Jimali Software Engineering

One of the main problems that affect the data integrity of passive RFID systems is the collision between the tags. A popular anticollision algorithm which dominates the standards in HF and UHF passive RFID systems is Framed Slotted Aloha (FSA) and some variations of FSA. Throughput and average time delay of the RFID system which determines the performance/efficiency of the system are reduced rapidly when the number of tags inside the interrogation zone is increased. Using larger frame sizes is not always the solution. This paper discusses and compares the existing protocols, and proposes a variation of FSA, called Progressing Scanning (PS) algorithm. The PS algorithm divides the tags in the interrogation zone into smaller groups and gives the ability to the reader to communicate each time with one of them. For performance analysis, the PS algorithm was evaluated with the parameters of a typical passive RFID system at 2.5 GHz. The results showed that the PS algorithm can improve the efficiency of the RFID system and provide a reliable solution for cases with a high density of tags in the area (over 800 tags).br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://ads.pheedo.com/click.phdo?s=b5dd09d3759389f0b4359e0f7d884186p=1img alt= style=border: 0; border=0 src=http://ads.pheedo.com/img.phdo?s=b5dd09d3759389f0b4359e0f7d884186p=1//a img alt= height=0 width=0 border=0 style=display:none src=http://a.rfihub.com/eus.gif?eui=2225/

In a heterogeneous wireless sensor network (WSN), relay nodes (RNs) are adopted to relay data packets from sensor nodes (SNs) to the base station (BS). The deployment of the RNs can have a significant impact on connectivity and lifetime of a WSN system. This paper studies the effects of random deployment strategies. We first discuss the biased energy consumption rate problem associated with uniform random deployment. This problem leads to insufficient energy utilization and shortened network lifetime. To overcome this problem, we propose two new random deployment strategies, namely, the lifetime-oriented deployment and hybrid deployment. The former solely aims at balancing the energy consumption rates of RNs across the network, thus extending the system lifetime. However, this deployment scheme may not provide sufficient connectivity to SNs when the given number of RNs is relatively small. The latter reconciles the concerns of connectivity and lifetime extension. Both single-hop and multi-hop communication models are considered in this paper. With a combination of theoretical analysis and simulated evaluation, this study explores the trade-off between connectivity and lifetime extension in the problem of RN deployment. It also provides a guideline for efficient deployment of RNs in a large scale heterogeneous WSN.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://ads.pheedo.com/click.phdo?s=e113fac7c0107b7bc76a7212976e0cb3p=1img alt= style=border: 0; border=0 src=http://ads.pheedo.com/img.phdo?s=e113fac7c0107b7bc76a7212976e0cb3p=1//a img alt= height=0 width=0 border=0 style=display:none src=http://a.rfihub.com/eus.gif?eui=2225/

Short Message Service (SMS) is the most popular mobile data service today. In Taiwan, a subscriber sends more than 200 short messages per year on average. The huge demand for SMS significantly increases network traffic, and it is essential that mobile operators should provide efficient SMS delivery mechanism. In this paper, we study the short message retransmission policies and derive some facts about these policies. Then we propose an analytic model to investigate the short message retransmission performance. The analytic model is validated against simulation experiments. We also collect SMS statistics from a commercial mobile telecommunications network. Our study indicates that the performance trends for the analytic/simulation models and the measured data are consistent.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://ads.pheedo.com/click.phdo?s=5b48fa61f11495d03d8d4b86c003e794p=1img alt= style=border: 0; border=0 src=http://ads.pheedo.com/img.phdo?s=5b48fa61f11495d03d8d4b86c003e794p=1//a img alt= height=0 width=0 border=0 style=display:none src=http://a.rfihub.com/eus.gif?eui=2225/

In Wireless Sensor and Actor Networks (WSANs), the collaborative operation of sensors enables the distributed sensing of a physical phenomenon, while actors collect and process sensor data and perform appropriate actions. In this paper, coordination and communication problems in WSANs with mobile actors are studied. First, a new location management scheme is proposed to handle the mobility of actors with minimal energy expenditure for the sensors, based on a hybrid strategy that includes location updating and location prediction. Actors broadcast location updates limiting their scope based on Voronoi diagrams, while sensors predict the movement of actors based on Kalman filtering of previously received updates. An optimal energy-aware forwarding rule is then derived for sensor-actor communication, based on geographical routing. Consequently, algorithms are proposed that allow controlling the delay of the data-delivery process based on power control, and deal with network congestion by forcing multiple actors to be recipients for traffic generated in the event area. Finally, a model is proposed to optimally assign tasks to actors and control their motion in a coordinated way to to accomplish the tasks based on the characteristics of the events. Performance evaluation shows the effectiveness of the proposed solution.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://ads.pheedo.com/click.phdo?s=0f481ce4784857b9266c9d3d26e04ff4p=1img alt= style=border: 0; border=0 src=http://ads.pheedo.com/img.phdo?s=0f481ce4784857b9266c9d3d26e04ff4p=1//a img alt= height=0 width=0 border=0 style=display:none src=http://a.rfihub.com/eus.gif?eui=2225/

IEEE Transactions on Mobile Computing

Technology innovations have shaped the IT Industry ever since its inception. Adoption of a particular innovation sometime becomes a key survival factor. Incumbents not adopting an innovation may loose market share. IBM lost market share to Microsoft who adopted the emergence of PC during the Mainframe era. Siebel who were late to realize the potential of SaaS lost market share to Salesforce. Evaluating a trend becomes important. In this attempt, #x201C;Diffusion of Innovations (DoI)#x201D; theory by Everett M. Rogers was found incomplete since the focus there is mostly on the #x201C;customer context#x201D;. However, historical analysis of IT innovations hint at influence by other contexts like the #x201C;Competitor Context#x201D; and the #x201C;Technical Intricacies Context#x201D;. #x201C;Business Ecosystem#x201D; play a very vital role in IT. This paper proposes a new framework to evaluate emerging trends. Emerging trends like SaaS, SOA and cloud computing have been analyzed using this framework.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://ads.pheedo.com/click.phdo?s=4c10cf9173a816e82d5a9664f7906de7p=1img alt= style=border: 0; border=0 src=http://ads.pheedo.com/img.phdo?s=4c10cf9173a816e82d5a9664f7906de7p=1//a img alt= height=0 width=0 border=0 style=display:none src=http://a.rfihub.com/eus.gif?eui=2225/

If you want to transform the business and cause a dramatic improvement for the enterprise, you#x2019;ll need to start by truly transforming your IT life cycle. It#x2019;s easy to believe that IT is truly reinventing itself by hiring new people, installing new tools, or with a new methodology. However, providing transformational leadership starts with the leadership itself along with the development of the three core competencies of Strategy, Solutions, and Implementation. They should be put into practice as concurrent activities because the #x201C;new IT you#x201D; will not be sustainable if your organization accomplishes these activities only one time. Your job in IT leadership is to develop all three competencies in your organization by cultivating the necessary skills and techniques, and then to cause each of them to happen continuously and concurrently. When these changes become the new habits of the organization, you are on the path to true transformation.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://ads.pheedo.com/click.phdo?s=49625ddb2a4e8ff970506a1f15fc3a40p=1img alt= style=border: 0; border=0 src=http://ads.pheedo.com/img.phdo?s=49625ddb2a4e8ff970506a1f15fc3a40p=1//a img alt= height=0 width=0 border=0 style=display:none src=http://a.rfihub.com/eus.gif?eui=2225/

Providing security in a distributed system requires more than user authentication with passwords or digital certificates and confidentiality in data transmission. Rigorous control of the executed tasks is needed in order to prevent malicious users from breaking policies, to identify the use of stolen passwords, and also to make possible rapid detection of known attacks. In this work, a solution for intrusion detection in grid and cloud computing environment is presented in which audit data is collected from the cloud and two intrusion detection techniques are applied. Analysis for anomaly detection is performed to verify if user actions correspond to known behavior profiles and knowledge analysis is performed to verify security policy violations and known attack patterns. This approach was evaluated in terms of performance results.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://ads.pheedo.com/click.phdo?s=76cf70ae6b4b0cbddabfc354dcb6f152p=1img alt= style=border: 0; border=0 src=http://ads.pheedo.com/img.phdo?s=76cf70ae6b4b0cbddabfc354dcb6f152p=1//a img alt= height=0 width=0 border=0 style=display:none src=http://a.rfihub.com/eus.gif?eui=2225/

This paper looks at how to define a set of #x201C;shall nots#x201D; requirements that is as complete as possible, starting with the elicitation and discovery through system integration and testing using a process called hazard mining. Hazard mining deeply probes the semantics of the code while the code executes in attempts to flush out forgotten hazards that need to be defended.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://ads.pheedo.com/click.phdo?s=1a09d771ca08065304decf5332125c88p=1img alt= style=border: 0; border=0 src=http://ads.pheedo.com/img.phdo?s=1a09d771ca08065304decf5332125c88p=1//a img alt= height=0 width=0 border=0 style=display:none src=http://a.rfihub.com/eus.gif?eui=2225/

Academic and management literature hypothesize that organizations that adopt organizational and management practices (#x201C;complementary factors#x201D;) that improve IT-business communications and partnership are better able to transform their business models. This implies that these firms obtain higher productivity and profitability from IT investments than organizations with comparable levels of IT investment. This study employs a stage-growth model (the #x201C;strategic alignment maturity model#x201D;) that focuses on the evolution of a bilateral and ideally co-adaptive relationship between business and information technology functions to empirically test that hypothesis. Employing the results of a survey of pharmaceutical industry business and IT executives, this research provides new insights into the relationship between IT-business strategic alignment maturity and firm-level productivity and profitability. This study also helps to quantify important #x201C;complementary factors#x201D; that provide transformational impact on a firm#x2019;s production function by its investments in IT.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://ads.pheedo.com/click.phdo?s=39f15e85ed9951efaf931ecc9773d7ecp=1img alt= style=border: 0; border=0 src=http://ads.pheedo.com/img.phdo?s=39f15e85ed9951efaf931ecc9773d7ecp=1//a img alt= height=0 width=0 border=0 style=display:none src=http://a.rfihub.com/eus.gif?eui=2225/

IT Professionalbr clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://ads.pheedo.com/click.phdo?s=0cc09a3e9cdcf97c75f1351e0742a98cp=1img alt= style=border: 0; border=0 src=http://ads.pheedo.com/img.phdo?s=0cc09a3e9cdcf97c75f1351e0742a98cp=1//a img alt= height=0 width=0 border=0 style=display:none src=http://a.rfihub.com/eus.gif?eui=2225/

Existing object tracking applications focus on finding the moving patterns of a single object or all objects. In contrast, we propose a distributed mining algorithm that identifies a group of objects with similar movement patterns. This information is important in some biological research domains, such as the study of animals' social behavior and wildlife migration. The proposed algorithm comprises a local mining phase and a cluster ensembling phase. In the local mining phase, the algorithm finds movement patterns based on local trajectories. Then, based on the derived patterns, we propose a new similarity measure to compute the similarity of moving objects and identify the local group relationships. To address the energy conservation issue in resource-constrained environments, the algorithm only transmits the local grouping results to the sink node for further ensembling. In the cluster ensembling phase, our algorithm combines the local grouping results to derive the group relationships from a global view. We further leverage the mining results to track moving objects efficiently. The results of experiments show that the proposed mining algorithm achieves good grouping quality, and the mining technique helps reduce the energy consumption by reducing the amount of data to be transmitted.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://ads.pheedo.com/click.phdo?s=e11cc48d09b8bb64e482f95205612314p=1img alt= style=border: 0; border=0 src=http://ads.pheedo.com/img.phdo?s=e11cc48d09b8bb64e482f95205612314p=1//a img alt= height=0 width=0 border=0 style=display:none src=http://a.rfihub.com/eus.gif?eui=2225/

Long time-series datasets are common in many domains, especially scientific domains. Applications in these fields often require comparing trajectories using similarity measures. Existing methods perform well for short time-series but their evaluation cost degrades rapidly for longer time-series. In this work, we develop a new time-series similarity measure called the Dictionary Compression Score (DCS) for determining time-series similarity. We also show that this method allows us to accurately and quickly calculate similarity for both short and long time-series. We use the well known Kolmogorov Complexity in information theory and the Lempel-Ziv compression framework as a basis to calculate similarity scores. We show that off-the-shelf compressors do not fair well for computing time-series similarity. To address this problem, we developed a novel dictionary-based compression technique to compute time-series similarity. We also develop heuristics to automatically identify suitable parameters for our method, thus removing the task of parameter tuning found in other existing methods. We have extensively compared DCS with existing similarity methods for classification. Our experimental evaluation shows that for long time-series datasets, DCS is accurate, and it is also significantly faster than existing methods.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://ads.pheedo.com/click.phdo?s=9144f1277e0efd43caa7bb8493423f9dp=1img alt= style=border: 0; border=0 src=http://ads.pheedo.com/img.phdo?s=9144f1277e0efd43caa7bb8493423f9dp=1//a img alt= height=0 width=0 border=0 style=display:none src=http://a.rfihub.com/eus.gif?eui=2225/

In this work, web-based metrics that compute the semantic similarity between words or terms are presented and compared with the state-of-the-art. Starting from the fundamental assumption that similarity of context implies similarity of meaning, relevant web documents are downloaded via a web search engine and the contextual information of words of interest is compared (context-based similarity metrics). The proposed algorithms work automatically, do not require any human annotated knowledge resources, e.g., ontologies, and can be generalized and applied to different languages. Context-based metrics are evaluated both on the Charles-Miller dataset and on a medical term dataset. It is shown that context-based similarity metrics significantly outperform co-occurrence based metrics, in terms of correlation with human judgment, for both tasks. In addition, the proposed unsupervised context-based similarity computation algorithms are shown to be competitive with state-of- the-art supervised semantic similarity algorithms that employ language-specific knowledge resources. Specifically, context-based metrics achieve correlation scores of up to 0.88 and 0.74 for the Charles-Miller and medical datasets, respectively. The effect of stop-word filtering is also investigated for word and term similarity computation. Finally, the performance of context-based term similarity metrics is evaluated as a function of the number of web documents used and for various feature weighting schemes.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://ads.pheedo.com/click.phdo?s=8da66f5aa55741167c76ef5bc4158b35p=1img alt= style=border: 0; border=0 src=http://ads.pheedo.com/img.phdo?s=8da66f5aa55741167c76ef5bc4158b35p=1//a img alt= height=0 width=0 border=0 style=display:none src=http://a.rfihub.com/eus.gif?eui=2225/

Visual methods have been widely studied and used in data cluster analysis, \textit{e.g.}, the VAT algorithm for visual analysis of cluster tendency. Given a pairwise dissimilarity matrix $\bm{D}$ of a set of $n$ objects, methods such as VAT generally represent $\bm{D}$ as an $n\times n$ image $\mathrm{I}(\tilde{\bm{D}})$ where the objects are reordered to highlight cluster structure as dark blocks along the diagonal of the image. A major limitation of such visual methods is their inability to highlight cluster structure in $\mathrm{I}(\tilde{\bm{D}})$ when $\bm{D}$ contains clusters with highly complex structure. In this paper, we address this limitation by proposing a Spectral VAT algorithm, where $\bm{D}$ is mapped to $\bm{D'}$ in an embedding space by spectral decomposition of the Laplacian matrix, and then reordered to $\bm{\tilde{D'}}$ using the VAT algorithm. We propose a strategy to automatically determine the number of clusters in $\mathrm{I}(\bm{\tilde{D'}})$, as well as a visual method for cluster formation from $\mathrm{I}(\bm{\tilde{D'}})$ based on the difference between diagonal blocks and off-diagonal blocks. In addition, we propose a sampling-based extended scheme to enable visual cluster tendency assessment and data partitioning for large data sets. Extensive experimental results on several synthetic and real-world data sets demonstrate the effectiveness of our algorithms.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://ads.pheedo.com/click.phdo?s=2e4757d8bff296146cf03ce77e21d653p=1img alt= style=border: 0; border=0 src=http://ads.pheedo.com/img.phdo?s=2e4757d8bff296146cf03ce77e21d653p=1//a img alt= height=0 width=0 border=0 style=display:none src=http://a.rfihub.com/eus.gif?eui=2225/

A major assumption in many machine learning and data mining systems is that the data must be from the same feature representations and that the data distributions in the training and test data are the same. However, in many real-world applications, this assumption does not hold. For example, we sometimes have a classification task in one task domain, but we only have sufficient training data in another task domain where the data may be in a different feature space or follow a different distribution. In these cases, knowledge transfer, if done successfully, would greatly benefit learning in our interested domain by avoiding expensive data labeling tasks. In recent years, \emph{transfer learning} has emerged as a new technique to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression and clustering problems. We discuss the relationship between transfer learning and other related research areas, such as domain adaptation, multi-task learning and sample selection bias as well as co-variate shift, and explore some potential future problems in knowledge transfer research.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://ads.pheedo.com/click.phdo?s=812db68c6f048c9bc0948db126e2b379p=1img alt= style=border: 0; border=0 src=http://ads.pheedo.com/img.phdo?s=812db68c6f048c9bc0948db126e2b379p=1//a img alt= height=0 width=0 border=0 style=display:none src=http://a.rfihub.com/eus.gif?eui=2225/

t-Closeness is a privacy model recently defined for data anonymization. A data set is said to satisfy t-closeness if, for each group of records sharing a combination of key attributes, the distance between the distribution of a confidential attribute in the group and the distribution of the attribute in the entire data set is no more than a threshold t. Here, we define a privacy measure in terms of information theory, similar to t-closeness. Then, we use the tools of that theory to show that our privacy measure can be achieved by the postrandomization method (PRAM) for masking in the discrete case, and by a form of noise addition in the general case.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://ads.pheedo.com/click.phdo?s=344a2258d848b4b9a28634bd5995a17cp=1img alt= style=border: 0; border=0 src=http://ads.pheedo.com/img.phdo?s=344a2258d848b4b9a28634bd5995a17cp=1//a img alt= height=0 width=0 border=0 style=display:none src=http://a.rfihub.com/eus.gif?eui=2225/

Taxonomies, representing hierarchical data, are a key knowledge source in multiple disciplines. Information processing across taxonomies is not possible unless they are appropriately merged for commonalities and differences. For taxonomy merging the first task is to identify common concepts between the taxonomies. Then these common concepts along with their associated concepts in the two taxonomies need to be integrated. Doing this in a conflict-free manner is a challenging task and generally requires human intervention. In this paper we explore the possibility of asymmetrically merging one taxonomy into another, automatically. Given one or more source taxonomies and a destination taxonomy, modeled as directed acyclic graphs, we present intuitive algorithms that merge relevant portions of the source taxonomies into the destination taxonomy. We prove that our algorithms are conflict-free, information-lossless and scalable. We also define precision and recall measures for evaluating enriched taxonomies, such as TA, the result of merging two taxonomies, with TI, the ideal merger. Our experiments indicate the effectiveness of our approach.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://ads.pheedo.com/click.phdo?s=8debb8b180ac44d9247a886853732393p=1img alt= style=border: 0; border=0 src=http://ads.pheedo.com/img.phdo?s=8debb8b180ac44d9247a886853732393p=1//a img alt= height=0 width=0 border=0 style=display:none src=http://a.rfihub.com/eus.gif?eui=2225/

Given a set of objects and their pairwise distances, we wish to determine a visual representation of the data. We use the quartet paradigm to compute a hierarchy of clusters of the objects. The method is based on an NP-hard graph optimization problem called the Minimum Quartet Tree Cost problem. This paper presents and compares several heuristic approaches to approximate the optimal hierarchy. The performance of the algorithms is tested through extensive computational experiments and it is shown that the Reduced Variable Neighbourhood Search heuristic is the most effective approach to the problem, obtaining high quality solutions in short computational running times.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://ads.pheedo.com/click.phdo?s=09edca5a1dd9842ab2a7af291279078fp=1img alt= style=border: 0; border=0 src=http://ads.pheedo.com/img.phdo?s=09edca5a1dd9842ab2a7af291279078fp=1//a img alt= height=0 width=0 border=0 style=display:none src=http://a.rfihub.com/eus.gif?eui=2225/

Researchers have rigorously studied the resampling, algorithms, and feature selection approaches to the class imbalance problem. No systematic studies have been conducted to understand how well these methods combat the class imbalance problem and which of these methods best manage the different challenges posed by imbalanced data sets. In particular, feature selection has rarely been studied outside of text classification problems. Additionally, no studies have looked at the additional problem of learning from small samples. This paper presents a first systematic comparison of the three types of methods and of seven feature selection metrics evaluated on small sample data sets from different applications. We evaluated the performance of these metrics using area under the receiver operating characteristic and area under the precision-recall curve. We compared each metric on the average performance across all problems and on the likelihood of a metric yielding the best performance on a specific problem. We examined the performance of these metrics inside each problem domain. Finally, we evaluated the efficacy of these metrics to see which perform best across algorithms. Our results showed that signal-to-noise ratio and Feature Assessment by Sliding Thresholds are great candidates for feature selection in most applications, especially when selecting very small numbers of features.br clear=both style=clear: both;/ br clear=both style=clear: both;/ a href=http://ads.pheedo.com/click.phdo?s=202c28198047167e5a243153ee58bc60p=1img alt= style=border: 0; border=0 src=http://ads.pheedo.com/img.phdo?s=202c28198047167e5a243153ee58bc60p=1//a img alt= height=0 width=0 border=0 style=display:none src=http://a.rfihub.com/eus.gif?eui=2225/