nils-herrmann/clean_scirepeval huggingface dataset:
Supporting Negative Authorization in Spatiotemporal Role Based Access Control

Role based access control (RBAC) has emerged as an effective solution for several access control problems of relevance today. To cope with the growing requirements, core RBAC has been extended over temporal, spatial and spatiotemporal dimensions. The various models developed so far predominantly deal with monotonic policies which allow access only if there is a corresponding positive authorization. However, in many practical situations, there is a need for specifying policies that deny access through negative authorization. The authorization may again depend on different spatiotemporal conditions. In this paper, the notion of user-role-permission (URP) relation is used to incorporate negative authorization in ESTARBAC, one of the existing spatiotemporal RBAC models. The proposed method has been compared with two other existing approaches. We also analyze how negative authorization can facilitate expressing some of the important access control policies relevant for an organization.
A 600 MHz half-bit level pipelined multiplier macrocell

In this paper a high throughput 8/spl times/8 bit multiplier in a 0.8 /spl mu/ CMOS process is described. A novel pipelining technique in NPCPL (normal process complementary pass transistor logic) allows fine grain pipelining with minimal overhead of area and latency. This is in contrast to conventional approaches where highly pipelined designs are constrained by area and latency overhead of pipeline latches. A two-stage full adder is employed in a carry-save array architecture. The 0.95 mm/spl times/0.87 mm multiplier core supports a throughput of 600 MHz with a power dissipation of 0.9 Watt. Since such a high speed clock cannot be routed from the external world, the clock is generated on-chip. The on-chip clock generator ensures generation of non-overlapping clocks under all conditions of process parameter variations. This along with proper design of clock buffers and clock distribution network minimises clock skew to allow high speed clocking.<<ETX>>
Credit Card Fraud Detection Using Hidden Markov Model

Due to a rapid advancement in the electronic commerce technology, the use of credit cards has dramatically increased. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In this paper, we model the sequence of operations in credit card transaction processing using a hidden Markov model (HMM) and show how it can be used for the detection of frauds. An HMM is initially trained with the normal behavior of a cardholder. If an incoming credit card transaction is not accepted by the trained HMM with sufficiently high probability, it is considered to be fraudulent. At the same time, we try to ensure that genuine transactions are not rejected. We present detailed experimental results to show the effectiveness of our approach and compare it with other techniques available in the literature.
A spectral filtering based deep learning for detection of logo and stamp

This paper presents a novel spectral filtering based deep learning algorithm (SFDL) for detecting logos and stamps in a scanned document image. In a document image, textual contents are main source of high spatial frequency components. Accordingly, the high frequency filtering is used to suppress the text symbols. In the next step, segmentation process is used for localizing the candidate regions of interests such as logos and stamps. Preprocessing of these candidate regions is essential before classification. The proposed preprocessing includes steps such as region fusion, resizing and key point based pooling. Finally, the preprocessed candidate regions are classified using deep convolutional neural network. The main advantage of the SFDL is its capability to detect logos without prior information or assumption about their locations in a document. The performance of the proposed SFDL algorithm is evaluated using publicly accessible document image database StaVer. It is observed that SFDL performs satisfactorily for detecting logo and stamp. The precision and recall measures of the proposed SFDL are compared with existing techniques. Experimental results show that recall and precision of logo detection are 86.8%, 97.2%, respectively. Similarly, recall and precision for stamp detection are 85.3% and 94.8%.
ANN- and PSO-Based Synthesis of On-Chip Spiral Inductors for RF ICs

This paper presents an efficient layout-level synthesis approach for RF planar on-chip spiral inductors. A spiral inductor is modeled using artificial neural networks in which the layout design parameters, namely, spiral outer diameter, number of turns, width of metal traces, and metal spacing, are taken as input. Inductance, quality factor (Q), and self-resonance frequency (SRF) form the output of the neural model. Particle-swarm optimization is used to explore the layout space to achieve a given target inductance meeting the SRF and other constraints. Our synthesis approach provides multiple sets of layout parameters that help a designer in the tradeoff analysis between conflicting objectives, such as area, Q, and SRF for a target-inductance value. We present several synthesis results which show good accuracy with respect to full-wave electromagnetic (EM) simulations. Since the proposed procedure does not require an EM simulation in the synthesis loop, it substantially reduces the cycle time in RF-circuit design optimization.
Detection of intrusive activity in databases by combining multiple evidences and belief update

In this paper, we propose an innovative approach for database intrusion detection which combines evidences from current as well as past behavior of users. It consists of four components, namely, rule-based component, belief combination component, security sensitive history database component and Bayesian learning component. The rule-based component consists of a set of well-defined rules which give independent evidences about a transaction's behavior. An extension of Dempster-Shafer's theory is used to combine multiple such evidences and an initial belief is computed. First level inferences are made about the transaction depending on this initial belief. Once the transaction is found to be suspicious, belief is updated according to its similarity with malicious or genuine transaction history using Bayesian learning. Experimental evaluation shows that the proposed intrusion detection system can effectively detect intrusive attacks in databases without raising too many false alarms.
RESTRAC: REference Sequence Based Space TRAnsformation for Clustering

Effective mining of large amount of DNA and RNA fragments obtained from next generation sequencing technologies, depends on the availability of efficient analytical tools to process them. One of the important aspects of this analysis, dealing with huge number of fragments, is partitioning them based on their level of similarities. In this paper we propose a space transformation based clustering approach to achieve this partitioning. In this approach, we transform each sequence by a set of reference sequences into a point in a multidimensional vector space and do the clustering in this vector space. We show through extensive analysis that the proposed transformation very closely preserve the clustering properties of the sequences using edit distance. Time for this transformation is linear with the number of sequences. The amount of time saving for this clustering is significant because in this approach edit distance calculations between two sequences are replaced by vector distance calculations between two corresponding feature vectors. We used agglomerative hierarchical clustering using single and average linkage because they are frequently used by the bioinformatics community. Agglomerative hierarchical clustering runs in quadratic time with the number of sequences and clustering time for this approach in the edit space can be prohibitive for large number of sequences. There exists greedy heuristic methods that perform clustering much faster but at the cost of significantly reduced cluster quality. We have applied our method to 16S rRNA fragment datasets obtained from different environmental samples. In these experiments, RESTRAC achieves up to five hundred times speed-up for single linkage and up to five times speed-up for average linkage while preserving good cluster quality.
A user interactive method for measuring lateral ventricle width in neonates

Ventriculomegaly is one of the most sensitive markers for abnormal development of the fetal central nervous system. So assessment of the size of lateral ventricles has become an important part of routine neurological assessment in neonates. We have proposed a method for measuring the width of lateral ventricles in term and preterm neonates from the coronal view of their brain ultrasound images. Based on the ultrasound image, the following steps are performed. 1) Ultrasound image is preprocessed to reduce the speckle noise. 2) The region of interest is extracted from the original image. 3) The image containing lateral ventricles and cavum septum pellucidum (CSP) is segmented. 4) Lateral ventricles and CSP are labeled. 5) Morphological operations are done to smooth the boundaries of lateral ventricles and to fill the cavities. 6) Lateral ventricle width is measured by means of the proposed image processing method.
A user friendly implementation for efficiently conducting Hammersmith Infant Neurological Examination

The aim of this work is to design a semi-automatic application that can be used as an aid by the doctors for smoothly conducting Hammersmith Infant Neurological Examination (HINE). A simplified version of the examination which provides a quantitative neurological assessment is used to design the application. The application includes a methodology of conducting HINE examination suited to inexperienced staff, applicable to both neonatal and post-neonatal infants. It also provides a facility to go through the previous records of a patient that can help in diagnosing patients with high risk of neurological disorder. A semi-automatic approach is proposed for skeleton generation. The application has been installed in hospitals and currently in operation. It is expected to increase the efficiency of conducting HINE using the proposed application.
Color-texture feature extraction using soft decision from the HSV color space

Color and texture are two important low-level features of an image. These features can be used either alone or in combination using appropriate weights for content-based retrieval of images. When only color is used, retrieval results for query images with texture patterns is found to be poor. If both the features are combined, the proportionate contribution of each feature depends on the query image. This involves a high computation overhead for capturing relevance feedback from the user. We propose a new soft-decision approach to the modeling of combined human visual perception of color and texture in a single feature vector called COLTEX. It shows encouraging results in content-based image retrieval applications. We have developed a Web-based system for demonstrating our work and for performing user queries.
Graph-Based Multiplayer Detection and Tracking in Broadcast Soccer Videos

In this paper, we propose a graph-based approach for detecting and tracking multiple players in broadcast soccer videos. In the first stage, the position of the players in each frame is determined by removing the non player regions. The remaining pixels are then grouped using a region growing algorithm to identify probable player candidates. A directed weighted graph is constructed, where probable player candidates correspond to the nodes of the graph while each edge links candidates in a frame with the candidates in next two consecutive frames. Finally, dynamic programming is applied to find the trajectory of each player. Experiments with several sequences from broadcasted videos of international soccer matches indicate that the proposed approach is able to track the players reasonably well even under varied illumination and ground conditions.
Semantic Cues Enhanced Multimodality Multistream CNN for Action Recognition

This paper addresses the issue of video-based action recognition by exploiting an advanced multistream convolutional neural network (CNN) to fully use semantics-derived multiple modalities in both spatial (appearance) and temporal (motion) domains, since the performance of the CNN-based action recognition methods heavily relates to two factors: semantic visual cues and the network architecture. Our work consists of two major parts. First, to extract useful human-related semantics accurately, we propose a novel spatiotemporal saliency-based video object segmentation (STS) model. By fusing different distinctive saliency maps, which are computed according to object signatures of complementary object detection approaches, a refined STS maps can be obtained. In this way, various challenges in the realistic video can be handled jointly. Based on the estimated saliency maps, an energy function is constructed to segment two semantic cues: the actor and one distinctive acting part of the actor. Second, we modify the architecture of the two-stream network (TS-Net) to design a multistream network that consists of three TS-Nets with respect to the extracted semantics, which is able to use deeper abstract visual features of multimodalities in multi-scale spatiotemporally. Importantly, the performance of action recognition is significantly boosted when integrating the captured human-related semantics into our framework. Experiments on four public benchmarks—JHMDB, HMDB51, UCF-Sports, and UCF101—demonstrate that the proposed method outperforms the state-of-the-art algorithms.
Minimum Near-Convex Shape Decomposition

Shape decomposition is a fundamental problem for part-based shape representation. We propose the minimum near-convex decomposition (MNCD) to decompose arbitrary shapes into minimum number of "near-convex" parts. The near-convex shape decomposition is formulated as a discrete optimization problem by minimizing the number of nonintersecting cuts. Two perception rules are imposed as constraints into our objective function to improve the visual naturalness of the decomposition. With the degree of near-convexity a user-specified parameter, our decomposition is robust to local distortions and shape deformation. The optimization can be efficiently solved via binary integer linear programming. Both theoretical analysis and experiment results show that our approach outperforms the state-of-the-art results without introducing redundant parts and thus leads to robust shape representation.
Robust 3D Hand Pose Estimation in Single Depth Images: From Single-View CNN to Multi-View CNNs

Articulated hand pose estimation plays an important role in human-computer interaction. Despite the recent progress, the accuracy of existing methods is still not satisfactory, partially due to the difficulty of embedded high-dimensional and non-linear regression problem. Different from the existing discriminative methods that regress for the hand pose with a single depth image, we propose to first project the query depth image onto three orthogonal planes and utilize these multi-view projections to regress for 2D heat-maps which estimate the joint positions on each plane. These multi-view heat-maps are then fused to produce final 3D hand pose estimation with learned pose priors. Experiments show that the proposed method largely outperforms state-of-the-art on a challenging dataset. Moreover, a cross-dataset experiment also demonstrates the good generalization ability of the proposed method.
Height Gradient Histogram (HIGH) for 3D Scene Labeling

RGB-D (color + 3D point cloud) based scene labeling has received much attention due to the affordable RGB-D sensors such as Microsoft Kinect. To fully utilize the RGB-D data, it is critical to develop robust features that can reliably describe the 3D shape information of the point cloud data. Previous work has proposed to extract SIFT-like features from the depth dimension data directly while ignored the important height dimension data of the 3D point cloud. In this paper, we propose to describe 3D scene using height gradient information and propose a new compact point cloud feature called Height Gradient Histogram (HIGH). Using Text on Boost as the pixel classifier, the experiments on two benchmarked 3D scene labeling datasets show that HIGH feature can well handle the intra-category variations of object class, and significantly improve class-average accuracy compared with the state-of-the-art results. We will publish the code of HIGH feature for the community.
Thematic Saliency Detection Using Spatial-Temporal Context

We propose a new measurement of video saliency termed thematic video saliency}. Video saliency is detected in terms of finding the thematic objects that frequently appear at the salient positions in the video scenes. By representing all image segments in the video as the spatial-temporal context, we build an affinity graph among them, and formulate the thematic object discovery as a novel cohesive sub-graph mining problem. A trust region algorithm is also proposed to solve the challenging optimization problem. Unlike individual image saliency or co-saliency analysis, our proposed video saliency fully incorporates the whole spatial-temporal video context. Experiments on our newly developed eye tracking dataset as well as other two datasets further validate the effectiveness of our method on video saliency detection.
Egocentric hand pose estimation and distance recovery in a single RGB image

Articulated hand pose recovery in egocentric vision is useful for in-air interaction with the wearable devices, such as the Google glasses. Despite the progress obtained with the depth camera, this task is still challenging with ordinary RGB cameras. In this paper we demonstrate the possibility to recover both the articulated hand pose and its distance from the camera with a single RGB camera in egocentric view. We address this problem by modeling the distance as a hidden variable and use the Conditional Regression Forest to infer the pose and distance jointly. Especially, we find that the pose estimation accuracy can be further enhanced by incorporating the hand part semantics. The experimental results show that the proposed method achieves good performance on both a synthesized dataset and several real-world color image sequences that are captured in different environments. In addition, our system runs in real-time at more than 10fps.
Manifold Kernel Sparse Representation of Symmetric Positive-Definite Matrices and Its Applications

The symmetric positive-definite (SPD) matrix, as a connected Riemannian manifold, has become increasingly popular for encoding image information. Most existing sparse models are still primarily developed in the Euclidean space. They do not consider the non-linear geometrical structure of the data space, and thus are not directly applicable to the Riemannian manifold. In this paper, we propose a novel sparse representation method of SPD matrices in the data-dependent manifold kernel space. The graph Laplacian is incorporated into the kernel space to better reflect the underlying geometry of SPD matrices. Under the proposed framework, we design two different positive definite kernel functions that can be readily transformed to the corresponding manifold kernels. The sparse representation obtained has more discriminating power. Extensive experimental results demonstrate good performance of manifold kernel sparse codes in image classification, face recognition, and visual tracking.
A framework based on deep learning and mathematical morphology for cabin door detection in an automated aerobridge docking system

In this paper, a cabin door detection framework based on deep learning and mathematical morphology is proposed. It is applied to an automated docking system for airplane cabin door. This system needs to work under any weather condition like rain, shine, day and night. Limited by the number of videos, just a small dataset based on actual airport operation is established for aerobridge docking process. As the training dataset is small, the trained detector cannot identify all the cabin doors in this dataset. Some of the cabin doors, which are not detected, can be identified with the combination of deep learning and mathematical morphology. Experimental results show that the integration of deep learning and mathematical morphology performs better than the simple deep learning method.
Minimum near-convex decomposition for robust shape representation

Shape decomposition is a fundamental problem for part-based shape representation. We propose a novel shape decomposition method called Minimum Near-Convex Decomposition (MNCD), which decomposes 2D and 3D arbitrary shapes into minimum number of “near-convex” parts. With the degree of near-convexity a user specified parameter, our decomposition is robust to large local distortions and shape deformation. The shape decomposition is formulated as a combinatorial optimization problem by minimizing the number of non-intersection cuts. Two major perception rules are also imposed into our scheme to improve the visual naturalness of the decomposition. The global optimal solution of this challenging discrete optimization problem is obtained by a dynamic subgradient-based branch-and-bound search. Both theoretical analysis and experiment results show that our approach outperforms the state-of-the-art results without introducing redundant parts. Finally we also show the superiority of our method in the application of hand gesture recognition.
Resolving Ambiguous Hand Pose Predictions by Exploiting Part Correlations

The positions of the hand joints are important high-level features for hand-based human-computer interaction. We present a novel method to predict the 3-D joint positions from the depth images and the parsed hand parts obtained with a pretrained classifier. The hand parts are utilized as the additional cue to resolve the multimodal predictions produced by the previous regression-based method without increasing the computational cost significantly. In addition, we further enforce the hand motion constraints to fuse the per-pixel prediction results. The posterior distribution of the joints is formulated as a weighted product of experts model based on the individual pixel predictions, which is maximized via the expectation-maximization algorithm on a learned low-dimensional space of the hand joint parameters. The experimental results show the proposed method improves the prediction accuracy considerably compared with the rivals that also regress for the joint locations from the depth images. Especially, we show that the regressor learned on synthesized dataset also gives accurate prediction on real-world depth images by enforcing the hand part correlations despite their discrepancies.
Fast Appearance Modeling for Automatic Primary Video Object Segmentation

Automatic segmentation of the primary object in a video clip is a challenging problem as there is no prior knowledge of the primary object. Most existing techniques thus adapt an iterative approach for foreground and background appearance modeling, i.e., fix the appearance model while optimizing the segmentation and fix the segmentation while optimizing the appearance model. However, these approaches may rely on good initialization and can be easily trapped in local optimal. In addition, they are usually time consuming for analyzing videos. To address these limitations, we propose a novel and efficient appearance modeling technique for automatic primary video object segmentation in the Markov random field (MRF) framework. It embeds the appearance constraint as auxiliary nodes and edges in the MRF structure, and can optimize both the segmentation and appearance model parameters simultaneously in one graph cut. The extensive experimental evaluations validate the superiority of the proposed approach over the state-of-the-art methods, in both efficiency and effectiveness.