causality task. Answering questions about intentional causality versus physical causality activated a network of regions that have traditionally been associated with Theory of Mind, including the medial prefrontal cortex (mPFC), the superior temporal sulcus and the temporal poles bilaterally. In addition, the precuneus bordering with posterior cingulate cortex, an area involved in self-awareness and self-related processing, was activated more when thinking about intentional causality. In the prospective memory task, activations were found in the right parietal cortex, frontopolar cortex (BA 10) and precuneus. Different subregions within the precuneus/posterior cingulate cortex were activated in both main effects of intentional causality and prospective memory. Therefore, the precuneus/posterior cingulate cortex subserves separately thinking about one's own intentions and consequent actions and bearing in mind an intention to make an action. Previous studies have shown that prospective memory, requiring the formation of an intention and the execution of a corresponding action, is associated with decreased activation in the dorsal mPFC, close to the region activated in Theory of Mind tasks. Here, we found that holding in mind an intention to act and at the same time thinking about an intentional action led to reduced activity in a dorsal section of the mPFC. This was a different region from a more anterior, inferior dorsal mPFC region that responded to intentional causality. This suggests that different regions of mPFC play different roles in thinking about intentions. (c) 2005 Elsevier Inc. All rights reserved.


title: ConoSurf: Open‐source 3D scanning system based on a conoscopic holography device for acquiring surgical surfaces

abstract: Background ::: A difficulty in computer-assisted interventions is acquiring the patient's anatomy intraoperatively. Standard modalities have several limitations: low image quality (ultrasound), radiation exposure (computed tomography) or high costs (magnetic resonance imaging). An alternative approach uses a tracked pointer; however, the pointer causes tissue deformation and requires sterilizing. Recent proposals, utilizing a tracked conoscopic holography device, have shown promising results without the previously mentioned drawbacks. ::: ::: Methods ::: We have developed an open-source software system that enables real-time surface scanning using a conoscopic holography device and a wide variety of tracking systems, integrated into pre-existing and well-supported software solutions. ::: ::: Results ::: The mean target registration error of point measurements was 1.46 mm. For a quick guidance scan, surface reconstruction improved the surface registration error compared with point-set registration. ::: ::: Conclusions ::: We have presented a system enabling real-time surface scanning using a tracked conoscopic holography device. Results show that it can be useful for acquiring the patient's anatomy during surgery.


title: Energy Comparison of Seven MPPT Techniques for PV Systems

abstract: In the future, solar energy will be a very important energy source. Several studies suppose that more than 45% of the energy in the world will be generated by photovoltaic array. Therefore it is necessary to concentrate our forces to reduce the application costs and to increment their performance. In order to reach the last aspect, it is important to note that the output characteristic of a photovoltaic array is nonlinear and changes with solar irradiation and cell’s temperature. Therefore a Maximum Power Point Tracking (MPPT) technique is needed to maximize the produced energy. This paper presents a comparative study of seven widely-adopted MPPT algorithms; their performance is evaluated using, for all the techniques, a common device with minimum hardware variations. In particular, this study compares the behaviors of each technique in presence of solar irradiation variations.


Crystallization kinetics study of dynamically vulcanized PA6/NBR/HNTs nanocomposites by nonisothermal differential scanning calorimetry


title: Stacked Graphs – Geometry & Aesthetics

abstract: In February 2008, the New York Times published an unusual chart of box office revenues for 7500 movies over 21 years. The chart was based on a similar visualization, developed by the first author, that displayed trends in music listening. This paper describes the design decisions and algorithms behind these graphics, and discusses the reaction on the Web. We suggest that this type of complex layered graph is effective for displaying large data sets to a mass audience. We provide a mathematical analysis of how this layered graph relates to traditional stacked graphs and to techniques such as ThemeRiver, showing how each method is optimizing a different ldquoenergy functionrdquo. Finally, we discuss techniques for coloring and ordering the layers of such graphs. Throughout the paper, we emphasize the interplay between considerations of aesthetics and legibility.


title: Maximum power point tracking controller using P and O algorithm for solar PV array on FPGA

abstract: The electrical circuit modelling of a photovoltaic module and the implementation MPPT controller on Field Programmable Gate Array (FPGA) is investigated in this article. The maximum power point location is always unknown at any instant from the PV cells thus necessitate the design of locating this maximum point using some controller. The widely used P and O algorithm is implemented on FPGA system. The design have been written in VHDL language and its simulation is done using Quartus II VHDL tools. Further, the VHDL-modules have also been simulated, synthesized and tested using ModelSim Altera Quartus II. The same algorithm is implemented on generalized PV model developed in MATLAB Simulink software and the outputs are compared with that of Altera. It has been carefully observed that we can satisfactorily extracted the MPP from both the system. The system is further evaluated for different insolation and ambient Temperature in order to analyze the various outputs in these conditions. Simulation results validated the proposed implementation of MPPT algorithm using VHDL program in FPGA.


OpenFlow Controllers over EstiNet Network Simulator and Emulator : Functional Validation and Performance Evaluation


title: Generic physiological features as predictors of player experience

abstract: This paper examines the generality of features extracted from heart rate (HR) and skin conductance (SC) signals as predictors of self-reported player affect expressed as pairwise preferences. Artificial neural networks are trained to accurately map physiological features to expressed affect in two dissimilar and independent game surveys. The performance of the obtained affective models which are trained on one game is tested on the unseen physiological and selfreported data of the other game. Results in this early study suggest that there exist features of HR and SC such as average HR and one and two-step SC variation that are able to predict affective states across games of different genre and dissimilar game mechanics.


title: Information security culture : A Behaviour Compliance Conceptual Framework

abstract: Understanding the complex dynamic and uncertain characteristics of organisational employees who perform authorised or unauthorised information security activities is deemed to be a very important and challenging task. This paper presents a conceptual framework for classifying and organising the characteristics of organisational subjects involved in these information security practices. Our framework expands the traditional Human Behaviour and the Social Environment perspectives used in social work by identifying how knowledge, skills and individual preferences work to influence individual and group practices with respect to information security management. The classification of concepts and characteristics in the framework arises from a review of recent literature and is underpinned by theoretical models that explain these concepts and characteristics. Further, based upon an exploratory study of three case organisations in Saudi Arabia involving extensive interviews with senior managers, department managers, IT managers, information security officers, and IT staff; this article describes observed information security practices and identifies several factors which appear to be particularly important in influencing information security behaviour. These factors include values associated with national and organisational culture and how they manifest in practice, and activities related to information security management.


title: Automatic annotation of human actions in video

abstract: This paper addresses the problem of automatic temporal annotation of realistic human actions in video using minimal manual supervision. To this end we consider two associated problems: (a) weakly-supervised learning of action models from readily available annotations, and (b) temporal localization of human actions in test videos. To avoid the prohibitive cost of manual annotation for training, we use movie scripts as a means of weak supervision. Scripts, however, provide only implicit, noisy, and imprecise information about the type and location of actions in video. We address this problem with a kernel-based discriminative clustering algorithm that locates actions in the weakly-labeled training data. Using the obtained action samples, we train temporal action detectors and apply them to locate actions in the raw video data. Our experiments demonstrate that the proposed method for weakly-supervised learning of action models leads to significant improvement in action detection. We present detection results for three action classes in four feature length movies with challenging and realistic video data.


title: Object permanence in five-month-old infants

abstract: A new method was devised to test object permanence in young infants. Fivemonth-old infants were habituated to a screen that moved back and forth through a 180-degree arc, in the manner of a drawbridge. After infants reached habituation, a box was centered behind the screen. Infants were shown two test events: a possible event and an impossible event. In the possible event, the screen stopped when it reached the occluded box; in the impossible event, the screen moved through the space occupied by the box. The results indicated that infants looked reliably longer at the impossible than at the possible event. This finding suggested that infants (1) understood that the box continued to exist, in its same location, after it was occluded by the screen, and (2) expected the screen to stop against the occluded box and were surprised, or puzzled, when it failed to do so. A control experiment in which the box was placed next to the screen provided support for this interpretation of the results. Together, the results of these experiments indicate that, contrary to Piaget’s (1954) claims, infants as young as 5 months of age understand that objects continue to exist when occluded. The results also indicate that 5-month-old infants realize that solid objects do not move through the space occupied by other solid objects.


title: Transferring knowledge across robots

abstract: One of the most impressive characteristics of human perception is its domain adaptation capability. Humans can recognize objects and places simply by transferring knowledge from their past experience. Inspired by that, current research in robotics is addressing a great challenge: building robots able to sense and interpret the surrounding world by reusing information previously collected, gathered by other robots or obtained from the web. But, how can a robot automatically understand what is useful among a large amount of information and perform knowledge transfer? In this paper we address the domain adaptation problem in the context of visual place recognition. We consider the scenario where a robot equipped with a monocular camera explores a new environment. In this situation traditional approaches based on supervised learning perform poorly, as no annotated data are provided in the new environment and the models learned from data collected in other places are inappropriate due to the large variability of visual information. To overcome these problems we introduce a novel transfer learning approach. With our algorithm the robot is given only some training data (annotated images collected in different environments by other robots) and is able to decide whether, and how much, this knowledge is useful in the current scenario. At the base of our approach there is a transfer risk measure which quantifies the similarity between the given and the new visual data. To improve the performance, we also extend our framework to take into account multiple visual cues. Our experiments on three publicly available datasets demonstrate the effectiveness of the proposed approach. We cast the visual place recognition problem within a transfer learning framework.A method to quantify the similarity between Source and Target data is proposed.The Kullback-Leibler divergence and the Earth Mover's Distance are compared.We extend our approach to integrate informations from multiple visual cues.We perform an extensive experimental evaluation on publicly available sequences.


title: Adaptive feature learning CNN for behavior recognition in crowd scene

abstract: Learning and recognizing 3-dimension (3D) adaptive features are important for crowd scene understanding in video surveillance research. Deep learning architectures such as Convolutional Neural Networks (CNN) have recently shown much success in various computer vision applications. Existing approaches such as hand-crafted method and 2D-CNN architectures are widely used in adaptive feature representations on image data. However, learning dynamic and temporal features in 3D scale features in videos remains an open problem. In this study, we proposed a novel technique 3D-scale Convolutional Neural Network (3DS-CNN), based on the decomposition of 3D feature maps into 2D spatio and 2D temporal feature representations. Extensive experiments on hundreds of video scene were demonstrated on publicly available crowd datasets. Quantitative and qualitative evaluations indicate that the proposed model display superior performance when compared to baseline approaches. The mean average precision of 95.30% was recorded on WWW crowd dataset.


title: Motion deblurring of faces

abstract: Face analysis lies at the heart of computer vision with remarkable progress in the past decades. Face recognition and tracking are tackled by building invariance to fundamental modes of variation such as illumination, 3D pose. A much less standing mode of variation is motion deblurring, which however presents substantial challenges in face analysis. Recent approaches either make oversimplifying assumptions, e.g. in cases of joint optimization with other tasks, or fail to preserve the highly structured shape/identity information. We introduce a two-step architecture tailored to the challenges of motion deblurring: the first step restores the low frequencies; the second restores the high frequencies, while ensuring that the outputs span the natural images manifold. Both steps are implemented with a supervised data-driven method; to train those we devise a method for creating realistic motion blur by averaging a variable number of frames. The averaged images originate from the \(2MF^2\) dataset with \(19\) million facial frames, which we introduce for the task. Considering deblurring as an intermediate step, we conduct a thorough experimentation on high-level face analysis tasks, i.e. landmark localization and face verification, on blurred images. The experimental evaluation demonstrates the superiority of our method.


title: PalmHashing: a novel approach for cancelable biometrics

abstract: We propose a novel cancelable biometric approach, known as PalmHashing, to solve the non-revocable biometric issue. The proposed method hashes palmprint templates with a set of pseudo-random keys to obtain a unique code called palmhash. The palmhash code can be stored in portable devices such tokens and smartcards for verification. Multiple sets of palmhash codes can be maintained in multiple applications. Thus the privacy and security of the applications can be greatly enhanced. When compromised, revocation can also be achieved via direct replacement of a new set of palmhash code. In addition, PalmHashing offers several advantages over contemporary biometric approaches such as clear separation of the genuine-imposter populations and zero EER occurrences. In this paper, we outline the implementation details of this method and also highlight its potentials in security-critical applications.


title: Performance of feature-selection methods in the classification of high-dimension data

abstract: Contemporary biological technologies produce extremely high-dimensional data sets from which to design classifiers, with 20,000 or more potential features being common place. In addition, sample sizes tend to be small. In such settings, feature selection is an inevitable part of classifier design. Heretofore, there have been a number of comparative studies for feature selection, but they have either considered settings with much smaller dimensionality than those occurring in current bioinformatics applications or constrained their study to a few real data sets. This study compares some basic feature-selection methods in settings involving thousands of features, using both model-based synthetic data and real data. It defines distribution models involving different numbers of markers (useful features) versus non-markers (useless features) and different kinds of relations among the features. Under this framework, it evaluates the performances of feature-selection algorithms for different distribution models and classifiers. Both classification error and the number of discovered markers are computed. Although the results clearly show that none of the considered feature-selection methods performs best across all scenarios, there are some general trends relative to sample size and relations among the features. For instance, the classifier-independent univariate filter methods have similar trends. Filter methods such as the t-test have better or similar performance with wrapper methods for harder problems. This improved performance is usually accompanied with significant peaking. Wrapper methods have better performance when the sample size is sufficiently large. ReliefF, the classifier-independent multivariate filter method, has worse performance than univariate filter methods in most cases; however, ReliefF-based wrapper methods show performance similar to their t-test-based counterparts.


title: Feel-good touch: finding the most pleasant tactile feedback for a mobile touch screen button

abstract: Earlier research has shown the benefits of tactile feedback for touch screen widgets in all metrics: performance, usability and user experience. In our current research the goal was to go deeper in understanding the characteristics of a tactile click for virtual buttons. More specifically we wanted to find a tactile click which is the most pleasant to use with a finger. We used two actuator solutions in a small mobile touch screen: piezo actuators or a standard vibration motor. We conducted three experiments: The first and second experiments aimed to find the most pleasant tactile feedback done with the piezo actuators or a vibration motor, respectively, and the third one combined and compared the results from the first two experiments. The results from the first two experiments showed significant differences for the perceived pleasantness of the tactile clicks, and we used these most pleasant clicks in the comparison experiment in addition to the condition with no tactile feedback. Our findings confirmed results from earlier studies showing that tactile feedback is superior to a nontactile condition when virtual buttons are used with the finger regardless of the technology behind the tactile feedback. Another finding suggests that the users perceived the feedback done with piezo actuators slightly more pleasant than the vibration motor based feedback, although not statistically significantly. These results indicate that it is possible to modify the characteristics of the virtual button tactile clicks towards the most pleasant ones, and on the other hand this knowledge can help designers to create better touch screen virtual buttons and keyboards.


title: permission. Learning to Hash with Binary Reconstructive Embeddings

abstract: Fast retrieval methods are increasingly critical for many large-scale analysis tasks, and there have been several recent methods that attempt to learn hash functions for fast and accurate nearest neighbor searches. In this paper, we develop an algorithm for learning hash functions based on explicitly minimizing the reconstruction error between the original distances and the Hamming distances of the corresponding binary embeddings. We develop a scalable coordinate-descent algorithm for our proposed hashing objective that is able to efficiently learn hash functions in a variety of settings. Unlike existing methods such as semantic hashing and spectral hashing, our method is easily kernelized and does not require restrictive assumptions about the underlying distribution of the data. We present results over several domains to demonstrate that our method outperforms existing state-of-the-art techniques.


title: A survey of energy harvesting sources for embedded systems

abstract: Historically, batteries have been the source of energy for most mobile, embedded and remote system applications. Now, with ubiquitous computing requirements in the fields of embedded systems, wireless sensor networks and low- power electronics such as MEMS devices, an alternative source of energy is required. Also with the limited capacity of finite power sources and the need for supplying energy for a lifetime of a system, there is a requirement for self- powered devices. The process of extracting energy from the surrounding environment is termed as energy harvesting. Energy harvesting, which originated from the windmill and water wheel, is widely being considered as a low- maintenance solution for a wide variety of applications. There are various forms of energy that can be scavenged, like thermal, mechanical, solar, acoustic, wind, and wave. This paper serves as a survey for identifying the sources of energy harvesting based on various technical papers available in the public domain.


title: Emotion drives attention: Detecting the snake in the grass.

abstract: Participants searched for discrepant fear-relevant pictures (snakes or spiders) in grid-pattern arrays of fear-irrelevant pictures belonging to the same category (flowers or mushrooms) and vice versa. Fear-relevant pictures were found more quickly than fear-irrelevant ones. Fear-relevant, but not fear-irrelevant, search was unaffected by the location of the target in the display and by the number of distractors, which suggests parallel search for fear-relevant targets and serial search for fear-irrelevant targets. Participants specifically fearful of snakes but not spiders (or vice versa) showed facilitated search for the feared objects but did not differ from controls in search for nonfeared fear-relevant or fear-irrelevant, targets. Thus, evolutionary relevant threatening stimuli were effective in capturing attention, and this effect was further facilitated if the stimulus was emotionally provocative.


title: Effect of horseback riding versus a dynamic and static horse riding simulator on sitting ability of children with cerebral palsy: a randomized controlled trial

abstract: [Purpose] A randomized controlled trial was conducted to investigate the immediate effects of horseback riding (HR) and a dynamic (DHS) and static (SHS) horse riding simulator (OSIM uGallop, Taiwan) on sitting ability of children with cerebral palsy. [Subjects and Methods] Thirty children with cerebral palsy were recruited and randomly assigned into three groups. Children received 30 minutes of exercise according to their assigned group. The Segmental Assessment of Trunk Control (SATCo) and Gross Motor Function Measure-66 (GMFM-66) sitting dimension were used to assess children in all groups both before and after the interventions. [Results] Sitting abilities were significantly improved after all interventions. Horseback riding showed the most improvement, followed by the dynamic and static horse riding simulator groups. Horseback riding also showed a significant improvement in the GMFM sitting dimension. [Conclusion] Horseback riding was the best intervention for promoting sitting ability of children with spastic cerebral palsy. However, a dynamic horse riding simulator can be a good surrogate for horseback riding when horseback riding is not available.