nils-herrmann/clean_scirepeval huggingface dataset:
SemiBoost: Boosting for Semi-Supervised Learning

Semi-supervised learning has attracted a significant amount of attention in pattern recognition and machine learning. Most previous studies have focused on designing special algorithms to effectively exploit the unlabeled data in conjunction with labeled data. Our goal is to improve the classification accuracy of any given supervised learning algorithm by using the available unlabeled examples. We call this as the Semi-supervised improvement problem, to distinguish the proposed approach from the existing approaches. We design a metasemi-supervised learning algorithm that wraps around the underlying supervised algorithm and improves its performance using unlabeled data. This problem is particularly important when we need to train a supervised learning algorithm with a limited number of labeled examples and a multitude of unlabeled examples. We present a boosting framework for semi-supervised learning, termed as SemiBoost. The key advantages of the proposed semi-supervised learning approach are: 1) performance improvement of any supervised learning algorithm with a multitude of unlabeled data, 2) efficient computation by the iterative boosting algorithm, and 3) exploiting both manifold and cluster assumption in training classification models. An empirical study on 16 different data sets and text categorization demonstrates that the proposed framework improves the performance of several commonly used supervised learning algorithms, given a large number of unlabeled examples. We also show that the performance of the proposed algorithm, SemiBoost, is comparable to the state-of-the-art semi-supervised learning algorithms.
Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A

Rapid progress in unconstrained face recognition has resulted in a saturation in recognition accuracy for current benchmark datasets. While important for early progress, a chief limitation in most benchmark datasets is the use of a commodity face detector to select face imagery. The implication of this strategy is restricted variations in face pose and other confounding factors. This paper introduces the IARPA Janus Benchmark A (IJB-A), a publicly available media in the wild dataset containing 500 subjects with manually localized face images. Key features of the IJB-A dataset are: (i) full pose variation, (ii) joint use for face recognition and face detection benchmarking, (iii) a mix of images and videos, (iv) wider geographic variation of subjects, (v) protocols supporting both open-set identification (1:N search) and verification (1:1 comparison), (vi) an optional protocol that allows modeling of gallery subjects, and (vii) ground truth eye and nose locations. The dataset has been developed using 1,501,267 million crowd sourced annotations. Baseline accuracies for both face detection and face recognition from commercial and open source algorithms demonstrate the challenge offered by this new unconstrained benchmark.
Automatic image analysis of plant root structures

The minirhizotron technique has provided agricultural scientists the opportunity of observing rhizosphere activities without destroying root structures. Nonetheless, the laborious analysis of the data still prohibits its wide applications. Advanced image understanding techniques are needed to derive satisfactory descriptions of plant root networks in an efficient and robust way. The paper presents a plant root image analysis system designed as a blackboard architecture with a hierarchy of data abstractions. Important properties of plant roots are used throughout the processing and multiple sources of information are combined to resolve uncertainties in image interpretation. Experimental results from some stages of the research are given which support the overall processing scheme.<<ETX>>
A CMOS VAX microprocessor with on-chip cache and memory management

A single-chip 32-b microprocessor designed in a 2-/spl mu/m (drawn) CMOS process with two layers of metal interconnect is described. The microprocessor implements the VAX architecture. A one-transistor dynamic RAM cell is used to build a 1-kbyte instruction and data cache. The chip contains 134000 transistors, is 9.4/spl times/9.7 mm/SUP 2/ large, operates at 5.0 V, and dissipates 1.5 W.
Speedup of fuzzy and possibilistic kernel c-means for large-scale clustering

The ubiquity of personal computing technology has produced an abundance of staggeringly large data sets — the Library of Congress has stored over 160 terabytes of web data and it is estimated that Facebook alone logs over 25 terabytes of data per day. There is a great need for systems by which one can elucidate the similarity and dissimilarity among and between groups in these data sets. Clustering is one way to find these groups. In this paper, we propose an approximation method for the fuzzy and possibilistic kernel c-means clustering algorithms. Our approximation constrains the cluster centers to be linear combinations of a size m randomly selected subset of the n input objects, where m << n. The proposed algorithm only requires an m × n rectangular portion of the full n × n kernel matrix and the n diagonal values, resulting in significant memory savings. Furthermore, the computational complexity of the c-means algorithm is substantially reduced. We demonstrate that up to 3 orders of magnitude of speedup are possible while achieving almost the same performance as the original kernel c-means algorithm.
Universal 3D Wearable Fingerprint Targets: Advancing Fingerprint Reader Evaluations

We present the design and manufacturing of high-fidelity universal 3D fingerprint targets, which can be imaged on a variety of fingerprint sensing technologies, namely, capacitive, contact optical, and contactless optical. Universal 3D fingerprint targets enable, for the first time, not only a repeatable and controlled evaluation of fingerprint readers but also the ability to conduct fingerprint reader interoperability studies. Fingerprint reader interoperability refers to how robust fingerprint recognition systems are to variations in the images acquired by different types of fingerprint readers. To build universal 3D fingerprint targets, we adopt a molding and casting framework consisting of: 1) digital mapping of fingerprint images to a negative mold; 2) CAD modeling a scaffolding system to hold the negative mold; 3) fabricating the mold and scaffolding system with a high resolution 3D printer; 4) producing or mixing a material with similar electrical, optical, and mechanical properties to that of the human finger; and 5) fabricating a 3D fingerprint target using controlled casting. Our experiments conducted with personal identity verification and Appendix F certified optical (contact and contactless) and capacitive fingerprint readers demonstrate the usefulness of universal 3D fingerprint targets for controlled and repeatable fingerprint reader evaluations and also fingerprint reader interoperability studies.
Integrating Range and Texture Information for 3D Face Recognition

The performance of face recognition systems that use two-dimensional images depends on consistent conditions w.r.t. lighting, pose, and facial appearance. We are developing a face recognition system that utilizes three-dimensional shape information to make the system more robust to arbitrary view, lighting, and facial appearance. For each subject, a 3D face model is constructed by integrating several 2.5D face scans from different viewpoints. A 2.5D scan is composed of one range image along with a registered 2D color image. The recognition engine consists of two components, surface matching and appearance-based matching. The surface matching component is based on a modified Iterative Closest Point (ICP) algorithm.The candidate list used for appearance matching is dynamically generated based on the output of the surface matching component, which reduces the complexity of the appearance-based matching stage. The 3D model in the gallery is used to synthesize new appearance samples with pose and illumination variations that are used for discriminant subspace analysis. The weighted sum rule is applied to combine the two matching components. A hierarchical matching structure is designed to further improve the system performance in both accuracy and efficiency. Experimental results are given for matching a database of 100 3D face models with 598 2.5D independent test scans acquired in different pose and lighting conditions, and with some smiling expression. The results show the feasibility of the proposed matching scheme.
Face Clustering: Representation and Pairwise Constraints

Clustering face images according to their latent identity has two important applications: 1) grouping a collection of face images when no external labels are associated with images, and 2) indexing for efficient large scale face retrieval. The clustering problem is composed of two key parts: representation and similarity metric for face images, and choice of the partition algorithm. We first propose a representation based on ResNet, which has been shown to perform very well in image classification problems. Given this representation, we design a clustering algorithm, Conditional Pairwise Clustering (ConPaC), which directly estimates the adjacency matrix only based on the similarities between face images. This allows a dynamic selection of number of clusters and retains pairwise similarities between faces. ConPaC formulates the clustering problem as a Conditional Random Field model and uses Loopy Belief Propagation to find an approximate solution for maximizing the posterior probability of the adjacency matrix. Experimental results on two benchmark face datasets (LFW and IJB-B) show that ConPaC outperforms well known clustering algorithms such as <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-means, spectral clustering, and approximate Rank-order. Additionally, our algorithm can naturally incorporate pairwise constraints to work in a semi-supervised way that leads to improved clustering performance. We also propose a <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-NN variant of ConPaC, which has a linear time complexity given a <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-NN graph, suitable for large datasets.
A control point theory for boundary representation and matching

There are several methods available for representing boundaries in two dimensions. The more widely known ones involve boundary modeling with run-length codes, chain codes and polygons, AR model coefficients, Fourier descriptors (FD's) and B-spline control points (CP's). In this paper, we present results showing the usefulness of CP's in boundary representation. CP's can yield large compression of the boundary data and they exercise local control over boundary shape. Because they can be normalized with respect to scale, translation and rotation, they are valuable in boundary matching applications. Finally, being the series coefficients of spline basis functions, CP's are useful features for boundaries detected in noisy environments.
Securing fingerprint template: Fuzzy vault with minutiae descriptors

Fuzzy vault has been shown to be an effective technique for securing fingerprint minutiae templates. Its security depends on the difficulty in identifying the set of genuine minutiae points among a mixture of genuine and chaff points and reconstructing the secure polynomial using the evaluations (ordinate values) available for each point in the vault. We show that the security of fuzzy vault can be improved by ldquoencryptingrdquo these polynomial evaluations using a fuzzy commitment scheme. This encryption makes it difficult for an adversary to decode the vault even if the correct set of minutiae is selected. We use minutiae descriptors, which capture orientation and ridge frequency information in a minutiapsilas neighborhood, for securing the polynomial evaluations. This modification leads to a significant increase in both the security (number of tries an adversary has to make in order to guess the secure key) and matching accuracy of the vault. We validate our results on FVC2002 DB2 and show that false accept rate (FAR) is reduced from 0.7% to 0.01% at a genuine accept rate (GAR) of 95%. At the same time, vault security as measured in terms of min-entropy, is increased from 31 bits to 47 bits in case a perfect code is used.
Biometric fusion: Does modeling correlation really matter?

Sources of information in a multibiometric system are often assumed to be statistically independent in order to simplify the design of the fusion algorithm. However, the independence assumption may not be always valid. In this paper, we analyze whether modeling the dependence between match scores in a multibiometric system has any effect on the fusion performance. Our analysis is based on the likelihood ratio (LR) based fusion framework, which guarantees optimal performance if the match score densities are known. We show that the assumption of independence between matchers has a significant negative impact on the performance of the LR fusion scheme only when (i) the dependence characteristics among genuine match scores is different from that of the impostor scores and (ii) the individual matchers are not very accurate.
Matching Forensic Sketches to Mug Shot Photos

The problem of matching a forensic sketch to a gallery of mug shot images is addressed in this paper. Previous research in sketch matching only offered solutions to matching highly accurate sketches that were drawn while looking at the subject (viewed sketches). Forensic sketches differ from viewed sketches in that they are drawn by a police sketch artist using the description of the subject provided by an eyewitness. To identify forensic sketches, we present a framework called local feature-based discriminant analysis (LFDA). In LFDA, we individually represent both sketches and photos using SIFT feature descriptors and multiscale local binary patterns (MLBP). Multiple discriminant projections are then used on partitioned vectors of the feature-based representation for minimum distance matching. We apply this method to match a data set of 159 forensic sketches against a mug shot gallery containing 10,159 images. Compared to a leading commercial face recognition system, LFDA offers substantial improvements in matching forensic sketches to the corresponding face images. We were able to further improve the matching performance using race and gender information to reduce the target gallery size. Additional experiments demonstrate that the proposed framework leads to state-of-the-art accuracys when matching viewed sketches.
Keystroke dynamics for user authentication

In this paper we investigate the problem of user authentication using keystroke biometrics. A new distance metric that is effective in dealing with the challenges intrinsic to keystroke dynamics data, i.e., scale variations, feature interactions and redundancies, and outliers is proposed. Our keystroke biometrics algorithms based on this new distance metric are evaluated on the CMU keystroke dynamics benchmark dataset and are shown to be superior to algorithms using traditional distance metrics.
On a taxonomy of facial features

After nearly a decade of intensive research in face recognition, no standard organization exists for grouping the salient information available in 2D face images into feature categories. At the same time, human verification of a subject's identity based on facial images lacks a consistent methodology. In this paper we propose a taxonomy of available facial features that: (i) serves as a precursor to studies on the individuality of facial features, (ii) follows a similar well established and accepted organization for fingerprint features, and (iii) contains features computable by both machines and humans as well as by machines alone. This manuscript is intended as a strawman of an organization of facial features, that would hopefully lead to a standardization of such features. Such a facial feature organization will (i) enable studies on the individuality of facial features, which has important ramifications for the acceptance of expert testimony in legal proceedings for determining the identity of an individual from a facial photograph, and (ii) help standardize the framework of commercial face recognition systems.
A Discriminative Model for Age Invariant Face Recognition

Aging variation poses a serious problem to automatic face recognition systems. Most of the face recognition studies that have addressed the aging problem are focused on age estimation or aging simulation. Designing an appropriate feature representation and an effective matching framework for age invariant face recognition remains an open problem. In this paper, we propose a discriminative model to address face matching in the presence of age variation. In this framework, we first represent each face by designing a densely sampled local feature description scheme, in which scale invariant feature transform (SIFT) and multi-scale local binary patterns (MLBP) serve as the local descriptors. By densely sampling the two kinds of local descriptors from the entire facial image, sufficient discriminatory information, including the distribution of the edge direction in the face image (that is expected to be age invariant) can be extracted for further analysis. Since both SIFT-based local features and MLBP-based local features span a high-dimensional feature space, to avoid the overfitting problem, we develop an algorithm, called multi-feature discriminant analysis (MFDA) to process these two local feature spaces in a unified framework. The MFDA is an extension and improvement of the LDA using multiple features combined with two different random sampling methods in feature and sample space. By random sampling the training set as well as the feature space, multiple LDA-based classifiers are constructed and then combined to generate a robust decision via a fusion rule. Experimental results show that our approach outperforms a state-of-the-art commercial face recognition engine on two public domain face aging data sets: MORPH and FG-NET. We also compare the performance of the proposed discriminative model with a generative aging model. A fusion of discriminative and generative models further improves the face matching accuracy in the presence of aging.
On the Scalability of Evidence Accumulation Clustering

This work focuses on the scalability of the Evidence Accumulation Clustering (EAC) method. We first address the space complexity of the co-association matrix. The sparseness of the matrix is related to the construction of the clustering ensemble. Using a split and merge strategy combined with a sparse matrix representation, we empirically show that a linear space complexity is achievable in this framework, leading to the scalability of EAC method to clustering large data-sets.
Large-scale evaluation of multimodal biometric authentication using state-of-the-art systems

We examine the performance of multimodal biometric authentication systems using state-of-the-art commercial off-the-shelf (COTS) fingerprint and face biometric systems on a population approaching 1,000 individuals. The majority of prior studies of multimodal biometrics have been limited to relatively low accuracy non-COTS systems and populations of a few hundred users. Our work is the first to demonstrate that multimodal fingerprint and face biometric systems can achieve significant accuracy gains over either biometric alone, even when using highly accurate COTS systems on a relatively large-scale population. In addition to examining well-known multimodal methods, we introduce new methods of normalization and fusion that further improve the accuracy.
An Intrinsic Dimensionality Estimator from Near-Neighbor Information

The intrinsic dimensionality of a set of patterns is important in determining an appropriate number of features for representing the data and whether a reasonable two- or three-dimensional representation of the data exists. We propose an intuitively appealing, noniterative estimator for intrinsic dimensionality which is based on nearneighbor information. We give plausible arguments supporting the consistency of this estimator. The method works well in identifying the true dimensionality for a variety of artificial data sets and is fairly insensitive to the number of samples and to the algorithmic parameters. Comparisons between this new method and the global eigenvalue approach demonstrate the utility of our estimator.
Face Recognition Performance under Aging

With the integration of face recognition technology into important identity applications, it is imperative that the effects of facial aging on face recognition performance are thoroughly understood. As face recognition systems evolve and improve, they should be periodically re-evaluated on large-scale longitudinal face datasets. In our study, we evaluate the performance of two state-of-the-art commercial off the shelf (COTS) face recognition systems on two large-scale longitudinal datasets of mugshots of repeat offenders. The largest of these two datasets has 147,784 images of 18,007 subjects with an average of 8 images per subject over an average time span of 8.5 years. We fit multilevel statistical models to genuine comparison scores (similarity between images of the same face) from the two COTS face matchers. This allows us to analyze the degradation in recognition performance due to elapsed time between a probe (query) and its enrollment (gallery) image. We account for face image quality to obtain a better estimate of trends due to aging, and analyze whether longitudinal trends in genuine scores differ by subject gender and race. Based on the results of our statistical model, we infer that the state-of-the-art COTS matchers can verify 99% of the subjects at a false accept rate (FAR) of 0.01% for up to 10.5 and 8.5 years of elapsed time. Beyond this time lapse of 8.5 years, there is a significant loss in face recognition accuracy. This study extends and confirms the findings of earlier longitudinal studies on face recognition.
The design and analysis of the clock distribution network for a 1.2 GHz Alpha microprocessor

Single-wire, synchronous clocking systems for increasingly large and complex microprocessors present major technical challenges: Die size increases whereas target clock skew and jitter typically remain a constant percentage of a decreasing cycle time. The clocking methodology of the present Alpha microprocessor handles such challenges by radically departing from a single chip-wide clock distribution, to better control clock skew, jitter and power dissipation. Four major clocks (one reference and three derived) are used to clock separate chip sections.
An introduction to biometric recognition

A wide variety of systems requires reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that the rendered services are accessed only by a legitimate user and no one else. Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones, and ATMs. In the absence of robust personal recognition schemes, these systems are vulnerable to the wiles of an impostor. Biometric recognition, or, simply, biometrics, refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics. By using biometrics, it is possible to confirm or establish an individual's identity based on "who she is", rather than by "what she possesses" (e.g., an ID card) or "what she remembers" (e.g., a password). We give a brief overview of the field of biometrics and summarize some of its advantages, disadvantages, strengths, limitations, and related privacy concerns.