nils-herrmann/clean_scirepeval huggingface dataset:
Integrated Procedure Automating Test Chip Layout, Place and Route, and Test Plan Development for Efficient Parametric Device and Process Design

This work describes a novel system for device development that automates and fully integrates the workflow from test chip construction, from placement and routing to electrical test program generation. In addition to accelerating test chip and test program development, this system facilitates parameterized data analysis, thereby providing a framework that finally allows the user to realize the full benefits of complex and elegant experimental device designs. By utilizing a centralized database and eliminating parameter re-entry, the automation provided by this integrated approach eliminates many of the sources for human error while maximizing reuse between technologies.
Modelling, synthesis and characterisation of occlusion in videos

Occlusion is one of the most challenging problems in many video processing applications such as surveillance, gait recognition, activity recognition and so on. Attempts have been made to develop algorithms for handling occlusion and evaluate their performance on various datasets. However, these studies are subjective in nature and the datasets are hardly characterised in terms of the level of occlusion, thereby precluding any form of quantitative comparison of performance. This shows a compelling need to design an explicit, unambiguous and quantitative model, which should be able to objectively represent occlusion in a video. This study proposes an occlusion model based on the position and pose uncertainties of the moving subjects in a video. The proposed occlusion model is able to characterise the level of occlusion present in a video. It is also employed to synthetically generate occlusion for walking sequences, thus providing a direction for controlled dataset generation against which human identification algorithms can be tested. Given an input video with a subject moving without any occlusion, a particle swarm optimisation-based parameter estimation methodology is presented that generates the desired level of occlusion. The proposed approaches have been tested on the TUM-IITKGP and PETS2010 datasets. Finally, as an application, the occlusion model has been used to generate an occluded gait datasets and the performances of different gait recognition algorithms have been compared under varying levels of occlusion.
3D-SIFT feature based brain atlas generation: An application to early diagnosis of Alzheimer's disease

In this paper, we propose a novel technique for brain atlas generation which is based on robust and invariant feature key-points computed on several human brain volumes. 3D scale-invariant feature transform (3D SIFT) has been used for detection and description of the feature key-points. By a Model Based MRI Alignment technique, the search space for key-point matching among different volumes has been reduced considerably. To obtain a set of invariant feature key-points from multiple brain volumes, a greedy approach has been introduced. The proposed technique has been used to generate a brain atlas by considering 30 normal human brain volumes of a population with ages ranging from 33 to 70 years. As an application, the set of invariant key-points has been used for an early diagnosis of Alzheimer's disease.
Authorization Management in Multi-cloud Collaboration Using Attribute-Based Access Control

Security in multi-cloud collaborative environment requires a fine-grained access control mechanism. Attribute Based Access Control (ABAC) is considered to be a suitable choice in such situations. However, identification of a correct set of ABAC rules is a crucial step in establishing secure collaborations among multiple clouds. In this paper, we formally define cross-domain rule mining as the problem of finding a minimal set of ABAC rules that allow access to the resources of one cloud by the users of another cloud. The problem is shown to be NP-Hard and a heuristic algorithm is proposed to solve it. Experiments on an extensive set of benchmark and synthetic data show encouraging results.
SKIP Prediction for Fast Rate Distortion Optimization in H.264

In H.264, the optimal coding mode for each macroblock (MB) is selected by exhaustively searching all MB modes in the multiple reference frames. The exhaustive mode search has a very high computational complexity. This paper proposes three different approaches along with their combination to reduce the complexity of the rate-distortion optimized mode decision process in H.264. These approaches are based on transform-domain properties and spatio-temporal correlation in video sequences. Experimental results demonstrate that the proposed methods achieve around 5-7 times improvement in average speedup with good prediction quality and comparable bitrate.
State-Based Modeling and Object Extraction From Echocardiogram Video

In this paper, we propose a hierarchical state-based model for representing an echocardiogram video. It captures the semantics of video segments from dynamic characteristics of objects present in each segment. Our objective is to provide an effective method for segmenting an echo video into view, state, and substate levels. This is motivated by the need for building efficient indexing tools to support better content management. The modeling is done using four different views, namely, short axis, long axis, apical four chamber, and apical two chamber. For view classification, an artificial neural network is trained with the histogram of a region of interest of each video frame. Object states are detected with the help of synthetic M-mode images. In contrast to traditional single M-mode, we present a novel approach named sweep M-mode for state detection. We also introduce radial M-mode for substate identification from color flow Doppler 2-D imaging. The video model described here represents the semantics of video segments using first-order predicates. Suitable operators have been defined for querying the segments. We have carried out experiments on 20 echo videos and compared the results with manual annotation done by two experts. View classification accuracy is 97.19%. Misclassification error of the state detection stage is less than 13%, which is within acceptable range since only frames at the state boundaries are found to be misclassified.
Towards role mining with restricted user-role assignment

RBAC (Role-Based Access Control) is a widely adopted access control model. However, migrating to RBAC requires identification of a complete and correct set of roles. Many bottom-up algorithms have been proposed for mining such roles. These algorithms attempt to find a minimal set of roles that is consistent with a given input configuration. But none of these consider any form of constraint on the maximum number of users that can be assigned to each role. This is a common requirement often termed as ‘cardinality constraint’ which has been widely referred in early RBAC literature. In this paper, we propose three algorithms which identify roles that satisfy such type of constraint. The algorithms vary in their degree of complexity and accuracy. Comparative performance analysis has been done using benchmark datasets.
A document image analysis system on parallel processors

The paper presents a document image processing system implemented on a set of parallel processors. A preprocessing stage is first used to correct skew from scanned document images. The corrected image is segmented and labelled in a two-step minimum containing rectangle (MCR) detection stage. Text block filtering (TBF) is then done heuristically and the filtered blocks are submitted to a multilayer perceptron (MLP) for recognition of characters. Smoothing of the document image is done during MLP-based character recognition to reduce the preprocessing time. It also reduces the formation of merged characters, a main source of recognition errors in conventional approaches. The MLP identifies the bold words during recognition which are used for automatic indexing of documents. Data is partitioned exploiting the inherent parallelism in a document image data. Communication overhead is small compared to the computation time so that a high degree of parallelization is achieved, reducing the total execution time.
Text-graphics separation to detect logo and stamp from color document images: A spectral approach

Text and graphics separation is an important task in the field of document image processing. This work aims at detecting graphics such as logos and stamps in a scanned document image. A novel spectral filtering based text-graphics separation algorithm (SFTGS) is presented here. The property of text that it is the major source of high spatial frequency components in a document image, is exploited in this algorithm. Accordingly high frequency filtering is used to separate the text symbols. This is followed by a segmentation process for delineating residual text and the graphics. The main advantage of SFTGS is that it works in a single pass, and can discriminate graphics and text without supervised training. Subsequently, the graphics segments are further categorized into two different classes, namely logos and stamps. In this case, we assume that these are the two classes of graphical objects present in the documents. The technique is evaluated using publicly available document dataset consisting of graphics as stamps and logos. The result is compared with existing approaches reported in the literature, and it is found that the proposed method performs superior to them. An overall performance of 89.1% recall and 96.9% precision is obtained for SFTGS.
An Administrative Model for Collaborative Management of ABAC Systems and Its Security Analysis

Attribute-based Access Control (ABAC) has been emerging as a suitable choice for large and federated enterprises due to its flexibility in expressing various types of security policies. Improved flexibility, however, results in higher design complexity and consequently, possibility of undesired flow of information. Reliance of access decision on the attribute values of subjects, objects and environment underscores the need for a formal way of managing attribute assignment in ABAC systems. Since large enterprises potentially have hundreds of subjects and thousands of resources, centralized management of attribute assignment is inexpedient. This paper introduces an attribute-based administrative model that supports decentralized administration of ABAC systems. The proposed model consists of a number of operations to administer the set of subjects and the set of subject attribute assignments in an ABAC system. We then suggest a methodology for analyzing the security properties of ABAC using Alloy analyzer in the presence of the proposed administrative model.
Object Level Frame Comparison for Video Shot Detection

We propose a new approach to temporal video segmentation in which shot boundaries are detected by object-level matching of video frames. I-frames are initially segmented using features extracted from the HSV color space. Two consecutive I-frames are compared in terms of the color, size and position of their objects. A shot boundary is detected if there is a strong mismatch between frame objects. We have developed a web-based video retrieval system for demonstrating our work and for performing user queries.
A novel high speed automatic layout system to place and route test structures for parametric test capability

In this paper, we created a generalized framework for the automated placement and routing of analog test structures. We exploited the concept of terminal properties when placing and routing the test structures and generated a library of place and routing strategies for different architectures. This new approach significantly reduces layout time, maximizes the reuse of place and route routines, and facilitates the introduction of a holistic parametric test design flow.
Recognition of an Indian script using multilayer perceptrons and fuzzy features

Presents a multi-stage character recognition system for an Indian script, namely Bengali (also called Bangla) using fuzzy features and multilayer perceptrons (MLP). The fuzzy features are extracted from the Hough transform of a character pixel pattern. We first define a number of fuzzy sets on the Hough transform accumulator cells. The fuzzy sets are then combined by t-norms to generate feature vectors from each character. A set of fuzzy linguistic vectors is next generated from these feature vectors. The MLPs used for the classification have the fuzzy features as inputs. The MLP outputs also represent the "belongingness" of an input pattern to different fuzzy character pattern classes. To improve the recognition accuracy of Bengali characters, we divide all the patterns into three distinct sets. Each set of characters is once again divided into a number of mutually exclusive character pattern classes. During recognition, the class of each pattern is first determined, followed by recognition of the actual character within that class. The recognition accuracy of the system is more than 98%.
Migrating from RBAC to temporal RBAC

The last two decades have witnessed an emergence of role-based access control (RBAC) as the de facto standard for access control. However, for organisations already having a deployed RBAC system, in many cases it may become necessary to associate a temporal dimension with the existing access control policies due to changing organisational requirements. In such cases, migration from RBAC to a temporal extension of RBAC becomes essential. Temporal RBAC (TRBAC) is one such RBAC extension. The process of creating a set of roles for implementing a TRBAC system is known as temporal role mining. Existing temporal role mining approaches typically assume that TRBAC is being deployed from scratch and do not consider it as a migration from an existing RBAC policy. In this study, the authors propose two temporal role mining approaches that enable migration from RBAC to TRBAC. These approaches make use of conventional (non-temporal) role mining algorithms. Apart from aiding the migration process, deriving the roles in this manner allows the flexibility of minimising any desired role mining metric. They experimentally evaluate the performance of both of the proposed approaches and show that they are both efficient and effective.
An efficient model-guided framework for alignment of brain MR image sequences

This paper proposes a method for alignment of human brain magnetic resonance (MR) image sequences in the brain based on a 3D human brain model (triangulated mesh). The brain model is composed of four components, namely, cerebrum, cerebellum, brain stem and pituitary gland which are represented by four different colors. Synthesized image sequences (cross-sections) are extracted from the model at regular intervals for sagittal and coronal views as done in MR imaging. The cerebellums are segmented from the sequence of MR images by using the method of active contouring and their sizes are determined. The areas of the cerebellums are computed from the cross-sections using the color information. To obtain the optimal synthesized cross-section sequence corresponding to the series of MR images, an efficient dynamic programming based computational technique has been developed that uses the normalized sizes of cerebellum in both the MR image sequences and the cross-sections.
Removal of Gray Rubber Stamps

Rubber stamps often overlap with original text content of a document, and hence obscure the text regions very badly. Removal of these stamp regions becomes a necessity for successful conversion of such documents into electronic format. Stamp removal from a document becomes more difficult when they are in gray scale, or text and stamp are of the same color. In this paper, we propose a technique to remove such stamps from overlapped regions by identifying stamp regions and stamp pixels.
Segmentation and histogram generation using the HSV color space for image retrieval

We have analyzed the properties of the HSV (hue, saturation and value) color space with emphasis on the visual perception of the variation in hue, saturation and intensity values of an image pixel. We extract pixel features by either choosing the hue or the intensity as the dominant property based on the saturation value of a pixel. The feature extraction method has been applied for both image segmentation as well as histogram generation applications - two distinct approaches to content based image retrieval (CBIR). Segmentation using this method shows better identification of objects in an image. The histogram retains a uniform color transition that enables us to do a window-based smoothing during retrieval. The results have been compared with those generated using the RGB color space.
Optimal Rule Mining for Dynamic Authorization Management in Collaborating Clouds Using Attribute-Based Access Control

Applications that span across multiple clouds are often found to be vulnerable to security threats. Such highly heterogeneous environments need a fine-grained access control mechanism like Attribute-based Access Control (ABAC) for enforcing security. A first step towards successfully deploying ABAC is to define an appropriate set of access control rules that establish the desired inter-cloud accesses. This becomes more challenging when the access requirements vary with time or the users and objects are updated quite frequently. We study the problem of formulation of an optimal set of ABAC rules for granting inter-cloud accesses in a dynamic environment. The problem being NP-Hard, we propose heuristic solutions. Extensive experiments on benchmark datasets show encouraging results.
DETECTION OF ON-FIELD ADVERTISEMENT BILLBOARDS FROM SOCCER TELECASTS

We propose a method for the identification of on-field billboards meant for advertisements in soccer telecasts. We use a three-step approach in which the first step is to identify shot boundaries and classify the frames as part of a long shot or a close shot. The regions that are expected to contain billboards are then segmented with the help of domain knowledge and a number of heuristics. Finally, we use a combination of local and global features to identify the particular billboards of each advertiser. For each detected billboard, its position and the percentage of visible area are recorded. The results can be conveniently used to determine the effectiveness of billboard advertising.
Policy Adaptation in Attribute-Based Access Control for Inter-Organizational Collaboration

In Attribute-Based Access Control (ABAC), attributes are defined as characteristics of subjects, objects as well as environment, and access is granted or denied based on the values of these attributes. With increasing number of organizations showing interest in migrating to ABAC, it is imperative that algorithmic techniques be developed to facilitate the process. While the traditional ABAC policy mining approaches support the development of an ABAC policy from existing Discretionary Access Control (DAC) or Role-Based Access Control (RBAC) systems, they do not handle adaptation to the policy of a similar organization. As the policy itself need not be developed ab initio in this process, it provides agility and a faster migration path, especially for organizations participating in collaborative sharing of data. With the set of objects and their attributes given, along with an access control policy to be adapted to, the problem is to determine an optimal assignment of attributes to subjects so that a set of desired accesses can be granted. Here, optimality is in the number of ABAC rules the subjects would require to use to gain access to various objects. Such an approach not only helps in assisting collaboration between organizations, but also ensures efficient evaluation of rules during policy enforcement. We show that the optimal policy adaptation problem is NP-Complete and present a heuristic solution.
A genetic algorithm for feature selection in a neuro-fuzzy OCR system

We have worked on the development of a character recognition system in the soft computing paradigm. In this paper we present a genetic algorithm used for feature selection with a Feature Quality Index (FQI) metric. We generate feature vectors by defining fuzzy sets on Hough transform of character pattern pixels. Each feature element is multiplied by a mask vector bit before reaching the input of a multilayer perceptron (MLP). The genetic algorithm operates on the bit string represented by the mask vector to select the best set of features. The method has been tested with three benchmark data sets and the results show a fast convergence of the genetic algorithm.
Efficient Bottom-Up Mining of Attribute Based Access Control Policies

Attribute Based Access Control (ABAC) is fast replacing traditional access control models due to its dynamic nature, flexibility and scalability. ABAC is often used in collaborative environments. However, a major hurdle to deploying ABAC is to precisely configure the ABAC policy. In this paper, we present an ABAC mining approach that can automatically discover the appropriate ABAC policy rules. We first show that the ABAC mining problem is equivalent to identifying a set of functional dependencies in relational databases that cover all of the records in a table. We also propose a more efficient algorithm, called ABAC-SRM which discovers the most general policy rules from a set of candidate rules. We experimentally show that ABAC-SRM is accurate and significantly more efficient than the existing state of the art.
Resistance Estimation of Lateral Power Arrays through Accurate Netlist Generation

An accurate and fast technique for estimation of resistance from a large lateral power array layout along with parasitics and interconnects is presented in this paper. We extract a resistive network for metallization utilizing the finite element method (FEM). The technique benefits in terms of computation from exploitation of the repetitive nature of metallization in a power array layout. Device channels are modeled by linear resistances as the power MOS operates mostly in the linear region. Since we avoid use of lumped models or extrapolation techniques for resistance modeling, a good level of accuracy is achieved. Through a proper indexing scheme, we maintain a one-to-one correspondence between the layout geometry and the netlist from which a current profile of the layout may be formed subsequently.
BLAST-SSAHA Hybridization for Credit Card Fraud Detection

A phenomenal growth in the number of credit card transactions, especially for online purchases, has recently led to a substantial rise in fraudulent activities. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. In real life, fraudulent transactions are interspersed with genuine transactions and simple pattern matching is not often sufficient to detect them accurately. Thus, there is a need for combining both anomaly detection as well as misuse detection techniques. In this paper, we propose to use two-stage sequence alignment in which a profile analyzer (PA) first determines the similarity of an incoming sequence of transactions on a given credit card with the genuine cardholder's past spending sequences. The unusual transactions traced by the profile analyzer are next passed on to a deviation analyzer (DA) for possible alignment with past fraudulent behavior. The final decision about the nature of a transaction is taken on the basis of the observations by these two analyzers. In order to achieve online response time for both PA and DA, we suggest a new approach for combining two sequence alignment algorithms BLAST and SSAHA.