1 10 7 https://repository.horizon.ac.id/files/original/065000afa0f88d2b32c10168d714dbb7.pdf 7c39655b986c610d4dd90739e783d3a7 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 10 NO. 2 (2021) MARCH 2021 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource E�cient Dynamic Group Signature Scheme with Veri�er Loc Subject The topic of the resource Lattice based cryptography; dy- namic group signatures; veri�er local revocation; time bound keys Description An account of the resource Revocation is an important feature of group signature schemes. Veri�er Local Revoca- tion (VLR) is a popular revocation mechanism that involves only veri�ers in the revocation process. In VLR, a revocation list is maintained to store the information about revoked users. The veri�cation cost of VLR based schemes is linearly proportional to the size of the revoca- tion list. In many applications, the size of the revocation list grows with time, which makes the veri�cation process expensive. In this paper, we propose a lattice based dynamic group signature using VLR and time bound keys to reduce the size of the revocation list to speed up the veri�cation process. In the proposed scheme, an expiration date is �xed for signing key of each group member, and veri�ers can �nd out (at constant cost) if a signature is generated using an expired key. Hence revocation information of members who are revoked before signing key expiry date (premature revocation) are kept in the revocation list, and other members are part of natural revocation. This leads to a signi�cant saving on the revocation check by assuming natural revocation accounts for a large fraction of the total revocation. This scheme also takes care of non-forgeability of signing key expiry date. Creator An entity primarily responsible for making the resource Abhilash M H,B B Amberker Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource March 2021 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/7c19d1f8d3ced302e8a4269f69a682e0.pdf ba3338ad77efa7a203f3d70b3b74fc53 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 10 NO. 2 (2021) MARCH 2021 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Predicting the Growth of e-Commerce using Trendline Analysis: A Case Study of Ogun State, Nigeria Subject The topic of the resource E-Commerce, adoption, diffusion, infusion, barriers, trendline. Description An account of the resource There is a growing interest from e-commerce planners and other planning agencies in the Information Technology world to measure and forecast the growth of e-commerce in developing countries like Nigeria. The difficulties lie in finding the best forecasting model that can incorporate both the internal and external barriers that influence the full adoption and diffusion of e-commerce. The study attempts to identify the relevant e-commerce tools and its spread in Ogun East Senatorial District as well as formulating a mathematical model for e-commerce adoption and diffusion. A well-structured questionnaire was used to collect data from 126 respondents and analyzed using Trendline, a built-in analysis tool in Microsoft® Office Excel version 2013. The study identified PCs/laptops, ATM cards, e-mail services, mobile money transfer, e-commerce Websites, and point-of-sales (POS) terminals as e-commerce tools used by the respondents. The results of the study show that majority of the e-commerce users/adopters were single female students between the ages of 21 and 30 years, with university education owing to a proportion of 63% of the respondents while the earliest adopted e-commerce tools in descending order were tablets/smartphones, PCs/laptops, ATM cards, and email services. The results further show that the most popularly-used tools were e-commerce websites (98% responses), email services (94% responses), mobile money transfer (94% responses), POS terminals (94% responses), tablets/smartphones (93% responses), PCs/laptops (87% responses) and ATM cards (80% responses). Based on the findings of this study, it is therefore recommended that government should promote the use and development of e-commerce, notably by reducing the costs of access to technology, through the liberation of trade in software and hardware. Creator An entity primarily responsible for making the resource Ebenezer N. Igwe1 and Olumuyiwa B. Alaba2,Olalere A. Abass Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource March 2021 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/5e394ffeff7ba315d1a1898d30620c45.pdf 767cafab7114fbd5b469de7cdacbfab4 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 10 NO. 2 (2021) MARCH 2021 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Comparative Study on South African Users’ Perception to Use, Choose, and Purchase a Smartphone Device through the Lens of the Social Shaping of Technology (SST) Theory Subject The topic of the resource Mobile communication; mobile technology device; smartphone device; socio-networking sites; social shaping technology; social capital; social influence; perceived mobility value; social networks Description An account of the resource Mobile technologies are increasingly making important contributions to the lives of many users. Various factors are said to influence the decision of users to remain connected and be in constant interaction with other users from the same or different social networks. This paper used the Social Shaping of Technology (SST) theory to assess the influence of users’ social networks on their decision to use, choose and purchase a smartphone device. The study used a quantitative research method to analyse data. Data were collected from a sample of young adults aged between 15 and 35 years residing in Durban, South Africa. The study revealed that social networking sites influence users’ decision to use, choose, and purchase a smartphone device. The findings also revealed factors that are perceived as influential in the perceptions of users about their social networks and their interactions with users from the same social networks. Creator An entity primarily responsible for making the resource Lufungula Osembe Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource March 2021 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/71fdfb308f85836d36ff6642fbf97d61.pdf 9d710fdcde06e4b01cddf4a0823e836b Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 10 NO. 2 (2021) MARCH 2021 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Risk-Based Design of Socio-Cyber-Physical Systems Subject The topic of the resource socio-cyber-physical system, design, failure, risk sources, safety, coexistence, risk management plan. Description An account of the resource The aim of risk management of socio-cyber-physical systems at designing is the integral safety, which ensures their coexistence with their vicinity throughout their life cycles. On the basis of present knowledge and experience, part of risks that threaten socio-cyber-physical systems shall be mitigated by preentive measures during their designing and manufacturing. Due to dynamic changes of the world, the conditions of socio-cyber-physical systems at operation change. If changes exceed the socio-cyber-physical systems´ safety limits which were insert-ed into their designs, the accidents or socio-cyber-physical sysems´ failures occur. The presented risk management plan is tool which ensures the prevention of such unaccepted situations and the safety. Creator An entity primarily responsible for making the resource Dana Prochazkova,Jan Prochazka Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource March 2021 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/6746d5106b4c49064bd9b9a2b3ef54ea.pdf 022c191152b171efc98a0f6b972754b3 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 10 NO. 2 (2021) MARCH 2021 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Research Methods in Machine Learning: A Content Analysis Subject The topic of the resource Research methods in machine learning, machine learning algorithms, machine learning techniques. Description An account of the resource Research methods in machine learning play a pivotal role since the accuracy and reliability of the results are influenced by the research methods used. The main aims of this paper were to explore current research methods in machine learning, emerging themes, and the implications of those themes in machine learning research. To achieve this the researchers analyzed a total of 100 articles published since 2019 in IEEE journals. This study revealed that Machine learning uses quantitative research methods with experimental research design being the de facto research approach. The study also revealed that researchers nowadays use more than one algorithm to address a problem. Optimal feature selection has also emerged to be a key thing that researchers are using to optimize the performance of Machine learning algorithms. Confusion matrix and its derivatives are still the main ways used to evaluate the performance of algorithms, although researchers are now also considering the processing time taken by an algorithm to execute. Python programming languages together with its libraries are the most used tools in creating, training, and testing models. The most used algorithms in addressing both classification and prediction problems are; Naïve Bayes, Support Vector Machine, Random Forest, Artificial Neural Networks, and Decision Tree. The recurring themes identified in this study are likely to open new frontiers in Machine learning research. Creator An entity primarily responsible for making the resource Jackson Kamiri,Geoffrey Mariga Source A related resource from which the described resource is derived March 2021 Date A point or period of time associated with an event in the lifecycle of the resource March 2021 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/d845046df51e8623b8e76b92e7f1c52f.pdf cfad3d0ac6e24980274d0684d03cbdd4 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 10 NO. 2 (2021) MARCH 2021 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Parallel Merging and Sorting on Linked List Subject The topic of the resource Parallel algorithms, optimal algorithms, EREW(Exclusive Read Exclusive Write), CREW(Concurrent Read Exclusive Write), CRCW(Concurrent Read Concurrent Write). https://repository.horizon.ac.id/files/original/6a96a83faab4645815de0593a440de5b.pdf bc86ede1e31c89f6ca0ba2507059ee37 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 10 NO. 2 (2021) MARCH 2021 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Developing Hybrid-Based Recommender System with Naïve Bayes Optimization to Increase Prediction Efficiency Subject The topic of the resource Collaborative filtering, Association rule mining, hybrid ensemble, recommender systems Description An account of the resource Commerce and entertainment world today have shifted to the digital platforms where customer preferences are suggested by recommender systems. Recommendations have been made using a variety of methods such as content-based, collaborative filtering-based or their hybrids. Collaborative systems are common recommenders, which use similar users’ preferences. They however have issues such as data sparsity, cold start problem and lack of scalability. When a small percentage of users express their preferences, data becomes highly sparse, thus affecting quality of recommendations. New users or items with no preferences, forms cold start issues affecting recommendations. High amount of sparse data affects how the user-item matrices are formed thus affecting the overall recommendation results. How to handle data input in the recommender engine while reducing data sparsity and increase its potential to scale up is proposed. This paper proposed development of hybrid model with data optimization using a Naïve Bayes classifier, with an aim of reducing data sparsity problem and a blend of collaborative filtering model and association rule mining-based ensembles, for recommending items with an aim of improving their predictions. Machine learning using python on Jupyter notebook was used to develop the hybrid. The models were tested using MovieLens 100k and 1M datasets. We demonstrate the final recommendations of the hybrid having new top ten highly rated movies with 68% approved recommendations. We confirm new items suggested to the active user(s) while less sparse data was input and an improved scaling up of collaborative filtering model, thus improving model efficacy and better predictions. Creator An entity primarily responsible for making the resource Ndung’u Rachael Njeri, Kamau Gabriel Ndung’u*, Wambugu Geoffrey Mariga Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource March 2021 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text