nils-herrmann/clean_scirepeval huggingface dataset:
Salient Region Detection by Fusing Bottom-Up and Top-Down Features Extracted From a Single Image

Recently, some global contrast-based salient region detection models have been proposed based on only the low-level feature of color. It is necessary to consider both color and orientation features to overcome their limitations, and thus improve the performance of salient region detection for images with low-contrast in color and high-contrast in orientation. In addition, the existing fusion methods for different feature maps, like the simple averaging method and the selective method, are not effective sufficiently. To overcome these limitations of existing salient region detection models, we propose a novel salient region model based on the bottom-up and top-down mechanisms: the color contrast and orientation contrast are adopted to calculate the bottom-up feature maps, while the top-down cue of depth-from-focus from the same single image is used to guide the generation of final salient regions, since depth-from-focus reflects the photographer's preference and knowledge of the task. A more general and effective fusion method is designed to combine the bottom-up feature maps. According to the degree-of-scattering and eccentricities of feature maps, the proposed fusion method can assign adaptive weights to different feature maps to reflect the confidence level of each feature map. The depth-from-focus of the image as a significant top-down feature for visual attention in the image is used to guide the salient regions during the fusion process; with its aid, the proposed fusion method can filter out the background and highlight salient regions for the image. Experimental results show that the proposed model outperforms the state-of-the-art models on three public available data sets.
Automatic Vulnerability Checking of IEEE 802.16 WiMAX Protocols through TLA+

Vulnerability analysis is indispensably the first step towards securing a network protocol, but currently remains mostly a best effort manual process with no completeness guarantee. Formal methods are proposed for vulnerability analysis and most existing work focus on security properties such as perfect forwarding secrecy and correctness of authentication. However, it remains unclear how to apply these methods to analyze more subtle vulnerabilities such as denial-of-service (DoS) attacks. To address this challenge, in this paper, we propose use of TLA+ to automatically check DoS vulnerability of network protocols with completeness guarantee. In particular, we develop new schemes to avoid state space explosion in property checking and to model attackers' capabilities for finding realistic attacks. As a case study, we successfully identify threats to IEEE 802.16 air interface protocols.
Real-time decoding algorithm in brain machine interfaces

The technology of invasive Brain-Computer Interfaces (BCIs) has been developed in last decades, for its possibility to restore motor function of the disabilities. The main task of BCI system is to translate the cortical neural activities into commands of direct brain-controlled prosthetic devices. To study how cortical signals simultaneously recorded from primary motor cortex (M1) neurons were used for external devices control, invasive brain-machine interfaces in rat were investigated. In these experiments, rats were trained to control a relay to obtain water by pressing a lever over a pressure threshold. Microwire array was implanted in rat's motor cortex to record neural activities, and pressure was recorded by a pressure sensor. After spike detecting and sorting, totally 22–58 neurons were found in all 15 channels per rat (except the reference electrode in the array). To compute the firing rate of individual unit, the numbers of spikes in a time bin (Δt =100ms) were counted. Meanwhile, the pressure signal was also computed into bin size (Δt =100ms), by averaging pressure value in the bin period. After that some decoders were designed to make mapping between neural activities and pressure, such as Optimum Linear Estimation(OLE), Kalman filter (KF), and Kernel-Based Membership combined with Curve Fitting algorithm (KMCF). These decoders can be easily learned using a few minutes of training data and provides real-time estimates of hand position every 100ms given the firing rates of neurons in motor cortex. The performances of these decoders were evaluated by Correlation Coefficient (CC) between real and predictive pressure value. The results showed that the KMCF decoder performed best in these experiment, which had higher CC than others (CC=0.94). The higher CC indicated that the activity of motor cortex (M1) neurons can be used for detection of the corresponding movement states and estimation of continuous kinematic parameters. The rats could use their neural activities to directly control the relay and successfully get rewards after about one week of training. In further, with the development of the motor-related BCIs technology, BCIs will be possibly used to motor function restoration of paralytics and greatly rehabilitate their capacity of life. Furthermore, the experiment results provide insights into the nature of the neural coding of movement.
A Depth-Bin-Based Graphical Model for Fast View Synthesis Distortion Estimation

During 3-D video communication, transmission errors, such as packet loss, could happen to the texture and depth sequences. View synthesis distortion will be generated when these sequences are used to synthesize virtual views according to the depth-image-based rendering method. A depth-value-based graphical model (DVGM) has been employed to achieve the accurate packet-loss-caused view synthesis distortion estimation (VSDE). However, the DVGM models the complicated view synthesis processes at depth-value level, which costs too much computation and is difficult to be applied in practice. In this paper, a depth-bin-based graphical model (DBGM) is developed, in which the complicated view synthesis processes are modeled at depth-bin level so that it can be used for the fast VSDE with 1-D parallel camera configuration. To this end, several depth values are fused into one depth bin, and a depth-bin-oriented rule is developed to handle the warping competition process. Then, the properties of the depth bin are analyzed and utilized to form the DBGM. Finally, a conversion algorithm is developed to convert the per-pixel input depth value probability distribution into the depth-bin format. Experimental results verify that our proposed method is 8– $32\times $ faster and requires 17%–60% less memory than the DVGM, with exactly the same accuracy.
Multi-Scale Analysis of Odd Gabor Transform for Edge Detection

This paper proposes an adaptive edge detection technique based on multi-scale analysis of odd Gabor transform. In order to obtain the odd Gabor transform based edge response efficiently, an improved scheme is suggested, which can avoid the inverse influence brought by the estimation error of gradient direction on the construction of odd Gabor filter templates. To sharpen the edge response, an adjacent scales multiplication in odd Gabor transform domain is introduced. Moreover an adaptive threshold determining method related to the exponential distribution property of sharpened edge response is also presented. The experimental results on both synthetic and real world images show that our scheme is robust and has good edge detection performance
Hop ID: A Virtual Coordinate based Routing for Sparse Mobile Ad Hoc Networks

Routing in wireless communication systems such as ad hoc networks remains a challenging problem given the limited wireless bandwidth, users' mobility, and potentially large scale. Recently, a thrust of research has addressed these problems-the on- demand routing, geographical routing, and virtual coordinates. In this paper, we focus on geographical routing that has been shown to achieve good scalability without flooding; however, this usually requires the availability of location information and can suffer from poor routing performance and severe dead end problems, especially in sparse networks. Specifically, we propose a new Hop ID routing scheme, which is a virtual coordinate-based routing protocol and does not require any location information. This achieves excellent routing performance comparable with that obtained by the shortest path routing schemes. In addition, we design efficient algorithms for setting up the system and adapt to the node mobility quickly and can effectively route out of dead ends. Extensive analysis and simulation show that the Hop ID-based routing achieves efficient routing for mobile ad hoc networks with various density, irregular topologies, and obstacles.
A 0.02 mm$^{2}$ 59.2 dB SFDR 4th-Order SC LPF With 0.5-to-10 MHz Bandwidth Scalability Exploiting a Recycling SC-Buffer Biquad

This paper reports a switched-capacitor (SC)-buffer Biquad that can be recycled efficiently as an ultra-compact low-pass filter (LPF) in nanoscale CMOS. It incorporates only passive-SC networks and open-loop unity-gain buffers; both are friendlier to technology downscaling than most conventional Biquads that use high-gain amplifiers and closed-loop negative feedback. Complex-pole pairs with independent Q factors are recursively realized in one clock period, while ensuring low crosstalk effect between the formations of each pole. Nonlinearity and parasitic effects are inherently low due to no internal gain. The fabricated 65 nm CMOS prototype is a 1x-recycling SC-buffer Biquad that is equivalent to a 4th-order Butterworth LPF with 75% buffer utilization. It occupies a die size of only 0.02 mm2 and exhibits 20x bandwidth tunability (0.5 to 10 MHz), linear with the clock rate. At 10 MHz bandwidth, the in-band IIP3 is +17.6 dBm and input-referred noise is 19.5 nV/ √Hz; they correspond to 59.2 dB SFDR and 0.013 fJ figure-of-merit which are favorably comparable with the recent art. The 1 dB compression point (P1dB) conforms to the out-of-band blocker profile of the LTE standard at a 20 dB front-end gain.
Robust Plane Detection Using Depth Information From a Consumer Depth Camera

The emerging of depth-camera technology is paving the way for a variety of new applications and it is believed that plane detection is one of them. In fact, planes are common in man-made living structures, thus their accurate detection can benefit many visual-based applications. The use of depth information allows detecting planes characterized by complex pattern and texture, where the texture-based plane detection algorithms usually fail. In this paper, we propose a robust depth-driven plane detection (DPD) algorithm which consists of two parts: the growing-based plane detection and a two-stage refinement. The proposed approach starts from the seed patch with the highest planarity and uses the estimated equation of the growing plane and a dynamic threshold function to steer the growing process. Aided with this mechanism, each seed patch can grow to its maximum extent, and then the next seed patch starts to grow. This process is iteratively repeated so as to detect all the planes. Moreover, the refinement is proposed to tackle two common problems suffered by growing-based approaches, the over-growing problem, and the under-growing problem. Validated by extensive experiments, the proposed DPD algorithm is able to accurately detect planes and robust to various testing conditions. In terms of applications, it can be used as the pre-processing step for a variety of applications, such as, planar object recognition, super-resolution of the time-of-flight depth images with intrinsically low resolution.
A Weighted K-Subset Load Balancing Algorithm for Distributed Slee in Next Generation Network

Load balancing with stale information is becoming an increasingly important problem for distributed systems. And k-subset algorithm is promising to address this problem. In this paper, the load balancing problem for distributed SLEE in NGN is studied. Considering the heterogeneity of the clustering system, a load model is designed, and a weighted k-subset (WK) algorithm is proposed. WK improves k-subset algorithm with a weight defined on the process capabilities and the resource capacities of the nodes. Experiments show that WK balances system load effectively, and outperforms k-subset in every situation by about 10%.
Towards Effectively Identifying RESTful Web Services

In recent years, RESTful Web services have been rapidly developed and deployed, because of the advantages of lightweight, flexibility and extensibility, etc. However, most RESTful services are described in heterogeneous and ordinary HTML pages, which makes them really difficult to be identified and crawled automatically from the Internet. In this paper we propose a hybrid classifier framework called co-NV for automatic identification of RESTful services on the Web. In our framework, web pages are analyzed and filtered according to the contents and structure characteristics of HTML documents, with Naïve Bayes classifier and Vector Space Model (VSM) respectively. Experiments with real RESTful services prove that our framework works effectively with high precision and recall rate, and is very practical.
STC: A Simple to Complex Framework for Weakly-Supervised Semantic Segmentation.

Recently, significant improvement has been made on semantic object segmentation due to the development of deep convolutional neural networks (DCNNs). Training such a DCNN usually relies on a large number of images with pixel-level segmentation masks, and annotating these images is very costly in terms of both finance and human effort. In this paper, we propose a simple to complex (STC) framework in which only image-level annotations are utilized to learn DCNNs for semantic segmentation. Specifically, we first train an initial segmentation network called Initial-DCNN with the saliency maps of simple images (i.e., those with a single category of major object(s) and clean background). These saliency maps can be automatically obtained by existing bottom-up salient object detection techniques, where no supervision information is needed. Then, a better network called Enhanced-DCNN is learned with supervision from the predicted segmentation masks of simple images based on the Initial-DCNN as well as the image-level annotations. Finally, more pixel-level segmentation masks of complex images (two or more categories of objects with cluttered background), which are inferred by using Enhanced-DCNN and image-level annotations, are utilized as the supervision information to learn the Powerful-DCNN for semantic segmentation. Our method utilizes 40K simple images from Flickr.com and 10K complex images from PASCAL VOC for step-wisely boosting the segmentation network. Extensive experimental results on PASCAL VOC 2012 segmentation benchmark well demonstrate the superiority of the proposed STC framework compared with other state-of-the-arts.
Image composition detection using object-based color consistency

An efficient approach for the blind image composite detection is proposed in this paper. Image composite is commonly used when an image forgery occurs. A forgery detector is devised to expose this kind of tampering through checking consistency of the color perception between object area and background area. Local color statistic and object-based color temperature metric are presented to assess color realism over the whole image. Low realism score is considered as the evidence of tampering. Tests on a refined composite image dataset demonstrate a promising performance with 70% precision under lower false positive possibility.
Multiview Cross-Media Hashing with Semantic Consistency

Cross-media hashing is used to handle both cross-media representation and indexing simultaneously. Most existing methods attempt to bridge the semantic gap by maximizing the correlation of heterogeneous instances describing the same information object. Although these methods guarantee that such instances are close in the commonly shared space, instances describing different objects but the same category may be scattered. We propose a new cross-media hashing scheme, multiview cross-media hashing with semantic consistency (MCMHSC), to address this problem. By fully exploiting the semantic correlation and complementary information among objects, MCMHSC builds discriminative hashing codes. Experiments on two public benchmark datasets show that our proposed scheme achieves comparable or better performance compared to state-of-the-art methods in terms of accuracy and time complexity.
Texture Characteristics Based Fast Coding Unit Partition in HEVC Intra Coding

High efficiency video coding (HEVC) is an emerging video compression standard, developed by the Joint Collaborative Team on Video Coding (JCT-VC). The aim of HEVC standardization effort is to save about 50% bit rate for equal perceptual video quality relative to H.264/AVC. Although HEVC provides greater bit rate saving, it also brings the enormous encoding complexity increase. In this paper, we propose a fast intra CU decision algorithm based on the texture characteristics of video. Furthermore, we also consider the coding bits of each CU as auxiliary information to refine the partition results. Experimental results show that the fast intra mode decision algorithm provides over 33% complexity reduction in terms of encoding time with negligible quality loss, compared with the original HEVC test model version HM-12.0+RExt-4.0rc2.
Multiple description coding for scalable video coding with redundant slice

Scalable video coding(SVC) is designed to provide adaptable capability for heterogeneous network structures with its scalabilities. Due to the introducing of inter-layer prediction, SVC coded bitstream is much vulnerable to the channel error. To solve this problem, a lot of schemes are proposed. However, they are either not compatible with the standard or just focus on one of scalabilities in SVC. In this paper, we propose a multiple description scheme that is compatible with SVC standard and supports all the scalabilities. The proposed scheme is based on the redundant slice in the SVC standard, in which each layer is composed of primary slice and redundant slice by interleaving. To deal with the mismatch error and propagated error, the quantization step of redundant slice and the mode of each macroblock is tuned by considering the prediction path in the same layer and between different layers, as well as the channel packet loss rate. The experimental results show that the proposed scheme is efficient for the packet loss channel.
Frequency-Domain Turbo Equalization for Single Carrier Mobile Broadband Systems

This paper investigates mobile broadband communications, which undergo time-varying radio channels with large multipath delay spread. Space-time block transmission and single carrier modulation are adopted at the transmitter to combat the inter-symbol interference resulted from, the multipath delay. The single carrier modems with frequency-domain equalization have similar performance and complexity as orthogonal frequency-division multiplexing, yet less sensitive to radio frequency impairments. At the receiver, we propose the turbo equalization that consists of a minimum mean-square error equalizer in the frequency domain and a channel decoder. In order to cope with the time-varying characteristics of the mobile channel, the received data is partitioned into blocks for the linear equalizer, and then combined as a decoding frame for the decoder. Simulation results show good performance of the proposed scheme with feasible computational complexity
Semi-Supervised Learning with Density-Sensitive Manifold graph

The key problem of Graph-Based Semi-Supervised Learning (GBSSL) methods is how to construct the graph structure under some assumptions. While distance information among graph nodes is investigated well for graph construction, the density information is not given enough attention. In this paper, we propose a novel GBSSL method, named Density-Sensitive Manifold Learning (DSML), which introduces density distribution into graph construction by calculating a new propagation coefficient matrix. The experimental results show that DSML scheme performs better than traditional GBSSL methods. More importantly, the new propagation coefficient matrix can be easily introduced into traditional GBSSL methods to improve their performance, which is also validated in the experiments.
Forensic estimation of gamma correction in digital images

In the digital era, digital photographs become pervasive and are frequently used to record event facts. Authenticity and integrity of such photos can be ascertained by discovering more information about the previously applied operations. In this paper, we propose a forensic scheme for identifying and reconstructing gamma correction operations in digital images. Statistical abnormity on image grayscale histograms, which is caused by the contrast enhancement, is analyzed theoretically and measured effectively. Graylevel mapping functions involved in gamma correction can be estimated blindly. Experiments both on globally and locally applied corrected images show the validity of our proposed gamma estimation algorithm.
Copy detection towards semantic mining for video retrieval

In large-scale video database, lots of different videos frequently share the similar content copied from the same source. Generally, those videos have certain semantic correlations, such as being of similar events and sharing the same topic. Mining these semantic correlations can greatly facilitate video search. However, as a preprocessing step, detecting and localizing the copy pair among videos, i.e. copy detection problem, plays a key role for precise semantic mining. To meet the requirements in semantic mining scenario, we propose a frame fusion based copy detection scheme. In this scheme, the copy detection problem is converted to HMM decoding problem with three relaxed constraints, where Viterbi algorithm is employed to automatically detect the copy pair. The experimental results show that the proposed approach achieves high localization accuracy even when the copied clip undergoes some complex transformations, while achieving comparable performance compared with state-of-the-art copy detection methods.
Performance Comparisons of Different Channel Codes in Distributed Video Coding

This paper introduces advanced channel codes turbo and LDPC to distributed video coding (DVC), and compares their performance in the whole system. From the simulation results, we can find that the LDPC codes are superior to turbo codes when the video sequence has high motion. Additionally, whether turbo or LDPC, the DVC system outperforms the conventional intra-frame coding though underperforms conventional motion-compensated inter-frame coding. But DVC has the advantage of easy encoding which can meet the demands of friendly up-linking media communication
Study of coordinative service bus based service execution platform

Enterprise Service Bus (ESB) is a promising tool to solve the problem of service composition at the background of service integration. Traditional ESB is not efficient enough for dynamical composition; and do not have the coordinative distributed execution feature, which can provide both effective and rapid service composition. This paper proposes a Coordinative Service Bus based Execution Platform at the basis of service coordination and service bus. Through creating Execution Binding Map (EBM), the platform uses a method of pre-binding optimal services to improve efficiency of service selecting and invoking. In addition, it uses a method of Multi-level Request Scheduling with Feedback to ensure the overall efficiency of the platform and to reduce the possibility of failure of partial services.