RelTextRank: An Open Source Framework for Building Relational Syntactic-Semantic Text Pair Representations


title: A 250kW transverse-laminated Synchronous Reluctance motor

abstract: Throughout the last 15 years, Synchronous Reluctance (SyR) motor drives have been presented as a competitive alternative to Induction Motor (IM) drives in many variable speed applications, at least in the small power range. Very few examples of SyR motors above the 100Nm torque size are present in the literature. The main advantage of the SyR motor lays in the absence of rotor Joule losses that permits to obtain a continuous torque that is higher than the one of an IM of the same size. The paper presents a 250kW, 1000rpm Synchronous Reluctance motor for industrial applications and its performance comparison with an Induction Motor with identical stator. The rotor of the SyR motor has been purposely designed to fit the stator and housing of the IM competitor. The experimental comparison of the two motors is presented, on a regenerative test bench where one the two motors under test can load the competitor and vice-versa. The results of the experimental tests confirm the assumption that the SyR motor gives more torque and then more power in continuous operation at rated windings temperature. However, the IM maintains a certain advantage in terms of flux-weakening capability that means a larger constant-power speed range.


title: Beyond Recommender Systems: Helping People Help Each Other

abstract: The Internet and World Wide Web have brought us into a world of endless possibilities: interactive Web sites to experience, music to listen to, conversations to participate in, and every conceivable consumer item to order. But this world also is one of endless choice: how can we select from a huge universe of items of widely varying quality? Computational recommender systems have emerged to address this issue. They enable people to share their opinions and benefit from each other’s experience. We present a framework for understanding recommender systems and survey a number of distinct approaches in terms of this framework. We also suggest two main research challenges: (1) helping people form communities of interest while respecting personal privacy, and (2) developing algorithms that combine multiple types of information to compute recommendations.


title: Development of dual-band microstrip bandpass filter based on split ring resonator

abstract: An approach to develop a dual-band microstrip bandpass filter (BPF) is proposed based on split ring resonator (SRR) structure. The reason of using SRR structure is due to its advantages such as having strong coupling connection between its loops and workable as lumped LC series. The proposed BPF is designed on a grounded 1.9mm thick high relative permittivity Roger RT/Duroid® 6010 dielectric substrate with the dimension of 25mm × 20mm. Prior hardware realization, some parametric studies are carried out by varying the gap of rings as well as the separation between rings. From the characterization, it shows that the realized SRR-based dual-band microstrip BPF prototype has the first-band center frequency of 0.99GHz with −3dB working bandwidth of 47.8MHz and the second-band center frequency of 1.99GHz with −3dB working bandwidth of 134MHz. In spite of the measured results have slightly differences with the simulated one especially for the second frequency band, in most cases the characteristics of realized prototype have good agreements with the design ones.


title: Magic pencil: generalized sketch inversion via generative adversarial nets

abstract: Human can draw the sketches to show their imagination, which are more specific and vivid comparing with the text. The traditional work on sketch are focusing on recognition, which could be seen as the dimension reduction mapping from the sketch images to the semantic space. Different from the existing work, in this paper, we propose a novel image synthetic approach named Magic Pencil, which aims to synthesize the real images based on the sketch. Specifically, we design a novel multi-task neural network based on generative adversarial nets composed of three components i.e. the generator, the discriminator, and the classifiers. The generator is used to generate the real images after we input the sketch image and the variational factors. While the discriminator is designed to judge the reality of the generated images. And the classifiers is created to validate the semantic consistency between the sketch input and the generated images. We show that our proposed multi-task neural network trained end-to-end achieves promised results without further processing. To train our proposed neural network, we collect a novel dataset based on the existing sketchy dataset. From the experimental results, we could observe that our method could generate the semantic consistency and nearly reality images.


title: A Survey on Systems Security Metrics

abstract: Security metrics have received significant attention. However, they have not been systematically explored based on the understanding of attack-defense interactions, which are affected by various factors, including the degree of system vulnerabilities, the power of system defense mechanisms, attack (or threat) severity, and situations a system at risk faces. This survey particularly focuses on how a system security state can evolve as an outcome of cyber attack-defense interactions. This survey concerns how to measure system-level security by proposing a security metrics framework based on the following four sub-metrics: (1) metrics of system vulnerabilities, (2) metrics of defense power, (3) metrics of attack or threat severity, and (4) metrics of situations. To investigate the relationships among these four sub-metrics, we propose a hierarchical ontology with four sub-ontologies corresponding to the four sub-metrics and discuss how they are related to each other. Using the four sub-metrics, we discuss the state-of-art existing security metrics and their advantages and disadvantages (or limitations) to obtain lessons and insight in order to achieve an ideal goal in developing security metrics. Finally, we discuss open research questions in the security metrics research domain and we suggest key factors to enhance security metrics from a system security perspective.


Can Human Resource Best Practice Contribute to a Judicial Definition of “ Unjustifiable ” in Employment Law ? The example of redundancy terminations in New Zealand


title: R$^3$-Net: A Deep Network for Multi-oriented Vehicle Detection in Aerial Images and Videos

abstract: Vehicle detection is a significant and challenging task in aerial remote sensing applications. Most existing methods detect vehicles with regular rectangle boxes and fail to offer the orientation of vehicles. However, the orientation information is crucial for several practical applications, such as the trajectory and motion estimation of vehicles. In this paper, we propose a novel deep network, called a rotatable region-based residual network (R3-Net), to detect multioriented vehicles in aerial images and videos. More specially, R3-Net is utilized to generate rotatable rectangular target boxes in a half coordinate system. First, we use a rotatable region proposal network (R-RPN) to generate rotatable region of interests (R-RoIs) from feature maps produced by a deep convolutional neural network. Here, a proposed batch averaging rotatable anchor strategy is applied to initialize the shape of vehicle candidates. Next, we propose a rotatable detection network (R-DN) for the final classification and regression of the R-RoIs. In R-DN, a novel rotatable position-sensitive pooling is designed to keep the position and orientation information simultaneously while downsampling the feature maps of R-RoIs. In our model, R-RPN and R-DN can be trained jointly. We test our network on two open vehicle detection image data sets, namely, DLR 3K Munich Data set and VEDAI Data set, demonstrating the high precision and robustness of our method. In addition, further experiments on aerial videos show the good generalization capability of the proposed method and its potential for vehicle tracking in aerial videos. The demo video is available at https://youtu.be/xCYD-tYudN0 .


title: Image Style Classification Based on Learnt Deep Correlation Features

abstract: This paper presents a comprehensive study of deep correlation features on image style classification. Inspired by that, correlation between feature maps can effectively describe image texture, and we design various correlations and transform them into style vectors, and investigate classification performance brought by different variants. In addition to intralayer correlation, interlayer correlation is proposed as well, and its effectiveness is verified. After showing the effectiveness of deep correlation features, we further propose a learning framework to automatically learn correlations between feature maps. Through extensive experiments on image style classification and artist classification, we demonstrate that the proposed learnt deep correlation features outperform several variants of convolutional neural network features by a large margin, and achieve the state-of-the-art performance.


title: Source code retrieval on StackOverflow using LDA

abstract: Internet code search is quite popular research area. StackOverflow allows developers to ask and answer questions about code. Previous approach to search code on StackOverflow uses tf-idf method that based on number of occurrences of words to recommend source code. This method has the disadvantage that variable or method identifiers are considered as normal words, even though identifiers are often a combination of two or more words. For example, there is an identifier named “randomString”. In that case, if we search using a keyword “random” the system probably will not recommend “randomString” because both words are different. Concept location can tackle this problem. Concept location has been used widely to obtain the correlation between code with a specific concepts or features. Previous research of concept location only focused on source code's comments, and relation among the objects within the source code. This research proposes a mechanism for finding code on StackOverflow uses Latent Dirichlet Allocation (LDA) using concept location in the preprocessing stage. Questions, answers, and code snippets about Java programming are downloaded from StackOverflow to a local repository. Corpuses are generated by extracting questions, answers and code snippets. Inferencing concept location from source code is created using LDA algorithm. Developers query concepts and then system will recommend source code based on the relevant concepts. The result of the experiment shows that the system is able to recommend source code with 48% average of precision and 58% average of recall.


title: UWB Tomographic Radar Imaging of Penetrable and Impenetrable Objects

abstract: In this paper, the capability of ultra-wide-band (UWB) sensor arrays for tomographic radar of electrically large objects is presented. The major concern when imaging is extended to real objects is to achieve a correct reconstruction of the object shape and its electric properties. A general framework based on a UWB bifocusing operator (UWB-BF) with good tomographic imaging capabilities is presented. This general approach provides a comprehensive under- standing of the basic tradeoffs with regard to sensing geometry and image quality parameters. Through numerical simulations and measurements applied to canonical as well as to complex objects, basic design criteria are assessed and the potential of UWB tomographic radar imaging is presented.


title: Depression Anxiety Stress Scales (DASS): Revisited

abstract: To evaluate psychometric properties of DASS, data obtained from 850 university students (Men = 306, women = 533, 11 cases did not report their sex) were analyzed. For men average age was 22.13 (SD = 2.27) and for women the mean age was 21.63 (SD = 1.79). Coefficient alphas for Depression, Anxiety, Stress subscales and full Scale (DASS-42) were .88, .85, .86, and .95 respectively and for DASS-21 these values were .79, .70, .76, and .89. For the total sample correlations among DASS-42 subscales were .75 to .81 and among DASS-21 subscales correlations were .61 to .67. For women correlations among DASS-42 subscales were .73 to .80 and among DASS-21 subscales correlations were .59 t0 .65 and for men these correlations were .77 to .83 and .63 to .70 respectively. Corrected item-total correlations and the correlations between each item and DASS subscales were within acceptable boundaries. This was true for both DASS-42 and DASS-21. All the correlations were significant (p < .0001). Principal component analysis with varimax rotation did not result in simple factor structure for any of the three subscales. Very few items loaded only on their corresponding factors. Most items loaded on two factors. Some items loaded on three factors also. The overall results indicate that for assessing depression, anxiety, and stress of Malaysian population DASS is a reliable and valid instrument. However, locally translated and adapted version of DASS is likely to serve better. Discussion includes implications of these findings.


title: Signal Processing for Finance, Economics, and Marketing: Concepts, framework, and big data applications

abstract: Economic data and financial markets are intriguing to researchers working on data and quantitative models. With rapid growth of and increasing access to data in digital form, finance, economics, and marketing data are poised to become one of the most important and tangible big data applications, owing not only to the relatively clean organization and structure of the data but also to clear application objectives and market demands. However, data-related economic studies often have different viewpoints from signal processing (SP). Also, many fundamental economics and business problems have been well formulated and studied in both theory and practice. The knowledge of foundational finance and economic theories will help SP and data researchers avoid reinventing the wheel and develop meaningful and useful research in these areas.


title: A High-Resolution Human Contact Network for Infectious Disease Transmission

abstract: The most frequent infectious diseases in humans--and those with the highest potential for rapid pandemic spread--are usually transmitted via droplets during close proximity interactions (CPIs). Despite the importance of this transmission route, very little is known about the dynamic patterns of CPIs. Using wireless sensor network technology, we obtained high-resolution data of CPIs during a typical day at an American high school, permitting the reconstruction of the social network relevant for infectious disease transmission. At 94% coverage, we collected 762,868 CPIs at a maximal distance of 3 m among 788 individuals. The data revealed a high-density network with typical small-world properties and a relatively homogeneous distribution of both interaction time and interaction partners among subjects. Computer simulations of the spread of an influenza-like disease on the weighted contact graph are in good agreement with absentee data during the most recent influenza season. Analysis of targeted immunization strategies suggested that contact network data are required to design strategies that are significantly more effective than random immunization. Immunization strategies based on contact network data were most effective at high vaccination coverage.


title: Six types of IT-business strategic alignment: an investigation of the constructs and their measurement

abstract: Top management has been concerned with IT-business strategic alignment (hereafter referred to as alignment) for the past 30 years. Consequently, alignment researchers have developed many models to explain how alignment generates value for firms. However, these models use inconsistent definitions and measures of alignment, which has led to conflicting results and has potentially inhibited the progress of research on this critical topic. This paper emphasizes the importance of demarcating the six alignment types that are sometimes confused in the literature into a single, unified model. It also reports on the development of definitions and measures of these six types of alignment including alignment between IT and business strategies (i.e., intellectual alignment), between IT and business infrastructures and processes (i.e., operational alignment), and across these two domains such that strategies are linked with infrastructures and processes (i.e., four types of cross-domain alignment). Analyzing survey data collected from 140 Chief Information Officers, we found each measure possesses desirable psychometric properties. Implications for theory and practice are discussed.


title: Geometry-Based Customization of Bending Modalities for 3D-Printed Soft Pneumatic Actuators

abstract: In this work, we propose a novel type of 3D-printed soft pneumatic actuator that allows geometry-based customization of bending modalities. While motion in the 3-D space has been achieved for several types of soft actuators, only 2-D bending has been previously modeled and characterized within the scope of 3D-printed soft pneumatic actuators. We developed the first type of 3D-printed soft pneumatic actuator, which, by means of the unique feature of customizable cubes at an angle with the longitudinal axis of the structure, is capable of helical motion. Thus, we characterize its mechanical behavior and formulate mathematical and FEA models to validate the experimental results. Variation to the pattern of the inclination angle along the actuator is then demonstrated to allow for complex 3D-bending modalities and the main applications in the fields of object manipulation and wearable robotics are finally discussed.


title: Deep water ROV design for the Mexican oil industry

abstract: This paper describes both the design and technical integration of a deep water ROV (Remotely Operated Vehicle) that is able to employ visual inspection in deep water, as well as to take samples with an underwater arm. Visual inspection is the essential application for a ROV, which is useful for; underwater structure inspection, underwater oil production systems inspection and underwater pipeline inspection. This paper mainly describes the ROV design, and explains in general manner other elements; such as the SCU, LARS and TMS. It is important to mention that the design phase is complete and it was financed by PEMEX (Federal Mexican Oil Company). The project has stalled, since we are waiting for the financial support from PEMEX for the system construction. A future objective is the design and construction of Work-Class ROVs, which can be used to perform PEMEX specific tasks.


title: Unified subspace learning for incomplete and unlabeled multi-view data

abstract: Class indicator matrix is learned for incomplete and unlabeled multi-view data.Preserving the inter-view and intra-view data similarity can improve performance.Running time is in the same magnitudes with that of the mainstream methods.Obtain best results for incomplete multi-view clustering and cross-modal retrieval. Multi-view data with each view corresponding to a type of feature set are common in real world. Usually, previous multi-view learning methods assume complete views. However, multi-view data are often incomplete, namely some samples have incomplete feature sets. Besides, most data are unlabeled due to a large cost of manual annotation, which makes learning of such data a challenging problem. In this paper, we propose a novel subspace learning framework for incomplete and unlabeled multi-view data. The model directly optimizes the class indicator matrix, which establishes a bridge for incomplete feature sets. Besides, feature selection is considered to deal with high dimensional and noisy features. Furthermore, the inter-view and intra-view data similarities are preserved to enhance the model. To these ends, an objective is developed along with an efficient optimization strategy. Finally, extensive experiments are conducted for multi-view clustering and cross-modal retrieval, achieving the state-of-the-art performance under various settings.


title: The Most-Cited Articles of the 21st Century

abstract: The aim of this paper is to collect the most-cited articles of the 21 century and to study how this group changed over time. Here the term “most-cited” is operationalized by considering yearly h-cores in the Web of Science. These h-cores are analysed in terms of authors, research areas, countries, institutions, journals and average number of authors per paper. We only consider publications of article or proceedings type. The research of some of the more prolific authors is on genetics and genomes publishing in multidisciplinary journals, such as Nature and Science, while the results show that writing a software tool for crystallography or molecular biology may help collecting large numbers of citations. English is the language of all articles in any h-core. The core institutions are largely those best placed in most rankings of world universities. Some attention is given on the relation between h-core articles and the information sciences. We conclude by stating that the notion of an h-core provides a new perspective on leading countries, articles and scientists. Conference Topic Citation and co-citation analysis Introduction The objective of this paper is to collect the most-cited articles of the 21 century and to study how this group changed over time. The term “most-cited” is operationalized by considering the h-core (Hirsch, 2005; Rousseau, 2006) in the Web of Science (WoS) for each period of time, starting with the period 2001-2005, continuing with 2001-2006 and ending with 20012013. These periods refer to the publication and the citation window. We recall that the h-core at a given moment in time, for instance on January 1, 2009, consists of the set of articles which at that time received a number of citations at least equal to their rank among all articles published during the period 2001-2008. This approach is different from the one taken in (Van Noorden et al., 2014) where a fixed number, concretely 100, of articles is considered. Furthermore, we study the papers making up the corresponding h-cores in terms of authors, research areas, countries, institutions, journals and average number of authors per paper. Methodology We have to point out that the 21 century starts on January 1, 2001. This implies that we only consider publications from 2001 on. Moreover, we only consider publications in Thomson Reuters’ Web of Science (WoS) and we restrict ourselves to publications of article or proceedings type. Although finding today’s h-core for a set of articles in the Web of Science is easy, finding an h-core in the past needs some specific knowledge of the tools available in the WoS. First one retrieves the set for which one wants to determine the h-core (ending in the year Y). Its


title: Goals in Enterprise Architecture Management -- Findings from Literature and Future Research Directions

abstract: The Management of Enterprise Architectures (EAM) is an evolving discipline within the information systems community. By taking a holistic point of view on an enterprise, considering its business and Information Technology (IT) elements, EAM aims at a better alignment of business and IT, cost savings and faster response times. In order to achieve those benefits academics as well as industry representatives already provide methods, models and tools. Nevertheless, researchers and practitioners call for means to demonstrate the payoff of EAM initiatives. A common management approach for explicitly tracking an individual's or team's performance and success relies on the definition of goals. To the authors' knowledge such an approach is not yet established within the EAM discipline. To improve this shortfall, this paper gives an overview on the current body of knowledge in the field of goals in EAM by means of an extensive literature study. Based on the analysis' results we present reoccurring properties of goals in EAM, possible interrelations and an initial classification. The paper concludes by providing topics for future research directions.


title: Identification of treatment response with social interactions

abstract: This paper develops a formal language for study of treatment response with social interactions, and uses it to obtain new findings on identification of potential outcome distributions. Defining a person's treatment response to be a function of the entire vector of treatments received by the population, I study identification when shape restrictions and distributional assumptions are placed on response functions. An early key result is that the traditional assumption of individualistic treatment response (ITR) is a polar case within the broad class of constant treatment response (CTR) assumptions, the other pole being unrestricted interactions. Important non-polar cases are interactions within reference groups and distributional interactions. I show that established findings on identification under assumption ITR extend to assumption CTR. These include identification with assumption CTR alone and when this shape restriction is strengthened to semi-monotone response. I next study distributional assumptions using instrumental variables. Findings obtained previously under assumption ITR extend when assumptions of statistical independence (SI) are posed in settings with social interactions. However, I find that random assignment of realized treatments generically has no identifying power when some persons are leaders who may affect outcomes throughout the population. Finally, I consider use of models of endogenous social interactions to derive restrictions on response functions. I emphasize that identification of potential outcome distributions differs from the longstanding econometric concern with identification of structural functions. This paper is a revised version of CWP01/10


title: SIMPLE-fying middlebox policy enforcement using SDN

abstract: Networks today rely on middleboxes to provide critical performance, security, and policy compliance capabilities. Achieving these benefits and ensuring that the traffic is directed through the desired sequence of middleboxes requires significant manual effort and operator expertise. In this respect, Software-Defined Networking (SDN) offers a promising alternative. Middleboxes, however, introduce new aspects (e.g., policy composition, resource management, packet modifications) that fall outside the purvey of traditional L2/L3 functions that SDN supports (e.g., access control or routing). This paper presents SIMPLE, a SDN-based policy enforcement layer for efficient middlebox-specific "traffic steering''. In designing SIMPLE, we take an explicit stance to work within the constraints of legacy middleboxes and existing SDN interfaces. To this end, we address algorithmic and system design challenges to demonstrate the feasibility of using SDN to simplify middlebox traffic steering. In doing so, we also take a significant step toward addressing industry concerns surrounding the ability of SDN to integrate with existing infrastructure and support L4-L7 capabilities.


title: BLAST: a Loosely Schema-aware Meta-blocking Approach for Entity Resolution

abstract: Identifying records that refer to the same entity is a fundamental step for data integration. Since it is prohibitively expensive to compare every pair of records, blocking techniques are typically employed to reduce the complexity of this task. These techniques partition records into blocks and limit the comparison to records co-occurring in a block. Generally, to deal with highly heterogeneous and noisy data (e.g. semi-structured data of the Web), these techniques rely on redundancy to reduce the chance of missing matches. ::: ::: Meta-blocking is the task of restructuring blocks generated by redundancy-based blocking techniques, removing superfluous comparisons. Existing meta-blocking approaches rely exclusively on schema-agnostic features. ::: ::: In this paper, we demonstrate how "loose" schema information (i.e., statistics collected directly from the data) can be exploited to enhance the quality of the blocks in a holistic loosely schema-aware (meta-)blocking approach that can be used to speed up your favorite Entity Resolution algorithm. We call it Blast (Blocking with Loosely-Aware Schema Techniques). We show how Blast can automatically extract this loose information by adopting a LSH-based step for efficiently scaling to large datasets. We experimentally demonstrate, on real-world datasets, how Blast outperforms the state-of-the-art unsupervised meta-blocking approaches, and, in many cases, also the supervised one.


title: Optimization of Envelope Tracking Power Amplifier for Base-Station Applications

abstract: We have proposed two methods of enhancing efficiency of an envelope tracking power amplifier (ET PA) from an interlock operation. The first is the utilization of sinking current. The sinking current is a critical efficiency reduction factor since it is a wasted power. To reduce the sinking current, the gate bias of the power amplifier (PA) is increased so that the sinking current is delivered to the PA and is utilized for amplification. The other one is the RF input shaping method. The input signal of ET PAs is a modulated RF signal, and the signal does not guarantee fully saturated operation of the PA at all power levels due to $g_{ m}$ nonlinearity of a device. To obtain the maximum efficiency for all of the envelope voltage, we have found the optimum RF input conditions and applied it to the input of the PA. To verify the methods, the proposed ET PA is implemented using a Cree CGH40045 GaN HEMT. For a long-term evolution 5-MHz signal with 6.5-dB peak-to-average power ratio, the PA delivers power-added efficiency of 58.76% with 40.14-dBm output power at 889 MHz.


title: Loneliness, social support, and preference for online social interaction: the mediating effects of identity experimentation online among children and adolescents

abstract: This study explores the practices of online social activities among children and adolescents in order to uncover the connections between preferences for online social interaction and loneliness, social support, and the mediating effect of identity experimentation online. Data were gathered from a random sample of 718 youngsters aged 9 to 19. Analyses revealed that individuals who are lonely and have a lower level of offline social support find opportunities for identity experimentation online more gratifying than those who are less lonely or not lonely. Both loneliness and social support offline were found significantly related to preference for online social interaction, but the relationships were mediated by identity experimentation online. Finally, it was found that age differences exist. In particular, individuals aged 9 to 14 who are lonely and those aged 15 to 19 with little social support show a significant preference for online social interaction. Implications for future research into identity exp...


title: Guiding the evolution of Genetic Network Programming with reinforcement learning

abstract: Genetic Network Programming (GNP) is one of the evolutionary algorithms. It adopts a directed graph structure to represent a solution to a given problem. Agents judge situations and execute actions sequentially following the node transitions in the graph. On one hand, GNP possesses an advantage of node reusability, which makes it possible to realize a compact graph structure that represents a solution. On the other hand, the compact structure suggests that any connection might play a significant role in the solution, i.e., a slight change to the connections could tremendously influence the performance of the agents for the given task. The conventional GNP, however, lacks an effective way to evaluate and to take advantage of the connections. This paper thus proposes a reinforcement learning approach to learn GNP's subgraphs that contain a relatively small number of connections, and further proposes a partial reconstruction approach to modify the solution with the obtained subgraphs. These two approaches are combined together to form a new evolutionary learning model named GNP with Evolution-oriented Reinforcement Learning (GNP-ERL). Some experiments are conducted on the Tileworld testbed to verify the effectiveness of GNP-ERL, and the simulation results demonstrate that it outperforms the conventional GNP in both training and testing phases.


title: Rhetorical Figuration as a Metric in Text Summarization

abstract: We show that surface-level markers of pragmatic intent can be used to recognize the important sentences in text and can thereby improve the performance of text summarization systems. In particular, we focus on using automated detection of rhetorical figures—characteristic syntactic patterns of persuasive language—to provide information for an additional metric to enhance the performance of the MEAD summarizer.


title: Declarative business artifact centric modeling of decision and knowledge intensive business processes

abstract: In this paper we address the problem of modeling collaborative decision and knowledge intensive business processes (sometimes referred to as Decision Intensive Processes, or DIP processes). DIP processes assist users in performing decision intensive tasks, and provide users with a guidance relevant to process execution context. DIP processes are by nature collaborative, data-driven, need to support various kinds of flexibility at design and run time, and need to integrate with external services and information sources. Such a combination presents significant challenges for contemporary business processes technologies. We present a solution based on a business artifacts paradigm (a.k.a. business entities with lifecycles) using a Guard-Stage-Milestone (GSM) model for declarative lifecycles specification. We introduce a CoreControl -- MicroProcess process design pattern, which allows a natural blending of a business functional process structure (usual for most business processes), with a decision & knowledge driven structure providing domain specific decision guidance to users. The proposed design pattern along with the declarative GSM BA approach provide suitable design primitives for DIP process, as demonstrated on a real problem from the supply chain solutions enablement domain.


Book Reviews From Discourse to Logic: Introduction to Modeltheoretic Semantics of Natural Language, Formal Logic and Discourse Representation Theory


title: Face Antispoofing Using Speeded-Up Robust Features and Fisher Vector Encoding

abstract: The vulnerabilities of face biometric authentication systems to spoofing attacks have received a significant attention during the recent years. Some of the proposed countermeasures have achieved impressive results when evaluated on intratests, i.e., the system is trained and tested on the same database. Unfortunately, most of these techniques fail to generalize well to unseen attacks, e.g., when the system is trained on one database and then evaluated on another database. This is a major concern in biometric antispoofing research that is mostly overlooked. In this letter, we propose a novel solution based on describing the facial appearance by applying Fisher vector encoding on speeded-up robust features extracted from different color spaces. The evaluation of our countermeasure on three challenging benchmark face-spoofing databases, namely the CASIA face antispoofing database, the replay-attack database, and MSU mobile face spoof database, showed excellent and stable performance across all the three datasets. Most importantly, in interdatabase tests, our proposed approach outperforms the state of the art and yields very promising generalization capabilities, even when only limited training data are used.


A Query Answering System for E-Learning Hindi Documents


title: Asynchronous Management Protocol Agent Application Data Model

abstract: This document describes an Application Data Model (ADM) for an ::: Asynchronous Management Protocol (AMP) Agent. The AMP Agent represents ::: a managed device in the Asynchronous Management Architecture. This ::: document is in compliance with the template provided by [I-D.birrane- ::: dtn-adm].


title: BALLERINA: automatic generation and clustering of efficient random unit tests for multithreaded code

abstract: Testing multithreaded code is hard and expensive. Each multithreaded unit test creates two or more threads, each executing one or more methods on shared objects of the class under test. Such unit tests can be generated at random, but basic generation produces tests that are either slow or do not trigger concurrency bugs. Worse, such tests have many false alarms, which require human effort to filter out. We present BALLERINA, a novel technique for automatic generation of efficient multithreaded random tests that effectively trigger concurrency bugs. BALLERINA makes tests efficient by having only two threads, each executing a single, randomly selected method. BALLERINA increases chances that such a simple parallel code finds bugs by appending it to more complex, randomly generated sequential code. We also propose a clustering technique to reduce the manual effort in inspecting failures of automatically generated multithreaded tests. We evaluate BALLERINA on 14 real-world bugs from 6 popular codebases: Groovy, Java JDK, jFreeChart, Log4j, Lucene, and Pool. The experiments show that tests generated by BALLERINA can find bugs on average 2X-10X faster than various configurations of basic random generation, and our clustering technique reduces the number of inspected failures on average 4X-8X. Using BALLERINA, we found three previously unknown bugs in Apache Pool and Log4j, one of which was already confirmed and fixed.


title: Many-Objective Evolutionary Algorithm: Objective Space Reduction and Diversity Improvement

abstract: Evolutionary algorithms have been successfully applied for exploring both converged and diversified approximate Pareto-optimal fronts in multiobjective optimization problems, two- or three-objective in general. However, when solving problems with many objectives, nearly all algorithms perform poorly due to the loss of selection pressure in fitness evaluation. An extremely large objective space could inadvertently deteriorate the effect of an evolutionary operator. In this paper, we propose a new approach to directly handle the challenges to solve many-objective optimization problems (MaOPs). This novel design includes two stages: first, the whole population quickly approaches a small number of “target” points near the true Pareto front; then, the proposed diversity improvement strategy is applied to facilitate these individuals to spread and well distribute. As a case study, the proposed algorithm based on this design is compared with five state-of-the-art algorithms. Experimental results show that the proposed method exhibits improved performance in both convergence and diversity for solving MaOPs.


Auditing Algorithms : Research Methods for Detecting Discrimination on Internet Platforms


Schema Theory


title: Singing-Related Activity of Identified HVC Neurons in the Zebra Finch

abstract: High vocal center (HVC) is part of the premotor pathway necessary for song production and is also a primary source of input to the anterior forebrain pathway (AFP), a basal ganglia-related circuit essential for vocal learning. We have examined the activity of identified HVC neurons of zebra finches during singing. Antidromic activation was used to identify three classes of HVC cells: neurons projecting to the premotor nucleus RA, neurons projecting to area X in the AFP, and putative HVC interneurons. HVC interneurons are active throughout the song and display tonic patterns of activity. Projection neurons exhibit highly phasic stereotyped firing patterns. X-projecting (HVC(X)) neurons burst zero to four times per motif, whereas RA-projecting neurons burst extremely sparsely—at most once per motif. The bursts of HVC projection neurons are tightly locked to the song and typically have a jitter of <1 ms. Population activity of interneurons, but not projection neurons, was significantly correlated with syllable patterns. Consistent with the idea that HVC codes for the temporal order in the song rather than for sound, the vocal dynamics and neural dynamics in HVC occur on different and uncorrelated time scales. We test whether HVC(X) neurons are auditory sensitive during singing. We recorded the activity of these neurons in juvenile birds during singing and found that firing patterns of these neurons are not altered by distorted auditory feedback, which is known to disrupt learning or to cause degradation of song already learned.


title: An Integrated Model For Online shopping, Using Selective Models

abstract: As in traditional shopping, customer acquisition and retention are critical issues in the success of an online store. Many factors impact how, and if, customers accept online shopping. Models presented in recent years, only focus on behavioral or technological aspects of online shopping. So far, a comprehensive and integrated model which covers both behavioral and technological aspects of online shopping does not exist. This study aims to present one such model. The main purpose of this study is to present an integrated model of online shopping intentions, such that both aspects of technology and behavior are covered. After an inclusive secondary research, factors which impact online shopping preference were identified, analyzed, and combined. Using the same approach, there relationships between the factors and their interactions were hypothesized. Finally, in order to test the hypotheses, data were collected through a well-structured questionnaire, and analyzed with the help of smart PLS Package. The results indicate that customers` attitudes towards shopping online, product/Service attributes, and online store attributes impact their preference for online shopping.


title: Data Profiling: A Tutorial

abstract: is to understand the dataset at hand and its metadata. The process of metadata discovery is known as data profiling. Profiling activities range from ad-hoc approaches, such as eye-balling random subsets of the data or formulating aggregation queries, to systematic inference of structural information and statistics of a dataset using dedicated profiling tools. In this tutorial, we highlight the importance of data profiling as part of any data-related use-case, and we discuss the area of data profiling by classifying data profiling tasks and reviewing the state-of-the-art data profiling systems and techniques. In particular, we discuss hard problems in data profiling, such as algorithms for dependency discovery and profiling algorithms for dynamic data and streams. We also pay special attention to visualizing and interpreting the results of data profiling. We conclude with directions for future research in the area of data profiling. This tutorial is based on our survey on profiling relational data [2].


title: Hair Segmentation Using Heuristically-Trained Neural Networks

abstract: We present a method for binary classification using neural networks (NNs) that performs training and classification on the same data using the help of a pretraining heuristic classifier. The heuristic classifier is initially used to segment data into three clusters of high-confidence positives, high-confidence negatives, and low-confidence sets. The high-confidence sets are used to train an NN, which is then used to classify the low-confidence set. Applying this method to the binary classification of hair versus nonhair patches, we obtain a 2.2% performance increase using the heuristically trained NN over the current state-of-the-art hair segmentation method.


title: The influence of system characteristics on e-learning use

abstract: The benefits of an e-learning system will not be maximized unless learners use the system. This study proposed and tested alternative models that seek to explain student intention to use an e-learning system when the system is used as a supplementary learning tool within a traditional class or a stand-alone distance education method. The models integrated determinants from the well-established technology acceptance model as well as system and participant characteristics cited in the research literature. Following a demonstration and use phase of the e-learning system, data were collected from 259 college students. Structural equation modeling provided better support for a model that hypothesized stronger effects of system characteristics on e-learning system use. Implications for both researchers and practitioners are discussed.


title: Natural Scene Statistics At the Centre of Gaze

abstract: Early stages of visual processing may exploit the characteristic structure of natural visual stimuli. This structure may differ from the intrinsic structure of natural scenes, because sampling of the environment is an active process. For example, humans move their eyes several times a second when looking at a scene. The portions of a scene that fall on the fovea are sampled at high spatial resolution, and receive a disproportionate fraction of cortical processing. We recorded the eye positions of human subjects while they viewed images of natural scenes. We report that active selection affected the statistics of the stimuli encountered by the fovea, and also by the parafovea up to eccentricities of 4 degrees. We found two related effects. First, subjects looked at image regions that had high spatial contrast. Second, in these regions, the intensities of nearby image points (pixels) were less correlated with each other than in images selected at random. These effects could serve to increase the information available to the visual system for further processing. We show that both of these effects can be simply obtained by constructing an artificial ensemble comprised of the highest-contrast regions of images.


title: 1.6kV, 2.9 mΩ cm2 normally-off p-GaN HEMT device

abstract: A p-GaN/AlGaN/GaN based normally-off HEMT device has been demonstrated on a Si substrate. Our p-GaN based device shows not only a high threshold voltage of 3 V but also low gate leakage current. Buffer and device breakdown voltages exceed 1600 V with 5.2 um GaN buffer thickness and specific on-state resistance is 2.9mΩ cm2. The calculated figure of merit is 921 MV2/Ωcm2, which is the highest value reported for the GaN E-mode devices.


title: Rainbow: Combining Improvements in Deep Reinforcement Learning

abstract: The deep reinforcement learning community has made several independent improvements to the DQN algorithm. However, it is unclear which of these extensions are complementary and can be fruitfully combined. This paper examines six extensions to the DQN algorithm and empirically studies their combination. Our experiments show that the combination provides state-of-the-art performance on the Atari 2600 benchmark, both in terms of data efficiency and final performance. We also provide results from a detailed ablation study that shows the contribution of each component to overall performance.


title: Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Predictions on Maximum Calibration Period and Acceptable Error Limits

abstract: $Objective$ : Pulse transit time (PTT) is being widely pursued for ubiquitous blood pressure (BP) monitoring. PTT-based systems may require periodic cuff calibrations but can still be useful for hypertension screening by affording numerous, out-of-clinic measurements that can be averaged. The objective was to predict the maximum calibration period that would not compromise accuracy and acceptable error limits in light of measurement averaging for PTT-based systems. $Methods$ : Well-known mathematical models and vast BP data were leveraged. Models relating PTT, age, and gender to BP were employed to determine the maximum time period for the PTT-BP calibration curve to change by $Results$ : The maximum calibration period was about 1 year for a 30 year old and declined linearly to about 6 months for a 70 year old. A PTT-based system with precision error of >12 mmHg for systolic BP could achieve the screening accuracy of the cuff-based approach via measurement averaging. $Conclusion$ : This theoretical study indicates that PTT-based BP monitoring is viable even with periodic calibration and seemingly high measurement errors. $Significance$ : The predictions may help guide the implementation, evaluation, and application of PTT-based BP monitoring systems in practice.


Factors That Impact Consumers' Intention to Shop on Foreign Online Stores


title: 3D IC testing using a chip prober and a transparent membrane probe card

abstract: We performed pre- and post-bond testing of static random access memory (SRAM)/flash memory chips using a chip prober with a transparent membrane probe card. By deformation of the polyethylene naphthalate film (base material of the probe card) during probing, low-load and damage-less probing was possible, and we successfully conducted pre-bond testing of 30–50-μm-thick SRAM/flash memory chips and post-bond testing of stacked chips.


title: An Overview of Botulinum Toxins: Past, Present, and Future

abstract: Botulinum toxin (BTX) has revolutionized the field of cosmetic medicine. With more than 11 million injections since 2002, 1 its administration is by far the most common cosmetic procedure being performed in the United States. This achievement is truly impressive for what may be the most toxic chemical on earth. Based on the estimated inhalational lethal dose, a single gram of BTX is capable of killing 1 million people. 2 Fortunately, a vial of BTX for cosmetic use contains about a 200 million-fold smaller quantity of active neurotoxin. The rapid ascent in popularity of BTX with both clinicians and patients can be attributed to its remarkable efficacy; predictable and reproducible results; excellent safety record; and the relative ease, comfort, and speed of administration. Over the past 9 years since its Food and Drug Administration (FDA) approval for the treatment of glabellar lines, physicians have explored myriad applications for BTX not only for the treatment of the aging face and neck but also for a long list of neuromuscular and glandular disorders, muscle contouring, and various pain syndromes. In keeping with the theme of this issue, this review focuses predominantly on aesthetic uses of BTX. The pharmacodynamics, clinical properties, and safety profiles of the 2 BTX products FDA approved for cosmetic


title: An X- and Ku-Band 8-Element Phased-Array Receiver in 0.18-$\mu{\hbox{m}}$ SiGe BiCMOS Technology

abstract: This paper demonstrates an 8-element phased array receiver in a standard 0.18-mum SiGe BiCMOS (1P6M, SiGe HBT ft ap 150 GHz) technology for X- and Ku-band applications. The array receiver adopts the All-RF architecture, where the phase shifting and power combining are done at the RF level. With the integrations of all the digital control circuitry and ESD protection for all I/O pads, the receiver consumes a current of 100 ~ 200 m A from a 3.3 V supply voltage. The receiver shows 1.5 ~ 24.5 dB of power gain per channel from a 50 Omega load at 12 GHz with bias current control, and an associated NF of 4.2 dB (@ max. gain) to 13.2 dB (@ min. gain). The RMS gain error is < 0.9 dB and the RMS phase error is < 6deg at 6-18 GHz for all 4-bit phase states. The measured group delay is 162.5 plusmn 12.5 ps for all phase states at 6-18 GHz. The RMS phase mismatch and RMS gain mismatch among the eight channels are < 2.7deg and 0.4 dB, respectively, for all 16 phase states, over 6-18 GHz. The 8-element array can operate instantaneously at any center frequency and with a wide bandwidth (3 to 6 GHz, depending on the center frequency) given primarily by the 3 dB gain variation in the 6-18 GHz range. To our knowledge, this is the first demonstration of an All-RF phased array on a silicon chip with very low RMS phase and gain errors at 6-18 GHz. The chip size is 2.2 times 2.45 mm2 including all pads.


title: Twitter Sentiment Analysis

abstract: This project addresses the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in them: positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. It is a rapidly expanding service with over 200 million registered users - out of which 100 million are active users and half of them log on twitter on a daily basis - generating nearly 250 million tweets per day. Due to this large amount of usage we hope to achieve a reflection of public sentiment by analysing the sentiments expressed in the tweets. Analysing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange. The aim of this project is to develop a functional classifier for accurate and automatic sentiment classification of an unknown tweet stream.


title: Synthesis and Characterization of Fluorescently Doped Mesoporous Nanoparticles for Two-Photon Excitation

abstract: The template-directed synthesis of mesoporous silica nanoparticles doped with water-soluble fluorescent dyes optimized for two-photon excitation is described. Two structurally related symmetrical two-photon dyes that possess pyridinium acceptor endgroups conjugated to a fluorenyl core are synthesized by Heck coupling. These dyes display bright fluorescence (Φ ≈ 0.35) under both one- (ϵ ≈ 6 × 104 M−1 cm−1) and two-photon excitation (σ2 ≈ 1000 GM) and were successfully encapsulated in silica nanoparticles via immobilization through noncovalent interactions. The nanoparticles present a mean diameter of 100 nm and a hexagonal network of mesopores as shown by TEM. Interestingly, the photophysical characteristics of the dyes are retained upon their immobilization into the silica matrix, leading to fluorescent silica nanoparticles with giant/unprecedented TPA cross-section (1 × 107 GM). Such nanospheres represent attractive nanoplatforms for the development of biotargeted biocompatible luminescent tracers.


title: A 3D Relative-Motion Context Constraint-Based MAP Solution for Multiple-Object Tracking Problems

abstract: Multi-object tracking (MOT), especially by using a moving monocular camera, is a very challenging task in the field of visual object tracking. To tackle this problem, the traditional tracking-by-detection-based method is heavily dependent on detection results. Occlusion and mis-detections will often lead to tracklets or drifting. In this paper, the tasks of MOT and camera motion estimation are formulated as finding a maximum a posteriori (MAP) solution of joint probability and synchronously solved in a unified framework. To improve performance, we incorporate the three-dimensional (3D) relative-motion model into a sequential Bayesian framework to track multiple objects and the camera's ego-motion estimation. A 3D relative-motion model that describes spatial relations among objects is exploited for predicting object states robustly and recovering objects when occlusion and mis-detections occur. Reversible jump Markov chain Monte Carlo (RJMCMC) particle filtering is applied to solve the posteriori estimation problem. Both quantitative and qualitative experiments with benchmark datasets and video collected on campus were conducted, which confirms that the proposed method is outperformed in many evaluation metrics.


title: Facial Skin Beautification Using Adaptive Region-Aware Masks

abstract: In this paper, we propose a unified facial beautification framework with respect to skin homogeneity, lighting, and color. A novel region-aware mask is constructed for skin manipulation, which can automatically select the edited regions with great precision. Inspired by the state-of-the-art edit propagation techniques, we present an adaptive edge-preserving energy minimization model with a spatially variant parameter and a high-dimensional guided feature space for mask generation. Using region-aware masks, our method facilitates more flexible and accurate facial skin enhancement while the complex manipulations are simplified considerably. In our beautification framework, a portrait is decomposed into smoothness, lighting, and color layers by an edge-preserving operator. Next, facial landmarks and significant features are extracted as input constraints for mask generation. After three region-aware masks have been obtained, a user can perform facial beautification simply by adjusting the skin parameters. Furthermore, the combinations of parameters can be optimized automatically, depending on the data priors and psychological knowledge. We performed both qualitative and quantitative evaluation for our method using faces with different genders, races, ages, poses, and backgrounds from various databases. The experimental results demonstrate that our technique is superior to previous methods and comparable to commercial systems, for example, PicTreat, ${\rm Portrait}{+}$ , and Portraiture.


title: Image Registration Techniques: A Survey

abstract: Image Registration is the process of aligning two or more images of the same scene with reference to a particular image. The images are captured from various sensors at different times and at multiple view-points. Thus to get a better picture of any change of a scene or object over a considerable period of time image registration is important. Image registration finds application in medical sciences, remote sensing and in computer vision. This paper presents a detailed review of several approaches which are classified accordingly along with their contributions and drawbacks. The main steps of an image registration procedure are also discussed. Different performance measures are presented that determine the registration quality and accuracy. The scope for the future research are presented as well.


title: Detection and classification of Eye Blink Artifact in electroencephalogram through Discrete Wavelet Transform and Neural Network

abstract: Electroencephalography (EEG) is the recording of electrical activity along the scalp of human brain. EEG is most often used to diagnose brain disorders i.e. epilepsy, sleep disorder, coma, brain death etc. EEG signals are frequently contaminated by Eye Blink Artifacts generated due to the opening and closing of eye lids during EEG recording. To analyse signal of EEG for diagnosis it is necessary that the EEG recording should be artifact free. This paper is based on the work to detect the presence of artifact and its actual position with extent in EEG recording. For the purpose of classification of artifact or non-artifact activity Artificial Neural Network (ANN) is used and for detection of contaminated zone the Discrete Wavelet Transform with level 6 Haar is used. The part of zone detection is necessary for further appropriate removal of artifactual activities from EEG recording without losing the background activity. The results demonstrated from the ANN classifier are very much promising such as- Sensitivity 98.21 %, Specificity 87.50 %, and Accuracy 95.83 %.


title: Securing cyberspace: Identifying key actors in hacker communities

abstract: As the computer becomes more ubiquitous throughout society, the security of networks and information technologies is a growing concern. Recent research has found hackers making use of social media platforms to form communities where sharing of knowledge and tools that enable cybercriminal activity is common. However, past studies often report only generalized community behaviors and do not scrutinize individual members; in particular, current research has yet to explore the mechanisms in which some hackers become key actors within their communities. Here we explore two major hacker communities from the United States and China in order to identify potential cues for determining key actors. The relationships between various hacker posting behaviors and reputation are observed through the use of ordinary least squares regression. Results suggest that the hackers who contribute to the cognitive advance of their community are generally considered the most reputable and trustworthy among their peers. Conversely, the tenure of hackers and their discussion quality were not significantly correlated with reputation. Results are consistent across both forums, indicating the presence of a common hacker culture that spans multiple geopolitical regions.


title: Modeling and characterization of the system-level Power Delivery Network for a dual-core ARM Cortex-A57 cluster in 28nm CMOS

abstract: Power delivery is a well-known challenge for high-end microprocessor systems. Comparatively, mobile computing platforms typically consume order-of-magnitude lower currents, but economic and volume constraints limit the quality of the Power Delivery Network. In addition, the trend towards GHz+ operating frequencies and the ubiquity of low-power techniques such as clock-gating and power-gating, make these systems susceptible to pathological AC transients. Consequently, mobile computing systems are ultimately limited by power-delivery. In this paper, we present the system-level Power Delivery Network (PDN) modeling, analysis and measurement results on a dual-core 64bit ARM Cortex-A57 compute cluster in 28nm CMOS. We present a comprehensive analysis of the PDN by characterizing the individual contribution of each constituent i.e. the PCB, package and the die. We present frequency- and time-domain simulation results and correlate that with measurement (both on-chip and off-chip). Our results demonstrate how complex software and micro-architectural interactions can trigger PDN resonances that ultimately lead to system failure.


title: TUT database for acoustic scene classification and sound event detection

abstract: We introduce TUT Acoustic Scenes 2016 database for environmental sound research, consisting of binaural recordings from 15 different acoustic environments. A subset of this database, called TUT Sound Events 2016, contains annotations for individual sound events, specifically created for sound event detection. TUT Sound Events 2016 consists of residential area and home environments, and is manually annotated to mark onset, offset and label of sound events. In this paper we present the recording and annotation procedure, the database content, a recommended cross-validation setup and performance of supervised acoustic scene classification system and event detection baseline system using mel frequency cepstral coefficients and Gaussian mixture models. The database is publicly released to provide support for algorithm development and common ground for comparison of different techniques.


title: A 0.5 V 1.1 MS/sec 6.3 fJ/Conversion-Step SAR-ADC With Tri-Level Comparator in 40 nm CMOS

abstract: This paper presents an extremely low-voltage operation and power efficient successive-approximation-register (SAR) analog-to-digital converter (ADC). Tri-level comparator is proposed to relax the speed requirement of the comparator and decrease the resolution of internal Digital-to-Analog Converter (DAC) by 1-bit. The internal charge redistribution DAC employs unit capacitance of 0.5 fF and ADC operates at nearly thermal noise limitation. To deal with the problem of capacitor mismatch, reconfigurable capacitor array and calibration procedure were developed. The prototype ADC fabricated using 40 nm CMOS process achieves 46.8 dB SNDR and 58.2 dB SFDR with 1.1 MS/sec at 0.5 V power supply. The FoM is 6.3-fJ/conversion step and the chip die area is only 160 μm × 70 μm.


title: VQA: Visual Question Answering

abstract: We propose the task of free-form and open-ended Visual Question Answering (VQA). Given an image and a natural language question about the image, the task is to provide an accurate natural language answer. Mirroring real-world scenarios, such as helping the visually impaired, both the questions and answers are open-ended. Visual questions selectively target different areas of an image, including background details and underlying context. As a result, a system that succeeds at VQA typically needs a more detailed understanding of the image and complex reasoning than a system producing generic image captions. Moreover, VQA is amenable to automatic evaluation, since many open-ended answers contain only a few words or a closed set of answers that can be provided in a multiple-choice format. We provide a dataset containing $$\sim $$~0.25 M images, $$\sim $$~0.76 M questions, and $$\sim $$~10 M answers (www.visualqa.org), and discuss the information it provides. Numerous baselines and methods for VQA are provided and compared with human performance. Our VQA demo is available on CloudCV (http://cloudcv.org/vqa).


title: Multi-style Generative Reading Comprehension

abstract: This study tackles generative reading comprehension (RC), which consists of answering questions based on textual evidence and natural language generation (NLG). We propose a multi-style abstractive summarization model for question answering, called Masque. The proposed model has two key characteristics. First, unlike most studies on RC that have focused on extracting an answer span from the provided passages, our model instead focuses on generating a summary from the question and multiple passages. This serves to cover various answer styles required for real-world applications. Second, whereas previous studies built a specific model for each answer style because of the difficulty of acquiring one general model, our approach learns multi-style answers within a model to improve the NLG capability for all styles involved. This also enables our model to give an answer in the target style. Experiments show that our model achieves state-of-the-art performance on the Q&A task and the Q&A + NLG task of MS MARCO 2.1 and the summary task of NarrativeQA. We observe that the transfer of the style-independent NLG capability to the target style is the key to its success.


title: Order Batching in Order Picking Warehouses: A Survey of Solution Approaches

abstract: Order picking is a warehouse function dealing with the retrieval of articles from their storage location in order to satisfy a given demand specified by customer orders. Of all warehouse operations, order picking is considered to include the most cost-intensive ones. Even though there have been different attempts to automate the picking process, manual order picking systems are still prevalent in practice. This article will focus on order batching, one of the main planning issues in order picking systems. Order Batching has been proven to be pivotal for the efficiency of order picking operations. With respect to the availability of information about the customer orders, order batching can be distinguished into static batching and dynamic batching. Improved order batching reduces the total picking time required to collect the requested articles. According to experience from practice, this can result in significant savings of labor cost and into a reduction of the customer order's delivery lead time. The aim of this contribution is to provide comprehensive insights into order batching by giving a detailed state-of-the-art overview of the different solution approaches which have been suggested in the literature. Corresponding to the available publications, the emphasis will be on static order batching. In addition to this, the paper will also review the existing literature for variants and extensions of static order batching (e.g. due dates, alternative objective functions). Furthermore, solution approaches for dynamic order batching problems (like time window batching) will be presented.


title: Design and Implementation of the Jomini Engine: Towards a Historical Massively Multiplayer Online Role-Playing Game

abstract: This technical paper describes the design and implementation of a game engine for historical massive multiplayer online role-playing games MMORPGs. We explore the game and system design space with a focus on historical accuracy and provide a detailed discussion of key design goals for the support of a large-scale, distributed and scalable MMORPG. The JominiEngine is a modular and extensible system architecture built on C# and integrates Riak, a non-SQL database, at the core of the persistent data store in order to facilitate scalability. "Overlord", the first instance of the engine is set in the medieval time period and uses rigorous UML design methodology for both game and system design. In order to enhance the immersive gaming experience, a separate Unity-based client can be used to interact with the game engine.


title: Robust Tree-based Causal Inference for Complex Ad Effectiveness Analysis

abstract: As the online advertising industry has evolved into an age of diverse ad formats and delivery channels, users are exposed to complex ad treatments involving various ad characteristics. The diversity and generality of ad treatments call for accurate and causal measurement of ad effectiveness, i.e., how the ad treatment causes the changes in outcomes without the confounding effect by user characteristics. Various causal inference approaches have been proposed to measure the causal effect of ad treatments. However, most existing causal inference methods focus on univariate and binary treatment and are not well suited for complex ad treatments. Moreover, to be practical in the data-rich online environment, the measurement needs to be highly general and efficient, which is not addressed in conventional causal inference approaches. In this paper we propose a novel causal inference framework for assessing the impact of general advertising treatments. Our new framework enables analysis on uni- or multi-dimensional ad treatments, where each dimension (ad treatment factor) could be discrete or continuous. We prove that our approach is able to provide an unbiased estimation of the ad effectiveness by controlling the confounding effect of user characteristics. The framework is computationally efficient by employing a tree structure that specifies the relationship between user characteristics and the corresponding ad treatment. This tree-based framework is robust to model misspecification and highly flexible with minimal manual tuning. To demonstrate the efficacy of our approach, we apply it to two advertising campaigns. In the first campaign we evaluate the impact of different ad frequencies, and in the second one we consider the synthetic ad effectiveness across TV and online platforms. Our framework successfully provides the causal impact of ads with different frequencies in both campaigns. Moreover, it shows that the ad frequency usually has a treatment effect cap, which is usually over-estimated by naive estimation.


title: Modelling and analysis of local field potentials for studying the function of cortical circuits

abstract: The past decade has witnessed a renewed interest in cortical local field potentials (LFPs)--that is, extracellularly recorded potentials with frequencies of up to ~500 Hz. This is due to both the advent of multielectrodes, which has enabled recording of LFPs at tens to hundreds of sites simultaneously, and the insight that LFPs offer a unique window into key integrative synaptic processes in cortical populations. However, owing to its numerous potential neural sources, the LFP is more difficult to interpret than are spikes. Careful mathematical modelling and analysis are needed to take full advantage of the opportunities that this signal offers in understanding signal processing in cortical circuits and, ultimately, the neural basis of perception and cognition.


How Can Memory-Augmented Neural Networks Pass a False-Belief Task?


title: Treebank based deep grammar acquisition and Part-Of-Speech Tagging for Sanskrit sentences

abstract: Sanskrit since many thousands of years has been the oriental language of India. It is the base for most of the Indian Languages. Ambiguity is inherent in the Natural Language sentences. Here, one word can be used in multiple senses. Morphology process takes word in isolation and fails to disambiguate correct sense of a word. Part-Of-Speech Tagging (POST) takes word sequences in to consideration to resolve the correct sense of a word present in the given sentence. Efficient POST have been developed for processing of English, Japanese, and Chinese languages but it is lacking for Indian languages. In this paper our work present simple rule-based POST for Sanskrit language. It uses rule based approach to tag each word of the sentence. These rules are stored in the database. It parses the given Sanskrit sentence and assigns suitable tag to each word automatically. We have tested this approach for 15 tags and 100 words of the language this rule based tagger gives correct tags for all the inflected words in the given sentence.


title: Comparison of recurrent rate of gingival pigmentation after treatment by liquid nitrogen and cryoprob in 18 months follows-up.

abstract: Background: Cryosurgery is one of the simplest, most effective gingival depigmentation methods. Repigmentation may happen after a while in each method. The aim of this study is to compare the recurrence rate after treatment by liquid nitrogen swap and a cryoprob in 18 months. Materials and Methods: A total of 26 patients with physiologic gingival pigmentation were selected. The anterior sextant was divided into left and right segments; each segment was treated randomly by swap technique or cryoprob. Standard photos were evaluated with photoshop software (Red, Green, Blue, Cyan, Magenta, Yellow, Black [RGB, CMYK]) before and at 2 week, 1, 3, 6, 9, 12, 15, 18 months after the treatment. The results were compared, by the independent t-test and repeated measure ANOVA thereafter least signifi cant difference post-hoc. The viewpoints of patients and physicians regarding the treatment outcomes were obtained by a questionnaire and consequently evaluated qualitatively by McNemar test (P 0.05). The mean values of CMYK signifi cantly decreased after 2 weeks in both methods (P 0.05). Qualitative evaluation showed the preference of the cryosurgery with swap method (P < 0.001). No signifi cant recurrence was observed during 18 months follow-up. Conclusion: Both methods of cryosurgery are appropriate in treatment of gingival depigmentation because no signifi cant recurrence was observed during 18 months follow-up. Key Words: Cryosurgery, gingiva, nitrogen, nitrous oxide, pigmentation


title: A Soldier Health Monitoring System for Military Applications

abstract: With recent advances in technology, various wearable sensors have been developed for the monitoring of human physiological parameters. A Body Sensor Network (BSN) consisting of such physiological and biomedical sensor nodes placed on, near or within a human body can be used for real-time health monitoring. In this paper, we describe an on-going effort to develop a system consisting of interconnected BSNs for real-time health monitoring of soldiers. We discuss the background and an application scenario for this project. We describe the preliminary prototype of the system and present a blast source localization application.


title: Music emotion classification and context-based music recommendation

abstract: Context-based music recommendation is one of rapidly emerging applications in the advent of ubiquitous era and requires multidisciplinary efforts including low level feature extraction and music classification, human emotion description and prediction, ontology-based representation and recommendation, and the establishment of connections among them. In this paper, we contributed in three distinctive ways to take into account the idea of context awareness in the music recommendation field. Firstly, we propose a novel emotion state transition model (ESTM) to model human emotional states and their transitions by music. ESTM acts like a bridge between user situation information along with his/her emotion and low-level music features. With ESTM, we can recommend the most appropriate music to the user for transiting to the desired emotional state. Secondly, we present context-based music recommendation (COMUS) ontology for modeling user's musical preferences and context, and for supporting reasoning about the user's desired emotion and preferences. The COMUS is music-dedicated ontology in OWL constructed by incorporating domain-specific classes for music recommendation into the Music Ontology, which includes situation, mood, and musical features. Thirdly, for mapping low-level features to ESTM, we collected various high-dimensional music feature data and applied nonnegative matrix factorization (NMF) for their dimension reduction. We also used support vector machine (SVM) as emotional state transition classifier. We constructed a prototype music recommendation system based on these features and carried out various experiments to measure its performance. We report some of the experimental results.


title: Profile-Based Control for Central Domestic Hot Water Distribution

abstract: A main goal of hot water distribution research is to improve the system's efficiency, i.e., to fulfill hot water requirements while minimizing energy and water losses. Central domestic hot water (CDHW) systems represent an important part of current installations worldwide, e.g., hotels, hospitals, sports centers, social facilities, and multifamily residential or apartment buildings. The optimization of such systems claims for forecasting capabilities and context-aware enhancements are based on patterns of use. Thus, the level of uncertainty is reduced, and systems are not forced to operate using blind/oversized/generic assumptions. This paper presents a novel control strategy based on habit profiles for the management of a CDHW system. A simulated environment is utilized to compare the introduced strategy with habitual performances. Simulations are supported by real databases concerning users' behavioral patterns. Results are promising and point to place profile-based strategies as a suitable approach for an optimized water and energy management in future buildings.


title: Sentiment analysis of top colleges in India using Twitter data

abstract: In today's world, opinions and reviews accessible to us are one of the most critical factors in formulating our views and influencing the success of a brand, product or service. With the advent and growth of social media in the world, stakeholders often take to expressing their opinions on popular social media, namely Twitter. While Twitter data is extremely informative, it presents a challenge for analysis because of its humongous and disorganized nature. This paper is a thorough effort to dive into the novel domain of performing sentiment analysis of people's opinions regarding top colleges in India. Besides taking additional preprocessing measures like the expansion of net lingo and removal of duplicate tweets, a probabilistic model based on Bayes' theorem was used for spelling correction, which is overlooked in other research studies. This paper also highlights a comparison between the results obtained by exploiting the following machine learning algorithms: Naive Bayes and Support Vector Machine and an Artificial Neural Network model: Multilayer Perceptron. Furthermore, a contrast has been presented between four different kernels of SVM: RBF, linear, polynomial and sigmoid.


title: Clustering in cooperative networks

abstract: Low power ad hoc wireless networks operate in conditions where channels are subject to fading. Cooperative diversity mitigates fading in these networks by establishing virtual antenna arrays through clustering the nodes. A cluster in a cooperative diversity network is a collection of nodes that cooperatively transmits a single packet. There are two types of clustering schemes: static and dynamic. In static clustering all nodes start and stop transmission simultaneously, and nodes do not join or leave the cluster while the packet is being transmitted. Dynamic clustering allows a node to join an ongoing cooperative transmission of a packet as soon as the packet is received. In this paper we take a broad view of the cooperative network by examining packet flows, while still faithfully implementing the physical layer at the bit level. We evaluate both clustering schemes using simulations on large multi-flow networks. We demonstrate that dynamically-clustered cooperative networks substantially outperform both statically-clustered cooperative networks and classical point-to-point networks.


title: Expert System for Identification of Sport Talents: Idea, Implementation and Results

abstract: In this chapter, detailed description of the complete expert system for identification of sport talents will be given. Current version of developed software based solution has the following characteristics: ability of forming a referent measurement database with the records of all potential and active sportsmen, diagnostics of their anthropological characteristics, sports talent recognition, advising and guiding amateurs into the sports activities suitable for their potential. Also, a comparison of the test results for the same person and for overall achievement monitoring through a longer time period is possible. Evaluation and tests of the presented fuzzy-based approach with some other approaches used for the evaluation of the morphology models suggest that it is capable of successful recognition of the sport compatible for the tested individual based on his/her morphological characteristics.


A Review Paper on Text Summarization


title: Determining Surface Orientations of Specular Surfaces by Using the Photometric Stereo Method

abstract: The orientation of patches on the surface of an object can be determined from multiple images taken with different illumination, but from the same viewing position. The method, referred to as photometric stereo, can be implemented using table lookup based on numerical inversion of reflectance maps. Here we concentrate on objects with specularly reflecting surfaces, since these are of importance in industrial applications. Previous methods, intended for diffusely reflecting surfaces, employed point source illumination, which is quite unsuitable in this case. Instead, we use a distributed light source obtained by uneven illumination of a diffusely reflecting planar surface. Experimental results are shown to verify analytic expressions obtained for a method employing three light source distributions.


Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy


Advances in Remote Sensing for Oil Spill Disaster Management: State-of-the-Art Sensors Technology for Oil Spill Surveillance


title: A first principle model for multiphase slugging flow in vertical risers

abstract: In this paper, we propose a simple model to represent the slugging flow regime appearing in vertical risers. We consider a one dimensional two-phase flow composed of a liquid phase and a gaseous compressible phase. The presented model can be applied to a wide class of systems, ranging from pure vertical risers to more complex geometries such as those found on actual sub sea petroleum facilities. Following ideas from the literature, we introduce a virtual valve located at the bottom of the riser. This allows us to reproduce observed periodic regimes. It also brings insight into the physics of the slugging phenomenon. Most importantly, this model reveals relatively easy to tune and seems suitable for control design. A tuning methodology is proposed along with a proof of the existence of a limit cycle under simplifying assumptions.


title: A Survey of Computer Vision-Based Human Motion Capture

abstract: A comprehensive survey of computer vision-based human motion capture literature from the past two decades is presented. The focus is on a general overview based on a taxonomy of system functionalities, broken down into four processes: initialization, tracking, pose estimation, and recognition. Each process is discussed and divided into subprocesses and/or categories of methods to provide a reference to describe and compare the more than 130 publications covered by the survey. References are included throughout the paper to exemplify important issues and their relations to the various methods. A number of general assumptions used in this research field are identified and the character of these assumptions indicates that the research field is still in an early stage of development. To evaluate the state of the art, the major application areas are identified and performances are analyzed in light of the methods presented in the survey. Finally, suggestions for future research directions are offered.


title: Texture Feature Extraction for Identification of Medicinal Plants and Comparison of Different Classifiers

abstract: This paper presents an automated system for recognizing the medicinal plant leaves that are taken from the suburbs of the western ghats region. The dataset comprises of 250 different leaf images, of five species. Texture analyses of the leaf images have been done in this work using the feature computation. The features include grey textures, grey tone spatial dependency matrices(GTSDM) and Local Binary Pattern(LBP) operators. For each leaf image, a feature vector is generated from the statistical values. 70% of the images in the dataset are the training dataset and the rest are included in the test set. Six different classifiers are used to classify the plant leaves based on feature values. When features are combined without any preprocessing, it yielded a classification performance of 94.7%.


title: Selective Unsupervised Feature Learning with Convolutional Neural Network (S-CNN)

abstract: Supervised learning of convolutional neural networks (CNNs) can require very large amounts of labeled data. Labeling thousands or millions of training examples can be extremely time consuming and costly. One direction towards addressing this problem is to create features from unlabeled data. In this paper we propose a new method for training a CNN, with no need for labeled instances. This method for unsupervised feature learning is then successfully applied to a challenging object recognition task. The proposed algorithm is relatively simple, but attains accuracy comparable to that of more sophisticated methods. The proposed method is significantly easier to train, compared to existing CNN methods, making fewer requirements on manually labeled training data. It is also shown to be resistant to overfitting. We provide results on some well-known datasets, namely STL-10, CIFAR-10, and CIFAR-100. The results show that our method provides competitive performance compared with existing alternative methods. Selective Convolutional Neural Network (S-CNN) is a simple and fast algorithm, it introduces a new way to do unsupervised feature learning, and it provides discriminative features which generalize well.


title: Fast data in the era of big data: Twitter's real-time related query suggestion architecture

abstract: We present the architecture behind Twitter's real-time related query suggestion and spelling correction service. Although these tasks have received much attention in the web search literature, the Twitter context introduces a real-time "twist": after significant breaking news events, we aim to provide relevant results within minutes. This paper provides a case study illustrating the challenges of real-time data processing in the era of "big data". We tell the story of how our system was built twice: our first implementation was built on a typical Hadoop-based analytics stack, but was later replaced because it did not meet the latency requirements necessary to generate meaningful real-time results. The second implementation, which is the system deployed in production today, is a custom in-memory processing engine specifically designed for the task. This experience taught us that the current typical usage of Hadoop as a "big data" platform, while great for experimentation, is not well suited to low-latency processing, and points the way to future work on data analytics platforms that can handle "big" as well as "fast" data.


title: A 12mW all-digital PLL based on class-F DCO for 4G phones in 28nm CMOS

abstract: We propose a new architecture of an all-digital PLL (ADPLL) for advanced cellular radios that is optimized for 28 nm CMOS. It is based on a wide tuning range, fine-resolution class-F DCO with only switchable metal capacitors and a phase-predictive TDC. The 8mW DCO emits -157 dBc/Hz at 20MHz offset at ~2 GHz, while fully satisfying metal density rules. The 0.4mW TDC clocked at 40MHz achieves PVT-stabilized 6 ps resolution for -108 dBc/Hz in-band phase noise. FREF spur is ultra-low at <;-94 dBc. The ADPLL supports a 2-point modulation and consumes 12mW while occupying 0.22mm2, thus demonstrating both 72% power and 38% area reductions over prior records.


title: The Dynamic Integrated Approach to teacher professional development: rationale and main characteristics

abstract: This paper refers to the Dynamic Integrated Approach (DIA) towards teacher professional development, which attempts to merge research findings on teacher effectiveness and teacher professional development. The theoretical framework and the major features of the DIA are presented. It is argued that the DIA can be effectively implemented through five steps: establishing clarity and consensus about aims and objectives; identifying needs and priorities for improvement through empirical investigation; provision of improvement guidelines, reflection opportunities and coaching on effective teaching by the Advisory and Research Team; establishing a formative evaluation mechanism; and finally establishing a summative evaluation system. Results of empirical studies providing support to the basic elements and the overall effectiveness of the DIA are also presented. Implications of the findings are discussed and suggestions for further research, particularly in exploring the conditions under which the DIA could have ...


title: Fog in Comparison to Cloud: A Survey

abstract: Fog computing is new buzz word in computing world after cloud computing. This new computing paradigm could be seen as an extension to cloud computing. Main aim of fog computing is to reduce the burden on cloud by gathering workloads, services, applications and huge data to near network edge. In this survey paper, we will discuss main characteristics of the Fog that are; 1.Mobility, 2.Location awareness, 3.Low latency, 4.Huge number of nodes, 5. Extensive geographical distribution, 6.Various real time applications and we explore the advantages and motivation of Fog computing, and analyze its applications for IOT.


title: Micropenis: Etiology, Diagnosis and Treatment Approaches

abstract: Micropenis is a medical diagnosis based on correct measurement of length. If stretched penile length is below the value corresponding to - 2.5 standard deviation of the mean in a patient with normal internal and external male genitalia, a diagnosis of micropenis is considered. Micropenis can be caused by a variety of factors including structural or hormonal defects of the hypothalamic-pituitary-gonadal axis. It can also be a component of a number of congenital syndromes. For the etiological evaluation, endocrinologic tests are important. This article reviews the etiology, diagnosis, treatment and management of micropenis. ::: ::: Conflict of interest:None declared.


title: Kernel regression for real-time building energy analysis

abstract: This study proposes a new technique for real-time building energy modelling and event detection using kernel regression. We show that this technique can exceed the performance of conventional neural network algorithms, and do so by a large margin when the available training dataset is small. Furthermore, unlike the synapse weights in a neural network, the parameters of our kernel regression models are amenable to human interpretation and can give useful information about the building being studied. We extensively test our proposed algorithms using a new dataset consisting of 1.5 years of power and environmental measurements for four buildings, in addition to benchmarking against the ASHRAE Predictor Shootout dataset. On the new dataset, our kernel regression algorithm gave the best prediction performance in three of four cases and significantly outperformed neural networks (the nearest competitor) with training sets of 1/2 a year or less.


title: CAPTCHA: Using Hard AI Problems for Security

abstract: We introduce CAPTCHA, an automated test that humans can pass, but current computer programs can't pass: any program that has high success over a CAPTCHA can be used to solve an unsolved Artificial Intelligence (AI) problem. We provide several novel constructions of CAPTCHAS. Since CAPTCHAs have many applications in practical security, our approach introduces a new class of hard problems that can be exploited for security purposes. Much like research in cryptography has had a positive impact on algorithms for factoring and discrete log, we hope that the use of hard AI problems for security purposes allows us to advance the field of Artificial Intelligence. We introduce two families of Al problems that can be used to construct CAPTCHAS and we show that solutions to such problems can be used for steganographic communication. CAPTCHAS based on these AI problem families, then, imply a win-win situation: either the problems remain unsolved and there is a way to differentiate humans from computers, or the problems are solved and there is a way to communicate covertly on some channels.


title: A unified model for high-power, air-core toroidal PCB inductors

abstract: In high-frequency and very-high-frequency power converters, which operate with switching frequencies between 3 and 300 MHz, efficient power inductors cannot be constructed with commercially available core materials. In this regime, air-core inductors are preferred, and the toroidal geometry best constrains the magnetic field. These inductors have been implemented as printed circuit board (PCB) traces. Here, we present the first unified electrical model for high-power, air-core toroidal inductors implemented as PCB traces, which includes closed-form calculations for the lumped capacitance, resistance, and inductance of the structure. In this regime, the self-resonant frequency (SRF) of the inductor may be of the same order of magnitude as the circuit operating frequency, so the capacitance must be carefully modeled and tightly controlled. The lumped capacitance is calculated by assuming that the impedance between two turns is well approximated by the characteristic impedance of a microstrip geometry. We fabricate inductors with inductances ranging from 44 nH to 10 μH and PCB heights of 1 mm, 1.6 mm, 2 mm, 3.2 mm, and 6.4 mm. For all inductors from 44 nH to 2 μH, the SRF is predicted within 10% and the modeled capacitance is within 1 pF of the measured value.


Butterfly Mixing: Accelerating Incremental-Update Algorithms on Clusters


title: Exploiting the map metaphor in a tool for software evolution

abstract: Software maintenance and evolution are the dominant activities in the software lifecycle. Modularization can separate design decisions and allow them to be independently evolved, but modularization often breaks down and complicated global changes are required. Tool support can reduce the costs of these unfortunate changes, but current tools are limited in their ability to manage information for large-scale software evolution. In this paper we argue that the map metaphor can serve as an organizing principle for the design of effective tools for performing global software changes. We describe the design of Aspect Browser, developed around the map metaphor, and discuss a case study of removing a feature from a 500,000 line program written in Fortran and C.


title: Aneugen‐induced micronuclei (MN) in human lymphocytes may be discerned using image analysis techniques when cell‐cycle stage is taken into account

abstract: We show that for the in vitro cytochalasin‐B human lymphocyte micronucleus (MN) test, the quantification of the DNA content of MN and the difference in DNA content between the two macronuclei in the binucleate cells without MN, as measured by image analysis, gives a first estimation of the aneugenic potential of a test compound. Cultures of isolated human lymphocytes were exposed either to γ‐rays as a clastogen or to carbendazim (MBC) as an an‐eugen. The lymphocytes were stained with Feulgen stain and the MN were analyzed for DNA content with a Magiscan 2A image analyzer. The mean DNA content of MN induced by MBC were statistically higher than γ‐irradiation‐induced MN. It was demonstrated that in culture the lymphocytes, as well as the MN, are in different stages of the cell cycle, but this will not affect the discriminating power of the MN DNA content when only G1 cells are considered, or when DNA content of the MN is expressed relative to the total genome. The identification of Gl and G2 cell populations from image analysis data was performed by extrapolation of DNA content data from Gl‐ and G2‐sorted lymphocytes with a FacStar plus flow sorter. It was demonstrated that in MBC‐treated cells the DNA rearrangement between the macronuclei in binucle‐ates without MN was on the average higher than in γ‐irradiated and untreated cells, which points to aneugenic effects of MBC without the formation of MN. In contrast to DNA content measurements, the area of the MN is not a reliable measure for discriminating clastogens from aneugens. © 1995 Wiley‐Liss, Inc. Copyright © 1995 Wiley‐Liss, Inc., A Wiley Company


COMPARISON OF PITCH TRACKERS FOR REAL-TIME GUITAR EFFECTS


title: Toward sociable robots

abstract: Abstract This paper explores the topic of social robots—the class of robots that people anthropomorphize in order to interact with them. From the diverse and growing number of applications for such robots, a few distinct modes of interaction are beginning to emerge. We distinguish four such classes: socially evocative, social interface, socially receptive, and sociable. For the remainder of the paper, we explore a few key features of sociable robots that distinguish them from the others. We use the vocal turn-taking behavior of our robot, Kismet, as a case study to highlight these points.


Clinical application of the Hybrid Assistive Limb (HAL) for gait training—a systematic review


Emotional intelligence in the classroom: Skill-based training for teachers and students