Kernel density estimation based on Ripley’s correction

In this paper, we investigate a technique inspired by Ripley’s circumference method to correct bias of density estimation of edges (or frontiers) of regions. The idea of the method was theoretical and difficult to implement. We provide a simple technique – based of properties of Gaussian kernels – to efficiently compute weights to correct border bias on frontiers of the region of interest, with an automatic selection of an optimal radius for the method. We illustrate the use of that technique to visualize hot spots of car accidents and campsite locations, as well as location of bike thefts.     

Global sensitivity analysis based on Gini’s mean difference

Global sensitivity analysis has been widely used to detect the relative contributions of input variables to the uncertainty of model output, and then more resources can be assigned to the important input variables to reduce the uncertainty of model output more efficiently. In this paper, a new kind of global sensitivity index based on Gini’s mean difference is proposed. The proposed sensitivity index is more robust than the variance-based first order sensitivity index for the cases with non-normal distributions. Through the decomposition of Gini’s mean difference, it shows that the proposed sensitivity index can be represented by the energy distance, which measures the difference between probability distributions. Therefore, the proposed sensitivity index also takes the probability distribution of model output into consideration. In order to estimate the proposed sensitivity index efficiently, an efficient Monte Carlo simulation method is also proposed, which avoids the nested sampling procedure. The test examples show that the proposed sensitivity index is more robust than the variance-based first order sensitivity index for the cases with non-normal distributions.     

Concurrent depth-first search algorithms based on Tarjan’s Algorithm

We present concurrent algorithms, based on depth-first search, for three problems relevant to model checking: given a state graph, to find its strongly connected components, which states are in loops, and which states are in “lassos”. Each algorithm is a variant of Tarjan’s Algorithm. Our algorithms typically exhibit a three- or four-fold speed-up over the corresponding sequential algorithms on an eight-core machine.     

Fuzzy approach for intrusion detection based on user’s commands

The article concerns the problem of detecting masqueraders in computer systems. A masquerader in a computer system is an intruder who pretends to be a legitimate user in order to gain access to protected resources. The article presents an intrusion detection method based on a fuzzy approach. Two types of user’s activity profiles are proposed along with the corresponding data structures. The solution analyzes the activity of the computer user in a relatively short period of time, building a user’s profile. The profile is based on the most recent activity of the user, therefore, it is named the local profile. Further analysis involves creating a more general structure based on a defined number of local profiles of one user, called the fuzzy profile. It represents a generalized behavior of the computer system user. The fuzzy profiles are used directly to detect abnormalities in users’ behavior, and thus possible intrusions. The proposed solution is prepared to be able to create user’s profiles based on any countable features derived from user’s actions in computer system (i.e., used commands, mouse and keyboard data, requested network resources). The presented method was tested using one of the commonly available standard intrusion data sets containing command names executed by users of a Unix system. Therefore, the obtained results can be compared with other approaches. The results of the experiments have shown that the method presented in this article is comparable with the best intrusion detection methods, tested with the same data set, in the matter of the obtained results. The proposed solution is characterized by a very low computational complexity, which has been confirmed by experimental results.     

Image betrayal checking based on organization’s watermarking in Internet

There is a growing need for confidential institutions to actively check the documents in Internet for detecting some leaked confidential images. This paper presents an efficient method of active image betrayal checking based on a new type of watermarking in addition to the user-specific fingerprint. The implemented organization’s watermarking, as a ‘fingerprint’ for a group of staffs, with a codeword length far shorter than that of the user-specific fingerprint, is extracted from a suspected image at the first stage of betrayal checking, for the purpose of reducing the number of suspected images by a great deal. Then, only the remaining suspected images are subjected to the time-consuming user-specific fingerprint checking. Experimental tests confirmed the improved efficiency of this method.     

Transformation of feature space based on Fisher’s linear discriminant

Linear transformation of data in multidimensional feature space based on Fisher’s criterion is considered. The case of two classes with arbitrary distributions is studied. We derived expressions for recurrent calculation of weight vectors which form new features. Example offered shows that the newly found features which represent the data more accurately make it possible to achieve linear separability of classes which remains impossible using the technique of principal components and the classic Fisher’s linear discriminant.     

Pose estimation from multiple cameras based on Sylvester’s equation

In this paper, we introduce a method to estimate the object’s pose from multiple cameras. We focus on direct estimation of the 3D object pose from 2D image sequences. Scale-Invariant Feature Transform (SIFT) is used to extract corresponding feature points from adjacent images in the video sequence. We first demonstrate that centralized pose estimation from the collection of corresponding feature points in the 2D images from all cameras can be obtained as a solution to a generalized Sylvester’s equation. We subsequently derive a distributed solution to pose estimation from multiple cameras and show that it is equivalent to the solution of the centralized pose estimation based on Sylvester’s equation. Specifically, we rely on collaboration among the multiple cameras to provide an iterative refinement of the independent solution to pose estimation obtained for each camera based on Sylvester’s equation. The proposed approach to pose estimation from multiple cameras relies on all of the information available from all cameras to obtain an estimate at each camera even when the image features are not visible to some of the cameras. The resulting pose estimation technique is therefore robust to occlusion and sensor errors from specific camera views. Moreover, the proposed approach does not require matching feature points among images from different camera views nor does it demand reconstruction of 3D points. Furthermore, the computational complexity of the proposed solution grows linearly with the number of cameras. Finally, computer simulation experiments demonstrate the accuracy and speed of our approach to pose estimation from multiple cameras.     

A novel corporate credit rating system based on Student’s-t hidden Markov models

Corporate credit rating systems have been an integral part of expert decision making of financial institutions for the last four decades. They are embedded into the pricing function determining the interest rate of a loan contact, and play crucial role in the credit approval process. However, the currently employed intelligent systems are based on assumptions that completely ignore two key characteristics of financial data, namely their heavy-tailed actual distributions, and their time-series nature. These unrealistic assumptions definitely undermine the performance of the resulting corporate credit rating systems used to inform expert decisions. To address these shortcomings, in this work we propose a novel corporate credit rating system based on Student’s-t hidden Markov models (SHMMs), which are a well-established method for modeling heavy-tailed time-series data: Under our approach, we use a properly selected set of financial ratios to perform credit scoring, which we model via SHMMs. We evaluate our method using a dataset pertaining to Greek corporations and SMEs; this dataset includes five-year financial data, and delinquency behavioral information. We perform extensive comparisons of the credit risk assessments obtained from our method with other models commonly used by financial institutions. As we show, our proposed system yields significantly more reliable predictions, offering a valuable new intelligent system to bank experts, to assist their decision making.     

Intelligent control using multiple models based on on-line learning

In this paper we deal with the problem of plants with large parameter variations under different operating modes. A novel intelligent control algorithm based on multiple models is proposed to improve the dynamical response performance. At the same time adaptive model bank is applied to establish models without prior system information. Multiple models and corresponding controllers are automatically established on-line by a conventionally adaptive model and a re-initialized one. A best controller is chosen by the performance function at every instant. The closed-loop system’s stability and asymptotical convergence of tracking error can be guaranteed. Simulation results have confirmed the validity of the proposed method.     

Term weighting for information retrieval based on term’s discrimination power

One of the most important research topics in Information Retrieval is term weighting for document ranking and retrieval, such as TFIDF, BM25, etc. We propose a term weighting method that utilizes past retrieval results consisting of the queries that contain a particular term, retrieval documents, and their relevance judgments. A term’s Discrimination Power(DP) is based on the difference degree of the term’s average weights obtained from between relevant and non-relevant retrieved document sets. The difference based DP performs better compared to ratio based DP introduced in the previous research. Our experimental result shows that a term weighting scheme based on the discrimination power method outperforms a TF*IDF based scheme.     

Small target detection based on bird’s visual information processing mechanism

Multimedia Tools and Applications - Detecting small targets in large fields of view is a challenging task. Nowadays, many targets detection models based on the convolutional neural network (CNN)...     

An improved low-cost yoking proof protocol based on Kazahaya’s flaws

Peris-Lopez et al. (J Netw Comput Appl 34:833–845, 2011) recently provided some guidelines that should be followed to design a secure yoking proof protocol. In addition, conforming to those guidelines and EPC C1-G2, they presented a yoking proof for medical systems based on low-cost RFID tags, named Kazahaya. In this paper, we compromise its security and show how a passive adversary can retrieve secret parameters of a patient’s tag in cost of \(O(2^{16})\) off-line PRNG evaluations. Nevertheless, to show other weaknesses of the protocol and rule out any possible improvement by increasing the length of the used PRNG, we present a forgery attack that proves that a generated proof at time \(t_n\) can be used to forge a valid proof for any desired time \(t_j\). The success probability of this attack is ‘1’ and the complexity is negligible. In addition, we present a new lightweight protocol based on 128-bit PRNG function to solve the problems of Kazahaya protocol. In terms of security, we evaluate the new protocol based on formal and informal methods and prove that the improved protocol is not vulnerable to RFID attacks.     

Classification of image distortion based on the generalized Benford’s law

Multimedia Tools and Applications - Distortion classification is an important step in blind image quality assessment. In this paper, a new image distortion classification algorithm is presented....     

Design of robust non-fragile H-infinity controller based on Delta operator theory

Considering that the controller parameters are of additive norm-bounded uncertainties when realized, a design method of robust non-fragile H-infinity controller for uncertain systems based on Delta operator theory is illustrated in this paper. A sufficient and necessary condition of the existence for the controller is given, which is presented in LMI forms. Finally, the designed method is used in the speed control system of a permanent magnet linear synchronous motor （PMLSM）. With the designed controller, the resulting speed closed-loop system is still stable and has the expected Hinfinity performance even if the sample period is reduced and the parameters of the controller and the controlled object are varied. The results show that the designed method is effective.     

COSA – A generic cognitive system architecture based on a cognitive model of human behavior

This article presents an overview of COSA, a cognitive system architecture, which is a generic framework proposing a unified architecture for cognitive systems. Conventional automation and similar systems lack the ability of cooperation and cognition, leading to serious deficiencies when acting in complex environments, especially in the context of human-computer interaction. Cognitive systems based on cognitive automation can overcome these deficiencies. Designing such artificial cognitive systems can be considered a very complex software development process. Although a number of developments of artificial cognitive systems have already demonstrated great functional potentials in field tests, the engineering approach of this kind of software is still a candidate for further improvement. Therefore, wide-spread application of cognitive systems has not been achieved yet. This article presents a new engineering approach for cognitive systems, implemented by the COSA framework, which may be a crucial step forward to achieve a wide-spread application of cognitive systems. The approach is based on a new concept of generating cognitive behaviour, the cognitive process (CP). The CP can be regarded as a model of the human information processing loop whose behaviour is solely driven by "a-priori knowledge". The main features of COSA are the implementation of the CP as its kernel and the separation of architecture from application leading to reduced development time and increased knowledge reuse. Additionally, separating the knowledge modelling process from behaviour generation enables the knowledge designer to use the knowledge representation that is best suited to his modelling problem. A first application based on COSA implements an autonomous unmanned air vehicle accomplishing a military reconnaissance mission. Some of the application experiences with the new approach are presented.     

Family verification based on similarity of individual family member’s facial segments

Humans process faces to recognize family resemblance and act accordingly. Undoubtedly, they are capable of recognizing their kin and family members. In this paper, we study the facts and valid assumptions of facial resemblance in family members’ facial segments. Our analysis and psychological studies show that the facial resemblance differs from member to member and depends on image segments. First, we estimate the degree of resemblance of each member’s image segment. Then, we propose a novel method to fuse similarity of each member’s facial image segments to perform family verification. Employing the proposed approach on the collected 5,400-sample family database achieves considerable improvement compared to the state-of-the-art fusion rule in three designated test scenarios. Experimental results also show that the proposed approach could estimate the similarity slightly more accurate than human perception. We believe the public availableness of the database may advance the development in this domain.     

Foundations of information technology based on Bunge’s systemist philosophy of reality

Software and Systems Modeling - General ontology is a prominent theoretical foundation for information technology analysis, design, and development. Ontology is a branch of philosophy which studies...     

The ELECTRE multicriteria analysis approach based on Atanassov’s intuitionistic fuzzy sets

In recent decades, intuitionistic fuzzy sets have been applied to many different fields; however, few current studies have used the ELECTRE method to solve multi-criteria decision-making problems with intuitionistic fuzzy information. The purpose of this paper is to develop a new method, the intuitionistic fuzzy ELECTRE method, for solving multi-criteria decision-making problems. Atanassov’s intuitionistic fuzzy set (A-IFS) characteristics are simultaneously concerned with the degree of membership, degree of non-membership, and intuitionistic index, and people can use A-IFS to describe uncertain situations in decision-making problems. We use the proposed method to rank all alternatives and determine the best alternative. The proposed method can also use imperfect or insufficient knowledge of data to deal with decision-making problems. Finally, two practical examples are given that illustrate the procedure of the proposed method.     

Flexible asynchronous simulation of iterated prisoner’s dilemma based on actor model

The wide range of applications of the Iterated prisoner’s dilemma (IPD) game made it a popular subject of study for the research community. As a consequence, numerous experiments have been conducted by researchers along the last decades. However, topics related with scaling simulation leveraging existing HPC infrastructure in the field of IPD did not always play a relevant role in such experimental work. The main contribution of this paper is a new simulation framework, based on asynchronous communication and its implementation oriented to distributed environments. Such framework is based on the modern Akka actor platform, that supports concurrent, distributed and resilient message-driven simulations; which are exemplified over the IPD game as a case study. We also present several interesting results regarding the introduction of asynchrony into the IPD simulation in order to obtain an efficient framework, so the whole simulation becomes scalable when using HPC facilities. The influence of asynchrony on the algorithm itself is also discussed, and the results show that it does not hamper the simulation.     

Early diagnosis model of Alzheimer’s Disease based on sparse logistic regression

Multimedia Tools and Applications - Accurate classification of Alzheimer’s Disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI) are critical for the effective treatment...