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Heuristic evaluation of stock exchange mobile application in Malaysia

Test Engineering and Management 2020年 83卷 11539-11551页

作者： Hussain, Azham Barakat, Mustafa M. Zaaba, Zarul Fitri School of Computing Universiti Utara Malaysia Kedah06010 Malaysia Department of Information Technology Ministry of Foreign Affair Republic of Iraq Iraq School of Computer Science Universiti Sains Malaysia Penang Malaysia

In recent years, heuristic evaluation has focused to improving the efficiency and effectiveness of applications. This technique is considered as the most effective tool that is used in human-computer interaction studies to test the usability of information systems. Moreover, this technique is widely adopted to analyze the usability of applications as it is inexpensive and quick. The objective of this study is to use the heuristic evaluation principals to assess the effectiveness of the stock exchange mobile application in Malaysia. Questionnaire survey method was used and data were collected by 25 participants from Kuala Lumpur Stock Exchange (KLSE). The heuristic evaluation was conducted by 25 experts to examine the usefulness of stock exchange mobile application. The results revealed that stock exchange application is an effective and useful application that has all required features. Meanwhile, findings also postulated that most of the users show agreeableness regarding the applications of principals of heuristics evaluation in this application. These heuristic principals can further used to avoid the usability gaps in this stock exchange application. This study also gives guidelines to designers for taking corrective measures during the designing of the applications for the stock exchange. Additionally, these guidelines help to provide solutions in the future that how designers may improve the user interface of these stock exchange applications that may improve the experience of users. © 2020 Mattingley Publishing. All rights reserved.

关键词： Financial markets

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Engineering Smart Contracts with Symboleo: Progress Report 2024 34

Engineering Smart Contracts with Symboleo: Progress Report 2...

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34th Annual International Conference on Collaborative Advances in Software and computing, CASCON 2024

作者： Mylopoulos, John Alfuhaid, Sofana Amyot, Daniel Anda, Amal Ahmed Logrippo, Luigi Meloche, Regan Rahimi-Kian, Ashkan Rajpal, Sahil Roveri, Marco Sivakumar, Durga Sousa-Dias, Daniel School of EECS University of Ottawa OttawaONK1N 6N5 Canada King Abdulaziz University Faculty of Computing and Information Technology Jeddah Saudi Arabia Université du Québec en Outaouais Département d'informatique et ingénierie GatineauQCJ8X 3X7 Canada IEMS Solution Ltd. Communitech KitchenerONN2G 1H6 Canada University of Trento Dept. of Information Engineering and Computer Science Trento Italy

ISBN: (纸本)9798331504830

Legal contracts have been used for millennia around the world as a foundation for business transactions. Smart con-tracts are cyber-physical systems that deploy Internet-of-Things (IoT) and blockchain technologies to monitor, automate, and control the execution of legal contracts. Because of the conceptual complexity of legal concepts and the technologies deployed, developing smart contracts is a time-consuming and error-prone task. The aim of this project is to develop concepts, tools and methods for engineering smart contracts. The contributions of the project consist of a specification language, called Symboleo, and related tools that are intended to make the engineering of smart contracts more systematic and cost effective. This paper summarizes earlier outcomes of the project and reports on new results obtained during the past year. © 2024 IEEE.

关键词： Smart contract

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Minimum Noise Variance in MI-DP Based Federated Learning

Minimum Noise Variance in MI-DP Based Federated Learning

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International Conference on Computer and Communications (ICCC)

作者： Zheng He Bin Dai School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China Peng Cheng Laboratory Shenzhen China School of Information Science and Technology Southwest Jiaotong University Chengdu China

As a method of distributed machine learning, federated learning can protect clients' private data exposed to external opponents. However, by analyzing the differences in the upload parameters of the clients, private information can still be leaked. This paper aims at the privacy security of client parameters in federated learning when the server is trusted. We propose to employ mutual information differential privacy to provide sharper privacy loss bounds than traditional differential privacy. Considering the case of adding Gaussian noise to learning parameters in federated learning, we obtain the lower bound of the standard deviation of the noise and prove that given a fixed privacy protection level, whether adding noise at the server or the client side, the utility of the learning model does not change. Our experiments show this result, and the model will converge under a certain privacy level measured via conditional mutual information.

关键词： Privacy Differential privacy Federated learning Gaussian noise Distributed databases Servers Computer security

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Categorization of the reviewers based on the preciseness of the consumers’ reviews in online retail marketplace 22

Categorization of the reviewers based on the preciseness of ...

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Proceedings of the 9th International Conference on Applied computing & information technology

作者： Anjan Dutta Janapriyo Maitra Partha Ghosh Punyasha Chatterjee Takaaki Goto Soumya Sen School of Mobile Computing and Communication Jadavpur University India and Computer Science and Technology Raja Ranjit Kishore Govt. Polytechnic India Techno International NewTown India Kingston School of Management and Science India School of Mobile Computing and Communication Jadavpur University India Faculty of Information Sciences and Arts Toyo University Japan A.K.Choudhury School of I.T University of Calcutta India

ISBN: (纸本)9781450397605

In the last decade, huge growth is observed in the online marketplace. Consumers are increasingly engaged in online shopping due to its operational flexibility, huge product search space and diversified products. In this virtual marketplace, consumers make the purchase decision not only based on product specifications given by the seller but also considers the product reviews given by the peer customers. On the other hand, inconsistent reviews make the consumers confused and thus have a negative impact on the overall sales of a business entity. Therefore, identifying the reviewers giving precise and consistent opinions about the products is important for business organizations. In this work, a novel methodology is suggested to classify reviewers into different categories based on the preciseness of the reviews. Business entities can utilize the precise reviewers for getting genuine product feedback and encourage this group to promote their products resulting in increased business growth.

关键词： NLTK precise customer groups Sentiment Analysis VADER polarity scores eWOM

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Application of NOMA for cellular-connected UAVs:opportunities and challenges

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science China(information sciences) 2021年第4期64卷 26-39页

作者： Wee Kiat NEW Chee Yen LEOW Keivan NAVAIE Yanshi SUN Zhiguo DING Wireless Communication Centre School of Electrical Engineering Faculty of EngineeringUniversiti Teknologi Malaysia School of Computing and Communications Lancaster University Key Laboratory of Wireless-Optical Communications Chinese Academy of SciencesSchool of Information Science and Technology University of Science and Technology of China School of Electrical and Electronic Engineering The University of Manchester

Unmanned aerial vehicles(UAVs) have gained considerable interests in numerous civil applications. To push forward its potentials, cellular-connected UAVs have been introduced. Nevertheless, cellular networks face several bottlenecks such as spectrum scarcity and limited concurrent connectivity. To address these issues, non-orthogonal multiple access(NOMA) can be adopted. NOMA provides several opportunities for cellular-connected UAVs such as larger rate region, balanced performance between system throughput and fairness, and reduced delay. In this paper, we review important findings of the related studies, and outline new opportunities and challenges in NOMA for cellular-connected UAVs. Monte-Carlo simulations are then performed to analyze the new aerial user's(AU)'s signal characteristics and evaluate the NOMA performance for co-existence of AU and terrestrial user(TU). Our preliminary results show that NOMA is a promising strategy for cellular-connected UAVs.

关键词： cellular-connected UAVs non-orthogonal multiple access signal characteristics aerial user rate terrestrial user rate

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Improving Student Performance Prediction Using a PCA-based Cuckoo Search Neural Network Algorithm

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Procedia Computer science 2023年 225卷 4598-4610页

作者： Maria Ali Muhammad daniyal liaquat Muhammad Nouman Atta Abdullah Khan Saima Anwar Lashari Dzati Athiar Ramli Institute of Computer Science and Information Technology the University of Agriculture Peshawar Pakistan College of Computing and Informatics Saudi Electronic University Riyadh KSA Intelligent Biometrics Group (IBG) School of Electrical and Electronic Engineering USM Engineering Campus University Sains Malaysia 14300 Nibong Tebal Pulau Pinang

The ANN is a commonly used network for pattern recognition, and has been trained for various tasks such as prediction, classification, and engineering. However, this model faces challenges such as local minima and slow convergence, which have been addressed through different strategies such as combining the artificial neural network (ANN) with optimised models like the cuckoo search (CS) algorithm. However, for large datasets, the hybrid ANN-based CS algorithm can lead to overfitting. To overcome this issue, the authors propose a new algorithm called Principal Component Analysis with Cuckoo Search Neural Network (PCACSNN). The performance of this algorithm is compared to other commonly used algorithms such as ANN, backpropagation neural network (BPNN), and cuckoo search backpropagation (CSBP), using the Mean Square Error (MSE) and accuracy on classification problems. The simulations were performed on the Student Performance dataset taken from the UCIMLR. The results show that the proposed model performs better than the other models, achieving high accuracy and low MSE for both mathematics and Portuguese student datasets. For the mathematics students, the suggested model attained an accuracy of (99.32%) with MSE of 2.77E-07 for 70% training data and an accuracy of 98.52% with MSE of 2.50E-04 for 30% training data. Similarly, for the Portuguese student dataset, the proposed model obtained (99.38%) accuracy with MSE of 1.09E-08 for 70% training data and 98.72% accuracy with MSE of 1.01E-04 for 30% training data.

关键词： Artificial Neural Network (ANN) Cuckoo Search (CS) Algorithm Portuguese Mathematics Accuracy Mean Square Error (MSE) Local Minima Slow Convergence Back Propagation

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Assessing Secure Cloud information Sharing Through Authentication and Encoded Indexing

Assessing Secure Cloud Information Sharing Through Authentic...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Pandiaraja P Karthik K Rajesh Kanna P C.Ushapriya P.C.Senthil Mahesh Muthumanickam K Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering M. Kumarasamy College of Engineering Karur Tamilnadu India Department of Computer Science and Engineering Bannari Amman Institute of Technology Erode Tamilnadu India Department of AI & ML St. Martin’s Engineering College Secunderabad Telangana India Department of Computer Science and Engineering Excel Engineering College Komarapalayam Tamilnadu India Department of Information Technology Kongunadu College of Engineering and Technology Trichy Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

The high usage of storage in cloud, ensuring the privacy and security of information stored in the cloud has come up with a difficult concern. One approach to address this issue is the use of techniques which are encrypted, where data is already encrypted being uploaded to the storage of cloud. However, traditional encryption methods hinder the ability to find and retrieve particular information from the secured information, as the information needs to be decrypted first. This limitation hampers the usability and efficiency of storage of cloud systems. To overcome the challenge, this research says a novel approach for keyword search on secured information in storage of cloud using an AES approach. The algorithm of AES is a maximum adopted symmetric encryption method familiar for efficiency and robustness. By leveraging the AES encryption approach, the solution assures the confidentiality of information stored while enabling efficient keyword-based search operations without the need for decryption. The proposed approach utilizes a combination of cryptographic techniques such as AES encryption and index structures to access efficient information findings on the secured information. A secure index is created during encryption process that enables for potent retrieval of encrypted data related to specific keywords. The encrypted data remains confidential, as only authorized users possess the necessary decryption keys to retrieve the plaintext information. During data outsourcing, data owner can set the access permission based on their user identity. Users should have specific identity with decryption key for accessing data from cloud. Here data manager can also provide particular period for accessing information from cloud. The results demonstrate that the keyword findings on secured information in storage of using the AES approach achieve a balance between data privacy and search functionality. The solution offers an effective means to securely store and retrieve data

关键词： Cloud computing Data privacy Privacy Organizations Robustness Encryption Outsourcing Usability Secure storage Indexing

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Training Learning Weights of Elman Neural Network Using Salp Swarm Optimization Algorithm

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Procedia Computer science 2023年 225卷 1974-1986页

作者： Qaedah Ali Musaeed Naji Mahdi Maria Ali Muhammad Nouman Atta Abdullah Khan Saima Anwar Lashari Dzati Athiar Ramli Institute of Computer Science and Information Technology the University of Agriculture Peshawar Pakistan College of Computing and Informatics Saudi Electronic University Riyadh KSA Intelligent Biometrics Group (IBG) School of Electrical and Electronic Engineering USM Engineering Campus University Sains Malaysia 14300 Nibong Tebal Pulau Pinang

Elman Neural Network (ENN) is one of the most common type of recurrent neural network (RNN). It is frequently applied to numerous applications such as classification and predication. However, the ENN model, suffers from serious flaws like network stagnancy and delayed convergence during training. To enhance the effectiveness of the ENN and address the mention problems, numerous studies have been conducted to speed up the learning process of ENN. Many optimization techniques are being used and each algorithm has its own characteristics in terms of efficiency. Some of the algorithms inspire from nature, while others come from the swarm's combined behaviour. Different numbers of optimization methods, including Particle Swarm Optimization (PSO), Bat Algorithm, and Cuckoo Search (CS) are applied to improve the ENN's learning process. As a result, this study suggested using Salp Swarm algorithm (SSA) to address these issues and speed up the ENN algorithm's learning process. The SSA, which is based on the typical behaviour of Salp swarms, is proposed in this research as a novel optimization technique. Due to its effectiveness and Salp intelligent behaviour, a novel Salp algorithm improves the learning process of the artificial neural network (ANN), and ENN models. Therefore this study proposed Salp swarm artificial neural network (SSANN) and Salp swarm Elman neural network (SSElmanNN). Performance of the suggested models is evaluated against ANN, back propagation neural network (BPNN), along with ENN in term of accuracy and Mean Square Error (MSE). Two datasets such as IRIS and Credit Card are used for simulation. The simulation results demonstrate that the suggested models outperform the other algorithms used in this study in terms of MSE and accuracy.

关键词： ANN ENN BPNN SSA MSE Iris and Credit Card

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Using Label-text Correlation and Deviation Punishment for Fine-grained Suicide Risk Detection in Social Media

Using Label-text Correlation and Deviation Punishment for Fi...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zepeng Li Zhengyi An Wenchuan Cheng Jiawei Zhou Bin Hu Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China School of Medical Technology Beijing Institute of Technology Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chines Academy of Sciences Shanghai China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Suicide causes serious harm to individuals, families and society, and becomes a social problem of widespread concern. Therefore, it is necessary to find and intervene individuals at risk of suicide as soon as possible. In recent years, social media data has successfully been leveraged for suicide risk detection. However, for fine-grained suicide risk detection, the existing models ignore the deviation between the predicted results and the real results when making wrong predictions, and do not pay attention to the semantic information contained in the labels. This paper proposes a deep learning model based on Label-Text Correlation and Deviation Punishment (LTC-DP). While learning the semantic relation adequately between the text and the corresponding label, the model can give different punishment adaptively according to the deviation degrees between the predicted results and the real result. The experimental results show that compared with the baseline model, the proposed model has better performance in fine-grained suicide risk detection. In addition, we release a fine-grained suicide risk detection data set based on Weibo, the data set is available at https://***/cxyazy/FGCSD-main.

关键词： Training Deep learning Adaptation models Correlation Social networking (online) Computational modeling Biological system modeling

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Efficiency Broadcast Base Safety Message BSM Through VANET Based on Transmit Packet Coding (TPC) 2

Efficiency Broadcast Base Safety Message BSM Through VANET B...

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2nd IEEE Global Conference on Life sciences and Technologies, LifeTech 2020

作者： Ayoob, Ayoob Khalil, Ghaith Yingzhuang, Liu Chowdhury, Morshed Al, Torana Huazhong University of Science and Technology School of Electronics and Information Engineering Wuhan430074 China University of Melbourne School of Computing and Information Systems Melbourne School of Engineering Melbourne Australia Deakin University School of Information Technology Burwood Australia

ISBN: (纸本)9781728170633

Vehicular networking has emerged with wireless technology concept that supports safety messaging exchange. VANETs provide a collection of interesting safety applications. Many of these applications rely on broadcasting of messages to other vehicles. Basic Safety Messages (BSMs) are an important message type used in safety applications in VANETs. The emergence of Vehicular Ad Hoc Networks (VANETs) is expected to be an important step toward achieving safety and efficiency in intelligent transportation systems (ITS). One important requirement of safety applications is that vehicles are able to communicate with neighboring vehicles, with very low latency and packet loss. The high mobility, unreliable channel quality and high message rates make this a challenging problem for VANETs. In this paper focus on how to enhance the security and scalability of the system for the VANET by improving BSM transfer quality using Transmit Packet Coding (TPC). Briefly discusses issues and challenges for disseminating reliability and low latency of BSM over VANETs. The simulation of different scenario of VANETs and dissemination of transfer BSM content over this network environment using NS2. The result shows suitability of proposed technique over different existing solutions towards effective and efficient techniques in term of delivery ratio and probability for BSM broadcasting over vehicular environments. © 2020 IEEE.

关键词： Vehicular ad hoc networks

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