nils-herrmann/clean_scirepeval huggingface dataset:
Active video-based surveillance system: the low-level image and video processing techniques needed for implementation

The importance of video surveillance techniques has considerably increased since the latest terrorist incidents. Safety and security have become critical in many public areas, and there is a specific need to enable human operators to remotely monitor the activity across large environments. For these reasons, multicamera systems are needed to provide surveillance coverage across a wide area, ensuring object visibility over a large range of depths. In the development of advanced visual-based surveillance systems, a number of key issues critical to its successful operation must be addressed. This article describes the low-level image and video processing techniques needed to implement a modern surveillance system. In particular, the change detection methods for both fixed and mobile cameras (pan and tilt) are introduced and the registration methods for multicamera systems with overlapping and nonoverlapping views are discussed.
Face detection for visual surveillance

In this paper, a real-time face detection system for color image sequences is presented. The system applies three different face detection methods and integrates the obtained results to achieve a greater location accuracy. The first method localizes the human head through outline analysis, focusing the attention of the system on a small image area. The second, a skin color method, is applied to the blobs to find skin regions (e.g., faces, hands, etc.). The third. principal component analysis, is used to reduce the dimensionality of the data set and to detect face patterns. Finally. the obtained face locations are fused to increase the detection reliability and to avoid false detections due to occlusions or unfavorable human poses. The proposed approach is used by a video-based surveillance system for monitoring indoor scenes.
Person Re-Identification Ranking Optimisation by Discriminant Context Information Analysis

Person re-identification is an open and challenging problem in computer vision. Existing re-identification approaches focus on optimal methods for features matching (e.g., metric learning approaches) or study the inter-camera transformations of such features. These methods hardly ever pay attention to the problem of visual ambiguities shared between the first ranks. In this paper, we focus on such a problem and introduce an unsupervised ranking optimization approach based on discriminant context information analysis. The proposed approach refines a given initial ranking by removing the visual ambiguities common to first ranks. This is achieved by analyzing their content and context information. Extensive experiments on three publicly available benchmark datasets and different baseline methods have been conducted. Results demonstrate a remarkable improvement in the first positions of the ranking. Regardless of the selected dataset, state-of-the-art methods are strongly outperformed by our method.
Remote feature learning for mobile re-identification

This work introduces a novel method for person re-identification using embedded smart cameras. State-of-the-art methods address the re-identification problem using global and local features, metric learning and feature transformation algorithms. Such methods require advanced systems with high computational capabilities. Nowadays, there is a growing interest in security applications using embedded cameras. Motivated by this we propose to study a new system that addresses the challenges posed by the reidentification problem using devices (e.g. smartphones, etc.) that have limited resources. In this work we introduce a novel client-server system that exploits a feature learning method to achieve a two-fold objective: (i) maximize the re-identification performance over time and (ii) reduce the required computational costs. In the training phase, state-of-the-art features are selected considering both the device capabilities and re-identification performance. During the detection phase, the re-identification performance are maximized by selecting the best features for a given input image. To demonstrate the performance of the proposed method we conduct the experiments using different mobile devices. Statistics about feature extraction and feature matching are presented together with re-identification results.
Correlation based fingerprint liveness detection

Fingerprint recognition systems are vulnerable to spoof attacks, which consist in presenting forged fingerprints to the sensor. Typical anti-spoofing mechanism is fingerprint liveness detection. Existing liveness detection methods are still not robust to spoofing materials, datasets and sensor variations. In particular, the performance of a liveness detection algorithm remarkably drops upon encountering spoof fabrication materials that were not used during the training stage. Likewise, a quintessential liveness detection method needs to be adapted and retrained to new spoofing materials, datasets and each sensor used for acquiring the fingerprints. In this paper, we propose a framework that first performs correlation mapping between live and spoof fingerprints and then uses a discriminative-generative classification scheme for spoof detection. Partial Least Squares (PLS) is utilized to learn the correlations. While, support vector machine (SVM) is combined with three generative classifiers, namely Gaussian Mixture Model, Gaussian Copula, and Quadratic Discriminant Analysis, for final classification. Experiments on the publicly available LivDet2011 and LivDet2013 datasets, show that the proposed method outperforms the existing methods alongside cross-spoof material and cross-sensor techniques.
Kernelized Saliency-Based Person Re-Identification Through Multiple Metric Learning

Person re-identification in a non-overlapping multi-camera scenario is an open and interesting challenge. While the task can hardly be completed by machines, we, as humans, are inherently able to sample those relevant persons' details that allow us to correctly solve the problem in a fraction of a second. Thus, knowing where a human might fixate to recognize a person is of paramount interest for re-identification. Inspired by the human gazing capabilities, we want to identify the salient regions of a person appearance to tackle the problem. Toward this objective, we introduce the following main contributions. A kernelized graph-based approach is used to detect the salient regions of a person appearance, later used as a weighting tool in the feature extraction process. The proposed person representation combines visual features either considering or not the saliency. These are then exploited in a pairwise-based multiple metric learning framework. Finally, the non-Euclidean metrics that have been separately learned for each feature are fused to re-identify a person. The proposed kernelized saliency-based person re-identification through multiple metric learning has been evaluated on four publicly available benchmark data sets to show its superior performance over the state-of-the-art approaches (e.g., it achieves a rank 1 correct recognition rate of 42.41% on the VIPeR data set).
Learning pairwise feature dissimilarities for person re-identification

This paper deals with person re-identification in a multi-camera scenario with non-overlapping fields of view. Signature based matching has been the dominant choice for state-of-the-art person re-identification across multiple non-overlapping cameras. In contrast we propose a novel approach that exploits pairwise dissimilarities between feature vectors to perform the re-identification in a supervised learning framework. To achieve the proposed objective we address the person re-identification problem as follows: i) we extract multiple features from two persons images and compare them using standard distance metrics. This gives rise to what we called distance feature vector; ii) we learn the set of positive and negative distance feature vectors and perform the re-identification by classifying the test distance feature vectors. We evaluate our approach on two publicly available benchmark datasets and we compare it with state-of-the-art methods for person re-identification.
MoBio_LivDet: Mobile biometric liveness detection

Biometric authentication is now being used ubiquitously as an alternative to passwords on mobile devices. However, current biometric systems are vulnerable to simple spoofing attacks. Several liveness detection methods have been proposed to determine whether there is a live person or an artificial replica in front of the biometric sensor. Yet, the problem is unsolved due to hardship in finding discriminative and computationally inexpensive features for spoofing attacks. Moreover, previous liveness detection approaches are not explicitly aimed for mobile biometric, thus principally unsuited for portable devices. Therefore, we build a software-based multi-biometric prototype that detects face, iris and fingerprint spoofing attacks on mobile devices. We present MoBio_LivDet (Mobile Biometric Liveness Detection), a novel approach that analyzes local features and global structures of the biometric images using a set of low-level feature descriptors and decision level fusion. The system allows user to balance the security level (robustness against spoofing) and convenience that they want. The proposed method is highly fast, simple, efficient, robust and does not require user-cooperation, thus making it extremely apt for mobile devices. Experimental analysis on publicly available face, iris and fingerprint data sets with real spoofing attacks show promising results.
Robust Painting Recognition and Registration for Mobile Augmented Reality

In this work we introduce a novel approach for painting recognition and registration for mobile Augmented Reality applications. To address the challenges of real-time painting recognition and registration we introduce three main contributions: i) A relevant painting region detector extracts the painting region from the given image. ii) Two local and global features are extracted from the relevant region to robustly match a painting database. iii) A RANSAC homography estimation method is used to overlay the additional content in an AR framework. Experiments have been carried out on a dataset built with publicly available images.
Stereo Localization Based on Network's Uncalibrated Camera Pairs

In this paper, a stereo framework for a robust real time localization of objects using network’s camera pairs is presented. The stereo system contains a combination of static and pan-tilt-zoom (PTZ) cameras instead of traditional dual head mounted cameras. The proposed novelty consists in applying stereo vision to heterogeneous cameras belonging to a video-surveillance network. First, a look-up-table (LUT) is built with the rectification transformations computed for some predefined pan and tilt values. Then, the LUT is used to compute rectification transformations by means of neural networks for any arbitrary pan and tilt settings. Different zoom levels are compensated by resizing images according to their focal ratio and by applying zero padding. Localization of any object is made using its 3D position information obtained by a modified stereo concept. Experimental results are presented for the localization of moving objects in a parking lot scenario.
Group sparse features for speech emotion perception in tensor space

With increasing demands for a natural interaction between human and machine, emotion perception from speech signals is becoming an important interaction interface. In this paper, we give a feature extraction framework for speech emotion recognition and present a novel method to extract emotion information based on group sparsity in tensor space. The speech signal is encoded as cortical representation in auditory system. We propose the group lasso nonnegative tensor factorization model to learn the multilinear factor matrices from tensor feature subspaces. l1/l2 constraint on multiple subspaces is imposed to recover the different groups of covariance for each factor (frequency, time, etc). The experimental results show that the proposed method can improve the multi-classes emotion recognition performance than state of the art baseline systems.
A Fast Parallel Implementation of Molecular Dynamics with the Morse Potential on a Heterogeneous Petascale Supercomputer

Molecular Dynamics (MD) simulations have been widely used in the study of macromolecules. To ensure an acceptable level of statistical accuracy relatively large number of particles are needed, which calls for high performance implementations of MD. These days heterogeneous systems, with their high performance potential, low power consumption, and high price-performance ratio, offer a viable alternative for running MD simulations. In this paper we introduce a fast parallel implementation of MD simulation with the Morse potential on Tianhe-1A, a petascale heterogeneous supercomputer. Our code achieves a speedup of 3.6× on one NVIDIA Tesla M2050 GPU (containing 14 Streaming Multiprocessors) compared to a 2.93GHz six-core Intel Xeon X5670 CPU. In addition, our code runs faster on 1024 compute nodes (with two CPUs and one GPU inside a node) than on 4096 GPU-excluded nodes, effectively rendering one GPU more efficient than six six-core CPUs. Our work shows that large-scale MD simulations can benefit enormously from GPU acceleration in petascale supercomputing platforms. Our performance results are achieved by using (1) a patch-cell design to exploit parallelism across the simulation domain, (2) a new GPU kernel developed by taking advantage of Newton's Third Law to reduce redundant force computation on GPUs, (3) two optimization methods including a dynamic load balancing strategy that adjusts the workload, and a communication overlapping method to overlap the communications between CPUs and GPUs.
New sampling-based design of simultaneous compensation of both dispersion and dispersion slope for multichannel fiber Bragg gratings

The design of a multichannel fiber Bragg grating to compensate simultaneously the fiber dispersion and dispersion slope is proposed and analyzed. It is found that constituent gratings with equal length would result in unequal channel bandwidth and large band edge sidelobes in broad bandwidth channels due to large dispersion slope. To overcome these short-comings, we propose a new design procedure in that both the physical length and apodization function of the constituent gratings be allowed to vary. This results in a multichannel grating with equal bandwidth and negligible band edge sidelobes. Of course, this grating compensates both the fiber dispersion and dispersion slope very well.
Study on Energy Saving of Chinese-fir Wood Carbonization Process Based on Moisture Absorption Characteristics

Chinese fir is one of the most important species in plantation-grown forest in South China and been widely used in many fields. However, the properties of strong moisture absorption and weak dimensional stability limit its development. The introduction of thermal treatment and carbonization has repaired this defect in certain extent but at the cost of high energy-consumption. In this paper, whole factor analysis method was used to find a novel and optimized process parameter, the results show as follows: 1) these 3 factors that carbonization temperature, carbonization time and environment humidity have significant influence on the moisture content and mechanical properties, especially on energy consumption. 2) Equilibrium moisture contents all tend to decrease with the increase of carbonization temperature, carbonization time and environment humidity, but much more energy are consumed only obtained light decrease. 3) The best process parameter is 160degC-180degC carbonization temperature, 2 hours carbonization and 22.45% environment humidity, under this condition, no only dimensional stability is improved, but also production cost is saved and energy consumption is decreased by nearly 50%.
Fast algorithm for adaptive beamforming of cyclic signals

There has been an explosive increase in the demand of radio channels in cellular communication systems. Due to the finite limit of the frequency spectrum the number of available channels is bounded. This constraint has become a bottleneck of next-generation wireless communication systems. Some array beamforming techniques have been proposed to solve this problem by the space-division multiple access (SDMA). In the paper, a fast adaptive beamforming algorithm for extracting cyclic signals is proposed. Similar to the SCORE algorithm, the new beamformer performs a blind signal extraction. Compared with the SCORE method it has simpler computation and faster convergence rate and higher SINR in many situations of interest.
On optimal subspaces for appearance-based object recognition

On the subject of optimal subspaces for appearance-based object recognition, it is generally believed that algorithms based on LDA (linear discriminant analysis) are superior to those based on PCA (principal components analysis), provided that relatively large training data sets are available. In this paper, we show that while this is generally true for classification with the nearest-neighbor classifier, it is not always the case with a maximum-likelihood classifier. We support our claim by presenting both intuitively plausible arguments and actual results on a large data set of human chromosomes. Our conjecture is that perhaps only when the underlying object classes are linearly separable would LDA be truly superior to other known subspaces of equal dimensionality.
Evaluation on Application Potential of Gigantochloa ligulata for Biomedicine

Gigantochloa ligulata is both a wide use as biomatrals and an antiquity as medicine. It contains many kinds of organosufide compounds. Therefore, Py-GC/MS was used to analyse and evaluate the application potential of Gigantochloa ligulata for biomedicine. The results indicated that the main components of the Gigantochloa ligulata by Py-GC/MS were carbon dioxide (19.67%), acetic acid (16.08%), acetone (10.86%), 1-propanol (8.13%), 2-propanone, 1-hydroxy- (5.12%), 1,6-anhydro-.beta.-d-glucopyranose (5.06%), 2-butanone, 1,1,1-trifluoro- (3.68%), r-(- )-cyclohexylethylamine (3.31%), 2,4(1h,3h)-pyrimidinedione, 5- (trifluoromethyl)- (3.01%), benzofuran, 2,3-dihydro- (2.85%), butanedial (2.59%), 1,2-ethanediol, monoacetate (1.84%), oxirane, 2,3-dimethyl-, cis- (1.81%), phenol, 2,6-dimethoxy-4-(2- propenyl)- (1.35%), phenol, 2,6-dimethoxy- (1.13%), phenol, 2- (1,1-dimethylethyl)- (1.03%), aminobenzoic acid (0.97%), 4- hydroxy-3-methylacetophenone (0.88%), phenol, 2-methoxy- (0.87%), phenol, 2-methoxy-4-(1-propenyl)- (0.84%), phenol, 2- methoxy-4-methyl- (0.7%), 1,3,6-trioxocane (0.63%), 2-propenoic acid, 3-(4-hydroxy-3-methoxyphenyl)- (0.59%), etc. so the the pyrolysis byproducts of Gigantochloa ligulata may be used as raw materials of rare bio medicines.
Optimum transmitter power control in cellular systems with heterogeneous SIR thresholds

Power control is of great importance to increase the capacity of cellular mobile systems. In early papers, optimum transmitter power control theory has been fully investigated for cellular systems under the assumption of system-wide homogeneous signal-to-interference ratio (SIR) threshold, a minimum required SIR also known as the protection ratio. In reality, the minimum required SIR for each mobile is a function of that mobile's fading rate and other channel characteristics. Therefore it varies from mobile to mobile and can also change as a function of time as individual mobiles change their speeds. A homogeneous SIR threshold would have to be set according to the required SIR in the worst case, which would result in reduced system performance. In this paper, we explore the optimum transmitter power control theory under the assumption of heterogeneous SIR thresholds. Distributed power control algorithms are also investigated. The efficiency of these algorithms is demonstrated through simulation of CDMA cellular systems.
Notice of Retraction Study of a soft starter of six-phase Brushless DC motor

This paper studies a soft starter of six-phase BLDC motor, presents a soft start method based on complex programmable logic device (CPLD), analyzes the soft starting process of six-phase BLDC motor and the influence of PWM carrier frequency and duty on starting process, employs back EMF zero crossing detection circuit to detect the rotor position, designs the hardware circuit and program of the soft starter, and finally verifies the validity of the proposed method by the experiment. The experimental results show that the soft starter based on CPLD is simple, reliable and practical.
Strong Modulation Instability in a Silicon–Organic Hybrid Slot Waveguide

In this paper, we investigate the strong modulation instability (MI) at telecommunication band in a silicon-organic hybrid slot waveguide. The organic material of polymer poly (bis para-toluene sulfonate) of 2, 4-hexadiyne 1, 6 diol (PTS), which has high third-order nonlinear refractive index and very low two-photon absorption, is used to fill the slot of the waveguide. The optical gain can be up to ~3600 m-1 with a low pump peak power of 300 mW. By using Gaussian pulses with width of 10 ps and peak power of 250 mW, deep modulation of the pump is achieved, and the ultrashort pulse trains with the periods of 27 and 24 fs are obtained in the anomalous and normal group-velocity dispersion regions, respectively.
cdma2000 Highly Detectable Pilot

Research and development on mobile phone positioning technologies are gaining momentum due to the potential in location based services. Mobile location is estimated based on measurements from satellites and terrestrial base stations. Satellite measurements often available in rural and suburban areas are less or not available in dense urban and indoor areas. Thus, base station measurements play an important role in mobile positioning. In this paper, we present the highly detectable pilot (HDP) that is being standardized in 3GPP2 for cdma2000 1xEV-DO. HDP allows mobile to detect significantly more base stations, and thus provides much better accuracy in location estimate.
The effect of APP on preparation process and flame retardancy of bagasse board

With bagasses as testing material, UF as adhesive and APP of liquid and powder as flame retardants, amount of flame retardants content, glue consumption and design density as testing factors, orthogonal experiment was conducted. Retardant and mechanical properties were tested by cone calorimeter and universal mechanical testing machine, respectively. The results showed that: with the increasing of APP adding content, curing time of UF reduced, viscosity of UF decreased and bonding strength reduced. The optimal parameters were: process of bagasse board was processed by liquid APP, the adding mount of APP was 12%, the amount of UF was 15%, density designed of board was 0.85g/cm3.
Secure Data Aggregation Scheme for Clustered Wireless Sensor Networks

This paper proposes a secure data aggregation scheme for clustered wireless sensor networks. In our design, the encrypted sensor readings were transmitted to the cluster head with MAC and the cluster head process the encrypted data aggregation without decryption. For the readings have the same value and come from different nodes, the cluster head remains the node's identifiers in data aggregation processing to provide the information of global data distribution. Compared with related works, except providing data privacy protection, our scheme had better performances in resilient against active attack, node compromise attack and DoS attack.