title: Effects of aromatherapy on sleep quality and anxiety of patients

abstract: BACKGROUND ::: In intensive care units (ICUs), patients cannot sleep well. Aromatherapy is used for depression, anxiety, relaxation and disorders related with sleep and stress. ::: ::: ::: AIM ::: This study aimed to investigate the effect of lavender essential oil on the sleep quality and anxiety level of patients in coronary ICU. ::: ::: ::: PARTICIPANTS ::: A total of 60 patients in coronary ICU participated in this study. ::: ::: ::: DESIGN ::: A randomized controlled study was conducted with 60 patients in a province located in the southeast of Turkey. ::: ::: ::: METHODS ::: After informing the patients in both groups about the study, they were administered a questionnaire, Pittsburgh Sleep Quality Index (PSQI) and the Beck Anxiety Inventory (BAI) scale. The patients in the intervention group were given 2% lavender essential oil via inhalation for 15 days after which they were administered the same scales again to evaluate the sleep quality and anxiety. As for the control group, they were administered the same scales again after 15 days without the inhalation of lavender essential oil. ::: ::: ::: RESULTS ::: Comparison of the PSQI and BAI scores of the patients in the control and intervention groups before and after the intervention showed statistically significant differences in the change in favour of the intervention group (p < 0·05). ::: ::: ::: CONCLUSION ::: Lavender essential oil increased quality of sleep and reduced level of anxiety in patients with coronary artery disease. ::: ::: ::: RELEVANCE TO CLINICAL PRACTICE ::: As a non-invasive, cheap, easily applicable, cost-effective, independent nursing intervention and appropriate for cardiac patients, lavender essential oil could be applied in ICUs.


title: UTTime: Temporal Relation Classification using Deep Syntactic Features

abstract: In this paper, we present a system, UTTime, which we submitted to TempEval-3 for Task C: Annotating temporal relations. The system uses logistic regression classifiers and exploits features extracted from a deep syntactic parser, including paths between event words in phrase structure trees and their path lengths, and paths between event words in predicateargument structures and their subgraphs. UTTime achieved an F1 score of 34.9 based on the graphed-based evaluation for Task C (ranked 2 nd ) and 56.45 for Task C-relationonly (ranked 1 st ) in the TempEval-3 evaluation.


title: Analyzing the eICU Collaborative Research Database

abstract: Patients in hospital intensive care units (ICUs) are physiologically fragile and unstable, generally have life-threatening conditions, and require close monitoring and rapid therapeutic interventions. Staggering amounts of data are collected in the ICU daily: multi-channel waveforms sampled hundreds of times each second, vital sign time series updated each second or minute, alarms and alerts, lab results, imaging results, records of medication and fluid administration, staff notes and more. Reducing the barriers to data access has the potential to accelerate knowledge generation and ultimately improve patient care. In this interactive tutorial we introduce the eICU Collaborative Research Database: a large, publicly available database created by the MIT Laboratory for Computational Physiology in partnership with the Philips eICU Research Institute. The database contains routinely collected data from over 200,000 admissions to intensive care units across the United States, with representation from 10-12% of US ICU beds. The data facilitates a breadth of research studies, such as investigations into treatment efficacy, discovery of clinical markers in illnesses, and the development of decision support models. Participants in the tutorial gain an overview of the eICU Collaborative Research Database, in particular being introduced to its structure, content, and limitations. Following this overview, participants explore a demo version of the database using a laptop in a hands-on project. This exercise requires minimal technical expertise and gives an insight into the type of study that can be carried out using the database. We also highlight the growing online community centered around secondary analysis of this data. All tutorial materials are open source and publicly available. The eICU Collaborative Research Database offers an unparalleled insight into ICU care. Access to the database is granted to legitimate researchers who request it, following completion of a training course in human subjects research and acceptance of a data use agreement. We anticipate that the research community will use this unique resource to further human knowledge in the field of critical care.


title: Rethinking the Mobile Food Journal: Exploring Opportunities for Lightweight Photo-Based Capture

abstract: Food choices are among the most frequent and important health decisions in everyday life, but remain notoriously difficult to capture. This work examines opportunities for lightweight photo-based capture in mobile food journals. We first report on a survey of 257 people, examining how they define healthy eating, their experiences and challenges with existing food journaling methods, and their ability to interpret nutritional information that can be captured in a food journal. We then report on interviews and a field study with 27 participants using a lightweight, photo-based food journal for between 4 to 8 weeks. We discuss mismatches between motivations and current designs, challenges of current approaches to food journaling, and opportunities for photos as an alternative to the pervasive but often inappropriate emphasis on quantitative tracking in mobile food journals.


title: Grid-free out-of-core voxelization to sparse voxel octrees on GPU

abstract: In this paper, we present the first grid-free, out-of-core GPU voxelization method. Our method combines efficient parallel triangle voxelization on GPU with out-of-core technologies in order to allow the processing of scenes with large triangle counts at a high resolution. We directly generate the voxelized data in a sparse voxel octree (SVO) representation, without any intermediate grid structure ("grid-free"). We apply triangle preprocessing and avoid atomic operations, thus leading to an optimized balanced GPU workload and efficient parallel triangle processing. Compared to existing out-of-core CPU approaches, we manage a proper handling of voxel attributes, i.e. all triangle attributes contributing to a voxel are accessible when calculating the voxel attribute. We test and compare our approach to state-of-the-art methods and demonstrate its viability in terms of speed, input triangle count, resolution and output quality.


title: Review of Lip-Reading Recognition

abstract: Lip-reading technology was introduced in this paper, including the development course, technical methods and application direction. Lip-reading technology mainly includes the lip detection and localization, feature extraction and recognition method and fusion method, the classification of the mouth and the corpus. This paper do a detailed comparative analysis for traditional methods and new technology, emphatically introduces the lip feature extraction.


title: Dual-band bandpass filter using stub loaded resonators with multiple transmission zeros

abstract: A dual-band bandpass filter using both an open stub-loaded resonator (OSLR) and a short stub-loaded resonator (SSLR) is presented. The use of SSLR has the advantage that a transmission zero can be flexibly controlled by tuning the length of the short stub, which can be employed as a new but convenient way to create transmission zeros in front of passbands in filter design. By further introducing a two-order planar transversal filter structure using the OSLR as well as the source-load coupling, a dual-band filter is implemented with four transmission zeros and exhibits high skirt selectivity based on the simulated result. Two passbands with center frequencies of 2.45 GHz and 3.5 GHz are achieved with compact size of about 0.18λ × 0.12λ at 2.45 GHz.


title: Triple P-Positive Parenting Program as a public health approach to strengthening parenting.

abstract: Parenting programs have considerable potential to improve the mental health and well-being of children, improve family relationships, and benefit the community at large. However, traditional clinical models of service delivery reach relatively few parents. A public health approach is needed to ensure that more parents benefit and that a societal-level impact is achieved. The Triple P-Positive Parenting Program is a comprehensive, multilevel system of parenting intervention that combines within a single intervention universal and more targeted interventions for high-risk children and their parents. With Triple P, the overarching goal is to enhance the knowledge, skills, and confidence of parents at a whole-of-population level and, in turn, to reduce the prevalence rates of behavioral and emotional problems in children and adolescents. The distinguishing features of the intervention and variables that influence its effective implementation are discussed. Self-regulation is a unifying concept that is applied throughout the entire system (e.g., to interactions between children, parents, service providers, and agencies involved in delivering the intervention). Challenges and future directions for the development of public health approaches to parenting are discussed.


title: Review of axial flux induction motor for automotive applications

abstract: Hybrid and electric vehicles have been the focus of many academic and industrial studies to reduce transport pollution; they are now established products. In hybrid and electric vehicles, the drive motor should have high torque density, high power density, high efficiency, strong physical structure and variable speed range. An axial flux induction motor is an interesting solution, where the motor is a double sided axial flux machine. This can significantly increase torque density. In this paper a review of the axial flux motor for automotive applications, and the different possible topologies for the axial field motor, are presented.


title: An Affordable Solution for Binocular Eye Tracking and Calibration in Head-mounted Displays

abstract: Immersion is the ultimate goal of head-mounted displays (HMD) for Virtual Reality (VR) in order to produce a convincing user experience. Two important aspects in this context are motion sickness, often due to imprecise calibration, and the integration of a reliable eye tracking. We propose an affordable hard- and software solution for drift-free eye-tracking and user-friendly lens calibration within an HMD. The use of dichroic mirrors leads to a lean design that provides the full field-of-view (FOV) while using commodity cameras for eye tracking. Our prototype supports personalizable lens positioning to accommodate for different interocular distances. On the software side, a model-based calibration procedure adjusts the eye tracking system and gaze estimation to varying lens positions. Challenges such as partial occlusions due to the lens holders and eye lids are handled by a novel robust monocular pupil-tracking approach. We present four applications of our work: Gaze map estimation, foveated rendering for depth of field, gaze-contingent level-of-detail, and gaze control of virtual avatars.


title: Nebula: Distributed edge cloud for data-intensive computing

abstract: Centralized cloud infrastructures have become the de-facto platform for data-intensive computing today. However, they suffer from inefficient data mobility due to the centralization of cloud resources, and hence, are highly unsuited for dispersed-data-intensive applications, where the data may be spread at multiple geographical locations. In this paper, we present Nebula: a dispersed cloud infrastructure that uses voluntary edge resources for both computation and data storage. We describe the lightweight Nebula architecture that enables distributed data-intensive computing through a number of optimizations including location-aware data and computation placement, replication, and recovery. We evaluate Nebula's performance on an emulated volunteer platform that spans over 50 PlanetLab nodes distributed across Europe, and show how a common data-intensive computing framework, MapReduce, can be easily deployed and run on Nebula. We show Nebula MapReduce is robust to a wide array of failures and substantially outperforms other wide-area versions based on a BOINC like model.


title: A Survey of Indoor Inertial Positioning Systems for Pedestrians

abstract: With the continual miniaturisation of sensors and processing nodes, Pedestrian Dead Reckoning (PDR) systems are becoming feasible options for indoor tracking. These use inertial and other sensors, often combined with domain-specific knowledge about walking, to track user movements. There is currently a wealth of relevant literature spread across different research communities. In this survey, a taxonomy of modern PDRs is developed and used to contextualise the contributions from different areas. Techniques for step detection, characterisation, inertial navigation and step-and-heading-based dead-reckoning are reviewed and compared. Techniques that incorporate building maps through particle filters are analysed, along with hybrid systems that use absolute position fixes to correct dead-reckoning output. In addition, consideration is given to the possibility of using smartphones as PDR sensing devices. The survey concludes that PDR techniques alone can offer good short- to medium- term tracking under certain circumstances, but that regular absolute position fixes from partner systems will be needed to ensure long-term operation and to cope with unexpected behaviours. It concludes by identifying a detailed list of challenges for PDR researchers.


DFT Studies on cis-1,4-Polymerization of Dienes Catalyzed by a Cationic Rare-Earth Metal Complex Bearing an Ancillary PNP Ligand


title: Memory leads the way to better computing

abstract: New non-volatile memory devices store information using different physical mechanisms from those employed in today's memories and could achieve substantial improvements in computing performance and energy efficiency.


title: A Syllable Based Word Recognition Model For Korean Noun Extraction

abstract: Noun extraction is very important for many NLP applications such as information retrieval, automatic text classification, and information extraction. Most of the previous Korean noun extraction systems use a morphological analyzer or a Part-of-Speech (POS) tagger. Therefore, they require much of the linguistic knowledge such as morpheme dictionaries and rules (e.g. morphosyntactic rules and morphological rules).This paper proposes a new noun extraction method that uses the syllable based word recognition model. It finds the most probable syllable-tag sequence of the input sentence by using automatically acquired statistical information from the POS tagged corpus and extracts nouns by detecting word boundaries. Furthermore, it does not require any labor for constructing and maintaining linguistic knowledge. We have performed various experiments with a wide range of variables influencing the performance. The experimental results show that without morphological analysis or POS tagging, the proposed method achieves comparable performance with the previous methods.


title: Lightweight highly secure PUF protocol for mutual authentication and secret message exchange

abstract: PUFs or Physically Unclonable Functions are emerging hardware security primitives that offer a lightweight alternative to standard security for highly restricted devices like RFIDs. PUFs, however, have been found to be vulnerable to modeling attacks and as such PUF-based authentication protocols had to employ cryptographic primitives to establish reliable security. We introduce a novel lightweight, PUF-based protocol that offers unlimited number of highly secure mutual authentications and secret message exchanges while utilizing no cryptographic or hash functions. The protocol uses simple binary operations, a strong PUF circuit and a true random number generator to establish cryptography-strength security making it the first lightweight PUF-based protocol that can truly match cryptographic algorithms in terms of reliable security and unlimited authentications.


title: Automated Curriculum Learning by Rewarding Temporally Rare Events

abstract: Reward shaping allows reinforcement learning (RL) agents to accelerate learning by receiving additional reward signals. However, these signals can be difficult to design manually, especially for complex RL tasks. We propose a simple and general approach that determines the reward of pre-defined events by their rarity alone. Here events become less rewarding as they are experienced more often, which encourages the agent to continually explore new types of events as it learns. The adaptiveness of this reward function results in a form of automated curriculum learning that does not have to be specified by the experimenter. We demonstrate that this \emph{Rarity of Events} (RoE) approach enables the agent to succeed in challenging VizDoom scenarios without access to the extrinsic reward from the environment. Furthermore, the results demonstrate that RoE learns a more versatile policy that adapts well to critical changes in the environment. Rewarding events based on their rarity could help in many unsolved RL environments that are characterized by sparse extrinsic rewards but a plethora of known event types.


Auto-Categorization of Businesses on Yelp


title: Rubik: Knowledge Guided Tensor Factorization and Completion for Health Data Analytics

abstract: Computational phenotyping is the process of converting heterogeneous electronic health records (EHRs) into meaningful clinical concepts. Unsupervised phenotyping methods have the potential to leverage a vast amount of labeled EHR data for phenotype discovery. However, existing unsupervised phenotyping methods do not incorporate current medical knowledge and cannot directly handle missing, or noisy data. We propose Rubik, a constrained non-negative tensor factorization and completion method for phenotyping. Rubik incorporates 1) guidance constraints to align with existing medical knowledge, and 2) pairwise constraints for obtaining distinct, non-overlapping phenotypes. Rubik also has built-in tensor completion that can significantly alleviate the impact of noisy and missing data. We utilize the Alternating Direction Method of Multipliers (ADMM) framework to tensor factorization and completion, which can be easily scaled through parallel computing. We evaluate Rubik on two EHR datasets, one of which contains 647,118 records for 7,744 patients from an outpatient clinic, the other of which is a public dataset containing 1,018,614 CMS claims records for 472,645 patients. Our results show that Rubik can discover more meaningful and distinct phenotypes than the baselines. In particular, by using knowledge guidance constraints, Rubik can also discover sub-phenotypes for several major diseases. Rubik also runs around seven times faster than current state-of-the-art tensor methods. Finally, Rubik is scalable to large datasets containing millions of EHR records.


title: On User studies and Information Needs

abstract: Apart from information retrieval there is virtually no other area of information science that has occasioned as much research effort and writing as ‘user studies’. Within user studies the investigation of ‘information needs’ has been the subject of much debate and no little confusion. The aim of this paper is to attempt to reduce this confusion by devoting attention to the definition of some concepts and by proposing the basis for a theory of the motivations for information‐seeking behaviour.


title: Modal-adversarial Semantic Learning Network for Extendable Cross-modal Retrieval

abstract: Cross-modal retrieval, e.g., using an image query to search related text and vice-versa, has become a highlighted research topic, to provide flexible retrieval experience across multi-modal data. Existing approaches usually consider the so-called non-extendable cross-modal retrieval task. In this task, they learn a common latent subspace from a source set containing labeled instances of image-text pairs and then generate common representation for the instances in a target set to perform cross-modal matching. However, these method may not generalize well when the instances of the target set contains unseen classes since the instances of both the source and target set are assumed to share the same range of classes in the non-extensive cross-modal retrieval task. In this paper, we consider a more practical issue of extendable cross-modal retrieval task where instances in source and target set have disjoint classes. We propose a novel framework, termed Modal-adversarial Semantic Learning Network (MASLN), to tackle the limitation of existing methods on this practical task. Specifically, the proposed MASLN consists two subnetworks of cross-modal reconstruction and modal-adversarial semantic learning. The former minimizes the cross-modal distribution discrepancy by reconstructing each modality data mutually, with the guidelines of class embeddings as side information in the reconstruction procedure. The latter generates semantic representation to be indiscriminative for modalities, while to distinguish the modalities from the common representation via an adversarial learning mechanism. The two subnetworks are jointly trained to enhance the cross-modal semantic consistency in the learned common subspace and the knowledge transfer to instances in the target set. Comprehensive experiment on three widely-used multi-modal datasets show its effectiveness and robustness on both non-extendable and extendable cross-modal retrieval task.


title: Use of CAD/CAM technologies in pediatric dentistry

abstract: AIM: The aim of this article is to present a review of the literature data about the possibilities of CAD-CAM technology usage in pediatric dentistry. METHODS: An electronic search of the literature from 2005 to 2018 was performed using two databases: Medline/PubMed and Embase. RESULTS: The first computer system helping with restorations - CEREC (initially Siemens, now Sirona) was implemented about 30 years ago. Many systems are already available to use both in the dental office and the technician`s laboratory. Now every type of ceramic material can be used in a restoration for almost all indications of aesthetic dentistry. The functional and aesthetic restorations for severely damaged primary and permanent children`s teeth require materials which must be biocompatible, mechanically durable during mastication and with unchanging colour. In the literature data there are evidences about Computer Aided Design/Computer Aided Manufacturing (CAD/CAM) technology usage in pediatric dentistry for dental restorations of extensive carious lesions, eroded and abraded teeth, primary teeth with absence of a permanent successor, dental dysplasia or dental trauma of hard tooth tissues. CONCLUSION: Ceramic materials and CAD/CAM technologies are increasingly being used in aesthetic dentistry both in adults and children.


title: Design of emerging digital services: a taxonomy

abstract: There has been a gigantic shift from a product based economy to one based on services, specifically digital services. From every indication it is likely to be more than a passing fad and the changes these emerging digital services represent will continue to transform commerce and have yet to reach market saturation. Digital services are being designed for and offered to users, yet very little is known about the design process that goes behind these developments. Is there a science behind designing digital services? By examining 12 leading digital services, we have developed a design taxonomy to be able to classify and contrast digital services. What emerged in the taxonomy were two broad dimensions; a set of fundamental design objectives and a set of fundamental service provider objectives. This paper concludes with an application of the proposed taxonomy to three leading digital services. We hope that the proposed taxonomy will be useful in understanding the science behind the design of digital services.


title: Object mapping, recognition, and localization from tactile geometry

abstract: We present a method for performing object recognition using multiple images acquired from a tactile sensor. The method relies on using the tactile sensor as an imaging device, and builds an object representation based on mosaics of tactile measurements. We then describe an algorithm that is able to recognize an object using a small number of tactile sensor readings. Our approach makes extensive use of sequential state estimation techniques from the mobile robotics literature, whereby we view the object recognition problem as one of estimating a consistent location within a set of object maps. We examine and test approaches based on both traditional particle filtering and histogram filtering. We demonstrate both the mapping and recognition / localization techniques on a set of raised letter shapes using real tactile sensor data.


title: maplab: An Open Framework for Research in Visual-inertial Mapping and Localization

abstract: Robust and accurate visual-inertial estimation is crucial to many of today's challenges in robotics. Being able to localize against a prior map and obtain accurate and drift-free pose estimates can push the applicability of such systems even further. Most of the currently available solutions, however, either focus on a single session use case, lack localization capabilities, or do not provide an end-to-end pipeline. We believe that only a complete system, combining state-of-the-art algorithms, scalable multisession mapping tools, and a flexible user interface, can become an efficient research platform. We, therefore, present maplab, an open, research-oriented visual-inertial mapping framework for processing and manipulating multisession maps, written in C++. On the one hand, maplab can be seen as a ready-to-use visual-inertial mapping and localization system. On the other hand, maplab provides the research community with a collection of multisession mapping tools that include map merging, visual-inertial batch optimization, and loop closure. Furthermore, it includes an online frontend that can create visual-inertial maps and also track a global drift-free pose within a localization map. In this letter, we present the system architecture, five use cases, and evaluations of the system on public datasets. The source code of maplab is freely available for the benefit of the robotics research community.


title: Shape-based recognition of wiry objects

abstract: We present an approach to the recognition of complex-shaped objects in cluttered environments based on edge information. We first use example images of a target object in typical environments to train a classifier cascade that determines whether edge pixels in an image belong to an instance of the desired object or the clutter. Presented with a novel image, we use the cascade to discard clutter edge pixels and group the object edge pixels into overall detections of the object. The features used for the edge pixel classification are localized, sparse edge density operations. Experiments validate the effectiveness of the technique for recognition of a set of complex objects in a variety of cluttered indoor scenes under arbitrary out-of-image-plane rotation. Furthermore, our experiments suggest that the technique is robust to variations between training and testing environments and is efficient at runtime.


title: Automated detection of neovascularization for proliferative diabetic retinopathy screening

abstract: Neovascularization is the primary manifestation of proliferative diabetic retinopathy (PDR) that can lead to acquired blindness. This paper presents a novel method that classifies neovascularizations in the 1-optic disc (OD) diameter region (NVD) and elsewhere (NVE) separately to achieve low false positive rates of neovascularization classification. First, the OD region and blood vessels are extracted. Next, the major blood vessel segments in the 1-OD diameter region are classified for NVD, and minor blood vessel segments elsewhere are classified for NVE. For NVD and NVE classifications, optimal region-based feature sets of 10 and 6 features, respectively, are used. The proposed method achieves classification sensitivity, specificity and accuracy for NVD and NVE of 74%, 98.2%, 87.6%, and 61%, 97.5%, 92.1%, respectively. Also, the proposed method achieves 86.4% sensitivity and 76% specificity for screening images with PDR from public and local data sets. Thus, the proposed NVD and NVE detection methods can play a key role in automated screening and prioritization of patients with diabetic retinopathy.


title: Transport of Correlations in a Harmonic Chain

abstract: We study the propagation of different types of correlations through a quantum bus formed by a chain of coupled harmonic oscillators. This includes steering, entanglement, mutual information, quantum discord, and Bell-like nonlocality. The whole system consists of the quantum bus (propagation medium) and other quantum harmonic oscillators (sources and receivers of quantum correlations) weakly coupled to the chain. We are particularly interested in using the point of view of transport to spot distinctive features displayed by different kinds of correlations. We found, for instance, that there are fundamental differences in the way steering and discord propagate, depending on the way they are defined with respect to the parties involved in the initial correlated state. We analyzed both the closed- and open-system dynamics as well as the role played by thermal excitations in the propagation of the correlations.


Social Collaboration Analytics for Enterprise Collaboration Systems: Providing Business Intelligence on Collaboration Activities


The Effect of Aromatherapy on Reduction of Physical Stress


title: Cascaded Fully Convolutional Networks for automatic prenatal ultrasound image segmentation

abstract: Computerized prenatal ultrasound (US) image segmentation methods can greatly improve the efficiency and objectiveness of the biometry interpretation. However, the boundary incompleteness and ambiguity in US images hinder the automatic solutions severely. In this paper, we propose a cascaded framework for fully automatic US image segmentation. A customized Fully Convolutional Network (FCN) was utilized to exploit feature extractions from multiple visual scales and distinguish the anatomy with a dense prediction map. To enhance the local spatial consistency of the prediction map, we further implant the FCN core into an Auto-Context scheme. By modifying the join operator in traditional Auto-Context scheme from parallelization to summation, our framework gains extra considerable improvement. We demonstrate the efficacy of our method on two challenging datasets: fetal head and abdomen US images. Extensive experimental results show that our method can bridge severe boundary incompleteness and achieves the best segmentation accuracy when compared with state-of-the-art methods.


title: Synthetic Aperture Radar (SAR) Interferometry for Assessing Wenchuan Earthquake (2008) Deforestation in the Sichuan Giant Panda Site

abstract: Abstract: Synthetic aperture radar (SAR) has been an unparalleled tool in cloudy and rainy regions as it allows observations throughout the year because of its all-weather, all-day operation capability. In this paper, the influence of Wenchuan Earthquake on the Sichuan Giant Panda habitats was evaluated for the first time using SAR interferometry and combining data from C-band Envisat ASAR and L-band ALOS PALSAR data. Coherence analysis based on the zero-point shifting indicated that the deforestation process was significant, particularly in habitats along the Min River approaching the epicenter after the natural disaster, and as interpreted by the vegetation deterioration from landslides, avalanches and debris flows. Experiments demonstrated that C-band Envisat ASAR data were sensitive to vegetation, resulting in an underestimation of deforestation; in contrast, L-band PALSAR data were capable of evaluating the deforestation process owing to a better penetration and the significant coherence gain on damaged forest areas. The percentage of damaged forest estimated by PALSAR decreased from 20.66% to 17.34% during 2009–2010, implying an approximate 3% recovery rate of forests in the earthquake


title: A multi-resonant gate driver for Very-High-Frequency (VHF) resonant converters

abstract: This paper presents the design and implementation of a Very-High-Frequency (VHF) multi-resonant gate drive circuit. The design procedure is greatly simplified compared with some VHF self-oscillating multi-resonant gate drivers presented in previous works. The proposed circuit has the potential to reduce long start-up time required in a self-oscillating resonant gate drive circuit and to better utilize fast transient capability of VHF converters. A prototype resonant gate driver is demonstrated and able to reduce 60 % of the gate driving loss of a 20 MHz 32 W Class-E power amplifier with Si MOSFET.


title: A bio inspired Energy-Aware Multi objective Chiropteran Algorithm (EAMOCA) for hybrid cloud computing environment

abstract: Resource scheduling is a momentous task of determining and formulating when an activity should start or end depending on the number of tasks, the availability of the resources, time and processor speed. A cloud is an aggregation of resources or services that are provided as service through network. In earlier version of cloud computing (grid computing) it was enough to find the subset of resources for the applications whereas cloud computing goes one step higher allocating resources to Virtual Machines (VM's) as well as scheduling tasks on the VM's. So resource scheduling is indispensable in cloud computing for load balancing and maximizing the utilization while minimizing the execution time and energy. Few approaches relating to energy salvation have certain disadvantages such as, time complexity and slow Convergence. Hence Energy-Aware Multi objective Chiropteran Algorithm (EAMOCA) is developed by bringing together the echo-localization and hibernation properties for scheduling resources as well as conserving energy. Promotion of energy salvation in cloud environment is achieved in a well delineated manner. By using the performance metrics such as total energy consumed by physical resources, SLA violation (CPU performance) and VM migration, we have manipulated our approach through real time implementation by setting up a private cloud employing VMware.


title: Comorbid depression and anxiety spectrum disorders

abstract: The relationship between depression and anxiety disorders has long been a matter of controversy. The overlap of symptoms associated with these disorders makes diagnosis, research, and treatment particularly difficult. Recent evidence suggests genetic and neurobiologic similarities between depressive and anxiety disorders. Comorbid depression and anxiety are highly prevalent conditions. Patients with panic disorder, generalized anxiety disorder, social phobia, and other anxiety disorders are also frequently clinically depressed. Approximately 85% of patients with depression also experience significant symptoms of anxiety. Similarly, comorbid depression occurs in up to 90% of patients with anxiety disorders. Patients with comorbid disorders do not respond as well to therapy, have a more protracted course of illness, and experience less positive treatment outcomes. One key to successful treatment of patients with mixed depressive and anxiety disorders is early recognition of comorbid conditions. Antidepressant medications, including the selective serotonin reuptake inhibitors, tricyclic antidepressants, and monoamine oxidase inhibitors, are highly effective in the management of comorbid depression and anxiety. The high rates of comorbid depression and anxiety argue for well-designed treatment studies in these populations. Depression and Anxiety 4:160–168, 1996/1997. © 1997 Wiley-Liss, Inc.


title: A swarm optimization algorithm inspired in the behavior of the social-spider

abstract: Swarm intelligence is a research field that models the collective behavior in swarms of insects or animals. Several algorithms arising from such models have been proposed to solve a wide range of complex optimization problems. In this paper, a novel swarm algorithm called the Social Spider Optimization (SSO) is proposed for solving optimization tasks. The SSO algorithm is based on the simulation of cooperative behavior of social-spiders. In the proposed algorithm, individuals emulate a group of spiders which interact to each other based on the biological laws of the cooperative colony. The algorithm considers two different search agents (spiders): males and females. Depending on gender, each individual is conducted by a set of different evolutionary operators which mimic different cooperative behaviors that are typically found in the colony. In order to illustrate the proficiency and robustness of the proposed approach, it is compared to other well-known evolutionary methods. The comparison examines several standard benchmark functions that are commonly considered within the literature of evolutionary algorithms. The outcome shows a high performance of the proposed method for searching a global optimum with several benchmark functions.


title: Real time pothole detection using Android smartphones with accelerometers

abstract: The importance of the road infrastructure for the society could be compared with importance of blood vessels for humans. To ensure road surface quality it should be monitored continuously and repaired as necessary. The optimal distribution of resources for road repairs is possible providing the availability of comprehensive and objective real time data about the state of the roads. Participatory sensing is a promising approach for such data collection. The paper is describing a mobile sensing system for road irregularity detection using Android OS based smart-phones. Selected data processing algorithms are discussed and their evaluation presented with true positive rate as high as 90% using real world data. The optimal parameters for the algorithms are determined as well as recommendations for their application.


title: Finding Matches in a Haystack: A Max-Pooling Strategy for Graph Matching in the Presence of Outliers

abstract: A major challenge in real-world feature matching problems is to tolerate the numerous outliers arising in typical visual tasks. Variations in object appearance, shape, and structure within the same object class make it harder to distinguish inliers from outliers due to clutters. In this paper, we propose a max-pooling approach to graph matching, which is not only resilient to deformations but also remarkably tolerant to outliers. The proposed algorithm evaluates each candidate match using its most promising neighbors, and gradually propagates the corresponding scores to update the neighbors. As final output, it assigns a reliable score to each match together with its supporting neighbors, thus providing contextual information for further verification. We demonstrate the robustness and utility of our method with synthetic and real image experiments.


title: Reflective and Impulsive Determinants of Social Behavior

abstract: This article describes a 2-systems model that explains social behavior as a joint function of reflective and impulsive processes. In particular, it is assumed that social behavior is controlled by 2 interacting systems that follow different operating principles. The reflective system generates behavioral decisions that are based on knowledge about facts and values, whereas the impulsive system elicits behavior through associative links and motivational orientations. The proposed model describes how the 2 systems interact at various stages of processing, and how their outputs may determine behavior in a synergistic or antagonistic fashion. It extends previous models by integrating motivational components that allow more precise predictions of behavior. The implications of this reflective–impulsive model are applied to various phenomena from social psychology and beyond. Extending previous dual-process accounts, this model is not limited to specific domains of mental functioning and attempts to integrate cognitive, motivational, and behavioral mechanisms.


title: A Survey of Question Answering for Math and Science Problem

abstract: Turing test was long considered the measure for artificial intelligence. But with the advances in AI, it has proved to be insufficient measure. We can now aim to mea- sure machine intelligence like we measure human intelligence. One of the widely accepted measure of intelligence is standardized math and science test. In this paper, we explore the progress we have made towards the goal of making a machine smart enough to pass the standardized test. We see the challenges and opportunities posed by the domain, and note that we are quite some ways from actually making a system as smart as a even a middle school scholar.


Survey of Event Correlation Techniques for Attack Detection in Early Warning Systems


title: A computational efficient covariance matrix update and a (1+1)-CMA for evolution strategies

abstract: First, the covariance matrix adaptation (CMA) with rank-one update is introduced into the (1+1)-evolution strategy. An improved implementation of the 1/5-th success rule is proposed for step size adaptation, which replaces cumulative path length control. Second, an incremental Cholesky update for the covariance matrix is developed replacing the computational demanding and numerically involved decomposition of the covariance matrix. The Cholesky update can replace the decomposition only for the update without evolution path and reduces the computational effort from O(n3) to O(n2). The resulting (1+1)-Cholesky-CMA-ES is an elegant algorithm and the perhaps simplest evolution strategy with covariance matrix and step size adaptation. Simulations compare the introduced algorithms to previously published CMA versions.


title: Analysis of natural rock slope deformations under temperature variation : A case from a cool temperate region in Japan

abstract: Abstract In this paper, natural rock slope deformation across fractures predominantly in a chert rock mass was monitored using six surface fracture displacement sensors, and the deformations arising from thermal stresses were predicted using (5 m × 5 m) two-dimensional (2D) finite element (FE) plane strain analysis coupled with a model for rock mass expansion due to freezing of pore water. A new and simple method to minimize displacement proportional to temperature (due to thermal response of chert rock mass and sensor) was proposed. By applying the method, the corrected displacement, u ′, can be well recognized. Under u ′, clear rock mass movement, which could be related to fracture growth, was observed. In addition, progressive fracture opening and closure were noted. Results from this study indicate insignificant influences of weather conditions on fracture/rock mass movement. Furthermore, under numerical analysis (FE), in the rock mass model (with 1-m deep fracture), tensile stresses that were large enough to induce fracture growth appeared at the fracture tip when temperature lowered. And in the rock slope model (with 1-m deep fracture), small tensile stresses, which were sufficient to cause fracture growth along the planes of weakness, were observed. This research suggests that freezing effects on deformation of chert rock mass are insignificant, and we tentatively suggest that thermal fatigue predominantly caused the permanent fracture deformations.


title: Mutant proteins of human interleukin 2. Renaturation yield, proliferative activity and receptor binding

abstract: Muteins, i.e. proteins altered by mutation of their genes, of interleukin 2 (Il2) were generated by oligonucleotide-directed mutagenesis in vitro. All acidic and basic residues conserved between man and mouse were exchanged as well as four lipophilic residues contained within four hydrophobic segments of the protein. The muteins were produced in Escherichia coli and submitted to a renaturation and purification protocol, before bioactivity and receptor binding of each of them was determined. All muteins besides two (K44/T125 and Q110/T125) could be renatured and purified. One mutein (K94/T125) exhibited a more than tenfold-improved renaturation yield. One amino exchange (Asp-20 to Asn) resulted in an about 20-fold reduction in proliferative activity and high-affinity receptor binding. The binding to the low-affinity Il2-binding protein (Tac antigen) was unimpaired. A second exchange (Arg-38 to Gln) had no effect on proliferative activity. The binding to both the high- and the low-affinity receptor, however, was reduced about 20-fold. Preliminary trials on the stability of these muteins by guanidinium hydrochloride denaturation studies detected no differences between wild-type interleukin 2 and muteins. It is suggested that Asp-20 forms part of the binding site for the large receptor subunit whereas Arg-38 is involved in the contact site to the small subunit.


title: Towards a process model of information systems implementation: the case of customer relationship management (CRM)

abstract: The failure rate of Customer Relationship Management (CRM) implementations is estimated to be greater than 65%. Lowering the failure rate and supporting the success of information systems (IS) are the ultimate goals of IS practitioners and researchers. However, most previous research in the area has focused on identifying factors such as critical success factors (CSFs) that are correlated with IS success. There has been little research on how IS implementation may lead to successful results. The state of knowledge in IS implementation may be likened to cooking with a list of ingredients but not the recipe. Drawing on process theory, this study examines the process of IS implementation by explaining how factors of IS implementation influence each other and how interactions among them produce results. Based on one successful case and two unsuccessful cases, we develop the process model of IS implementation, by which the process of IS implementation and the dynamics of IS success can be explained. The proposed model facilitates an understanding of how repeating patterns of IS failure can be reversed, and could serve to guide new IS implementation projects.


title: Interactive visualization of streaming data with Kernel Density Estimation

abstract: In this paper, we discuss the extension and integration of the statistical concept of Kernel Density Estimation (KDE) in a scatterplot-like visualization for dynamic data at interactive rates. We present a line kernel for representing streaming data, we discuss how the concept of KDE can be adapted to enable a continuous representation of the distribution of a dependent variable of a 2D domain. We propose to automatically adapt the kernel bandwith of KDE to the viewport settings, in an interactive visualization environment that allows zooming and panning. We also present a GPU-based realization of KDE that leads to interactive frame rates, even for comparably large datasets. Finally, we demonstrate the usefulness of our approach in the context of three application scenarios - one studying streaming ship traffic data, another one from the oil & gas domain, where process data from the operation of an oil rig is streaming in to an on-shore operational center, and a third one studying commercial air traffic in the US spanning 1987 to 2008.


title: An Efficient Randomized Algorithm for Detecting Circles

abstract: Detecting circles from a digital image is very important in shape recognition. In this paper, an efficient randomized algorithm (RCD) for detecting circles is presented, which is not based on the Hough transform (HT). Instead of using an accumulator for saving the information of the related parameters in the HT-based methods, the proposed RCD does not need an accumulator. The main concept used in the proposed RCD is that we first randomly select four edge pixels in the image and define a distance criterion to determine whether there is a possible circle in the image; after finding a possible circle, we apply an evidence-collecting process to further determine whether the possible circle is a true circle or not. Some synthetic images with different levels of noises and some realistic images containing circular objects with some occluded circles and missing edges have been taken to test the performance. Experimental results demonstrate that the proposed RCD is faster than other HT-based methods for the noise level between the light level and the modest level. For a heavy noise level, the randomized HT could be faster than the proposed RCD, but at the expense of massive memory requirements.


title: A Deep Network Model for Paraphrase Detection in Short Text Messages

abstract: Abstract This paper is concerned with paraphrase detection, i.e., identifying sentences that are semantically identical. The ability to detect similar sentences written in natural language is crucial for several applications, such as text mining, text summarization, plagiarism detection, authorship authentication and question answering. Recognizing this importance, we study in particular how to address the challenges with detecting paraphrases in user generated short texts, such as Twitter, which often contain language irregularity and noise, and do not necessarily contain as much semantic information as longer clean texts. We propose a novel deep neural network-based approach that relies on coarse-grained sentence modelling using a convolutional neural network (CNN) and a recurrent neural network (RNN) model, combined with a specific fine-grained word-level similarity matching model. More specifically, we develop a new architecture, called DeepParaphrase, which enables to create an informative semantic representation of each sentence by (1) using CNN to extract the local region information in form of important n-grams from the sentence, and (2) applying RNN to capture the long-term dependency information. In addition, we perform a comparative study on state-of-the-art approaches within paraphrase detection. An important insight from this study is that existing paraphrase approaches perform well when applied on clean texts, but they do not necessarily deliver good performance against noisy texts, and vice versa. In contrast, our evaluation has shown that the proposed DeepParaphrase-based approach achieves good results in both types of texts, thus making it more robust and generic than the existing approaches.


title: ITS, The End of the World as We Know It: Transitioning AIED into a Service-Oriented Ecosystem

abstract: Advanced learning technologies are reaching a new phase of their evolution where they are finally entering mainstream educational contexts, with persistent user bases. However, as AIED scales, it will need to follow recent trends in service-oriented and ubiquitous computing: breaking AIED platforms into distinct services that can be composed for different platforms (web, mobile, etc.) and distributed across multiple systems. This will represent a move from learning platforms to an ecosystem of interacting learning tools. Such tools will enable new opportunities for both user-adaptation and experimentation. Traditional macro-adaptation (problem selection) and step-based adaptation (hints and feedback) will be extended by meta-adaptation (adaptive system selection) and micro-adaptation (event-level optimization). The existence of persistent and widely-used systems will also support new paradigms for experimentation in education, allowing researchers to understand interactions and boundary conditions for learning principles. New central research questions for the field will also need to be answered due to these changes in the AIED landscape.


title: Role of IL-33 in inflammation and disease

abstract: Interleukin (IL)-33 is a new member of the IL-1 superfamily of cytokines that is expressed by mainly stromal cells, such as epithelial and endothelial cells, and its expression is upregulated following pro-inflammatory stimulation. IL-33 can function both as a traditional cytokine and as a nuclear factor regulating gene transcription. It is thought to function as an 'alarmin' released following cell necrosis to alerting the immune system to tissue damage or stress. It mediates its biological effects via interaction with the receptors ST2 (IL-1RL1) and IL-1 receptor accessory protein (IL-1RAcP), both of which are widely expressed, particularly by innate immune cells and T helper 2 (Th2) cells. IL-33 strongly induces Th2 cytokine production from these cells and can promote the pathogenesis of Th2-related disease such as asthma, atopic dermatitis and anaphylaxis. However, IL-33 has shown various protective effects in cardiovascular diseases such as atherosclerosis, obesity, type 2 diabetes and cardiac remodeling. Thus, the effects of IL-33 are either pro- or anti-inflammatory depending on the disease and the model. In this review the role of IL-33 in the inflammation of several disease pathologies will be discussed, with particular emphasis on recent advances.


title: DeepConvLSTM on single accelerometer locomotion recognition

abstract: This project aims to evaluate the deep neural network architecture Deep-ConvLSTM to classify locomotive human activities using data from a single accelerometer. The evaluation involves comparisons ...


title: Teaching Programming Broadly and Deeply: The Kernel Language Approach

abstract: We present the kernel language approach; a new way to teach programming that situates most of the widely known programming paradigms (including imperative, object-oriented, concurrent, logic, and functional) into a uniform setting that shows their deep relationships and how to use them together. Widely different practical languages (exemplified by Java, Haskell, Prolog, and Erlang) with their rich panoplies of abstractions and syntax are explained by straightforward translations into closely related kernel languages, simple languages that consist of small numbers of programmer-significant concepts. Kernel languages are easy to understand and have a simple formal semantics that can be used by practicing programmers to reason about correctness and complexity.


title: Subspace Clustering of Categorical and Numerical Data With an Unknown Number of Clusters

abstract: In clustering analysis, data attributes may have different contributions to the detection of various clusters. To solve this problem, the subspace clustering technique has been developed, which aims at grouping the data objects into clusters based on the subsets of attributes rather than the entire data space. However, the most existing subspace clustering methods are only applicable to either numerical or categorical data, but not both. This paper, therefore, studies the soft subspace clustering of data with both of the numerical and categorical attributes (also simply called mixed data for short). Specifically, an attribute-weighted clustering model based on the definition of object-cluster similarity is presented. Accordingly, a unified weighting scheme for the numerical and categorical attributes is proposed, which quantifies the attribute-to-cluster contribution by taking into account both of intercluster difference and intracluster similarity. Moreover, a rival penalized competitive learning mechanism is further introduced into the proposed soft subspace clustering algorithm so that the subspace cluster structure as well as the most appropriate number of clusters can be learned simultaneously in a single learning paradigm. In addition, an initialization-oriented method is also presented, which can effectively improve the stability and accuracy of $k$ -means-type clustering methods on numerical, categorical, and mixed data. The experimental results on different benchmark data sets show the efficacy of the proposed approach.


title: Sparsity: Optimization Framework for Sparse Matrix Kernels

abstract: Sparse matrix-vector multiplication is an important computational kernel that performs poorly on most modern processors due to a low compute-to-memory ratio and irregular memory access patterns. Optimization is difficult because of the complexity of cache-based memory systems and because performance is highly dependent on the non-zero structure of the matrix. The SPARSITY system is designed to address these problems by allowing users to automatically build sparse matrix kernels that are tuned to their matrices and machines. SPARSITY combines traditional techniques such as loop transformations with data structure transformations and optimization heuristics that are specific to sparse matrices. It provides a novel framework for selecting optimization parameters, such as block size, using a combination of performance models and search. In this paper we discuss the optimization of two operations: a sparse matrix times a dense vector and a sparse matrix times a set of dense vectors. Our experience indicates that register level optimizations are effective for matrices arising in certain scientific simulations, in particular finite-element problems. Cache level optimizations are important when the vector used in multiplication is larger than the cache size, especially for matrices in which the non-zero structure is random. For applications involving multiple vectors, reorganizing the computation to perform the entire set of multiplications as a single operation produces significant speedups. We describe the different optimizations and parameter selection techniques and evaluate them on several machines using over 40 matrices taken from a broad set of application domains. Our results demonstrate speedups of up to 4X for the single vector case and up to 10X for the multiple vector case.


title: Generation of High Dynamic Range Illumination from a Single Image for the Enhancement of Undesirably Illuminated Images

abstract: This paper presents an algorithm that enhances undesirably illuminated images by generating and fusing multi-level illuminations from a single image.The input image is first decomposed into illumination and reflectance components by using an edge-preserving smoothing filter. Then the reflectance component is scaled up to improve the image details in bright areas. The illumination component is scaled up and down to generate several illumination images that correspond to certain camera exposure values different from the original. The virtual multi-exposure illuminations are blended into an enhanced illumination, where we also propose a method to generate appropriate weight maps for the tone fusion. Finally, an enhanced image is obtained by multiplying the equalized illumination and enhanced reflectance. Experiments show that the proposed algorithm produces visually pleasing output and also yields comparable objective results to the conventional enhancement methods, while requiring modest computational loads.


title: A Methodology for assessing Agile Software Development Approaches

abstract: Agile methods provide an organization or a team the flexibility to adopt a selected subset of principles and practices based on their culture, their values, and the types of systems that they develop. More specifically, every organization or team implements a customized agile method, tailored to better accommodate its needs. However, the extent to which a customized method supports the organizational objectives, or rather the 'goodness' of that method is questionable. Existing agile assessment approaches focus on a comparative analysis, or are limited in scope and application. In this research, we propose a structured, systematic and comprehensive approach to assess the 'goodness' of agile methods. We examine an agile method based on (1) its adequacy, (2) the capability of the organization to support the adopted principles and practices specified by the method, and (3) the method's effectiveness. We propose the Objectives, Principles and Practices (OPP) Framework to guide our assessment. The Framework identifies (1) objectives of the agile philosophy, (2) principles that support the objectives, (3) practices that are reflective of the principles, (4) the linkages between the objectives, principles and practices, and (5) indicators for each practice to assess the effectiveness of the practice and the extent to which the organization supports its implementation. In this document, we discuss our solution approach, preliminary results, and future work.


title: Deep Generative Stochastic Networks Trainable by Backprop

abstract: We introduce a novel training principle for probabilistic models that is an alternative to maximum likelihood. The proposed Generative Stochastic Networks (GSN) framework is based on learning the transition operator of a Markov chain whose stationary distribution estimates the data distribution. The transition distribution of the Markov chain is conditional on the previous state, generally involving a small move, so this conditional distribution has fewer dominant modes, being unimodal in the limit of small moves. Thus, it is easier to learn because it is easier to approximate its partition function, more like learning to perform supervised function approximation, with gradients that can be obtained by backprop. We provide theorems that generalize recent work on the probabilistic interpretation of denoising autoencoders and obtain along the way an interesting justification for dependency networks and generalized pseudolikelihood, along with a definition of an appropriate joint distribution and sampling mechanism even when the conditionals are not consistent. GSNs can be used with missing inputs and can be used to sample subsets of variables given the rest. We validate these theoretical results with experiments on two image datasets using an architecture that mimics the Deep Boltzmann Machine Gibbs sampler but allows training to proceed with simple backprop, without the need for layerwise pretraining.


title: Improved J48 Classification Algorithm for the Prediction of Diabetes

abstract: research work deals with efficient data mining procedure for predicting the diabetes from medical records of patients. Diabetes is a very common disease these days in all populations and in all age groups. Diabetes contributes to heart disease, increases the risks of developing kidney disease, nerve damage, blood vessel damage and blindness. So mining the diabetes data in efficient manner is a critical issue. The Pima Indians Diabetes Data Set is used in this paper; which collects the information of patients with and without having diabetes. The modified J48 classifier is used to increase the accuracy rate of the data mining procedure. The data mining tool WEKA has been used as an API of MATLAB for generating the J-48 classifiers. Experimental results showed a significant improvement over the existing J-48 algorithm. KeywordsDecision Tree, MATLAB, Data Mining, Diabetes, WEKA.


title: Ultra-wearable capacitive coupled and common electrode-free ECG monitoring system

abstract: Nowadays, transfer of the health care from ambulance to patient's home needs higher demand on patient's mobility, comfort and acceptance of the system. Therefore, the goal of this study is to proof the concept of a system which is ultra-wearable, less constraining and more suitable for long term measurements than conventional ECG monitoring systems which use conductive electrolytic gels for low impedance electrical contact with skin. The developed system is based on isolated capacitive coupled electrodes without any galvanic contact to patient's body and does not require the common right leg electrode. Measurements performed under real conditions show that it is possible to acquire well known ECG waveforms without the common electrode when the patient is sitting and even during walking. Results of the validation process demonstrate that the system performance is comparable to the conventional ECG system while the wearability is increased.


title: Age-old practices in the ‘ New World ’ : A study of gift-giving between teenage mobile phone users

abstract: In this paper, we present an overview of the data collected from an ethnographic study of teenagers and their use of mobile phones. Through the data, we suggest that teenagers use their phones to participate in social practices that closely resemble forms of ritualised gift-giving. Such practices, we claim, shape the way teenagers understand and thus use their phones. We go onto show that this insight into everyday, phone-mediated activities has practical implications for mobile phone design. Using an example, we describe how teenagers' gift-giving practices can inform design, providing an initial means to conceptualise future emerging technologies


title: Automatic document metadata extraction using support vector machines

abstract: Automatic metadata generation provides scalability and usability for digital libraries and their collections. Machine learning methods offer robust and adaptable automatic metadata extraction. We describe a support vector machine classification-based method for metadata extraction from header part of research papers and show that it outperforms other machine learning methods on the same task. The method first classifies each line of the header into one or more of 15 classes. An iterative convergence procedure is then used to improve the line classification by using the predicted class labels of its neighbor lines in the previous round. Further metadata extraction is done by seeking the best chunk boundaries of each line. We found that discovery and use of the structural patterns of the data and domain based word clustering can improve the metadata extraction performance. An appropriate feature normalization also greatly improves the classification performance. Our metadata extraction method was originally designed to improve the metadata extraction quality of the digital libraries Citeseer [S. Lawrence et al., (1999)] and EbizSearch [Y. Petinot et al., (2003)]. We believe it can be generalized to other digital libraries.


title: Increasing the Capacity of Magnetic Induction Communications in RF-Challenged Environments

abstract: Magnetic Induction (MI) techniques enable efficient wireless communications in dense media with high material absorptions, such as underground soil medium and oil reservoirs. A wide range of novel and important applications in such RF-challenged environments can be realized based on the MI communication mechanism. Despite the potential advantages, the major bottleneck of the MI communication is the limited channel capacity due to the low MI bandwidth. In this paper, the Spread Resonance (RS) strategy is developed for the MI communication in RF-challenged environments which greatly increases the MI channel capacity. Specifically, instead of using the same resonant frequency for all the MI coils, the spread resonance strategy allocates different resonant frequencies for different MI relay and transceiver coils. An optimization solution for the resonant frequency allocation is formulated to maximize the MI channel capacity which captures multiple unique MI effects, including the parasitic capacitor in each MI coil, the Eddy currents in various transmission media with limited conductivities, and the random direction of each coil. Numerical evaluations are provided to validate the significant channel capacity improvements by the proposed SR strategy for MI communication systems.


title: Automatic discovery of technology trends from patent text

abstract: Patent text is a rich source to discover technological progresses, useful to understand the trend and forecast upcoming advances. For the importance in mind, several researchers have attempted textual-data mining from patent documents. However, previous mining methods are limited in terms of readability, domain-expertise, and adaptability. In this paper, we first formulate the task of technological trend discovery and propose a method for discovering such a trend. We complement a probabilistic approach by adopting linguistic clues and propose an unsupervised procedure to discover technological trends. Based on the experiment, our method is promising not only in its accuracy, 77% in R-precision, but also in its functionality and novelty of discovering meaningful technological trends.


title: Modeling reader's emotional state response on document's typographic elements

abstract: We present the results of an experimental study towards modeling the reader's emotional state variations induced by the typographic elements in electronic documents. Based on the dimensional theory of emotions we investigate how typographic elements, like font style (bold, italics, bold-italics) and font (type, size, color and background color), affect the reader's emotional states, namely, Pleasure, Arousal, and Dominance (PAD). An experimental procedure was implemented conforming to International Affective Picture System guidelines and incorporating the Self-Assessment Manikin test. Thirty students participated in the experiment. The stimulus was a short paragraph of text for which any content, emotion, and/or domain dependent information was excluded. The Analysis of Variance revealed the dependency of (a) all the three emotional dimensions on font size and font/background color combinations and (b) the Pleasure dimension on font type and font style. We introduce a set of mapping rules showing how PAD vary on the discrete values of font style and font type elements. Moreover, we introduce a set of equations describing the PAD dimensions' dependency on font size. This novel model can contribute to the automated reader's emotional state extraction in order, for example, to enhance the acoustic rendition of the documents, utilizing text-to-speech synthesis.


title: The link prediction problem for social networks

abstract: Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link prediction problem, and develop approaches to link prediction based on measures the "proximity" of nodes in a network. Experiments on large co-authorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures.


title: Associative Activation and Its Relation to Exploration and Exploitation in the Brain

abstract: Associative activation is commonly assumed to rely on associative strength, such that if A is strongly associated with B, B is activated whenever A is activated. We challenged this assumption by examining whether the activation of associations is state dependent. In three experiments, subjects performed a free-association task while the level of a simultaneous load was manipulated in various ways. In all three experiments subjects in the low-load conditions provided significantly more diverse and original associations compared with subjects in the high-load conditions, who exhibited high consensus. In an additional experiment, we found increased semantic priming of immediate associations under high load and of remote associations under low load. Taken together, these findings imply that activation of associations is an exploratory process by default, but is narrowed to exploiting the more immediate associations under conditions of high load. We propose a potential mechanism for processing associations in ...


title: Towards a sandbox for the deobfuscation and dissection of PHP malware

abstract: The creation and proliferation of PHP-based Remote Access Trojans (or web shells) used in both the compromise and post exploitation of web platforms has fuelled research into automated methods of dissecting and analysing these shells. Current malware tools disguise themselves by making use of obfuscation techniques designed to frustrate any efforts to dissect or reverse engineer the code. Advanced code engineering can even cause malware to behave differently if it detects that it is not running on the system for which it was originally targeted. To combat these defensive techniques, this paper presents a sandbox-based environment that aims to accurately mimic a vulnerable host and is capable of semi-automatic semantic dissection and syntactic deobfuscation of PHP code.


title: OpenAI Gym

abstract: OpenAI Gym is a toolkit for reinforcement learning research. It includes a growing collection of benchmark problems that expose a common interface, and a website where people can share their results and compare the performance of algorithms. This whitepaper discusses the components of OpenAI Gym and the design decisions that went into the software.


title: An exact algorithm for solving the vertex separator problem

abstract: Given G = (V, E) a connected undirected graph and a positive integer β(|V|), the vertex separator problem is to find a partition of V into no-empty three classes A, B, C such that there is no edge between A and B, max{|A|, |B|} ? β(|V|) and |C| is minimum. In this paper we consider the vertex separator problem from a polyhedral point of view. We introduce new classes of valid inequalities for the associated polyhedron. Using a natural lower bound for the optimal solution, we present successful computational experiments.


title: Implementation Differences of Hospital Information System (HIS) in Malaysian Public Hospitals

abstract: Hospital Information System (HIS) is important to ::: healthcare sector especially in public hospitals as they need to ::: serve the public with high-quality healthcare treatments. HIS ::: helps to improve patients care services. Thus, the Malaysian ::: Government has introduced three (3) categories of HIS namely ::: Total Hospital Information System (THIS), Intermediate ::: Hospital Information System (IHIS), and Basic Hospital ::: Information System (BHIS) among Malaysian public hospitals. ::: However, only 15.2% of the Malaysian public hospitals are ::: implementing the system. Moreover, there is limited number of ::: empirical studies on HIS implementation in Malaysia. Thus, ::: this paper aims to investigate issues and challenges in HIS ::: implementation for each category of HIS by using in-depth ::: interviews. Nine participants were involved in the interviews. ::: The interview data were transcribed verbatim and analysed ::: based on Content and Thematic Analysis using NVivo software. ::: The results showed that different category of HIS faced ::: different issues and challenges.


title: High repetition rate charging a Marx type generator

abstract: Summary form only given, as follows. Resistive ladder networks are commonly used as the charging and isolation means for Marx-type high voltage generators. The resistive charging network, however, suffers from several serious limitations. Namely, the efficiency is limited to 50% and the long charge time limits the PRR (pulse repetition rate). The efficiency can be considerably improved by replacing the resistive ladder with inductor elements; however, the PRR is only slightly improved. In this paper it is shown that by introducing mutual coupling, k, between the two parallel inductors in each stage of the, ladder network, the effective inductance during the charging mode is decreased by a factor of (1-k)/(1+k). Since it is feasible to achieve a coupling, k, on the order of 0.99, this speeds up the charging time by about an order of magnitude compared to resistive or conventional inductive charging. During the erected or discharge mode, the inductors provide isolation between stages and must not excessively rob energy from the energy store. The addition of mutual coupling is beneficial because during the discharge cycle, the effective inductances of the ladder elements are actually increased by a factor (1+k). The Marx switches cause a re-arrangement of the coupled inductors from parallel during the charging to series during the discharge modes. This results in a much faster charging time, by reducing the effective inductance by (1-k)/(1+k); while providing an effective isolation inductance that is (1+k) greater than the uncoupled value. The practical design of the coupled inductors for high voltage applications is discussed and modeled simulations of the performance are included.


title: Recent Advances in Flexible and Stretchable Bio‐Electronic Devices Integrated with Nanomaterials

abstract: Flexible and stretchable electronics and optoelectronics configured in soft, water resistant formats uniquely address seminal challenges in biomedicine. Over the past decade, there has been enormous progress in the materials, designs, and manufacturing processes for flexible/stretchable system subcomponents, including transistors, amplifiers, bio-sensors, actuators, light emitting diodes, photodetector arrays, photovoltaics, energy storage elements, and bare die integrated circuits. Nanomaterials prepared using top-down processing approaches and synthesis-based bottom-up methods have helped resolve the intrinsic mechanical mismatch between rigid/planar devices and soft/curvilinear biological structures, thereby enabling a broad range of non-invasive, minimally invasive, and implantable systems to address challenges in biomedicine. Integration of therapeutic functional nanomaterials with soft bioelectronics demonstrates therapeutics in combination with unconventional diagnostics capabilities. Recent advances in soft materials, devices, and integrated systems are reviewes, with representative examples that highlight the utility of soft bioelectronics for advanced medical diagnostics and therapies.


title: Dhalion: Self-Regulating Stream Processing in Heron

abstract: In recent years, there has been an explosion of large-scale real-time analytics needs and a plethora of streaming systems have been developed to support such applications. These systems are able to continue stream processing even when faced with hardware and software failures. However, these systems do not address some crucial challenges facing their operators: the manual, time-consuming and error-prone tasks of tuning various configuration knobs to achieve service level objectives (SLO) as well as the maintenance of SLOs in the face of sudden, unpredictable load variation and hardware or software performance degradation. ::: ::: In this paper, we introduce the notion of self-regulating streaming systems and the key properties that they must satisfy. We then present the design and evaluation of Dhalion, a system that provides self-regulation capabilities to underlying streaming systems. We describe our implementation of the Dhalion framework on top of Twitter Heron, as well as a number of policies that automatically reconfigure Heron topologies to meet throughput SLOs, scaling resource consumption up and down as needed. We experimentally evaluate our Dhalion policies in a cloud environment and demonstrate their effectiveness. We are in the process of open-sourcing our Dhalion policies as part of the Heron project.


title: The embryology of conjoined twins

abstract: INTRODUCTION ::: Attention is drawn to the spontaneous incidence of twinning, both dizygotic and monozygotic in different mammalian species. Conjoined twinning, however, only arises when the twinning event occurs at about the primitive streak stage of development, at about 13-14 days after fertilisation in the human, and is exclusively associated with the monoamniotic monochorionic type of placentation. It is believed that the highest incidence of conjoined twinning is encountered in the human. While monozygotic twinning may be induced experimentally following exposure to a variety of agents, the mechanism of induction of spontaneous twinning in the human remains unknown. All agents that are capable of acting as a twinning stimulus are teratogenic, and probably act by interfering with the spindle apparatus. ::: ::: ::: DISCUSSION ::: The incidence of the various types of conjoined twinning is discussed. Information from the largest study to date indicates that the spontaneous incidence is about 10.25 per million births. The most common varieties encountered were thoraco-omphalopagus (28%), thoracopagus (18.5%), omphalopagus (10%), parasitic twins (10%) and craniopagus (6%). Of these, about 40% were stillborn, and 60% liveborn, although only about 25% of those that survived to birth lived long enough to be candidates for surgery. Conjoined twinning occurs by the incomplete splitting of the embryonic axis and, with the exception of parasitic conjoined twins, all are symmetrical and "the same parts are always united to the same parts". Fusion of monozygotic twins is no longer believed to be the basis of conjoined twinning. Accounts are provided of the anatomical features of each of the commonly encountered varieties.


title: Robustness May Be at Odds with Accuracy

abstract: We show that there exists an inherent tension between the goal of adversarial robustness and that of standard generalization. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. We demonstrate that this trade-off between the standard accuracy of a model and its robustness to adversarial perturbations provably exists even in a fairly simple and natural setting. These findings also corroborate a similar phenomenon observed in practice. Further, we argue that this phenomenon is a consequence of robust classifiers learning fundamentally different feature representations than standard classifiers. These differences, in particular, seem to result in unexpected benefits: the representations learned by robust models tend to align better with salient data characteristics and human perception.


title: Gastroenterology Ontology Construction Using Synonym Identification and Relation Extraction

abstract: Ontology plays an increasingly important role in knowledge management and the semantic Web. However, ontology cannot perform well in realistic diagnosis reasoning unless it contains timely and accurate medical information and its individual items display all attributes of the categories they belong to. In this paper, we present a method that extracts synonyms along with concepts and their relationships for gastroenterology ontology construction. Specifically, we reuse the existing ontology as the basis for ontology completion. In addition, we conduct synonym identification through a combined application of global context features, local context features, and medical-specific features, and incorporate dependency information into deep neural networks for relation extraction. The extracted information is merged for ontology completion. Experimental results demonstrate that the proposed synonym identification and relation extraction method achieves the best performance compared with state-of-the-art methods and also builds a more complete ontology compared with existing gastroenterology disease ontologies. Our results are reproducible, and we will release the source code and ontology of this work after publication: https://github.com/shenyingpku/gastrointestinal_owl


The TANH Transformation


title: Understanding modern device drivers

abstract: Device drivers are the single largest contributor to operating-system kernel code with over 5 million lines of code in the Linux kernel, and cause significant complexity, bugs and development costs. Recent years have seen a flurry of research aimed at improving the reliability and simplifying the development of drivers. However, little is known about what constitutes this huge body of code beyond the small set of drivers used for research. In this paper, we study the source code of Linux drivers to understand what drivers actually do, how current research applies to them and what opportunities exist for future research. We determine whether assumptions made by most driver research, such as that all drivers belong to a class, are indeed true. We also analyze driver code and abstractions to determine whether drivers can benefit from code re-organization or hardware trends. We develop a set of static-analysis tools to analyze driver code across various axes. Broadly, our study looks at three aspects of driver code (i) what are the characteristics of driver code functionality and how applicable is driver research to all drivers, (ii) how do drivers interact with the kernel, devices, and buses, and (iii) are there similarities that can be abstracted into libraries to reduce driver size and complexity? We find that many assumptions made by driver research do not apply to all drivers. At least 44% of drivers have code that is not captured by a class definition, 28% of drivers support more than one device per driver, and 15% of drivers do significant computation over data. From the driver interactions study, we find USB bus offers an efficient bus interface with significant standardized code and coarse-grained access, ideal for executing drivers in isolation. We also find that drivers for different buses and classes have widely varying levels of device interaction, which indicates that the cost of isolation will vary by class. Finally, from our driver similarity study, we find 8% of all driver code is substantially similar to code elsewhere and may be removed with new abstractions or libraries.


title: Design and Implementation of an Ontology-Based Clinical Reminder System to Support Chronic Disease Healthcare

abstract: Improving quality of healthcare for people with chronic conditions requires informed and knowledgeable healthcare providers and patients. Decision support and clinical information system are two of the main components to support improving chronic care. In this paper, we describe an ontology-based information and knowledge management framework that is important for chronic disease care management. Ontology-based knowledge acquisition and modeling based on knowledge engineering approach provides an effective mechanism in capturing expert opinion in form of clinical practice guidelines. The framework focuses on building of healthcare ontology and clinical reminder system that link clinical guideline knowledge with patient registries to support evidenced-based healthcare. We describe implementation and approaches in integrating clinical reminder services to existing healthcare provider environment by focusing on augmenting decision making and improving quality of patient care services.


title: Towards a Reduced-Wire Interface for CMUT-Based Intravascular Ultrasound Imaging Systems

abstract: Having intravascular ultrasound (IVUS) imaging capability on guide wires used in cardiovascular interventions may eliminate the need for separate IVUS catheters and expand the use of IVUS in a larger portion of the vasculature. High frequency capacitive micro machined ultrasonic transducer (CMUT) arrays should be integrated with interface electronics and placed on the guide wire for this purpose. Besides small size, this system-on-a-chip (SoC) front-end should connect to the back-end imaging system with a minimum number of wires to preserve the critical mechanical properties of the guide wire. We present a 40 MHz CMUT array interface SoC, which will eventually use only two wires for power delivery and transmits image data using a combination of analog-to-time conversion (ATC) and an impulse radio ultra-wideband (IR-UWB) wireless link. The proof-of-concept prototype ASIC consumes only 52.8 mW and occupies 4.07 $\text{mm}^{2}$ in a 0.35- $\mu\text{m}$ standard CMOS process. A rectifier and regulator power the rest of the SoC at 3.3 V from a 10 MHz power carrier that is supplied through a 2.4 m micro-coax cable with an overall efficiency of 49.1%. Echo signals from an 8-element CMUT array are amplified by a transimpedance amplifier (TIA) array and down-converted to baseband by quadrature sampling using a 40 MHz clock, derived from the power carrier. The ATC generates pulse-width-modulated (PWM) samples at 2 $\times$ 10 MS/s with 6 bit resolution, while the entire system achieved 5.1 ENOB. Preliminary images from the prototype system are presented, and alternative data transmission and possible future directions towards practical implementation are discussed.


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title: Text mining and ontologies in biomedicine: making sense of raw text

abstract: The volume of biomedical literature is increasing at such a rate that it is becoming difficult to locate, retrieve and manage the reported information without text mining, which aims to automatically distill information, extract facts, discover implicit links and generate hypotheses relevant to user needs. Ontologies, as conceptual models, provide the necessary framework for semantic representation of textual information. The principal link between text and an ontology is terminology, which maps terms to domain-specific concepts. This paper summarises different approaches in which ontologies have been used for text-mining applications in biomedicine.


title: A hybrid systems model to simulate cyber interdependencies between critical infrastructures

abstract: Critical infrastructures (CI) depend on communications and information technology infrastructure (CITI) for many of their key functionalities, which is known as cyber interdependency. In this paper, we presented a cyber interdependency simulation technique that is based on the idea that overall performance of a CI network is determined by the behaviour of its components at multiple time scales. Accordingly, for cyber interdependency simulation, we decomposed CI network into two time scales; regular CI events are captured in large time-steps and data communication network events are captured in smaller time-steps. Two different simulators are used for each of these partitions and results from both simulations are synchronised appropriately. A real life example is presented to demonstrate the accuracy and usefulness of our approach. The hybrid simulation model gives significant flexibility to plug-in domain specific models into the critical infrastructure simulator for accurate and efficient solution.


title: D-Bees: A Novel Method Inspired by Bee Colony Optimization for Solving Word Sense Disambiguation

abstract: Abstract Word sense disambiguation is an early problem in the field of computational linguistics, and is defined as identifying the sense (or senses) that most likely represents a word, or a sequence of words in a given context. Word sense disambiguation was recently addressed as a combinatorial optimization problem in which the goal is to find a sequence of senses that maximizes the semantic relatedness among the target words. In this paper, we propose a novel algorithm for solving the word sense disambiguation problem, namely D-Bees, that is inspired by the bee colony optimization meta-heuristic in which several artificial bee agents collaborate to solve the problem. The D-Bees algorithm is evaluated on a standard SemEval 2007 task 7 coarse-grained English all-words corpus and is compared to the genetic and simulated annealing algorithms as well as an ant colony algorithm. It will follow that the bee and ant colony optimization approaches perform on par achieving better results than the genetic and simulated annealing algorithms on the given dataset.


title: Abnormalities in emotion processing within cortical and subcortical regions in criminal psychopaths: evidence from a functional magnetic resonance imaging study using pictures with emotional content

abstract: Abstract Background Neurobiology of psychopathy is important for our understanding of current neuropsychiatric questions. Despite a growing interest in biological research in psychopathy, its neural underpinning remains obscure. Methods We used functional magnetic resonance imaging to study the influence of affective contents on brain activation in psychopaths. Series containing positive and negative pictures from the International Affective Picture System were shown to six male psychopaths and six male control subjects while 100 whole-brain echo-planar-imaging measurements were acquired. Differences in brain activation were evaluated using BrainVoyager software 4.6. Results In psychopaths, increased activation through negative contents was found right-sided in prefrontal regions and amygdala. Activation was reduced right-sided in the subgenual cingulate and the temporal gyrus, and left-sided in the dorsal cingulate and the parahippocampal gyrus. Increased activation through positive contents was found left-sided in the orbitofrontal regions. Activation was reduced in right medial frontal and medial temporal regions. Conclusions These findings underline the hypotheses that psychopathy is neurobiologically reflected by dysregulation and disturbed functional connectivity of emotion-related brain regions. These findings may be interpreted within a framework including prefrontal regions that provide top-down control to and regulate bottom-up signals from limbic areas. Because of the small sample size, the results of this study have to be regarded as preliminary.


title: 3D path following control for UAVs using L1 adaptive method

abstract: This paper considers the 3D path following problem of an Unmanned Aerial Vehicle (UAV), the objective is to steer the UAV along a given reference path, while the desired transient and steady performance is satisfied. To solve the problem, we make use of the typical multi-loop control structure in which the guidance loop controller directs the aircraft to the desired path, while the inner loop controller stabilizes the nonlinear dynamics and tracks the command generated by outer loop. By introducing a virtual target, the proposed Lyapunov based kinematic controller for outer loop can make the UAV asymptotically approach the defined path with smooth movements. Moreover, the developed inner loop controller is based on L1 adaptive control method, which can provide guaranteed transient performance and guaranteed robustness in the presence of fast adaptation, without introducing or enforcing persistence of excitation, without any gain scheduling in the controller parameters, and without resorting to high-gain feedback. Finally, the controllers are validated by simulation.


title: Automatic Facial Expression Recognition System Using Deep Network-Based Data Fusion

abstract: This paper presents a novel automatic facial expressions recognition system (AFERS) using the deep network framework. The proposed AFERS consists of four steps: 1) geometric features extraction; 2) regional local binary pattern (LBP) features extraction; 3) fusion of both the features using autoencoders; and 4) classification using Kohonen self-organizing map (SOM)-based classifier. This paper makes three distinct contributions. The proposed deep network consisting of autoencoders and the SOM-based classifier is computationally more efficient and performance wise more accurate. The fusion of geometric features with LBP features using autoencoders provides better representation of facial expression. The SOM-based classifier proposed in this paper has been improved by making use of a soft-threshold logic and a better learning algorithm. The performance of the proposed approach is validated on two widely used databases (DBs): 1) MMI and 2) extended Cohn-Kanade (CK+). An average recognition accuracy of 97.55% in MMI DB and 98.95% in CK+ DB are obtained using the proposed algorithm. The recognition results obtained from fused features are found to be distinctly superior to both recognition using individual features as well as recognition with a direct concatenation of the individual feature vectors. Simulation results validate that the proposed AFERS is more efficient as compared to the existing approaches.


title: Multimode crystal oscillator for power management unit with digitally controlled AGC loop in 0.18μm CMOS technology

abstract: In this paper, a novel multimode crystal oscillator approach is proposed for a Power Management Unit (PMU). It consists of a digitally controlled Automatic Gain Control (AGC) loop allowing multiple power modes to meet the system's low phase noise and power consumption requirements. A new technique with a programmable switched capacitor array controlled by a counter is presented preserving the integrity of the clock during the mode transition. A 32.768 kHz crystal oscillator using the proposed idea was fabricated in a 0.18 μm CMOS technology. Measurement results show that it achieves phase noise of −120 dBc/Hz at 1 kHz offset in normal operating mode and only consumes 860nA current off 3.3V backup battery in RTC-only (low-power) mode including all the digital supporting circuitry. It also shows the seamless transition between the operating modes.


title: Overview of narrowband IoT in LTE Rel-13

abstract: In 3GPP Rel-13, a narrowband system, named Narrowband Internet of Things (NB-IoT), has been introduced to provide low-cost, low-power, wide-area cellular connectivity for the Internet of Things. This system, based on Long Term Evolution (LTE) technology, supports most LTE functionalities albeit with essential simplifications to reduce device complexity. Further optimizations to increase coverage, reduce overhead and reduce power consumption while increasing capacity have been introduced as well. The design objectives of NB-IoT include low-complexity devices, high coverage, long device battery life, and massive capacity. Latency is relaxed although a delay budget of 10 seconds is the target for exception reports. This paper provides an overview of NB-IoT design, including salient features from the physical and higher layers. Illustrative results with respect to performance objectives are also provided. Finally, NB-IoT enhancements in LTE Rel-14 are briefly outlined.


title: Dual- and Multiband U-Slot Patch Antennas

abstract: A wide band patch antenna fed by an L-probe can be designed for dual- and multi-band application by cutting U-slots on the patch. Simulation and measurement results are presented to illustrate this design.