nils-herrmann/clean_scirepeval huggingface dataset:
Deep Collective Inference

Moore, John A. MS, Purdue University, December 2016. Deep Collective Inference. Major Professor: Jennifer Neville. Collective inference is widely used to improve classification in network datasets. However, despite recent advances in deep learning and the successes of recurrent neural networks (RNNs), researchers have only just recently begun to study how to apply RNNs to heterogeneous graph and network datasets. There has been recent work on using RNNs for unsupervised learning in networks (e.g., graph clustering, node embedding) and for prediction (e.g., link prediction, graph classification), but there has been little work on using RNNs for node-based relational classification tasks. In this paper, we provide an end-to-end learning framework using RNNs for collective inference. Our main insight is to transform a node and its set of neighbors into an unordered sequence (of varying length) and use an LSTM-based RNN to predict the class label as the output of that sequence. We develop a collective inference method, which we refer to as Deep Collective Inference (DCI), that uses semi-supervised learning in partially-labeled networks and two label distribution correction mechanisms for imbalanced classes. We compare to several alternative methods on seven network datasets. DCI achieves up to a 12% reduction in error compared to the best alternative and a 25% reduction in error on average over all methods, for all label proportions.
Déjà Image-Captions: A Corpus of Expressive Descriptions in Repetition

We present a new approach to harvesting a large-scale, high quality image-caption corpus that makes a better use of already existing web data with no additional human efforts. The key idea is to focus on Déjà Image-Captions: naturally existing image descriptions that are repeated almost verbatim – by more than one individual for different images. The resulting corpus provides association structure between 4 million images with 180K unique captions, capturing a rich spectrum of everyday narratives including figurative and pragmatic language. Exploring the use of the new corpus, we also present new conceptual tasks of visually situated paraphrasing, creative image captioning, and creative visual paraphrasing.
Dog breed classification via landmarks

Object recognition is an important problem with a wide range of applications. It is also a challenging problem, especially for animal categorization as the differences among breeds can be subtle. In this paper, based on statistical techniques for landmark-based shape representation, we propose to model the shape of dog breed as points on the Grassmann manifold. We consider the dog breed categorization as the classification problem on this manifold. The proposed scheme is tested on a dataset including 8,351 images of 133 different breeds. Experimental results demonstrate the advocated scheme outperforms state of the art approaches by nearly 20%.
The jabberwocky programming environment for structured social computing

We present Jabberwocky, a social computing stack that consists of three components: a human and machine resource management system called Dormouse, a parallel programming framework for human and machine computation called ManReduce, and a high-level programming language on top of ManReduce called Dog. Dormouse is designed to enable cross-platform programming languages for social computation, so, for example, programs written for Mechanical Turk can also run on other crowdsourcing platforms. Dormouse also enables a programmer to easily combine crowdsourcing platforms or create new ones. Further, machines and people are both first-class citizens in Dormouse, allowing for natural parallelization and control flows for a broad range of data-intensive applications. And finally and importantly, Dormouse includes notions of real identity, heterogeneity, and social structure. We show that the unique properties of Dormouse enable elegant programming models for complex and useful problems, and we propose two such frameworks. ManReduce is a framework for combining human and machine computation into an intuitive parallel data flow that goes beyond existing frameworks in several important ways, such as enabling functions on arbitrary communication graphs between human and machine clusters. And Dog is a high-level procedural language written on top of ManReduce that focuses on expressivity and reuse. We explore two applications written in Dog: bootstrapping product recommendations without purchase data, and expert labeling of medical images.
Newer Understanding of Specific Anatomic Targets in the Aging Face as Applied to Injectables: Superficial and Deep Facial Fat Compartments--An Evolving Target for Site-Specific Facial Augmentation.

The identification of specific fat compartments of the face has greatly improved the plastic surgeon's approach to facial rejuvenation. These superficial and deep compartments are discretely partitioned into multiple independent units by fascial barriers and undergo age-dependant volumetric changes. This knowledge has created a topographical map allowing for the direct and precise augmentation of those compartments that are deflated preferentially. These include the deep medial cheek, nasolabial, superficial middle, and lateral cheek compartments. Once this volume loss has been addressed, the overlying superficial musculoaponeurotic system and skin envelope can be treated to address laxity and bridge the compartments, creating a smooth cheek contour. Facial augmentation can be performed alone in the correct patient; however, it most often complements face-lifting. It is, therefore, important to have a thorough understanding of this anatomy and the changes that occur during aging.
Evolution of Attacks, Threat Models, and Solutions for Virtualized Systems

Virtualization technology enables Cloud providers to efficiently use their computing services and resources. Even if the benefits in terms of performance, maintenance, and cost are evident, however, virtualization has also been exploited by attackers to devise new ways to compromise a system. To address these problems, research security solutions have evolved considerably over the years to cope with new attacks and threat models. In this work, we review the protection strategies proposed in the literature and show how some of the solutions have been invalidated by new attacks, or threat models, that were previously not considered. The goal is to show the evolution of the threats, and of the related security and trust assumptions, in virtualized systems that have given rise to complex threat models and the corresponding sophistication of protection strategies to deal with such attacks. We also categorize threat models, security and trust assumptions, and attacks against a virtualized system at the different layers—in particular, hardware, virtualization, OS, and application.
Hatman: Intra-cloud Trust Management for Hadoop

Data and computation integrity and security are major concerns for users of cloud computing facilities. Many production-level clouds optimistically assume that all cloud nodes are equally trustworthy when dispatching jobs; jobs are dispatched based on node load, not reputation. This increases their vulnerability to attack, since compromising even one node suffices to corrupt the integrity of many distributed computations. This paper presents and evaluates Hatman: the first full-scale, data-centric, reputation-based trust management system for Hadoop clouds. Hatman dynamically assesses node integrity by comparing job replica outputs for consistency. This yields agreement feedback for a trust manager based on EigenTrust. Low overhead and high scalability is achieved by formulating both consistency-checking and trust management as secure cloud computations; thus, the cloud's distributed computing power is leveraged to strengthen its security. Experiments demonstrate that with feedback from only 100 jobs, Hatman attains over 90% accuracy when 25% of the Hadoop cloud is malicious.
The Emerging Field of Emotion Regulation : An Integrative Review

The emerging field of emotion regulation studies how individuals influence which emotions they have, when they have them, and how they experience and express them. This review takes an evolutionary perspective and characterizes emotion in terms of response tendencies. Emotion regulation is defined and distinguished from coping, mood regulation, defense, and affect regulation. In the increasingly specialized discipline of psychology, the field of emotion regulation cuts across traditional boundaries and provides common ground. According to a process model of emotion regulation, emotion may be regulated at five points in the emotion generative process: (a) selection of the situation, (b) modification of the situation, (c) deployment of attention, (d) change of cognitions, and (e) modulation of responses. The field of emotion regulation promises new insights into age-old questions about how people manage their emotions.
Emergent Tangled Graph Representations for Atari Game Playing Agents

Organizing code into coherent programs and relating different programs to each other represents an underlying requirement for scaling genetic programming to more difficult task domains. Assuming a model in which policies are defined by teams of programs, in which team and program are represented using independent populations and coevolved, has previously been shown to support the development of variable sized teams. In this work, we generalize the approach to provide a complete framework for organizing multiple teams into arbitrarily deep/wide structures through a process of continuous evolution; hereafter the Tangled Program Graph (TPG). Benchmarking is conducted using a subset of 20 games from the Arcade Learning Environment (ALE), an Atari 2600 video game emulator. The games considered here correspond to those in which deep learning was unable to reach a threshold of play consistent with that of a human. Information provided to the learning agent is limited to that which a human would experience. That is, screen capture sensory input, Atari joystick actions, and game score. The performance of the proposed approach exceeds that of deep learning in 15 of the 20 games, with 7 of the 15 also exceeding that associated with a human level of competence. Moreover, in contrast to solutions from deep learning, solutions discovered by TPG are also very ‘sparse’. Rather than assuming that all of the state space contributes to every decision, each action in TPG is resolved following execution of a subset of an individual’s graph. This results in significantly lower computational requirements for model building than presently the case for deep learning.
Hybrid Visual Servoing With Hierarchical Task Composition for Aerial Manipulation

In this letter, a hybrid visual servoing with a hierarchical task-composition control framework is described for aerial manipulation, i.e., for the control of an aerial vehicle endowed with a robot arm. The proposed approach suitably combines into a unique hybrid-control framework the main benefits of both image-based and position-based control schemes. Moreover, the underactuation of the aerial vehicle has been explicitly taken into account in a general formulation, together with a dynamic smooth activation mechanism. Both simulation case studies and experiments are presented to demonstrate the performance of the proposed technique.
A Highly Efficient and Linear Power Amplifier for 28-GHz 5G Phased Array Radios in 28-nm CMOS

This paper presents the first linear bulk CMOS power amplifier (PA) targeting low-power fifth-generation (5G) mobile user equipment integrated phased array transceivers. The output stage of the PA is first optimized for power-added efficiency (PAE) at a desired error vector magnitude (EVM) and range given a challenging 5G uplink use case scenario. Then, inductive source degeneration in the optimized output stage is shown to enable its embedding into a two-stage transformer-coupled PA; by broadening interstage impedance matching bandwidth and helping to reduce distortion. Designed and fabricated in 1P7M 28 nm bulk CMOS and using a 1 V supply, the PA achieves +4.2 dBm/9% measured Pout/PAE at -25 dBc EVM for a 250 MHz-wide 64-quadrature amplitude modulation orthogonal frequency division multiplexing signal with 9.6 dB peak-to-average power ratio. The PA also achieves 35.5%/10% PAE for continuous wave signals at saturation/9.6 dB back-off from saturation. To the best of the authors' knowledge, these are the highest measured PAE values among published K-and Ka-band CMOS PAs.
A supervised extreme learning committee for food recognition

Food recognition is an emerging topic in computer vision. The problem is being addressed especially in health-oriented systems where it is used as a support for food diary applications. The goal is to improve current food diaries, where the users have to manually insert their daily food intake, with an automatic recognition of the food type, quantity and consequent calories intake estimation. In addition to the classical recognition challenges, the food recognition problem is characterized by the absence of a rigid structure of the food and by large intra-class variations. To tackle such challenges, a food recognition system based on a committee classification is proposed. The aim is to provide a system capable of automatically choosing the optimal features for food recognition out of the existing plethora of available ones (e.g., color, texture, etc.). Following this idea, each committee member, i.e., an Extreme Learning Machine, is trained to specialize on a single feature type. Then, a Structural Support Vector Machine is exploited to produce the final ranking of possible matches by filtering out the irrelevant features and thus merging only the relevant ones. Experimental results show that the proposed system outperforms state-of-the-art works on four publicly available benchmark datasets.
Methods for Using Textual Entailment in Open-Domain Question Answering

Work on the semantics of questions has argued that the relation between a question and its answer(s) can be cast in terms of logical entailment. In this paper, we demonstrate how computational systems designed to recognize textual entailment can be used to enhance the accuracy of current open-domain automatic question answering (Q/A) systems. In our experiments, we show that when textual entailment information is used to either filter or rank answers returned by a Q/A system, accuracy can be increased by as much as 20% overall.
Software-in-the-Loop simulation for early-stage testing of AUTOSAR software component

In Embedded Software, the early-stage testing of source code is important since it may reduce the future development cost. However, at the automotive domain, the conventional software testing methods depend on actual system and hardware such as Hardware-in-the-Loop simulator and vehicle, then the early-stage testing method of automotive software is still in immature status. Therefore the testing of automotive software component is delayed until they are developed enough to run at the actual hardware. In this paper, we propose a method to do dynamic analysis and test the AUTOSAR software component by Software-in-the-Loop simulation without target system hardware. It provides rapid prototyping environment to validate the behavior of automotive software and helps to improve the quality of software component from the early error detection.
Coherent Hierarchical Culling: Hardware Occlusion Queries Made Useful

We present a simple but powerful algorithm for optimizing the usage of hardware occlusion queries in arbitrary complex scenes. Our method minimizes the number of issued queries and reduces the delays due to the latency of query results. We reuse the results of occlusion queries from the last frame in order to initiate and schedule the queries in the next frame. This is done by processing nodes of a spatial hierarchy in a front-to-back order and interleaving occlusion queries with rendering of certain previously visible nodes. The proposed scheduling of the queries makes use of spatial and temporal coherence of visibility. Despite its simplicity, the algorithm achieves good culling efficiency for scenes of various types. The implementation of the algorithm is straightforward and it can be easily integrated in existing real-time rendering packages based on common hierarchical data structures.
The Apriori Algorithm – a Tutorial

Association rules are ”if-then rules” with two measures which quantify the support and confidence of the rule for a given data set. Having their origin in market basked analysis, association rules are now one of the most popular tools in data mining. This popularity is to a large part due to the availability of efficient algorithms. The first and arguably most influential algorithm for efficient association rule discovery is Apriori. In the following we will review basic concepts of association rule discovery including support, confidence, the apriori property, constraints and parallel algorithms. The core consists of a review of the most important algorithms for association rule discovery. Some familiarity with concepts like predicates, probability, expectation and random variables is assumed.
A Low-Phase-Noise Wide-Tuning-Range Oscillator Based on Resonant Mode Switching

In this paper we will present a low-phase-noise wide-tuning-range oscillator suitable for scaled CMOS processes. It switches between the two resonant modes of a high-order LC resonator that consists of two identical LC tanks coupled by capacitor and transformer. The mode switching method does not add lossy switches to the resonator and thus doubles frequency tuning range without degrading phase noise performance. Moreover, the coupled resonator leads to 3 dB lower phase noise than a single LC tank, which provides a way of achieving low phase noise in scaled CMOS process. Finally, the novel way of using inductive and capacitive coupling jointly decouples frequency separation and tank impedances of the two resonant modes, and makes it possible to achieve balanced performance. The proposed structure is verified by a prototype in a low power 65 nm CMOS process, which covers all cellular bands with a continuous tuning range of 2.5-5.6 GHz and meets all stringent phase noise specifications of cellular standards. It uses a 0.6 V power supply and achieves excellent phase noise figure-of-merit (FoM) of 192.5 dB at 3.7 GHz and >; 188 dB across the entire tuning range. This demonstrates the possibility of achieving low phase noise and wide tuning range at the same time in scaled CMOS processes.
SimBow at SemEval-2017 Task 3: Soft-Cosine Semantic Similarity between Questions for Community Question Answering

This paper describes the SimBow system submitted at SemEval2017-Task3, for the question-question similarity subtask B. The proposed approach is a supervised combination of different unsupervised textual similarities. These textual similarities rely on the introduction of a relation matrix in the classical cosine similarity between bag-of-words, so as to get a softcosine that takes into account relations between words. According to the type of relation matrix embedded in the soft-cosine, semantic or lexical relations can be considered. Our system ranked first among the official submissions of subtask B.
Investigating emotion with music: an fMRI study.

The present study used pleasant and unpleasant music to evoke emotion and functional magnetic resonance imaging (fMRI) to determine neural correlates of emotion processing. Unpleasant (permanently dissonant) music contrasted with pleasant (consonant) music showed activations of amygdala, hippocampus, parahippocampal gyrus, and temporal poles. These structures have previously been implicated in the emotional processing of stimuli with (negative) emotional valence; the present data show that a cerebral network comprising these structures can be activated during the perception of auditory (musical) information. Pleasant (contrasted to unpleasant) music showed activations of the inferior frontal gyrus (IFG, inferior Brodmann's area (BA) 44, BA 45, and BA 46), the anterior superior insula, the ventral striatum, Heschl's gyrus, and the Rolandic operculum. IFG activations appear to reflect processes of music-syntactic analysis and working memory operations. Activations of Rolandic opercular areas possibly reflect the activation of mirror-function mechanisms during the perception of the pleasant tunes. Rolandic operculum, anterior superior insula, and ventral striatum may form a motor-related circuitry that serves the formation of (premotor) representations for vocal sound production during the perception of pleasant auditory information. In all of the mentioned structures, except the hippocampus, activations increased over time during the presentation of the musical stimuli, indicating that the effects of emotion processing have temporal dynamics; the temporal dynamics of emotion have so far mainly been neglected in the functional imaging literature.
Uncovering Latent Style Factors for Expressive Speech Synthesis

Prosodic modeling is a core problem in speech synthesis. The key challenge is producing desirable prosody from textual input containing only phonetic information. In this preliminary study, we introduce the concept of “style tokens” in Tacotron, a recently proposed end-to-end neural speech synthesis model. Using style tokens, we aim to extract independent prosodic styles from training data. We show that without annotation data or an explicit supervision signal, our approach can automatically learn a variety of prosodic variations in a purely data-driven way. Importantly, each style token corresponds to a fixed style factor regardless of the given text sequence. As a result, we can control the prosodic style of synthetic speech in a somewhat predictable and globally consistent way.
Telecommunications Demand and Pricing Structure: An Econometric Analysis

The main objective of this paper is to analyse residential demand by traffic destination, using a translogarithmic indirect utility function. We focus on five traffic directions, in order to construct a model adapted to evaluate the characteristics of telecommunications demand in a competitive market. The resulting price elasticities express high reactivity to own price changes for the main traffic directions, as well as little interactions between the different types of traffic. Moreover the high values of income elasticities confirm the importance of income effects when analysing residential telecommunications demand. This model shows useful for welfare analysis. The computation of customers’ income equivalent variation shows, on average, a higher willingness to pay for some traffic directions than the bill actually paid. Finally we show that the optimal prices for the operator, in a cost minimisation point of view, are higher than the observed prices for local and national traffic directions. This emphasises the existence of important cross-subsidies among the different segments of customers.