Ahmet Burak Can

SORT: A Self-ORganizing Trust Model for Peer-to-Peer Systems

Open nature of peer-to-peer systems exposes them to malicious activity. Building trust relationships among peers can mitigate attacks of malicious peers. This paper presents distributed algorithms that enable a peer to reason about trustworthiness of other peers based on past interactions and recommendations. Peers create their own trust network in their proximity by using local information available and do not try to learn global trust information. Two contexts of trust, service, and recommendation contexts, are defined to measure trustworthiness in providing services and giving recommendations. Interactions and recommendations are evaluated based on importance, recentness, and peer satisfaction parameters. Additionally, recommenders trustworthiness and confidence about a recommendation are considered while evaluating recommendations. Simulation experiments on a file sharing application show that the proposed model can mitigate attacks on 16 different malicious behavior models. In the experiments, good peers were able to form trust relationships in their proximity and isolate malicious peers.

GenTrust: A genetic trust management model for peer-to-peer systems

Abstract In recent years, peer-to-peer systems have attracted significant interest by offering diverse and easily accessible sharing environments to users. However, this flexibility of P2P systems introduces security vulnerabilities. Peers often interact with unknown or unfamiliar peers and become vulnerable to a wide variety of attacks. Therefore, having a robust trust management model is critical for such open environments in order to exclude unreliable peers from the system. In this study, a new trust model for peer-to-peer networks called GenTrust is proposed. GenTrust has evolved by using genetic programming. In this model, a peer calculates the trustworthiness of another peer based on the features extracted from past interactions and the recommendations. Since the proposed model does not rely on any central authority or global trust values, it suits the decentralized nature of P2P networks. Moreover, the experimental results show that the model is very effective against various attackers, namely individual, collaborative, and pseudospoofing attackers. An analysis on features is also carried out in order to explore their effects on the results. This is the first study which investigates the use of genetic programming on trust management.

A Plant Recognition Approach Using Shape and Color Features in Leaf Images

Recognizing plants is a vital problem especially for biologists, chemists, and environmentalists. Plant recognition can be performed by human experts manually but it is a time consuming and low-efficiency process. Automation of plant recognition is an important process for the fields working with plants. This paper presents an approach for plant recognition using leaf images. Shape and color features extracted from leaf images are used with k-Nearest Neighbor, Support Vector Machines, Naive Bayes, and Random Forest classification algorithms to recognize plant types. The presented approach is tested on 1897 leaf images and 32 kinds of leaves. The results demonstrated that success rate of plant recognition can be improved up to 96% with Random Forest method when both shape and color features are used.

A weighted rule based method for predicting malignancy of pulmonary nodules by nodule characteristics

Predicting malignancy of solitary pulmonary nodules from computer tomography scans is a difficult and important problem in the diagnosis of lung cancer. This paper investigates the contribution of nodule characteristics in the prediction of malignancy. Using data from Lung Image Database Consortium (LIDC) database, we propose a weighted rule based classification approach for predicting malignancy of pulmonary nodules. LIDC database contains CT scans of nodules and information about nodule characteristics evaluated by multiple annotators. In the first step of our method, votes for nodule characteristics are obtained from ensemble classifiers by using image features. In the second step, votes and rules obtained from radiologist evaluations are used by a weighted rule based method to predict malignancy. The rule based method is constructed by using radiologist evaluations on previous cases. Correlations between malignancy and other nodule characteristics and agreement ratio of radiologists are considered in rule evaluation. To handle the unbalanced nature of LIDC, ensemble classifiers and data balancing methods are used. The proposed approach is compared with the classification methods trained on image features. Classification accuracy, specificity and sensitivity of classifiers are measured. The experimental results show that using nodule characteristics for malignancy prediction can improve classification results.

A computer program (TSecSoft) to determine mineral percentages using photographs obtained from thin sections

The geomechanical behavior of rocks is controlled mainly by their mineral content and texture. For this reason, determining the mineral content of rocks is highly important for their genetic classification and understanding their geomechanical behavior. Conventionally, the mineral content of rocks has been determined by point counting on thin sections. However, this process is exhaustive and time-consuming. This study presents a computer program, TSecSoft, that determines the mineral content of rocks. TSecSoft is developed with MATLAB10a, and the MATLAB scripts (m-files) are converted to a standalone application using MATLAB Deployment Toolbox. After automatically obtaining an initial segmentation, the user can correct segments to produce perfect segmentation. When correcting segments, the user can merge segments or divide one segment into several using the TSecSoft pen function. To assess the TSecSofts performance, point counting and TSecSoft are used on six different thin sections prepared from granitic rock specimens, and their results are compared. The correlation coefficients of the mineral percentage values obtained from point counting and TSecSoft are considerably high. All results indicate that a useful and time-saving tool has been produced for determining the mineral percentages of rocks.

RECOGNITION OF BASIC HUMAN ACTIONS USING DEPTH INFORMATION

Human action recognition using depth sensors is an emerging technology especially in game console industry. Depth information can provide robust features about 3D environments and increase accuracy of action recognition in short ranges. This paper presents an approach to recognize basic human actions using depth information obtained from the Kinect sensor. To recognize actions, features extracted from angle and displacement information of joints are used. Actions are classified using support vector machines and random forest (RF) algorithm. The model is tested on HUN-3D, MSRC-12, and MSR Action 3D datasets with various testing approaches and obtained promising results especially with the RF algorithm. The proposed approach produces robust results independent from the dataset with simple and computationally cheap features.

A Multimodal Approach for Recognizing Human Actions Using Depth Information

Human action recognition using depth information is a trending technology especially in human computer interaction. Depth information may provide more robust features to increase accuracy of action recognition. This paper presents an approach to recognize basic human actions using the depth information from RGB-D sensors. Features obtained from a trained skeletal model and raw depth data are studied. Angle and displacement features derived from the skeletal model were the most useful in classification. However, HOG descriptors of gradient and depth history images derived from depth data also improved classification performance when used with skeletal model features. Actions are classified with the random forest algorithm. The model is tested on MSR Action 3D dataset and compared with some of the recent methods in literature. According to the experiments, the proposed model produces promising results.

Evolving a Trust Model for Peer-to-Peer Networks Using Genetic Programming

Peer-to-peer (P2P) systems have attracted significant interest in recent years. In P2P networks, each peer act as both a server or a client. This characteristic makes peers vulnerable to a wide variety of attacks. Having robust trust management is very critical for such open environments to exclude unreliable peers from the system. This paper investigates the use of genetic programming to asses the trustworthiness of peers without a central authority. A trust management model is proposed in which each peer ranks other peers according to local trust values calculated automatically based on the past interactions and recommendations. The experimental results have shown that the model could successfully identify malicious peers without using a central authority or global trust values and, improve the system performance.

Action recognition with skeletal volume and deep learning

The use of depth sensors in activity recognition is a technology that emerges in human computer interaction and motion recognition. In this study, an approach to identify single-person activities using deep learning on depth image sequences is presented. First, a 3D volumetric template is generated using skeletal information obtained from a depth video. The generated 3D volume is used for extracting features by taking images from different angles at different volumes. Actions are recognized by extracting deep features using AlexNet model [1] and Histogram of Oriented Gradients (HOG) features from these images. The proposed method has been tested with MSRAction3D [2] and UTHKinect-Action3D [2] datasets. The obtained results were comparable to similar studies in the literature.

A Comparison of Feature Detectors and Descriptors in RGB-D SLAM Methods

In RGB-D based SLAM methods, robot motion is generally computed by detecting and matching feature points in image frames obtained from an RGB-D sensor. Thus, feature detectors and descriptors used in a SLAM method significantly affect the performance. In this work, impacts of feature detectors and descriptors on the performance of an RGB-D based SLAM method are studied. SIFT, SURF, BRISK, ORB, FAST, GFTT, STAR feature detectors and SIFT, SURF, BRISK, ORB, BRIEF, FREAK feature descriptors are evaluated in terms of accuracy and speed.