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How My Students and I (Re)Discovered the Joy of Computing in CS2

ACM Inroads 2023年第2期14卷 36-39页

作者： Chávez, Victoria C. Computer Science & Learning Sciences Northwestern University 2120 Campus Drive EvanstonIL60208 United States

Ask students what the hardest CS courses are, and they will likely mention data structures and algorithms (DSA) amongst the top three. In fact, DSA courses are included thrice in the current top 5 video results for "hardest CS classes" on YouTube. Students often bemoan about their nightmarish experiences and the high failure rates. How can we make CS2-DSA courses more joyful? How can we build on the incredible work being done in AP CS and CS0 courses through efforts such as "Passion, Beauty, Joy and Awe" (PBJA) of computing [4,5] and "Beauty and Joy of Computing" (BJC) [5]? In this piece, I reflect on my own experiences teaching a DSA course for the first and second time and highlight some of the ways my students and I were able to (re)-discover the joy of computing in CS2. © 2023 Association for Computing Machinery. All rights reserved.

关键词： Students

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Designing for youth interpreter professional development: A sociotechnologically-framed participatory design approach 15

Designing for youth interpreter professional development: A ...

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14th International Conference on Interaction Design and Children, IDC 2015

作者： Jimenez Pazmino, Priscilla F. Slattery, Brian Lyons, Leilah Hunt, Benjamin Computer Science Learning Sciences University of Illinois - Chicago United States

ISBN: (纸本)9781450335904

Informal science institutions (ISIs) are beginning to adopt mobile technology to support interpreters (docents), but not much is known about how to design these supports. One approach to designing technology for new scenarios is participatory design (PD), where end-users are involved as experts in the task domain who can help envision the application of technology. However, in our context end-users are often youth interpreters who are emerging professionals. This poses a challenge because traditional PD methods trust that the users can represent the task domain. Novice professionals may not yet fully understand the task domain, but eliciting their needs and visions is still important for producing a tool they will find useful. A design approach is needed that captures the requirements for supporting expert task execution as an underlying structure for the tool, while nonetheless eliciting and respecting the special needs of novices. We developed and applied two different framing strategies (one technological, one sociotechnological) to traditional PD methods to help youth non-expert interpreters generate task-relevant design ideas. We report results from using these strategies in an exploratory fashion and discuss opportunities for future research on PD methods that can address the needs of youth as emerging professionals. Copyright is held by the owner/author(s).

关键词： informal education participatory design sociotechnological participatory design design methodology interpreters professional development teenagers

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COSMO: Combination of Selective Memorization for Low-cost Vision-and-Language Navigation

arXiv

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arXiv 2025年

作者： Zhang, Siqi Qiao, Yanyuan Wang, Qunbo Yan, Zike Wu, Qi Wei, Zhihua Liu, Jing School of Computer Science and Technology Tongji University China Institute of Automation Chinese Academy of Sciences China Australian Institute for Machine Learning The University of Adelaide Australia Tsinghua University China

Vision-and-Language Navigation (VLN) tasks have gained prominence within artificial intelligence research due to their potential application in fields like home assistants. Many contemporary VLN approaches, while based on transformer architectures, have increasingly incorporated additional components such as external knowledge bases or map information to enhance performance. These additions, while boosting performance, also lead to larger models and increased computational costs. In this paper, to achieve both high performance and low computational costs, we propose a novel architecture with the combination of selective memorization (COSMO). Specifically, COSMO integrates state-space modules and transformer modules, and incorporates two VLN-customized selective state space modules: the Round Selective Scan (RSS) and the Cross-modal Selective State Space Module (CS3). RSS facilitates comprehensive inter-modal interactions within a single scan, while the CS3 module adapts the selective state space module into a dual-stream architecture, thereby enhancing the acquisition of cross-modal interactions. Experimental validations on three mainstream VLN benchmarks, REVERIE, R2R, and R2R-CE, not only demonstrate competitive navigation performance of our model but also show a significant reduction in computational costs. © 2025, CC BY-NC-SA.

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Using Semantic Web Technologies to Improve the Extract Transform Load Model

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computers, Materials & Continua 2021年第8期68卷 2711-2726页

作者： Amena Mahmoud Mahmoud Y.Shams O.M.Elzeki Nancy Awadallah Awad Department of Computer Science Kafrelshiekh UniversityKafrelshiekhEgypt Department of Machine Learning Kafrelsheikh UniversityKafrelshiekhEgypt Department of Computer Science Mansoura UniversityMansouraEgypt Department of Computer and Information Systems Sadat Academy for Management SciencesCairoEgypt

Semantic Web(SW)provides new opportunities for the study and application of big data,massive ranges of data sets in varied formats from multiple *** studies focus on potential SW technologies for resolving big data problems,such as structurally and semantically heterogeneous data that result from the variety of data formats(structured,semi-structured,numeric,unstructured text data,email,video,audio,stock ticker).SW offers information semantically both for people and machines to retain the vast volume of data and provide a meaningful output of unstructured *** the current research,we implement a new semantic Extract Transform Load(ETL)model that uses SW technologies for aggregating,integrating,and representing data as linked ***,geospatial data resources are aggregated from the internet,and then a semantic ETL model is used to store the aggregated data in a semantic model after converting it to Resource Description Framework(RDF)format for successful integration and *** principal contribution of this research is the synthesis,aggregation,and semantic representation of geospatial data to solve problems.A case study of city data is used to illustrate the semantic ETL model’s *** results show that the proposed model solves the structural and semantic heterogeneity problems in diverse data sources for successful data aggregation,integration,and representation.

关键词： Semantic web big data ETL model linked data geospatial data

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Enhancing the Prediction of Lymph Node Metastasis in Early Breast Cancer Using Deep learning on Routine Full-Breast Mammograms

Research Square

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Research Square 2025年

作者： Zhang, Daqu Dihge, Looket Bendahl, Pär-Ola Arvidsson, Ida Dustler, Magnus Ellbrant, Julia Gulis, Kim Hjärtström, Malin Ohlsson, Mattias Rejmer, Cornelia Schmidt, David Zackrisson, Sophia Edén, Patrik Rydén, Lisa Centre for Environmental and Climate Science Computational Science for Health and Environment Lund University Lund Sweden Department of Clinical Sciences Division of Surgery Lund University Skåne University Hospital Lund Sweden Department of Plastic and Reconstructive Surgery Skåne University Hospital Malmö Sweden Department of Clinical Sciences Division of Oncology Lund University Lund Sweden Centre for Mathematical Sciences Division of Computer Vision and Machine Learning Lund University Lund Sweden Department of Translational Medicine Diagnostic Radiology Lund University Malmö Lund Sweden Department of Surgery and Gastroenterology Skåne University Hospital Malmö Sweden Department of Surgery Kristianstad Central Hospital Kristianstad Sweden Department of Clinical Sciences Anesthesiology and Intensive Care Lund University Lund Sweden Department of Medical Imaging and Physiology Skåne University Hospital Malmö Sweden

Background: With a trend toward de-escalation of axillary surgery in breast cancer, prediction models incorporating imaging modalities can help reassess the need for surgical axillary staging. Although mammography is routinely performed for breast cancer imaging, its potential in nodal staging remains underutilized. This study aims to employ advancements in deep learning (DL) to comprehensively evaluate the potential of routine mammograms for predicting lymph node metastasis (LNM) in preoperative clinical settings. Methods: This retrospective study included 1,265 cN0 T1-T2 breast cancer patients, comprising 368 node-positive and 897 node-negative cases, diagnosed from 2009-2017 at three Swedish institutions. Patients diagnosed in 2017 were assigned to the independent test set (n=123, site 2) and the external test set (n=103, site 3), while the remaining patients (n=1,039, site 1 and 2) were used for model development and double cross-validation. A neck module, in conjunction with a ResNet backbone pretrained on unlabeled mammograms, was developed to extract global information from full-breast or region-of-interest (ROI) mammograms by predicting five cancer outcomes. Clinicopathological characteristics were combined with the learned mammogram features to predict LNM collaboratively. The models were evaluated using area under the receiver operating characteristic (ROC) curve (AUC), calibration, and decision curve analysis. Results: Compared to models using only clinical variables, incorporating full-breast mammograms with preoperative clinical variables improved the ROC AUC from 0.690 ± 0.063 (SD) to 0.774 ± 0.057 in the independent test set and from 0.584 ± 0.068 to 0.637 ± 0.063 in the external test set. The combined model showed good calibration and, at sensitivity ≥ 90%, achieved a better net benefit, and a higher sentinel lymph node biopsy reduction rate of 41.7% in the independent test set. Full-breast mammograms showed comparable ability to tumor ROIs in predic

关键词： Self-supervised learning

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TEMPORAL DISTRIBUTION SHIFT IN REAL-WORLD PHARMACEUTICAL DATA: IMPLICATIONS FOR UNCERTAINTY QUANTIFICATION IN QSAR MODELS

arXiv

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arXiv 2025年

作者： Friesacher, Hannah Rosa Svensson, Emma Winiwarter, Susanne Mervin, Lewis Arany, Adam Engkvist, Ola Molecular AI Discovery Sciences AstraZeneca R&D Gothenburg431 83 Sweden ESAT-STADIUS KU Leuven 3000 Belgium Molecular AI Discovery Sciences AstraZeneca R&D CambridgeCB2 0AA United Kingdom ELLIS Unit Linz & Institute for Machine Learning Johannes Kepler University Linz Linz4040 Austria Department of Computer Science and Engineering Chalmers University of Technology Gothenburg412 96 Sweden BioPharmaceuticals R&D AstraZeneca Gothenburg431 83 Sweden

The estimation of uncertainties associated with predictions from quantitative structure-activity relationship (QSAR) models can accelerate the drug discovery process by identifying promising experiments and allowing an efficient allocation of resources. Several computational tools exist that estimate the predictive uncertainty in machine learning models. However, deviations from the i.i.d. setting have been shown to impair the performance of these uncertainty quantification methods. We use a real-world pharmaceutical dataset to address the pressing need for a comprehensive, large-scale evaluation of uncertainty estimation methods in the context of realistic distribution shifts over time. We investigate the performance of several uncertainty estimation methods, including ensemble-based and Bayesian approaches. Furthermore, we use this real-world setting to systematically assess the distribution shifts in label and descriptor space and their impact on the capability of the uncertainty estimation methods. Our study reveals significant shifts over time in both label and descriptor space and a clear connection between the magnitude of the shift and the nature of the assay. Moreover, we show that pronounced distribution shifts impair the performance of popular uncertainty estimation methods used in QSAR models. This work highlights the challenges of identifying uncertainty quantification methods that remain reliable under distribution shifts introduced by real-world data. Copyright © 2025, The Authors. All rights reserved.

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Patch-based topic model for group detection

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science China(Information sciences) 2017年第11期60卷 235-241页

作者： Mulin CHEN Qi WANG Xuelong LI School of Computer Science and Center for Optical Imagery Analysis and Learning Northwestern Polytechnical University Unmanned System Research Institute Northwestern Polytechnical University Center for Optical Imagery Analysis and Learning Xi'an Institute of Optics and Precision MechanicsChinese Academy of Sciences

Pedestrians in crowd scenes tend to connect with each other and form coherent groups. In order to investigate the collective behaviors in crowds, plenty of studies have been conducted on group ***, most of the existing methods are limited to discover the underlying semantic priors of individuals. By segmenting the crowd image into patches, this paper proposes the Patch-based Topic Model(PTM) for group detection. The main contributions of this study are threefold:(1) the crowd dynamics are represented by patchlevel descriptor, which provides a macroscopic-level representation;(2) the semantic topic label of each patch are inferred by integrating the Latent Dirichlet Allocation(LDA) model and the Markov Random Fields(MRF);(3) the optimal group number is determined automatically with an intro-class distance evaluation *** results on real-world crowd videos demonstrate the superior performance of the proposed method over the state-of-the-arts.

关键词： group detection collective behavior crowd analysis latent topic

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Hybrid Deep learning for International Trading: Integrating LSTM and Transformer Models

Hybrid Deep Learning for International Trading: Integrating ...

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Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： T. S. Akhila J. Relin Francis Raj D. Kani Jesintha R. Santhana Krishnan V. Vinoth Kumar P. Sundaravadivel Department of Electronics and Communication Engineering Dhanalakshmi College of Engineering Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Machine Learning Saveetha Engineering College Chennai India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

This research proposes a hybrid deep learning model combining Long Short-Term Memory (LSTM) networks and Transformers to enhance decision-making in international trading. The model leverages advanced techniques such as RoBERTa for text tokenization, FinBERT for sentiment analysis, and XLM-Roberta for multilingual language processing. LSTM is employed to analyze sequential numerical data like historical prices and trading volumes, providing accurate price and volatility forecasts. The Transformer component processes textual data to extract sentiment classifications and event-driven market impacts, facilitating a comprehensive understanding of market conditions. A fully connected fusion layer integrates insights from both components, enabling the model to classify market conditions holistically. The optimization process utilizes the Adam optimizer with learning rate scheduling to ensure efficient and effective training. Loss functions like Mean Squared Error (MSE) and Cross-Entropy Loss are employed to optimize numerical predictions and categorical outcomes, respectively. By combining temporal analysis with sentiment-driven insights, this hybrid model offers improved price forecasting, advanced multilingual sentiment analysis, and event-driven market condition predictions. The proposed approach aims to provide traders with actionable insights, enabling more informed decision-making in the highly dynamic and multilingual environment of international trading.

关键词： Deep learning Adaptation models Sentiment analysis Analytical models Predictive models Transformers Data models Numerical models Multilingual Long short term memory

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learning electricity with multiple representations 14

Learning electricity with multiple representations

引用

14th International Conference on Interaction Design and Children, IDC 2015

作者： Beheshti, Elham Obiorah, Mmachi Horn, Michael S. Computer Science and Learning Sciences Northwestern University EvanstonIL United States

ISBN: (纸本)9781450335904

Electrical circuits are difficult to understand. In this paper, we introduce Spark, a museum exhibit that enables learners to interact with multiple representations of electrical circuits at the same time. The goal is to familiarize children with fundamental concepts of electricity such as current and resistance. We tested our design with 6 parent-child dyads at a major science museum. Our study investigates how families make sense of representations at the level of circuits and at the level of electrons and ions. Our findings show a wide variety of visitor interactions with the exhibit, in particular when connecting the two representations of a circuit. © 2015 ACM.

关键词： Agent-based modeling Electrical circuits Iterative design Multiple representations Museums learning

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A statistic criterion for reducing indeterminacy in linear causal modeling

A statistic criterion for reducing indeterminacy in linear c...

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2nd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2013

作者： Bontempi, Gianluca Machine Learning Group Computer Science Department Faculty of Sciences Brussels Belgium

ISBN: (纸本)9789898565419

Inferring causal relationships from observational data is still an open challenge in machine learning. State-of-the-art approaches often rely on constraint-based algorithms which detect v-structures in triplets of nodes in order to orient arcs. These algorithms are destined to fail when confronted with completely connected triplets. This paper proposes a criterion to deal with arc orientation also in presence of completely linearly connected triplets. This criterion is then used in a Relevance-Causal (RC) algorithm, which combines the original causal criterion with a relevance measure, to infer causal dependencies from observational data. A set of simulated experiments on the inference of the causal structure of linear networks shows the effectiveness of the proposed approach.

关键词： Linear networks

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