Initialization Requirement in Developing of Mobile Learning 'Molearn' for Biology Students Using Inquiry-based learning

Inquiry-based learning is kind of learning activities that involves students’ entire capabilities in exploring and investigating particular objects or phenomenon using critical thinking skills. Recently, information technology tangibly contributes in any education aspects, including the existence of e-learning, a widely spreading learning model in the 21st century education. This study aims at initializing needs of developing mobile learning ‘Molearn’ based on inquiry-based method. By cooperating with Biology teacher community in senior high school, ‘Molearn’ provides IT-based medium in Biology learning process

Low-Power Computer Vision: Improve the Efficiency of Artificial Intelligence

Energy efficiency is critical for running computer vision on battery-powered systems, such as mobile phones or UAVs (unmanned aerial vehicles, or drones). This book collects the methods that have won the annual IEEE Low-Power Computer Vision Challenges since 2015. The winners share their solutions and provide insight on how to improve the efficiency of machine learning systems

A literature review of Artificial Intelligence applications in railway systems

Nowadays it is widely accepted that Artificial Intelligence (AI) is significantly influencing a large number of domains, including railways. In this paper, we present a systematic literature review of the current state-of-the-art of AI in railway transport. In particular, we analysed and discussed papers from a holistic railway perspective, covering sub-domains such as maintenance and inspection, planning and management, safety and security, autonomous driving and control, revenue management, transport policy, and passenger mobility. This review makes an initial step towards shaping the role of AI in future railways and provides a summary of the current focuses of AI research connected to rail transport. We reviewed about 139 scientific papers covering the period from 2010 to December 2020. We found that the major research efforts have been put in AI for rail maintenance and inspection, while very limited or no research has been found on AI for rail transport policy and revenue management. The remaining sub-domains received mild to moderate attention. AI applications are promising and tend to act as a game-changer in tackling multiple railway challenges. However, at the moment, AI research in railways is still mostly at its early stages. Future research can be expected towards developing advanced combined AI applications (e.g. with optimization), using AI in decision making, dealing with uncertainty and tackling newly rising cybersecurity challenges

Field Testing of a Stochastic Planner for ASV Navigation Using Satellite Images

We introduce a multi-sensor navigation system for autonomous surface vessels (ASV) intended for water-quality monitoring in freshwater lakes. Our mission planner uses satellite imagery as a prior map, formulating offline a mission-level policy for global navigation of the ASV and enabling autonomous online execution via local perception and local planning modules. A significant challenge is posed by the inconsistencies in traversability estimation between satellite images and real lakes, due to environmental effects such as wind, aquatic vegetation, shallow waters, and fluctuating water levels. Hence, we specifically modelled these traversability uncertainties as stochastic edges in a graph and optimized for a mission-level policy that minimizes the expected total travel distance. To execute the policy, we propose a modern local planner architecture that processes sensor inputs and plans paths to execute the high-level policy under uncertain traversability conditions. Our system was tested on three km-scale missions on a Northern Ontario lake, demonstrating that our GPS-, vision-, and sonar-enabled ASV system can effectively execute the mission-level policy and disambiguate the traversability of stochastic edges. Finally, we provide insights gained from practical field experience and offer several future directions to enhance the overall reliability of ASV navigation systems.Comment: 33 pages, 20 figures. Project website https://pcctp.github.io. arXiv admin note: text overlap with arXiv:2209.1186

Monte Carlo Method with Heuristic Adjustment for Irregularly Shaped Food Product Volume Measurement

Volume measurement plays an important role in the production and processing of food products. Various methods have been proposed to measure the volume of food products with irregular shapes based on 3D reconstruction. However, 3D reconstruction comes with a high-priced computational cost. Furthermore, some of the volume measurement methods based on 3D reconstruction have a low accuracy. Another method for measuring volume of objects uses Monte Carlo method. Monte Carlo method performs volume measurements using random points. Monte Carlo method only requires information regarding whether random points fall inside or outside an object and does not require a 3D reconstruction. This paper proposes volume measurement using a computer vision system for irregularly shaped food products without 3D reconstruction based on Monte Carlo method with heuristic adjustment. Five images of food product were captured using five cameras and processed to produce binary images. Monte Carlo integration with heuristic adjustment was performed to measure the volume based on the information extracted from binary images. The experimental results show that the proposed method provided high accuracy and precision compared to the water displacement method. In addition, the proposed method is more accurate and faster than the space carving method

Combating Misinformation in the Age of LLMs: Opportunities and Challenges

Misinformation such as fake news and rumors is a serious threat on information ecosystems and public trust. The emergence of Large Language Models (LLMs) has great potential to reshape the landscape of combating misinformation. Generally, LLMs can be a double-edged sword in the fight. On the one hand, LLMs bring promising opportunities for combating misinformation due to their profound world knowledge and strong reasoning abilities. Thus, one emergent question is: how to utilize LLMs to combat misinformation? On the other hand, the critical challenge is that LLMs can be easily leveraged to generate deceptive misinformation at scale. Then, another important question is: how to combat LLM-generated misinformation? In this paper, we first systematically review the history of combating misinformation before the advent of LLMs. Then we illustrate the current efforts and present an outlook for these two fundamental questions respectively. The goal of this survey paper is to facilitate the progress of utilizing LLMs for fighting misinformation and call for interdisciplinary efforts from different stakeholders for combating LLM-generated misinformation.Comment: 9 pages for the main paper, 35 pages including 656 references, more resources on "LLMs Meet Misinformation" are on the website: https://llm-misinformation.github.io

Fluorescence-based strategies to investigate the structure and dynamics of aptamer-ligand complexes

This work was funded by the Scottish Universities Physics Alliance (SUPA), the University of St Andrews, the Engineering and Physical Sciences ResearchCouncil (EPSRC), the Canadian Institutes of Health Research (CIHR). Funding for open access charge: University of St Andrews.In addition to the helical nature of double-stranded DNA and RNA, single-stranded oligonucleotides can arrange themselves into tridimensional structures containing loops, bulges, internal hairpins and many other motifs. This ability has been used for more than two decades to generate oligonucleotide sequences, so-called aptamers, that can recognize certain metabolites with high affinity and specificity. More recently, this library of artificially-generated nucleic acid aptamers has been expanded by the discovery that naturally occurring RNA sequences control bacterial gene expression in response to cellular concentration of a given metabolite. The application of fluorescence methods has been pivotal to characterize in detail the structure and dynamics of these aptamer-ligand complexes in solution. This is mostly due to the intrinsic high sensitivity of fluorescence methods and also to significant improvements in solid-phase synthesis, post-synthetic labelling strategies and optical instrumentation that took place during the last decade. In this work, we provide an overview of the most widely employed fluorescence methods to investigate aptamer structure and function by describing the use of aptamers labelled with a single dye in fluorescence quenching and anisotropy assays. The use of 2-aminopurine as a fluorescent analog of adenine to monitor local changes in structure and fluorescence resonance energy transfer (FRET) to follow long-range conformational changes is also covered in detail. The last part of the review is dedicated to the application of fluorescence techniques based on single-molecule microscopy, a technique that has revolutionized our understanding of nucleic acid structure and dynamics. We finally describe the advantages of monitoring ligand-binding and conformational changes, one molecule at a time, to decipher the complexity of regulatory aptamers and summarize the emerging folding and ligand-binding models arising from the application of these single-molecule FRET microscopy techniques.Publisher PDFPeer reviewe