January 21-22, 2023, Virtual Conference
Abdeen Mustafa Omer, Energy Research Institute (ERI), Nottingham, United Kingdom
The rapid growth during the last decade has been accompanied by active construction, which in some instances neglected the impact on the environment and human activities. Policies to promote the rational use of electric energy and to preserve natural non-renewable resources are of paramount importance. Low energy design of urban environment and buildings in densely populated areas requires consideration of wide range of factors, including urban setting, transport planning, energy system design and architectural and engineering details. The focus of the world’s attention on environmental issues in recent years has stimulated response in many countries, which have led to a closer examination of energy conservation strategies for conventional fossil fuels. One way of reducing building energy consumption is to design buildings, which are more economical in their use of energy for heating, lighting, cooling, ventilation and hot water supply. However, exploitation of renewable energy in buildings and agricultural greenhouses can, also, significantly contribute towards reducing dependency on fossil fuels. This will also contribute to the amelioration of environmental conditions by replacing conventional fuels with renewable energies that produce no air pollution or greenhouse gases. This study describes various designs of low energy buildings. It also, outlines the effect of dense urban building nature on energy consumption, and its contribution to climate change. Measures, which would help to save energy in buildings, are also presented.
Renewable technologies, Built environment, Sustainable development, Mitigation measures.
Arthur Gomes1 and Meghang Nagavekar2, 1Department of Mechatronics Engineering, Manipal Institute of Technology, Manipal, India, 2Department of Electronics and Instrumentation Engineering, Manipal Institute of Technology, Manipal, India
ROS (robot operating system) is rapidly becoming the operational framework of choice for all robotic control and navigation applications. Consumer and industrial robots are increasingly being integrated with ROS packages. This paper proposes a wireless controller system for teleoperating any ROS compatible robot platform. The controller is itself a microcontroller-based device with a long-range serial transceiver. The microcontroller of choice is the STM32F103C8 and the transceiver is an HC-12 serial communication module. An identical transceiver module is also utilized at the ROS master’s end to establish point to point communication for transmission of serialized ROS messages. The system maintains steady connection without loss of bandwidth. The system also ensures that ROS messages from other nodes can be made available to the controller. The controller can be programmed for direct integration with ROS open-source framework and packages without any requirement for middleware. It is efficient, effective and easy to debug. A controller of this description could potentially be applied in areas requiring realtime teleoperation of ROS based robotic systems like manipulators, mobile robots and drones. The system is reliable, fast and upgradable as per the user's requirements.
ROS, Controller, Wireless communication, Teleoperation.
Alghafiqe Alhloul and Abu Alam, Department of Computer Science, Gloucestershire University, Cheltenham, Uk.
Cyberbullying is a new emerged problem with last effecting consequences. The prevalence of cyberbullying toward minority creates a mental issue for the victims. Due the recent COVID-19 pandemic, the usage of social media specially twitter is increased and consequently this hostile behavior is increased too. Cyberbullying takes various forms and, in the twitter, it takes the textual format. In recent years many machine and deep learning model are developed for automatic cyberbullying detection. Motivated by previous researches we proposed a novel combination between the attention layer and convolutional pooling layer to extract key words from the tweets efficiently. We evaluated the proposed model using 47000 labelled tweets based on classes of cyberbullying such as Age, Ethnicity, Gender, Religion, Other type of cyberbullying, Not cyberbullying. Experimental results indicated 97.10% accuracy and 97.12% F1-score to classify tweets into mention classes. To evaluate the proposed model, we compared the proposed mode using combination of machine learning and deep learning models. Our finding indicates that the proposed model has extracted the most important keywords in the tweets and degraded information which are not related to the assault.
Cyberbullying, Self-attention, Convolution network, Machine learning , Deep learning.
Mohamed Mihoub1,2, Ahmed Al Balushi1, Marwa Al Badai1, Siham Al Samahi1, Khadija Al Sadi1, Nour Al Mayahi1, 1Department of Engineering, College of Engineering and Technology, University of Technology and Applied Sciences, Suhar, Oman, 1,2Higher Institute of Applied Science and Technology of Sousse, Tunisia
The project deals with the mobility of autonomous medical robots inside the hospital premises. Instead of using multiple beacons radiating electromagnetic waves for recognizing its location (which may be harmful for patients), the presented robot uses a stereo camera and an IMU sensor for calculating its movement in a manner copied from human behaviour. The desired trajectory is recorded on the robot controller, in the form of points. The robot goes from point to point with a good accuracy allowing the use of the robot for moving in new areas with the same performance and lower cost.
Mobile robot, Depth Camera, Stereo Camera, Mecanum Wheels.
David Noever and Matt Ciolino, PeopleTec, Inc., Huntsville, Alabama, USA
This research revisits the classic Turing test and compares recent large language models such as ChatGPT for their abilities to reproduce human-level comprehension and compelling text generation. Two task challenges- summary and question answering- prompt ChatGPT to produce original content (98-99%) from a single text entry and sequential questions initially posed by Turing in 1950. We score the original and generated content against the OpenAI GPT-2 Output Detector from 2019, and establish multiple cases where the generated content proves original and undetectable (98%). The question of a machine fooling a human judge recedes in this work relative to the question of "how would one prove it?" The original contribution of the work presents a metric and simple grammatical set for understanding the writing mechanics of chatbots in evaluating their readability and statistical clarity, engagement, delivery, overall quality, and plagiarism risks. While Turing's original prose scores at least 14% below the machine-generated output, whether an algorithm displays hints of Turing's true initial thoughts (the "Lovelace 2.0" test) remains unanswerable.
Large language Models (LM), Generative Pre-trained Transformer (GPT-3), Turing test.
Zuzana Špitálová, Oliver Leontiev and Patrik Harmaňoš, Institute of Computer Engineering and Applied Informatics Faculty of Informatics and Information Technology, Slovak University of Technology, Bratislava, Slovakia
In the last years, there is a demand for the possibilities of transporting goods using shared services. That requires more subjects participated in this process and modern technologies. This results in city infrastructure requirements which enable the smart city creation. Devices, used for the delivery and other processes inside the city, can help to gain data from the environment. That data can be further used for defining the improvements related to the daily city life. We designed the system for delivery process which can help to achieve the described situation. It consists of two parts, the frontend and the backend. The frontend part represents the graphical interface. The backend one represents the database for the gained data. The main idea of our work is the system, which enables everyone to participate in the delivery process.
Smart City, Delivery System, PostrgreSQL, ReactNative, API.
Younes El koudia, Jarou Tarik, Abdouni Jawad, Sofia El Idrissi and Elmahdi Nasri, Advanced Systems Engineering Laboratory, National School of Applied Sciences, Kenitra, Morocco
The work show in this pepper progresses through a sequence of physics-based increasing fidelity models that are used to design the robot controllers that respect the limits of the robot capabilities, develop a reference simple controller applicable to a large subset of tracking conditions, which include mostly non-invasive or highly dynamic movements and define path geometry following the control problem and develop both a simple geometric control and a dynamic model predictive control approach.
Robot, tracking, path geometry, geometric control, predictive control.
Tjada Nelson, Austin O’Brien and Cherie Noteboom, Beacom College of Computer & Cyber Sciences, Dakota State University, Madison, South Dakota
With a text mining and bibliometrics approach, this study reviews the literature on the evolution of malware classification using machine learning. This work takes literature from 2008 to 2022 on the subject of using machine learning for malware classification to understand the impact of this technology on malware classification. Throughout this study, we seek to answer three main research questions: RQ1: Is the application of machine learning for malware classification growing? RQ2: What is the most common machine-learning application for malware classification? RQ3: What are the outcomes of the most common machine learning applications? The analysis of 2186 articles resulting from a data collection process from peer-reviewed databases shows the trajectory of the application of this technology on malware classification as well as trends in both the machine learning and malware classification fields of study. This study performs quantitative and qualitative analysis using statistical and N-gram analysis techniques and a formal literature review to answer the proposed research questions. The research reveals methods such as support vector machines and random forests to be standard machine learning methods for malware classification in efforts to detect maliciousness or categorize malware by family. Machine learning is a highly researched technology with many applications, from malware classification and beyond.
Malware, Malware Classification, Machine Learning.
Khalid AlNujaidi and Ghadah AlHabib, Computer Science Department, Prince Mohammad Bin Fahd University, Khobar, Saudi Arabia
As the population grows and more land is being used for urbanization, ecosystems are disrupted by our roads and cars. This expansion of infrastructure cuts through wildlife territories, leading to many instances of Wildlife-Vehicle Collision (WVC). These instances of WVC are a global issue that is having a global socio-economic impact, resulting in billions of dollars in property damage and, at times, fatalities for vehicle occupants. In Saudi Arabia, this issue is similar, with instances of Camel-Vehicle Collision (CVC) being particularly deadly due to the large size of camels, which results in a 25% fatality rate [4]. The focus of this work is to test different object detection. The Deep Learning (DL) object detection models used in the experiments are: CenterNet, EfficientDet, Faster R-CNN, and SSD. Results of the experiments show that CenterNet performed the best in terms of accuracy and was the most efficient in training. These results prove that such models can be deployed to develop a system to help combat this issue, which is the vision of this work.
Wildlife-Vehicle Collision, Camel-Vehicle Collision, Deep Learning, Object Detection, Computer Vision.