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1,393 results for “traces”
Nutrient concentration in seawater samples, collected from the underway supply, CTD and trace metal rosettes in the Southern Ocean during the austral summer of 2016/2017, on board the Antarctic Circumnavigation Expedition (ACE).
<p><strong>Dataset abstract</strong></p> <p>This dataset presents concentrations of nutrients (µmol/L): nitrous oxide (NOx), nitrate, nitrite, ammonium (NH4), phosphate (PO4) and silicic acid (Si), measured in samples of seawater during the Antarctic Circumnavigation Expedition (ACE). Samples were collected from CTD and trace metal rosette (TMR) deployments, as well as from the underway water supply on board, then analysed by flow injection following certified standards. This data was collected to support physical, chemical and biological oceanography studies in the Southern Ocean, conducted as part of ACE during the austral summer of 2016/2017.</p> <p><strong>Dataset contents</strong></p> <ul> <li>ace_seawater_nutrient_data.csv, data file, comma-separated values</li> <li>data_analysis_nutrient_concentration_standards.csv, metadata, comma-separated values</li> <li>quality_checking_crm_analysis_results.csv, metadata, comma-separated values</li> <li>nutrient_concentrations_along_track_visualisation.png, metadata, portable network graphics</li> <li>ace_seawater_nutrients_comparison_plots_tmr_ctd.pdf, metadata, portable document format</li> <li>data_file_header.txt, metadata, text</li> <li>README.txt, metadata text</li> <li>change_log.txt</li> </ul> <p><strong>Change log</strong></p> <p>v1.1 - various changes to data and metadata, described below</p> <ul> <li>added further data points to data file (those sampled from trace metal rosette)</li> <li>added ACE station number and ACE event number to data file</li> <li>reordered columns in data file</li> <li>updated README with information about sampling of trace metal rosette</li> <li>updated README with new citation, license, dataset contact and dataset contents, updated abstract</li> <li>updated data_file_header with new data fields</li> <li>added comparison plot of CTD and TMR data points</li> <li>updated caption of plot of nutrient concentrations along track (only from CTD samples)</li> </ul> <p>v1.0 - initial release of dataset</p> <p><strong>Dataset license</strong></p> <p>This nutrient concentration dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Edge workload traces from Aeneas, Julius, and MR-Leo
<p><strong>Overview</strong></p> <p>The emerging field of edge computing is suffering from a lack of representative data to evaluate rapidly emerging new algorithms or techniques. It is a critical issue as this complex paradigm has numerous different use cases which translate to a highly diverse set of workload types.</p> <p>In this dataset, we collect traces for three available edge applications: Aeneas [1], Julius [2], and MR-Leo [3]. They are provided open-source. Read our article [4] for explanations about the gathering of these traces and an analysis of them.</p> <p>[1] https://www.readbeyond.it/aeneas/<br> [2] https://github.com/julius-speech/julius<br> [3] https://gitlab.liu.se/ida-rtslab/public-code/2019_mrleo_server<br> [4] K. Toczé, N. Schmitt, U. Kargén, A. Aral, and I. Brandic, <strong>Edge Workload Trace Gathering and Analysis for Benchmarking</strong>, in <em>6th IEEE International Conference on Fog and Edge Computing 2022 (ICFEC 2022)</em>, IEEE, 2022. DOI: <a href="https://doi.org/10.1109/ICFEC54809.2022.00012">10.1109/ICFEC54809.2022.00012</a></p> <p><strong>Details about the hardware used for the trace collection</strong></p> <p>The Aeneas trace was gathered using a an HP Elitebook 840 G5 running Ubuntu 18.10. It has 16 GB RAM and an Intel Core i7-8550U CPU (1.8 GHz, 4 cores, 8 threads). Aeneas version 1.7.3 was used.</p> <p>We collect traces for Julius on four Off-The-Shelve servers with varying performance as depicted in the following table. Because Julius is a single-core application, performance is mostly dictated by the clock speed of the CPU. Each server is running Ubuntu 18.04.4 LTS with kernel version 4.15.0-108-generic.<br> As some audio files caused Julius to crash, all files are converted from the original Ogg Vorbis format to WAV by applying \texttt{ffmpeg -i audio.ogg -acodec pcm\_s16le -ac 1 -ar 16000}, resulting in a total of 5724 audio files.<br> </p> <table> <caption>System under test servers for the Julius speech recognition application. (All CPUs are Intel Xeon)</caption> <tbody> <tr> <td>Server</td> <td>CPU @ Clock (Cores / Threads)</td> <td>Memory @ Clock</td> </tr> <tr> <td>A</td> <td>E3-1230 v5 @ 3.4GHz (4/8)</td> <td>1x16GB @ 2133MHz</td> </tr> <tr> <td>B</td> <td>E5-2640 v3 @ 2.6GHz (8/16)</td> <td>2x16GB @ 2133MHz</td> </tr> <tr> <td>C</td> <td>E5-2650 v3 @ 2.3GHz (10/20)</td> <td>2x16GB @ 2133MHz</td> </tr> <tr> <td>D</td> <td>E5-2650 v4 @ 2.2GHz (12/24)</td> <td>2x16GB @ 2133MHz</td> </tr> </tbody> </table> <p>The MR-Leo trace is gathered using a an HP Elitebook 840 G5 running Ubuntu 18.10. It has 16 GB RAM and an Intel Core i7-8550U CPU (1.8 GHz, 4 cores, 8 threads). The MR-Leo implementation using ORB-SLAM2 is used, in the replay mode on the edge server, meaning that the input video was directly streamed on the edge server, and not from an end device.</p> <p><strong>Details about the task id field</strong></p> <p>The task id for the Aeneas trace looks as follows: A_1_3 where the A stands for Aeneas, the first number corresponds to the example used for this task (see Figure 3 in parenthesis) and the second number identify the run number in which the data is collected.</p> <p>The task id field in the trace file uses the format J_C_3_4 where J stands for Julius, the second letter corresponds to the server on which the measurement has been taken (see Table above), the first number is the file that is converted (see Figure 4), and the second number is the run number.</p> <p>The task id field of the MR-Leo trace looks as follows: M_1_4_78. The M stands for MR-Leo, the first number identifies the video, the second number identifies the run number and the last number is the frame number.</p>
Patterns of Knowledge Circulation in Early Modern East-Central Europe: Tracing Jewish Kabbalistic Textual Units in Multiple-Text Manuscripts
<p>This presentation will provide initial insights into the first comprehensive study of the networks of production and circulation of Jewish esoteric texts in early modern East-Central Europe (1500-1750). This study, which is funded by the German Research Foundation and has begun in 2019, aims to collect datasets and quantitatively analyse the transmission and reception of Jewish esoteric traditions recorded in manuscript form. In doing so, the study makes use of network analysis methods and utilises digital database environment (provided by LAB 1100) that facilitates analysis and visualisation of data with complex temporal, geographical and relational attributes.<br> The scholarship on Jewish books, both in manuscripts and in print, and kabbalah has centred chiefly on studying individual figures, narrowing the focus of study to singular individuals and groups. Such an approach, although important, stops short of unearthing broader patterns and dissemination of ideas, which could be available through analysis of a larger selection of sources. Virtually no study has been to date devoted to examining broader networks of esoteric textual knowledge exchange from the perspective of the material evidence of such interactions. Such examination may offer a more comprehensive view on East-Central European dynamics of creating, transmitting, and re-appropriating kabbalistic and esoteric sources.<br> Of special interest is the phenomenon of copying, transcription and compiling of kabbalistic textual units in multiple-text manuscripts, often of complex codicological structure, which abound as the most universal medium of dissemination for esoteric (kabbalistic) texts. In recent years, substantial methodological changes occurred in the field of manuscript studies, which emphasise positioning and contextualizing manuscripts as material objects within their social and cultural milieus, and rediscovers the network of ‘manuscript cultures,’ i.e. ‘the urban micro-cultures,’ which left their imprints on both the external aspects and the contents of the codices. The current presentation will highlight potentials and challenges of network analysis for the study of circulation of textual units and their variants in multiple complex and composite manuscripts, whose transcription gives evidence to conscious decisions of those involved in their dissemination and subsequent transmission in East-Central Europe. As the study will have run for just over one year by the date of the conference, the presentation will refrain from offering final conclusions, but remain open to discussions and suggestions from scholars deploying similar methods to the study of manuscripts and manuscript cultures.</p>
Probe Trace _ Logo
<p>Project's logo in 3 different file format: Microsoft Word Object format, Image (Enhanced Metafile), Image (PNG)</p>
Experimental Data for: Comparing Trace Visualizations for Program Comprehension through Controlled Experiments
<p>For efficient and effective program comprehension, it is essential to provide software engineers with appropriate visualizations of the program's execution traces. Empirical studies, such as controlled experiments, are required to assess the effectiveness and efficiency of proposed visualization techniques.</p> <p>We present controlled experiments to compare the trace visualization tools EXTRAVIS and ExplorViz in typical program comprehension tasks. We replicate the first controlled experiment with a second one targeting a differently sized software system. In addition to a thorough analysis of the strategies chosen by the participants, we report on common challenges comparing trace visualization techniques. Besides our own replication of the first experiment, we provide a package containing all our experimental data to facilitate the verifiability, reproducibility and further extensibility of our presented results.</p> <p>Although subjects spent similar time on program comprehension tasks with both tools for a small-sized system, analyzing a larger software system resulted in a significant efficiency advantage of 28 percent less time spent by using ExplorViz. Concerning the effectiveness (correct solutions for program comprehension tasks), we observed a significant improvement of correctness for both object system sizes of 39 and 61 percent with ExplorViz.</p> <p>This package contains the experimental data.</p>
Time independent traces from NAS MPI benchmarks (LU,IS,FT)
<p>Time independent traces from NAS MPI benchmarks (LU,IS,FT) that runs on Grid'5000 testbed on the graphene cluster (node 105 to143) using a dedicated switch (no network contention) and with 1 MPI process per node.</p> <p>These traces are made to be used by a distributed system simulator to replay the jobs executions.</p>
Efficient Large-Scale Trace Checking Using MapReduce
<p>The problem of checking a logged event trace against a temporal logic specification arises in many practical cases. Unfortunately, known algorithms for an expressive logic like MTL (Metric Temporal Logic) do not scale with respect to two crucial dimensions: the length of the trace and the size of the time interval for which logged events must be buffered to check satisfaction of the specification. The former issue can be addressed by distributed and parallel trace checking algorithms that can take advantage of modern cloud computing and programming frameworks like MapReduce. Still, the latter issue remains open with current state-of-the-art approaches. </p> <p>In this paper we address this memory scalability issue by proposing a new semantics for MTL, called lazy semantics. This semantics can evaluate temporal formulae and boolean combinations of temporal-only formulae at any arbitrary time instant. We prove that lazy semantics is more expressive than standard point-based semantics and that it can be used as a basis for a correct parametric decomposition of any MTL formula into an equivalent one with smaller, bounded time intervals. We use lazy semantics to extend our previous distributed trace checking algorithm for MTL. We evaluate the proposed algorithm in terms of memory scalability and time/memory tradeoffs.</p>
Figure 11. - Fruits and galls produced by Epicephala species on Glochidionobovatum. A Fruit produced after pollination by Epicephalaobovatella (Tomogashima, Wakayama) B Gall induced on female flower by Epicephalacorruptrix (Takae, Okinawa) C Cross section of the gall induced by Epicephalacorruptrix. Arrow indicates the galled locule with feeding trace of Epicephala larva. Note that the irregularly developed ovules of the galled locule have merged indistinguishablly to septa. Scale bar 2 mm.
Figure 11. - Fruits and galls produced by Epicephala species on Glochidionobovatum. A Fruit produced after pollination by Epicephalaobovatella (Tomogashima, Wakayama) B Gall induced on female flower by Epicephalacorruptrix (Takae, Okinawa) C Cross section of the gall induced by Epicephalacorruptrix. Arrow indicates the galled locule with feeding trace of Epicephala larva. Note that the irregularly developed ovules of the galled locule have merged indistinguishablly to septa. Scale bar 2 mm.
Results for paper "Optimal trace inequality constants for interior penalty discontinuous Galerkin discretisations of elliptic operators using arbitrary elements with non-constant Jacobians"
<p>Results for paper "Optimal trace inequality constants for interior penalty discontinuous Galerkin discretisations of elliptic operators using arbitrary elements with non-constant Jacobians"</p>
RSSI Traces from the WSN-Testbed at the I4, Friedrich-Alexander University Erlangen-Nuremberg
<p>This is a one week RSSI-trace from an office-based WSN-Testbed at the I4, Friedrich-Alexander University Erlangen-Nuremberg. 9 Tmote Sky node were used to sample the RSSI at approximately 8192 Hz.</p> <p>The data was collected using base_rssi2.c, which is part of the code provided with https://doi.org/10.5281/zenodo.582277. It can be decoded using the code in /tools/scala in the the same repo.</p> <p>The trace was taken in the week of 2015-05-06.</p>
The FORTH-TRACE dataset for human activity recognition of simple activities and postural transitions using a Body Area Network
<p>The dataset is collected from 15 participants wearing 5 Shimmer wearable sensor nodes on the locations listed in Table 1. The participants performed a series of 16 activities (7 basic and 9 postural transitions), listed in Table 2.</p> <p>The captured signals are the following:</p> <ul> <li>3-axis accelerometer</li> <li>3-axis gyroscope</li> <li>3-axis magnetometer</li> </ul> <p>The sampling rate of the devices is set to 51.2 Hz.</p> <p>DATASET FILES</p> <p>The dataset contains the following files:</p> <ul> <li>partX/partXdev1.csv</li> <li>partX/partXdev2.csv</li> <li>partX/partXdev3.csv</li> <li>partX/partXdev4.csv</li> <li>partX/partXdev5.csv</li> </ul> <p>Where X corresponds to the participant ID, and numbers 1-5 to the device IDs indicated in Table 1.</p> <p>Each .csv file has the following format:</p> <ul> <li>Column1: Device ID</li> <li>Column2: accelerometer x</li> <li>Column3: accelerometer y</li> <li>Column4: accelerometer z</li> <li>Column5: gyroscope x</li> <li>Column6: gyroscope y</li> <li>Column7: gyroscope z</li> <li>Column8: magnetometer x</li> <li>Column9: magnetometer y</li> <li>Column10: magnetometer z</li> <li>Column11: Timestamp</li> <li>Column12: Activity Label</li> </ul> <p>Table 1: LOCATIONS</p> <ol> <li>Left Wrist</li> <li>Right Wrist</li> <li>Torso</li> <li>Right Thigh</li> <li>Left Ankle</li> </ol> <p>Table 2: ACTIVITY LABELS</p> <p>(Arrows (->) indicate transitions between activities)</p> <ol> <li>stand</li> <li>sit</li> <li>sit and talk</li> <li>walk</li> <li>walk and talk</li> <li>climb stairs (up/down)</li> <li>climb stairs (up/down) and talk</li> <li>stand -> sit</li> <li>sit -> stand</li> <li>stand -> sit and talk</li> <li>sit and talk -> stand</li> <li>stand -> walk</li> <li>walk -> stand</li> <li>stand -> climb stairs (up/down), stand -> climb stairs (up/down) and talk</li> <li>climb stairs (up/down) -> walk</li> <li>climb stairs (up/down) and talk -> walk and talk</li> </ol>
Tracing the evolution of short-period binaries with super-synchronous fast-rotators
<p>Dataset for the triple scenario (see Sec. 5 in Britavskiy et al.)<br><br>*_triple.txt contain zero age main sequence triple configurations compatible with the "triple merger scenario" described in the paper.<br>*.npy are binary version of the corresponding txt for faster loading. <br><br>The txt files where generated with <a href="https://zenodo.org/api/records/10028333/draft/files/ML_stability.py/content">ML_stability.py.</a> <br><a href="https://zenodo.org/api/records/10028333/draft/files/plot_P_unstable.py/content">plot_P_unstable.py</a> generates fig. 10 and <a href="https://zenodo.org/api/records/10028333/draft/files/plot_min_a_in.py/content">lot_min_a_in.py</a> fig. 9, the other python files are libraries of functions called by these.<br><a href="https://zenodo.org/api/records/10028333/draft/files/mlp_model_trip_ghost.pkl/content">mlp_model_trip_ghost.pkl</a> is the dynamical stability classifier from <a href="https://ui.adsabs.harvard.edu/abs/2023MNRAS.525.2388V/abstract">Vynatheya et al. 2023</a> (ghost orbit method), used by <a href="https://zenodo.org/api/records/10028333/draft/files/classify_trip.py/content">classify_trip.py</a> to determine the probability of dynamical stability of a given system.</p>
Extended reference scenarios (ERS) file (including only the temperature and trace species climatology)
<p>Extended reference scenarios are store in a single NetCDF file providing the atmospheric state (pressure, temperature and composition) expected to be seen by CAIRT for different conditions (altitude, latitude, season, time, solar activity and volcanic activity). </p>
Learning by Viewing: Generating Test Inputs for Games by Integrating Human Gameplay Traces in Neuroevolution
<p>Replication package for the paper "Learning by Viewing: Generating Test Inputs for Games by Integrating Human Gameplay Traces in Neuroevolution" </p><p> </p><p>Although automated test generation is common in many programming domains, games still challenge test generators due to their heavy randomisation and hard-to-reach program states. Neuroevolution combined with search-based software testing principles has been shown to be a promising approach for testing games, but the co-evolutionary search for optimal network topologies and weights involves unreasonably long search durations. Humans, on the other hand, tend to be quick in picking up basic gameplay. In this paper, we therefore aim to improve the evolutionary search for game input generators by integrating knowledge about human gameplay behaviour. To this end, we propose a novel way of systematically recording human gameplay traces, and integrating these traces into the evolutionary search for networks using traditional gradient descent as a mutation operator. Experiments conducted on eight diverse Scratch games demonstrate that the proposed approach reduces the required search time from five hours down to only 30 minutes on average.</p>
Supplementary Tables for Can leafhoppers help us trace the impact of climate change on agriculture?
<p>Supplementary Tables for the Preprint entitled: Can leafhoppers help us trace the impact of climate change on agriculture? to be posted in bioRxiv. </p> <p><strong>Table S1. </strong>Detailed information on the strawberry fields included in this study.</p> <p><strong>Table S2</strong>. Detailed information on the weather stations used to retrieve temperature and precipitation data used in this study </p> <p><strong>Table S3. </strong>Strawberry samples analyzed in this study with symptoms resembling strawberry green petal phytoplasma disease during both growing seasons studied here.</p> <p><strong>Table S4.</strong> The geographic location of all the strawberry green petal phytoplasma disease cases reported to the provincial laboratory in expertise in diagnostic and phytopathology in the last decade.</p> <p><strong>Table S5.</strong> Leafhopper species and the number of specimens per species analyzed by phytoplasma-specific PCR to detect the presence of the pathogen.</p> <p><strong>Table S6.</strong> Detailed information on the leafhoppers incubated with strawberry plants during the phytoplasma transmission assays.</p> <p><strong>Table S7.</strong> Detailed information on <em>Macosteles quadrilineatus</em> used to study the leafhopper microbiome.</p> <p><strong>Table S8. </strong>Detailed information on the insecticides used by strawberry growers during both grow seasons included in the study and those treatments selected for further statistic analyses.</p> <p><strong>Table S9. </strong>Identification and number of leafhopper species captured in strawberry fields in each geographic region screened in this study.</p> <p><strong>Table S10. </strong>Detailed information of diversity indexes Shannon and Simpson calculated using the data collected in this study.</p> <p><strong>Table S11.</strong> Fixed days and temperature values used during leafhopper populations modelling.</p> <p><strong>Table S12.</strong> Detailed information on the taxonomy of the phytoplasma strain SbGPQ affecting strawberry plants in eastern Canada by hybridization and illumine sequencing and by PCR amplification, cloning and Sanger sequencing.</p> <p><strong>Table S13.</strong> Detailed information on <em>Macosteles quadrilineatus</em> microbiome including OTUs, reads, and metadata information.</p> <p><strong>Table S14.</strong> Detailed information on the core microbiome for <em>Macosteles quadrilineatus</em> captured during each growing season and in common for all the leafhoppers analyzed during this study.</p> <p><strong>Table S15. </strong><span>BIC values for models selection. </span></p>
Fives Input dataset (Cobalt & Darshan traces, combined and preprocessed)
<p>Dataset made of aggregated and curated Cobalt and Darshan logs from the Theta HPC platform at ALCF.</p> <p>Cobalt and Darshan logs were obtained from ALCF Public Data repository (https://reports.alcf.anl.gov/data/index.html) and cover the year 2022. This data was generated from resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. In order to use the scripts contained within this archive, these datasets must be downloaded and placed in the directory '2022' at the root of the extracted archive.</p> <p>The Darshan logs used in this datasets are originillay available in an aggregated form. The levels of details are usually the following : </p> <ul> <li>job (reservation made to a resource manager for some platform resources)</li> <li>application run (application running inside the job, on the reserved resources ; there may be multiple ones, sequentially or in parallel, during a job's execution)</li> <li>I/O operation (read or write registered to a file from a process of an application)</li> </ul> <p>Darshan CSV files for Theta contain job and application runs informations, but individual I/O of each application run is aggregated into a single entry.</p> <p>This resource is organised as a single archive containing:</p> <ul> <li>YAML files with our datasets, at various granularity levels (in 'preprocessed_datastets' directory): <ul> <li>48 files containing each<strong> 1 month worth of job traces</strong> for one of <strong>3 job classes</strong> (4 files per month, one per job class and one with all job classes) </li> <li>4 files containing each the entire year worth of job traces ; 1 file per job class, 1 file with all job classes.</li> </ul> </li> <li>A Jupyter Lab notebook, which contains the necessary routines to create aformentionned datasets from raw logs files from ALCF, for the Theta system</li> <li>A requirements.txt file, describing required Python packages and their versions.</li> <li>Various empty directories meant to receive outputs from the Jupyter notebook.</li> </ul>
Dataset for: A Deep-Learning Technique to Locate Cryptographic Operations in Side-Channel Traces
<p>This dataset is part of "A Deep-Learning Technique to Locate Cryptographic Operations in Side-Channel Traces" available <a href="https://www.arxiv.org/abs/2402.19037" target="_blank" rel="noopener">online</a>.</p> <p>The source code for testing the dataset is available on <a href="https://github.com/hardware-fab/DL-to-locate-COs-for-SCA">GitHub</a>.</p> <p>The dataset is organized as follows:</p> <ul> <li><strong>\training</strong>: contains three subsets, i.e., train, valid, and test. <br> Each subset consists of two .npy files: <ul> <li><em>_set</em>: it contains the side-channel traces that are preprocessed accordingly.</li> <li> <em>_labels</em>: itcontains the target labels for training the CNN, labeling each data as <em>cipher start</em>, <em>cipher rest</em>, or <em>noise</em>.</li> </ul> </li> <li><strong>\inference</strong>: contains two files as a demo of the inference pipeline.<br> One file is the is the side-channel trace containing an undefined number of AES encryptions. The other file is a list of plaintexts matching the AES encryptions to test a CPA attack.</li> </ul> <p><strong>Cite:</strong></p> <blockquote> <pre><code>@INPROCEEDINGS{10546758, author={Chiari, Giuseppe and Galli, Davide and Lattari, Francesco and Matteucci, Matteo and Zoni, Davide}, booktitle={2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)}, title={A Deep- Learning Technique to Locate Cryptographic Operations in Side-Channel Traces}, year={2024}, pages={1-6}, doi={10.23919/DATE58400.2024.10546758}}</code></pre> </blockquote> <p>This repository is protected by copyright and licensed under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a> license.</p> <p>© 2024 hardware-fab</p>
Dataset for: Hound: Locating Cryptographic Primitives in Desynchronized Side-Channel Traces Using Deep-Learning
<p>This dataset is part of "Hound: Locating Cryptographic Primitives in Desynchronized Side-Channel Traces Using Deep-Learning" [1] available <a href="https://arxiv.org/pdf/2408.06296">online</a>.</p> <p>The source code for testing the dataset is available on <a href="https://github.com/hardware-fab/Hound">GitHub</a>.</p> <p>The dataset is organized as follows:</p> <ul> <li><strong>/training</strong>: Contains three subsets: <em>train</em>, <em>valid</em>, and <em>test</em>. Each subset consists of two <em>.npy</em> files: <ul> <li><em><strong>_set</strong></em>: Contains the preprocessed side-channel traces.</li> <li><strong><em>_labels</em></strong>: Contains the target labels for training the CNN, labeling each data as `CP start`, `CP spare`, or `noise`.</li> </ul> </li> <li><strong>/inference</strong>: Contains files for two demos: consecutive AES executions and AES executions interleaved with noisy applications. Each demo consists of two <em>.npy</em> files: <ul> <li><strong>aes_</strong>: Contains the side-channel traces to input into Hound.</li> <li><strong>gt_</strong>: Contains the ground truth for checking the correctness of Hound segmentation.</li> </ul> </li> </ul> <p>This repository is protected by copyright and licensed under the <a href="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</a> license.</p> <p>© 2024 hardware-fab</p> <blockquote> <p>[1] D. Galli, G. Chiari and D. Zoni, "Hound: Locating Cryptographic Primitives in Desynchronized Side-Channel Traces using Deep-Learning," 2024 IEEE 42nd International Conference on Computer Design (ICCD), Milan, Italy, 2024, pp. 114-121, doi: 10.1109/ICCD63220.2024.00027.</p> </blockquote>
Recovering Trace Links In Software Architecture Documentation
<p>Replication Package for the dissertation "Recovering Trace Links In Software Architecture Documentation" by Jan Keim.</p> <p>The ZIP-file contains the replication package. Additionally, there is the OVA-file, which is a file to be imported as virtual machine (e.g., in VirtualBox). The virtual machine contains the replication package and everything required to run the experiments like Java, maven, dependencies etc. is installed.</p>
Supplementary material for "Tracing emerging contaminants from the Baltic Sea and North Sea in fjord waters in southern Norway with rare earth elements as far-field tracers"
<p><span>Dataset presented and discussed in the manuscript of the research article “</span><span>Tracing emerging contaminants from the Baltic Sea and North Sea in fjord waters in southern Norway with rare earth elements as far-field tracers</span><span><span>” by Zocher et al. The manuscript will be submitted to <em>Environmental Pollution</em> and was prepared by the following authors: </span></span></p> <p> </p> <p><span><span>Anna-Lena Zocher (1), Tomasz Maciej Ciesielski (2,3), Stefania Piarulli (4), Julia Farkas (4) and Michael Bau (1). </span></span></p> <p><span> </span></p> <p><span><span>(1) School of Science, Constructor University, Bremen, Germany</span></span></p> <p><span><span>(2) Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway</span></span></p> <p><span><span>(3) </span></span><span><span>Department of Arctic Technology, The University Centre in Svalbard (UNIS), Longyearbyen, Norway</span></span></p> <p><span><span>(4) SINTEF Ocean, Trondheim, Norway</span></span></p> <p> </p> <p><span>This work was conducted within the ELEMENTARY project, and we appreciate funding from the Norwegian Research Council (grant No. 301236).</span></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.