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9,302 results for “DETECT”
Detection of Erythromycin Resistant Genes in Group B Streptococcus strains isolated in Georgia
<p>figure 1 - <strong>Antibiotic Susceptibility Test Results </strong><a href="#_msocom_1">[ac1]</a> </p> <p> <a href="#_msoanchor_1">[ac1]</a>Results of Antibiotic Susceptibility Tests</p> <p><strong>figure 2 - </strong></p> <p><strong>Positive PCR results</strong><a href="#_msocom_1">[ac1]</a> </p> <p> <a href="#_msoanchor_1">[ac1]</a>Positive results for presence of the genes in resistant strains</p> <p> </p>
Supplementary material for: Inconsistency Detection in Natural Language Requirements using ChatGPT: a Preliminary Evaluation
<p>Supplementary material including both data and annotations. For each document we present:</p> <ol> <li>annotated results of the chatGPT answers;</li> <li>annotated results of the manual analysis</li> <li>The requirements and the grund truth pairs. Originals requirements are marked with "1" in the third column, mutants are marked with "0". </li> </ol>
Detection of proton-dependent peptide uptake into liposomes by bacterial peptide transporter DtpB using pyranine assay
<p>For more details please see:</p> <p><strong>Plasticity of the binding pocket in peptide transporters underpins promiscuous substrate recognition</strong></p> <p>https://dx.doi.org/10.1016/j.celrep.2023.112831</p>
Object detection for graphical user interface: old fashioned or deep learning or a combination? - Model&Datasets
<p>This repo contains the datasets, trained models, and data splitting in ESEC/FSE 2020 "Object detection for graphical user interface: old fashioned or deep learning or a combination?" paper.</p>
Vulnerability Detection for Smart Contracts: A Systematic Literature Review - Supplementary Material
<p>Supplementary Material refers to the paper "Vulnerability Detection for Smart Contracts: A Systematic Literature Review" and contains the following information:</p> <p>a) DataAnalysis.xls - An Excel file created for analysis (i.e., tables and graphics).</p> <p> </p>
IoMT-TrafficData: A Dataset for Benchmarking Intrusion Detection in IoMT
<h3><strong>Article Information<br></strong></h3> <p>The work involved in developing the dataset and benchmarking its use of machine learning is set out in the article ‘IoMT-TrafficData: Dataset and Tools for Benchmarking Intrusion Detection in Internet of Medical Things’. DOI: 10.1109/ACCESS.2024.3437214.</p> <p>Please do cite the aforementioned article when using this dataset. </p> <h3><strong>Abstract</strong></h3> <p>The increasing importance of securing the Internet of Medical Things (IoMT) due to its vulnerabilities to cyber-attacks highlights the need for an effective intrusion detection system (IDS). In this study, our main objective was to develop a Machine Learning Model for the IoMT to enhance the security of medical devices and protect patients’ private data. To address this issue, we built a scenario that utilised the Internet of Things (IoT) and IoMT devices to simulate real-world attacks. We collected and cleaned data, pre-processed it, and provided it into our machine-learning model to detect intrusions in the network. Our results revealed significant improvements in all performance metrics, indicating robustness and reproducibility in real-world scenarios. This research has implications in the context of IoMT and cybersecurity, as it helps mitigate vulnerabilities and lowers the number of breaches occurring with the rapid growth of IoMT devices. The use of machine learning algorithms for intrusion detection systems is essential, and our study provides valuable insights and a road map for future research and the deployment of such systems in live environments. By implementing our findings, we can contribute to a safer and more secure IoMT ecosystem, safeguarding patient privacy and ensuring the integrity of medical data.</p> <h3><strong>ZIP Folder Content</strong></h3> <p>The ZIP folder comprises two main components: <strong>Captures</strong> and <strong>Datasets</strong>. Within the captures folder, we have included all the captures used in this project. These captures are organized into separate folders corresponding to the type of network analysis: BLE or IP-Based. Similarly, the datasets folder follows a similar organizational approach. It contains datasets categorized by type: <strong>BLE</strong>, <strong>IP-Based Packet</strong>, and <strong>IP-Based Flows</strong>.</p> <p>To cater to diverse analytical needs, the datasets are provided in two formats: CSV (Comma-Separated Values) and pickle. The CSV format facilitates seamless integration with various data analysis tools, while the pickle format preserves the intricate structures and relationships within the dataset.</p> <p>This organization enables researchers to easily locate and utilize the specific captures and datasets they require, based on their preferred network analysis type or dataset type. The availability of different formats further enhances the flexibility and usability of the provided data.</p> <h3><strong>Datasets' Content</strong></h3> <p>Within this dataset, three sub-datasets are available, namely <strong>BLE, IP-Based Packet, and IP-Based Flows</strong>. Below is a table of the features selected for each dataset and consequently used in the evaluation model within the provided work.</p> <p>Identified Key Features Within Bluetooth Dataset</p> <table> <tbody> <tr> <td><strong>Feature</strong></td> <td><strong>Meaning</strong></td> </tr> <tr> <td>btle.advertising_header</td> <td>BLE Advertising Packet Header</td> </tr> <tr> <td>btle.advertising_header.ch_sel</td> <td>BLE Advertising Channel Selection Algorithm</td> </tr> <tr> <td>btle.advertising_header.length</td> <td>BLE Advertising Length</td> </tr> <tr> <td>btle.advertising_header.pdu_type</td> <td>BLE Advertising PDU Type</td> </tr> <tr> <td>btle.advertising_header.randomized_rx</td> <td>BLE Advertising Rx Address</td> </tr> <tr> <td>btle.advertising_header.randomized_tx</td> <td>BLE Advertising Tx Address</td> </tr> <tr> <td>btle.advertising_header.rfu.1</td> <td>Reserved For Future 1</td> </tr> <tr> <td>btle.advertising_header.rfu.2</td> <td>Reserved For Future 2</td> </tr> <tr> <td>btle.advertising_header.rfu.3</td> <td>Reserved For Future 3</td> </tr> <tr> <td>btle.advertising_header.rfu.4</td> <td>Reserved For Future 4</td> </tr> <tr> <td>btle.control.instant</td> <td>Instant Value Within a BLE Control Packet</td> </tr> <tr> <td>btle.crc.incorrect</td> <td>Incorrect CRC</td> </tr> <tr> <td>btle.extended_advertising</td> <td>Advertiser Data Information</td> </tr> <tr> <td>btle.extended_advertising.did</td> <td>Advertiser Data Identifier</td> </tr> <tr> <td>btle.extended_advertising.sid</td> <td>Advertiser Set Identifier</td> </tr> <tr> <td>btle.length</td> <td>BLE Length</td> </tr> <tr> <td>frame.cap_len</td> <td>Frame Length Stored Into the Capture File</td> </tr> <tr> <td>frame.interface_id</td> <td>Interface ID</td> </tr> <tr> <td>frame.len</td> <td>Frame Length Wire</td> </tr> <tr> <td>nordic_ble.board_id</td> <td>Board ID</td> </tr> <tr> <td>nordic_ble.channel</td> <td>Channel Index</td> </tr> <tr> <td>nordic_ble.crcok</td> <td>Indicates if CRC is Correct</td> </tr> <tr> <td>nordic_ble.flags</td> <td>Flags</td> </tr> <tr> <td>nordic_ble.packet_counter</td> <td>Packet Counter</td> </tr> <tr> <td>nordic_ble.packet_time</td> <td>Packet time (start to end)</td> </tr> <tr> <td>nordic_ble.phy</td> <td>PHY</td> </tr> <tr> <td>nordic_ble.protover</td> <td>Protocol Version</td> </tr> </tbody> </table> <p> </p> <p>Identified Key Features Within IP-Based Packets Dataset</p> <table> <tbody> <tr> <td><strong>Feature</strong></td> <td><strong>Meaning</strong></td> </tr> <tr> <td>http.content_length</td> <td>Length of content in an HTTP response</td> </tr> <tr> <td>http.request</td> <td>HTTP request being made</td> </tr> <tr> <td>http.response.code</td> <td>Sequential number of an HTTP response</td> </tr> <tr> <td>http.response_number</td> <td>Sequential number of an HTTP response</td> </tr> <tr> <td>http.time</td> <td>Time taken for an HTTP transaction</td> </tr> <tr> <td>tcp.analysis.initial_rtt</td> <td>Initial round-trip time for TCP connection</td> </tr> <tr> <td>tcp.connection.fin</td> <td>TCP connection termination with a FIN flag</td> </tr> <tr> <td>tcp.connection.syn</td> <td>TCP connection initiation with SYN flag</td> </tr> <tr> <td>tcp.connection.synack</td> <td>TCP connection establishment with SYN-ACK flags</td> </tr> <tr> <td>tcp.flags.cwr</td> <td>Congestion Window Reduced flag in TCP</td> </tr> <tr> <td>tcp.flags.ecn</td> <td>Explicit Congestion Notification flag in TCP</td> </tr> <tr> <td>tcp.flags.fin</td> <td>FIN flag in TCP</td> </tr> <tr> <td>tcp.flags.ns</td> <td>Nonce Sum flag in TCP</td> </tr> <tr> <td>tcp.flags.res</td> <td>Reserved flags in TCP</td> </tr> <tr> <td>tcp.flags.syn</td> <td>SYN flag in TCP</td> </tr> <tr> <td>tcp.flags.urg</td> <td>Urgent flag in TCP</td> </tr> <tr> <td>tcp.urgent_pointer</td> <td>Pointer to urgent data in TCP</td> </tr> <tr> <td>ip.frag_offset</td> <td>Fragment offset in IP packets</td> </tr> <tr> <td>eth.dst.ig</td> <td>Ethernet destination is in the internal network group</td> </tr> <tr> <td>eth.src.ig</td> <td>Ethernet source is in the internal network group</td> </tr> <tr> <td>eth.src.lg</td> <td>Ethernet source is in the local network group</td> </tr> <tr> <td>eth.src_not_group</td> <td>Ethernet source is not in any network group</td> </tr> <tr> <td>arp.isannouncement</td> <td>Indicates if an ARP message is an announcement</td> </tr> </tbody> </table> <p> </p> <p>Identified Key Features Within IP-Based Flows Dataset</p> <table> <tbody> <tr> <td><strong>Feature</strong></td> <td><strong>Meaning</strong></td> </tr> <tr> <td>proto</td> <td>Transport layer protocol of the connection</td> </tr> <tr> <td>service</td> <td>Identification of an application protocol</td> </tr> <tr> <td>orig_bytes</td> <td>Originator payload bytes</td> </tr> <tr> <td>resp_bytes</td> <td>Responder payload bytes</td> </tr> <tr> <td>history</td> <td>Connection state history</td> </tr> <tr> <td>orig_pkts</td> <td>Originator sent packets</td> </tr> <tr> <td>resp_pkts</td> <td>Responder sent packets</td> </tr> <tr> <td>flow_duration</td> <td>Length of the flow in seconds</td> </tr> <tr> <td>fwd_pkts_tot</td> <td>Forward packets total</td> </tr> <tr> <td>bwd_pkts_tot</td> <td>Backward packets total</td> </tr> <tr> <td>fwd_data_pkts_tot</td> <td>Forward data packets total</td> </tr> <tr> <td>bwd_data_pkts_tot</td> <td>Backward data packets total</td> </tr> <tr> <td>fwd_pkts_per_sec</td> <td>Forward packets per second</td> </tr> <tr> <td>bwd_pkts_per_sec</td> <td>Backward packets per second</td> </tr> <tr> <td>flow_pkts_per_sec</td> <td>Flow packets per second</td> </tr> <tr> <td>fwd_header_size</td> <td>Forward header bytes</td> </tr> <tr> <td>bwd_header_size</td> <td>Backward header bytes</td> </tr> <tr> <td>fwd_pkts_payload</td> <td>Forward payload bytes</td> </tr> <tr> <td>bwd_pkts_payload</td> <td>Backward payload bytes</td> </tr> <tr> <td>flow_pkts_payload</td> <td>Flow payload bytes</td> </tr> <tr> <td>fwd_iat</td> <td>Forward inter-arrival time</td> </tr> <tr> <td>bwd_iat</td> <td>Backward inter-arrival time</td> </tr> <tr> <td>flow_iat</td> <td>Flow inter-arrival time</td> </tr> <tr> <td>active</td> <td>Flow active duration</td> </tr> </tbody> </table>
Tailored machine learning models for functional RNA detection in genome-wide screens
<p>The prediction of noncoding RNA and protein coding genetic loci has received<br> considerable attention in comparative genomics aiming in particular<br> at the identification of properties of nucleotide sequences that are<br> informative of their biological role in the cell. We present here a<br> software framework for the alignment-based training, evaluation and<br> application of machine learning models with user-defined<br> parameters. Instead of focusing on the one-size-fits-all approach of<br> pervasive \is annotation pipelines, we offer a framework for the<br> structured generation and evaluation of models based on arbitrary<br> features and input data, focusing on stable and explainable results.<br> Furthermore, we showcase the usage of our software package in a<br> full-genome screen of Drosophila melanogaster and evaluate<br> our results against the well-known but much less flexible program<br> RNAz.</p> <p> </p>
Datasets and Detection Rules for UIGuard
<p> </p> <p>1. Dark Pattern Detection Rules- Final.xlsx: heuristics rules we built from the integrated taxonomies</p> <p>2. User Study.pdf: a brief version of our user study procedure</p> <p>3. rico_test_annotations6352_final.json: Dark pattern Annotation for Rico test set.</p> <p>Format: { filename: { "image_size": {"width": int, "height": int}, "instances": {dp_type1: [indicator1, indicator2, ...], dp_type2: [...] } }</p> <p>examples: {"38452.jpg": {"image_size": {"width": 540, "height": 960}, "instances": { "II-PRE-Nocheckbox": [ {"label": "PRE-PP", "bbox": [63, 294, 462, 355]} ], "FA-Privacy": [{"label": "PRE-PP", "bbox": [63, 294, 462, 355]}], } } }</p> <p>The filename is same as the filename in Rico dataset</p> <p> </p> <p><strong>Source code can be seen in https://github.com/chenjshnn/UIST23-UIGuard </strong></p>
miRCoop-v2: Detecting synergistic miRNA interactions in cancer
<p>miRCoop-v2: Detecting synergistic miRNA interactions in cancer</p>
Retention time with molecular mass detection for pemetrexed, PEG, GABA, Pemetrexed-PEG conjugate, Pemetrexed-GABA conjugate
<p>This data represent the Retention time with molecular mass detection for pemetrexed, PEG, GABA, Pemetrexed-PEG conjugate, Pemetrexed-GABA conjugate</p>
A Novel Credit Card Fraud Detection Method
<p>Credit card fraud can lead to significant financial losses for both individuals and financial institutions. In this paper, we propose a novel method called CTCN, which uses Conditional Tabular Generative Adversarial Networks (CTGAN) and Temporal Convolutional Network (TCN) for credit card fraud detection. Our approach includes an oversampling algorithm that uses CTGAN to balance the dataset, and Neighborhood Cleaning Rule (NCL) to filter out majority class samples that overlap with the minority class. We generate synthetic minority class samples that conform to the original data distribution, resulting in a balanced dataset. We then employ TCN to analyze transaction sequences and capture long-term dependencies between data, revealing potential relationships between transaction sequences, thus achieving accurate credit card fraud detection. Experiments on three public datasets demonstrate that our proposed method outperforms current machine learning and deep learning methods, as measured by recall, F1-Score, and AUC-ROC.</p>
Supplementary Material for "Automated detection of an insect-infested keystone vegetation phenotype using airborne LiDAR"
<p>Ecologists, foresters and conservation practitioners need "biodiversity scanners" to effectively inventory biodiversity, audit conservation progress and track changes in ecosystem function. Quantifying biological diversity using airborne LiDAR remains challenging, especially for small invertebrates. However, insect aggregations can drastically alter landscapes and vegetation, and these "extended phenotypes" could serve as environmental landmarks of insect presence in LiDAR data. To test the feasibility of this approach, we studied the symbiotic ants that alter canopy shapes of whistling thorn acacia, a keystone tree species of the black cotton soils of east African savannas. We demonstrate a protocol of LiDAR data collection, training data preparation (including a customizable tree-segmentation algorithm) and convolutional neural network-based classification for the detection of ant-infested, within-species acacia tree phenotypic variations. Surveying ant occupancy of 402 hectares of 9,680 acacia trees took 1,000 work hours, while surveyed patterns of ant distribution was replicated by trained classifier based on an hour-long airborne LiDAR collection. We suggest that large scale surveys of insect occupancy (or insect-vectored disease) can be automated through a combination of airborne LiDAR and machine learning.</p>
Molecular and structural insights into H2 indicator supraparticles: lowering the limit of detection by tuning incorporated catalyst nanoparticles
<p>Raw data from DRIFTS experiments, TEM and SEM microscopy, Zeta potential measurements, and TGA measurements for the article "Molecular and structural insights into H2 indicator supraparticles: lowering the limit of detection by tuning incorporated catalyst nanoparticles" published in Chemistry of Materials <a href="https://doi.org/10.1021/acs.chemmater.3c01105">https://doi.org/10.1021/acs.chemmater.3c01105</a>.</p>
VISIONE Feature Repository for VBS: Multi-Modal Features and Detected Objects from V3C1+V3C2 Dataset
<p>This repository contains a diverse set of features extracted from the V3C1+V3C2 dataset, sourced from the Vimeo Creative Commons Collection. These features were utilized in the VISIONE system [Amato et al. 2023, Amato et al. 2022] during the latest editions of the Video Browser Showdown (VBS) competition (<a href="https://www.videobrowsershowdown.org/">https://www.videobrowsershowdown.org/</a>).</p> <p>The original V3C1+V3C2 dataset, provided by NIST, can be downloaded using the instructions provided at <a href="https://videobrowsershowdown.org/about-vbs/existing-data-and-tools/">https://videobrowsershowdown.org/about-vbs/existing-data-and-tools/</a>.</p> <p>It comprises 7,235 video files, amounting for 2,300h of video content and encompassing 2,508,113 predefined video segments.</p> <p>We subdivided the predefined video segments longer than 10 seconds into multiple segments, with each segment spanning no longer than 16 seconds. As a result, we obtained a total of 2,648,219 segments. For each segment, we extracted one frame, specifically the middle one, and computed several features, which are described in detail below.</p> <p>This repository is released under a Creative Commons Attribution license. If you use it in any form for your work, please cite the following paper:</p> <blockquote> <pre>@inproceedings{amato2023visione, title={VISIONE at Video Browser Showdown 2023}, author={Amato, Giuseppe and Bolettieri, Paolo and Carrara, Fabio and Falchi, Fabrizio and Gennaro, Claudio and Messina, Nicola and Vadicamo, Lucia and Vairo, Claudio}, booktitle={International Conference on Multimedia Modeling}, pages={615--621}, year={2023}, organization={Springer} }</pre> </blockquote> <p> </p> <p>This repository comprises the following files:</p> <ul> <li><strong><em>msb.tar.gz </em></strong> contains tab-separated files (.tsv) for each video. Each tsv file reports, for each video segment, the timestamp and frame number marking the start/end of the video segment, along with the timestamp of the extracted middle frame and the associated identifier ("id_visione"). </li> <li><em><strong>extract-keyframes-from-msb.tar.gz</strong></em> contains a Python script designed to extract the middle frame of each video segment from the MSB files. To run the script successfully, please ensure that you have the original V3C videos available.</li> <li><strong><em>features-aladin.tar.gz<sup>†</sup></em> </strong>contains <a href="https://github.com/mesnico/ALADIN">ALADIN</a> [Messina N. et al. 2022] features extracted for all the segment's middle frames. </li> <li><em><strong>features-clip-laion.tar.gz<sup>†</sup></strong></em> contains <a href="https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K">CLIP ViT-H/14 - LAION-2B </a>[Schuhmann et al. 2022] features extracted for all the segment's middle frames.</li> <li><em><strong>features-clip-openai.tar.gz<sup>†</sup> </strong></em>contains <a href="https://huggingface.co/openai/clip-vit-large-patch14">CLIP ViT-L/14</a> [Radford et al. 2021] features extracted for all the segment's middle frames. </li> <li><em><strong>features-clip2video.tar.gz<sup>†</sup> </strong></em>contains <a href="https://github.com/CryhanFang/CLIP2Video">CLIP2Video</a> [Fang H. et al. 2021] extracted for all the video segments. <strong> </strong>In particular 1) we concatenate consecutive short segments so to create segments at least 3 seconds long; 2) we downsample the obtained segments to 2.5 fps; 3) we feed the network with the first min(36, n) frames, where n is the number of frames of the segment. Notice that the minimum processed length consists of 7 frames, given that the segment is no shorter than 3s. </li> <li><em><strong>objects-frcnn-oiv4.tar.gz<sup>*</sup> </strong></em>contains the objects detected using <a href="http://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1">Faster R-CNN+Inception ResNet</a> (trained on the Open Images V4 [Kuznetsova et al. 2020]). </li> <li><em><strong>objects-mrcnn-lvis.tar.gz<sup>*</sup></strong></em> contains the objects detected using Mask R-CNN [He et al. 2017] (trained on LVIS).</li> <li><em><strong>objects-vfnet64-coco.tar.gz<sup>*</sup></strong></em> contains the objects detected using VfNet [Zhang et al. 2021] (trained on COCO dataset).</li> </ul> <p>*Please be sure to use the <strong>v2 version </strong>of this repository, since v1 feature files may contain inconsistencies that have now been corrected</p> <p><em><strong>*Note on the object annotations:</strong></em> Within an object archive, there is a jsonl file for each video, where each row contains a record of a video segment (the <em>"_id"</em> corresponds to the <em>"id_visione"</em> used in the msb.tar.gz) . Additionally, there are three arrays representing the objects detected, the corresponding scores, and the bounding boxes. The format of these arrays is as follows:</p> <ul> <li><em>"object_class_names"</em>: vector with the class name of each detected object.</li> <li><em>"object_scores"</em>: scores corresponding to each detected object.</li> <li><em>"object_boxes_yxyx"</em>: bounding boxes of the detected objects in the format <em>(ymin, xmin, ymax, xmax).</em></li> </ul> <p> </p> <p><em><strong><sup>†</sup>Note on the cross-modal features: </strong></em>The extracted multi-modal features (ALADIN, CLIPs, CLIP2Video) enable internal searches within the V3C1+V3C2 dataset using the query-by-image approach (features can be compared with the dot product). However, to perform searches based on free text, the text needs to be transformed into the joint embedding space according to the specific network being used. Please be aware that t<strong>he service for transforming text into features is not provided within this repository and should be developed independently using the original feature repositories linked above.</strong></p> <p>We have plans to release the code in the future, allowing the reproduction of the VISIONE system, including the instantiation of all the services to transform text into cross-modal features. However, this work is still in progress, and the code is not currently available.</p> <p> </p> <p><strong>References:</strong></p> <p>[Amato et al. 2023] Amato, G.et al., 2023, January. VISIONE at Video Browser Showdown 2023. In International Conference on Multimedia Modeling (pp. 615-621). Cham: Springer International Publishing.</p> <p>[Amato et al. 2022] Amato, G. et al. (2022). VISIONE at Video Browser Showdown 2022. In: , et al. MultiMedia Modeling. MMM 2022. Lecture Notes in Computer Science, vol 13142. Springer, Cham. </p> <p>[Fang H. et al. 2021] Fang H. et al., 2021. Clip2video: Mastering video-text retrieval via image clip. arXiv preprint arXiv:2106.11097.</p> <p>[He et al. 2017] He, K., Gkioxari, G., Dollár, P. and Girshick, R., 2017. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 2961-2969).</p> <p>[Kuznetsova et al. 2020] Kuznetsova, A., Rom, H., Alldrin, N., Uijlings, J., Krasin, I., Pont-Tuset, J., Kamali, S., Popov, S., Malloci, M., Kolesnikov, A. and Duerig, T., 2020. The open images dataset v4. International Journal of Computer Vision, 128(7), pp.1956-1981.</p> <p>[Lin et al. 2014] Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P. and Zitnick, C.L., 2014, September. Microsoft coco: Common objects in context. In European conference on computer vision (pp. 740-755). Springer, Cham.</p> <p>[Messina et al. 2022] Messina N. et al., 2022, September. Aladin: distilling fine-grained alignment scores for efficient image-text matching and retrieval. In Proceedings of the 19th International Conference on Content-based Multimedia Indexing (pp. 64-70).</p> <p>[Radford et al. 2021] Radford A. et al., 2021, July. Learning transferable visual models from natural language supervision. In International conference on machine learning (pp. 8748-8763). PMLR.</p> <p>[Schuhmann et al. 2022] Schuhmann C. et al., 2022. Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in Neural Information Processing Systems, 35, pp.25278-25294.</p> <p>[Zhang et al. 2021] Zhang, H., Wang, Y., Dayoub, F. and Sunderhauf, N., 2021. Varifocalnet: An iou-aware dense object detector. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8514-8523).</p>
EMBEDWATCH: Dynamic Root Cause Detection of Spatial Memory Errors in Embedded Systems
<p>Dataset of firmwares used for the experiments of the paper <em>EMBEDWATCH: Dynamic Root Cause Detection of Spatial Memory Errors in Embedded Systems</em></p>
CASENet: Deep Category-Aware Semantic Edge Detection
<p><strong>Introduction</strong></p> <p>This dataset includes initial and trained model weights (caffemodel files) for SBD/Cityscapes datasets which is used in our CASENet software available (<a href="https://github.com/merlresearch/CASENet">https://github.com/merlresearch/CASENet</a>).</p> <p><strong>Citation</strong></p> <p>If you use the data, please cite the following <a href="https://www.merl.com/publications/TR2017-100">TR2017-100</a>:</p> <pre><code>@inproceedings{Yu2017jul, author = {Yu, Zhiding and Feng, Chen and Liu, Ming-Yu and Ramalingam, Srikumar}, title = {CASENet: Deep Category-Aware Semantic Edge Detection}, booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = 2017, month = jul, doi = {10.1109/CVPR.2017.191}, url = {https://www.merl.com/publications/TR2017-100} }</code></pre> <p>Copyright and License<br> The CASENet dataset is released under<a href="https://creativecommons.org/licenses/by-sa/4.0/"> CC-BY-SA-4.0 license</a>.</p> <p><br> All data:</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2017, 2023 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre> <p><br> </p>
Analysis of Anomaly Detection for Artificial Intelligence of Things: A Systematic Literature Mapping
<p>This data set contains the characteristics extracted from each of the works selected from the literary review carried out following the PRISMA methodology.</p>
Experimental Results for the PRICAI 2023 Paper ``Detecting AI Planning Modelling Mistakes -- Potential Errors and Benchmark Domains''
<p>This repository contains data related to the following paper:</p> <p>An overview of which domains have been tested on which software, refer to the following paper:<br> @InProceedings{Sleath2023PossibleModelingErrors,<br> author = {Kayleigh Sleath and Pascal Bercher},<br> title = {Detecting AI Planning Modelling Mistakes -- Potential Errors and Benchmark Domains},<br> booktitle = {Proceedings of the 20th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2023)},<br> year = {2023},<br> publisher = {Springer}<br> }</p> <p>Specifically, it contains:</p> <p>- the benchmarks used in the paper.<br> - screenshots of the output of call commands to the respective software artifacts<br> (the actual call commands are only contained in some of them)<br> - excel tables collecting the results</p> <p>Please note:</p> <p>(1) These benchmark problems are just copied in here for the sake of completeness and transparency. But if you are actually interested in using them, please use the newest version, which might have corrections and additional test cases. You find it here: https://github.com/ProfDrChaos/flawedPlanningModels</p> <p>(2) Please also note that the screenshots of our tests might not perfectly fit the folder structure of our benchmarks because we made some minor restructurings after the paper submission. Specifically, some test cases that we classified as syntactical in the submission were then changed into a semantic ones for the camera-ready version. (Hence the paths in the screenshots or tables might be slightly different.)</p> <p>(3) Also note that by the time you read this, several errors undetected by these systems at the time of the evaluation might be resolved by now. (We included the version numbers of the tested software.)</p>
The data used in correspondence on the article entitled "Reply: Correspondence on NanoVar's performance outlined by Jiang T. et al. in 'Long-read sequencing settings for efficient structural variation detection based on comprehensive evaluation'."
<p>The data used in correspondence on the article entitled “Reply: Correspondence on NanoVar’s performance outlined by Jiang T. et al. in ‘Long-read sequencing settings for efficient structural variation detection based on comprehensive evaluation’.”. It contains the benchmarking results evaluated by Truvari. The running log files of NanoVar are also included in this repository.</p>
Supplementary Materials for "Challenges in the detection and attribution of Northern Hemisphere surface temperature trends since 1850"
<p><strong>Supplementary Materials for</strong>:</p> <p>R. Connolly, W. Soon, M. Connolly, S. Baliunas, J. Berglund, C.J. Butler, R.G. Cionco, A.G. Elias, V. Fedorov, H. Harde, G.W. Henry, D.V. Hoyt, O. Humlum, D.R. Legates, N. Scafetta, J.-E. Solheim, L. Szarka, V.M. Velasco Herrera, H. Yan and W.J. Zhang (<strong>2023</strong>). "Challenges in the detection and attribution of Northern Hemisphere surface temperature trends since 1850". <em>Research in Astronomy and Astrophysics</em>. <a href="https://doi.org/10.1088/1674-4527/acf18e">https://doi.org/10.1088/1674-4527/acf18e</a></p> <p><strong>Description:</strong></p> <p>A dataset for the detection and attribution of Northern Hemisphere surface temperature trends since 1850.</p> <table> <caption>Contents of dataset</caption> <thead> <tr> <th scope="col">Tab</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>NH temperatures</td> <td>5 different Northern Hemisphere (NH) annual surface temperature (ST) estimates in temperature anomalies (°C) relative to 1901-2000 averages.</td> </tr> <tr> <td>TSI 1850-2018</td> <td>27 different Total Solar Irradiance (TSI) reconstructions. At 1 Astronomical Unit (W/m2) relative to 1901-2000 average.</td> </tr> <tr> <td>IPCC AR6 forcings</td> <td>IPCC AR6 radiative forcings (RF) time series (for the 1850-2018 period) in units of W/m2</td> </tr> <tr> <td>Fitting statistics</td> <td>Fitting statistics and results from all fits</td> </tr> <tr> <td>Key results</td> <td>Summary statistics and results</td> </tr> <tr> <td>Fits - Rural-only</td> <td>Multi-linear regression fitting results for Rural-only ST</td> </tr> <tr> <td>Fits - Urban & rural</td> <td>Multi-linear regression fitting results for Urban & rural ST</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Fits - SST</td> </tr> </tbody> </table> </td> <td>Multi-linear regression fitting results for Sea surface temperature (SST)</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Fits - Tree-rings</td> </tr> </tbody> </table> </td> <td>Multi-linear regression fitting results for tree-ring proxy based ST</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Fits - Glaciers</td> </tr> </tbody> </table> </td> <td>Multi-linear regression fitting results for glacier-length proxy based ST</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Model - Rural-only</td> </tr> </tbody> </table> </td> <td>Breakdown of model components for fits to Rural-only ST</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Model - Urban & rural</td> </tr> </tbody> </table> </td> <td>Breakdown of model components for fits to urban & rural ST</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Model - SST</td> </tr> </tbody> </table> </td> <td>Breakdown of model components for fits to SST</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Model - Tree-rings</td> </tr> </tbody> </table> </td> <td>Breakdown of model components for fits to tree-ring proxy-based ST</td> </tr> <tr> <td> <table> <tbody> <tr> <td>Model - Glaciers</td> </tr> </tbody> </table> </td> <td>Breakdown of model components for fits to glacier-length proxy-based ST</td> </tr> </tbody> </table> <p> </p>
ScienceDex guides
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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.