Fuzzy Signature Research Articles

Private mutual authentications with fuzzy matching

A private mutual authentication, or unlinkable secret handshake, originally allows two members from the same organisations to anonymously authenticate each other. In this paper, a new privacy-preserving mutual authentication protocol is constructed to achieve fuzzy private matching. The proposed protocol supports more flexible threshold-based appropriate matching under the multiple-groups environment, which is not limited to authenticate between members from the same groups. Our new protocol is constructed from the fuzzy identity-based encryption scheme and constant-size group signature. Without using the random oracle, the new protocol is proved secure by assuming the intractability of the decisional bilinear Diffie-Hellman problems. Compared with previous works, our proposed protocol can adapt to many different applications.

A new approach to signature recognition using the fuzzy method

The paper presents a new fuzzy approach to off-line handwritten signature recognition. The solution is based on characteristic feature extraction. After finding signature's center of gravity a number of lines are drawn through it at different angles. Cross points of generated lines and signature sample, which are further grouped and sorted, are treated as the set of features. On the basis of such structures, obtained from a chosen number of learning samples, a fuzzy model is created, called the fuzzy signature. During a verification phase the level of conformity of an input sample and the fuzzy signature is calculated. The extension in feature extraction as well as proposed fuzzy model has never been employed before. It needs to be emphasized that information stored within the verification system cannot be used to recreate the original signatures collected at the enrolment phase. The fact is particularly valuable for large databases and systems where storage safety is crucial. The solution is very flexible and allows the user to extend an intuitive structure of fuzzy sets by employing dynamic features, making the approach an on-line method. The results obtained should be still improved, similarly to the case of other known biometric systems related to signature recognition. However, the presented technique can be easily utilized in applications where FAR coefficient should be very low and is more important than FRR ratio.

Decision Support System for Evaluating Existing Apartment Buildings Based on Fuzzy Signatures

In historical district at European cities it is a major problem how to take decision on renovating or replacing existing buildings. This problem is imminent in Budapest (Hungary) in many traditional districts such as the Ferencváros district where we selected a compound area for further examination. By financial aid for the renovation of these buildings which awarded by Municipal Assembly of this district in question there is much uncertainty and confusion concerning how to decide whether or not and how to reconstruct a building where new private owners apply for support. In this paper we propose a formal evaluation method based on fuzzy signature rule bases (the formal being a special case of L-fuzzy object). Using the available expert knowledge we propose a fuzzy signature model including relevance weights and weighted aggregations for each node and parent node, respectively, so that as a result a single membership value may be calculated for each building in question. Linguistic labels for decision (such as worthless, average, highly valuable, etc.) are generated from the values thus obtained. Such linguistic calculations might be of help for the Municipal Assembly awarding financial support. A complete example wit 26 buildings is presented.

Complex Structured Decision Making Model: A hierarchical frame work for complex structured data

We introduce a hierarchical framework we call Complex Structured Decision Making model for complexly structured knowledge representation in intelligent decision making. We show that our model extends non-hierarchical (flat) decision making models to hierarchical decision making models that are similar to comprehensible human decision making processes. Further, we make an argument that hierarchial representation of knowledge in a Complex Structured Decision Making Model simplifies the approximation of aggregation functions to easily adapt to the underline relation of the system. Additionally, using a real world complex structured data set, we show that hierarchically organized Fuzzy Integrals, e.g. Choquet Integral, and Sugeno Integral and Fuzzy Signatures outperform these non-hierarchical Fuzzy Integrals.

An Improved Concurrent Signature Scheme

Wang et al have designed a new fuzzy signature scheme based on the discrete logarithm. This scheme has proposed a signature protocol in which the signature can be fairly exchanged between signers. This paper has put forward an improved concurrent signature scheme, which can reduce the numbers of implementing positive protocols and negative protocols when exchange and open the signatures and improve the efficiency of the algorithm. At the same time, the correctness, the non-forgeability and the fairness of the signature can be increased.

An Improved Method for Protein Similarity Searching by Alignment of Fuzzy Energy Signatures

Describing protein structures in terms of their energy features can be a key to understand how proteins work and interact to each other in cellular reactions. This can be also a base to compare proteins and search protein similarities. In the paper, we present protein comparison by the alignment of protein energy signatures. In the alignment, components of energy signatures are represented as fuzzy numbers. This modification improves the decision making while establishing the alignment path and guarantees the approximate character of the method, at the same time. The effectiveness of the developed alignment algorithm is tested by incorporating it in the new FS-EAST method (Fuzzy Signatures – Energy Alignment Search Tool), which allows to seek structurally similar regions of proteins.

Fuzzy methods and eye gaze for cooperative robot communication

We introduce our fuzzy signature and pattern matching with possibility calculation based approach for modelling the communication between human-control robot and a pair of assistant robots for completing cooperative tasks in a simulated environment. We show that a sophisticated extension using computerised recognition of eye gaze with fuzzy modelling based interpretation of possible intentions effectively eliminates the need of any physical control from the human's side. The experiment results show a good improvement of time saved by the use of our eye gaze intention in the context.

퍼지 시그너쳐 집합을 이용한 마이크로어레이 데이터 검색

마이크로어레이 데이터는 수천가지 유전자의 발현정보를 포함할 수 있으며, 여기에서 의미있는 패턴을 추출하여 추가적인 분석을 위한 목적으로 활용되고 있다. 다수의 샘플 또는 실험에 대해서 마이크로어레이 데이터가 수집된 경우에 분석자가 관심을 갖는 유전자들이나 샘플들을 효과적으로 검색하는 것이 필요한 경우가 있다. 이 논문에서는 단순한 조건뿐만 아니라 복잡한 조건을 정의하여 원하는 특성을 만족하는 유전자나 샘플을 추출하는 방법으로 퍼지 시그너쳐 집합을 활용하는 방법을 제안한다. 퍼지 시그너쳐는 벡터값을 값을 갖는 퍼지 집합을 확장한 것으로, 벡터의 각 요소가 다시 벡터가 되는 것을 허용하는 재귀적인 구조이다. 퍼지 시그너쳐 집합은 단말 원소가 구간 [0,1] 사이에서 정의된 퍼지집합이라는 것을 제외하면 퍼지 시그너쳐와 같은 구조를 가진다. 이 논문에서는 각 내부 노드에 대해서 명시적으로 결합 연산자를 지정하도록 하고, 결합 연산을 위해 비교연산자를 사용할 수 있도록 확장한 퍼지 시그너쳐 집합을 소개한다. 또한 확장된 퍼지 시그너쳐 집합을 마이크로어레이 데이터 검색을 위해 사용하는 방법과 이를 사용한 예를 보인다. Microarray data sets could contain thousands of gene expression levels and have been considered as an important source from which meaningful patterns could be extracted for further analysis in biological studies. It is sometimes necessary to retrieve out specific genes or samples of analyst's interest in an effective way. This paper is concerned with a method to make use of fuzzy signature set in order to filter out genes or samples which satisfy complicated constraints as well as simple ones. Fuzzy signatures are an extension of vector valued fuzzy sets, in which elements of the vector are allowed to have a vector. Fuzzy signature sets are similar to fuzzy signatures except that their leaf elements are fuzzy sets defined on the interval [0,1]. This paper introduces an extension of fuzzy signature sets which specifies aggregation operators at each internal node and comparison operators for aggregation. It also shows how to use the extended fuzzy signature sets in microarray data retrieval and some examples of its usage.

Learning Weights from Observations for Hierarchical Fuzzy Signatures

Learning Weights from Observations for Hierarchical Fuzzy Signatures

Fuzzy Training in Supervised Image Classification

Abstract When classifying imagery of reality that seldom presents itself with hand boundaries but transitions and gradual interfaces between biophysical phenomena, remote sensing researchers have proposed a fuzzy partition matrix that is more representative of the real situation than the conventional hard classification. In achieving a complete fuzzy approach in supervised classification, this paper discusses the fuzzification of the training stage to improve the classification performance. “Fuzzy training” is proposed to cope with data uncertainty in the early stage of image classification in order to derive statistical parameters that more closely resemble reality. A fuzzy parameter estimator (FPE) was developed with a modified Bezdek's Fuzzy c-Means engine and empirically evaluated. Classification results based on the fuzzy spectral signatures were superior to results obtained using conventional training methods.