query: A Unified Bayesian Model of Scripts, Frames and Language

positive: Learning Latent Personas of Film Characters We present two latent variable models for learning character types, or personas, in film, in which a persona is defined as a set of mixtures over latent lexical classes. These lexical classes capture the stereotypical actions of which a character is the agent and patient, as well as attributes by which they are described. As the first attempt to solve this problem explicitly, we also present a new dataset for the text-driven analysis of film, along with a benchmark testbed to help drive future work in this area.

positive: VerbOcean: Mining the Web for Fine-Grained Semantic Verb Relations Broad-coverage repositories of semantic relations between verbs could benefit many NLP tasks. We present a semi-automatic method for extracting fine-grained semantic relations between verbs. We detect similarity, strength, antonymy, enablement, and temporal happens-before relations between pairs of strongly associated verbs using lexicosyntactic patterns over the Web. On a set of 29,165 strongly associated verb pairs, our extraction algorithm yielded 65.5% accuracy. Analysis of error types shows that on the relation strength we achieved 75% accuracy. We provide the resource, called VERBOCEAN, for download at http://semantics.isi.edu/ocean/.

positive: Global Learning of Typed Entailment Rules Extensive knowledge bases of entailment rules between predicates are crucial for applied semantic inference. In this paper we propose an algorithm that utilizes transitivity constraints to learn a globally-optimal set of entailment rules for typed predicates. We model the task as a graph learning problem and suggest methods that scale the algorithm to larger graphs. We apply the algorithm over a large data set of extracted predicate instances, from which a resource of typed entailment rules has been recently released (Schoenmackers et al., 2010). Our results show that using global transitivity information substantially improves performance over this resource and several baselines, and that our scaling methods allow us to increase the scope of global learning of entailment-rule graphs.

positive: Improving neural networks by preventing co-adaptation of feature detectors When a large feedforward neural network is trained on a small training set, it typically performs poorly on held-out test data. This overfitting is greatly reduced by randomly omitting half of the feature detectors on each training case. This prevents complex co-adaptations in which a feature detector is only helpful in the context of several other specific feature detectors. Instead, each neuron learns to detect a feature that is generally helpful for producing the correct answer given the combinatorially large variety of internal contexts in which it must operate. Random “dropout” gives big improvements on many benchmark tasks and sets new records for speech and object recognition. A feedforward, artificial neural network uses layers of non-linear hidden units between its inputs and its outputs. By adapting the weights on the incoming connections of these hidden units it learns feature detectors that enable it to predict the correct output when given an input vector [15]. If the relationship between the input and the correct output is complicated and the network has enough hidden units to model it accurately, there will typically be many different settings of the weights that can model the training set almost perfectly, especially if there is only a limited amount of labeled training data. Each of these weight vectors will make different predictions on held-out test data and almost all of them will do worse on the test data than on the training data because the feature detectors have been tuned to work well together on the training data but not on the test data. Overfitting can be reduced by using “dropout” to prevent complex co-adaptations on the training data. On each presentation of each training case, each hidden unit is randomly omitted from the network with a probability of 0.5, so a hidden unit cannot rely on other hidden units being present. Another way to view the dropout procedure is as a very efficient way of performing model averaging with neural networks. A good way to reduce the error on the test set is to average the predictions produced by a very large number of different networks. The standard way to do this is to train many separate networks and then to apply each of these networks to the test data, but this is computationally expensive during both training and testing. Random dropout makes it possible to train a huge number of different networks in a reasonable time. There is almost certainly a different network for each presentation of each training case but all of these networks share the same weights for the hidden units that are present. We use the standard, stochastic gradient descent procedure for training the dropout neural networks on mini-batches of training cases, but we modify the penalty term that is normally used to prevent the weights from growing too large. Instead of penalizing the squared length (L2 norm) of the whole weight vector, we set an upper bound on the L2 norm of the incoming weight vector for each individual hidden unit. If a weight-update violates this constraint, we renormalize the weights of the hidden unit by division. Using a constraint rather than a penalty prevents weights from growing very large no matter how large the proposed weight-update is. This makes it possible to start with a

positive: Finding scientific topics. A first step in identifying the content of a document is determining which topics that document addresses. We describe a generative model for documents, introduced by Blei, Ng, and Jordan [Blei, D. M., Ng, A. Y. & Jordan, M. I. (2003) J. Machine Learn. Res. 3, 993-1022], in which each document is generated by choosing a distribution over topics and then choosing each word in the document from a topic selected according to this distribution. We then present a Markov chain Monte Carlo algorithm for inference in this model. We use this algorithm to analyze abstracts from PNAS by using Bayesian model selection to establish the number of topics. We show that the extracted topics capture meaningful structure in the data, consistent with the class designations provided by the authors of the articles, and outline further applications of this analysis, including identifying "hot topics" by examining temporal dynamics and tagging abstracts to illustrate semantic content.

negative: A MATLAB-Simulink-Based PV Module Model and Its Application Under Conditions of Nonuniform Irradiance The performance of a photovoltaic (PV) module is mostly affected by array configuration, irradiance, and module temperature. It is important to understand the relationship between these effects and the output power of the PV array. This paper presents a MATLAB-Simulink-based PV module model which includes a controlled current source and an S-Function builder. The modeling scheme in S-Function builder is deduced by some predigested functions. Under the conditions of nonuniform irradiance, the model is practically validated by using different array configurations in testing platform. The comparison experiments indicate that I-V and P-V characteristic curves of simulation match the measurements from outdoor experiment well. Under the conditions of nonuniform irradiance, both simulation and experiment show that the output power of a PV array gets more complicated due to multiple peaks. Moreover, the proposed model can also simulate electric circuit and its maximum power point tracking (MPPT) in the environment of MATLAB-Simulink. The experiments show that the proposed model has good predictability in the general behaviors of MPPT under the conditions of both nonuniform and uniform irradiance.

negative: QuickCut: An Interactive Tool for Editing Narrated Video We present QuickCut, an interactive video editing tool designed to help authors efficiently edit narrated video. QuickCut takes an audio recording of the narration voiceover and a collection of raw video footage as input. Users then review the raw footage and provide spoken annotations describing the relevant actions and objects in the scene. QuickCut time-aligns a transcript of the annotations with the raw footage and a transcript of the narration to the voiceover. These aligned transcripts enable authors to quickly match story events in the narration with semantically relevant video segments and form alignment constraints between them. Given a set of such constraints, QuickCut applies dynamic programming optimization to choose frame-level cut points between the video segments while maintaining alignments with the narration and adhering to low-level film editing guidelines. We demonstrate QuickCut's effectiveness by using it to generate a variety of short (less than 2 minutes) narrated videos. Each result required between 14 and 52 minutes of user time to edit (i.e. between 8 and 31 minutes for each minute of output video), which is far less than typical authoring times with existing video editing workflows.

negative: Towards improving bug tracking systems with game mechanisms Low bug report quality and human conflicts pose challenges to keep bug tracking systems productive. This work proposes to address these issues by applying game mechanisms to bug tracking systems. We investigate the use of game mechanisms in Stack Overflow, an online community organized to resolve computer programming related problems, for which the improvements we seek for bug tracking systems also turn out to be relevant. The results of our Stack Overflow investigation show that its game mechanisms could be used to address these issues by motivating contributors to increase contribution frequency and quality, by filtering useful contributions, and by creating an agile and dependable moderation system. We proceed by mapping these mechanisms to open-source bug tracking systems, and find that most benefits are applicable. Additionally, our results motivate tailoring a reward and reputation system and summarizing bug reports as future directions for increasing the benefits of game mechanisms in bug tracking systems.

negative: Anti-jamming performance of cognitive radio networks under multiple uncoordinated jammers in fading environment In this paper, we study both the jamming capability of the cognitive-radio-based jammers and the anti-jamming capability of the cognitive radio networks (CRN), by considering multiple uncooperative jammers and independent Rayleigh flat-fading propagations. A Markov model of CRN transmission is set up for the cross-layer analysis of the anti-jamming performance. The transitional probabilities are derived analytically by considering a smart jamming attack strategy. Average throughput expression is obtained and verified by simulations. The results indicate that CRN communications can be extremely susceptible to smart jamming attacks targeting the CRN spectrum sensing and channel switching procedures.

negative: Usability Analysis of Visual Programming Environments: A 'Cognitive Dimensions' Framework The cognitive dimensions framework is a broad-brush evaluation technique for interactive devices and for non-interactive notations. It sets out a small vocabulary of terms designed to capture the cognitively-relevant aspects of structure, and shows how they can be traded off against each other. The purpose of this paper is to propose the framework as an evaluation technique for visual programming environments. We apply it to two commercially-available dataflow languages (with further examples from other systems) and conclude that it is effective and insightful; other HCI-based evaluation techniques focus on different aspects and would make good complements. Insofar as the examples we used are representative, current VPLs are successful in achieving a good ‘closeness of match’, but designers need to consider the ‘viscosity’ (resistance to local change) and the ‘secondary notation’ (possibility of conveying extra meaning by choice of layout, colour, etc.).

negative: Customer churn analysis : Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry Retaining customers is one of the most critical challenges in the maturing mobile telecommunications service industry. Using customer transaction and billing data, this study investigates determinants of customer churn in the Korean mobile telecommunications service market. Results indicate that call quality-related factors influence customer churn; however, customers participating in membership card programs are also more likely to churn, which raises questions about program effectiveness. Furthermore, heavy users also tend to churn. In order to analyze partial and total defection, this study defines changes in a customer’s status from active use (using the service on a regular basis) to non-use (deciding not to use it temporarily without having churned yet) or suspended (being suspended by the service provider) as partial defection and from active use to churn as total defection. Thus, mediating effects of a customer’s partial defection on the relationship between the churn determinants and total defection are analyzed and their implications are discussed. Results indicate that some churn determinants influence customer churn, either directly or indirectly through a customer’s status change, or both; therefore, a customer’s status change explains the relationship between churn determinants and the probability of churn. r 2006 Elsevier Ltd. All rights reserved.

negative: Building Skeleton Models via 3-D Medial Surface/Axis Thinning Algorithms

negative: Machine learning applications in genetics and genomics The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets. Here, we provide an overview of machine learning applications for the analysis of genome sequencing data sets, including the annotation of sequence elements and epigenetic, proteomic or metabolomic data. We present considerations and recurrent challenges in the application of supervised, semi-supervised and unsupervised machine learning methods, as well as of generative and discriminative modelling approaches. We provide general guidelines to assist in the selection of these machine learning methods and their practical application for the analysis of genetic and genomic data sets.

negative: Real AdaBoost : boosting for credit scorecards and similarity to WOE logistic regression Adaboost is a machine learning algorithm that builds a series of small decision trees, adapting each tree to predict difficult cases missed by the previous trees and combining all trees into a single model. We will discuss the AdaBoost methodology and introduce the extension called Real AdaBoost. Real AdaBoost comes from a strong academic pedigree: its authors are pioneers of machine learning and the method has well-established empirical and theoretical support spanning 15 years. Practically speaking, Real AdaBoost is able to produce readable credit scorecards and offers attractive features including variable interaction and adaptive, stage-wise binning. We will contrast Real AdaBoost to the dominant methodology for creating credit scorecards: stepwise weight of evidence logistic regression (SWOELR). Real AdaBoost is remarkably similar to SWOELR and is well positioned to serve as a benchmark for SWOELR models; it may even offer a statistical framework by which we can understand the power of SWOELR. We offer a macro to generate Real AdaBoost models in SAS. INTRODUCTION Financial institutions (FIs) must develop a wide range of models for marketing, fraud detection, loan adjudication, etc. Modeling has undergone a recent renaissance as machine learning has exploded – spurned by the availability of advanced statistical techniques, the ubiquity of powerful computers to execute these techniques, and the well-publicized successes of the companies who have embraced these methods (Parloff 2016). Modeling departments within some FIs face opposing demands: executives want some of the famed value of advanced methods, while government regulators, internal deployment teams and front-line staff want models that are easy to implement, interpret and understand. In this paper we review Real AdaBoost, a machine learning technique that may offer a middle-ground between powerful, but opaque machine learning methods and transparent conventional methods. CONSUMER RISK MODELS One field of modeling where FIs must often strike a balance between power and transparency is consumer risk modeling. Consumer risk modeling involves ranking customers by their credit worthiness (the likelihood they will repay a loan): first by identifying customer characteristics that indicate risk of delinquency, and then combining them mathematically to calculate a relative risk score for each customer (common characteristics include: past loan delinquency, high credit utilization, etc.). CREDIT SCORECARDS In order to keep the consumer risk models as transparent as possible, many FIs require that the final output of the model be in the form of a scorecard (an example is shown in Table 1). Credit scorecards are a popular way to represent customer risk models due to their simplicity, readability, and the ease with which business expertise can be incorporated during the modeling process (Maldonado et al. 2013). A scorecard lists a number of characteristics that indicate risk and each characteristic is subdivided into a small number of bins defined by ranges of values for that characteristic (e.g., credit utilization: 30-80% is a bin for the credit utilization characteristic). Each bin is assigned a number of score points, a value derived from a statistical model and proportional to the risk of that bin (SAS 2012). A customer will fall into one and only one bin per characteristic and the final score of the applicant is the sum of the points assigned by each bin (plus an intercept). This final score is proportional to consumer risk. The procedure for developing scorecards is termed stepwise weight of evidence logistic regression (SWOELR) and is implemented in the Credit Scoring add-on in SAS® Enterprise MinerTM.

negative: Approximation Algorithms for Degree-Constrained Minimum-Cost Network-Design Problems We study network-design problems with two different design objectives: the total cost of the edges and nodes in the network and the maximum degree of any node in the network. A prototypical example is the degree-constrained node-weighted Steiner tree problem: We are given an undirected graph G(V,E) , with a nonnegative integral function d that specifies an upper bound d(v) on the degree of each vertex v ∈ V in the Steiner tree to be constructed, nonnegative costs on the nodes, and a subset of k nodes called terminals. The goal is to construct a Steiner tree T containing all the terminals such that the degree of any node v in T is at most the specified upper bound d(v) and the total cost of the nodes in T is minimum. Our main result is a bicriteria approximation algorithm whose output is approximate in terms of both the degree and cost criteria—the degree of any node v ∈ V in the output Steiner tree is O(d(v) log  k) and the cost of the tree is O(log  k) times that of a minimum-cost Steiner tree that obeys the degree bound d(v) for each node v . Our result extends to the more general problem of constructing one-connected networks such as generalized Steiner forests. We also consider the special case in which the edge costs obey the triangle inequality and present simple approximation algorithms with better performance guarantees.

negative: Construction and Refinement of Panoramic Mosaics with Global and Local Alignment This paper presents techniques for constructing full view panoramic mosaics from sequences of images. Our representation associates a rotation matrix (and optionally a focal length) with each input image, rather than explicitly projecting all of the images onto a common surface (e.g., a cylinder). In order to reduce accumulated registration errors, we apply global alignment (block adjustment) to the whole sequence of images, which results in an optimal image mosaic (in the least-squares sense). To compensate for small amounts of motion parallax introduced by translations of the camera and other unmodeled distortions, we develop a local alignment (deghosting) technique which warps each image based on the results of pairwise local image registrations. By combining both global and local alignment, we significantly improve the quality of our image mosaics, thereby enabling the creation of full view panoramic mosaics with hand-held cameras.

negative: A multi-temporal InSAR method incorporating both persistent scatterer and small baseline approaches [1] Synthetic aperture radar (SAR) interferometry is a technique that provides high-resolution measurements of the ground displacement associated with many geophysical processes. Advanced techniques involving the simultaneous processing of multiple SAR acquisitions in time increase the number of locations where a deformation signal can be extracted and reduce associated error. Currently there are two broad categories of algorithms for processing multiple acquisitions, persistent scatterer and small baseline methods, which are optimized for different models of scattering. However, the scattering characteristics of real terrains usually lay between these two end-member models. I present here a new method that combines both approaches, to extract the deformation signal at more points and with higher overall signal-to-noise ratio than can either approach alone. I apply the combined method to data acquired over Eyjafjallajökull volcano in Iceland, and detect time-varying ground displacements associated with two intrusion events. Citation: Hooper, A., (2008), A multi-temporal InSAR method incorporating both persistent scatterer and small baseline approaches, Geophys. Res. Lett., 35, L16302, doi:10.1029/ 2008GL034654.

negative: Machine Learning Methods for Optical Character Recognition Success of optical character recognition depends on a number of factors, two of which are feature extraction and classi cation algorithms. In this paper we look at the results of the application of a set of classi ers to datasets obtained through various basic feature extraction methods.

negative: Type-2 fuzzy ontology-based semantic knowledge for collision avoidance of autonomous underwater vehicles The volume of obstacles encountered in the marine environment is rapidly increasing, which makes the development of collision avoidance systems more challenging. Several fuzzy ontology-based simulators have been proposed to provide a virtual platform for the analysis of maritime missions. However, due to the simulators’ limitations, ontologybased knowledge cannot be utilized to evaluate maritime robot algorithms and to avoid collisions. The existing simulators must be equipped with smart semantic domain knowledge to provide an efficient framework for the decision-making system of AUVs. This article presents type-2 fuzzy ontology-based semantic knowledge (T2FOBSK) and a simulator for marine users that will reduce experimental time and the cost of marine robots and will evaluate algorithms intelligently. The system reformulates the user’s query to extract the positions of AUVs and obstacles and convert them to a proper format for the simulator. The simulator uses semantic knowledge to calculate the degree of collision risk and to avoid obstacles. The available type-1 fuzzy ontology-based approach cannot extract intensively blurred data from the hazy marine environment to offer actual solutions. Therefore, we propose a type-2 fuzzy ontology to provide accurate information about collision risk and the marine environment during real-time marine operations. Moreover, the type-2 fuzzy ontology is designed using Protégé OWL-2 tools. The DL query and SPARQL query are used to evaluate the ontology. The distance to closest point of approach (DCPA), time to closest point of approach (TCPA) and variation of compass degree (VCD) are used to calculate the degree of collision risk between AUVs and obstacles. The experimental and simulation results show that the proposed architecture is highly efficient and highly productive for marine missions and the real-time decision-making system of AUVs. 2014 Elsevier Inc. All rights reserved.

negative: Tracking Time Evolving Data Streams for Short-Term Traffic Forecasting Data streams have arisen as a relevant topic during the last few years as an efficient method for extracting knowledge from big data. In the robust layered ensemble model (RLEM) proposed in this paper for short-term traffic flow forecasting, incoming traffic flow data of all connected road links are organized in chunks corresponding to an optimal time lag. The RLEM model is composed of two layers. In the first layer, we cluster the chunks by using the Graded Possibilistic c-Means method. The second layer is made up by an ensemble of forecasters, each of them trained for short-term traffic flow forecasting on the chunks belonging to a specific cluster. In the operational phase, as a new chunk of traffic flow data presented as input to the RLEM, its memberships to all clusters are evaluated, and if it is not recognized as an outlier, the outputs of all forecasters are combined in an ensemble, obtaining in this a way a forecasting of traffic flow for a short-term time horizon. The proposed RLEM model is evaluated on a synthetic data set, on a traffic flow data simulator and on two real-world traffic flow data sets. The model gives an accurate forecasting of the traffic flow rates with outlier detection and shows a good adaptation to non-stationary traffic regimes. Given its characteristics of outlier detection, accuracy, and robustness, RLEM can be fruitfully integrated in traffic flow management systems.

negative: Gujarati Script Recognition : A Review This paper is step to locate the researchers, their track in the way for recognizing the characters from various regional scripts in India. This survey paper would provide a path for developing recognition tools for Indian scripts where there is still a scope of recognition accuracy. It provides the reasons for researchers to work for recognition of Indian scripts. In this paper, the various scripts along with their special properties, there feature extraction and recognition techniques are described. Simultaneously a detailed comparison is made on the fronts of techniques used and recognition of Gujarati script.

negative: Survey on security threats of smartphones in Internet of Things Internet of Things (IoT) is an emerging concept which will interconnect billions of devices (such as smartphones, sensors and other networking devices), to communicate with each other. This interconnection of devices can be beneficial in many ways such as improving location based services and timely coordination. However, this also impose severe security concerns specially on smartphones which contains sensitive data. In this paper, we provide a comprehensive analysis of different security issues in smartphones which are integral part of IoT. We provide an easy and concise overview of different security threats. Our categorization helps in identifying specific security breach and applying relevant countermeasures.

negative: Text Document Clustering based on Phrase Similarity using Affinity Propagation Affinity propagation (AP) was recently introduced as an un-supervised learning algorithm for exemplar based clustering. In this paper novel text document clustering algorithm has been developed based on vector space model, phrases and affinity propagation clustering algorithm. Proposed algorithm can be called Phrase affinity clustering (PAC). PAC first finds the phrase by ukkonen suffix tree construction algorithm, second finds the vector space model using tf-idf weighting scheme of phrase. Third calculate the similarity matrix form VSD using cosine similarity . In Last affinity propagation algorithm generate the clusters . F-Measure ,Purity and Entropy of Proposed algorithm is better than GAHC ,ST-GAHC and ST-KNN on OHSUMED ,RCV1 and News group data sets.

negative: Development of a credible and integrated electronic voting machine based on contactless IC cards, biometrie fingerprint credentials and POS printer In recent times there has been a decline in the confidence of common people over electronic voting machines (EVMs). More anticipated than what the reality actually is, today's automated vote casting methods have faced immense controversy related to being vulnerable to hacking and questions have been raised about their transparency and security. This paper proposes the design and development of a novel tamper resistant electronic voting system that aims to mitigate the recurring issues and flaws of existing voting machines of today. To elucidate the system in brief, multiple layered verification process would be carried out on a potential voter by the means of fingerprint recognition and a Near Field Communication (NFC) smart card entry in order to authenticate his or her identity. Subsequently, the person would cast the vote by pressing a button corresponding to a particular candidate which would be recorded in the system providing the vote caster a visual confirmation. The final vote would then be printed out spontaneously onto a ballot box using a POS (Point of Sales) printer for an added level of validation. This system not only prevents multiple vote casts, but also eliminates the discrepancies that commonly arise with a person claiming not to have voted, whereas his or her name is present in the list of vote casters. The project is an incremental extension of a previous research.

negative: LLC resonant converter burst mode control with constant burst time and optimal switching pattern In this paper, a novel burst mode control solution for LLC resonant converters is proposed for the highest efficiency and small output voltage ripple. The burst time is set to be constant, the off time is modulated by load conditions. During the burst time, three pulse switching pattern is implemented to keep output voltage low frequency ripple minimal. The switch first turned on is controlled to be last turned off to reduce the initial hard switching turn on loss when burst starts. The pulse width of first switch is optimized to settle LLC to the steady-state under the highest efficiency load condition in one pulse. Zero current detection (ZCD) of the resonant current is implemented to achieve first switch partial ZVS, further increasing light load efficiency.

negative: A self-powered ultra-fast DC solid state circuit breaker using a normally-on SiC JFET This paper introduces a new self-powered solid state circuit breaker (SSCB) concept using a normally-on SiC JFET as the main static switch and a fast-starting isolated DC/DC converter as the protection driver. The new SSCB detects short circuit faults by sensing its drain-source voltage rise, and draws power from the fault condition to turn and hold off the SiC JFET. The new two-terminal SSCB can be directly placed in a circuit branch without requiring any external power supply or additional wiring. A unique low power isolated DC/DC converter is designed and optimized to provide a fast reaction to a short circuit event. The SSCB prototypes have experimentally demonstrated a fault current interruption capability up to 180 amperes at a DC bus voltage of 400 volts within 0.8 microseconds. DC circuit protection applications provide a unique market opportunity for wide bandgap power semiconductor devices outside the conventional focus on power electronic converter applications.

negative: Noninvasive in vivo glucose sensing on human subjects using mid-infrared light. Mid-infrared quantum cascade laser spectroscopy is used to noninvasively predict blood glucose concentrations of three healthy human subjects in vivo. We utilize a hollow-core fiber based optical setup for light delivery and collection along with a broadly tunable quantum cascade laser to obtain spectra from human subjects and use standard chemo-metric techniques (namely partial least squares regression) for prediction analysis. Throughout a glucose concentration range of 80-160 mg/dL, we achieve clinically accurate predictions 84% of the time, on average. This work opens a new path to a noninvasive in vivo glucose sensor that would benefit the lives of hundreds of millions of diabetics worldwide.

negative: FCM : THE FUZZY c-MEANS CLUSTERING ALGORITHM nThis paper transmits a FORTRAN-IV coding of the fuzzy c-means (FCM) clustering program. The FCM program is applicable to a wide variety of geostatistical data analysis problems. This program generates fuzzy partitions and prototypes for any set of numerical data. These partitions are useful for corroborating known substructures or suggesting substructure in unexplored data. The clustering criterion used to aggregate subsets is a generalized least-squares objective function. Features of this program include a choice of three norms (Euclidean, Diagonal, or Mahalonobis), an adjustable weighting factor that essentially controls sensitivity to noise, acceptance of variable numbers of clusters, and outputs that include several measures of cluster validity.

negative: Relational versus non-relational database systems for data warehousing Relational database systems have been the dominating technology to manage and analyze large data warehouses. Moreover, the ER model, the standard in database design has a close relationship with the relational model. Recently, there has been a surge of alternative technologies for large scale analytic processing, most of which are not based on the relational model. Out of these proposals, distributed file systems together with MapReduce have become strong competitors to relational database systems to analyze large data sets, exploiting parallel processing. Moreover, there is progress on using MapReduce to evaluate relational queries. With that motivation in mind, this panel will compare pros and cons of each technology for data warehousing and will identify research issues, considering practical aspects like ease of use, programming flexibility and cost; as well as technical aspects like data modeling, storage, hardware, scalability, query processing, fault tolerance and data mining.

negative: Variance Reduction Methods II 1 Importance Sampling For this problem, however, the usual approach would be completely inadequate since approximating θ to any reasonable degree of accuracy would require n to be inordinately large. For example, on average we would have to set n ≈ 2.7014× 10 in order to obtain just one non-zero value of I. Clearly this is impractical and a much smaller value of n would have to be used. Using a much smaller value of n, however, would almost inevitably result in an estimate, θ̂n = 0, and an approximate confidence interval [L,U ] = [0, 0]! So the naive approach does not work. We could try to use the variance reduction techniques we have seen in the course so far, but they would provide little, if any, help.

negative: Constraint Programming in OPL OPL is a modeling language for mathematical programming and combinatorial optimization problems. It is the rst modeling language to combine high-level algebraic and set notations from model-ing languages with a rich constraint language and the ability to specify search procedures and strategies that is the essence of constraint programming. In addition, OPL models can be controlled and composed using OPLSCRIPT, a script language that simpliies the development of applications that solve sequences of models, several instances of the same model, or a combination of both as in column-generation applications. This paper illustrates some of the functionalities of OPL for constraint programming using frequency allocation, sport-scheduling, and job-shop scheduling applications. It also illustrates how OPL models can be composed using OPLSCRIPT on a simple connguration example.

negative: You Only Look Once: Unified, Real-Time Object Detection We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. Our unified architecture is extremely fast. Our base YOLO model processes images in real-time at 45 frames per second. A smaller version of the network, Fast YOLO, processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background. Finally, YOLO learns very general representations of objects. It outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.

negative: Librispeech: An ASR corpus based on public domain audio books This paper introduces a new corpus of read English speech, suitable for training and evaluating speech recognition systems. The LibriSpeech corpus is derived from audiobooks that are part of the LibriVox project, and contains 1000 hours of speech sampled at 16 kHz. We have made the corpus freely available for download, along with separately prepared language-model training data and pre-built language models. We show that acoustic models trained on LibriSpeech give lower error rate on the Wall Street Journal (WSJ) test sets than models trained on WSJ itself. We are also releasing Kaldi scripts that make it easy to build these systems.

negative: DevOps for Developers ed descriptions of machines by using a DSL while enjoying the full power of scripting languages (in both Puppet and Chef, you can describe behavior in the Ruby language (a dynamic, general-purpose object-oriented programming language), see http://www.ruby-lang. org/en/). Declarative descriptions of target behavior (i.e., what the system must be). Thus, running the scripts will always lead to the same end result. Management of code in version control. By using a version control system as the leading medium, you do not need to adjust the machines manually (which is not reproducible). Synchronization of environments by using a version control system and automatic provisioning of environments. Continuous integration servers, such as Jenkins, simply have to listen to the path in the version control system to detect changes. Then the configuration management tool (e.g., Puppet) ensures that the corresponding machines apply the behavior that is described in version control. Using tools such as Jenkins (see Chapter 8) and Puppet and Vagrant (see Chapter 9), complete setups, including virtualizations, can be managed automatically. Sharing of scripts (e.g., Puppet manifests). A cross-functional team that includes development and operations can develop this function. Sharing the scripts in the version control system enables all parties, particularly development and operations, to use those scripts to set up their respective environments: test environments (used by development) and production environments (managed by operations). Automation is an essential backbone of DevOps (see Chapter 3 and Chapter 8 for more information on automation). Automation is the use of solutions to reduce the need for human work. Automation can ensure that the software is built the same way each time, that the team sees every change made to the software, and that the software is tested and reviewed in the same way every day so that no defects slip through or are introduced through human error. In software development projects, a high level of automation is a prerequisite for quickly delivering the best quality and for obtaining feedback from stakeholders early and often. Automating aspects of DevOps helps to make parts of the process transparent for the whole team and also helps deploy software to different target environments in the same way. You can best improve what you measure; and to measure something usefully, you need a process that delivers results in a reproducible way. DevOps addresses aspects similar to those tackled by Agile development, but the former focuses on breaking down the walls between developers and operations workers. The challenge is to communicate the benefits of DevOps to both development and operations teams. Both groups may be reluctant to start implementing the shift toward DevOps because their day is already full of activities. So why should they be concerned with the work of others? Why should DEVOPS FOR DEVELOPERS 31 operations want to use unfamiliar tools and adjust their daily routines when their self-made, isolated solutions have worked just fine for years? Because of this resistance, the incentives and commitment provided by upper management are important. Incentives alone are not enough: unified processes and tool chains are also important. Upper management will also resist by questioning the wisdom of implementing DevOps if the concrete benefits are not visible. Better cash flow and improved time to market are hard to measure. Management asks questions that address the core problems of software engineering while ignoring the symptoms: how can the company achieve maximal earnings in a short period of time? How can requirements be made stable and delivered to customers quickly? These results and visions should be measured with metrics that are shared by development and operations. Existing metrics can be further used or replaced by metrics that accurately express business value. One example of an end-to-end metric is the cycle time, which we will discuss in detail in Chapter 3.

negative:
BigDataScript: a scripting language for data pipelines MOTIVATION
The analysis of large biological datasets often requires complex processing pipelines that run for a long time on large computational infrastructures. We designed and implemented a simple script-like programming language with a clean and minimalist syntax to develop and manage pipeline execution and provide robustness to various types of software and hardware failures as well as portability.

RESULTS
We introduce the BigDataScript (BDS) programming language for data processing pipelines, which improves abstraction from hardware resources and assists with robustness. Hardware abstraction allows BDS pipelines to run without modification on a wide range of computer architectures, from a small laptop to multi-core servers, server farms, clusters and clouds. BDS achieves robustness by incorporating the concepts of absolute serialization and lazy processing, thus allowing pipelines to recover from errors. By abstracting pipeline concepts at programming language level, BDS simplifies implementation, execution and management of complex bioinformatics pipelines, resulting in reduced development and debugging cycles as well as cleaner code.

AVAILABILITY AND IMPLEMENTATION
BigDataScript is available under open-source license at http://pcingola.github.io/BigDataScript.

negative: Conversion of urdu nastaliq to roman urdu using OCR This paper deals with Urdu Nastaliq, which is a popular script of writing Urdu language. The complex and cursive nature of nastaliq makes optical character recognition for this script very challenging. Segmentation of urdu nastaliq is also very complex due to different levels and shapes of characters according to their position in a word. Character based segmentation technique for urdu names has been proposed in this paper. After segmenting each character, the characters are matched to their templates already saved in the database. The proposed technique handles many complexities in conversion of urdu names to roman urdu including vowel sounds produced by a single character due to diacritics and different voices of ye, ain and other such characters. At the end names written in nastaliq script are converted into roman urdu.

negative: Orange: From Experimental Machine Learning to Interactive Data Mining Orange (www.ailab.si/orange) is a suite for machine learning and data mining. It can be used though scripting in Python or with visual programming in Orange Canvas using GUI components called widgets. In the demonstration we will show how to easily prototype stateof-the-art machine learning algorithms through Orange scripting, and design powerful and interactive data exploration applications through visual programming. 1 Orange, a Component-Based Framework Orange is a comprehensive, component-based framework for machine learning and data mining. It is intended for both experienced users and researchers in machine learning who want to prototype new algorithms while reusing as much of the code as possible, and for those just entering the field who can either write short Python scripts for data analysis or enjoy in the powerful while easy-to-use visual programming environment. Orange includes a range of techniques, such as data management and preprocessing, supervised and unsupervised learning, performance analysis, and a range of data and model visualization techniques. 1.1 Scripting in Python As a framework, Orange is comprised of several layers. The core design principle was to use C++ to code the basic data representation and manipulation and all time-complex procedures, such as most learning algorithms and data preprocessing. Tasks that are less time consuming are coded in Python. Python is a popular object-oriented scripting language known for its simplicity and power, and often used for prototyping and as a ”glue-language” for components written in other languages. The interface between C++ and Python provides a tight integration: Python scripts can access and manipulate Orange objects as if they were implemented in Python. On the other hand, components defined in Python can be used by the C++ core. For instance, one can use classification tree as implemented within Orange (in C++) but prototype a component for attribute selection in Python. For Orange, we took special care to implement machine learning methods so that they are assembled from a set of reusable components one can either use in the new algorithms, or replace them with prototypes written in Python. Just for a taste, here is a simple Python script, which, using Orange, reads the data, reports on the number of instances and attributes, builds two classifiers and outputs predicted and true class of the first five instances. import orange data = orange.ExampleTable(’voting.tab’) print ’Instances:’, len(data), ’Attributes:’, len(data.domain.attributes) nbc = orange.BayesLearner(data) knn = orange.kNNLearner(data, k=10) for i in range(5): print nbc(data[i]), knn(data[i]), ’vs. true class’, data[i].getClass() Another, a bit more complicated script below, implements a classification tree learner where node attributes that split the data are chosen at random by a function randomChoice, which is used in place of data splitting component of Orange’s classification tree inducer. The script builds a standard and random tree from the data, and reports on their sizes. import orange, random def randomChoice(instances, *args): attr = random.choice(instances.domain.attributes) cl = orange.ClassifierFromVar(whichVar=attr, classVar=attr) return cl, attr.values, None, 1 treeLearner = orange.TreeLearner() rndLearner = orange.TreeLearner() rndLearner.split = randomChoice data = orange.ExampleTable(’voting.tab’) tree = treeLearner(data) rndtree = rndLearner(data) print tree.treesize(), ’vs.’, rndtree.treesize() 1.2 Visual Programming Component-based approach was also used for graphical user’s interface (GUI). Orange’s GUI is made of widgets, which are essentially a GUI wrappers around data analysis algorithms implemented in Orange and Python. Widgets communicate through channels, and a particular set of connected widgets is called a schema. Orange schemas can be either set in Python scripts, or, preferably, are put together by means of visual programming in an application called Orange Canvas. Besides ease-of-use and flexibility, data exploration widgets were carefully design to support interaction. Clicking on a classification tree node in the tree visualization widget, for example, outputs the corresponding data instances making them available for further analysis. Any visualization of predictive or visualization models where their elements are associated with particular subsets of instances, attributes, data domains, etc., behave in the similar way. A snapshot of Orange Canvas with an example schema is shown in Fig. 1. Fig. 1. Snapshot of Orange Canvas with a schema that takes a microarray data, performs k-means clustering, and evaluates the performance of two different supervised learning methods when predicting the cluster label. Clustered data is visualized in the Heat Map widget, which sends any selected data subset to the Scatterplot widget. 2 Orange Demonstration at ECML-PKDD The demonstration at ECML-PKDD will feature an introduction to Orange, give a tutorial on how to do machine learning by scripting, and show how to use component-based programming to design a new induction algorithm. The second part of the demonstration will demonstrate Orange widgets and visual programming, and will show hot to use these to build classifiers, find interaction between attributes, and do a range of interesting data visualizations. Demonstration will be live, e.g. we will write scripts and visually program at the stage rather than show transparencies. The demonstration will use the data from medicine (predictive models) and functional genomics (data mining of microarray and sequence data). CD’s with installations and copies of the white-papers will be handed out. 3 On Significance and Contribution Orange is an open-source framework that features both scripting and visual programming. Because of component-based design in C++ and integration with Python, Orange should appeal to machine learning researchers for the speed of execution and ease of prototyping of new methods. Graphical user’s interface is provided through visual programming and carefully designed widgets that support interactive data exploration. Component-based design, both on the level of procedural and visual programming, flexibility in combining components to design new machine learning methods and data mining applications, and user and developer-friendly environment are also the most significant attributes of Orange and those where Orange can make its contribution to the community.