title: VLA Radio Observations of the HST Frontier Fields Cluster Abell 2744: The Discovery of New Radio Relics

abstract: Cluster mergers leave distinct signatures in the ICM in the form of shocks and diffuse cluster radio sources that provide evidence for the acceleration of relativistic particles. However, the physics of particle acceleration in the ICM is still not fully understood. Here we present new 1-4 GHz Jansky Very Large Array (VLA) and archival Chandra observations of the HST Frontier Fields Cluster Abell 2744. In our new VLA images, we detect the previously known $\sim2.1$ Mpc radio halo and $\sim1.5$ Mpc radio relic. We carry out a radio spectral analysis from which we determine the relic's injection spectral index to be $\alpha_{\rm{inj}} = -1.12 \pm 0.19$. This corresponds to a shock Mach number of $\mathcal{M}$ = 2.05$^{+0.31}_{-0.19}$ under the assumption of diffusive shock acceleration. We also find evidence for spectral steepening in the post-shock region. We do not find evidence for a significant correlation between the radio halo's spectral index and ICM temperature. In addition, we observe three new polarized diffuse sources and determine two of these to be newly discovered giant radio relics. These two relics are located in the southeastern and northwestern outskirts of the cluster. The corresponding integrated spectral indices measure $-1.81 \pm 0.26$ and $-0.63 \pm 0.21$ for the SE and NW relics, respectively. From an X-ray surface brightness profile we also detect a possible density jump of $R=1.39^{+0.34}_{-0.22}$ co-located with the newly discovered SE relic. This density jump would correspond to a shock front Mach number of $\mathcal{M}=1.26^{+0.25}_{-0.15}$.


title: The Mean Free Path of Electrons in Polar Crystals

abstract: Polar crystals of stoichiometric composition at low temperatures are insulators of electricity. If, however, electrons are raised into the normally empty conduction band of energy levels, either through the absorption of light or the thermal energy of surrounding atoms, the crystal can conduct. The purpose of this note is to calculate the mean free path of such electrons, and hence their mobility (velocity of drift in unit field). The results obtained will be compared with experimental material obtained from semi-conductors and from substances which show photoconductivity.


title: An Adaptive Intelligent Management System of Advertising for Social Networks: A Case Study of Facebook

abstract: At present, social networks are strongly used for personal or professional purposes. The social networks have accumulated a large amount of information, which can be used in different ways. One of such possible purposes is advertising. In particular, when a company pays for advertising in a social network, it wants to get an ideal deployment of the ads on the network. The ideal behavior aims at reducing the high costs to pay, the number of hours to design advertising, the difficulty in the creation of ads by lots, among other things. In particular, the optimization of the production of advertising on social networks, based on the performance of the ads, is not an easy task. If we make an incorrect design, this can result in losses for a company. In this sense, this paper presents an intelligent system of management of social network advertising, based on data mining techniques, to automatically produce ads. In addition, we test our adaptive mechanism of automated production (generation) of online advertising on Facebook. Our system carries out automatic modifications and improvements of ads. Facebook is one of the more popular social networks, being widely used by companies for advertising, in order to create contents attracting the attention of users.


title: On the Significance of Process Comprehension for Conducting Targeted ICS Attacks

abstract: The exploitation of Industrial Control Systems (ICSs) has been described as both easy and impossible, where is the truth? PostStuxnet works have included a plethora of ICS focused cyber security research activities, with topics covering device maturity, network protocols, and overall cyber security culture. We often hear the notion of ICSs being highly vulnerable due to a lack of inbuilt security mechanisms, considered a low hanging fruit to a variety of low skilled threat actors. While there is substantial evidence to support such a notion, when considering targeted attacks on ICS, it is hard to believe an attacker with limited resources, such as a script kiddie or hacktivist, using publicly accessible tools and exploits alone, would have adequate knowledge and resources to achieve targeted operational process manipulation, while simultaneously evade detection. Through use of a testbed environment, this paper provides two practical examples based on a Man-In-The-Middle scenario, demonstrating the types of information an attacker would need obtain, collate, and comprehend, in order to begin targeted process manipulation and detection avoidance. This allows for a clearer view of associated challenges, and illustrate why targeted ICS exploitation might not be possible for every malicious actor.


title: How understanding the neurobiology of complex post-traumatic stress disorder can inform clinical practice: a social cognitive and affective neuroscience approach

abstract: Objective: In this review, we examine the relevance of the social cognitive and affective neuroscience (SCAN) paradigm for an understanding of the psychology and neurobiology of complex post‐traumatic stress disorder (PTSD) and its effective treatment. Method: The relevant literature pertaining to SCAN and PTSD was reviewed. Results: We suggest that SCAN offers a novel theoretical paradigm for understanding psychological trauma and its numerous clinical outcomes, most notably problems in emotional/self‐awareness, emotion regulation, social emotional processing and self‐referential processing. A core set of brain regions appear to mediate these collective psychological functions, most notably the cortical midline structures, the amygdala, the insula, posterior parietal cortex and temporal poles, suggesting that problems in one area (e.g. emotional awareness) may relate to difficulties in another (e.g. self‐referential processing). We further propose, drawing on clinical research, that the experiences of individuals with PTSD related to chronic trauma often reflect impairments in multiple social cognitive and affective functions. Conclusion: It is important that the assessment and treatment of individuals with complex PTSD not only addresses traumatic memories but also takes a SCAN‐informed approach that focuses on the underlying deficits in emotional/self‐awareness, emotion regulation, social emotional processing and self‐referential processing.


title: Model Predictive-Based Maximum Power Point Tracking for Grid-Tied Photovoltaic Applications Using a Z-Source Inverter

abstract: This paper presents a model predictive-based maximum power point tracking (MPPT) method for a photovoltaic energy harvesting system based on a single-stage grid-tied Z -source inverter. First, it provides a brief review of Z -source inverters, MPPT methods and model predictive control. Next, it introduces the proposed model predictive-based MPPT method. Finally, it provides experimental results to verify the theoretical outcomes.


title: Automating Direct Speech Variations in Stories and Games

abstract: Dialogue authoring in large games requires not only content creation but the subtlety of its delivery, which can vary from character to character. Manually authoring this dialogue can be tedious, time-consuming, or even altogether infeasible. This paper utilizes a rich narrative representation for modeling dialogue and an expressive natural language generation engine for realizing it, and expands upon a translation tool that bridges the two. We add functionality to the translator to allow direct speech to be modeled by the narrative representation, whereas the original translator supports only narratives told by a third person narrator. We show that we can perform character substitution in dialogues. We implement and evaluate a potential application to dialogue implementation: generating dialogue for games with big, dynamic, or procedurally-generated open worlds. We present a pilot study on human perceptions of the personalities of characters using direct speech, assuming unknown personality types at the time of authoring.


title: TweetCred: Real-Time Credibility Assessment of Content on Twitter

abstract: During sudden onset crisis events, the presence of spam, rumors and fake content on Twitter reduces the value of information contained on its messages (or "tweets"). A possible solution to this problem is to use machine learning to automatically evaluate the credibility of a tweet, i.e. whether a person would deem the tweet believable or trustworthy. This has been often framed and studied as a supervised classification problem in an off-line (post-hoc) setting. In this paper, we present a semi-supervised ranking model for scoring tweets according to their credibility. This model is used in TweetCred, a real-time system that assigns a credibility score to tweets in a user's timeline. TweetCred, available as a browser plug-in, was installed and used by 1,127 Twitter users within a span of three months. During this period, the credibility score for about 5.4 million tweets was computed, allowing us to evaluate TweetCred in terms of response time, effectiveness and usability. To the best of our knowledge, this is the first research work to develop a real-time system for credibility on Twitter, and to evaluate it on a user base of this size.


title: Question taxonomy and implications for automatic question generation

abstract: Many Automatic Question Generation (AQG) approaches have been proposed focusing on reading comprehension support; however, none of them addressed academic writing. We conducted a large-scale case study with 25 supervisors and 36 research students enroled in an Engineering Research Method course. We investigated trigger questions, as a form of feedback, produced by supervisors, and how they support these students' literature review writing. In this paper, we identified the most frequent question types according to Graesser and Person's Question Taxonomy and discussed how the human experts generate such questions from the source text. Finally, we proposed a more practical Automatic Question Generation Framework for supporting academic writing in engineering education.


title: Design and operation of Wi-Fi agribot integrated system

abstract: Robotics in agriculture is not a new concept; in controlled environments (green houses), it has a history of over 20 years. Research has been performed to develop harvesters for cherry tomatoes, cucumbers, mushrooms, and other fruits. In horticulture, robots have been introduced to harvest citrus and apples. In this paper autonomous robot for agriculture (AgriBot) is a prototype and implemented for performing various agricultural activities like seeding, weeding, spraying of fertilizers, insecticides. AgriBot is controlled with a Arduino Mega board having At mega 2560 microcontroller. The powerful Raspberry Pi a mini computer is used to control and monitor the working of the robot. The Arduino Mega is mounted on a robot allowing for access to all of the pins for rapid prototyping. Its hexapod body can autonomously walk in any direction, avoiding objects with its ultrasonic proximity sensor. Its walking algorithms allow it to instantly change direction and walk in any new direction without turning its body. An underbody sensory array allows the robot to know if a seed has been planted in the area at the optimal spacing and depth. AgriBot can then dig a hole, plant a seed in the hole, cover the seed with soil, and apply any pre-emergence fertilizers and/or herbicides along with the marking agent. AgriBot can then signal to other robots in the immediate proximity that it needs help planting in that area or that this area has been planted and to move on by communicating through Wi-Fi.


title: A history of robots: from science fiction to surgical robotics

abstract: Surgical robotics is an evolving field with great advances having been made over the last decade. The origin of robotics was in the science-fiction literature and from there industrial applications, and more recently commercially available, surgical robotic devices have been realized. In this review, we examine the field of robotics from its roots in literature to its development for clinical surgical use. Surgical mills and telerobotic devices are discussed, as are potential future developments.


title: Debbie, the Debate Bot of the Future

abstract: Chatbots are a rapidly expanding application of dialogue systems with companies switching to bot services for customer support, and new applications for users interested in casual conversation. One style of casual conversation is argument; many people love nothing more than a good argument. Moreover, there are a number of existing corpora of argumentative dialogues, annotated for agreement and disagreement, stance, sarcasm and argument quality. This paper introduces Debbie, a novel arguing bot, that selects arguments from conversational corpora, and aims to use them appropriately in context. We present an initial working prototype of Debbie, with some preliminary evaluation and describe future work.


title: OpenStack: Toward an Open-Source Solution for Cloud Computing

abstract: Cloud computing is a quite new concept for which the resources are virtualized, dynamically extended and provided as a service on the Internet. In this paper, we present a comparative study between some of the IaaS (Infrastructure as a Service) commonly used to select the best suited one for deployment and research development in the field of cloud computing. The aim is to provide the computer industry with the opportunity to build a hosting architecture, massively scalable which is completely open source, while overcoming the constraints and the use of proprietary technologies. Then, we present the solution OpenStack retained by the comparative study. We discuss in detail its functional and architectural system. We finish by a discussion of the motivation of our choice of the IaaS solution. General Terms:


title: Super-resolution through neighbor embedding

abstract: In this paper, we propose a novel method for solving single-image super-resolution problems. Given a low-resolution image as input, we recover its high-resolution counterpart using a set of training examples. While this formulation resembles other learning-based methods for super-resolution, our method has been inspired by recent manifold teaming methods, particularly locally linear embedding (LLE). Specifically, small image patches in the lowand high-resolution images form manifolds with similar local geometry in two distinct feature spaces. As in LLE, local geometry is characterized by how a feature vector corresponding to a patch can be reconstructed by its neighbors in the feature space. Besides using the training image pairs to estimate the high-resolution embedding, we also enforce local compatibility and smoothness constraints between patches in the target high-resolution image through overlapping. Experiments show that our method is very flexible and gives good empirical results.


title: Don't Use Large Mini-Batches, Use Local SGD

abstract: Mini-batch stochastic gradient methods (SGD) are state of the art for distributed training of deep neural networks. Drastic increases in the mini-batch sizes have lead to key efficiency and scalability gains in recent years. However, progress faces a major roadblock, as models trained with large batches often do not generalize well. Local SGD can offer the same communication vs. computation pattern as mini-batch SGD---thus is as efficient as mini-batch SGD from a systems perspective---but instead of performing a single large-batch update in each round, it performs several local parameter updates sequentially. We extensively study the communication efficiency vs. performance trade-offs associated with local SGD and provide a new variant, called \emph{post-local SGD}. We show that it significantly improves the generalization performance compared to large-batch training and converges to flatter minima.


title: SSID Oracle Attack on Undisclosed Wi-Fi Preferred Network Lists

abstract: User’s location privacy concerns have been further raised by today’s Wi-Fi technology omnipresence. Preferred Network Lists (PNLs) are a particularly interesting source of private location information, as devices are storing a list of previously used hotspots. Privacy implications of a disclosed PNL have been covered by numerous papers, mostly focusing on passive monitoring attacks. Nowadays, however, more and more devices no longer transmit their PNL in clear, thus mitigating passive attacks. Hidden PNLs are still vulnerable against active attacks whereby an attacker mounts a fake SSID hotspot set to one likely contained within targeted PNL. If the targeted device has this SSID in the corresponding PNL, it will automatically initiate a connection with the fake hotspot thus disclosing this information to the attacker. By iterating through different SSIDs (from a predefined dictionary) the attacker can eventually reveal a big part of the hidden PNL. Considering user mobility, executing active attacks usually has to be done within a short opportunity window, while targeting nontrivial SSIDs from user’s PNL. The existing work on active attacks against hidden PNLs often neglects both of these challenges. In this paper we propose a simple mathematical model for analyzing active SSID dictionary attacks, allowing us to optimize the effectiveness of the attack under the above constraints (limited window of opportunity and targeting nontrivial SSIDs). Additionally, we showcase an example method for building an effective SSID dictionary using top-N recommender algorithm and validate our model through simulations and extensive real-life tests.


title: Bitcoin -- The Miner's Dilemma

abstract: An open distributed system can be secured by requiring participants to present proof of work and rewarding them for participation. The Bitcoin digital currency introduced this mechanism, which is adopted by almost all contemporary digital currencies and related services.A natural process leads participants of such systems to form pools, where members aggregate their power and share the rewards. Experience with Bitcoin shows that the largest pools are often open, allowing anyone to join. It has long been known that a member can sabotage an open pool by seemingly joining it but never sharing its proofs of work. The pool shares its revenue with the attacker, and so each of its participants earns less.We define and analyze a game where pools use some of their participants to infiltrate other pools and perform such an attack. With any number of pools, no-pool-attacks is not a Nash equilibrium. We study the special cases where either two pools or any number of identical pools play the game and the rest of the participants are uninvolved. In both of these cases there exists an equilibrium that constitutes a “tragedy of the commons” where the participating pools attack one another and earn less than they would have if none had attacked.For two pools, the decision whether or not to attack is the miner’s dilemma, an instance of the iterative prisoner’s dilemma. The game is played daily by the active Bitcoin pools, which apparently choose not to attack. If this balance breaks, the revenue of open pools might diminish, making them unattractive to participants.


title: Implementing RSA for sensor nodes in smart cities

abstract: In smart city construction, wireless sensor networks (WSNs) are normally deployed to collect and transmit real-time data. The nodes of the WSN are embedded facility that integrated sensors and data processing modules. For security and privacy concerns, cryptography methods are required for data protection. However, the Rivest-Shamir-Adleman (RSA) cryptosystem, known as the the most popular and deployed public key algorithm, is still hardly implemented on embedded devices because of the intense computation required from its inherent arithmetic operations. Even though, different methods have being proposed for more efficient RSA implementations such as utilizing the Chinese remainder theorem, various modular exponentiation methods, and optimized modular arithmetic methods. In this paper, we propose an efficient multiplication for long integers on the sensor nodes equipped with 16-bit microcontrollers. Combined with this efficient multiplication, we obtain a faster Montgomery multiplication. The combined optimized Montgomery multiplication, the Chinese remainder theorem, and the m-ary exponentiation method allowed for execution times of less than 44.6 × 106 clock cycles for RSA decryption, a new speed record for the RSA implementation on MSP430 microcontrollers.


title: Incremental Fuzzy Clustering With Multiple Medoids for Large Data

abstract: As an important technique of data analysis, clustering plays an important role in finding the underlying pattern structure embedded in unlabeled data. Clustering algorithms that need to store all the data into the memory for analysis become infeasible when the dataset is too large to be stored. To handle such large data, incremental clustering approaches are proposed. The key idea behind these approaches is to find representatives (centroids or medoids) to represent each cluster in each data chunk, which is a packet of the data, and final data analysis is carried out based on those identified representatives from all the chunks. In this paper, we propose a new incremental clustering approach called incremental multiple medoids-based fuzzy clustering (IMMFC) to handle complex patterns that are not compact and well separated. We would like to investigate whether IMMFC is a good alternative to capturing the underlying data structure more accurately. IMMFC not only facilitates the selection of multiple medoids for each cluster in a data chunk, but also has the mechanism to make use of relationships among those identified medoids as side information to help the final data clustering process. The detailed problem formulation, updating rules derivation, and the in-depth analysis of the proposed IMMFC are provided. Experimental studies on several large datasets that include real world malware datasets have been conducted. IMMFC outperforms existing incremental fuzzy clustering approaches in terms of clustering accuracy and robustness to the order of data. These results demonstrate the great potential of IMMFC for large-data analysis.


title: Online Human Action Detection using Joint Classification-Regression Recurrent Neural Networks

abstract: Human action recognition from well-segmented 3D skeleton data has been intensively studied and has been attracting an increasing attention. Online action detection goes one step further and is more challenging, which identifies the action type and localizes the action positions on the fly from the untrimmed stream data. In this paper, we study the problem of online action detection from streaming skeleton data. We propose a multi-task end-to-end Joint Classification-Regression Recurrent Neural Network to better explore the action type and temporal localization information. By employing a joint classification and regression optimization objective, this network is capable of automatically localizing the start and end points of actions more accurately. Specifically, by leveraging the merits of the deep Long Short-Term Memory (LSTM) subnetwork, the proposed model automatically captures the complex long-range temporal dynamics, which naturally avoids the typical sliding window design and thus ensures high computational efficiency. Furthermore, the subtask of regression optimization provides the ability to forecast the action prior to its occurrence. To evaluate our proposed model, we build a large streaming video dataset with annotations. Experimental results on our dataset and the public G3D dataset both demonstrate very promising performance of our scheme.


title: Simulation of DPCM and ADM Systems

abstract: Through years, Digital Communication systems, Pulse Coded Modulation (PCM), Linear Delta Modulation (LDM), Differential Pulse Coded Modulation (DPCM), and Adaptive Delta Modulation (ADM), have proven their unlimited advantages over analog communication systems, in term of error minimization, and distances of transmission enhancement. However two of these systems, the Pulse Coded Modulation and Linear Delta Modulation, still have some weaknesses limiting their advantages, these limitations negatively affect the communication process causing quantization error, slope overload distortion and granular noise. On the other hand, communication engineers have developed two additional digital communication systems which are the Adaptive Delta Modulation (ADM) and the Differential Pulse Coded Modulation (DPCM) in order to solve the aforementioned problems. This paper discusses the implementation and simulation of the aforementioned digital communication systems using Simulink (The Math Works, Inc., Natick, MA, USA) showing the effect of different types of noise when applied to the channel, thus, proving the importance of DPCM and ADM systems in eliminating such effects and ensuring a successful transfer of data.


title: Deep neural network based acoustic model parameter reduction using manifold regularized low rank matrix factorization

abstract: In this paper, we propose a deep neural network (DNN) model parameter reduction based on manifold regularized low rank matrix factorization to reduce the computational complexity of acoustic model for low resource embedded devices. One of the most common DNN model parameter reduction techniques is truncated singular value decomposition (TSVD). TSVD reduces the number of parameters by approximating a target matrix with a low rank one in terms of minimizing the Euclidean norm. In this work, we questioned whether the Euclidean norm is appropriate as objective function to factorize DNN matrices because DNN is known to learn nonlinear manifold of acoustic features. Therefore, in order to exploit the manifold structure for robust parameter reduction, we propose manifold regularized matrix factorization approach. The proposed method was evaluated on TIMIT phone recognition domain.


title: A Study of Voice Activity Detection Techniques for NIST Speaker Recognition Evaluations

abstract: Since 2008, interview-style speech has become an important part of the NIST speaker recognition evaluations (SREs). Unlike telephone speech, interview speech has lower signal-to-noise ratio, which necessitates robust voice activity detectors (VADs). This paper highlights the characteristics of interview speech files in NIST SREs and discusses the difficulties in performing speech/non-speech segmentation in these files. To overcome these difficulties, this paper proposes using speech enhancement techniques as a pre-processing step for enhancing the reliability of energy-based and statistical-model-based VADs. A decision strategy is also proposed to overcome the undesirable effects caused by impulsive signals and sinusoidal background signals. The proposed VAD is compared with the ASR transcripts provided by NIST, VAD in the ETSI-AMR Option 2 coder, satistical-model (SM) based VAD, and Gaussian mixture model (GMM) based VAD. Experimental results based on the NIST 2010 SRE dataset suggest that the proposed VAD outperforms these conventional ones whenever interview-style speech is involved. This study also demonstrates that (1) noise reduction is vital for energy-based VAD under low SNR; (2) the ASR transcripts and ETSI-AMR speech coder do not produce accurate speech and non-speech segmentations; and (3) spectral subtraction makes better use of background spectra than the likelihood-ratio tests in the SM-based VAD. The segmentation files produced by the proposed VAD can be found in http://bioinfo.eie.polyu.edu.hk/ssvad.


title: Methods for population-based eQTL analysis in human genetics

abstract: Gene expression is a critical process in biological system that is influenced and modulated by many factors including genetic variation. Expression Quantitative Trait Loci (eQTL) analysis provides a powerful way to understand how genetic variants affect gene expression. For genome wide eQTL analysis, the number of genetic variants and that of genes are large and thus the search space is tremendous. Therefore, eQTL analysis brings about computational and statistical challenges. In this paper, we provide a comprehensive review of recent advances in methods for eQTL analysis in population-based studies. We first present traditional pairwise association methods, which are widely used in human genetics. To account for expression heterogeneity, we investigate the methods for correcting confounding factors. Next, we discuss newly developed statistical learning methods including Lasso-based models. In the conclusion, we provide an overview of future method development in analyzing eQTL associations. Although we focus on human genetics in this review, the methods are applicable to many other organisms.


title: On the development of intelligent optical inspections

abstract: The automated optical inspection (AOI) has replaced the manual optical inspection of printed circuit boards (PCBs) in nearly all manufacturing lines. Programming the inspection machines is time consuming and requires a good understanding of the optical inspection domain and printed circuit boards. The process of creating such inspection programs needs to be simplified to enable the user to get to inspection results easier and faster. Over the last few years better and affordable computational hardware helped developing the field of artificial neural networks, called deep learning, to an extent, that it surpassed other state of the art approaches and is used in many optical classification tasks since then. With the ongoing trend of Industry 4.0 AOI systems started not only producing but also storing vast amounts of data. This has the potential to aid the improvement of the inspection machines. This paper will discuss key aspects of AOI as well as basics of deep learning and proposes a new way to integrate these techniques for the manufacturers and users of AOI machines. Additionally, the process of programming AOI inspections will be faster.


title: Revising the definition of Alzheimer's disease: a new lexicon

abstract: Summary Alzheimer's disease (AD) is classically defined as a dual clinicopathological entity. The recent advances in use of reliable biomarkers of AD that provide in-vivo evidence of the disease has stimulated the development of new research criteria that reconceptualise the diagnosis around both a specific pattern of cognitive changes and structural/biological evidence of Alzheimer's pathology. This new diagnostic framework has stimulated debate about the definition of AD and related conditions. The potential for drugs to intercede in the pathogenic cascade of the disease adds some urgency to this debate. This paper by the International Working Group for New Research Criteria for the Diagnosis of AD aims to advance the scientific discussion by providing broader diagnostic coverage of the AD clinical spectrum and by proposing a common lexicon as a point of reference for the clinical and research communities. The cornerstone of this lexicon is to consider AD solely as a clinical and symptomatic entity that encompasses both predementia and dementia phases.


title: A survey of fault tolerance mechanisms and checkpoint/restart implementations for high performance computing systems

abstract: In recent years, High Performance Computing (HPC) systems have been shifting from expensive massively parallel architectures to clusters of commodity PCs to take advantage of cost and performance benefits. Fault tolerance in such systems is a growing concern for long-running applications. In this paper, we briefly review the failure rates of HPC systems and also survey the fault tolerance approaches for HPC systems and issues with these approaches. Rollback-recovery techniques which are most often used for long-running applications on HPC clusters are discussed because they are widely used for long-running applications on HPC systems. Specifically, the feature requirements of rollback-recovery are discussed and a taxonomy is developed for over twenty popular checkpoint/restart solutions. The intent of this paper is to aid researchers in the domain as well as to facilitate development of new checkpointing solutions.


title: Key performance indicators for traffic intensive web‐enabled business processes

abstract: Purpose – Intensive traffic often occurs in web‐enabled business processes hosted by travel industry and government portals. An extreme case for intensive traffic is flash crowd situations when the number of web users spike within a short time due to unexpected events caused by political unrest or extreme weather conditions. As a result, the servers hosting these business processes can no longer handle overwhelming service requests. To alleviate this problem, process engineers usually analyze audit trail data collected from the application server and reengineer their business processes to withstand unexpected surge in the visitors. However, such analysis can only reveal the performance of the application server from the internal perspective. This paper aims to investigate this issue.Design/methodology/approach – This paper proposes an approach for analyzing key performance indicators of traffic intensive web‐enabled business processes from audit trail data, web server logs, and stress testing logs.Finding...


Emotion Detection from Text : Survey


title: Diode-clamped multilevel converters: a practicable way to balance DC-link voltages

abstract: The converter topologies identified as diode-clamped multilevel (DCM) or, equivalently, as multipoint clamped (MPC), are rarely used in industrial applications, owing to some serious drawbacks involving mainly the stacked bank of capacitors that constitutes their multilevel DC link. The balance of the capacitor voltages is not possible in all operating conditions when the MPC converter possesses a passive front end. On the other hand, in AC/DC/AC power conversion, the back-to-back connection of a multilevel rectifier with a multilevel inverter allows the balance of the DC-link capacitor voltages and, at the same time, it offers the power-factor-correction capability at the mains AC input. An effective balancing strategy suitable for MPC conversion systems with any number of DC-link capacitors is presented here. The strategy has been carefully studied to optimize the converter efficiency. The simulation results related to a high-power conversion system (up to 10 MW) characterized by four intermediate DC-link capacitors are shown.


title: Window Size Impact in Human Activity Recognition

abstract: Signal segmentation is a crucial stage in the activity recognition process; however, this has been rarely and vaguely characterized so far. Windowing approaches are normally used for segmentation, but no clear consensus exists on which window size should be preferably employed. In fact, most designs normally rely on figures used in previous works, but with no strict studies that support them. Intuitively, decreasing the window size allows for a faster activity detection, as well as reduced resources and energy needs. On the contrary, large data windows are normally considered for the recognition of complex activities. In this work, we present an extensive study to fairly characterize the windowing procedure, to determine its impact within the activity recognition process and to help clarify some of the habitual assumptions made during the recognition system design. To that end, some of the most widely used activity recognition procedures are evaluated for a wide range of window sizes and activities. From the evaluation, the interval 1–2 s proves to provide the best trade-off between recognition speed and accuracy. The study, specifically intended for on-body activity recognition systems, further provides designers with a set of guidelines devised to facilitate the system definition and configuration according to the particular application requirements and target activities.


Chapter 3 The Origins Of Dualism


title: Real-time global illumination using precomputed light field probes

abstract: We introduce a new data structure and algorithms that employ it to compute real-time global illumination from static environments. Light field probes encode a scene's full light field and internal visibility. They extend current radiance and irradiance probe structures with per-texel visibility information similar to a G-buffer and variance shadow map. We apply ideas from screen-space and voxel cone tracing techniques to this data structure to efficiently sample radiance on world space rays, with correct visibility information, directly within pixel and compute shaders. From these primitives, we then design two GPU algorithms to efficiently gather real-time, viewer-dependent global illumination onto both static and dynamic objects. These algorithms make different tradeoffs between performance and accuracy. Supplemental GLSL source code is included.


title: Socially-Informed Timeline Generation for Complex Events

abstract: Existing timeline generation systems for complex events consider only information from traditional media, ignoring the rich social context provided by user-generated content that reveals representative public interests or insightful opinions. We instead aim to generate socially-informed timelines that contain both news article summaries and selected user comments. We present an optimization framework designed to balance topical cohesion between the article and comment summaries along with their informativeness and coverage of the event. Automatic evaluations on real-world datasets that cover four complex events show that our system produces more informative timelines than state-of-the-art systems. In human evaluation, the associated comment summaries are furthermore rated more insightful than editor's picks and comments ranked highly by users.


title: A Survey on Hierarchical Attribute Set based Encryption (HASBE) Access Control Model for Cloud Computing

abstract: ABSTRACT Cloud computing refers to the application and service that run on a distributed system using virtualized resources and access by common internet protocol and networking standard. Cloud computing virtualizes system by pooling and sharing resources. System and resources can be monitored from central infrastructure as needed. It requires high security because of now a days companies going to put more essential and huge amount of data on cloud. That’s why the reason traditional access control is not enough For the High security .so that the attribute-based encryption (ABE) have been proposed for access control of outsourced data in cloud computing with the complex access control policy.in this paper, we have proposed hierarchical attribute-set-based encryption (HASBE) access control by extending cipher-text policy and attribute-set-based encryption (ASBE) with a hierarchical structure of users. HASBE provides Flexibility, scalability and fine-grained access control with efficient user revocation but the hierarchical structure of the domain hierarchy is to complex and there is no sub-domain level user hierarchy which increases system response time and decreases the system performance. So we are proposing HASBE scheme by creating a sub domain in to the user level Hierarchy that reduce the complexity of the hierarchy and also improve the system performance.


title: Test Code Quality and Its Relation to Issue Handling Performance

abstract: Automated testing is a basic principle of agile development. Its benefits include early defect detection, defect causelocalization and removal of fear to apply changes to the code. Therefore, maintaining high quality test code is essential. This study introduces a model that assesses test code quality by combining source code metrics that reflect three main aspects of test codequality: completeness, effectiveness and maintainability. The model is inspired by the Software Quality Model of the SoftwareImprovement Group which aggregates source code metrics into quality ratings based on benchmarking. To validate the model we assess the relation between test code quality, as measured by the model, and issue handling performance. An experiment isconducted in which the test code quality model is applied to $18$ open source systems. The test quality ratings are tested for correlation with issue handling indicators, which are obtained by mining issue repositories. In particular, we study the (1) defect resolution speed, (2) throughput and (3) productivity issue handling metrics. The results reveal a significant positive correlation between test code quality and two out of the three issue handling metrics (throughput and productivity), indicating that good test code quality positively influences issue handling performance.


title: Expression of type-IV collagenases in human tumor cell lines that can form liver colonies in chick embryos.

abstract: Chick embryo has been used as a model system for evaluating the metastatic potential of tumor cells. We have previously demonstrated that expression of the tissue inhibitor of matrix metalloproteinase-1 (TIMP-I) gene can suppress liver colonization of tumor cells in chick embryo, probably by inhibiting the activity of matrix metalloproteinases (MMP) produced by tumor cells. In an attempt to identify MMP associated with liver colonization, we examined 24 human tumor cell lines for their potential to form metastatic colonies in chick-embryo liver after the cells had been inoculated into the chorioallantoic membrane (CAM) vein. We compared the results with the mRNA expression of MMP (MMP-1, MMP-2, MMP-3, MMP-9) studied previously. Three of 8 cell lines from mesenchymal tumors (fibrosarcoma HT1080, osteosarcomas SK-ES and MNNG/ HOS) and 2 of 16 cell lines from epithelial tumors (gastric carcinoma KKLS and bladder carcinoma T24) proliferated in the livers. MMP-2 and MMP-9 were the enzymes whose transcripts were more frequently expressed in these 5 metastatic cell lines (MMP-1; 2/5, MMP-2; 4/5, MMP-3; 0/5, MMP-9; 3/5), but other cell lines that did not form liver colonies expressed the transcripts at lower frequency (MMP-2; 7/19, MMP-9; 3/19). Although either or both MMP-2 and MMP-9 transcripts were expressed in 4 of the 5 metastatic cell lines, they were undetectable in T24 cells. However, induced expression of both enzymes was detected by immunostaining in the T24 cells colonized in the liver. Thus, type-IV collagenases expressed by tumor cells may play a role in facilitating colonization in chick embryos. © 1994 Wiley-Liss, Inc.


title: Personalized tour recommendation based on user interests and points of interest visit durations

abstract: Tour recommendation and itinerary planning are challenging tasks for tourists, due to their need to select Points of Interest (POI) to visit in unfamiliar cities, and to select POIs that align with their interest preferences and trip constraints. We propose an algorithm called PERSTOUR for recommending personalized tours using POI popularity and user interest preferences, which are automatically derived from real-life travel sequences based on geotagged photos. Our tour recommendation problem is modelled using a formulation of the Orienteering problem, and considers user trip constraints such as time limits and the need to start and end at specific POIs. In our work, we also reflect levels of user interest based on visit durations, and demonstrate how POI visit duration can be personalized using this time-based user interest. Using a Flickr dataset of four cities, our experiments show the effectiveness of PERSTOUR against various baselines, in terms of tour popularity, interest, recall, precision and F1-score. In particular, our results show the merits of using time-based user interest and personalized POI visit durations, compared to the current practice of using frequency-based user interest and average visit durations.


title: Learning Framework for Robust Obstacle Detection, Recognition, and Tracking

abstract: This paper introduces a general framework for detection, recognition, and tracking preceding vehicles and pedestrians based on a deep learning approach. The proposed framework combines a novel deep learning approach with the use of multiple sources of local patterns and depth information to yield robust on-road vehicle and pedestrian detection, recognition, and tracking. The proposed system is first based on robust obstacle detection to identify obstacles appearing along the road that are likely to be vehicles and pedestrians, implemented as an efficient adaptive U-V disparity algorithm. Second, the results from the obstacle detection stage are input into a novel vehicle and pedestrian recognition system based on a deep learning model that processes multiple sources of depth information and local patterns. Finally, the results from the recognition stage are used to track detected vehicles or pedestrians in the next frame by means of a proposed tracking and validation model. The proposed framework has been thoroughly evaluated by inputting several vehicle and pedestrian data sets that were collected under various driving conditions. Experimental results show that this framework provides robust vehicle and pedestrian detection, recognition, and tracking with high accuracy, and also satisfies the real-time requirements of driver assistance systems.


title: An empirical analysis of the IEEE 802.11 MAC layer handoff process

abstract: IEEE 802.11 based wireless networks have seen rapid growth and deployment in the recent years. Critical to the 802.11 MAC operation, is the handoff function which occurs when a mobile node moves its association from one access point to another. In this paper, we present an empirical study of this handoff process at the link layer, with a detailed breakup of the latency into various components. In particular, we show that a MAC layer function - probe is the primary contributor to the overall handoff latency. In our study, we observe that the latency is significant enough to affect the quality of service for many applications (or network connections). Further we find variations in the latency from one hand-off to another as well as with APs and STAs used from different vendors. Finally, we discuss optimizations on the probe phase which can potentially reduce the probe latency by as much as 98% (and a minimum of 12% in our experiments). Based on the study, we draw some guidelines for future handoff schemes.


title: DeepSign: Deep Learning for Automatic Malware Signature Generation and Classification

abstract: This paper presents a novel deep learning based method for automatic malware signature generation and classification. The method uses a deep belief network (DBN), implemented with a deep stack of denoising autoencoders, generating an invariant compact representation of the malware behavior. While conventional signature and token based methods for malware detection do not detect a majority of new variants for existing malware, the results presented in this paper show that signatures generated by the DBN allow for an accurate classification of new malware variants. Using a dataset containing hundreds of variants for several major malware families, our method achieves 98.6% classification accuracy using the signatures generated by the DBN. The presented method is completely agnostic to the type of malware behavior that is logged (e.g., API calls and their parameters, registry entries, websites and ports accessed, etc.), and can use any raw input from a sandbox to successfully train the deep neural network which is used to generate malware signatures.


title: Bayesian Unsupervised Word Segmentation with Nested Pitman-Yor Language Modeling

abstract: In this paper, we propose a new Bayesian model for fully unsupervised word segmentation and an efficient blocked Gibbs sampler combined with dynamic programming for inference. Our model is a nested hierarchical Pitman-Yor language model, where Pitman-Yor spelling model is embedded in the word model. We confirmed that it significantly outperforms previous reported results in both phonetic transcripts and standard datasets for Chinese and Japanese word segmentation. Our model is also considered as a way to construct an accurate word n-gram language model directly from characters of arbitrary language, without any "word" indications.


title: Riemannian $L^{p}$ center of mass: Existence, uniqueness, and convexity

abstract: Let be a complete Riemannian manifold and a probability measure on . Assume . We derive a new bound (in terms of , the injectivity radius of and an upper bound on the sectional curvatures of ) on the radius of a ball containing the support of which ensures existence and uniqueness of the global Riemannian center of mass with respect to . A significant consequence of our result is that under the best available existence and uniqueness conditions for the so-called ``local'' center of mass, the global and local centers coincide. In our derivation we also give an alternative proof for a uniqueness result by W. S. Kendall. As another contribution, we show that for a discrete probability measure on , under the existence and uniqueness conditions, the (global) center of mass belongs to the closure of the convex hull of the masses. We also give a refined result when is of constant curvature.


title: Arterial speed studies with taxi equipped with global positioning receivers as probe vehicle

abstract: The paper describes a new methodology for performing arterial speed studies using taxis equipped GPS receivers as probe cars. It documents the data collection and data reduction, as well as analyses that illustrate the capabilities of the GPS/GIS methodology. The data collection procedure uses taxis equipped with GPS receivers to automatically collect time, local coordinates, and speed at regular sampling periods, for example every one second. The data reduction procedure filters and aggregates GPS data to compute travel time and speed along highway segments. Finally, the developed methodology is implemented with 100 taxis equipped with GPS receivers of Guangzhou City. The results demonstrate the power of the methodology proposed in this paper on travel time.


title: Chromosome banding studies in two patients with XXXXY syndrome.

abstract: In 2 adult male patients with 49 chromosomes, an XXXXY sex chromosome constitution was confirmed by trypsin-Giemsa banding sites. Clinical findings as well as fingerprint ridge counts were typical of the syndrome. Primary hypogonadism was documented by finding low serum testosterone and raised serum LH and FSH levels. Several radiological abnormalities, not previously described in this syndrome, were seen in 1 patient.


title: Shallowing Deep Networks: Layer-Wise Pruning Based on Feature Representations

abstract: Recent surge of Convolutional Neural Networks (CNNs) has brought successes among various applications. However, these successes are accompanied by a significant increase in computational cost and the demand for computational resources, which critically hampers the utilization of complex CNNs on devices with limited computational power. In this work, we propose a feature representation based layer-wise pruning method that aims at reducing complex CNNs to more compact ones with equivalent performance. Different from previous parameter pruning methods that conduct connection-wise or filter-wise pruning based on weight information, our method determines redundant parameters by investigating the features learned in the convolutional layers and the pruning process is operated at a layer level. Experiments demonstrate that the proposed method is able to significantly reduce computational cost and the pruned models achieve equivalent or even better performance compared to the original models on various datasets.


title: Examination of the Effects of Degeneration on Vertebral Artery by Using Neural Network in Cases With Cervical Spondylosis

abstract: The scope of this study is to diagnose vertebral arterial inefficiency by using Doppler measurements from both right and left vertebral arterials. Total of 96 patients' Doppler measurements, consisting of 42 of healthy, 30 of spondylosis, and 24 of clinically proven vertebrobasillary insufficiency (VBI), were examined. Patients' age and sex information as well as RPSN, RPSVN, LPSN, LPSVN, and TOTALVOL medical parameters obtained from vertebral arterials were classified by neural networks, and the performance of said classification reached up to 93.75% in healthy, 83.33% in spondylosis, and 97.22% in VBI cases. The area under ROC curve, which is a direct indication of repeating success ratio, is calculated as 92.3%, and the correlation coefficient of the classification groups is 0.9234. It is also demonstrated that those medical parameters of age and systolic velocity, which were applied into the neural networks, were more effective in developing vertebral deficiency.


title: Microplastic Ingestion by Zooplankton

abstract: Small plastic detritus, termed "microplastics", are a widespread and ubiquitous contaminant of marine ecosystems across the globe. Ingestion of microplastics by marine biota, including mussels, worms, fish, and seabirds, has been widely reported, but despite their vital ecological role in marine food-webs, the impact of microplastics on zooplankton remains under-researched. Here, we show that microplastics are ingested by, and may impact upon, zooplankton. We used bioimaging techniques to document ingestion, egestion, and adherence of microplastics in a range of zooplankton common to the northeast Atlantic, and employed feeding rate studies to determine the impact of plastic detritus on algal ingestion rates in copepods. Using fluorescence and coherent anti-Stokes Raman scattering (CARS) microscopy we identified that thirteen zooplankton taxa had the capacity to ingest 1.7-30.6 μm polystyrene beads, with uptake varying by taxa, life-stage and bead-size. Post-ingestion, copepods egested faecal pellets laden with microplastics. We further observed microplastics adhered to the external carapace and appendages of exposed zooplankton. Exposure of the copepod Centropages typicus to natural assemblages of algae with and without microplastics showed that 7.3 μm microplastics (>4000 mL(-1)) significantly decreased algal feeding. Our findings imply that marine microplastic debris can negatively impact upon zooplankton function and health.


title: Recurrent Neural Networks for Emotion Recognition in Video

abstract: Deep learning based approaches to facial analysis and video analysis have recently demonstrated high performance on a variety of key tasks such as face recognition, emotion recognition and activity recognition. In the case of video, information often must be aggregated across a variable length sequence of frames to produce a classification result. Prior work using convolutional neural networks (CNNs) for emotion recognition in video has relied on temporal averaging and pooling operations reminiscent of widely used approaches for the spatial aggregation of information. Recurrent neural networks (RNNs) have seen an explosion of recent interest as they yield state-of-the-art performance on a variety of sequence analysis tasks. RNNs provide an attractive framework for propagating information over a sequence using a continuous valued hidden layer representation. In this work we present a complete system for the 2015 Emotion Recognition in the Wild (EmotiW) Challenge. We focus our presentation and experimental analysis on a hybrid CNN-RNN architecture for facial expression analysis that can outperform a previously applied CNN approach using temporal averaging for aggregation.


title: Power spectrum analysis of heart rate fluctuation: a quantitative probe of beat-to-beat cardiovascular control.

abstract: Power spectrum analysis of heart rate fluctuations provides a quantitative noninvasive means of assessing the functioning of the short-term cardiovascular control systems. We show that sympathetic and parasympathetic nervous activity make frequency-specific contributions to the heart rate power spectrum, and that renin-angiotensin system activity strongly modulates the amplitude of the spectral peak located at 0.04 hertz. Our data therefore provide evidence that the renin-angiotensin system plays a significant role in short-term cardiovascular control in the time scale of seconds to minutes.


title: Maxout Networks

abstract: We consider the problem of designing models to leverage a recently introduced approximate model averaging technique called dropout. We define a simple new model called maxout (so named because its output is the max of a set of inputs, and because it is a natural companion to dropout) designed to both facilitate optimization by dropout and improve the accuracy of dropout's fast approximate model averaging technique. We empirically verify that the model successfully accomplishes both of these tasks. We use maxout and dropout to demonstrate state of the art classification performance on four benchmark datasets: MNIST, CIFAR-10, CIFAR-100, and SVHN.


Unraveling the Tuskegee Study of Untreated Syphilis


Analysis of Productivity Measures and the Factors Affecting Accurate Measurement of Productivity


title: Easy over hard: a case study on deep learning

abstract: While deep learning is an exciting new technique, the benefits of this method need to be assessed with respect to its computational cost. This is particularly important for deep learning since these learners need hours (to weeks) to train the model. Such long training time limits the ability of (a)~a researcher to test the stability of their conclusion via repeated runs with different random seeds; and (b)~other researchers to repeat, improve, or even refute that original work. For example, recently, deep learning was used to find which questions in the Stack Overflow programmer discussion forum can be linked together. That deep learning system took 14 hours to execute. We show here that applying a very simple optimizer called DE to fine tune SVM, it can achieve similar (and sometimes better) results. The DE approach terminated in 10 minutes; i.e. 84 times faster hours than deep learning method. We offer these results as a cautionary tale to the software analytics community and suggest that not every new innovation should be applied without critical analysis. If researchers deploy some new and expensive process, that work should be baselined against some simpler and faster alternatives.


title: Verifiable Anonymous Identities and Access Control in Permissioned Blockchains

abstract: In this paper we address the issue of identity and access control within shared permissioned blockchains. We propose the ChainAchor system that provides anonymous but verifiable identities for entities on the blockchain. ChainAchor also provides access control to entities seeking to submit transactions to the blockchain to read/verify transactions on the the permissioned blockchain. Consensus nodes enforce access control to the shared permissioned blockchain by a simple look-up to a (read-only) list of anonymous members' public-keys. ChainAnchor also provides unlinkability of transactions belonging to an entity on the blockchain. This allows for an entity to optionally disclose their identity when a transaction is called into question (e.g. regulatory or compliance requirements), but without affecting the anonymity and unlinkability of their remaining transactions.


title: Making collection operations optimal with aggressive JIT compilation

abstract: Functional collection combinators are a neat and widely accepted data processing abstraction. However, their generic nature results in high abstraction overheads -- Scala collections are known to be notoriously slow for typical tasks. We show that proper optimizations in a JIT compiler can widely eliminate overheads imposed by these abstractions. Using the open-source Graal JIT compiler, we achieve speedups of up to 20x on collection workloads compared to the standard HotSpot C2 compiler. Consequently, a sufficiently aggressive JIT compiler allows the language compiler, such as Scalac, to focus on other concerns. In this paper, we show how optimizations, such as inlining, polymorphic inlining, and partial escape analysis, are combined in Graal to produce collections code that is optimal with respect to manually written code, or close to optimal. We argue why some of these optimizations are more effectively done by a JIT compiler. We then identify specific use-cases that most current JIT compilers do not optimize well, warranting special treatment from the language compiler.


title: Memristor-based XOR gate for full adder

abstract: Memristor technology is regarded as a potential solution to the memory bottleneck in Von Neumann Architecture by putting storage and computation integrated in the same physical location. In this paper, we proposed a nonvolatile exclusive-OR (XOR) logic gate with 5 memristors, which can execute operation in a single step. Moreover, based on the XOR logic gate, a full adder was presented and simulated by SPICE. Compared to other logic gate and full adder, the proposed circuits have benefits of simpler architecture, higher speed and lower power consumption. This paper provides a memristor-based element as a solution to the future alternative Computation-In-Memory architecture.


title: 3D Reconstruction of Background and Objects Moving on Ground Plane Viewed from a Moving Camera

abstract: We present a novel method to obtain a 3D Euclidean reconstruction of both the background and moving objects in a video sequence. We assume that, multiple objects are moving rigidly on a ground plane observed by a moving camera. The video sequence is first segmented into static background and motion blobs by a homography-based motion segmentation method. Then classical "Structure from Motion" (SfM) techniques are applied to obtain a Euclidean reconstruction of the static background. The motion blob corresponding to each moving object is treated as if there were a static object observed by a hypothetical moving camera, called a "virtual camera". This virtual camera shares the same intrinsic parameters with the real camera but moves differently due to object motion. The same SfM techniques are applied to estimate the 3D shape of each moving object and the pose of the virtual camera. We show that the unknown scale of moving objects can be approximately determined by the ground plane, which is a key contribution of this paper. Another key contribution is that we prove that the 3D motion of moving objects can be solved from the virtual camera motion with a linear constraint imposed on the object translation. In our approach, a planartranslation constraint is formulated: "the 3D instantaneous translation of moving objects must be parallel to the ground plane". Results on real-world video sequences demonstrate the effectiveness and robustness of our approach.


title: Opinion integration through semi-supervised topic modeling

abstract: Web 2.0 technology has enabled more and more people to freely express their opinions on the Web, making the Web an extremely valuable source for mining user opinions about all kinds of topics. In this paper we study how to automatically integrate opinions expressed in a well-written expert review with lots of opinions scattering in various sources such as blogspaces and forums. We formally define this new integration problem and propose to use semi-supervised topic models to solve the problem in a principled way. Experiments on integrating opinions about two quite different topics (a product and a political figure) show that the proposed method is effective for both topics and can generate useful aligned integrated opinion summaries. The proposed method is quite general. It can be used to integrate a well written review with opinions in an arbitrary text collection about any topic to potentially support many interesting applications in multiple domains.


title: An error correcting parser for context free grammars that takes less than cubic time

abstract: The problem of parsing has been studied extensively for various formal grammars. Given an input string and a grammar, the parsing problem is to check if the input string belongs to the language generated by the grammar. A closely related problem of great importance is one where the input are a string ${\cal I}$ and a grammar $G$ and the task is to produce a string ${\cal I}'$ that belongs to the language generated by $G$ and the `distance' between ${\cal I}$ and ${\cal I}'$ is the smallest (from among all the strings in the language). Specifically, if ${\cal I}$ is in the language generated by $G$, then the output should be ${\cal I}$. Any parser that solves this version of the problem is called an {\em error correcting parser}. In 1972 Aho and Peterson presented a cubic time error correcting parser for context free grammars. Since then this asymptotic time bound has not been improved under the (standard) assumption that the grammar size is a constant. In this paper we present an error correcting parser for context free grammars that runs in $O(T(n))$ time, where $n$ is the length of the input string and $T(n)$ is the time needed to compute the tropical product of two $n\times n$ matrices. ::: In this paper we also present an $\frac{n}{M}$-approximation algorithm for the {\em language edit distance problem} that has a run time of $O(Mn^\omega)$, where $O(n^\omega)$ is the time taken to multiply two $n\times n$ matrices. To the best of our knowledge, no approximation algorithms have been proposed for error correcting parsing for general context free grammars.


title: Thin a-SiC:H Films Formed by Remote Hydrogen Microwave Plasma CVD using Dimethylsilane and Trimethylsilane Precursors

abstract: The effect of substrate temperature (TS) on the chemical structure, surface morphology, density, and adhesion to a substrate of a-SiC:H films is reported. The increase in TS from 30 °C to 400 °C causes the elimination of organic moieties from the film and the formation of Si-carbidic network. The films produced at high TS are morphologically homogeneous, dense, and strongly adhere to coatings.


title: Applying Goal-Driven Autonomy to StarCraft

abstract: One of the main challenges in game AI is building agents that can intelligently react to unforeseen game situations. In real-time strategy games, players create new strategies and tactics that were not anticipated during development. In order to build agents capable of adapting to these types of events, we advocate the development of agents that reason about their goals in response to unanticipated game events. This results in a decoupling between the goal selection and goal execution logic in an agent. We present a reactive planning implementation of the Goal-Driven Autonomy conceptual model and demonstrate its application in StarCraft. Our system achieves a win rate of 73% against the built-in AI and outranks 48% of human players on a competitive ladder server.


title: Clustering of time series data—a survey

abstract: Time series clustering has been shown effective in providing useful information in various domains. There seems to be an increased interest in time series clustering as part of the effort in temporal data mining research. To provide an overview, this paper surveys and summarizes previous works that investigated the clustering of time series data in various application domains. The basics of time series clustering are presented, including general-purpose clustering algorithms commonly used in time series clustering studies, the criteria for evaluating the performance of the clustering results, and the measures to determine the similarity/dissimilarity between two time series being compared, either in the forms of raw data, extracted features, or some model parameters. The past researchs are organized into three groups depending upon whether they work directly with the raw data either in the time or frequency domain, indirectly with features extracted from the raw data, or indirectly with models built from the raw data. The uniqueness and limitation of previous research are discussed and several possible topics for future research are identified. Moreover, the areas that time series clustering have been applied to are also summarized, including the sources of data used. It is hoped that this review will serve as the steppingstone for those interested in advancing this area of research.


abstract: Linguistic structure is an evolutionary trade-off between simplicity and expressivity Kenny Smith (kenny@ling.ed.ac.uk), Monica Tamariz & Simon Kirby Language Evolution and Computation Research Unit, School of Philosophy, Psychology & Language Sciences, University of Edinburgh, Dugald Stewart Building, 3 Charles Street, Edinburgh, EH8 9AD, UK Abstract Language exhibits structure: a species-unique system for ex- pressing complex meanings using complex forms. We present a review of modelling and experimental literature on the evo- lution of structure which suggests that structure is a cultural adaptation in response to pressure for expressivity (arising dur- ing communication) and compressibility (arising during learn- ing), and test this hypothesis using a new Bayesian iterated learning model. We conclude that linguistic structure can and should be explained as a consequence of cultural evolution in response to these two pressures. Keywords: language; structure; cultural evolution; learning; communication Introduction Human language is unique among the communication sys- tems of the natural world in that it is exhibits a rich combina- torial and compositional structure: language provides a gen- erative system for productively combining meaningless ele- ments (e.g. speech sounds) to form meaning-bearing units (morphemes), which are further recombined to yield com- plex units (phrases) whose meaning is derived in a predictable manner from the meaning of their component parts and their manner of composition. This allows us massive expressive potential: at least at a first approximation, anything you can think you can express in language. No other species has a communication system providing anything approaching this expressive power: why do humans? One explanation for the presence of structure 1 in human language appeals to biological evolution under natural selec- tion (Pinker & Bloom, 1990): language is fundamentally a biological trait, being underpinned by some innate language- specific apparatus; the ability to communicate propositions which a structured language provides is adaptive, since it facilitates social interaction and ultimately increases fitness; therefore, structure in language represents a biological adap- tation to facilitate communication. A second account explains structure in language as a consequence of cultural, rather than biological, evolution (Christiansen & Chater, 2008). Rather than language structure reflecting an evolved domain-specific learning apparatus, the idea is that languages have adapted over repeated episodes of learning and production (a process sometimes called iterated learning) in response to weaker, domain-general constraints arising from the biases of lan- guage learners. We have previously termed this evolutionary process cultural selection for learnability (Brighton, Kirby, & Smith, 2005). A range of models and experiments show 1 We use the term structure as a shorthand for combinatoriality and/or compositionality. that cultural selection for learnability leads to the evolution of structure, under certain assumptions about the nature of transmission and the biases of language learners. Under a strong interpretation of this account, language’s function for communication could be seen as an epiphenomenon: struc- tured language provides a powerful medium for communica- tion, but language structure is not ‘for’ communication. Here we present a new model of iterated learning, moti- vated by recent experimental work, which goes some way to reconcile these two viewpoints. We draw from the biologi- cal account the insight that the alignment between language’s apparent function as a system for expressing propositions and its structure, tailor-made for just such a purpose, is unlikely to be a fortuitous coincidence. We draw from cultural evo- lutionary account two insights: 1) biological evolution is not the only evolutionary process which might act to shape lan- guage: cultural evolution, a necessary consequence of the fact that language is socially learned, is a second such mechanism; 2) selection for learnability will impact on language during its transmission. This model suggests that structure arises from cultural evolution when language is under pressure to be ex- pressive and learnable: pressure for expressivity arises from language use in communication, language learning by naive individuals introduces a pressure for simplicity arising from domain-general preferences for compressibility in learning. Crucially, both must be in play: pressure for expressivity or simplicity alone does not lead to structure. Structure in lan- guage is a linguistic adaptation, not a biological adaptation, in response to competing pressures for expressivity and learn- ability (Kirby, Cornish, & Smith, 2008; Steels, 2012). Cultural evolution of structure: previous work Models of the cultural evolution of linguistic structure typ- ically emphasise the role of learnability constraints in driv- ing the evolution of compositionality. Specifically, language is under pressure to be compressible: to allow the forma- tion of compressed mental representations, i.e. simple gram- mars. This pressure for compressibility is inherent in learn- ing (Chater & Vitanyi, 2003), and can be amplified by other constraints acting on language transmission (e.g. the mis- match between the infinite expressivity of languages and the finite set of data from which such languages must be learned). Learning and transmission therefore favour languages which admit to compressed representations, i.e. which permit gen- eralisations. Recursive compositionality is one such general- isation (e.g. Steels, 1998; Kirby, 2002; Brighton, Smith, & Kirby, 2005), and therefore represents an adaptation by lan-


title: Fragment Allocation in Distributed Database Design

abstract: On a Wide Area Network (WAN), fragment allocation is a major issue in distributed database design since it concerns the overall performance of distributed database systems. Here we propose a simple and comprehensive model that reflects transaction behavior in distributed databases. Based on the model and transaction information, two heuristic algorithms are developed to find a near-optimal allocation such that the total communication cost is minimized as much as possible. The results show that the fragment allocation found by the algorithms is close to being an optimal one. Some experiments were also conducted to verify that the cost formulas can truly reflect the communication cost in the real world.


title: Orange: Data Mining Fruitful and Fun - A Historical Perspective

abstract: Orange (http://orange.biolab.si) is a general-purpose machine learning and data mining tool. Its multilayer architecture is suitable for different kinds of users, from data mining beginners to programmers who prefer a scripting interface. In this paper we outline the history of Orange’s development and present its achievements, current status, and future challenges. Povzetek: Orange kot sploono orodje za stojno uˇ cenje in odkrivanje znanj iz podatkov, zgodovina, trenutna podoba in prihodnji izzivi.


title: Performance evaluation of local colour invariants

abstract: In this paper, we compare local colour descriptors to grey-value descriptors. We adopt the evaluation framework of Mikolayzcyk and Schmid. We modify the framework in several ways. We decompose the evaluation framework to the level of local grey-value invariants on which common region descriptors are based. We compare the discriminative power and invariance of grey-value invariants to that of colour invariants. In addition, we evaluate the invariance of colour descriptors to photometric events such as shadow and highlights. We measure the performance over an extended range of common recording conditions including significant photometric variation. We demonstrate the intensity-normalized colour invariants and the shadow invariants to be highly distinctive, while the shadow invariants are more robust to both changes of the illumination colour, and to changes of the shading and shadows. Overall, the shadow invariants perform best: they are most robust to various imaging conditions while maintaining discriminative power. When plugged into the SIFT descriptor, they show to outperform other methods that have combined colour information and SIFT. The usefulness of C-colour-SIFT for realistic computer vision applications is illustrated for the classification of object categories from the VOC challenge, for which a significant improvement is reported.


title: Detection and Control of Hypertension in the Population The United States Experience

abstract: Abstract Trends in prevalence, awareness, treatment, and control of hypertension in the adult US population are reported. The data are from the National Health and Nutrition Examination Surveys (NHANES), carried out in four separate surveys, the last being NHANES III, 1988–1991. The age-adjusted prevalence of hypertension at ≥ 160/95 mm Hg declined from 20% to 14%, and at > 140/90 mm Hg it declined from 36.3% to 20.4% in NHANES III. Hypertension awareness increased significantly to as high as 89% for those with blood pressures ≥ 160/95 mm Hg. For all people with blood pressure ≥ 160/95 mm Hg, nearly 64% have it controlled below that level, but only 29% have their blood pressure controlled below 140/90 mm Hg. Although the data from these surveys are encouraging, there are still too many people in the United States with uncontrolled hypertension.


An E-voting Protocol Based on Blockchain.


title: An Efficient Globally Optimal Algorithm for Asymmetric Point Matching

abstract: Although the robust point matching algorithm has been demonstrated to be effective for non-rigid registration, there are several issues with the adopted deterministic annealing optimization technique. First, it is not globally optimal and regularization on the spatial transformation is needed for good matching results. Second, it tends to align the mass centers of two point sets. To address these issues, we propose a globally optimal algorithm for the robust point matching problem in the case that each model point has a counterpart in scene set. By eliminating the transformation variables, we show that the original matching problem is reduced to a concave quadratic assignment problem where the objective function has a low rank Hessian matrix. This facilitates the use of large scale global optimization techniques. We propose a modified normal rectangular branch-and-bound algorithm to solve the resulting problem where multiple rectangles are simultaneously subdivided to increase the chance of shrinking the rectangle containing the global optimal solution. In addition, we present an efficient lower bounding scheme which has a linear assignment formulation and can be efficiently solved. Extensive experiments on synthetic and real datasets demonstrate the proposed algorithm performs favorably against the state-of-the-art methods in terms of robustness to outliers, matching accuracy, and run-time.


title: CardiacNET: Segmentation of Left Atrium and Proximal Pulmonary Veins from MRI Using Multi-View CNN

abstract: Anatomical and biophysical modeling of left atrium (LA) and proximal pulmonary veins (PPVs) is important for clinical management of several cardiac diseases. Magnetic resonance imaging (MRI) allows qualitative assessment of LA and PPVs through visualization. However, there is a strong need for an advanced image segmentation method to be applied to cardiac MRI for quantitative analysis of LA and PPVs. In this study, we address this unmet clinical need by exploring a new deep learning-based segmentation strategy for quantification of LA and PPVs with high accuracy and heightened efficiency. Our approach is based on a multi-view convolutional neural network (CNN) with an adaptive fusion strategy and a new loss function that allows fast and more accurate convergence of the backpropagation based optimization. After training our network from scratch by using more than 60K 2D MRI images (slices), we have evaluated our segmentation strategy to the STACOM 2013 cardiac segmentation challenge benchmark. Qualitative and quantitative evaluations, obtained from the segmentation challenge, indicate that the proposed method achieved the state-of-the-art sensitivity (90%), specificity (99%), precision (94%), and efficiency levels (10s in GPU, and 7.5 min in CPU).


title: Dynamic Difficulty Adjustment through an Adaptive AI

abstract: Dynamic Difficulty Adjustment (DDA) consists in an alternative to the static game balancing performed in game design. DDA is done during execution, tracking the player's performance and adjusting the game to present proper challenges to the player. This approach seems appropriate to increase the player entertainment, since it provides balanced challenges, avoiding boredom or frustration during the gameplay. This paper presents a mechanism to perform the dynamic difficulty adjustment during a game match. The idea is to dynamically change the game AI, adapting it to the player skills. We implemented three different AIs to match player behaviors: beginner, regular and experienced in the game Defense of the Ancient (DotA), a modification (MOD) of the game Warcraft III. We performed a series of experiments and, after comparing all results, the presented mechanism was able to keep up with the player's abilities on 85% of all experiments. The remaining 15% failed to suit the player's need because the adjustment did not occur on the right moment.


title: Bangladeshi Sign Language Recognition using fingertip position

abstract: Sign language is the only means of communication for deaf and dump people which uses manual communication and body language to convey meaning. For any sign language, an interpreter is essential to communicate with deaf and dump people. To enhance interaction with community, Sign Language Recognition (SLR) is a growing field of research now a days. The task of SLR is language specific and a number of prominent works are available for few major languages. On the hand, the works are very few for Bangladeshi Sign Language (BSL) although Bangla is a major language and Bangladesh has a large community of deaf and dump people. In this study a BSL recognition scheme has been investigated based on fingertip position. The method considered relative tip positions of five fingers in two dimension space and position vectors are used to train artificial neural network (ANN) for recognition purpose. The method seems efficient with respect to ANN training with pixel values of image as of previous studies. The proposed method has been tested on a prepared data set of 518 images of 37 signs and achieved 99% recognition rate. The proposed method is found better than exiting BSL recognition methods.


title: Drug-induced alterations in the activity of rat brain cholinergic enzymes. I. In vitro and in vivo effect of amphetamine.

abstract: Abstract The effect of acute and chronic administration of d-amphetamine sulphate on rat brain acetylcholinesterase and choline acetylase activities has been studied. High concentrations (10 −2 M) of amphetamine were found to activate both AChE and ChAc activities in crude extracts from rat brain. Daily amphetamine injections into rats produced after some time an increase in ChAc activity of the brain but had no significant effect on its AChE activity. There was a marked inhibition in eating behaviour (95%) following the first day of treatment. Eating behaviour increased to about 40% of the control level after 14 days of treatment. There was a gradual reduction in body weight. The progressive changes in drug-induced eating behaviour and body weight showed some correlation to changes in ChAc activity. The possibility of amphetamine-induced adrenergic activity interacting with cholinergic function iis discussed.


title: Expert consensus document: The International Scientific Association for Probiotics and Prebiotics consensus statement on the scope and appropriate use of the term probiotic

abstract: Probiotics are widely regarded as live microorganisms that, when administered in sufficient amounts, confer a health benefit, but guidance is needed on the most appropriate use of the term. This Consensus Statement outlines recommendations for the scope and definition of the term 'probiotic' as determined by an expert panel convened by the International Scientific Association for Probiotics and Prebiotics in October 2013.


title: Phace: physics-based face modeling and animation

abstract: We present a novel physics-based approach to facial animation. Contrary to commonly used generative methods, our solution computes facial expressions by minimizing a set of non-linear potential energies that model the physical interaction of passive flesh, active muscles, and rigid bone structures. By integrating collision and contact handling into the simulation, our algorithm avoids inconsistent poses commonly observed in generative methods such as blendshape rigs. A novel muscle activation model leads to a robust optimization that faithfully reproduces complex facial articulations. We show how person-specific simulation models can be built from a few expression scans with a minimal data acquisition process and an almost entirely automated processing pipeline. Our method supports temporal dynamics due to inertia or external forces, incorporates skin sliding to avoid unnatural stretching, and offers full control of the simulation parameters, which enables a variety of advanced animation effects. For example, slimming or fattening the face is achieved by simply scaling the volume of the soft tissue elements. We show a series of application demos, including artistic editing of the animation model, simulation of corrective facial surgery, or dynamic interaction with external forces and objects.


title: pwnPr3d: An Attack-Graph-Driven Probabilistic Threat-Modeling Approach

abstract: In this paper we introduce pwnPr3d, a probabilistic threat modeling approach for automatic attack graph generation based on network modeling. The aim is to provide stakeholders in organizations with a holistic approach that both provides high-level overview and technical details. Unlike many other threat modeling and attack graph approaches that rely heavily on manual work and security expertise, our language comes with built-in security analysis capabilities. pwnPr3d generates probability distributions over the time to compromise assets.


title: Intercellular Nanotubes Mediate Bacterial Communication

abstract: Bacteria are known to communicate primarily via secreted extracellular factors. Here we identify a previously uncharacterized type of bacterial communication mediated by nanotubes that bridge neighboring cells. Using Bacillus subtilis as a model organism, we visualized transfer of cytoplasmic fluorescent molecules between adjacent cells. Additionally, by coculturing strains harboring different antibiotic resistance genes, we demonstrated that molecular exchange enables cells to transiently acquire nonhereditary resistance. Furthermore, nonconjugative plasmids could be transferred from one cell to another, thereby conferring hereditary features to recipient cells. Electron microscopy revealed the existence of variously sized tubular extensions bridging neighboring cells, serving as a route for exchange of intracellular molecules. These nanotubes also formed in an interspecies manner, between B. subtilis and Staphylococcus aureus, and even between B. subtilis and the evolutionary distant bacterium Escherichia coli. We propose that nanotubes represent a major form of bacterial communication in nature, providing a network for exchange of cellular molecules within and between species.


title: DeepMedic for Brain Tumor Segmentation

abstract: Accurate automatic algorithms for the segmentation of brain tumours have the potential of improving disease diagnosis, treatment planning, as well as enabling large-scale studies of the pathology. In this work we employ DeepMedic [1], a 3D CNN architecture previously presented for lesion segmentation, which we further improve by adding residual connections. We also present a series of experiments on the BRATS 2015 training database for evaluating the robustness of the network when less training data are available or less filters are used, aiming to shed some light on requirements for employing such a system. Our method was further benchmarked on the BRATS 2016 Challenge, where it achieved very good performance despite the simplicity of the pipeline.


title: Human resource information systems: a current assessment

abstract: Human resource information systems (HRIS) have become a major MIS subfunction within the personnel areas of many large corporations. This article traces the development of HRIS as an entity independent of centralized MIS, assesses its current operation and technological base, and considers its future role in the firm, especially its relationship to the centralized MIS function. The results of a survey of HRIS professionals from 171 U.S. corporations are described in order to provide an overview of the current design, operations, and effectiveness of HRIS. The findings of the survey are discussed in terms of their implications for management of human resource information systems.


title: A Survey of the Use of Adobe Flash Local Shared Objects to Respawn HTTP Cookies

abstract: Website developers can use Adobe’s Flash Player product to store information locally on users’ disks with Local Shared Objects (LSOs). LSOs can be used to store state information and user identifiers, and thus can be used for similar purposes as HTTP cookies. In a paper by Soltani et al, researchers documented at least four instances of “respawning,” where users deleted their HTTP cookies only to have the HTTP cookies recreated based on LSO data. In addition, the Soltani team found half of the 100 most popular websites used Flash technologies to store information about users. Both respawning and using LSOs to store data about users can reduce online privacy. One year later, we visited popular websites plus 500 randomly-selected websites to determine if respawning still occurs. We found no instances at all of respawning in a randomly-selected group of 500 websites. We found two instances of respawning in the most popular 100 websites. While our methods are different from the Soltani team and we cannot compare directly, our results suggest respawning is not increasing, and may be waning. As in the Soltani study, we found LSOs with unique identifiers. In the 100 most popular websites, LSOs were set at 20, and 9 used their LSOs to store unique identifiers. In 500 randomly selected sites, LSOs were set at 41, and 17 used their LSOs to store unique identifiers. Unique identifiers may, or may not, be keys into back-end databases to perform cookie-style tracking. However, unique identifiers could be benign, for example, uniquely identifying a specific animation or music clip. While we can use contextual information like variable names to guess what a given unique identifier is for, using our study methods we cannot conclusively determine how companies use unique identifiers. We cannot quantify how many, if any, sites are using unique identifiers in LSOs for any purpose that might have privacy implications. Even assuming a pessimistic worst case where all websites with unique identifiers in LSOs are using them to track users, the percentage of such sites studied is low — 9% of the top 100, and only 3.4% of the randomly-selected 500 sites we studied. However, over 40% of the LSOs in each data set used unique identifiers, and especially with the top 100 sites, many people could be affected. Because we found sites using LSOs as unique identifiers, we believe further study is needed to determine if these sites are using LSOs to evade users’ choices. However, without visibility into back-end databases, it is difficult to determine how unique identifiers are used. We conclude our paper with policy options and a discussion of implications for industry self-regulation of Internet privacy.


title: A micromouse maze sovling simulator

abstract: A 2D micromouse solving maze simulator is designed to simulate and examine algorithms for IEEE micromouse competition preparation purpose. 6 types of algorithms are included currently for choosing to solve a maze and record the track. Contour map of the maze cells is calculated as the micromouse finding the path to the end point. The maze solving and running process with steps counts are displayed at real time animatedly. A partition-center algorithm is introduced and verified with the simulator.


Parkinson disease: Deep brain stimulation might alleviate parkinsonism by reducing excessive synchronization in primary motor cortex


title: Transportation Modes Classification Using Sensors on Smartphones

abstract: This paper investigates the transportation and vehicular modes classification by using big data from smartphone sensors. The three types of sensors used in this paper include the accelerometer, magnetometer, and gyroscope. This study proposes improved features and uses three machine learning algorithms including decision trees, K-nearest neighbor, and support vector machine to classify the user’s transportation and vehicular modes. In the experiments, we discussed and compared the performance from different perspectives including the accuracy for both modes, the executive time, and the model size. Results show that the proposed features enhance the accuracy, in which the support vector machine provides the best performance in classification accuracy whereas it consumes the largest prediction time. This paper also investigates the vehicle classification mode and compares the results with that of the transportation modes.


Synthesis Lectures on Information Concepts, Retrieval, and Services


title: Spin Transfer Torques

abstract: This tutorial article introduces the physics of spin transfer torques in magnetic devices. We provide an elementary discussion of the mechanism of spin transfer torque, and review the theoretical and experimental progress in this field. Our intention is to be accessible to beginning graduate students. This is the introductory paper for a cluster of"Current Perspectives"articles on spin transfer torques published in volume 320 of the Journal of Magnetism and Magnetic Materials. This article is meant to set the stage for the others which follow it in this cluster; they focus in more depth on particularly interesting aspects of spin-torque physics and highlight unanswered questions that might be productive topics for future research.


title: Fitting smooth surfaces to dense polygon meshes

abstract: Recent progress in acquiring shape from range data permits the acquisition of seamless million-polygon meshes from physical models. While dense polygon meshes are an adequate representation for some applications, many users prefer smooth surface representations for reasons of compactness, control, manufacturability, or appearance. In this thesis, we present algorithms and an end-to-end software system for converting dense irregular polygon meshes of arbitrary topology into tensor product B-spline surface patches with accompanying displacement maps. This choice of representation yields a coarse but efficient model suitable for interactive modification and animation and a fine but more expensive model suitable for rendering. ::: The first step in our process consists of interactively painting patch boundaries onto the polygonal surface. In many applications, the placement of patch boundaries is considered part of the creative process and is not amenable to automation. We present efficient techniques for representing, creating and editing curves on dense polygonal surfaces. ::: The second step in our process consists of finding a gridded resampling of each bounded section of the mesh. Our resampling algorithm lays a grid of springs across the polygon mesh, then iterates between relaxing this grid and subdividing it. This grid provides a parameterization for the mesh section, which is initially unparameterized. Our parameterization algorithm is automatic, efficient, and robust, even for complex polygonal surfaces. Prior algorithms have lacked one or more of these properties, making them unusable for dense meshes. Our strategy also provides the user a flexible method to design parameterizations--an ability that previous literature in surface approximation does not address. ::: The third and final step of our process consists of fitting a hybrid of B-spline surfaces and displacement maps to our gridded re-sampling. The displacement map is an image representation of the error between the fitted B-spline surfaces and our spring grid. Since displacement maps are just images our hybrid representation facilitates the use of image processing operators for manipulating the geometric detail of an object. ::: Our resampling and fitting steps are fast enough to surface a million polygon mesh in under 10 minutes--important for an interactive system.


title: Taking the Edge off with Espresso: Scale, Reliability and Programmability for Global Internet Peering

abstract: We present the design of Espresso, Google's SDN-based Internet peering edge routing infrastructure. This architecture grew out of a need to exponentially scale the Internet edge cost-effectively and to enable application-aware routing at Internet-peering scale. Espresso utilizes commodity switches and host-based routing/packet processing to implement a novel fine-grained traffic engineering capability. Overall, Espresso provides Google a scalable peering edge that is programmable, reliable, and integrated with global traffic systems. Espresso also greatly accelerated deployment of new networking features at our peering edge. Espresso has been in production for two years and serves over 22% of Google's total traffic to the Internet.