nils-herrmann/clean_scirepeval huggingface dataset:
Multimedia event detection with multimodal feature fusion and temporal concept localization

We present a system for multimedia event detection. The developed system characterizes complex multimedia events based on a large array of multimodal features, and classifies unseen videos by effectively fusing diverse responses. We present three major technical innovations. First, we explore novel visual and audio features across multiple semantic granularities, including building, often in an unsupervised manner, mid-level and high-level features upon low-level features to enable semantic understanding. Second, we show a novel Latent SVM model which learns and localizes discriminative high-level concepts in cluttered video sequences. In addition to improving detection accuracy beyond existing approaches, it enables a unique summary for every retrieval by its use of high-level concepts and temporal evidence localization. The resulting summary provides some transparency into why the system classified the video as it did. Finally, we present novel fusion learning algorithms and our methodology to improve fusion learning under limited training data condition. Thorough evaluation on a large TRECVID MED 2011 dataset showcases the benefits of the presented system.
Text categorization based on regularization extreme learning machine

This article proposes a novel approach for text categorization based on a regularization extreme learning machine (RELM) in which its weights can be obtained analytically, and a bias-variance trade-off could be achieved by adding a regularization term into the linear system of single-hidden layer feedforward neural networks. To fit the input scale of RELM, the latent semantic analysis was used to represent text for dimensionality reduction. Moreover, a classification algorithm based on RELM was developed including the uni-label (i.e., a document can only be assigned to a unique category) and multi-label (i.e., a document can be assigned to multiple categories simultaneously) situations. The experimental results in two benchmarks show that the proposed method can produce good performance in most cases, and it could learn faster than popular methods such as feedforward neural networks or support vector machine.
Head tracking for the Oculus Rift

We present methods for efficiently maintaining human head orientation using low-cost MEMS sensors. We particularly address gyroscope integration and compensation of dead reckoning errors using gravity and magnetic fields. Although these problems have been well-studied, our performance criteria are particularly tuned to optimize user experience while tracking head movement in the Oculus Rift Development Kit, which is the most widely used virtual reality headset to date. We also present novel predictive tracking methods that dramatically reduce effective latency (time lag), which further improves the user experience. Experimental results are shown, along with ongoing research on positional tracking.
Research Paper Recommender Systems: A Random-Walk Based Approach

Every day researchers from all over the world have to filter the huge mass of existing research papers with the crucial aim of finding out useful publications related to their current work. In this paper we propose a research paper recommending algorithm based on the citation graph and random-walker properties. The PaperRank algorithm is able to assign a preference score to a set of documents contained in a digital library and linked one each other by bibliographic references. A data set of papers extracted by ACM portal has been used for testing and very promising performances have been measured
Algorithms for Designing Pop-Up Cards

We prove that every simple polygon can be made as a (2D) pop-up card/book that opens to any desired angle between 0 and 360◦. More precisely, given a simple polygon attached to the two walls of the open pop-up, our polynomial-time algorithm subdivides the polygon into a single-degree-of-freedom linkage structure, such that closing the pop-up flattens the linkage without collision. This result solves an open problem of Hara and Sugihara from 2009. We also show how to obtain a more efficient construction for the special case of orthogonal polygons, and how to make 3D orthogonal polyhedra, from pop-ups that open to 90◦, 180◦, 270◦, or 360◦.
Efficient Selective-ID Secure Identity-Based Encryption Without Random Oracles

We construct two efficient Identity Based Encryption (IBE) systems that are selective identity secure without the random oracle model. Selective identity secure IBE is a slightly weaker security model than the standard security model for IBE. In this model the adversary must commit ahead of time to the identity that it intends to attack, whereas in the standard model the adversary is allowed to choose this identity adaptively. Our first secure IBE system extends to give a selective identity Hierarchical IBE secure without random oracles.
Large-scale JPEG steganalysis using hybrid deep-learning framework

Deep learning frameworks have achieved overwhelming superiority in many fields of pattern recognition in recent years. However, the application of deep learning frameworks in image steganalysis is still in its initial stage. In this paper we firstly proved that the convolution phase and the quantization & truncation phase are not learnable for deep neural networks. Then on the basis of the theoretical analysis, we proposed a new hybrid deep-learning framework for JPEG steganalysis, which is made up of two hybrid parts. The first part is hand-crafted. It corresponds to the the convolution phase and the quantization & truncation phase of the rich models. The second part is a compound deep neural network containing three CNN subnets in which the model parameters are learnable during the training procedure. We have conducted extensive experiments on large-scale dataset extracted from ImageNet. Primary dataset used in our experiments contains one million images, while our largest dataset contains ten million images. The large-scale experiments show that our proposed framework outperforms all other steganalytic models (hand-crafted or deeplearning based) in the literature. Furthermore, the experimental results revealed that our proposed framework possesses some great features, including well attacking-target transfer ability and insensitive to altered JPEG block artifact.
Analysis and Control of a Three-Phase, Unity Power Factor  $Y$-Rectifier

This paper presents the analysis and operation of a three-phase pulsewidth modulation rectifier system formed by the star-connection of three single-phase boost rectifier modules (Y-rectifier) without a mains neutral point connection. The current forming operation of the Y-rectifier is analyzed and it is shown that the phase current has the same high quality and low ripple as the Vienna rectifier. The isolated star point of Y-rectifier results in a mutual coupling of the individual phase module outputs and has to be considered for control of the module dc link voltages. An analytical expression for the coupling coefficients of the Y-rectifier phase modules is derived. Based on this expression, a control concept with reduced calculation effort is designed and it provides symmetric loading of the phase modules and solves the balancing problem of the dc link voltages. The analysis also provides insight that enables the derivation of a control concept for two phase operation, such as in the case of a mains phase failure. The theoretical and simulated results are proved by experimental analysis on a fully digitally controlled, 5.4-kW prototype.
"Everything I Disagree With is #FakeNews": Correlating Political Polarization and Spread of Misinformation

An important challenge in the process of tracking and detecting the dissemination of misinformation is to understand the gap in the political views between people that engage with the so called ”fake news”. A possible factor responsible for this gap is opinion polarization, which may prompt the general public to classify content that they disagree or want to discredit as fake. In this work, we study the relationship between political polarization and content reported by Twier users as related to ”fake news”. We investigate how polarization may create distinct narratives on what misinformation actually is. We perform our study based on two datasets collected from Twier. e rst dataset contains tweets about US politics in general, from which we compute the political leaning of each user towards the Republican and Democratic Party. In the second dataset, we collect tweets and URLs that co-occurred with ”fake news” related keywords and hashtags, such as #FakeNews and #AlternativeFact, as well as reactions towards such tweets and URLs. We then analyze the relationship between polarization and what is perceived as misinformation, and whether users are designating information that they disagree as fake. Our results show an increase in the polarization of users and URLs (in terms of their associated political viewpoints) for information labeled with fakenews keywords and hashtags, when compared to information not labeled as ”fake news”. We discuss the impact of our ndings on the challenges of tracking ”fake news” in the ongoing bale against misinformation.
Metamorphic testing: a new approach for generating next test cases

In software testing, a set of test cases is constructed according to some predeened selection criteria. The software is then examined against these test cases. Three interesting observations have been made on the current artifacts of software testing. Firstly, an error-revealing test case is considered useful while a successful test case which does not reveal software errors is usually not further investigated. Whether these successful test cases still contain useful information for revealing software errors has not been properly studied. Secondly, no matter how extensive the testing has been conducted in the development phase, errors may still exist in the software 5. These errors, if left undetected, may eventually cause damage to the production system. The study of techniques for uncovering software errors in the production phase is seldom addressed in the literature. Thirdly, as indicated by Weyuker in 66, the availability of test oracles is pragmatically unattainable in most situations. However, the availability of test oracles is generally assumed in conventional software testing techniques. In this paper, we propose a novel test case selection technique that derives new test cases from the successful ones. The selection aims at revealing software errors that are possibly left undetected in successful test cases which may be generated using some existing strategies. As such, the proposed technique augments the eeectiveness of existing test selection strategies. The technique also helps uncover software errors in the production phase and can be used in the absence of test oracles.
Ku-band 200-W Pulsed Power Amplifier Based on Waveguide Spatially Power-Combining Technique for Industrial Applications

A Ku-band 200-W pulsed solid-state power amplifier has been presented and designed by using a hybrid radial-/rectangular-waveguide spatially power-combining technique. The hybrid radial-/rectangular-waveguide power-dividing/power-combining circuit employed in this design provides not only a high power-combining efficiency over a wide bandwidth but also efficient heat sinking for the active power devices. A simple design approach of the presented power-dividing/power-combining structure has been developed. The measured small-signal gain of the pulsed power amplifier is about 51.3 dB over the operating frequency range, while the measured maximum output power at 1-dB compression is 209 W at 13.9 GHz, with an active power-combining efficiency of about 91%. Furthermore, the active power-combining efficiency is greater than 82% from 13.75 to 14.5 GHz.
Texture Synthesis with Spatial Generative Adversarial Networks

Generative adversarial networks (GANs) [7] are a recent approach to train generative models of data, which have been shown to work particularly well on image data. In the current paper we introduce a new model for texture synthesis based on GAN learning. By extending the input noise distribution space from a single vector to a whole spatial tensor, we create an architecture with properties well suited to the task of texture synthesis, which we call spatial GAN (SGAN). To our knowledge, this is the first successful completely data-driven texture synthesis method based on GANs.
A divide et impera strategy for automatic classification of retinal vessels into arteries and veins

The first pathologic alterations of the retina are seen in the vessel network. These modifications affect very differently arteries and veins, and the appearance and entity of the modification differ as the retinopathy becomes milder or more severe. In order to develop an automatic procedure for the diagnosis and grading of retinopathy, it is necessary to be able to discriminate arteries from veins. The problem is complicated by the similarity in the descriptive features of these two structures and by the contrast and luminosity variability of the retina. We developed a new algorithm for classifying the vessels, which exploits the peculiarities of retinal images. By applying a divide et imperaapproach that partitioned a concentric zone around the optic disc into quadrants, we were able to perform a more robust local classification analysis. The results obtained by the proposed technique were compared with those provided by a manual classification on a validation set of 443 vessels and reached an overall classification error of 12 %, which reduces to 7 % if only the diagnostically important retinal vessels are considered.
Task Specific Adversarial Cost Function

The cost function used to train a generative model should fit the purpose of the model. If the model is intended for tasks such as generating perceptually correct samples, it is beneficial to maximise the likelihood of a sample drawn from the model, Q, coming from the same distribution as the training data, P . This is equivalent to minimising the Kullback-Leibler (KL) distance, KL[Q‖P ]. However, if the model is intended for tasks such as retrieval or classification it is beneficial to maximise the likelihood that a sample drawn from the training data is captured by the model, equivalent to minimising KL[P‖Q]. The cost function used in adversarial training optimises the Jensen-Shannon entropy which can be seen as an even interpolation between KL[Q‖P ] and KL[P‖Q]. Here, we propose an alternative adversarial cost function which allows easy tuning of the model for either task. Our task specific cost function is evaluated on a dataset of hand-written characters in the following tasks: Generation, retrieval and one-shot learning.
Aggregating Local Deep Features for Image Retrieval

Several recent works have shown that image descriptors produced by deep convolutional neural networks provide state-of-the-art performance for image classification and retrieval problems. It also has been shown that the activations from the convolutional layers can be interpreted as local features describing particular image regions. These local features can be aggregated using aggregating methods developed for local features (e.g. Fisher vectors), thus providing new powerful global descriptor. In this paper we investigate possible ways to aggregate local deep features to produce compact descriptors for image retrieval. First, we show that deep features and traditional hand-engineered features have quite different distributions of pairwise similarities, hence existing aggregation methods have to be carefully re-evaluated. Such re-evaluation reveals that in contrast to shallow features, the simple aggregation method based on sum pooling provides the best performance for deep convolutional features. This method is efficient, has few parameters, and bears little risk of overfitting when e.g. learning the PCA matrix. In addition, we suggest a simple yet efficient query expansion scheme suitable for the proposed aggregation method. Overall, the new compact global descriptor improves the state-of-the-art on four common benchmarks considerably.
Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision

Understanding the 3D world is a fundamental problem in computer vision. However, learning a good representation of 3D objects is still an open problem due to the high dimensionality of the data and many factors of variation involved. In this work, we investigate the task of single-view 3D object reconstruction from a learning agent’s perspective. We formulate the learning process as an interaction between 3D and 2D representations and propose an encoder-decoder network with a novel projection loss defined by the perspective transformation. More importantly, the projection loss enables the unsupervised learning using 2D observation without explicit 3D supervision. We demonstrate the ability of the model in generating 3D volume from a single 2D image with three sets of experiments: (1) learning from single-class objects; (2) learning from multi-class objects and (3) testing on novel object classes. Results show superior performance and better generalization ability for 3D object reconstruction when the projection loss is involved.
CPU-assisted GPGPU on fused CPU-GPU architectures

This paper presents a novel approach to utilize the CPU resource to facilitate the execution of GPGPU programs on fused CPU-GPU architectures. In our model of fused architectures, the GPU and the CPU are integrated on the same die and share the on-chip L3 cache and off-chip memory, similar to the latest Intel Sandy Bridge and AMD accelerated processing unit (APU) platforms. In our proposed CPU-assisted GPGPU, after the CPU launches a GPU program, it executes a pre-execution program, which is generated automatically from the GPU kernel using our proposed compiler algorithms and contains memory access instructions of the GPU kernel for multiple thread-blocks. The CPU pre-execution program runs ahead of GPU threads because (1) the CPU pre-execution thread only contains memory fetch instructions from GPU kernels and not floating-point computations, and (2) the CPU runs at higher frequencies and exploits higher degrees of instruction-level parallelism than GPU scalar cores. We also leverage the prefetcher at the L2-cache on the CPU side to increase the memory traffic from CPU. As a result, the memory accesses of GPU threads hit in the L3 cache and their latency can be drastically reduced. Since our pre-execution is directly controlled by user-level applications, it enjoys both high accuracy and flexibility. Our experiments on a set of benchmarks show that our proposed pre-execution improves the performance by up to 113% and 21.4% on average.
Reinforcement learning for control: Performance, stability, and deep approximators

Reinforcement learning (RL) offers powerful algorithms to search for optimal controllers of systems with nonlinear, possibly stochastic dynamics that are unknown or highly uncertain. This review mainly covers artificial-intelligence approaches to RL, from the viewpoint of the control engineer. We explain how approximate representations of the solution make RL feasible for problems with continuous states and control actions. Stability is a central concern in control, and we argue that while the control-theoretic RL subfield called adaptive dynamic programming is dedicated to it, stability of RL largely remains an open question. We also cover in detail the case where deep neural networks are used for approximation, leading to the field of deep RL, which has shown great success in recent years. With the control practitioner in mind, we outline opportunities and pitfalls of deep RL; and we close the survey with an outlook that – among other things – points out some avenues for bridging the gap between control and artificial-intelligence RL techniques.
Symmetry-based text line detection in natural scenes

Recently, a variety of real-world applications have triggered huge demand for techniques that can extract textual information from natural scenes. Therefore, scene text detection and recognition have become active research topics in computer vision. In this work, we investigate the problem of scene text detection from an alternative perspective and propose a novel algorithm for it. Different from traditional methods, which mainly make use of the properties of single characters or strokes, the proposed algorithm exploits the symmetry property of character groups and allows for direct extraction of text lines from natural images. The experiments on the latest ICDAR benchmarks demonstrate that the proposed algorithm achieves state-of-the-art performance. Moreover, compared to conventional approaches, the proposed algorithm shows stronger adaptability to texts in challenging scenarios.
Can emotions be truly group level? Evidence regarding four conceptual criteria.

Recent advances in understanding prejudice and intergroup behavior have made clear that emotions help explain people's reactions to social groups and their members. Intergroup emotions theory (D. M. Mackie, T. Devos, & E. R. Smith, 2000; E. R. Smith, 1993) holds that intergroup emotions are experienced by individuals when they identify with a social group, making the group part of the psychological self. What differentiates such group-level emotions from emotions that occur purely at the individual level? The authors argue that 4 key criteria define group-level emotions: Group emotions are distinct from the same person's individual-level emotions, depend on the person's degree of group identification, are socially shared within a group, and contribute to regulating intragroup and intergroup attitudes and behavior. Evidence from 2 studies supports all 4 of these predictions and thus points to the meaningfulness, coherence, and functionality of group-level emotions.
Efficient Methods for Inferring Large Sparse Topic Hierarchies

Latent variable topic models such as Latent Dirichlet Allocation (LDA) can discover topics from text in an unsupervised fashion. However, scaling the models up to the many distinct topics exhibited in modern corpora is challenging. “Flat” topic models like LDA have difficulty modeling sparsely expressed topics, and richer hierarchical models become computationally intractable as the number of topics increases. In this paper, we introduce efficient methods for inferring large topic hierarchies. Our approach is built upon the Sparse Backoff Tree (SBT), a new prior for latent topic distributions that organizes the latent topics as leaves in a tree. We show how a document model based on SBTs can effectively infer accurate topic spaces of over a million topics. We introduce a collapsed sampler for the model that exploits sparsity and the tree structure in order to make inference efficient. In experiments with multiple data sets, we show that scaling to large topic spaces results in much more accurate models, and that SBT document models make use of large topic spaces more effectively than flat LDA.