nils-herrmann/clean_scirepeval huggingface dataset:
A Comparative Study of Margin Noise Removal Algorithms on MarNR: A Margin Noise Dataset of Document Images

Margin noise removal is an important step prior to segmentation and optical character recognition (OCR) of a page. Presence of this noise results in erroneous output by the segmentation algorithms and OCR systems. In this paper, we present a margin noise removal dataset MarNR. A comparative study of four margin noise removal algorithms is also presented in this paper. For the purpose of evaluation, we have considered seven metrics. The metrics Hamming distance, noise ratio, and page content removal aim to evaluate a margin noise removal algorithm either on the quantity of noise removed or on the original content of the image retrieved. We also consider margin noise removal as a bi-classification task and four metrics of evaluation are defined using confusion matrices obtained experimentally over a labeled test dataset explicitly generated for evaluating the margin noise removal algorithms. The dataset consists of various document images with variation in layout and margin noises. The labeled dataset is also made public for comparative study of different margin noise removal algorithms.
Security Analysis of Temporal-RBAC Using Timed Automata

Role Based Access Control (RBAC) is arguably the most common access control mechanism today due to its applicability at various levels of authorization in a system. Time varying nature of access control in RBAC administered systems is often implemented through Temporal-RBAC - an extension of RBAC in the temporal domain. In this paper, we propose an initial approach towards verification of security properties of a Temporal-RBAC system. Each role is mapped to a timed automaton. A controller automaton is used to activate and deactivate various roles. Security properties are specified using Computation Tree Logic (CTL) and are verified with the help of a model checking tool named Uppaal. We have specifically considered reachability, safety and liveness properties to show the usefulness of our approach.
Removal of hand-drawn annotation lines from document images by digital-geometric analysis and inpainting

Performance of an OCR system is badly affected due to presence of hand-drawn annotation lines in various forms, such as underlines, circular lines, and other text-surrounding curves. Such annotation lines are drawn by a reader usually in free hand in order to summarize some text or to mark the keywords within a document page. In this paper, we propose a generalized scheme for detection and removal of these hand-drawn annotations from a scanned document page. An underline drawn by hand is roughly horizontal or has a tolerable undulation, whereas for a hand-drawn curved line, the slope usually changes at a gradual pace. Based on this observation, we detect the cover of an annotation object-be it straight or curved-as a sequence of straight edge segments. The novelty of the proposed method lies in its ability to compute the exact cover of the annotation object, even when it touches or passes through any text character. After getting the annotation cover, an effective method of inpainting is used to quantify the regions where text reconstruction is needed. We have done our experimentation with various documents written in English, and some results are presented here to show the efficiency and robustness of the proposed method.
Security analysis and implementation of web-based telemedicine services with a four-tier architecture

Security of telemedicine applications is not often given adequate importance by the developers and healthcare administrators primarily to reduce cost. Though some security safeguards are employed by these applications to comply with existing medical data security and privacy regulations, these are not adequate in todaypsilas context. Moreover, in a Web-based application environment not only the data but also the application itself is vulnerable to attackers. Keeping these concerns in mind, we present the design of a Web-based, four-tier telemedicine system named iMedik which is accessible over desktops as well as handheld devices. We have illustrated how the proposed system differs from existing three-tier Web applications. The compliance status of the application with HIPAA Security Guidelines has also been noted. The security measures described in our approach look into the four-tier architecture from an attackerpsilas viewpoint and present a simple road map for developing secure e-health application with anywhere, anytime availability.
A probabilistic adaptive algorithm for constructing hierarchical meshes

Recent advancement in the field of video communication requires an efficient video compression technique which can transmit a good quality video with very low bandwidth. Mesh based video compression techniques are often used to achieve this goal. A mesh is formed on the boundary of an object inside a video frame and nodes of the mesh are transmitted to describe the motion of the object. Motion vectors at the nodes decide on the coarse-to-fine hierarchy of a mesh. Sometimes this technique fails to construct a proper hierarchical mesh for an object and cannot capture its motion properly. This paper introduces a new probabilistic approach to construct a hierarchical mesh which can describe the motion of an object with fare accuracy without knowing the proper shape of the object. This algorithm can also sense the amount of motion and select the reference frames accordingly in proper places so that minimum information is required to be transmitted without compromising on the quality of the video.
Resistance Estimation for Lateral Power Arrays Through Accurate Netlist Generation

Estimation of resistance of power devices has become critical for improving the efficiency of on-chip power-management circuits. In this paper, we present an efficient technique for estimation of resistance of a large lateral power-array layout along with parasitics. We extract a resistive network for metalizations utilizing the finite-element method. The method primarily benefits in terms of computational speed from reuse methodology facilitated by repetitive structure of the metal interconnect layers. Device channels are modeled by linear resistances as the power MOS operates mostly in the linear region. Since we avoid use of heuristic-based lumped models or extrapolation techniques for resistance modeling, a good level of accuracy is achieved.
A comparative analysis of two distance measures in color image databases

The Euclidean distance measure has been used in comparing feature vectors of images, while the cosine angle distance measure is used in document retrieval. We theoretically analyze these two distance measures based on feature vectors normalized by image size and experiment with them in the context of a color image database. We find that the cosine angle distance, in general, works equally well for image databases. We show, for a given query vector, the characteristics of feature vectors that will be favored by one measure but not by the other. We compute k-nearest neighbors for query images using both Euclidean and cosine angle distance for a small image database. The experimental data corroborate our theoretical results.
A wide-band lumped element compact CAD model of Si-based planar spiral inductor for RFIC design

In this paper, a new lumped element compact Si-based on-chip spiral inductor model is presented for wide-band application. It consists of a new substrate network and underpass oxide leakage component to model the reduction in equivalent series resistance at higher frequencies, taking into account the substrate loss due to eddy current effect. The model accurately estimates the equivalent circuit parameters and inductor behavior over a wide frequency range up to 20GHz. The proposed model has been verified with the measured results of octagonal spiral inductors fabricated on a six-metal BiCMOS7 process. Application of global optimizations genetic algorithm (GA) and particle swarm optimization (PSO) are discussed. PSO is used for the extraction of model parameters that speeds up the convergence and overcomes the sub-optimal solution of conventional optimization algorithm.
Characteristics of weighted feature vector in content-based image retrieval applications

Color and texture feature vectors of an image are always considered to be an important attribute in content-based image retrieval system. Both of these feature vectors of an image can be combined for the performance enhancement of the content-based image retrieval system. One of the standard ways of extracting color feature from an image is to generate a color histogram. Using Haar wavelet or Daubechies' wavelet the texture feature of an image can be extracted. These two feature vectors and the feature vectors in the database are normalized so that the value of a bin is always between [0,1]. During retrieval, both color and texture feature vectors of query image is combined, weighted and compared with the color and texture feature vectors of each of the database images using Manhattan distance metric. The retrieved result is dependent on the weight given to each of the feature vector. We have done a detailed study of the performance of different combination of weights to color (w/sub c/) and texture (w/sub t/) features on a large database of images. Different combination weights are used in for evaluation and the results shows that texture feature vector weight (W/sub t/) in the range of W/sub c/ /spl plusmn/0.1 to w/sub c/ /spl plusmn/0.2 perform better than the other combinations.
Information fusion from multiple cameras for gait-based re-identification and recognition

In this study, the authors present a fully automated frontal (i.e. employing front and back views only) gait recognition approach using the depth information captured by multiple Kinect RGB-D cameras. Limited depth sensing range restricts each of these Kinects to record only a part of a complete gait cycle of a walking subject. Hence, information from more than one Kinect is fused together to examine which features of a gait cycle can be conveniently extracted from the sequences captured independently by these cameras. To achieve this, it is imperative that the same subject be re-identified as he moves from the field of view of one camera to another. The authors use a set of soft-biometric features computed from the skeleton stream provided by Kinect software development kit) for doing automatic re-identification. To enable such information fusion and also to handle missing components even after re-identification, features are extracted at the granularity of small fractions of a gait cycle. Experiments carried out on a data set with gait videos captured by Kinects respectively from the back and front views show promising results.
Approximate SAD computation for real-time low power video encoders

This paper presents a heuristic approach to reduce the computational power requirement of SAD cost function used in the motion estimation phase of video encoding. The proposed technique selects the best matched macroblock based on partial SAD sums. This results in lower power consumption with marginal loss in image quality. Different levels to trade-off image quality with computational power have also been proposed. The mathematical intuition in support of the heuristics is discussed. Our approach shows good performance, especially at low bitrates.
Robust tracking of facial feature points with 3D Active Shape Models

Exact 3D tracking of facial feature points is appealing for many applications in human-machine interaction. In this work a 3D Active Shape Model (ASM) that can be shifted, scaled, and rotated is used to track the points. The efficient Gauss-Newton method is applied to estimate the 3D ASM, rotation, translation, and scale parameters. If the head turns to one side, some points might be occluded but they are still considered for the estimation of the parameters. A robust error norm that reduces (or ideally cancels) the influence of occluded points is applied. With some algebraic transformations the computational cost per frame can be further reduced. The proposed algorithm is evaluated on the basis of the Airplane Behavior Corpus.
A two-step algorithm and its parallelization for the generation of minimum containing rectangles for document image segmentation

In document processing, segmentation is done to uniquely identify each foreground connected region of an image by specifying its minimum containing rectangle (MCR). MCR is the rectangle with minimum dimensions that completely encloses a geometric pattern. We present a two-step MCR detection algorithm and its parallelization method. The first step determines the boundary of each connected component in a document image. This reduces resource requirements and speeds up the subsequent rectangle detection step. The rectangle detection step determines MCRs of the connected components from the detected boundaries. A comparison is made between a single-step and the two-step approaches of MCR detection. Both the boundary detection and the rectangle detection steps are parallelized and implemented on transputers to reduce the total processing time.
A Fuzzy System for Impact Analysis of Advertisement Billboards in Soccer Telecast

Advertisement billboards placed along the periphery of a soccer field in popular tournaments are used to promote specific products or the brand image of a company. In this paper, we introduce a fuzzy logic based approach for estimating the visual impact of such billboards when broadcasted through the television medium. The present system estimates the persistence effect of a billboard on human mind by using a two stage fuzzy rule based system. In the first phase, a shot level analysis is carried out, which is followed by an inter shot analysis to estimate the overall impact. In both the stages, parameters of the fuzzy set membership functions are tuned using the Particle Swarm Optimization algorithm. The system works on top of a billboard detection system and the results have been compared against a user survey.
A fuzzy inferencing system for gait recognition

This paper proposes a method for the identification of individuals from their gait using fuzzy logic. Gait signature is first extracted in the form of a spatiotemporal representation called Gait Energy Image (GEI). Since the dimension of GEI is very high, we use fuzzy principal component analysis (FPCA) for dimension reduction. Unlike traditional PCA, it helps to get rid of the problems of outliers and missing data. Classification of test pattern is done based on two aspects, namely, proximity towards each class and the count of training samples corresponding to each class that are close to the test sample. The impreciseness and subjective nature of these two terms motivate us to use fuzzy logic which is capable of capturing and modeling the uncertainty inherent to the problem domain. We introduce a new gait recognition method based on a fuzzy inferencing system. Feature vectors resulting from the FPCA transformation are used by the fuzzy inferencing system to compute the class assignment. Comprehensive experiments carried out on CMU's Mobo dataset show promising classification rates. The performance has been compared with other approaches and is found to attain improved result.
Friends are forever? Evolution of active friendship clusters in online social networks

The goal of this paper is to study how friendship clusters evolve in online social networks. Results obtained from our work on Facebook data indicate that the set of friends who actively interact during a particular time interval is only a fraction of the total number of listed friends and this set of active friends tends to evolve with time. Interaction footprints on the timeline are used to cluster such active friends. Since there is a temporal variation in these clusters, it is modeled as a graph, wherein nodes represent cluster memberships and edge weights represent the number of common members between a pair of nodes. Novel methodologies for predicting the size and membership of the next active cluster based on the features extracted from past interaction of a user are introduced and their performance evaluated.
Toward Automating Hammersmith Pulled-To-Sit Examination of Infants Using Feature Point Based Video Object Tracking

Hammersmith Infant Neurological Examination (HINE) is a set of tests used for grading neurological development of infants on a scale of 0 to 3. These tests help in assessing neurophysiological development of babies, especially preterm infants who are born before (the fetus reaches) the gestational age of 36 weeks. Such tests are often conducted in the follow-up clinics of hospitals for grading infants with suspected disabilities. Assessment based on HINE depends on the expertise of the physicians involved in conducting the examinations. It has been noted that some of these tests, especially pulled-to-sit and lateral tilting, are difficult to assess solely based on visual observation. For example, during the pulled-to-sit examination, the examiner needs to observe the relative movement of the head with respect to torso while pulling the infant by holding wrists. The examiner may find it difficult to follow the head movement from the coronal view. Video object tracking based automatic or semi-automatic analysis can be helpful in this case. In this paper, we present a video based method to automate the analysis of pulled-to-sit examination. In this context, a dynamic programming and node pruning based efficient video object tracking algorithm has been proposed. Pulled-to-sit event detection is handled by the proposed tracking algorithm that uses a 2-D geometric model of the scene. The algorithm has been tested with normal as well as marker based videos of the examination recorded at the neuro-development clinic of the SSKM Hospital, Kolkata, India. It is found that the proposed algorithm is capable of estimating the pulled-to-sit score with sensitivity (80%-92%) and specificity (89%-96%).
Access Control Model for Web Services with Attribute Disclosure Restriction

Web service is a programmable interface accessible through a network. In this paper we focus on the scenario in which different organizations use Web services to collaborate, share knowledge, integrate services and for providing value added services to customers. As a test case, we consider health care application in which different hospitals can give various types of services to other hospitals. We find attribute based access control (ABAC) model to be quite suitable for access control in Web services. However, there is a need to enforce user's security policy to decide only which attributes should be disclosed so that users can reveal their attributes to service providers according to their need. We extend the ABAC model with user attribute disclosure restriction and propose a framework for defining and applying security policies
A Structured Soft (Max-Min) Decision Trees for Patient Specific Fuzzy Classifier in the Classification of Epilepsy Risk Levels from EEG Signals

The objective of this research is to investigate the feasibility of structured Soft (max-min) decision trees in optimization of fuzzy outputs for the classification of epilepsy risk levels from EEG (electroencephalogram) signals. The fuzzy pre classifier is used to classify the risk levels of epilepsy based on extracted parameters like energy, variance, peaks, sharp and spike waves, duration, events and covariance from the EEG signals of the patient. Soft decision tree (post classifier with max-min criteria) six types are applied on the classified data to identify the optimized risk level (singleton) which characterizes the patient's epilepsy risk level. The efficacy of the above methods is compared based on the bench mark parameters such as performance index (PI), sensitivity, specificity and quality value (QV).
A comparative study on texture features used for segmentation of images rich in texture

A comparative study based method to select appropriate texture feature for image segmentation using K-means clustering algorithm is proposed in this paper. We study and record the performances based on three features namely, Contourlet, Gabor and Tamura.  Tamura feature is proposed that produces better result than the conventional one. Results of our experiment suggest that, for a given class of images, segmentation algorithm using Contourlet and Gabor with similar feature space perform equally well. On the other hand, performance of conventional Tamura feature lacks consistency but Tamura with multiple coarseness and directions improves segmentation.
Specification and Verification of Separation of Duty Constraints in Attribute-Based Access Control

Constraints form an important aspect of any access control system and are often regarded as one of the principle motivations behind developing different access control models. The two primary concerns related to a constraint are its specification and enforcement. Among the various types of constraints, enforcement of the Separation of Duty (SoD) constraint is considered to be the most important in commercial applications. In this paper, we introduce the problem of SoD specification, verification, and enforcement in attribute-based access control (ABAC) systems. We then demonstrate the effect of modifications in the different components of ABAC on enforcement. We also analyze the complexity of the enforcement problem and provide a methodology for solving it. Experiments on a wide range of data sets show encouraging results.
Stamp and logo detection from document images by finding outliers

Stamps and logos are generally used for authenticating the source of a document. For automatic document processing, identification and segmentation of stamps and logos are essential. In the past, methods to detect stamps and logos were limited to specific shapes, colors, or training data. However, stamps and logos can be of any shape or color. In this paper, we have proposed a novel stamp and logo detection technique. Our approach is based on the fact that stamps and logos, in general, are not the primary contents of a document. This fact motivates us to propose an outlier detection technique for the same purpose in a feature space. Based on some geometric features, the detected outliers are classified as stamps and logos. Our method shows good performance in case of separating them from text. Moreover, this technique is capable of detecting logos as well as chromatic and achromatic stamps.
A two-state Markov chain model of degraded document images

We propose a two-state Markov chain model of degraded document images. The model generates random and burst noise to simulate isolated pixel reversal as well as blurring of a larger document region. In the random state, the probability of pixel inversion is low compared to that in the burst state. However, the model remains in the random state for a much longer period of time. Validation of the model has been done using the statistical methodology of (Kanungo et al., 1995). To estimate the parameters efficiently, we use a genetic algorithm (GA) to search through the parameter space in which the model parameter values are encoded into a concatenated bit string to form the chromosomes. We also show how the accuracy of an optical character recognition system with dictionary search varies with two derived parameters of the proposed noise model.