Статьи журнала - International Journal of Information Technology and Computer Science
Все статьи: 1195
Association Rule Hiding by Positions Swapping of Support and Confidence
Статья научная
Many strategies had been proposed in the literature to hide the information containing sensitive items. Some use distributed databases over several sites, some use data perturbation, some use clustering and some use data distortion technique. Present paper focuses on data distortion technique. Algorithms based on this technique either hide a specific rule using data alteration technique or hide the rules depending on the sensitivity of the items to be hidden. The proposed approach is based on data distortion technique where the position of the sensitive items is altered but its support is never changed. The proposed approach uses the idea of representative rules to prune the rules first and then hides the sensitive rules. Experimental results show that proposed approach hides the more number of rules in minimum number of database scans compared to existing algorithms based on the same approach i.e. data distortion technique.
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Asymmetric Concealed Data Aggregation Techniques in Wireless Sensor Networks: A Survey
Статья научная
The wireless communication nature of remotely deployed sensor nodes make the attacks more easily to be happened in wireless sensor networks (WSNs). But traditional security algorithms are infeasible in WSNs due to the limited computing, communication power, storage, band width and energy of sensor nodes. So energy efficient secure data aggregation schemes are necessary in resource constrained WSNs. Concealed Data Aggregation (CDA) based on privacy homomorphism (PH) gives a critical solution for energy efficient secure data aggregation in WSNs. PH based algorithms allow aggregation to be happened on cipher texts. Thus, it eliminates the power consuming decryption operations at the aggregator node for the data aggregation and further encryption for the secure transmission of aggregated data. It also avoids the aggregator node from the burden of keeping the secret key information and thereby it achieves energy efficiency and reduces the frequency of node compromise attacks in aggregator nodes. Among the CDA techniques, asymmetric PH based CDA techniques are exploring due to their combination with elliptic curve cryptography having reduced key size. We present an overview of asymmetric concealed data aggregation techniques that achieve both end to end data confidentiality and non delayed data aggregation.
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Asynchronous Data Fusion With Parallel Filtering Frame
Статья научная
This paper studies the design of data fusion algorithm for asynchronous system with integer times sampling. Firstly, the multisensor asynchronous samplings is mapped to the basic axis, accordingly a sampling sequence of single sensor can be taken. Secondly, aiming at the sensor with the densest sampling points, the modified parallel filtering is given. Afterwards, the sequential filtering fusion method is introduced to deal with the case that there are multiple mapped measurements at some sampling point. Finally, a novel parallel filtering fusion algorithm for asynchronous system with integer times sampling is proposed. Besides, a judgment scheme to distinguish measurement number at every sampling point in the fusion period is also designed. One simple computer numerical value simulation is demonstrated to validate the effectiveness of the judgment scheme and the proposed asynchronous fusion algorithm.
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Attacking image watermarking and steganography - a survey
Статья научная
Image hiding techniques include steganography and watermarking. Steganography procedures are directed to keep the secure information from eavesdropping and perturbations. On the other hand, watermarking algorithms are used for keeping the watermark robust to attacks. When the attacker tries to perturb the carrier image to remove the watermark, the image quality will be degraded to level that makes it useless. Data hiding is essential in many applications such as communication channel security, data security and forgery detection. Watermarking is used in copyright protection. Image hiding attacks can be active or passive. In active attack, the attacker changes the content of the data. While in passive attacks the attacker tries to guess the secure data by eavesdropping. In this paper, we discuss the image data hiding attacks that directed to both secure message and carrier image. First, message attacks such as Oracle and Template attacks will be discussed. Second, the carrier image attacks are presented in two broad categories, namely passive and active attacks. Finally, the paper conclusion will be presented. The paper presented image data hiding attack types in professional and well-organized categories.
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AuMID: An Authentication Mechanism based on Identity Tag under Future Internet Architecture
Статья научная
It has been commonly recognized that the current Internet faces serious security and scaling problems. To address these problems, the architecture of ID/locator separation is the focus of future Internet development. However, the relevant authentication mechanism has not been proposed under this architecture. In this paper, we advance a new authentication mechanism called AuMID under ID/locator separation architecture, and describe the detailed procedures of access authentication and handoff authentication, and simultaneously give the deployment of authentication centers. Besides, AuMID uniquely introduces the Identity Tag which represents the terminal’s identity information to implement the sustainable authentication for the terminal. This mechanism adopts the challenge-response approach and achieves the double-way authentication between the terminal and access network. At the same time, by the use of Identify Tag AuMID successfully guarantees the authenticity of the source under ID/locator separation architecture. In conclusion, this paper gives a qualitative analysis for the scalability and security of this AuMID and an evaluation of handoff authentication delay.
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Augmented Random Search for Quadcopter Control: An alternative to Reinforcement Learning
Статья научная
Model-based reinforcement learning strategies are believed to exhibit more significant sample complexity than model-free strategies to control dynamical systems, such as quadcopters. This belief that Model-based strategies that involve the use of well-trained neural networks for making such high-level decisions always give better performance can be dispelled by making use of Model-free policy search methods. This paper proposes the use of a model-free random searching strategy, called Augmented Random Search (ARS), which is a better and faster approach of linear policy training for continuous control tasks like controlling a Quadcopter’s flight. The method achieves state-of-the-art accuracy by eliminating the use of too much data for the training of neural networks that are present in the previous approaches to the task of Quadcopter control. The paper also highlights the performance results of the searching strategy used for this task in a strategically designed task environment with the help of simulations. Reward collection performance over 1000 episodes and agent’s behavior in flight for augmented random search is compared with that of the behavior for reinforcement learning state-of- the-art algorithm, called Deep Deterministic policy gradient(DDPG) Our simulations and results manifest that a high variability in performance is observed in commonly used strategies for sample efficiency of such tasks but the built policy network of ARS-Quad can react relatively accurately to step response providing a better performing alternative to reinforcement learning strategies.
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Augmented reality based campus guide application using feature points object detection
Статья научная
These days though GPS is a very common navigation system, still using GPS for everybody is not possible due to the lack of technological awareness. So people follow the traditional method of asking location to the local people around. After taking external guidance, even if one reaches the destination place, one is not able to understand the significance of that place. In this paper, we have discussed an implementation of a Mobile Augmented Reality based application called “ARCampusGo". With this application, one has to just scan the structure/monument to view the details about it. Along with it, this application also provides the names of nearby structure/monuments. One can select any of them and then the route to the selected monument/structure from the current location is rendered. This application renders rich, easy and interactive visual experience to the user. The performance and usability of “ARCampusGo" are evaluated during different daytime and nighttime with the different number of users. The user experience and feedback are considered for performance measurement and enhancement.
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Статья научная
Sentiment analysis is a critical component in natural language processing applications, particularly for text classification. By employing state-of-the-art techniques such as ensemble methods, transfer learning and deep learning architectures, our methodology significantly enhances the robustness and precision of sentiment predictions. We systematically investigate the impact of various NLP models, including recurrent neural networks and transformer-based architectures, on sentiment classification tasks. Furthermore, we introduce a novel ensemble method that combines the strengths of multiple classifiers to improve the predictive ability of the system. The results demonstrate the potential of integrating state-of-the-art Natural Language Processing (NLP) models with ensemble classifiers to advance sentiment analysis. This lays the foundation for a more advanced comprehension of textual sentiments in diverse applications.
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Authorship attribution for Bengali language using the fusion of N-gram and Naive Bayes algorithms
Статья научная
This research shows the authorship attribution for three Bengali writers using both Naïve Bayes method and a new method proposed by us which performs better than Naïve Bayes for authorship attribution. Though a lot of works exist in the field of authorship attribution for other languages (especially English); the amount of work in this field for Bengali language is very low. For this experiment, we make our own dataset having 107380 words and 21198 unique words. For both methods, we pre-process our dataset to be compatible to work with the method experiments. For our dataset, Naïve Bayes gives an accuracy of 86% while our method gives an accuracy of 95%. The main inspiration behind our method is that every author has a nature to write some adjacent words and some single words repeatedly.
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Статья научная
Analog circuits are of great importance in electronic system design. Analog circuit design consists of circuit topology design and component values design. These two aspects are both essential to computer aided analog circuit evolving. However, Traditional GA is not very efficient in evolving circuit component’s values. This paper proposed a hybrid algorithm HME-GA (GA with hyper-mutation and elitist strategies). The advantage of HME-GA is that, it not only concentrates on evolving circuit topology, but also pays attention to evolving circuit component’s values. Experimental results show that, the proposed algorithm performs much better than traditional GA. HME-GA is an efficient tool for analog circuit design. Evolutionary technology has been demonstrated to be very useful in computer aided analog circuit design. More potential of evolutionary methods on analog circuit design is waiting for exploring.
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Automated Client-side Sanitizer for Code Injection Attacks
Статья научная
Web applications are useful for various online services. These web applications are becoming ubiquitous in our daily lives. They are used for multiple purposes such as e-commerce, financial services, emails, healthcare services and many other captious services. But the presence of vulnerabilities in the web application may become a serious cause for the security of the web application. A web application may contain different types of vulnerabilities. Cross-site scripting is one of the type of code injection attacks. According to OWASP TOP 10 vulnerability report, Cross-site Scripting (XSS) is among top 5 vulnerabilities. So this research work aims to implement an effective solution for the prevention of cross- site scripting vulnerabilities. In this paper, we implemented a novel client-side XSS sanitizer that prevents web applications from XSS attacks. Our sanitizer is able to detect cross-site scripting vulnerabilities at the client-side. It strengthens web browser, because modern web browser do not provide any specific notification, alert or indication of security holes or vulnerabilities and their presence in the web application.
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Статья научная
This paper illustrates architecture for a multi agent system in healthcare domain. The architecture is generic and designed in form of multiple layers. One of the layers of the architecture contains many proactive, co-operative and intelligent agents such as resource management agent, query agent, pattern detection agent and patient management agent. Another layer of the architecture is a collection of libraries to auto-generate code for agents using soft computing techniques. At this stage, codes for artificial neural network and fuzzy logic are developed and encompassed in this layer. The agents use these codes for development of neural network, fuzzy logic or hybrid solutions such as neuro-fuzzy solution. Third layer encompasses knowledge base, metadata and other local databases. The multi layer architecture is supported by personalized user interfaces for friendly interaction with its users. The framework is generic, flexible, and designed for a distributed environment like the Web; with minor modifications it can be employed on grid or cloud platform. The paper also discusses detail design issues, suitable applications and future enhancement of the work.
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Automatic spoken language recognition with neural networks
Статья научная
Translation has become very important in our generation as people with completely different cultures and languages are networked together through the Internet. Nowadays one can easily communicate with anyone in the world with the services of Google Translate and/or other translation applications. Humans can already recognize languages that they have priory been exposed to. Even though they might not be able to translate, they can have a good idea of what the spoken language is. This paper demonstrates how different Neural Network models can be trained to recognize different languages such as French, English, Spanish, and German. For the training dataset voice samples were choosed from Shtooka, VoxForge, and Youtube. For testing purposes, not only data from these websites, but also personally recorded voices were used. At the end, this research provides the accuracy and confidence level of multiple Neural Network architectures, Support Vector Machine and Hidden Markov Model, with the Hidden Markov Model yielding the best results reaching almost 70 percent accuracy for all languages.
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Automating text simplification using pictographs for people with language deficits
Статья научная
Automating text simplification is a challenging research area due to the compound structures present in natural languages. Social involvement of people with language deficits can be enhanced by providing them with means to communicate with the outside world, for instance using the internet independently. Using pictographs instead of text is one of such means. This paper presents a system which performs text simplification by translating text into pictographs. The proposed system consists of a set of phases. First, a simple summarization technique is used to decrease the number of sentences before converting them to pictures. Then, text preprocessing is performed including processes such as tokenization and lemmatization. The resulting text goes through a spelling checker followed by a word sense disambiguation algorithm to find words which are most suitable to the context in order to increase the accuracy of the result. Clearly, using WSD improves the results. Furthermore, when support vector machine is used for WSD, the system yields the best results. Finally, the text is translated into a list of images. For testing and evaluation purposes, a test corpus of 37 Basic English sentences has been manually constructed. Experiments are conducted by presenting the list of generated images to ten normal children who are asked to reproduce the input sentences based on the pictographs. The reproduced sentences are evaluated using precision, recall, and F-Score. Results show that the proposed system enhances pictograph understanding and succeeds to convert text to pictograph with precision, recall and F-score of over 90% when SVM is used for word sense disambiguation, also all these techniques are not combined together before which increases the accuracy of the system over all other studies.
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Automation in Software Source Code Development
Статья научная
The Continuous Integration idea lays beneath almost all automation aspects of the software development process. On top of that idea are built other practices extending it: Continuous Delivery and Continuous Deployment, automating even more aspects of software development. The purpose of this paper is to describe those practices, including debug process automation, to emphasize the importance of automating not only unit tests, and to provide an example of complex automation of the web application development process.
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Статья научная
In recent years, the use of cloud computing has increased exponentially to satisfy computing needs in both big and small organizations. However, the high amounts of power consumed by cloud data centres have raised concern. A major cause of power wastage in cloud computing is inefficient utilization of computing resources. In Infrastructure as a Service, the inefficiency is caused when users request for more resources for virtual machines than is required. In this paper, we propose a technique for automatic virtual machine sizing and resource usage prediction using neural networks, for multi-tenant Infrastructure as a Service cloud service model. The proposed technique aims at reducing energy wastage in data centres by efficient resource utilization. An evaluation of our technique on CloudSim Plus cloud simulator and WEKA shows that effective VM sizing not only achieves energy savings but also reduces the cost of using cloud services from a customer perspective.
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Beampattern for Multiple Antennas in Hybrid Terrestrial Satellite Communications System (HTSCS)
Статья научная
The hybrid architecture of Terrestrial and Satellite networks discussed in this paper utilizes frequency reuse. However, at the same time the frequency reuse results in Co-Channel Interference (CCI). The CCI is caused by the mobile users to the satellite end because of the strong receiver on the satellite end. Mainly, this paper will focus on to tone down the CCI and would also show that how the OFDM based adaptive beamforming can be employed to mitigate this interference. The technique which is being used to mitigate this interference is Pre-FFT adaptive beamforming also called as time domain beamforming. In this paper, main task is to mitigate the CCI which is induced by the mobile users to the satellite end and will be considered that there are J users. Out of these J users there is one desired user and rest are interferers. When the interfered data is received at the satellite end, the Pre-FFT adaptive beamforming extracts the desired user data from the interferers by applying the complex weights to the received symbol. The weight for the next symbol is then updated by Least Mean Square (LMS) algorithm and then is applied to it. This process is carried out till all the desired user data is extracted from the interference signal.
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Статья научная
This paper explores the proposed solutions based on blockchain technology's potential to solve supply chain management issues. The problems include lack of traceability and transparency, scalability and cost issues, sustainability, efficiency, patchwork logistics, and bullwhip effect issues. In this paper, we have suggested some solutions with the help of blockchain technology. The solutions can solve multiple significant issues in supply chain management. Our blockchain-based solutions can provide a secure and visible record of all transactions and data along the supply chain, which can improve traceability and transparency, a decentralized and efficient method of data processing and exchange that can also increase scalability and reduce cost, a transparent and accountable way to track and verify sustainability-related data. Our method can enable more streamlined and automated tracking and data sharing, helping to reduce the risk of delays and inefficiencies while mitigating the risk of the bullwhip effect by providing real-time visibility and enabling better communication and collaboration between parties. The paper discusses the implications and challenges of implementing blockchain in supply chain management.
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Big Data Analytics Maturity Model for SMEs
Статья научная
Small and medium-sized enterprises (SMEs) are the backbone of the global economy, constituting 90% of all businesses. Despite being widely adopted by large businesses who have reported numerous benefits including increased profitability and increased efficiency and a survey in 2017 of 50 Fortune 1000 and leading firms’ executives indicated that 48.4% of respondents confirmed they are achieving measurable results from their Big Data investments, with 80.7% confirming that they have generated business. Big Data Analytics is adopted by only 10% of SMEs. The paper outlines a review of Big Data Maturity Models and discusses their positive features and limitations. Previous research has analysed the barriers to adoption of Big Data Analytics in SMEs and a scoring tool has been developed to help SMEs adopt Big Data Analytics. The paper demonstrates that the scoring tool could be translated and compared to a Maturity Model to provide a visual representation of Big Data Analytics maturity and help SMEs to understand where they are on the journey. The paper outlines a case study to show a comparison to provide intuitive visual model to assist top management to improve their competitive advantage.
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Статья научная
In Healthcare and Medical diagnosis, Patient Satisfaction surveys are a valuable information resource and if studied adequately can contribute significantly to recognize the performance of the hospitals and recommend it. The analysis of measurements concerning patient satisfaction can act as a valid indicator for giving recommendations to the patient about a specific hospital, as well as can provide insights to improve the services for healthcare organizations. The primary objective of the proposed research is to carry out an in-depth investigation of all the measurements in HCAHPS survey dataset and distinguish those that contribute considerably to the hospital suggestions. This work performs predictive analysis by building multiple classification models, each of which examined and evaluated to determine the efficiency in predicting the target variable, i.e., whether the hospital is recommended or not, based on specific set of measurements that contribute to it. All the models built as a part of research specified the same list of measure id is that help in deriving the target. It provides an insight into how caregiver interaction, emphasizes on the services rendered by the caregiver and overall patient experience makes a hospital highly valued and preferred. An in depth-analysis is conducted to derive the implementation results and have been stated in the later part of the paper.
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