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Intelligent Biometric Information Management

Intelligent Information Management 2010年第9期2卷 499-511页

作者： harry wechsler 不详

We advance here a novel methodology for robust intelligent biometric information management with inferences and predictions made using randomness and complexity concepts. Intelligence refers to learning, adap- tation, and functionality, and robustness refers to the ability to handle incomplete and/or corrupt adversarial information, on one side, and image and or device variability, on the other side. The proposed methodology is model-free and non-parametric. It draws support from discriminative methods using likelihood ratios to link at the conceptual level biometrics and forensics. It further links, at the modeling and implementation level, the Bayesian framework, statistical learning theory (SLT) using transduction and semi-supervised lea- rning, and Information Theory (IY) using mutual information. The key concepts supporting the proposed methodology are a) local estimation to facilitate learning and prediction using both labeled and unlabeled data;b) similarity metrics using regularity of patterns, randomness deficiency, and Kolmogorov complexity (similar to MDL) using strangeness/typicality and ranking p-values;and c) the Cover – Hart theorem on the asymptotical performance of k-nearest neighbors approaching the optimal Bayes error. Several topics on biometric inference and prediction related to 1) multi-level and multi-layer data fusion including quality and multi-modal biometrics;2) score normalization and revision theory;3) face selection and tracking;and 4) identity management, are described here using an integrated approach that includes transduction and boosting for ranking and sequential fusion/aggregation, respectively, on one side, and active learning and change/ outlier/intrusion detection realized using information gain and martingale, respectively, on the other side. The methodology proposed can be mapped to additional types of information beyond biometrics.

关键词： Authentication Biometrics Boosting Change Detection Complexity Cross-Matching Data Fusion Ensemble Methods Forensics Identity Management Imposters Inference Intelligent Information Management Margin gain MDL Multi-Sensory Integration Outlier Detection P-Values Quality Randomness Ranking Score Normalization Semi-Supervised Learning Spectral Clustering Strangeness Surveillance Tracking Typicality Transduction

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Cyberspace Security Using Adversarial Learning and Conformal Prediction

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Intelligent Information Management 2015年第4期7卷 195-222页

作者： harry wechsler Department of Computer Science George Mason University Fairfax VA USA

This paper advances new directions for cyber security using adversarial learning and conformal prediction in order to enhance network and computing services defenses against adaptive, malicious, persistent, and tactical offensive threats. Conformal prediction is the principled and unified adaptive and learning framework used to design, develop, and deploy a multi-faceted?self-managing defensive shield to detect, disrupt, and deny intrusive attacks, hostile and malicious behavior, and subterfuge. Conformal prediction leverages apparent relationships between immunity and intrusion detection using non-conformity measures characteristic of affinity, a typicality, and surprise, to recognize patterns and messages as friend or foe and to respond to them accordingly. The solutions proffered throughout are built around active learning, meta-reasoning, randomness, distributed semantics and stratification, and most important and above all around adaptive Oracles. The motivation for using conformal prediction and its immediate off-spring, those of semi-supervised learning and transduction, comes from them first and foremost supporting discriminative and non-parametric methods characteristic of principled demarcation using cohorts and sensitivity analysis to hedge on the prediction outcomes including negative selection, on one side, and providing credibility and confidence indices that assist meta-reasoning and information fusion.

关键词： Active Learning Adversarial Learning Anomaly Detection Change Detection Conformal Prediction Cyber Security Data Mining Denial and Deception Human Factors Insider Threats Intrusion Detection Meta-Reasoning Moving Target Defense Performance Evaluation Randomness Semi-Supervised Learning Sequence Analysis Statistical Learning Transduction

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Face Recognition across Time Lapse Using Convolutional Neural Networks

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Journal of Information Security 2016年第3期7卷 141-151页

作者： Hachim El Khiyari harry wechsler Department of Computer Science George Mason University Fairfax VA USA

Time lapse, characteristic of aging, is a complex process that affects the reliability and security of biometric face recognition systems. This paper reports the novel use and effectiveness of deep learning, in general, and convolutional neural networks (CNN), in particular, for automatic rather than hand-crafted feature extraction for robust face recognition across time lapse. A CNN architecture using the VGG-Face deep (neural network) learning is found to produce highly discriminative and interoperable features that are robust to aging variations even across a mix of biometric datasets. The features extracted show high inter-class and low intra-class variability leading to low generalization errors on aging datasets using ensembles of subspace discriminant classifiers. The classification results for the all-encompassing authentication methods proposed on the challenging FG-NET and MORPH datasets are competitive with state-of-the-art methods including commercial face recognition engines and are richer in functionality and interoperability than existing methods as it handles mixed biometric datasets, e.g., FG-NET and MORPH.

关键词： Aging Authentication Biometrics Convolutional Neural Networks (CNN) Deep Learning Ensemble Methods Face Recognition Interoperability Security

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Intelligent Evidence-Based Management for Data Collection and Decision-Making Using Algorithmic Randomness and Active Learning

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Intelligent Information Management 2011年第4期3卷 142-159页

作者： harry wechsler Shen-Shyang Ho 不详

We describe here a comprehensive framework for intelligent information management (IIM) of data collection and decision-making actions for reliable and robust event processing and recognition. This is driven by algorithmic information theory (AIT), in general, and algorithmic randomness and Kolmogorov complexity (KC), in particular. The processing and recognition tasks addressed include data discrimination and multilayer open set data categorization, change detection, data aggregation, clustering and data segmentation, data selection and link analysis, data cleaning and data revision, and prediction and identification of critical states. The unifying theme throughout the paper is that of “compression entails comprehension”, which is realized using the interrelated concepts of randomness vs. regularity and Kolmogorov complexity. The constructive and all encompassing active learning (AL) methodology, which mediates and supports the above theme, is context-driven and takes advantage of statistical learning, in general, and semi-supervised learning and transduction, in particular. Active learning employs explore and exploit actions characteristic of closed-loop control for evidence accumulation in order to revise its prediction models and to reduce uncertainty. The set-based similarity scores, driven by algorithmic randomness and Kolmogorov complexity, employ strangeness / typicality and p-values. We propose the application of the IIM framework to critical states prediction for complex physical systems;in particular, the prediction of cyclone genesis and intensification.

关键词： Active Learning Algorithmic Information Theory Algorithmic Randomness Evidence-Based Management Kolmogorov Complexity P-Values Transduction Critical States Prediction

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Age Invariant Face Recognition Using Convolutional Neural Networks and Set Distances

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Journal of Information Security 2017年第3期8卷 174-185页

作者： Hachim El Khiyari harry wechsler Department of Computer Science George Mason University Fairfax VA USA

Biometric security systems based on facial characteristics face a challenging task due to variability in the intrapersonal facial appearance of subjects traced to factors such as pose, illumination, expression and aging. This paper innovates as it proposes a deep learning and set-based approach to face recognition subject to aging. The images for each subject taken at various times are treated as a single set, which is then compared to sets of images belonging to other subjects. Facial features are extracted using a convolutional neural network characteristic of deep learning. Our experimental results show that set-based recognition performs better than the singleton-based approach for both face identification and face verification. We also find that by using set-based recognition, it is easier to recognize older subjects from younger ones rather than younger subjects from older ones.

关键词： Aging Biometrics Convolutional Neural Networks (CNN) Deep Learning Image Set-Based Face Recognition (ISFR) Transfer Learning

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Real-Time Timing Channel Detection in a Software-Defined Networking Virtual Environment

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Intelligent Information Management 2015年第6期7卷 283-302页

作者： Anyi Liu Jim X. Chen harry wechsler Department of Computer Science Indiana University—Purdue University Fort Wayne Fort Wayne USA Department of Computer Science George Mason University Fairfax USA

Despite extensive research, timing channels (TCs) are still known as a principal category of threats that aim to leak and transmit information by perturbing the timing or ordering of events. Existing TC detection approaches use either signature-based approaches to detect known TCs or anomaly-based approach by modeling the legitimate network traffic in order to detect unknown TCs. Un-fortunately, in a software-defined networking (SDN) environment, most existing TC detection approaches would fail due to factors such as volatile network traffic, imprecise timekeeping mechanisms, and dynamic network topology. Furthermore, stealthy TCs can be designed to mimic the legitimate traffic pattern and thus evade anomalous TC detection. In this paper, we overcome the above challenges by presenting a novel framework that harnesses the advantages of elastic re-sources in the cloud. In particular, our framework dynamically configures SDN to enable/disable differential analysis against outbound network flows of different virtual machines (VMs). Our framework is tightly coupled with a new metric that first decomposes the timing data of network flows into a number of using the discrete wavelet-based multi-resolution transform (DWMT). It then applies the Kullback-Leibler divergence (KLD) to measure the variance among flow pairs. The appealing feature of our approach is that, compared with the existing anomaly detection approaches, it can detect most existing and some new stealthy TCs without legitimate traffic for modeling, even with the presence of noise and imprecise timekeeping mechanism in an SDN virtual environment. We implement our framework as a prototype system, OBSERVER, which can be dynamically deployed in an SDN environment. Empirical evaluation shows that our approach can efficiently detect TCs with a higher detection rate, lower latency, and negligible performance overhead compared to existing approaches.

关键词： Covert Channel Timing Channel Intrusion Detection Virtualization Software-Defined Network

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PATTERN RECOGNITION USING ADAPTIVE GABOR PYRAMIDS

PATTERN RECOGNITION USING ADAPTIVE GABOR PYRAMIDS

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International Conference on Signal Processing(ICSP’93)

作者： harry wechsler Victor Chen Department of Computer Science George Mason University

＜正＞This paper describes an integrated methodology for the development and implementation of pattern recognition systems using Gabor pyramids,distributed associative memories（DAMs）,and active and selective percept...

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Bile acids,gut microbiota,and therapeutic insights in hepatocellular carcinoma

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Cancer Biology & Medicine 2024年第2期21卷 144-162页

作者： Yang Song harry CH Lau Xiang Zhang Jun Yu Institute of Digestive Disease and Department of Medicine and Therapeutics State Key Laboratory of Digestive DiseaseLi Ka Shing Institute of Health SciencesCUHK Shenzhen Research InstituteThe Chinese University of Hong KongHong KongChina Department of Gastroenterology Zhongshan Hospital Xiamen UniversityXiamen 361004China

Hepatocellular carcinoma(HCC)is a prevalent and aggressive liver malignancy.The interplay between bile acids(BAs)and the gut microbiota has emerged as a critical factor in HCC development and progression.Under normal conditions,BA metabolism is tightly regulated through a bidirectional interplay between gut microorganisms and BAs.The gut microbiota plays a critical role in BA metabolism,and BAs are endogenous signaling molecules that help maintain liver and intestinal homeostasis.Of note,dysbiotic changes in the gut microbiota during pathogenesis and cancer development can disrupt BA homeostasis,thereby leading to liver inflammation and fibrosis,and ultimately contributing to HCC development.Therefore,understanding the intricate interplay between BAs and the gut microbiota is crucial for elucidating the mechanisms underlying hepatocarcinogenesis.In this review,we comprehensively explore the roles and functions of BA metabolism,with a focus on the interactions between BAs and gut microorganisms in HCC.Additionally,therapeutic strategies targeting BA metabolism and the gut microbiota are discussed,including the use of BA agonists/antagonists,probiotic/prebiotic and dietary interventions,fecal microbiota transplantation,and engineered bacteria.In summary,understanding the complex BA-microbiota crosstalk can provide valuable insights into HCC development and facilitate the development of innovative therapeutic approaches for liver malignancy.

关键词： Bile acid gut microbiota hepatocellular carcinoma therapeutics microbiota modulation

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Polar Models of Culture-Regressivism Versus Progressivism

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Cultural and Religious Studies 2024年第4期12卷 193-209页

作者： harry W.Adams Prairie View A&M University TXUSA

The culture wars that simmer within any nation may have been escalating recently;but regardless of their national settings or milieus,many of these wars are informed by two opposing paradigms of culture.This paper analyzes two of these leading paradigms,designated here as Regressivism and Progressivism.Other theorists have long chronicled the differing strengths and weaknesses of these paradigms,taking the former as expressing more conservative,traditional,and nationalistic values,and the latter as expressing more liberal,pluralistic,and cosmopolitan values.Going beyond these perennial distinctions,I argue that Progressivism is more benign and beneficial than the former-by meeting basic human needs better and supporting more effective adaptation to changing exigencies.I also argue that Progressivism does not express merely subjective or relativistic preferences and values,but objectively preferable and quantifiable ones,that benefit not only our personal lives more but also our global village and communal lives more.

关键词： cultural pluralism conservatism nationalism cosmopolitanism communitarianism

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Corrosion Test of the Steel Plate in a WJ-8 Fastener for High Speed Rail

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Journal of Transportation Technologies 2024年第1期14卷 16-30页

作者： Zhiyong Wang Zhiping Zeng Hualiang (harry) Teng Department of Mechanical Engineering University of Nevada Las Vegas USA School of Civil Engineering Central South University Changsha China Department of Civil and Environmental Engineering and Construction University of Nevada Las Vegas USA

It was found that the steel plate in the composite plate in the WJ-8 fastener used in high speed rail is rusty. The objective of this study is to test the zinc coating of the steel plate. A literature review was conducted to identify the zinc coating techniques, and the companies that can provide different coating service was identified. A salt fog chamber was built that was in compliance with the ANSI B117 code, and the steel plates that were coated by the identified companies were tested using the salt fog chamber. The results indicated that the coating technique that had the best performance in preventing corrosion was the Greenkote plates with passivation. The galvanized option had the roughest coating layer, and it was the most reactive in the salt water solution. This makes it non-ideal for the dynamic rail environment because the increased friction of the plate could damage the supports, especially during extreme temperatures that would cause the rail to expand or contract. Greenkote with Phosphate and ArmorGalv also provided increased corrosion prevention with a smooth, strong finish, but it had more rust on the surface area than the Greenkote with ELU passivation. The ArmorGalv sample had more rust on the surface area than the Greenkote samples. This may not be a weakness in the ArmorGalv process;rather, it likely was the result of this particular sample not having the added protection of a colored coating.

关键词： Steel Plate for High Speed Rail Fastening Steel Corrosion Zinc Coating Salt-Fog Chamber

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