title: ERAP: ECC Based RFID Authentication Protocol

abstract: RFID tags are a new generation of small devices used for identification in many applications today. RFID authentication plays an important role in applications where security and privacy is a major concern. As an example, RFID has gained appreciation as an emerging technology to thwart counterfeiting problems. Public key cryptography (PKC) provides an impeccable solution to the counterfeiting problem. One recent family of public key cryptosystem is elliptic curve cryptography (ECC) which is a better choice than RSA cryptographic system because of its shorter key length. In this paper, we propose a secure, mutual offline authentication protocol which is based on ECC. Finally, we present security analysis of our proposed authentication protocol.


title: Developments of Fuel Cell and Hydrogen Technology at NRC's Institute for Fuel Cell Innovation

abstract: National Research Council (NRC) as the premier research and development organization within the government of Canada has the mandate of providing vital scientific and technological services to research and industrial communities. The NRC Institute for Fuel Cell Innovation (IFCI) is leading NRC’s National Fuel Cell Program and is working closely with academic, government, and industrial organizations to support fuel cell cluster in Vancouver and across Canada and to fulfill the innovation needs of Canadian fuel cell companies. The key programs at IFCI include: Proton Exchange Membrane Fuel Cells (PEMFC), Solid Oxide Fuel Cells (SOFC), Hydrogen generation and infrastructure, and technology demonstration. NRC-IFCI’s impact on the fuel cell industry can be seen through the development and transfer of targeted and collaborative research projects addressing strategic and current technical gaps and providing infrastructure for research, development and demonstration. IFCI has been a catalyst in the coordination of industry’s responses to current commercialization barriers. This paper presents the latest research and development activities as well as demonstrations at NRC-IFCI.


title: Evolutionary dynamics of multi-agent learning: a survey

abstract: The interaction of multiple autonomous agents gives rise to highly dynamic and nondeterministic environments, contributing to the complexity in applications such as automated financial markets, smart grids, or robotics. Due to the sheer number of situations that may arise, it is not possible to foresee and program the optimal behaviour for all agents beforehand. Consequently, it becomes essential for the success of the system that the agents can learn their optimal behaviour and adapt to new situations or circumstances. The past two decades have seen the emergence of reinforcement learning, both in single and multi-agent settings, as a strong, robust and adaptive learning paradigm. Progress has been substantial, and a wide range of algorithms are now available. An important challenge in the domain of multi-agent learning is to gain qualitative insights into the resulting system dynamics. In the past decade, tools and methods from evolutionary game theory have been successfully employed to study multi-agent learning dynamics formally in strategic interactions. This article surveys the dynamical models that have been derived for various multi-agent reinforcement learning algorithms, making it possible to study and compare them qualitatively. Furthermore, new learning algorithms that have been introduced using these evolutionary game theoretic tools are reviewed. The evolutionary models can be used to study complex strategic interactions. Examples of such analysis are given for the domains of automated trading in stock markets and collision avoidance in multi-robot systems. The paper provides a roadmap on the progress that has been achieved in analysing the evolutionary dynamics of multi-agent learning by highlighting the main results and accomplishments.


title: A survey of techniques for internet traffic classification using machine learning

abstract: The research community has begun looking for IP traffic classification techniques that do not rely on `well known? TCP or UDP port numbers, or interpreting the contents of packet payloads. New work is emerging on the use of statistical traffic characteristics to assist in the identification and classification process. This survey paper looks at emerging research into the application of Machine Learning (ML) techniques to IP traffic classification - an inter-disciplinary blend of IP networking and data mining techniques. We provide context and motivation for the application of ML techniques to IP traffic classification, and review 18 significant works that cover the dominant period from 2004 to early 2007. These works are categorized and reviewed according to their choice of ML strategies and primary contributions to the literature. We also discuss a number of key requirements for the employment of ML-based traffic classifiers in operational IP networks, and qualitatively critique the extent to which the reviewed works meet these requirements. Open issues and challenges in the field are also discussed.


title: An Encoder-Decoder Model for ICD-10 Coding of Death Certificates

abstract: Information extraction from textual documents such as hospital records and healthrelated user discussions has become a topic of intense interest. The task of medical concept coding is to map a variable length text to medical concepts and corresponding classification codes in some external system or ontology. In this work, we utilize recurrent neural networks to automatically assign ICD-10 codes to fragments of death certificates written in English. We develop end-to-end neural architectures directly tailored to the task, including basic encoder-decoder architecture for statistical translation. In order to incorporate prior knowledge, we concatenate cosine similarities vector among the text and dictionary entry to the encoded state. Being applied to a standard benchmark from CLEF eHealth 2017 challenge, our model achieved F-measure of 85.01% on a full test set with significant improvement as compared to the average score of 62.2% for all official participants approaches.


title: Long Distance Pronominalisation and Global Focus

abstract: Our corpus of descriptive text contains a significant number of long-distance pronominal references (8.4% of the total). In order to account for how these pronouns are intepreted, we re-examine Grosz and Sidner's theory of the attentional state, and in particular the use of the global focus to supplement centering theory. Our corpus evidence concerning these long-distance pronominal references, as well as studies of the use of descriptions, proper names and ambiguous uses of pronouns, lead us to conclude that a discourse focus stack mechanism of the type proposed by Sidner is essential to account for the use of these referring expressions. We suggest revising the Grosz & Sidner framework by allowing for the possibility that an entity in a focus space may have special status.


title: Best-path vs. multi-path overlay routing

abstract: Time-varying congestion on Internet paths and failures due to software, hardware, and configuration errors often disrupt packet delivery on the Internet.Many aproaches to avoiding these problems use multiple paths between two network locations. These approaches rely on a path-independence assumption in order to work well; i.e., they work best when the problems on different paths between two locations are uncorrelated in time.This paper examines the extent to which this assumption holds on the Internet by analyzing 14 days of data collected from 30 nodes in the RON testbed. We examine two problems that manifest themselves---congestion-triggered loss and path failures---and find that the chances of losing two packets between the same hosts is nearly as high when those packets are sent through an intermediate node (60%) as when they are sent back-to-back on the same path (70%). In so doing, we also compare two different ways of taking advantage of path redundancy proposed in the literature: mesh routing based on packet replication, and reactive routing based on adaptive path selection.


Large-Scale Pattern-Based Information Extraction from the World Wide Web


title: Detecting and Preventing"Multiple-Account"Cheating in Massive Open Online Courses

abstract: We describe a cheating strategy enabled by the features of massive open online courses (MOOCs) and detectable by virtue of the sophisticated data systems that MOOCs provide. The strategy, Copying Answers using Multiple Existences Online (CAMEO), involves a user who gathers solutions to assessment questions using a "harvester" account and then submits correct answers using a separate "master" account. We use "clickstream" learner data to detect CAMEO use among 1.9 million course participants in 115 MOOCs from two universities. Using conservative thresholds, we estimate CAMEO prevalence at 1,237 certificates, accounting for 1.3% of the certificates in the 69 MOOCs with CAMEO users. Among earners of 20 or more certificates, 25% have used the CAMEO strategy. CAMEO users are more likely to be young, male, and international than other MOOC certificate earners. We identify preventive strategies that can decrease CAMEO rates and show evidence of their effectiveness in science courses.


title: Evaluating access control policies through model checking

abstract: We present a model-checking algorithm which can be used to evaluate access control policies, and a tool which implements it. The evaluation includes not only assessing whether the policies give legitimate users enough permissions to reach their goals, but also checking whether the policies prevent intruders from reaching their malicious goals. Policies of the access control system and goals of agents must be described in the access control description and specification language introduced as RW in our earlier work. The algorithm takes a policy description and a goal as input and performs two modes of checking. In the assessing mode, the algorithm searches for strategies consisting of reading and writing steps which allow the agents to achieve their goals no matter what states the system may be driven into during the execution of the strategies. In the intrusion detection mode, a weaker notion of strategy is used, reflecting the willingness of intruders to guess the value of attributes which they cannot read.


title: Characterizing large-scale use of a direct manipulation application in the wild

abstract: Examining large-scale, long-term application use is critical to understanding how an application meets the needs of its user community. However, there have been few published analyses of long-term use of desktop applications, and none that have examined applications that support creating and modifying content using direct manipulation. In this paper, we present an analysis of 2 years of usage data from an instrumented version of the GNU Image Manipulation Program, including data from over 200 users. In the course of our analysis, we show that previous findings concerning the sparseness of command use and idiosyncrasy of users' command vocabularies extend to a new domain and interaction style. These findings motivate continued research in adaptive and mixed-initiative interfaces. We also describe the novel application of a clustering technique to characterize a user community's higher-level tasks from low-level logging data.


title: Force‐feedback teleoperation of an industrial robot in a nuclear spent fuel reprocessing plant

abstract: Purpose – Aims to describe how to make an industrial robot work as a telemanipulator with force feedback, in order to carry out various tasks for remote handling in nuclear fuel cycle plants.Design/methodology/approach – The robot Staubli RX170 (used as a slave arm) has been fitted with a force‐torque sensor and an electronic system for sensors' signals multiplexing. The overall system has been made tolerant to γ radiation up to a 10 kGy integrated dose. The industrial robot has been coupled to a master arm with force feedback capability and to the computer assisted teleoperation controller TAO2000 developed by CEA‐LIST.Findings – The result of the maintenance operation reported in the paper, carried out with a Staubli RX170 robot at AREVA/COGEMA La Hague plant, illustrates the validity of this approach and demonstrates how remote handling can benefit from this new technology.Originality/value – Introduces the teleoperation of industrial robots as a new solution for the maintenance of nuclear facilities. ...


title: Microwave metamaterial absorber for sensing applications

abstract: Abstract A metamaterial absorber (MA) based sensor is designed and analysed for various important applications including pressure, temperature, density, and humidity sensing. Material parameters, as well as equivalent circuit model have been extracted and explained. After obtaining a perfect absorption (PA) at around 6.46 GHz and 7.68 GHz, surface current distributions at resonance points have been explained. Since bandwidth and applicability to different sensor applications are important for metamaterial sensor applications, we have realized distinctive sensor demonstrations for pressure, temperature, moisture content and density and the obtained results have been compared with the current literature. The proposed structure uses the changes on the overall system resonance frequency which is caused by the sensor layer’s dielectric constant that varies depending on the electromagnetic behaviour of the sample placed in. This model can be adapted to be used in sensor applications including industrial, medical and agricultural products.


title: High-degree cubature Kalman filter

abstract: The cubature Kalman filter (CKF), which is based on the third degree spherical-radial cubature rule, is numerically more stable than the unscented Kalman filter (UKF) but less accurate than the Gauss-Hermite quadrature filter (GHQF). To improve the performance of the CKF, a new class of CKFs with arbitrary degrees of accuracy in computing the spherical and radial integrals is proposed. The third-degree CKF is a special case of the class. The high-degree CKFs of the class can achieve the accuracy and stability performances close to those of the GHQF but at lower computational cost. A numerical integration problem and a target tracking problem are utilized to demonstrate the necessity of using the high-degree cubature rules to improve the performance. The target tracking simulation shows that the fifth-degree CKF can achieve higher accuracy than the extended Kalman filter, the UKF, the third-degree CKF, and the particle filter, and is computationally much more efficient than the GHQF.


title: Recognising Nested Named Entities in Biomedical Text

abstract: Although recent named entity (NE) annotation efforts involve the markup of nested entities, there has been limited focus on recognising such nested structures. This paper introduces and compares three techniques for modelling and recognising nested entities by means of a conventional sequence tagger. The methods are tested and evaluated on two biomedical data sets that contain entity nesting. All methods yield an improvement over the baseline tagger that is only trained on flat annotation.


title: RiFCN: Recurrent Network in Fully Convolutional Network for Semantic Segmentation of High Resolution Remote Sensing Images

abstract: Semantic segmentation in high resolution remote sensing images is a fundamental and challenging task. Convolutional neural networks (CNNs), such as fully convolutional network (FCN) and SegNet, have shown outstanding performance in many segmentation tasks. One key pillar of these successes is mining useful information from features in convolutional layers for producing high resolution segmentation maps. For example, FCN nonlinearly combines high-level features extracted from last convolutional layers; whereas SegNet utilizes a deconvolutional network which takes as input only coarse, high-level feature maps of the last convolutional layer. However, how to better fuse multi-level convolutional feature maps for semantic segmentation of remote sensing images is underexplored. In this work, we propose a novel bidirectional network called recurrent network in fully convolutional network (RiFCN), which is end-to-end trainable. It has a forward stream and a backward stream. The former is a classification CNN architecture for feature extraction, which takes an input image and produces multi-level convolutional feature maps from shallow to deep; while in the later, to achieve accurate boundary inference and semantic segmentation, boundary-aware high resolution feature maps in shallower layers and high-level but low-resolution features are recursively embedded into the learning framework (from deep to shallow) to generate a fused feature representation that draws a holistic picture of not only high-level semantic information but also low-level fine-grained details. Experimental results on two widely-used high resolution remote sensing data sets for semantic segmentation tasks, ISPRS Potsdam and Inria Aerial Image Labeling Data Set, demonstrate competitive performance obtained by the proposed methodology compared to other studied approaches.


title: Differences in Kinematics and Electromyographic Activity between Men and Women during the Single-Legged Squat *

abstract: Background: Numerous factors have been identified as potentially increasing the risk of anterior cruciate ligament injury in the female athlete. However, differences between the sexes in lower extremity coordination, particularly hip control, are only minimally understood. Hypothesis: There is no difference in kinematic or electromyographic data during the single-legged squat between men and women. Study Design: Descriptive comparison study. Methods: We kinematically and electromyographically analyzed the single-legged squat in 18 intercollegiate athletes (9 male, 9 female). Subjects performed five single-legged squats on their dominant leg, lowering themselves as far as possible and then returning to a standing position without losing balance. Results: Women demonstrated significantly more ankle dorsiflexion, ankle pronation, hip adduction, hip flexion, hip external rotation, and less trunk lateral flexion than men. These factors were associated with a decreased ability of the women to maintain a varus knee position during the squat as compared with the men. Analysis of all eight tested muscles demonstrated that women had greater muscle activation compared with men. When each muscle was analyzed separately, the rectus femoris muscle activation was found to be statistically greater in women in both the area under the linear envelope and maximal activation data. Conclusions: Under a physiologic load in a position commonly assumed in sports, women tend to position their entire lower extremity and activate muscles in a manner that could increase strain on the anterior cruciate ligament. © 2003 American Orthopaedic Society for Sports Medicine


title: Am. J. Hum. Genet. 76:449–462, 2005 Accounting for Decay of Linkage Disequilibrium in Haplotype Inference

abstract: Although many algorithms exist for estimating haplotypes from genotype data, none of them take full account of both the decay of linkage disequilibrium (LD) with distance and the order and spacing of genotyped markers. Here, we describe an algorithm that does take these factors into account, using a flexible model for the decay of LD with distance that can handle both "blocklike" and "nonblocklike" patterns of LD. We compare the accuracy of this approach with a range of other available algorithms in three ways: for reconstruction of randomly paired, molecularly determined male X chromosome haplotypes; for reconstruction of haplotypes obtained from trios in an autosomal region; and for estimation of missing genotypes in 50 autosomal genes that have been completely resequenced in 24 African Americans and 23 individuals of European descent. For the autosomal data sets, our new approach clearly outperforms the best available methods, whereas its accuracy in inferring the X chromosome haplotypes is only slightly superior. For estimation of missing genotypes, our method performed slightly better when the two subsamples were combined than when they were analyzed separately, which illustrates its robustness to population stratification. Our method is implemented in the software package PHASE (v2.1.1), available from the Stephens Lab Web site.


title: Decision making and neuropsychiatry

abstract: Abnormal decision making is a central feature of neuropsychiatric disorders. Recent investigations of the neural substrates underlying decision making have involved qualitative assessment of the cognition of decision making in clinical lesion studies (in patients with frontal lobe dementia) and neuropsychiatric disorders such as mania, substance abuse and personality disorders. A neural network involving the orbitofrontal cortex, ventral striatum and modulatory ascending neurotransmitter systems has been identified as having a fundamental role in decision making and in the neural basis of neuropsychiatric diseases. This network accounts for the dissociations among decision-making deficits in different clinical populations. Ultimately, a more refined and sophisticated characterization of such deficits might guide the early diagnosis and cognitive and therapeutic rehabilitation of these patients.


title: Smart data pricing models for the internet of things: a bundling strategy approach

abstract: The Internet of Things (IoT) has emerged as a new paradigm for the future Internet. In IoT, devices are connected to the Internet and thus are a huge data source for numerous applications. In this article, we focus on addressing data management in IoT through using a smart data pricing (SDP) approach. With SDP, data can be managed flexibly and efficiently through intelligent and adaptive incentive mechanisms. Moreover, data is a major source of revenue for providers and partners. We propose a new pricing scheme for IoT service providers to determine the sensing data buying price and IoT service subscription fee offered to sensor owners and service users, respectively. Additionally, we adopt the bundling strategy that allows multiple providers to form a coalition and offer their services as a bundle, attracting more users and achieving higher revenue. Finally, we outline some important open research issues for SDP and IoT.


title: Automatic Test Cases Generation from Software Specifications

abstract: A new technique is proposed in this paper to extend the Integrated Classification Tree Methodology (ICTM) developed by Chen et al. [13] This software assists testers to construct test cases from functional specifications. A Unified Modelling Language (UML) class diagram and Object Constraint Language (OCL) are used in this paper to represent the software specifications. Each classification and associated class in the software specification is represented by classes and attributes in the class diagram. Software specification relationships are represented by associated and hierarchical relationships in the class diagram. To ensure that relationships are consistent, an automatic methodology is proposed to capture and control the class relationships in a systematic way. This can help to reduce duplication and illegitimate test cases, which improves the testing efficiency and minimises the time and cost of the testing. The methodology introduced in this paper extracts only the legitimate test cases, by removing the duplicate test cases and those incomputable with the software specifications. Large amounts of time would have been needed to execute all of the test cases; therefore, a methodology was proposed which aimed to select a best testing path. This path guarantees the highest coverage of system units and avoids using all generated test cases. This path reduces the time and cost of the testing.


title: A novel 6-Gbps half-rate SST transmitter with impedance calibration and adjustable pre-emphasis

abstract: A novel half-rate source-series-terminated (SST) transmitter in 65nm bulk CMOS technology is presented in this paper. Compared to previous half-rate SST transmitters, the proposed one consists of four binary-weighted slices increasing proportionally as 1x, 2x, 4x and 8x and the range of pre-emphasis level is increased greatly by the clock-match block to adapt to different channel. The half-rate transmitter can adjust the pre-emphasis level from 1.2dB to 23dB. The transmitter output impedance is adjustable from 33ohms to 64ohms. A power consumption of 24mW is measured at a transmit rate of 6 GB/s which is power-efficient compared to previous half-rate SST transmitter.


title: Comment: Understanding Simpson’s Paradox

abstract: I thank the editor, Ronald Christensen, for the opportunity to discuss this important topic and to comment on the article by Armistead. Simpson’s paradox is often presented as a compelling demonstration of why we need statistics education in our schools. It is a reminder of how easy it is to fall into a web of paradoxical conclusions when relying solely on intuition, unaided by rigorous statistical methods. In recent years, ironically, the paradox assumed an added dimension when educators began using it to demonstrate the limits of statistical methods, and why causal, rather than statistical considerations are necessary to avoid those paradoxical conclusions (Wasserman 2004; Arah 2008; Pearl 2009, pp. 173–182). My comments are divided into three parts. First, I will give a brief summary of the history of Simpson’s paradox and how it has been treated in the statistical literature in the past century. Next, I will ask what is required to declare the paradox “resolved,” and argue that modern understanding of causal inference has met those requirements. Finally, I will answer specific questions raised in Armistead’s article and show how the resolution of Simpson’s paradox can be taught for fun and progress.


title: LinkNBed: Multi-Graph Representation Learning with Entity Linkage

abstract: Knowledge graphs have emerged as an important model for studying complex multi-relational data. This has given rise to the construction of numerous large scale but incomplete knowledge graphs encoding information extracted from various resources. An effective and scalable approach to jointly learn over multiple graphs and eventually construct a unified graph is a crucial next step for the success of knowledge-based inference for many downstream applications. To this end, we propose LinkNBed, a deep relational learning framework that learns entity and relationship representations across multiple graphs. We identify entity linkage across graphs as a vital component to achieve our goal. We design a novel objective that leverage entity linkage and build an efficient multi-task training procedure. Experiments on link prediction and entity linkage demonstrate substantial improvements over the state-of-the-art relational learning approaches.


What clinical findings can be used to diagnose deep venous thrombosis?


title: Relationship between quality of life and distress in infertility: a validation study of the Dutch FertiQoL.

abstract: BACKGROUND ::: This study examined the relationship between emotional distress as measured by the Hospital Anxiety and Depression Scale (HADS) and the Fertility Quality of Life (FertiQoL) questionnaire. ::: ::: ::: METHODS ::: The FertiQoL and HADS were distributed to a random sample of 785 patients attending 29 Dutch clinics for medically assisted reproduction. FertiQoL was psychometrically tested for reliability. Pearson's correlations were calculated between subscales of FertiQoL and HADS. Using an independent t-test, differences between patient subgroups were computed for both instruments. The threshold for clinically meaningful depression/anxiety on the HADS subscales was used to ascertain the critical threshold for high distress on the FertiQoL scales. ::: ::: ::: RESULTS ::: FertiQoL and HADS were completed by 583 patients (response 74%). Reliability of FertiQoL scales was high (reliability coefficient between 0.72 and 0.91). Significant negative correlations were found between FertiQoL subscales and HADS scores for anxiety and depression, ranging from -0.29 to -0.71. Means on FertiQoL scales and HADS scales of couples undergoing an assisted reproductive technology (ART) treatment and a non-ART treatment did not differ significantly. Patients scoring above the HADS threshold for pathology on anxiety had an average FertiQoL score of 58.8, whereas patients exceeding the HADS depression threshold had a FertiQoL total score of 51.9 (range 0-100). ::: ::: ::: CONCLUSIONS ::: Our study confirms the expected negative relation between quality of life as measured by FertiQoL and anxiety and depression. The data support that FertiQoL reliably measures QoL in women facing infertility. FertiQoL enables clinicians to tailor care more specifically to the patient in a comprehensive way.


Learning Technical Drawing with Augmented Reality and Holograms


title: Security Issues and Challenges for Cyber Physical System

abstract: In this paper, we investigate the security challenges and issues of cyber-physical systems. (1)We abstract the general workflow of cyber physical systems, (2)identify the possible vulnerabilities, attack issues, adversaries characteristics and a set of challenges that need to be addressed, (3)then we also propose a context-aware security framework for general cyber-physical systems and suggest some potential research areas and problems.


title: Asymmetric Tri-training for Unsupervised Domain Adaptation

abstract: It is important to apply models trained on a large number of labeled samples to different domains because collecting many labeled samples in various domains is expensive. To learn discriminative representations for the target domain, we assume that artificially labeling the target samples can result in a good representation. Tri-training leverages three classifiers equally to provide pseudo-labels to unlabeled samples; however, the method does not assume labeling samples generated from a different domain. In this paper, we propose the use of an asymmetric tri-training method for unsupervised domain adaptation, where we assign pseudo-labels to unlabeled samples and train the neural networks as if they are true labels. In our work, we use three networks asymmetrically, and by asymmetric, we mean that two networks are used to label unlabeled target samples, and one network is trained by the pseudo-labeled samples to obtain target-discriminative representations. Our proposed method was shown to achieve a state-of-the-art performance on the benchmark digit recognition datasets for domain adaptation.


Social Networking: Addictive, Compulsive, Problematic, or Just Another Media Habit?


title: Semantic full-text search with broccoli

abstract: We combine search in triple stores with full-text search into what we call \emph{semantic full-text search}. We provide a fully functional web application that allows the incremental construction of complex queries on the English Wikipedia combined with the facts from Freebase. The user is guided by context-sensitive suggestions of matching words, instances, classes, and relations after each keystroke. We also provide a powerful API, which may be used for research tasks or as a back end, e.g., for a question answering system. Our web application and public API are available under \url{http://broccoli.cs.uni-freiburg.de}.


title: Task Scheduling Optimization in Cloud Computing Based on Heuristic Algorithm

abstract: Cloud computing is an emerging technology and it allows users to pay as you need and has the high performance. Cloud computing is a heterogeneous system as well and it holds large amount of application data. In the process of scheduling some intensive data or computing an intensive application, it is acknowledged that optimizing the transferring and processing time is crucial to an application program. In this paper in order to minimize the cost of the processing we formulate a model for task scheduling and propose a particle swarm optimization (PSO) algorithm which is based on small position value rule. By virtue of comparing PSO algorithm with the PSO algorithm embedded in crossover and mutation and in the local research, the experiment results show the PSO algorithm not only converges faster but also runs faster than the other two algorithms in a large scale. The experiment results prove that the PSO algorithm is more suitable to cloud computing.


Artificial Intelligence in Design: An Overview


title: Towards Realistic Predictors

abstract: A new class of predictors, denoted realistic predictors, is defined. These are predictors that, like humans, assess the difficulty of examples, reject to work on those that are deemed too hard, but guarantee good performance on the ones they operate on. In this paper, we talk about a particular case of it, realistic classifiers. The central problem in realistic classification, the design of an inductive predictor of hardness scores, is considered. It is argued that this should be a predictor independent of the classifier itself, but tuned to it, and learned without explicit supervision, so as to learn from its mistakes. A new architecture is proposed to accomplish these goals by complementing the classifier with an auxiliary hardness prediction network (HP-Net). Sharing the same inputs as classifiers, the HP-Net outputs the hardness scores to be fed to the classifier as loss weights. Alternatively, the output of classifiers is also fed to HP-Net in a new defined loss, variant of cross entropy loss. The two networks are trained jointly in an adversarial way where, as the classifier learns to improve its predictions, the HP-Net refines its hardness scores. Given the learned hardness predictor, a simple implementation of realistic classifiers is proposed by rejecting examples with large scores. Experimental results not only provide evidence in support of the effectiveness of the proposed architecture and the learned hardness predictor, but also show that the realistic classifier always improves performance on the examples that it accepts to classify, performing better on these examples than an equivalent nonrealistic classifier. All of these make it possible for realistic classifiers to guarantee a good performance.


Secure Autonomous Cyber-Physical Systems Through Verifiable Information Flow Control


Utilizing Argument Mining Techniques for Argumentative Dialogue Systems.


title: Task-Aware Image Downscaling

abstract: Image downscaling is one of the most classical problems in computer vision that aims to preserve the visual appearance of the original image when it is resized to a smaller scale. Upscaling a small image back to its original size is a difficult and ill-posed problem due to information loss that arises in the downscaling process. In this paper, we present a novel technique called task-aware image downscaling to support an upscaling task. We propose an auto-encoder-based framework that enables joint learning of the downscaling network and the upscaling network to maximize the restoration performance. Our framework is efficient, and it can be generalized to handle an arbitrary image resizing operation. Experimental results show that our task-aware downscaled images greatly improve the performance of the existing state-of-the-art super-resolution methods. In addition, realistic images can be recovered by recursively applying our scaling model up to an extreme scaling factor of x128. We also validate our model’s generalization capability by applying it to the task of image colorization.


title: 21.6 A 12nW always-on acoustic sensing and object recognition microsystem using frequency-domain feature extraction and SVM classification

abstract: IoT devices are becoming increasingly intelligent and context-aware. Sound is an attractive sensory modality because it is information-rich but not as computationally demanding as alternatives such as vision. New applications of ultra-low power (ULP), ‘always-on’ intelligent acoustic sensing includes agricultural monitoring to detect pests or precipitation, infrastructure health tracking to recognize acoustic symptoms, and security/safety monitoring to identify dangerous conditions. A major impediment for the adoption of always-on, context-aware sensing is power consumption, particularly for ultra-small IoT devices requiring long-term operation without battery replacement. To sustain operation with a 1mm2 solar cell in ambient light (100lux) or achieve a lifetime of 10 years using a button cell battery (2mAh), <20nW power consumption must be achieved, which is more than 2 orders of magnitude lower than current state-of-the-art acoustic sensing systems [1,2]. More broadly a previous ULP signal acquisition IC [3] consumes just 3nW while 64nW ECG monitoring system [4] includes back-end classification, however there are no sub-20nW complete sensing systems with both analog frontend and digital backend.


title: Regressing Robust and Discriminative 3D Morphable Models with a Very Deep Neural Network

abstract: The 3D shapes of faces are well known to be discriminative. Yet despite this, they are rarely used for face recognition and always under controlled viewing conditions. We claim that this is a symptom of a serious but often overlooked problem with existing methods for single view 3D face reconstruction: when applied in the wild, their 3D estimates are either unstable and change for different photos of the same subject or they are over-regularized and generic. In response, we describe a robust method for regressing discriminative 3D morphable face models (3DMM). We use a convolutional neural network (CNN) to regress 3DMM shape and texture parameters directly from an input photo. We overcome the shortage of training data required for this purpose by offering a method for generating huge numbers of labeled examples. The 3D estimates produced by our CNN surpass state of the art accuracy on the MICC data set. Coupled with a 3D-3D face matching pipeline, we show the first competitive face recognition results on the LFW, YTF and IJB-A benchmarks using 3D face shapes as representations, rather than the opaque deep feature vectors used by other modern systems.


title: The Effect of Corporate Social Responsibility on Organizational Commitment

abstract: 1. INTRODUCTIONOver the years, corporate social responsibility (CSR) has been explored from different perspectives - normative, institutional, stakeholder, integrative, political, transactional - and this multifaceted approach greatly contributed to our understanding of CSR, even if it still remains an "essentially contested concept" (Gond and Moon, 2011). While CSR is often broadly understood as "actions that appear to further some social good, beyond the interest of the firm and that which is required by law" (McWilliams and Siegel 2001:117), the firm's interests have been a great driver of CSR research, visible in the exploration of the link between CSR and firm's (mostly financial) performance (Waddock and Graves, 1997; McWilliams and Siegel, 2001; Orlitzky et al., 2003). Evidence points to the increasing usage of CSR programs as a way of achieving competitive advantage (Matten and Moon, 2008). At the other end of the spectrum, the link between CSR and other organizational outcomes, such as organizational commitment, job satisfaction, and turnover intentions, have remained relatively unexplored (Brammer et al, 2007; Hansen et al., 2010; Turker, 2009b; Lee et al., 2013). While there exist different motivations for CSR, more researchers are beginning to understand and analyze CSR in the context of strategic and value orientation (Jonker and de Witte, 2006), shifting the focus of research from tailoring CSR for better financial performance to that of the overall betterment through CSR (value creation) in the long term, benefitting both stakeholders and the organization. CSR can therefore be redefined as not going beyond firm's interest, but aligning CSR with company interest to create shared value (Porter and Kramer, 2011).In studying how CSR relates to organizational outcomes, researchers have recently become interested in employees' role (Bolton et al., 2011; Kim et al., 2010). Although scholarly discussions have been pointing to CSR being a participative and integrative process (McLagan, 1999), it often occurs that in practice top management dictates the desired values, without employee involvement (Bolton et al., 2011). As internal stakeholders, employees can have a significant impact on both the development and implementation of CSR strategy. Evidence, although limited, points to the positive relationship between CSR and affective organizational commitment (Brammer et al., 2007; Turker, 2009b).There exists an agreement among scholars that CSR, if it is to have a positive impact on organizational outcomes, must be integrated into organization's business strategy and its organizational culture (i.e. Jonker and de Witte, 2006; Collier and Esteban 2007; Carlisle and Faulkner, 2004). Involvement in CSR can range from implementing activities in order to improve company's image and increase short-term profit to the more strategic and more value-building approach which over time becomes embedded into the organizational culture. The relationship between CSR and positive outcomes has not been empirically tested while accounting for embedded CSR culture. In order to better understand the effect of CSR on organizational commitment, this paper explores whether employee engagement in CSR and CSR values, as two elements of CSR organizational culture, mediate the impact of CSR on organizational commitment. Research results are relevant to academics studying relationship between CSR and various outcomes, especially affective organizational commitment, as well as to practitioners who can use these findings to create programs that will help embed CSR culture into their organizations and improve organizational commitment.2. RESEARCH BACKGROUND AND HYPOTHESES DEVELOPMENT2.1. CSR and organizational commitmentPropositions such as "doing well by doing good" and "what is good for society is good for the company" prompted researchers to address the effect of CSR on various organizational outcomes, including workplace attitudes and behavior. …


title: Analogic CNN algorithms for some image compression and restoration tasks

abstract: This paper presents analogic cellular neural network universal machine algorithms for some important image processing tasks. Templates are given for skeletonizing black-and-white and grey-scale images. A method is presented enhancing the quality of copying machines by removing scratches from the copied images. Finally, a similar method is used to enlarge images. All templates use only a nearest neighborhood, therefore being well suited for VLSI implementation. >


title: Training working memory updating in young adults

abstract: Working memory updating (WMU) is a core mechanism in the human mental architecture and a good predictor of a wide range of cognitive processes. This study analyzed the benefits of two different WMU training procedures, near transfer effects on a working memory measure, and far transfer effects on nonverbal reasoning. Maintenance of any benefits a month later was also assessed. Participants were randomly assigned to: an adaptive training group that performed two numerical WMU tasks during four sessions; a non-adaptive training group that performed the same tasks but on a constant and less demanding level of difficulty; or an active control group that performed other tasks unrelated with working memory. After the training, all three groups showed improvements in most of the tasks, and these benefits were maintained a month later. The gain in one of the two WMU measures was larger for the adaptive and non-adaptive groups than for the control group. This specific gain in a task similar to the one trained would indicate the use of a better strategy for performing the task. Besides this nearest transfer effect, no other transfer effects were found. The adaptability of the training procedure did not produce greater improvements. These results are discussed in terms of the training procedure and the feasibility of training WMU.


title: Multilayer transition-metal dichalcogenide channel Thin-Film Transistors

abstract: We show that multilayered transition-metal dichalcogenides such as multilayer MoS 2 present a compelling case for Thin-Film Transistors (TFTs) for large-area display technology. Through a combined structural, optical, and electronic characterization of multilayer MoS 2 TFTs, supported by density-functional theory based bandstructure calculations, we show the inherently attractive properties of these materials for such applications. We find that the current modulation of such devices is high, the current saturation is robust, normally-off operation is feasible, effective field-effect mobility at RT exceeds 100 cm2/V.s, and the channel can be operated in both accumulation and inversion modes. These properties make multilayer MoS 2 more feasible than single layer versions to maintain processing robustness.


title: An Awkward Disparity between BLEU / RIBES Scores and Human Judgements in Machine Translation

abstract: Automatic evaluation of machine translation (MT) quality is essential in developing high quality MT systems. Despite previous criticisms, BLEU remains the most popular machine translation metric. Previous studies on the schism between BLEU and manual evaluation highlighted the poor correlation between MT systems with low BLEU scores and high manual evaluation scores. Alternatively, the RIBES metric—which is more sensitive to reordering—has shown to have better correlations with human judgements, but in our experiments it also fails to correlate with human judgements. In this paper we demonstrate, via our submission to the Workshop on Asian Translation 2015 (WAT 2015), a patent translation system with very high BLEU and RIBES scores and very poor human judgement scores.


title: Speaker Verification and Spoken Language Identification using a Generalized I-vector Framework with Phonetic Tokenizations and Tandem Features

abstract: This paper presents a generalized i-vector framework with phonetic tokenizations and tandem features for speaker verification as well as language identification. First, the tokens for calculating the zero-order statistics is extended from the MFCC trained Gaussian Mixture Models (GMM) components to phonetic phonemes, 3-grams and tandem feature trained GMM components using phoneme posterior probabilities. Second, given the calculated zero-order statistics (posterior probabilities on tokens), the feature used to calculate the first-order statistics is also extended from MFCC to tandem features and is not necessarily the same feature employed by the tokenizer. Third, the zero-order and first-order statistics vectors are then concatenated and represented by the simplified supervised i-vector approach followed by the standard back end modeling methods. We study different system setups with different tokens and features. Finally, selected effective systems are fused at the score level to further improve the performance. Experimental results are reported on the NIST SRE 2010 common condition 5 female part task and the NIST LRE 2007 closed set 30 seconds task for speaker verification and language identification, respectively. The proposed generalized i-vector framework outperforms the i-vector baseline by relatively 45% in terms of equal error rate (EER) and norm minDCF values. Index Terms: speaker verification, language identification, generalized i-vector, phonetic tokenization, tandem feature


title: Big data search for environmental telemetry

abstract: This paper presents how Exalead CloudView is used to search for environmental parameters in big data. In particular, given an environmental application, we propose to leverage trivial and non-trivial connections between different sensor signals, in order to find patterns that are likely to provide innovative solutions to existing problems and to establish different data models. The aggregation of such data models will provide evidence of connections between different events and environmental responses to those triggers, faster and better than trivial mining sensors data. As a consequence, the software has a significant potential for matching environmental applications and challenges that are related in non-obvious ways.


title: HILDA: A discourse parser using support vector machine classification. Dialogue and Discourse

abstract: Discourse structures have a central role in several computational tasks, such as question-answering or dialogue generation. In particular, the framework of the Rhetorical Structure Theory (RST) offers a sound formalism for hierarchical text organization. In this article, we present HILDA, an implemented discourse parser based on RST and Support Vector Machine (SVM) classification. SVM classifiers are trained and applied to discourse segmentation and relation labeling. By combining labeling with a greedy bottom-up tree building approach, we are able to create accurate discourse trees in linear time complexity. Importantly, our parser can parse entire texts, whereas the publicly available parser SPADE (Soricut and Marcu 2003) is limited to sentence level analysis. HILDA outperforms other discourse parsers for tree structure construction and discourse relation labeling. For the discourse parsing task, our system reaches 78.3% of the performance level of human annotators. Compared to a state-of-the-art rule-based discourse parser, our system achieves an performance increase of 11.6%.


title: Sharp Finite-Time Iterated-Logarithm Martingale Concentration

abstract: We give concentration bounds for martingales that are uniform over finite times and extend classical Hoeffding and Bernstein inequalities. We also demonstrate our concentration bounds to be optimal with a matching anti-concentration inequality, proved using the same method. Together these constitute a finite-time version of the law of the iterated logarithm, and shed light on the relationship between it and the central limit theorem.


title: Decision support for diffuse pollution management

abstract: The effort to manage diffuse pollution at the catchment scale is an ongoing challenge that needs to take into account trade-offs between environmental and economic objectives. Best Management Practices (BMPs) are gaining ground as a means to address the problem, but their application (and impact) is highly dependant on the characteristics of the crops and of the land in which they are to be applied. In this paper, we demonstrate a new methodology and associated decision support tool that suggests the optimal location for placing BMPs to minimise diffuse surface water pollution at the catchment scale, by determining the trade-off among economic and multiple environmental objectives. The decision support tool consists of a non-point source (NPS) pollution estimator, the SWAT (Soil and Water Assessment Tool) model, a genetic algorithm (GA), which serves as the optimisation engine for the selection and placement of BMPs across the agricultural land of the catchment, and of an empirical economic function for the estimation of the mean annual cost of BMP implementation. In the proposed decision support tool, SWAT was run a number of times equal to the number of tested BMPs, to predict nitrates nitrogen (N-NO3) and total phosphorus (TP) losses from all the agricultural Hydrologic Response Units (HRUs) and possible BMPs implemented on them. The results were then saved in a database which was subsequently used for the optimisation process. Fifty different BMPs, including sole or combined changes in livestock, crop, soil and nutrient application management in alfalfa, corn and pastureland fields, were evaluated in the reported application of the tool in a catchment in Greece, by solving a three-objective optimisation process (cost, TP and N-NO3). The relevant two-dimensional trade-off curves of cost-TP, cost-N-NO3 and N-NO3-TP are presented and discussed. The strictest environmental target, expressed as a 45% reduction of TP at the catchment outlet, which also resulted in a 25% reduction of the annual N-NO3 yield was met at an affordable annual cost of 25 @?/person by establishing an optimal combination of BMPs. The methodology could be used to assist in a more cost-effective implementation of environmental legislation.


Language , Ideology and Power : a Critical Approach to Political Discourse


title: Smart Cities in Taiwan: A Perspective on Big Data Applications

abstract: In this paper, we discuss the concept of a smart city based on information and communication technology (ICT), analyze the objectives of smart city development in Taiwan, and explain the supporting technologies that make such development possible. Subsequently, we propose a hierarchical structure framework of smart city systems with levels of complexity ranging from low to high and interconnections and interactive relationships in five dimensions: the Internet of Things (IoT), cloud computing, Big Data, Mobile Network, and smart business. We integrate each key resource of the core operation systems of cities to promote the innovative operation of cities and further optimize city development. We then propose a Big Data platform data flow framework that uses information from ubiquitous sensor networks and information equipment to analyze the Big Data application process of smart cities and determine the resulting advantages and challenges. Additionally, we analyze the current state of development of smart cities in Taiwan. Finally, we discuss a new philosophy of smart city development and provide a practical blueprint for the formation, operation, and development of the smart cities with the aim of creating a bright future for the smart cities of Taiwan.


title: Detection of Memory Leaks in C/C++ Code via Machine Learning

abstract: Memory leaks are one of the primary causes of software aging. Despite of recent countermeasures in C/C++ such as smart pointers, leak-related defects remain a troublesome issue in C/C++ code, especially in legacy applications.We propose an approach for automatic detection of memory leaks in C/C++ programs based on characterizing memory allocation sites via the age distribution of the non-disposed memory chunks allocated by such a site (the so-called GenCount-technique introduced for Java by Vladimir Šor). We instrument malloc and free calls in C/C++ and collect for each allocation site data on the number of allocated memory fragments, their lifetimes, and sizes. Based on this data we compute feature vectors and train a machine learning classifier to differentiate between leaky and defect-free allocation sites.Our evaluation uses applications from SPEC CPU2006 suite with injected memory leaks resembling real leaks. The results show that even out-of-the-box classification algorithms can achieve high accuracy, with precision and recall values of 0.93 and 0.88, respectively.


title: Gender differences in learning style preferences among undergraduate physiology students

abstract: Students have individual learning style preferences including visual (V; learning from graphs, charts, and flow diagrams), auditory (A; learning from speech), read-write (R; learning from reading and writing), and kinesthetic (K; learning from touch, hearing, smell, taste, and sight). These preferences can be assessed using the VARK questionnaire. We administered the VARK questionnaire to undergraduate physiology majors enrolled in a capstone physiology laboratory at Michigan State University; 48 of the 86 students (55.8%) who returned the completed questionnaire voluntarily offered gender information. The responses were tallied and assessed for gender difference in learning style preference; 54.2% of females and only 12.5% of males preferred a single mode of information presentation. Among the female students, 4.2% of the students preferred V, 0% of the students preferred A, 16.7% of the students preferred printed words (R), and 33.3% of the students preferred using all their senses (K). In contrast, male students were evenly distributed in preference, with 4.2% of the students preferring A, R, or K, respectively, while 0% of the students preferred V. Furthermore, 45.8% of female and 87.5% of male respondents preferred multiple modes [female: 2 modes (12.5%), 3 modes (12.5%), and 4 modes (20.8%); males: 2 modes (16.7%), 3 modes (12.5%), and 4 modes (58.3%)] of presentation. In summary, a majority of male students preferred multimodal instruction, specifically, four modes (VARK), whereas a majority of female students preferred single-mode instruction with a preference toward K. Thus, male and female students have significantly different learning styles. It is the responsibility of the instructor to address this diversity of learning styles and develop appropriate learning approaches.


WikOLAP: Integration of Wiki and OLAP Systems


title: 3D Reconstruction of Human Motion and Skeleton from Uncalibrated Monocular Video

abstract: This paper introduces a new model-based approach for simultaneously reconstructing 3D human motion and full-body skeletal size from a small set of 2D image features tracked from uncalibrated monocular video sequences. The key idea of our approach is to construct a generative human motion model from a large set of preprocessed human motion examples to constrain the solution space of monocular human motion tracking. In addition, we learn a generative skeleton model from prerecorded human skeleton data to reduce ambiguity of the human skeleton reconstruction. We formulate the reconstruction process in a nonlinear optimization framework by continuously deforming the generative models to best match a small set of 2D image features tracked from a monocular video sequence. We evaluate the performance of our system by testing the algorithm on a variety of uncalibrated monocular video sequences.


title: A Comprehensive Comparison between Neural Style Transfer and Universal Style Transfer

abstract: Style transfer aims to transfer arbitrary visual styles to content images. We explore algorithms adapted from two papers that try to solve the problem of style transfer while generalizing on unseen styles or compromised visual quality. Majority of the improvements made focus on optimizing the algorithm for real-time style transfer while adapting to new styles with considerably less resources and constraints. We compare these strategies and compare how they measure up to produce visually appealing images. We explore two approaches to style transfer: neural style transfer with improvements and universal style transfer. We also make a comparison between the different images produced and how they can be qualitatively measured.


title: A semantic differential study of the influence of aesthetic properties on product pleasure

abstract: In this paper, the influence of the aesthetic properties of products on inducing the feeling of product pleasure on consumers is investigated. In the context of this study, aesthetic properties are considered as a synonym for visual aspects of the products. It is essential to solicit the perspective of a consumer, as different perspectives lead to different perceptions of the same product. The perception about a product is the determining factor for the emotions evoked in the consumer, which will evidently affect the extent of the pleasure. Semantic Differential (SD) method is used in order to examine the evaluation of table-clock samples with dissimilar aesthetic properties. In the SD questionnaire, 14 image-word pairs are employed for the evaluation of 8 table-clock designs, which are presented to 40 subjects. The results indicate that the property 'form' is utterly effective in consumers' perception. The effects of the other aesthetic properties are discussed as well as the classification of the products with respect to the adjectives.


title: SLSB: Improving the Steganographic Algorithm LSB

abstract: This paper presents a novel steganographic algorithm based on the spatial domain: Selected Least Significant Bits (SLSB). It works with the least significant bits of one of the pixel color components in the image and changes them according to the message’s bits to hide. The rest of bits in the pixel color component selected are also changed in order get the nearest color to the original one in the scale of colors. This new method has been compared with others that work in the spatial domain and the great difference is the fact that the LSBs bits of every pixel color component are not used to embed the message, just those from pixel color component selected.


title: Printed circuit board defect detection using mathematical morphology and MATLAB image processing tools

abstract: various concentrated work on detection of defects on printed circuit boards (PCBs) have been done, but it is also crucial to classify these defects in order to analyze and identify the root causes of the defects. This project is aimed in detecting and classifying the defects on bare single layer PCBs by introducing a hybrid algorithm by combining the research done by Heriansyah et al [1] and Khalid [2]. This project proposes a PCB defect detection and classification system using a morphological image segmentation algorithm [1] and simple the image processing theories [2]. Based on initial studies, some PCB defects can only exist in certain groups. Thus, it is obvious that the image processing algorithm could be improved by applying a segmentation exercise. This project uses template and test images of single layer, bare, grayscale computer generated PCBs. The research improves Khalid [2] work by increasing the number of defect categories from 5 to 7, with each category classifying a minimum of 1 to a maximum 4 different types of defects and a total of 13 out of 14 defects were classified.


title: Excision of the polydnavirus chromosomal integrated EP 1 sequence of the parasitoid wasp Cotesia congregata ( Braconidae , Microgastinae ) at potential recombinase binding sites Ste !

abstract: Cotesia congregata polydnavirus (CcPDV) is essential for successful parasitism of Manduca sexta larvae by the braconid wasp Cotesia congregata. To determine the molecular mechanisms for the vertical transmission of CcPDV in the wasps, we analysed the different forms of the virus sequences containing the gene encoding the early parasitism-specific protein 1 (EP1). By a detailed molecular analysis, we demonstrated that the EP1 sequences are present in wasp DNA in two forms: a circular form as seen in the virus particles and a form integrated into the wasp genome. Moreover, we showed that the integrated form of the EP1 sequences is able to excise in the ovary cells. A fragment corresponding to an EP1 'empty locus' (without the viral sequence) was PCR-amplified from ovarian DNA. Comparison of the sequences isolated from the EP1 circle, the integrated form and the empty locus revealed that the extremities of the EP1 genomic sequences constitute a direct repeat. Strikingly, these sequences contain a potential binding site for a recombinase of the Hin family located in close vicinity to the position where the DNA strand exchange occurs. Thus, the data bear upon the possibility that the bracovirus circles are excised via a mechanism related to the Hin mediated Conservative Specific-Specific Recombination (CSSR) of prokaryotes.


title: Deep learning with convolutional neural network in radiology

abstract: Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques.


title: Single-Switch Cell Voltage Equalizer Using Multistacked Buck-Boost Converters Operating in Discontinuous Conduction Mode for Series-Connected Energy Storage Cells

abstract: The cell voltage imbalance of series-connected energy storage cells, such as supercapacitors (SCs) and lithium-ion cells, causes premature deterioration and a decrease in the available energies of the cells. Various equalization techniques have been developed for diminishing such imbalances. However, since the number of switches, sensors, and/or multiwinding transformers present in conventional equalizers is directly proportional to the number of series connections of the cells, the circuit complexity and cost of the equalizers are prone to increase with the number of series connections. In this paper, single-switch cell voltage equalizers using multistacked buck-boost converters, such as the single-ended primary inductor converter (SEPIC), Zeta, and Cuk converters, are proposed. These equalizers consist of passive components and a single switch, significantly reducing the complexity of the circuit when compared with that of conventional equalizers. In addition, when the proposed equalizers operate in discontinuous conduction mode, feedback control is not required to limit currents flowing through cells and circuit components. The proposed equalizers are compared with conventional topologies in terms of the number of active and passive components required. Operating analyses were conducted under both cell-voltage-balanced and -imbalanced conditions. Experimental equalization tests were performed for four series-connected SCs using the SEPIC-based single-switch equalizer. The energies of the series-connected SCs were preferentially redistributed by the equalizer to the cell(s) having the lowest voltage, resulting in the elimination of the cell voltage imbalance and subsequent uniformity of the cell voltages.


title: Deep Unsupervised Learning using Nonequilibrium Thermodynamics

abstract: A central problem in machine learning involves modeling complex data-sets using highly flexible families of probability distributions in which learning, sampling, inference, and evaluation are still analytically or computationally tractable. Here, we develop an approach that simultaneously achieves both flexibility and tractability. The essential idea, inspired by non-equilibrium statistical physics, is to systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process. We then learn a reverse diffusion process that restores structure in data, yielding a highly flexible and tractable generative model of the data. This approach allows us to rapidly learn, sample from, and evaluate probabilities in deep generative models with thousands of layers or time steps, as well as to compute conditional and posterior probabilities under the learned model. We additionally release an open source reference implementation of the algorithm.


title: Targeted Apoptosis of Senescent Cells Restores Tissue Homeostasis in Response to Chemotoxicity and Aging

abstract: The accumulation of irreparable cellular damage restricts healthspan after acute stress or natural aging. Senescent cells are thought to impair tissue function, and their genetic clearance can delay features of aging. Identifying how senescent cells avoid apoptosis allows for the prospective design of anti-senescence compounds to address whether homeostasis can also be restored. Here, we identify FOXO4 as a pivot in senescent cell viability. We designed a FOXO4 peptide that perturbs the FOXO4 interaction with p53. In senescent cells, this selectively causes p53 nuclear exclusion and cell-intrinsic apoptosis. Under conditions where it was well tolerated in vivo, this FOXO4 peptide neutralized doxorubicin-induced chemotoxicity. Moreover, it restored fitness, fur density, and renal function in both fast aging XpdTTD/TTD and naturally aged mice. Thus, therapeutic targeting of senescent cells is feasible under conditions where loss of health has already occurred, and in doing so tissue homeostasis can effectively be restored.


title: Lower bounds and reduction procedures for the bin packing problem

abstract: The bin packing problem, in which a set of items of various sizes has to be packed into a minimum number of identical bins, has been extensively studied during the past fifteen years, mainly with the aim of finding fast heuristic algorithms to provide good approximate solutions. We present lower bounds and a dominance criterion and derive a reduction algorithm. Lower bounds are evaluated through an extension of the concept of worst-case performance. For both lower bounds and reduction algorithm an experimental analysis is provided.


title: Proposing a Software Process Model for Follow the Sun Development.

abstract: Many software organizations are restructuring their software development groups by extending operations to offshore software development centers. Thus, a Follow the Sun (FTS) development is a potential strategy for these organizations. FTS can help with reducing the software development life cycle duration and consequently the time-to-market. However, while the FTS concept looks promising in theory, it appears to be difficult in practice and software organizations have a pressing need for support in how to successfully implement FTS in a global software environment. In this paper, we combine the results from prior work in FTS and a design validation method conducted by experts to propose a software process model for FTS development, named FTS-SPM (Follow the Sun Software Process Model). Our paper describes how we built the FTS-SPM and draws recommendations for software organizations interested in practicing FTS.


title: Generalized Commuting Matrices and Their Eigenvectors for DFTs, Offset DFTs, and Other Periodic Operations

abstract: It is well known that some matrices (such as Dickinson-Steiglitz's matrix) can commute with the discrete Fourier transform (DFT) and that one can use them to derive the complete and orthogonal DFT eigenvector set. Recently, Candan found the general form of the DFT commuting matrix. In this paper, we further extend the previous work and find the general form of the commuting matrix for any periodic, quasi-periodic, and offset quasi-periodic operations. Using the general commuting matrix, we can derive the complete and orthogonal eigenvector sets for offset DFTs, DCTs of types 1, 4, 5, and 8, DSTs of types 1, 4, 5, and 8, discrete Hartley transforms of types 1 and 4, the Walsh transform, and the projection operation (the operation that maps a whole vector space into a subspace) successfully. Moreover, several novel ways of finding DFT eigenfunctions are also proposed. Furthermore, we also extend our theories to the continuous case, i.e., if a continuous transform is periodic, quasi-periodic, or offset quasi-periodic (such as the FT and some cyclic operations in optics), we can find the general form of the commuting operation and then find the complete and orthogonal eigenfunctions set for the continuous transform.


title: On Skyline Groups

abstract: We formulate and investigate the novel problem of finding the skyline k-tuple groups from an n-tuple data set-i.e., groups of k tuples which are not dominated by any other group of equal size, based on aggregate-based group dominance relationship. The major technical challenge is to identify effective anti-monotonic properties for pruning the search space of skyline groups. To this end, we first show that the anti-monotonic property in the well-known Apriori algorithm does not hold for skyline group pruning. Then, we identify two anti-monotonic properties with varying degrees of applicability: order-specific property which applies to SUM, MIN, and MAX as well as weak candidate-generation property which applies to MIN and MAX only. Experimental results on both real and synthetic data sets verify that the proposed algorithms achieve orders of magnitude performance gain over the baseline method.


title: Lazy Decision Trees

abstract: Lazy learning algorithms, exemplified by nearest-neighbor algorithms, do not induce a concise hypothesis from a given training set; the inductive process is delayed until a test instance is given. Algorithms for constructing decision trees, such as C4.5, ID3, and CART create a single "best" decision tree during the training phase, and this tree is then used to classify test instances. The tests at the nodes of the constructed tree are good on average, but there may be better tests for classifying a specific instance. We propose a lazy decision tree algorithm--LAZYDT--that conceptually constructs the "best" decision tree for each test instance. In practice, only a path needs to be constructed, and a caching scheme makes the algorithm fast. The algorithm is robust with respect to missing values without resorting to the complicated methods usually seen in induction of decision trees. Experiments on real and artificial problems are presented.


title: Query recommendation using query logs in search engines

abstract: In this paper we propose a method that, given a query submitted to a search engine, suggests a list of related queries The related queries are based in previously issued queries, and can be issued by the user to the search engine to tune or redirect the search process The method proposed is based on a query clustering process in which groups of semantically similar queries are identified The clustering process uses the content of historical preferences of users registered in the query log of the search engine The method not only discovers the related queries, but also ranks them according to a relevance criterion Finally, we show with experiments over the query log of a search engine the effectiveness of the method.


title: Visualization of Multidimensional Design and Optimization Data Using Cloud Visualization

abstract: As our ability to generate more and more data for increasingly large engineering models improves, the need for methods for managing that data becomes greater. Information management from a decision-making perspective involves being able to capture and represent significant information to a designer so that they can make effective and efficient decisions. However, most visualization techniques used in engineering, such as graphs and charts, are limited to two-dimensional representations and at most three-dimensional representations. In this paper, we present a new visualization technique to capture and represent engineering information in a multidimensional context. The new technique, Cloud Visualization, is based upon representing sets of points as clouds in both the design and performance spaces. The technique is applicable to both single and multiobjective optimization problems and the relevant issues with each type of problem are discussed. A multiobjective case study is presented to demonstrate the application and usefulness of the Cloud Visualization techniques.Copyright © 2002 by ASME


title: Locality sensitive semi-supervised feature selection

abstract: In many computer vision tasks like face recognition and image retrieval, one is often confronted with high-dimensional data. Procedures that are analytically or computationally manageable in low-dimensional spaces can become completely impractical in a space of several hundreds or thousands dimensions. Thus, various techniques have been developed for reducing the dimensionality of the feature space in the hope of obtaining a more manageable problem. The most popular feature selection and extraction techniques include Fisher score, Principal Component Analysis (PCA), and Laplacian score. Among them, PCA and Laplacian score are unsupervised methods, while Fisher score is supervised method. None of them can take advantage of both labeled and unlabeled data points. In this paper, we introduce a novel semi-supervised feature selection algorithm, which makes use of both labeled and unlabeled data points. Specifically, the labeled points are used to maximize the margin between data points from different classes, while the unlabeled points are used to discover the geometrical structure of the data space. We compare our proposed algorithm with Fisher score and Laplacian score on face recognition. Experimental results demonstrate the efficiency and effectiveness of our algorithm.


title: A New Large Urdu Database for Off-Line Handwriting Recognition

abstract: A new large Urdu handwriting database, which includes isolated digits, numeral strings with/without decimal points, five special symbols, 44 isolated characters, 57 Urdu words (mostly financial related), and Urdu dates in different patterns, was designed at Centre for Pattern Recognition and Machine Intelligence (CENPARMI). It is the first database for Urdu off-line handwriting recognition. It involves a large number of Urdu native speakers from different regions of the world. Moreover, the database has different formats --- true color, gray level and binary. Experiments on Urdu digits recognition has been conducted with an accuracy of 98.61%. Methodologies in image pre-processing, gradient feature extraction and classification using SVM have been described, and a detailed error analysis is presented on the recognition results.


title: A Classification of Trust Computation Models for Service-Oriented Internet of Things Systems

abstract: Future Internet of Things (IoT) systems will connect the physical world into cyberspace everywhere and everything via billions of smart objects and are expected to have a high economic impact. To date there is little work on trust computation in IoT environments for security enhancement, especially for dealing with misbehaving owners of IoT devices that provide services to other IoT devices in the system. In this paper we classify trust computation models to-date for IoT systems. Our approach is to classify existing trust computation models based on five design dimensions: trust composition, trust propagation, trust aggregation, trust update, and trust formation. We summarize advantages and drawbacks of each dimension's options, and highlight the effectiveness of defense mechanisms against malicious attacks. We also summarize the most and least studied trust computation techniques in the literature and provide insight on the effectiveness of trust computation techniques as applying to IoT systems. Finally, we identify gaps in IoT trust computation research and suggest future research directions.


title: Sex bias in neuroscience and biomedical research

abstract: Female mammals have long been neglected in biomedical research. The NIH mandated enrollment of women in human clinical trials in 1993, but no similar initiatives exist to foster research on female animals. We reviewed sex bias in research on mammals in 10 biological fields for 2009 and their historical precedents. Male bias was evident in 8 disciplines and most prominent in neuroscience, with single-sex studies of male animals outnumbering those of females 5.5 to 1. In the past half-century, male bias in non-human studies has increased while declining in human studies. Studies of both sexes frequently fail to analyze results by sex. Underrepresentation of females in animal models of disease is also commonplace, and our understanding of female biology is compromised by these deficiencies. The majority of articles in several journals are conducted on rats and mice to the exclusion of other useful animal models. The belief that non-human female mammals are intrinsically more variable than males and too troublesome for routine inclusion in research protocols is without foundation. We recommend that when only one sex is studied, this should be indicated in article titles, and that funding agencies favor proposals that investigate both sexes and analyze data by sex.


title: Automatic Arabic License Plate Recognition

abstract:  Abstract—Automatic License Plate (LP) recognition uses optical character recognition to read LPs on vehicles, such system is essential for traffic control, theft control, electronic toll collection, parking lots access and/or billing. The proposed Arabic LP recognition system is applied to Saudi Arabian LPs which have two different formats. Therefore, this system recognizes both Arabic and Indian numerals as well as limited Arabic and Latin alphabets. The system goes through many preprocessing steps in order to produce segmented characters of the LPs images. The feature extraction step uses the count of black pixels from the horizontal projection profiles in addition to the black pixel distributions in divided zones of the character image. The recognition is performed separately based on different regions; therefore, applying the recognition process to specific regions of the LPs; speeds up the processing due to the limited number of comparisons. The recognition step uses both a distance classifier and Neural Network (NN) classifier to discriminate between the different characters. This proposed system provides more importance to the preprocessing stage whose success guarantees the successful performance of the whole system. Also, the Arabic characters are verified against the corresponding Latin characters and similarly the Arabic numerals to their corresponding Indian numerals. The system is tested on 462 correctly localized LPs providing a 98.63% character recognition rate and a total of 94.9% accepted LPs.


title: Curvature compensated BiCMOS bandgap with 1 V supply voltage

abstract: In this paper we present a bandgap circuit capable of generating a reference voltage of 0.54 V. The circuit, implemented in a submicron BiCMOS technology, operates with a supply voltage of 1 V. In the bandgap circuit proposed we use a non-conventional operational amplifier which achieves virtually zero systematic offset, operating directly from the 1 V power supply. The bandgap architecture used allows a straightforward implementation of the curvature compensation method. The proposed circuit achieves 5 ppm / K of accuracy without requiring additional operational amplifiers or complex circuits.


title: Deep Ordinal Regression Network for Monocular Depth Estimation

abstract: Monocular depth estimation, which plays a crucial role in understanding 3D scene geometry, is an ill-posed problem. Recent methods have gained significant improvement by exploring image-level information and hierarchical features from deep convolutional neural networks (DCNNs). These methods model depth estimation as a regression problem and train the regression networks by minimizing mean squared error, which suffers from slow convergence and unsatisfactory local solutions. Besides, existing depth estimation networks employ repeated spatial pooling operations, resulting in undesirable low-resolution feature maps. To obtain high-resolution depth maps, skip-connections or multilayer deconvolution networks are required, which complicates network training and consumes much more computations. To eliminate or at least largely reduce these problems, we introduce a spacing-increasing discretization (SID) strategy to discretize depth and recast depth network learning as an ordinal regression problem. By training the network using an ordinary regression loss, our method achieves much higher accuracy and faster convergence in synch. Furthermore, we adopt a multi-scale network structure which avoids unnecessary spatial pooling and captures multi-scale information in parallel. The proposed deep ordinal regression network (DORN) achieves state-of-the-art results on three challenging benchmarks, i.e., KITTI [16], Make3D [49], and NYU Depth v2 [41], and outperforms existing methods by a large margin.


Creative Exercises in General Chemistry: A Student-Centered Assessment.


A Review of Remote Patient Monitoring System : Potentials , Challenges and Current Issues


title: Agricultural uses of plant biostimulants

abstract: Background ::: Plant biostimulants are diverse substances and microorganisms used to enhance plant growth. The global market for biostimulants is projected to increase 12 % per year and reach over $2,200 million by 2018. Despite the growing use of biostimulants in agriculture, many in the scientific community consider biostimulants to be lacking peer-reviewed scientific evaluation.


title: Differential symbolic execution

abstract: Detecting and characterizing the effects of software changes is a fundamental component of software maintenance. Version differencing information can be used to perform version merging, infer change characteristics, produce program documentation, and guide program re-validation. Existing techniques for characterizing code changes, however, are imprecise leading to unnecessary maintenance efforts. In this paper, we introduce a novel extension and application of symbolic execution techniques that computes a precise behavioral characterization of a program change. This technique, which we call differential symbolic execution (DSE), exploits the fact that program versions are largely similar to reduce cost and improve the quality of analysis results. We define the foundational concepts of DSE, describe cost-effective tool support for DSE, and illustrate its potential benefit through an exploratory study that considers version histories of two Java code bases.


title: An online method for tight-tolerance insertion tasks for string and rope

abstract: This paper presents a fast tight-tolerance threading technique for string and rope. Instead of relying on simulations of these deformable objects to plan a path or compute control actions, we control the movement of the string with a virtual magnetic vector field emanating from the narrow openings we wish to thread through. We compute an approximate Jacobian to move the tip of the string through the vector field and propose a method to promote alignment of the head of the string to the opening. We also propose a method for re-grasping the string based on the relationship between the string's configuration, the orientation of the opening, and direction of gravity. This re-grasping method in conjunction with our controller can be used to thread the string through a sequence of openings. We evaluated our method in simulation (with simulated sensor noise) and on the Da Vinci surgical robot. Our results suggest that our method is quite robust to errors in sensing, and is capable of real-world threading tasks with the da Vinci robot, where the diameter of the string (3.5mm) and opening (4.9mm) differ by only 1.4 mm.


title: Color Image Segmentation Using a Spatial K-Means Clustering Algorithm

abstract: This paper details the implementation of a new adaptive technique for color-texture segmentation that is a generalization of the standard K-Means algorithm. The standard K-Means algorithm produces accurate segmentation results only when applied to images defined by homogenous regions with respect to texture and color since no local constraints are applied to impose spatial continuity. In addition, the initialization of the K-Means algorithm is problematic and usually the initial cluster centers are randomly picked. In this paper we detail the implementation of a novel technique to select the dominant colors from the input image using the information from the color histograms. The main contribution of this work is the generalization of the K-Means algorithm that includes the primary features that describe the color smoothness and texture complexity in the process of pixel assignment. The resulting color segmentation scheme has been applied to a large number of natural images and the experimental data indicates the robustness of the new developed segmentation algorithm.


title: Self-Guiding Multimodal LSTM—When We Do Not Have a Perfect Training Dataset for Image Captioning

abstract: In this paper, a self-guiding multimodal LSTM (sg-LSTM) image captioning model is proposed to handle uncontrolled imbalanced real-world image-sentence dataset. We collect FlickrNYC dataset from Flickr as our testbed with 306,165 images and the original text descriptions uploaded by the users are utilized as the ground truth for training. Descriptions in FlickrNYC dataset vary dramatically ranging from short term-descriptions to long paragraph-descriptions and can describe any visual aspects, or even refer to objects that are not depicted. To deal with the imbalanced and noisy situation and to fully explore the dataset itself, we propose a novel guiding textual feature extracted utilizing a multimodal LSTM (m-LSTM) model. Training of m-LSTM is based on the portion of data in which the image content and the corresponding descriptions are strongly bonded. Afterwards, during the training of sg-LSTM on the rest training data, this guiding information serves as additional input to the network along with the image representations and the ground-truth descriptions. By integrating these input components into a multimodal block, we aim to form a training scheme with the textual information tightly coupled with the image content. The experimental results demonstrate that the proposed sg-LSTM model outperforms the traditional state-of-the-art multimodal RNN captioning framework in successfully describing the key components of the input images.


title: Hierarchical graph indexing

abstract: Traffic analysis, in the context of Telecommunications or Internet and Web data, is crucial for large network operations. Data in such networks is often provided as large graphs with hundreds of millions of vertices and edges. We propose efficient techniques for managing such graphs at the storage level in order to facilitate its processing at the interface level(visualization). The methods are based on a hierarchical decomposition of the graph edge set that is inherited from a hierarchical decomposition of the vertex set. Real time navigation is provided by an efficient two level indexing schema called the gkd*-tree. The first level is a variation of a kd-tree index that partitions the edge set in a way that conforms to the hierarchical decomposition and the data distribution (the gkd-tree). The second level is a redundant R-tree that indexes the leaf pages of the gkd-tree. We provide computational results that illustrate the superiority of the gkd-tree against conventional indexes like the kd-tree and the R*-tree both in creation as well as query response times.


title: Anesthesia following overfilling of a root canal sealer into the mandibular canal: A case report

abstract: The surgical treatment of a case of anesthesia that occurred with the extrusion of Endomethasone root canal sealer into the mandibular canal is presented. Endomethasone is a neurotoxic root canal sealer containing paraformaldehyde and eugenol. The literature indicates immediate surgical decompression on the extrusion of Endomethasone into the mandibular canal. In our case, the decompression surgery was done 3 weeks after the endodontic mishap. The nearly complete resolution of anesthesia 4 months following the decompression surgery suggests that the neurotoxic effects of Endomethasone are still reversible after 3 weeks.


title: Network Anomalies Detection Using Statistical Technique: A Chi- Square approach

abstract: Intrusion Detection System is used to detect suspicious activities is one form of defense. However, the sheer size of the network logs makes human log analysis intractable. Furthermore, traditional intrusion detection methods based on pattern matching techniques cannot cope with the need for faster speed to manually update those patterns. Anomaly detection is used as a part of the intrusion detection system, which in turn use certain data mining techniques. Data mining techniques can be applied to the network data to detect possible intrusions. The foremost step in application of data mining techniques is the selection of appropriate features from the data. This paper aims to build an Intrusion Detection System that can detect known and unknown intrusion automatically. Under a data mining framework, the IDS are trained with statistical algorithm, named Chi-Square statistics. This study shows the plan, implementation and the analyze of these threats by using a Chi-Square statistic technique, in order to prevent these attacks and to make a Network Intrusion detection system (NIDS). This proposed model is used to detect anomaly-based network to see how effective this statistical technique in detecting intrusions.


title: Measuring Catastrophic Forgetting in Neural Networks

abstract: Deep neural networks are used in many state-of-the-art systems for machine perception. Once a network is trained to do a specific task, e.g., bird classification, it cannot easily be trained to do new tasks, e.g., incrementally learning to recognize additional bird species or learning an entirely different task such as flower recognition. When new tasks are added, typical deep neural networks are prone to catastrophically forgetting previous tasks. Networks that are capable of assimilating new information incrementally, much like how humans form new memories over time, will be more efficient than re-training the model from scratch each time a new task needs to be learned. There have been multiple attempts to develop schemes that mitigate catastrophic forgetting, but these methods have not been directly compared, the tests used to evaluate them vary considerably, and these methods have only been evaluated on small-scale problems (e.g., MNIST). In this paper, we introduce new metrics and benchmarks for directly comparing five different mechanisms designed to mitigate catastrophic forgetting in neural networks: regularization, ensembling, rehearsal, dual-memory, and sparse-coding. Our experiments on real-world images and sounds show that the mechanism(s) that are critical for optimal performance vary based on the incremental training paradigm and type of data being used, but they all demonstrate that the catastrophic forgetting problem has yet to be solved.


DL 4 MD : A Deep Learning Framework for Intelligent Malware Detection


title: Learning Simpler Language Models with the Differential State Framework

abstract: Learning useful information across long time lags is a critical and difficult problem for temporal neural models in tasks such as language modeling. Existing architectures that address the issue are often complex and costly to train. The differential state framework (DSF) is a simple and high-performing design that unifies previously introduced gated neural models. DSF models maintain longer-term memory by learning to interpolate between a fast-changing data-driven representation and a slowly changing, implicitly stable state. Within the DSF framework, a new architecture is presented, the delta-RNN. This model requires hardly any more parameters than a classical, simple recurrent network. In language modeling at the word and character levels, the delta-RNN outperforms popular complex architectures, such as the long short-term memory (LSTM) and the gated recurrent unit (GRU), and, when regularized, performs comparably to several state-of-the-art baselines. At the subword level, the delta-RNN's performance ...


title: Regulatory Challenges for Cartilage Repair Technologies

abstract: In the United States, few Food and Drug Administration (FDA)–approved options exist for the treatment of focal cartilage and osteochondral lesions. Developers of products for cartilage repair face many challenges to obtain marketing approval from the FDA. The objective of this review is to discuss the necessary steps for FDA application and approval for a new cartilage repair product. FDA Guidance Documents, FDA Panel Meetings, scientific organization recommendations, and clinicaltrials.gov were reviewed to demonstrate the current thinking of FDA and the scientific community on the regulatory process for cartilage repair therapies. Cartilage repair therapies can receive market approval from FDA as medical devices, drugs, or biologics, and the specific classification of product can affect the nonclinical, clinical, and regulatory strategy to bring the product to market. Recent FDA guidance gives an outline of the required elements to bring a cartilage repair product to market, although these standards are often very general. As a result, companies have to carefully craft their study patient population, comparator group, and clinical endpoint to best showcase their product’s attributes. In addition, regulatory strategy and manufacturing process validation need to be considered early in the clinical study process to allow for timely product approval following the completion of clinical study. Although the path to regulatory approval for a cartilage repair therapy is challenging and time-consuming, proper clinical trial planning and attention to the details can eventually save companies time and money by bringing a product to the market in the most expeditious process possible.


title: A Hierarchical Bayesian Language Model Based On Pitman-Yor Processes

abstract: We propose a new hierarchical Bayesian n-gram model of natural languages. Our model makes use of a generalization of the commonly used Dirichlet distributions called Pitman-Yor processes which produce power-law distributions more closely resembling those in natural languages. We show that an approximation to the hierarchical Pitman-Yor language model recovers the exact formulation of interpolated Kneser-Ney, one of the best smoothing methods for n-gram language models. Experiments verify that our model gives cross entropy results superior to interpolated Kneser-Ney and comparable to modified Kneser-Ney.