5,815 research outputs found
Incorporating Interactive Electronic Storybooks into Shared Reading Programs by Kindergarten Teachers: A Multiple Case Study
This qualitative study investigated how two kindergarten teachers used interactive electronic storybooks (referred to as e-storybooks) for shared reading, as well as their attitudes towards adopting this tool as a resource for shared reading. The research inquiry was guided by three research questions: 1) What instructional strategies did the kindergarten teachers employ to try to achieve pedagogical effectiveness of the e-storybooks for shared reading programs? 2) What problems did the kindergarten teachers encounter in utilizing the e-storybooks for shared reading programs, and how did they try to overcome them? 3) What were the kindergarten teachers’ attitudes towards adopting e-storybooks as a resource for their reading block
The Study of Electronic Design Public Elective Courses Teaching in Colleges and Universities by Tina Pro
AbstractWith the continuous development of EDA technology, its advantage in electronic courses teaching in college and universities is becoming increasingly prominent. The introduction of Tina Pro technology into the teaching of Electronic Design public elective courses in colleges and universities can improve the non electronic students’ interesting in learning Electronic Design, let the Electronic Design get a more effective and wider promotion, and improve the students’ innovation ability and manipulative ability
High-quality Image Restoration from Partial Mixed Adaptive-Random Measurements
A novel framework to construct an efficient sensing (measurement) matrix,
called mixed adaptive-random (MAR) matrix, is introduced for directly acquiring
a compressed image representation. The mixed sampling (sensing) procedure
hybridizes adaptive edge measurements extracted from a low-resolution image
with uniform random measurements predefined for the high-resolution image to be
recovered. The mixed sensing matrix seamlessly captures important information
of an image, and meanwhile approximately satisfies the restricted isometry
property. To recover the high-resolution image from MAR measurements, the total
variation algorithm based on the compressive sensing theory is employed for
solving the Lagrangian regularization problem. Both peak signal-to-noise ratio
and structural similarity results demonstrate the MAR sensing framework shows
much better recovery performance than the completely random sensing one. The
work is particularly helpful for high-performance and lost-cost data
acquisition.Comment: 16 pages, 8 figure
Redox potential control in anaerobic Clostridium beijerinckii fermentation using single-use vessels
Redox potential is an important physiochemical factor which measures the tendency of the medium to acquire electrons. In Clostridium beijerinckii fermentation, redox potential indicates the status of the NAD(P)+ pool regeneration which directs the electron flow leading to solvent production including butanol. In this study, anaerobic C. beijerinckii fermentation was conducted in Eppendorf BioBLU® 3f Single-Use Vessels controlled by the BioFlo® 120 bioprocess control station. The parameters being monitored throughout the fermentation were redox potential and pH using ISM® redox/pH sensors. The objectives of this study were (1) to investigate the effects of redox control on the growth and butanol production of C. beijerinckii; and (2) to validate the suitability of the BioFlo 120 and BioBLU 3f Single-Use Vessel for anaerobic fermentation applications. When C. beijerinckii was grown without redox control, a continuous change of redox potential was observed in the broth. When fermentation ended at 124 h, the optical density at 600 nm (OD600) was 0.8, glucose consumption was 33 % and butanol production was limited. When the redox potential was controlled at -500 mV by redox sensor guided addition of Na2S·9H2O solution, the OD600 was 1.6, glucose consumption was 51 %, and butanol production showed a 2-fold increase. In summary, with the combination of ISM redox sensor and BioBLU Single-Use Vessel, the high variability of redox potential during C. beijerinckii fermentation can be actively controlled to drastically increase biomass growth and solvent production
Prompt Stealing Attacks Against Large Language Models
The increasing reliance on large language models (LLMs) such as ChatGPT in
various fields emphasizes the importance of ``prompt engineering,'' a
technology to improve the quality of model outputs. With companies investing
significantly in expert prompt engineers and educational resources rising to
meet market demand, designing high-quality prompts has become an intriguing
challenge. In this paper, we propose a novel attack against LLMs, named prompt
stealing attacks. Our proposed prompt stealing attack aims to steal these
well-designed prompts based on the generated answers. The prompt stealing
attack contains two primary modules: the parameter extractor and the prompt
reconstruction. The goal of the parameter extractor is to figure out the
properties of the original prompts. We first observe that most prompts fall
into one of three categories: direct prompt, role-based prompt, and in-context
prompt. Our parameter extractor first tries to distinguish the type of prompts
based on the generated answers. Then, it can further predict which role or how
many contexts are used based on the types of prompts. Following the parameter
extractor, the prompt reconstructor can be used to reconstruct the original
prompts based on the generated answers and the extracted features. The final
goal of the prompt reconstructor is to generate the reversed prompts, which are
similar to the original prompts. Our experimental results show the remarkable
performance of our proposed attacks. Our proposed attacks add a new dimension
to the study of prompt engineering and call for more attention to the security
issues on LLMs
An Empirical Analysis of Search Engine Advertising: Sponsored Search in Electronic Markets
The phenomenon of sponsored search advertising—where advertisers pay a fee to Internet search engines to be displayed alongside organic (nonsponsored) Web search results—is gaining ground as the largest source of revenues for search engines. Using a unique six-month panel data set of several hundred keywords collected from a large nationwide retailer that advertises on Google, we empirically model the relationship between different sponsored search metrics such as click-through rates, conversion rates, cost per click, and ranking of advertisements. Our paper proposes a novel framework to better understand the factors that drive differences in these metrics. We use a hierarchical Bayesian modeling framework and estimate the model using Markov Chain Monte Carlo methods. Using a simultaneous equations model, we quantify the relationship between various keyword characteristics, position of the advertisement, and the landing page quality score on consumer search and purchase behavior as well as on advertiser\u27s cost per click and the search engine\u27s ranking decision. Specifically, we find that the monetary value of a click is not uniform across all positions because conversion rates are highest at the top and decrease with rank as one goes down the search engine results page. Though search engines take into account the current period\u27s bid as well as prior click-through rates before deciding the final rank of an advertisement in the current period, the current bid has a larger effect than prior click-through rates. We also find that an increase in landing page quality scores is associated with an increase in conversion rates and a decrease in advertiser\u27s cost per click. Furthermore, our analysis shows that keywords that have more prominent positions on the search engine results page, and thus experience higher click-through or conversion rates, are not necessarily the most profitable ones—profits are often higher at the middle positions than at the top or the bottom ones. Besides providing managerial insights into search engine advertising, these results shed light on some key assumptions made in the theoretical modeling literature in sponsored search
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