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opencc-zeroJun 2024View details →

Dataset: Kornit Digital Ltd. (KRNT) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

opencc-zeroJun 2024View details →

Dataset: HIVE Digital Technologies Ltd. (HIVE) Stock Performance

Fig. 3 in Possible Use Of Nationwide Digital Soil Database On Predicting Roe Deer Antler Weight

Fig. 3. The ten-year mean values (1997–2006) of roe deer antler weights for each game management unit in Hungary. Source: National Game Management Database of Hungary

Fig. 2 in Possible Use Of Nationwide Digital Soil Database On Predicting Roe Deer Antler Weight

Fig. 2. The spatial distribution of main soil types, based on the soil types and subtypes from the Agrotopography Map of Hungary. (* For abbreviations see Table 1). Source: Hungarian Academy of Sciences, Research Institute of Soil Science and Agricultural Chemistry

Fig. 5 in Possible Use Of Nationwide Digital Soil Database On Predicting Roe Deer Antler Weight

Fig. 5. The means and standard deviations of soil-evaluation-numbers (SEN; soil fertility index representing the natural fertility of different soils in the percentage of the fertility of the most fertile soil) and percentages of land-use types (agriculture and forest) for each main soil types* in Hungary.

Fig. 1 in Possible Use Of Nationwide Digital Soil Database On Predicting Roe Deer Antler Weight

Fig. 1. The values of soil-evaluation-number (representing the natural fertility of different soils in the percentage of the fertility of the most fertile soil) from the Agrotopography Map of Hungary. (* 1, <10%; 2, 10–20%; 3, 20–30%; 4, 30–40%; 5, 40–50%; 6, 50–60%; 7, 60–70%; 8, 70–80%; 9, 80–90%; 10, 90–100%). Source: Hungarian Academy of Sciences, Research Institute of Soil Science and Agricul-

opencc-by-4.0Jul 2024View details →

"Wissen schaffen (lassen!?)". Workflows mit Generativer KI in den Digital Humanities

Die rasante Entwicklung von generativen KI-Technologien stellt eine bedeutende Veränderung für die Forschungspraxis nicht nur in den Digital Humanities dar. Dieser Vortrag untersucht den Einsatz von GPT-4-Tier LLM (Gemini Advanced und Claude 3) sowie deren Möglichkeiten und Grenzen in verschiedenen Forschungsprojekten der Digital Humanities. Der Fokus liegt dabei auf Workflows wie der Datenerfassung, Transkription, Übersetzung, Datenmodellierung, Datengenerierung oder -analyse sowie der Visualisierung geisteswissenschaftlicher Daten. Anhand ausgewählter Fallstudien wird die Integration von generativer KI in diese Prozesse dargestellt, wobei sowohl die Automatisierung von Standardaufgaben als auch die Unterstützung komplexerer, analytischer und anspruchsvoller Tätigkeiten wie Datenmodellierung thematisiert werden. Haben generative KI-Modelle, wenn sie im Einklang mit menschlicher Expertise und komplementären Systemen eingesetzt werden, das Potenzial, die Effizienz und Tiefe (digitaler) geisteswissenschaftlicher Forschung zu steigern? Die Studie betont auch die Notwendigkeit, die Grenzen und Herausforderungen, wie die Abhängigkeit von großen Technologieunternehmen beim Einsatz von generativer KI in den Digital Humanities kritisch zu hinterfragen.

Fig. 23. Left manual digit II in The Osteology Of Balaur Bondoc, An Island-Dwelling Dromaeosaurid (Dinosauria: Theropoda) From The Late Cretaceous Of Romania

Fig. 23. Left manual digit II of Balaur bondoc (EME PV.313). Left phalanx II-1 (A–F), left phalanx II- 2 (G–L), and left phalanx II-3 (M–N) in extensor (A, G), flexor (B, H), lateral (C, I, M), medial (D, J, N), proximal (E, K), and distal (F, L) views. Abbreviations: ygr, proximal bifurcations of the Y-shaped groove. Scale bar equals 1 cm.

Fig. 22. Right manual digit I in The Osteology Of Balaur Bondoc, An Island-Dwelling Dromaeosaurid (Dinosauria: Theropoda) From The Late Cretaceous Of Romania

Fig. 22. Right manual digit I of Balaur bondoc (EME PV.313). Right phalanx I-1 (A–F) and right phalanx I-2 (G–H) in extensor (A), flexor (B), lateral (C, H), medial (D, G), proximal (E), and distal (F) views. Abbreviations: for, foramen; ft, flexor tubercle. Scale bar equals 1 cm.

Fig. 21. Left manual digit I in The Osteology Of Balaur Bondoc, An Island-Dwelling Dromaeosaurid (Dinosauria: Theropoda) From The Late Cretaceous Of Romania

Fig. 21. Left manual digit I of Balaur bondoc (EME PV.313). Left phalanx I-1 (A–F) and left phalanx I- 2 (G–H) in extensor (A), flexor (B), lateral (C, G), medial (D, H), proximal (E), and distal (F) views. Abbreviations: for, foramen; ft, flexor tubercle; gr, groove separating flexor tubercle from ventral surface of ungual. Scale bar equals 1 cm.

Fig. 24. Right manual digit II in The Osteology Of Balaur Bondoc, An Island-Dwelling Dromaeosaurid (Dinosauria: Theropoda) From The Late Cretaceous Of Romania

Fig. 24. Right manual digit II of Balaur bondoc (EME PV.313). Right phalanx II-1 (A–F), right phalanx II-2 (G–L), and right phalanx II-3 (M–N) in extensor (A, G), flexor (B, H), lateral (C, I, N), medial (D, J, M), proximal (E, K), and distal (F, L) views. Scale bar equals 1 cm.

opencc-by-4.0Jul 2024View details →

Data of Digital Scholarly Edition of the Diary of the Travel of Heinrich XI. Reuß-Greiz 1740-1742

Open the record for dataset details and reuse information.

Figure 6. (a1), (a2), (a3), (a4), (a5), (a6), (a7) and (a8) watermarked image is degraded respectively through JPEG2000 compression, JPEG compression, median filtering, adding Salt&Pepper noise, rotating, center cropping, surrounding cropping and scaling. (b1), (b2), (b3), (b4), (b5), (b6), (b7) and (b8) The corresponding extracted watermarks.-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme

This paper has described a scheme for digital watermarking of still images based on discrete wavelet transform. In the proposed method, the embedded logo watermark can be extracted without access to the original image. It has been confirmed that the proposed watermarking method is able to extract the embedded logo watermark from the watermarked images that have degraded through compression, filtering, cropping and scaling. Although this algorithm is not robust against rotation, it can completely extract the watermark from watermarked images that lose about 35% of their areas by cropping attack.

Figure 1. (a) Original watermark (b) extracted watermarks after compression(c) merged watermark-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme

Therefore, each bit of the logo watermark is stored in one coefficient of a sub-block to keep the capacity of watermarking fixed. When a region of the watermarked image is destroyed; the whole watermark can be extracted using other regions of the watermarked image by merging extracted watermarks. Figure 1 shows result of merging logo watermarks that were extracted from a compressed (with JPEG2000 algorithm) watermarked image.

Figure 4. (a) The original "Hookah" image (b) Watermarked "Hookah" with Q=35 (c) The original "Baby" image (d) Watermarked "Baby" with Q=35-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme

A set of distortions is applied to the watermarked image and the watermark is extracted from the distorted image. We used bit correct rate (BCR) to evaluate our proposed algorithm and it is calculated from the following equation [6].

Figure 2. LL2 sub-band is divided into sub-block-Discrete Wavelet Transform Method: A New Optimized Robust Digital Image Watermarking Scheme

In the following experiments, two gray-level images with size of 512 by 512, “Baby” and “Hookah” are the test images. The binary image “IAU” with size of 32 by 32 is used in our simulations as a watermark. Figure 3 shows the watermark. In the experiments Haar wavelet filter was used for discrete wavelet transform. The level of wavelet decomposition (n) and the number of sub-blocks (K) were also assumed to be 2 and 16 respectively. The proposed watermarking algorithm is evaluated from the point view of embedded watermark transparency and robustness; the result of each is shown in next two sections.

opencc-by-4.0Nov 2016View details →

Brain Journal-The Status of Positive Psychology Strengths within the Romanian School in the Digital Society -Figure 1. Pie chart vitalitysuccess

The Pie charts above illustrate the distribution of responses for the "Vitality" strength. It can be noticed how the lower values are much more frequent than the high ones, in terms of promoting this strength in school. Thus, the participants at this study consider that school does not attach enough importance to this strength, even though it has proven to be so mandatory in assuring the students' well-being and personal success.

opencc-by-4.0Jan 2016View details →

Figure 3. Impact of the Boom in digital data in learning-Micro Learning: A Modernized Education System-

Figure 3 portrays the research guiding the impact of the boom in digital data in knowledge codification. The current status shows that 80% of the respondents are interested in learning via electronic devices, followed by e-mails at 75%. 72% and 70% of respondents opt for video clips and sound and voice recording. 65% of the respondents selected images followed by graphical display at 61%. 50% of the respondents selected Journals. Further observation from the figure reveals that books and reference volumes had a very insignificant impact, as expected by merely 45% and 30% of the respondents. This directed us towards the necessity for micro learning, and encourages the increase of the usage of electronic devices.

opencc-by-sa-4.0Apr 2018View details →

Digital Agathemerus Project

This release includes all the primary data produced between 2015 and 2018, which form the backbone of the Digital Agathemerus Project. For more information, go to <a href="http://www.digitalagathemerus.org">http://www.digitalagathemerus.org</a>.