50 research outputs found
Initial location selection of electric vehicles charging infrastructure in urban city through clustering algorithm
Transportation is one of the critical sectors worldwide, mainly based on fossil fuels, especially internal combustion engines. In a developing country, heightened dependence on fossil fuels affected energy sustainability issues, greenhouse gas emissions, and increasing state budget allocation towards fuel subsidies. Moreover, shifting to electric vehicles (EVs) with alternative energy, primely renewable energy sources, is considered a promising alternative to decreasing dependence on fossil fuel consumption. The availability of a sufficient EV charging station infrastructure is determined as an appropriate strategy and rudimentary requirement to optimize the growth of EV users, especially in urban cities. This study aims to utilize the k-mean algorithm’s clustering method to group and select a potential EV charging station location in Jakarta an urban city in Indonesia. This study proposed a method for advancing the layout location’s comprehensive suitability. An iterative procedure determines the most suitable value for K as centroids. The K value is evaluated by cluster silhouette coefficient scores to acquire the optimized numeral of clusters. The results show that 95 potential locations are divided into 19 different groups. The suggested initial EV charging station location was selected and validated by silhouette coefficient scores. This research also presents the maps of the initially selected locations and clustering
Retrieve-Cluster-Summarize: An Alternative to End-to-End Training for Query-specific Article Generation
Query-specific article generation is the task of, given a search query,
generate a single article that gives an overview of the topic. We envision such
articles as an alternative to presenting a ranking of search results. While
generative Large Language Models (LLMs) like chatGPT also address this task,
they are known to hallucinate new information, their models are secret, hard to
analyze and control. Some generative LLMs provide supporting references, yet
these are often unrelated to the generated content. As an alternative, we
propose to study article generation systems that integrate document retrieval,
query-specific clustering, and summarization. By design, such models can
provide actual citations as provenance for their generated text. In particular,
we contribute an evaluation framework that allows to separately trains and
evaluate each of these three components before combining them into one system.
We experimentally demonstrate that a system comprised of the best-performing
individual components also obtains the best F-1 overall system quality.Comment: 5 pages, 1 figure
Using Active Learning to Teach Critical and Contextual Studies: One Teaching Plan, Two Experiments, Three Videos.
Since the 1970s, art and design education at UK universities has existedas a divided practice; on the one hand applying active learning in thestudio and on the other hand enforcing passive learning in the lecturetheatre. As a result, art and design students are in their vast majorityreluctant about modules that may require them to think, read and writecritically during their academic studies. This article describes, evaluatesand analyses two individual active learning experiments designed todetermine if it is possible to teach CCS modules in a manner thatencourages student participation. The results reveal that opting foractive learning methods improved academic achievement, encouragedcooperation, and enforced an inclusive classroom. Furthermore, andcontrary to wider perception, the article demonstrates that activelearning methods can be equally beneficial for small-size as well aslarge-size groups
Practical approaches to delivering pandemic impacted laboratory teaching
#DryLabsRealScience is a community of practice established to support life science educators with the provision of laboratory-based classes in the face of the COVID-19 pandemic and restricted access to facilities. Four key
approaches have emerged from the innovative work shared with the network: videos, simulations, virtual/augmented reality, and datasets, with each having strengths and weaknesses. Each strategy was used pre-COVID and has a sound theoretical underpinning; here, we explore how the pandemic has forced
their adaptation and highlight novel utilisation to support student learning in the laboratory environment during the challenges faced by remote and blended teaching
The 15th International CDIO Conference: Proceedings – Full Papers
We discuss a conceptual thesis structure model and visual tool for enhancing the writing process in the context of an engineering Master’s thesis. Our model is based on visualizing the thesis as a series of funnels that adjust the writing focus to the desired scope in each individual chapter. At the end of the thesis, the focus is widened back into the original topic area with a reflection on how the solutions proposed in the thesis have impacted or potentially will impact the field. Using our model gives students the opportunity to write a good Master’s thesis in various engineering disciplines. In our experience, the Focus Funnel approach has been very useful and effective, resulting in an overall improvement in the quality of engineering Master’s theses in our degree program.</p
A comparison of the CAR and DAGAR spatial random effects models with an application to diabetics rate estimation in Belgium
When hierarchically modelling an epidemiological phenomenon on a finite collection of sites in space, one must always take a latent spatial effect into account in order to capture the correlation structure that links the phenomenon to the territory. In this work, we compare two autoregressive spatial models that can be used for this purpose: the classical CAR model and the more recent DAGAR model. Differently from the former, the latter has a desirable property: its ρ parameter can be naturally interpreted as the average neighbor pair correlation and, in addition, this parameter can be directly estimated when the effect is modelled using a DAGAR rather than a CAR structure. As an application, we model the diabetics rate in Belgium in 2014 and show the adequacy of these models in predicting the response variable when no covariates are available
A Statistical Approach to the Alignment of fMRI Data
Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods
COMMUNITY DETECTION IN GRAPHS
Thesis (Ph.D.) - Indiana University, Luddy School of Informatics, Computing, and Engineering/University Graduate School, 2020Community detection has always been one of the fundamental research topics in graph mining. As a type of unsupervised or semi-supervised approach, community detection aims to explore node high-order closeness by leveraging graph topological structure. By grouping similar nodes or edges into the same community while separating dissimilar ones apart into different communities, graph structure can be revealed in a coarser resolution. It can be beneficial for numerous applications such as user shopping recommendation and advertisement in e-commerce, protein-protein interaction prediction in the bioinformatics, and literature recommendation or scholar collaboration in citation
analysis. However, identifying communities is an ill-defined problem. Due to the No Free Lunch theorem [1], there is neither gold standard to represent perfect community partition nor universal methods that are able to detect satisfied communities for all tasks under various types of graphs. To have a global view of this research topic, I summarize state-of-art community detection methods by categorizing them based on graph types, research tasks and methodology frameworks. As academic exploration on community detection grows rapidly in recent years, I hereby particularly focus on the state-of-art works published in the latest decade, which may leave out some classic models published decades ago. Meanwhile, three subtle community detection tasks are proposed and assessed in this dissertation as well. First, apart from general models which consider only graph structures, personalized community detection considers user need as auxiliary information to guide community detection. In the end, there will be fine-grained communities for nodes better matching user needs while coarser-resolution communities for the rest of less relevant nodes. Second, graphs always suffer from the sparse connectivity issue. Leveraging conventional models directly on such graphs may hugely distort the quality of generate communities. To tackle such a problem, cross-graph techniques are involved to propagate external graph information as a support for target graph community detection. Third, graph community structure supports a natural language processing (NLP) task to depict node intrinsic characteristics by generating node summarizations via a text generative model. The contribution of this dissertation is threefold. First, a decent amount of researches are reviewed and summarized under a well-defined taxonomy. Existing works about methods, evaluation and applications are all addressed in the literature review. Second, three novel community detection tasks are demonstrated and associated models are proposed and evaluated by comparing with state-of-art baselines under various datasets. Third, the limitations of current works are pointed out and future research tracks with potentials are discussed as well
The 15th International CDIO Conference: Proceedings – Full Papers
The 15th international CDIO conference was held at Aarhus University from 25 June 2019 to 27 June 2019 with activities on 24 and 28 June. The main theme of the 15th International CDIO Conference was CHANGE in Engineering Education.
The conference programme included:
Keynotes
General presentations
Working groups
Workshops
Round tables
Social events
CDIO Academy (A CDIO experience for students