title: The Parasocial Contact Hypothesis

abstract: We propose a communication analogue to Allport's (1954) Contact Hypothesis called the Parasocial Contact Hypothesis (PCH). If people process mass-mediated parasocial interaction in a manner similar to interpersonal interaction, then the socially beneficial functions of intergroup contact may result from parasocial contact. We describe and test the PCH with respect to majority group members' level of prejudice in three studies, two involving parasocial contact with gay men (Six Feet Under and Queer Eye for the Straight Guy) and one involving parasocial contact with comedian and male transvestite Eddie Izzard. In all three studies, parasocial contact was associated with lower levels of prejudice. Moreover, tests of the underlying mechanisms of PCH were generally supported, suggesting that parasocial contact facilitates positive parasocial responses and changes in beliefs about the attributes of minority group categories.


title: ERCP in Patients With Cirrhosis: “Risky Business” Or “Just Do It”?

abstract: Acute-on-chronic-liver-failure (ACLF) describes acute deterioration of liver function in patients with decompensated cirrhosis resulting in organ failure, and can occur as a procedural complication. In the current issue, Leal et al found that, compared to in-patients with cirrhosis who did not undergo interventions, ACLF was significantly more common among undergoing ERCP, but not more so than other non-ERCP interventions. ACLF risk increased significantly, however, if an ERCP-related AE occurred. ACLF can be a consequence of both the indication for and a complication of ERCP. When the indication is strong, the benefit of ERCP still outweighs risk among patients with cirrhosis.


title: Research of Fan-Out Panel Level Package (FOPLP) manufacturing process using single-sided adhesive tape

abstract: Fan-Out Panel Level Package (FOPLP) is well-known process to extend for Fan-Out Wafer Level Package (FOWLP) efficiently in terms of area-fill factor, large-area processability and manufacturing cost. However, they typically require us many unique and novel equipment and thus enormous initial cost tremendously. Here we report new FOPLP manufacturing processes involving single-sided adhesive tapes. We investigated that the warpage of molded package panel, called “panel warpage”, will be critically affected by an internal residual stress in the panel which depends on several parameters of the adhesive tapes such as tape thickness and thermomechanical properties of the molding material.


title: Automatic Extrinsic Calibration of a Camera and a 3D LiDAR Using Line and Plane Correspondences

abstract: In this paper, we address the problem of extrinsic calibration of a camera and a 3D Light Detection and Ranging (LiDAR) sensor using a checkerboard. Unlike previous works which require at least three checkerboard poses, our algorithm reduces the minimal number of poses to one by combining 3D line and plane correspondences. Besides, we prove that parallel planar targets with parallel boundaries provide the same constraints in our algorithm. This allows us to place the checkerboard close to the LiDAR so that the laser points better approximate the target boundary without loss of generality. Moreover, we present an algorithm to estimate the similarity transformation between the LiDAR and the camera for the applications where only the correspondences between laser points and pixels are concerned. Using a similarity transformation can simplify the calibration process since the physical size of the checkerboard is not needed. Meanwhile, estimating the scale can yield a more accurate result due to the inevitable measurement errors of the checkerboard size and the LiDAR intrinsic scale factor that transforms the LiDAR measurement to the metric measurement. Our algorithm is validated through simulations and experiments. Compared to the plane-only algorithms, our algorithm can obtain more accurate result by fewer number of poses. This is beneficial to the large-scale commercial application.


title: RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information

abstract: Distantly-supervised Relation Extraction (RE) methods train an extractor by automatically aligning relation instances in a Knowledge Base (KB) with unstructured text. In addition to relation instances, KBs often contain other relevant side information, such as aliases of relations (e.g., founded and co-founded are aliases for the relation founderOfCompany). RE models usually ignore such readily available side information. In this paper, we propose RESIDE, a distantly-supervised neural relation extraction method which utilizes additional side information from KBs for improved relation extraction. It uses entity type and relation alias information for imposing soft constraints while predicting relations. RESIDE employs Graph Convolution Networks (GCN) to encode syntactic information from text and improves performance even when limited side information is available. Through extensive experiments on benchmark datasets, we demonstrate RESIDE's effectiveness. We have made RESIDE's source code available to encourage reproducible research.


Blockchain and Its Applications


title: Popular Nutrition-Related Mobile Apps: A Feature Assessment

abstract: Background: A key challenge in human nutrition is the assessment of usual food intake. This is of particular interest given recent proposals of eHealth personalized interventions. The adoption of mobile phones has created an opportunity for assessing and improving nutrient intake as they can be used for digitalizing dietary assessments and providing feedback. In the last few years, hundreds of nutrition-related mobile apps have been launched and installed by millions of users. Objective: This study aims to analyze the main features of the most popular nutrition apps and to compare their strategies and technologies for dietary assessment and user feedback. Methods: Apps were selected from the two largest online stores of the most popular mobile operating systems—the Google Play Store for Android and the iTunes App Store for iOS—based on popularity as measured by the number of installs and reviews. The keywords used in the search were as follows: calorie(s), diet, diet tracker, dietician, dietitian, eating, fit, fitness, food, food diary, food tracker, health, lose weight, nutrition, nutritionist, weight, weight loss, weight management, weight watcher, and ww calculator. The inclusion criteria were as follows: English language, minimum number of installs (1 million for Google Play Store) or reviews (7500 for iTunes App Store), relation to nutrition (ie, diet monitoring or recommendation), and independence from any device (eg, wearable) or subscription. Results: A total of 13 apps were classified as popular for inclusion in the analysis. Nine apps offered prospective recording of food intake using a food diary feature. Food selection was available via text search or barcode scanner technologies. Portion size selection was only textual (ie, without images or icons). All nine of these apps were also capable of collecting physical activity (PA) information using self-report, the global positioning system (GPS), or wearable integrations. Their outputs focused predominantly on energy balance between dietary intake and PA. None of these nine apps offered features directly related to diet plans and motivational coaching. In contrast, the remaining four of the 13 apps focused on these opportunities, but without food diaries. One app—FatSecret—also had an innovative feature for connecting users with health professionals, and another—S Health—provided a nutrient balance score. Conclusions: The high number of installs indicates that there is a clear interest and opportunity for diet monitoring and recommendation using mobile apps. All the apps collecting dietary intake used the same nutrition assessment method (ie, food diary record) and technologies for data input (ie, text search and barcode scanner). Emerging technologies, such as image recognition, natural language processing, and artificial intelligence, were not identified. None of the apps had a decision engine capable of providing personalized diet advice. [JMIR Mhealth Uhealth 2016;4(3):e85]


title: Contrast Enhancement Based on Layered Difference Representation of 2D Histograms

abstract: A novel contrast enhancement algorithm based on the layered difference representation of 2D histograms is proposed in this paper. We attempt to enhance image contrast by amplifying the gray-level differences between adjacent pixels. To this end, we obtain the 2D histogram h(k, k + l ) from an input image, which counts the pairs of adjacent pixels with gray-levels k and k + l , and represent the gray-level differences in a tree-like layered structure. Then, we formulate a constrained optimization problem based on the observation that the gray-level differences, occurring more frequently in the input image, should be more emphasized in the output image. We first solve the optimization problem to derive the transformation function at each layer. We then combine the transformation functions at all layers into the unified transformation function, which is used to map input gray-levels to output gray-levels. Experimental results demonstrate that the proposed algorithm enhances images efficiently in terms of both objective quality and subjective quality.


Word Embedding and WordNet Based Metaphor Identification and Interpretation


BIOAVAILABILITY OF METALS


title: A molecular dynamics study of round and flattened carbon nanotube structures

abstract: Large diameter single wall carbon nanotubes exist with both round right cylinder and barbell-like flattened cross sections. Using an adaptive intermolecular reactive bond order potential, we examine the characteristics of both configurations by using molecular dynamics simulations. We then continue to examine the dynamical transition from the flattened to round state by charge injection. Predictions based on these simulations provide a basis for the design of active fluid transport devices, and nanoscale mechanical systems/motors.


title: Mining entity attribute synonyms via compact clustering

abstract: Entity attribute values, such as "lord of the rings" for movie.title or "infant" for shoe.gender, are atomic components of entity expressions. Discovering alternative surface forms of attribute values is important for improving entity recognition and retrieval. In this work, we propose a novel compact clustering framework to jointly identify synonyms for a set of attribute values. The framework can integrate signals from multiple information sources into a similarity function between attribute values. And the weights of these signals are optimized in an unsupervised manner. Extensive experiments across multiple domains demonstrate the effectiveness of our clustering framework for mining entity attribute synonyms.


title: Analysis of the Anatomic Changes of the Aging Facial Skeleton Using Computer-Assisted Tomography

abstract: PURPOSE ::: The bony skeleton serves as the scaffolding for the soft tissues of the face; however, age-related changes of bony morphology are not well defined. This study sought to compare the anatomic relationships of the facial skeleton and soft tissue structures between young and old men and women. ::: ::: ::: METHODS ::: A retrospective review of CT scans of 100 consecutive patients imaged at Duke University Medical Center between 2004 and 2007 was performed using the Vitrea software package. The study population included 25 younger women (aged 18-30 years), 25 younger men, 25 older women (aged 55-65 years), and 25 older men. Using a standardized reference line, the distances from the anterior corneal plane to the superior orbital rim, lateral orbital rim, lower eyelid fat pad, inferior orbital rim, anterior cheek mass, and pyriform aperture were measured. Three-dimensional bony reconstructions were used to record the angular measurements of 4 bony regions: glabellar, orbital, maxillary, and pyriform aperture. ::: ::: ::: RESULTS ::: The glabellar (p = 0.02), orbital (p = 0.0007), maxillary (p = 0.0001), and pyriform (p = 0.008) angles all decreased with age. The maxillary pyriform (p = 0.003) and infraorbital rim (p = 0.02) regressed with age. Anterior cheek mass became less prominent with age (p = 0.001), but the lower eyelid fat pad migrated anteriorly over time (p = 0.007). ::: ::: ::: CONCLUSIONS ::: The facial skeleton appears to remodel throughout adulthood. Relative to the globe, the facial skeleton appears to rotate such that the frontal bone moves anteriorly and inferiorly while the maxilla moves posteriorly and superiorly. This rotation causes bony angles to become more acute and likely has an effect on the position of overlying soft tissues. These changes appear to be more dramatic in women.


title: Low cost underwater gliders for littoral marine research

abstract: Current off-the-shelf underwater gliders (UGs) are large, heavy, expensive, and difficult to modify, both in hardware and software, which limits their use for multivehicle coordination experiments and deployment in high risk environments. To address these challenges, the Nonlinear and Autonomous Systems Laboratory (NAS Lab) at Michigan Tech has designed two types of UGs for concept development, testing, and problem solving, and additionally, for scaffolding advanced interest and education in engineering. The first, a Glider for Underwater Presentation and Promoting Interest in Engineering (GUPPIE), nationally targets high school students and undergraduates to provide these students a UG platform for hands-on experience in concept development, testing, and problem solving. The second platform is a Glider for Autonomous Littoral Underwater Research (GALUR). At 10% of the cost of current models, the GALUR was designed to serve as a low cost multivehicle control testbed for disparate research groups who need to test and validate control algorithms. Still a highly-maneuverable UG, the GALUR allows researchers to address underwater communication issues by implementing control strategies for individual and multiple vehicle underwater data collection and mapping. Through the process of developing and testing these two UGs, the research team is ultimately working toward their long term goal of developing a fleet of low cost highly maneuverable underwater gliders. This paper details the challenges and milestones of the development process, and outlines the future research trajectory and goals.


title: Investigation of Speech Separation as a Front-End for Noise Robust Speech Recognition

abstract: Recently, supervised classification has been shown to work well for the task of speech separation. We perform an in-depth evaluation of such techniques as a front-end for noise-robust automatic speech recognition (ASR). The proposed separation front-end consists of two stages. The first stage removes additive noise via time-frequency masking. The second stage addresses channel mismatch and the distortions introduced by the first stage; a non-linear function is learned that maps the masked spectral features to their clean counterpart. Results show that the proposed front-end substantially improves ASR performance when the acoustic models are trained in clean conditions. We also propose a diagonal feature discriminant linear regression (dFDLR) adaptation that can be performed on a per-utterance basis for ASR systems employing deep neural networks and HMM. Results show that dFDLR consistently improves performance in all test conditions. Surprisingly, the best average results are obtained when dFDLR is applied to models trained using noisy log-Mel spectral features from the multi-condition training set. With no channel mismatch, the best results are obtained when the proposed speech separation front-end is used along with multi-condition training using log-Mel features followed by dFDLR adaptation. Both these results are among the best on the Aurora-4 dataset.


title: COSS: Content-Based Subscription as an IoT Service

abstract: Publish/subscribe (pub/sub) systems are widely used in numerous Internet-Of-Things (IoT) applications such as environment monitoring, supply chain tracing, healthcare, and vehicle networks. In these applications, publishers (e.g. Smart devices, sensors) are continuously generating large volume of data with an extremely high throughput, whereas subscribers are only interested in a small portion of the data. Recently, content-based subscription systems have raised more and more attentions by the researchers where subscribers can specify rules on the content of messages that are composed of many attributes. For example, in traffic monitoring, an operator is only interested in the data within a specified area defined by constraints on latitude and longitude instead of the whole map. In this paper, we present COSS, the first Content-based Subscription Service for IoT with natural multi-tenant support and easy-to-use REST APIs. Moreover, we investigate in the problem of Balanced Rule Engine Partitioning for content-based subscription under the Tenant-Message-Rule (TMR) model. We show the NP-hardness of the problem and design a heuristics to enable COSS to adaptively adjust the message distribution according to the workload history, and to scale on both the high data throughput of IoT workloads and multi-tenant. Extensive experiments show that COSS offers high performance and scalability for content-based subscription in terms of the number of tenants, and the data throughput of the messages.


title: WiFi-aware as a connectivity solution for IoT pairing IoT with WiFi aware technology: Enabling new proximity based services

abstract: Internet of Things is referred to a combination of physical devices having sensors and connection capabilities enabling them to interact with each other (machine to machine) and can be controlled remotely via cloud engine. Success of an IoT device depends on the ability of systems and devices to securely sample, collect, and analyze data, and then transmit over link, protocol, or media selections based on stated requirements, all without human intervention. Among the requirements of the IoT, connectivity is paramount. It's hard to imagine that a single communication technology can address all the use cases possible in home, industry and smart cities. Along with the existing low power technologies like Zigbee, Bluetooth and 6LoWPAN, 802.11 WiFi standards are also making its way into the market with its own advantages in high range and better speed. Along with IEEE, WiFi Alliance has a new standard for the proximity applications. Neighbor Awareness Network (NAN) popularly known as WiFi Aware is that standard which enables low power discovery over WiFi and can light up many proximity based used cases. In this paper we discuss how NAN can influence the emerging IoT market as a connectivity solution for proximity assessment and contextual notifications with its benefits in some of the scenarios. When we consider WiFi the infrastructure already exists in terms of access points all around in public and smart phones or tablets come with WiFi as a default feature hence enabling NAN can be easy and if we can pair them with IoT, many innovative use cases can evolve.


title: Efficient Deep Learning for Stereo Matching

abstract: In the past year, convolutional neural networks have been shown to perform extremely well for stereo estimation. However, current architectures rely on siamese networks which exploit concatenation followed by further processing layers, requiring a minute of GPU computation per image pair. In contrast, in this paper we propose a matching network which is able to produce very accurate results in less than a second of GPU computation. Towards this goal, we exploit a product layer which simply computes the inner product between the two representations of a siamese architecture. We train our network by treating the problem as multi-class classification, where the classes are all possible disparities. This allows us to get calibrated scores, which result in much better matching performance when compared to existing approaches.


title: Encoder-Decoder Shift-Reduce Syntactic Parsing

abstract: Starting from NMT, encoder-decoder neu- ral networks have been used for many NLP problems. Graph-based models and transition-based models borrowing the en- coder components achieve state-of-the-art performance on dependency parsing and constituent parsing, respectively. How- ever, there has not been work empirically studying the encoder-decoder neural net- works for transition-based parsing. We apply a simple encoder-decoder to this end, achieving comparable results to the parser of Dyer et al. (2015) on standard de- pendency parsing, and outperforming the parser of Vinyals et al. (2015) on con- stituent parsing.


title: FaaSter, better, cheaper : the prospect of serverless scientific computing and HPC

abstract: The adoption of cloud computing facilities and programming models differs vastly between different application domains. Scalable web applications, low-latency mobile backends and on-demand provisioned databases are typical cases for which cloud services on the platform or infrastructure level exist and are convincing when considering technical and economical arguments. Applications with specific processing demands, including high-performance computing, high-throughput computing and certain flavours of scientific computing, have historically required special configurations such as compute- or memory-optimised virtual machine instances. With the rise of function-level compute instances through Function-as-a-Service (FaaS) models, the fitness of generic configurations needs to be re-evaluated for these applications. We analyse several demanding computing tasks with regards to how FaaS models compare against conventional monolithic algorithm execution. Beside the comparison, we contribute a refined FaaSification process for legacy software and provide a roadmap for future work.


Between MDPs and Semi-MDPs: Learning, Planning, and Representing Knowledge at Multiple Temporal Scales


title: A document corpus browser for in-depth reading

abstract: Software tools, including Web browsers, e-books, electronic document formats, search engines, and digital libraries are changing the way people read, making it easier for them to find and view documents. However, while these tools provide significant help with short-term reading projects involving small numbers of documents, they provide less help with longer-term reading projects, in which a topic is to be understood in depth by reading many documents. For such projects, readers must find and manage many documents and citations, remember what has been read, and prioritize what to read next. This paper describes three integrated software tools that facilitate in-depth reading. A first tool extracts citation information from documents. A second finds on-line documents from their citations. The last is a document corpus browser that uses a zoomable user interface to show a corpus at multiple granularities while supporting reading tasks that take days, weeks, or longer. We describe these tools and the design principles that motivated them.


title: Intelligent Bug Algorithm (IBA): A Novel Strategy to Navigate Mobile Robots Autonomously

abstract: This research proposed an intelligent obstacle avoidance algorithm to navigate an autonomous mobile robot. The presented Intelligent Bug Algorithm (IBA) over performs and reaches the goal in relatively less time as compared to existing Bug algorithms. The improved algorithm offers a goal oriented strategy by following smooth and short trajectory. This has been achieved by continuously considering the goal position during obstacle avoidance. The proposed algorithm is computationally inexpensive and easy to tune. The paper also presents the performance comparison of IBA and reported Bug algorithms. Simulation results of robot navigation in an environment with obstacles demonstrate the performance of the improved algorithm.


title: A Simple Yet Effective Balanced Edge Partition Model for Parallel Computing

abstract: Graph edge partition models have recently become an appealing alternative to graph vertex partition models for distributed computing due to their flexibility in balancing loads and their performance in reducing communication cost [6, 16]. In this paper, we propose a simple yet effective graph edge partitioning algorithm. In practice, our algorithm provides good partition quality (and better than similar state-of-the-art edge partition approaches, at least for power-law graphs) while maintaining low partition overhead. In theory, previous work [6] showed that an approximation guarantee of O(dmax√ log n log k) apply to the graphs with m=Ω(k2) edges (k is the number of partitions). We further rigorously proved that this approximation guarantee hold for all graphs. ::: We show how our edge partition model can be applied to parallel computing. We draw our example from GPU program locality enhancement and demonstrate that the graph edge partition model does not only apply to distributed computing with many computer nodes, but also to parallel computing in a single computer node with a many-core processor.


title: Evaluating Bayesian networks' precision for detecting students' learning styles

abstract: Students are characterized by different learning styles, focusing on different types of information and processing this information in different ways. One of the desirable characteristics of a Web-based education system is that all the students can learn despite their different learning styles. To achieve this goal we have to detect how students learn: reflecting or acting; steadily or in fits and starts; intuitively or sensitively. In this work, we evaluate Bayesian networks at detecting the learning style of a student in a Web-based education system. The Bayesian network models different aspects of a student behavior while he/she works with this system. Then, it infers his/her learning styles according to the modeled behaviors. The proposed Bayesian model was evaluated in the context of an Artificial Intelligence Web-based course. The results obtained are promising as regards the detection of students' learning styles. Different levels of precision were found for the different dimensions or aspects of a learning style.


title: AMBER: Automatic Supervision for Multi-Attribute Extraction

abstract: The extraction of multi-attribute objects from the deep web is the bridge between the unstructured web and structured data. Existing approaches either induce wrappers from a set of human-annotated pages or leverage repeated structures on the page without supervision. What the former lack in automation, the latter lack in accuracy. Thus accurate, automatic multi-attribute object extraction has remained an open challenge. ::: AMBER overcomes both limitations through mutual supervision between the repeated structure and automatically produced annotations. Previous approaches based on automatic annotations have suffered from low quality due to the inherent noise in the annotations and have attempted to compensate by exploring multiple candidate wrappers. In contrast, AMBER compensates for this noise by integrating repeated structure analysis with annotation-based induction: The repeated structure limits the search space for wrapper induction, and conversely, annotations allow the repeated structure analysis to distinguish noise from relevant data. Both, low recall and low precision in the annotations are mitigated to achieve almost human quality (more than 98 percent) multi-attribute object extraction. ::: To achieve this accuracy, AMBER needs to be trained once for an entire domain. AMBER bootstraps its training from a small, possibly noisy set of attribute instances and a few unannotated sites of the domain.


title: Complex Contagions: A Decade in Review

abstract: Since the publication of “Complex Contagions and the Weakness of Long Ties” in 2007, complex contagions have been studied across an enormous variety of social domains. In reviewing this decade of research, we discuss recent advancements in applied studies of complex contagions, particularly in the domains of health, innovation diffusion, social media, and politics. We also discuss how these empirical studies have spurred complementary advancements in the theoretical modeling of contagions, which concern the effects of network topology on diffusion, as well as the effects of individual-level attributes and thresholds. In synthesizing these developments, we suggest three main directions for future research. The first concerns the study of how multiple contagions interact within the same network and across networks, in what may be called an ecology of contagions. The second concerns the study of how the structure of thresholds and their behavioral consequences can vary by individual and social context. The third area concerns the roles of diversity and homophily in the dynamics of complex contagion, including both diversity of demographic profiles among local peers and the broader notion of structural diversity within a network. Throughout this discussion, we make an effort to highlight the theoretical and empirical opportunities that lie ahead.


title: Consensus on media violence effects: Comment on Bushman, Gollwitzer, and Cruz (2015).

abstract: Craig A. Anderson, Department of Psychology, Iowa State University; Luca Andrighetto, Dip. di Scienze della Formazione, University of Genova; Bruce D. Bartholow, Department of Psychological Sciences, University of Missouri; Laurent Begue, Laboratoire Interuniversitaire de Psychologie, University of Grenoble; Paul Boxer, Department of Psychology, Rutgers University; Jeanne Funk Brockmyer, Department of Psychology, University of Toledo; Melinda C. R. Burgess, Department of Psychology, Southwestern Oklahoma State University; Esther Calvete, Deusto International Research School, Universidad de Deusto; Joanne Cantor, Department of Communication Arts, University of Wisconsin; Sarah M. Coyne, School of Family Life, Brigham Young University; Karen Dill-Shackleford, Fielding Graduate University; Ed Donnerstein, Department of Communication, University of Arizona; Alessandro Gabbiadini, University of Milano Bicocca; Bryan Gibson, Department of Psychology, Central Michigan University; Youssef Hasan, Qatar University; Adam K. Lueke, Department of Psychology, Central Michigan University; Izaskun Orue, Dpto. de Personalidad, Evaluacion y Tratamiento psicologico, Universidad de Deusto; Paolo Riva, Department of Psychology, University of Milano-Bicocca; Victor C. Strasburger, School of Medicine, University of New Mexico; Chiara Volpato, Department of Psychology, University of MilanoBicocca; Wayne Warburton, Department of Psychology, Macquarie University. Correspondence concerning this article should be addressed to Craig A. Anderson, Department of Psychology, Iowa State University, Ames, IA 50011-3180. E-mail: caa@iastate.edu T hi s do cu m en t is co py ri gh te d by th e A m er ic an Ps yc ho lo gi ca l A ss oc ia tio n or on e of its al lie d pu bl is he rs .


title: iSphere: Self-Luminous Spherical Drone Display

abstract: We present iSphere, a flying spherical display that can display high resolution and bright images in all directions from anywhere in 3D space. Our goal is to build a new platform which can physically and directly emerge arbitrary bodies in the real world. iSphere flies by itself using a built-in drone and creates a spherical display by rotating arcuate multi light-emitting diode (LED) tapes around the drone. As a result of the persistence of human vision, we see it as a spherical display flying in the sky. The proposed method yields large display surfaces, high resolution, drone mobility, high visibility and 360° field of view. Previous approaches fail to match these characteristics, because of problems with aerodynamics and payload. We construct a prototype and validate the proposed method. The unique characteristics and benefits of flying spherical display surfaces are discussed and we describe application scenarios based on iSphere such as guidance, signage and telepresence.


title: Spherical Wavelet Descriptors for Content-based 3D Model Retrieval

abstract: The description of 3D shapes with features that possess descriptive power and invariant under similarity transformations is one of the most challenging issues in content based 3D model retrieval. Spherical harmonics-based descriptors have been proposed for obtaining rotation invariant representations. However, spherical harmonic analysis is based on latitude-longitude parameterization of a sphere which has singularities at each pole. Consequently, features near the two poles are over represented while features at the equator are under-sampled, and variations of the north pole affects significantly the shape function. In this paper we discuss these issues and propose the usage of spherical wavelet transform as a tool for the analysis of 3D shapes represented by functions on the unit sphere. We introduce three new descriptors extracted from the wavelet coefficients, namely: (1) a subset of the spherical wavelet coefficients, (2) the L1 and, (3) the L2 energies of the spherical wavelet sub-bands. The advantage of this tool is three fold; First, it takes into account feature localization and local orientations. Second, the energies of the wavelet transform are rotation invariant. Third, shape features are uniformly represented which makes the descriptors more efficient. Spherical wavelet descriptors are natural extension of 3D Zernike moments and spherical harmonics. We evaluate, on the Princeton Shape Benchmark, the proposed descriptors regarding computational aspects and shape retrieval performance.


DELAYED SKIP CONNECTIONS FOR MUSIC CONTENT DRIVEN MOTION GENERATION


title: Learning disability prediction tool using ANN and ANFIS

abstract: Learning Disability (LD) is a neurological condition that affects a child's brain and impairs his ability to carry out one or many specific tasks. LD affects about 15 % of children enrolled in schools. The prediction of LD is a vital and intricate job. The aim of this paper is to design an effective and powerful tool, using the two intelligent methods viz., Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System, for measuring the percentage of LD that affected in school-age children. In this study, we are proposing some soft computing methods in data preprocessing for improving the accuracy of the tool as well as the classifier. The data preprocessing is performed through Principal Component Analysis for attribute reduction and closest fit algorithm is used for imputing missing values. The main idea in developing the LD prediction tool is not only to predict the LD present in children but also to measure its percentage along with its class like low or minor or major. The system is implemented in Mathworks Software MatLab 7.10. The results obtained from this study have illustrated that the designed prediction system or tool is capable of measuring the LD effectively.


title: A hybrid approach to the sentiment analysis problem at the sentence level

abstract: The objective of this article is to present a hybrid approach to the Sentiment Analysis problem at the sentence level. This new method uses natural language processing (NLP) essential techniques, a sentiment lexicon enhanced with the assistance of SentiWordNet, and fuzzy sets to estimate the semantic orientation polarity and its intensity for sentences, which provides a foundation for computing with sentiments. The proposed hybrid method is applied to three different data-sets and the results achieved are compared to those obtained using Naive Bayes and Maximum Entropy techniques. It is demonstrated that the presented hybrid approach is more accurate and precise than both Naive Bayes and Maximum Entropy techniques, when the latter are utilised in isolation. In addition, it is shown that when applied to datasets containing snippets, the proposed method performs similarly to state of the art techniques.


Effect of Language, Speaking Style and Speaker on Long-Term F0 Estimation


title: Scamander and the Rivers of Hades in Homer

abstract: At odyssey 10.488-95, in response to Odysseus' request that he and his men leave her island, Circe states that they must venture to Hades to consult with the Theban seer Teiresias. She gives Odysseus some ba? sic instructions on how to get there and what to do there (10.504-40): he should cross Ocean and beach his ship where there is a remote coastline and a grove of Persephone. He should then go himself to the house of Hades, where he should dig a pit and pour a libation. The location of Hades in the passage is further identified as a meeting point of four separate rivers: Acheron, Pyriphlegethon, Cocytus, and Styx:


title: FPGA Hardware Acceleration of Monte Carlo Simulations for the Ising Model

abstract: A two-dimensional Ising model with nearest-neighbors ferromagnetic interactions is implemented in a Field Programmable Gate Array (FPGA) board. Extensive Monte Carlo simulations were carried out using an efficient hardware representation of individual spins and a combined global-local LFSR random number generator. Consistent results regarding the descriptive properties of magnetic systems, like energy, magnetization and susceptibility are obtained while a speed-up factor of approximately six times is achieved in comparison to previous FPGA-based published works and almost $10^4$ times in comparison to a standard CPU simulation. A detailed description of the logic design used is given together with a careful analysis of the quality of the random number generator used. The obtained results confirm the potential of FPGAs for analyzing the statistical mechanics of magnetic systems.


title: Bayesian community-wide culture-independent microbial source tracking

abstract: SourceTracker finds the proportion and origin of contaminants in a given sample. Its database will prove useful in screening of metagenomic datasets for contaminants.


title: A User-Friendly Log Viewer for Storage Systems

abstract: System log files contains messages emitted from several modules within a system and carries valuable information about the system state such as device status and error conditions and also about the various tasks within the system such as program names, execution path, including function names and parameters, and the task completion status. For customers with remote support, the system collects and transmits these logs to a central enterprise repository, where these are monitored for alerts, problem forecasting, and troubleshooting. Very large log files limit the interpretability for the support engineers. For an expert, a large volume of log messages may not pose any problem; however, an inexperienced person may get flummoxed due to the presence of a large number of log messages. Often it is desired to present the log messages in a comprehensive manner where a person can view the important messages first and then go into details if required. In this article, we present a user-friendly log viewer where we first hide the unimportant or inconsequential messages from the log file. A user can then click a particular hidden view and get the details of the hided messages. Messages with low utility are considered inconsequential as their removal does not impact the end user for the aforesaid purpose such as problem forecasting or troubleshooting. We relate the utility of a message to the probability of its appearance in the due context. We present machine-learning-based techniques that computes the usefulness of individual messages in a log file. We demonstrate identification and discarding of inconsequential messages to shrink the log size to acceptable limits. We have tested this over real-world logs and observed that eliminating such low value data can reduce the log files significantly (30p to 55p), with minimal error rates (7p to 20p). When limited user feedback is available, we show modifications to the technique to learn the user intent and accordingly further reduce the error.


title: An event-centric approach to authoring stories in crowds

abstract: We present a graphical authoring tool for creating complex narratives in large, populated areas with crowds of virtual humans. With an intuitive drag-and-drop interface, our system enables an untrained author to assemble story arcs in terms of narrative events that seamlessly control either principal characters or choreographed heterogeneous crowds within the same conceptual structure. Smart Crowds allow groups of characters to be dynamically assembled and scheduled with ambient activities, while also permitting individual characters to be selected from the crowd and featured more prominently as an individual in a story with more sophisticated behavior. Our system runs in real-time at interactive rates with no pause or costly pre-computation step between creating a story and simulating it, making this approach ideal for storyboarding or pre-visualization of narrative sequences.


title: OXIDATIVE CHANGES AND PHOTOSYNTHESIS IN OAT PLANTS GROWN IN AS-CONTAMINATED SOIL

abstract: Summary. The effect of different arsenic concentrations on some physiological parameters in oats cultivar Hanza 152 was studied. Arsenic was applied as H 3 AsO 4 at concentrations of 40, 80 and 160 mg (As) per kg soil. The plants were grown in pots with 5 kg soil/pot. Physiological analysis showed a small negative effect of arsenic at concentration of 40 mg (As) per kg soil, but the higher dosages of 80 and 160 mg (As) per kg soil, generated stress in oats plant and as a consequence leaf gas-exchange was suppressed (14% and 25% for photosynthesis rate and 13% and 20% for transpiration intensity). The chlorophyll fluorescence ratio F v /F m decreased. The chlorophyll and protein content also decreased. The peroxidase activity and lipid peroxidation increased, considerably at 160 mg arsenic, per kg soil, which is a typical plant reaction to the oxidative stress.


title: Efficient and Expressive Knowledge Base Completion Using Subgraph Feature Extraction

abstract: We explore some of the practicalities of using random walk inference methods, such as the Path Ranking Algorithm (PRA), for the task of knowledge base completion. We show that the random walk probabilities computed (at great expense) by PRA provide no discernible benefit to performance on this task, so they can safely be dropped. This allows us to define a simpler algorithm for generating feature matrices from graphs, which we call subgraph feature extraction (SFE). In addition to being conceptually simpler than PRA, SFE is much more efficient, reducing computation by an order of magnitude, and more expressive, allowing for much richer features than paths between two nodes in a graph. We show experimentally that this technique gives substantially better performance than PRA and its variants, improving mean average precision from .432 to .528 on a knowledge base completion task using the NELL KB.


title: A camera flash based projector system for true scale metric reconstruction

abstract: Computer vision techniques have been applied for rapid and accurate structure recovery in many fields. Most methods perform poorly in areas containing little or no texture and in presence of repetitive patterns. We present a portable, cost-effective pattern projector system powered by the flash of a camera, to aid the reconstruction of such areas. No calibration is required between the camera-projector, projector-scene or pattern. We demonstrate the effectiveness of our system on various representative surfaces like metal, clay, porcelain and natural fibres with different inherent colors/textures. A pipeline is presented to automatically generate textured, true scale metric models, which can be used for quantitative studies or visualization. The practability of our system is explored in the specific area of digital archiving of historically significant objects.


title: Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability

abstract: We explore the concept of co-design in the context of neural network verification. Specifically, we aim to train deep neural networks that not only are robust to adversarial perturbations but also whose robustness can be verified more easily. To this end, we identify two properties of network models - weight sparsity and so-called ReLU stability - that turn out to significantly impact the complexity of the corresponding verification task. We demonstrate that improving weight sparsity alone already enables us to turn computationally intractable verification problems into tractable ones. Then, improving ReLU stability leads to an additional 4-13x speedup in verification times. An important feature of our methodology is its "universality," in the sense that it can be used with a broad range of training procedures and verification approaches.


title: Evidence-based lean logic profiles for conceptual data modelling languages

abstract: Multiple logic-based reconstruction of conceptual data modelling languages such as EER, UML Class Diagrams, and ORM exists. They mainly cover various fragments of the languages and none are formalised such that the logic applies simultaneously for all three modelling language families as unifying mechanism. This hampers interchangeability, interoperability, and tooling support. In addition, due to the lack of a systematic design process of the logic used for the formalisation, hidden choices permeate the formalisations that have rendered them incompatible. We aim to address these problems, first, by structuring the logic design process in a methodological way. We generalise and extend the DSL design process to apply to logic language design more generally and, in particular, by incorporating an ontological analysis of language features in the process. Second, availing of this extended process, of evidence gathered of language feature usage, and of computational complexity insights from Description Logics (DL), we specify logic profiles taking into account the ontological commitments embedded in the languages. The profiles characterise the minimum logic structure needed to handle the semantics of conceptual models, enabling the development of interoperability tools. There is no known DL language that matches exactly the features of those profiles and the common core is small (in the tractable $\mathcal{ALNI}$). Although hardly any inconsistencies can be derived with the profiles, it is promising for scalable runtime use of conceptual data models.


An Optimization Framework for Recovery of Speech from Phase-Encoded Spectrograms


title: A dual memory theory of the testing effect

abstract: A new theoretical framework for the testing effect—the finding that retrieval practice is usually more effective for learning than are other strategies—is proposed, the empirically supported tenet of which is that separate memories form as a consequence of study and test events. A simplest case quantitative model is derived from that framework for the case of cued recall. With no free parameters, that model predicts both proportion correct in the test condition and the magnitude of the testing effect across 10 experiments conducted in our laboratory, experiments that varied with respect to material type, retention interval, and performance in the restudy condition. The model also provides the first quantitative accounts of (a) the testing effect as a function of performance in the restudy condition, (b) the upper bound magnitude of the testing effect, (c) the effect of correct answer feedback, (d) the testing effect as a function of retention interval for the cases of feedback and no feedback, and (e) the effect of prior learning method on subsequent learning through testing. Candidate accounts of several other core phenomena in the literature, including test-potentiated learning, recognition versus cued recall training effects, cued versus free recall final test effects, and other select transfer effects, are also proposed. Future prospects and relations to other theories are discussed.


title: All-Digital Low-Dropout Regulator With Adaptive Control and Reduced Dynamic Stability for Digital Load Circuits

abstract: Digitally implementable LDOs embedded within digital functional units augment their analog counterparts for ultrafine-grained power management in digital ICs. Digital load circuits represent load currents with large and infrequent current transients and require a wide voltage range of operation, preferably down to the threshold voltage ( $V_{{\rm TH}}$ ) of the transistor. This paper presents a discrete-time, fully digital, scan-programmable LDO macro in a low-power 0.13-μm technology operating down to 1.07×, the transistor $V_{{\rm TH}}$ , and featuring greater than 90% current efficiency across a 50× current range through fine-grained clock gating and adaptive control. An 8× improvement in transient response time to large load steps is achieved through switched mode control. Both transient and steady-state operation models and measurements of the LDO are presented.


title: An Exploratory Study of How Developers Seek, Relate, and Collect Relevant Information during Software Maintenance Tasks

abstract: Much of software developers' time is spent understanding unfamiliar code. To better understand how developers gain this understanding and how software development environments might be involved, a study was performed in which developers were given an unfamiliar program and asked to work on two debugging tasks and three enhancement tasks for 70 minutes. The study found that developers interleaved three activities. They began by searching for relevant code both manually and using search tools; however, they based their searches on limited and misrepresentative cues in the code, environment, and executing program, often leading to failed searches. When developers found relevant code, they followed its incoming and outgoing dependencies, often returning to it and navigating its other dependencies; while doing so, however, Eclipse's navigational tools caused significant overhead. Developers collected code and other information that they believed would be necessary to edit, duplicate, or otherwise refer to later by encoding it in the interactive state of Eclipse's package explorer, file tabs, and scroll bars. However, developers lost track of relevant code as these interfaces were used for other tasks, and developers were forced to find it again. These issues caused developers to spend, on average, 35 percent of their time performing the mechanics of navigation within and between source files. These observations suggest a new model of program understanding grounded in theories of information foraging and suggest ideas for tools that help developers seek, relate, and collect information in a more effective and explicit manner


title: Elastic Scaling for Data Stream Processing

abstract: This article addresses the profitability problem associated with auto-parallelization of general-purpose distributed data stream processing applications. Auto-parallelization involves locating regions in the application's data flow graph that can be replicated at run-time to apply data partitioning, in order to achieve scale. In order to make auto-parallelization effective in practice, the profitability question needs to be answered: How many parallel channels provide the best throughput? The answer to this question changes depending on the workload dynamics and resource availability at run-time. In this article, we propose an elastic auto-parallelization solution that can dynamically adjust the number of channels used to achieve high throughput without unnecessarily wasting resources. Most importantly, our solution can handle partitioned stateful operators via run-time state migration, which is fully transparent to the application developers. We provide an implementation and evaluation of the system on an industrial-strength data stream processing platform to validate our solution.


title: Children and adults as intuitive scientists

abstract: The metaphor of children and lay adults as intuitive scientists has gained wide acceptance. Although useful in one sense, pertaining to scientific understanding, in another, pertaining to the process of scientific thinking, the metaphor may be fundamentally misleading. Research is reviewed indicating that processes of scientific thinking differ significantly in children, lay adults, and scientists. Hence, it is the instruments of scientific thinking, not just the products, that undergo "strong restructuring" (Carey, 1986). A framework for conceptualizing development of scientific thinking processes is proposed, centering on progressive differentiation and coordination of theory and evidence. This development is metacognitive, as well as strategic. It requires thinking about theories, rather than merely with them, and thinking about evidence, rather than merely being influenced by it, and, hence, reflects the attainment of control over the interaction of theories and evidence in one's own thinking. The metaphor of the lay adult—or the child—as an intuitive scientist has gained wide acceptance in the last decade. As the scientist explores the environment, constructs models as a basis for understanding it, and revises those models as new evidence is generated, so do lay people endeavor to make sense of their environments by processing data and constructing mental models based on these data. The highly influential volume by Nisbett and Ross (1980) and seminal research by Tversky and Kahneman that is highlighted in the Nisbett and Ross book have done much to promote the metaphor. More recently, it has been promoted as well by the literature on scientific understanding and conceptual change, discussed later, and to an extent by the general theory of induction proposed by Holland, Holyoak, Nisbett, and Thagard (1986), a theory whose principles are intended to apply to all forms of induction from the very simplest forms of concept formation to the thinking of scientists. As the research summarized by Nisbett and Ross documents, the particular inference rules that the intuitive scientist uses to interpret evidence and make inductive inferences are likely to be faulty. The intuitive scientist, for example, makes inferences based on insufficient sample size and overlooks base rates. Nevertheless, the process and goal of this intuitive scientific activity are thought to be analogous in the layperson and the professional scientist. In this article, I have a particular concern with the thinking of children and the ways in which it may or may not resemble that of scientists. I therefore focus on the metaphor of child as scientist, although I consider as well the thinking of lay adults and its resemblance to that of scientists. I explore two quite different senses in which this metaphor might be taken and conclude that, although it may be useful and productive in one sense, the child-as-scientist metaphor in another sense may be fundamentally misleading.


title: SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays

abstract: Chest X-ray (CXR) is one of the most commonly prescribed medical imaging procedures, often with over 2-10x more scans than other imaging modalities such as MRI, CT scan, and PET scans. These voluminous CXR scans place significant workloads on radiologists and medical practitioners. Organ segmentation is a crucial step to obtain effective computer-aided detection on CXR. In this work, we propose Structure Correcting Adversarial Network (SCAN) to segment lung fields and the heart in CXR images. SCAN incorporates a critic network to impose on the convolutional segmentation network the structural regularities emerging from human physiology. During training, the critic network learns to discriminate between the ground truth organ annotations from the masks synthesized by the segmentation network. Through this adversarial process the critic network learns the higher order structures and guides the segmentation model to achieve realistic segmentation outcomes. Extensive experiments show that our method produces highly accurate and natural segmentation. Using only very limited training data available, our model reaches human-level performance without relying on any existing trained model or dataset. Our method also generalizes well to CXR images from a different patient population and disease profiles, surpassing the current state-of-the-art.


title: An Experimentally Verified Active Gate Control Method for the Series Connection of IGBT/Diodes

abstract: The series connection of insulated gate bipolar transistor (IGBT)/diode devices allows the operation at voltage levels higher than the rated voltage of one IGBT/diode. However, due to individual parameter differences of the series-connected IGBT/diodes, it is difficult to ensure a proper voltage balance between them, and transient or steady-state voltage unbalances could cause the failure of these devices. This paper presents an active gate driver developed by the authors that is suitable for the series connection of IGBTs. The proposed active gate driver achieves the transient and steady-state voltage balance between the series-connected IGBT/diode devices. The effectiveness of the gate driver and the active gate control method has been experimentally validated, and promising results have been obtained.


title: A Comparative Study of Supervised Machine Learning Techniques for Spam E-mail Filtering

abstract: Unsolicited e-mail (Spam) has become a major issue for each e-mail user. In recent days it is very difficult to filter spam emails as these emails are written or generated in a very special way so that anti-spam filters cannot detect such emails. This Paper compares and discusses performance measures of certain categories of supervised machine learning techniques such as Bayes algorithms, lazy algorithms, tree algorithms, neural network, and support vector machines for classifying a spam e-mail corpus maintained by UCI Machine Learning Repository. The objective of this study is to consider the content of the emails, learn a finite dataset available and to develop a classification model that will able to predict whether an e-mail is spam or not.


title: Efficient algorithms for processing XPath queries

abstract: Our experimental analysis of several popular XPath processors reveals a striking fact: Query evaluation in each of the systems requires time exponential in the size of queries in the worst case. We show that XPath can be processed much more efficiently, and propose main-memory algorithms for this problem with polynomial-time combined query evaluation complexity. Moreover, we show how the main ideas of our algorithm can be profitably integrated into existing XPath processors. Finally, we present two fragments of XPath for which linear-time query processing algorithms exist and another fragment with linear-space/quadratic-time query processing.


title: Digital Social Norm Enforcement: Online Firestorms in Social Media

abstract: Actors of public interest today have to fear the adverse impact that stems from social media platforms. Any controversial behavior may promptly trigger temporal, but potentially devastating storms of emotional and aggressive outrage, so called online firestorms. Popular targets of online firestorms are companies, politicians, celebrities, media, academics and many more. This article introduces social norm theory to understand online aggression in a social-political online setting, challenging the popular assumption that online anonymity is one of the principle factors that promotes aggression. We underpin this social norm view by analyzing a major social media platform concerned with public affairs over a period of three years entailing 532,197 comments on 1,612 online petitions. Results show that in the context of online firestorms, non-anonymous individuals are more aggressive compared to anonymous individuals. This effect is reinforced if selective incentives are present and if aggressors are intrinsically motivated.


title: On the position accuracy of mobile robot localization based on particle filters combined with scan matching

abstract: Many applications in mobile robotics and especially industrial applications require that the robot has a precise estimate about its pose. In this paper, we analyze the accuracy of an integrated laser-based robot pose estimation and positioning system for mobile platforms. For our analysis, we used a highly accurate motion capture system to precisely determine the error in the robot's pose. We are able to show that by combining standard components such as Monte-Carlo localization, KLD sampling, and scan matching, an accuracy of a few millimeters at taught-in reference locations can be achieved. We believe that this is an important analysis for developers of robotic applications in which pose accuracy matters.


title: Time Domain Model for Costas Loop Based QPSK Receiver

abstract: A complete Matlab model is made for a millimeter wave wireless communication system including a four-phase Costas loop for carrier recovery and QPSK demodulation. Simulation results are presented to demonstrate the dynamic behaviour of the Costas loop and the effects of noise, linearity and bandwidth limitations. The time domain results are compared to the frequency domain transfer function derived from the Costas loop parameters.


title: 4 Motivation in a Language MOOC: Issues for Course Designers

abstract: Whilst several existing studies on foreign language learning have explored motivation in more traditional settings (Dornyei, 2003), this paper presents one of the first studies on the motivation of participants in a MOOC. The MOOC, Travailler en francais (https://sites.google.com/site/mooctravailleren-francais/home), was a 5-week open online course for learners of French at level B1 of the CEFR, and aimed to develop language and employability skills for working in a francophone country. It took place in early 2014 and attracted more than 1000 participants. Intrinsic motivation (Wigfield & Eccles, 2000) is directly linked to one's enjoyment of accomplishing a task. We conducted a study based on the cognitive variables of the Self-Determination Theory (Deci & Ryan, 1985), and adapted the Intrinsic Motivation Inventory to the context of a MOOC in order to understand the expectancy beliefs and task values of participants engaging with the MOOC. Participants answered a 40 Likert-type questions on enjoyment/ interest (i.e. I will enjoy doing this MOOC very much), perceived competence (i.e. I think I will be able to perform successfully in the MOOC), effort (i.e. I will put a lot of effort in this MOOC), value/usefulness (i.e. I think that doing this MOOC will be useful for developing my skills), felt pressure and tension (i.e. I think I might feel pressured while doing the MOOC) and relatedness (i.e. I think I will feel like I can really trust the other participants). Results highlight significant factors that could directly influence intrinsic motivation for learning in a MOOC environment. The chapter makes recommendations for LMOOC designers based on the emerging profile of MOOC participants, on their motivation and self-determination, as well as on the pressures they might feel, including time pressures. Finally, the extent to which participants relate to each other, and are able to engage in social learning and interaction, is a real challenge for LMOOC designers.


title: Roles in agile software development teams

abstract: One of the key elements in describing a software development method is the roles that are assigned to the members of the software team. This article describes our experience in assigning roles to students who are involved in the development of software projects, working in Extreme Programming teams. This experience, which is based on 25 such projects, teaches us that a personal role for each teammate increases personal responsibility while maintaining the essence of the software development method. In this paper we discuss ways in which different software development methods address the place of roles in a software development team. We also share our experience in refining role specifications and suggest a way to achieve and measure progress by using the perspective of the different roles.


title: Memory Bandwidth: the True Bottleneck of SIMD Multimedia Performance on a Superscalar Processor

abstract: This paper presents the performance of DSP, image and 3D applications on recent general-purpose microprocessors using streaming SIMD ISA extensions (integer and floating point). The 9 benchmarks benchmark we use for this evaluation have been optimized for DLP and caches use with SIMD extensions and data prefetch. The result of these cumulated optimizations is a speedup that ranges from 1.9 to 7.1. ::: ::: All the benchmarks were originaly computation bound and 7 becomes memory bandwidth bound with the addition of SIMD and data prefetch. Quadrupling the memory bandwidth has no effect on original kernels but improves the performance of SIMD kernels by 15-55%.


Routing and Internet Gateway Selection in Aeronautical Ad Hoc Networks


title: Leveraging the VTA-TVM Hardware-Software Stack for FPGA Acceleration of 8-bit ResNet-18 Inference

abstract: We present a full-stack design to accelerate deep learning inference with FPGAs. Our contribution is two-fold. At the software layer, we leverage and extend TVM, the end-to-end deep learning optimizing compiler, in order to harness FPGA-based acceleration. At the the hardware layer, we present the Versatile Tensor Accelerator (VTA) which presents a generic, modular, and customizable architecture for TPU-like accelerators. Our results take a ResNet-18 description in MxNet and compiles it down to perform 8-bit inference on a 256-PE accelerator implemented on a low-cost Xilinx Zynq FPGA, clocked at 100MHz. Our full hardware acceleration stack will be made available for the community to reproduce, and build upon at http://github.com/uwsaml/vta.


title: Unity in Diversity: Learning Distributed Heterogeneous Sentence Representation for Extractive Summarization

abstract: Automated multi-document extractive text summarization is a widely studied research problem in the field of natural language understanding. Such extractive mechanisms compute in some form the worthiness of a sentence to be included into the summary. While the conventional approaches rely on human crafted document-independent features to generate a summary, we develop a data-driven novel summary system called HNet, which exploits the various semantic and compositional aspects latent in a sentence to capture document independent features. The network learns sentence representation in a way that, salient sentences are closer in the vector space than non-salient sentences. This semantic and compositional feature vector is then concatenated with the document-dependent features for sentence ranking. Experiments on the DUC benchmark datasets (DUC-2001, DUC-2002 and DUC-2004) indicate that our model shows significant performance gain of around 1.5-2 points in terms of ROUGE score compared with the state-of-the-art baselines.


title: When is a Convolutional Filter Easy To Learn?

abstract: We analyze the convergence of (stochastic) gradient descent algorithm for learning a convolutional filter with Rectified Linear Unit (ReLU) activation function. Our analysis does not rely on any specific form of the input distribution and our proofs only use the definition of ReLU, in contrast with previous works that are restricted to standard Gaussian input. We show that (stochastic) gradient descent with random initialization can learn the convolutional filter in polynomial time and the convergence rate depends on the smoothness of the input distribution and the closeness of patches. To the best of our knowledge, this is the first recovery guarantee of gradient-based algorithms for convolutional filter on non-Gaussian input distributions. Our theory also justifies the two-stage learning rate strategy in deep neural networks. While our focus is theoretical, we also present experiments that illustrate our theoretical findings.


title: Cost-Effective Training of Deep CNNs with Active Model Adaptation

abstract: Deep convolutional neural networks have achieved great success in various applications. However, training an effective DNN model for a specific task is rather challenging because it requires a prior knowledge or experience to design the network architecture, repeated trial-and-error process to tune the parameters, and a large set of labeled data to train the model. In this paper, we propose to overcome these challenges by actively adapting a pre-trained model to a new task with less labeled examples. Specifically, the pre-trained model is iteratively fine tuned based on the most useful examples. The examples are actively selected based on a novel criterion, which jointly estimates the potential contribution of an instance on optimizing the feature representation as well as improving the classification model for the target task. On one hand, the pre-trained model brings plentiful information from its original task, avoiding redesign of the network architecture or training from scratch; and on the other hand, the labeling cost can be significantly reduced by active label querying. Experiments on multiple datasets and different pre-trained models demonstrate that the proposed approach can achieve cost-effective training of DNNs.


title: State of municipal solid waste management in Delhi, the capital of India.

abstract: Delhi is the most densely populated and urbanized city of India. The annual growth rate in population during the last decade (1991-2001) was 3.85%, almost double the national average. Delhi is also a commercial hub, providing employment opportunities and accelerating the pace of urbanization, resulting in a corresponding increase in municipal solid waste (MSW) generation. Presently the inhabitants of Delhi generate about 7000tonnes/day of MSW, which is projected to rise to 17,000-25,000tonnes/day by the year 2021. MSW management has remained one of the most neglected areas of the municipal system in Delhi. About 70-80% of generated MSW is collected and the rest remains unattended on streets or in small open dumps. Only 9% of the collected MSW is treated through composting, the only treatment option, and rest is disposed in uncontrolled open landfills at the outskirts of the city. The existing composting plants are unable to operate to their intended treatment capacity due to several operational problems. Therefore, along with residue from the composting process, the majority of MSW is disposed in landfills. In absence of leachate and landfill gas collection systems, these landfills are a major source of groundwater contamination and air pollution (including generation of greenhouse gases). This study describes and evaluates the present state of municipal solid waste management in Delhi. The paper also summarizes the proposed policies and initiatives of the Government of Delhi and the Municipal Corporation of Delhi to improve the existing MSW management system.


title: Gender Differences in Self-Concept and Psychological Well-Being in Old Age A Meta-Analysis

abstract: Because of women's higher risk of being widowed, having health problems, and needing care, one might expect them to have a more negative self-concept and lower subjective well-being (SWB). However, women may also have greater access to sources of SWB (e.g., relations to adult children) and may engage in processes to protect the self (e.g., lowered aspirations). Meta-analysis was used to synthesize findings from 300 empirical studies on gender differences in life satisfaction, happiness, self-esteem, loneliness, subjective health, and subjective age in late adulthood. Older women reported significantly lower SWB and less positive self-concept than men on all measures, except subjective age, although gender accounted for less than 1% of the variance in well-being and self-concept. Smaller gender differences in SWB were found in younger than in older groups. Statistically controlling for gender differences in widowhood, health, and socioeconomic status decreased gender differences in SWB. Cohort differences in SWB are reported as well.


title: Exposure-Robust Alignment of Differently Exposed Images

abstract: This letter presents a novel exposure-robust method to align differently exposed images. First, a directional mapping approach is introduced to normalize differently exposed images so as to alleviate the effect of saturation. Then, a non-parametric local binary pattern (LBP) is employed to represent intensity-invariant features of these images. An efficient two-stage alignment is proposed for motion estimation. Experiments on a variety of synthesized and real image sequences demonstrate that the proposed method is less sensitive to the reference image, and robust to 12 exposure values (EV) increments, which is superior to existing methods.


title: Expectation Propagation for Approximate Inference: Free Probability Framework

abstract: We study asymptotic properties of expectation propagation (EP) -- a method for approximate inference originally developed in the field of machine learning. Applied to generalized linear models, EP iteratively computes a multivariate Gaussian approximation to the exact posterior distribution. The computational complexity of the repeated update of covariance matrices severely limits the application of EP to large problem sizes. In this study, we present a rigorous analysis by means of free probability theory that allows us to overcome this computational bottleneck if specific data matrices in the problem fulfill certain properties of asymptotic freeness. We demonstrate the relevance of our approach on the gene selection problem of a microarray dataset.


title: Perceiving Learning at a Glance: A Systematic Literature Review of Learning Dashboard Research

abstract: This paper presents a systematic literature review of the state-of-the-art of research on learning dashboards in the fields of Learning Analytics and Educational Data Mining. Research on learning dashboards aims to identify what data is meaningful to different stakeholders and how data can be presented to support sense-making processes. Learning dashboards are becoming popular due to the increased use of educational technologies, such as Learning Management Systems (LMS) and Massive Open Online Courses (MOOCs). The initial search of five main academic databases and GScholar resulted in 346 papers out of which 55 papers were included in the final analysis. Our review distinguishes different kinds of research studies as well as various aspects of learning dashboards and their maturity regarding evaluation. As the research field is still relatively young, most studies are exploratory and proof-of-concept. The review concludes by offering a definition for learning dashboards and by outlining open issues and future lines of work in the area of learning dashboards. There is a need for longitudinal research in authentic settings and studies that systematically compare different dashboard designs.


title: Trend Following Trading under a Regime Switching Model

abstract: This paper is concerned with the optimality of a trend following trading rule. The idea is to catch a bull market at its early stage, ride the trend, and liquidate the position at the first evidence of the subsequent bear market. We characterize the bull and bear phases of the markets mathematically using the conditional probabilities of the bull market given the up to date stock prices. The optimal buying and selling times are given in terms of a sequence of stopping times determined by two threshold curves. Numerical experiments are conducted to validate the theoretical results and demonstrate how they perform in a marketplace.


title: Pattern Discovery for Wide-Window Open Information Extraction in Biomedical Literature

abstract: Open information extraction is an important task in Biomedical domain. The goal of the OpenIE is to automatically extract structured information from unstructured text with no or little supervision. It aims to extract all the relation tuples from the corpus without requiring pre-specified relation types. The existing tools may extract ill-structured or incomplete information, or fail on the Biomedical literature due to the long and complicated sentences. In this paper, we propose a novel pattern-based information extraction method for the wide-window entities (WW-PIE). WW-PIE utilizes dependency parsing to break down the long sentences first and then utilizes frequent textual patterns to extract the high-quality information. The pattern hierarchical grouping organize and structure the extractions to be straightforward and precise. Consequently, comparing with the existing OpenIE tools, WW-PIE produces structured output that can be directly used for downstream applications. The proposed WW-PIE is also capable in extracting n-ary and nested relation structures, which is less studied in the existing methods. Extensive experiments on real-world biomedical corpus from PubMed abstracts demonstrate the power of WW-PIE at extracting precise and well-structured information.


Ouroboros Praos: An adaptively-secure, semi-synchronous proof-of-stake protocol


title: Preventing Filicide in Families With Autistic Children

abstract: Autism is a complex neurodevelopmental disorder that affects social behaviors and parent-child interaction. It has been associated with an increased risk of social victimization, and a recent rise in number of acts of filicide of developmentally disabled children has included several cases of autism. In this article, possible risk factors for filicidal behavior in families with autistic children and prevention strategies are reviewed.


title: The Impact of Education on Economic Growth: The Case of India

abstract: There exists an enormous interest in clarification of the relationship between education and economic growth. Over the past 30 years, there have been conducted studies by economists about the connection between education and economic growth. There are actually many publications which provide strong evidence that suggests a correlation between the two. This paper attempts to build upon previous publications and to introduce a unique insight along with contemporary evidence about the relationship between education and economic growth in India from 1975 to 2016 by foc using on primary, secondary and tertiary levels of education. The relationships are examined by utilization of econometric estimations with the Granger Causality Method and the Cointegration Method. These methods are used to create models that could shed light on the claim that education plays a central and significant role in economic growth of India which could consequently be used as an example for similar countries in Asia or around the world. The findings of this work show that there is compelling evidence proving a positive connection between education levels and economic growth in India which might influence governmental actions and shape the future of India.


title: Normalized Blind Deconvolution

abstract: We introduce a family of novel approaches to single-image blind deconvolution, i.e., the problem of recovering a sharp image and a blur kernel from a single blurry input. This problem is highly ill-posed, because infinite (image, blur) pairs produce the same blurry image. Most research effort has been devoted to the design of priors for natural images and blur kernels, which can drastically prune the set of possible solutions. Unfortunately, these priors are usually not sufficient to favor the sharp solution. In this paper we address this issue by looking at a much less studied aspect: the relative scale ambiguity between the sharp image and the blur. Most prior work eliminates this ambiguity by fixing the \(L^1\) norm of the blur kernel. In principle, however, this choice is arbitrary. We show that a careful design of the blur normalization yields a blind deconvolution formulation with remarkable accuracy and robustness to noise. Specifically, we show that using the Frobenius norm to fix the scale ambiguity enables convex image priors, such as the total variation, to achieve state-of-the-art results on both synthetic and real datasets.


title: Sequential Word Spotting in Historical Handwritten Documents

abstract: In this work we present a handwritten word spotting approach that takes advantage of the a priori known order of appearance of the query words. Given an ordered sequence of query word instances, the proposed approach performs a sequence alignment with the words in the target collection. Although the alignment is quite sparse, i.e. the number of words in the database is higher than the query set, the improvement in the overall performance is sensitively higher than isolated word spotting. As application dataset, we use a collection of handwritten marriage licenses taking advantage of the ordered index pages of family names.


title: Freeze view touch and finger gesture based interaction methods for handheld augmented reality interfaces

abstract: Interaction techniques for handheld mobile Augmented Reality (AR) often focus on device-centric methods based around touch input. However, users may not be able to easily interact with virtual objects in mobile AR scenes if they are holding the handheld device with one hand and touching the screen with the other, while at the same time trying to maintain visual tracking of an AR marker. In this paper we explore novel interaction methods for handheld mobile AR that overcomes this problem. We investigate two different approaches; (1) freeze view touch and (2) finger gesture based interaction. We describe how each method is implemented and present findings from a user experiment comparing virtual object manipulation with these techniques to more traditional touch methods.


title: A Two-Parameter Calibration Technique Tracking Temperature Variations for Current Source Mismatch

abstract: This brief presents a foreground calibration technique for current source mismatch that is insensitive to temperature variations. Two calibration digital-to-analog converters (CAL DACs) provide correction currents to compensate two current source mismatch components caused by the threshold voltage mismatch and the current factor mismatch. Each CAL DAC has the same temperature dependence as its corresponding mismatch component. The sum current of the two CAL DACs automatically tracks the temperature variations of the current source mismatch with no need for temperature information. To obtain the CAL DAC input codes, two different bias current settings are used instead of calibrating at two separate temperatures. This scheme significantly reduces the calibration time and cost. A 16-bit DAC with this calibration technique is fabricated in a 65-nm CMOS process. The measurement results show that the matching of the most significant bit currents is improved from intrinsic 12 to 16 bits across the temperature range from −40 °C to 85 °C. The proposed calibration technique achieves superior temperature stability compared to the previously published schemes.


title: Generating Cancelable Fingerprint Templates

abstract: Biometrics-based authentication systems offer obvious usability advantages over traditional password and token-based authentication schemes. However, biometrics raises several privacy concerns. A biometric is permanently associated with a user and cannot be changed. Hence, if a biometric identifier is compromised, it is lost forever and possibly for every application where the biometric is used. Moreover, if the same biometric is used in multiple applications, a user can potentially be tracked from one application to the next by cross-matching biometric databases. In this paper, we demonstrate several methods to generate multiple cancelable identifiers from fingerprint images to overcome these problems. In essence, a user can be given as many biometric identifiers as needed by issuing a new transformation "key". The identifiers can be cancelled and replaced when compromised. We empirically compare the performance of several algorithms such as Cartesian, polar, and surface folding transformations of the minutiae positions. It is demonstrated through multiple experiments that we can achieve revocability and prevent cross-matching of biometric databases. It is also shown that the transforms are noninvertible by demonstrating that it is computationally as hard to recover the original biometric identifier from a transformed version as by randomly guessing. Based on these empirical results and a theoretical analysis we conclude that feature-level cancelable biometric construction is practicable in large biometric deployments


title: Beyond Extrastriate Body Area (EBA) and Fusiform Body Area (FBA): Context Integration in the Meaning of Actions

abstract: Our ability to identify and interpret the actions and intentions of other people in a meaningful way is the bedrock of social cognition. Visual perception of human body is a critical component of this complex task as long as it provides cues which enable the observer to make the required inferences to accurately extract the meaning of daily action events. ::: ::: During the last decade, neuroimaging studies have identified two brain regions of the extrastriate visual cortex that are highly sensitive to the perception of human bodies and body parts. These regions are the extrastriate body area (EBA), located at the posterior inferior temporal sulcus/middle temporal gyrus (Downing et al., 2001) and the fusiform body area (FBA) found ventrally in the fusiform gyrus (Peelen and Downing, 2005; Schwarzlose et al., 2005). Evidence derived from fMRI studies has shown that both areas become significantly activated in response to body/body parts stimuli visually presented in different formats like photos, line drawings, stick figures, and silhouettes compared to control stimuli like faces/face parts, tools/tool parts, and scenes (Downing et al., 2001; Peelen and Downing, 2005; Schwarzlose et al., 2005; Spiridon et al., 2006; Weiner and Grill-Spector, 2010). Recently, it has been suggested that EBA and FBA can be functionally dissociated, with a more selective activation for local body parts in EBA relative to more holistic images of the human body in FBA (Taylor et al., 2007). ::: ::: Based on these findings, many authors have claimed that EBA/FBA should be directly involved in complex functions such as perceiving goal-directed actions and other higher-level related processes (Costantini et al., 2005; Saxe et al., 2006; Moro et al., 2008; Marsh et al., 2010; Kuhn et al., 2011). However, as suggested by Downing and Peelen (2011), it might be more accurate to interpret the activity of these regions in terms of populations of neurons that selectively encode and make explicit low-level visual features of human bodies like body shape and posture. According to this hypothesis, the comprehension of meaningful actions could be supported by a more distributed neural network where visual information extracted by EBA/FBA is integrated with the contextual information processed in other parts of the brain. Although this hypothesis seems to be more plausible, the authors do not give further information on how this integration might be accomplished or, more specifically, about which other cortical areas would be actively engaged in this network. ::: ::: In order to address this issue, we propose a functional neuroanatomic model for the contextual processing of goal-directed actions where the general perceptual processing provided by EBA/FBA is integrated in a larger fronto-insular–temporal network. ::: ::: When we witness a simple event, our brain integrates the information about people, objects, and the interactions among them into a coherent meaningful representation. For instance, object recognition is thought to be instantiated by cognitive structures that integrate information about the identity of the objects that tend to co-occur in a given context with previously learned information about their possible relationships (Bar, 2004). These structures can be thought of as a set of expectations about what is more probable to see or not to see in a given context, enabling us to make predictions and accurately disambiguate incoming information. We proposed that in a fronto-insular–temporal “social context network” (SCN), several frontal areas update and associate ongoing contextual information in relation to episodic memory and target-context associations (Sigala et al., 2008; Bar, 2009; Burgess et al., 2009). The temporal regions [e.g., the parahippocampal cortex (PHC), hippocampus, and amygdala] may index the value learning of target-context associations (Langston and Wood, 2010). Finally, the insular cortex would coordinate internal and external milieus in an inner motivational state (Singer et al., 2009; Ibanez et al., 2010a). See Figure Figure11. ::: ::: ::: ::: Figure 1 ::: ::: Lateral view of the left hemisphere showing the proposed fronto-insular–temporal network (light-blue, violet, and green regions of interest, respectively). In this context network, prefrontal areas (PFC) such as frontopolar and dorsolateral prefrontal ... ::: ::: ::: ::: This contextual dimension of action understanding is also supported by ERP studies on the N400-like component, an ongoing negativity elicited when a meaningful action is incongruent (unexpected) with a previous context. N400 seems to be a specific context integration component (Bar, 2004). For example, videos and pictures of everyday-life actions, co-speech gestures, and semantic processing of current motor events (Sitnikova et al., 2003; Aravena et al., 2010; Ibanez et al., 2010b, 2011; Proverbio et al., 2010) have shown that, as the action-related stimulus becomes more expected/congruent with the context in which it is embedded, the N400 amplitude is reduced compared to incongruent/unexpected conditions. These findings suggest that when the previous context builds up meaning, processing of upcoming stimulus that fit with that context is facilitated. Evidence derived from lesion studies, MEG, and intracranial recordings includes the left superior/middle temporal gyrus, the anterior-medial temporal lobe, the PHC and fusiform gyrus as well as frontopolar, orbital, and dorsolateral prefrontal regions as the possible sources of this N400 effect (Halgren et al., 2002; Van Petten and Luka, 2006). ::: ::: Finally, as well as frontal and temporal regions, the insular cortex plays a crucial role in the proposed network. This region has been recently implicated in the contextual integration of interoceptive information (conscious representation about one's body physiological state and motivational drives) with external stimuli (sensory current environment) into a global feeling state (Craig, 2002; Ibanez et al., 2010a). Moreover, anterior insular cortex has also shown to be recruited during motivational decision-making in uncertain contexts, suggesting that this area also mediates risk behavior when the available information is not sufficient to predict an outcome (Singer et al., 2009). ::: ::: Overall, the SCN provides an empirically testable set of hypotheses regarding contextual update, contextual prediction, and target-context association in action meaning paradigms. For instance, we expect to observe the engagement of the SCN during action meaning processing. This prediction is partially confirmed since frontal, temporal, or insular activations have been previously observed together with EBA and FBA during action paradigms (Kable and Chatterjee, 2006; Lamm et al., 2007; Lamm and Decety, 2008; Hodzic et al., 2009a,b; Cross et al., 2010; Kret et al., 2011). Additionally, a more straight empirical testing would be provided by direct contextual manipulation of action-related stimuli. The use of frames, background information or multimodal designs (as used in other domains of contextual studies, e.g., Bar, 2004) adapted to action meaning tasks would provide simple experimental shortcuts. An ideal experimental approach would comprise a battery of tasks that vary the degree of context for action/non-action stimuli, in order to test the relative engagement of the SCN in EBA/FBA activation during action and non-action processing. We expect that, while manipulating the contextual information (e.g., by increasing its influence), stronger activation in frontal, temporal, and insular regions rather than in EBA/FBA would be observed. Furthermore, if the contextual information being processed crucially requires the extraction of specific information regarding body/body parts (e.g., imagine a task where body posture is important for disentangle the emotional state of a person), we expect that activity in EBA/FBA will be enhanced as much as in the other regions of the SCN. ::: ::: In brief, we suggest that action meaning is beyond EBA and FBA through the integration of contextual information processed by a distributed fronto-insular–temporal network. Moreover, action meaning is not an amodal, invariant, immutable representation in a brain area, but instead a polymodal, context-sensitive, constructive, and distributed process. Similar to context integration during visual object recognition (Bar, 2004), information of body appearance and posture in EBA/FBA should be integrated within a SCN in order to process action meaning. We propose a multimodal system of action meaning in which expectations (frontal areas) of external information (including body processing in EBA/FBA), interacts with their semantic association (temporal regions) and the current internal motivational states (insula) in order to get a specific significance of an event. Thus, a context–facilitation large-scale distributed neural network may process and influence the EBA/FBA activity in a top-down manner.


title: GSM based automatic energy meter reading system with instant billing

abstract: The technology of e-metering (Electronic Metering) has gone through rapid technological advancements and there is increased demand for a reliable and efficient Automatic Meter Reading (AMR) system. This paper presents the design of a simple low cost wireless GSM energy meter and its associated web interface, for automating billing and managing the collected data globally. The proposed system replaces traditional meter reading methods and enables remote access of existing energy meter by the energy provider. Also they can monitor the meter readings regularly without the person visiting each house. A GSM based wireless communication module is integrated with electronic energy meter of each entity to have remote access over the usage of electricity. A PC with a GSM receiver at the other end, which contains the database acts as the billing point. Live meter reading from the GSM enabled energy meter is sent back to this billing point periodically and these details are updated in a central database. A new interactive, user friendly graphical user interface is developed using Microsoft visual studio .NET framework and C#. With proper authentication, users can access the developed web page details from anywhere in the world. The complete monthly usage and due bill is messaged back to the customer after processing these data.


title: Acute myocardial infarction with simultaneous occlusions of two major coronary arteries in a young man.

abstract: Simultaneous formation of the thrombi in two different coronary arteries is a very rare event. We present a 34-year-old man with acute myocardial infarction due to simultaneous occlusion of the two major coronary arteries. His only risk factor was smoking 40 cigarettes daily. Emergency arteriography revealed a total occlusion of the proximal left anterior descending artery (LAD) and the left circumflex coronary artery (LCx). We performed primary angioplasty to the LAD and instituted thrombolytic therapy to the LCx. During intra-aortic balloon pumping and medical treatment, the patient had no symptoms of angina. He underwent a second catheterization 4 weeks after primary angioplasty. After intravenous ergonovine provocation, coronary arteriography revealed diffuse vasospasm of the LAD and the LCx. These data suggest that habitual heavy smoking and coronary spasm may have been causatory factors for myocardial infarction in this case.