انجمن کامپیوتر ایران

Web access prediction has attracted significant attention in recent years. Web prefetching and some personalization systems use prediction algorithms. Most current applications that predict the next user web page have an offline component that does the data preparation task and an online section that provides personalized content to the users based on their current navigational activities. In this paper we present an online prediction model that does not have an offline component and fit in the memory with good prediction accuracy. Our algorithm is based on LZ78 and LZW algorithms that are adapted for modeling the user navigation in web. Our model decreases computational complexities which is a serious problem in developing online prediction systems. A performance evaluation is presented using real web logs. This evaluation shows that our model needs much less memory than PPM family of algorithms with good prediction accuracy.

In this paper, we address the Heart Sound signal modeling problem. The approach taken is based on sparse and redundant representations on an overcomplete dictionary. We apply matching pursuit (MP) and orthogonal matching pursuit (OMP) on two sets of normal and pathological phonocardiograms (PCGs). The dictionary includes classical Gabor wavelets or time-frequency atoms which are the product of a sinusoid and a Gaussian window function. The normalized root-mean-square error (NRMSE) was computed between the original and the reconstructed signals. The results show that the OMP method is very suitable to the transient and complex properties of the PCG’s, as it yielded excellent NRMSE’s around 1.61% for normal sounds and 5.19% for pathological murmurs.

Keystroke dynamics-based authentication, KDA, verifies users via their typing patterns. To authenticate users based on their typing samples, it is required to find out the resemblance of a typing sample and the training samples of a user regardless of the text typed. In this paper, a measure is proposed to find the distance between a typing sample and a set of samples of a user. For each digraph, histogram-based density estimation is used to find the pdf of its duration time. This measure is combined with another measure which is based on the two samples distances. Experimental results show considerable decrease in FAR while FRR remains constant.

Concept graph is a graph that represents the relationships between language concepts. In this structure the relationship between any two words is demonstrated by a weighted edge such that the value of this weight is interpreted as the degree of the relevance of two words. Having this graph, we can obtain most relevant words to a special term. In this paper, we propose a method for improving the classification of documents from unknown sources by means of concept graph. In our method, initially some features are selected from a training set by a well-known feature selection algorithm. Then, by extracting most relevant words for each class from the concept graph, a more effective feature set is produced. Our experimental results identify an improvement of 1% and 8% in precision and recall measures, respectively.

با افز ايش قيمت ابرکامپ يوترها ا ز طرف ي و ني از به مناب ع محاسباتي با حجم وسيع از طرف ديگر، محقق ين کام پيوتر را بر آن داشته است تا به سراغ استفاده از شبکهاي از منابع محاسبا تي به نا م گريد Grid) روي آورند. گونهاي از گريد به نام گريد محاسباتي Computing Grid مجموعهاي از منابع توزيع شده را براي حل مس ايل با ميزان محاسبات بالا در علوم، مهندس ي و تجارت فراهم آورده اس ت . براي استفاده بهينه از قابليتهاي اين سيستم توزيع شده، الگوريتمه اي زمانبندي کارامدي موردنياز است . در اي ن مقاله الگو ر يتم زمانبن دي جديدي به کمک برنامهسازي ژنتيک و بر مبن اي دو الگور يتم پاي هاي Min-Min و Max-Min طراحي شده است که س ع ي در استفاده از مزاياي اين دو، رفع معايب آنه ا و کوتاه کردن مدت زمانبن د ي نموده است. با مقايسه کارا يي الگو ريتم جدي د با دو الگو ر يتم Min-Min و Max-Min نتايج قابل قبولي بدست آمد.