Intelligent computing

The growing adoption of Artificial Intelligence (AI) in the decentralized finance space has opened new opportunities to improve fraud detection, smart contracts, liquidity pooling, efficiency, and scalability of decentralized finance (DeFi) platforms. Despite the global expansion of AI-powered DeFi applications, Nigeria faces unique challenges such as regulatory uncertainty, high fraud penetration, low digital literacy, and infrastructural gaps, which hinder full integration and trust in AI-enabled DeFi systems. This study is motivated by the need to understand how AI can foster inclusive and transparent financial ecosystems. Therefore, the study examined the effects of AI adoption on decentralized finance in Nigeria. It adopts a survey research design, with a sample size of 400 active DeFi participants determined through Cochran formula. Regression analysis was performed to examine the relationships between variables using the Statistical Package for Social Sciences (SPSS Version 23). Findings reveal that stakeholders' perceptions have a positive and significant association on AI's role in building trust and security in Nigeria (0.627; 0.000<0.01). Perceived barriers also have a positive and significant association with the integration of artificial intelligence (AI) into DeFi systems by blockchain developers and financial professionals in Nigeria (0.506; 0.000<0.01). DeFi users have a significant and positive association with AI-driven tools used in decision-making for decentralized financial activities in Nigeria (0.551; 0.000<0.01). Regulatory changes have a significant and positive association with AI adoption in Nigeria (0.519; 0.000<0.01). AI practitioners have a significant and positive association with the development of decentralized finance in Nigeria (0.614; 0.000<0.01). Based on these findings, the study concludes that AI is a powerful tool that can potentially revolutionize the face of decentralized finance in Nigeria. The study further recommended that policymakers and regulators develop and scale an adaptive AI-DeFi regulatory framework by developing a tiered regulatory sandbox specific to DeFi-AI platforms, enabling controlled experimentation under policy oversight and a national registry of certified AI-DeFi practitioners.

Crop yield prediction from UAV images has significant potential in accelerating and revolutionizing crop breeding pipelines. Although convolutional neural networks (CNN) provide easy, accurate and efficient solutions over traditional machine learning models in computer vision applications, a CNN training requires large number of ground truth data, which is often difficult to collect in the agricultural context. The major objective of this study was to develope an end-to-end 3D CNN model for plot-scale soybean yield prediction using multitemporal UAV-based RGB images with approximately 30,000 sample plots. A low-cost UAV-RGB system was utilized and multitemporal images from 13 different experimental fields were collected at Argentina in 2021. Three commonly used 2D CNN architectures (i.e., VGG, ResNet and DenseNet) were transformed into 3D variants to incorporate the temporal data as the third dimension. Additionally, multiple spatiotemporal resolutions were considered as data input and the CNN architectures were trained with different combinations of input shapes. The results reveal that: (a) DenseNet provided the most efficient result (R 2 0.69) in terms of accuracy and model complexity, followed by VGG (R 2 0.70) and ResNet (R 2 0.65); (b) Finer spatiotemporal resolution did not necessarily improve the model performance but increased the model complexity, while the coarser resolution achieved comparable results; and (c) DenseNet showed lower clustering patterns in its prediction maps compared to the other models. This study clearly identifies that multitemporal observation with UAV-based RGB images provides enough information for the 3D CNN architectures to accurately estimate soybean yield non-destructively and efficiently.

Open Web Application Security Project (OWASP) Top 10 algorithm for Web Applications (WA) security audits using Vulnerability Assessment and Penetration Testing (VAPT) processes. The study places particular emphasis on enhancing the VAPT process by optimizing the OWASP algorithm. To achieve this, the research utilizes desk documents to gain knowledge of WA cybersecurity audits and their associated tools. It also delves into archives to explore VAPT processes and identify techniques, methods, and tools for VAPT automation. Furthermore, the research proposes a prototype optimization that streamlines the two steps of VAPT using the OWASP Top 10 algorithm through an experimental procedure. The results are obtained within a virtual environment, which employs black box testing methods as the primary means of data acquisition and analysis. In this experimental setting, the OWASP algorithm demonstrates an impressive level of precision, achieving a precision rate exceeding 90%. It effectively covers all researched vulnerabilities, thus justifying its optimization. This research contributes significantly to the enhancement of the OWASP algorithm and benefits the offensive security community. It plays a crucial role in ensuring compliance processes for professionals and analysts in the security and software development fields.

The physical limitations of CMOS technology triggered several research for finding an alternative technology. QCA is one of the emerging nanotechnologies which is gaining attention as a substitute of CMOS. The main potential of QCA is its ultra low power consumption, less area overhead, and high speed. Majority and inverter gates are the basic gates in QCA, which together works as a universal logic gate to implement any QCA circuit. This paper proposes an efficient methodology for optimal QCA circuit synthesis of arbitrary multi-output boolean functions. A multi-objective genetic algorithm based approach is used to reduce worst case delay and gate count of a QCA circuit. Different importance is given to worst case delay, no. of majority and no. of inverter gates. Several efficient techniques are used in order to achieve the optimal result and reduce the computational complexity furthermore additional methodologies are used to eliminate redundancies from the final solution. Comparison of the obtained results with the existing best techniques indicates, the proposed technique outperforms in terms of worst case delay and gate count.

Loan default prediction is one of the most important and critical problem faced by many banks and other financial institutions as it has a huge effect on their survival and profit. Many traditional methods exist for mining information about a loan application and have been greatly studied and applied in the past. These methods seem to be underperforming as there have been reported increases in the amount of bad loans and defaulters among many financial institutions. In this paper, gradient boosting algorithm called XGBoost was used for loan default prediction. The prediction is based on a loan data from a leading bank taking into consideration data sets from both the loan application and the demographic of the applicant. Similarly, important evaluation metrics such as Accuracy, Recall, precision, F1-Score and ROC area of the analysis were used. The paper provides an effective basis for loan credit approval in order to identify risky customers from a large number of loan applications using predictive modeling. The full utilization of this model will assist financial institutions in knowing a risking customer that may default in loan payment before lending.

Syllables preserve within unit co-articulation effects, which results syllables to be a good choice as speech unit for speech database of many languages. Punjabi is a syllabic language, so syllables have been selected as the basic speech unit for the preparation of speech database for Punjabi. In order to select a minimal syllable set covering almost whole Punjabi word set to be stored in the speech database, all the available Punjabi syllables have been statistically analyzed over a large Punjabi corpus having more than 104 million words. This statistical analysis reveals very interesting and important facts about the actual use of Punjabi syllables by its speaker and writers. From the results of this analysis a relatively smaller syllable set of about first ten thousand (0.86% of total available 1156740 syllables) most frequently occurring syllables having cumulative frequency of occurrence less than 99.81% have been selected. Also, frequency of occurrence of these syllables at the three positions (starting, middle and end) in the words of corpus has been obtained, for detailed storage and better utilization of these syllables in certain applications like Text-to-Speech synthesis.

In the ever-evolving landscape of web development, client-side storage mechanisms have become pivotal in enhancing user experience and application performance. This paper delves into the various client-side storage options available in modern web applications, including cookies, local storage, session storage, and IndexedDB. By exploring their functionalities, use cases, and limitations, we aim to provide a comprehensive understanding of how these mechanisms can be effectively utilized. The paper also discusses implementation considerations, best practices, and security concerns, emphasizing the importance of compliance with regulations like GDPR and CCPA. Through comparative analysis and real-world examples—particularly in e-commerce and content platforms—we highlight strategies for selecting the appropriate storage solutions. Finally, we explore future trends and potential areas for further research, offering insights into how emerging technologies may shape the future of client-side storage.

As the increasing number of telemedical applications over the Internet using mobile devices (PDAs, Tablet PC, various medical devices, and so on), for access of medical data in terms of telemedicine and remote patient telemonitoring, the necessity of new powerful encryption techniques becomes a crucial issue. In this paper is presented a fully functional software encryption system based on a combination of a one-dimensional hybrid cellular automaton (CA) and a programmable cellular automaton (PCA) used for securing of medical data sent over the internet. The experimental results are based on the powerful of cellular automata based encryption systems, already validated in my previously works. The proposed algorithm belongs to the class of symmetric key systems.

We present an efficient document representation learning framework, Document Vector through Corruption (Doc2VecC). Doc2VecC represents each document as a simple average of word embeddings. It ensures a representation generated as such captures the semantic meanings of the document during learning. A corruption model is included, which introduces a data-dependent regularization that favors informative or rare words while forcing the embeddings of common and non-discriminative ones to be close to zero. Doc2VecC produces significantly better word embeddings than Word2Vec. We compare Doc2VecC with several state-of-the-art document representation learning algorithms. The simple model architecture introduced by Doc2VecC matches or out-performs the state-of-the-art in generating high-quality document representations for sentiment analysis, document classification as well as semantic relatedness tasks. The simplicity of the model enables training on billions of words per hour on a single machine. At the same time, the model is very efficient in generating representations of unseen documents at test time.

In this paper we discuss the "Cloud Computing" architecture, cloud services, layers and types of cloud and propose a cloud infrastructure prototype for distributed university campus. Cloud Computing can be defined as providing resources and capabilities of Information Technology (e.g., applications, storages, communication, collaboration, infrastructure) via services offered by cloud computing providers. Cloud Computing has various characteristics as shared infrastructure, self-service, pay-peruse model, dynamic and virtualized, elastic and scalable. Nowadays, because of the increasing popularity of Cloud Computing many giant IT companies such as Microsoft, IBM, Google, and Amazon interest developing new cloud environments due to advantages of the Cloud Computing technology include cost, availability, and scalability. A Cloud Computing service has ubiquitous access through a Web browser or mobile device with APIs or special desktop applications developed by cloud service provider. Use of Cloud Computing on universities has many benefits such as accessing the file storages, e-mails, databases, educational resources, research applications and tools anywhere for faculty, administrators, staff, students and other users in university, on demand. Furthermore, cloud computing reduces universities' IT complexity and cost. It is argued that cloud computing paradigms and characteristic, service and deployment model of cloud computing technology in first section of this paper. Then we discuss the implementation of cloud services at universities and various opportunities and benefits of cloud services for universities. Finally, we present suggested cloud infrastructure prototype for distributed campus.

This tudy investigates the impact of blockchain technology on sustainable real estate supply chain management, specifically focusing on material traceability and transparency. The findings highlight the importance of considering blockchain as a viable solution for enhancing material traceability and transparency in sustainable real estate supply chains as panacea for preventing incessant building collapse in Nigeria. The study contributes to the understanding of blockchain technology's role in real estate supply chain management and provides insights for industry practitioners, policymakers, and researchers. In this study, a quantitative approach was employed, which included distributing a questionnaire to 300 participants representing various sectors within the real estate industry. Out of the total, 235 responses were received. A regression analysis was conducted to examine the impact of blockchain technology on material traceability and transparency in real estate supply chain. The regression analysis showed a statistically significant positive relationship (β = 0.347, p < 0.005), and (β = 0.358, p < 0.005) indicating that the adoption of blockchain technology has significant impact on improving material traceability and transparency respectively. While challenges exist in the use of blockchain, such as scalability, future work should focus on practical implementation through pilot projects and the development of standardised protocols. It is recommended that industry stakeholders collaborate with regulatory bodies to establish guidelines and standards specific to blockchain implementation in the real estate sector in Nigeria.

Machine learning and deep learning
algorithms have the potential to revolutionize network
data analytics in 5G cellular networks. With the increase
in the number of connected devices and the explosion of
data generated by these devices, traditional methods of
network data analytics are becoming increasingly
inadequate. However, this function faces several
challenges, including managing massive amounts of data,
processing data in real-time, addressing privacy
concerns, integrating with other network systems, and
requiring skilled professionals. Overcoming these
challenges will enable the network data analytics
function to achieve its objectives and improve the overall
performance and reliability of 5G cellular networks.
Machine learning and deep learning algorithms enable
the automatic identification of patterns and insights in
large volumes of data, thereby facilitating real-time
decision-making and proactive network management.
These algorithms can be used for a wide range of
network data analytics functions, including network
optimization, anomaly detection, prediction of network
failures, and dynamic spectrum management. Deep
learning algorithms have shown significant promise in
processing unstructured network data such as network
logs, video, and images. As the 5G network continues to
grow and evolve, the use of machine learning and deep
learning algorithms in network data analytics is likely to
become increasingly essential for ensuring network
efficiency, reliability, and security.

Computer vision, medical image analysis, and autonomous vehicles are just a few of the many areas that have found success using Convolutional Neural Networks (CNNs), a potent tool for picture recognition. Choosing the correct CNN architecture and training method, however, can be difficult when working with huge datasets. The paper compares different convolutional neural network (CNN) designs and training strategies to determine which is the most effective for photo recognition. A variety of designs and training techniques for convolutional neural networks (CNNs) for image recognition are covered in the literature review, which covers earlier research on the subject. This paper outlines the pros and cons of popular convolutional neural network (CNN) designs including LeNet, AlexNet, VGG, and ResNet. Common training methods are also discussed in the literature, such as SGD, Adam, and BN. The research relied on the CIFAR-10 dataset, which included sixty thousand 10-category colour images at a resolution of thirty-two by thirty-two pixels. As a first step in the data preparation procedure, we normalised the pixel values and added some random rotations and flips to the training set. Using three distinct training methods—SGD, Adam, and SGD with BN—the researchers constructed and trained seven distinct CNN architectures, namely LeNet, AlexNet, VGG-16, VGG-19, ResNet-50, ResNet-101, and ResNet-152. With a 94.7% success rate, ResNet-152 proved to be the best architecture for the CIFAR-10 dataset. ResNet-101 and VGG-19 were just behind, both reaching 93.7% accuracy. With 152 layers, ResNet-152 outperformed VGG-19, which has only 19 layers, demonstrating that deeper networks beat shallower ones. Improved performance of the CNN architectures, leading to faster convergence and higher accuracy, was achieved by incorporating BN into the SGD training technique. This study sheds light on the relative merits of various convolutional neural network (CNN) designs and training methods for picture recognition. High accuracy in image recognition tasks is achieved by carefully choosing the CNN architecture and training technique, as shown by the findings. Adding BN to SGD enhanced performance, highlighting the importance of the training strategy in the study. These results have real-world consequences since they may guide future CNN designs by experts in the area of image recognition. Research in the future might look into how well these CNN designs and training methods work with other datasets, as well as investigate other cutting-edge approaches like adversarial training and transfer learning. Computer vision, medical image processing, and autonomous cars are just a few of the many potential applications of convolutional neural networks (CNNs) that could be made possible by this type of research.

RFID tags (radio frequency identification devices) are in essence transceivers consisting of three components that make up a sophisticated transponder. Once activated, the tag transmits data back to a receiving antenna: the technology does not require human intervention and further benefits from the fact that no line of sight is needed to control/operate the system. The tags can have both read and write abilities and their performance characteristics can be tailored/changed to accommodate a range of situations. As RFID becomes ubiquitous, the proliferation of RFID system suggests that it will be all pervasive and there is no doubt that RFID is to have a tremendous impact on all major construction projects and built environment facilities. As the systems become more widespread, so the technology itself becomes smaller and cheaper. Some popular RFID applications include: supply chain management, baggage handling, library information systems, rental car, inventory control, hospitals and animal identification Within this paper we argue that utility provision (the hidden services) is an area where RFID technology would be able to identify location of buried pipes and others underground equipment. Results from field trials are considered and issues and concerns relating to developing such an application of RFID are discussed.

In this paper we perform event analysis on a challenging surveillance dataset without any artificial events. We analyze low-level and high-level features such as motion vectors, change detection and pedestrian detections for recognition of events. We performed detection of three events namely person runs, elevator no entry and opposing flow. The event detection is performed on 60 hours of TRECVid 2008 event detection dataset.

The potential use of Automatic Speech Recognition to assist receptive communication is explored. The opportunities and challenges that this technology presents students and staff to provide captioning of speech online or in classrooms for deaf or hard of hearing students and assist blind, visually impaired or dyslexic learners to read and search learning material more readily by augmenting synthetic speech with natural recorded real speech is also discussed and evaluated. The automatic provision of online lecture notes, synchronised with speech, enables staff and students to focus on learning and teaching issues, while also benefiting learners unable to attend the lecture or who find it difficult or impossible to take notes at the same time as listening, watching and thinking.

In today's era, the growing ratio of Gastrointestinal (GI) diseases in human beings has become a crucial point of notice and must be diagnosed as early as possible. There are various methods to diagnose abdomen-related problems using medical imaging techniques like ultrasound, endoscopy, Colonoscopy, abdominal CT scan and digital X-ray, etc. Endoscopy is one of the most efficient medical imaging techniques for diagnosing gastrointestinal (GI) diseases. Manual diagnosis of endoscopic images may have a possibility of committing mistakes in properly detecting gastrointestinal disorders because tiny particles are involved in endoscopic images and may be responsible for critical disorders. However, manual diagnosis may ignore such information because of less efficiency of vision and observation. To avoid such problems, various models based on soft computing and neuro-fuzzy techniques have been proposed to detect and classify various gastrointestinal disorders. In this article, the authors propose a systematic review of previous research that has been carried out using intelligent computing methods. Here, various conventional approaches are discussed and compared. This review research shows performance limitations due to complex data models, heterogeneous datasets and the absence of intelligent feature selection methods in diagnosing gastrointestinal disorders.

Virtualization brings compelling features to individual computer systems and organizations allowing for the concurrent execution of multiple operating systems and applications on the same physical server. However, for all the performance improvements offered by adopting virtualization technologies, one of the major obstacles to widespread adoption of virtualization is the concern about security in the technology. Therefore, the security implications in virtualization environments must be addressed and understood because of the exploitation to compromise the operation of missioncritical systems. In this paper, we first discuss common exploits of security properties in virtualized computing environments and analyze their security vulnerabilities from the perspective of attackers. Consequently, we identify the main areas of virtualized information system design and operation in which security concerns must be addressed. Finally, we present our recommendations and future trends for trusted virtualized computing environments.

Analyzing binary programs without source code is critical for cybersecurity professionals. This paper presents an undergraduate binary reverse engineering course design that culminates with a comprehensive binary exploitation competition. Our approach challenges students to develop tools that automatically detect and exploit program vulnerabilities. We hypothesize that this competition presents a unique opportunity to exercise the core competencies of binary reverse engineering. We share our detailed design, labs, experiences, and lessons learned from this course for others to build on our initial success. CCS CONCEPTS • Social and professional topics ! Model curricula; • Software and its engineering ! Software reverse engineering.

In today's era, the growing ratio of Gastrointestinal (GI) diseases in human beings has become a crucial point of notice and must be diagnosed as early as possible. There are various methods to diagnose abdomen-related problems using medical imaging techniques like ultrasound, endoscopy, Colonoscopy, abdominal CT scan and digital X-ray, etc. Endoscopy is one of the most efficient medical imaging techniques for diagnosing gastrointestinal (GI) diseases. Manual diagnosis of endoscopic images may have a possibility of committing mistakes in properly detecting gastrointestinal disorders because tiny particles are involved in endoscopic images and may be responsible for critical disorders. However, manual diagnosis may ignore such information because of less efficiency of vision and observation. To avoid such problems, various models based on soft computing and neuro-fuzzy techniques have been proposed to detect and classify various gastrointestinal disorders. In this article, the authors propose a systematic review of previous research that has been carried out using intelligent computing methods. Here, various conventional approaches are discussed and compared. This review research shows performance limitations due to complex data models, heterogeneous datasets and the absence of intelligent feature selection methods in diagnosing gastrointestinal disorders.

The travelling salesman problem (TSP) is probably one of the most famous problems in combinatorial optimization. There are many techniques to solve the TSP problem such as Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Simulated Annealing (SA). In this paper, we conduct a comparison study to evaluate the performance of these three algorithms in terms of execution time and shortest distance. JAVA programing is used to implement the algorithms using three benchmarks on the same platform conditions. Among the three algorithms, we found out that the Simulated Annealing has the shortest time in execution(<1s) but for the shortest distance, it comes in the second order. Furthermore, in term of shortest distance between the cities, ACO performs better than GA and SA. However, ACO comes in the last order in term of time execution.

Electricity is the nervous system in our live on earth and it is involved in many electronic devices and systems. In this paper, the end use of electricity was forecasted in the medium-term scale. The medium-term forecasting is conducted using the following forecasting algorithms: Auto-Regressive Integrated Moving Average (ARIMA), Hybrid ARIMA, and Linear Regression with horizon value equals five (2018)(2019)(2020)(2021)(2022). After evaluating tested algorithms using MAPE it is noticed that Hybrid ARIMA was the best forecasting algorithm to be used with MAPE 1.77%, while ARIMA MAPE equals 4.81% and linear regression MAPE equals 7.04%. The deviation of the forecasted 5 years (2018)(2019)(2020)(2021)(2022) is decreasing by 1.21% with comparison to the last 5 years (2013)(2014)(2015)(2016)(2017).

The combination of forecasting and planning has increased prominence within few decades and now receives considerable attention from both academics and practitioners. Palestine municipalities are facing complex circumstances because of the climate change (lack of rainfall) and due to the Israeli occupation. Water resources are decreasing by the passage of time. Therefore, it is necessary to focus on water service projects for best exploit of resources given the growth of population. In this research, we forecasted the consumption used by citizen in the medium-term future and forecasted revenues from water service. We conducted the forecasting using the following forecasting algorithms: Auto-Regressive Integrated Moving Average (ARIMA), Hybrid ARIMA, Singular Spectrum Analysis (SSA), and Linear Regression. We applied them on dataset collected from KhanYounis municipality (KHM)department of customer services. We found that the best algorithm is Hybrid ARIMA which gave the Mean Absolute Percentage Error (MAPE) of 17.38%. Finally, we forecasted the whole city of KhanYounis city. Generally, we found that the maximum water consumption for the overall city after five years will increase to about 8.4% compared to 2017, but the minimum water revenue will decrease to about 3.8% compared to 2017.

Natural resources are limited and very important in our industrial life and development. Oil is considered the black gold and it is included in hundreds of industrial fields. In this paper, the oil production amounts produced from the world continents and regions

The travelling salesman problem (TSP) is probably one of the most famous problems in combinatorial optimization. There are many techniques to solve the TSP problem such as Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Simulated Annealing (SA).In this paper, we conduct a comparison study to evaluate the performance of these three algorithms in terms of execution time and shortest distance. JAVA programing is used to implement the algorithms using three benchmarks on the same platform conditions. Among the three algorithms, we found out that the Simulated Annealing has the shortest time in execution(<1s) but for the shortest distance, it comes in the second order. Furthermore, in term of shortest distance between the cities, ACO performs better than GA and SA. However, ACO comes in the last order in term of time execution.

Prototype pollution is a critical vulnerability that affects JavaScript environments, including Node.js. This vulnerability arises from the dynamic nature of JavaScript, allowing attackers to manipulate the prototype of objects and inject malicious properties into them. In Node.js applications, prototype pollution can lead to severe security threats, including Remote Code Execution (RCE) and Cross-Site Scripting (XSS) attacks. Research in the Prototype pollution vulnerability detection and exploitation in Node.js has seen significant advancements. Various techniques, such as Symbolic/ Concolic testing, static analysis, and dynamic taint analysis, have been employed to effectively detect and exploit prototype pollution vulnerabilities. Tools and frameworks, such as UOPF (Undefined-oriented Programming Framework) and Silent Spring, have been developed to automate detecting and chaining prototype pollution gadgets in Node.js template engines. Prototype pollution vulnerabilities in Node.js are particularly challenging due to the complex nature of JavaScript applications and the diversity of libraries and frameworks used in Node.js development. Therefore, researchers continue to explore new methods and techniques to improve the detection and mitigation of prototype pollution vulnerabilities in Node.js environments.

Modern European societies face growing terrorism threats alongside with other criminal activities and security incidents. Furthermore, the European zone experiences a migration crisis that reinforces the need for development of innovative surveillance systems. The primary objective of this paper is to conduct firstly a cyber-threat assessment of now-days surveillance systems and secondly to concretely assess specific systems developed in various research projects.

Schema matching is a basic problem in many database application domains, such as data integration. The problem of schema matching can be formulated as follows, "given two schemas, S i and S j , find the most plausible correspondences between the elements of S i and S j , exploiting all available information, such as the schemas, instance data, and auxiliary sources" [24]. Given the rapidly increasing number of data sources to integrate and due to database heterogeneities, manually identifying schema matches is a tedious, time consuming, error-prone, and therefore expensive process. As systems become able to handle more complex databases and applications, their schemas become large, further increasing the number of matches to be performed. Thus, automating this process, which attempts to achieve faster and less labor-intensive, has been one of the main tasks in data integration. However, it is not possible to determine fully automatically the different correspondences between schemas, primarily because of the differing and often not explicated or documented semantics of the schemas. Several solutions in solving the issues of schema matching have been proposed. Nevertheless, these solutions are still limited, as they do not explore most of the available information related to schemas and thus affect the result of integration. This paper presents an approach for matching schemas of heterogeneous relational databases that utilizes most of the information related to schemas, which indirectly explores the implicit semantics of the schemas, that further improves the results of the integration.

Resource efficiency and quality of service (QoS) are both long-pursuit goals for cloud providers over the last decade. However, hardly any cloud platform can exactly achieve them perfectly even until today. Improving resource efficiency or resource utilization often could cause complicated resource contention between colocated cloud applications on different resources, spanning from the underlying hardware to the software stack, leading to unexpected performance degradation. The low-entropy cloud proposes a new software-hardware codesigned technology stack to holistically curb performance interference from the bottom up and obtain both high resource efficiency and high quality of application performance. In this paper, we introduce a new computer architecture for the low-entropy cloud stack, called labeled von Neumann architecture (LvNA), which incorporates a set of label-powered control mechanisms to enable shared components and resources on chip to differentiate, isolate, and prio...

Network intrusions may illicitly retrieve data/information, or prevent legitimate access. Reliable detection of network intrusions is an important problem, misclassification of an intrusion is an issue by the resultant overall reduction of accuracy of detection. A variety of potential methods exist to develop an improved system to perform classification more accurately. Feature selection is one area that may be utilized to successfully improve performance by initially identifying sets and subsets of features that are relevant and non-redundant. Within this paper explicit pairings of features have been investigated in order to determine if the presence of pairings has a positive effect on classification, potentially increasing the accuracy of detecting intrusions correctly. In particular, classification using the ensemble algorithm, StackingC, with F-Measure performance and derived Information Gain Ratio, as well as their subsequent correlation as a combined measure, are presented.

In today’s technical world, the intellectual computing of a efficient human-computer interaction (HCI) or human alternative and augmentative communication (HAAC) is essential in our lives. Hand gesture recognition is one of the most important techniques that can be used to build up a gesture based interface system for HCI or HAAC application. Therefore, suitable development of gesture recognition method is necessary to design advance hand gesture recognition system for successful applications like robotics, assistive systems, sign language communication, virtual reality etc. However, the variation of illumination, rotation, position and size of gesture images, efficient feature representation, and classification are the main challenges towards the development of a real time gesture recognition system. The aim of this work is to develop a framework for vision based static hand gesture recognition which overcomes the challenges of illumination, rotation, size and position variation of...

Network intrusions may illicitly retrieve data/information, or prevent legitimate access. Reliable detection of network intrusions is an important problem, misclassification of an intrusion is an issue by the resultant overall reduction of accuracy of detection. A variety of potential methods exist to develop an improved system to perform classification more accurately. Feature selection is one area that may be utilized to successfully improve performance by initially identifying sets and subsets of features that are relevant and non-redundant. Within this paper explicit pairings of features have been investigated in order to determine if the presence of pairings has a positive effect on classification, potentially increasing the accuracy of detecting intrusions correctly. In particular, classification using the ensemble algorithm, StackingC, with F-Measure performance and derived Information Gain Ratio, as well as their subsequent correlation as a combined measure, are presented.

Developers of Software Defined Network (SDN) faces a lack of or difficulty in getting a physical environment to test their inventions and developments. That drives them to use a virtual environment for their experiments. This work addresses the differences between the SDN virtual environment and physical SDN switches, which leads to equip a more realistic SDN virtual environment. Consequently, this paper presents a precise performance evaluation and comparison of off-the-shelf SDN devices, HP Aruba 3810M, with Open Virtual Switch (OVS) inside Mininet emulator. This work examines the variability of the path delay, throughput, packet losses and jitter of SDN in a different windows size of the packets and network background loads. Our conducted experiments consider a number of protocols such as ICMP, TCP and UDP. In order to evaluate the network latency accurately, a new asynchronous latency measurement technique is proposed. The developed technique shows more precise results in comparison to other techniques. Furthermore, the work focuses on extracting the flow-setup latency, caused by the external SDN controller when setting flow rules into the switch. The comparison of results shows a dissimilarity in the behaviour of SDN hardware and the Mininet emulator. The SDN hardware exposed higher latency and flowsetup time due to extra resources of delay, which the emulator does not possess.

Articulation via target-oriented approaches have been commonly used in robotics. Movement of a robotic arm can involve targeting via a forward or inverse kinematics approach to reach the target. We attempted to transform the task of controlling the motor articulation to a machine learning approach. Towards this goal, we built an online robotic arm to extract articulation datasets and have used SVM and Naïve Bayes techniques to predict multi-joint articulation. For controlling the preciseness and efficiency, we developed pick and place tasks based on pre-marked positions and extracted training datasets which were then used for learning. We have used classification as a scheme to replace prediction-correction approach as usually attempted in traditional robotics. This study reports significant classification accuracy and efficiency on real and synthetic datasets generated by the device. The study also suggests SVM and Naïve Bayes algorithms as alternatives for computational intensive prediction-correction learning schemes for articulator movement in laboratory environments.

This paper describes the architecture of a computer system conceived as an intelligent assistant for public transport management. The goal of the system is to help operators of a control center in making strategic decisions about how to solve problems of a fleet of buses in an urban network. The system uses artificial intelligence techniques to simulate the decision processes. In particular, a complex knowledge model has been designed by using advanced knowledge engineering methods that integrates three main tasks: diagnosis, prediction and planning. Finally, the paper describes two particular applications developed following this architecture for the cities of Torino (Italy) and Vitoria (Spain).

Mobile Access to the Web of Data is currently a real challenge in developing countries, mainly characterized by limited Internet connectivity and high penetration of mobile devices with the limited resources (such as cache and memory). In this paper, we survey and compare proposed solutions (such as models and architectures) that could contribute to solving this problem of mobile access to the Web of Data with intermittent Internet access. These solutions are discussed in relation to the underlying network architectures and data models considered. We present a conceptual study of peer-to-peer solutions based on gossip protocols dedicated to design the connected overlay networks. In addition, we provide a detailed analysis of client-server and data replication systems generally designed to ensure the local availability of data on the system. We conclude with some recommendations to achieve a connected architecture that provides mobile contributors with local access to the Web of data.

The emergence of heterogeneous systems has been very notable recently. Still their programming is a complex task. The co-execution of a single OpenCL kernel on several devices is a challenging endeavour, requiring considering the different computing capabilities of the devices and application behaviour. OmpSs is a framework for task based parallel applications, that does not support coexecution between several devices. This paper presents an extension of OmpSs that solves two main issues. First, the automatic distribution of datasets and the management of device memory address spaces. Second, the implementation of a set of load balancing algorithms to adapt to the particularities of applications and systems. All this is accomplished with negligible impact on the programming. Experimental results reveal that the use of all the devices in the system is beneficial in terms performance and energy consumption. Also, the Auto-Tune algorithm gives the best overall results without requiring manual parameter tuning.

The extensive use of cloud computing is changing the way of dealing with information and communication technology. Cloud computing has created a new concept to deal with software services and hardware infrastructure. Some benefits are immediate; for instance, to allow students to share their information easily and to discover new experiences of the education system. However, despite the comprehensive literature, there is still limited studies related to the applied factors that influence the adoption of cloud technology at higher education institutions. Such limitations in the literature arise clearly in developing countries. This paper attempts to enhance the understanding of related factors affecting the adoption of cloud computing in the educational context of developing nations. A fieldwork study for collecting data has been conducted at higher education institutions in the kingdom of Jordan. This study applied an interpretive paradigm with a qualitative research method to investigate the main enablers of and barriers to cloud computing adoption at the universities in developing countries in general and in the case of Jordan in particular.

In this paper, we propose to learn word embeddings based on the recent fixedsize ordinally forgetting encoding (FOFE) method, which can almost uniquely encode any variable-length sequence into a fixed-size representation. We use FOFE to fully encode the left and right context of each word in a corpus to construct a novel word-context matrix, which is further weighted and factorized using truncated SVD to generate low-dimension word embedding vectors. We have evaluated this alternative method in encoding word-context statistics and show the new FOFE method has a notable effect on the resulting word embeddings. Experimental results on several popular word similarity tasks have demonstrated that the proposed method outperforms many recently popular neural prediction methods as well as the conventional SVD models that use canonical count based techniques to generate word context matrices.

A secure and efficient pseudo-random number generator is crucial for the security of a stream cipher or block cipher in a cryptosystem. This paper is devoted to the design and analysis of a new robust Pseudo-Chaotic Number Generator (PCNG) based on discrete chaotic maps over a finite field, and a multiplexing mechanism. The proposed PCNG contains three discrete chaotic maps: Piece-Wise Linear Chaotic Map (PWLCM), skew tent and logistic map. XOR operators are applied on these maps to form three optional intermediate outputs that are selected by the multiplexing mechanism to produce the final pseudo-chaotic sequence. The XOR operators and multiplexing mechanism can increase the dynamic pseudo-chaotic properties significantly. The PCNG is designed over a finite 2 32 field, has a simple structure and can be easily implemented. It has been implemented on a new stream cipher. Security analyses and statistical experiments have been carried out and the results have proven that the proposed PCNG can generate effective pseudorandom numbers and the stream cipher exhibits good security properties with large secret key space and high key sensitivity. Therefore, the proposed PCNG can be successfully applied in cryptosystems.

Wi-Fi and Zigbee networks are widely deployed in wireless industrial control and monitoring applications. The security protocols used in these wireless networks rely on stream cipher to encrypt data before being transmitted. These protocols are secure but they don't respect the real-time requirement of industrial control. Chaotic systems are able to produce stream cipher with higher randomicity, which looks like stochastic noise. Based on two perturbed piecewise linear chaotic map (PWLCM), we propose a new high speed chaotic cryptographic scheme. It requires a little memory capacity, and also appears to be very secure. The proposed generator is easily realized and succeeds all the statistical tests. In this paper, we will explain briefly the design of our proposed generator and show its efficiency through encryption tests.

This paper describes the development and the implementation of an omnidirectional multiple stereo cameras vision system. The vision system is compact and light enough to operate on board a commercially available off the shelf miniature UAV (Unmanned Aerial Vehicle)-Quadrotor. The vision system contains several stereo cameras ruggedly fixed onboard the UAV-Quadrotor, it is oriented in such a way that it has a 360 degrees omnidirectional visual coverage. The paper demonstrates that by combining several stereo cameras, it can provide and combine depth information and optical flow data in real time to an on-board image processing computer. One can estimate the position and the orientation such as roll, pitch and yaw of the UAV in 3D (three dimensions) space accurately without the aid of GPS (Global Positing System), IMU (Initial Measurement Unit) or any other external navigation or orientation aid. This method can be described as "Simultaneous Localization and Exploration Oriented Visual Navigation". It is a development of a vision system capable of navigating autonomously an UAV-Quadrotor through random free spaces within an unknown complex environment and without any mapping, it simply performs by detecting and avoiding obstacles calculating the required "through flight path".

In this article, a multi-agent approach modeled by the UML (Unified Modeling Language) is used to solve the piloting problem of Quality Management System QMS. Indeed, this multi-agent modeling of QMS proposes the use a micro framework via a dynamic diagram and macro through a static diagram for the agents development. From this modeling, an information system can be implemented in order to ensure the QMS conformity and effectiveness to the requirements of the series standards ISO 9000, ISO 9001, ISO 9004. In this context, an industrial company example will be proposed and the improvement prospects will be illustrated there. 2. A brief recall on the new multi-agent approach for modeling QMS Referring to the identified requirements of ISO 9000, ISO 9001 and ISO 9004 [10] [11] [12] and the foundations of the process approach; we can identify five types of processes namely:-A direction process that will listen to customer expectations, setting objectives, providing resources, decision-making and confirmation of actions for improvement. 1. Introduction

In the Software-as-a-Service model one single application instance is usually shared between different tenants to decrease operational costs. However, sharing at this level may lead to undesired influence from one tenant onto the performance observed by the others. Intentionally, the application does not manage hardware resources and the responsible OS is not aware of application level entities like tenants. Consequently, it is difficult to control the performance of individual tenants to make sure they are isolated. In this paper we present an overview and classification of methods to ensure performance isolation based on request admission control. Further, informational requirements of these methods are discussed.

In this paper, an AR (Augmented reality) system based on a multimodal user interface is proposed and its core techniques are described. The objective of this work is to realize certain task that operators can use the virtual robot to ameliorate the interaction with the real world using AR environment. VC++ and OpenGL are used to create this application. AR applications (ARToolKit) is used to detect the real-time position and orientation of camera and to facilite the operator’s observation and operation. Experiments prove that the system is reliable and efficient. Simulation result is obtained and it is very useful for designers to ameliorate the interaction with the real world.

Face recognition is the broad area of researchers for exploring new techniques. The main part of the face recognition is feature extraction. Feature extraction is the form of dimensionality reduction. When the input data to an algorithm is too large to be processed then the input data will be transformed into a reduced representation set of features. Transforming the input data into the set of features is called feature extraction. For the feature extraction and image processing we use rectangular feature. The rectangular feature is the techniques where we have consider pixel values of the gray scale image. In the rectangular feature xmin, ymin, height and width are considering for calculation the pixel values on particular point in an image. We have to calculate face feature vector using principal component analysis (PCA). We have taken three features eyes, lips and nose for feature extraction these features are used for face recognition. On these four rectangular features we have ...

Communication networks can be transformed through the implementation of computing, which can improve their overall performance, security, and energy efficiency. Nevertheless, several obstacles must be overcome before computing can be fully integrated into communication networks. This paper examines the challenges and research opportunities in the field of computing in communication networks. To take full advantage of computing, new algorithms and techniques must be developed, and machine learning must be explored. Big data analytics and the integration of edge and fog computing with computing techniques are also promising areas for research. The challenges that must be addressed include ethical and privacy concerns, energy consumption, integration with 5G networks, IoT, and cloud computing, and the development of resilient computing systems that can identify and respond to cyber threats. Overcoming these challenges will ensure that computing remains an essential tool for designing and optimizing communication networks.

One of the most important aspects of financial risk is credit risk management. Effective credit rating models are crucial for the credit institution in assessing credit applications, they have been widely studied in the field of statistics and machine learning. Given that small improvements in credit rating systems can generate significant profits, any improvement is of high interest to banks and financial institutions. The ensemble methods are a set of algorithms whose individual decisions are combined to perform classification tasks. In this work, we propose an enhanced experimental comparative study of five ensemble methods associated with seven base classifiers using six public credit scoring datasets. Four popular evaluation metrics, including area under the curve (AUC), accuracy, false positive rate (FPR) and Time taken to build the model, are employed to measure the performance of models. The experimental results and statistical tests show that Pegasos model has a better overall performance than the other methods analyzed her for Boosting and Credal Decision Tree (CDT) model has a better overall performance than the other algorithms in the case of Bagging, Random Subspace, DECORATE and Rotation Forest.

This paper presents a solution to design and implement a hardware error detection and correction circuit using associative memories. This type of memory allows search of a binary value stored, having input data a partial (or modified) amount of this value. This property can be used in communication, for detection and correction of errors. In our analysis, the obtained experimental results were compared with performances of other hardware systems.