title: Visualizing cyber security: Usable workspaces

abstract: The goal of cyber security visualization is to help analysts increase the safety and soundness of our digital infrastructures by providing effective tools and workspaces. Visualization researchers must make visual tools more usable and compelling than the text-based tools that currently dominate cyber analysts' tool chests. A cyber analytics work environment should enable multiple, simultaneous investigations and information foraging, as well as provide a solution space for organizing data. We describe our study of cyber-security professionals and visualizations in a large, high-resolution display work environment and the analytic tasks this environment can support. We articulate a set of design principles for usable cyber analytic workspaces that our studies have brought to light. Finally, we present prototypes designed to meet our guidelines and a usability evaluation of the environment.


A Lattice Interpretation Of The Chinese Wall Policy


title: Relational Marginal Problems: Theory and Estimation

abstract: In the propositional setting, the marginal problem is to find a (maximum-entropy) distribution that has some given marginals. We study this problem in a relational setting and make the following contributions. First, we compare two different notions of relational marginals. Second, we show a duality between the resulting relational marginal problems and the maximum likelihood estimation of the parameters of relational models, which generalizes a well-known duality from the propositional setting. Third, by exploiting the relational marginal formulation, we present a statistically sound method to learn the parameters of relational models that will be applied in settings where the number of constants differs between the training and test data. Furthermore, based on a relational generalization of marginal polytopes, we characterize cases where the standard estimators based on feature's number of true groundings needs to be adjusted and we quantitatively characterize the consequences of these adjustments. Fourth, we prove bounds on expected errors of the estimated parameters, which allows us to lower-bound, among other things, the effective sample size of relational training data.


title: Class Imbalances versus Class Overlapping: an Analysis of a Learning System Behavior

abstract: Several works point out class imbalance as an obstacle on applying machine learning algorithms to real world domains. However, in some cases, learning algorithms perform well on several imbalanced domains. Thus, it does not seem fair to directly correlate class imbalance to the loss of performance of learning algorithms. In this work, we develop a systematic study aiming to question whether class imbalances are truly to blame for the loss of performance of learning systems or whether the class imbalances are not a problem by themselves. Our experiments suggest that the problem is not directly caused by class imbalances, but is also related to the degree of overlapping among the classes.


title: Text summarization using unsupervised deep learning

abstract: Unsupervised extractive summarization of emails using a deep auto-encoder with excellent performance.Ensemble Noisy Auto-Encoder runs noisy inputs through one trained network, enhancing performance.Summaries are highly informative and semantically similar to human abstracts. We present methods of extractive query-oriented single-document summarization using a deep auto-encoder (AE) to compute a feature space from the term-frequency (tf) input. Our experiments explore both local and global vocabularies. We investigate the effect of adding small random noise to local tf as the input representation of AE, and propose an ensemble of such noisy AEs which we call the Ensemble Noisy Auto-Encoder (ENAE). ENAE is a stochastic version of an AE that adds noise to the input text and selects the top sentences from an ensemble of noisy runs. In each individual experiment of the ensemble, a different randomly generated noise is added to the input representation. This architecture changes the application of the AE from a deterministic feed-forward network to a stochastic runtime model. Experiments show that the AE using local vocabularies clearly provide a more discriminative feature space and improves the recall on average 11.2%. The ENAE can make further improvements, particularly in selecting informative sentences. To cover a wide range of topics and structures, we perform experiments on two different publicly available email corpora that are specifically designed for text summarization. We used ROUGE as a fully automatic metric in text summarization and we presented the average ROUGE-2 recall for all experiments.


title: Towards Computational Baby Learning: A Weakly-Supervised Approach for Object Detection

abstract: Intuitive observations show that a baby may inherently possess the capability of recognizing a new visual concept (e.g., chair, dog) by learning from only very few positive instances taught by parent(s) or others, and this recognition capability can be gradually further improved by exploring and/or interacting with the real instances in the physical world. Inspired by these observations, we propose a computational model for weakly-supervised object detection, based on prior knowledge modelling, exemplar learning and learning with video contexts. The prior knowledge is modeled with a pre-trained Convolutional Neural Network (CNN). When very few instances of a new concept are given, an initial concept detector is built by exemplar learning over the deep features the pre-trained CNN. The well-designed tracking solution is then used to discover more diverse instances from the massive online weakly labeled videos. Once a positive instance is detected/identified with high score in each video, more instances possibly from different view-angles and/or different distances are tracked and accumulated. Then the concept detector can be fine-tuned based on these new instances. This process can be repeated again and again till we obtain a very mature concept detector. Extensive experiments on Pascal VOC-07/10/12 object detection datasets [9] well demonstrate the effectiveness of our framework. It can beat the state-of-the-art full-training based performances by learning from very few samples for each object category, along with about 20,000 weakly labeled videos.


title: Cloud robotics: A software architecture: For heterogeneous large-scale autonomous robots

abstract: The paper proposes a software architecture for cloud robotics which intends three subsystems in the cloud environment: Middleware Subsystem, Background Tasks Subsystem, and Control Subsystem. The architecture invokes cloud technologies such as cloud computing, cloud storage, and other networking platforms arranged on the assistances of congregated infrastructure and shared services for robotics, for instance Robot Operating System (ROS). Since the architecture is looking for reliable, scalable, and distributed system for the heterogeneous large-scale autonomous robots, Infrastructure as a Service (IaaS) is chosen among the cloud services. Three major tasks can be handled by the proposed software architecture Computing, Storage, and Networking. Hadoop-MapReduce provides the appropriate framework in the cloud environment to process and handle these tasks.


title: Phase Noise and Fundamental Sensitivity of Oscillator-Based Reactance Sensors

abstract: This paper investigates the fundamental sensitivity of oscillator-based reactance sensors, which are widely used in numerous types of biomedical sensing applications. We first show that the intrinsic sensitivity is limited by the $1/{ f}^{3}$ phase noise of the sensing oscillators. To achieve sensor detection sensitivity below this limit, a correlated double counting (CDC) noise suppression scheme is proposed to cancel the correlated $1/{ f}^{3}$ phase noise in differential frequency detections. The suppression effect of the CDC scheme is thoroughly modeled. Moreover, the CDC scheme is extended to a high-order configuration, called the Interleaving-N CDC, to further improve the frequency resolution. In addition, we show that the weighting sequence on the Interleaving-N CDC data can be optimized as a digital noise filter to maximize the noise suppression. Given a sensing oscillator with any phase-noise profile, a general weighting optimization method is proposed based on the minimum variance distortion less response. As an example, an oscillator-based inductive magnetic sensor array in a 45-nm CMOS silicon-on-insulator process is implemented with the proposed CDC scheme. It achieves a noise suppression of 10.4 dB with basic CDC sheme and a frequency resolution of 0.128 parts per million for Interleaving-N CDC scheme, both with negligible power overhead. This enables inductance-change detection sensitivity of 0.41 fH for a low- $Q$ on-chip 1.6-nH inductor with a quality factor of only 4.95.


title: Young people's use of online social networking sites – a uses and gratifications perspective

abstract: Purpose – The purpose of this paper is to explore why young people use and participate in social networking sites (SNSs) with specific reference to Bebo.Design/methodology/approach – A qualitative approach is employed in this paper with a view to exploring the uses and gratifications (U and G) that girls aged 12‐14 years, both seek and obtain from the Bebo SNS. The research is conducted in a school setting in Ireland.Findings – The findings indicate that the participants are actively using Bebo for their own personal motives and gratifications in terms of presenting and managing a certain identity and persona in a social context. Furthermore, the relatively impersonal nature of the online environment is seen to especially facilitate the young participants in negotiating the practicalities and difficulties that can arise offline, in terms of forging identities and managing relationships.Originality/value – U and G theory has attracted criticism in terms of a perceived limitation that it only serves to offe...


title: Chemical Speciation and Bioavailability of Mercury in the Sediment of the Riparian Zone of Three Gorges Reservoir Area,China

abstract: Thirty-six sediment samples in 4 typical sites along the riparian zone of Three Gorges Reservoir Area,China,were analyzed to investigate the speciation and bioavailability of mercury(Hg) in them.The result showed that there was just slightly contaminated by mercury,and lower methylation potential was found in sediment of WLFZ.The concentrations of methyl mercury(MeHg) in the sediment of the riparian zone in July and September were 0.128±0.028 ng/g and 0.031±0.027 ng/g,respectively.The MeHg:THg ratio in the sediment in July and September was 0.296±0.154% and 0.069±0.081%,respectively,a triple difference.The MeHg:THg ratio differed with different types of sediment as well,being greater in flooding sediment than in semi-flooding sediment and the value of drying sediment being the least.The content of different Hg fractions of the sediments collected in July was the highest in Hg-e(46.99%),followed in order by Hg-s(36.28%),Hg-o(9.57%),Hg-w(4.49%)and Hg-h(2.67%).The content of different Hg fractions of the sediments collected in September was the highest in Hg-s(46.47%),followed in order by Hg-e(34.73%),Hg-o(14.18%),Hg-h(2.40%) and Hg-w(2.21%).In other words,Hg-s was the main fraction of sediments in September.


title: Semantically Far Inspirations Considered Harmful?: Accounting for Cognitive States in Collaborative Ideation

abstract: Collaborative ideation systems can help people generate more creative ideas by exposing them to ideas different from their own. However, there are competing theoretical views on whether and when such exposure is helpful. Associationist theory suggests that exposing ideators to ideas that are semantically far from their own maximizes novel combinations of ideas. In contrast, SIAM theory cautions that systems should offer far ideas only when ideators reach an impasse (a cognitive state in which they have exhausted ideas within a particular category), and offer near ideas during productive ideation (a cognitive state in which they are actively exploring ideas within a category), which maximizes exploration within categories. Our research compares these theoretical recommendations. In an online experiment, 245 participants generated ideas for a themed wedding; we detected and validated participants' cognitive states using a combination of behavioral and neuroimaging data. Receiving far ideas during productive ideation resulted in slower ideation and less within-category exploration, without significant benefits for novelty, compared to receiving no inspirations. Participants were also more likely to hit an impasse when receiving far ideas during productive ideation. These findings suggest that far inspirational ideas can harm creativity if received during productive ideation.


title: Multi-stage Programming with Dependent Types

abstract: Dependent types and multi-stage programming have both been used, separately, in programming language design and implementation. Each technique has its own advantages — with dependent types, we can verify aspects of interpreters and compilers such as type safety and stack invariants. Multi-stage programming, on the other hand, can give the implementor access to underlying compiler technology; a staged interpreter is a translator. In this paper, we investigate the combination of these techniques. We implement an interpreter for a simply typed lambda calculus, using dependent types to guarantee correctness properties by construction. We give explicit proofs of these correctness properties, then add staging annotations to generate a translator from the interpreter. In this way, we have constructed a verified compiler from a verified staged interpreter. We illustrate the application of the technique by considering a simple staged interpreter that provides guarantees for some simple resource bound properties, as might be found in a domain specific language for real-time embedded systems.


title: A prototype single-chip spectrum analyzer with integrated frequency-synthesized tuning

abstract: A variety of applications such as spectrum monitoring systems and dynamic spectrum access systems motivate the development of on-chip capability to measure the frequency spectrum of the local radio environment. Toward this end, a prototype of a simple single-chip frequency-synthesized spectrum analyzer is presented. The design is targeted for applications where the spectrum analyzer would be a component of a larger complex system. Therefore, a simple architecture is considered in order to minimize chip area and reduce power consumption. Although the proposed spectrum analyzer architecture is uncomplicated, it nevertheless provides useful baseline capability for measurement of the frequency spectrum. A prototype was fabricated in a standard 0.5 micron CMOS process, and measured results are given from 20 to 200 MHz.


title: GPS Digital Tracking Loops Design for High Dynamic Launching Vehicles

abstract: This paper describes the design of digital tracking loops for GPS receivers in a high dynamics environment, without external aiding. We adopted the loop structure of a frequency-locked loop (FLL)-assisted phase-locked loop (PLL) and design it to track accelerations steps, as those occurring in launching vehicles. We used a completely digital model of the loop where the FLL and PLL parts are jointly designed, as opposed to the classical discretized analog model with separately designed FLL and PLL. The new approach does not increase the computational burden. We performed simulations and real RF signal experiments of a fixed-point implementation of the loop, showing that reliable tracking of steps up to 40 g can be achieved


title: On the equivalence of linear discriminant analysis and least squares

abstract: Linear discriminant analysis (LDA) is a popular dimensionality reduction and classification method that simultaneously maximizes between-class scatter and minimizes within-class scatter. In this paper, we verify the equivalence of LDA and least squares (LS) with a set of dependent variable matrices. The equivalence is in the sense that the LDA solution matrix and the LS solution matrix have the same range. The resulting LS provides an intuitive interpretation in which its solution performs data clustering according to class labels. Further, the fact that LDA and LS have the same range allows us to design a two-stage algorithm that computes the LDA solution given by generalized eigenvalue decomposition (GEVD), much faster than computing the original GEVD. Experimental results demonstrate the equivalence of the LDA solution and the proposed LS solution.


title: Preferential precipitation on dislocation loops in Te-doped gallium arsenide

abstract: Abstract Heat-treated tellurium-doped gallium arsenide contains prismatic and faulted dislocation loops with precipitate particles lying on the dislocation core. The position of the precipitates on the loops has been studied revealing preferential precipitation on 〈110〉 line segments for both faulted and prismatic loops. From a consideration of the atomic arrangements at the dislocation cores it is concluded that the precipitates preferentially decorate the prismatic loops where the dislocation core is identical to either a βss or αgs Lomer dislocation. Precipitates on the {111} faulted loops, however, can appear on all possible 〈110〉 dislocation segments. The faulted loops are thought to consist of a layer of gallium telluride, and, since this is a defect lattice containing gallium vacancies, β type dislocation core structures can occur along all six 〈110〉 directions. By analogy with the faulted loops, preferential precipitation on prismatic loops is believed to occur on βss Lomer dislocations.


title: Toward Social Internet of Vehicles: Concept, Architecture, and Applications

abstract: The main vision of the Internet of Things (IoT) is to equip real-life physical objects with computing and communication power so that they can interact with each other for the social good. As one of the key members of IoT, Internet of Vehicles (IoV) has seen steep advancement in communication technologies. Now, vehicles can easily exchange safety, efficiency, infotainment, and comfort-related information with other vehicles and infrastructures using vehicular ad hoc networks (VANETs). We leverage on the cloud-based VANETs theme to propose cyber-physical architecture for the Social IoV (SIoV). SIoV is a vehicular instance of the Social IoT (SIoT), where vehicles are the key social entities in the machine-to-machine vehicular social networks. We have identified the social structures of SIoV components, their relationships, and the interaction types. We have mapped VANETs components into IoT-A architecture reference model to offer better integration of SIoV with other IoT domains. We also present a communication message structure based on automotive ontologies, the SAE J2735 message set, and the advanced traveler information system events schema that corresponds to the social graph. Finally, we provide the implementation details and the experimental analysis to demonstrate the efficacy of the proposed system as well as include different application scenarios for various user groups.


title: Dipiperidinoethane neurotoxicity clarified

abstract: Abstract By systemic administration, dipiperidinoethane (DPE) induces a seizure-linked brain damage syndrome similar to that induced by kainic acid. Here, we report that DPE does not reproduce this syndrome when injected into the amygdala, but an oxidized derivative (DPE-di-N-oxide) does. It is pointed out that DPE-di-N-oxide, a postulated in vivo metabolite of DPE, bears a closer structural resemblance to the cholinergic agonist oxotremorine than to kainic acid. On the basis of this and related evidence we propose that a cholinergic mechanism may play a role in this type of seizure-brain damage syndrome.


title: Privileged Multi-label Learning

abstract: This paper presents privileged multi-label learning (PrML) to explore and exploit the relationship between labels in multi-label learning problems. We suggest that for each individual label, it cannot only be implicitly connected with other labels via the low-rank constraint over label predictors, but also its performance on examples can receive the explicit comments from other labels together acting as an \emph{Oracle teacher}. We generate privileged label feature for each example and its individual label, and then integrate it into the framework of low-rank based multi-label learning. The proposed algorithm can therefore comprehensively explore and exploit label relationships by inheriting all the merits of privileged information and low-rank constraints. We show that PrML can be efficiently solved by dual coordinate descent algorithm using iterative optimization strategy with cheap updates. Experiments on benchmark datasets show that through privileged label features, the performance can be significantly improved and PrML is superior to several competing methods in most cases.


title: LSTM-CRF for Drug-Named Entity Recognition

abstract: Drug-Named Entity Recognition (DNER) for biomedical literature is a fundamental facilitator of Information Extraction. For this reason, the DDIExtraction2011 (DDI2011) and DDIExtraction2013 (DDI2013) challenge introduced one task aiming at recognition of drug names. State-of-the-art DNER approaches heavily rely on hand-engineered features and domain-specific knowledge which are difficult to collect and define. Therefore, we offer an automatic exploring words and characters level features approach: a recurrent neural network using bidirectional long short-term memory (LSTM) with Conditional Random Fields decoding (LSTM-CRF). Two kinds of word representations are used in this work: word embedding, which is trained from a large amount of text, and character-based representation, which can capture orthographic feature of words. Experimental results on the DDI2011 and DDI2013 dataset show the effect of the proposed LSTM-CRF method. Our method outperforms the best system in the DDI2013 challenge.


title: TUIO: A Protocol for Table-Top Tangible User Interfaces

abstract: In this article we present Tuio, a simple yet versatile protocol designed specifically to meet the requirements of table-top tangible user interfaces. Inspired by the idea of interconnecting various existing table interfaces such as the reacTable* , being developed in Barcelona and the tDesk from Bielefeld, this protocol defines common properties of controller objects on the table surface as well as of finger and hand gestures performed by the user. Currently this protocol has been implemented within a fiducial marker-based computer vision engine developed for the reacTable* project. This fast and robust computer vision engine is based on the original d-touch concept, which is also included as an alternative to the newer fiducial tracking engine. The computer vision framework has been implemented on various standard platforms and can be extended with additional sensor components. We are currently working on the tracking of finger-tips for gestural control within the table interface. The Tuio protocol has been implemented using OpenSound Control [4] and is therefore usable on any platform supporting this protocol. At the moment we have working implementations for Java, C++, PureData, Max/MSP, SuperCollider and Flash.


title: High accuracy signal parameter estimation algorithm for calibration of PMU devices

abstract: This article describes the implementation of a high accuracy signal parameter estimation algorithm for estimating signal parameters of Phasor Measurement Unit (PMU). The algorithm presented is compared with standard four parameter sine fit algorithm, showing improved performance for harmonically distorted signals, which are commonly present in power line signals.


title: Efficacy in the margins of NHL with ibrutinib

abstract: In this issue of Blood, Noy et al report on the efficacy of single-agent ibrutinib in patients with relapsed or refractory marginal zone lymphoma (MZL). They demonstrate an overall response rate of 48% and a median progression-free survival of 14 months, establishing the Bruton tyrosine kinase (BTK) inhibitor ibrutinib as a therapeutic option for this population.1


title: Automatic Extraction of Manhattan-World Building Masses from 3D Laser Range Scans

abstract: We propose a novel approach for the reconstruction of urban structures from 3D point clouds with an assumption of Manhattan World (MW) building geometry; i.e., the predominance of three mutually orthogonal directions in the scene. Our approach works in two steps. First, the input points are classified according to the MW assumption into four local shape types: walls, edges, corners, and edge corners. The classified points are organized into a connected set of clusters from which a volume description is extracted. The MW assumption allows us to robustly identify the fundamental shape types, describe the volumes within the bounding box, and reconstruct visible and occluded parts of the sampled structure. We show results of our reconstruction that has been applied to several synthetic and real-world 3D point data sets of various densities and from multiple viewpoints. Our method automatically reconstructs 3D building models from up to 10 million points in 10 to 60 seconds.


title: Fabrication of well aligned P(VDF-TrFE) nanofibers with asymmetric comb-shape electrode

abstract: This paper presents an efficient fabrication process of highly aligned P(VDF-TrFE) nanofibers based on conventional far-field electrospinning (CFES) technique. By attaching a comb-shape electrode onto a fast rotating drum, the process combines the advantages of plane electrode and rotating-drum-collecting methods, which leads to the orientation of the deposited nanofibers and enhances it. Additionally, the high rotating speed also contributes to the fast and continuous deposition of the nanofibers with good alignment. Fourier transform infrared (FTIR) spectra and X-ray pattern (XRD) indicate that P(VDF-TrFE) has formed crystalline of β-phase, which possesses piezoelectric characteristic. Since the deposited nanofibers are suspended over the electrodes, they can be easily transferred to other substrates which make them very flexible and convenience for further treatment and various applications. As a demonstration, we used this fabricated nanofiber to scavenge mechanical energy into electricity. Under the bend frequency of 3Hz, a segment of 1×2cm nanofibers can generate open-circuit voltage of 149mV and short-circuit current of 3.06nA, which shows the capability of this well oriented P(VDF-TrFE) nanofibers can harvest mechanical energy from tiny deformations.


title: Tuned Models of Peer Assessment in MOOCs

abstract: In massive open online courses (MOOCs), peer grading serves as a critical tool for scaling the grading of complex, open-ended assignments to courses with tens or hundreds of thousands of students. But despite promising initial trials, it does not always deliver accurate results compared to human experts. In this paper, we develop algorithms for estimating and correcting for grader biases and reliabilities, showing significant improvement in peer grading accuracy on real data with 63,199 peer grades from Coursera's HCI course offerings --- the largest peer grading networks analysed to date. We relate grader biases and reliabilities to other student factors such as student engagement, performance as well as commenting style. We also show that our model can lead to more intelligent assignment of graders to gradees.


title: Simultaneous estimation of cortical activity during social interactions by using EEG hyperscannings

abstract: In this paper we show how the possibility of recording simultaneously the cerebral neuroelectric activity in different subjects (EEG hyperscanning) during the execution of different tasks could return useful information about the “internal” cerebral state of the subjects. We present the results obtained by EEG hyperscannings during ecological task (such as the execution of a card game) as well as that obtained in a series of couples of subjects during the performance of the Prisoner's Dilemma Game. The simultaneous recordings of couples of interacting subjects allows to observe and to model directly the neural signature of human interactions in order to understand the cerebral processes generating and generated by social cooperation or competition. Results obtained in a study of different groups recorded during the card game revealed a larger activity in prefrontal and anterior cingulated cortex in different frequency bands for the player that leads the game when compared to other players. Results collected in a population of 10 subjects during the performance of the Prisoner's Dilemma suggested that the most consistently activated structure is the orbitofrontal region (roughly described by the Brodmann area 10) during the condition of competition in both the tasks. It could be speculated whether the pattern of cortical connectivity between different cortical areas in different subjects could be employed as a tool for assessing the outcome of the task in advance.


title: Path Planning and Obstacle Avoidance for Autonomous Mobile Robots: A Review

abstract: Recent advances in the area of mobile robotics caused growing attention of the armed forces, where the necessity for unmanned vehicles being able to carry out the dull and dirty operations, thus avoid endangering the life of the military personnel. UAV offers a great advantage in supplying reconnaissance data to the military personnel on the ground, thus lessening the life risk of the troops. In this paper we analyze various techniques for path planning and obstacle avoidance and cooperation issues for multiple mobile robots. We also present a generic dynamics and control model for steering a UAV along a collision free path from a start to a goal position.


title: CACTI-P: Architecture-level modeling for SRAM-based structures with advanced leakage reduction techniques

abstract: This paper introduces CACTI-P, the first architecture-level integrated power, area, and timing modeling framework for SRAM-based structures with advanced leakage power reduction techniques. CACTI-P supports modeling of major leakage power reduction approaches including power-gating, long channel devices, and Hi-k metal gate devices. Because it accounts for implementation overheads, CACTI-P enables in-depth study of architecture-level tradeoffs for advanced leakage power management schemes. We illustrate the potential applicability of CACTI-P in the design and analysis of leakage power reduction techniques of future manycore processors by applying nanosecond scale power-gating to different levels of cache for a 64 core multithreaded architecture at the 22nm technology. Combining results from CACTI-P and a performance simulator, we find that although nanosecond scale power-gating is a powerful way to minimize leakage power for all levels of caches, its severe impacts on processor performance and energy when being used for L1 data caches make nanosecond scale power-gating a better fit for caches closer to main memory.


title: Finding event correlations in federated wireless sensor networks

abstract: Event correlation engines help us find events of interest inside raw sensor data streams and help reduce the data volume, simultaneously. This paper discusses some of the challenges faced in finding event correlations over federated wireless sensor networks (WSNs) including high data volumes, uncertain or missing data, application-specific dependencies and widely varying data ranges and sampling frequencies. Analysis over real geo-tracking data of moving objects confirms some of these challenges. Federation at the data layer above the WSNs is presented as a feasible alternative.


title: Direct visual odometry for a fisheye-stereo camera

abstract: We present a direct visual odometry algorithm for a fisheye-stereo camera. Our algorithm performs simultaneous camera motion estimation and semi-dense reconstruction. The pipeline consists of two threads: a tracking thread and a mapping thread. In the tracking thread, we estimate the camera pose via semi-dense direct image alignment. To have a wider field of view (FoV) which is important for robotic perception, we use fisheye images directly without converting them to conventional pinhole images which come with a limited FoV. To address the epipolar curve problem, plane-sweeping stereo is used for stereo matching and depth initialization. Multiple depth hypotheses are tracked for selected pixels to better capture the uncertainty characteristics of stereo matching. Temporal motion stereo is then used to refine the depth and remove false positive depth hypotheses. Our implementation runs at an average of 20 Hz on a low-end PC. We run experiments in outdoor environments to validate our algorithm, and discuss the experimental results. We experimentally show that we are able to estimate 6D poses with low drift, and at the same time, do semi-dense 3D reconstruction with high accuracy. To the best of our knowledge, there is no other existing semi-dense direct visual odometry algorithm for a fisheye-stereo camera.


title: Deep belief network based semantic taggers for spoken language understanding.

abstract: This paper investigates the use of deep belief networks (DBN) for semantic tagging, a sequence classification task, in spoken language understanding (SLU). We evaluate the performance of the DBN based sequence tagger on the well-studied ATIS task and compare our technique to conditional random fields (CRF), a state-of-the-art classifier for sequence classification. In conjunction with lexical and named entity features, we also use dependency parser based syntactic features and part of speech (POS) tags [1]. Under both noisy conditions (output of automatic speech recognition system) and clean conditions (manual transcriptions), our deep belief network based sequence tagger outperforms the best CRF based system described in [1] by an absolute 2% and 1% F-measure, respectively.Upon carrying out an analysis of cases where CRF and DBN models made different predictions, we observed that when discrete features are projected onto a continuous space during neural network training, the model learns to cluster these features leading to its improved generalization capability, relative to a CRF model, especially in cases where some features are either missing or noisy.


title: Automated Tracking and Grasping of a Moving Object with a Robotic Hand-Eye System

abstract: An attempt to achieve a high level of interaction between a real-time vision system capable of tracking moving objects in 3-D and a robot arm with gripper that can be used to pick up a moving object is described. The interplay of hand-eye coordination in dynamic grasping tasks such as grasping of parts on a moving conveyor system, assembly of articulated parts, or for grasping from a mobile robotic system is explored. The goal is to build an integrated sensing and actuation system that can operate in dynamic as opposed to static environments. The system built addresses three distinct problems in using robotic hand-eye coordination for grasping moving objects: fast computation of 3-D motion parameters from vision, predictive control of a moving robotic arm to track a moving object, and interception and grasping. The system operates at approximately human arm movement rates. Experimental results in which a moving model train is tracked, stably grasped, and picked up by the system are presented. The algorithms developed to relate sensing to actuation are quite general and applicable to a variety of complex robotic tasks. >


title: Entity Synonyms for Structured Web Search

abstract: Nowadays, there are many queries issued to search engines targeting at finding values from structured data (e.g., movie showtime of a specific location). In such scenarios, there is often a mismatch between the values of structured data (how content creators describe entities) and the web queries (how different users try to retrieve them). Therefore, recognizing the alternative ways people use to reference an entity, is crucial for structured web search. In this paper, we study the problem of automatic generation of entity synonyms over structured data toward closing the gap between users and structured data. We propose an offline, data-driven approach that mines query logs for instances where content creators and web users apply a variety of strings to refer to the same webpages. This way, given a set of strings that reference entities, we generate an expanded set of equivalent strings (entity synonyms) for each entity. Our framework consists of three modules: candidate generation, candidate selection, and noise cleaning. We further study the cause of the problem through the identification of different entity synonym classes. The proposed method is verified with experiments on real-life data sets showing that we can significantly increase the coverage of structured web queries with good precision.


title: Direct-on-line-start permanent-magnet-assisted synchronous reluctance machine with ferrite magnets

abstract: The possibility of applying low-cost ferrite magnets in a direct-on-line-start permanent-magnet-assisted synchronous reluctance machine (DOL PMaSynRM) for industrial applications, such as pump or ventilation systems was studied. The main target of this machine topology was to achieve a higher efficiency class compared to a counterpart induction machine (IM). Because of the weak properties of the ferrite magnets the motor had to be optimized for a low magnetic stress starting from the permanent magnets' point of view. The optimization procedure includes finding of a suitable rotor construction with a proper number of barriers, thickness of the barriers and thickness of the ferrite magnets. More detailed attention was dedicated to the analysis and elimination of irreversible demagnetization risk of ferrite magnets during start up period.


An Exact Algorithm for Finding Minimum Oriented Bounding Boxes


title: Analysis of Ultrasound Kidney Images Using Content Descriptive Multiple Features for Disorder Identification and ANN Based Classification

abstract: The objective of this work is to provide a set of most significant content descriptive feature parameters to identify and classify the kidney disorders with ultrasound scan. The ultrasound images are initially pre-processed to preserve the pixels of interest prior to feature extraction. In total 28 features are extracted, the analysis of features value shows that 13 features are highly significant in discrimination. This resultant feature vector is used to train the multilayer back propagation network. The network is tested with the unknown samples. The outcome of multi-layer back propagation network is verified with medical experts and this confirms classification efficiency of 90.47%, 86.66%, and 85.71% for the classes considered respectively. The study shows that feature extraction after pre-processing followed by ANN based classification significantly enhance objective diagnosis and provides the possibility of developing computer-aided diagnosis system


title: Statistical models of appearance for eye tracking and eye-blink detection and measurement

abstract: A statistical active appearance model (AAM) is developed to track and detect eye blinking. The model has been designed to be robust to variations of head pose or gaze. In particular we analyze and determine the model parameters which encode the variations caused by blinking. This global model is further extended using a series of sub-models to enable independent modeling and tracking of the two eye regions. Several methods to enable measurement and detection of eye-blink are proposed and evaluated. The results of various tests on different image databases are presented to validate each model.


Brain Tumor Extraction in MRI images using Clustering and Morphological Operations Techniques


title: Improved Bayesian inference for the stochastic block model with application to large networks

abstract: An efficient MCMC algorithm is presented to cluster the nodes of a network such that nodes with similar role in the network are clustered together. This is known as block-modeling or block-clustering. The model is the stochastic blockmodel (SBM) with block parameters integrated out. The resulting marginal distribution defines a posterior over the number of clusters and cluster memberships. Sampling from this posterior is simpler than from the original SBM as transdimensional MCMC can be avoided. The algorithm is based on the allocation sampler. It requires a prior to be placed on the number of clusters, thereby allowing the number of clusters to be directly estimated by the algorithm, rather than being given as an input parameter. Synthetic and real data are used to test the speed and accuracy of the model and algorithm, including the ability to estimate the number of clusters. The algorithm can scale to networks with up to ten thousand nodes and tens of millions of edges.


title: User Traffic Profile for Traffic Reduction and Effective Bot C&C Detection

abstract: Bots are malicious software components used for generating spams, launching denial of service attacks, phishing, identity theft and information exfiltration and such other illegal activities. Bot detection is an area of active research in recent times. Here we propose a bot detection mechanism for a single host. A user traffic profile is used to filter out normal traffic generated by the host. The remaining suspicious traffic is subject to detailed analysis. The nature of detailed analysis is derived from a characterization of bot traffic. The detection system is tested using two real world bots. Performance evaluation results show that the proposed system achieves a high detection rate (100%) and a low false positive rate (0–8%). The traffic filtering yielded an average reduction in traffic by 70%.


title: A microwave broadband triplexer used in satellite television systems

abstract: This paper presents the plane type triplexer. Consists three band-pass filter designed at 1.2/1.9/2.75 GHz. Bandwidth is 500 MHz. Maximum size is 47.5 × 23 mm2. This triplexer is applied on commercial set-top box (STB). Characteristic impedance is 75 ohm. The first part is analysis the common filter types and use Chebyshev type II to simulate each filter. Second, combine three filters by shunting way. It has to prevent signal flow at the center frequencies of the other two filters. As a result, combine three networks can accomplish the required characteristics for a triplexer. Finally, show the simulation result and verify the property. The simulated results and measurements of the filters are not similar. We propose a better way to improve and fabricate it.


title: Large-Scale Distributed Graph Computing Systems: An Experimental Evaluation

abstract: With the prevalence of graph data in real-world applications (e.g., social networks, mobile phone networks, web graphs, etc.) and their ever-increasing size, many distributed graph computing systems have been developed in recent years to process and analyze massive graphs. Most of these systems adopt Pregel's vertex-centric computing model, while various techniques have been proposed to address the limitations in the Pregel framework. However, there is a lack of comprehensive comparative analysis to evaluate the performance of various systems and their techniques, making it difficult for users to choose the best system for their applications. We conduct extensive experiments to evaluate the performance of existing systems on graphs with different characteristics and on algorithms with different design logic. We also study the effectiveness of various techniques adopted in existing systems, and the scalability of the systems. The results of our study reveal the strengths and limitations of existing systems, and provide valuable insights for users, researchers and system developers.


title: Deep Reinforcement Learning for Solving the Vehicle Routing Problem

abstract: We present an end-to-end framework for solving Vehicle Routing Problem (VRP) using deep reinforcement learning. In this approach, we train a single model that finds near-optimal solutions for problem instances sampled from a given distribution, only by observing the reward signals and following feasibility rules. Our model represents a parameterized stochastic policy, and by applying a policy gradient algorithm to optimize its parameters, the trained model produces the solution as a sequence of consecutive actions in real time, without the need to re-train for every new problem instance. Our method is faster in both training and inference than a recent method that solves the Traveling Salesman Problem (TSP), with nearly identical solution quality. On the more general VRP, our approach outperforms classical heuristics on medium-sized instances in both solution quality and computation time (after training). Our proposed framework can be applied to variants of the VRP such as the stochastic VRP, and has the potential to be applied more generally to combinatorial optimization problems.


title: Efficient learning of timeseries shapelets

abstract: In timeseries classification, shapelets are subsequences of timeseries with high discriminative power. Existing methods perform a combinatorial search for shapelet discovery. Even with speedup heuristics such as pruning, clustering, and dimensionality reduction, the search remains computationally expensive. In this paper, we take an entirely different approach and reformulate the shapelet discovery task as a numerical optimization problem. In particular, the shapelet positions are learned by combining the generalized eigenvector method and fused lasso regularizer to encourage a sparse and blocky solution. Extensive experimental results show that the proposed method is orders of magnitudes faster than the state-of-the-art shapelet-based methods, while achieving comparable or even better classification accuracy.


title: Deep learning for human part discovery in images

abstract: This paper addresses the problem of human body part segmentation in conventional RGB images, which has several applications in robotics, such as learning from demonstration and human-robot handovers. The proposed solution is based on Convolutional Neural Networks (CNNs). We present a network architecture that assigns each pixel to one of a predefined set of human body part classes, such as head, torso, arms, legs. After initializing weights with a very deep convolutional network for image classification, the network can be trained end-to-end and yields precise class predictions at the original input resolution. Our architecture particularly improves on over-fitting issues in the up-convolutional part of the network. Relying only on RGB rather than RGB-D images also allows us to apply the approach outdoors. The network achieves state-of-the-art performance on the PASCAL Parts dataset. Moreover, we introduce two new part segmentation datasets, the Freiburg sitting people dataset and the Freiburg people in disaster dataset. We also present results obtained with a ground robot and an unmanned aerial vehicle.


title: Sensitivity Improved SPR Biosensor Based on the MoS2/Graphene–Aluminum Hybrid Structure

abstract: MoS2-graphene-based hybrid structures are biocompatible and useful in the field of biosensors. Herein, we propose a heterostructured MoS2/aluminum (Al) film/MoS2/graphene as a highly sensitive surface plasmon resonance (SPR) biosensor based on the Otto configuration. The sensitivity of the proposed biosensor is enhanced by using three methods. First, prisms of different refractive index have been discussed and it is found that sensitivity can be enhanced by using a low refractive index prism. Second, the influence of the thickness of the air layer on the sensitivity is analyzed and the optimal thickness of air is obtained. Finally, the sensitivity improvement and mechanism by using molybdenum disulfide (MoS2)–graphene hybrid structure is revealed. The maximum sensitivity ∼ 190.83°/RIU is obtained with six layers of MoS2 coating on both surfaces of Al thin film.


title: A user model neural network for a personal news service

abstract: User modelling has been widely applied to pedantic situations, where we are attempting to infer the user's knowledge. In teaching it is important to know that the user has mastered the elementary concepts before proceeding with the advanced topics. However, the application of user modelling to information retrieval demands a quite different type of user model. Here we construct a user model for browsing, where the user is uncertain of exactly which information he desires. This requires a more inexact and robust user model, that can quickly give guidance to the system. We propose a user model based on neural networks that can be constructed incrementally. Performance of the model shows some promise for this approach. We discuss the advantages and limitations of the approach and its implications for user modelling.


title: Application of the Double Metaphone Algorithm to Amharic Orthography

abstract: The Metaphone algorithm applies the phonetic encoding of orthographic sequences to simplify words prior to comparison. While Metaphone has been highly successful for the English language, for which it was designed, it may not be applied directly to Ethiopian languages. The paper details how the principles of Metaphone can be applied to Ethiopic script and uses Amharic as a case study. Match results improve as specific considerations are made for Amharic writing practices. Results are shown to improve further when common errors from Amharic input methods are considered.


title: A methodical approach to extracting interesting objects

abstract: This paper presents a fully automated object extraction system for web documents. Our methodology consists of a layered framework and a set of algorithms. A distinct feature of our approach is the full automation of both the extraction of data object regions from dynamic web pages and the identification of the correct object-boundary separators. We implemented the methodology in the XWRAPElite object extraction system and evaluated the system using more than 3200 pages over 75 diverse websites. Our experiments show three important and interesting results: First, our algorithms for identifying the minimal object-rich subtree achieves a 96% success rate over all the web pages we have tested. Second, our algorithms for discovering and extracting object separator tags reach the success rate of 95%. Most significantly, the overall system achieves a precision between 96% and 100% (it returns only correct objects) and excellent recall (between 95% and 96%, with very few significant objects left out). The minimal subtree extraction algorithms and the object-boundary identification algorithms are fast, about 87 milliseconds per page with an average page size of 30KB.


title: SystemML: Declarative Machine Learning on Spark

abstract: The rising need for custom machine learning (ML) algorithms and the growing data sizes that require the exploitation of distributed, data-parallel frameworks such as MapReduce or Spark, pose significant productivity challenges to data scientists. Apache SystemML addresses these challenges through declarative ML by (1) increasing the productivity of data scientists as they are able to express custom algorithms in a familiar domain-specific language covering linear algebra primitives and statistical functions, and (2) transparently running these ML algorithms on distributed, data-parallel frameworks by applying cost-based compilation techniques to generate efficient, low-level execution plans with in-memory single-node and large-scale distributed operations. This paper describes SystemML on Apache Spark, end to end, including insights into various optimizer and runtime techniques as well as performance characteristics. We also share lessons learned from porting SystemML to Spark and declarative ML in general. Finally, SystemML is open-source, which allows the database community to leverage it as a testbed for further research.


title: Packing Sparse Convolutional Neural Networks for Efficient Systolic Array Implementations: Column Combining Under Joint Optimization

abstract: This paper describes a novel approach of packing sparse convolutional neural networks into a denser format for efficient implementations using systolic arrays. By combining multiple sparse columns of a convolutional filter matrix into a single dense column stored in the systolic array, the utilization efficiency of the systolic array can be substantially increased (e.g., 8x) due to the increased density of nonzero weights in the resulting packed filter matrix. In combining columns, for each row, all filter weights but the one with the largest magnitude are pruned. The remaining weights are retrained to preserve high accuracy. We study the effectiveness of this joint optimization for both high utilization efficiency and classification accuracy with ASIC and FPGA designs based on efficient bit-serial implementations of multiplier-accumulators. We demonstrate that in mitigating data privacy concerns the retraining can be accomplished with only fractions of the original dataset (e.g., 10% for CIFAR-10). We present analysis and empirical evidence on the superior performance of our column combining approach against prior arts under metrics such as energy efficiency (3x) and inference latency (12x).


title: RBNet: A Deep Neural Network for Unified Road and Road Boundary Detection

abstract: Accurately detecting road and its boundary on the images is an essential task for vision-based autonomous driving systems. However, prevailing methods either only detect road or add an extra processing stage to detect road boundary. In this work, we introduce a deep neural network, called Road and road Boundary detection Network (RBNet), that can detect both road and road boundary in a single process. In specific, we first investigate the contextual relationship between the road structure and its boundary arrangement and then model them with a Bayesian network. By implementing the Bayesian model, the RBNet can learn to simultaneously estimate the probabilities of a pixel on the image belonging to the road and road boundary. Comprehensive evaluations are carried out based on the well-known road benchmark, which can demonstrate the compelling performance of the proposed method.


title: Large-Area Synthesis of High-Quality and Uniform Graphene Films on Copper Foils

abstract: Graphene has been attracting great interest because of its distinctive band structure and physical properties. Today, graphene is limited to small sizes because it is produced mostly by exfoliating graphite. We grew large-area graphene films of the order of centimeters on copper substrates by chemical vapor deposition using methane. The films are predominantly single-layer graphene, with a small percentage (less than 5%) of the area having few layers, and are continuous across copper surface steps and grain boundaries. The low solubility of carbon in copper appears to help make this growth process self-limiting. We also developed graphene film transfer processes to arbitrary substrates, and dual-gated field-effect transistors fabricated on silicon/silicon dioxide substrates showed electron mobilities as high as 4050 square centimeters per volt per second at room temperature.


title: Investigation of Nonlinear Pupil Dynamics by Recurrence Quantification Analysis

abstract: Pupil is controlled by the autonomous nervous system (ANS). It shows complex movements and changes of size even in conditions of constant stimulation. The possibility of extracting information on ANS by processing data recorded during a short experiment using a low cost system for pupil investigation is studied. Moreover, the significance of nonlinear information contained in the pupillogram is investigated. We examined 13 healthy subjects in different stationary conditions, considering habitual dental occlusion (HDO) as a weak stimulation of the ANS with respect to the maintenance of the rest position (RP) of the jaw. Images of pupil captured by infrared cameras were processed to estimate position and size on each frame. From such time series, we extracted linear indexes (e.g., average size, average displacement, and spectral parameters) and nonlinear information using recurrence quantification analysis (RQA). Data were classified using multilayer perceptrons and support vector machines trained using different sets of input indexes: the best performance in classification was obtained including nonlinear indexes in the input features. These results indicate that RQA nonlinear indexes provide additional information on pupil dynamics with respect to linear descriptors, allowing the discrimination of even a slight stimulation of the ANS. Their use in the investigation of pathology is suggested.


Clinical Decision Support Systems : A discussion on different methodologies used in Health Care


title: A Nonlinear Mapping Approach to Stain Normalization in Digital Histopathology Images Using Image-Specific Color Deconvolution

abstract: Histopathology diagnosis is based on visual examination of the morphology of histological sections under a microscope. With the increasing popularity of digital slide scanners, decision support systems based on the analysis of digital pathology images are in high demand. However, computerized decision support systems are fraught with problems that stem from color variations in tissue appearance due to variation in tissue preparation, variation in stain reactivity from different manufacturers/batches, user or protocol variation, and the use of scanners from different manufacturers. In this paper, we present a novel approach to stain normalization in histopathology images. The method is based on nonlinear mapping of a source image to a target image using a representation derived from color deconvolution. Color deconvolution is a method to obtain stain concentration values when the stain matrix, describing how the color is affected by the stain concentration, is given. Rather than relying on standard stain matrices, which may be inappropriate for a given image, we propose the use of a color-based classifier that incorporates a novel stain color descriptor to calculate image-specific stain matrix. In order to demonstrate the efficacy of the proposed stain matrix estimation and stain normalization methods, they are applied to the problem of tumor segmentation in breast histopathology images. The experimental results suggest that the paradigm of color normalization, as a preprocessing step, can significantly help histological image analysis algorithms to demonstrate stable performance which is insensitive to imaging conditions in general and scanner variations in particular.


title: Serum, retinal, choroidal vitreal vitamin E concentrations in human infants.

abstract: Vitamin E is being used as an antioxidant in preterm neonates in an attempt to decrease the severity of retinopathy of prematurity. However, its efficacy may depend on many factors, a major one being its concentration in the retina. There is very little information available on the alpha-tocopherol levels in the human eye tissue. The present study was carried out to determine the total alpha-tocopherol levels in the retina, choroid, and vitreous and to compare the retinal levels with the serum levels obtained at the time of death. Thirteen whole eye donations were available during the past 22 months for the study. The data from this small number of cases showed that retinal levels of alpha-tocopherol were higher than choroidal and vitreal levels; higher serum levels were associated with higher retinal levels; and both serum and retinal alpha-tocopherol levels were lower in the unsupplemented (group A) infants. Further studies on pharmacokinetics of vitamin E in human newborns and its distribution are needed.


Machine Vision


title: Consumption of yerba mate ( Ilex paraguariensis ) improves serum lipid parameters in healthy dyslipidemic subjects and provides an additional LDL-cholesterol reduction in individuals on statin therapy.

abstract: The objective of this study was to verify the effect of yerba mate (Ilex paraguariensis) consumption on lipid and lipoprotein levels in humans. One hundred and two individuals participated of this single-blind controlled trial. Normolipidemic (n = 15), dyslipidemic (n = 57), and hypercholesterolemic subjects on long-term statin therapy (n = 30) ingested 330 mL, 3 times/day, of green or roasted yerba mate infusions for 40 days. In normolipidemic subjects, yerba mate consumption reduced LDL-cholesterol by 8.7% (p < 0.05). Compared with the baseline period, yerba mate intake by dyslipidemic individuals for 20 and 40 days lowered LDL-cholesterol by 8.1 and 8.6% (p < 0.001) and non-HDL cholesterol by 5.4 and 6.5% (p < 0.01). After 20 days of yerba mate intake, apolipoprotein B was reduced by 6.0% (p < 0.05) and HDL-cholesterol was increased by 4.4% (p < 0.01). In all participants triglyceride levels remained unchanged. The consumption of yerba mate by hypercholesterolemic individuals on statin therapy promoted...


title: Early and Late Level Fusion of Deep Convolutional Neural Networks for Visual Concept Recognition

abstract: Visual concept recognition is an active research field in the last decade. Related to this attention, deep learning architectures are showing great promise in various computer vision domains including image classification, object detection, event detection and action recognition in videos. In this study, we investigate various aspects of convolutional neural networks for visual concept recognition. We analyze recent studies and different network architectures both in terms of running time and accuracy. In our proposed visual concept recognition system, we first discuss various important properties of popular convolutional network architecture under consideration. Then we describe our method for feature extraction at different levels of abstraction. We present extensive empirical information along with best practices for big data practitioners. Using these best practices we propose efficient fusion mechanisms both for single and multiple network models. We present state-of-the-art results on benchmark datasets while keeping computational costs at low level. Our results show that these state-of-the-art results can be reached without using extensive data augmentation techniques.


title: Automatic Set Expansion for List Question Answering

abstract: This paper explores the use of set expansion (SE) to improve question answering (QA) when the expected answer is a list of entities belonging to a certain class. Given a small set of seeds, SE algorithms mine textual resources to produce an extended list including additional members of the class represented by the seeds. We explore the hypothesis that a noise-resistant SE algorithm can be used to extend candidate answers produced by a QA system and generate a new list of answers that is better than the original list produced by the QA system. We further introduce a hybrid approach which combines the original answers from the QA system with the output from the SE algorithm. Experimental results for several state-of-the-art QA systems show that the hybrid system performs better than the QA systems alone when tested on list question data from past TREC evaluations.


title: Learning Collaborative Information Filters

abstract: Predicting items a user would like on the basis of other users’ ratings for these items has become a well-established strategy adopted by many recommendation services on the Internet. Although this can be seen as a classification problem, algorithms proposed thus far do not draw on results from the machine learning literature. We propose a representation for collaborative filtering tasks that allows the application of virtually any machine learning algorithm. We identify the shortcomings of current collaborative filtering techniques and propose the use of learning algorithms paired with feature extraction techniques that specifically address the limitations of previous approaches. Our best-performing algorithm is based on the singular value decomposition of an initial matrix of user ratings, exploiting latent structure that essentially eliminates the need for users to rate common items in order to become predictors for one another's preferences. We evaluate the proposed algorithm on a large database of user ratings for motion pictures and find that our approach significantly outperforms current collaborative filtering algorithms.


title: Predicting the severity of bug reports using classification algorithms

abstract: Bug triaging is the process of prioritizing the bug reports based on the severity and is driven by the business needs and available resources. Majority times, only few of the reported bugs are selected to be fixed. The selected bugs are prioritized (ordered) based on their severity (e.g. the bug inhibits an important feature of the product, the bug affects a large number of users) and then fixed according to their priority. If the severity is assigned incorrectly then time and resources may be wasted to fix that bug. Hence there is a need for new techniques to avoid this misuse of resources. In this paper, bagging ensemble method is used for predicting the severity of bug reports. Also, bagging ensemble method is compared with C4.5 classifier. The results have shown that bagging ensemble method gives better accuracy compared to C4.5 general classifier on the given dataset.


title: Morbidity Associated with Colostomy Reversal After Cytoreductive Surgery and HIPEC

abstract: Background ::: Cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC) has improved the survival in selected colorectal cancer patients with peritoneal metastases. In these patients, the risk of a low anastomosis is sometimes diminished through the creation of a colostomy. Currently, the morbidity and mortality associated with the reversal of the colostomy in this population is unknown.


The Biological Bases of Empathy


title: Job-aware Scheduling in Eagle: Divide and Stick to Your Probes

abstract: We present Eagle, a new hybrid data center scheduler for data-parallel programs. Eagle dynamically divides the nodes of the data center in partitions for the execution of long and short jobs, thereby avoiding head-of-line blocking. Furthermore, it provides job awareness and avoids stragglers by a new technique, called Sticky Batch Probing (SBP). The dynamic partitioning of the data center nodes is accomplished by a technique called Succinct State Sharing (SSS), in which the distributed schedulers are informed of the locations where long jobs are executing. SSS is particularly easy to implement with a hybrid scheduler, in which the centralized scheduler places long jobs. With SBP, when a distributed scheduler places a probe for a job on a node, the probe stays there until all tasks of the job have been completed. When finishing the execution of a task corresponding to probe P, rather than executing a task corresponding to the next probe P' in its queue, the node may choose to execute another task corresponding to P. We use SBP in combination with a distributed approximation of Shortest Remaining Processing Time (SRPT) with starvation prevention. We have implemented Eagle as a Spark plugin, and we have measured job completion times for a subset of the Google trace on a 100-node cluster for a variety of cluster loads. We provide simulation results for larger clusters, different traces, and for comparison with other scheduling disciplines. We show that Eagle outperforms other state-of-the-art scheduling solutions at most percentiles, and is more robust against mis-estimation of task duration.


title: NADiA - Towards Neural Network Driven Virtual Human Conversation Agents

abstract: Advances in artificial intelligence and machine learning - in particular neural networks - have given rise to a new generation of virtual assistants and chatbots. Within this work, we describe the motivation and architecture of NADiA - Neurally Animated Dialog Agent - which leverages both the user's verbal input and facial expressions for multi-modal conversation. NADiA combines a neural language model that generates conversational responses, a convolutional neural network for facial expression analysis, and virtual human technology that is deployed on a mobile phone.


Multimodal Named Entity Disambiguation for Noisy Social Media Posts


Deep Reinforcement Learning for NLP


title: Deep Material-Aware Cross-Spectral Stereo Matching

abstract: Cross-spectral imaging provides strong benefits for recognition and detection tasks. Often, multiple cameras are used for cross-spectral imaging, thus requiring image alignment, or disparity estimation in a stereo setting. Increasingly, multi-camera cross-spectral systems are embedded in active RGBD devices (e.g. RGB-NIR cameras in Kinect and iPhone X). Hence, stereo matching also provides an opportunity to obtain depth without an active projector source. However, matching images from different spectral bands is challenging because of large appearance variations. We develop a novel deep learning framework to simultaneously transform images across spectral bands and estimate disparity. A material-aware loss function is incorporated within the disparity prediction network to handle regions with unreliable matching such as light sources, glass windshields and glossy surfaces. No depth supervision is required by our method. To evaluate our method, we used a vehicle-mounted RGB-NIR stereo system to collect 13.7 hours of video data across a range of areas in and around a city. Experiments show that our method achieves strong performance and reaches real-time speed.


title: A literature review of recommender systems in the television domain

abstract: A literature review about recommender systems in the television domain was performed.Recommender systems were categorized according to seven research questions (RQs).282 relevant research papers were collected between 2003 and 2015 (until May) using a research methodology.Preliminary findings about the research papers were presented.We presented and discussed the results of this literature review according to RQs. Recommender Systems (RSs) are software tools and techniques providing suggestions of relevant items to users. These systems have received increasing attention from both academy and industry since the 1990s, due to a variety of practical applications as well as complex problems to solve. Since then, the number of research papers published has increased significantly in many application domains (books, documents, images, movies, music, shopping, TV programs, and others). One of these domains has our attention in this paper due to the massive proliferation of televisions (TVs) with computational and network capabilities and due to the large amount of TV content and TV-related content available on the Web. With the evolution of TVs and RSs, the diversity of recommender systems for TV has increased substantially. In this direction, it is worth mentioning that we consider "recommender systems for TV" as those that make recommendations of both TV-content and any content related to TV. Due to this diversity, more investigation is necessary because research on recommender systems for TV domain is still broader and less mature than in other research areas. Thus, this literature review (LR) seeks to classify, synthesize, and present studies according to different perspectives of RSs in the television domain. For that, we initially identified, from the scientific literature, 282 relevant papers published from 2003 to May, 2015. The papers were then categorized and discussed according to different research and development perspectives: recommended item types, approaches, algorithms, architectural models, output devices, user profiling and evaluation. The obtained results can be useful to reveal trends and opportunities for both researchers and practitioners in the area.


Probabilistic Default Reasoning with Strict and Defeasible Conditional Constraints


title: Liquid Hot Water Pretreatment of Cellulosic Biomass

abstract: Lignocellulosic biomass is an abundant and renewable resource for fuel ethanol production. However, the lignocellulose is recalcitrant to enzymatic hydrolysis because of its structural complexity. Controlled-pH liquid hot water (LHW) pretreatment of cellulosic feedstock improves its enzymatic digestibility by removing hemicellulose and making the cellulose more accessible to cellulase enzymes. The removed hemicellulose is solubilized in the liquid phase of the pretreated feedstock as oligosaccharides. Formation of monomeric sugars during the LHW pretreatment is minimal. The LHW pretreatment is carried out by cooking the feedstock in process water at temperatures between 160 and 190 degrees C and at a pH of 4-7. No additional chemicals are needed. This chapter presents the detailed procedure of the LHW pretreatment of lignocellulosic biomass.


title: Understanding Internet Banking Adoption and Use Behavior: A Hong Kong Perspective

abstract: This study investigates adoption/use behavior within the context of Hong Kong Internet Banking services. A research framework based on the extended Technology Acceptance Model (TAM2) and Social Cognitive Theory is developed to identify factors that would influence the adoption and continue use of Internet Banking. Structural Equation Modeling (SEM) is employed to examine the entire pattern of inter-correlations among the eight proposed constructs, and to test related propositions empirically. A survey involving a total of 499 university students is conducted and confirmatory factor analysis used to determine the measurement efficacies. The results reveal that both subjective norm and computer self-efficacy indirectly play significant roles in influencing the intention to adopt Internet Banking. Perceived ease of use has a significant indirect effect on intention to adopt/use through perceived usefulness, while its direct effect on intention to adopt is not significant.


title: Glucose-responsive insulin by molecular and physical design.

abstract: The concept of a glucose-responsive insulin (GRI) has been a recent objective of diabetes technology. The idea behind the GRI is to create a therapeutic that modulates its potency, concentration or dosing relative to a patient's dynamic glucose concentration, thereby approximating aspects of a normally functioning pancreas. From the perspective of the medicinal chemist, the GRI is also important as a generalized model of a potentially new generation of therapeutics that adjust potency in response to a critical therapeutic marker. The aim of this Perspective is to highlight emerging concepts, including mathematical modelling and the molecular engineering of insulin itself and its potency, towards a viable GRI. We briefly outline some of the most important recent progress toward this goal and also provide a forward-looking viewpoint, which asks if there are new approaches that could spur innovation in this area as well as to encourage synthetic chemists and chemical engineers to address the challenges and promises offered by this therapeutic approach.


title: Analysis and Design of Energy Regenerative Snubber for Transformer Isolated Converters

abstract: An energy regenerative passive snubber for transformer isolated converters is proposed. The snubber is implemented on the transformer's primary and secondary windings. The proposed snubber significantly reduces the voltage spike across the switch caused by the transformer's primary inductance upon switch turn-off and facilitates the fast ramping up of the transformer secondary current. In addition, the proposed snubber provides lossless zero voltage turn off and zero current turn on conditions for the power switch. Experimental example of a flyback converter has shown measured efficiency exceeding 90%. This paper describes the principle of operation and presents approximate theoretical analysis and design guidelines of the proposed snubber. Simulation and experimental results are also reported. The proposed energy regenerating snubber is best suited for flyback and SEPIC converters and can also be adapted to other transformer isolated topologies.


title: Response of Cotton to Early-Season Square Abscission under Elevated CO

abstract: A field study was performed to quantify the compensation capacity of cotton (Gossypium hirsutum L.) for simulated damage of square buds loss by manual removal method during early growing season in 2004 and 2005 in combination with elevated CO 2 relative to ambient CO 2 . Square buds of cotton plants were wholly removed manually for 1 wk (SRI treatment) and two consecutive weeks (SR2 treatment) in contrast to no square bud removal (SRO treatment) after squaring stage, and their compensation ability is quantified by measuring plant growth and production. Two levels of CO 2 (ambient and double-ambient) and three types of manual removal of square buds (SR1 and SR2 vs. SRO) were deployed in a completely randomized design with six treatment combinations. Cotton plants grown in elevated CO 2 had significantly higher leaf area per plant on each sampling date for SR1 treatment compared with SR0 treatment in 2004 and 2005. Significantly higher seedcotton yield, maturity, and harvested biomass were also observed for SR0, SRI, and SR2 treatments under elevated CO 2 relative to ambient CO 2 in 2004 and 2005. Moreover, there were significant interactions between CO 2 level X square removal treatment on seedcotton yield and boll maturity, and significance between square removal treatment X investigation year on plant harvested biomass. Results from these studies provide a profile for developing strategies for future management of cotton ecosystems in Northern China.


title: SARFID on drone: Drone-based UHF-RFID tag localization

abstract: This paper presents a localization system for objects tagged with UHF-RFID passive tags, where the reader is attached to a drone. The system implements the phase-based SARFID technique to locate static tags with respect to a UHF-RFID reader attached to a commercial drone. The reader antenna trajectory is achieved through a Global Positioning System. The bi-dimensional tag position is estimated with centimeter order accuracy. Only one reader antenna is required, without any reference tag.


title: Interference Management in Femtocells

abstract: Increase in system capacity and data rates can be achieved efficiently in a wireless system by getting the transmitter and receiver closer to each other. Femtocells deployed in the macrocell significantly improve the indoor coverage and provide better user experience. The femtocell base station called as Femtocell Access Point (FAP) is fully user deployed and hence reduces the infrastructure, maintenance and operational cost of the operator while at the same time providing good Quality of Service (QoS) to the end user and high network capacity gains. However, the mass deployment of femtocell faces a number of challenges, among which interference management is of much importance, as the fundamental limits of capacity and achievable data rates mainly depends on the interference faced by the femtocell network. To cope with the technical challenges including interference management faced by the femtocells, researchers have suggested a variety of solutions. These solutions vary depending on the physical layer technology and the specific scenarios considered. Furthermore, the cognitive capabilities, as a functionality of femtocell have also been discussed in this survey. This article summarises the main concepts of femtocells that are covered in literature and the major challenges faced in its large scale deployment. The main challenge of interference management is discussed in detail with its types in femtocells and the solutions proposed over the years to manage interference have been summarised. In addition an overview of the current femtocell standardisation and the future research direction of femtocells have also been provided.


title: Cybersemiotics: A New Foundation for Transdisciplinary Theory of Information, Cognition, Meaningful Communication and the Interaction Between Nature and Culture

abstract: Cybersemiotics constructs a non-reductionist framework in order to integrate third person knowledge from the exact sciences and the life sciences with first person knowledge described as the qualities of feeling in humanities and second person intersubjective knowledge of the partly linguistic communicative interactions, on which the social and cultural aspects of reality are based. The modern view of the universe as made through evolution in irreversible time, forces us to view man as a product of evolution and therefore an observer from inside the universe. This changes the way we conceptualize the problem and the role of consciousness in nature and culture. The theory of evolution forces us to conceive the natural and social sciences as well as the humanities together in one theoretical framework of unrestricted or absolute naturalism, where consciousness as well as culture is part of nature. But the theories of the phenomenological life world and the hermeneutics of the meaning of communication seem to defy classical scientific explanations. The humanities therefore send another insight the opposite way down the evolutionary ladder, with questions like: What is the role of consciousness, signs and meaning in the development of our knowledge about evolution? Phenomenology and hermeneutics show the sciences that their prerequisites are embodied living conscious beings imbued with meaningful language and with a culture. One can see the world view that emerges from the work of the sciences as a reconstruction back into time of our present ecological and evolutionary self- understanding as semiotic intersubjective conscious cultural and historical creatures, but unable to handle the aspects of meaning and conscious awareness and therefore leaving it out of the story. Cybersemiotics proposes to solve the dualistic paradox by starting in the middle with semiotic cognition and communication as a basic sort of reality in which all our knowledge is created and then suggests that knowledge develops into four aspects of human reality: Our surrounding nature described by the physical and chemical natural sciences, our corporality described by the life sciences such as biology and medicine, our inner world of subjective experience described by phenomenologically based investigations and our social world described by the social sciences. I call this alternative model to the positivistic hierarchy the cybersemiotic star. The article explains the new understanding of Wissenschaft that emerges from Peirce's and Luhmann's conceptions.


title: Asynchronous-Logic QDI Quad-Rail Sense-Amplifier Half-Buffer Approach for NoC Router Design

abstract: We propose a low area overhead and power-efficient asynchronous-logic quasi-delay-insensitive (QDI) sense-amplifier half-buffer (SAHB) approach with quad-rail (i.e., 1-of-4) data encoding. The proposed quad-rail SAHB approach is targeted for area- and energy-efficient asynchronous network-on-chip (ANoC) router designs. There are three main features in the proposed quad-rail SAHB approach. First, the quad-rail SAHB is designed to use four wires for selecting four ANoC router directions, hence reducing the number of transistors and area overhead. Second, the quad-rail SAHB switches only one out of four wires for 2-bit data propagation, hence reducing the number of transistor switchings and dynamic power dissipation. Third, the quad-rail SAHB abides by QDI rules, hence the designed ANoC router features high operational robustness toward process-voltage-temperature (PVT) variations. Based on the 65-nm CMOS process, we use the proposed quad-rail SAHB to implement and prototype an 18-bit ANoC router design. When benchmarked against the dual-rail counterpart, the proposed quad-rail SAHB ANoC router features 32% smaller area and dissipates 50% lower energy under the same excellent operational robustness toward PVT variations. When compared to the other reported ANoC routers, our proposed quad-rail SAHB ANoC router is one of the high operational robustness, smallest area, and most energy-efficient designs.


title: Exposing photo manipulation with inconsistent reflections

abstract: The advent of sophisticated photo editing software has made it increasingly easier to manipulate digital images. Often visual inspection cannot definitively distinguish the resulting forgeries from authentic photographs. In response, forensic techniques have emerged to detect geometric or statistical inconsistencies that result from specific forms of photo manipulation. In this article we describe a new forensic technique that focuses on geometric inconsistencies that arise when fake reflections are inserted into a photograph or when a photograph containing reflections is manipulated. This analysis employs basic rules of reflective geometry and linear perspective projection, makes minimal assumptions about the scene geometry, and only requires the user to identify corresponding points on an object and its reflection. The analysis is also insensitive to common image editing operations such as resampling, color manipulations, and lossy compression. We demonstrate this technique with both visually plausible forgeries of our own creation and commercially produced forgeries.


NEURAL NETWORK MODEL OF ARTIFICIAL INTELLIGENCE FOR HANDWRITING RECOGNITION


FPGA Implementation of High Throughput Digital QPSK Modulator using Verilog HDL


title: Islamic microfinance : an emerging market niche

abstract: An estimated 72 percent of people living in Muslim-majority countries do not use formal financial services. Even when financial services are available, some people view conventional products as incompatible with the financial principles set forth in Islamic law. In recent years, some microfinance institutions (MFIs) have stepped in to service low-income Muslim clients who demand products consistent with Islamic financial principles - leading to the emergence of Islamic microfinance as a new market niche. The supply of Islamic microfinance is very concentrated in a few countries, with the top three countries (Indonesia, Bangladesh, and Afghanistan) accounting for 80 percent of global outreach. Nevertheless, demand for Islamic microfinance products is strong. This focus note provides an overview of the current state of the Islamic microfinance sector and identifies possible challenges to its growth. It is intended as an introduction to Islamic microfinance primarily for the donor community and other potential entrants into the market.


title: Things Rank and Gross in Nature: A Review and Synthesis of Moral Disgust.

abstract: Much like unpalatable foods, filthy restrooms, and bloody wounds, moral transgressions are often described as "disgusting." This linguistic similarity suggests that there is a link between moral disgust and more rudimentary forms of disgust associated with toxicity and disease. Critics have argued, however, that such references are purely metaphorical, or that moral disgust may be limited to transgressions that remind us of more basic disgust stimuli. Here we review the evidence that moral transgressions do genuinely evoke disgust, even when they do not reference physical disgust stimuli such as unusual sexual behaviors or the violation of purity norms. Moral transgressions presented verbally or visually and those presented as social transactions reliably elicit disgust, as assessed by implicit measures, explicit self-report, and facial behavior. Evoking physical disgust experimentally renders moral judgments more severe, and physical cleansing renders them more permissive or more stringent, depending on the object of the cleansing. Last, individual differences in the tendency to experience disgust toward physical stimuli are associated with variation in moral judgments and morally relevant sociopolitical attitudes. Taken together, these findings converge to support the conclusion that moral transgressions can in fact elicit disgust, suggesting that moral cognition may draw upon a primitive rejection response. We highlight a number of outstanding issues and conclude by describing 3 models of moral disgust, each of which aims to provide an account of the relationship between moral and physical disgust.


title: Denver: a metropolitan public school scoliosis survey.

abstract: A successful method for screening pupils for scoliosis in a metropolitan public school system is outlined. The incidence of scoliosis increased to greater than 3% in the seventh-grade population. Approximately 15% of the patients with established diagnoses required active management.


title: The effect of pulsatile gonadotropin-releasing hormone and estradiol administration on luteinizing hormone and follicle-stimulating hormone concentrations in pituitary stalk-sectioned ovariectomized pony mares.

abstract: Hourly pulses of gonadotropin-releasing hormone (GnRH) or bi-daily injections of estradiol (E2) can increase luteinizing hormone (LH) secretion in ovariectomized, anestrous pony mares. However, the site (pituitary versus hypothalamus) of positive feedback of estradiol on gonadotropin secretion has not been described in mares. Thus, one of our objectives involved investigating the feedback of estradiol on the pituitary. The second objective consisted of determining if hourly pulses of GnRH could re-establish physiological LH and FSH concentrations after pituitary stalk-section (PSS), and the third objective was to describe the declining time trends of LH and FSH secretion after PSS. During summer months, ovariectomized pony mares were divided into three groups: Group 1 (control, n = 2), Group 2 (pulsatile GnRH (25 micrograms/hr), n = 3), and Group 3 (estradiol (5 mg/12 hr), n = 3). All mares were stalk-sectioned and treatment begun immediately after stalk-section. Blood samples were collected every 30 min for 8 h on the day before surgery (D0) and 5 d post surgery (D5) to facilitate the comparison of gonadotropin levels before and after pituitary stalk-section. Additionally, jugular blood samples were collected every 12 hr beginning the evening of surgery, allowing for evaluation of the gonadotropin secretory time trends over the 10 d of treatment. On Day 10, animals were euthanized to confirm pituitary stalk-section and to submit tissue for messenger RNA analysis (parallel study). Plasma samples were assayed for LH and FSH by RIA. Mean LH secretion decreased from Day 0 to Day 5 in Groups 1 and 3, whereas LH secretion tended (P < 0.08) to decrease in Group 2 mares. On Day 5, LH was higher (P < 0.01) in Group 2 (17.26 +/- 3.68 ng/ml: LSMEANS = SEM), than either Group 1 (2.65 +/- 4.64 ng/ml) or Group 3 (4.28 +/- 3.68 ng/ml). Group 1 did not differ from Group 3 on Day 5 (P < 0.40). Similarly, mean FSH levels decreased in all groups after surgery, yet Group 2 mares had significantly (P < 0.001) higher FSH concentrations (17.66 +/- 1.53 ng/ml) than Group 1 or Group 3 (8.34 +/- 1.84 and 7.69 +/- 1.63 ng/ml, respectively). Regression analysis of bi-daily LH and FSH levels indicated that the time trends were not parallel. These findings indicate: 1) Pituitary stalk-section lowered LH and FSH to undetectable levels within 5 d after surgery. 2) pulsatile administration of GnRH (25 micrograms/hr) maintained LH and FSH secretion, although concentrations tended to be lower than on Day 0, and 3) E2 did not stimulate LH or FSH secretion.


title: WoLMIS: a labor market intelligence system for classifying web job vacancies

abstract: In the last decades, an increasing number of employers and job seekers have been relying on Web resources to get in touch and to find a job. If appropriately retrieved and analyzed, the huge number of job vacancies available today on on-line job portals can provide detailed and valuable information about the Web Labor Market dynamics and trends. In particular, this information can be useful to all actors, public and private, who play a role in the European Labor Market. This paper presents WoLMIS, a system aimed at collecting and automatically classifying multilingual Web job vacancies with respect to a standard taxonomy of occupations. The proposed system has been developed for the Cedefop European agency, which supports the development of European Vocational Education and Training (VET) policies and contributes to their implementation. In particular, WoLMIS allows analysts and Labor Market specialists to make sense of Labor Market dynamics and trends of several countries in Europe, by overcoming linguistic boundaries across national borders. A detailed experimental evaluation analysis is also provided for a set of about 2 million job vacancies, collected from a set of UK and Irish Web job sites from June to September 2015.


title: Sparse Filtering

abstract: Unsupervised feature learning has been shown to be effective at learning representations that perform well on image, video and audio classification. However, many existing feature learning algorithms are hard to use and require extensive hyperparameter tuning. In this work, we present sparse filtering, a simple new algorithm which is efficient and only has one hyperparameter, the number of features to learn. In contrast to most other feature learning methods, sparse filtering does not explicitly attempt to construct a model of the data distribution. Instead, it optimizes a simple cost function – the sparsity of l2-normalized features – which can easily be implemented in a few lines of MATLAB code. Sparse filtering scales gracefully to handle high-dimensional inputs, and can also be used to learn meaningful features in additional layers with greedy layer-wise stacking. We evaluate sparse filtering on natural images, object classification (STL-10), and phone classification (TIMET), and show that our method works well on a range of different modalities.


title: A Hybrid Trust-Based Recommender System for Online Communities of Practice

abstract: The needs for life-long learning and the rapid development of information technologies promote the development of various types of online Community of Practices (CoPs). In online CoPs, bounded rationality and metacognition are two major issues, especially when learners face information overload and there is no knowledge authority within the learning environment. This study proposes a hybrid, trust-based recommender system to mitigate above learning issues in online CoPs. A case study was conducted using Stack Overflow data to test the recommender system. Important findings include: (1) comparing with other social community platforms, learners in online CoPs have stronger social relations and tend to interact with a smaller group of people only; (2) the hybrid algorithm can provide more accurate recommendations than celebrity-based and content-based algorithm and; (3) the proposed recommender system can facilitate the formation of personalized learning communities.


title: A walking pattern generation method with feedback and feedforward control for humanoid robots

abstract: This paper proposes a new walking pattern generation method for humanoid robots. The proposed method consists of feedforward control and feedback control for walking pattern generation. The pole placement method as a feedback controller changes the poles of system in order to generate more stable and smoother walking pattern. The advanced pole-zero cancelation by series approximation(PZCSA) as a feedforward controller plays a role of reducing the inherent property of linear inverted pendulum model (LIPM), that is, non-minimum phase property due to an unstable zero of LIPM and tracking efficiently the desired zero moment point (ZMP). The efficiency of the proposed method is verified by three simulations such as arbitrary walking step length, arbitrary walking phase time and sudden change of walking path.


title: A recommender system for efficient discovery of new anomalies in large-scale access logs

abstract: We present a novel, non-standard recommender system for large-scale security policy management(SPM). Our system Helios discovers and recommends unknown and unseen anomalies in large-scale access logs with minimal supervision and no starting information on users and items. Typical recommender systems assume availability of user- and item-related information, but such information is not usually available in access logs. To resolve this problem, we first use discrete categorical labels to construct categorical combinations from access logs in a bootstrapping manner. Then, we utilize rank statistics of entity rank and order categorical combinations for recommendation. From a double-sided cold start, with minimal supervision, Helios learns to recommend most salient anomalies at large-scale, and provides visualizations to security experts to explain rationale behind the recommendations. Our experiments show Helios to be suitable for large-scale applications: from cold starts, in less than 60 minutes, Helios can analyze roughly 4.6 billion records in logs of 400GB with about 300 million potential categorical combinations, then generate ranked categorical combinations as recommended discoveries. We also show that, even with limited computing resources, Helios accelerates unknown and unseen anomaly discovery process for SPM by 1 to 3 orders of magnitude, depending on use cases. In addition, Helios' design is flexible with metrics and measurement fields used for discoveries and recommendations. Overall, our system leads to more efficient and customizable SPM processes with faster discoveries of unseen and unknown anomalies.


title: Synthesizing Musical Accompaniments with Bayesian Belief Networks

abstract: This paper discusses recent work in creating a computer program that plays the role of a sensitive musical accompanist. An accompanist must synthesize a number of different sources of information including a real-time analysis of the soloist’s acoustic signal, an understanding of the timing relationships represented in the musical score, the interpretation of the soloist learned through rehearsals, and musical constraints on the way in which the accompaniment can be played. A probabilistic framework is presented in which all of these knowledge sources can be represented and learned from actual data. This model then forms the basis of an approach to musical accompaniment using the machinery of Bayesian belief networks. A demonstration is provided from J.S. Bach’s Cantata 12.