nils-herrmann/clean_scirepeval huggingface dataset:
Learning Visual Sentiment Distributions via Augmented Conditional Probability Neural Network

Visual sentiment analysis is raising more and more attention with the increasing tendency to express emotions through images. While most existing works assign a single dominant emotion to each image, we address the sentiment ambiguity by label distribution learning (LDL), which is motivated by the fact that image usually evokes multiple emotions. Two new algorithms are developed based on conditional probability neural network (CPNN). First, we propose BCPNN which encodes image label into a binary representation to replace the signless integers used in CPNN, and employ it as a part of input for the neural network. Then, we train our ACPNN model by adding noises to ground truth label and augmenting affective distributions. Since current datasets are mostly annotated for single-label learning, we build two new datasets, one of which is relabeled on the popular Flickr dataset and the other is collected from Twitter. These datasets contain 20,745 images with multiple affective labels, which are over ten times larger than the existing ones. Experimental results show that the proposed methods outperform the state-of-theart works on our large-scale datasets and other publicly available benchmarks.  In recent years, lots of attention has been paid to affective image classification (Jou et al. 2015; Joshi et al. 2011; Chen et al. 2015). Most of these works are conducted by psychological studies (Lang 1979; Lang, Bradley, and Cuthbert 1998), and focus on manual design of features and classifiers (You et al. 2015a). As defined as a singlelabel learning (SLL) problem which assigns a single emotional label to each image, previous works (You et al. 2016; Sun et al. 2016) have performed promising results. However, image sentiment may be the mixture of all components from different regions rather than a single representative emotion. Meanwhile, different people may have different emotional reactions to the same image, which is caused by a variety of elements like the different culture background and various recognitions from unique experiences (Peng et al. 2015). Furthermore, even a single viewer may have multiple reactions to one image. Figure 1 shows examples from a widely used dataset, i.e. Abstract Paintings Copyright c
TableQA: Question Answering on Tabular Data

Tabular data is difficult to analyze and to search through, yielding for new tools and interfaces that would allow even non tech-savvy users to gain insights from open datasets without resorting to specialized data analysis tools and without having to fully understand the dataset structure. The goal of our demonstration is to showcase answering natural language questions from tabular data, and to discuss related system configuration and model training aspects. Our prototype is publicly available and open-sourced (see demo )
Write Fast, Read in the Past: Causal Consistency for Client-Side Applications

Client-side apps (e.g., mobile or in-browser) need cloud data to be available in a local cache, for both reads and updates. For optimal user experience and developer support, the cache should be consistent and fault-tolerant. In order to scale to high numbers of unreliable and resource-poor clients, and large database, the system needs to use resources sparingly. The SwiftCloud distributed object database is the first to provide fast reads and writes via a causally-consistent client-side local cache backed by the cloud. It is thrifty in resources and scales well, thanks to consistent versioning provided by the cloud, using small and bounded metadata. It remains available during faults, switching to a different data centre when the current one is not responsive, while maintaining its consistency guarantees. This paper presents the SwiftCloud algorithms, design, and experimental evaluation. It shows that client-side apps enjoy the high performance and availability, under the same guarantees as a remote cloud data store, at a small cost.
Deep Learning for Object Saliency Detection and Image Segmentation

In this paper, we propose several novel deep learning methods for object saliency detection based on the powerful convolutional neural networks. In our approach, we use a gradient descent method to iteratively modify an input image based on the pixel-wise gradients to reduce a cost function measuring the class-specific objectness of the image. The pixel-wise gradients can be efficiently computed using the back-propagation algorithm. The discrepancy between the modified image and the original one may be used as a saliency map for the image. Moreover, we have further proposed several new training methods to learn saliency-specific convolutional nets for object saliency detection, in order to leverage the available pixel-wise segmentation information. Our methods are extremely computationally efficient (processing 20-40 images per second in one GPU). In this work, we use the computed saliency maps for image segmentation. Experimental results on two benchmark tasks, namely Microsoft COCO and Pascal VOC 2012, have shown that our proposed methods can generate high-quality salience maps, clearly outperforming many existing methods. In particular, our approaches excel in handling many difficult images, which contain complex background, highly-variable salient objects, multiple objects, and/or very small salient objects.
Toward an Ethics of Algorithms : Convening , Observation , Probability , and Timeliness

Part of understanding the meaning and power of algorithms means asking what new demands they might make of ethical frameworks, and how they might be held accountable to ethical standards. I develop a definition of networked information algorithms (NIAs) as assemblages of institutionally situated code, practices, and norms with the power to create, sustain, and signify relationships among people and data through minimally observable, semiautonomous action. Starting from Merrill’s prompt to see ethics as the study of ‘‘what we ought to do,’’ I examine ethical dimensions of contemporary NIAs. Specifically, in an effort to sketch an empirically grounded, pragmatic ethics of algorithms, I trace an algorithmic assemblage’s power to convene constituents, suggest actions based on perceived similarity and probability, and govern the timing and timeframes of ethical action. University of Southern California, Los Angeles, CA, USA Corresponding Author: Mike Ananny, University of Southern California, 3502 Watt Way, Los Angeles, CA 90089, USA.  ananny@usc.edu Science, Technology, & Human Values 1-25 a The Author(s) 2015 Reprints and permission: sagepub.com/journalsPermissions.nav
An Intelligent Model for Pairs Trading Using Genetic Algorithms

Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice.
A more pragmatic Web 3 . 0 : Linked Blockchain Data

Linked Data is proclaimed as the Semantic Web done right. The Semantic Web is an incomplete dream so far, but a homogeneous revolutionary platform as a network of Blockchains could be the solution to this not optimal reality. This research paper introduces some initial hints and ideas about how a futuristic Internet that might be composed and powered by Blockchains networks would be constructed and designed to interconnect data and meaning, thus allow reasoning. An industrial application where Blockchain and Linked Data fits perfectly as a Supply Chain management system is also researched.
Rate-distortion methods for image and video compression

Rate-Distortion Methods for Image and Video Compression: An. Or Laplacian p.d.f.s and optimal bit allocation techniques to ensure that bits.Rate-Distortion Methods for Image and Video Compression. Coding Parameters: chosen on input-by-input rampant caries pdf basis to optimize. In this article we provide an overview of rate-distortion R-D based optimization techniques and their practical application to image and video. Rate-distortion methods for image and video compression. Enter the password to open this PDF file. EE368b Image and Video Compression. Lower the bit-rate R by allowing some acceptable distortion. Consideration of a specific coding method. Bit-rate at least R.rate-distortion R-D based optimization techniques and their practical application to. Area of R-D optimized image and video coding see 1, 2 and many of the. Such Intra coding alone is in common use as ramones guitar tab pdf a video coding method today.  A step higher in bit rate, picture quality, and popularity.coding, rate distortion RD optimization, soft decision quantization SDQ. RD methods for video compression can be classified into two categories. Practical SDQ include without limitation SDQ in JPEG image coding and H. However, since we know that most lossy compression techniques operate on data. In image and video compression, the human perception models are less well. The conditional PDF QY Xy x that minimize rate for a given distortion D.The H. 264AVC video coding standard has been recently proposed by the Joint. MB which determine the overall rate and the distortion of the coded.  The picture encoding process in the proposed method. Selection of λ and.fact, operational rate-distortion methods have come into wide use for image and video coders. In previous work, de Queiroz applied this technique to finding.
Two-Handed Virtual Manipulation

We discuss a two-handed user interface designed to support three-dimesional neurosurgical visualization. By itself, this system is a “point design,” an example of an advanced user interface technique. In this work, we argue that in order to understand why interaction techniques do or do not work, and to suggest possibilities for new techniques, it is important to move beyond point design and to introduce careful scientific measurement of human behavioral principles. In particular, we argue that the common-sense viewpoint that “two hands save time by working in parallel” may not always be an effective way to think about two-handed interface design because the hands do not necessarily work in parallel (there is a structure to two-handed manipulation) and because two hands do more than just save time over one hand (two hands provide the user with more information and can structure how the user thinks about a task). To support these claims, we present an interface design developed in collaboration with neurosurgeons which has undergone extensive informal usability testing, as well as a pair of formal experimental studies which investigate behavioral aspects of two-handed virtual object manipulation. Our hope is that this discussion will help others to apply the lessons in our neurosurgery application to future two-handed user interface designs.
Design and implementation of a connected farm for smart farming system

Agriculture has been one of the most important industries in human history since it provides humans with absolutely indispensable resources such as food, fiber, and energy. The agriculture industry could be further developed by employing new technologies, in particular, the Internet of Things (IoT). In this paper, we present a connected farm based on IoT systems, which aims to provide smart farming systems for end users. A detailed design and implementation for connected farms are illustrated, and its advantages are explained with service scenarios compared to previous smart farms. We hope this work will show the power of IoT as a disruptive technology helping across multi industries including agriculture.
Adjusting the Outputs of a Classifier to New a Priori Probabilities: A Simple Procedure

It sometimes happens (for instance in case control studies) that a classifier is trained on a data set that does not reflect the true a priori probabilities of the target classes on real-world data. This may have a negative effect on the classification accuracy obtained on the real-world data set, especially when the classifier's decisions are based on the a posteriori probabilities of class membership. Indeed, in this case, the trained classifier provides estimates of the a posteriori probabilities that are not valid for this real-world data set (they rely on the a priori probabilities of the training set). Applying the classifier as is (without correcting its outputs with respect to these new conditions) on this new data set may thus be suboptimal. In this note, we present a simple iterative procedure for adjusting the outputs of the trained classifier with respect to these new a priori probabilities without having to refit the model, even when these probabilities are not known in advance. As a by-product, estimates of the new a priori probabilities are also obtained. This iterative algorithm is a straightforward instance of the expectation-maximization (EM) algorithm and is shown to maximize the likelihood of the new data. Thereafter, we discuss a statistical test that can be applied to decide if the a priori class probabilities have changed from the training set to the real-world data. The procedure is illustrated on different classification problems involving a multilayer neural network, and comparisons with a standard procedure for a priori probability estimation are provided. Our original method, based on the EM algorithm, is shown to be superior to the standard one for a priori probability estimation. Experimental results also indicate that the classifier with adjusted outputs always performs better than the original one in terms of classification accuracy, when the a priori probability conditions differ from the training set to the real-world data. The gain in classification accuracy can be significant.
Survey on Intrusion Detection System using Machine Learning Techniques

In today’s world, almost everybody is affluent with computers and network based technology is growing by leaps and bounds. So, network security has become very important, rather an inevitable part of computer system. An Intrusion Detection System (IDS) is designed to detect system attacks and classify system activities into normal and abnormal form. Machine learning techniques have been applied to intrusion detection systems which have an important role in detecting Intrusions. This paper reviews different machine approaches for Intrusion detection system. This paper also presents the system design of an Intrusion detection system to reduce false alarm rate and improve accuracy to detect intrusion.
The Economics of Multi-Hop Ride Sharing - Creating New Mobility Networks Through IS

Ride sharing allows to share costs of traveling by car, e.g., for fuel or highway tolls. Furthermore, it reduces congestion and emissions by making better use of vehicle capacities. Ride sharing is hence beneficial for drivers, riders, as well as society. While the concept has existed for decades, ubiquity of digital and mobile technology and user habituation to peer-to-peer services and electronic markets have resulted in particular growth in recent years. This paper explores the novel idea of multi-hop ride sharing and illustrates how Information Systems can leverage its potential. Based on empirical ride sharing data, we provide a quantitative analysis of the structure and the economics of electronic ride sharing markets. We explore the potential and competitiveness of multi-hop ride sharing and analyze its implications for platform operators. We find that multi-hop ride sharing proves competitive against other modes of transportation and has the potential to greatly increase ride availability and city connectedness, especially under high reliability requirements. To fully realize this potential, platform operators should implement multi-hop search, assume active control of pricing and booking processes, improve coordination of transfers, enhance data services, and try to expand their market share.
On the Benefit of Synthetic Data for Company Logo Detection

In this paper we explore the benefits of synthetically generated data for the task of company logo detection with deep-learned features in the absence of a large training set. We use pre-trained deep convolutional neural networks for feature extraction and use a set of support vector machines for classifying those features. In order to generate sufficient training examples we synthesize artificial training images. Using a bootstrapping process, we iteratively add new synthesized examples from an unlabeled dataset to the training set. Using this setup we are able to obtain a performance which is close to the performance of the full training set.
XPIWIT - an XML pipeline wrapper for the Insight Toolkit

The Insight Toolkit offers plenty of features for multidimensional image analysis. Current implementations, however, often suffer either from a lack of flexibility due to hard-coded C++ pipelines for a certain task or by slow execution times, e.g. caused by inefficient implementations or multiple read/write operations for separate filter execution. We present an XML-based wrapper application for the Insight Toolkit that combines the performance of a pure C++ implementation with an easy-to-use graphical setup of dynamic image analysis pipelines. Created XML pipelines can be interpreted and executed by XPIWIT in console mode either locally or on large clusters. We successfully applied the software tool for the automated analysis of terabyte-scale, time-resolved 3D image data of zebrafish embryos.



XPIWIT is implemented in C++ using the Insight Toolkit and the Qt SDK. It has been successfully compiled and tested under Windows and Unix-based systems. Software and documentation are distributed under Apache 2.0 license and are publicly available for download at https://bitbucket.org/jstegmaier/xpiwit/downloads/.



Supplementary data are available at Bioinformatics online.
A rapid prototyping software infrastructure for user interfaces in ubiquitous augmented reality

Recent user interface concepts, such as multimedia, multimodal, wearable, ubiquitous, tangible, or augmented-reality-based (AR) interfaces, each cover different approaches that are all needed to support complex human–computer interaction. Increasingly, an overarching approach towards building what we call ubiquitous augmented reality (UAR) user interfaces that include all of the just mentioned concepts will be required. To this end, we present a user interface architecture that can form a sound basis for combining several of these concepts into complex systems. We explain in this paper the fundamentals of DWARF’s user interface framework (DWARF standing for distributed wearable augmented reality framework) and an implementation of this architecture. Finally, we present several examples that show how the framework can form the basis of prototypical applications.
VLSI Implementation of Neural Networks

Currently, fuzzy controllers are the most popular choice for hardware implementation of complex control surfaces because they are easy to design. Neural controllers are more complex and hard to train, but provide an outstanding control surface with much less error than that of a fuzzy controller. There are also some problems that have to be solved before the networks can be implemented on VLSI chips. First, an approximation function needs to be developed because CMOS neural networks have an activation function different than any function used in neural network software. Next, this function has to be used to train the network. Finally, the last problem for VLSI designers is the quantization effect caused by discrete values of the channel length (L) and width (W) of MOS transistor geometries. Two neural networks were designed in 1.5 microm technology. Using adequate approximation functions solved the problem of activation function. With this approach, trained networks were characterized by very small errors. Unfortunately, when the weights were quantized, errors were increased by an order of magnitude. However, even though the errors were enlarged, the results obtained from neural network hardware implementations were superior to the results obtained with fuzzy system approach.
The neuroprotective effects of bilingualism upon the inferior parietal lobule: A Structural Neuroimaging Study in Aging Chinese Bilinguals

It is a timely issue to understand the impact of bilingualism upon brain structure in healthy aging and upon cognitive decline given evidence of its neuroprotective effects. Plastic changes induced by bilingualism were reported in young adults in the left inferior parietal lobule (LIPL) and its right counterpart (RIPL) (Mechelli et al., 2004). Moreover, both age of second language (L2) acquisition and L2 proficiency correlated with increased grey matter (GM) in the LIPL/RIPL. However it is unknown whether such findings replicate in older bilinguals. We examined this question in an aging bilingual population from Hong Kong. Results from our Voxel Based Morphometry study show that elderly bilinguals relative to a matched monolingual control group also have increased GM volumes in the inferior parietal lobules underlining the neuroprotective effect of bilingualism. However, unlike younger adults, age of L2 acquisition did not predict GM volumes. Instead, LIPL and RIPL appear differentially sensitive to the effects of L2 proficiency and L2 exposure with LIPL more sensitive to the former and RIPL more sensitive to the latter. Our data also intimate that such * Corresponding author. University Vita-Salute San Raffaele, Via Olgettina 58, 20132 Milan,  þ39 0226434888. E-mail addresses: abutalebi.jubin@hsr.it, jubin@hku.hk (
A Neural Probabilistic Model for Context Based Citation Recommendation

Automatic citation recommendation can be very useful for authoring a paper and is an AI-complete problem due to the challenge of bridging the semantic gap between citation context and the cited paper. It is not always easy for knowledgeable researchers to give an accurate citation context for a cited paper or to find the right paper to cite given context. To help with this problem, we propose a novel neural probabilistic model that jointly learns the semantic representations of citation contexts and cited papers. The probability of citing a paper given a citation context is estimated by training a multi-layer neural network. We implement and evaluate our model on the entire CiteSeer dataset, which at the time of this work consists of 10,760,318 citation contexts from 1,017,457 papers. We show that the proposed model significantly outperforms other stateof-the-art models in recall, MAP, MRR, and nDCG.
Understandable Big Data: A survey

This survey presents the concept of Big Data. Firstly, a definition and the features of Big Data are given. Secondly, the different steps for Big Data data processing and the main problems encountered in big data management are described. Next, a general overview of an architecture for handling it is depicted. Then, the problem of merging Big Data architecture in an already existing information system is discussed. Finally this survey tackles semantics (reasoning, coreference resolution, entity linking, information extraction, consolidation, paraphrase resolution, ontology alignment) in the Big Data context.