title: RNN Approaches to Text Normalization: A Challenge

abstract: This paper presents a challenge to the community: given a large corpus of written text aligned to its normalized spoken form, train an RNN to learn the correct normalization function. We present a data set of general text where the normalizations were generated using an existing text normalization component of a text-to-speech system. This data set will be released open-source in the near future. ::: We also present our own experiments with this data set with a variety of different RNN architectures. While some of the architectures do in fact produce very good results when measured in terms of overall accuracy, the errors that are produced are problematic, since they would convey completely the wrong message if such a system were deployed in a speech application. On the other hand, we show that a simple FST-based filter can mitigate those errors, and achieve a level of accuracy not achievable by the RNN alone. ::: Though our conclusions are largely negative on this point, we are actually not arguing that the text normalization problem is intractable using an pure RNN approach, merely that it is not going to be something that can be solved merely by having huge amounts of annotated text data and feeding that to a general RNN model. And when we open-source our data, we will be providing a novel data set for sequence-to-sequence modeling in the hopes that the the community can find better solutions. ::: The data used in this work have been released and are available at: this https URL


title: Large-Scale Automatic Species Identification

abstract: The crowd-sourced Naturewatch GBIF dataset is used to obtain a species classification dataset containing approximately 1.2 million photos of nearly 20 thousand different species of biological organisms observed in their natural habitat. We present a general hierarchical species identification system based on deep convolutional neural networks trained on the NatureWatch dataset. The dataset contains images taken under a wide variety of conditions and is heavily imbalanced, with most species associated with only few images. We apply multi-view classification as a way to lend more influence to high frequency details, hierarchical fine-tuning to help with class imbalance and provide regularisation, and automatic specificity control for optimising classification depth. Our system achieves 55.8% accuracy when identifying individual species and around 90% accuracy at an average taxonomy depth of 5.1—equivalent to the taxonomic rank of “family”—when applying automatic specificity control.


title: An audio-based real-time beat tracking system for music with or without drum-sounds

abstract: This paper describes a real-time beat tracking system that recognizes a hierarchical beat structure comprising the quarter-note, half-note, and measure levels in real-world audio signals sampled from popular-music compact discs. Most previous beat-tracking systems dealt with MIDI signals and had difficulty in processing, in real time, audio signals containing sounds of various instruments and in tracking beats above the quarter-note level. The system described here can process music with drums and music without drums and can recognize the hierarchical beat structure by using three kinds of musical knowledge: of onset times, of chord changes, and of drum patterns. This paper also describes several applications of beat tracking, such as beat-driven real-time computer graphics and lighting control.


title: Predicting purchase behaviors from social media

abstract: In the era of social commerce, users often connect from e-commerce websites to social networking venues such as Facebook and Twitter. However, there have been few efforts on understanding the correlations between users' social media profiles and their e-commerce behaviors. This paper presents a system for predicting a user's purchase behaviors on e-commerce websites from the user's social media profile. We specifically aim at understanding if the user's profile information in a social network (for example Facebook) can be leveraged to predict what categories of products the user will buy from (for example eBay Electronics). The paper provides an extensive analysis on how users' Facebook profile information correlates to purchases on eBay, and analyzes the performance of different feature sets and learning algorithms on the task of purchase behavior prediction.


title: Gender nonconforming youth: current perspectives

abstract: Beginning with a case vignette, a discussion follows of the reformulation of theories of gender development taking into consideration the recent upsurge of gender nonconforming and transgender youth presenting for gender services and also in the culture at large. The three predominant models of pediatric gender care are reviewed and critiqued, along with a presentation of the recently developed interdisciplinary model of gender care optimal in the treatment of gender nonconforming youth seeking either puberty blockers or cross-sex hormones.


title: Why Deep Learning Works: A Manifold Disentanglement Perspective

abstract: Deep hierarchical representations of the data have been found out to provide better informative features for several machine learning applications. In addition, multilayer neural networks surprisingly tend to achieve better performance when they are subject to an unsupervised pretraining. The booming of deep learning motivates researchers to identify the factors that contribute to its success. One possible reason identified is the flattening of manifold-shaped data in higher layers of neural networks. However, it is not clear how to measure the flattening of such manifold-shaped data and what amount of flattening a deep neural network can achieve. For the first time, this paper provides quantitative evidence to validate the flattening hypothesis. To achieve this, we propose a few quantities for measuring manifold entanglement under certain assumptions and conduct experiments with both synthetic and real-world data. Our experimental results validate the proposition and lead to new insights on deep learning.


title: Neuroaesthetics and beyond: new horizons in applying the science of the brain to the art of dance

abstract: Throughout history, dance has maintained a critical presence across all human cultures, defying barriers of class, race, and status. How dance has synergistically co-evolved with humans has fueled a rich debate on the function of art and the essence of aesthetic experience, engaging numerous artists, historians, philosophers, and scientists. While dance shares many features with other art forms, one attribute unique to dance is that it is most commonly expressed with the human body. Because of this, social scientists and neuroscientists are turning to dance and dancers to help answer questions of how the brain coordinates the body to perform complex, precise, and beautiful movements. In the present paper, we discuss how recent advances in neuroscientific methods provide the tools to advance our understanding of not only the cerebral phenomena associated with dance learning and observation but also the neural underpinnings of aesthetic appreciation associated with watching dance. We suggest that future work within the fields of dance neuroscience and neuroaesthetics have the potential to provide mutual benefits to both the scientific and artistic communities.


title: Trustworthy Sensing in an Untrusted IoT Environment

abstract: Contemporary IoT environments, such as smart buildings, require end-users to trust data collection policies published by the systems. There are several reason why such a trust is misplaced - IoT systems may not be honest, may inadvertently collect data without user’s knowledge, or may fall victim to cyberattacks that hijack IoT devices transferring user data to a malicious third-party leading to the loss of individual’s privacy. To address such concerns, we are developing IoTtrust, a framework to ensure trust and confidence in IoT systems (and applications). textbfIoTtrust includes a simple yet powerful policy language, contracts between subjects and IoT space, and mechanisms to empower each party to undeniably attest the contract validity.


title: Semantic Image Retrieval by Uniting Deep Neural Networks and Cognitive Architectures

abstract: Image and video retrieval by their semantic content has been an important and challenging task for years, because it ultimately requires bridging the symbolic/subsymbolic gap. Recent successes in deep learning enabled detection of objects belonging to many classes greatly outperforming traditional computer vision techniques. However, deep learning solutions capable of executing retrieval queries are still not available. We propose a hybrid solution consisting of a deep neural network for object detection and a cognitive architecture for query execution. Specifically, we use YOLOv2 and OpenCog. Queries allowing the retrieval of video frames containing objects of specified classes and specified spatial arrangement are implemented.


title: Gradient-based inverse risk-sensitive reinforcement learning

abstract: We address the problem of inverse reinforcement learning in Markov decision processes where the agent is risksensitive. In particular, we model risk-sensitivity in a reinforcement learning framework by making use of models of human decision-making having their origins in behavioral psychology and economics. We propose a gradient-based inverse reinforcement learning algorithm that minimizes a loss function defined on the observed behavior. We demonstrate the performance of the proposed technique on two examples, the first of which is the canonical Grid World example and the second of which is an MDP modeling passengers' decisions regarding ride-sharing. In the latter, we use pricing and travel time data from a ride-sharing company to construct the transition probabilities and rewards of the MDP.


title: Vehicle tracking using extended object methods: An approach for fusing radar and laser

abstract: Combining data from heterogeneous sensors allows to enhance tracking systems by increasing the field of view, incorporating redundancy, and improving the performance by exploiting complementary sensor characteristics. This paper proposes a new vehicle tracking approach for vehicle environment perception that fuses radar and laser data. A Random-Finite-Set-based tracking filter, which permits a clear mathematical formulation of the multi-object problem, is used as fusion center. In combination with extended object measurement models that work on the raw sensor data directly, the filter uses all available information without the need for further preprocessing routines, considers object interdependencies, and works in ambiguous situations. The results are evaluated using experimental data from a test vehicle.


title: All-digital PLL and GSM/EDGE transmitter in 90nm CMOS

abstract: A 1.2V 42mA all-digital PLL and polar transmitter for a single-chip GSM/EDGE transceiver is implemented in 90nm CMOS. It transmits GMSK with 0.5/spl deg/ rms phase error and achieves -165dBc/Hz phase noise at 20MHz offset, with 10 /spl mu/s settling time. A digitally controlled 6dBm class-E PA modulates the amplitude and meets the EDGE spectral mask with 3.5% EVM.


title: Cloud-based malware detection for evolving data streams

abstract: Data stream classification for intrusion detection poses at least three major challenges. First, these data streams are typically infinite-length, making traditional multipass learning algorithms inapplicable. Second, they exhibit significant concept-drift as attackers react and adapt to defenses. Third, for data streams that do not have any fixed feature set, such as text streams, an additional feature extraction and selection task must be performed. If the number of candidate features is too large, then traditional feature extraction techniques fail. In order to address the first two challenges, this article proposes a multipartition, multichunk ensemble classifier in which a collection of v classifiers is trained from r consecutive data chunks using v-fold partitioning of the data, yielding an ensemble of such classifiers. This multipartition, multichunk ensemble technique significantly reduces classification error compared to existing single-partition, single-chunk ensemble approaches, wherein a single data chunk is used to train each classifier. To address the third challenge, a feature extraction and selection technique is proposed for data streams that do not have any fixed feature set. The technique's scalability is demonstrated through an implementation for the Hadoop MapReduce cloud computing architecture. Both theoretical and empirical evidence demonstrate its effectiveness over other state-of-the-art stream classification techniques on synthetic data, real botnet traffic, and malicious executables.


title: Abstract Rethinking interference theory: Executive control and the mechanisms of forgetting

abstract: Interference provides an account of one of the most basic problems in the science of memory: forgetting. Historically, theories of this process were shaped by models of associative learning prevalent when interference research began. In this article, I argue that we should reconsider the long-standing conceptualization of interference as a learning phenomenon and reframe interference as arising from systems that achieve mental and behavioral control. Specifically, it is argued that forgetting is not a passive side effect of storing new memories, but results from inhibitory control mechanisms recruited to override prepotent responses. In support of this idea, I discuss two control situations in which response override is necessary—selection and stopping—and show how these situations have direct parallels in retrieval. I then review evidence that in both of these situations, the need to override prepotent, distracting memories is supported by inhibitory mechanisms that ultimately cause forgetting. The theoretical properties of these inhibitory effects are outlined, along with critical factors known to modulate or mask inhibition. The relation between this executive control theory of forgetting and classical accounts of interference is discussed.


title: Calibrating Noise to Sensitivity in Private Data Analysis

abstract: We continue a line of research initiated in [10, 11] on privacy-preserving statistical databases. Consider a trusted server that holds a database of sensitive information. Given a query function f mapping databases to reals, the so-called true answer is the result of applying f to the database. To protect privacy, the true answer is perturbed by the addition of random noise generated according to a carefully chosen distribution, and this response, the true answer plus noise, is returned to the user. Previous work focused on the case of noisy sums, in which f = Σ i g(x i ), where x i denotes the ith row of the database and g maps database rows to [0,1]. We extend the study to general functions f, proving that privacy can be preserved by calibrating the standard deviation of the noise according to the sensitivity of the function f. Roughly speaking, this is the amount that any single argument to f can change its output. The new analysis shows that for several particular applications substantially less noise is needed than was previously understood to be the case. The first step is a very clean characterization of privacy in terms of indistinguishability of transcripts. Additionally, we obtain separation results showing the increased value of interactive sanitization mechanisms over non-interactive.


title: A Malicious Attack on the Machine Learning Policy of a Robotic System

abstract: The field of robotics has matured using artificial intelligence and machine learning such that intelligent robots are being developed in the form of autonomous vehicles. The anticipated widespread use of intelligent robots and their potential to do harm has raised interest in their security. This research evaluates a cyberattack on the machine learning policy of an autonomous vehicle by designing and attacking a robotic vehicle operating in a dynamic environment. The primary contribution of this research is an initial assessment of effective manipulation through an indirect attack on a robotic vehicle using the Q learning algorithm for real-time routing control. Secondly, the research highlights the effectiveness of this attack along with relevant artifact issues.


title: Machine Learning-Assisted Optical Performance Monitoring in Fiber-Optic Networks

abstract: We review machine learning (ML)-based optical performance monitoring (OPM) techniques in optical communications. Recent applications of ML-assisted OPM in different aspects of fiber-optic networking including cognitive fault detection and management, network equipment failure prediction, and dynamic planning and optimization of software-defined networks are also discussed.


title: Network Traffic Fingerprinting Based on Approximated Kernel Two-Sample Test

abstract: Many applications and communication protocols exhibit unique communication patterns that can be exploited to identify them in network traffic. This paper proposes a method to represent these patterns compactly, such that they can be used in different analytical tasks. The method treats each communication as a set of observations of a random variable with unknown probability distribution. This view allows us to derive the representation from a distance between two probability distributions used in maximum mean discrepancy—a non-parametric kernel test. The representation (and distance) can be then easily used in various algorithms for identification of communicating application and data analysis, independently of the specific type of input data.


title: Blood–brain barrier breakdown as a therapeutic target in traumatic brain injury

abstract: Traumatic brain injury (TBI) is the leading cause of death in young adults and children. The treatment of TBI in the acute phase has improved substantially; however, the prevention and management of long-term complications remain a challenge. Blood-brain barrier (BBB) breakdown has often been documented in patients with TBI, but the role of such vascular pathology in neurological dysfunction has only recently been explored. Animal studies have demonstrated that BBB breakdown is involved in the initiation of transcriptional changes in the neurovascular network that ultimately lead to delayed neuronal dysfunction and degeneration. Brain imaging data have confirmed the high incidence of BBB breakdown in patients with TBI and suggest that such pathology could be used as a biomarker in the clinic and in drug trials. Here, we review the neurological consequences of TBI, focusing on the long-term complications of such injuries. We present the clinical evidence for involvement of BBB breakdown in TBI and examine the primary and secondary mechanisms that underlie such pathology. We go on to consider the consequences of BBB injury, before analyzing potential mechanisms linking vascular pathology to neuronal dysfunction and degeneration, and exploring possible targets for treatment. Finally, we highlight areas for future basic research and clinical studies into TBI.


title: A state-of-the-art survey on vehicular mechatronics focusing on by-wire systems.

abstract: This report is the result of a survey of the current state of the art/ practice in vehicular mechatronics. It summarizes a large quantity of scientific papers and theses, as well as white papers an ...


title: Adaptive Multi-Resource Allocation for Cloudlet-Based Mobile Cloud Computing System

abstract: Mobile cloud computing utilizing cloudlet is an emerging technology to improve the quality of mobile services. In this paper, to better overcome the main bottlenecks of the computation capability of cloudlet and the wireless bandwidth between mobile devices and cloudlet, we consider the multi-resource allocation problem for the cloudlet environment with resource-intensive and latency-sensitive mobile applications. The proposed multi-resource allocation strategy enhances the quality of mobile cloud service, in terms of the system throughput (the number of admitted mobile applications) and the service latency. We formulate the resource allocation model as a semi-Markov decision process under the average cost criterion, and solve the optimization problem using linear programming technology. Through maximizing the long-term reward while meeting the system requirements of the request blocking probability and service time latency, an optimal resource allocation policy is calculated. From simulation result, it is indicated that the system adaptively adjusts the allocation policy about how much resource to allocate and whether to utilize the distant cloud according to the traffic of mobile service requests and the availability of the resource in the system. Our algorithm outperforms greedy admission control over a broad range of environments.


title: Batch Mode Active Learning for Regression With Expected Model Change

abstract: While active learning (AL) has been widely studied for classification problems, limited efforts have been done on AL for regression. In this paper, we introduce a new AL framework for regression, expected model change maximization (EMCM), which aims at choosing the unlabeled data instances that result in the maximum change of the current model once labeled. The model change is quantified as the difference between the current model parameters and the updated parameters after the inclusion of the newly selected examples. In light of the stochastic gradient descent learning rule, we approximate the change as the gradient of the loss function with respect to each single candidate instance. Under the EMCM framework, we propose novel AL algorithms for the linear and nonlinear regression models. In addition, by simulating the behavior of the sequential AL policy when applied for $k$ iterations, we further extend the algorithms to batch mode AL to simultaneously choose a set of $k$ most informative instances at each query time. Extensive experimental results on both UCI and StatLib benchmark data sets have demonstrated that the proposed algorithms are highly effective and efficient.


title: A Comparison of Internet Protocol (IPv6) Security Guidelines

abstract: The next generation of the Internet Protocol (IPv6) is currently about to be introduced in many organizations. However, its security features are still a very novel area of expertise for many practitioners. This study evaluates guidelines for secure deployment of IPv6, published by the U.S. NIST and the German federal agency BSI, for topicality, completeness and depth. The later two are scores defined in this paper and are based on the Requests for Comments relevant for IPv6 that were categorized, weighted and ranked for importance using an expert survey. Both guides turn out to be of practical value, but have a specific focus and are directed towards different audiences. Moreover, recommendations for possible improvements are presented. Our results could also support strategic management decisions on security priorities as well as for the choice of security guidelines for IPv6 roll-outs.


title: Analysis of Torque Capability and Quality in Vernier Permanent-Magnet Machines

abstract: Vernier permanent-magnet (VPM) machines have been obtaining a lot of attention over the past few years due to several advantages, such as their high torque density and simple mechanical structures. Moreover, it is found that the torque ripple of VPM machines is ultralow, even without specific design measures such as a short pitch, skewing slots/poles, magnet shaping technology, etc. This paper presents theoretical analysis and comprehensive simulations on the torque ripple of VPM machines. First, a general instantaneous torque equation of VPM machines is proposed to analyze torque features and the effect of parameters on the torque performance of VPM machines. Subsequently, based on the general torque equation and a finite-element algorithm, it is verified that torque smoothness is the inherent characteristic of VPM machines, and the torque ripple of VPM machines can be below 0.2%. Furthermore, it is demonstrated that the torque density of a VPM machine is 40% larger than that of a regular permanent-magnet machine. All these advantages demonstrate that VPM machines can obtain much better steady and dynamic drive performance. Finally, all the theoretical analyses are verified by experiments on a VPM prototype.


title: Extended-enterprise systems' impact on enterprise risk management

abstract: Purpose – This article aims to focus on raising awareness of the limitations of traditional “enterprise‐centric” views of enterprise risk management that ignore the risks that are inherited from key business and supply chain partners. In essence, enterprise systems implementations have allowed organizations to couple their operations more tightly with other business partners, particularly in the area of supply chain management, and in the process enterprise systems applications are redefining the boundaries of the entity in terms of risk management concerns and the scope of financial audits. Design/methodology/approach – The prior literature that has begun to explore aspects of assessing key risk components in these relationships is reviewed with an eye to highlighting the limitations of what is understood about risk in interorganizational relationships. This analysis of the prior research establishes the basis for the logical formation of a framework for future enterprise risk management research in the area of e‐commerce relationships. Findings – Conclusions focus on the overall framework of risks that should be considered when interorganizational relationships are critical to an enterprise's operations and advocate an “extended‐enterprise” view of enterprise risk management. Research limitations/implications – The framework introduced in this paper provides guidance for future research in the area of interorganizational systems control and risk assessment. Practical implications – The framework further highlights areas of risk that auditors and corporate risk managers should consider in assessing the risk inherited through interorganizational relationships. Originality/value – The paper highlights the need to shift from an enterprise‐centric view of risk management to an extended‐enterprise risk management view.


title: Driver Fatigue Detection: A Survey

abstract: Driver fatigue is an important factor in a large number of accidents. There has been much work done in driver fatigue detection. This paper presents a comprehensive survey of research on driver fatigue detection and provides structural categories for the methods which have been proposed. The methods of fatigue detection mainly focused on measures of the driver's state, driver performance and the combination of the driver's state and performance. The measures of driver's state included PERCLOS, mouth shape and head position; the measures of driver performance included lane tracking and tracking of distance between vehicles. These approaches are presented and discussed in detail. Some typical driver monitoring systems are also introduced in this paper. Finally, summary and conclusions are presented.


title: Electronic version of the third volume of the General Catalogue of Variable Stars with improved coordinates

abstract: We present a new electronic version of the second volume of the fourth edition of the General Catalogue of Variable Stars (GCVS), which contains data on 13480 variable stars in the constellations Cygnus-Orion (the order of constellations in the Catalogue follows the Latin alphabet). The new version takes into account the Name Lists of Variable Stars from no. 67 to 76 for the same constellations. The main distinctive feature of the new version is that it contains improved equatorial J2000.0 coordinates for 13446 stars (including those for 5052 stars with an allowance made for proper motions), based on the identifications with positional catalogs using finding charts, as well as on our new measurements. We searched for a number of stars on original plates from the collections of several observatories and using digital sky survey images. The new version also includes a file of remarks to the second and third GCVS volumes. Apart from a complete update of the positional information, we took into account several corrections that were found to be necessary after the publication of the second GCVS volume (1985). We present a list of references to new Internet resources.


title: Associative Array Model of SQL, NoSQL, and NewSQL Databases

abstract: The success of SQL, NoSQL, and NewSQL databases is a reflection of their ability to provide significant functionality and performance benefits for specific domains, such as financial transactions, internet search, and data analysis. The BigDAWG polystore seeks to provide a mechanism to allow applications to transparently achieve the benefits of diverse databases while insulating applications from the details of these databases. Associative arrays provide a common approach to the mathematics found in different databases: sets (SQL), graphs (NoSQL), and matrices (NewSQL). This work presents the SQL relational model in terms of associative arrays and identifies the key mathematical properties that are preserved within SQL. These properties include associativity, commutativity, distributivity, identities, annihilators, and inverses. Performance measurements on distributivity and associativity show the impact these properties can have on associative array operations. These results demonstrate that associative arrays could provide a mathematical model for polystores to optimize the exchange of data and execution queries.


title: The surface of vulnerability: An analytical framework for examining environmental change

abstract: Abstract This paper introduces an analytical framework for evaluating the vulnerability of people and places to environmental and social forces. The framework represents the relative vulnerability of a variable of concern (e.g. such as agricultural yield) to a set of disturbing forces (e.g. climate change, market fluctuations) by a position on a three-dimensional analytical surface, where vulnerability is defined as a function of sensitivity, exposure, and the state relative to a threshold of damage. The surface is presented as a tool to help identify relative vulnerability in order to prioritize actions and assess the vulnerability implications of management and policy decisions.


title: Symmetry-invariant optimization in deep networks

abstract: Recent works have highlighted scale invariance or symmetry that is present in the weight space of a typical deep network and the adverse effect that it has on the Euclidean gradient based stochastic gradient descent optimization. In this work, we show that these and other commonly used deep networks, such as those which use a max-pooling and sub-sampling layer, possess more complex forms of symmetry arising from scaling based reparameterization of the network weights. We then propose two symmetry-invariant gradient based weight updates for stochastic gradient descent based learning. Our empirical evidence based on the MNIST dataset shows that these updates improve the test performance without sacrificing the computational efficiency of the weight updates. We also show the results of training with one of the proposed weight updates on an image segmentation problem.


ULCL - An Ultra-lightweight Cryptographic Library for Embedded Systems


title: Tiny-DSOD: Lightweight Object Detection for Resource-Restricted Usages

abstract: Object detection has made great progress in the past few years along with the development of deep learning. However, most current object detection methods are resource hungry, which hinders their wide deployment to many resource restricted usages such as usages on always-on devices, battery-powered low-end devices, etc. This paper considers the resource and accuracy trade-off for resource-restricted usages during designing the whole object detection framework. Based on the deeply supervised object detection (DSOD) framework, we propose Tiny-DSOD dedicating to resource-restricted usages. Tiny-DSOD introduces two innovative and ultra-efficient architecture blocks: depthwise dense block (DDB) based backbone and depthwise feature-pyramid-network (D-FPN) based front-end. We conduct extensive experiments on three famous benchmarks (PASCAL VOC 2007, KITTI, and COCO), and compare Tiny-DSOD to the state-of-the-art ultra-efficient object detection solutions such as Tiny-YOLO, MobileNet-SSD (v1 & v2), SqueezeDet, Pelee, etc. Results show that Tiny-DSOD outperforms these solutions in all the three metrics (parameter-size, FLOPs, accuracy) in each comparison. For instance, Tiny-DSOD achieves 72.1% mAP with only 0.95M parameters and 1.06B FLOPs, which is by far the state-of-the-arts result with such a low resource requirement.


title: Named Entity Recognition on Code-Switched Data: Overview of the CALCS 2018 Shared Task

abstract: In the third shared task of the Computational Approaches to Linguistic Code-Switching (CALCS) workshop, we focus on Named Entity Recognition (NER) on code-switched social-media data. We divide the shared task into two competitions based on the English-Spanish (ENG-SPA) and Modern Standard Arabic-Egyptian (MSA-EGY) language pairs. We use Twitter data and 9 entity types to establish a new dataset for code-switched NER benchmarks. In addition to the CS phenomenon, the diversity of the entities and the social media challenges make the task considerably hard to process. As a result, the best scores of the competitions are 63.76% and 71.61% for ENG-SPA and MSA-EGY, respectively. We present the scores of 9 participants and discuss the most common challenges among submissions.


The Rural Exodus; The Problem of the Village and the Town


title: Voice Helper: A Mobile Assistive System for Visually Impaired Persons

abstract: In this information society, visually impaired persons need assistive tools for operating digital devices so that they could get and apply digital information while learning, living and working. The reader, which transforms digital information to text and then to voice by TTS (Text-To-Speech), is widely used to help visually impaired persons to operate devices. This paper focused on designing and implementing an assistive system for visually impaired persons while using Android smart phones. The system, Voice Helper (VH), integrates open sources and also enhances many functions of them. VH includes the message reader, text file reader, OCR reader, voice dialer and visually disabled dialer to facilitate daily activities for visually impaired persons. In addition, it provides the Navigation Reader for walking and riding, which is based on Google Maps and supports more detailed voice guidance for the distance to a destination and directions of moving. The operating environment of VH is integrated and verified by visually impaired persons.


title: Effects of a secure attachment relationship on right brain development, affect regulation, and infant mental health

abstract: Over the last ten years the basic knowledge of brain structure and function has vastly ex- panded, and its incorporation into the developmental sciences is now allowing for more complex and heuristic models of human infancy. In a continuation of this effort, in this two-part work I integrate current interdisciplinary data from attachment studies on dyadic affective communications, neuroscience on the early developing right brain, psychophysiology on stress systems, and psychiatry on psychopath- ogenesis to provide a deeper understanding of the psychoneurobiological mechanisms that underlie infant mental health. In this article I detail the neurobiology of a secure attachment, an exemplar of adaptive infant mental health, and focus upon the primary caregiver's psychobiological regulation of the infant's maturing limbic system, the brain areas specialized for adapting to a rapidly changing environment. The infant's early developing right hemisphere has deep connections into the limbic and autonomic nervous systems and is dominant for the human stress response, and in this manner the attachment relationship facilitates the expansion of the child's coping capcities. This model suggests that adaptive infant mental health can be fundamentally defined as the earliest expression of flexible strategies for coping with the novelty and stress that is inherent in human interactions. This efficient right brain function is a resilience factor for optimal development over the later stages of the life cycle.


title: SMA: A System-Level Mutual Authentication for Protecting Electronic Hardware and Firmware

abstract: Due to the enhanced capability of adversaries, electronic systems are now increasingly vulnerable to counterfeiting and piracy. The majority of counterfeit systems today are of cloned type, which have been on the rise in the recent years. Ensuring the security of such systems is of great concern as an adversary can create a backdoor or insert a malware to bypass security modules. The reliability of such systems could also be questionable as the components used in these systems may be counterfeit and/or of inferior quality. It is of prime importance to develop solutions that can prevent an adversary from creating these non-authentic systems. In this paper, we present a novel system-level mutual authentication approach for both the hardware and firmware. The hardware authenticates the firmware by verifying the checksum during the power-up. On the other hand, firmware verifies the identity of the hardware and cannot produce correct results unless it receives a unique hardware fingerprint, which we call as system ID. We propose two secure protocols, TIDP and TIDS, to construct the system ID and authenticate the system by using this unique ID. We show that our approach is resistant to various known attacks.


title: Hashing based indexing techniques for content based image retrieval: A survey

abstract: Image retrieval has been an active research field and classified as concept-based and content-based image retrieval systems. One of the key challenges is to design indexing technique for fast and accurate retrieval of images. Unsupervised techniques are more practical due to its learning binary codes property. Hashing based methods highly depends on the quality of hash functions and constructing effective hash functions is a challenging task. This paper presents a detailed study of recent hashing based techniques for analysis of the future scope research in hashing based indexing techniques.


title: Responsive and Enforced Interrupt Handling for Real-Time System Virtualization

abstract: The increasing performance of modern processors makes virtualization a viable solution for consolidating real-time systems into a single hardware platform. Although real-time task scheduling in a virtual machine can benefit from hierarchical scheduling, unbounded interrupt handling time and vulnerability to interrupt storms make practitioners hesitant to virtualize interrupt-driven real-time applications. In this paper, we propose vINT, an interrupt handling scheme designed for real-time system virtualization. vINT provides a pseudo-VCPU abstraction dedicated for interrupt handling, which overcomes the limits imposed by the timing parameters of virtual CPUs in an analyzable way. vINT also accounts for and enforces interrupt handling and resulting execution flows within a guest virtual machine. vINT does not require any change to the guest OS code, so it can be used for virtualizing proprietary, closed-source OSs. We analyze interrupt handling time as well as VCPU and task schedulability, with and without vINT. Our experimental results indicate that vINT achieves timely interrupt handling while providing as good task schedulability as when it is not used. Our case study based on a prototype implementation on the KVM hyper visor shows that vINT yields significant benefits in reducing interrupt handling time and in protecting real-time tasks against interrupt storms permeating into the virtual machine.


title: Neural models of memory

abstract: Abstract Neural models assist in characterizing the processes carried out by cortical and hippocampal memory circuits. Recent models of memory have addressed issues including recognition and recall dynamics, sequences of activity as the unit of storage, and consolidation of intermediate-term episodic memory into long-term memory.


title: TIOSA: Testing VM Interoperability at an OS and Application Level -- A Hypervisor Testing Method and Interoperability Survey

abstract: Virtualization is the foundation of modern, cloud-based applications. The existence of virtual machines (VM) that host the components of such applications enables their portability and scalability. VMs are used in cloud infrastructures, and with dynamic operational requirements there is a need to move VMs within and across different clouds. The successful migration of VMs from one cloud to another should not always be considered as given. The goal of this paper is to devise and implement methods and conduct functional tests towards evaluating interoperability in cloud environments. We suggest a methodology for assessing the interoperability across different systems and we conduct a survey with a series of hypervisors and operating systems.


title: Fuzzy cognitive maps for modeling complex systems

abstract: This paper presents Fuzzy Cognitive Maps as an approach in modeling the behavior and operation of complex systems. This technique is the fusion of the advances of the fuzzy logic and cognitive maps theories, they are fuzzy weighted directed graphs with feedback that create models that emulate the behavior of complex decision processes using fuzzy causal relations. There are some applications in diverse domains (manage, multiagent systems, etc.) and novel works (dynamical characteristics, learning procedures, etc.) to improve the performance of these systems. First the description and the methodology that this theory suggests is examined, also some ideas for using this approach in the control process area, and then the implementation of a tool based on Fuzzy Cognitive Maps is described. The application of this theory in the field of control and systems might contribute to the progress of more intelligent and independent control systems. Fuzzy Cognitive Maps have been fruitfully used in decision making and simulation of complex situation and analysis.


Ventricular Volume and Mass by CMR


title: Dynamic thermal management for high-performance microprocessors

abstract: With the increasing clock rate and transistor count of today's microprocessors, power dissipation is becoming a critical component of system design complexity. Thermal and power-delivery issues are becoming especially critical for high-performance computing systems. In this work, we investigate dynamic thermal management as a technique to control CPU power dissipation. With the increasing usage of clock gating techniques, the average power dissipation typically seen by common applications is becoming much less than the chip's rated maximum power dissipation. However system designers still must design thermal heat sinks to withstand the worse-case scenario. We define and investigate the major components of any dynamic thermal management scheme. Specifically we explore the tradeoffs between several mechanisms for responding to periods of thermal trauma and we consider the effects of hardware and software implementations. With approximate dynamic thermal management, the CPU can be designed for a much lower maximum power rating, with minimal performance impact for typical applications.


title: Interferometric synthetic aperture radar (InSAR)—its past, present and future

abstract: Introduction Very simply, interferometric synthetic aperture radar (InSAR) involves the use of two or more synthetic aperture radar (SAR) images of the same area to extract landscape topography and its deformation patterns. A SAR system transmits electromagnetic waves at a wavelength that can range from a few millimeters to tens of centimeters and therefore can operate during day and night under all-weather conditions. Using SAR processing technique (Curlander and McDonough, 1991), both the intensity and phase of the reflected (or backscattered) radar signal of each ground resolution element (a few meters to tens of meters) can be calculated in the form of a complex-valued SAR image that represents the reflectivity of the ground surface. The amplitude or intensity of the SAR image is determined primarily by terrain slope, surface roughness, and dielectric constants, whereas the phase of the SAR image is determined primarily by the distance between the satellite antenna and the ground targets. InSAR imaging utilizes the interaction of electromagnetic waves, referred to as interference, to measure precise distances between the satellite antenna and ground resolution elements to derive landscape topography and its subtle change in elevation.


title: Ontology-based sentiment analysis model of customer reviews for electronic products

abstract: This paper reports on a generalizable system model design that analyzes the unstructured customer reviews inside the posts about electronic products on social networking ::: websites. For the purposes of this study, posts on social ::: networking websites have been mined and the keywords are ::: extracted from such posts. The extracted keywords and the ::: ontologies of electronic products and emotions form the base for the sentiment analysis model which is used to understand online consumer behavior in the market. In order to enhance system accuracy, negating and enhancing terms are considered in the proposed model. Sentiment analysis is demonstrated to be extremely important to system accuracy.


title: Evolution, challenge, and outlook of TSV, 3D IC integration and 3d silicon integration

abstract: 3D integration consists of 3D IC packaging, 3D IC integration, and 3D Si integration. They are different and in general the TSV (through-silicon via) separates 3D IC packaging from 3D IC/Si integrations since the latter two use TSV but 3D IC packaging does not. TSV (with a new concept that every chip or interposer could have two surfaces with circuits) is the heart of 3D IC/Si integrations and is the focus of this investigation. The origin of 3D integration is presented. Also, the evolution, challenges, and outlook of 3D IC/Si integrations are discussed as well as their road maps are presented. Finally, a few generic, low-cost, and thermal-enhanced 3D IC integration system-in-packages (SiPs) with various passive TSV interposers are proposed.


The Roles of Adipokines, Proinflammatory Cytokines, and Adipose Tissue Macrophages in Obesity-Associated Insulin Resistance in Modest Obesity and Early Metabolic Dysfunction


title: From Shortest-Path to All-Path: The Routing Continuum Theory and Its Applications

abstract: As a crucial operation, routing plays an important role in various communication networks. In the context of data and sensor networks, routing strategies such as shortest-path, multi-path and potential-based (“all-path”) routing have been developed. Existing results in the literature show that the shortest path and all-path routing can be obtained from $L_{1}$ and $L_{2}$ flow optimization, respectively. Based on this connection between routing and flow optimization in a network, in this paper we develop a unifying theoretical framework by considering flow optimization with mixed (weighted) $L_{1}/L_{2}$ -norms. We obtain a surprising result: as we vary the trade-off parameter $\theta$ , the routing graphs induced by the optimal flow solutions span from shortest-path to multi-path to all-path routing—this entire sequence of routing graphs is referred to as the routing continuum. We also develop an efficient iterative algorithm for computing the entire routing continuum. Several generalizations are also considered, with applications to traffic engineering, wireless sensor networks, and network robustness analysis.


title: Matchmaker: constructing constrained texture maps

abstract: Texture mapping enhances the visual realism of 3D models by adding fine details. To achieve the best results, it is often necessary to force a correspondence between some of the details of the texture and the features of the model.The most common method for mapping texture onto 3D meshes is to use a planar parameterization of the mesh. This, however, does not reflect any special correspondence between the mesh geometry and the texture. The Matchmaker algorithm presented here forces user-defined feature correspondence for planar parameterization of meshes. This is achieved by adding positional constraints to the planar parameterization. Matchmaker allows users to introduce scores of constraints while maintaining a valid one-to-one mapping between the embedding and the 3D surface. Matchmaker's constraint mechanism can be used for other applications requiring parameterization besides texture mapping, such as morphing and remeshing.Matchmaker begins with an unconstrained planar embedding of the 3D mesh generated by conventional methods. It moves the constrained vertices to the required positions by matching a triangulation of these positions to a triangulation of the planar mesh formed by paths between constrained vertices. The matching triangulations are used to generate a new parameterization that satisfies the constraints while minimizing the deviation from the original 3D geometry.


title: Knowledge Management through Ontologies

abstract: Most enterprises agree that knowledge is an essential asset for success and survival on a increasingly competitive and global market. This awareness is one of the main reasons for the exponential growth of knowledge management in the past decade. Our approach to knowledge management is based on ontologies, and makes knowledge assets intelligently accessible to people in organizations. Most company-vital knowledge resides in the heads of people, and thus successful knowledge management does not only consider technical aspects but also social ones. In this paper, we describe an approach to intelligent knowledge management that explicitly takes into account the social issues involved. The proof of concept is given by a large-scale initiative involving knowledge management of a virtual organization.


title: A GaN-based 97% efficient hybrid switched-capacitor converter with lossless regulation capability

abstract: DC-DC converters for large conversion ratio applications face challenges such as high device stress and large inductor size. Switched-capacitor (SC) converters are known to have reduced device stress and no inductors, but the associated large transient current and poor efficiency at regulated output have prevented their wide adoptions. In this work, a complete solution is proposed, including the control and design considerations for a magnetic hybrid type of SC converter, so that the capacitor charging loss is eliminated and high efficiency is maintained with voltage regulation. Compared to conventional buck converters, the proposed converter offers the advantages of higher efficiency and significantly reduced inductor size. A 170 V to 24 V, 70 W prototype with a wide input and output voltage operating range is implemented using GaN transistors. A low profile heat-sink is designed and built by 3D printing to sufficiently cool the converter. A peak efficiency of 97% and a power density of 276 W/in3 are achieved, which compare favorably to both conventional SC converters and buck converters.


title: NDVI Short-Term Forecasting Using Recurrent Neural Networks

abstract: In this paper predictions of the Normalized Difference Vegetation Index (NDVI) data recorded by satellites over Ventspils Municipality in Courland, Latvia are discussed. NDVI is an important variable for vegetation forecasting and management of various problems, such as climate change monitoring, energy usage monitoring, managing the consumption of natural resources, agricultural productivity monitoring, drought monitoring and forest fire detection. Artificial Neural Networks (ANN) are computational models and universal approximators, which are widely used for nonlinear, non-stationary and dynamical process modeling and forecasting. In this paper Elman Recurrent Neural Networks (ERNN) are used to make one-step-ahead prediction of univariate NDVI time series.


MeshLab: an Open-Source Mesh Processing Tool


title: Deploying SDP for machine learning

abstract: We discuss the use in machine learning of a general type of convex optimisation problem known as semi-definite programming (SDP) (1). We intend to argue that SDP's arise quite naturally in a variety of situations, accounting for their omnipresence in modern machine learning approaches, and we provide examples in support.


title: A new approach for sparse matrix vector product on NVIDIA GPUs

abstract: The sparse matrix vector product (SpMV) is a key operation in engineering and scientific computing and, hence, it has been subjected to intense research for a long time. The irregular computations involved in SpMV make its optimization challenging. Therefore, enormous effort has been devoted to devise data formats to store the sparse matrix with the ultimate aim of maximizing the performance. Graphics Processing Units (GPUs) have recently emerged as platforms that yield outstanding acceleration factors. SpMV implementations for NVIDIA GPUs have already appeared on the scene. This work proposes and evaluates a new implementation of SpMV for NVIDIA GPUs based on a new format, ELLPACK-R, that allows storage of the sparse matrix in a regular manner. A comparative evaluation against a variety of storage formats previously proposed has been carried out based on a representative set of test matrices. The results show that, although the performance strongly depends on the specific pattern of the matrix, the implementation based on ELLPACK-R achieves higher overall performance. Moreover, a comparison with standard state-of-the-art superscalar processors reveals that significant speedup factors are achieved with GPUs. Copyright © 2010 John Wiley & Sons, Ltd.


Gender variation in Lung function parameters in Indian Adolescents


title: Datalog: Bag Semantics via Set Semantics

abstract: Duplicates in data management are common and problematic. In this work, we present a translation of Datalog under bag semantics into a well-behaved extension of Datalog, the so-called warded Datalog^+/-, under set semantics. From a theoretical point of view, this allows us to reason on bag semantics by making use of the well-established theoretical foundations of set semantics. From a practical point of view, this allows us to handle the bag semantics of Datalog by powerful, existing query engines for the required extension of Datalog. This use of Datalog^+/- is extended to give a set semantics to duplicates in Datalog^+/- itself. We investigate the properties of the resulting Datalog^+/- programs, the problem of deciding multiplicities, and expressibility of some bag operations. Moreover, the proposed translation has the potential for interesting applications such as to Multiset Relational Algebra and the semantic web query language SPARQL with bag semantics.


title: E.: Intelligent navigation systems for building evacuation

abstract: Intelligent navigation systems can significantly improve the evacuation of civilians in urban emergencies by providing dynamic directions to the evacuees as the hazard spreads. In this paper, we propose two distributed and adaptive systems for building evacuation. The first system, called intelligent evacuation system (IES), is composed of a wireless network of static decision nodes (DNs) installed in the building which provide movement decision support to evacuees by directing them to exits via less hazardous routes. The second system, called opportunistic communications based evacuation system (OCES), is based on mobile communication nodes (CNs) carried by civilians, which form an opportunistic network to exchange information on the situation and provide directions to civilians. We evaluate our systems’ performance with simulation experiments of a three-floor office building. Our results indicate that both systems can greatly improve the outcome of an emergency.


title: LexRank: Graph-based Lexical Centrality as Salience in Text Summarization

abstract: We introduce a stochastic graph-based method for computing relative importance of textual units for Natural Language Processing. We test the technique on the problem of Text Summarization (TS). Extractive TS relies on the concept of sentence salience to identify the most important sentences in a document or set of documents. Salience is typically defined in terms of the presence of particular important words or in terms of similarity to a centroid pseudo-sentence. We consider a new approach, LexRank, for computing sentence importance based on the concept of eigenvector centrality in a graph representation of sentences. In this model, a connectivity matrix based on intra-sentence cosine similarity is used as the adjacency matrix of the graph representation of sentences. Our system, based on LexRank ranked in first place in more than one task in the recent DUC 2004 evaluation. In this paper we present a detailed analysis of our approach and apply it to a larger data set including data from earlier DUC evaluations. We discuss several methods to compute centrality using the similarity graph. The results show that degree-based methods (including LexRank) outperform both centroid-based methods and other systems participating in DUC in most of the cases. Furthermore, the LexRank with threshold method outperforms the other degree-based techniques including continuous LexRank. We also show that our approach is quite insensitive to the noise in the data that may result from an imperfect topical clustering of documents.


title: 60-GHz Substrate Integrated Waveguide Fed Cavity-Backed Aperture-Coupled Microstrip Patch Antenna Arrays

abstract: Cavity-backed patch antenna arrays with full corporate substrate integrated waveguide (SIW) feed networks are demonstrated at V-band for applications with the needs of high-gain antennas. A prototype of $16 \times 16$ radiating elements with a waveguide transition is fabricated by applying standard printed circuit board (PCB) facilities. A gain up to 30.1 dBi with a 3-dB gain bandwidth of 16.1%, an impedance bandwidth of 15.3% for $\mathrm{SWR} \,{ , and symmetrically broadside radiation patterns with $-40\,\mathrm{dB}$ cross-polarizations are achieved. The performance of the proposed antenna array is also systematically evaluated. The result serves as a reference for designing large antenna arrays operating at millimeter-wave frequencies.


title: Signature Segmentation from Document Images

abstract: In this paper we propose a novel method for the extraction of signatures from document images. Instead of using a human defined set of features a part-based feature extraction method is used. In particular, we use the Speeded Up Robust Features (SURF) to distinguish the machine printed text from signatures. Using SURF features makes the approach generally more useful and reliable for different resolution documents. We have evaluated our system on the publicly available Tobacco-800 dataset in order to compare it to previous work. Finally, all signatures were found in the images and less than half of the found signatures are false positives. Therefore, our system can be applied for practical use.


Towards Deep Learning in genome-Wide Association Interaction studies


Spam Corpus Creation for TREC


Compressed Time Delay Neural Network for Small-Footprint Keyword Spotting.


title: Rule-Based Network Intrusion Detection System for Port Scanning with Efficient Port Scan Detection Rules Using Snort

abstract: In the field of network security, researchers have implemented different models to secure the network. Intrusion Detection System is also one of them and Snort is an open source tool for Intrusion Detection and Prevention System. Today intrusion Detection System is a growing technology in network security and mostly researchers have focused in this field, some of them used signature or rule-based technique and some are anomaly based techniques to improve security of network. In this paper we propose a rule-base Intrusion Detection System with our self generated new Efficient Port Scan Detection Rules (EPSDR). These rules will be used to detect naive port scan attacks in real time network using Snort and Basic Analysis Security Engine (BASE). BASE is used to view the snort results in font-end web page because Snort has no graphic user interface. In This rule-based Intrusion Detection System we will match the signature with our Efficient Port Scan Detection Rules (EPSDR) from captured packet. As a definition of signature based IDS this new EPSDR based IDS will be useful to reduce the false positive alarm.


title: High-Gain Low-Profile Circularly Polarized Slotted SIW Cavity Antenna for MMW Applications

abstract: A high-gain low-profile circularly polarized substrate integrated waveguide cavity antenna is proposed using TE 440 high-order mode. This antenna is simply excited by a coaxial probe. A linear-to-circular convertor is used in order to achieve circular polarization (CP). The low-profile, lightweight antenna offers both left- and right-hand CP. The implemented antenna exhibits a measured gain of almost 16 dBi and simulated radiation efficiency of about 96% with good measured return loss. Measured axial ratio is almost less than 1.6 dB. Furthermore, the antenna shows a good performance radiation pattern. Measured and simulated results show a good agreement. This antenna is considered as an appropriate candidate for use in future fifth-generation wireless systems.


title: Fine-grained change detection in structured text documents

abstract: Detecting and understanding changes between document revisions is an important task. The acquired knowledge can be used to classify the nature of a new document revision or to support a human editor in the review process. While purely textual change detection algorithms offer fine-grained results, they do not understand the syntactic meaning of a change. By representing structured text documents as XML documents we can apply tree-to-tree correction algorithms to identify the syntactic nature of a change. Many algorithms for change detection in XML documents have been propsed but most of them focus on the intricacies of generic XML data and emphasize speed over the quality of the result. Structured text requires a change detection algorithm to pay close attention to the content in text nodes, however, recent algorithms treat text nodes as black boxes. We present an algorithm that combines the advantages of the purely textual approach with the advantages of tree-to-tree change detection by redistributing text from non-overlapping common substrings to the nodes of the trees. This allows us to not only spot changes in the structure but also in the text itself, thus achieving higher quality and a fine-grained result in linear time on average. The algorithm is evaluated by applying it to the corpus of structured text documents that can be found in the English Wikipedia.


title: Message Understanding Conference-6: A Brief History

abstract: We have recently completed the sixth in a series of "Message Understanding Conferences" which are designed to promote and evaluate research in information extraction. MUC-6 introduced several innovations over prior MUCs, most notably in the range of different tasks for which evaluations were conducted. We describe some of the motivations for the new format and briefly discuss some of the results of the evaluations.


title: The Genetics of Aortopathies in Clinical Cardiology

abstract: Aortopathies pose a significant healthcare burden due to excess early mortality, increasing incidence, and underdiagnosis. Understanding the underlying genetic causes, early diagnosis, timely surveillance, prophylactic repair, and family screening are keys to addressing these diseases. Next-generation sequencing continues to expand our understanding of the genetic causes of heritable aortopathies, rapidly clarifying their underlying molecular pathophysiology and suggesting new potential therapeutic targets. This review will summarize the pathogenetic mechanisms and management of heritable genetic aortopathies with attention to specific forms of both syndromic and nonsyndromic disorders, including Marfan syndrome, Loeys-Dietz syndrome, vascular Ehlers-Danlos syndrome, and familial thoracic aortic aneurysm and dissection.


title: Processor scheduling in shared memory multiprocessors

abstract: Existing work indicates that the commonly used “single queue of runnable tasks” approach to scheduling shared memory multiprocessors can perform very poorly in a multiprogrammed parallel processing environment. A more promising approach is the class of “two-level schedulers” in which the operating system deals solely with allocating processors to jobs while the individual jobs themselves perform task dispatching on those processors. In this paper we compare two basic varieties of two-level schedulers. Those of the first type, static, make a single decision per job regarding the number of processors to allocate to it. Once the job has received its allocation, it is guaranteed to have exactly that number of processors available to it whenever it is active. The other class of two-level scheduler, dynamic, allows each job to acquire and release processors during its execution. By responding to the varying parallelism of the jobs, the dynamic scheduler promises higher processor utilizations at the cost of potentially greater scheduling overhead and more complicated application level task control policies. Our results, obtained via simulation, highlight the tradeoffs between the static and dynamic approaches. We investigate how the choice of policy is affected by the cost of switching a processor from one job to another. We show that for a wide range of plausible overhead values, dynamic scheduling is superior to static scheduling. Within the class of static schedulers, we show that, in most cases, a simple “run to completion” scheme is preferable to a round-robin approach. Finally, we investigate different techniques for tuning the allocation decisions required by the dynamic policies and quantify their effects on performance. We believe our results are directly applicable to many existing shared memory parallel computers, which for the most part currently employ a simple “single queue of tasks” extension of basic sequential machine schedulers. We plan to validate our results in future work through implementation and experimentation on such a system.


title: Knowledge processing in decisions under fuzziness and uncertainty

abstract: Some decision making applications require to encode statistics and fuzziness. In this work two frameworks are considered: coherent conditional probabilities and possibilities, which allow to give a rigorous interpretation of membership function. A comparison of the two interpretations is given to employ a general Bayesian inferential approach able to embed fuzzy information.


title: Deep Learning for Ontology Reasoning

abstract: In this work, we present a novel approach to ontology reasoning that is based on deep learning rather than logic-based formal reasoning. To this end, we introduce a new model for statistical relational learning that is built upon deep recursive neural networks, and give experimental evidence that it can easily compete with, or even outperform, existing logic-based reasoners on the task of ontology reasoning. More precisely, we compared our implemented system with one of the best logic-based ontology reasoners at present, RDFox, on a number of large standard benchmark datasets, and found that our system attained high reasoning quality, while being up to two orders of magnitude faster.


title: Postsurgical contact dermatitis due to povidone iodine: a diagnostic dilemma.

abstract: We present 7 cases of postsurgical contact dermatitis due to povidone iodine. The diagnosis was based on the clinical manifestations, the history of exposure, the site of the lesions, and the results of patch tests. This type of dermatitis can develop in the area of surgery or at distant sites exposed to povidone iodine during the surgical intervention. Patch tests with 10% povidone iodine in petrolatum were positive in all patients. Based on the results of the same tests in a control group, we recommend the use of petrolatum rather than water as the vehicle for the diagnosis of this form of contact dermatitis. Repeated open application tests with a commercially available solution of povidone iodine were negative. We conclude that the presence of the solution under occlusion during surgery is necessary both for the symptoms to develop and for the diagnosis to be made. This condition may be underdiagnosed.


title: Interface-Based Design of Real-Time Systems with Hierarchical Scheduling

abstract: In interface-based design, components are described by a component interface. In contrast to a component description that describes what a component does, a component interface describes how a component can be used, and a well designed component interface provides enough information to decide whether two or more components can work together properly in a system. Real-Time Interfaces expand the idea of interfacebased design to the area of real-time system design, where the term of working together properly refers to questions like: Does the composed system satisfy all requested real-time properties such as delay and throughput constraints? In this work, we extend the theory of Real-Time Interfaces and prove its applicability for the design of systems with hierarchical scheduling. We introduce a component system for interface-based design of systems with mixed FP, RM and EDF scheduling. We then further extend the ability for hierarchic scheduling by introducing server components. The introduced component system with Real-Time Interfaces not only allows interface-based design of complex real-time systems with hierarchical scheduling, but also inherently enables detailed schedulability analysis of such systems.


title: CMOS RC oscillator using 0.35 micron for portable mosquito-repel circuit

abstract: Mosquito-borne diseases, such as malaria and dengue, are major health concerns in the Philippines. This research provides a CMOS RC oscillator that operates at frequency based on the wing-beat frequency of male mosquitoes and dragonflies, in order to produce ultrasonic signal that repels biting female mosquitoes. This work presents the design and chip layout of CMOS RC oscillator which comprised of inverting amplifiers, diffusion chip resistor, and poly1-insulator-poly2 chip capacitor, and employed the TSMC 0.35 micron dual-polysilicon quad-metal mixed-mode technology. This microelectronic CMOS oscillator can be further developed into portable and wearable mosquito-repel circuits, and can help improve the non-occurrence of malaria and dengue in the country.


title: Modelling out-of-vocabulary words for robust speech recognition

abstract: This thesis concerns the problem of unknown or out-of-vocabulary (OOV) words in continuous speech recognition. ::: We propose a novel approach for handling OOV words within a single-stage recognition framework. To achieve this goal, an explicit and detailed model of OOV words is constructed and then used to augment the closed-vocabulary search space of a standard speech recognizer. This OOV model achieves open-vocabulary recognition through the use of more flexible subword units that can be concatenated during recognition to form new phone sequences corresponding to potential new words. Examples of such subword units are phones, syllables, or some automatically-learned multi-phone sequences. Subword units have the attractive property of being a closed set, and thus are able to cover any new words, and can conceivably cover most utterances with partially spoken words as well. ::: The main challenge with such an approach is ensuring that the OOV model does not absorb portions of the speech signal corresponding to in-vocabulary (IV) words. In dealing with this challenge, we explore several research issues related to designing the subword lexicon, language model, and topology of the OOV model. We present a dictionary-based approach for estimating subword language models. Such language models are utilized within the subword search space to help recognize the underlying phonetic transcription of OOV words. We also propose a data-driven iterative bottom-up procedure for automatically creating a multi-phone subword inventory. Starting with individual phones, this procedure uses the maximum mutual information principle to successively merge phones to obtain longer subword units. ::: The thesis also extends this OOV approach to modelling multiple classes of OOV words. ::: In addition, the thesis examines an approach for combining OOV modelling with recognition confidence scoring. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)


title: Study of spontaneous recurrent seizures and morphological alterations after status epilepticus induced by intrahippocampal injection of pilocarpine

abstract: Abstract Epileptic seizures are clinical manifestations of neuronal discharges characterized by hyperexcitability and/or hypersynchrony in the cortex and other subcortical regions. The pilocarpine (PILO) model of epilepsy mimics temporal lobe epilepsy (TLE) in humans. In the present study, we used a more selective approach: microinjection of PILO into the hilus of the dentate gyrus (H-PILO). Our main goal was to evaluate the behavioral and morphological alterations present in this model of TLE. Seventy-six percent of all animals receiving H-PILO injections had continuous seizures called status epilepticus (SE). A typical pattern of evolution of limbic seizures during the SE with a latency of 29.3 ± 16.3 minutes was observed using an analysis of behavioral sequences. During the subsequent 30 days, 71% of all animals exhibited spontaneous recurrent seizures (SRSs) during a daily 8-hour videotaping session. These SRSs had a very conspicuous and characteristic pattern detected by behavioral sequences or neuroethological analysis. Only the animals that had SE showed positive Neo-Timm staining in the inner molecular layer of the dentate gyrus (sprouting) and reduced cell density in Ammon's horn pyramidal cell subfield CA1. However, no correlation between the intensity of sprouting and the mean number and total number of SRSs was found. Additionally, using Fluoro-Jade staining, we observed neurodegeration in the hilus and pyramidal cell subfields CA3 and CA1 24 hours after SE. These data indicate that H-PILO is a reliable, selective, efficient, low-mortality model that mimics the acute and chronic behavioral and morphological aspects of TLE.


Ion-hose instability in a long-pulse linear induction accelerator


title: PPChecker: Plagiarism Pattern Checker in Document Copy Detection

abstract: Nowadays, most of documents are produced in digital format, in which they can be easily accessed and copied. Document copy detection is a very important tool for protecting the author's copyright. We present PPChecker, a document copy detection system based on plagiarism pattern checking. PPChecker calculates the amount of data copied from the original document to the query document, based on linguistically-motivated plagiarism patterns. Experiments performed on CISI document collection show that PPChecker produces better decision information for document copy detection than existing systems.


title: On the optimality of multiantenna broadcast scheduling using zero-forcing beamforming

abstract: Although the capacity of multiple-input/multiple-output (MIMO) broadcast channels (BCs) can be achieved by dirty paper coding (DPC), it is difficult to implement in practical systems. This paper investigates if, for a large number of users, simpler schemes can achieve the same performance. Specifically, we show that a zero-forcing beamforming (ZFBF) strategy, while generally suboptimal, can achieve the same asymptotic sum capacity as that of DPC, as the number of users goes to infinity. In proving this asymptotic result, we provide an algorithm for determining which users should be active under ZFBF. These users are semiorthogonal to one another and can be grouped for simultaneous transmission to enhance the throughput of scheduling algorithms. Based on the user grouping, we propose and compare two fair scheduling schemes in round-robin ZFBF and proportional-fair ZFBF. We provide numerical results to confirm the optimality of ZFBF and to compare the performance of ZFBF and proposed fair scheduling schemes with that of various MIMO BC strategies.


title: Partial Shading Detection and Smooth Maximum Power Point Tracking of PV Arrays under PSC

abstract: One of the most important issues in the operation of a photovoltaic (PV) system is extracting maximum power from the PV array, especially in partial shading condition (PSC). Under PSC, P-V characteristic of PV arrays will have multiple peak points, only one of which is global maximum. Conventional maximum power point tracking (MPPT) methods are not able to extract maximum power in this condition. In this paper, a novel two-stage MPPT method is presented to overcome this drawback. In the first stage, a method is proposed to determine the occurrence of PSC, and in the second stage, using a new algorithm that is based on ramp change of the duty cycle and continuous sampling from the P-V characteristic of the array, global maximum power point of array is reached. P&O algorithm is then re-activated to trace small changes of the new MPP. Open loop operation of the proposed method makes its implementation cheap and simple. The method is robust in the face of changing environmental conditions and array characteristics, and has minimum negative impact on the connected power system. Simulations in Matlab/Simulink and experimental results validate the performance of the proposed methods.


title: Brain effective connectivity modeling for alzheimer's disease by sparse gaussian bayesian network

abstract: Recent studies have shown that Alzheimer's disease (AD) is related to alteration in brain connectivity networks. One type of connectivity, called effective connectivity, defined as the directional relationship between brain regions, is essential to brain function. However, there have been few studies on modeling the effective connectivity of AD and characterizing its difference from normal controls (NC). In this paper, we investigate the sparse Bayesian Network (BN) for effective connectivity modeling. Specifically, we propose a novel formulation for the structure learning of BNs, which involves one L1-norm penalty term to impose sparsity and another penalty to ensure the learned BN to be a directed acyclic graph - a required property of BNs. We show, through both theoretical analysis and extensive experiments on eleven moderate and large benchmark networks with various sample sizes, that the proposed method has much improved learning accuracy and scalability compared with ten competing algorithms. We apply the proposed method to FDG-PET images of 42 AD and 67 NC subjects, and identify the effective connectivity models for AD and NC, respectively. Our study reveals that the effective connectivity of AD is different from that of NC in many ways, including the global-scale effective connectivity, intra-lobe, inter-lobe, and inter-hemispheric effective connectivity distributions, as well as the effective connectivity associated with specific brain regions. These findings are consistent with known pathology and clinical progression of AD, and will contribute to AD knowledge discovery.


title: Regular and irregular progressive edge-growth tanner graphs

abstract: We propose a general method for constructing Tanner graphs having a large girth by establishing edges or connections between symbol and check nodes in an edge-by-edge manner, called progressive edge-growth (PEG) algorithm. Lower bounds on the girth of PEG Tanner graphs and on the minimum distance of the resulting low-density parity-check (LDPC) codes are derived in terms of parameters of the graphs. Simple variations of the PEG algorithm can also be applied to generate linear-time encodeable LDPC codes. Regular and irregular LDPC codes using PEG Tanner graphs and allowing symbol nodes to take values over GF(q) (q>2) are investigated. Simulation results show that the PEG algorithm is a powerful algorithm to generate good short-block-length LDPC codes.


title: Estimation of ambient light and transmission map with common convolutional architecture

abstract: This paper presents a method for effective ambient light and transmission estimation in underwater images using a common convolutional network architecture. The estimated ambient light and the transmission map are used to dehaze the underwater images. Dehazing underwater images is especially challenging due to the unknown and significantly varying ambient light in underwater environments. Unlike common dehazing methods, the proposed method is capable of estimating ambient light along with the transmission map thereby improving the reconstruction quality of the dehazed images. We evaluate the dehazing performance of the proposed method on real underwater images and also compare our method to current state-of-the-art techniques.


title: Optimized shape semantic graph representation for object understanding and recognition in point clouds

abstract: To understand and recognize the three-dimensional (3-D) objects represented as point cloud data, we use an optimized shape semantic graph (SSG) to describe 3-D objects. Based on the decomposed components of an object, the boundary surface of different components and the topology of components, the SSG gives a semantic description that is consistent with human vision perception. The similarity measurement of the SSG for different objects is effective for distinguishing the type of object and finding the most similar one. Experiments using a shape database show that the SSG is valuable for capturing the components of the objects and the corresponding relations between them. The SSG is not only suitable for an object without any loops but also appropriate for an object with loops to represent the shape and the topology. Moreover, a two-step progressive similarity measurement strategy is proposed to effectively improve the recognition rate in the shape database containing point-sample data.


Deep Learning-Based Telephony Speech Recognition in the Wild


title: Android mobile app for real-time bilateral Arabic sign language translation using leap motion controller

abstract: This paper introduces an android mobile App for real-time bilateral Arabic sign language translation. The system is designed to operate on isolated sign language words. The mobile App has four main components; sign language capture for training, sign language capture for translation, voice to sign language translation and a sign language quiz game. Therefore, the Mobile App can be used for bilateral communication with the deaf community. Acquisition of sign language words is performed using a portable Leap Motion Controller that is connected to the USB of the mobile phone through an OTG adapter. We use simple feature extraction approaches based on statistical features such as mean, variance and covariance. We also use simple classification approach based on the minimum distance classifier. Classification results performed using 15 different sign language words revealed that excellent classification results are achieved. In comparison to a similar system that uses data gloves, our system has the advantage of higher accuracy and the advantage of real-time processing using a mobile App.


title: Efficient Unsupervised Temporal Segmentation of Motion Data

abstract: We introduce a method for automated temporal segmentation of human motion data into distinct actions and compositing motion primitives based on self-similar structures in the motion sequence. We use neighborhood graphs for the partitioning and the similarity information in the graph is further exploited to cluster the motion primitives into larger entities of semantic significance. The method requires no assumptions about the motion sequences at hand and no user interaction is required for the segmentation or clustering. In addition, we introduce a feature bundling preprocessing technique to make the segmentation more robust to noise, as well as a notion of motion symmetry for more refined primitive detection. We test our method on several sensor modalities, including markered and markerless motion capture as well as on electromyograph and accelerometer recordings. The results highlight our system's capabilities for both segmentation and for analysis of the finer structures of motion data, all in a completely unsupervised manner.


title: WORKING PAPER 41 The 15-D Measure of Health Related Quality of Life: Reliability, Validity and Sensitivity of its Health State Descriptive System

abstract: The 15D is a generic, 15-dimensional, standardised, self-administered measure of HRQOL, that can be used both as a profile and as a single index score measure. This paper examines the acceptability, reliability, validity and sensitivity of two versions (15D.1 and 15D.2) of its health state descriptive system as a profile measure compared with the Nottingham Health Profile (NHP), SF-20 and EuroQol by using several data sets and methods. The response and completion rates show that the acceptability is comparable to NHP, SF-20 and EuroQol. Reliability in terms of repeatability is high, even higher than for NHP. There is substantial evidence of content and construct validity (cross-sectional and longitudinal) and depression-related criterion validity. On roughly comparable dimensions the discriminatory power of 15D.1 appears to be superior to NHP, at least equivalent to SF-20, that of 15D.2 superior to EuroQol and 15D.1, and the responsiveness of 15D.1 to change seems to be similar to NHP and SF-20. The remaining 9-10 dimensions of 15D provide a large extra reserve in these respects. It is concluded that as a profile measure 15D performs equally well as NHP and SF-20, in some respects even better. The properties as a single index score measure will be explored separately.


title: Logistic Regression in Rare Events Data

abstract: We study rare events data, binary dependent variables with dozens to thousands of times fewer ones (events, such as wars, vetoes, cases of political activism, or epidemiological infections) than zeros (“nonevents”). In many literatures, these variables have proven difficult to explain and predict, a problem that seems to have at least two sources. First, popular statistical procedures, such as logistic regression, can sharply underestimate the probability of rare events. We recommend corrections that outperform existing methods and change the estimates of absolute and relative risks by as much as some estimated effects reported in the literature. Second, commonly used data collection strategies are grossly inefficient for rare events data. The fear of collecting data with too few events has led to data collections with huge numbers of observations but relatively few, and poorly measured, explanatory variables, such as in international conflict data with more than a quarter-million dyads, only a few of which are at war. As it turns out, more efficient sampling designs exist for making valid inferences, such as sampling all available events (e.g., wars) and a tiny fraction of nonevents (peace). This enables scholars to save as much as 99% of their (nonfixed) data collection costs or to collect much more meaningful explanatory variables. We provide methods that link these two results, enabling both types of corrections to work simultaneously, and software that implements the methods developed.


title: Discrimination Power of Short-Term Heart Rate Variability Measures for CHF Assessment

abstract: In this study, we investigated the discrimination power of short-term heart rate variability (HRV) for discriminating normal subjects versus chronic heart failure (CHF) patients. We analyzed 1914.40 h of ECG of 83 patients of which 54 are normal and 29 are suffering from CHF with New York Heart Association (NYHA) classification I, II, and III, extracted by public databases. Following guidelines, we performed time and frequency analysis in order to measure HRV features. To assess the discrimination power of HRV features, we designed a classifier based on the classification and regression tree (CART) method, which is a nonparametric statistical technique, strongly effective on nonnormal medical data mining. The best subset of features for subject classification includes square root of the mean of the sum of the squares of differences between adjacent NN intervals (RMSSD), total power, high-frequencies power, and the ratio between low- and high-frequencies power (LF/HF). The classifier we developed achieved sensitivity and specificity values of 79.3% and 100 %, respectively. Moreover, we demonstrated that it is possible to achieve sensitivity and specificity of 89.7% and 100 %, respectively, by introducing two nonstandard features ΔAVNN and ΔLF/HF, which account, respectively, for variation over the 24 h of the average of consecutive normal intervals (AVNN) and LF/HF. Our results are comparable with other similar studies, but the method we used is particularly valuable because it allows a fully human-understandable description of classification procedures, in terms of intelligible “if ... then ...” rules.


title: RF link budget analysis in urban propagation microcell environment for mobile radio communication systems link planning

abstract: This paper presents a study to design and analysis of an RF link budget analysis model with a primary focus on the real urban propagation microcell environment to analyze the mobile signal loss factors during the propagation and to calculate the transmitting signal power at the transmitter end. The link budget analysis prediction system utilizes a combined model that demonstrates appropriateness to reliable link design in order to develop a flexible RF link budget analysis prediction model for mobile communication systems cellular network link planning in urban propagation microcell environment.


title: Learning Fast Low-Rank Projection for Image Classification

abstract: Rooted in a basic hypothesis that a data matrix is strictly drawn from some independent subspaces, the low-rank representation (LRR) model and its variations have been successfully applied in various image classification tasks. However, this hypothesis is very strict to the LRR model as it cannot always be guaranteed in real images. Moreover, the hypothesis also prevents the sub-dictionaries of different subspaces from collaboratively representing an image. Fortunately, in supervised image classification, low-rank signal can be extracted from the independent label subspaces (ILS) instead of the independent image subspaces (IIS). Therefore, this paper proposes a projective low-rank representation (PLR) model by directly training a projective function to approximate the LRR derived from the labels. To the best of our knowledge, PLR is the first attempt to use the ILS hypothesis to relax the rigorous IIS hypothesis in the LRR models. We further prove a low-rank effect that the representations learned by PLR have high intraclass similarities and large interclass differences, which are beneficial to the classification tasks. The effectiveness of our proposed approach is validated by the experimental results on three databases.


title: Optimizing energy consumption of GUIs in Android apps: a multi-objective approach

abstract: The wide diffusion of mobile devices has motivated research towards optimizing energy consumption of software systems— including apps—targeting such devices. Besides efforts aimed at dealing with various kinds of energy bugs, the adoption of Organic Light-Emitting Diode (OLED) screens has motivated research towards reducing energy consumption by choosing an appropriate color palette. Whilst past research in this area aimed at optimizing energy while keeping an acceptable level of contrast, this paper proposes an approach, named GEMMA (Gui Energy Multi-objective optiMization for Android apps), for generating color palettes using a multi- objective optimization technique, which produces color solutions optimizing energy consumption and contrast while using consistent colors with respect to the original color palette. An empirical evaluation that we performed on 25 Android apps demonstrates not only significant improvements in terms of the three different objectives, but also confirmed that in most cases users still perceived the choices of colors as attractive. Finally, for several apps we interviewed the original developers, who in some cases expressed the intent to adopt the proposed choice of color palette, whereas in other cases pointed out directions for future improvements


Spring 3-19-2013 An extension of the UTAUT 2 in a healthcare context


title: Efficient and Dynamic Simplification of Line Drawings Abstract

abstract: In this paper we present a pipeline for rendering dynamic 2D/3D line drawings efficiently. Our main goal is to create efficient static renditions and coherent animations of line drawings in a setting where lines can be added, deleted and arbitrarily transformed on-the-fly. Such a dynamic setting enables us to handle interactively sketched 2D line data, as well as arbitrarily transformed 3D line data in a unified manner. We evaluate the proximity of screen projected strokes to simplify them while preserving their continuity. We achieve this by using a special data structure that facilitates efficient proximity calculations in a dynamic setting. This on-the-fly proximity evaluation also facilitates generation of appropriate visibility cues to mitigate depth ambiguities and visual clutter for 3D line data. As we perform all these operations using only line data, we can create line drawings from 3D models without any surface information. We demonstrate the effectiveness and applicability of our approach by showing several examples with initial line representations obtained from a variety of sources: 2D and 3D hand-drawn sketches and 3D salient geometry lines obtained from 3D surface representations.


title: A Survey of Indoor Positioning and Object Locating Systems

abstract: This paper investigated various indoor positioning techniques and presented a comprehensive study about their advantages and disadvantages. Infrared, Ultrasonic and RF technologies are used in different indoor positioning systems. RFID positioning systems based on RSSI technology are the most recent developments. Positioning accuracy was greatly improved by using integrated RFID technologies.


title: Social networks that matter: Twitter under the microscope

abstract: Scholars, advertisers and political activists see massive online social networks as a representation of social interactions that can be used to study the propagation of ideas, social bond dynamics and viral marketing, among others. But the linked structures of social networks do not reveal actual interactions among people. Scarcity of attention and the daily rythms of life and work makes people default to interacting with those few that matter and that reciprocate their attention. A study of social interactions within Twitter reveals that the driver of usage is a sparse and hidden network of connections underlying the “declared” set of friends and followers.


title: Evidence for Optimal Integration of Visual Feature Representations across Saccades

abstract: We explore the visual world through saccadic eye movements, but saccades also present a challenge to visual processing by shifting externally stable objects from one retinal location to another. The brain could solve this problem in two ways: by overwriting preceding input and starting afresh with each fixation or by maintaining a representation of presaccadic visual features in working memory and updating it with new information from the remapped location. Crucially, when multiple objects are present in a scene the planning of eye movements profoundly affects the precision of their working memory representations, transferring limited memory resources from fixation toward the saccade target. Here we show that when humans make saccades, it results in an update of not just the precision of representations but also their contents. When multiple item colors are shifted imperceptibly during a saccade the perceived colors are found to fall between presaccadic and postsaccadic values, with the weight given to each input varying continuously with item location, and fixed relative to saccade parameters. Increasing sensory uncertainty, by adding color noise, biases updating toward the more reliable input, which is consistent with an optimal integration of presaccadic working memory with a postsaccadic updating signal. We recover this update signal and show it to be tightly focused on the vicinity of the saccade target. These results reveal how the nervous system accumulates detailed visual information from multiple views of the same object or scene. ::: ::: SIGNIFICANCE STATEMENT This study examines the consequences of saccadic eye movements for the internal representation of visual objects. A saccade shifts the image of a stable visual object from one part of the retina to another. We show that visual representations are built up over these different views of the same object, by combining information obtained before and after each saccade. The weights given to presaccadic and postsaccadic information are determined by the relative reliability of each input. This provides evidence that the visual system combines inputs over time in a statistically optimal way.