nils-herrmann/clean_scirepeval huggingface dataset:
You Are What You Watch and When You Watch: Inferring Household Structures From IPTV Viewing Data

What you watch and when you watch say a lot about you, and such information at the aggregated level across a user population obviously provides significant insights for social and commercial applications. In this paper, we propose a model for inferring household structures based on analyzing users' viewing behaviors in Internet Protocol Television (IPTV) systems. We emphasize extracting features of viewing behaviors based on the dynamic of watching time and TV programs and training a classifier for inferring household structures according to the features. In the training phase, instead of merely using the limited labeled samples, we apply semisupervised learning strategy to obtain a graph-based model for classifying household structures from users' features. We test the proposed model on China Telecom IPTV data and demonstrate its utility in census research and system simulation. The demographic characteristics inferred by our approach match well with the population census data of Shanghai, and the inference of household structures of IPTV users gives encouraging results compared with the ground truth obtained by surveys, which opens the door for leveraging IPTV viewing data as a complementary way for time- and resource-consuming census tracking. On the other hand, the proposed model can also synthesize trace data for the simulations of IPTV systems, which provides us with a new strategy for system simulation.
Rain removal via residual generation cascading

Single-image rain removal has always been challenging, due to its inherent ill-posed nature. In this paper, we propose a novel rain removal pipeline. This pipeline features two processing pathways: 1) a bottom-up pathway which iteratively performs down sampling and subtraction operations, yielding a Laplacian pyramid (LP) with decreasing scales, to bypass rain details; and 2) a top-down pathway which is built on image series obtained by the above bottom-up process and generate a series of image components based on conditional generative adversarial nets (CGAN), aiming at removing rain streaks while preserving image details. Generated components are further fused via scale-space aggregation, and descriptive image intrinsic information contained in different scales is interacted to jointly generate high quality de-rained image. Extensive experiments on synthetic and real-world rainy image datasets both demonstrate the effectiveness of the proposed rain streaks removal approach.
Separation of weak reflection from a single superimposed image using gradient profile sharpness

It is a massively ill-posed problem to separate a superimposed image into an object image of our interested object and an interference image of reflection. Previous studies relied on redundant information introduced by multiple exposure or multi-view configurations in the separation. Later some new methods proposed tailor-made constraints to remove reflection in specific conditions for a single superimposed image. However, the separated results of these methods always have a lot of residuals or a few tone distortions. In this paper, we aim to realize a clear separation of weak reflection for a single superimposed image. Since the reflection is weak and always out of focus, the resulted interference image would have a smoother edge map than the object image. We utilize this smoothness constraint to obtain an initial separation by classifying gradients according to GPS (gradient profile sharpness) computation. Then we propose a gradient validation framework to reduce the structural correlation between the object image and the interference image. This framework can well correct the misclassified gradients obtained in the initial separation. The experimental results demonstrate that our method can generate promising separation results with little residual or color distortions.
Google image search refinement: Finding text in images using local features

In this paper, we implement a Google image search refinement that utilizes local features to find Chinese characters in search results, including following stages: (1) Chinese characters images and their SIFT features (SDB) are generated offline, (2) A text-based image search results are retrieved from the Google, (3) SIFT features of results are matched to query-text SDB using MPLSH, (4) A geometric verification algorithm is used to find the query-text and rerank results. Experiment results show that our approach is simple and effective in recognition of text in natural images, and is helpful to refine the web image search.
Part-based human gait identification under clothing and carrying condition variations

Gait recognition has already achieved satisfactory performance on small databases under ideal conditions. Most of the existing approaches represent gait pattern using a locomotion model or statistic model of human silhouette. However, it is still a challenging task to conduct human gait identification under variations of clothing and carrying condition in real scenes. In this paper, an adaptive part-based feature selection method is proposed to filter out interference feature blocks and a matching procedure is performed to identify the correct subject. Compared with the state-of-the-art methods on a large standard dataset, the proposed method shows an encouraging computational complexity reduction and performance improvement in identification rates.
Error-resilience packet scheduling for low bit-rate video streaming over wireless channels

In the case of low bit-rate video streaming over wireless channels, burst channel errors possibly cause consecutive frame losses. Considering the source-channel characteristics of the specific case, this paper presents a novel error-resilience packet scheduling scheme based on the reference frame selection (RFS) without back-channel, which is called RFS-based packet scheduling (RFS-PS). By applying the delayed packet scheduling scheme in a group-of-picture (GOP) according to different delay requirements, the probability of simultaneous losses between different prediction chains can be reduced. Simulation results show that the proposed RFS-PS scheme can spread out burst packet losses into different chains, and effectively increase error resilience of low bit-rate video streaming over wireless channels
No-reference image blur assessment based on gradient profile sharpness

The increasing number of digital images and videos has boosted the need of no-reference objective image and video quality assessment (QA). In this paper, we focus on proposing a perceptual-based no-reference image blur assessment using the feature of gradient profile sharpness (GPS). We first build a triangle model to represent the gradient profiles of edge pixels, and propose GPS based on the triangle model. Then a metric for blur assessment is extracted from the distribution histogram of GPS. Experimental results show that the proposed blur metric correlates well with the perceived blurriness, and it can achieve competitive performance with the state-of-the-art no-reference metrics on public image dataset.
Discrete hyper-graph matching

This paper focuses on the problem of hyper-graph matching, by accounting for both unary and higher-order affinity terms. Our method is in line with the linear approximate framework while the problem is iteratively solved in discrete space. It is empirically found more efficient than many extant continuous methods. Moreover, it avoids unknown accuracy loss by heuristic rounding step from the continuous approaches. Under weak assumptions, we prove the iterative discrete gradient assignment in general will trap into a degenerating case - an m-circle solution path where m is the order of the problem. A tailored adaptive relaxation mechanism is devised to detect the degenerating case and makes the algorithm converge to a fixed point in discrete space. Evaluations on both synthetic and real-world data corroborate the efficiency of our method.
Feature Analysis of Spammers in Social Networks with Active Honeypots: A Case Study of Chinese Microblogging Networks

In this poster we report our study on the microblog spammers with samples attracted by 50 honeyspots from two popular Chinese microblogging networks: Sina Weibo (weibo.com), and Ten cent Weibo (t.QQ.com) in seven months. We studied their features such as social information, activity, account age and spamming strategy. Several distinguishing characteristics of spammers on these two social network communities are observed, which can be helpful to the further study on automatic detection of microblog spammers. To our best knowledge our work is the first of its kind on the analysis of features of Chinese micloblog spammers.
Learning a convolutional neural network for fractional interpolation in HEVC inter coding

Motion compensated prediction (MCP) is an effective technology for video coding to improve compression efficiency. Fractional sample precision prediction is utilized in HEVC to further remove temporal redundancy, and finite impulse response (FIR) filters designed using decomposition of the discrete cosine transform are applied to generate samples that do not fall on the integer positions. However, the coefficients of these DCT-based interpolation filters are fixed, which may not be able to adapt to varied video content. Inspired by the remarkable success of convolutional neural network (CNN) in the single image super-resolution task, we propose to learn a convolutional neural network for fractional interpolation in HEVC inter prediction. Compared with super-resolution, there is one big difference in fractional interpolation — fractional interpolation needs to maintain samples at integer positions while super-resolution generates a whole high-resolution image. Another difference is no real ground truth is available in fractional interpolation process. To overcome these two challenges, we introduce a constraint strategy to the training phase of the original super-resolution network as well as a specially designed preprocessing step which reuses the DCTIF interpolation process. Unlike other previous work, our proposed approach simultaneously generating the fractional positions from one network and experimental results show our proposed approach achieves 0.45% BD-Rate reduction under the low-delay-P configuration on average.
Review of ITU-T parametric models for compressed video quality estimation

This paper presents a review of parametric models for video quality estimation standardized in the past few years. The focus of this paper is the estimation of quality degradation caused by the compression artifacts. The models introduced in the paper contain the work of Telecommunication Standardization Section of the International Telecommunication Union (ITU-T) Study Group 9 and Study Group 12, standardized as ITU-T Rec. G.1070, ITU-T Rec. P.1201 series, ITU-T Rec. P.1202 series and ITU-T Rec. J.343.1. The core module estimating coding quality of each model is described with key algorithms and parametric formulas. The review of each model is presented and the strengths and weakness of each model are remarked. Finally, future work towards the development of an updated parametric QoE model for latest HEVC/H.265 is discussed.
Camshift Guided Particle Filter for Visual Tracking

Particle filter and mean shift are two important methods for tracking object in video sequence, and they are extensively studied by researchers. As their strength complements each other, some effort has been initiated in  to combine these two algorithms, on which the advantage of computational efficiency is focused. In this paper, we extend this idea by exploring even more intrinsic relationship between mean shift and particle filter, and propose a new algorithm, CamShift guided particle filter (CAMSGPF). In CAMSGPF, two basic algorithms - CamShift and particle filter - can work cooperatively and benefit from each other, so that the overall performance is improved and some redundancy in algorithms can be removed. Experimental results show that the proposed method can track objects robustly in various environments, and is much faster than the existing methods.
High-power gyrotron development at Forschungszentrum Karlsruhe for fusion applications

In the first part of this paper, the status of the 140-GHz continuously operated gyrotrons with an output power of 1 MW for the stellarator Wendelstein 7-X will be described. With the first series tube, an output power of 1000 kW has been achieved in short pulse operation (milliseconds) with an electron beam current of 40 A, and of 1150 kW at 50 A. With a pulse length of 3 min limited by the available high-voltage (HV) power supply, an output power of 920 kW at an electron beam current of about 40 A with an efficiency of 45% and a mode purity of 97.5% has been obtained. At a reduced beam current of 29 A, an output power of 570 kW was measured with a pulse length of 1893 s without significant increase in tube pressure. The energy content of this pulse is almost 1.1 GJ. For the next fusion plasma device, International Thermonuclear Experimental Reactor (ITER), gyrotrons with a higher output power of about 2 MW are desirable. In short-pulse experiments, the feasibility of the fabrication of coaxial cavity gyrotrons with an output power up to 2-MW, continuous wave (CW), has been demonstrated, and the information necessary for a technical design has been obtained. The development of a long-pulse 2-MW coaxial cavity gyrotron started within a European cooperation. In parallel to the design and fabrication of an industrial prototype gyrotron, a short-pulse preprototype gyrotron has been operated to verify the design of critical components. An output power of 1.2 MW with an efficiency of 20% has been achieved. The development of frequency tunable gyrotrons operating in the range from 105 to 140 GHz for stabilization of current driven plasma instabilities in fusion plasma devices (neoclassical tearing modes) is another task in the development of gyrotrons at the Forschungszentrum Karlsruhe.
Efficient Spatio-temporal Segmentation for Extracting Moving Objects in Video Sequences

Extraction of moving objects is an important and fundamental research topic for many digital video applications. This paper addresses an efficient spatio-temporal segmentation scheme to extract moving objects from video sequences. The temporal segmentation yields a temporal mask that indicates moving regions and static regions for each frame. For localization of moving objects, a block-based motion detection method considering a novel feature measure is proposed to detect changed regions. These changed regions are coarse and need accurate spatial compensation. An edge-based morphological dilation method is presented to achieve the anisotropic expansion of the changed regions. Furthermore, to solve the temporarily stopping problem of moving objects, the inertia information of moving objects is considered in the temporal segmentation. The spatial segmentation based on the watershed algorithm is performed to provide homogeneous regions with closed and precise boundaries. It considers the global information to improve the accuracy of the boundaries. To reduce over-segmentation in the watershed segmentation, a novel mean filter is proposed to suppress some minima. A fusion of the spatial and temporal segmentation results produces complete moving objects faithfully. Compared with the reference algorithms, the fusion threshold in our scheme is fixed for different sequences. Experiments on typical sequences have successfully demonstrated the validity of the proposed scheme.
A new unsupervised convolutional neural network model for Chinese scene text detection

As one of the most popular deep learning models, convolution neural network (CNN) has achieved huge success in image information extraction. Traditionally CNN is trained by supervised learning method with labeled data and used as a classifier by adding a classification layer in the end. Its capability of extracting image features is largely limited due to the difficulty of setting up a large training dataset. In this paper, we propose a new unsupervised learning CNN model, which uses a so-called convolutional sparse auto-encoder (CSAE) algorithm pre-train the CNN. Instead of using labeled natural images for CNN training, the CSAE algorithm can be used to train the CNN with unlabeled artificial images, which enables easy expansion of training data and unsupervised learning. The CSAE algorithm is especially designed for extracting complex features from specific objects such as Chinese characters. After the features of articficial images are extracted by the CSAE algorithm, the learned parameters are used to initialize the first CNN convolutional layer, and then the CNN model is fine-trained by scene image patches with a linear classifier. The new CNN model is applied to Chinese scene text detection and is evaluated with a multilingual image dataset, which labels Chinese, English and numerals texts separately. More than 10% detection precision gain is observed over two CNN models.
Bamboo: A fast descriptor based on AsymMetric pairwise BOOsting

A robust hash, or content-based fingerprint, is a succinct representation of the perceptually most relevant parts of a multimedia object. A key requirement of fingerprinting is that elements with perceptually similar content should map to the same fingerprint, even if their bit-level representations are different. In this work we propose BAMBOO (Binary descriptor based on AsymMetric pairwise BOOsting), a binary local descriptor that exploits a combination of content-based fingerprinting techniques and computationally efficient filters (box filters, Haar-like features, etc.) applied to image patches. In particular, we define a possibly large set of filters and iteratively select the most discriminative ones resorting to an asymmetric pair-wise boosting technique. The output values of the filtering process are quantized to one bit, leading to a very compact binary descriptor. Results show that such descriptor leads to compelling results, significantly outperforming binary descriptors having comparable complexity (e.g., BRISK), and approaching the discriminative power of state-of-the-art descriptors which are significantly more complex (e.g., SIFT and BinBoost).
LAURA — LocAlization and Ubiquitous monitoRing of pAtients for health care support

This works illustrates the LAURA system, which performs localization, tracking and monitoring of patients hosted at nursing institutes by exploiting a wireless sensor network based on the IEEE 801.15.4 (Zigbee) standard. We focus on the indoor personal localization module, which leverages a method based on received signal strength measurements, together with a particle filter to perform tracking of moving patients. We discuss the implementation and dimensioning of the localization and tracking system using commercial hardware, and we test the LAURA system in real environment, both with static and moving patients, achieving an average localization error lower than 2 m in 80% of the cases. The data sets containing the real measurements of received signal strengths collected during the experiments are made publicly available to enable reproducible research.
A Mathematical Programming Approach to Task Offloading in Visual Sensor Networks

This work studies how visual analysis tasks based on feature extraction can be speeded up in the context of Visual Sensor Networks. The main catch is for the camera node to leverage the presence of neighboring sensor nodes and offload the task, thus parallelizing its execution. We propose two mathematical programming formulations for the optimal visual task offloading problem: the first one targets the minimization of the overall task completion time while enforcing energy consumption constraints onto the nodes; the second maximizes the overall sensor network lifetime subject to a temporal constraint on the task completion time. The aforementioned formulations are used to characterize the achievable speed-up and consequent energy consumption in representative visual sensor network topologies.
Semantically improved genome-wide prediction of Gene Ontology annotations

Genomic annotations describing structural and functional features of genes and gene products through controlled terminologies and ontologies are extremely valuable, especially for computational analyses aimed at inferring new biomedical knowledge, which rely on available annotations. Yet, they are incomplete, especially for recently studied genomes, and only some of available annotations represent highly reliable human curated information. In order to help and speedup the time-consuming curation process and improve available annotations, computational methods able to provide prioritized lists of predicted annotations are paramount. Starting from a previous work on automatic prediction of Gene Ontology annotations based on singular value decomposition (SVD) of gene-to-term annotation matrix, here we propose a novel prediction algorithm that incorporates gene clustering based on gene functional similarity computed on Gene Ontology annotations. We tested both prediction methods performing k-fold cross-validation on two organism genomes, Saccharomyces cerevisiae (SGD) and Drosophila melanogaster (FlyBase). Results demonstrate effectiveness of our approach.
Multi-view coding of local features in visual sensor networks

Local visual features extracted from multiple camera views are employed nowadays in several application scenarios, such as object recognition, disparity matching, image stitching and many others. In several cases, local features need to be transmitted or stored on resource-limited devices, thus calling for efficient coding techniques. While recent works have addressed the problem of efficiently compressing local features extracted from still images or video sequences, in this paper we propose and evaluate an architecture for coding features extracted from multiple, overlapping views. The proposed Multi-View Feature Coding architecture can be applied to either real-valued or binary features, and allows to obtain bitrate reductions in the order of 10-20% with respect to simulcast coding.
Cost-Aware Rank Join with Random and Sorted Access

In this paper, we address the problem of joining ranked results produced by two or more services on the web. We consider services endowed with two kinds of access that are often available: 1) sorted access, which returns tuples sorted by score; 2) random access, which returns tuples matching a given join attribute value. Rank join operators combine objects of two or more relations and output the k combinations with the highest aggregate score. While the past literature has studied suitable bounding schemes for this setting, in this paper we focus on the definition of a pulling strategy, which determines the order of invocation of the joined services. We propose the Cost-Aware with Random and Sorted access (CARS) pulling strategy, which is derived at compile-time and is oblivious of the query-dependent score distributions. We cast CARS as the solution of an optimization problem based on a small set of parameters characterizing the joined services. We validate the proposed strategy with experiments on both real and synthetic data sets. We show that CARS outperforms prior proposals and that its overall access cost is always within a very short margin from that of an oracle-based optimal strategy. In addition, CARS is shown to be robust w.r.t. the uncertainty that may characterize the estimated parameters.