nils-herrmann/clean_scirepeval huggingface dataset:
Video concept detection by learning from web images: A case study on cross domain learning

Concept detection is probably the most important research problem in the area of multimedia. The need to model with sufficient and diverse training instances, however, makes the task computationally and resourcefully expensive. Meanwhile, the popularity of social media has generated massive amount of weakly tagged images which could be leveraged for concept model learning. Therefore, in this paper, we consider exploring weakly taggedWeb images to shed some light on video concept detection. Particularly, two sets of Web images downloaded from Flickr are utilized as training data for concept detection on two real-world large-scale video datasets released by TRECVID. Our experiments are conducted under different settings with and without transfer learning. The results indicate that Web images are helpful in the case of few available training instances in video domain, which is a common case of many real-world applications.
Uniqueness wavelet descriptor for plane closed curves

Using the periodized wavelets and considering the orthonormal bases with dilation factor two, an inhered uniqueness property in the discrete periodized wavelet transform (DPWT) is found and proved. The uniqueness property denotes the one-to-one mapping characteristic between the circular shift in the original data sequence and a combination vector in which each element is the sign of the summation of the high frequency band components in each decomposition level. Based on this uniqueness property, a new wavelet descriptor called the uniqueness wavelet descriptor (UWD) is proposed which has the properties of scaling, translation, rotation, and starting point shift invariance so that it can be used as the features of a plane closed curve for pattern recognition.
Analysis of Swarm Behavior of Users' GPS Data Based on Boids Model

Modern smart phones are equipped with a wide variety of sensors including GPS, accelerometer, gyroscope, or even biometric sensor. Many intelligent context-aware applications are thus also become popular. In particular, by using GPS data, many Location Based Services (LBSs) have been developed and deployed. In the literature, analysis based on the collection of GPS data from a large amount of users has also been widely studied, such as user classification, location prediction, personalized travel route recommendation, etc. However, analyzing the swarm behavior of a group of users based on their GPS data has received little attention. In this paper, we perform both quantitative and qualitative analysis on the swarm behavior of a group of users based on the Boids model proposed by Craig Reynolds. Specifically, we are interested in discovering the features of Boids model by analyzing the GPS trajectories of a group of people. Our results confirm that the moving of a group of people exhibits flocking behavior and can be explained by the features of Boids model.
Channel Thickness Effect on High-Frequency Performance of Poly-Si Thin-Film Transistors

In this letter, we present the high-frequency performances of polycrystalline silicon (poly-Si) thin-film transistors (TFTs) with a nominal gate length of 0.22 μm. Owing to the short gate length and adoption of salicide process, cutoff frequency (fT) of 17 GHz and maximum oscillation frequency of ~ 21 GHz are obtained. The result suggests that the poly-Si TFT technology is applicable to RF integrated circuits up to 2 GHz. In addition, we also investigate the effects of channel thickness on the high-frequency characteristics of poly-Si TFTs. We find that the variation of fT with channel thickness is mainly due to the change in transconductance.
Exploiting Web Images for Video Highlight Detection With Triplet Deep Ranking

Highlight detection from videos has been widely studied due to the fast growth of video contents. However, most existing approaches to highlight detection, either handcraft feature based or deep learning based, heavily rely on human-curated training data, which is very expensive to obtain and, thus, hinders the scalability to large datasets and unlabeled video categories. We observe that the largely available Web images can be applied as a weak supervision for highlight detection. For example, the top-ranked images in reference to the query “skiing” returned by a search engine may contain considerable positive samples of “skiing” highlights. Motivated by this observation, we propose a novel triplet deep ranking approach to video highlight detection using Web images as a weak supervision. The approach handles the relative preference of highlight scores between highlighting frames, nonhighlighting frames, and Web images by the triplet ranking constraints. Our approach can iteratively train two interdependent deep models (i.e., a triplet highlight model and a pairwise noise model) to deal with the noisy Web images in a single framework. We train the two models with relative preferences to generalize the capability regardless of the categories of training data. Therefore, our approach is fully category independent and exploits weakly supervised Web images. We evaluate our approach on two challenging datasets and achieve impressive results compared with the state-of-the-art pairwise ranking support vector machines, a robust recurrent autoencoder, and spatial deep convolution neural networks. We also empirically verify through cross-dataset evaluation that our category-independent model is fairly generalizable even if two different datasets do not share exactly the same categories.
Complex spectrogram enhancement by convolutional neural network with multi-metrics learning

This paper aims to address two issues existing in the current speech enhancement methods: 1) the difficulty of phase estimations; 2) a single objective function cannot consider multiple metrics simultaneously. To solve the first problem, we propose a novel convolutional neural network (CNN) model for complex spectrogram enhancement, namely estimating clean real and imaginary (RI) spectrograms from noisy ones. The reconstructed RI spectrograms are directly used to synthesize enhanced speech waveforms. In addition, since log-power spectrogram (LPS) can be represented as a function of RI spectrograms, its reconstruction is also considered as another target. Thus a unified objective function, which combines these two targets (reconstruction of RI spectrograms and LPS), is equivalent to simultaneously optimizing two commonly used objective metrics: segmental signal-to-noise ratio (SSNR) and log-spectral distortion (LSD). Therefore, the learning process is called multi-metrics learning (MML). Experimental results confirm the effectiveness of the proposed CNN with RI spectrograms and MML in terms of improved standardized evaluation metrics on a speech enhancement task.
Multi-mode interleaved boost converter for photovoltaic power systems with low-voltage ride-through capability

The objective of this paper is to propose a multi-mode interleaved boost converter for three-phase photovoltaic (PV) power systems with low-voltage ride-through (LVRT) capability. The proposed interleaved boost converter is able to continuously extract active power from the PV arrays during the grid voltage sag. After the grid voltage returns to its normal condition, the PV inverter can quickly achieve its maximum power point tracking (MPPT) operation. Also, the boost converter with multi-channel interleaved structure can increase power conversion efficiency but decrease input current ripple. Furthermore, the three-phase inverter with the maximum current limitation control can provide maximum reactive power under the allowable current amplitude during the voltage sag as well as output the demanded reactive/rated current ratio to meet different LVRT codes. Experimental results obtained from a 3 kVA prototype circuit demonstrate the performance of the proposed circuit topology.
Efficient mode selection for D2D communications: An interference upper bound approach

Device-to-device (D2D) communication is an emerging technique to enhance the throughout and reduce energy consumption of future cellular networks that are expected to serve a tremendous amount of wireless devices. With the D2D communication functionality, the data link between two users in the proximity can be established directly without routing through the base station (BS). The optimal mode selection policy is to choose the communication route (i.e., either direct or through the BS) that offers the higher throughput, but its implementation, which requires network wide information about the interference level of every user, is far from trivial. We propose a simple yet effective mode selection scheme that uses an upper bound in replacement of the exact interference level to choose the communication mode. We prove in which conditions that the proposed scheme performs as good as the optimal mode selection scheme. Simulations are performed to evaluate the performance of the proposed scheme. The proposed scheme can achieve the comparable performance to the optimal scheme in terms of sum throughput but with much lower implementation complexity.
Empirical Study on Rare Query Characteristics

User behavior analysis has played an important role in Web information retrieval. Rare queries, whose frequencies are rather low, are usually ignored in existing studies due to the data sparseness. Little has been known about the mass of rare queries on either the information need or the user behavior. In this paper, we make an empirical study of users' behavior on rare queries using a large scale search log. Features concerning query, resource and post-query actions are analyzed, based on which we propose a practical categorization framework and obtain an overview of rare query composition. Further, we study the characteristics of several most commonly occurring types of rare queries, and suggest improving the search performance of them separately. This work gives more insights into understanding the long tail of queries and will be helpful for Web search in terms of rare queries.
Dynamic Nano-scale Surface Profilometry Using Stroboscopic Interferometry

Dynamic 3-D nano-scale surface profilometry using stroboscopic interferometry was successfully developed. An optical microscopy based on stroboscopic interferometry was developed to achieve full-field vibratory out-of-plane surface profilometry and system characterization. To increase the measurement bandwidth of the developed system, an innovative image processing algorithm based on deconvolution principle was developed to improve the signal to noise ratio of the detected interferometric data. The method provides an excellent way to increase the measurement bandwidth without adding any significant hardware in a stroboscopic interferometric framework. Meanwhile, an innovative detection algorithm based on image contrast measure was developed for automatic identification of accurate resonant modes. To verify the effectiveness of the developed methodology, a cross microbeam was measured to analyze the full-field resonant vibratory modes and dynamic characteristics. The experimental results confirm that the dynamic behavior of the tested microcantilever beams can be accurately characterized and 5 nm of vertical measurement accuracy as well as tens micrometers of vertical measurement range can be achieved. The measured results were satisfactorily consistent with the theoretical simulation outcomes from ANSYS.
Accelerating processing speed in pathway research based on GPU

Genome-wide association study (GWAS) has become an effective and successful method to identify disease loci by considering SNPs independently. However, it may be invalid for uncovering the disease loci that not reaching a stringent genome-wide significance threshold. As a result, multi-SNP GWAS is developing rapidly as a complement to traditional GWAS. However, the high computational cost becomes a major limitation for it. The graphical processing unit (GPU) is a programmable graphics processor which has powerful parallel computing ability. And with the development, GPUs have been feasible for many scientific studies. Hence, we are motivated to use GPUs for pathway-based GWAS to improve computational efficiency. The experiment results attained showed the speed-up ratio can reach up to more than 160.
Superconducting infrared detector arrays with integrated processing circuitry

Thin-film Josephson junctions may be used as infrared detectors that function by a thermal sensing mechanism. In addition to the potential for high sensitivity to a broad range of optical wavelengths, they are ideally suited for integration with superconducting electronics on a single wafer. To develop these arrays, work was directed along two avenues: (1) maximizing the sensitivity of individual Josephson junction detector/SQUID amplifier units, and (2) development of superconducting on-chip processing circuitry-multiplexers and analog-to-digital converters. Superconducting detector/amplifier circuits were designed for low-noise performance. Amplification in these chips has been successfully demonstrated. Square 8*8 arrays integrated with various elements of superconducting processing circuitry have also been fabricated and tested. These use niobium Josephson junctions and 65 mu m*85 mu m absorber areas, and have been tested at 4.2 K. One such array includes circuitry for multiplexing all detectors, and was used successfully to form an image that was presented in real time on a computer terminal display. The output of a single detector was also used to drive a superconducting analog-to-digital converter.
A simplified cloud computing network architecture using future internet technologies

Cloud computing is expected to provide quick, agile, stable and reliable services. Existing Cloud computing network architecture is very complexity. In order to improve security, management and energy saving in cloud computing network, this article proposes a simplified cloud computing network architecture based on OpenFlow, Autonomic and Identifier Locator split technologies. This new network architecture design is more cost effective, on-demand provision, and secure than existing cloud network architecture. This novel cloud computing network architecture had been implemented and experimented.
Digitally controlled oscillator with storage based randomization for spurs reduction

This paper presents a storage based randomization method for spurs reduction in digitally controlled oscillator (DCO). The storage size is very compact as compared with conventional large size. A modified linear feedback shift register is applied for random address generator. The proposed method has been demonstrated in field programmable gate array (FPGA) with a digitally controlled oscillator. The spurious-free dynamic range (SFDR) of proposed method is 32.3 dB as compared with conventional method's 28.9 dB. The proposed method has successfully reduced the spurs with very small storage for randomization.
3-D Shape-Adaptive Directional Wavelet Transform for Object-Based Scalable Video Coding

This paper presents a 3-D shape-adaptive directional wavelet coding technique for object-based scalable video coding. This technique includes 3-D object-based directional threading and extensions of weighted adaptive lifting (WAL) scheme. The 3-D object-based directional threading, which unifies the concept of temporal motion threading and 2-D spatial directional threading, provides the opportunities to align a series of video object planes to form a 3-D video object and exploit the spatio-temporal correlation inside the 3-D video object. The WAL scheme, which is extended from 2-D frame-based image coding to 3-D object-based video coding, is employed to decompose the 3-D video object into a 3-D multispatio-temporal resolution video object pyramid for object-based scalable video coding. Experimental results show that the proposed 3-D shape-adaptive directional wavelet coding technique consistently outperforms MPEG-4 and other wavelet-based schemes for coding arbitrarily shaped video objects.
Jaccard index compensation for object segmentation evaluation

In this paper, we propose an objective metric for quality evaluation of individual object segmentation in images. Using the Jaccard Index as a base, additional compensation terms are integrated into our metric. These terms not only allow our metric to combine region-based and boundary-based methods, they also describe human visual tolerance and saturation. This metric can also can adaptively adjust “perception function” under different image sizes. The experiment on our subjective object segmentation quality assessment database demonstrates that the proposed metric performs well in matching subjective ratings.
3D subband coder for very low bit rates

A video coder based on 3D subband coding system (SBC) targetted for very low bit rate applications is developed and simulated. It employs QMF subband analysis to decompose an input sequence into 4 temporal bands. A low-complexity block-based motion detection scheme is then applied to all bands followed by motion classification and reduced motion search on the baseband which is H.261-like coded and the high temporal bands are vector-quantised (VQ) with a bank of codebooks (switched codebook VQ). Simulations are performed at 9.6 kbit/sand 14.4 kbit/s at 5 frame/s for QCIF format and results showed that it achieved better performance than the ITU-T short-term reference model SlM3 coder at similar bit rates. Subjective evaluation favours the 3D SBC coder because it does not suffer from 'blocking' artifacts.<<ETX>>
A Fast Macroblock Mode Decision Algorithm for H.264

H.264 (JVT) is a new video coding standard jointly developed by ITU-T and ISO MPEG. H.264 uses many advanced techniques and has achieved superior performance compared with other video coding standards. In these techniques, multiple block size is an important part and could get nearly 20% bit rate reduction. However, searching all block sizes has remarkably increased the computational complexity of the H.264 codec. So an efficient mode selection method is very necessary here. In this paper, the authors propose a fast mode decision algorithm. The main techniques include: Pskip mode early detection based on transform domain, mode prediction, and early termination by using adaptive threshold. The authors also use post-search technique to keep coding quality and intra mode skip detection to save computational cost. Experiment results show that our algorithm saves a lot of computational cost. Compared with full mode selection, our new algorithm could keep the image quality quite well with almost the same bit rate
Full-Reference Video Quality Assessment by Decoupling Detail Losses and Additive Impairments

Video quality assessment plays a fundamental role in video processing and communication applications. In this paper, we study the use of motion information and temporal human visual system (HVS) characteristics for objective video quality assessment. In our previous work, two types of spatial distortions, i.e., detail losses and additive impairments, are decoupled and evaluated separately for spatial quality assessment. The detail losses refer to the loss of useful visual information that will affect the content visibility, and the additive impairments represent the redundant visual information in the test image, such as the blocking or ringing artifacts caused by data compression and so on. In this paper, a novel full-reference video quality metric is developed, which conceptually comprises the following processing steps: 1) decoupling detail losses and additive impairments within each frame for spatial distortion measure; 2) analyzing the video motion and using the HVS characteristics to simulate the human perception of the spatial distortions; and 3) taking into account cognitive human behaviors to integrate frame-level quality scores into sequence-level quality score. Distinguished from most studies in the literature, the proposed method comprehensively investigates the use of motion information in the simulation of HVS processing, e.g., to model the eye movement, to predict the spatio-temporal HVS contrast sensitivity, to implement the temporal masking effect, and so on. Furthermore, we also prove the effectiveness of decoupling detail losses and additive impairments for video quality assessment. The proposed method is tested on two subjective quality video databases, LIVE and IVP, and demonstrates the state-of-the-art performance in matching subjective ratings.
Locating facial region of a head-and-shoulders color image

This paper addresses our proposed method to automatically locate the person's face from a given image that consists of a head-and-shoulders view of the person and a complex background scene. The method involves a fast, simple and yet robust algorithm that exploits the spatial distribution characteristics of human skin color. It first uses the chrominance component of the input image to detect pixels with skin color appearance. Then, bused on the spatial distribution of the detected skin-color pixels and their corresponding luminance values, the algorithm employs some regularization processes to reinforce regions of skin-color pixels that are more likely to belong to the facial regions and eliminate those that are not. The performance of the face localization algorithm is illustrated by some simulation results carried out on various head-and-shoulders test images.
Object-based rate control for MPEG-4 video object coding

In this paper, we develop an object-based rate control algorithm for MPEG-4 arbitrarily shaped video object coding. An improved quadratic rate-distortion model for texture information is derived, which is more accurate for arbitrarily shaped video objects. We also introduce an accurate rate-distortion model for shape information. Combining the rate-distortion models for texture and shape information, an object level rate control algorithm is proposed. To achieve constant bit rate, the quantizer is adjusted based on buffer fullness. The tradeoff between picture quality and buffer fullness is considered. Comparing with the rate control algorithm for the MPEG-4 verification model, our proposed scheme can achieve a more stable buffer without sacrificing picture quality.
Boundary-Guided Optimization Framework for Saliency Refinement

Salient object detection has made a rapid progress in recent years. To improve the quality of initial saliency maps, existing algorithms typically refine them via a neighbor-constrained smoothing model, which assigns similar saliency values to neighboring regions. Since the adjacent regions could also cross the boundary between the salient object and background, these spatial distance-based methods easily cause false detection by involving the background regions that are close to the salient objects. To address this problem, we propose a boundary-guided optimization framework to jointly improve the region smoothness and correct the false detect regions. Specifically, we introduce a latent segmentation variable to regularize the consistency between the refined saliency map and the latent segmentation mask, which penalizes high (low) saliency values of the regions lying outside (inside) the estimated object boundary. To optimize the proposed objective function, we decompose the primary problem into two subproblems: submodular optimization problem and convex optimization problem. The submodular optimization problem can be quickly optimized using the off-the-shelf technique while the convex optimization can be solved with a closed-form solution. The experimental results show that the proposed method consistently improve the performances of eight state-of-the-art salient object detection algorithms on three datasets, including latest deep convolutional neural network-based algorithms. Meanwhile, our method outperforms state-of-the-art saliency refinement algorithms.