126 research outputs found
Daily Streamflow Forecasting Using AutoML and Remote-Sensing-Estimated Rainfall Datasets in the Amazon Biomes
No full textReliable streamflow forecasting is crucial for several tasks related to water-resource management, including planning reservoir operations, power generation via Hydroelectric Power Plants (HPPs), and flood mitigation, thus resulting in relevant social implications. The present study is focused on the application of Automated Machine-Learning (AutoML) models to forecast daily streamflow in the area of the upper Teles Pires River basin, located in the region of the Amazon biomes. The latter area is characterized by extensive water-resource utilization, mostly for power generation through HPPs, and it has a limited hydrological data-monitoring network. Five different AutoML models were employed to forecast the streamflow daily, i.e., auto-sklearn, Tree-based Pipeline Optimization Tool (TPOT), H2O AutoML, AutoKeras, and MLBox. The AutoML input features were set as the time-lagged streamflow and average rainfall data sourced from four rain gauge stations and one streamflow gauge station. To overcome the lack of training data, in addition to the previous features, products estimated via remote sensing were leveraged as training data, including PERSIANN, PERSIANN-CCS, PERSIANN-CDR, and PDIR-Now. The selected AutoML models proved their effectiveness in forecasting the streamflow in the considered basin. In particular, the reliability of streamflow predictions was high both in the case when training data came from rain and streamflow gauge stations and when training data were collected by the four previously mentioned estimated remote-sensing products. Moreover, the selected AutoML models showed promising results in forecasting the streamflow up to a three-day horizon, relying on the two available kinds of input features. As a final result, the present research underscores the potential of employing AutoML models for reliable streamflow forecasting, which can significantly advance water-resource planning and management within the studied geographical area
Generative Artificial Intelligence and Regulations: Can We Plan a Resilient Journey Toward the Safe Application of Generative Artificial Intelligence?
No full textThe rapid advancements of Generative Artificial Intelligence (GenAI) technologies, such as the well-known OpenAI ChatGPT and Microsoft Copilot, have sparked significant societal, economic, and regulatory challenges. Indeed, while the latter technologies promise unprecedented productivity gains, they also raise several concerns, such as job loss and displacement, deepfakes, and intellectual property violations. The present article aims to explore the present regulatory landscape of GenAI across the major global players, highlighting the divergent approaches adopted by the United States, United Kingdom, China, and the European Union. By drawing parallels with other complex global issues such as climate change and nuclear proliferation, this paper argues that the available traditional regulatory frameworks may be insufficient to address the unique challenges posed by GenAI. As a result, this article introduces a resilience-focused regulatory approach that emphasizes aspects such as adaptability, swift incident response, and recovery mechanisms to mitigate potential harm. By analyzing the existing regulations and suggesting potential future directions, the present article aims to contribute to the ongoing discourse on how to effectively govern GenAI technologies in a rapidly evolving regulatory landscape
Opinion mining from machine translated Bangla reviews with stacked contractive auto-encoders
No full textIn the last years, online users have been sharing more and more opinions, reviews, and comments on the web. Opinion mining is the automatic process of getting the subject of such opinions, and recently it has been attracting great commercial and academic interest. Several methods were presented for performing opinion mining in Bangla language, however they reported limited performance. In the present article, we considered the only two publicly datasets available for opinion mining in the Bangla language. We machine translated the datasets into the English language and we preprocessed them by extracting textual frequency based features. Then, we designed two stacked contractive auto-encoders based architectures to perform opinion mining in Bangla language, one for each dataset. The classifiers were trained on the machine translated version on the two datasets in a stacked learning fashion. The proposed classifiers achieved improved performance, with respect to accuracy (>= 96%), precision (>= 93%), recall (>= 94%), and F1 score (>= 94%), reported in the past state of the art works. Furthermore, the experimental results showed that both the machine translation procedure and the stacked learning frameworks improved the final classification performance
Aspect extraction from bangla reviews through stacked auto-encoders
No full textInteractions between online users are growing more and more in recent years, due to the latest developments of the web. People share online comments, opinions, and reviews about many topics. Aspect extraction is the automatic process of understanding the topic (the aspect) of such comments, which has obtained huge interest from commercial and academic points of view. For instance, reviews available in webshops (like eBay, Amazon, Aliexpress, etc.) can help the customers in purchasing products and automatic analysis of reviews would be useful, as sometimes it is almost impossible to read all the available ones. In recent years, aspect extraction in the Bangla language has been regarded more and more as a task of growing importance. In the previous literature, a few methods have been introduced to classify Bangla texts according to the aspect they were focused on. This kind of research is limited mainly due to the lack of publicly available datasets for aspect extraction in the Bangla language. We take into account the only two publicly available datasets, recently published, collected for the task of aspect extraction in the Bangla language. Then, we introduce several classification methods based on stacked auto-encoders, as far as we know never exploited in the task of aspect extraction in Bangla, and we achieve better aspect classification performance with respect to the state-of-the-art: The experiments show an average improvement of 0.17, 0.31 and 0.30 (across the two datasets), respectively in precision, recall and F1-score, reported in the state-of-the-art works that tackled the problem
Will the machine like your image? Automatic assessment of beauty in images with machine learning techniques
No full textAlthough the concept of image quality has been a subject of study for the image processing community for more than forty years (where, with the term “quality”, we are referring to the accuracy with which an image processing system captures, processes, stores, compresses, transmits, and displays the signals that compose an image), notions related to aesthetics of photographs and images have only appeared for about ten years within the community. Studies devoted to aesthetics of images are multiplying today, taking advantage of the latest machine learning techniques and mostly due to the proliferation of huge communities and websites, specialized in digital photography sharing and archiving, such as Flickr, Imgur, DeviantArt, and Instagram. In this review, we examine the latest advances of computer methods that aim at computationally distinguishing high-quality from low-quality photos and images, relying on machine learning techniques. The paper is organized as follows: First, we introduce many approaches to aesthetics, studied in philosophy, neurobiology, experimental psychology, and sociology, to see what lighting they propose to researchers. Such points of view let us explain the weakness of the current consensus on the difficult aesthetics problem and the importance of the ongoing debates on it. Then, we analyze the work done in the community of pattern recognition and artificial intelligence on the task of automatic aesthetic assessment, and we both compare and critically examine the presented results. Finally, we describe many issues that have not been addressed, and starting from these, we outline some possible future directions
A Review of Facial Landmark Extraction in 2D Images and Videos Using Deep Learning
No full textThe task of facial landmark extraction is fundamental in several applications which involve facial analysis, such as facial expression analysis, identity and face recognition, facial animation, and 3D face reconstruction. Taking into account the most recent advances resulting from deep-learning techniques, the performance of methods for facial landmark extraction have been substantially improved, even on in-the-wild datasets. Thus, this article presents an updated survey on facial landmark extraction on 2D images and video, focusing on methods that make use of deep-learning techniques. An analysis of many approaches comparing the performances is provided. In summary, an analysis of common datasets, challenges, and future research directions are provided
Analisi teorico-preliminare e sperimentale del sistema di riposizionamento delle testing mass nell’esperimento L.I.S.A.
No full textvolume in stamp
How geometrical tolerances affect the measurement of reciprocal alignment of two different assemblies: a case study
No full textOften a designer has the problem to apply a suitable system of
geometrical and dimensional tolerances to an assembly. The
right solution is not unique, in fact it depends on the chosen
parameters. If the tolerances have to be optimized, some
important parameters have to be taken into account, e.g. the
efficiency of each prescription, or if this last is reachable, or it
can be verified and how much the realization costs.
The authors opinion is that a statistical approach based on the
Monte Carlo Method is very useful when the tolerances chains
are complex.
This paper shows an application of this method in order to
verify the functional alignment between two assemblies and a
critical analysis of the uncertainty in phase both of the
component design and test.
This study has been developed thanks to the strict requirements
imposed by ESA (European Space Agency) on the components
that Thales Alenia Space has to realize within the LISA
Pathfinder experiment.
The very critical aspect of this work is to reciprocally align two
cylindrical elements of two different assemblies. The
specifications require 100 μm as maximum linear displacement
and 300 μrad as maximum angular displacement. Moreover this
prescriptions have to be verified also when the two elements are
independently moving.
To be able to reach such strict accuracy level the components
have been assembled in an ISO 100 class cleanroom and the
work space was a 3D Coordinate-Measuring Machine (CMM).
The cylindrical elements have a 10 mm diameter, so the value
of the measurement uncertainty associated with the alignment
check is fundamental.
Starting from the different uncertainty sources, the
measurability and verifiability of the alignment have been
considered and evaluated.
The overall uncertainty has been assessed by numerical
simulations which have taken into account the dimensional,
geometrical and form tolerances as well as the instrumental
uncertainty of the 3D CMM. This estimation has been
positively validated by a session of repeated measurements.
Numerical simulations have also allowed performing a
sensitivity analysis, in order to give information about which
sources more contribute to the overall uncertainty
Enhancing micropositioning accuracy of a six axis hexapod through uncertainty evaluation
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