en
jalali
1391
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gregorian
2012
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online
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Route Lookup Algorithms Using the Novel Idea of Coded Prefix Trees
This paper introduces a new prefix matching algorithm called “Coded Prefix Search” and its improved version called “Scalar Prefix Search” using a coding concept for prefixes which can be implemented on a variety of trees especially limited height balanced trees for both IPv4 and IPv6 prefixes. Using this concept, each prefix is treated as a number. The main advantage of the proposed algorithms compared to Trie-based solutions is that the number of node accesses does not depend on IP address length in both search and update procedures. Therefore, applying this concept to balanced trees, causes the search and update node access complexities to be O(log n) where nis the number of prefixes. Also, compared to the existing range-based solutions, it does not need to store both end points of a prefix or to store ranges. Finally, compared to similar tree based solutions; it exhibits good storage requirements while it supports faster incremental updates. These properties make the algorithm capable of potential hardware implementation.
Coded Prefix, Scalar Prefix, Route Lookup, Longest Matching Prefix
1
12
http://ijict.itrc.ac.ir/browse.php?a_code=A-10-27-141&slc_lang=en&sid=1
2018/10/9
1397/7/17
2018/10/9
1397/7/17
Mohammad
Behdadfar
003194753284600476
003194753284600476
Yes
Hossein
Saidi
003194753284600477
003194753284600477
No
Masoud Reza
Hashemi
003194753284600478
003194753284600478
No
fa
JPEG Compressed Domain Face Recognition:Different Stages and Different Features
JPEG compression standard is widely used for reducing the volume of images that are stored or transmitted via networks. In biometrics datasets, face images are usually stored in JPEG compressed format, and should be fully decompressed to be used in a face recognition system. Recently, in order to reduce the time and complexity of decompression step, face recognition in compressed domain is considered as an emerging topic in face recognition systems. In this paper, we have tested different feature spaces, including PCA and ICA in various stages of JPEG compressed domain. The goal of these tests was to determine the best stage in JPEG compressed domain and the best features to be used in face recognition process, regarding the trade-off between the decompression overhead reduction and recognition accuracy.The experiments were conducted on FERET and FEI face databases, and results have been compared in various stages of JPEG compressed domain. The results show the superiority of zigzag scanned stagecompared to other stages and ICAfeature space compared to other feature spaces,both in terms of recognition accuracy and computational complexity.
Face Recognition, JPEG Compressed Domain, JPEG Decompression, Face Database, Feature Extraction
13
23
http://ijict.itrc.ac.ir/browse.php?a_code=A-10-27-142&slc_lang=en&sid=1
2018/10/92018/10/9
1397/7/17
2018/10/92018/10/9
1397/7/17
Mohammad Shahram
Moin
003194753284600479
003194753284600479
Yes
Alireza
Sepas Moghaddam
003194753284600480
003194753284600480
No
fa
An Automatic Image Annotation Technique Based on Coverage Ratio of Tags
In this paper we propose an automatic image annotation technique.The proposed techniqueis based oncoverage ratio of tags byemployingboth image content and metadata. The images in a reference imageset areemployed to automatically annotate a given image according to the coverage ratio of the tags in the reference image.Tagsand content descriptorsas well as coverage ratio of the tagsare generated for the entire images in the reference image set. The color and texture content descriptors are employed to retrieve similar images for an un-annotated image from reference image data set and the tags in the metadata of the retrieved images are used to annotate the un-annotated image. Simulation results indicate that the proposed technique outperforms another automatic annotation technique that uses similar content descriptors both in average precision and average recall.
coverage ratio, image content, image metadata, discrete wavelet transform, color histogram, automatic annotation
25
32
http://ijict.itrc.ac.ir/browse.php?a_code=A-10-27-143&slc_lang=en&sid=1
2018/10/92018/10/92018/10/9
1397/7/17
2018/10/92018/10/92018/10/9
1397/7/17
Ali
Abdolhoseini
003194753284600481
003194753284600481
Yes
Farzad
Zargari
003194753284600482
003194753284600482
No
fa
IECA: Intelligent Effective Crawling Algorithm for Web Pages
Obtaining important pages rapidly can be very useful when a crawler cannot visit the entire Webin a reasonable amount of time.Several Crawling algorithms such as Partial PageRank,Batch PageRank, OPIC, and FICA have been proposed, but they have high time complexity or low throughput. To overcome these problems, we propose a new crawling algorithm called IECA which is easy to implement with low time O(E*logV)and memory complexity O(V) -Vand Eare the number of nodes and edges in the Web graph, respectively. Unlike the mentioned algorithms, IECA traverses the Web graph only once and the importance of the Web pages is determined based on the logarithmic distance and weight of the incoming links. To evaluate IECA, we use threedifferent Web graphs such as the UK-2005, Web graph of university of California, Berkeley-2008, and Iran-2010. Experimental results show that our algorithm outperforms other crawling algorithms in discovering highly important pages.
search engines, Web crawling, Web graph, logarithmic distance, reinforcement learning, World Wide Web
33
42
http://ijict.itrc.ac.ir/browse.php?a_code=A-10-27-144&slc_lang=en&sid=1
2018/10/92018/10/92018/10/92018/10/9
1397/7/17
2018/10/92018/10/92018/10/92018/10/9
1397/7/17
Mohammad Amin
Golshani
003194753284600483
003194753284600483
Yes
Ali Mohammad
Zareh Bidoki
003194753284600484
003194753284600484
No
fa
Context Aware Mental Model Sharing in Dynamic Inaccessible Environments
One of the well-known cognitive concepts, commonly used in multi-agent systems, is the Shared Mental Model (SMM). In this paper we introduce a context aware mental model sharing strategy in a dynamic heterogeneous multi-agent environment in order to maximize collaboration via minimizing mental conflicts. This strategy uses a context aware architecture that is composed of three primary layers and a cross layer part which facilitates mental model sharing between agents. We model a complex inaccessible environment with specific dynamisms where agents must share their mental models in order to make correct decisions. Our proposed strategy is compared with other methods applying some important criteria such as shared information accuracy, communication load and performance in time constraint situations. Our findings may be interpreted as strong evidence that our method enables heterogeneous agents for a qualified teamwork as well as facilitating collective commitments.
shared mental model, mental model, dynamic environment, context awareness, sharing method
43
53
http://ijict.itrc.ac.ir/browse.php?a_code=A-10-27-145&slc_lang=en&sid=1
2018/10/92018/10/92018/10/92018/10/92018/10/9
1397/7/17
2018/10/92018/10/92018/10/92018/10/92018/10/9
1397/7/17
Sajjad
Salehi
003194753284600485
003194753284600485
Yes
Fattaneh
Taghiyareh
003194753284600486
003194753284600486
No
Mohammad
Saffar
003194753284600487
003194753284600487
No
Kambiz
Badie
003194753284600488
003194753284600488
No
fa
Artificial Immune Classifier (aiCLS): An Immune Inspired Supervised Machine Learning Method
Artificial immune systems have been proven to be efficient in pattern recognition, data clustering and data classification. The proposed method is a novel artificial immune classifier called aiCLS based on aiNET. Artificial immune network (aiNET) is an efficient data analysis and clustering algorithm capable of clustering simple datasets through complex ones. Hidden capabilities of aiNET for supervised learning were significantly considered by aiCLS. The proposed method takes a local optimization approach to classification problem. It generates local optimum cells to recognize any given training antigen. Concatenation of these cells results in a global optimum classifier. The novelty of aiCLS has been discussed from both computational and immunological aspects. From the computational aspect, aiCLS is a fast one-shot learner algorithm with regard to the proposed “iterative clonal selection”. From the immunological aspect, aiCLS introduces a novel clonal suppression method called “dissimilarity proportional clonal suppression (DPCS)”, which increases data reduction and convergence to local optimum for any given antigen. DPCS alters convergence through a greedy suppression, which takes antibody-antigen affinity into account. The experimental results show that aiCLS outperforms artificial immune recognition system (AIRS) on UCI benchmark datasets in both classification accuracy and data reduction.
Artificial Immune System, Immune Network, Classifier, Dissimilarity proportional clonal suppression, Supervised Learning
55
67
http://ijict.itrc.ac.ir/browse.php?a_code=A-10-27-146&slc_lang=en&sid=1
2018/10/92018/10/92018/10/92018/10/92018/10/92018/10/9
1397/7/17
2018/10/92018/10/92018/10/92018/10/92018/10/92018/10/9
1397/7/17
Seyed Amir
Ehsani
003194753284600489
003194753284600489
Yes
Amir Masoud
Eftekhari Moghadam
003194753284600490
003194753284600490
No
fa
An Improved Recommender System Based on Forgetting Mechanism for User Interest-Drifting
Highly effective recommender systems may still face users’ interest drifting. One of the main strategies for handling interest-drifting is forgetting mechanism. Current approaches based on forgetting mechanism have some drawbacks: (i) Drifting times are not considered to be detected in user interest over time. (ii) They are not adaptive to the evolving nature of user’s interest. Until now, there hasn’t been any study to overcome these problems. This paper discusses the above drawbacks and presents a novel recommender system, named WmIDForg, using web usage mining, web content mining techniques, and forgetting mechanism to address user interest-drift problem. We try to detect evolving and time-variant patterns of users' interest individually, and then dynamically use this information to predict favorite items of the user better over time. The experimental results on EachMovie dataset demonstrate our methodology increases recommendations precision 6.80% and 1.42% in comparison with available approaches with and without interest-drifting respectively.
web recommender system, users’ interest drifting, forgettin mechachanism, web usage mining, web content mining, time-based hybrid weight
69
77
http://ijict.itrc.ac.ir/browse.php?a_code=A-10-27-147&slc_lang=en&sid=1
2018/10/92018/10/92018/10/92018/10/92018/10/92018/10/92018/10/9
1397/7/17
2018/10/92018/10/92018/10/92018/10/92018/10/92018/10/92018/10/9
1397/7/17
Rozita
Tavakolian
003194753284600491
003194753284600491
Yes
Mohammad
Taghi Hamidi Beheshti
003194753284600492
003194753284600492
No
Nasrollah
Moghaddam Charkari
003194753284600493
003194753284600493
No