nils-herrmann/clean_scirepeval huggingface dataset:
Are Elephants Bigger than Butterflies? Reasoning about Sizes of Objects

Human vision greatly benefits from the information about sizes of objects. The role of size in several visual reasoning tasks has been thoroughly explored in human perception and cognition. However, the impact of the information about sizes of objects is yet to be determined in AI. We postulate that this is mainly attributed to the lack of a comprehensive repository of size information. In this paper, we introduce a method to automatically infer object sizes, leveraging visual and textual information from web. By maximizing the joint likelihood of textual and visual observations, our method learns reliable relative size estimates, with no explicit human supervision. We introduce the relative size dataset and show that our method outperforms competitive textual and visual baselines in reasoning about size comparisons.
Adaptive Multilingual Sentence Boundary Disambiguation

The sentence is a standard textual unit in natural language processing applications. In many languages the punctuation mark that indicates the end-of-sentence boundary is ambiguous; thus the tokenizers of most NLP systems must be equipped with special sentence boundary recognition rules for every new text collection. As an alternative, this article presents an efficient, trainable system for sentence boundary disambiguation. The system, called Satz, makes simple estimates of the parts of speech of the tokens immediately preceding and following each punctuation mark, and uses these estimates as input to a machine learning algorithm that then classifies the punctuation mark. Satz is very fast both in training and sentence analysis, and its combined robustness and accuracy surpass existing techniques. The system needs only a small lexicon and training corpus, and has been shown to transfer quickly and easily from English to other languages, as demonstrated on French and German.
Security and privacy on internet of things

There are billions of Internet of things (IoT) devices connecting to the Internet and the number is increasing. As a still ongoing technology, IoT can be used in different fields, such as agriculture, healthcare, manufacturing, energy, retailing and logistics. IoT has been changing our world and the way we live and think. However, IoT has no uniform architecture and there are different kinds of attacks on the different layers of IoT, such as unauthorized access to tags, tag cloning, sybil attack, sinkhole attack, denial of service attack, malicious code injection, and man in middle attack. IoT devices are more vulnerable to attacks because it is simple and some security measures can not be implemented. We analyze the privacy and security challenges in the IoT and survey on the corresponding solutions to enhance the security of IoT architecture and protocol. We should focus more on the security and privacy on IoT and help to promote the development of IoT.
Inference Compilation and Universal Probabilistic Programming

We introduce a method for using deep neural networks to amortize the cost of inference in models from the family induced by universal probabilistic programming languages, establishing a framework that combines the strengths of probabilistic programming and deep learning methods. We call what we do “compilation of inference” because our method transforms a denotational specification of an inference problem in the form of a probabilistic program written in a universal programming language into a trained neural network denoted in a neural network specification language. When at test time this neural network is fed observational data and executed, it performs approximate inference in the original model specified by the probabilistic program. Our training objective and learning procedure are designed to allow the trained neural network to be used as a proposal distribution in a sequential importance sampling inference engine. We illustrate our method on mixture models and Captcha solving and show significant speedups in the efficiency of inference.
Applied Geostatistics with Sgems: A User's Guide

Applied Geostatistics with SGeMS: A User's Guide PetraSim: A graphical user interface for the TOUGH2 family of multiphase flow and transport codes. Applied Geostatistics with SGeMS: A User's Guide · Certain Death in Sierra Treatise on Fungi as Experimental Systems for Basic and Applied Research. Baixe grátis o arquivo SGeMS User's Guide enviado para a disciplina de Applied Geostatistics with SGeMS: A Users' Guide · S-GeMS Tutorial Notes. Applied Geostatistics with SGeMS: A User's Guide · Certain Death in Sierra Leone: Introduction to Stochastic Calculus Applied to Finance, Second Edition. Build Native Cross-Platform Apps with Appcelerator: A beginner's guide for Web Developers Applied GeostAtistics with SGeMS: A User's guide (Repost).
Watson, more than a Semantic Web search engine

In this tool report, we present an overview of the Watson system, a Semantic Web search engine providing various functionalities not only to find and locate ontologies and semantic data online, but also to explore the content of these semantic documents. Beyond the simple facade of a search engine for the Semantic Web, we show that the availability of such a component brings new possibilities in terms of developing semantic applications that exploit the content of the Semantic Web. Indeed, Watson provides a set of APIs containing high level functions for finding, exploring and querying semantic data and ontologies that have been published online. Thanks to these APIs, new applications have emerged that connect activities such as ontology construction, matching, sense disambiguation and question answering to the Semantic Web, developed by our group and others. In addition, we also describe Watson as a unprecedented research platform for the study the Semantic Web, and of formalised knowledge in general.
Fast Stochastic Methods for Nonsmooth Nonconvex Optimization

We analyze stochastic algorithms for optimizing nonconvex, nonsmooth finite-sum problems, where the nonconvex part is smooth and the nonsmooth part is convex. Surprisingly, unlike the smooth case, our knowledge of this fundamental problem is very limited. For example, it is not known whether the proximal stochastic gradient method with constant minibatch converges to a stationary point. To tackle this issue, we develop fast stochastic algorithms that provably converge to a stationary point for constant minibatches. Furthermore, using a variant of these algorithms, we show provably faster convergence than batch proximal gradient descent. Finally, we prove global linear convergence rate for an interesting subclass of nonsmooth nonconvex functions, that subsumes several recent works. This paper builds upon our recent series of papers on fast stochastic methods for smooth nonconvex optimization [22, 23], with a novel analysis for nonconvex and nonsmooth functions.
Fault Effects in FlexRay-Based Networks with Hybrid Topology

This paper investigates fault effects and error propagation in a FlexRay-based network with hybrid topology that includes a bus subnetwork and a star subnetwork. The investigation is based on about 43500 bit-flip fault injection inside different parts of the FlexRay communication controller. To do this, a FlexRay communication controller is modeled by Verilog HDL at the behavioral level. Then, this controller is exploited to setup a FlexRay-based network composed of eight nodes (four nodes in the bus subnetwork and four nodes in the star subnetwork). The faults are injected in a node of the bus subnetwork and a node of the star subnetwork of the hybrid network Then, the faults resulting in the three kinds of errors, namely, content errors, syntax errors and boundary violation errors are characterized. The results of fault injection show that boundary violation errors and content errors are negligibly propagated to the star subnetwork and syntax errors propagation is almost equal in the both bus and star subnetworks. Totally, the percentage of errors propagation in the bus subnetwork is more than the star subnetwork.
Deep Reinforcement Learning: An Overview

In recent years, a specific machine learning method called deep learning has gained huge attraction, as it has obtained astonishing results in broad applications such as pattern recognition, speech recognition, computer vision, and natural language processing. Recent research has also been shown that deep learning techniques can be combined with reinforcement learning methods to learn useful representations for the problems with high dimensional raw data input. This paper reviews the recent advances in deep reinforcement learning with a focus on the most used deep architectures such as autoencoders, convolutional neural networks and recurrent neural networks which have successfully been come together with the reinforcement learning framework.
What you want (and do not want) affects what you see (and do not see): avoidance social goals and social events.

Two studies examined the influence of approach and avoidance social goals on memory for and evaluation of ambiguous social information. Study 1 found that individual differences in avoidance social goals were associated with greater memory of negative information, negatively biased interpretation of ambiguous social cues, and a more pessimistic evaluation of social actors. Study 2 experimentally manipulated social goals and found that individuals high in avoidance social motivation remembered more negative information and expressed more dislike for a stranger in the avoidance condition than in the approach condition. Results suggest that avoidance social goals are associated with emphasizing potential threats when making sense of the social environment.
Structured Embedding via Pairwise Relations and Long-Range Interactions in Knowledge Base

We consider the problem of embedding entities and relations of knowledge bases into low-dimensional continuous vector spaces (distributed representations). Unlike most existing approaches, which are primarily efficient for modelling pairwise relations between entities, we attempt to explicitly model both pairwise relations and long-range interactions between entities, by interpreting them as linear operators on the low-dimensional embeddings of the entities. Therefore, in this paper we introduces path ranking to capture the long-range interactions of knowledge graph and at the same time preserve the pairwise relations of knowledge graph; we call it structured embedding via pairwise relation and longrange interactions (referred to as SePLi). Comparing with the-state-of-the-art models, SePLi achieves better performances of embeddings.
Joint Subcarrier and CPU Time Allocation for Mobile Edge Computing

In mobile edge computing systems, mobile devices can offload compute-intensive tasks to a nearby \emph{cloudlet}, so as to save energy and extend battery life. Unlike a fully-fledged cloud, a cloudlet is a small-scale datacenter deployed at a wireless access point, and thus is highly constrained by both radio and compute resources. We show in this paper that separately optimizing the allocation of either compute or radio resource - as most existing works did - is highly \emph{suboptimal}: the congestion of compute resource leads to the waste of radio resource, and vice versa. To address this problem, we propose a joint scheduling algorithm that allocates both radio and compute resources coordinately. Specifically, we consider a cloudlet in an Orthogonal Frequency-Division Multiplexing Access (OFDMA) system with multiple mobile devices, where we study subcarrier allocation for task offloading and CPU time allocation for task execution in the cloudlet. Simulation results show that the proposed algorithm significantly outperforms per- resource optimization, accommodating more offloading requests while achieving salient energy saving.
L-Systems: From The Theory To Visual Models Of Plants

Recent advances in computer graphics have made it possible to visualize mathematical models of biological structures and processes with unprecedented realism. The resulting images, animations, and interactive systems are useful as research and educational tools in developmental biology and ecology. Prospective applications also include computer-assisted landscape architecture, design of new varieties of plants, and crop yield prediction. In this paper we revisit foundations of the applications of L-systems to the modeling of plants, and we illustrate them using recently developed sample models.
A service-oriented middleware for building context-aware services

The advancement of wireless networks and mobile computing necessitates more advanced applications and services to be built with context-awareness enabled and adaptability to their changing contexts. Today, building context-aware services is a complex task due to the lack of an adequate infrastructure support in pervasive computing environments. In this article, we propose a ServiceOriented Context-Aware Middleware (SOCAM) architecture for the building and rapid prototyping of context-aware services. It provides efficient support for acquiring, discovering, interpreting and accessing various contexts to build context-aware services. We also propose a formal context model based on ontology using Web Ontology Language to address issues including semantic representation, context reasoning, context classification and dependency. We describe our context model and the middleware architecture, and present a performance study for our prototype in a smart home environment. q 2004
Speaker-independent phone recognition using hidden Markov models

In this paper, we extend hidden Markov modeling to speaker-independent phone recognition. Using multiple codebooks of various LPC parameters and discrete HMMs, we obtain a speakerindependent phone recognition accuracy of 58.8% to 73.8% on the TIMTT database, depending on the type of acoustic and language models used. In comparison, the performance of expert spectrogram readers is only 69% without use of higher level knowledge. We also introduce the co-occurrence smoothing algorithm which enables accurate recognition even with very limited training data. Since our results were evaluated on a standard database, they can be used as benchmarks to evaluate future systems. This research was partly sponsored by a National Science Foundation Graduate Fellowship, and by Defense Advanced Research Projects Agency Contract N00039-85-C-0163. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of the National Science Foundation, the Defense Advanced Research Projects Agency, or the US Government.
Field trials and testing of the OctArm continuum manipulator

This paper describes the results of field trials and associated testing of the OctArm series of multi-section continuous backbone "continuum" robots. This novel series of manipulators has recently (Spring 2005) undergone a series of trials including open-air and in-water field tests. Outcomes of the trials, in which the manipulators demonstrated the ability for adaptive and novel manipulation in challenging environments, are described. Implications for the deployment of continuum robots in a variety of applications are discussed
ASSESSING TEACHING PRESENCE IN A COMPUTER CONFERENCING CONTEXT

This paper presents a tool developed for the purpose of assessing teaching presence in online courses that make use of computer conferencing, and preliminary results from the use of this tool. The method of analysis is based on Garrison, Anderson, and Archer’s  model of critical thinking and practical inquiry in a computer conferencing context. The concept of teaching presence is constitutively defined as having three categories – design and organization, facilitating discourse, and direct instruction. Indicators that we search for in the computer conference transcripts identify each category. Pilot testing of the instrument reveals interesting differences in the extent and type of teaching presence found in different graduate level online courses.
ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI

Compressive sensing (CS) is an effective approach for fast Magnetic Resonance Imaging (MRI). It aims at reconstructing MR images from a small number of under-sampled data in k-space, and accelerating the data acquisition in MRI. To improve the current MRI system in reconstruction accuracy and speed, in this paper, we propose two novel deep architectures, dubbed ADMM-Nets in basic and generalized versions. ADMM-Nets are defined over data flow graphs, which are derived from the iterative procedures in Alternating Direction Method of Multipliers (ADMM) algorithm for optimizing a general CS-based MRI model. They take the sampled k-space data as inputs and output reconstructed MR images. Moreover, we extend our network to cope with complex-valued MR images. In the training phase, all parameters of the nets, e.g., transforms, shrinkage functions, etc., are discriminatively trained end-to-end. In the testing phase, they have computational overhead similar to ADMM algorithm but use optimized parameters learned from the data for CS-based reconstruction task. We investigate different configurations in network structures and conduct extensive experiments on MR image reconstruction under different sampling rates. Due to the combination of the advantages in model-based approach and deep learning approach, the ADMM-Nets achieve state-of-the-art reconstruction accuracies with fast computational speed.
ON AN S-BAND RECTENNA ARRAY FOR WIRELESS MICROWAVE POWER TRANSMISSION

The microwave power transmission is an approach for wireless power transmission. As an important component of a microwave wireless power transmission systems, microwave rectennas are widely studied. A rectenna based on a microstrip dipole antenna and a microwave rectifier with high conversion efficiency were designed at 2.45 GHz. The dipole antenna achieved a gain of 5.2 dBi, a return loss greater than 10 dB, and a bandwidth of 20%. The microwave to DC (MW-DC) conversion efficiency of the rectifier was measured as 83% with 20 dBm input power and 600 Ω load. There are 72 rectennas to form an array with an area of 50 cm by 50 cm. The measured results show that the arrangement of the rectenna connection is an effective way to improve the total conversion efficiency, when the microwave power distribution is not uniform on rectenna array. The experimental results show that the highest microwave power transmission efficiency reaches 67.6%.
Implementation of Training Convolutional Neural Networks

Deep learning refers to the shining branch of machine learning that is based on learning levels of representations. Convolutional Neural Networks (CNN) is one kind of deep neural network. It can study concurrently. In this article, we gave a detailed analysis of the process of CNN algorithm both the forward process and back propagation. Then we applied the particular convolutional neural network to implement the typical face recognition problem by java. Then, a parallel strategy was proposed in section4. In addition, by measuring the actual time of forward and backward computing, we analysed the maximal speed up and parallel efficiency theoretically.
Neural Attention Models for Sequence Classification: Analysis and Application to Key Term Extraction and Dialogue Act Detection

Recurrent neural network architectures combining with attention mechanism, or neural attention model, have shown promising performance recently for the tasks including speech recognition, image caption generation, visual question answering and machine translation. In this paper, neural attention model is applied on two sequence labeling tasks, dialogue act detection and key term extraction. In the sequence labeling tasks, the model input is a sequence, and the output is the label of the input sequence. The major difficulty of sequence labeling is that when the input sequence is long, it can include many noisy or irrelevant part. If the information in the whole sequence is treated equally, the noisy or irrelevant part may degrade the classification performance. The attention mechanism is helpful for sequence classification task because it is capable of highlighting important part among the entire sequence for the classification task. The experimental results show that with the attention mechanism, discernible improvements were achieved in the sequence labeling task considered here. The roles of the attention mechanism in the tasks are further analyzed and visualized in this paper.
Learning Real and Boolean Functions: When Is Deep Better Than Shallow

We describe computational tasks – especially in vision – that correspond to compositional/hierarchical functions. While the universal approximation property holds both for hierarchical and shallow networks, we prove that deep (hierarchical) networks can approximate the class of compositional functions with the same accuracy as shallow networks but with exponentially lower VC-dimension as well as the number of training parameters. This leads to the question of approximation by sparse polynomials (in the number of independent parameters) and, as a consequence, by deep networks. We also discuss connections between our results and learnability of sparse Boolean functions, settling an old conjecture by Bengio. This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF 1231216. HNM was supported in part by ARO Grant W911NF-15-1-0385. ar X iv :1 60 3. 00 98 8v 1 [ cs .L G ] 3 M ar 2 01 6
A Light Transport Framework for Lenslet Light Field Cameras

Light field cameras capture full spatio-angular information of the light field, and enable many novel photographic and scientific applications. It is often stated that there is a fundamental trade-off between spatial and angular resolution, but there has been limited understanding of this trade-off theoretically or numerically. Moreover, it is very difficult to evaluate the design of a light field camera because a new design is usually reported with its prototype and rendering algorithm, both of which affect resolution.
 In this article, we develop a light transport framework for understanding the fundamental limits of light field camera resolution. We first derive the prefiltering model of lenslet-based light field cameras. The main novelty of our model is in considering the full space-angle sensitivity profile of the photosensor—in particular, real pixels have nonuniform angular sensitivity, responding more to light along the optical axis rather than at grazing angles. We show that the full sensor profile plays an important role in defining the performance of a light field camera. The proposed method can model all existing lenslet-based light field cameras and allows to compare them in a unified way in simulation, independent of the practical differences between particular prototypes. We further extend our framework to analyze the performance of two rendering methods: the simple projection-based method and the inverse light transport process. We validate our framework with both flatland simulation and real data from the Lytro light field camera.