Miracle-dz/UniFAR-FTUs huggingface dataset:
U-Net and YOLO: AIML Models for Lane and Object Detection in Real-Time

In recent years, significant research and funding have been directed towards the creation of self-driving cars. Real-time object and lane detection on roadways is a crucial aspect of autonomous driving systems. In this research, we offer a software-based self-driving vehicle system that detects objects and lanes using computer vision techniques. Our system can detect variety of things, from vehicles to lanes, and is built to achieve results with as little latency as possible. Currently in detection algorithms there has a significant amount theory suggesting the use of CNNs (Convolutional Neural Network) which learns and generates arrays such as feature maps. Multiple layers of these layers generate parameters that help the next learning layer and are therefore recognized for their exceptional performance in recognition and detection tasks. Using actual driving circumstances, we evaluate our approach and discuss its challenges and limitations. We also take into account prospective strategies for improving the system's performance and functionality in the future. Here we have followed a U-Net model architecture which calls features from earlier Convolutional layers unlike the normal neural network architecture and therefore is considered to be one of the best models for segmentation tasks. Object Detection has recently gained a lot of attention as Drones and other devices which need camera feedback has increased, and in this there has already been a lot of research conducted such Mask-RCNN, Faster-RCNN, etc. but out of all the Networks, the benchmark which YOLO has set is record breaking. Multiple little variations of YOLO are available in market because of it relatively higher speed and accuracy then others. Our research has implemented these models and can help develop secure and efficient transport networks, and it has substantial implications for the development of autonomous driving technologies. Results show that U-Net has competed with a good rmse score. YOLO model requires relatively low complications and has shown Higher accuracy in the the field of object detection till date. We have tried and tested with CULane dataset for Lane Detection as the data provides lane markings as annotation while Custom Dataset for our object detection that mainly focuses on identification of classes related to traffic ecosystem model.
Resident-funded retirement village valuations: complications with the application of the DCF

Retirement Village assets are different from traditional residential assets due to their operation in accordance with statutory legislation. Designed for independent living, retirement villages provide either detached or semi-detached residential dwellings with car parking and small private yards with community facilities providing a shared congregational area for village activities and socialising. In essence, the village operator provides the land and buildings to the residents who pay an amount on entry for the right of occupation. On departure from the units an agreed proportion of either the original purchase price or the sale price is paid to the outgoing resident. As on-going levies are typically offset by on-going operational expenses, the market value of the operator’s interest in the Retirement Village is therefore predominantly based upon the estimated future income from Deferred Management Fees and Capital Gain upon roll-over receivable by the operator in accordance with the respective residency agreements. Given the lumpiness of these payments, there is general acceptance that the most appropriate approach to valuation is through Discounted Cash Flow (DCF) analysis. There is however inconsistency between valuers across Australia in how they undertake their DCF analysis, leading to differences in reported values and subsequent confusion among users of valuation services. To give guidance to valuers and enhance confidence from users of valuation services this paper investigates the five major elements of discounted cash flow methodology, namely cash flows, escalation factors, holding period, terminal value and discount rate.
Parameter estimation for coupled unsaturated flow and transport

This paper deals with the estimation of soil hydraulic and transport parameters from transient unsaturated flow and tracer experiments using a combined simulations-optimization approach. Hydraulic properties are defined by a modified form of van Genuchten's (1980) parametric model for two-phase permeability-saturation-pressure relations, and transport properties are defined by an empirical parametric dispersion model. A nonlinear weighted least squares algorithm is used to estimate unknown model parameters by minimizing deviations between concentrations, water contents, and pressure heads obtained from hypothetical infiltration/redistribution/evaporation experiments, and those predicted by solving a numerical model of coupled unsaturated flow and transport. Simultaneous estimation of hydraulic and transport properties is found to yield smaller estimation errors for model parameters than sequential inversion of hydraulic properties from water content and pressure head data followed by inversion for transport properties from concentration data. Effects of random noise in data measurements, soil layering, and choice of improper parametric model on the parameter estimation process are discussed.
Improving Early Detection of Suicide Ideation using Oversampling Methods

According to the World Health Organization, suicide is a major social problem and is the fourth leading cause of death worldwide. Mental health, financial distress and loss of social ties are among the most common causes of suicide. With the widespread use of social media in the 21st century and the freedom of expression that comes with it, people with suicidal thoughts often share their thoughts through these digital platforms. Early detection of suicidal thoughts is essential to prevent harm and save lives. To this end, this article proposes an oversampling method to detect suicidal ideation. The proposed approach uses SMOTE and ADASYN techniques to equalize the sample size and then implements neural networks and machine learning models for early detection of suicidal thoughts. To evaluate the effectiveness of the proposed method, we apply it to balanced and unbalanced data sets and measure its precision, memory, F1 score and accuracy. In addition, we create the ROC curve to confirm the effectiveness of the proposed method. Our experimental results show that the proposed approach is superior to existing methods for suicidal ideation detection.
Dividing and Conquering the Layered Land

Il mondo piatto è un mondo in cui il tempo scorre scandito da un unico orologio, in un’unica direzione, orizzontale. Un po’ noioso, direte voi. Gli abitanti di questo mondo possono muoversi nel futuro, e, quelli più furbi, anche nel passato. Il futuro non ha fine, il passato ha invece un inizio, uno zero (ma qualcuno non ci crede). Tutto qui. Il mondo a strati è un po’ più eccitante. Ci sono molti strati, disposti uno sopra l’altro. Il tempo di ogni strato è scandito da un orologio. La particolarità è questa: ogni tick di un orologio di qualche strato corrisponde ad un certo numero di tick dell’orologio dello strato che sta sotto, ed è una frazione del tick dell’orologio della strato che sta sopra. Il bello del mondo a strati è che i loro abitanti, oltre a spostarsi avanti e indietro nello strato in cui si trovano, possono, sorpresa sorpresa, muoversi verticalmente, cambiare strato, e vivere più lentamente, prendendo le cose con più calma, se vanno verso su, oppure vivere in modo più frenetico, precipitando un po’ le cose, se vanno verso giù (si dice che gli abitanti-giovani prediligano gli strati bassi, e, man mano che maturano, salgano verso l’alto). La dimensione dell’universo a strati non la conosce nessuno. Gli abitanti-scienziati fanno tre ipotesi: esiste un numero finito di strati, quindi, non si può salire per sempre, e neppure scendere per sempre. Oppure, esistono infiniti strati. In questo caso, due soluzioni vengono studiate: vi è uno strato in cima, e poi giù, all’infinito, senza mai fermarsi. Oppure, vi è uno strato in basso, e poi su, all’infinito, senza limite. In the flat land time elapses according to a unique clock, in one direction, horizontally. A bit boring, you may say. The inhabitants of this land may move in the future, and, the smartest ones, in the past too. Future has no end, while past has a starting point, a zero (but someone does not believe it). That’s all. The layered land is a bit more exiting. The are several layers, one above the other. Each layer has its own clock. The peculiarity is the following: every tick of a clock of some layer corresponds to a certain number of ticks of the clock of the layer below, and is a fraction of the tick of the clock of the layer above. The beauty of the layered land is that people living here may move back and forth on some layer, and, surprise surprise, they may also move vertically, changing the layer, going upward, where life is slower and calmer, or going downward, where things get faster and frantic (they say that young people prefer living on low layers and, when they get older and wiser, they move upward). The dimension of the layered universe is unknown. Science people make three hypothesis: either there exists a finite number of layers, and hence one can move neither arbitrarily upward nor arbitrarily downward, or there are infinitely many layers. In this case, two solutions are studied: either there exists a coarsest layer on the top, and then downward unbounded, or there is a finest layer at the bottom, and then upward unbounded.
Self-Attention Mechanism based Visual Detection for Transmission Line Pins

The visual detection of transmission lines is a key component to reduce the safety hazards in energy transmission tasks. However, due to the complex background of the defect target, the small size of the target, and the slight difference between the defect and normal bolts, detecting the lack of bolts and pins on transmission lines is a challenging problem to traditional methods. Inspired by strong feature learning ability of deep neural networks, we propose an efficient method based on fusion self-attention mechanism to actively address the issue of detecting bolts and pins on transmission lines. Specifically, the proposed method is designed to achieve classification and position location regression by fusing scale and spatial attention mechanisms in a unified manner. The extracted self-attention features are uniformly fused and utilized through the Fusion Attention Head. In order to address the problem of complex backgrounds for detecting pins, we propose a pin detection model by adding Fusion Attention Head which can uniformly fused and utilized the extracted self-attention features. Furthermore, we proposed a generalized Focal Loss is used to replace the traditional Focal Loss to achieve a joint representation of classification and quality estimation. Finally, we test the proposed model on a field-collected dataset of transmission line defects and compared it with the Faster R-CNN and YOLOv4. The experimental results demonstrate that the average accuracy is improved by 17%.
Psychological Stress Assessment Method Based on Learning Using Privileged Information Framework

At present, research mainly uses single modal and multimodal physiological signals to identify human psychological states, but these methods generally have the drawbacks of low evaluation accuracy, poor applicability, and weak effectiveness. To address the above shortcomings, this article proposes a psychological stress assessment method based on the privileged information learning paradigm framework. This method uses the SVM+ algorithm under the privileged information learning paradigm framework to predict from the WESAD dataset and compares its performance with other algorithms. According to the experimental results, the SVM+ algorithm has classification accuracy and robustness compared to other comparative algorithms. The experiment in this article can provide reference for subsequent research on convenient psychological stress assessment.
Edge Computing-Based Cloud Platform for Snakebite Assisted Diagnosis

Snakebite is a major global public health problem, with more than 5 million snakebite victims recorded each year. Timely diagnosis and appropriate treatment of snakebite are the keys to preventing serious complications and improving the patient’s prognosis. However, most medical institutions lack experience in snakebite treatment, which is prone to misdiagnosis or even missed diagnosis, and serious cases endanger the lives of patients. Artificial intelligence is widely used in computer-aided diagnosis systems (CAD) and plays a vital role in snakebite recognition. In recent years, research has tended to build models, ignoring data preparation and analysis. Therefore, there is an urgent need for a rapid and accurate diagnostic method for snakebite to assist clinical diagnostic decision-making. We propose a snakebite auxiliary diagnosis system combining edge computing and machine learning technology. The system has the advantages of simple deployment, strong flexibility, and simple operation and is not limited by the equipment conditions of some grassroots hospitals. Experimental results show that this system can quickly and accurately diagnose the types of venomous snakes, overcome the limitations of traditional snakebite diagnosis methods in various aspects, and provide reliable real-time service tools for medical professionals, especially those in remote areas, allowing rapid diagnosis and treatment of snakebite cases, ultimately reducing morbidity and mortality.
Res BCDU-SCB Net: A Lung CT Image Segmentation based on SC-BLSTM

Due to the unique physiological structure of human lung tissue, segmentation of the lung parenchymal regions is a very challenging task. Traditional image segmentation methods have certain limitations on practical applications. Therefore, lung tissue segmentation methods based on deep learning have become a research hotspot in recent years. Res BCDU-Net may overcome the problem of high false positives in other semi-automatic segmentation, but it still has the problem of low accuracy in edge segmentation, which is probably caused by the unclear input of a single CT image. Therefore, we propose to utilize three consecutive CT images as the input, which employs inter-slice context to restrain the false positive. When a single input CT image is not clear, the accuracy of lung segmentation results can be improved by referring to its previous and next slices. Based on the input of three continuous images, the spatial information in the process of lung segmentation is added in this paper. We build a Res BCDU-SCB network and collect and integrate spatial information through SC-BLSTM module. An SC-attention module is designed to take both finer-grained spatial information and rich semantic information into account. In this paper, we append BLSTM (Bidirectional Convolutional Long Short-term Memory) module with SC-attention module, named SC-BLSTM, to decrease performance degradation caused by ambiguous boundaries and variable lung parenchymal shapes. The results of extensive experiments on lung CT images (LIDC-IDRI database) show that it may improve the final segmentation accuracy and reduce the false positives, which confirms the effectiveness of our proposed method.
A Survey on Collaborative Learning for Intelligent Autonomous Systems

This survey examines approaches to promote Collaborative Learning in distributed systems for emergent Intelligent Autonomous Systems (IAS). The study involves a literature review of Intelligent Autonomous Systems based on Collaborative Learning, analyzing aspects in four dimensions: computing environment, performance concerns, system management, and privacy concerns, mapping the significant requirements of systems to the emerging Artificial intelligence models. Furthermore, the article explores Collaborative Learning Taxonomy for IAS to demonstrate the correlation between IoT, Big Data, and Human-in-the-Loop. Several technological open issues exist in the aforementioned domains (such as in applications of autonomous driving, robotics in healthcare, cyber security, and others) to effectively achieve the future deployment of Intelligent Autonomous Systems. This Survey aims to organize concepts around IAS, indicating the approaches used to extract knowledge from data in Collaborative Learning for IAS, and identifying open issues. Moreover, it presents a guide to overcoming the existing challenges in decision-making mechanisms with IAS, providing a holistic vision of Big Data and Human-in-the-Loop.
Scalable Multi-Modal Learning for Cross-Link Channel Prediction in Massive IoT Networks

Tomorrow's massive-scale IoT sensor networks are poised to drive uplink traffic demand, especially in areas of dense deployment. To meet this demand, however, network designers leverage tools that often require accurate estimates of Channel State Information (CSI), which incurs a high overhead and thus reduces network throughput. Furthermore, the overhead generally scales with the number of clients, and so is of special concern in such massive IoT sensor networks. While prior work has used transmissions over one frequency band to predict the channel of another frequency band on the same link, this paper takes the next step in the effort to reduce CSI overhead: predict the CSI of a nearby but distinct link. We propose Cross-Link Channel Prediction (CLCP), a technique that leverages multi-view representation learning to predict the channel response of a large number of users, thereby reducing channel estimation overhead further than previously possible. CLCP's design is highly practical, exploiting existing transmissions rather than dedicated channel sounding or extra pilot signals. We have implemented CLCP for two different Wi-Fi versions, namely 802.11n and 802.11ax, the latter being the leading candidate for future IoT networks. We evaluate CLCP in two large-scale indoor scenarios involving both line-of-sight and non-line-of-sight transmissions with up to 144 different 802.11ax users and four different channel bandwidths, from 20 MHz up to 160 MHz. Our results show that CLCP provides a 2× throughput gain over baseline and a 30% throughput gain over existing prediction algorithms.
An experimental streamflow reconstruction for the River Murray, Australia, 1783–1988

We present an experimental reconstruction of River Murray streamflow to assess present‐day variations in the context of the past two centuries. Nine annually resolved paleoclimate proxy records from the Australasian region are used to develop a reconstruction of streamflow from 1783 to 1988. An ensemble of reconstructions is presented, providing probabilistic estimates of River Murray flows for each year back in time. The best‐estimate reconstruction captures approximately 23% (50%) of annual (decadal) naturalized streamflow variability. High and low streamflow phases and their association with decadal climate variability in the Pacific are discussed. Reconstructed River Murray streamflow shows considerable variation since 1783. We estimate that there is a 2.3% chance that the 1998–2008 record low decadal streamflow deficit has been exceeded since European settlement. Stochastic simulations of the decadal variations in River Murray streamflow are computed using the paleostreamflow reconstruction to estimate model parameters. From these simulations, we estimate that the 1998–2008 streamflow deficit has an approximate 1 in 1500 year return period. As climate models are assessed relative to short instrumental records, future projections of decadal‐scale variations in Murray‐Darling Basin (MDB) streamflow may be inadequately represented. Given the immense socioeconomic importance of Australia's “food bowl,” future paleoclimate and modeling efforts should be directed at understanding variability at this scale. This would greatly enhance our capacity to estimate regional sensitivity of the MDB's hydroclimate to further anthropogenic influences.
Deception in Drone Surveillance Missions: Strategic vs. Learning Approaches

Unmanned Aerial Vehicles (UAVs) have been used for surveillance operations, search and rescue missions, and delivery services. Given their importance and versatility, they naturally become targets for cyberattacks. Denial-of-Service (DoS) attacks are commonly considered to exhaust their resources or crash UAVs (or drones). This work proposes a unique proactive defense using honey drones (HD) for UAVs during surveillance operations. These HDs use lightweight virtual machines to lure and redirect potential DoS attacks. Both the choice of target by the attacker and the HD's deceptive tactics are influenced by the strength of the radio signal. However, a critical trade-off exists in that stronger signals can deplete battery life, while weaker signals can negatively affect the connectivity of a drone fleet network. To address this, we formulate an optimization problem to select the best strategies for an attacker or defender in selecting their signal strength level. We propose a novel HD-based defense to identify the optimal setting using deep reinforcement learning (DRL) or game theory and compare their performance with that of non-HD-based methods, such as Intrusion Detection Systems and ContainerDrone. Our experiments demonstrate the unique benefits and superior efficacy of each HD-based defense across various attack scenarios.
Comparative survey analysis of the CNN and LBPH Face Recognition Learning Algorithms: A Survey of Learning Approaches and Performance Evaluation

Face recognition technology has made it possible for computers to recognize human faces. It is an application that uses algorithms to extract distinctive traits from facial images and compares them with a database of recognized faces. Facial recognition has numerous real-world uses in social networking, healthcare, access control, security and surveillance, and more. Due to the acquisition and processing of sensitive personal data, it has also sparked questions about ethics and privacy. The Deep Learning Convolutional Neural Network and the Local Binary Pattern Histogram (LBPH) are two face recognition algorithms that are compared in this paper. The study aims to evaluate the performance of these algorithms under different conditions, including variations in pose, expression and illumination. The relevant theories of LBPH and CNN are first briefly discussed, and the performance comparison method is then described. The choice between these two algorithms will depend on the particular needs of the application and the resources that are available. Overall, this work offers insightful information regarding the advantages and disadvantages of LBPH and CNN for face recognition, assisting in the selection of an algorithm for practical applications.
Does reducing river nutrient discharge reduce coastal eutrophication in Belgian waters

1. Management Unit of the North Sea Mathematical Models (MUMM), Royal Belgian Institute for Natural Sciences (RBINS), Gulledelle 100, 1200 Brussels, Belgium. * Corresponding author: Genevieve Lacroix: Tel: +32 (0)2 773.21. 00, Fax: +32 (0)2 773.69. 72, E-mail: G.Lacroix@mumm.ac. be 2. Universite Libre de Bruxelles (ULB), Ecologie des systemes aquatiques (ESA), Campus de la Plaine CP 221, Boulevard du Triomphe, 1050 Bruxelles, Belgium.