Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust adaptive machine learning algorithms for distributed signal processing

Κατανεμημένα δίκτυα τα οποία απαρτίζονται από έναν μεγάλο αριθμό κόμβων, π. χ. Δίκτυα Ασύρματων Αισθητήρων, προσωπικοί υπολογιστές, φορητοί υπολογιστές, έξυπνα τηλέφωνα, κλπ. , οι οποίοι συνεργάζονται με σκοπό την επίτευξη ενός κοινού στόχου, αποτελούν μια υποσχόμενη τεχνολογία η οποία βρίσκει εφαρμογή σε πολλά μοντέρνα προβλήματα. Τυπικά παραδείγματα τέτοιων εφαρμογών είναι τα εξής: κατανεμημένη επίβλεψη περιβάλλοντος, εύρεση ακουστικής πηγής, εκτίμηση φάσματος, κλπ. Συνεργατικοί μηχανισμοί δύνανται να βελτιώσουν σημαντικά την διαδικασία εκμάθησης, μέσω της οποίας οι κόμβοι επιτυγχάνουν τον κοινό στόχο τους. Η παρούσα διατριβή μελετά το πρόβλημα της προσαρμοστικής μάθησης σε κατανεμημένα δίκτυα, εστιάζοντας στο πρόβλημα της κατανεμημένης εκτίμησης παραμέτρων. Ένα σύνολο από κόμβους λαμβάνουν πληροφορία, η οποία σχετίζεται με συγκεκριμένες παραμέτρους, και η εκτίμηση των εν λόγω παραμέτρων αποτελεί τον στόχο μας. Προς αυτήν την κατεύθυνση, οι κόμβοι λαμβάνουν υπόψη τόσο τις τοπικές μετρήσεις, όσο και την πληροφορία η οποία λαμβάνεται από την συνεργασία με τους υπόλοιπους κόμβους του δικτύου. Στα πλαίσια της παρούσας διατριβής, η συνεργασία μεταξύ των κόμβων ακολουθεί την φιλοσοφία της κατανεμημένης βελτιστοποίησης μέσω διάχυσης και οι προτεινόμενοι αλγόριθμοι ανήκουν στην οικογένεια APSM. Αρχικά, εύρωστοι αλγόριθμοι με βάση τον APSM προτείνονται. Ο στόχος είναι η «εναρμόνιση» της πληροφορίας, η οποία λαμβάνεται από την γειτονιά, με την τοπική πληροφορία. Η εν λόγω «εναρμόνιση» επιτυγχάνεται μέσω προβολής της πληροφορίας της γειτονιάς πάνω σε ένα κυρτό σύνολο, το οποίο κατασκευάζεται με βάση τοπικές μετρήσεις. Στην συνέχεια, αντιμετωπίζεται σενάριο κατά το οποίο ένα υποσύνολο των κόμβων του δικτύου δυσλειτουργεί και παράγει μετρήσεις, οι οποίες έχουν υποβαθμιστεί σημαντικά από τον θόρυβο. Για την επίλυση του εν λόγω προβλήματος γίνεται χρήση της συνάρτησης κόστους Huber, η οποία είναι εύρωστη στην ύπαρξη ακραίων τιμών θορύβου. Επιπλέον, μελετήθηκε το πρόβλημα της προσαρμοστικής εκτίμησης αραιών διανυσμάτων στα πλαίσια της κατανεμημένης μάθησης. Οι κόμβοι του δικτύου αναζητούν άγνωστο, αραιό διάνυσμα, το οποίο αποτελείται από μικρό αριθμό μη μηδενικών συντελεστών. Περιορισμοί σταθμισμένης l1 νόρμας καθώς και προβολές μεταβλητής μετρικής, οι οποίες ευνoούν αραιές λύσεις χρησιμοποιούνται. Τέλος, προτείνονται αλγόριθμοι, οι οποίοι οδηγούν σε μείωση της πληροφορίας που αποστέλλεται στο δίκτυο, περιορίζοντας τις εκτιμήσεις να βρίσκονται πάνω σε έναν Krylov υπόχωρο. Οι προτεινόμενοι αλγόριθμοι έχουν υψηλή απόδοση ενώ ταυτόχρονα οι απαιτούμενοι πόροι εύρους ζώνης και η πολυπλοκότητα παραμένουν σε λογικά επίπεδα.
Dubito Ergo Sum: Exploring AI Ethics

We paraphrase Descartes’ famous dictum in the area of AI ethics where the “I doubt and therefore I am” is suggested as a necessary aspect of morality. Therefore AI, which cannot doubt itself, cannot possess moral agency. Of course, this is not the end of the story. We explore various aspects of the human mind that substantially differ from AI, which includes the sensory grounding of our knowing, the act of understanding, and the significance of being able to doubt ourselves. The foundation of our argument is the discipline of ethics, one of the oldest and largest knowledge projects of human history, yet, we seem only to be beginning to get a grasp of it. After a couple of thousand years of studying the ethics of humans, we (humans) arrived at a point where moral psychology suggests that our moral decisions are intuitive, and all the models from ethics become relevant only when we explain ourselves. This recognition has a major impact on what and how we can do regarding AI ethics. We do not offer a solution, we explore some ideas and leave the problem open, but we hope somewhat better understood than before our study.
Toward a Theoretical Foundation of Policy Optimization for Learning Control Policies

Gradient-based methods have been widely used for system design and optimization in diverse application domains. Recently, there has been a renewed interest in studying theoretical properties of these methods in the context of control and reinforcement learning. This article surveys some of the recent developments on policy optimization, a gradient-based iterative approach for feedback control synthesis that has been popularized by successes of reinforcement learning. We take an interdisciplinary perspective in our exposition that connects control theory, reinforcement learning, and large-scale optimization. We review a number of recently developed theoretical results on the optimization landscape, global convergence, and sample complexityof gradient-based methods for various continuous control problems, such as the linear quadratic regulator (LQR), [Formula: see text] control, risk-sensitive control, linear quadratic Gaussian (LQG) control, and output feedback synthesis. In conjunction with these optimization results, we also discuss how direct policy optimization handles stability and robustness concerns in learning-based control, two main desiderata in control engineering. We conclude the survey by pointing out several challenges and opportunities at the intersection of learning and control.
Programming Wireless Sensor Networks : Fundamental Concepts and State of the Art LUCA

ions, middleware. ACM Reference Format: Mottola, L. and Picco, G. P. 2011. Programming wireless sensor networks: Fundamental concepts and state of the art. ACM Comput. Surv. 43, 3, Article 19 (April 2011), 51 pages. DOI = 10.1145/1922649.1922656 http://doi.acm. org/10.1145/1922649.1922656
Danish entry 3.0: AI enabled biosecurity system for enhanced protection

This research presents a proof-of-concept for an intelligent biosecurity system specifically designed for use in livestock and poultry production settings. The system, known as the Danish Entry, employs a combination of multiple sensors and an image recognition system to enhance biosecurity measures on the farm. The camera integrated into the system is connected to a computer that runs a visual recognition model capable of identifying potential breaches in biosecurity protocols. The proposed design aims to reduce the likelihood of farm personnel misusing biosecurity procedures and transporting pathogens into the farm. Furthermore, it facilitates slower human traffic flow through the physical barriers within a Danish Entry, thus increasing visual detection accuracy. This system presents a promising solution for improving biosecurity in the livestock and poultry industry using technology and automated approaches.
Real World Music Object Recognition

We present solutions to two of the most pressing issues in contemporary optical music recognition (OMR). We improve recognition accuracy on low-quality, real-world (i.e. containing ageing, lighting, or dirt artefacts among others) input data and provide confidence-rated model outputs to enable efficient human post-processing. Specifically, we present (i) a sophisticated input augmentation scheme that can reduce the gap between sanitised benchmarks and realistic tasks through a combination of synthetic data and noisy perturbations of real-world documents; (ii) an adversarial discriminative domain adaptation method that can be employed to improve the performance of OMR systems on low-quality data; (iii) a combination of model ensembles and prediction fusion, which generates trustworthy confidence ratings for each prediction. We evaluate our contributions on a newly created test set consisting of manually annotated pages of varying real-world quality, sourced from International Music Score Library Project ( IMSLP ) / the Petrucci Music Library. With the presented data augmentation scheme, we achieve a doubling in detection performance from 36.0% to 73.3% on noisy real-world data compared to state-of-the-art training. This result is then combined with robust confidence ratings paving the way for OMR to be deployed in the real world. Additionally, we show the merits of unsupervised adversarial domain adaptation for OMR raising the 36.0% baseline to 48.9% . All our code and data are freely available at: https://github.com/raember/s2anet/tree/ TISMIR_publication .
Measuring, modeling, and managing a dairy farm’s dynamic responses using the Internet of Things (IoT)

Precision Dairy Farming is a term that describes the benefit of using digital technologies to measure physiological, behavioral, and production indicators for individual animals. SomaDetect ™ has developed a series of algorithms to monitor individual cow performance and health at every milking as a major advancement for Precision Dairy Farming (PDF). The methods used include a light-scattering sensor, cloud-based IoT infrastructure, and data processing algorithms to develop actionable shortlists that are delivered via software to the farm users. The system has been tested across two sizes of farms: small (<200-cow herd) and large (>3000-cow herd), and across a four-month period to test for temporal stability. In this paper, the approach used is described in detail and provides examples to show how the data can be used to derive economic value for dairy farms via actionable shortlists. Results from the various studies presented demonstrate that the shortlists provided by the SomaDetect ecosystem facilitate data-driven, practical, strategic management decision changes. These management interventions drive farm Key Performance Indicators (KPIs) and enable several valuable impacts such as reducing the Bulk Tank Somatic Cell Count (BTSCC), enabling Selective Dry Cow Therapy (SDCT), and identifying cows at risk of early embryonic death as early as 18 days following insemination. The example data show that this new technology, combined with good management approaches, can provide significant value for dairy farms.
Junior secondary school students' conceptions about plate tectonics

ABSTRACT There are ongoing calls for research that identifies students’ conceptions about geographical phenomena. In response, this study investigates junior secondary school students’ (N = 95) conceptions about plate tectonics. Student response data was generated from semi-structured interviews-about-instances and a two-tiered multiple-choice test instrument developed and validated by the researchers. There were three main findings: (1) students held many alternative conceptions about plate tectonics, most of which have not been reported in previous research; (2) students’ alternative conceptions most commonly concerned the formation of landforms at tectonic plate boundaries; and (3) students were particularly confused about the cause of subduction at an oceanic-continental convergent plate boundary. The findings of this study can assist geography teachers and researchers to develop innovative pedagogies that consider students’ pre-instructional alternative conceptions and promote conceptual change learning.
Kinome-wide synthetic lethal screen identi�es PANK4 as modulator of resistance in glioblastoma

Temozolomide (TMZ) represents the cornerstone of therapy for glioblastoma (GBM). However, acquisition of resistance limits its therapeutic potential and therefore poses the need to identify new therapeutic combinations that could improve treatment outcomes. Despite the human kinome has proved to be an undisputable source of druggable targets, our knowledge remains con�ned to a limited fraction of it, with a multitude of under-investigated proteins yet to be characterised. Using a kinome-wide RNAi screen, we found that abrogation of pantothenate kinase 4 (PANK4) enhances the antiproliferative effects of TMZ in GBM in vitro. Further validation of our top-hit across various TMZ-resistant GBM cell models, patient-derived GBM cell lines and tissue samples, as well as in vivo studies, corroborated the potential translational signi�cance of our �ndings. We showed that PANK4 expression is induced during TMZ treatment, and its expression is associated with a worse clinical outcome. Using a Tandem Mass Tag (TMT)-based quantitative proteomic approach, a comprehensive global protein dynamics analysis was undertaken to identify key response signatures upon PANK4 knockdown, in the presence or absence of TMZ. We revealed that silencing of PANK4 leads to a marked downregulation of a subset of proteins involved in cellular detoxi�cation. More speci�cally, as cells undergo genotoxic stress during TMZ exposure, PANK4 depletion represents a synthetic vulnerability, focal point that can lead to critical cellular damage, accumulation of toxic metabolites, and subsequent cell death. Taken together, we unveil a previously unreported role for PANK4 in mediating therapeutic resistance to TMZ in GBM.
Prognostics for an actuator with the combination of support vector regression and particle filter

The accurate prognostics for actuator malfunctions is a challenging task. Developing reliable prognostic methods is vital for providing reasonable preventive maintenance schedules and preventing unexpected failures. Particle filter has been proved to be a traditional approach to deal with actuator prognostic problems. However, the measurement function in the particle filter algorithm cannot be obtained in the prediction process, this paper presents a hybrid framework combining support vector regression (SVR) and particle filter (PF). The SVR output prediction results are employed as the “measurements” for the subsequent PF algorithm. To accomplish the accurate prognostics for actuator fault of civil aircraft, an improved PF based on Kendall correlation coefficient is put forward to solve the problem of particles’ degeneracy. The experimental results are presented, demonstrating that the SVR-PF hybrid approach has satisfactory performance with better prognostics accuracy and higher fault resolution than traditional approaches.
SOCIAL MEDIA FOR PROMOTION AND MARKETING IN FAMILY FARMING: a resource application perspective

: Family farming provides food diversity worldwide. If, on the one hand, there is its productive efficiency, within the scope of the management of diverse cultures, on the other hand, there is the challenge in the promotion and commercialization of family farming products. This article seeks to, through a Systematic Literature Review (SLR) and the use of the StArt tool, analyze the research published in the databases Scopus and Web of Science, so as to verify the use of social media for promotion and commercialization in family farming. The results indicate that the use of social media in family farming is beneficial, bringing positive results, however, it lacks incentives, investments, and attention from public policies.
Institute for Mathematical Physics an Application of Young's Tower Method: Exponential Decay of Correlations in Multidimensional Dispersing Billiards an Application of Young's Tower Method: Exponential Decay of Correlations in Multidimensional Dispersing Billiards

We investigate multidimensional dispersing billiards with finite horizon and no corner points. We give an overview of the proof of exponential correlation decay and the central limit theorem for Hölder-continuous observables, following the lines of our paper [4]. The proof is conditional: an assumption on the complexity of the singularity structure has to be made.
Analysis of drinking behavior of beef cattle using computer vision

Monitoring the drinking behavior of animals is of great importance for livestock farming. Measuring drinking time is labor demanding and thus, it is still a challenge in most livestock production systems. Computer vision technology using a low-cost camera system can be useful in overcoming this issue. Therefore, the aim of this research was to develop a computer vision system for monitoring beef cattle drinking behavior. A data acquisition system, including an RGB-D camera and an ultrasonic sensor was developed to record the beef cattle drinking actions. We developed an algorithm for tracking the beef cattle key body parts, such as head-ear-neck position, using a state-of-the-art deep learning architecture DeepLabCut. The extracted key points were analyzed using a Long Short-Term Memory (LSTM) model to classify drinking and non-drinking periods. A total of 70 videos were used to train and test the model and 8 videos were used for validating purposes. During the testing, the model achieved 97.35% accuracy. The results of this study will guide us to meet the immediate needs and expand farme rs’ capability in monitoring an imal health and well-being.
Hippocampus { Spatial Models

The hippocampus is one of the most studied areas of the brain. It has attracted interest due to its position many synapses removed from sensory transducers or motor-e ectors, the putative role of hippocampal damage in human amnesia, the discovery of long term potentiation (LTP, see SYNAPTIC PLASTICITY) and the discovery that cell ring is spatially coded. It has also been studied for its role in neurological disorders including epilepsy, schizophrenia and Alzheimer's disease. Bilateral damage to the hippocampus and nearby structures in patient H.M. , as treatment for epilepsy, produced a profound retrograde and anterograde amnesia, prompting extensive cross-species research to uncover the speci c memory de cit that results from hippocampal damage (the most prominent of which, in the rat, appears to be a de cit in spatial navigation).
A reliable and efficient deep learning model integrating convolutional neural network and transformer structure for fine-grained classification of chicken Eimeria species

Chicken coccidiosis is a disease caused by Eimeria spp. and costs the broiler industry more than 14 billion dollars per year globally. Different chicken Eimeria species vary significantly in pathogenicity and virulence, so the classification of different chicken Eimeria species is of great significance for the epidemiological survey and related prevention and control. A new hybrid model integrating Transformer structure and the residual module in convolutional neural network (CNN), named Residual-Transformer-Fine-Grained (ResTFG), was proposed and evaluated for fine-grained classification of microscopic images of seven chicken Eimeria species. The results showed that ResTFG achieved the best performance with high accuracy and computationally efficient compared with traditional models. Specifically, the parameters, inference speed and overall accuracy of ResTFG are 1.95M, 256 FPS and 96.9%, respectively, which are 10.9 times lighter, 1.5 times faster and 2.7% higher in accuracy than the benchmark model. In addition, the results of ablation experiments showed that CNN or Transformer alone had model accuracies of only 89.8% and 87.0%. This study invented a reliable, computationally efficient, and promising deep learning model for the automatic fine-grain classification of chicken Eimeria species, which could potentially be embedded in microscopic devices at an affordable price for producers to improve the work efficiency of researchers and to be extended to other parasite ova, and applied to other agricultural tasks
Computer vision on the edge: A computing framework for high-throughput phenotyping in livestock operations

Computer vision systems (CVS) have gained attention in agriculture as a powerful technology for high-throughput phenotyping. However, implementing CVS in commercial farms can be challenging due to the massive amounts of data generated and the limitation of Internet bandwidth and latency when using on-premises or cloud computing. To address this challenge, we propose utilizing an edge computing system, which enables real-time monitoring and large-scale data collection by processing data locally at the edge devices. In this study, we developed a low-cost edge computing system designed primarily to video-image livestock animals in farm conditions. The system is based on 3D cameras (n = 29), known as edge devices, each connected to an edge node, here a single-board computer (Nvidia Jetson Nano). The edge nodes communicate through a local Wi-Fi network to an edge server, which also uses an Nvidia Jetson Nano that monitors and manages the edge nodes. The edge computing framework was programmed using the Bash command language interpreter. Log files are generated daily for monitoring the edge system, ensuring the smooth operation of the entire system. One of the key advantages of this system is that it allows for image recording in real-time (24/7) and can process data on the edge system, store it locally, or transfer it to a cloud system. Additionally, other sensing technologies, such as wearable and weather sensors, can be connected to the edge system, expanding the range of data that can be collected. This breakthrough in using high-throughput digital technologies to collect animal and environmental information has the potential to revolutionize how data is collected and processed on farms.
Recovery of Coloured Surface Reflectances Using the Photometric Stereo Method

We discuss a fast and efficient method to estimate the surface reflectance values with respect to different wavelength of light. Qualitative and quantitative evaluations show that surfaces rendered using surface reflectances obtained by the proposed method are more realistic when compared with conventional rendering methods. 1 Centre for Image Technology and Robotics, Tamaki Campus, Building 731, The University of Auckland, Morrin Road, Glen Innes, Auckland 1005, New Zealand. 2 Semiconductor Products Group, Agilent Technologies, Santa Clara CA 95054 USA. 3 Department of Electrical Engineering, I-Shou Univeristy, Kaohsiung, Taiwan You are granted permission for the non-commercial reproduction, distribution, display, and performance of this technical report in any format, BUT this permission is only for a period of 45 (forty-five) days from the most recent time that you verified that this technical report is still available from the CITR Tamaki web site under terms that include this permission. All other rights are reserved by the author(s). Recovery of Coloured Surface Reflectances Using the Photometric Stereo Method Chia-Yen Chen, Reinhard Klette, Ramakrishna Kakarala and Chi-Fa Chen
Organic matter and water management strategies to reduce methane and nitrous oxide emissions from rice fields in Vietnam

The reduction of CH4 and N2O emissions from rice paddies is of utmost importance in minimizing the impact of rice production on global warming. A field experiment was therefore conducted in farmers' field in Hanoi, Vietnam to examine whether the use of straw compost or straw biochar, in combination with the safe alternate wetting and drying (AWD) has the potential to suppress both CH4 and N2O emissions from rice paddies while maintaining the rice yield. The study compared the proposed strategies with local farmers' practice of permanent flooding (PF) and farmyard manure (FYM) incorporation, respectively. A control treatment without organic matter incorporation in both AWD and PF water regimes was also included in the study; all treatments received equal amounts of mineral fertilizer. Gas emissions were monitored using the closed chamber method at seven-day intervals during the first 50 days and at 15-day intervals thereafter. Addition of FYM, straw compost and biochar increased CH4 emissions by 230%, 150% and 38%, respectively, when compared with the control treatments in both the AWD and PF water regimes. Within AWD, FYM increased N2O emissions by 30%, straw compost and biochar displayed similar amount of N2O emissions as the control treatment. Within PF, N2O emissions under FYM and straw compost were 40% and 35% higher than the control treatment, respectively, and biochar once again displayed similar amount of N2O emissions as the control treatment. Yield difference was not significant (p > 0.05) between any of the treatments. These results indicated that the straw compost incorporation might not reduce the global warming potential (GWP) and yield-scaled GWP of rice production, whereas biochar in combination with AWD has the potential to maintain the GWP and yield-scaled GWP of rice production at lower level than the farmers' practice. a 2014 Elsevier B.V. All rights reserved.