query: Individuality and Alignment in Generated Dialogues

positive: SRILM - an extensible language modeling toolkit SRILM is a collection of C++ libraries, executable programs, and helper scripts designed to allow both production of and experimentation with statistical language models for speech recognition and other applications. SRILM is freely available for noncommercial purposes. The toolkit supports creation and evaluation of a variety of language model types based on N-gram statistics, as well as several related tasks, such as statistical tagging and manipulation of N-best lists and word lattices. This paper summarizes the functionality of the toolkit and discusses its design and implementation, highlighting ease of rapid prototyping, reusability, and combinability of tools.

positive: Efficient Realization of Coordinate Structures in Combinatory Categorial Grammar We describe a chart realization algorithm for Combinatory Categorial Grammar (CCG), and show how it can be used to efficiently realize a wide range of coordination phenomena, including argument cluster coordination and gapping. The algorithm incorporates three novel methods for improving the efficiency of chart realization: (i) using rules to chunk the input logical form into sub-problems to be solved independently prior to further combination; (ii) pruning edges from the chart based on the n-gram score of the edge’s string, in comparison to other edges with equivalent categories; and (iii) formulating the search as a best-first anytime algorithm, using n-gram scores to sort the edges on the agenda. The algorithm has been implemented as an extension to the OpenCCG open source CCG parser, and initial performance tests indicate that the realizer is fast enough for practical use in natural language dialogue systems.

positive: Two decades of statistical language modeling: where do we go from here? Statistical language models estimate the distribution of various natural language phenomena for the purpose of speech recognition and other language technologies. Since the first significant model was proposed in 1980, many attempts have been made to improve the state of the art. We review them, point to a few promising directions, and argue for a Bayesian approach to integration of linguistic theories with data.

positive: Statistical language modeling using the CMU-cambridge toolkit The CMU Statistical Language Modeling toolkit was re leased in in order to facilitate the construction and testing of bigram and trigram language models It is currently in use in over academic government and industrial laboratories in over countries This paper presents a new version of the toolkit We outline the con ventional language modeling technology as implemented in the toolkit and describe the extra e ciency and func tionality that the new toolkit provides as compared to previous software for this task Finally we give an exam ple of the use of the toolkit in constructing and testing a simple language model

positive: An Architecture for Action, Emotion, and Social Behavior The Oz project at Carnegie Mellon is studying the construction of artistically e ective simulated worlds. Such worlds typically include several agents, which must exhibit broad behavior. To meet this need, we are developing an agent architecture, called Tok, that presently supports reactivity, goals, emotions, and social behavior. Here we brie y introduce the requirements of our application, summarize the Tok architecture, and describe a particular agent we have constructed that exhibits the desired social behavior. 1 THE OZ PROJECT AND BROAD AGENTS 1 1 The Oz Project and Broad Agents The Oz project at Carnegie Mellon University is developing technology for artistically interesting simulated worlds [3]. We want to let human users participate in dramatically e ective worlds that include moderately competent, emotional agents. We work with artists in the CMU Drama and English Departments, to help focus our technology on genuine artistic needs. An Oz world has four primary components. There is a simulated physical environment, a set of automated agents which help populate the world, a user interface to allow one or more people to participate in the world [14], and a planner concerned with the long term structure of the user's experience [2]. One of the keys to an artistically engaging experience is for the user to be able to \suspend disbelief". That is, the user must be able to imagine that the world portrayed is real, without being jarred out of this belief by the world's behavior. The automated agents, in particular, must not be blatantly unreal. Thus, part of our e ort is aimed at producing agents with a broad set of capabilities, including goal-directed reactive behavior, emotional state and behavior, social knowledge and behavior, and some natural language abilities. For our purpose, each of these capacities can be as limited as is necessary to allow us to build broad, integrated agents [4]. Oz worlds can be simpler than the real world, but they must retain su cient complexity to serve as interesting artistic vehicles. The complexity level seems to be somewhat higher, but not exceptionally higher, than typical AI micro-worlds. Despite these simpli cations, we nd that our agents must deal with imprecise and erroneous perceptions, with the need to respond rapidly, and with a general inability to fully model the agent-rich world they inhabit. Thus, we suspect that some of our experience with broad agents in Oz may transfer to the domain of social, real-world robots. Building broad agents is a little studied area. Much work has been done on building reactive systems [1, 6, 7, 10, 11, 22], natural language systems (which we do not discuss here), and even emotion systems [9, 18, 20]. There has been growing interest in integrating action and learning (see [16]) and some very interesting work on broader integration [23, 19]. However, we are aware of no other e orts to integrate the particularly wide range of capabilities needed in the Oz domain. Here we present our e orts, focusing on the structure of a particular agent designed to exhibit goaldirected reactive behavior, emotion, and some social behavior. Further discussion of the integration issues can be found in [5].

negative: A source code linearization technique for detecting plagiarized programs It is very important to detect plagiarized programs in the field of computer science education. Therefore, many tools and algorithms have been developed for this purpose. Generally, these tools are operated in two phases. In phase 1, a program plagiarism detecting tool generates an intermediate representation from a given program set. The intermediate representation should reflect the structural characterization of the program. Most tools use the parse tree or token sequence by intermediate representation. In phase 2, the program looks for plagiarized material and evaluates the similarity of two programs. It is helpful to announce the plagiarized metarials between two programs to the instructor. In this paper, we present the static tracing method in order to improve program plagiarism detection accuracy. The static tracing method statically executes a program at the syntax-level and then extracts predefined keywords according to the order of the executed functions. The result of experiment proves this method can detect plagiarism more effectively than the previously released plagiarism detecting method.

negative: Silicon-Filled Rectangular Waveguides and Frequency Scanning Antennas for mm-Wave Integrated Systems We present a technology for the manufacturing of silicon-filled integrated waveguides enabling the realization of low-loss high-performance millimeter-wave passive components and high gain array antennas, thus facilitating the realization of highly integrated millimeter-wave systems. The proposed technology employs deep reactive-ion-etching (DRIE) techniques with aluminum metallization steps to integrate rectangular waveguides with high geometrical accuracy and continuous metallic side walls. Measurement results of integrated rectangular waveguides are reported exhibiting losses of 0.15 dB/ λg at 105 GHz. Moreover, ultra-wideband coplanar to waveguide transitions with 0.6 dB insertion loss at 105 GHz and return loss better than 15 dB from 80 to 110 GHz are described and characterized. The design, integration and measured performance of a frequency scanning slotted-waveguide array antenna is reported, achieving a measured beam steering capability of 82 ° within a band of 23 GHz and a half-power beam-width (HPBW) of 8.5 ° at 96 GHz. Finally, to showcase the capability of this technology to facilitate low-cost mm-wave system level integration, a frequency modulated continuous wave (FMCW) transmit-receive IC for imaging radar applications is flip-chip mounted directly on the integrated array and experimentally characterized.

negative: Development of a continuum robot using pneumatic artificial muscles This paper presents the design concept of an intrinsic spatial continuum robot, of which arm segments are actuated by pneumatic artificial muscles. Since pneumatic artificial muscles supported the arm segments of the robot instead of a passive spring, the developed continuum robot was not only capable of following a given trajectory, but also varying its stiffness according to given external environment. Experiment results revealed that the proposed continuum robot showed versatile movements by controlling the pressure of supply air to pneumatic artificial muscles, and the well-coordinated pattern matching scheme based on low-cost webcams gave good performance in estimating the position and orientation of the end-effector of the spatial continuum robot.

negative: Introduction of speech log-spectral priors into dereverberation based on Itakura-Saito distance minimization It has recently been shown that a multi-channel linear prediction can effectively achieve blind speech dereverberation based on maximum-likelihood (ML) estimation. This approach can estimate and cancel unknown reverberation processes from only a few seconds of observation. However, one problem with this approach is that speech distortion may increase if we iterate the dereverberation more than once based on Itakura-Saito (IS) distance minimization to further reduce the reverberation. To overcome this problem, we introduce speech log-spectral priors into this approach, and reformulate it based on maximum a posteriori (MAP) estimation. Two types of priors are introduced, a Gaussian mixture model (GMM) of speech log spectra, and a GMM of speech mel-frequency cepstral coefficients. In the formulation, we also propose a new versatile technique to integrate such log-spectral priors with the IS distance minimization in a computationally efficient manner. Preliminary experiments show the effectiveness of the proposed approach.

negative: Disentangled Representation Learning for Text Style Transfer This paper tackles the problem of disentangling the latent variables of style and content in language models. We propose a simple, yet effective approach, which incorporates auxiliary objectives: a multi-task classification objective, and dual adversarial objectives for label prediction and bag-ofwords prediction, respectively. We show, both qualitatively and quantitatively, that the style and content are indeed disentangled in the latent space, using this approach. This disentangled latent representation learning method is applied to attribute (e.g. style) transfer on non-parallel corpora. We achieve similar content preservation scores compared to previous state-of-the-art approaches, and significantly better style-transfer strength scores. Our code is made publicly available for replicability and extension purposes .

negative: Abstractive Multi-document Summarization with Semantic Information Extraction This paper proposes a novel approach to generate abstractive summary for multiple documents by extracting semantic information from texts. The concept of Basic Semantic Unit (BSU) is defined to describe the semantics of an event or action. A semantic link network on BSUs is constructed to capture the semantic information of texts. Summary structure is planned with sentences generated based on the semantic link network. Experiments demonstrate that the approach is effective in generating informative, coherent and compact summary.

negative: Opportunistic Computation Offloading in Mobile Edge Cloud Computing Environments The dynamic mobility and limitations in computational power, battery resources, and memory availability are main bottlenecks in fully harnessing mobile devices as data mining platforms. Therefore, the mobile devices are augmented with cloud resources in mobile edge cloud computing (MECC) environments to seamlessly execute data mining tasks. The MECC infrastructures provide compute, network, and storage services in one-hop wireless distance from mobile devices to minimize the latency in communication as well as provide localized computations to reduce the burden on federated cloud systems. However, when and how to offload the computation is a hard problem. In this paper, we present an opportunistic computation offloading scheme to efficiently execute data mining tasks in MECC environments. The scheme provides the suitable execution mode after analyzing the amount of unprocessed data, privacy configurations, contextual information, and available on-board local resources (memory, CPU, and battery power). We develop a mobile application for online activity recognition and evaluate the proposed scheme using the event data stream of 5 million activities collected from 12 users for 15 days. The experiments show significant improvement in execution time and battery power consumption resulting in 98% data reduction.

negative: Large-scale audio feature extraction and SVM for acoustic scene classification This work describes a system for acoustic scene classification using large-scale audio feature extraction. It is our contribution to the Scene Classification track of the IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (D-CASE). The system classifies 30 second long recordings of 10 different acoustic scenes. From the highly variable recordings, a large number of spectral, cepstral, energy and voicing-related audio features are extracted. Using a sliding window approach, classification is performed on short windows. SVM are used to classify these short segments, and a majority voting scheme is employed to get a decision for longer recordings. On the official development set of the challenge, an accuracy of 73 % is achieved. SVM are compared with a nearest neighbour classifier and an approach called Latent Perceptual Indexing, whereby SVM achieve the best results. A feature analysis using the t-statistic shows that mainly Mel spectra are the most relevant features.

negative: A fractional order fuzzy PID controller for binary distillation column control Expert and intelligent control schemes have recently emerged out as a promising solution with robustness which can efficiently deal with the nonlinearities, along with various types of modelling uncertainties, present in different real world systems e.g. binary distillation column. This paper is an attempt to propose an intelligent control system which takes the form of a Fractional Order Fuzzy Proportional – Integral – Derivative (FOFPID) controller which is investigated as a solution to deal with the complex dynamic nature of the distillation column. The FOFPID controller is an extension of an existing formula based self tuning fuzzy Proportional Integral controller structure, which varies its gains at run time in accordance with the instantaneous error and rate of change of error. The FOFPID controller is a Takagi-Sugeno (TS) model based fuzzy adaptive controller comprising of non-integer order of integration and differentiation operators used in the controller. It has been observed that inclusion of non-integer order of the integration and differentiation operators made the controller scheme more robust. For the performance evaluation of the proposed scheme, the performance of FOFPID controller is compared with that of its integer order counterpart, a Fuzzy Proportional – Integral – Derivative (FPID) controller. The parameters of both the controllers were optimized for minimum integral of absolute error (IAE) using a bio-inspired global optimization algorithm, Genetic Algorithm (GA). Intensive LabVIEW TM simulation studies were performed which included setpoint tracking with and without uncertainties, disturbance rejection, and noise suppression investigations. For testing the parameter uncertainty handling capability of the proposed controller, uncertain and time varying relative volatility and uncertain tray hydraulic constant were applied. Also, for the disturbance rejection studies, intensive simulations were conducted, which included two most common causes of disturbance i.e. variation in feed composition and variation in feed flow rate. All the simulation investigations clearly suggested that FOFPID controller provided superior performance over FPID controller for each case study i.e. setpoint tracking, disturbance rejection, noise suppression and parameter uncertainties.

negative: A three-component scattering model for polarimetric SAR data An approach has been developed that involves the fit of a combination of three simple scattering mechanisms to polarimetric SAR observations. The mechanisms are canopy scatter from a cloud of randomly oriented dipoles, evenor double-bounce scatter from a pair of orthogonal surfaces with different dielectric constants and Bragg scatter from a moderately rough surface. This composite scattering model is used to describe the polarimetric backscatter from naturally occurring scatterers. The model is shown to describe the behavior of polarimetric backscatter from tropical rain forests quite well by applying it to data from NASA/Jet Propulsion Laboratory’s (JPL’s) airborne polarimetric synthetic aperture radar (AIRSAR) system. The model fit allows clear discrimination between flooded and nonflooded forest and between forested and deforested areas, for example. The model is also shown to be usable as a predictive tool to estimate the effects of forest inundation and disturbance on the fully polarimetric radar signature. An advantage of this model fit approach is that the scattering contributions from the three basic scattering mechanisms can be estimated for clusters of pixels in polarimetric SAR images. Furthermore, it is shown that the contributions of the three scattering mechanisms to the HH, HV, and VV backscatter can be calculated from the model fit. Finally, this model fit approach is justified as a simplification of more complicated scattering models, which require many inputs to solve the forward scattering problem.

negative: Stacked Patch Antenna With Dual-Polarization and Low Mutual Coupling for Massive MIMO Massive multiple input and multiple output (MIMO) has attracted significant interests in both academia and industry. It has been considered as one of most promising technologies for 5G wireless systems. The large-scale antenna array for base stations naturally becomes the key to deploy the Massive MIMO technologies. In this communication, we present a dual-polarized antenna array with 144 ports for Massive MIMO operating at 3.7 GHz. The proposed array consists of 18 low profile subarrays. Each subarray consists of four single units. Each single antenna unit consists of one vertically polarized port and one horizontally polarized port connected to power splitters, which serve as a feeding network. A stacked patch design is used to construct the single unit with the feeding network, which gives higher gain and lower mutual coupling within the size of a conversional dual-port patch antenna. Simulation results of the proposed single antenna unit, sub-array, and Massive MIMO array are verified by measurement.

negative: UIMA: an architectural approach to unstructured information processing in the corporate research environment IBM Research has over 200 people working on Unstructured Information Management (UIM) technologies with a strong focus on Natural Language Processing (NLP). These researchers are engaged in activities ranging from natural language dialog, information retrieval, topic-tracking, named-entity detection, document classification and machine translation to bioinformatics and open-domain question answering. An analysis of these activities strongly suggested that improving the organization’s ability to quickly discover each other's results and rapidly combine different technologies and approaches would accelerate scientific advance. Furthermore, the ability to reuse and combine results through a common architecture and a robust software framework would accelerate the transfer of research results in NLP into IBM’s product platforms. Market analyses indicating a growing need to process unstructured information, specifically multilingual, natural language text, coupled with IBM Research’s investment in NLP, led to the development of middleware architecture for processing unstructured information dubbed UIMA. At the heart of UIMA are powerful search capabilities and a data-driven framework for the development, composition and distributed deployment of analysis engines. In this paper we give a general introduction to UIMA focusing on the design points of its analysis engine architecture and we discuss how UIMA is helping to accelerate research and technology transfer. 1 Motivation and Objectives We characterize a UIM application as a software system that analyzes large volumes of unstructured information while consulting structured information sources to discover, organize and present knowledge relevant to the application’s end-user. We define structured information as information whose intended meaning is unambiguous and explicitly represented in the structure or format of the data. The canonical example of structured information is a relational database table. We define unstructured information as information whose intended meaning is only loosely implied by its form. The canonical example is a natural language document; other examples include voice, audio, image and video. To appear in a special issue of the Journal of Natural Language Engineering 2004 2 Ferrucci and Lally Do Not Distirubte without written permission from authors Market indicators suggest a rapid increase in the use of text and voice analytics to a growing class of commercially viable applications (Roush 2003). National security interests are driving applications to involve an even broader set of UIM technologies including image and video analytics. Emerging and commercially important UIM application areas include life sciences, e-commerce, technical support, advanced search, and national and business intelligence. In six major labs spread out over the globe, IBM Research has over 200 people working on Unstructured Information Management (UIM) technologies with a primary focus on Natural Language Processing (NLP). These researchers are engaged in activities ranging from natural language dialog, information retrieval, topic-tracking, named-entity detection, document classification and machine translation to bioinformatics and open-domain question answering. Each group is developing different technical and engineering approaches to process unstructured information in pursuit of their specific research agenda in the UIM space. While IBM Research’s independent UIM technologies fare well in scientific venues, accelerated development of integrated, robust applications is becoming increasingly important to IBM’s commercial interests. Furthermore the rapid integration of different alogorthims and techniques is also a means to advance scientific results. For example, experiments in statistical name-entity detection or machine translation may benefit from using a deep parser as a feature generator. Facilitating the rapid integration of an independently produced deep parser with statistical algorithm implementations can accelerate the research cycle time as well as the time to product. Some of the challenges that face a large research organization in efficiently leveraging and integrating its (and third-party) UIM assets include: • Organizational Structure. IBM Research is largely organized in three to five person teams geographically dispersed among different labs throughout world. This structure makes it more difficult to jointly develop and reuse technologies. • Skills Alignment. There are inefficiencies in having PhDs trained and specialized in, for example, computational linguistics or statistical machine learning, develop and apply the requisite systems expertise to deliver their work into product architectures and/or make it easily usable by other researchers. To appear in a special issue of the Journal of Natural Language Engineering 2004 3 Ferrucci and Lally Do Not Distirubte without written permission from authors • Development Inefficiencies. Research results are delivered in a myriad of interfaces and technologies. Reuse and integration often requires too much effort dedicated to technical adaptation. Too often work is replicated. • Speed to product. Software Products introduce their own architectures, interfaces and implementation technologies. Technology transfer from the lab to the products often becomes a significant engineering or rewrite effort for researchers. A common framework and engineering discipline could help address the challenges listed above. The Unstructured Information Management Architecture (UIMA) project was initiated within IBM Research based on the premise that a common software architecture for developing, composing and delivering UIM technologies, if adopted by the organization, would facilitate technology reuse and integration, enable quicker scientific experimentation and speed the path to product. IBM’s Unstructured Information Management Architecture (UIMA) high-level objectives are two fold: 1) Accelerate scientific advances by enabling the rapid combination of UIM technologies (e.g., natural language processing, audio and video analysis, information retrieval, etc.). 2) Accelerate transfer of UIM technologies to product by providing an architecture and associated framework implementations that promote reuse and support flexible deployment options. 2 The Unstructured Information Management Architecture (UIMA) UIMA is a software architecture for developing UIM applications. The UIMA high-level architecture, illustrated in Figure 1, defines the roles, interfaces and communications of large-grained components essential for UIM applications. These include components capable of analyzing unstructured artifacts, integrating and accessing structured sources and storing, indexing and searching for artifacts based on discovered semantic content resulting from analyses. A primary design point for UIMA is that it should admit the implementation of middleware frameworks that provide a component-based infrastructure for developing UIM applications suitable for vastly different deployment environments. These should range from lightweight and embeddable implementations to highly scalable implementations that are meant to exploit clusters of machines and provide high throughput, high availability serviced-based offerings. While the primary focus of current UIMA framework implementations is squarely on natural language To appear in a special issue of the Journal of Natural Language Engineering 2004 4 Ferrucci and Lally Do Not Distirubte without written permission from authors UIM Application Analysis Collection Level Analysis Document, Collection & Metadata Store Knowledge Source Access Collection Processing Manager (CPM) Knowledge & Data Bases Unstructured Information (Text) Analysis Engines Crawlers Analysis Engine Directory Acquisition Unstructured Information Analysis Structured Information Access Semantic Search Engine Indices Indices Documents Collections Metadata Knowledge Source Adapter Directory Access

negative:
On composition of a federated web search result page: using online users to provide pairwise preference for heterogeneous verticals Modern web search engines are federated --- a user query is sent to the numerous specialized search engines called verticals like web (text documents), News, Image, Video, etc. and the results returned by these engines are then aggregated and composed into a search result page (SERP) and presented to the user. For a specific query, multiple verticals could be relevant, which makes the placement of these vertical results within blocks of textual web results challenging: how do we represent, assess, and compare the relevance of these heterogeneous entities?
 In this paper we present a machine-learning framework for SERP composition in the presence of multiple relevant verticals. First, instead of using the traditional label generation method of human judgment guidelines and trained judges, we use a randomized online auditioning system that allows us to evaluate triples of the form query, web block, vertical>. We use a pairwise click preference to evaluate whether the web block or the vertical block had a better users' engagement. Next, we use a hinged feature vector that contains features from the web block to create a common reference frame and augment it with features representing the specific vertical judged by the user. A gradient boosted decision tree is then learned from the training data. For the final composition of the SERP, we place a vertical result at a slot if the score is higher than a computed threshold. The thresholds are algorithmically determined to guarantee specific coverage for verticals at each slot.
 We use correlation of clicks as our offline metric and show that click-preference target has a better correlation than human judgments based models. Furthermore, on online tests for News and Image verticals we show higher user engagement for both head and tail queries.

negative: Soft start-up for high frequency LLC resonant converter with optimal trajectory control This paper investigates the soft start-up for high frequency LLC resonant converter with optimal trajectory control. Two methods are proposed to realize soft start-up for high frequency LLC converter by commercial low-cost microcontrollers (MCU). Both methods can achieve soft start-up with minimum stress and optimal energy delivery. One method is mixed-signal implementation by sensing resonant tank to minimize the digital delay. Another method is digital implementation with look-up table. Experimental results are demonstrated on 500kHz 1kW 400V/12V LLC converter.

negative: Grammar Variational Autoencoder s m i l e s ! c h a i n atom ! b r a c k e t _ a t o m | a l i p h a t i c _ o r g a n i c | a r o m a t i c _ o r g a n i c a l i p h a t i c _ o r g a n i c ! ’B’ | ’C’ | ’N’ | ’O’ | ’S ’ | ’P ’ | ’F ’ | ’ I ’ | ’ Cl ’ | ’ Br ’ a r o m a t i c _ o r g a n i c ! ’ c ’ | ’ n ’ | ’ o ’ | ’ s ’ b r a c k e t _ a t o m ! ’ [ ’ BAI ’ ] ’ BAI ! i s o t o p e symbol BAC | symbol BAC | i s o t o p e symbol | symbol BAC ! c h i r a l BAH | BAH | c h i r a l BAH ! hc ou n t BACH | BACH | h co u n t BACH ! c h a r g e c l a s s | c h a r g e | c l a s s symbol ! a l i p h a t i c _ o r g a n i c | a r o m a t i c _ o r g a n i c i s o t o p e ! DIGIT | DIGIT DIGIT | DIGIT DIGIT DIGIT DIGIT ! ’1 ’ | ’2 ’ | ’3 ’ | ’4 ’ | ’5 ’ | ’6 ’ | ’7 ’ | ’8 ’ c h i r a l ! ’@’ | ’@@’ hc ou n t ! ’H’ | ’H’ DIGIT c h a r g e ! ’ ’ | ’ ’ DIGIT | ’ ’ DIGIT DIGIT | ’+ ’ | ’+ ’ DIGIT | ’+ ’ DIGIT DIGIT bond ! ’ ’ | ’= ’ | ’# ’ | ’ / ’ | ’ \ ’ r i n g b o n d ! DIGIT | bond DIGIT branched_a tom ! atom | atom RB | atom BB | atom RB BB RB ! RB r i n g b o n d | r i n g b o n d BB ! BB b ra n ch | b r a n ch b ra nc h ! ’ ( ’ c h a i n ’ ) ’ | ’ ( ’ bond c h a i n ’ ) ’ c h a i n ! branched_a tom | c h a i n branched_a tom | c h a i n bond branched_a tom

negative: Fashion-focused creative commons social dataset In this work, we present a fashion-focused Creative Commons dataset, which is designed to contain a mix of general images as well as a large component of images that are focused on fashion (i.e., relevant to particular clothing items or fashion accessories). The dataset contains 4810 images and related metadata. Furthermore, a ground truth on image's tags is presented. Ground truth generation for large-scale datasets is a necessary but expensive task. Traditional expert based approaches have become an expensive and non-scalable solution. For this reason, we turn to crowdsourcing techniques in order to collect ground truth labels; in particular we make use of the commercial crowdsourcing platform, Amazon Mechanical Turk (AMT). Two different groups of annotators (i.e., trusted annotators known to the authors and crowdsourcing workers on AMT) participated in the ground truth creation. Annotation agreement between the two groups is analyzed. Applications of the dataset in different contexts are discussed. This dataset contributes to research areas such as crowdsourcing for multimedia, multimedia content analysis, and design of systems that can elicit fashion preferences from users.

negative: Memory versus non-linearity in reservoirs Reservoir Computing (RC) is increasingly being used as a conceptually simple yet powerful method for using the temporal processing of recurrent neural networks (RNN). However, because fundamental insight in the exact functionality of the reservoir is as yet still lacking, in practice there is still a lot of manual parameter tweaking or brute-force searching involved in optimizing these systems. In this contribution we aim to enhance the insights into reservoir operation, by experimentally studying the interplay of the two crucial reservoir properties, memory and non-linear mapping. For this, we introduce a novel metric which measures the deviation of the reservoir from a linear regime and use it to define different regions of dynamical behaviour. Next, we study the relationship of two important reservoir parameters, input scaling and spectral radius, on two properties of an artificial task, namely memory and non-linearity.

negative: Improving feature selection techniques for machine learning As a commonly used technique in data preprocessing for machine learning, feature selection identifies important features and removes irrelevant, redundant or noise features to reduce the dimensionality of feature space. It improves efficiency, accuracy and comprehensibility of the models built by learning algorithms. Feature selection techniques have been widely employed in a variety of applications, such as genomic analysis, information retrieval, and text categorization. Researchers have introduced many feature selection algorithms with different selection criteria. However, it has been discovered that no single criterion is best for all applications. We proposed a hybrid feature selection framework called based on genetic algorithms (GAs) that employs a target learning algorithm to evaluate features, a wrapper method. We call it hybrid genetic feature selection (HGFS) framework. The advantages of this approach include the ability to accommodate multiple feature selection criteria and find small subsets of features that perform well for the target algorithm. The experiments on genomic data demonstrate that ours is a robust and effective approach that can find subsets of features with higher classification accuracy and/or smaller size compared to each individual feature selection algorithm. A common characteristic of text categorization tasks is multi-label classification with a great number of features, which makes wrapper methods time-consuming and impractical. We proposed a simple filter (non-wrapper) approach called Relation Strength and Frequency Variance (RSFV) measure. The basic idea is that informative features are those that are highly correlated with the class and distribute most differently among all classes. The approach is compared with two well-known feature selection methods in the experiments on two standard text corpora. The experiments show that RSFV generate equal or better performance than the others in many cases. INDEX WORDS: Feature selection, Gene selection, Term selection, Dimension Reduction, Genetic algorithm, Text categorization, Text classification IMPROVING FEATURE SELECTION TECHNIQUES FOR

negative: Calculating the maximum execution time of real-time programs In real-time systems, the timing behavior is an important property of each task. It has to be guaranteed that the execution of a task does not take longer than the specified amount of time. Thus, a knowledge about the maximum execution time of programs is of utmost importance. This paper discusses the problems for the calculation of the maximum execution time (MAXT... MAximum eXecution Time). It shows the preconditions which have to be met before the MAXT of a task can be calculated. Rules for the MAXT calculation are described. Triggered by the observation that in most cases the calculated MAXT far exceeds the actual execution time, new language constructs are introduced. These constructs allow programmers to put into their programs more information about the behavior of the algorithms implemented and help to improve the self checking property of programs. As a consequence, the quality of MAXT calculations is improved significantly. In a realistic example, an improvement fator of 11 has been achieved.

negative: Comparative Study of Chronic Kidney Disease Prediction using KNN and SVM Chronic kidney disease (CKD), also known as chronic renal disease. Chronic kidney disease involves conditions that damage your kidneys and decrease their ability to keep you healthy. You may develop complications like high blood pressure, anemia (low blood count), weak bones, poor nutritional health and nerve damage. . Early detection and treatment can often keep chronic kidney disease from getting worse. Data Mining is the term used for knowledge discovery from large databases. The task of data mining is to make use of historical data, to discover regular patterns and improve future decisions, follows from the convergence of several recent trends: the lessening cost of large data storage devices and the everincreasing ease of collecting data over networks; the expansion of robust and efficient machine learning algorithms to process this data; and the lessening cost of computational power, enabling use of computationally intensive methods for data analysis. Machine learning, has already created practical applications in such areas as analyzing medical science outcomes, detecting fraud, detecting fake users etc. Various data mining classification approaches and machine learning algorithms are applied for prediction of chronic diseases. The objective of this research work is to introduce a new decision support system to predict chronic kidney disease. The aim of this work is to compare the performance of Support vector machine (SVM) and K-Nearest Neighbour (KNN) classifier on the basis of its accuracy, precision and execution time for CKD prediction. From the experimental results it is observed that the performance of KNN classifier is better than SVM. Keywords—Data Mining, Machine learning, Chronic kidney disease, Classification, K-Nearest Neighbour, Support vector machine.

negative:
Prader-Willi syndrome: a review of clinical, genetic, and endocrine findings INTRODUCTION
Prader-Willi syndrome (PWS) is a multisystemic complex genetic disorder caused by lack of expression of genes on the paternally inherited chromosome 15q11.2-q13 region. There are three main genetic subtypes in PWS: paternal 15q11-q13 deletion (65-75 % of cases), maternal uniparental disomy 15 (20-30 % of cases), and imprinting defect (1-3 %). DNA methylation analysis is the only technique that will diagnose PWS in all three molecular genetic classes and differentiate PWS from Angelman syndrome. Clinical manifestations change with age with hypotonia and a poor suck resulting in failure to thrive during infancy. As the individual ages, other features such as short stature, food seeking with excessive weight gain, developmental delay, cognitive disability and behavioral problems become evident. The phenotype is likely due to hypothalamic dysfunction, which is responsible for hyperphagia, temperature instability, high pain threshold, hypersomnia and multiple endocrine abnormalities including growth hormone and thyroid-stimulating hormone deficiencies, hypogonadism and central adrenal insufficiency. Obesity and its complications are the major causes of morbidity and mortality in PWS.

METHODS
An extensive review of the literature was performed and interpreted within the context of clinical practice and frequently asked questions from referring physicians and families to include the current status of the cause and diagnosis of the clinical, genetics and endocrine findings in PWS.

CONCLUSIONS
Updated information regarding the early diagnosis and management of individuals with Prader-Willi syndrome is important for all physicians and will be helpful in anticipating and managing or modifying complications associated with this rare obesity-related disorder.

negative: Spotlight: Optimizing Device Placement for Training Deep Neural Networks Training deep neural networks (DNNs) requires an increasing amount of computation resources, and it becomes typical to use a mixture of GPU and CPU devices. Due to the heterogeneity of these devices, a recent challenge is how each operation in a neural network can be optimally placed on these devices, so that the training process can take the shortest amount of time possible. The current state-of-the-art solution uses reinforcement learning based on the policy gradient method, and it suffers from suboptimal training times. In this paper, we propose Spotlight, a new reinforcement learning algorithm based on proximal policy optimization, designed specifically for finding an optimal device placement for training DNNs. The design of our new algorithm relies upon a new model of the device placement problem: by modeling it as a Markov decision process with multiple stages, we are able to prove that Spotlight achieves a theoretical guarantee on performance improvements. We have implemented Spotlight in the CIFAR-10 benchmark and deployed it on the Google Cloud platform. Extensive experiments have demonstrated that the training time with placements recommended by Spotlight is 60.9% of that recommended by the policy gradient method.

negative: Percutaneous interventions for left atrial appendage exclusion: options, assessment, and imaging using 2D and 3D echocardiography. Percutaneous left atrial appendage (LAA) exclusion is an evolving treatment to prevent embolic events in patients with nonvalvular atrial fibrillation. In the past few years multiple percutaneous devices have been developed to exclude the LAA from the body of the left atrium and thus from the systemic circulation. Two- and 3-dimensional transesophageal echocardiography (TEE) is used to assess the LAA anatomy and its suitability for percutaneous closure to select the type and size of the closure device and to guide the device implantation procedure in conjunction with fluoroscopy. In addition, 2- and 3-dimensional TEE is also used to assess the effectiveness of device implantation acutely and on subsequent follow-up examination. Knowledge of the implantation options that are currently available along with their specific characteristics is essential for choosing the appropriate device for a given patient with a specific LAA anatomy. We present the currently available LAA exclusion devices and the echocardiographic imaging approaches for evaluation of the LAA before, during, and after LAA occlusion.

negative:
Biomedical ontology alignment: an approach based on representation learning BACKGROUND
While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic similarity information becomes inscribed onto fields of pre-trained word vectors. The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance.

RESULTS
An ontology matching system derived using the proposed framework achieved an F-score of 94% on an alignment scenario involving the Adult Mouse Anatomical Dictionary and the Foundational Model of Anatomy ontology (FMA) as targets. This compares favorably with the best performing systems on the Ontology Alignment Evaluation Initiative anatomy challenge. We performed additional experiments on aligning FMA to NCI Thesaurus and to SNOMED CT based on a reference alignment extracted from the UMLS Metathesaurus. Our system obtained overall F-scores of 93.2% and 89.2% for these experiments, thus achieving state-of-the-art results.

CONCLUSIONS
Our proposed representation learning approach leverages terminological embeddings to capture semantic similarity. Our results provide evidence that the approach produces embeddings that are especially well tailored to the ontology matching task, demonstrating a novel pathway for the problem.

negative: The Media and Technology Usage and Attitudes Scale: An empirical investigation Current approaches to measuring people's everyday usage of technology-based media and other computer-related activities have proved to be problematic as they use varied outcome measures, fail to measure behavior in a broad range of technology-related domains and do not take into account recently developed types of technology including smartphones. In the present study, a wide variety of items, covering a range of up-to-date technology and media usage behaviors. Sixty-six items concerning technology and media usage, along with 18 additional items assessing attitudes toward technology, were administered to two independent samples of individuals, comprising 942 participants. Factor analyses were used to create 11 usage subscales representing smartphone usage, general social media usage, Internet searching, e-mailing, media sharing, text messaging, video gaming, online friendships, Facebook friendships, phone calling, and watching television in addition to four attitude-based subscales: positive attitudes, negative attitudes, technological anxiety/dependence, and attitudes toward task-switching. All subscales showed strong reliabilities and relationships between the subscales and pre-existing measures of daily media usage and Internet addiction were as predicted. Given the reliability and validity results, the new Media and Technology Usage and Attitudes Scale was suggested as a method of measuring media and technology involvement across a variety of types of research studies either as a single 60-item scale or any subset of the 15 subscales.

negative: Agenda-Based User Simulation for Bootstrapping a POMDP Dialogue System This paper investigates the problem of bootstrapping a statistical dialogue manager without access to training data and proposes a new probabilistic agenda-based method for simulating user behaviour. In experiments with a statistical POMDP dialogue system, the simulator was realistic enough to successfully test the prototype system and train a dialogue policy. An extensive study with human subjects showed that the learned policy was highly competitive, with task completion rates above 90%. 1 Background and Introduction 1.1 Bootstrapping Statistical Dialogue Managers One of the key advantages of a statistical approach to Dialogue Manager (DM) design is the ability to formalise design criteria as objective reward functions and to learn an optimal dialogue policy from real dialogue data. In cases where a system is designed from scratch, however, it is often the case that no suitable in-domain data is available for training the DM. Collecting dialogue data without a working prototype is problematic, leaving the developer with a classic chicken-and-egg problem. Wizard-of-Oz (WoZ) experiments can be carried out to record dialogues, but they are often time-consuming and the recorded data may show characteristics of humanhuman conversation rather than typical human-computer dialogue. Alternatively, human-computer dialogues can be recorded with a handcrafted DM prototype but neither of these two methods enables the system designer to test the implementation of the statistical DM and the learning algorithm. Moreover, the size of the recorded corpus (typically 10 dialogues) usually falls short of the requirements for training a statistical DM (typically 10 dialogues). 1.2 User Simulation-Based Training In recent years, a number of research groups have investigated the use of a two-stage simulation-based setup. A statistical user model is first trained on a limited amount of dialogue data and the model is then used to simulate dialogues with the interactively learning DM (see Schatzmann et al. (2006) for a literature review). The simulation-based approach assumes the presence of a small corpus of suitably annotated in-domain dialogues or out-of-domain dialogues with a matching dialogue format (Lemon et al., 2006). In cases when no such data is available, handcrafted values can be assigned to the model parameters given that the model is sufficiently simple (Levin et al., 2000; Pietquin and Dutoit, 2005) but the performance of dialogue policies learned this way has not been evaluated using real users.

negative: CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. The sensitivity of the commonly used progressive multiple sequence alignment method has been greatly improved for the alignment of divergent protein sequences. Firstly, individual weights are assigned to each sequence in a partial alignment in order to down-weight near-duplicate sequences and up-weight the most divergent ones. Secondly, amino acid substitution matrices are varied at different alignment stages according to the divergence of the sequences to be aligned. Thirdly, residue-specific gap penalties and locally reduced gap penalties in hydrophilic regions encourage new gaps in potential loop regions rather than regular secondary structure. Fourthly, positions in early alignments where gaps have been opened receive locally reduced gap penalties to encourage the opening up of new gaps at these positions. These modifications are incorporated into a new program, CLUSTAL W which is freely available.

negative: User simulation for spoken dialogue systems: learning and evaluation We propose the “advanced” n-grams as a new technique for simulating user behaviour in spoken dialogue systems, and we compare it with two methods used in our prior work, i.e. linear feature combination and “normal” n-grams. All methods operate on the intention level and can incorporate speech recognition and understanding errors. In the linear feature combination model user actions (lists of〈 speech act, task 〉 pairs) are selected, based on features of the current dialogue state which encodes the whole history of the dialogue. The user simulation based on “normal” n-grams treats a dialogue as a sequence of lists of 〈 speech act, task 〉 pairs. Here the length of the history considered is restricted by the order of the n-gram. The “advanced” n-grams are a variation of the normal ngrams, where user actions are conditioned not only on speech acts and tasks but also on the current status of the tasks, i.e. whether the information needed by the application (in our case flight booking) has been provided and confirmed by the user. This captures elements of goal-directed user behaviour. All models were trained and evaluated on the C OMMUNICATOR corpus, to which we added annotations for user actions and dialogue context. We then evaluate how closely the synthetic responses resemble the real user responses by comparing the user response generated by each user simulation model in a given dialogue context (taken from the annotated corpus) with the actual user response. We propose the expected accuracy, expected precision, and expected recall evaluation metrics as opposed to standard precision and recall used in prior work. We also discuss why they are more appropriate metrics for evaluating user simulation models compared to their standard counterparts. The advanced n-grams produce higher scores than the normal n-grams for small values of n, which proves their strength when little amount of data is available to train larger ngrams. The linear model produces the best expected accuracy but with respect to expected precision and expected recall it is outperformed by the large n-grams even though it is trained using more information. As a task-based evaluation, we also run each of the user simulation models against a system policy trained on the same corpus. Here the linear feature combination model outperforms the other methods and the advanced n-grams outperform the normal ngrams for all values of n, which again shows their potential. We also calculate the perplexity of the different user models.

negative: Stakeholder engagement, social auditing and corporate sustainability Purpose – To identify the applicability of social auditing as an approach of engaging stakeholders in assessing and reporting on corporate sustainability and its performance. Design/methodology/approach – Drawing upon the framework of AA1000 and the social auditing studies, this paper links stakeholder engagement, social auditing and corporate sustainability with a view to applying dialogue-based social auditing to address corporate sustainability. Findings – This paper identifies a “match” between corporate sustainability and social auditing, as both aim at improving the social, environmental and economic performance of an organisation, considering the well-being of a wider range of stakeholders and requiring the engagement of stakeholders in the process. This paper suggests that social auditing through engaging stakeholders via dialogue could be applied to build trusts, identify commitment and promote co-operation amongst stakeholders and corporations. Research limitations/implications – This research requires further empirical research into the practicality of social auditing in addressing corporate sustainability and the determination of the limitations of dialogue-based social auditing. Practical implications – Social auditing has been identified as a useful mechanism of balancing differing interests among stakeholders and corporations in a democratic business society. The application of social auditing in developing and achieving corporate sustainability has apparently practical implications. Originality/value – This paper examines the applicability of dialogue-based social auditing in helping business to move towards sustainability. Social auditing as a process of assessing and reporting on corporate social and environmental performance through engaging stakeholders via dialogue could be applied to build trusts, identify commitment and promote cooperation amongst stakeholders and corporations.

negative: Basic local alignment search tool. A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.

negative: Large-Scale Analysis of Soccer Matches Using Spatiotemporal Tracking Data Although the collection of player and ball tracking data is fast becoming the norm in professional sports, large-scale mining of such spatiotemporal data has yet to surface. In this paper, given an entire season's worth of player and ball tracking data from a professional soccer league (≈400,000,000 data points), we present a method which can conduct both individual player and team analysis. Due to the dynamic, continuous and multi-player nature of team sports like soccer, a major issue is aligning player positions over time. We present a "role-based" representation that dynamically updates each player's relative role at each frame and demonstrate how this captures the short-term context to enable both individual player and team analysis. We discover role directly from data by utilizing a minimum entropy data partitioning method and show how this can be used to accurately detect and visualize formations, as well as analyze individual player behavior.

negative: Domain Generalization with Adversarial Feature Learning In this paper, we tackle the problem of domain generalization: how to learn a generalized feature representation for an "unseen" target domain by taking the advantage of multiple seen source-domain data. We present a novel framework based on adversarial autoencoders to learn a generalized latent feature representation across domains for domain generalization. To be specific, we extend adversarial autoencoders by imposing the Maximum Mean Discrepancy (MMD) measure to align the distributions among different domains, and matching the aligned distribution to an arbitrary prior distribution via adversarial feature learning. In this way, the learned feature representation is supposed to be universal to the seen source domains because of the MMD regularization, and is expected to generalize well on the target domain because of the introduction of the prior distribution. We proposed an algorithm to jointly train different components of our proposed framework. Extensive experiments on various vision tasks demonstrate that our proposed framework can learn better generalized features for the unseen target domain compared with state-of-the-art domain generalization methods.