Computer Science and Engineering

The goal of this paper is to present a novel approach for assessment of the quality of Gaming software in terms of factors formulated by correlating its diversified attributes. All possible descriptive properties of customers, users and software products of the class Games were gathered under the three categories of 'customer attributes', 'user attributes' and 'engineering attributes'. The attributes were then put into category to category correlation and reduced. Some feed backs from the environment of functioning of the system were also considered to verify the reduction procedure and use the attributes for the purpose.

This paper presents a scheme for purposeful enhancement of domain knowledgebases which are supposed to be repositories of public information and also to be subject to frequent interaction by non-technical people through natural languages. In place of rigorous syntactic study of natural language queries, an acceptable alternative in the form of gradual simplification and recognition of useful semantic structures is proposed. Localization of matching procedures by isolating significant phrases and disambiguation of complicatedly ordered phrase components by heuristic rules are emphasized. Use of finite state machines for recognition and generation of data structures is fruitfully accommodated. Besides, techniques of inexact string matching for mapping query elements to database details have been incorporated to justify the usability of the enhancements.

This paper presents an optimized architecture and weights of three layered ANN designing method using sensitivity analysis and adaptive particle swarm optimization (SA-APSO). The optimized ANN architecture determination means to look for near minimal number of neurons in the ANN and finding the efficient connecting weights of it in such a way so that the ANN can achieve better performance for solving different problems. The proposed algorithm designs the ANN into two phases. In the first phase it tries to prune the neurons from ANN using sensitivity analysis to achieve the near minimal ANN structure and therefore it tries to optimize the weight matrices for further performance enhancement by adaptive particle swarm optimization. In the SA phase the authors use impact factor and correlation coefficients for pruning lower salient neurons. Initially it tries to prune the neurons having less impacts in the performance of ANN based on their impact factor values. Therefore it tries to lessen more neurons through merging the similar neurons in the ANN using correlation coefficient among the neuron pairs. In the optimization part it applied adaptive particle swarm optimization to optimize the connecting weight matrices to attain better performance. In the optimization by APSO, a special type of PSO, the authors' use training and validation fitness functions to emphasis on avoiding overfitting and more adapted with ANN, and to achieve effective weight matrices of ANN. To evaluate SA-APSO, it is applied on the dataset of Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA) to do short term load forecasting (STLF). Results show that the proposed SA-APSO is able to design smaller architecture and attain excellent accuracy.

Excessive locking and cumulative updates in Distributed Shared Memory (DSM) not only reduces the parallelism for block access but also causes a serious degradation in response time for a dense network. This paper proposes a new consistency model in DSM named Last Update Consistency (LUC) model, where the model uses logical clock counter to keep the DSM consistent. The logical clock always increases never decreases. So the increasing order of the logical clock value is used to provide the request to the DSM. In this model, multiple nodes can perform READ operations over the same block at a time. For WRITE operation over the same block, only the last modification will exist and the earlier WRITE operations will be treated as obsolete WRITE and should be discarded. The experimental and analytical analysis showed that the proposed model effectively reduces the unnecessary network traffic and cumulative block updates that exist in the Sequential Consistency Model and Release Consistency Model. Index Terms-View Consistency Model, Timestamp Ordering Protocol, Distributed Shared Memory. Rubaiyat Islam Rafat is currently a researcher at University of Dhaka, Bangladesh. He received his MS degree in information technology from Institute of Information Technology, University of Dhaka, Bangladesh, in 2009 and BS degree in computer science and engineering from Ahsaullah University of Science and Technology, Bangladesh, in 2005.

Unlike conventional infrastructure based wireless networks such as, wireless cellular networks, Mobile Ad Hoc Networks (MANET) contains rapidly deployable, self organizing and self maintaining capability features. Moreover, they can be formed on the fly as needed. More concerns for MANET’s security arise due to its high popularity to its users. Now-a days, more interests are given in Public Key Cryptography (PKC) rather than Secret Key Cryptography because of its strong security. However, in the context of wireless communication, although PKC offers robust solutions for many security problems, the excessive amount of required computational resources remarkably limit the usage of PKC.

Using Elliptic Curve Cryptography (ECC) offers higher strength per key bit in comparison with other PKCs. The computational power needed to break 1024 bit RSA is equal to the computational power needed to break 163 bit ECC. Unlike the ordinary Discrete Logarithm Problem (DLP) and the Integer Factorization Problem (IFP) for PKCs, no sub exponential-time algorithm is known for the Elliptic Curve Discrete Logarithm Problem (ECDLP). For this reason, the strength-per-key-bit is substantially greater in an algorithm that uses elliptic curves rather than existing RSA or El-Gamal PKC.

This thesis paper gives an introduction to ECC, how it is used in the implementation of Digital Signature Algorithms (ECDSA) and provides a novel security framework based on ECC and ECDSA for MANET. In the proposed framework, namely Optimized Pretty Good Privacy (OPGP), a composition of ECC and ECDSA is introduced for enhancing security. In OPGP, the authentication, non repudiation, integrity of information is provided by ECDSA and confidentiality is provided by using ECC. The optimized ECC and ECDSA succeed to provide same security level of existing RSA, by performing relatively lower computations.

This paper studies key management and security issues in mobile ad hoc networks (MANETs). Lack of strong identification results in unauthorized entities access to the sensitive data, and leads to loos of confidentiality and integrity, which causes many vulnerabilities and attacks in the network .We present the key management scheme as a combination of Identity-Based (User's Identification), Unique Transmission's time Factor and Threshold Cryptography for ad hoc networks. It is a certificateless solution which eliminates the need for public key distribution and certificates in public key management schemes. This scheme is also efficient in computation since small unique factor enhance the authentication of entities. Index Terms-Identity based cryptography, key management, security, mobile ad hoc network.

Despite being subjected to an economic downturn that is yet to recover, computers and information technology (IT) continue to drive and influence innovation, growth and development in almost all sectors of the world economy. The gains in efficiency and productivity obtained from the optimum use of emerging computer and IT technologies are having lasting impacts on the global society. These technologies are also playing a vital role in human health care and in enhancing our knowledge and understanding of the environment by supporting advanced modelling underpinned by high performance algorithms and processing paradigms. At the core of the continuing uptake and impact of computer and IT technologies is the research that is conducted worldwide in these fields. During the past decade the IEEE International Conference on Computer and Information Technology (ICCIT) has been successful in bringing together academics, researchers, IT professionals and managers to disseminate and discuss the up to date research findings in these fields. This Special Issue presents selected papers from the twelfth conference of the series (ICCIT 2009) held during December 21-23, 2009 at the Independent University Bangladesh. The first one was held in Dhaka, Bangladesh, in 1998. Since then the conference has grown to one of the largest computer and IT related research conferences in the South Asian region, with participation of academics and researchers from many countries around the world. Authors are required to submit full length papers to the conference for review. A double blind review process is followed whereby each paper is reviewed by at least two independent reviewers of high international standing. The acceptance rate of papers in recent years has been around 32% or less. This is an indication of the quality of work the papers accepted for the conference demonstrates. Starting in 2008 the proceedings of ICCIT are included in IEEExplore. The 2009 ICCIT papers are no exception. In 2009, a total of 473 full papers were submitted to the conference of which 150 were accepted after reviews conducted by an international program committee comprising 84 members from 16 countries with assistance from 96 reviewers. This was tantamount to an acceptance rate of 31.7%. Form the 150 papers accepted for the conference only 14 highly ranked papers were invited for this Special Issue. The authors were invited to enhance their conference papers significantly, with at least 30% extension, and submit the same for review. Of those only eight papers survived the review process and have been selected for inclusion in this Special Issue. The authors of these papers represent academic and/or research institutions from Bangladesh, Canada, Japan, Norway and the United States. These eight papers cover six domains of computing namely, application-driven algorithms, data mining, neural networks, distributed systems, content sharing and high-performance digital processors. Fourteen reviewers from five countries have assisted the guest editors in reviewing the papers submitted to the Special Issue during two rounds of review process. They have given immensely to the process by responding to the guest editors in the shortest possible time and by dedicating their valuable time to ensure that the Special Issue contains high-quality papers with significant contributions. The guest editors would like to express their sincere gratitude to all

We design new serial and parallel approximation algorithms for computing a maximum weight b-matching in an edge-weighted graph with a submodular objective function. This problem is NP-hard; the new algorithms have approximation ratio 1/3, and are relaxations of the Greedy algorithm that rely only on local information in the graph, making them parallelizable. We have designed and implemented Local Lazy Greedy algorithms for both serial and parallel computers. We have applied the approximate submodular b-matching algorithm to assign tasks to processors in the computation of Fock matrices in quantum chemistry on parallel computers. The assignment seeks to reduce the run time by balancing the computational load on the processors and bounding the number of messages that each processor sends. We show that the new assignment of tasks to processors provides a four fold speedup over the currently used assignment in the NWChemEx software on 8000 processors on the Summit supercomputer at Oak R...

We survey recent work on approximation algorithms for computing degree-constrained subgraphs in graphs and their applications in combinatorial scientific computing. The problems we consider include maximization versions of cardinality matching, edge-weighted matching, vertex-weighted matching and edge-weighted $b$ -matching, and minimization versions of weighted edge cover and $b$ -edge cover. Exact algorithms for these problems are impractical for massive graphs with several millions of edges. For each problem we discuss theoretical foundations, the design of several linear or near-linear time approximation algorithms, their implementations on serial and parallel computers, and applications. Our focus is on practical algorithms that yield good performance on modern computer architectures with multiple threads and interconnected processors. We also include information about the software available for these problems.

We describe two new 3/2-approximation algorithms and a new 2-approximation algorithm for the minimum weight edge cover problem in graphs. We show that one of the 3/2-approximation algorithms, the Dual Cover algorithm, computes the lowest weight edge cover relative to previously known algorithms as well as the new algorithms reported here. The Dual Cover algorithm can also be implemented to be faster than the other 3/2-approximation algorithms on serial computers. Many of these algorithms can be extended to solve the b-Edge Cover problem as well. We show the relation of these algorithms to the K-Nearest Neighbor graph construction in semi-supervised learning and other applications.

We explore the problem of sharing data that pertains to individuals with anonymity guarantees, where each user requires a desired level of privacy. We propose the first sharedmemory as well as distributed memory parallel algorithms for the adaptive anonymity problem that achieves this goal, and produces high quality anonymized datasets. The new algorithm is based on an optimization procedure that iteratively computes weights on the edges of a dissimilarity matrix, and at each iteration computes a minimum weighted b-Edge Cover in the graph. We describe how a 2-approximation algorithm for computing the b-Edge Cover can be used to solve the adaptive anonymity problem in parallel. We are able to solve adaptive anonymity problems with hundreds of thousands of instances and hundreds of features on a supercomputer in under five minutes. Our algorithm scales up to 8K cores on a distributed memory supercomputer, while also providing good speedups on shared memory multiprocessors. On smaller problems where an a Belief Propagation algorithm is feasible, our algorithm is two orders of magnitude faster.

We describe a paradigm for designing parallel algorithms via approximation, and illustrate it on the b-Edge Cover problem. A b-Edge Cover of minimum weight in a graph is a subset C of its edges such that at least a specified number b(v) of edges in C is incident on each vertex v, and the sum of the edge weights in C is minimum. The Greedy algorithm and a variant, the LSE algorithm, provide 3/2-approximation guarantees in the worst-case for this problem, but these algorithms have limited parallelism. Hence we design two new 2-approximation algorithms with greater concurrency. The MCE algorithm reduces the computation of a b-Edge Cover to that of finding a b'-Matching, by exploiting the relationship between these subgraphs in an approximation context. The LSENW is derived from the LSE algorithm using static edge weights rather than dynamically computing effective edge weights. This relaxation gives S-LSE a worse approximation guarantee but makes it more amenable to parallelization...

This presentation was made for Decision Support System course under Dr. Vatcharaporn Esichaikul in Asian Institute of Technology Master's Program. This presentation is on a published paper titled "An ontology-based Web mining method for unemployment rate prediction" by Ziang Li, Wei Xu, Likuan Zhang  and Raymond Y.K. Lau. This presentation was made for academic purpose and also published for academic purposes only. All rights reserved by the authors and the publisher of the paper.

Numerous meta-heuristic algorithms have been influenced by nature. Over the past couple of decades, their quantity has been significantly escalating. The majority of these algorithms attempt to emulate natural biological and physical phenomena. This research concentrates on the Flower Pollination algorithm, which is one of several bio-inspired algorithms. The original approach was suggested for pollen grain exploration and exploitation in confined space using a specific global pollination and local pollination strategy. As a "swarm intelligence" meta-heuristic algorithm, its strength lies in locating the vicinity of the optimum solution rather than identifying the minimum. A modification to the original method is detailed in this work. This research found that by changing the specific value of "switch probability" with dynamic values of different dimension sizes and functions, the outcome was mainly improved over the original flower pollination method.

The chronicle of prime numbers travel back thousands of years in human history. Not only the traits of prime numbers have surprised people, but also all those endeavors made for ages to find a pattern in the appearance of prime numbers has been captivating them. Until recently, it was firmly believed that prime numbers do not maintain any pattern of occurrence among themselves. This statement is conferred not to be completely true. This paper is also an attempt to discover a pattern in the occurrence of prime numbers. This work intends to introduce some mathematical well-known equations that point to the existence of a simplistic pattern in the number of primes within the range of a number and its square. We assume that the rigorous evaluation of the perceived pattern may benefit in many aspects such as applications of encryption, algorithms concerning prime numbers, and many more.

Dozens of new tools and technologies are being incorporated to help developers, which is becoming a source of consternation as they struggle to choose one over the others. For example, there are at least ten frameworks available to developers for developing web applications, posing a conundrum in selecting the best one that meets their needs. As a result, developers are continuously searching for all of the benefits and drawbacks of each API, framework, tool, and so on. One of the typical approaches is to examine all of the features through official documentation and discussion. This approach is time-consuming, often makes it difficult to determine which aspects are the most important to a particular developer and whether a particular aspect is important to the community at large. In this paper, we have used a benchmark API aspects dataset (Opiner) collected from StackOverflow posts and observed how Transformer models (BERT, RoBERTa, DistilBERT, and XLNet) perform in detecting software aspects in textual developer discussion with respect to the baseline Support Vector Machine (SVM) model. Through extensive experimentation, we have found that transformer models improve the performance of baseline SVM for most of the aspects, i.e.

In modern capital market the price of a stock is often considered to be highly volatile and unpredictable because of various social, financial, political and other dynamic factors. With calculated and thoughtful investment, stock market can ensure a handsome profit with minimal capital investment, while incorrect prediction can easily bring catastrophic financial loss to the investors. This paper introduces the application of a recently introduced machine learning model-the Transformer model, to predict the future price of stocks of Dhaka Stock Exchange (DSE), the leading stock exchange in Bangladesh. The transformer model has been widely leveraged for natural language processing and computer vision tasks, but, to the best of our knowledge, has never been used for stock price prediction task at DSE. Recently the introduction of time2vec encoding to represent the time series features has made it possible to employ the transformer model for the stock price prediction. This paper concentrates on the application of transformer-based model to predict the price movement of eight specific stocks listed in DSE based on their historical daily and weekly data. Our experiments demonstrate promising results and acceptable root mean squared error on most of the stocks.

Stock market investment have been an ideal form of investment for many years. Investing capitals smartly in stock market yields high profit returns. But there are many companies available in a market. Currently there are more than 345 active companies who have stocks in Dhaka Stock Exchange (DSE). Analyzing all these companies is quite impossible. However, many companies tend to move together. This study aims at finding which companies in DSE have a close connection and move alongside each other. By analyzing this relation, the investors and traders will be able to analyze a lot of companies' statistics from a calculating just a handful number of companies. The conducted experiment yielded promising results. It was found that though the system was not given anything other than technical data, it was able to identify companies that show domain specific outcomes. In other words, a relation between technical data and fundamental data was discovered from the conducted experiment.