volume-17-Issue 1 (2022)
Latest Articles
Towards Low-Cost IoT and LPWAN-Based Flood Forecast and Monitoring System
JUSPN, volume-17, Issue 1 (2022) , PP 43 - 49
Published: 04 Aug 2022
DOI: 10.5383/JUSPN.17.01.006
by Nassima Tadrist, Olivier Debauche, Saïd Mahmoudi, Adriano Guttadauria from University of Blida 1 Saâd Dahlab, Department of Water and Environmental Sciences, BP 270 Route Soumâa, Blida, Algeria, 09000 , University of Liège, TERRA / BioDynE - DEAL, Passage des Déportés 2, Gembloux, Belgium, 5030, University of Mons, Infortech Institute / Faculty of Engineering - ILIA, Place du Parc 20, Mons, Belgium, 7000
Abstract: t The recent floods have shown that the classic monitoring systems for watercourses are no longer adapted because other phenomena such as the insufficient capacity and/or obstruction of drainage networks, the modification of cultivation practices and rotations, the increase in the size of plots linked to the reparcelling, the urbanization of floodable areas, etc. The combination of all these causes, plus the modification of the water regime, implies an increase in the risk of flooding and an adapted monitoring that is no longer limited to watercourses in order to give early warning of the risk of flooding by runoff. The Internet of Things (IoT) and the availability of microcontrollers and sensors with low data rates and long ranges, as well as low-power wide area networks (LPWANs), allow for much more advanced monitoring systems. read more... read less...
Keywords: Monitoring System, Warning System, Flood, Runoff, Moody Flood, Flash Flood, Runoff Flooding, Sewer, IoT, LPWAN, LoRaWan, NB-IoT
Towards Performance of NLP Transformers on URL-Based Phishing Detection for Mobile Devices
JUSPN, volume-17, Issue 1 (2022) , PP 35 - 42
Published: 04 Aug 2022
DOI: 10.5383/JUSPN.17.01.005
by Hossein Shirazi, Katherine Haynes, Indrakshi Ray from Computer Science Department, Colorado State University, Fort Collins, CO, USA and Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA
Abstract: Hackers are increasingly launching phishing attacks via SMS and social media. Games and dating apps introduce yet another attack vector. However, current deep learning-based phishing detection applications do not apply to mobile devices due to the computational burden. We propose a lightweight phishing detection algorithm that distinguishes phishing from legitimate websites solely from URLs to be used in mobile devices. As a baseline performance, we apply Artificial Neural Networks (ANNs) to URL-based and HTML-based website features. A model search results in 15 ANN models with accuracies >96%, comparable to state-of-the-art approaches. Next, we test the performance of deep ANNs on URLbased features only; however, all models perform poorly with the highest accuracy of 86.2%, indicating that URL-based features alone are not adequate to detect phishing websites even with deep ANNs. Since language transformers learn to represent context-dependent text sequences, we hypothesize that they will be able to learn directly from the text in URLs to distinguish between legitimate and malicious websites. We apply three state-of-the-art deep transformers (BERT, ELECTRA, and RoBERTa) for phishing detection. Testing custom and standard vocabularies, we find that pre-trained transformers available for immediate use (with fine-tuning) outperform the model trained with the custom URL-based vocabulary. In addition, we test a thinner BERT transformer which is suitable for lightweight devices like mobiles, called MobileBERT. Our results emphasize that evaluation metrics of this model are competitive to other models in this study, yet the testing time is significantly less, making this model a choice for embedding phishing detection algorithms in mobile phones. Using pre-trained transformers to predict phishing websites from only URLs has five advantages: 1) requires little training time (230 to 320 s), 2) is more easily updatable than feature-based approaches because no pre-processing of URLs is required, 3) is safer to use because phishing websites can be predicted without physically visiting the malicious sites, 4) is easily deployable for real-time detection and is applicable to run on mobile devices, and 5) using a mobile specific transformer yields comparable performance and predicts 3 times faster than the other transformer models in this study. read more... read less...
Keywords: Social Engineering Attack, Phishing Detection, Deep learning, Transformers, Mobile Application
The way it made me feel – Creating and evaluating an in-app feedback tool for mobile apps
JUSPN, volume-17, Issue 1 (2022) , PP 27 - 34
Published: 04 Aug 2022
DOI: 10.5383/JUSPN.17.01.004
by Simon André Scherr, Mher Ter-Tovmasyan, Frauke Neugebauer, Steffen Hupp, Frank Elberzhager from Fraunhofer IESE, Kaiserslautern, Germany, 67663
Abstract: Mobile apps are becoming increasingly important in everyone's daily life. The success of an app is linked to high user acceptance. Therefore, it is necessary to capture users' expectations, needs, and problems regarding an app in any situation. By continuously capturing and analyzing user feedback, developers can evaluate the level of user acceptance. There are various feedback channels, such as app stores, social networks, and within the app, which can be used to capture user feedback. As we already have experience with feedback from app stores and social networks, we wanted to investigate inapp feedback approaches and thus conducted a mapping study to understand the state of the art of these approaches.We analyzed 36 publications and derived requirements for in-app feedback tools. Based on that, we defined requirements for an in-app feedback tool to describe its prototypical realization. Then we performed an evaluation regarding user acceptance of our tool with 33 participants. The evaluation showed a high rate of acceptance for the tool among the participants. The results also highlighted improvement areas for our tool, such as optimizing the rate of requests for feedback. We plan to address these aspects in future work and to continue improving our tool. read more... read less...
Keywords: Mobile Apps, Software Quality, User Feedback, User Experience, In-App Feedback
From Collective Memory to Map Services
JUSPN, volume-17, Issue 1 (2022) , PP 19 - 26
Published: 04 Aug 2022
DOI: 10.5383/JUSPN.17.01.003
by Konstantinos Koukoulis, Dimitrios Koukopoulos from University of Patras, Department of History and Archaeology, Agrinio, Greece and Hellenic Open University, Patras, Greece
Abstract: The route followed by a refugees’ group towards its destination can, in many cases, be regarded as the reference point around which the collective memory of such a group of people is intertwined. Such a route enriches people's memories with common experiences, targeting places and interactions among refugees and locals and may affect the collective memory of such people positively or negatively. A crucial point in the modern paradigm of smart cities is the quality of life. To achieve quality of life for its citizens a smart city should establish ways to reduce alienation among the different groups that constitute the city's palimpsest. Understanding the different cultural identities and improvement of social cohesion between different people groups is one of the basic vehicles towards this goal. In this paper, we attempt to give a first answer to such problems proposing and implementing specific services in the context of a crowdsourcing system for collective memory management using interactive maps. We demonstrate a basic usage scenario to show the strength of the implemented services, along with a two-step evaluation showing positive results. read more... read less...
Keywords: Crowdsourcing, Collective Memory Management, Big Data, Mobile Services and Platforms
Small Towns and Regional Municipalities Implement SMART Solutions, Identified Issues, and Challenges
JUSPN, volume-17, Issue 1 (2022) , PP 11 - 18
Published: 04 Aug 2022
DOI: 10.5383/JUSPN.17.01.002
by Peter Balco, Dorota Košecká, Peter Bajzík from Comenius University, Faculty of Management, Odbojárov 10, P. O. Box 95, Bratislava, 82005, Slovakia, ATOS IT Solutions and Services s.r.o., Pribinova 19, Bratislava Slovakia
Abstract: In the last decade, SMART services and solutions projects have been concentrated mainly in large and economically strong cities where large populations are concentrated. This is place where the potential is found that predicts return on investment as well as further development. As not all cities are predestined for this type of project, we were interested in how small towns and cities perceive their potential to engage in the implementation of such projects. We believe that the topic of SMART solutions should not be a significant priority only for large cities. We decided to analyze the needs of small cities in terms of implementing SMART solutions. We also tried to identify the challenges as well as the requirements to accelerate this process. In our analysis, we focused on the Slovak Republic, which is a good candidate for such research due to its structure of cities and municipalities. In the process of data collection, we approached more than 2,744 s mall towns and municipalities with a population of up to 5,000 with a request for information, and 547 town and municipality representatives responded to the questionnaire. The results of the research show an interesting and clear finding, s mall towns and rural areas also want SMART. In the research, we identified several not simple problems that need to be solved for the successful implementation of these goals read more... read less...
Keywords: SMART CITY, clusters, SMART Services, SMART Villages, SMART regions
Remote Collaboration Needs for New Work: Concepts, Procedure and Evaluation
JUSPN, volume-17, Issue 1 (2022) , PP 01 - 09
Published: 04 Aug 2022
DOI: 10.5383/JUSPN.17.01.001
by Sven Storck, Kathleen Späth, Claudia Nass Bauer, Frank Elberzhager from Fraunhofer IESE, Kaiserslautern, Germany, 67663
Abstract: More freedom, more flexibility, and reduced travel time for knowledge workers are just a few advantages of new work models, which have been discussed for several years now. Moreover, the problem of rural depopulation can be addressed by this concept. In the research project “Digital Teams”, we aim to develop a digital open-source platform to support and optimize the digital work environment for distributed teams in rural areas, especially in the knowledge work context. In this article, we focus on the research and design aspects of the project. We provide insights on how we have used the design thinking approach for our research and the development of the UX- and UI-design concepts. We are focusing on an ecosystem concept, which provides all relevant services for knowledge workers in their daily work life, rather than focusing on a specific remote collaboration purpose. We present initial evaluation results, which tend to be positive and give an outlook on future work. read more... read less...
Keywords: New Work, Design Thinking, Collaboration, Groupware, Rural Depopulation