title: Alternative metrics in scientometrics: a meta-analysis of research into three altmetrics

abstract: Alternative metrics are currently one of the most popular research topics in scientometric research. This paper provides an overview of research into three of the most important altmetrics: microblogging (Twitter), online reference managers (Mendeley and CiteULike) and blogging. The literature is discussed in relation to the possible use of altmetrics in research evaluation. Since the research was particularly interested in the correlation between altmetrics counts and citation counts, this overview focuses particularly on this correlation. For each altmetric, a meta-analysis is calculated for its correlation with traditional citation counts. As the results of the meta-analyses show, the correlation with traditional citations for micro-blogging counts is negligible (pooled r = 0.003), for blog counts it is small (pooled r = 0.12) and for bookmark counts from online reference managers, medium to large (CiteULike pooled r = 0.23; Mendeley pooled r = 0.51).


title: Development of an Unmanned Aerial Vehicle (UAV) for hyper resolution vineyard mapping based on visible, multispectral, and thermal imagery

abstract: Unmanned Aerial Vehicles(UAV) are an exciting new remote sensing tool capable of acquiring high resolution spatial data. This study has developed a UAV capable of collecting hyper resolution visible, multispectral and thermal imagery for application to Precision Viticulture (PV). Traditional modes of data collection are not well suited to the detection of subtle but important changes in vineyard structure given low temporal and spatial resolutions. Mapping with UAVs has the potential to provide imagery at an unprecedented spatial and temporal resolution. We present a technical description of our UAV and its payload options including visible imagery which is processed using feature matching and photogrammetric techniques to create Digital Surface Models(DSMs) of the vineyards. A thermal infrared camera is used to map soil moisture enabling assessment of irrigation efficiency, and a six-band multispectral camera enables the calculation of vegetation indices that relate to vineyard vigour and health.


title: The long reads ahead: de novo genome assembly using the MinION

abstract: Nanopore technology provides a novel approach to DNA sequencing that yields long, label-free reads of constant quality. The first commercial implementation of this approach, the MinION, has shown promise in various sequencing applications. This review gives an up-to-date overview of the MinION's utility as a de novo sequencing device. It is argued that the MinION may allow for portable and affordable de novo sequencing of even complex genomes in the near future, despite the currently error-prone nature of its reads. Through continuous updates to the MinION hardware and the development of new assembly pipelines, both sequencing accuracy and assembly quality have already risen rapidly. However, this fast pace of development has also lead to a lack of overview of the expanding landscape of analysis tools, as performance evaluations are outdated quickly. As the MinION is approaching a state of maturity, its user community would benefit from a thorough comparative benchmarking effort of de novo assembly pipelines in the near future. An earlier version of this article can be found on bioRxiv .


title: Patch Fed Planar Dielectric Slab Extended Hemi-Elliptical Lens Antenna

abstract: We present the results of a parametric study devised to allow us to optimally design a patch fed planar dielectric slab waveguide extended hemi-elliptical lens antenna. The lens antenna, 11lambda times 13lambda in the lens plane and 0.6lambda thick, constructed from polystyrene and weighing only 90 g is fabricated and characterized at 28.5 GHz for both single and multiple operating configurations. The lens when optimized for single beam operation achieves 18.5 dB measured gain (85% aperture efficiency), 40deg and 4.1deg half power beam width for E plane and H plane respectively and 10% impedance bandwidth for -10 dB return loss. While for optimized for multiple beam operation it is shown that the lens can accommodate up to 9 feeds and that beam symmetry can be maintained over a scan angle of 27deg with a gain of 14.9 to 17.7 dB, and first side lobe levels of -11 to -7 dB respectively. Over the frequency range 26 to 30 GHz the lens maintains a worst case return loss of -10 dB and port to port feed isolation of better than -25 dB. Further it is shown that residual leaked energy from the structure is less than -48 dBm at 1 cm, thus making a low profile enclosure possible. We also show that by simultaneous excitation of two adjacent ports we can obtain difference patterns with null depths of up to -36 dB.


title: ConDist: a context-driven categorical distance measure

abstract: A distance measure between objects is a key requirement for many data mining tasks like clustering, classification or outlier detection. However, for objects characterized by categorical attributes, defining meaningful distance measures is a challenging task since the values within such attributes have no inherent order, especially without additional domain knowledge. In this paper, we propose an unsupervised distance measure for objects with categorical attributes based on the idea that categorical attribute values are similar if they appear with similar value distributions on correlated context attributes. Thus, the distance measure is automatically derived from the given data set. We compare our new distance measure to existing categorical distance measures and evaluate on different data sets from the UCI machine-learning repository. The experiments show that our distance measure is recommendable, since it achieves similar or better results in a more robust way than previous approaches.


title: Object recognition in floor plans by graphs of white connected components

abstract: In this paper we describe the object location module of a complete floor plan understanding system. The approach relies on a Region Adjacency Graph (RAG) whose nodes correspond to connected components of the background. Two types of node attributes are proposed and are used to locate and recognize objects similar to the models or to generalize the recognition to different shapes. The tests are made on two benchmark datasets and on one collection of floor plans downloaded from Internet and containing heterogeneous images.


title: Modeling the switching performance of a MOSFET in the high side of a non-isolated buck converter

abstract: This paper uses generic equations to model the switching performance of metal oxide semiconductor field effect transistors (MOSFETs). Two MOSFETs with different gate structures are analyzed and switching times are presented, which are compared against practical measurements. It is shown that it is possible to get a reasonable accuracy of the switching performance of a MOSFET in a nonsynchronous buck converter, but care must be taken to ensure the correct values of the parameters are used. It is also shown that the MOSFET with the thick bottom oxide gate provides a faster switching instant when compared to a conventional U-Trench gated MOSFET.


Chapter 3 Data Mining for Counter-Terrorism


Automated Number Plate Recognition Using Hough Lines and Template Matching


title: Towards Monitoring of Novel Statements in the News

abstract: In media monitoring users have a clearly defined information need to find so far unknown statements regarding certain entities or relations mentioned in natural-language text. However, commonly used keyword-based search technologies are focused on finding relevant documents and cannot judge the novelty of statements contained in the text. In this work, we propose a new semantic novelty measure that allows to retrieve statements, which are both novel and relevant, from natural-language sentences in news articles. Relevance is defined by a semantic query of the user, while novelty is ensured by checking whether the extracted statements are related, but non-existing in a knowledge base containing the currently known facts. Our evaluation performed on English news texts and on CrunchBase as the knowledge base demonstrates the effectiveness, unique capabilities and future challenges of this novel approach to novelty.


title: Clinical manifestations and endoscopic findings of amebic colitis in a United States-Mexico border city: a case series

abstract: BackgroundInvasive amebiasis is not frequently seen in the United States. It is associated with considerable morbidity in patients residing in or traveling to endemic areas. We report a case series of patients with amebic colitis in a United States-Mexico border city to alert physicians to the varied clinical manifestations.Case presentationNine patients were diagnosed with amebic colitis. Mean age was 56 (38–83), 6 were males, and all were Hispanic. Common symptoms were diarrhea (56 %), hematochezia (33 %) and abdominal bloating (11 %). The diagnosis of amebic colitis was established in the following ways: 8 patients by colonoscopy with biopsy, 1 by surgery for colonic obstruction. The diagnosis of amebic colitis was confirmed in 8 patients (89 %) by amebic trophozoites present in histopathologic sections. One patient was diagnosed with amebic colitis based upon clinical symptoms, colitis on colonoscopy and visualization of amebic trophozoites on stool examination. In the 8 patients in whom colonoscopy was done, 6 (75 %) had inflammation with rectosigmoid involvement and 5 (62.5 %) had ulcerations. Infection resolved after treatment with metronidazole in most patients; however, one patient developed a liver abscess and another had a colonic perforation and later developed a liver abscess.ConclusionThe occurrence of amebic colitis in this United States-Mexico border city hospital population was low, but in some cases potentially life-threatening. Physicians should be alert to the less common presentations of amebic colitis, such as overt gastrointestinal bleeding, exacerbation of inflammatory bowel disease, and the incidental finding of association with colon cancer, or a surgical abdomen. Rectosigmoid involvement was typically found on colonoscopy.


title: On a battery/supercapacitor powered SRM drive for EV with integrated on-board charger

abstract: This paper develops a battery/supercapacitor powered switched-reluctance motor (SRM) drive for electric vehicle (EV) propulsion. Its on-board integrated charger is formed using the embedded motor drive components. The boosted motor drive DC-link voltage is established by an interleaved DC/DC boost converter from the battery. The super-capacitor (SC) is interfaced to the DC-link via a DC/DC buck converter. And it is connected to the battery bank by a diode to assist the battery in acceleration and deceleration. Good driving and regenerative braking performances are achieved by proper control. In idle condition, the grid-to-vehicle (G2V) battery charging can be conducted by the formed on-board integrated charger with good line drawn power quality. The established charger consists of a full-bridge boost switch-mode rectifier (SMR) and a buck DC/DC converter based charger.


Design and Analysis of a Reconfi gurable Hierarchical Temporal Memory Architecture


title: A NPR System for Generating Floral Patterns based on L-System

abstract: In history, the decorative pattern represents the design of art. The styles of decorative patterns are unique according to different countries and cultures. Because of the decorative floral pattern giving human an elegant and abundance impression, it is applied to many fields including product packaging, advertising or multimedia materials design. In this paper, we simulate the plant growth to create the decorative floral patterns. One of plant growth simulation approach is L-System (Lindenmayer System). Due to plant growth information can be stored in the L-system grammar formats, the styles of decorative patterns can be controlled by production rules which users define. However , defining a complex L-system grammar structure by non-professional users is clearly a very difficult task. It requires expert with both botanic and programming knowledge to manipulate. Therefore, we propose a user-friendly CAD system for generating L- grammar based on a sketch-based interface. Users can draw arbitrary lines or curves on the canvas for automatically creating decorative floral patterns. In addition, we integrate the Simeco system with CUDA (Compute Unified Device Architecture) for improving the inefficiency of modeling and simulating plants.


title: A multilabel text classification algorithm for labeling risk factors in SEC form 10-K

abstract: This study develops, implements, and evaluates a multilabel text classification algorithm called the multilabel categorical K-nearest neighbor (ML-CKNN). The proposed algorithm is designed to automatically identify 25 types of risk factors with specific meanings reported in Section 1A of SEC form 10-K. The idea of ML-CKNN is to compute a categorical similarity score for each label by the K-nearest neighbors in that category. ML-CKNN is tailored to achieve the goal of extracting risk factors from 10Ks. The proposed algorithm can perfectly classify 74.94p of risk factors and 98.75p of labels. Moreover, ML-CKNN is empirically shown to outperform ML-KNN and other multilabel algorithms. The extracted risk factors could be valuable to empirical studies in accounting or finance.


title: Contrastive Explanations for Reinforcement Learning in terms of Expected Consequences

abstract: Machine Learning models become increasingly proficient in complex tasks. However, even for experts in the field, it can be difficult to understand what the model learned. This hampers trust and acceptance, and it obstructs the possibility to correct the model. There is therefore a need for transparency of machine learning models. The development of transparent classification models has received much attention, but there are few developments for achieving transparent Reinforcement Learning (RL) models. In this study we propose a method that enables a RL agent to explain its behavior in terms of the expected consequences of state transitions and outcomes. First, we define a translation of states and actions to a description that is easier to understand for human users. Second, we developed a procedure that enables the agent to obtain the consequences of a single action, as well as its entire policy. The method calculates contrasts between the consequences of a policy derived from a user query, and of the learned policy of the agent. Third, a format for generating explanations was constructed. A pilot survey study was conducted to explore preferences of users for different explanation properties. Results indicate that human users tend to favor explanations about policy rather than about single actions.


title: Vehicular ad hoc networks (VANETs): Current state, challenges, potentials and way forward

abstract: Recent advances in wireless communication technologies and auto-mobile industry have triggered a significant research interest in the field of VANETs over the past few years. VANET consists of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications supported by wireless access technologies such as IEEE 802.11p. This innovation in wireless communication has been envisaged to improve road safety and motor traffic efficiency in near future through the development of Intelligent Transport Systems (ITS). Hence, government, auto-mobile industries and academia are heavily partnering through several ongoing research projects to establish standards for VANETs. The typical set of VANET application areas, such as vehicle collision warning and traffic information dissemination have made VANET an interested field of wireless communication. This paper provides an overview on current research state, challenges, potentials of VANETs as well the way forward to achieving the long awaited ITS.


Hand Gesture Recognition Using PCA


title: Development of robot legs inspired by bi-articular muscle-tendon complex of cats

abstract: Within the limbs of typical animals, there exist bi-articular muscles crossing two joints. It is known that the bi-articular muscles of the felid play an important role in the locomotion. Also the muscle-tendon complex, composed of the gastrocnemius muscle and the Achilles' tendon that cross the knee joint and the ankle joint contributes much to the movements such as running and jumping particularly. Besides, because the muscle-tendon complex has the function for absorbing shocking, it is utilized for soft landing from high places. To achieve high performance for jumping and landing motion like cats, we are developing robot legs inspired by the bi-articular muscle-tendon complex of cats. The leg consists of hip, knee and ankle joints. For the knee and ankle joints, a four-bar linkage mechanism with one elastic linkage, in which the knee joint is driven by an electric rotary motor and the ankle joint is passive, is applied. By this mechanism, basic functions of the bi-articular muscle-tendon complex of felids like cats can be realized and the performance for jumping and landing can be improved. In this paper, the new leg mechanism is described. Moreover, a prototype of a pair of the hind legs of the quadruped robot using the new mechanism has been developed. The results of jumping and landing experiments are shown to validate the effectiveness of the mechanism.


title: Caloric restriction and intermittent fasting: Two potential diets for successful brain aging

abstract: The vulnerability of the nervous system to advancing age is all too often manifest in neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. In this review article we describe evidence suggesting that two dietary interventions, caloric restriction (CR) and intermittent fasting (IF), can prolong the health-span of the nervous system by impinging upon fundamental metabolic and cellular signaling pathways that regulate life-span. CR and IF affect energy and oxygen radical metabolism, and cellular stress response systems, in ways that protect neurons against genetic and environmental factors to which they would otherwise succumb during aging. There are multiple interactive pathways and molecular mechanisms by which CR and IF benefit neurons including those involving insulin-like signaling, FoxO transcription factors, sirtuins and peroxisome proliferator-activated receptors. These pathways stimulate the production of protein chaperones, neurotrophic factors and antioxidant enzymes, all of which help cells cope with stress and resist disease. A better understanding of the impact of CR and IF on the aging nervous system will likely lead to novel approaches for preventing and treating neurodegenerative disorders.


title: Alzheimer Mechanisms and Therapeutic Strategies

abstract: There are still no effective treatments to prevent, halt, or reverse Alzheimer's disease, but research advances over the past three decades could change this gloomy picture. Genetic studies demonstrate that the disease has multiple causes. Interdisciplinary approaches combining biochemistry, molecular and cell biology, and transgenic modeling have revealed some of its molecular mechanisms. Progress in chemistry, radiology, and systems biology is beginning to provide useful biomarkers, and the emergence of personalized medicine is poised to transform pharmaceutical development and clinical trials. However, investigative and drug development efforts should be diversified to fully address the multifactoriality of the disease.


Vertical Slit Transistor Based Integrated Circuits ( VeSTICs ) : Overview and Highlights of Feasibility Study


title: Face spoofing detection from single images using micro-texture analysis

abstract: Current face biometric systems are vulnerable to spoofing attacks. A spoofing attack occurs when a person tries to masquerade as someone else by falsifying data and thereby gaining illegitimate access. Inspired by image quality assessment, characterization of printing artifacts, and differences in light reflection, we propose to approach the problem of spoofing detection from texture analysis point of view. Indeed, face prints usually contain printing quality defects that can be well detected using texture features. Hence, we present a novel approach based on analyzing facial image textures for detecting whether there is a live person in front of the camera or a face print. The proposed approach analyzes the texture of the facial images using multi-scale local binary patterns (LBP). Compared to many previous works, our proposed approach is robust, computationally fast and does not require user-cooperation. In addition, the texture features that are used for spoofing detection can also be used for face recognition. This provides a unique feature space for coupling spoofing detection and face recognition. Extensive experimental analysis on a publicly available database showed excellent results compared to existing works.


title: MALT: distributed data-parallelism for existing ML applications

abstract: Machine learning methods, such as SVM and neural networks, often improve their accuracy by using models with more parameters trained on large numbers of examples. Building such models on a single machine is often impractical because of the large amount of computation required. We introduce MALT, a machine learning library that integrates with existing machine learning software and provides data parallel machine learning. MALT provides abstractions for fine-grained in-memory updates using one-sided RDMA, limiting data movement costs during incremental model updates. MALT allows machine learning developers to specify the dataflow and apply communication and representation optimizations. Through its general-purpose API, MALT can be used to provide data-parallelism to existing ML applications written in C++ and Lua and based on SVM, matrix factorization and neural networks. In our results, we show MALT provides fault tolerance, network efficiency and speedup to these applications.


title: OMA DM-based remote software fault management for mobile devices

abstract: Mobile devices (e.g. mobile handsets or PDAs) have gained much functionality and intelligence with the growth of mobile network technologies and the increased use of mobile services. As a consequence, mobile devices have become more complex and many related problems have occurred. Specifically, sudden rebooting and freezing problems caused by software faults decrease the availability of the mobile device and cause inconvenience to end-users. To solve such problems, academia and industry have focused on Open Mobile Alliance (OMA) Device Management (DM); this method is the international de facto standard for mobile device management. In this paper, we propose a software fault management method to remotely determine and correct problems of mobile devices based on OMA DM. We present a definition of management objects and a method to collect them from mobile devices using the OMA DM protocol. We also present a method for debugging and correcting software faults using the collected information. Finally, we present a prototype implementation and performance evaluation to validate our proposed method. Results of the performance evaluation show that our proposed method is efficient and scalable in regard to network traffic overhead and response time.


Puddle : An Operating System for Reliable , High-Level Programming of Digital Microfluidic Devices


HPC Containers in Use


title: Driver Action Prediction Using Deep (Bidirectional) Recurrent Neural Network

abstract: Advanced driver assistance systems (ADAS) can be significantly improved with effective driver action prediction (DAP). Predicting driver actions early and accurately can help mitigate the effects of potentially unsafe driving behaviors and avoid possible accidents. In this paper, we formulate driver action prediction as a timeseries anomaly prediction problem. While the anomaly (driver actions of interest) detection might be trivial in this context, finding patterns that consistently precede an anomaly requires searching for or extracting features across multi-modal sensory inputs. We present such a driver action prediction system, including a real-time data acquisition, processing and learning framework for predicting future or impending driver action. The proposed system incorporates camera-based knowledge of the driving environment and the driver themselves, in addition to traditional vehicle dynamics. It then uses a deep bidirectional recurrent neural network (DBRNN) to learn the correlation between sensory inputs and impending driver behavior achieving accurate and high horizon action prediction. The proposed system performs better than other existing systems on driver action prediction tasks and can accurately predict key driver actions including acceleration, braking, lane change and turning at durations of 5sec before the action is executed by the driver.


title: Generating legal arguments and predictions from case texts

abstract: In this paper, we present methods for automatically finding abstract, legally relevant concepts in case texts and demonstrate how they can be used to make predictions of case outcomes, given the texts as inputs.In a set of experiments to test these methods, we focus on the open question of how best to represent legal text for finding abstract concepts. We compare different ways of representing legal case texts in order to test whether adding domain knowledge and some linguistic information can improve performance.We found that replacing individual names by roles in the case texts led to better indexing, and that adding certain syntactic and semantic information, in the form of Propositional Patterns that capture a sense of "who did what", led to better prediction. Our experiments also showed that of three learning algorithms, Nearest Neighbor worked best in learning how to identify indexing concepts in texts.In these experiments, we introduced a prototype system that can reason with text cases; it analyzes a case, predicts its outcome considering other cases in the database, and explains the prediction, all starting with a textual description of the case's facts as input.


title: Face Recognition and Drunk Classification Using Infrared Face Images

abstract: The aim of this study is to propose a system that is capable of recognising the identity of a person, indicating whether the person is drunk using only information extracted from thermal face images. The proposed system is divided into two stages, face recognition and classification. In the face recognition stage, test images are recognised using robust face recognition algorithms: Weber local descriptor (WLD) and local binary pattern (LBP). The classification stage uses Fisher linear discriminant to reduce the dimensionality of the features, and those features are classified using a classifier based on a Gaussian mixture model, creating a classification space for each person, extending the state-of-the-art concept of a “DrunkSpace Classifier.” The system was validated using a new drunk person database, which was specially designed for this work. The main results show that the performance of the face recognition stage was 100% with both algorithms, while the drunk identification saw a performance of 86.96%, which is a very promising result considering 46 individuals for our database in comparison with others that can be found in the literature.


title: A Review of Adaptive Line Enhancers for Noise Cancellation

abstract: This paper provides a literature review on Adaptive Line Enhancer (ALE) methods based on adaptive noise cancellation systems. Such methods have been used in various applications, including communication systems, biomedical engineering, and industrial applications. Developments in ALE in noise cancellation are reviewed, including the principles, adaptive algorithms, and recent modifications on the filter design proposed to increase the convergence rate and reduce the computational complexity for future implementation. The advantages and drawbacks of various adaptive algorithms, such as the Least Mean Square, Recursive Least Square, Affine Projection Algorithm, and their variants, are discussed in this review. Design modifications of filter structures used in ALE are also evaluated. Such filters include Finite Impulse Response, Infinite Impulse Response, lattice, and nonlinear adaptive filters. These structural modifications aim to achieve better adaptive filter performance in ALE systems. Finally, a perspective of future research on ALE systems is presented for further consideration.


title: Challenges in Enterprise Adoption of Agile Methods – A Survey

abstract: Agile methods are a departure from plan-driven traditional approaches, where the focus is on generating early releases of working software using collaborative techniques, code refactoring, and on-site customer involvement. Research and surveys have shown that agile methodologies are an efficient way of producing software with significant advantages in production costs, time-to-market, complexity, and quality improvement over heavy-weight traditional methodologies. Even with such apparent advantages the information technology industry has not seen large-scale adoption of agile methods. In this survey paper, the major challenges in adopting agile practices by enterprises are addressed. Drawing information from the literature issues like framework for agile organizational change and adoption strategies are examined. Inputs from the industry suggest that most organizations are best suited in adopting a combination of traditional and agile method. There is no agile methodology that can be universally applied and have to be tailored to integrate into existing processes.


title: Modularity in design of products and systems

abstract: Modularity refers to the use of common units to create product variants. This paper aims at the development of models and solution approaches to the modularity problem for mechanical, electrical, and mixed process products (e.g., electromechanical products). To interpret various types of modularity, e.g., component-swapping, component-sharing and bus modularity, a matrix representation of the modularity problem is presented. The decomposition approach is used to determine modules for different products. The representation and solution approaches presented are illustrated with numerous examples. The paper presents a formal approach to modularity allowing for optimal forming of modules even in the situation of insufficient availability of information. The modules determined may be shared across different products.


title: Organizational impact of system quality, information quality, and service quality

abstract: Increased organizational dependence on information systems drives management attention towards improving information systems' quality. A recent survey shows that ''Improve IT quality'' is one of the top concerns facing IT executives. As IT quality is a multidimensional measure, it is important to determine what aspects of IT quality are critical to organizations to help Chief Information Officers (CIOs) to devise effective IT quality improvement strategies. In this research, we model the relationship between information systems' (IS) quality and organizational impact. We hypothesize greater organizational impact in situations in which system quality, information quality and service quality are high. We also hypothesize a positive relationship between system quality and information quality. We test our hypotheses using survey data. Our structural equation model exhibits a good fit with the observed data. Our results show that IS service quality is the most influential variable in this model (followed by information quality and system quality), thus highlighting the importance of IS service quality for organizational performance. This paper contributes theoretically to IS success models through the system quality-to-information quality and IS quality-to-organizational impact links. Implications of our results for practice and research are discussed.


title: Feasibility of Embedded DRAM Cells on FinFET Technology

abstract: In this paper, we analyze the suitability of implementing embedded DRAM (eDRAM) cells on FinFET technology compared to classical planar MOSFETs. The results show a significant improvement in overall cell performance for multi-gate devices. While pFinFET-based memories showed better cell behavior and variability robustness, mixed n/pFinFET cells had the highest working frequency and a negligible impact on degradation. Finally, we show that a multiple fin-height strategy can be used to reduce the layout area of the eDRAM cells (>10%).


title: Novel RC sinusoidal oscillator using second-generation current conveyor

abstract: A novel second-generation current conveyor (CCII)-based resistance-capacitance (RC) sinusoidal oscillator operating over a wide dynamic range is described. The oscillation condition and the oscillation frequency can be adjusted independently by two control resistors. The circuit proposed makes use of grounded capacitors and is suitable for implementation with the commercially available integrated circuit chip AD844 from Analog Devices which implements a CCII+. The circuit enjoys low sensitivities and is suitable for integration. Experimental results are given, showing close agreement with the theoretical conclusions.


title: Research Challenges in Nextgen Service Orchestration

abstract: Fog/edge computing, function as a service, and programmable infrastructures, like software-defined networking or network function virtualisation, are becoming ubiquitously used in modern Information Technology infrastructures. These technologies change the characteristics and capabilities of the underlying computational substrate where services run (e.g. higher volatility, scarcer computational power, or programmability). As a consequence, the nature of the services that can be run on them changes too (smaller codebases, more fragmented state, etc.). These changes bring new requirements for service orchestrators, which need to evolve so as to support new scenarios where a close interaction between service and infrastructure becomes essential to deliver a seamless user experience. Here, we present the challenges brought forward by this new breed of technologies and where current orchestration techniques stand with regards to the new challenges. We also present a set of promising technologies that can help tame this brave new world.


Technical Challenges of Forensic Investigations in Cloud Computing Environments


title: Seeing, Listening, Drawing: Interferences between Sensorimotor Modalities in the Use of a Tablet Musical Interface

abstract: Audio, visual, and proprioceptive actions are involved when manipulating a graphic tablet musical interface. Previous works suggested a possible dominance of the visual over the auditory modality in this situation. The main goal of the present study is to examine the interferences between these modalities in visual, audio, and audio-visual target acquisition tasks. Experiments are based on a movement replication paradigm, where a subject controls a cursor on a screen or the pitch of a synthesized sound by changing the stylus position on a covered graphic tablet. The experiments consisted of the following tasks: (1) a target acquisition task that was aimed at a visual target (reaching a cue with the cursor displayed on a screen), an audio target (reaching a reference note by changing the pitch of the sound played in headsets), or an audio-visual target, and (2) the replication of the target acquisition movement in the opposite direction. In the return phase, visual and audio feedback were suppressed. Different gain factors perturbed the relationships among the stylus movements, visual cursor movements, and audio pitch movements. The deviations between acquisition and return movements were analyzed. The results showed that hand amplitudes varied in accordance with visual, audio, and audio-visual perturbed gains, showing a larger effect for the visual modality. This indicates that visual, audio, and audio-visual actions interfered with the motor modality and confirms the spatial representation of pitch reported in previous studies. In the audio-visual situation, vision dominated over audition, as the latter had no significant influence on motor movement. Consequently, visual feedback is helpful for musical targeting of pitch on a graphic tablet, at least during the learning phase of the instrument. This result is linked to the underlying spatial organization of pitch perception. Finally, this work brings a complementary approach to previous studies showing that audition may dominate over vision for other aspects of musical sound (e.g., timing, rhythm, and timbre).


title: Visual Translation Embedding Network for Visual Relation Detection

abstract: Visual relations, such as person ride bike and bike next to car, offer a comprehensive scene understanding of an image, and have already shown their great utility in connecting computer vision and natural language. However, due to the challenging combinatorial complexity of modeling subject-predicate-object relation triplets, very little work has been done to localize and predict visual relations. Inspired by the recent advances in relational representation learning of knowledge bases and convolutional object detection networks, we propose a Visual Translation Embedding network (VTransE) for visual relation detection. VTransE places objects in a low-dimensional relation space where a relation can be modeled as a simple vector translation, i.e., subject + predicate ≈ object. We propose a novel feature extraction layer that enables object-relation knowledge transfer in a fully-convolutional fashion that supports training and inference in a single forward/backward pass. To the best of our knowledge, VTransE is the first end-toend relation detection network. We demonstrate the effectiveness of VTransE over other state-of-the-art methods on two large-scale datasets: Visual Relationship and Visual Genome. Note that even though VTransE is a purely visual model, it is still competitive to the Lu’s multi-modal model with language priors [27].


title: Using Meta-Learning to Initialize Bayesian Optimization of Hyperparameters

abstract: Model selection and hyperparameter optimization is crucial in applying machine learning to a novel dataset. Recently, a sub-community of machine learning has focused on solving this problem with Sequential Model-based Bayesian Optimization (SMBO), demonstrating substantial successes in many applications. However, for expensive algorithms the computational overhead of hyperparameter optimization can still be prohibitive. In this paper we explore the possibility of speeding up SMBO by transferring knowledge from previous optimization runs on similar datasets; specifically, we propose to initialize SMBO with a small number of configurations suggested by a metalearning procedure. The resulting simple MI-SMBO technique can be trivially applied to any SMBO method, allowing us to perform experiments on two quite different SMBO methods with complementary strengths applied to optimize two machine learning frameworks on 57 classification datasets. We find that our initialization procedure mildly improves the state of the art in low-dimensional hyperparameter optimization and substantially improves the state of the art in the more complex problem of combined model selection and hyperparameter optimization.


title: Learning modular neural network policies for multi-task and multi-robot transfer

abstract: Reinforcement learning (RL) can automate a wide variety of robotic skills, but learning each new skill requires considerable real-world data collection and manual representation engineering to design policy classes or features. Using deep reinforcement learning to train general purpose neural network policies alleviates some of the burden of manual representation engineering by using expressive policy classes, but exacerbates the challenge of data collection, since such methods tend to be less efficient than RL with low-dimensional, hand-designed representations. Transfer learning can mitigate this problem by enabling us to transfer information from one skill to another and even from one robot to another. We show that neural network policies can be decomposed into "task-specific" and "robot-specific" modules, where the task-specific modules are shared across robots, and the robot-specific modules are shared across all tasks on that robot. This allows for sharing task information, such as perception, between robots and sharing robot information, such as dynamics and kinematics, between tasks. We exploit this decomposition to train mix-and-match modules that can solve new robot-task combinations that were not seen during training. Using a novel neural network architecture, we demonstrate the effectiveness of our transfer method for enabling zero-shot generalization with a variety of robots and tasks in simulation for both visual and non-visual tasks.


title: A systematic review of security requirements engineering

abstract: One of the most important aspects in the achievement of secure software systems in the software development process is what is known as Security Requirements Engineering. However, very few reviews focus on this theme in a systematic, thorough and unbiased manner, that is, none of them perform a systematic review of security requirements engineering, and there is not, therefore, a sufficiently good context in which to operate. In this paper we carry out a systematic review of the existing literature concerning security requirements engineering in order to summarize the evidence regarding this issue and to provide a framework/background in which to appropriately position new research activities.


title: Yedrouj-Net: An efficient CNN for spatial steganalysis

abstract: For about 10 years, detecting the presence of a secret message hidden in an image was performed with an Ensemble Classifier trained with Rich features. In recent years, studies such as Xu et al. have indicated that well-designed convolutional Neural Networks (CNN) can achieve comparable performance to the two-step machine learning approaches. In this paper, we propose a CNN that outperforms the state-ofthe-art in terms of error probability. The proposition is in the continuity of what has been recently proposed and it is a clever fusion of important bricks used in various papers. Among the essential parts of the CNN, one can cite the use of a pre-processing filterbank and a Truncation activation function, five convolutional layers with a Batch Normalization associated with a Scale Layer, as well as the use of a sufficiently sized fully connected section. An augmented database has also been used to improve the training of the CNN. Our CNN was experimentally evaluated against S-UNIWARD and WOW embedding algorithms and its performances were compared with those of three other methods: an Ensemble Classifier plus a Rich Model, and two other CNN steganalyzers.


title: Higher-order organization of complex networks

abstract: Networks are a fundamental tool for understanding and modeling complex systems in physics, biology, neuroscience, engineering, and social science. Many networks are known to exhibit rich, lower-order connectivity patterns that can be captured at the level of individual nodes and edges. However, higher-order organization of complex networks---at the level of small network subgraphs---remains largely unknown. Here we develop a generalized framework for clustering networks based on higher-order connectivity patterns. This framework provides mathematical guarantees on the optimality of obtained clusters and scales to networks with billions of edges. The framework reveals higher-order organization in a number of networks including information propagation units in neuronal networks and hub structure in transportation networks. Results show that networks exhibit rich higher-order organizational structures that are exposed by clustering based on higher-order connectivity patterns.


title: Efficient Neural Architecture Search via Parameter Sharing

abstract: We propose Efficient Neural Architecture Search (ENAS), a fast and inexpensive approach for automatic model design. In ENAS, a controller learns to discover neural network architectures by searching for an optimal subgraph within a large computational graph. The controller is trained with policy gradient to select a subgraph that maximizes the expected reward on the validation set. Meanwhile the model corresponding to the selected subgraph is trained to minimize a canonical cross entropy loss. Thanks to parameter sharing between child models, ENAS is fast: it delivers strong empirical performances using much fewer GPU-hours than all existing automatic model design approaches, and notably, 1000x less expensive than standard Neural Architecture Search. On the Penn Treebank dataset, ENAS discovers a novel architecture that achieves a test perplexity of 55.8, establishing a new state-of-the-art among all methods without post-training processing. On the CIFAR-10 dataset, ENAS designs novel architectures that achieve a test error of 2.89%, which is on par with NASNet (Zoph et al., 2018), whose test error is 2.65%.


title: Data Mapping Diagrams for Data Warehouse Design with UML

abstract: In Data Warehouse (DW) scenarios, ETL (Extraction, Transformation, Loading) processes are responsible for the extraction of data from heterogeneous operational data sources, their transformation (conversion, cleaning, normalization, etc.) and their loading into the DW. In this paper, we present a framework for the design of the DW back-stage (and the respective ETL processes) based on the key observation that this task fundamentally involves dealing with the specificities of information at very low levels of granularity including transformation rules at the attribute level. Specifically, we present a disciplined framework for the modeling of the relationships between sources and targets in different levels of granularity (including coarse mappings at the database and table levels to detailed inter-attribute mappings at the attribute level). In order to accomplish this goal, we extend UML (Unified Modeling Language) to model attributes as first-class citizens. In our attempt to provide complementary views of the design artifacts in different levels of detail, our framework is based on a principled approach in the usage of UML packages, to allow zooming in and out the design of a scenario.


title: Unsupervised Feature Selection with Controlled Redundancy (UFeSCoR)

abstract: Features selected by a supervised/ unsupervised technique often include redundant or correlated features. While use of correlated features may result in an increase in the design and decision making cost, removing redundancy completely can make the system vulnerable to measurement errors. Most feature selection schemes do not account for redundancy at all, while a few supervised methods try to discard correlated features. We propose a novel unsupervised feature selection scheme ( UFeSCoR ), which not only discards irrelevant features, but also selects features with controlled redundancy. Here, the number of selected features can also be directed. Our algorithm optimizes an objective function, which tries to select a specified number of features, with a controlled level of redundancy, such that the topology of the original data set can be maintained in the reduced dimension. Here, we have used Sammon’s error as a measure of preservation of topology. We demonstrate the effectiveness of the algorithm in terms of choosing relevant features, controlling redundancy, and selecting a given number of features using several data sets. We make a comparative study with five unsupervised feature selection methods. Our results reveal that the proposed method can select useful features with controlled redundancy.


Personalized Recommendation Combining User Interest and Social Circle


title: Design and experiments for a transformable solar-UAV

abstract: Aerial robotic platforms are an increasingly sought-after solution for a variety of sensing, monitoring, and transportation challenges. However, as invaluable as unmanned aerial vehicles (UAVs) have been for these applications, fixed-wing and multi-rotor systems each have individual limitations. Fixed-wing UAVs are generally capable of high-altitude surveillance and long flight times, while quad-rotors are most effective when used for their maneuverability and close-quarters surveying. This paper improves upon the prototypes discussed in [1] by creating a series of three next-generation prototypes to isolate the aspects of solar powered fixed-wing flight, quad-rotor flight, and transformation modes of the SUAV:Q platform. Improvements to the transformation mechanism, airframe design, variable pitch propulsion system, and custom-designed power electronics are presented along with validation of the designs through empirical testing.


title: Generative art inspired by nature, using NodeBox

abstract: NodeBox is a free application for producing generative art. This paper gives an overview of the nature-inspired functionality in NodeBox and the artworks we created using it. We demonstrate how it can be used for evolutionary computation in the context of computer games and art, and discuss some of our recent research with the aim to simulate (artistic) brainstorming using language processing techniques and semantic networks.


title: Digital graphic novels: Technology enhanced narrative for learning

abstract: The effectiveness of digital graphic novels (DGN) has never been measured from the perspective of interaction design (IxD) although previous studies regarded DGN as having materiality and multisensoriality characteristics which were crucial for most purposes and territories, especially for students’ learning.Teaching a dry graduate course with most of the traditional methods has been too dull to reinforce concepts taught in the classroom. This paper proposes DGN for learning difficult subjects such as ‘research methods’ and ‘Islamic values’.The DGN however, must be of a well-crafted content based on abstractions of real life events Methods of abstracting the content and enhancing the narratives in producing a DGN are provided.The DGN prototypes were developed and experimented with real graduate students.The results have proven a number of interesting facts about IxD in DGN and the desirability of DGN as one of the promising learning tools.


title: Rethinking the concept of kokoro no kea (care for mind) for disaster victims in Japan

abstract: ABSTRACT The Japanese concept of kokoro no kea (care for mind) is a local notion based on the global concept of post-traumatic stress disorder (PTSD). This localized concept has contributed to raising awareness of the need for post-disaster psycho-social support and establishing a disaster mental health system. Despite its intention to reduce the mental health-related stigma by expressing that mind can be injured in the same way as body can, the reluctance to seek support still persists. This paper aims to explore the challenges underlying the notion of kokoro no kea in changing the help-seeking behaviour of disaster victims through examining Japanese cultural concepts and political history. The literature review suggests that the semantic ambiguity embedded in the concept does not fully reflect the double-natured structure of consciousness in Japan which is related to an interdependent self in a society where cohesiveness and social inclusion are emphasized. Nor is the concept able to adequately signify the way that societal power relations have produced the collective experience of suffering. This paper concludes by reiterating that the cultural configuration of the self, language use, and the socio-political aspect of collective trauma are crucial elements in addressing the mental health needs of disaster victims.


Obfuscating Gender in Social Media Writing


A Graphbased Framework for Multi-Task Multi-View Learning


title: Comedo-DCIS is a precursor lesion for basal-like breast carcinoma: identification of a novel p63/Her2/neu expressing subgroup

abstract: // Malathy P.V. Shekhar 1,2 , Ikuko Kato 1,2 , Pratima Nangia-Makker 1,2 , and Larry Tait 2 1 Department of Oncology, Wayne State University, Detroit, MI, U.S.A. 2 Karmanos Cancer Institute, Detroit, MI, U.S.A. Correspondence: Malathy P.V. Shekhar, email: // Keywords : breast cancer, p63, Her2/neu, cytokeratin, EGFR Received : January 11, 2013 Accepted : February 22, 2013 Published : February 24, 2013 Abstract Basal breast cancer comprises ~15% of invasive ductal breast cancers, and presents as high-grade lesions with aggressive clinical behavior. Basal breast carcinomas express p63 and cytokeratin 5 (CK5) antigens characteristic of the myoepithelial lineage, and typically lack Her2/neu and hormone receptor expression. However, there is limited data about the precursor lesions from which they emerge. Here we wished to determine whether comedo-ductal carcinoma in situ (comedo-DCIS), a high-risk in situ breast lesion, serve as precursors for basal-like breast cancer. To determine this link, p63, CK5, Her2/neu, epidermal growth factor receptor (EGFR), estrogen receptor (ER) and progesterone receptor (PgR) expression were analyzed by immunohistochemistry in 17 clinical comedo- and 12 noncomedo-DCIS cases, and in tumors derived from unfractionated and CK5-overexpressing subpopulation (MCF10DCIS.com-CK5 high ) of MCF10DCIS.com cells, a model representative of clinical comedo-DCIS. p63 and Her2/neu coexpression was analyzed by immunofluorescence double labeling. A novel p63/CK5/Her2/neu expressing subpopulation of cells that are ER - /PgR - /EGFR - were identified in the myoepithelial and luminal areas of clinical comedo-DCIS and tumors derived from unfractionated MCF10DCIS.com and MCF10DCIS.com-CK5 high cells. These data suggest that p63 and Her2/neu expressors may share a common precursor intermediate. P63, but not Her2/neu, expression was significantly associated ( P = 0.038) with microinvasion/recurrence of clinical comedo-DCIS, and simultaneous expression of p63 and Her2/neu was marginally associated ( P = 0.067) with comedo-DCIS. These data suggest that p63/Her2/neu expressing precursor intermediate in comedo-DCIS may provide a cellular basis for emergence of p63+/Her2/neu- or p63+/Her2/neu+ basal-like breast cancer, and that p63/Her2/neu coexpression may serve as biomarkers for identification of this subgroup of basal-like breast cancers.


title: Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/bdm.495 When Effortful Thinking Influences Judgmental Anchoring: Differential Effects of Forewarning and Incentives on Self-generated and Externally Provided Anchors

abstract: Two experiments examined the impact of financial incentives and forewarnings on judgmental anchoring effects, or the tendency for judgments of uncertain qualities to be biased in the direction ofsalient anchor values. Previous research has found no effect of either manipulation on the magnitude of anchoring effects. We argue, however, that anchoring effects are produced by multiple mechanisms—one involving an effortful process of adjustment from ‘‘self-generated’’ anchors, and another involving the biased recruitment of anchor-consistent information from ‘‘externally provided’’ anchors— and that only the former should be influenced by incentives and forewarning. Two studies confirmed these predictions, showing that responses to ‘‘self-generated’’ anchors are influenced by both incentives and forewarnings whereas responses to ‘‘externally provided’’ anchors are not. Discussion focuses on the implications of these effects for debiasing efforts. Copyright # 2005 John Wiley & Sons, Ltd.


title: Fast color transfer for camouflage applications

abstract: Color transfer techniques have been widely developed to manipulate color information of image data in many applications. In this paper, color transfer research is presented for camouflage applications, along with the challenges facing many state-of-the-art color transfer methods. To address these challenges, a new fast color transfer algorithm is proposed. This new fast color transfer algorithm follows a classical color transfer framework, consisting of two parts: domain color extraction and color replacement. However, unlike other existing color transfer algorithms, this algorithm performs domain color extraction using a sampling process to reduce the dataset size of the classification process within the algorithm. Once the dataset size of the information to be classified is reduced, corresponding computational cost is largely decreased, thus improving the speed of the color transfer algorithm. Following previous domain color extraction process to collected domain color information, color replacement is conducted to change the color distribution of the target images (camouflage images) to be similar to those of the source images (operational background images). The entire algorithm is processed in a luminance-chrominance-independent (Lαβ) color model space. Experimental results show the performance of the proposed algorithm.


title: Reintroduction of Antidepressant Therapy across Pregnancy in Women Who Previously Discontinued Treatment

abstract: Background: Pregnancy has frequently been described as a time of emotional well-being conferring ‘protection’ against psychiatric disorders. However, data to support this impression


title: Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling

abstract: Syntactic features play an essential role in identifying relationship in a sentence. Previous neural network models directly work on raw word sequences or constituent parse trees, thus often suffer from irrelevant information introduced when subjects and objects are in a long distance. In this paper, we propose to learn more robust relation representations from shortest dependency paths through a convolution neural network. We further take the relation directionality into account and propose a straightforward negative sampling strategy to improve the assignment of subjects and objects. Experimental results show that our method outperforms the state-of-theart approaches on the SemEval-2010 Task 8 dataset.


title: A codebook-free and annotation-free approach for fine-grained image categorization

abstract: Fine-grained categorization refers to the task of classifying objects that belong to the same basic-level class (e.g. different bird species) and share similar shape or visual appearances. Most of the state-of-the-art basic-level object classification algorithms have difficulties in this challenging problem. One reason for this can be attributed to the popular codebook-based image representation, often resulting in loss of subtle image information that are critical for fine-grained classification. Another way to address this problem is to introduce human annotations of object attributes or key points, a tedious process that is also difficult to generalize to new tasks. In this work, we propose a codebook-free and annotation-free approach for fine-grained image categorization. Instead of using vector-quantized codewords, we obtain an image representation by running a high throughput template matching process using a large number of randomly generated image templates. We then propose a novel bagging-based algorithm to build a final classifier by aggregating a set of discriminative yet largely uncorrelated classifiers. Experimental results show that our method outperforms state-of-the-art classification approaches on the Caltech-UCSD Birds dataset.


title: Hybrid Collaborative Recommendation via Semi-AutoEncoder

abstract: In this paper, we first present a novel structure of AutoEncoder, namely Semi-AutoEncoder. We generalize it into a designated hybrid collaborative filtering model, which is able to predict ratings as well as to generate personalized top-N recommendations. Experimental results on two real-world datasets demonstrate its state-of-the-art performances.


title: Analysis of nonuniform ESD current distribution in deep submicron NMOS transistors

abstract: This paper presents a detailed study of the nonuniform bipolar conduction phenomenon under electrostatic discharge (ESD) events in single-finger NMOS transistors and analyzes its implications for the design of ESD protection for deep-submicron CMOS technologies. It is shown that the uniformity of the bipolar current distribution under ESD conditions is severely degraded depending on device finger width (W) and significantly influenced by the substrate and gate-bias conditions as well. This nonuniform current distribution is identified as a root cause of the severe reduction in ESD failure threshold current for the devices with advanced silicided processes. Additionally, the concept of an intrinsic second breakdown triggering current (I/sub t2i/) is introduced, which is substrate-bias independent and represents the maximum achievable ESD failure strength for a given technology. With this improved understanding of ESD behavior involved in advanced devices, an efficient design window can be constructed for robust deep submicron ESD protection.


title: Novel Design for a Rectenna to Collect Pulse Waves at 2.4 GHz

abstract: A novel rectifying circuit topology is proposed for converting electromagnetic pulse waves (PWs), that are collected by a wideband antenna, into dc voltage. The typical incident signal considered in this paper consists of 10-ns pulses modulated around 2.4 GHz with a repetition period of 100 ns. The proposed rectifying circuit topology comprises a double-current architecture with inductances that collect the energy during the pulse delivery as well as an output capacitance that maintains the dc output voltage between the pulses. Experimental results show that the efficiency of the rectifier reaches 64% for a mean available incident power of 4 dBm. Similar performances are achieved when a wideband antenna is combined with the rectifier in order to realize a rectenna. By increasing the repetition period of the incident PWs to 400 ns, the rectifier still operates with an efficiency of 52% for a mean available incident pulse power of −8 dBm. Finally, the proposed PW rectenna is tested for a wireless energy transmission application in a low- $Q$ cavity. The time reversal technique is applied to focus PWs around the desired rectenna. Results show that the rectenna is still efficient when noisy PW is handled.


title: Steerable interfaces for pervasive computing spaces

abstract: This paper introduces a new class of interactive interfaces that can be moved around to appear on ordinary objects and surfaces anywhere in a space. By dynamically adapting the form, function, and location of an interface to suit the context of the user, such steerable interfaces have the potential to offer radically new and powerful styles of interaction in intelligent pervasive computing spaces. We propose defining characteristics of steerable interfaces and present the first steerable interface system that combines projection, gesture recognition, user tracking, environment modeling and geometric reasoning components within a system architecture. Our work suggests that there is great promise and rich potential for further research on steerable interfaces.


title: A Lightweight Secure Data Sharing Scheme for Mobile Cloud Computing

abstract: With the popularity of cloud computing, mobile devices can store/retrieve personal data from anywhere at any time. Consequently, the data security problem in mobile cloud becomes more and more severe and prevents further development of mobile cloud. There are substantial studies that have been conducted to improve the cloud security. However, most of them are not applicable for mobile cloud since mobile devices only have limited computing resources and power. Solutions with low computational overhead are in great need for mobile cloud applications. In this paper, we propose a lightweight data sharing scheme (LDSS) for mobile cloud computing. It adopts CP-ABE, an access control technology used in normal cloud environment, but changes the structure of access control tree to make it suitable for mobile cloud environments. LDSS moves a large portion of the computational intensive access control tree transformation in CP-ABE from mobile devices to external proxy servers. Furthermore, to reduce the user revocation cost, it introduces attribute description fields to implement lazy-revocation, which is a thorny issue in program based CP-ABE systems. The experimental results show that LDSS can effectively reduce the overhead on the mobile device side when users are sharing data in mobile cloud environments.


SEMI-PARAMETRIC TOPOLOGICAL MEMORY


title: Design Considerations of a 15-kV SiC IGBT-Based Medium-Voltage High-Frequency Isolated DC–DC Converter

abstract: A dual active bridge (DAB) is a zero-voltage switching (ZVS) high-power isolated dc–dc converter. The development of a 15-kV SiC insulated-gate bipolar transistor switching device has enabled a noncascaded medium voltage (MV) isolated dc–dc DAB converter. It offers simple control compared to a cascaded topology. However, a compact-size high frequency (HF) DAB transformer has significant parasitic capacitances for such voltage. Under high voltage and high $dV/dT$ switching, the parasitics cause electromagnetic interference and switching loss. They also pose additional challenges for ZVS. The device capacitance and slowing of $dV/dT$ play a major role in deadtime selection. Both the deadtime and transformer parasitics affect the ZVS operation of the DAB. Thus, for the MV-DAB design, the switching characteristics of the devices and MV HF transformer parasitics have to be closely coupled. For the ZVS mode, the current vector needs to be between converter voltage vectors with a certain phase angle defined by deadtime, parasitics, and desired converter duty ratio. This paper addresses the practical design challenges for an MV-DAB application.


title: DevelopinganNLP andIR-basedAlgorithm forAnalyzingGene-diseaseRelationships

abstract: Objectives: High-throughput techniques such as cDNA microarray, oligonucleotide arrays, and serial analysis of gene expression (SAGE) have been developed and used to automatically screen huge amounts of gene expression data. However, researchers usually spend lots of time and money on discovering gene-disease relationships by utilizing these techniques. We prototypically implemented an algorithm that can provide some kind of predicted results for biological researchers before they proceed with experiments, and it is very helpful for them to discover gene-disease relationships more efficiently. Methods: Due to the fast development of computer technology, many information retrieval techniques have been applied to analyze huge digital biomedical databases available worldwide. Therefore we highly expect that we can apply information retrieval (IR) technique to extract useful information for the relationship of specific diseases and genes from MEDLINE articles. Furthermore, we also applied natural language processing (NLP) methods to do the semantic analysis for the relevant articles to discover the relationships between genes and diseases. Results: We have extracted gene symbols from our literature collection according to disease MeSH classifications. We have also built an IR-based retrieval system, “Biomedical Literature Retrieval System (BLRS)“ and applied the N-gram model to extract the relationship features which can reveal the relationship between genes and diseases. Finally, a relationship network of a specific disease has been built to represent the gene-disease relationships. Conclusions: A relationship feature is a functional word that can reveal the relationship between one single gene and a disease. By incorporating many modern IR techniques, we found that BLRS is a very powerful information discovery tool for literature searching. A relationship network which contains the information on gene symbol, relationship feature, and disease MeSH term can provide an integrated view to discover gene-disease relationships.


title: The Human Genetic History of East Asia: Weaving a Complex Tapestry

abstract: East Asia encompasses a wide variety of environments, peoples, cultures and languages. Although this review focuses on East Asia, no geographic region can be considered in isolation in terms of human population history, and migrations to and from East Asia have had a major impact. Here, we review the following topics: the initial colonization of East Asia, the direction of migrations between southeast Asia and northern Asia, the genetic relationships of East Asian hunter-gatherers and the genetic impact of various social practices on East Asian populations. By necessity we focus on insights derived from mitochondrial DNA and/or Y-chromosome data; ongoing and future studies of genome-wide SNP or multi-locus re-sequencing data, combined with the use of simulation, model-based methods to infer demographic parameters, will undoubtedly provide additional insights into the population history of East Asia.


Abstraction-Based Livelock/Deadlock Checking for Hardware Verification


title: A 128-element microstrip array antenna at Ku-band for Satellite communication

abstract: A 128-element broadband dual-frequency and dual-polarized microstrip array antenna at Ku-band for Satellite communication is described in this paper. It consists of four 4×8-element broadband dual-frequency and dual-polarized microstrip antenna arrays and a 1:4 Wilkinson microstrip power divider. The measurement results show that the relative impedance bandwidths are 10.7% for horizontal polarization channels and 5.02% for vertical polarization channels, which VSWR less than 1.5 are obtained, respectively. The isolation between two ports is less than -28dB, The maximum gain of the antenna array is achieved about 26.2 dB and 24.1 dB for two various band, respectively.


title: Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives

abstract: A state-of-the-art research on Big Data in SCM is reviewed.A discussion from analyzing current movements on the Big Data for SCM in service and manufacturing world-wide is presented.Current challenges, opportunities, and future perspectives Big Data are highlighted. Data from service and manufacturing sectors is increasing sharply and lifts up a growing enthusiasm for the notion of Big Data. This paper investigates representative Big Data applications from typical services like finance & economics, healthcare, Supply Chain Management (SCM), and manufacturing sector. Current technologies from key aspects of storage technology, data processing technology, data visualization technique, Big Data analytics, as well as models and algorithms are reviewed. This paper then provides a discussion from analyzing current movements on the Big Data for SCM in service and manufacturing world-wide including North America, Europe, and Asia Pacific region. Current challenges, opportunities, and future perspectives such as data collection methods, data transmission, data storage, processing technologies for Big Data, Big Data-enabled decision-making models, as well as Big Data interpretation and application are highlighted. Observations and insights from this paper could be referred by academia and practitioners when implementing Big Data analytics in the service and manufacturing sectors.


title: Demonstration of a 50 kW and 100 kHz SiC high power density converter for aerospace application

abstract: The high power density converter is required due to the strict demands of volume and weight in more electric aircraft, which makes SiC extremely attractive for this application. In this work, a prototype of 50 kW SiC high power density converter with the topology of two-level three-phase voltage source inverter is demonstrated. This converter is driven at high switching speed based on the optimization in switching characterization. It operates at a switching frequency up to 100 kHz and a low dead time of 250 ns. And the converter efficiency is measured to be 99% at 40 kHz and 97.8% at 100 kHz.


title: A Higher-Order Hidden Markov Chain-Modulated Model for Asset Allocation

abstract: This paper presents an analysis of asset allocation strategies when the asset returns are governed by a discrete-time higher-order hidden Markov model (HOHMM), also called the weak hidden Markov model. We assume the drifts and volatilities of the asset returns switch over time according to the state of the HOHMM, in which the probability of the current state depends on the information from previous time-steps. The “switching” and “mixed” strategies are studied. We use a multivariate filtering technique in conjunction with the EM algorithm to obtain estimates of model parameter at a given time. This, in turn, aids investors in determining the optimal investment strategy for the next time step. Numerical implementation is applied to data on Russell 3000 value and growth indices. We benchmark the respective performances of portfolio using three classical investment measures.


title: FingerSound: Recognizing unistroke thumb gestures using a ring

abstract: We introduce FingerSound, an input technology to recognize unistroke thumb gestures, which are easy to learn and can be performed through eyes-free interaction. The gestures are performed using a thumb-mounted ring comprising a contact microphone and a gyroscope sensor. A K-Nearest-Neighbor(KNN) model with a distance function of Dynamic Time Warping (DTW) is built to recognize up to 42 common unistroke gestures. A user study, where the real-time classification results were given, shows an accuracy of 92%-98% by a machine learning model built with only 3 training samples per gesture. Based on the user study results, we further discuss the opportunities, challenges and practical limitations of FingerSound when deploying it to real-world applications in the future.


title: CNN-N-Gram for HandwritingWord Recognition

abstract: Given an image of a handwritten word, a CNN is employed to estimate its n-gram frequency profile, which is the set of n-grams contained in the word. Frequencies for unigrams, bigrams and trigrams are estimated for the entire word and for parts of it. Canonical Correlation Analysis is then used to match the estimated profile to the true profiles of all words in a large dictionary. The CNN that is used employs several novelties such as the use of multiple fully connected branches. Applied to all commonly used handwriting recognition benchmarks, our method outperforms, by a very large margin, all existing methods.


title: Biometric Template Protection Using Spiral Cube: Performance and Security Analysis

abstract: Personal authentication systems based on biometrics have given rise to new problems and challenges related to the protection of personal data, issues of less concern in traditional authentication systems. The irrevocability of biometric templates makes biometric systems very vulnerable to several attacks. In this paper we present a new approach for biometric template protection. Our objective is to build a non-invertible transformation, based on random projection, which meets the requirements of revocability, diversity, security and performance. In this context, we use the chaotic behavior of logistic map to build the projection vectors using a methodology that makes the construction of the projection matrix depend on the biometric template and its identity. The proposed approach has been evaluated and compared with Biohashing and BioPhasor using a rigorous security analysis. Our extensive experimental results using several databases (e.g., face, finger-knuckle and iris), show that the proposed technique has the ability to preserve and increase the performance of protected systems. Moreover, it is demonstrated that the security of the proposed approach is sufficiently robust to possible attacks keeping an acceptable balance between discrimination, diversity and non-invertibility.


Data mining differential clinical outcomes associated with drug regimens using adverse event reporting data


title: PHONOLOGICAL OVERGENERATION IN PANINIAN SYSTEM

abstract: In this paper an attempt is made to study the problem of overgeneration that is caused by the application of the system of P******ini The system of P******ini is made up of certain rules stated by him and his commentators namely, K***ty***yana and Patanjali . These rules are supposed to produce the forms that are used in the language, i.e. Sanskrit. However, sometimes the technical application of these rules produces such forms which are not actually used in the language. In fact, sometimes it is beyond human capacities to use such forms. In the present paper two such cases dealing with the phonological overgeneration are studied and possible solutions are proposed to avoid the problem.


title: Surveillance of Vaccination Coverage among Adult Populations — United States, 2015

abstract: PROBLEM/CONDITION ::: Overall, the prevalence of illness attributable to vaccine-preventable diseases is greater among adults than among children. Adults are recommended to receive vaccinations based on their age, underlying medical conditions, lifestyle, prior vaccinations, and other considerations. Updated vaccination recommendations from CDC are published annually in the U.S. Adult Immunization Schedule. Despite longstanding recommendations for use of many vaccines, vaccination coverage among U.S. adults is low. ::: ::: ::: PERIOD COVERED ::: August 2014-June 2015 (for influenza vaccination) and January-December 2015 (for pneumococcal, tetanus and diphtheria [Td] and tetanus and diphtheria with acellular pertussis [Tdap], hepatitis A, hepatitis B, herpes zoster, and human papillomavirus [HPV] vaccination). ::: ::: ::: DESCRIPTION OF SYSTEM ::: The National Health Interview Survey (NHIS) is a continuous, cross-sectional national household survey of the noninstitutionalized U.S. civilian population. In-person interviews are conducted throughout the year in a probability sample of households, and NHIS data are compiled and released annually. The survey objective is to monitor the health of the U.S. population and provide estimates of health indicators, health care use and access, and health-related behaviors. ::: ::: ::: RESULTS ::: Compared with data from the 2014 NHIS, increases in vaccination coverage occurred for influenza vaccine among adults aged ≥19 years (a 1.6 percentage point increase compared with the 2013-14 season to 44.8%), pneumococcal vaccine among adults aged 19-64 years at increased risk for pneumococcal disease (a 2.8 percentage point increase to 23.0%), Tdap vaccine among adults aged ≥19 years and adults aged 19-64 years (a 3.1 percentage point and 3.3 percentage point increase to 23.1% and to 24.7%, respectively), herpes zoster vaccine among adults aged ≥60 years and adults aged ≥65 years (a 2.7 percentage point and 3.2 percentage point increase to 30.6% and to 34.2%, respectively), and hepatitis B vaccine among health care personnel (HCP) aged ≥19 years (a 4.1 percentage point increase to 64.7%). Herpes zoster vaccination coverage in 2015 met the Healthy People 2020 target of 30%. Aside from these modest improvements, vaccination coverage among adults in 2015 was similar to estimates from 2014. Racial/ethnic differences in coverage persisted for all seven vaccines, with higher coverage generally for whites compared with most other groups. Adults without health insurance reported receipt of influenza vaccine (all age groups), pneumococcal vaccine (adults aged 19-64 years at increased risk), Td vaccine (adults aged ≥19 years, 19-64 years, and 50-64 years), Tdap vaccine (adults aged ≥19 years and 19-64 years), hepatitis A vaccine (adults aged ≥19 years overall and among travelers), hepatitis B vaccine (adults aged ≥19 years, 19-49 years, and among travelers), herpes zoster vaccine (adults aged ≥60 years), and HPV vaccine (males and females aged 19-26 years) less often than those with health insurance. Adults who reported having a usual place for health care generally reported receipt of recommended vaccinations more often than those who did not have such a place, regardless of whether they had health insurance. Vaccination coverage was higher among adults reporting one or more physician contacts in the past year compared with those who had not visited a physician in the past year, regardless of whether they had health insurance. Even among adults who had health insurance and ≥10 physician contacts within the past year, depending on the vaccine, 18.2%-85.6% reported not having received vaccinations that were recommended either for all persons or for those with specific indications. Overall, vaccination coverage among U.S.-born adults was higher than that among foreign-born adults, with few exceptions (influenza vaccination [adults aged 19-49 years and 50-64 years], hepatitis A vaccination [adults aged ≥19 years], and hepatitis B vaccination [adults aged ≥19 years with diabetes or chronic liver conditions]). ::: ::: ::: INTERPRETATION ::: Coverage for all vaccines for adults remained low but modest gains occurred in vaccination coverage for influenza (adults aged ≥19 years), pneumococcal (adults aged 19-64 years with increased risk), Tdap (adults aged ≥19 years and adults aged 19-64 years), herpes zoster (adults aged ≥60 years and ≥65 years), and hepatitis B (HCP aged ≥19 years); coverage for other vaccines and groups with vaccination indications did not improve. The 30% Healthy People 2020 target for herpes zoster vaccination was met. Racial/ethnic disparities persisted for routinely recommended adult vaccines. Missed opportunities to vaccinate remained. Although having health insurance coverage and a usual place for health care were associated with higher vaccination coverage, these factors alone were not associated with optimal adult vaccination coverage. HPV vaccination coverage for males and females has increased since CDC recommended vaccination to prevent cancers caused by HPV, but many adolescents and young adults remained unvaccinated. ::: ::: ::: PUBLIC HEALTH ACTIONS ::: Assessing factors associated with low coverage rates and disparities in vaccination is important for implementing strategies to improve vaccination coverage. Evidence-based practices that have been demonstrated to improve vaccination coverage should be used. These practices include assessment of patients' vaccination indications by health care providers and routine recommendation and offer of needed vaccines to adults, implementation of reminder-recall systems, use of standing-order programs for vaccination, and assessment of practice-level vaccination rates with feedback to staff members. For vaccination coverage to be improved among those who reported lower coverage rates of recommended adult vaccines, efforts also are needed to identify adults who do not have a regular provider or insurance and who report fewer health care visits.


title: Use of WordNet Hypernyms for Answering What-Is Questions

abstract: We present a preliminary analysis of the use of WordNet hypernyms for answering “What-is” questions. We analyse the approximately 130 definitional questions in the TREC10 corpus with respect to our technique of Virtual Annotation (VA), which has previously been shown to be effective on the TREC9 definitional question set and other questions. We discover that VA is effective on a subset of the TREC10 definitional questions, but that some of these questions seem to need a user model to generate correct answers, or at least answers that agree with the NIST judges. Furthermore, there remains a large enough subset of definitional questions that cannot benefit at all from the WordNet isa-hierarchy, prompting the need to investigate alternative external resources.


title: A Job Scheduling Design for Visualization Services Using GPU Clusters

abstract: Modern large-scale heterogeneous computers incorporating GPUs offer impressive processing capabilities. It is desirable to fully utilize such systems for serving multiple users concurrently to visualize large data at interactive rates. However, as the disparity between data transfer speed and compute speed continues to increase in heterogeneous systems, data locality becomes crucial for performance. We present a new job scheduling design to support multi-user exploration of large data in a heterogeneous computing environment, achieving near optimal data locality and minimizing I/O overhead. The targeted application is a parallel visualization system which allows multiple users to render large volumetric data sets in both interactive mode and batch mode. We present a cost model to assess the performance of parallel volume rendering and quantify the efficiency of job scheduling. We have tested our job scheduling scheme on two heterogeneous systems with different configurations. The largest test volume data used in our study has over two billion grid points. The timing results demonstrate that our design effectively improves data locality for complex multi-user job scheduling problems, leading to better overall performance of the service.


title: CASTLE: fast concurrent internal binary search tree using edge-based locking

abstract: We present a new lock-based algorithm for concurrent manipulation of a binary search tree in an asynchronous shared memory system that supports search, insert and delete operations. Some of the desirable characteristics of our algorithm are: (i) a search operation uses only read and write instructions, (ii) an insert operation does not acquire any locks, and (iii) a delete operation only needs to lock up to four edges in the absence of contention. Our algorithm is based on an internal representation of a search tree and it operates at edge-level (locks edges) rather than at node-level (locks nodes); this minimizes the contention window of a write operation and improves the system throughput. Our experiments indicate that our lock-based algorithm outperforms existing algorithms for a concurrent binary search tree for medium-sized and larger trees, achieving up to 59% higher throughput than the next best algorithm.


title: Demystifying GPU microarchitecture through microbenchmarking

abstract: Graphics processors (GPU) offer the promise of more than an order of magnitude speedup over conventional processors for certain non-graphics computations. Because the GPU is often presented as a C-like abstraction (e.g., Nvidia's CUDA), little is known about the characteristics of the GPU's architecture beyond what the manufacturer has documented. This work develops a microbechmark suite and measures the CUDA-visible architectural characteristics of the Nvidia GT200 (GTX280) GPU. Various undisclosed characteristics of the processing elements and the memory hierarchies are measured. This analysis exposes undocumented features that impact program performance and correctness. These measurements can be useful for improving performance optimization, analysis, and modeling on this architecture and offer additional insight on the decisions made in developing this GPU.


title: The Study of Consumer Perception on Corporate Social Responsibility towards Consumers Attitude and Purchase Behavior

abstract: In today?s competitive conditions which many of competitive indices of companies are similar, corporate social responsibility and morality have found a special place. Environmental issues like environmental pollution, greenhouse gases, ozone depletion, paying more attention to humanitarian (philanthropic) activities, morality, and economic issues and etc. make the importance of corporate social responsibility more prominent, so it can be used as a factor for developing a positive attitude to the product by the customers, thereby you can benefit from the market by making yourself different than other competitors and gaining the competitive advantage in order to increase the market share. This paper examines the impact of CSR on customer buying behavior regarding the role of mediator towards the company. Statistical population of the study is consisted of all consumers of dairy products companies including Pegah and Kaleh in Ahwaz . All the customers were selected by Cluster Sampling method and due to the infinite populations in statistical research and by considering the error level of 0.05 and the estimate accuracy rate of 0.07 and the success ratio of 0.5, around 200 households were estimated. Wong Szeki (2012) and Galbreath (2010) questionnaires have been used for collecting data. Research findings indicate that costumer positive attitude to corporate social responsibility has positive and direct impact on buying behavior.


title: The use of Social Networking Sites (SNSs) by the faculty members of the School of Library & Information Science, PAAET, Kuwait

abstract: – The purpose of this study is to describe the usage of Social Networking Sites (SNSs) by the faculty members of the School of Library and Information Science (SLIS), at the College of Basic Education, the Public Authority for Applied Education and Training (PAAET), Kuwait. , – A survey conducted to collect data from 33 faculty members of whom only 21 members were using SNSs, representing 63.6 per cent of the total sample, and 12 members were not using SNSs, representing 36.4 per cent of the total sample. This study revealed that SNSs are used moderately by the faculty members. , – This study showed that faculty members who were using SNSs tend to be males, aged between 41 and 50 years, PhD holders, ranked as assistant professors, full-time members, specialized in information technologies with a relatively new experience of teaching ranged from one to five years, and most of the faculty members who were not using SNSs tended to be also males, aged between 41 and 60 years, PhD holders, ranked as lecturers, full-time members specialized in organization of information with a teaching experience ranged from 16 to 20 years. More than half of the faculty members were using SNSs for three years to less than six years, and a large number of them were using SNSs several times a week and were accessing these sites more from their school office, home and school laboratory. There are no any statistical significant differences between the demographic data of participants (gender, age and education level) and either their use or non-use of SNSs. There are no significant differences between the academic rank, teaching status and teaching experience of faculty and their use of SNSs. However, there is a significant relation between the faculty’s area of teaching and their use of SNSs. Faculty members were interested in the use of SNSs. YouTube, Twitter, Facebook and blogs respectively were used mostly by faculty members, but Twitter, Facebook and YouTube were the most famous SNSs they have profiles on. Faculty members have adopted SNSs mainly for the purpose of communicating with others, finding and sharing information with peers and students as well. Tasks on SNSs made by faculty members were mostly to make communication, send/receive messages and find general and specific information. Faculty members’ profiles on SNSs were mostly on Twitter, Facebook, YouTube, blogs, wikis and podcasting respectively. Faculty members confirmed that the use of YouTube, Facebook, blogs, Twitter, wikis and podcasting respectively was at least effective and the use of YouTube, Facebook, Twitter, Blogs and Wikis respectively was at least fairly useful fairly easy to them. Faculty members are in general agreement about the effectiveness of SNSs especially for disseminating and sharing information, communication and informal collaboration. The study showed also that there is no gender-related difference among the faculty in terms of their usage of SNSs. The study revealed also that the time was the most important barrier both SNSs users and non-users faced at PAAET’s SLIS. Other barriers like trust about SNSs, training and skills were significant to SNSs users in this study, and barriers like interests in SNSs, awareness of them and trust about them were respectively the most important barriers to SNSs non-users. The study recommended that a further research is needed to examine more additional aspects of using SNSs among faculty members that may affect their use like the technical, legal, ethical and intellectual aspects. More information is needed to investigate why some faculty members do not use SNSs especially for educational purposes. A qualitative study of the perception and opinions of faculty members would provide much important data about that. A further research is also needed to specify the relation between the use of these sites and each area of study separately. Due to the lack of awareness and knowledge about the use of SNSs, shortage of language skills and training, this study recommended that SNSs non-users should be provided with necessary assistance to foster their skills towards such usage. A future study is needed to compare experiences of faculty members and students regarding the use of SNSs in educational practices and may look at how communicational uses of these sites have influenced educational uses. , – This study involved a single and certain academic institution, namely PAAET. Therefore, findings, conclusions and recommendations may not be applicable and reasonable to be generalized on all Kuwaiti academic institutions. , – This paper provides valuable insight into the usage of SNSs by a very important client group. , – This study is the first one of its kind conducted about the usage of SNSs by faculty members at a library school of one of the two public academic institutions in the state of Kuwait to examine and investigate more specific information about SNSs and related innovative topics.


Indian Paper Currency Authentication System using Image processing


title: Performance Analysis of mmWave Ad Hoc Networks

abstract: Ad hoc networks provide an on-demand, infrastructure-free means to communicate between soldiers in war zones, aid workers in disaster areas, or consumers in device-to-device (D2D) applications. Unfortunately, ad hoc networks are limited by interference due to nearby transmissions. Millimeter-wave (mmWave) devices offer several potential advantages for ad hoc networks, including reduced interference due to directional antennas and building blockages, not to mention huge bandwidth channels for large data rates. This paper uses a stochastic geometry approach to characterize the one-way and two-way signal-to-interference ratio distribution of a mmWave ad hoc network with directional antennas, random blockages, and ALOHA channel access. The interference-to-noise ratio shows that a fundamental limitation of an ad hoc network, interference, may still be an issue. The performance of mmWave ad hoc networks is bounded by the transmission capacity and area spectral efficiency. The results show that mmWave networks can support much higher densities and larger spectral efficiencies, even in the presence of blockage, compared with lower frequency communication for certain link distances. Due to the increased bandwidth, the rate coverage of mmWave can be much greater than lower frequency devices.


Comparative Study on Protein of M 2 Generation in Wild Chickpea Treated with EMS and Gamma Radiation Independently and in Combination Dr .


MLSeq package : Machine Learning Interface to RNA-Seq Data


title: Compiling Database Application Programs

abstract: There is a trend towards increased specialization of data management software for performance reasons. In this paper, we study the automatic specialization and optimization of database application programs -- sequences of queries and updates, augmented with control flow constructs as they appear in database scripts, UDFs, transactional workloads and triggers in languages such as PL/SQL. We show how to build an optimizing compiler for database application programs using generative programming and state-of-the-art compiler technology. ::: We evaluate a hand-optimized low-level implementation of TPC-C, and identify the key optimization techniques that account for its good performance. Our compiler fully automates these optimizations and, applied to this benchmark, outperforms the manually optimized baseline by a factor of two. By selectively disabling some of the optimizations in the compiler, we derive a clinical and precise way of obtaining insight into their individual performance contributions.


title: Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition

abstract: Fine-grained visual recognition is challenging because it highly relies on the modeling of various semantic parts and fine-grained feature learning. Bilinear pooling based models have been shown to be effective at fine-grained recognition, while most previous approaches neglect the fact that inter-layer part feature interaction and fine-grained feature learning are mutually correlated and can reinforce each other. In this paper, we present a novel model to address these issues. First, a cross-layer bilinear pooling approach is proposed to capture the inter-layer part feature relations, which results in superior performance compared with other bilinear pooling based approaches. Second, we propose a novel hierarchical bilinear pooling framework to integrate multiple cross-layer bilinear features to enhance their representation capability. Our formulation is intuitive, efficient and achieves state-of-the-art results on the widely used fine-grained recognition datasets.


Cross-domain recommender systems : A survey of the State of the Art