C-AMTE: A location mechanism for flexible cache management in chip multiprocessors

This paper describes Constrained Associative-Mapping-of-Tracking-Entries (C-AMTE), a scalable mechanism to facilitate flexible and efficient distributed cache management in large-scale chip multiprocessors (CMPs). C-AMTE enables fast locating of cache blocks in CMP cache schemes that employ one-to-one or one-to-many associative mappings. C-AMTE stores in per-core data structures tracking entries to avoid on-chip interconnect traffic outburst or long distance directory lookups. Simulation results using a full system simulator demonstrate that C-AMTE achieves improvement in cache access latency by up to 34.4%, close to that of a perfect location strategy.     

He’s homotopy perturbation method: A strongly promising method for solving non-linear systems of the mixed Volterra–Fredholm integral equations

In this paper, He’s homotopy perturbation method is applied to solve non-linear systems of mixed Volterra–Fredholm integral equations. Two examples are presented to illustrate the ability of the method. Also comparisons are made between the Adomian decomposition method and the homotopy perturbation method. The results reveal that He’s homotopy perturbation method is very effective and simple and in these examples leads to the exact solutions.     

High sensitive optode for selective determination of Ni based on the covalently immobilized thionine in agarose membrane

A novel Ni²⁺ optode was prepared by covalent immobilization of thionine, 3,7-diamine-5-phenothiazoniom thionineacetate, in a transparent agarose membrane. Influences of various experimental parameters on Ni²⁺ sensing, including the reaction time, the solution pH and the concentration of reagents were investigated. Under the optimized conditions, a linear response was obtained for Ni²⁺ concentrations ranging from 1.00 × 10⁻¹⁰ to 1.00 × 10⁻⁷ mol l⁻¹ with an R² value of 0.9985. The detection limit (3σ) of the method for Ni²⁺ was 9.30 × 10⁻¹¹ mol l⁻¹. The influence of several potentially interfering ions such as Ag⁺, Hg²⁺, Cd²⁺, Zn²⁺, Pb²⁺, Cu²⁺, Mn²⁺, Co³⁺, Cr³⁺, Al³⁺ and Fe³⁺ on the determination of Ni²⁺ was studied and no significant interference was observed. The membrane showed a good durability and short response time with no evidence of reagent leaching. The membrane was successfully applied for the determination of Ni²⁺ in environmental water samples.     

A note on the stability of fractional order systems

In this paper, a new approach is suggested to investigate stability in a family of fractional order linear time invariant systems with order between 1 and 2. The proposed method relies on finding a linear ordinary system that possesses the same stability property as the fractional order system. In this way, instead of performing the stability analysis on the fractional order systems, the analysis is converted into the domain of ordinary systems which is well established and well understood. As a useful consequence, we have extended two general tests for robust stability check of ordinary systems to fractional order systems.     

Face recognition based on 3D ridge images obtained from range data

In this paper, we present an approach for 3D face recognition from frontal range data based on the ridge lines on the surface of the face. We use the principal curvature, k_max, to represent the face image as a 3D binary image called ridge image. The ridge image shows the locations of the ridge points around the important facial regions on the face (i.e., the eyes, the nose, and the mouth). We utilized the robust Hausdorff distance and the iterative closest points (ICP) for matching the ridge image of a given probe image to the ridge images of the facial images in the gallery. To evaluate the performance of our approach for 3D face recognition, we performed experiments on GavabDB face database (a small size database) and Face Recognition Grand Challenge V2.0 (a large size database). The results of the experiments show that the ridge lines have great capability for 3D face recognition. In addition, we found that as long as the size of the database is small, the performance of the ICP-based matching and the robust Hausdorff matching are comparable. But, when the size of the database increases, ICP-based matching outperforms the robust Hausdorff matching technique.     

Active Grid Information Server for grid computing

We describe the Active Grid Information Server for performing expressive resource discovery searches and resource management in a grid environment. We represent server meta-data, such as their CPU power, storage capacity and bandwidth as points in a multi-dimensional space and then express queries as predicates over these points. The design of the system is driven by its application as a part of the information infrastructure for computational grids. Such grids provide an infrastructure for sharing computing resources; an information infrastructure is their inherent part which collects resource data and provides search functionality. Our approach complements current solutions such as MDS by adding Event Condition Action rules and an ability to efficiently handle dynamic attributes. An Event Condition Action rule-based system can support ad hoc, adaptive, flexible, and dynamic schedulers that are modifiable at runtime. We evaluate our Active Grid Information Server by evaluating Event Condition Action rules for termination, confluence, and conflict.     

Exploiting an antivirus interface

We propose a technique for defeating signature-based malware detectors by exploiting information disclosed by antivirus interfaces. This information is leveraged to reverse engineer relevant details of the detector's underlying signature database, revealing binary obfuscations that suffice to conceal malware from the detector. Experiments with real malware and antivirus interfaces on Windows operating systems justify the effectiveness of our approach.     

A hybrid recommendation technique based on product category attributes

Recommender systems are powerful tools that allow companies to present personalized offers to their customers and defined as a system which recommends an appropriate product or service after learning the customers’ preferences and desires. Extracting users’ preferences through their buying behavior and history of purchased products is the most important element of such systems. Due to users’ unlimited and unpredictable desires, identifying their preferences is very complicated process. In most researches, less attention has been paid to user’s preferences varieties in different product categories. This may decrease quality of recommended items. In this paper, we introduced a technique of recommendation in the context of online retail store which extracts user preferences in each product category separately and provides more personalized recommendations through employing product taxonomy, attributes of product categories, web usage mining and combination of two well-known filtering methods: collaborative and content-based filtering. Experimental results show that proposed technique improves quality, as compared to similar approaches.     

A novel ACO–GA hybrid algorithm for feature selection in protein function prediction

Protein function prediction is an important problem in functional genomics. Typically, protein sequences are represented by feature vectors. A major problem of protein datasets that increase the complexity of classification models is their large number of features. Feature selection (FS) techniques are used to deal with this high dimensional space of features. In this paper, we propose a novel feature selection algorithm that combines genetic algorithms (GA) and ant colony optimization (ACO) for faster and better search capability. The hybrid algorithm makes use of advantages of both ACO and GA methods. Proposed algorithm is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. The performance of proposed algorithm is compared to the performance of two prominent population-based algorithms, ACO and genetic algorithms. Experimentation is carried out using two challenging biological datasets, involving the hierarchical functional classification of GPCRs and enzymes. The criteria used for comparison are maximizing predictive accuracy, and finding the smallest subset of features. The results of experiments indicate the superiority of proposed algorithm.