A learning environment for augmented reality mobile learning

There are many tools that enable the development of the augmented reality (AR) activities, some of them can even create and generate an AR experience where the incorporation of 3D objects is simple. However AR tools used in education are different from the general tools limited to the reproduction of virtual content. The purpose of this paper is to present a learning environment based on augmented reality, which can be used both by teachers to develop quality AR educational resources and by students to acquire knowledge in an area. Common problems teachers have in applying AR have been taken into account by producing an authoring tool for education which includes the following characteristics: (1) the ability to incorporate diverse multimedia resources such as video, sound, images and 3D objects in an easy manner, 2) the ability to incorporate tutored descriptions into elements which are being displayed (thus, the student is provided with an additional information, description or narrative about the resource, while the content gets adapted and personalized), (3) the possibility for the teacher to incorporate multiple choice questions (MCQ) into the virtual resource (useful for instant feedback to students on their understanding, it can be useful for the student to know what are the most important points of that issue and for the teacher to assess whether the student distinguishes different concepts) and (4) a library of virtual content where all resources are available in a simple and transparent way for any user. In this study ARLE is used to add AR technologies into notes or books created by the teacher, thereby supplementing both the theoretical and practical content without any programming skills needed on the designers' behalf. In addition to presenting system architecture and the examples of its educational use, a survey concerning use of AR amongst teachers in Spain has been conducted.


Qualitative Simulation

Qualitative simulation is a key inference process in qualitative causal reasoning. However, the precise meaning of the different proposals and their relation with differential equations is often unclear. In this paper, we present a precise definition of qualitative structure and behavior descriptions as abstractions of differential equations and continuously differentiable functions. We present a new algorithm for qualitative simulation that generalizes the best features of existing algorithms, and allows direct comparisons among alternate approaches. Starting with a set of constraints abstracted from a differential equation, we prove that the OSIM algorithm is guaranteed to produce a qualitative behavior corresponding to any solution to the original equation. We also show that any qualitative simulation algorithm will sometimes produce spurious qualitative behaviors: ones which do not correspond to any mechanism satisfying the given constraints. These observations suggest specific types of care that must be taken in designing applications of qualitative causal reasoning systems, and in constructing and validating a knowledge base of mechanism descriptions.


Anticipatory affect: neural correlates and consequences for choice.

'Anticipatory affect' refers to emotional states that people experience while anticipating significant outcomes. Historically, technical limitations have made it difficult to determine whether anticipatory affect influences subsequent choice. Recent advances in the spatio-temporal resolution of functional magnetic resonance imaging, however, now allow researchers to visualize changes in neural activity seconds before choice occurs. We review evidence that activation in specific brain circuits changes during anticipation of monetary incentives, that this activation correlates with affective experience and that activity in these circuits may influence subsequent choice. Specifically, an activation likelihood estimate meta-analysis of cued response studies indicates that nucleus accumbens (NAcc) activation increases during gain anticipation relative to loss anticipation, while anterior insula activation increases during both loss and gain anticipation. Additionally, anticipatory NAcc activation correlates with self-reported positive arousal, whereas anterior insula activation correlates with both self-reported negative and positive arousal. Finally, NAcc activation precedes the purchase of desirable products and choice of high-risk gambles, whereas anterior insula activation precedes the rejection of overpriced products and choice of low-risk gambles. Together, these findings support a neurally plausible framework for understanding how anticipatory affect can influence choice.


Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics

Recent workload trends indicate rapid growth in the deployment of machine learning, genomics and scientific workloads on cloud computing infrastructure. However, efficiently running these applications on shared infrastructure is challenging and we find that choosing the right hardware configuration can significantly improve performance and cost. The key to address the above challenge is having the ability to predict performance of applications under various resource configurations so that we can automatically choose the optimal configuration. Our insight is that a number of jobs have predictable structure in terms of computation and communication. Thus we can build performance models based on the behavior of the job on small samples of data and then predict its performance on larger datasets and cluster sizes. To minimize the time and resources spent in building a model, we use optimal experiment design, a statistical technique that allows us to collect as few training points as required. We have built Ernest, a performance prediction framework for large scale analytics and our evaluation on Amazon EC2 using several workloads shows that our prediction error is low while having a training overhead of less than 5% for long-running jobs.


Bootstrapping the Linked Data Web

Most knowledge sources on the Data Web were extracted from structured or semi-structured data. Thus, they encompass solely a small fraction of the information available on the document-oriented Web. In this paper, we present BOA, an iterative bootstrapping strategy for extracting RDF from unstructured data. The idea behind BOA is to use the Data Web as background knowledge for the extraction of natural language patterns that represent predicates found on the Data Web. These patterns are used to extract instance knowledge from natural language text. This knowledge is nally fed back into the Data Web, therewith closing the loop. We evaluate our approach on two data sets using DBpedia as background knowledge. Our results show that we can extract several thousand new facts in one iteration with very high accuracy. Moreover, we provide the rst repository of natural language representations of predicates found on the Data Web.


Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System

Precision Farming (PF) management strategies are commonly based on estimations of within-field yield potential, often derived from remotely-sensed products, e.g., Vegetation Index (VI) maps. These well-established means, however, lack important information, like crop height. Combinations of VI-maps and detailed 3D Crop Surface Models (CSMs) enable advanced methods for crop yield prediction. This work utilizes an Unmanned Aircraft System (UAS) to capture standard RGB imagery datasets for corn grain yield prediction at three earlyto mid-season growth stages. The imagery is processed into simple VI-orthoimages for crop/non-crop classification and 3D CSMs for crop height determination at different spatial resolutions. Three linear regression models are tested on their prediction ability using site-specific (i) unclassified mean heights, (ii) crop-classified mean heights and (iii) a combination of crop-classified mean heights with according crop coverages. The models show determination coefficients R of up to 0.74, whereas model (iii) performs best with imagery captured at the end of stem elongation and intermediate spatial resolution (0.04 m·px−1). Following these results, combined spectral and spatial modeling, based on aerial images and CSMs, proves to be a suitable method for mid-season corn yield prediction. Remote Sens. 2014, 11 10336


You are what you eat? Vegetarianism, health and identity.

This paper examines the views of 'health vegetarians' through a qualitative study of an online vegetarian message board. The researcher participated in discussions on the board, gathered responses to questions from 33 participants, and conducted follow-up e-mail interviews with 18 of these participants. Respondents were predominantly from the United States, Canada and the UK. Seventy per cent were female, and ages ranged from 14 to 53 years, with a median of 26 years. These data are interrogated within a theoretical framework that asks, 'what can a vegetarian body do?' and explores the physical, psychic, social and conceptual relations of participants. This provides insights into the identities of participants, and how diet and identity interact. It is concluded that vegetarianism is both a diet and a bodily practice with consequences for identity formation and stabilisation.


Digital Material Fabrication Using Mask-Image-Projection-based Stereolithography

Purpose – The purpose of this paper is to present a mask-image-projection-based Stereolithography process that can combine two base materials with various concentrations and structures to produce a solid object with desired material characteristics. Stereolithography is an additive manufacturing process in which liquid photopolymer resin is cross-linked and converted to solid. The fabrication of digital material requires frequent resin changes during the building process. The process presented in this paper attempts to address the related challenges in achieving such fabrication capability. Design/methodology/approach – A two-channel system design is presented for the multi-material mask-imageprojection-based Stereolithography process. In such a design, a coated thick film and linear motions in two axes are used to reduce the separation force of a cured layer. The material cleaning approach to thoroughly remove resin residue on built surfaces is presented for the developed process. Based on a developed testbed, experimental studies were conducted to verify the effectiveness of the presented process on digital material fabrication. Findings – The proposed two-channel system can reduce the separation force of a cured layer by an order of magnitude in the bottom-up projection system. The developed two-stage cleaning approach can effectively remove resin residue on built surfaces. Several multi-material designs have been fabricated to highlight the capability of the developed multi-material mask-image-projection-based Stereolithography process. Research limitations/implications – A proof-of-concept testbed has been developed. Its building speed and accuracy can be further improved. Our tests were limited to the same type of liquid resins. In addition, the removal of trapped air is a challenge in the presented process. Originality/value – This paper presents a novel and a pioneering approach towards digital material fabrication based on the Stereolithography process. This research contributes to the additive manufacturing development by significantly expanding the selection of base materials in fabricating solid objects with desired material characteristics.


Calibrating Large-area Mask Projection Stereolithography for Its Accuracy and Resolution Improvements

Solid freeform fabrication (SFF) processes based on mask image projection such as digital micro-mirror devices (DMD) have the potential to be fast and inexpensive. More and more research and commercial systems have been developed based on such digital devices. However, a digital light processing (DLP) projector based on DMD has limited resolution and certain image blurring. In order to use a DLP projector in the large-area mask projection stereolithography, it is critical to plan mask images in order to achieve high accuracy and resolution. Based on our previous work on optimized pixel blending, we present a calibration method for capturing the non-uniformity of a projection image by a low cost off-the-shelf DLP projector. Our method is based on two calibration systems, a geometric calibration system that can calibrate the position, shape, size, and orientation of a pixel and an energy calibration system that can calibrate the light intensity of a pixel. Based on both results, the light intensity at various grayscale levels can be approximated for each pixel. Developing a library of such approximation functions is critical for the optimized pixel blending to generate a better mask image plan. Experimental results verify our calibration results.


Projection micro-stereolithography using digital micro-mirror dynamic mask

We present in this paper the development of a high-resolution projection micro-stereolithography (P SL) process by using the Digital Micromirror Device (DMDTM, Texas Instruments) as a dynamic mask. This unique technology provides a parallel fabrication of complex three-dimensional (3D) microstructures used for micro electro-mechanical systems (MEMS). Based on the understanding of underlying mechanisms, a process model has been developed with all critical parameters obtained from the experimental measurement. By coupling the experimental measurement and the process model, the photon-induced curing behavior of the resin has been quantitatively studied. The role o erty of the r n d ©


A Layerless Additive Manufacturing Process based on CNC Accumulation

Most current additive manufacturing processes are layer-based, that is building a physical model layer-by-layer. By converting 3-dimensional geometry into 2-dimensional contours, the layer-based approach can dramatically simplify the process planning steps. However, there are also drawbacks associated with the layer-based approach such as inconsistent material properties between various directions. In a recent NSF workshop on additive manufacturing, it is suggested to investigate alternative non-layer based approaches. In this paper, we present an additive manufacturing process without planar layers. In the developed testbed, an additive tool based on a fiber optics cable and a UV-LED has been developed. By merging such tools inside a liquid resin tank, we demonstrate its capability of building various 2D and 3D structures. The technical challenges related to the development of such a process are discussed. Some potential applications including part repairing and building around inserts have also been demonstrated.


Single‐cell sequencing maps gene expression to mutational phylogenies in PDGF‐ and EGF‐driven gliomas

Glioblastoma multiforme (GBM) is the most common and aggressive type of primary brain tumor. Epidermal growth factor (EGF) and platelet-derived growth factor (PDGF) receptors are frequently amplified and/or possess gain-of-function mutations in GBM However, clinical trials of tyrosine-kinase inhibitors have shown disappointing efficacy, in part due to intra-tumor heterogeneity. To assess the effect of clonal heterogeneity on gene expression, we derived an approach to map single-cell expression profiles to sequentially acquired mutations identified from exome sequencing. Using 288 single cells, we constructed high-resolution phylogenies of EGF-driven and PDGF-driven GBMs, modeling transcriptional kinetics during tumor evolution. Descending the phylogenetic tree of a PDGF-driven tumor corresponded to a progressive induction of an oligodendrocyte progenitor-like cell type, expressing pro-angiogenic factors. In contrast, phylogenetic analysis of an EGFR-amplified tumor showed an up-regulation of pro-invasive genes. An in-frame deletion in a specific dimerization domain of PDGF receptor correlates with an up-regulation of growth pathways in a proneural GBM and enhances proliferation when ectopically expressed in glioma cell lines. In-frame deletions in this domain are frequent in public GBM data.


A very-high output impedance current mirror for very-low voltage biomedical analog circuits

In this paper, we present the design of a new very-high output impedance CMOS current mirror with enhanced output voltage compliance. The proposed current mirror uses MOS current dividers to sample the output current and a feedback action is used to force it to be equal to the input current; yielding very high impedance with a very large output voltage range. The proposed implementation yields an increase of the output impedance by a factor of about gmro compared with that of the super-Wilson current mirror, thus offering a potential solution to mitigate the effect of the low output impedance of ultra-deep submicron CMOS transistors used in sub 1-V current mirrors and current sources. A NMOS version of the proposed current mirror circuit was implemented using STMicroelectronics 1-V 90-nm CMOS process and simulated using Spectre to validate its performance. The output current is mirrored with a transfer error lower than 1% down to an output voltage as low as 80 mV for an input current of 5 muA, and 111 mV when the input current is increased to 50 muA.


Dune: Safe User-level Access to Privileged CPU Features

Dune is a system that provides applications with direct but safe access to hardware features such as ring protection, page tables, and tagged TLBs, while preserving the existing OS interfaces for processes. Dune uses the virtualization hardware in modern processors to provide a process, rather than a machine abstraction. It consists of a small kernel module that initializes virtualization hardware and mediates interactions with the kernel, and a user-level library that helps applications manage privileged hardware features. We present the implementation of Dune for 64bit x86 Linux. We use Dune to implement three userlevel applications that can benefit from access to privileged hardware: a sandbox for untrusted code, a privilege separation facility, and a garbage collector. The use of Dune greatly simplifies the implementation of these applications and provides significant performance advantages.


Applications of approximation algorithms to cooperative games

The Internet, which is intrinsically a common playground for a large number of players with varying degrees of collaborative and sel sh motives, naturally gives rise to numerous new game theoretic issues. Computational problems underlying solutions to these issues, achieving desirable economic criteria, often turn out to be NP-hard. It is therefore natural to apply notions from the area of approximation algorithms to these problems. The connection is made more meaningful by the fact that the two areas of game theory and approximation algorithms share common methodology { both heavily use machinery from the theory of linear programming. Various aspects of this connection have been explored recently by researchers [8, 10, 15, 20, 21, 26, 27, 29]. In this paper we will consider the problem of sharing the cost of a jointly utilized facility in a \fair" manner. Consider a service providing company whose set of possible customers, also called users, is U . For each set S U C(S) denotes the cost incurred by the company to serve the users in S. The function C is known as the cost function. For concreteness, assume that the company broadcasts news of common interest, such as nancial news, on the net. Each user, i, has a utility, u0i, for receiving the news. This utility u 0 i is known only to user i. User i enjoys a bene t of u0i xi if she gets the news at the price xi. If she does not get the news then her bene t is 0. Each user is assumed to be sel sh, and hence in order to maximize bene t, may misreport her utility as some other number, say ui. For the rest of the discussion, the utility of user i will mean the number ui. A cost sharing mechanism determines which users receive the broadcast and at what price. The mechanism is strategyproof if the dominant strategy of each user is to reveal the


Automatic generation of destination maps

Destination maps are navigational aids designed to show anyone within a region how to reach a location (the destination). Hand-designed destination maps include only the most important roads in the region and are non-uniformly scaled to ensure that all of the important roads from the highways to the residential streets are visible. We present the first automated system for creating such destination maps based on the design principles used by mapmakers. Our system includes novel algorithms for selecting the important roads based on mental representations of road networks, and for laying out the roads based on a non-linear optimization procedure. The final layouts are labeled and rendered in a variety of styles ranging from informal to more formal map styles. The system has been used to generate over 57,000 destination maps by thousands of users. We report feedback from both a formal and informal user study, as well as provide quantitative measures of success.


Aspect Extraction with Automated Prior Knowledge Learning

Aspect extraction is an important task in sentiment analysis. Topic modeling is a popular method for the task. However, unsupervised topic models often generate incoherent aspects. To address the issue, several knowledge-based models have been proposed to incorporate prior knowledge provided by the user to guide modeling. In this paper, we take a major step forward and show that in the big data era, without any user input, it is possible to learn prior knowledge automatically from a large amount of review data available on the Web. Such knowledge can then be used by a topic model to discover more coherent aspects. There are two key challenges: (1) learning quality knowledge from reviews of diverse domains, and (2) making the model fault-tolerant to handle possibly wrong knowledge. A novel approach is proposed to solve these problems. Experimental results using reviews from 36 domains show that the proposed approach achieves significant improvements over state-of-the-art baselines.


Application Protocols and Wireless Communication for IoT: A Simulation Case Study Proposal

The current Internet of Things (IoT) solutions require support at different network layers, from higher level applications to lower level media-based support. This paper presents some of the main application requirements for IoT, characterizing architecture, Quality of Service (QoS) features, security mechanisms, discovery service resources and web integration options and the protocols that can be used to provide them (e.g. CoAP, XMPP, DDS, MQTT-SN, AMQP). As examples of lower-level requirements and protocols, several wireless network characteristics (e.g. ZigBee, Z-Wave, BLE, LoRaWAN, SigFox, IEEE 802.11af, NB-IoT) are presented. The variety of possible applications scenarios and the heterogeneity of enabling technologies combined with a large number of sensors and devices, suggests the need for simulation and modeling tactics to describe how the previous requirements can be met. As a potential solution, the creation of simulation models and the usage of the OMNET++ simulation tool to enable meaningful IoT simulation is discussed. The analysis of the behavior of IoT applications is proposed for two use cases: Wireless Sensor Networks (WSN) for home and industrial automation, and Low Power Wide Area (LPWA) networks for smart meters, smart buildings, and smart cities.


Integrating Surface and Abstract Features for Robust Cross-Domain Chinese Word Segmentation

Current character-based approaches are not robust for cross domain Chin ese word segmentation. In this paper, we alleviate this problem by deriving a novel enhanced ch aracterbased generative model with a new abstract aggregate candidate-feature, which indicates if th given candidate prefers the corresponding position-tag of the longest dictionary matching wo rd. Since the distribution of the proposed feature is invariant across domains, our m del thus possesses better generalization ability. Open tests on CIPS-SIGHAN-2010 show that the enhance d gen rative model achieves robust cross-domain performance for various OOV cov erage rates and obtains the best performance on three out of four domains. The enhanced gen erative model is then further integrated with a discriminative model which also utilizes dictionary information . This integrated model is shown to be either superior or comparable to all other models repo rted in the literatur e on every domain of this task.


Neural Networks and Graph Algorithms with Next-Generation Processors

The use of graphical processors for distributed computation revolutionized the field of high performance scientific computing. As the Moore's Law era of computing draws to a close, the development of non-Von Neumann systems: neuromorphic processing units, and quantum annealers; again are redefining new territory for computational methods. While these technologies are still in their nascent stages, we discuss their potential to advance computing in two domains: machine learning, and solving constraint satisfaction problems. Each of these processors utilize fundamentally different theoretical models of computation. This raises questions about how to best use them in the design and implementation of applications. While many processors are being developed with a specific domain target, the ubiquity of spin-glass models and neural networks provides an avenue for multi-functional applications. This provides hints at the future infrastructure needed to integrate many next-generation processing units into conventional high-performance computing systems.


Identification of extract method refactoring opportunities for the decomposition of methods

The extraction of a code fragment into a separate method is one of the most widely performed refactoring activities, since it allows the decomposition of large and complex methods and can be used in combination with other code transformations for fixing a variety of design problems. Despite the significance of Extract Method refactoring towards code quality improvement, there is limited support for the identification of code fragments with distinct functionality that could be extracted into new methods. The goal of our approach is to automatically identify Extract Method refactoring opportunities which are related with the complete computation of a given variable (complete computation slice) and the statements affecting the state of a given object (object state slice). Moreover, a set of rules regarding the preservation of existing odule decomposition dependences is proposed that exclude refactoring opportunities corresponding to slices whose extraction could possibly cause a change in program behavior. The proposed approach has been evaluated regarding its ability to capture slices of code implementing a distinct functionality, its ability to resolve existing design flaws, its impact on the cohesion of the decomposed and extracted methods, and its ability to preserve program behavior. Moreover, precision and recall have been computed employing the refactoring depe opportunities found by in


WHY PEOPLE STAY : USING JOB EMBEDDEDNESS TO PREDICT VOLUNTARY TURNOVER

A new construct, entitled job embeddedness, is introduced. Assessing factors from on and off the job, it includes an individual’s (a) links to other people, teams and groups, (b) perception of their fit with their job, organization and community and (c) what they say they would have to sacrifice if they left their job. A measure of job embeddedness is developed with two samples. The results show that job embeddedness predicts the key outcomes of both intent to leave and voluntary turnover, and explains significant incremental variance over and above job satisfaction, organizational commitment, job alternatives and job search. Implications for theory and practice are discussed.


Real-Time Biologically Inspired Action Recognition from Key Poses Using a Neuromorphic Architecture

Intelligent agents, such as robots, have to serve a multitude of autonomous functions. Examples are, e.g., collision avoidance, navigation and route planning, active sensing of its environment, or the interaction and non-verbal communication with people in the extended reach space. Here, we focus on the recognition of the action of a human agent based on a biologically inspired visual architecture of analyzing articulated movements. The proposed processing architecture builds upon coarsely segregated streams of sensory processing along different pathways which separately process form and motion information (Layher et al., 2014). Action recognition is performed in an event-based scheme by identifying representations of characteristic pose configurations (key poses) in an image sequence. In line with perceptual studies, key poses are selected unsupervised utilizing a feature-driven criterion which combines extrema in the motion energy with the horizontal and the vertical extendedness of a body shape. Per class representations of key pose frames are learned using a deep convolutional neural network consisting of 15 convolutional layers. The network is trained using the energy-efficient deep neuromorphic networks (Eedn) framework (Esser et al., 2016), which realizes the mapping of the trained synaptic weights onto the IBM Neurosynaptic System platform (Merolla et al., 2014). After the mapping, the trained network achieves real-time capabilities for processing input streams and classify input images at about 1,000 frames per second while the computational stages only consume about 70 mW of energy (without spike transduction). Particularly regarding mobile robotic systems, a low energy profile might be crucial in a variety of application scenarios. Cross-validation results are reported for two different datasets and compared to state-of-the-art action recognition approaches. The results demonstrate, that (I) the presented approach is on par with other key pose based methods described in the literature, which select key pose frames by optimizing classification accuracy, (II) compared to the training on the full set of frames, representations trained on key pose frames result in a higher confidence in class assignments, and (III) key pose representations show promising generalization capabilities in a cross-dataset evaluation.


Hopelessness Depression : A Theory-Based Subtype of Depression

We present a revision of the 1978 reformulated theory of helplessness and depression and call it the hopelessness theory of depression. Although the 1978 reformulation has generated a vast amount of empirical work on depression over the past 10 years and recently has been evaluated as a model of depression, we do not think that it presents a clearly articulated theory of depression. We build on the skeletal logic of the 1978 statement and (a) propose a hypothesized subtype of depression— hopelessness depression, (b) introduce hopelessness as a proximal sufficient cause of the symptoms of hopelessness depression, (c) deemphasize causal attributions because inferred negative consequences and inferred negative characteristics about the self are also postulated to contribute to the formation of hopelessness and, in turn, the symptoms of hopelessness depression, and (d) clarify the diathesisstress and causal mediation components implied, but not explicitly articulated, in the 1978 statement. We report promising findings for the hopelessness theory and outline the aspects that still need to be tested.


SSS: a hybrid architecture applied to robot navigation

This paper describes a new three layer architecture, SSS, for robot control. It combines a servo-control layer, a Òsubsumption" layer, and a symbolic layer in a way that allows the advantages of each technique to be fully exploited. The key to this synergy is the interface between the individual subsystems. We describe how to build situation recognizers that bridge the gap between the servo and subsumption layers, and event detectors that link the subsumption and symbolic layers. The development of such a combined system is illustrated by a fully implemented indoor navigation example. The resulting real robot, called ÒTJ", is able automatically map office building environments and smoothly navigate through them at the rapid speed of 2.6 feet per second.


Deep Face Recognition: A Survey

Face recognition made tremendous leaps in the last five years with a myriad of systems proposing novel techniques substantially backed by deep convolutional neural networks (DCNN). Although face recognition performance sky-rocketed using deep-learning in classic datasets like LFW, leading to the belief that this technique reached human performance, it still remains an open problem in unconstrained environments as demonstrated by the newly released IJB datasets. This survey aims to summarize the main advances in deep face recognition and, more in general, in learning face representations for verification and identification. The survey provides a clear, structured presentation of the principal, state-of-the-art (SOTA) face recognition techniques appearing within the past five years in top computer vision venues. The survey is broken down into multiple parts that follow a standard face recognition pipeline: (a) how SOTA systems are trained and which public data sets have they used; (b) face preprocessing part (detection, alignment, etc.); (c) architecture and loss functions used for transfer learning (d) face recognition for verification and identification. The survey concludes with an overview of the SOTA results at a glance along with some open issues currently overlooked by the community.


Beyond the right hemisphere: brain mechanisms mediating vocal emotional processing

Vocal perception is particularly important for understanding a speaker's emotional state and intentions because, unlike facial perception, it is relatively independent of speaker distance and viewing conditions. The idea, derived from brain lesion studies, that vocal emotional comprehension is a special domain of the right hemisphere has failed to receive consistent support from neuroimaging. This conflict can be reconciled if vocal emotional comprehension is viewed as a multi-step process with individual neural representations. This view reveals a processing chain that proceeds from the ventral auditory pathway to brain structures implicated in cognition and emotion. Thus, vocal emotional comprehension appears to be mediated by bilateral mechanisms anchored within sensory, cognitive and emotional processing systems.


Generating English from Abstract Meaning Representations

We present a method for generating English sentences from Abstract Meaning Representation (AMR) graphs, exploiting a parallel corpus of AMRs and English sentences. We treat AMR-to-English generation as phrase-based machine translation (PBMT). We introduce a method that learns to linearize tokens of AMR graphs into an English-like order. Our linearization reduces the amount of distortion in PBMT and increases generation quality. We report a Bleu score of 26.8 on the standard AMR/English test set.


A Novel Distributed SDN-Secured Architecture for the IoT

Due to their rapid evolution, mobile devices demand for more dynamic and flexible networking services. A major challenges of future mobile networks is the increased mobile traffic. With the recent upcoming technologies of network programmability like Software-Defined Network (SDN), it may be integrated to create a new communication platform for Internet of Things (IoT). In this work, we present how to determine the effectiveness of an approach to build a new secured network architecture based on SDN and clusters. Our proposed scheme is a starting point for some experiments providing perspective over SDN deployment in a cluster environment. With this aim in mind, we suggest a routing protocol that manages routing tasks over Cluster-SDN. By using network virtualization and OpenFlow technologies to generate virtual nodes, we simulate a prototype system controlled by SDN. With our testbed, we are able to manage 500 things. We can analyze every OpenFlow messages and we have discovered that with a particular flow, the things can exchange information unlike the routing principle.


Logistic Regression and Collaborative Filtering for Sponsored Search Term Recommendation

Sponsored search advertising is largely based on bidding on individual terms. The richness of natural languages permits web searchers to express their information needs in myriad ways. Advertisers have difficulty discovering all the terms that are relevant to their products or services. We examine the performance of logistic regression and collaborative filtering models on two different data sources to predict terms relevant to a set of seed terms describing an advertiser’s product or service.


Removal of High-Density Salt-and-Pepper Noise in Images With an Iterative Adaptive Fuzzy Filter Using Alpha-Trimmed Mean

Suppression of impulse noise in images is an important problem in image processing. In this paper, we propose a novel adaptive iterative fuzzy filter for denoising images corrupted by impulse noise. It operates in two stages-detection of noisy pixels with an adaptive fuzzy detector followed by denoising using a weighted mean filter on the “good” pixels in the filter window. Experimental results demonstrate the algorithm to be superior to state-of-the-art filters. The filter is also shown to be robust to very high levels of noise, retrieving meaningful detail at noise levels as high as 97%.


Ant colony optimization for routing and load-balancing: survey and new directions

Although an ant is a simple creature, collectively a colony of ants performs useful tasks such as finding the shortest path to a food source and sharing this information with other ants by depositing pheromone. In the field ofant colony optimization (ACO), models ofcollective intelligenceof ants are transformed into useful optimization techniques that find applications in computer networking. In this survey, the problem-solving paradigm of ACO is explicated and compared to traditional routing algorithms along the issues of routing information, routing overhead and adaptivity. The contributions of this survey include 1) providing a comparison and critique of the state-of-the-art approaches for mitigatingstagnation (a major problem in many ACO algorithms), 2) surveying and comparing three major research in applying ACO in routing and load-balancing, and 3) discussing new directions and identifying open problems. The approaches for mitigating stagnation discussed include:evaporation, aging, pheromone smoothingand limiting, privileged pheromone layingandpheromone-heuristic control . The survey on ACO in routing/load-balancing includes comparison and critique of ant-based control and its ramifications, AntNetand its extensions, as well as ASGAand SynthECA. Discussions on new directions include an ongoing work of the authors in applyingmultiple ant colony optimizationin load-balancing.


It Makes Sense: A Wide-Coverage Word Sense Disambiguation System for Free Text

Word sense disambiguation (WSD) systems based on supervised learning achieved the best performance in SensEval and SemEval workshops. However, there are few publicly available open source WSD systems. This limits the use of WSD in other applications, especially for researchers whose research interests are not in WSD. In this paper, we present IMS, a supervised English all-words WSD system. The flexible framework of IMS allows users to integrate different preprocessing tools, additional features, and different classifiers. By default, we use linear support vector machines as the classifier with multiple knowledge-based features. In our implementation, IMS achieves state-of-the-art results on several SensEval and SemEval tasks.


NASARI: a Novel Approach to a Semantically-Aware Representation of Items

The semantic representation of individual word senses and concepts is of fundamental importance to several applications in Natural Language Processing. To date, concept modeling techniques have in the main based their representation either on lexicographic resources, such as WordNet, or on encyclopedic resources, such as Wikipedia. We propose a vector representation technique that combines the complementary knowledge of both these types of resource. Thanks to its use of explicit semantics combined with a novel cluster-based dimensionality reduction and an effective weighting scheme, our representation attains state-of-the-art performance on multiple datasets in two standard benchmarks: word similarity and sense clustering. We are releasing our vector representations at http://lcl.uniroma1.it/nasari/.


De-Conflated Semantic Representations

One major deficiency of most semantic representation techniques is that they usually model a word type as a single point in the semantic space, hence conflating all the meanings that the word can have. Addressing this issue by learning distinct representations for individual meanings of words has been the subject of several research studies in the past few years. However, the generated sense representations are either not linked to any sense inventory or are unreliable for infrequent word senses. We propose a technique that tackles these problems by de-conflating the representations of words based on the deep knowledge that can be derived from a semantic network. Our approach provides multiple advantages in comparison to the previous approaches, including its high coverage and the ability to generate accurate representations even for infrequent word senses. We carry out evaluations on six datasets across two semantic similarity tasks and report state-of-the-art results on most of them.


Neural Sequence Learning Models for Word Sense Disambiguation

Word Sense Disambiguation models exist in many flavors. Even though supervised ones tend to perform best in terms of accuracy, they often lose ground to more flexible knowledge-based solutions, which do not require training by a word expert for every disambiguation target. To bridge this gap we adopt a different perspective and rely on sequence learning to frame the disambiguation problem: we propose and study in depth a series of end-to-end neural architectures directly tailored to the task, from bidirectional Long Short-Term Memory to encoder-decoder models. Our extensive evaluation over standard benchmarks and in multiple languages shows that sequence learning enables more versatile all-words models that consistently lead to state-of-the-art results, even against word experts with engineered features.


Your Exploit is Mine: Automatic Shellcode Transplant for Remote Exploits

Developing a remote exploit is not easy. It requires a comprehensive understanding of a vulnerability and delicate techniques to bypass defense mechanisms. As a result, attackers may prefer to reuse an existing exploit and make necessary changes over developing a new exploit from scratch. One such adaptation is the replacement of the original shellcode (i.e., the attacker-injected code that is executed as the final step of the exploit) in the original exploit with a replacement shellcode, resulting in a modified exploit that carries out the actions desired by the attacker as opposed to the original exploit author. We call this a shellcode transplant. Current automated shellcode placement methods are insufficient because they over-constrain the replacement shellcode, and so cannot be used to achieve shellcode transplant. For example, these systems consider the shellcode as an integrated memory chunk and require that the execution path of the modified exploit must be same as the original one. To resolve these issues, we present ShellSwap, a system that uses symbolic tracing, with a combination of shellcode layout remediation and path kneading to achieve shellcode transplant. We evaluated the ShellSwap system on a combination of 20 exploits and 5 pieces of shellcode that are independently developed and different from the original exploit. Among the 100 test cases, our system successfully generated 88% of the exploits.


Manufacturing Problem Solving in a Job Shop — Research in Layout Planning

For ensuring efficient operation of a job shop, it is important to minimize waste, which has no value addition to the final product. For a job shop, minimizing movement is considered as the highest priority for waste prevention. For this reason, the layout for a job shop should be designed in such a way to ensure the lowest possible cost for production by reducing nonvalue added activities, such as movement of work-in-process. An effective and efficient way of layout planning for a job shop is a key for solving movement inefficiencies and facilitating communication and interaction between workers and supervisors. This involves relocation of equipment and machinery to streamline materials flow. The primary objective of relocation is to avoid flow conflicts, reduce process time, and increase efficiency of labor usage. Proximity of the most frequently used machines minimizes the movement cost significantly, which eventually minimizes the cost of production. This paper describes the research done in process flow improvements in a job shop manufacturing steel components. The literature focused mainly on mathematical modeling with assumptions that are not applicable for a typical small-scale job shop operation. However, this was overcome by collecting material movement data over three months and analyzing the information using a From-To chart. By analyzing the chart, the actual loads between departments for the operation period were tabulated in available plant space. From this information, the inter-departmental flow was shown by a model. This provides the basic layout pattern, which was improved. A second step was to determine the cost of this layout by multiplying the material handling cost by the number of loads moved between each pair of departments. As a recommendation for solving the problem, two layout models have been developed for ensuring the lowest movement cost. Introduction Transportation is considered as one of the seven wastes for lean manufacturing, and effective layout planning is considered as a key to overcome this kind of waste. It is stated, “Double handling and excessive movements are likely to cause damage and deterioration with the distance of communication between processes” [1]. Therefore, layout planning has clear impact with the quality and quantity of the final products by reducing waste and improving efficiency.


High-power GaN MMIC PA Over 40–4000MHz

We report a high-performance GaN MMIC power amplifier operating from 40MHz to 4,000MHz. The MMIC achieved 80W pulsed (100us pulse width and 10% duty cycle) output power (P5dB) with 54% efficiency at 40MHz, 50W with about 30% efficiency across most of the mid band, and gradually decreases to 30W with 22% efficiency at 4000MHz. Power gain is 25dB across the 40-4000MHz band. This ultra wideband performance is achieved by both tailoring the device output impedance, and using a unique wide-band, circuit-matching topology. Detailed design techniques of both the device and the matching circuit will be presented.


Team assembly mechanisms determine collaboration network structure and team performance.

Agents in creative enterprises are embedded in networks that inspire, support, and evaluate their work. Here, we investigate how the mechanisms by which creative teams self-assemble determine the structure of these collaboration networks. We propose a model for the self-assembly of creative teams that has its basis in three parameters: team size, the fraction of newcomers in new productions, and the tendency of incumbents to repeat previous collaborations. The model suggests that the emergence of a large connected community of practitioners can be described as a phase transition. We find that team assembly mechanisms determine both the structure of the collaboration network and team performance for teams derived from both artistic and scientific fields.


Internet of Things Business Models

Almost all businesses are aware of the potential gains that the Internet of Things (IoT) has to offer, they are unsure of how to approach it. This article proposes a business model that builds on Holler et al., (2014) [1]. The model consists of three dimensions: “Who, Where, and Why”. “Who” describes collaborating partners, which builds the “Value Network”. “Where” describes sources of value co-creation rooted in the layer model of digitized objects, and “Why” describes how partners benefit from collaborating within the value network. With the intention of addressing “How”, the proposed framework has integrated the IoT strategy category, tactics, and value chain elements. The framework is then validated through the case studies of some successful players who are either the Awardees of the IoT Award 2014 or the ICT Award 2015 of Hong Kong.


Automatic 3D face reconstruction from single images or video

This paper presents a fully automated algorithm for reconstructing a textured 3D model of a face from a single photograph or a raw video stream. The algorithm is based on a combination of Support Vector Machines (SVMs) and a Morphable Model of 3D faces. After SVM face detection, individual facial features are detected using a novel regression- and classification-based approach, and probabilistically plausible configurations of features are selected to produce a list of candidates for several facial feature positions. In the next step, the configurations of feature points are evaluated using a novel criterion that is based on a Morphable Model and a combination of linear projections. To make the algorithm robust with respect to head orientation, this process is iterated while the estimate of pose is refined. Finally, the feature points initialize a model-fitting procedure of the Morphable Model. The result is a high resolution 3D surface model.


Polymer Nanoparticles for Smart Drug Delivery

In the recent decades, polymers are widely used as biomaterials due to their favorable properties such as good biocompatibility, easy design and preparation, a variety of structures and interesting bio-mimetic character. Especially in the field of smart drug delivery, polymer played a significant role because it can deliver therapeutic agents directly into the intended site of action, with superior efficacy. The ideal requirements for designing nano-particulate delivery system are to effectively be controlled particle size, surface character; enhance permeation, flexibility, solubility and release of therapeutically active agents in order to attain the target and specific activity at a predetermined rate and time. The smart drug delivery systems have been successfully made by the advances in polymer science in the bio-nano‐ technology field. Recently, these advances have been found in various medical applications for nano-scale structures in smart drug delivery. The smart drug delivery systems should possess some important feature such as pre-scheduled rate, self controlled, targeted, predetermined time and monitor the delivery. The smart drug delivery system enhances the polymer nanoparticle better stage to their therapy regimen. They are drug carriers of natural, semi-synthetic, and synthetic polymeric nature at the nano-scale to micro-scale range. The polymeric particles are collectively named as spheres and capsules. The most of the polymeric nanoparticles with surfactants offer stability of various forms of active drugs and have useful to smart release properties. There are numerous biological applications have been reported for the nano-scale to micro-scale sized particles, such as site-targeted, controlled, and enhanced bioavailability of hydrophobic drugs [1-4]. Due to the nanoparticles size the drugs have been targeting into various applications, such as, various cancers targeting has been shown to be promising [5]. Moreover, polymeric particles proved their effectiveness in stabilizing and protecting the drug molecules such as proteins, peptides, or DNA molecules from various environmental hazards degradation [2-4, 6, 7]. So these polymers are affording the potential for various protein and gene delivery. Numerous methods had been available to fabricate


Deep neural networks for recognizing online handwritten mathematical symbols

This paper presents application of deep learning to recognize online handwritten mathematical symbols. Recently various deep learning architectures such as Convolution neural network (CNN), Deep neural network (DNN) and Long short term memory (LSTM) RNN have been applied to fields such as computer vision, speech recognition and natural language processing where they have been shown to produce state-of-the-art results on various tasks. In this paper, we apply max-out-based CNN and BLSTM to image patterns created from online patterns and to the original online patterns, respectively and combine them. We also compare them with traditional recognition methods which are MRF and MQDF by carrying out some experiments on CROHME database.


The HAWKwood Database

We present a database consisting of wood pile images, which can be used as a benchmark to evaluate the performance of wood pile detection and surveying algorithms. We distinguish six database categories which can be used for different types of algorithms. Images of real and synthetic scenes are provided, which consist of 7655 images divided into 354 data sets. Depending on the category the data sets either include ground truth data or forestry specific measurements with which algorithms may be compared.


Temperature-aware microarchitecture: Modeling and implementation

With cooling costs rising exponentially, designing cooling solutions for worst-case power dissipation is prohibitively expensive. Chips that can autonomously modify their execution and power-dissipation characteristics permit the use of lower-cost cooling solutions while still guaranteeing safe temperature regulation. Evaluating techniques for this dynamic thermal management (DTM), however, requires a thermal model that is practical for architectural studies.This paper describes HotSpot, an accurate yet fast and practical model based on an equivalent circuit of thermal resistances and capacitances that correspond to microarchitecture blocks and essential aspects of the thermal package. Validation was performed using finite-element simulation. The paper also introduces several effective methods for DTM: "temperature-tracking" frequency scaling, "migrating computation" to spare hardware units, and a "hybrid" policy that combines fetch gating with dynamic voltage scaling. The latter two achieve their performance advantage by exploiting instruction-level parallelism, showing the importance of microarchitecture research in helping control the growth of cooling costs.Modeling temperature at the microarchitecture level also shows that power metrics are poor predictors of temperature, that sensor imprecision has a substantial impact on the performance of DTM, and that the inclusion of lateral resistances for thermal diffusion is important for accuracy.


A 3 ppm 1.5 × 0.8 mm 2 1.0 µA 32.768 kHz MEMS-Based Oscillator

This paper describes the first 32 kHz low-power MEMS-based oscillator in production. The primary goal is to provide a small form-factor oscillator (1.5 × 0.8 mm 2 ) for use as a crystal replacement in space-constrained mobile devices. The oscillator generates an output frequency of 32.768 kHz and its binary divisors down to 1 Hz. The frequency stability over the industrial temperature range (-40 °C to 85 °C) is ±100 ppm as an oscillator (XO) or ±3 ppm with optional calibration as a temperature compensated oscillator (TCXO). Supply currents are 0.9 μA for the XO and 1.0 μA for the TCXO at supply voltages from 1.4 V to 4.5 V. The MEMS resonator is a capacitively-transduced tuning fork at 524 kHz. The circuitry is fabricated in 180 nm CMOS and includes low power sustaining circuit, fractional-N PLL, temperature sensor, digital control, and low swing driver.


ITIL in small to medium-sized enterprises software companies: towards an implementation sequence

Information Technology Infrastructure Library, ITIL framework, is a set of comprehensive publications providing descriptive guidance on the management of IT processes, functions, roles and responsibilities related to Information Technology Service Management. However, and in spite of its repercussion and popularity, the ITIL framework does not suggest an implementation order for their processes. This decision constitutes the first challenge that an organization must overcome when starting an ITIL implementation, the enterprise size being one of the leading factors to be considered in the decision making process. In the scenario of Small and Medium Enterprises dedicated to producing software, this paper is devoted to investigating which processes are the most selected to start the implementation of ITIL in these organizations. This is done by means of two different instruments. Firstly, a systematic literature review on the topic and secondly, a survey conducted among experts and practitioners. Results show in both cases that Incident Management Process should be the first process when implementing ITIL framework.


Efficiently inferring community structure in bipartite networks

Bipartite networks are a common type of network data in which there are two types of vertices, and only vertices of different types can be connected. While bipartite networks exhibit community structure like their unipartite counterparts, existing approaches to bipartite community detection have drawbacks, including implicit parameter choices, loss of information through one-mode projections, and lack of interpretability. Here we solve the community detection problem for bipartite networks by formulating a bipartite stochastic block model, which explicitly includes vertex type information and may be trivially extended to k-partite networks. This bipartite stochastic block model yields a projection-free and statistically principled method for community detection that makes clear assumptions and parameter choices and yields interpretable results. We demonstrate this model's ability to efficiently and accurately find community structure in synthetic bipartite networks with known structure and in real-world bipartite networks with unknown structure, and we characterize its performance in practical contexts.


Fast Fourier Transforms for Nonequispaced Data Alok Dutt Yale University 1993 Two groups of algorithms are presented generalizing the fast Fourier transform (FFT) to the case of non-integer frequencies and nonequispaced nodes on the interval [-7r, 7r]. These schemes are based on combinations of certain analytical considerations with the classical fast Fourier transform, and generalize both the forward and backward FFTs. The number of arithmetic operations required by each of the algorithms is proportional to Nlog N + Nlog(I/e), where 6 is the desired precision of computations and N is the number of nodes. Several related algorithms are also presented, each of which utilizes a similar set of techniques from analysis and linear algebra. These include an efficient version of the Fast Multipole Method in one dimension and fast algorithms for the evaluation, integration and differentiation of Lagrange polynomial interpolants. Several numerical examples are used to illustrate the efficiency of the approach, and to compare the performances of the two sets of nonuniform FFT algorithms. The work of this author was supported in part by the Office of Naval Research under Grant N00014-89-J-1527 and in part by the National Science Foundation under Grant DMS9012751. Approved for public release: distribution is unlimited.


Value of Information Systems and Products : Understanding the Users ’ Perspective and Values

Developers aim at providing value through their systems and products. However, value is not financial only, but depends on usage and users’ perceptions of value. In this paper, we clarify the concept of value from the users’ perspective and the role of user involvement in providing value. First, theories and approaches of psychology, marketing and human-computer interaction are reviewed. Secondly, the concept of ‘user values’ is suggested to clarify the concept of value from the user’s point of view and a category framework of user values is presented to make them more concrete and easier to identify. Thirdly, the activities and methods for adopting user values in development work are discussed. The analysis of the literature shows that value has been considered in multiple ways in development. However, users’ perspectives have received less attention. As a conclusion, we draw future research directions for value-centered design and propose that user involvement is essential in identifying user values, interpreting the practical meaning of the values and implementing them in development work.


Quadtree Convolutional Neural Networks

This paper presents a Quadtree Convolutional Neural Network (QCNN) for efficiently learning from image datasets representing sparse data such as handwriting, pen strokes, freehand sketches, etc. Instead of storing the sparse sketches in regular dense tensors, our method decomposes and represents the image as a linear quadtree that is only refined in the non-empty portions of the image. The actual image data corresponding to non-zero pixels is stored in the finest nodes of the quadtree. Convolution and pooling operations are restricted to the sparse pixels, leading to better efficiency in computation time as well as memory usage. Specifically, the computational and memory costs in QCNN grow linearly in the number of non-zero pixels, as opposed to traditional CNNs where the costs are quadratic in the number of pixels. This enables QCNN to learn from sparse images much faster and process high resolution images without the memory constraints faced by traditional CNNs. We study QCNN on four sparse image datasets for sketch classification and simplification tasks. The results show that QCNN can obtain comparable accuracy with large reduction in computational and memory costs.


A 0 . 3-μ m CMOS 8Gb / s 4-PAM Serial Link Transceiver

An 8-Gb/s 0.3-µm CMOS transceiver uses multilevel signaling (4-PAM) and transmit pre-shaping in combination with receive equalization to reduce ISI due to channel low-pass effects. High on-chip frequencies are avoided by multi-plexing and demultiplexing the data directly at the pads. Timing recovery takes advantage of a novel frequency acquisition scheme and a linear PLL with a loop bandwidth >30MHz, phase margin >48 and capture range of 20MHz without a frequency acquisition aid. The transmitted 8-Gbps data is successfully detected by the receiver after a 10-m coaxial cable. The 2mm x 2mm chip consumes 1.1W at 8Gbps with a 3-V supply.


SDN/NFV-enabled satellite communications networks: Opportunities, scenarios and challenges

In the context of next generation 5G networks, the satellite industry is clearly committed to revisit and revamp the role of satellite communications. As major drivers in the evolution of (terrestrial) fixed and mobile networks, Software Defined Networking (SDN) and Network Function Virtualisation (NFV) technologies are also being positioned as central technology enablers towards improved and more flexible integration of satellite and terrestrial segments, providing satellite network further service innovation and business agility by advanced network resources management techniques. Through the analysis of scenarios and use cases, this paper provides a description of the benefits that SDN/NFV technologies can bring into satellite communications towards 5G. Three scenarios are presented and analysed to delineate different potential improvement areas pursued through the introduction of SDN/NFV technologies in the satellite ground segment domain. Within each scenario, a number of use cases are developed to gain further insight into specific capabilities and to identify the technical challenges stemming from them.


Multi-Sensor Conflict Measurement and Information Fusion

In sensing applications where multiple sensors observe the same scene, fusing sensor outputs can provide improved results. However, if some of the sensors are providing lower quality outputs, e.g. when one or more sensors has a poor signal-tonoise ratio (SNR) and therefore provides very noisy data, the fused results can be degraded. In this work, a multi-sensor conflict measure is proposed which estimates multi-sensor conflict by representing each sensor output as interval-valued information and examines the sensor output overlaps on all possible n-tuple sensor combinations. The conflict is based on the sizes of the intervals and how many sensors output values lie in these intervals. In this work, conflict is defined in terms of how little the output from multiple sensors overlap. That is, high degrees of overlap mean low sensor conflict, while low degrees of overlap mean high conflict. This work is a preliminary step towards a robust conflict and sensor fusion framework. In addition, a sensor fusion algorithm is proposed based on a weighted sum of sensor outputs, where the weights for each sensor diminish as the conflict measure increases. The proposed methods can be utilized to (1) assess a measure of multi-sensor conflict, and (2) improve sensor output fusion by lessening weighting for sensors with high conflict. Using this measure, a simulated example is given to explain the mechanics of calculating the conflict measure, and stereo camera 3D outputs are analyzed and fused. In the stereo camera case, the sensor output is corrupted by additive impulse noise, DC offset, and Gaussian noise. Impulse noise is common in sensors due to intermittent interference, a DC offset a sensor bias or registration error, and Gaussian noise represents a sensor output with low SNR. The results show that sensor output fusion based on the conflict measure shows improved accuracy over a simple averaging fusion strategy.


Active Imitation Learning via Reduction to I.I.D. Active Learning

In standard passive imitation learning, the goal is to learn a target policy by passively observing full execution trajectories of it. Unfortunately, generating such trajectories can require substantial expert effort and be impractical in some cases. In this paper, we consider active imitation learning with the goal of reducing this effort by querying the expert about the desired action at individual states, which are selected based on answers to past queries and the learner’s interactions with an environment simulator. We introduce a new approach based on reducing active imitation learning to i.i.d. active learning, which can leverage progress in the i.i.d. setting. Our first contribution, is to analyze reductions for both non-stationary and stationary policies, showing that the label complexity (number of queries) of active imitation learning can be substantially less than passive learning. Our second contribution, is to introduce a practical algorithm inspired by the reductions, which is shown to be highly effective in four test domains compared to a number of alternatives.


Judgment under emotional certainty and uncertainty: the effects of specific emotions on information processing.

The authors argued that emotions characterized by certainty appraisals promote heuristic processing, whereas emotions characterized by uncertainty appraisals result in systematic processing. The 1st experiment demonstrated that the certainty associated with an emotion affects the certainty experienced in subsequent situations. The next 3 experiments investigated effects on processing of emotions associated with certainty and uncertainty. Compared with emotions associated with uncertainty, emotions associated with certainty resulted in greater reliance on the expertise of a source of a persuasive message in Experiment 2, more stereotyping in Experiment 3, and less attention to argument quality in Experiment 4. In contrast to previous theories linking valence and processing, these findings suggest that the certainty appraisal content of emotions is also important in determining whether people engage in systematic or heuristic processing.


Human Pose Regression by Combining Indirect Part Detection and Contextual Information

In this paper, we propose an end-to-end trainable regression approach for human pose estimation from still images. We use the proposed Soft-argmax function to convert feature maps directly to joint coordinates, resulting in a fully differentiable framework. Our method is able to learn heat maps representations indirectly, without additional steps of artificial ground truth generation. Consequently, contextual information can be included to the pose predictions in a seamless way. We evaluated our method on two very challenging datasets, the Leeds Sports Poses (LSP) and the MPII Human Pose datasets, reaching the best performance among all the existing regression methods and comparable results to the state-of-the-art detection based approaches.


Tachyon : Memory Throughput I / O for Cluster Computing Frameworks

As ever more big data computations start to be in-memory, I/O throughput dominates the running times of many workloads. For distributed storage, the read throughput can be improved using caching, however, the write throughput is limited by both disk and network bandwidth due to data replication for fault-tolerance. This paper proposes a new file system architecture to enable frameworks to both read and write reliably at memory speed, by avoiding synchronous data replication on writes.


Automatic Acquisition of Hyponyms from Large Text Corpora

We describe a method for the automatic acquisition of the hyponymy lexical relation from unrestricted text. Two goals motivate the approach: (i) avoidance of the need for pre-encoded knowledge and (ii) applicability across a wide range of text. We identify a set of lexicosyntactic patterns that are easily recognizable, that occur frequently and across text genre boundaries, and that indisputably indicate the lexical relation of interest. We describe a method for discovering these patterns and suggest that other lexical relations will also be acquirable in this way. A subset of the acquisition algorithm is implemented and the results are used to augment and critique the structure of a large hand-built thesaurus. Extensions and applications to areas such as information retrieval are suggested.


Open domain event extraction from twitter

Tweets are the most up-to-date and inclusive stream of in- formation and commentary on current events, but they are also fragmented and noisy, motivating the need for systems that can extract, aggregate and categorize important events. Previous work on extracting structured representations of events has focused largely on newswire text; Twitter's unique characteristics present new challenges and opportunities for open-domain event extraction. This paper describes TwiCal-- the first open-domain event-extraction and categorization system for Twitter. We demonstrate that accurately extracting an open-domain calendar of significant events from Twitter is indeed feasible. In addition, we present a novel approach for discovering important event categories and classifying extracted events based on latent variable models. By leveraging large volumes of unlabeled data, our approach achieves a 14% increase in maximum F1 over a supervised baseline. A continuously updating demonstration of our system can be viewed at http://statuscalendar.com; Our NLP tools are available at http://github.com/aritter/ twitter_nlp.


TwiNER: named entity recognition in targeted twitter stream

Many private and/or public organizations have been reported to create and monitor targeted Twitter streams to collect and understand users' opinions about the organizations. Targeted Twitter stream is usually constructed by filtering tweets with user-defined selection criteria e.g. tweets published by users from a selected region, or tweets that match one or more predefined keywords. Targeted Twitter stream is then monitored to collect and understand users' opinions about the organizations. There is an emerging need for early crisis detection and response with such target stream. Such applications require a good named entity recognition (NER) system for Twitter, which is able to automatically discover emerging named entities that is potentially linked to the crisis. In this paper, we present a novel 2-step unsupervised NER system for targeted Twitter stream, called TwiNER. In the first step, it leverages on the global context obtained from Wikipedia and Web N-Gram corpus to partition tweets into valid segments (phrases) using a dynamic programming algorithm. Each such tweet segment is a candidate named entity. It is observed that the named entities in the targeted stream usually exhibit a gregarious property, due to the way the targeted stream is constructed. In the second step, TwiNER constructs a random walk model to exploit the gregarious property in the local context derived from the Twitter stream. The highly-ranked segments have a higher chance of being true named entities. We evaluated TwiNER on two sets of real-life tweets simulating two targeted streams. Evaluated using labeled ground truth, TwiNER achieves comparable performance as with conventional approaches in both streams. Various settings of TwiNER have also been examined to verify our global context + local context combo idea.


Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data

In this paper, we present an overview of generalized expectation criteria (GE), a simple, robust, scalable method for semi-supervised training using weakly -labeled data. GE fits model parameters by favoring models that match certain expectation constrai nts, such as marginal label distributions, on the unlabeled data. This paper shows how to apply generali z d expectation criteria to two classes of parametric models: maximum entropy models and condition al random fields. Experimental results demonstrate accuracy improvements over supervise d training and a number of other stateof-the-art semi-supervised learning methods for these mod els.


Towards Instrument Segmentation for Music Content Description: a Critical Review of Instrument Classification Techniques

A system capable of describing the musical content of any kind of soundfile or soundstream, as it is supposed to be done in MPEG7-compliant applications, should provide an account of the different moments where a certain instrument can be listened to. This segmentation according to instrument taxonomies must be solved with different strategies than segmentation according to perceptual features. In this paper we concentrate on reviewing the different techniques that have been so far proposed for automatic classification of musical instruments. Although the ultimate goal should be the segmentation of complex sonic mixtures, it is still far from being solved. Therefore, the practical approach is to reduce the scope of the classification systems to only deal with isolated, and out-of-context, sounds. There is an obvious tradeoff in endorsing this strategy: we gain simplicity and tractability, but we lose contextual and time-dependent cues that can be exploited as relevant features for classifying the sounds.


Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems

Particle swarm optimization (PSO) is a population-based swarm intelligence algorithm that shares many similarities with evolutionary computation techniques. However, the PSO is driven by the simulation of a social psychological metaphor motivated by collective behaviors of bird and other social organisms instead of the survival of the fittest individual. Inspired by the classical PSO method and quantum mechanics theories, this work presents novel quantum-behaved PSO (QPSO) approaches using mutation operator with Gaussian probability distribution. The application of Gaussian mutation operator instead of random sequences in QPSO is a powerful strategy to improve the QPSO performance in preventing premature convergence to local optima. In this paper, new combinations of QPSO and Gaussian probability distribution are employed in well-studied continuous optimization problems of engineering design. Two case studies are described and evaluated in this work. Our results indicate that Gaussian QPSO approaches handle such problems efficiently in terms of precision and convergence and, in most cases, they outperform the results presented in the literature. 2009 Elsevier Ltd. All rights reserved.


Positive Youth Development , Participation in Community Youth Development Programs , and Community Contributions of Fifth-Grade Adolescents : Findings From the First Wave Of the 4-H Study of Positive Youth Development

The 4-H Study of Positive Youth Development (PYD), a longitudinal investigation of a diverse sample of 1,700 fifth graders and 1,117 of their parents, tests developmental contextual ideas linking PYD, youth contributions, and participation in community youth development (YD) programs, representing a key ecological asset. Using data from Wave 1 of the study, structural equation modeling procedures provided evidence for five first-order latent factors representing the “Five Cs” of PYD (competence, confidence, connection, character, and caring), and for their convergence on a second-order, PYD latent construct. A theoretical construct, youth “contribution,” was also created and examined. Both PYD and YD program participation independently related to contribution. The importance of longitudinal analyses for extending the present results is discussed.


A Flexible AC Distribution System Device for a Microgrid

This paper presents a flexible ac distribution system device for microgrid applications. The device aims to improve the power quality and reliability of the overall power distribution system that the microgrid is connected to. The control design employs a new model predictive control algorithm which allows faster computational time for large power systems by optimizing the steady-state and the transient control problems separately. Extended Kalman filters are also employed for frequency tracking and to extract the harmonic spectra of the grid voltage and the load currents in the microgrid. The design concept is verified through different test case scenarios to demonstrate the capability of the proposed device and the results obtained are discussed.


A Hierarchical Game Framework for Resource Management in Fog Computing

Supporting real-time and mobile data services, fog computing has been considered as a promising technology to overcome long and unpredicted delay in cloud computing. However, as resources in FNs are owned by independent users or infrastructure providers, the ADSSs cannot connect and access data services from the FNs directly, but can only request data service from the DSOs in the cloud. Accordingly, in fog computing, the DSOs are required to communicate with FNs and allocate resources from the FNs to the ADSSs. The DSOs provide virtualized data services to the ADSSs, and the FNs, motivated by the DSOs, provide data services in the physical network. Nevertheless, with fog computing added as the intermediate layer between the cloud and users, there are challenges such as the resource allocation in the virtualized network between the DSOs and ADSSs, the asymmetric information problem between DSOs and ADSSs, and the resource matching from the FNs to the ADSSs in the physical network. In this article, we propose a three-layer hierarchical game framework to solve the challenges in fog computing networks. In the proposed framework, we apply the Stackelberg sub-game for the interaction between DSOs and ADSSs, moral hazard modeling for the interaction between DSOs and FNs, and the student project allocation matching sub-game for the interaction between FNs and ADSSs. The purpose is to obtain stable and optimal utilities for each DSO, FN, and ADSS in a distributed fashion.


Social Learning Theory and Developmental Psychology : The Legacies of

Social learning theory began as an attempt by Robert Sears and others to meld psychoanalytic and stimulus-response learning theory into a comprehensive explanation of human behavior, drawing on the clinical richness of the former and the rigor of the latter. Albert Bandura abandoned the psychoanalytic and drive features of the approach, emphasizing instead cognitive and informationprocessing capacities that mediate social behavior. Both theories were intended as a general framework for the understanding of human behavior, and their developmental aspects remain to be worked out in detail. Nevertheless, Bandura has provided a strong theoretical beginning: The theory appears to be capable of accounting well for existing developmental data as well as guiding new investigation.


Compact Half Diamond Dual-Band Textile HMSIW On-Body Antenna

A novel wearable dual-band textile antenna, designed for optimal on-body performance in the 2.4 and 5.8 GHz Industrial, Scientific and Medical bands, is proposed. By using brass eye-lets and a combination of conducting and non-conductive textile materials, a half-mode substrate integrated waveguide cavity with ground plane is realized that is very compact and flexible, while still directing radiation away from the wearer. Additional miniaturization is achieved by adding a row of shorting vias and slots. Beside excellent free space performance in the 2.4 and 5.8 GHz bands, respectively, with measured impedance bandwidth of 4.9% and 5.1%, maximal measured free-space gain of 4.1 and 5.8 dBi, and efficiency of 72.8% and 85.6%, very stable on-body performance is obtained, with minimal frequency detuning when deploying the antenna on the human body and when bent around cylinders with radii of 75 and 40 mm. At 2.45 and 5.8 GHz, respectively, the measured on-body gain is 4.4 and 5.7 dBi, with sufficiently small calculated SAR values of 0.55 and 0.90 W/kg. These properties make the proposed antenna excellently suited for wearable on-body systems.


A machine learning approach for medication adherence monitoring using body-worn sensors

One of the most important challenges in chronic disease self-management is medication non-adherence, which has irrevocable outcomes. Although many technologies have been developed for medication adherence monitoring, the reliability and cost-effectiveness of these approaches are not well understood to date. This paper presents a medication adherence monitoring system by user-activity tracking based on wrist-band wearable sensors. We develop machine learning algorithms that track wrist motions in real-time and identify medication intake activities. We propose a novel data analysis pipeline to reliably detect medication adherence by examining single-wrist motions. Our system achieves an accuracy of 78.3% in adherence detection without need for medication pillboxes and with only one sensor worn on either of the wrists. The accuracy of our algorithm is only 7.9% lower than a system with two sensors that track motions of both wrists.


Cost Effective Genetic Algorithm for Workflow Scheduling in Cloud Under Deadline Constraint

Cloud computing is becoming an increasingly admired paradigm that delivers high-performance computing resources over the Internet to solve the large-scale scientific problems, but still it has various challenges that need to be addressed to execute scientific workflows. The existing research mainly focused on minimizing finishing time (makespan) or minimization of cost while meeting the quality of service requirements. However, most of them do not consider essential characteristic of cloud and major issues, such as virtual machines (VMs) performance variation and acquisition delay. In this paper, we propose a meta-heuristic cost effective genetic algorithm that minimizes the execution cost of the workflow while meeting the deadline in cloud computing environment. We develop novel schemes for encoding, population initialization, crossover, and mutations operators of genetic algorithm. Our proposal considers all the essential characteristics of the cloud as well as VM performance variation and acquisition delay. Performance evaluation on some well-known scientific workflows, such as Montage, LIGO, CyberShake, and Epigenomics of different size exhibits that our proposed algorithm performs better than the current state-of-the-art algorithms.


Measuring #GamerGate: A Tale of Hate, Sexism, and Bullying

2009-2014 PhD in Computer Science, University of California Santa Barbara, Santa Barbara, CA. Dissertation title: “Stepping Up the Cybersecurity Game: Protecting Online Services from Malicious Activity” 2014 MSc in Computer Science, University of California Santa Barbara, Santa Barbara, CA. 2006-2009 Laurea Specialistica in Computer Engineering (MSc equivalent), Università degli Studi di Genova, Genova, Italy. Thesis title: “A Distributed System for Intrusion Prevention” 2003-2006 Laurea Triennale in Computer Engineering (BSc equivalent), Università degli Studi di Genova, Genova, Italy. Thesis title: “Computer Security in a Linux System” (in Italian) 1998-2003 Liceo Classico A. D’Oria, High School, Genova, Italy. Focus on humanities


A fast-transient LDO based on buffered flipped voltage follower

In this work, the analysis of the flipped voltage follower (FVF) based single-transistor-control (STC) LDO is given. Two evolved versions of FVF, cascaded FVF (CAFVF) and level shifted FVF (LSFVF), are studied. Then, a buffered FVF (BFVF) for LDO application is proposed, combining the virtues of both CAFVF and LSFVF structures. It alleviates the minimum loading requirement of FVF and the simulation result shows that it has faster transient response and better load regulation.


Double hierarchy hesitant fuzzy linguistic term set and MULTIMOORA method: A case of study to evaluate the implementation status of haze controlling measures

In recent years, hesitant fuzzy linguistic term sets (HFLTSs) have been studied by many scholars and are becoming gradually mature. However, some shortcomings of HFLTS also emerged. To describe the complex linguistic terms or linguistic term sets more accurately and reasonably, in this paper, we introduce the novel concepts named double hierarchy linguistic term set (DHLTS) and double hierarchy hesitant fuzzy linguistic term set (DHHFLTS). The operational laws and properties of the DHHFLTSs are developed as well. Afterwards, we investigate the multiple criteria decision making model with double hierarchy hesitant fuzzy linguistic information. We develop a double hierarchy hesitant fuzzy linguistic MULTIMOORA (DHHFL-MULTIMOORA) method to solve it. Furthermore, we apply the DHHFL-MULTIMOORA method to deal with a practical case about selecting the optimal city in China by evaluating the implementation status of haze controlling measures. Some comparisons between the DHHFL-MULTIMOORA method and the hesitant fuzzy linguistic TOPSIS method are provided to show the advantages of the proposed method. © 2017 Elsevier B.V. All rights reserved. l p v t t s c e P t t g


Behavioral activation and inhibition systems and the severity and course of depression.

Theorists have proposed that depression is associated with abnormalities in the behavioral activation (BAS) and behavioral inhibition (BIS) systems. In particular, depressed individuals are hypothesized to exhibit deficient BAS and overactive BIS functioning. Self-reported levels of BAS and BIS were examined in 62 depressed participants and 27 nondepressed controls. Clinical functioning was assessed at intake and at 8-month follow-up. Relative to nondepressed controls, depressed participants reported lower BAS levels and higher BIS levels. Within the depressed group, lower BAS levels were associated with greater concurrent depression severity and predicted worse 8-month outcome. Levels of both BIS and BAS showed considerable stability over time and clinical state. Overall, results suggest that BAS dysregulation exacerbates the presentation and course of depressive illness.


Direct voltage control of DC-DC boost converters using model predictive control based on enumeration

This paper presents a model predictive control (MPC) approach for the dc-dc boost converter. Based on a hybrid model of the converter suitable for both continuous and discontinuous conduction mode an objective function is formulated, which is to be minimized. The proposed MPC scheme, utilized as a voltage-mode controller, achieves regulation of the output voltage to its reference, without requiring a subsequent current control loop. Simulation and experimental results are provided to demonstrate the merits of the proposed control methodology, which include fast transient response and robustness.


A Logit Model of Brand Choice Calibrated on Scanner Data

Optical scanning of the Universal Product Code in supermarkets provides a new level of detail and completeness in household panel data and makes possible the construction of more comprehensive brand choice models than hitherto possible. A multinomial logit model calibrated on 32 weeks of purchases of regular ground coffee by 100 households shows high statistical significance for the explanatory variables of brand loyalty, size loyalty, presence/absence of store promotion, regular shelf price and promotional price cut. The model is quite parsimonious in that the coefficients of these variables are modeled to be the same for all coffee brand-sizes. Considering its parsimony, the calibrated model predicts surprisingly well the share of purchases by brand-size in a hold-out sample of 100 households over the 32 week calibration period and a subsequent 20 week forecast period. Discrepencies in prediction are conjectured to be due in part to missing variables. Three short term market response measures are calculated from the model: regular shelf price elasticity of share, percent share increase from a promotion with a median price cut, and promotional price cut elasticity of share. Responsiveness varies across brand-sizes in a systematic way with large share brand-sizes showing less responsiveness in percentage terms. On the basis of the model a quantitative picture emerges of groups of loyal customers who are relatively insensitive to marketing actions and a pool of switchers who are quite responsive.


Global H∞ Consensus of Multi-Agent Systems with Lipschitz Nonlinear Dynamics

Abstract: This paper addresses the global consensus problems of a class of nonlinear multi-agent systems with Lipschitz nonlinearity and directed communication graphs, by using a distributed consensus protocol based on the relative states of neighboring agents. A two-step algorithm is presented to construct a protocol, under which a Lipschitz multi-agent system without disturbances can reach global consensus for a strongly connected directed communication graph. Another algorithm is then given to design a protocol which can achieve global consensus with a guaranteed H∞ performance for a Lipschitz multiagent system subject to external disturbances. The case with a leader-follower communication graph is also discussed. Finally, the effectiveness of the theoretical results is demonstrated through a network of single-link manipulators.


Few-Shot Image Recognition by Predicting Parameters from Activations

In this paper, we are interested in the few-shot learning problem. In particular, we focus on a challenging scenario where the number of categories is large and the number of examples per novel category is very limited, e.g. 1, 2, or 3. Motivated by the close relationship between the parameters and the activations in a neural network associated with the same category, we propose a novel method that can adapt a pre-trained neural network to novel categories by directly predicting the parameters from the activations. Zero training is required in adaptation to novel categories, and fast inference is realized by a single forward pass. We evaluate our method by doing few-shot image recognition on the ImageNet dataset, which achieves the state-of-the-art classification accuracy on novel categories by a significant margin while keeping comparable performance on the large-scale categories. We also test our method on the MiniImageNet dataset and it strongly outperforms the previous state-of-the-art methods.


Field test of autonomous loading operation by wheel loader

The authors have been conducting research on an autonomous system for loading operation by wheel loader. Experimental results at a field test site using full-size model (length: 6.1m) will be described in this paper. Basic structure of system consists of three sub systems: measuring and modeling of environment, task planning and motion control. The experimental operation includes four cycles of scooping and loading to dump truck. The experimental results prove that the developed system performed the autonomous operation smoothly and completed the mission.


Feature-Oriented Software Product Lines

Inevitably, reading is one of the requirements to be undergone. To improve the performance and quality, someone needs to have something new every day. It will suggest you to have more inspirations, then. However, the needs of inspirations will make you searching for some sources. Even from the other people experience, internet, and many books. Books and internet are the recommended media to help you improving your quality and performance.


DaTuM: Dynamic tone mapping technique for OLED display power saving based on video classification

The adoption of the latest OLED (organic light emitting diode) technology does not change the fact that screen is still one of the most energy-consuming modules in modern smartphones. In this work, we found that video streams from the same video category share many common power consumption features on OLED screens. Therefore, we are able to build a Hidden Markov Model (HMM) classifier to categorize videos based on OLED screen power characteristics. Using this HMM classifier, we propose a video classification based dynamic tone mapping (DTM) scheme, namely, DaTuM, to remap output color range and minimize the power-hungry color compositions on OLED screens for power saving. Experiment shows that DaTuM scheme averagely reduces OLED screen power by 17.8% with minimum display quality degradation. Compared to DTM scheme based on official category info provided by the video sources and one state-of-the-art scheme, DaTuM substantially enhances OLED screens' power efficiency and display quality controllability.


Coordinate Noun Phrase Disambiguation in a Generative Parsing Model

In this paper we present methods for improving the disambiguation of noun phrase (NP) coordination within the framework of a lexicalised history-based parsing model. As well as reducing noise in the data, we look at modelling two main sources of information for disambiguation: symmetry in conjunct structure, and the dependency between conjunct lexical heads. Our changes to the baseline model result in an increase in NP coordination dependency f-score from 69.9% to 73.8%, which represents a relative reduction in f-score error of 13%.


The max-min hill-climbing Bayesian network structure learning algorithm

We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and search-and-score techniques in a principled and effective way. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. In our extensive empirical evaluation MMHC outperforms on average and in terms of various metrics several prototypical and state-of-the-art algorithms, namely the PC, Sparse Candidate, Three Phase Dependency Analysis, Optimal Reinsertion, Greedy Equivalence Search, and Greedy Search. These are the first empirical results simultaneously comparing most of the major Bayesian network algorithms against each other. MMHC offers certain theoretical advantages, specifically over the Sparse Candidate algorithm, corroborated by our experiments. MMHC and detailed results of our study are publicly available at http://www.dsl-lab.org/supplements/mmhc_paper/mmhc_index.html.


Ordering-Based Search: A Simple and Effective Algorithm for Learning Bayesian Networks

One of the basic tasks for Bayesian networks (BNs) is that of learning a network structure from data. The BN-learning problem is NPhard, so the standard solution is heuristic search. Many approaches have been proposed for this task, but only a very small number outperform the baseline of greedy hill-climbing with tabu lists; moreover, many of the proposed algorithms are quite complex and hard to implement. In this paper, we propose a very simple and easy-toimplement method for addressing this task. Our approach is based on the well-known fact that the best network (of bounded in-degree) consistent with a given node ordering can be found very efficiently. We therefore propose a search not over the space of structures, but over the space of orderings, selecting for each ordering the best network consistent with it. This search space is much smaller, makes more global search steps, has a lower branching factor, and avoids costly acyclicity checks. We present results for this algorithm on both synthetic and real data sets, evaluating both the score of the network found and in the running time. We show that orderingbased search outperforms the standard baseline, and is competitive with recent algorithms that are much harder to implement.


Learning Bayesian Networks: The Combination of Knowledge and Statistical Data