Estimation of linear trend onset in time series

We propose a method to detect the onset of linear trend in a time series and estimate the change point T from the profile of a linear trend test statistic, computed on consecutive overlapping time windows along the time series. We compare our method to two standard methods for trend change detection and evaluate them with Monte Carlo simulations for different time series lengths, autocorrelation strengths, trend slopes and distribution of residuals. The proposed method turns out to estimate T better for small and correlated time series. The methods were also applied to global temperature records suggesting different turning points.     

Hull-form optimization in calm and rough water

The paper presents a formal methodology for the hull form optimization in calm and rough water using wash waves and selected dynamic responses, respectively. Parametric hull form modeling is used to generate the variant hull forms with some of the form parameters modified, which are evaluated in the optimization scheme based on evolutionary strategies. Rankine-source panel method and strip theories are used for the hydrodynamic evaluation. The methodology is implemented in the optimization of a double-chine, planing hull form. Furthermore, a dual-stage optimization strategy is applied on a modern fast displacement ferry. The effect of the selected optimization parameters is presented and discussed.     

Lossless fitness inheritance in genetic algorithms for decision trees

When genetic algorithms are used to evolve decision trees, key tree quality parameters can be recursively computed and re-used across generations of partially similar decision trees. Simply storing instance indices at leaves is sufficient for fitness to be piecewise computed in a lossless fashion. We show the derivation of the (substantial) expected speedup on two bounding case problems and trace the attractive property of lossless fitness inheritance to the divide-and-conquer nature of decision trees. The theoretical results are supported by experimental evidence.     

Providing advanced remote medical treatment services through pervasive environments

Advances on sensor technology, wireless environments and data mining introduce new possibilities in the healthcare sector, realizing the anytime-anywhere access to medical information. Towards this direction, integration of packet-switched networks and sensor devices can be effective in deploying assistive environments, such as home monitoring for elderly or patients. In this paper we describe a policy-based architecture that utilizes wireless sensor devices, advanced network topologies and software agents to enable remote monitoring of patients and elderly people; through the aforementioned technologies we achieve continuous monitoring of a patient’s condition and we can proceed when necessary with proper actions. We also present a software framework and network architecture that realizes the provision of remote medical services, in compliance with the imposed security and privacy requirements. A proof of concept prototype is also deployed, along with an evaluation of the overall architecture’s performance.     

A computer-aided system for malignancy risk assessment of nodules in thyroid US images based on boundary features

In this paper, a novel computer-based approach is proposed for malignancy risk assessment of thyroid nodules in ultrasound images. The proposed approach is based on boundary features and is motivated by the correlation which has been addressed in medical literature between nodule boundary irregularity and malignancy risk. In addition, local echogenicity variance is utilized so as to incorporate information associated with local echogenicity distribution within nodule boundary neighborhood. Such information is valuable for the discrimination of high-risk nodules with blurred boundaries from medium-risk nodules with regular boundaries. Analysis of variance is performed, indicating that each boundary feature under study provides statistically significant information for the discrimination of thyroid nodules in ultrasound images, in terms of malignancy risk. k-nearest neighbor and support vector machine classifiers are employed for the classification tasks, utilizing feature vectors derived from all combinations of features under study. The classification results are evaluated with the use of the receiver operating characteristic. It is derived that the proposed approach is capable of discriminating between medium-risk and high-risk nodules, obtaining an area under curve, which reaches 0.95.     

A note on the exponential Poisson distribution: A nested EM algorithm

An EM type algorithm for estimating the parameters of the exponential Poisson distribution has recently been proposed. This algorithm uses a Newton Raphson approach at the M-step. An alternative approach, where the Newton Raphson step is replaced by another EM algorithm is described. This approach has simple closed form expressions, making use of a nested EM scheme where the M-step itself is solved with an EM algorithm. An advantage of this algorithm is that it provides estimates in the admissible range when the initial values are in the admissible range, and avoids overflow problems that may occur during Newton-Raphson iterations and, in certain cases, it can be faster. Some simulation evidence on the speed of the new approach compared to the one that uses Newton-Raphson is provided.     

The geometric element transformation method for mixed mesh smoothing

The geometric element transformation method (GETMe) is a geometry-based smoothing method for mixed and non-mixed meshes. It is based on a simple geometric transformation applicable to elements bounded by polygons with an arbitrary number of nodes. The transformation, if applied iteratively, leads to a regularization of the polygons. Global mesh smoothing is accomplished by averaging the new node positions obtained by local element transformations. Thereby, the choice of transformation parameters as well as averaging weights can be based on the element quality which leads to high quality results. In this paper, a concept of an enhanced transformation approach is presented and a proof for the regularizing effect of the transformation based on eigenpolygons is given. Numerical examples confirm that the GETMe approach leads to superior mesh quality if compared to other geometry-based methods. In terms of quality it can even compete with optimization-based techniques, despite being conceptually significantly simpler.     

SDriver: Location-specific signatures prevent SQL injection attacks

SQL injection attacks involve the construction of application input data that will result in the execution of malicious SQL statements. Many web applications are prone to SQL injection attacks. This paper proposes a novel methodology of preventing this kind of attacks by placing a secure database driver between the application and its underlying relational database management system. To detect an attack, the driver uses stripped-down SQL queries and stack traces to create SQL statement signatures that are then used to distinguish between injected and legitimate queries. The driver depends neither on the application nor on the RDBMS and can be easily retrofitted to any system. We have developed a tool, SDriver, that implements our technique and used it on several web applications with positive results.     

Utilizing bloom filters for detecting flooding attacks against SIP based services

Any application or service utilizing the Internet is exposed to both general Internet attacks and other specific ones. Most of the times the latter are exploiting a vulnerability or misconfiguration in the provided service and/or in the utilized protocol itself. Consequently, the employment of critical services, like Voice over IP (VoIP) services, over the Internet is vulnerable to such attacks and, on top of that, they offer a field for new attacks or variations of existing ones. Among the various threats–attacks that a service provider should consider are the flooding attacks, at the signaling level, which are very similar to those against TCP servers but have emerged at the application level of the Internet architecture. This paper examines flooding attacks against VoIP architectures that employ the Session Initiation Protocol (SIP) as their signaling protocol. The focus is on the design and implementation of the appropriate detection method. Specifically, a bloom filter based monitor is presented and a new metric, named session distance, is introduced in order to provide an effective protection scheme against flooding attacks. The proposed scheme is evaluated through experimental test bed architecture under different scenarios. The results of the evaluation demonstrate that the required time to detect such an attack is negligible and also that the number of false alarms is close to zero.     

On the efficiency of user identification: a system-based approach

In the Internet era, users’ fundamental privacy and anonymity rights have received significant research and regulatory attention. This is not only a result of the exponential growth of data that users generate when accomplishing their daily task by means of computing devices with advanced capabilities, but also because of inherent data properties that allow them to be linked with a real or soft identity. Service providers exploit these facts for user monitoring and identification, albeit impacting users’ anonymity, based mainly on personal identifiable information or on sensors that generate unique data to provide personalized services. In this paper, we report on the feasibility of user identification using general system features like memory, CPU and network data, as provided by the underlying operating system. We provide a general framework based on supervised machine learning algorithms both for distinguishing users and informing them about their anonymity exposure. We conduct a series of experiments to collect trial datasets for users’ engagement on a shared computing platform. We evaluate various well-known classifiers in terms of their effectiveness in distinguishing users, and we perform a sensitivity analysis of their configuration setup to discover optimal settings under diverse conditions. Furthermore, we examine the bounds of sampling data to eliminate the chances of user identification and thus promote anonymity. Overall results show that under certain configurations users’ anonymity can be preserved, while in other cases users’ identification can be inferred with high accuracy, without relying on personal identifiable information.