DORIS-MAE: Scientific Document Retrieval using Multi-level Aspect-based Queries

In scientific research, the ability to effectively retrieve relevant documents based on complex, multifaceted queries is critical. Existing evaluation datasets for this task are limited, primarily due to the high cost and effort required to annotate resources that effectively represent complex queries. To address this, we propose a novel task, Scientific DOcument Retrieval using Multi-level Aspect-based quEries (DORIS-MAE), which is designed to handle the complex nature of user queries in scientific research. We developed a benchmark dataset within the field of computer science, consisting of 100 human-authored complex query cases. For each complex query, we assembled a collection of 100 relevant documents and produced annotated relevance scores for ranking them. Recognizing the significant labor of expert annotation, we also introduce Anno-GPT, a scalable framework for validating the performance of Large Language Models (LLMs) on expert-level dataset annotation tasks. LLM annotation of the DORIS-MAE dataset resulted in a 500x reduction in cost, without compromising quality. Furthermore, due to the multi-tiered structure of these complex queries, the DORIS-MAE dataset can be extended to over 4,000 sub-query test cases without requiring additional annotation. We evaluated 17 recent retrieval methods on DORIS-MAE, observing notable performance drops compared to traditional datasets. This highlights the need for better approaches to handle complex, multifaceted queries in scientific research. Our dataset and codebase are available at https://github.com/Real-Doris-Mae/Doris-Mae-Dataset.Comment: To appear in NeurIPS 2023 Datasets and Benchmarks Trac

Development of modified apple polysaccharide capped silver nanoparticles loaded with mesalamine for effective treatment of ulcerative colitis

The objective of study was to develop modified apple polysaccharide (MAP) based silver nanoparticles (AgNPs) loaded with mesalamine (MES) for effective treatment of ulcerative colitis in acetic acid induced rat model. AgNPs were prepared by reducing silver nitrate using MAP solution. The size and zeta potential of AgNPs was 89 ± 3 nm and −16.3 ± 1.54 mV and AgNPs loaded with MES (AgNPs-MES) was 101 ± 9 nm and −14.27 ± 2.16 mV. The dissolution study revealed about 54% drug release after 5 h indicating release of drug at the colonic site. The in vivo study was carried out on acetic acid induced ulcerative colitis rats and efficacy of treatment was assessed through evaluation of disease activity index and level of antioxidants as well as tumor necrosis factor-α after 7th and 14th day of induction of colitis. Histopathological evaluation of colonic tissue was also carried out. The results revealed that AgNPs-MES (high dose) provided better therapeutic efficacy for the treatment of UC as compared to its low dose, MES alone, MES-MAP, AgNPs alone and MAP alone. It was concluded that MAP based AgNPs loaded with MES were successfully formulated and found to be effective in treating ulcerative colitis

Energy management of AC microgrid: deterministic and stochastic framework

by Dhanapala PrudhvirajM.Tech

Characterization of YouTube Video Streaming Traffic

Online digital videos have made a revolutionary evolution since the social networking sites such as YouTube and Hulu have emerged. These websites facilitate video accessable and only a click away. Ever increasing internet traffic and a very significant increase in the use of videos in social networking has led to the problem of network congestion. Consequently, it becomes essential and imperative to analyze the traffic flow and comprehend how it is being delivered from the server. If the flow of traffic is analyzed appropriately and if the analysis methodology is understood properly, the service providers can understand the reasons for network congestion and avoid them. Given the context, a few research studies have thrown light on video delivery procedure of YouTube with emphasis on methodology. A few other works have examined the location of video storage and the strategy involved in sending the video packets, yet the packet delivery strategy for different types of videos (based on popularity) has not been explained in detail. The present research fills this gap. This research explores the origin of the source of the video and the exact strategy being followed by YouTube in delivering popular and non-popular videos. This thesis also discusses the analysis methodology of packet delivery in detail.Online digital video har gjort en revolutionerande utveckling sedan de sociala nätverkssajter som YouTube och Hulu har uppstått. Dessa webbplatser underlätta video tillgång? Kompetent och bara ett klick bort. Ständigt ökande internettrafik och en mycket betydande ökning av användningen av video i sociala nätverk har lett till problem med överbelastning på nätet. Därför blir det viktigt och nödvändigt att analysera trafiken fl? Ow och förstå hur det blir levererad från servern. Om? Trafikflödet analyseras korrekt och om den analysmetod förstås ordentligt, kan tjänsteleverantörerna förstå orsakerna till överbelastning av nätet och undvika dem. Med tanke på sammanhanget, har ett fåtal forskningsstudier kastat ljus på video leverans förfarande för YouTube med tyngdpunkt på metodik. Några andra verk har undersökt platsen för videolagring och strategin involverad i att skicka videopaketen, men paketet leverans strategi för? Olika typer av videoklipp (baserat på popularitet) inte har förklarats i detalj. Den nuvarande forskningen? Fyller denna lucka. Denna forskning undersöker ursprunget till källan av videon och den exakta strategin följs av YouTube i att leverera populära och icke-populära filmer. Denna avhandling diskuterar också analysmetodik för paketleverans i detalj

Characterization of YouTube Video Streaming Traffic

Online digital videos have made a revolutionary evolution since the social networking sites such as YouTube and Hulu have emerged. These websites facilitate video accessable and only a click away. Ever increasing internet traffic and a very significant increase in the use of videos in social networking has led to the problem of network congestion. Consequently, it becomes essential and imperative to analyze the traffic flow and comprehend how it is being delivered from the server. If the flow of traffic is analyzed appropriately and if the analysis methodology is understood properly, the service providers can understand the reasons for network congestion and avoid them. Given the context, a few research studies have thrown light on video delivery procedure of YouTube with emphasis on methodology. A few other works have examined the location of video storage and the strategy involved in sending the video packets, yet the packet delivery strategy for different types of videos (based on popularity) has not been explained in detail. The present research fills this gap. This research explores the origin of the source of the video and the exact strategy being followed by YouTube in delivering popular and non-popular videos. This thesis also discusses the analysis methodology of packet delivery in detail.Online digital video har gjort en revolutionerande utveckling sedan de sociala nätverkssajter som YouTube och Hulu har uppstått. Dessa webbplatser underlätta video tillgång? Kompetent och bara ett klick bort. Ständigt ökande internettrafik och en mycket betydande ökning av användningen av video i sociala nätverk har lett till problem med överbelastning på nätet. Därför blir det viktigt och nödvändigt att analysera trafiken fl? Ow och förstå hur det blir levererad från servern. Om? Trafikflödet analyseras korrekt och om den analysmetod förstås ordentligt, kan tjänsteleverantörerna förstå orsakerna till överbelastning av nätet och undvika dem. Med tanke på sammanhanget, har ett fåtal forskningsstudier kastat ljus på video leverans förfarande för YouTube med tyngdpunkt på metodik. Några andra verk har undersökt platsen för videolagring och strategin involverad i att skicka videopaketen, men paketet leverans strategi för? Olika typer av videoklipp (baserat på popularitet) inte har förklarats i detalj. Den nuvarande forskningen? Fyller denna lucka. Denna forskning undersöker ursprunget till källan av videon och den exakta strategin följs av YouTube i att leverera populära och icke-populära filmer. Denna avhandling diskuterar också analysmetodik för paketleverans i detalj

Metamorphic Testing of Image Classification and Consistency Analysis Using Clustering

Testing deep learning systems requires expensive labeled data. In recent years, researchers began to leverage metamorphic testing to address this issue. However, metamorphic relations on image data remain poorly understood. To gain a deeper understanding of these metamorphic relations, we survey common image operations modeling covariate shift, manually classify and categorize the underlying metamorphic relations, and conduct experiments to validate our classifications. In our experiments, we train three popular convolutional neural network architectures on an image classification task. Next, we apply metamorphic operations on input test images and measure the change in classification accuracy and cross-entropy loss. A hierarchical clustering algorithm cluster these results and plots a dendrogram. We compare the groups from manual classification and the clusters from the algorithm to provide key insights. We find that Affine and Noise relations are consistent. Furthermore, we recommend metamorphic relationships to save time and better test deep learning systems in the future.</p

Design Development of Helmet Visor for Effective Vision

&lt;p&gt;Motorcyclists use helmets for safety, and visors are used to prevent dirt particles from entering into the eyes. Visors can also be used to reduce the intensity of light. However, some of the presently available visors are not suitable for light intensity reduction, while some are too expensive to afford by all. This project investigates the effect of developed helmet visor on visibility by practical experiments and finds a suitable alternative for the standard helmet visor. The values obtained from the experiments are validated against analytical calculations.&lt;/p&gt;</jats:p