| 1. |
- Dalipi, Fisnik, Senior lecturer, et al.
(författare)
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Sentiment Analysis of Students’ Feedback in MOOCs : A Systematic Literature Review
- 2021
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Ingår i: Frontiers in Artificial Intelligence. - : Frontiers Media S.A.. - 2624-8212. ; 4
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Forskningsöversikt (refereegranskat)abstract
- In recent years, sentiment analysis (SA) has gained popularity among researchers in various domains, including the education domain. Particularly, sentiment analysis can be applied to review the course comments in massive open online courses (MOOCs), which could enable instructors to easily evaluate their courses. This article is a systematic literature review on the use of sentiment analysis for evaluating students’ feedback in MOOCs, exploring works published between January 1, 2015, and March 4, 2021. To the best of our knowledge, this systematic review is the first of its kind. We have applied a stepwise PRISMA framework to guide our search process, by searching for studies in six electronic research databases (ACM, IEEE, ScienceDirect, Springer, Scopus, and Web of Science). Our review identified 40 relevant articles out of 440 that were initially found at the first stage. From the reviewed literature, we found that the research has revolved around six areas: MOOC content evaluation, feedback contradiction detection, SA effectiveness, SA through social network posts, understanding course performance and dropouts, and MOOC design model evaluation. In the end, some recommendations are provided and areas for future research directions are identified.
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| 2. |
- Hedayati, Soudabeh, et al.
(författare)
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MapReduce scheduling algorithms in Hadoop : a systematic study
- 2023
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Ingår i: Journal of Cloud Computing. - : Springer. - 2192-113X. ; 12
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Tidskriftsartikel (refereegranskat)abstract
- Hadoop is a framework for storing and processing huge volumes of data on clusters. It uses Hadoop Distributed File System (HDFS) for storing data and uses MapReduce to process that data. MapReduce is a parallel computing framework for processing large amounts of data on clusters. Scheduling is one of the most critical aspects of MapReduce. Scheduling in MapReduce is critical because it can have a significant impact on the performance and efficiency of the overall system. The goal of scheduling is to improve performance, minimize response times, and utilize resources efficiently. A systematic study of the existing scheduling algorithms is provided in this paper. Also, we provide a new classification of such schedulers and a review of each category. In addition, scheduling algorithms have been examined in terms of their main ideas, main objectives, advantages, and disadvantages.
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| 3. |
- Katerina, Zdravkova, et al.
(författare)
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Integration of Large Language Models into Higher Education : A Perspective from Learners
- 2024
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Ingår i: 2<em>023 International Symposium on Computers in Education (SIIE)</em>, Setúbal, Portugal, 2023. - : IEEE. - 9798350329315 - 9798350329322
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Konferensbidrag (refereegranskat)abstract
- Large language models (LLMs) are being criticized for copyright infringement, inadvertent bias in training data, a danger to human innovation, the possibility of distributing incorrect or misleading information, and prejudice. Due to their popularity among students, the introduction of many comparable apps, and the inability to resist unfair and fraudulent student usage, their educational use needs to be adapted and harmonized. The incorporation of LLMs should be defined not only by pedagogues and educational institutions, but also by students who will actively utilize them to learn and prepare assignments. In order to find out what students from two universities think and suggest about LLMs use in education, they were asked to give their contribution by answering the survey that was conducted at the beginning of the spring semester of academic 2022/23. Their feedback was quantitatively and qualitatively analyzed, showing in a better light what students think about LLMs and how and why they would use them. Based on the analysis, the authors propose an original strategy for integrating LLMs into education. The proposed approach is also adapted for those students who are not interested in using LLMs and for those who prefer the hybrid mode by combining their own research with LLMs generated recommendations. The authors expect that by implementing the proposed strategy, schools will benefit from a better education in which research, creativity, academic honesty, recognition of false information, and the ability to improve knowledge will prevail.
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| 4. |
- Maleki, Neda, et al.
(författare)
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DeltaBin : An Efficient Binary Data Format for Low Power IoT Devices
- 2023
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Ingår i: <em>2023 International Conference on Computer, Information and Telecommunication Systems (CITS), Genoa, Italy, 2023</em>. - Genoa, Italy : IEEE Press. - 9798350336108 - 9798350336092
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Konferensbidrag (refereegranskat)abstract
- The Internet of Things (IoT) notion is quickly influencing t he architectures of data-driven systems d ue to the ever-increasing rapid technological progress in all sectors. The IoT involves the collection and exchange of data from a large number of interconnected devices or sensors. The collected data is structured and transmitted in a variety of different data formats such as JSON, CBOR, BSON, or simply a binary format. The data format used by an IoT device can have a significant i mpact on t he efficiency of its data transmission. In general, using a more compact and efficient data format can help to reduce t he amount of data that needs to be transmitted, which can improve the overall speed and performance of the device. For example, using a binary data format rather than a text-based format can often result in smaller data sizes and faster transmission times. Similarly, using a binary format in a more compressed form can further help to reduce the size of the data being transmitted, which can further improve the efficiency of the transmission. In this paper, we propose Delta Binary (i.e., DeltaBin) to reduce the binary data format by transmitting only changed data. We assess DeltaBin using a real IoT deployment scenario.
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| 5. |
- Maleki, Neda, et al.
(författare)
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DynaSens : Dynamic Scheduling for IoT Devices Sustainability
- 2022
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Ingår i: 2022 International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications, CoBCom 20222022. - : IEEE. - 9781665485982
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Konferensbidrag (refereegranskat)abstract
- The Internet of Things (IoT) have shown numerous potential applications that can enhance our quality of life. IoT is becoming a core technology to bring smart homes, smart cities, and smart industries into reality. However, with potential benefits comes a challenge of sustainability, and one major concern is to minimize energy consumption. In a citywide area, managing the operation of such large-scale IoT networking is one of the complex tasks. One of the ways is to utilize dynamic sensing scheduling where the IoT device goes to the sleep mode and prevents unnecessary data transmission. In this paper, we propose a dynamic sensing (DynaSens) algorithm for an IoT-based waste management system. This algorithm helps to reduce the waste bin overflowing, thus, provides better sanitation, and it is also helpful in reducing the fuel cost of waste collection vehicles. Our work utilizes measured values such as current consumption, LiDAR measurement time, and LoRa transmission time as the input data for the simulation experiment to evaluate energy consumption. We also assessed DynaSens using a real dataset obtained from a recycling house. We use Pycom LoPy4 micro-controller as a development board. For a number of garbage-thrown scenarios, DynaSens enables longer battery longevity by reducing the repeated execution of the same tasks. © 2022 IEEE.
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| 6. |
- Maleki, Neda, et al.
(författare)
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Unraveling Energy Consumption Patterns : Insights Through Data Analysis and Predictive Modeling
- 2023
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Ingår i: 15th International Conference on Applied Energy.
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Konferensbidrag (refereegranskat)abstract
- Most of the utility meters in Sweden are connected using the Internet of Things (IoT) technology. This opens new possibilities for understanding society’s energy consumption dynamics and making citizens aware of their power consumption usage. In this study, we investigate the patterns of electricity consumption using machine learning methods. We collected metered data from Kalmar Energi company, the electrical grid for Kalmar city in Sweden. In addition, we collected the Kalmar weather and electricity price data from the Swedish Meteorological and Hydrological Institute (SMHI) and Nordpool, the European leading power market, respectively. We comprehensively analyze the electricity consumption data to assess the changes in overall electricity demand during the year 2021 in the city of Kalmar. This information can be of significant benefit to other regions seeking to improve their sustainability and energy consumption practices. For analysis and energy consumption prediction, we utilize two forecasting models, i.e., Random Forest (RF) and XGBoost. RF model results show a high level of accuracy with the achieved R-squared (R2) value of 0.91 compared to XGBoost value of 0.87.
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| 7. |
- Manzoni, Pietro, et al.
(författare)
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Crowdsourcing Through TinyML as aWay to Engage End-Users in IoT Solutions
- 2023. - 1
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Ingår i: Mobile Crowdsourcing. - Switzerland : Springer. - 9783031323973 - 9783031323966 ; , s. 359-387
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- This book offers the latest research results in recent development on the principles, techniques and applications in mobile crowdsourcing. It presents state-of-the-art content and provides an in-depth overview of the basic background in this related field. Crowdsourcing involves a large crowd of participants working together to contribute or produce goods and services for the society. The early 21st century applications of crowdsourcing can be called crowdsourcing 1.0, which includes businesses using crowdsourcing to accomplish various tasks, such as the ability to offload peak demand, access cheap labor, generate better results in a timely matter, and reach a wider array of talent outside the organization. Mobile crowdsensing can be described as an extension of crowdsourcing to the mobile network to combine the idea of crowdsourcing with the sensing capacity of mobile devices. As a promising paradigm for completing complex sensing and computation tasks, mobile crowdsensing serves the vital purpose of exploiting the ubiquitous smart devices carried by mobile users to make conscious or unconscious collaboration through mobile networks. Considering that we are in the era of mobile internet, mobile crowdsensing is developing rapidly and has great advantages in deployment and maintenance, sensing range and granularity, reusability, and other aspects. Due to the benefits of using mobile crowdsensing, many emergent applications are now available for individuals, business enterprises, and governments. In addition, many new techniques have been developed and are being adopted.
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| 8. |
- Mohammadian, Mehrdad, et al.
(författare)
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Persis : A Persian Font Recognition Pipeline Using Convolutional Neural Networks
- 2022
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Ingår i: <em>2022 12th International Conference on Computer and Knowledge Engineering (ICCKE)</em>, Mashhad, Iran, Islamic Republic of. - : IEEE. ; , s. 196-204
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Konferensbidrag (refereegranskat)abstract
- What happens if we see a suitable font for our design work but we do not know its name? Visual Font Recognition (VFR) systems are used to identify the font typeface in an image. These systems can assist graphic designers in identifying fonts used in images. A VFR system also aids in improving the speed and accuracy of Optical Character Recognition (OCR) systems. In this paper, we proposed the first publicly available datasets in the field of Persian font recognition and employed Convolutional Neural Networks (CNN) to address the Persian font recognition problem. The results show that the proposed pipeline obtained 78.0% top-1 accuracy on our new datasets, 89.1% in the IDPL-PFOD dataset, and 94.5% in the KAFD dataset. Furthermore, the average time spent in the entire pipeline for one sample of our proposed datasets is 0.54 and 0.017 seconds for CPU and GPU, respectively. We conclude that CNN methods can be used to recognize Persian fonts without the need for additional pre-processing steps such as feature extraction, binarization, normalization, etc.
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| 9. |
- Musaddiq, Arslan, et al.
(författare)
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Industry-Academia Cooperation : Applied IoT Research for SMEs in South-East Sweden
- 2023
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Ingår i: Internet of Things. GIoTS 2022. - Cham : Springer. - 9783031209352 - 9783031209369 ; , s. 397-410
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Konferensbidrag (refereegranskat)abstract
- This paper presents the activities of the Applied IoT Lab at the Department of Computer Science and Media Technology, Linnaeus University (LNU), Kalmar, Sweden. The lab is actively engaged in IoT-based educational programs, including a series of workshops and pilot cases. The lab is funded by the European Union and two Swedish counties – Kalmar and Kronoberg. The workshops and pilot cases are part of the research project named IoT Lab for Small and Medium-sized Enterprises (SMEs). One of the lab’s main objectives is to strengthen and support local companies with IoT. The project IoT Lab for SMEs also aims to spread knowledge and inspire the local community about the possibilities of using IoT technologies by organizing open lab days, in-depth lectures, and seminars. This paper introduces Applied IoT Lab at LNU, its educational programs, and industry-academic cooperation, including workshops and a number of ongoing pilot cases.
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| 10. |
- Musaddiq, Arslan, et al.
(författare)
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Integrating Object Detection and Wide Area Network Infrastructure for Sustainable Ferry Operation
- 2023
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Ingår i: <em>2023 IEEE International Conference on Imaging Systems and Techniques (IST)</em>, Copenhagen, Denmark. - : IEEE. - 9798350330830 - 9798350330847
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Konferensbidrag (refereegranskat)abstract
- Low-Power Wide-Area Network (LPWAN) technologies offer new opportunities for data collection, transmission, and decision-making optimization. Similarly, a wide range of use cases of computer vision and object detection algorithms can be found across different industries. This paper presents a case study focusing on the utilization of LPWAN infrastructure, specifically the Helium network, coupled with computer vision and object detection algorithms, to optimize passenger ferry operation. The passenger ferry called M/S Dessi operates between Kalmar and Färjestaden in Sweden during the summer season. By implementing an Edge-computing solution, real-time data collection and communication are achieved, enabling accurate measurement of passenger flow. This approach is superior to traditional methods of collecting passenger data, such as manual counting or CCTV surveillance. Real-time passenger data is invaluable for traffic planning, crowd prediction, revenue enhancement, and speed and fuel optimization. The utilization of the Helium network ensures reliable and long-distance data transmission, extending the system’s applicability to multiple ferries and distant locations. The proposed approach can be utilized to integrate passenger ferries that operate in close proximity to urban areas into society’s digital transformation efforts. This study highlights the potential of LPWAN, computer vision, and object detection in enhancing passenger ferry operations, contributing to enhanced efficiency and sustainability.
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