Naveen Aggarwal

Web hypermedia content management system effort estimation model

This study aims at creation of a well defined estimation model which can be used to estimate the effort required for designing and developing the web hypermedia content management systems. The data from the different content management system projects are studied and the linear regression approach is used to finalize the model. This model also provides guidelines to calculate phase wise distribution of effort. The model is designed to help project manager to estimate effort at the very early stage of requirement analysis. A set of questionnaire is used to estimate the complexity of the project, which has to be filled after completing the initial requirement analysis. Final effort is estimated using the project size and the different adjustment factors. For better calculation of these adjustments factors, these are categorized into three categories based on their characteristics such as Production and General system characteristics. This model is proposed to be used differently for the different types of projects. These projects are categorized based on their size and total/build effort ratio. The size of the project is estimated by using the modified object point analysis approach. The estimated effort is further phase wise distributed for better scheduling of the project. Another questionnaire is used to refine the model and it has to be filled by the project managers after completing the project. The proposed model is validated by studying twelve completed projects taken from industry and seventy different projects completed by the students. The proposed model shows a great improvement as compared to the earlier models used in effort estimation of CMS projects.

A review of task scheduling based on meta-heuristics approach in cloud computing

Heterogeneous distributed computing systems are the emerging for executing scientific and computationally intensive applications. Cloud computing in this context describes a paradigm to deliver the resource-like computing and storage on-demand basis using pay-per-use model. These resources are managed by data centers and dynamically provisioned to the users based on their availability, demand and quality parameters required to be satisfied. The task scheduling onto the distributed and virtual resources is a main concern which can affect the performance of the system. In the literature, a lot of work has been done by considering cost and makespan as the affecting parameters for scheduling the dependent tasks. Prior work has discussed the various challenges affecting the performance of dependent task scheduling but did not consider storage cost, failure rate-related challenges. This paper accomplishes a review of using meta-heuristics techniques for scheduling tasks in cloud computing. We presented the taxonomy and comparative review on these algorithms. Methodical analysis of task scheduling in cloud and grid computing is presented based on swarm intelligence and bio-inspired techniques. This work will enable the readers to decide suitable approach for suggesting better schemes for scheduling user’s application. Future research issues have also been suggested in this research work.

Video content authentication techniques: a comprehensive survey

In this digital day and age, we are becoming increasingly dependent on multimedia content, especially digital images and videos, to provide a reliable proof of occurrence of events. However, the availability of several sophisticated yet easy-to-use content editing software has led to great concern regarding the trustworthiness of such content. Consequently, over the past few years, visual media forensics has emerged as an indispensable research field, which basically deals with development of tools and techniques that help determine whether or not the digital content under consideration is authentic, i.e., an actual, unaltered representation of reality. Over the last two decades, this research field has demonstrated tremendous growth and innovation. This paper presents a comprehensive and scrutinizing bibliography addressing the published literature in the field of passive-blind video content authentication, with primary focus on forgery/tamper detection, video re-capture and phylogeny detection, and video anti-forensics and counter anti-forensics. Moreover, the paper intimately analyzes the research gaps found in the literature, provides worthy insight into the areas, where the contemporary research is lacking, and suggests certain courses of action that could assist developers and future researchers explore new avenues in the domain of video forensics. Our objective is to provide an overview suitable for both the researchers and practitioners already working in the field of digital video forensics, and for those researchers and general enthusiasts who are new to this field and are not yet completely equipped to assimilate the detailed and complicated technical aspects of video forensics.

Risk factors for contrast-induced nephropathy after coronary angiography

Contrast-induced nephropathy (CIN) is of concern after the use of radiocontrast media for coronary angiography (CAG) and percutaneous coronary intervention (PCI). We studied the incidence of CIN and its risk factors in patients undergoing CAG. In this prospective study, we included all patients with normal renal parameters undergoing CAG with nonionic radiocontrast media. We excluded patients with known chronic kidney disease, baseline creatinine more than 1.5 mg/dL, significant hypotension, anemia, and patients with acute myocardial infarction undergoing emergency PCI. Serum creatinine was done at baseline and serially for seven days after the procedure. Appropriate statistical tests were used to analyze the results and P <0.05 was considered statistically significant. The study population (n = 500, 348 males and 152 females) had a mean age of 56.6 ± 12.5 years. Twelve patients (2.4%) developed CIN and were equally distributed irrespective of the age, diabetes, or PCI procedure. CIN was observed to be more common in patients with hypertension than in those without hypertension (P = 0.0158). The total volume of contrast administered to CIN group (175 ± 59.3) was not significant as compared to that of non-CIN (159.1 ± 56) group (P = 0.334). None of the patients in our study required renal replacement therapy, and there was no mortality. CIN is observed in 2.4% of patients undergoing CAG and had a self-limiting course. Hypertension is the only observed risk factor, and further large-scale studies are necessary to delineate the novel risk factors for CIN in the general population with normal kidney function.

Smart patrolling

Road surface monitoring is an important problem in providing smooth road infrastructure to the commuters. The key to road condition monitoring is to detect road potholes and bumps, which affect the driving comfort and transport safety. This paper presents a smartphone based sensing and crowdsourcing technique to detect the road surface conditions. The in-built sensors of the smartphone like accelerometer and GPS1 have been used to observe the road conditions. It has been observed that several techniques in the past have been proposed using these sensors. Such techniques either use fixed threshold values which are road or vehicle condition dependent or use machine learning based classified training which requires intensive and continuous training. The motivation of our work is to improve classification accuracy of detecting road surface conditions using DTW2 technique which has not been researched on data based on motion sensors. The main features of DTW is its ability to automatically cope with time deformations and different speeds associated with time data, its simplicity is to be used in resource constrained devices such as smartphones and also the simplicity in its training procedure which is must as fast as compared to techniques such as SVM,3 HMM4 and ANN.5 Our technique shows better accuracy and efficiency with detection rate of 88.66% and 88.89% for potholes and bumps respectively, when compared with the existing techniques with the use of the proposed technique, prioritization of the road repair and maintenance can be decided based on real-time data and facts.

Improving public transportation through crowd-sourcing

Commuting on roads in densely populated cities of the developing world is fraught with high delays and uncertainties. Wide use of public transportation can ease the load on the road infrastructure, but such use is not convenient, partly due to the unpredictable nature. In this work, our goal is to improve the usability of public transportation, through better information. Such information can lead to better planning and predictability for commuters. We take a crowd-sourced approach where information about transportation units as well as road conditions is crowd-sourced from commuters. The information is then processed and made available to other commuters. In this context, this paper presents a naming framework we have developed, which will enable flexible and scalable content-driven data gathering and dissemination. Based on a preliminary implementation of the framework, we present various field-experiment results which shed light on the practicality of the proposed approach as well as on technical issues which need further careful addressing.

Novel Research in the Field of Shot Boundary Detection – A Survey

Segregating a video sequence into shots is the first step toward video-content analysis and content-based video browsing and retrieval. A shot may be defined as a sequence of consecutive frames taken by a single uninterrupted camera. Shots are the basic building blocks of videos and their detection provides the basis for higher level content analysis, indexing and categorization. The problem of detecting where one shot ends and the next begins is known as Shot Boundary Detection (SBD). Over the past two decades, numerous SBD techniques have been proposed in the literature. This paper presents a brief survey of all the major novel and latest contributions in this field of digital video processing.

Malicious data classification using structural information and behavioral specifications in executables

With the rise in the underground Internet economy, automated malicious programs popularly known as malwares have become a major threat to computers and information systems connected to the internet. Properties such as self healing, self hiding and ability to deceive the security devices make these software hard to detect and mitigate. Therefore, the detection and the mitigation of such malicious software is a major challenge for researchers and security personals. The conventional systems for the detection and mitigation of such threats are mostly signature based systems. Major drawback of such systems are their inability to detect malware samples for which there is no signature available in their signature database. Such malwares are known as zero day malware. Moreover, more and more malware writers uses obfuscation technology such as polymorphic and metamorphic, packing, encryption, to avoid being detected by antivirus. Therefore, the traditional signature based detection system is neither effective nor efficient for the detection of zero-day malware. Hence to improve the effectiveness and efficiency of malware detection system we are using classification method based on structural information and behavioral specifications. In this paper we have used both static and dynamic analysis approaches. In static analysis we are extracting the features of an executable file followed by classification. In dynamic analysis we are taking the traces of executable files using NtTrace within controlled atmosphere. Experimental results obtained from our algorithm indicate that our proposed algorithm is effective in extracting malicious behavior of executables. Further it can also be used to detect malware variants.

A review on Video Quality Assessment

With the advancement in technology, more and more video content are available on Internet. Video content such as news, sports, entertainment plays a very important role in everybodys life. But before reaching at the user end due to processing and transmission the videos may get distorted. In this paper various artifacts, distortions which gets introduced in videos are discussed. The assessment of quality of video depends upon the type of distortion. In this paper we also discussed two Video Quality Assessment techniques namely: Subjective and Objective assessment. These methods provide the users QoE (Quality of experience) and QoS (Quality of service). However, subjective assessment is considered to the easiest method for evaluation as the evaluation is done with the help of users. But it is very time consuming and tedious. So, most of modern applications are inclined to use the objective assessment. Different VQA performance metrics used for objective assessment are analyzed.

Finding occupancy in buses using crowdsourced data from smartphones

In the present scenario, developing countries like India are facing huge traffic congestion problems. Commuters have to wait long hours for arrival of buses, and when the bus arrives it is often found to be overcrowded, causing inconvenience in the commuters and discouraging them to use public transit system. The ITS(Intelligent Transport System) developed so far does provide arrival time of buses in real time but such systems are rare which provide the passenger occupancy in real time. Most of such installations use extortionate devices like passenger counting devices, cameras etc installed on the buses and at the bus stops. In this paper we propose a cost effective user participation based mode of collecting information about occupancy level of public transportation system using the potential of smartphones. Smartphones have inbuilt sensors like GPS which can be used to extract locational intelligence of the commuters. Hence, information gets crowdsourced from commuters and they themselves can provide information about occupancy level of a bus using their smartphones. The information so collected is stored in a historical database which is analyzed and processed to obtain occupancy level patterns for different routes on different days. The patterns observed are used to make predictions of occupancy level in a bus. Our results show that it is possible to achieve an accuracy to a level of 91.86 percent.