nils-herrmann/clean_scirepeval huggingface dataset:
A notion of task relatedness yielding provable multiple-task learning guarantees

The approach of learning multiple “related” tasks simultaneously has proven quite successful in practice; however, theoretical justification for this success has remained elusive. The starting point for previous work on multiple task learning has been that the tasks to be learned jointly are somehow “algorithmically related”, in the sense that the results of applying a specific learning algorithm to these tasks are assumed to be similar. We offer an alternative approach, defining relatedness of tasks on the basis of similarity between the example generating distributions that underlie these tasks. We provide a formal framework for this notion of task relatedness, which captures a sub-domain of the wide scope of issues in which one may apply a multiple task learning approach. Our notion of task similarity is relevant to a variety of real life multitask learning scenarios and allows the formal derivation of generalization bounds that are strictly stronger than the previously known bounds for both the learning-to-learn and the multitask learning scenarios. We give precise conditions under which our bounds guarantee generalization on the basis of smaller sample sizes than the standard single-task approach.
Benefactive/Malefactive Event and Writer Attitude Annotation

This paper presents an annotation scheme for events that negatively or positively affect entities (benefactive/malefactive events) and for the attitude of the writer toward their agents and objects. Work on opinion and sentiment tends to focus on explicit expressions of opinions. However, many attitudes are conveyed implicitly, and benefactive/malefactive events are important for inferring implicit attitudes. We describe an annotation scheme and give the results of an inter-annotator agreement study. The annotated corpus is available online.
The State of the art in distributed query processing

Distributed data processing is becoming a reality. Businesses want to do it for many reasons, and they often must do it in order to stay competitive. While much of the infrastructure for distributed data processing is already there (e.g., modern network technology), a number of issues make distributed data processing still a complex undertaking: (1) distributed systems can become very large, involving thousands of heterogeneous sites including PCs and mainframe server machines; (2) the state of a distributed system changes rapidly because the load of sites varies over time and new sites are added to the system; (3) legacy systems need to be integrated—such legacy systems usually have not been designed for distributed data processing and now need to interact with other (modern) systems in a distributed environment. This paper presents the state of the art of query processing for distributed database and information systems. The paper presents the “textbook” architecture for distributed query processing and a series of techniques that are particularly useful for distributed database systems. These techniques include special join techniques, techniques to exploit intraquery paralleli sm, techniques to reduce communication costs, and techniques to exploit caching and replication of data. Furthermore, the paper discusses different kinds of distributed systems such as client-server, middleware (multitier), and heterogeneous database systems, and shows how query processing works in these systems.
Learning the click-through rate for rare/new ads from similar ads

Ads on the search engine (SE) are generally ranked based on their Click-through rates (CTR). Hence, accurately predicting the CTR of an ad is of paramount importance for maximizing the SE's revenue. We present a model that inherits the click information of rare/new ads from other semantically related ads. The semantic features are derived from the query ad click-through graphs and advertisers account information. We show that the model learned using these features give a very good prediction for the CTR values.
TrBagg: A Simple Transfer Learning Method and its Application to Personalization in Collaborative Tagging

The aim of transfer learning is to improve prediction accuracy on a target task by exploiting the training examples for tasks that are related to the target one. Transfer learning has received more attention in recent years, because this technique is considered to be helpful in reducing the cost of labeling. In this paper, we propose a very simple approach to transfer learning: TrBagg, which is the extension of bagging. TrBagg is composed of two stages: Many weak classifiers are first generated as in standard bagging, and these classifiers are then filtered based on their usefulness for the target task. This simplicity makes it easy to work reasonably well without severe tuning of learning parameters. Further, our algorithm equips an algorithmic scheme to avoid negative transfer. We applied TrBagg to personalized tag prediction tasks for social bookmarks Our approach has several convenient characteristics for this task such as adaptation to multiple tasks with low computational cost.
InterpNET: Neural Introspection for Interpretable Deep Learning

Humans are able to explain their reasoning. On the contrary, deep neural networks are not. This paper attempts to bridge this gap by introducing a new way to design interpretable neural networks for classification, inspired by physiological evidence of the human visual system’s inner-workings. This paper proposes a neural network design paradigm, termed InterpNET, which can be combined with any existing classification architecture to generate natural language explanations of the classifications. The success of the module relies on the assumption that the network’s computation and reasoning is represented in its internal layer activations. While in principle InterpNET could be applied to any existing classification architecture, it is evaluated via an image classification and explanation task. Experiments on a CUB bird classification and explanation dataset show qualitatively and quantitatively that the model is able to generate high-quality explanations. While the current state-of-the-art METEOR score on this dataset is 29.2, InterpNET achieves a much higher METEOR score of 37.9.
Guidelines for Using Multiple Views in Information Visualization

A multiple view system uses two or more distinct views to support the investigation of a single conceptual entity. Many such systems exist, ranging from computer-aided design (CAD) systems for chip design that display both the logical structure and the actual geometry of the integrated circuit to overview-plus-detail systems that show both an overview for context and a zoomed-in-view for detail. Designers of these systems must make a variety of design decisions, ranging from determining layout to constructing sophisticated coordination mechanisms. Surprisingly, little work has been done to characterize these systems or to express guidelines for their design. Based on a workshop discussion of multiple views, and based on our own design and implementation experience with these systems, we present eight guidelines for the design of multiple view systems.
A proposed method for predicting US presidential election by analyzing sentiment in social media

US Presidential election is an event anticipated by US citizens and people around the world. By utilizing the big data provided by social media, this research aims to make a prediction of the party or candidate that will win the US presidential election 2016. This paper proposes two stages in research methodology which is data collection and implementation. Data used in this research are collected from Twitter. The implementation stage consists of preprocessing, sentiment analysis, aggregation, and implementation of Electoral College system to predict the winning party or candidate. The implementation of Electoral College will be limited only by using winner take all basis for all states. The implementations are referring from previous works with some addition of methods. The proposed method still unable to use real time data due to random user location value gathered from Twitter REST API, and researchers will be working on it for future works.
Efficient personalized pagerank with accuracy assurance

Personalize PageRank (PPR) is an effective relevance (proximity) measure in graph mining. The goal of this paper is to efficiently compute single node relevance and top-k/highly relevant nodes without iteratively computing the relevances of all nodes. Based on a "random surfer model", PPR iteratively computes the relevances of all nodes in a graph until convergence for a given user preference distribution. The problem with this iterative approach is that it cannot compute the relevance of just one or a few nodes. The heart of our solution is to compute single node relevance accurately in non-iterative manner based on sparse matrix representation, and to compute top-k/highly relevant nodes exactly by pruning unnecessary relevance computations based on upper/lower relevance estimations. Our experiments show that our approach is up to seven orders of magnitude faster than the existing alternatives.
Experimental research on locomotion characters of water strider and movement realization on a water strider robot

Water strider can slide agilely on water surface at high speed. To study its locomotion characters, movements of water strider are recorded by a high speed camera. The trajectories and angle variations of water strider leg are obtained from the photo series, and provide basic information for bionic robot design. Thus a water strider robot based on surface tension is proposed. The driving mechanism is designed to replicate the trajectory of water strider's middle leg.
Worldwide Influenza Surveillance through Twitter

We evaluate the performance of Twitter-based influenza surveillance in ten English-speaking countries across four continents. We find that tweets are positively correlated with existing surveillance data provided by government agencies in these countries, with r values ranging from .37–.81. We show that incorporating Twitter data into a strong autoregressive baseline reduces mean squared error in 80 to 100 percent of locations depending on the lag, with larger improvements when reporting delays are longer.
Real-Time Facial Segmentation and Performance Capture from RGB Input

PRIOR WORK We introduce the concept of unconstrained real-time 3D facial performance capture through explicit semantic segmentation in the RGB input. To ensure robustness, cutting edge supervised learning approaches rely on large training datasets of face images captured in the wild. While impressive tracking quality has been demonstrated for faces that are largely visible, any occlusion due to hair, accessories, or hand-to-face gestures would result in significant visual artifacts and loss of tracking accuracy. The modeling of occlusions has been mostly avoided due to its immense space of appearance variability. To address this curse of high dimensionality, we perform tracking in unconstrained images assuming non-face regions can be fully masked out. Along with recent breakthroughs in deep learning, we demonstrate that pixel-level facial segmentation is possible in real-time by repurposing convolutional neural networks designed originally for general semantic segmentation. We develop an efficient architecture based on a two-stream deconvolution network with complementary characteristics, and introduce carefully designed training samples and data augmentation strategies for improved segmentation accuracy and robustness. We adopt a state-of-the-art regression-based facial tracking framework with segmented face images as training, and demonstrate accurate and uninterrupted facial performance capture in the presence of extreme occlusion and even side views. Furthermore, the resulting segmentation can be directly used to composite partial 3D face models on the input images and enable seamless facial manipulation tasks, such as virtual make-up or face replacement. SEGMENTATION NETWORK pooling 5 output probability map convolution network deconvolution network DeconvNet VGG-16 FCN-8s fusion input frame + + pooling 3 pooling 4 + facial performance capture semantical segmentation RGB input [Cao et al. 2014] RGB-D input [Hsieh et al. 2015] FCN [Long et al. 2015] DeconvNet [Noh et al. 2015] RESULTS face data hand data cropping / occlusion negative samples input image labeled segmentation input image with occlusion augmentation
Content Based Recommendation System Using SOM and Latent Dirichlet Allocation Model

The content-based recommendation systems, is a systems that recommend any information to a user based upon a description of the page or document and a profile of the user’s interests. Content-based recommendation systems may be used in a variety of domains ranging from recommending web pages, news articles, restaurants, television programs, and items for sale. Although the details of various systems differ, content-based recommendation systems share in common a means for describing the items that may be recommended, a means for creating a profile of the user that describes the types of items the user likes, and a means of comparing items to the user profile to determine what to recommend. This paper presents a novel content-based recommendation method which recommends web pages content of a user’s recent interests in a page. Traditionally, a web page is recommended based on a comparison between a user’s profile and web contents that are represented as a set of feature keywords. This paper proposes a new approach to representing and extracting page using SOM and Latent Dirichlet Allocation (LDA) Model. Most of the techniques presented are based on URL or title of pages for recommendation this method is only based on content of a page for recommendation. This paper proposed Content Based Recommendation System Using Latent Dirichlet Allocation Model.  SOM, LDA, Recommendation system, data mining.
A Novel Bit Level Time Series Representation with Implication of Similarity Search and Clustering

Because time series are a ubiquitous and increasingly prevalent type of data, there has been much research effort devoted to time series data mining recently. As with all data mining problems, the key to effective and scalable algorithms is choosing the right representation of the data. Many high level representations of time series have been proposed for data mining. In this work, we introduce a new technique based on a bit level approximation of the data. The representation has several important advantages over existing techniques. One unique advantage is that it allows raw data to be directly compared to the reduced representation, while still guaranteeing lower bounds to Euclidean distance. This fact can be exploited to produce faster exact algorithms for similarly search. In addition, we demonstrate that our new representation allows time series clustering to scale to much larger datasets.
A Low-Profile High-Gain and Wideband Filtering Antenna With Metasurface

A low-profile, high-gain, and wideband metasurface (MS)-based filtering antenna with high selectivity is investigated in this communication. The planar MS consists of nonuniform metallic patch cells, and it is fed by two separated microstrip-coupled slots from the bottom. The separation between the two slots together with a shorting via is used to provide good filtering performance in the lower stopband, whereas the MS is elaborately designed to provide a sharp roll-off rate at upper band edge for the filtering function. The MS also simultaneously works as a high-efficient radiator, enhancing the impedance bandwidth and antenna gain of the feeding slots. To verify the design, a prototype operating at 5 GHz has been fabricated and measured. The reflection coefficient, radiation pattern, antenna gain, and efficiency are studied, and reasonable agreement between the measured and simulated results is observed. The prototype with dimensions of 1.3 λ0 × 1.3 λ0 × 0.06 λ0 has a 10-dB impedance bandwidth of 28.4%, an average gain of 8.2 dBi within passband, and an out-of-band suppression level of more than 20 dB within a very wide stop-band.
Digital image forensics

The article explains how photo-response nonuniformity (PRNU) of imaging sensors can be used for a variety of important digital forensic tasks, such as device identification, device linking, recovery of processing history, and detection of digital forgeries. The PRNU is an intrinsic property of all digital imaging sensors due to slight variations among individual pixels in their ability to convert photons to electrons. Consequently, every sensor casts a weak noise-like pattern onto every image it takes. This pattern, which plays the role of a sensor fingerprint, is essentially an unintentional stochastic spread-spectrum watermark that survives processing, such as lossy compression or filtering. This tutorial explains how this fingerprint can be estimated from images taken by the camera and later detected in a given image to establish image origin and integrity. Various forensic tasks are formulated as a two-channel hypothesis testing problem approached using the generalized likelihood ratio test. The performance of the introduced forensic methods is briefly illustrated on examples to give the reader a sense of the performance.
Towards a Load Balancing in a three-level cloud computing network

Network bandwidth and hardware technology are developing rapidly, resulting in the vigorous development of the Internet. A new concept, cloud computing, uses low-power hosts to achieve high reliability. The cloud computing, an Internet-based development in which dynamically scalable and often virtualized resources are provided as a service over the Internet has become a significant issue. The cloud computing refers to a class of systems and applications that employ distributed resources to perform a function in a decentralized manner. Cloud computing is to utilize the computing resources (service nodes) on the network to facilitate the execution of complicated tasks that require large-scale computation. Thus, the selecting nodes for executing a task in the cloud computing must be considered, and to exploit the effectiveness of the resources, they have to be properly selected according to the properties of the task. However, in this study, a two-phase scheduling algorithm under a three-level cloud computing network is advanced. The proposed scheduling algorithm combines OLB (Opportunistic Load Balancing) and LBMM (Load Balance Min-Min) scheduling algorithms that can utilize more better executing efficiency and maintain the load balancing of system.
Quantum Computing for Computer Scientists

The multidisciplinary field of quantum computing strives to exploit some of the uncanny aspects of quantum mechanics to expand our computational horizons. Quantum Computing for Computer Scientists takes readers on a tour of this fascinating area of cutting-edge research. Written in an accessible yet rigorous fashion, this book employs ideas and techniques familiar to every student of computer science. The reader is not expected to have any advanced mathematics or physics background. After presenting the necessary prerequisites, the material is organized to look at different aspects of quantum computing from the specific standpoint of computer science. There are chapters on computer architecture, algorithms, programming languages, theoretical computer science, cryptography, information theory, and hardware. The text has step-by-step examples, more than two hundred exercises with solutions, and programming drills that bring the ideas of quantum computing alive for today’s computer science students and researchers.
Cross-lingual Distillation for Text Classification

Cross-lingual text classification(CLTC) is the task of classifying documents written in different languages into the same taxonomy of categories. This paper presents a novel approach to CLTC that builds on model distillation, which adapts and extends a framework originally proposed for model compression. Using soft probabilistic predictions for the documents in a label-rich language as the (induced) supervisory labels in a parallel corpus of documents, we train classifiers successfully for new languages in which labeled training data are not available. An adversarial feature adaptation technique is also applied during the model training to reduce distribution mismatch. We conducted experiments on two benchmark CLTC datasets, treating English as the source language and German, French, Japan and Chinese as the unlabeled target languages. The proposed approach had the advantageous or comparable performance of the other state-of-art methods.
Neural reuse: a fundamental organizational principle of the brain.

An emerging class of theories concerning the functional structure of the brain takes the reuse of neural circuitry for various cognitive purposes to be a central organizational principle. According to these theories, it is quite common for neural circuits established for one purpose to be exapted (exploited, recycled, redeployed) during evolution or normal development, and be put to different uses, often without losing their original functions. Neural reuse theories thus differ from the usual understanding of the role of neural plasticity (which is, after all, a kind of reuse) in brain organization along the following lines: According to neural reuse, circuits can continue to acquire new uses after an initial or original function is established; the acquisition of new uses need not involve unusual circumstances such as injury or loss of established function; and the acquisition of a new use need not involve (much) local change to circuit structure (e.g., it might involve only the establishment of functional connections to new neural partners). Thus, neural reuse theories offer a distinct perspective on several topics of general interest, such as: the evolution and development of the brain, including (for instance) the evolutionary-developmental pathway supporting primate tool use and human language; the degree of modularity in brain organization; the degree of localization of cognitive function; and the cortical parcellation problem and the prospects (and proper methods to employ) for function to structure mapping. The idea also has some practical implications in the areas of rehabilitative medicine and machine interface design.
A Rotating Synthetic Aperture Radar Imaging Concept for Robot Navigation

This paper introduces a concept for robot navigation based on a rotating synthetic aperture short-range radar scanner. It uses an innovative broadband holographic reconstruction algorithm, which overcomes the typical problem of residual phase errors caused by an imprecisely measured aperture position and moving targets. Thus, it is no longer necessary to know the exact trajectory of the synthetic aperture radar to get a high-resolution image, which is a major advantage over the classical holographic reconstruction algorithm. However, the developed algorithm is not only used to compute a high-resolution 360 ° 2-D image after each turn of the radar platform while the robot is moving, but also to calculate the relative residual radial velocity between the moving radar scanner system and all targets in the environment. This allows us to determine the exact velocity of the robotic system on which the radar scanner is mounted, and thus to obtain the exact radar trajectory, if there are stationary targets like walls in the environment.