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2,863 results for “Challenge”
Overcoming Challenges in DevOps Education through Teaching Methods
<p>DevOps is a set of practices that deals with coordination between development and operation teams and ensures rapid and reliable new software releases that are essential in industry. DevOps education assumes the vital task of preparing new professionals in these practices using appropriate teaching methods. However, there are insufficient studies investigating teaching methods in DevOps. We performed an analysis based on interviews to identify teaching methods and their relationship with educational challenges. Our findings show that <strong>project-based learning</strong> and <strong>collaborative learning</strong> are emerging as the most relevant teaching methods.</p>
Dataset: "The challenge of ionisation chamber dosimetry in ultra-short pulsed high dose-rate Very High Energy Electron beams"
<p>Dataset for the paper entitled "The challenge of ionisation chamber dosimetry in ultra-short pulsed high dose-rate Very High Energy Electron beams"</p>
nnU-Net weights for Prostate segmentation used in PI-CAI Challenge - Phase 1
<p>nnU-Net weights for Prostate segmentation used in PI-CAI Challenge - Phase 1.</p> <p>A baseline nnU-Net was trained to segment the prostate gland, using the 1500 cases training data from PI-CAI dataset. The network took as input 3 modalities (T2w, ADC, high DWIb), and the ground truth was AI-derived prostate masks.</p> <p>See Abstract "Deep learning for detection and diagnosis of prostate cancer from bpMRI and PSA: Guerbet’s contribution to the PI-CAI 2022 Grand Challenge" for further information.</p>
FIGURE 1 in A new genus of Chalcidoidea (Hymenoptera) from Chile with challenging taxonomic position
FIGURE 1. Lautaroderus malalcahuello Gumovsky gen. et sp. n., holotype (♀). A–C, habitus views. D, E, posterior mesosoma (mesoscutellum, metascutellum and propodeum). Abbreviations: mtsc, metascutellum; psp, propodeal spiracle.
FIGURE 4 in A new genus of Chalcidoidea (Hymenoptera) from Chile with challenging taxonomic position
FIGURE 4. Aoridus campbelli Yoshimoto, holotype (♀). A, head and mesosoma. B, head. C, part of mesosoma showing basal part of fore wing (the region basal to the red dashed line is the speculum and the regions of excessive pilosity are indicated by red arrows). Abbreviation: fs, frontal sulcus.
FIGURE 3 in A new genus of Chalcidoidea (Hymenoptera) from Chile with challenging taxonomic position
FIGURE 3. Lautaroderus malalcahuello Gumovsky gen. et sp. n., holotype (♀). A, G, head and anterior part of mesosoma: A, latero-ventral view; G, dorso-frontal view. B, metasoma and posterior part of mesosoma. C, ventral part of body and legs with labelled tarsi. D‒F, tarsi: D, fore tarsus; E, hind tarsus; F, mid tarsus. Abbreviations: bc, basitarsal comb; ca, calcar; fs, frontal sulcus; ftr, fore tarsus; htr, hind tarsus; mtr, mid tarsus; sg, scrobal groove; msp, mesothoracic spiracle; pp, prepectus.
FIGURE 2 in A new genus of Chalcidoidea (Hymenoptera) from Chile with challenging taxonomic position
FIGURE 2. Lautaroderus malalcahuello Gumovsky gen. et sp. n., holotype (♀). A, head and mesosoma. B, face. C, lower face. D, E, head and antenna. Abbreviations: an1 and an2, anelli; cly, clypeus; C1‒C3, club segments; fs, frontal sulcus; F1‒F4, funiculars; mtsc, metascutellum.
DISPLACE Challenge @ Interspeech-2023
<p>The <strong>DIarization of SPeaker and LAnguage in Conversational Environments [DISPLACE]</strong> challenge entails a first of kind task to perform speaker and language diarization on the same data, as the data contains multi-speaker social conversations in multi-lingual code-mixed speech. In multi-lingual communities, social conversations frequently involve code-mixed and code-switched speech. In such cases, various speech processing systems need to perform the speaker and language segmentation before any downstream task. The current speaker diarization systems are not equipped to handle multi-lingual conversations, while the language recognition systems may not be able to handle the same talker speaking in multiple languages within the same recording. </p> <p>With this motivation, the DISPLACE challenge attempts to benchmark and improve Speaker Diarization (SD) in multi-lingual settings and Language Diarization (LD) in multi-speaker settings, using the same underlying dataset. For this challenge, a natural multi-lingual, multi-speaker conversational dataset will be distributed for development and evaluation purposes. There will be no training data given and the participants will be free to use any resource for training the models. The challenge reflects the theme of Interspeech 2023 - "<em>Inclusive Spoken Language Science and Technology – Breaking Down Barriers</em>" in its true sense. </p> <p>Registrations are open for this challenge which will contain two tracks - a) Speaker diarization track and b) Language diarization track. </p> <p>For more details, dates and to register, kindly visit the DISPLACE challenge website: <a href="https://displace2023.github.io/">https://displace2023.github.io/</a></p> <p>We look forward to your team challenging to 'displace' the state-of-the-art in speaker, language diarization. </p>
DCASE 2023 Challenge Task 2 Evaluation Dataset
<p><strong>Description</strong></p> <p>This dataset is the "evaluation dataset" for the <a href="https://dcase.community/challenge2023/task-first-shot-unsupervised-anomalous-sound-detection-for-machine-condition-monitoring">DCASE 2023 Challenge Task 2 "First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring"</a>.</p> <p>The data consists of the normal/anomalous operating sounds of seven types of real/toy machines. Each recording is a single-channel audio that includes both a machine's operating sound and environmental noise. The duration of recordings varies from 6 to 18 sec, depending on the machine type. The following seven types of real/toy machines are used:</p> <ul> <li>Vacuum</li> <li>ToyTank</li> <li>ToyNscale</li> <li>ToyDrone</li> <li>bandsaw</li> <li>grinder</li> <li>shaker</li> </ul> <p> </p> <p><strong>Definition</strong></p> <p>We first define key terms in this task: "machine type," "section," "source domain," "target domain," and "attributes.".</p> <ul> <li>"Machine type" indicates the type of machine, which in the development dataset is one of seven: fan, gearbox, bearing, slide rail, valve, ToyCar, and ToyTrain.</li> <li>A section is defined as a subset of the dataset for calculating performance metrics.</li> <li>The source domain is the domain under which most of the training data and some of the test data were recorded, and the target domain is a different set of domains under which some of the training data and some of the test data were recorded. There are differences between the source and target domains in terms of operating speed, machine load, viscosity, heating temperature, type of environmental noise, signal-to-noise ratio, etc.</li> <li>Attributes are parameters that define states of machines or types of noise.</li> </ul> <p> </p> <p><strong>Dataset</strong></p> <p>This dataset consists of seven machine types. For each machine type, one section is provided, and the section is a complete set of training and test data. For each section, this dataset provides (i) 990 clips of normal sounds in the source domain for training, (ii) ten clips of normal sounds in the target domain for training. The source/target domain of each sample is provided. Additionally, the attributes of each sample in the training and test data are provided in the file names and attribute csv files.</p> <p> </p> <p><strong>Recording procedure</strong></p> <p>Normal/anomalous operating sounds of machines and its related equipment are recorded. Anomalous sounds were collected by deliberately damaging target machines. For simplifying the task, we use only the first channel of multi-channel recordings; all recordings are regarded as single-channel recordings of a fixed microphone. We mixed a target machine sound with environmental noise, and only noisy recordings are provided as training/test data. The environmental noise samples were recorded in several real factory environments. We will publish papers on the dataset to explain the details of the recording procedure by the submission deadline.</p> <p> </p> <p><strong>Directory structure</strong></p> <p>- /dev_data </p> <p> - /raw<br> - /Vacuum<br> - /test <br> - /section_00_0001.wav <br> - ... <br> - /section_00_0200.wav <br> - /ToyTank (The other machine types have the same directory structure as Vacuum.) <br> - /ToyNscale<br> - /ToyDrone<br> - /bandsaw<br> - /grinder<br> - /shaker</p> <p><strong>Condition of use</strong></p> <p>This dataset was created jointly by <strong>Hitachi, Ltd. </strong>and <strong>NTT Corporation</strong> and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you use this dataset, please cite all the following papers. We will publish a paper on the description of the DCASE 2023 Task 2, so pleasure make sure to cite the paper, too.</p> <ul> <li>Noboru Harada, Daisuke Niizumi, Yasunori Ohishi, Daiki Takeuchi, and Masahiro Yasuda. <em>First-shot anomaly detection for machine condition monitoring: A domain generalization baseline. In arXiv e-prints: 2303.00455</em>, 2023. [<a href="https://arxiv.org/pdf/2303.00455.pdf">URL</a>]</li> <li>Kota Dohi, Tomoya Nishida, Harsh Purohit, Ryo Tanabe, Takashi Endo, Masaaki Yamamoto, Yuki Nikaido, and Yohei Kawaguchi. <em>MIMII DG: sound dataset for malfunctioning industrial machine investigation and inspection for domain generalization task.</em> In Proceedings of the 7th Detection and Classification of Acoustic Scenes and Events 2022 Workshop (DCASE2022), 31-35. Nancy, France, November 2022, . [<a href="https://arxiv.org/pdf/2205.13879.pdf">URL</a>]</li> <li>Noboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Masahiro Yasuda, and Shoichiro Saito. <em>ToyADMOS2: another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions.</em> In Proceedings of the 6th Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021), 1–5. Barcelona, Spain, November 2021. [<a href="https://dcase.community/documents/workshop2021/proceedings/DCASE2021Workshop_Harada_6.pdf">URL</a>]</li> </ul> <p> </p> <p><strong>Contact</strong></p> <p>If there is any problem, please contact us:</p> <ul> <li>Kota Dohi, <a href="mailto:kota.dohi.gr@hitachi.com">kota.dohi.gr@hitachi.com</a></li> <li>Keisuke Imoto, <a href="mailto:keisuke.imoto@ieee.org">keisuke.imoto@ieee.org</a></li> <li>Noboru Harada, <a href="mailto:noboru@ieee.org">noboru@ieee.org</a></li> <li>Daisuke Niizumi, <a href="mailto:daisuke.niizumi.dt@hco.ntt.co.jp">daisuke.niizumi.dt@hco.ntt.co.jp</a></li> <li>Yohei Kawaguchi, <a href="mailto:yohei.kawaguchi.xk@hitachi.com">yohei.kawaguchi.xk@hitachi.com</a></li> </ul>
Challenges of ELA-based Function Evolution using Genetic Programming - Reproducability files
<p>This repository contains the data and code for the paper "Challenges of ELA-based Function Evolution using Genetic<br> Programming"</p> <p> </p> <p>This repository consists of separated folders, which contain the following data:</p> <p>## Code:</p> <p>This is the main code used to run the GP functions. The main executable is 'main_gp.py', which executes a single run of the GP system (based on the passed-in argument, which is an index from 0-71 in our experiments).</p> <p>The data for the BBOB functions are generated using the 'preliminary' folder and the 'get_ela_preliminary.py' file.</p> <p> </p> <p>## Data_GP:</p> <p>This contains the full logs from each GP run, separated by target function and dimension.</p> <p> </p> <p>## data_random_func:</p> <p>This contains the same kind of data but for the Random Function Generator.</p> <p> </p> <p>## Reproducibility:</p> <p>This contains all code used to analyse and visualize the resulting data. The notebook is structured in the same way as the paper, separated by figure.</p>
Forest Types Show Divergent Biophysical Responses After Fire: Challenges to Ecological Modeling
<p>Datasets and scripts (MATLAB and R) used to document patterns of post-fire biophysical dynamics in seven forest types and 21 Level III ecoregions of the western United States.</p>
Data Repository for Chip-Chat: Challenges and Opportunities in Conversational Hardware Design
<p><strong># Data Repository for Chip-Chat: Challenges and Opportunities in Conversational Hardware Design</strong></p><p>This repository accompanies the manuscript accepted at MLCAD 2023, titled "Chip-Chat: Challenges and Opportunities in Conversational Hardware Design".</p><p>It contains the following:</p><p>- `free-chat-gpt4-tt03` - this contains the data used for the paper, which examines free-form process when exploring the potential applications for LLMs in hardware design. The task here was to generate the Verilog for a full (albeit small) processor design. Here, the chats are presented (and annotated) in the `/chats` subdirectory, which also includes a python script for extracting metadata (presented in table IV in the manuscript). Note that this directory also includes `/assembler` which provides a basic assembler (also written in Python) to make it easier to write demo programs (examples included) for the processor.</p><p>- `scripted-benchmarks` - this contains additional data not used in the paper, which examines a more rigid process when exploring the potential applications for LLMs in hardware design. Here, each model chats are separated by subdirectory.</p><p>- `scripted-benchmarks-gpt4-tt03` - this contains just the benchmarks not used in the paper, made by the first run of GPT-4, which were used for tapeout in Tiny Tapeout 3.</p><p>The two tt03 directories contain the GitHub action scripts required to invoke OpenLane and produce synthesis files, as well as used to perform simulation tests.</p>
Current situation of the hospitalization of persons without family in Japan and related medical challenges
<p>This study aims to determine the approximate number of hospitalizations of persons without family and the medical challenges they encounter in hospitals across Japan. Self-administered questionnaires were mailed to 4,000 randomly selected hospitals nationwide to investigate the actual conditions and problems, decision-making processes, and use of the government-recommended Guidelines for the hospitalization of, and decision-making support for, persons without family. </p>
Challenges of copro-parasitological surveys in wild Iberian ibex (Capra pyrenaica) populations addressed through a combination of molecular and statistical tools
<p><span>Copro-parasitological surveys in wildlife face challenges due to the secretive nature of many species and the unknown performance of the diagnostic tests employed. To overcome these issues, we used a combination of hierarchical models (site-occupancy and N-mixture models) applied to copro-parasitological data obtained from faecal samples assigned to the host species by molecular methods in the Iberian ibex in north-western Iberian Peninsula. The aims were to compare the performance of four diagnostic tests (Mini-FLOTAC, McMaster, Willis flotation, and natural sedimentation) and to use this methodological approach (molecular analysis with hierarchical models) to better estimate positivity proportion and shedding intensity in a wild ibex population. Pooled faecal samples were collected, and those confirmed by molecular analyses to be the host species in question were included in the study. Hierarchical models confirmed different performances of each diagnostic test, with Mini-FLOTAC showing higher sensitivity for eimeriid coccidia, Willis flotation (for proportion positive) and McMaster (for shedding intensity) in gastrointestinal Strongylida, and equal performance of MiniFlotac/Willis flotation (for proportion positive) and MiniFlotac/McMaster (for shedding intensity) in <em>Moniezia</em> spp. This study employed a combination of molecular and statistical methods that improved the estimates of prevalence and shedding intensity and allowed us to compare the performance of four diagnostic tests while assessing the effect of covariates. Such improvements are critical to enhancing inference in non-invasive wildlife copro-parasitological studies.</span></p>
Comparison Metrics Microscale Simulation Challenge for Wind Resource Assessment - Perdigão
<p>Simulation results of the "Comparison Metrics Microscale Simulation Challenge for Wind Resource Assessment". Simulations with various models and tools were performed of the Perdigão site. The wind speed profiles for nine metmast positions and the AEP values for two metmast positions are made available. At each position the wind speed profiles and AEP values are given for twelve wind direction sectors.</p>
Data from: Identifying and overcoming breastfeeding challenges using two-way short message service, a randomized controlled trial
<p>Breastfeeding challenges, such as pain and perception of insufficient milk supply, prevent many women from exclusively breastfeeding their babies for the six-month duration recommended by the World Health Organization. Mobile health strategies may help women identify and overcome breastfeeding challenges. This study evaluated a two-way short message service (SMS) intervention that delivered breastfeeding messages to women in Kiambu County, Kenya, and its impact on rates of exclusive breastfeeding and breastfeeding behavior at 1, 4, and 6 months post-delivery. Women in the control group received a general package of messages containing postnatal information; women in the intervention group received these messages, plus messages related to breastfeeding and surveys which offered more information about breastfeeding challenges. Both groups of women had access to a help desk agent who responded to questions via SMS.</p> <p>There was no significant difference in the rate of exclusive breastfeeding between the intervention and control at any time point (p=.861). Women in the intervention group were more likely to report experiencing breastfeeding challenges such as pain and perceived insufficient milk supply than those in the control group (32% vs. 24%, p=0.00). Of the women in the intervention group who reported experiencing breastfeeding challenges, 70% of them found a resolution to their challenge, and 23% of them resolved their challenges using SMS. SMS may help women in low-resource settings to identify and overcome their breastfeeding challenges.</p>
PROMISE12: Data from the MICCAI Grand Challenge: Prostate MR Image Segmentation 2012
<p>This repository contains all data associated with the 'Prostate MR Image Segmentation'-challenge 2012 on https://promise12.grand-challenge.org/. The goal of this challenge was to compare interactive and (semi)-automatic segmentation algorithms for MRI of the prostate. </p> <p> </p>
New Challenges for Gender Equality in AI: Navigating the Ethical and Social Implications.- 1st SPATIAL podcast episode
<p>Gender bias can penetrate AI systems through multiple avenues, posing a significant challenge in ensuring fair and equitable outcomes. From historical biases ingrained in the data to biased data selection methods, these factors contribute to biased AI systems. Recognizing the implications of gender bias in AI is crucial, as it impacts various domains such as social media advertising, job recruitment, smart devices, facial recognition, and voice recognition. To strive for equity, it is essential to understand the interconnectedness of different social and political identities, emphasizing intersectionality and the need to avoid generalizing experiences.</p> <p>In the first episode of the <a href="https://spatial-h2020.eu/">SPATIAL </a>podcast, we had the privilege of conversing with Marcus Westberg, postdoctoral researcher and project manager at <a href="https://www.tudelft.nl/">TU Delft</a>, the coordinator of the SPATIAL project.</p>
Managing Human Factors in Automated Vehicle Development: Towards Challenges and Practices
<p>This data set complements our publication with the title:</p> <p>"Managing Human Factors in Automated Vehicle Development: Towards Challenges and Practices" to appear in Proceedings of IEEE 31st International Requirements Engineering Conference (RE'23), Hannover, Germany, 2023 </p> <p>To support our research, we are providing two files:</p> <p>a) Interview Guide<br> b) Overview of Themes</p> <p>Please note that the themes mentioned in the theme file are not directly relevant to this specific paper. They relate to another study conducted using the same dataset. We have included these themes to demonstrate that the dataset was used for two separate papers, each examining different aspects.</p>
Dataset of the Paper "Demystifying Practices, Challenges and Expected Features of Using GitHub Copilot"
<p>This dataset collected from Stack Overflow (SO) and GitHub was used to conduct an empirical study on investigating the practices, challenges, and expected features of using GitHub Copilot. We provide below a brief description of each file in the dataset:</p> <p><strong>1. Dataset (SO).xlsx</strong></p> <p>contains the IDs and URLs of the labelled posts which are related to Copilot from SO, and the data extracted from these related SO posts.</p> <p><strong>2. Dataset (GitHub).xlsx</strong></p> <p>contains the discussion IDs and URLs in the Copilot category of GitHub Discussions, and the data extracted from the relevant discussions.</p> <p><strong>3. Extracted Data (SO+GitHub).xlsx</strong></p> <p>provides the final results of the data extracted from SO posts and GitHub discussions.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.