Miracle-dz/UniFAR-FTUs huggingface dataset:
Collaborative Dynamic Watchdog Mechanism for Detection and Isolation of Malicious Nodes through Clustering in Wireless Sensor Networks

One of the most important characteristics of Wireless sensor networks (WSN) is the cooperation among the nodes that are present over the network. Since WSN consists of both static and dynamic nodes, the cooperation has to be managed in an effective manner. But this cooperation activity consumes more cost in terms of resources. The cooperation among nodes is a cost consuming activity as some of the nodes in the working environment can be malicious and selfish which will affect network's overall performance. The malicious nodes are nodes which affect the other nodes in the network whereas selfish nodes are nodes that will not work in a cooperative manner in the network. There is a well-known mechanism called watchdog which is used for identifying these malicious and selfish nodes. In the existing system, the watchdogs have been implemented in a local manner. The local watchdog will take charge of its own node. Implementation of local watchdogs leads to lack of precision when detecting selfish nodes. To overcome this, a Collaborative watchdog mechanism through Clustering (CCOCOWOA) has been proposed for identifying selfish nodes and for recovering the network. This mechanism will work based on the information that will be gathered when a contact occurs with selfish nodes and it will be quickly propagated. It is also efficient by energy and communication cost compared to COCOWA based approach for detecting selfish nodes.
An Information Entropy and Ensemble Learning Approach for DR-DOS Detection within Aviation Networks

Fifth Generation (5G) architecture brings a new wave of innovation into the development of the future aviation environment. However, the deployment of Internet of Things (IoT) infrastructure weakens the current closed loop systems deployed within the airport operational domain; caused by the expansion of attack surfaces due to an increased number of endpoints. We propose a solution to detect multiple Distributed Reflective Denial-of-Service (DRDoS) attack types utilizing stacked ensemble structure of Bayesian Statistics, J48 (C4.5) Decision Trees and Multi-Layer Perception algorithms, to identify DRDoS attacks. The proposed solution gives a 94.1% accuracy on the CIC-2019 dataset.
Utilizing Synthetic Data for VV&C of Machine Learning Applications

Machine Learning (ML) offers a revolutionary method to build algorithms from data. This data-driven methodology has proven to be very effective in progressing fields ranging from natural language processing to autonomy. Simultaneously, the technique has also opened significant challenges in system validation with important issues when used in safety-critical systems. If data forms function, how does one know the generated function is safe? Further, fundamental to the ML approach is the availability of vast amounts of data for training a ML model. For a variety of important applications, the availability of data is challenging.In this paper, we propose a unique computational paradigm which combines the inherent advantages of ML with algorithmic transformational methods. Using this technique, one can generate a large amount of synthetic data which preserve the "signature" of the original dataset while building variations that aid the ML training process. Further, the nature of the algorithmic transformations offers insights into the validation and verification (V&V) scenario generation process. An example is provided of the methodology exercised in a maintenance application for the infrastructure delivering the safety critical Automatic Dependent Surveillance-Broadcast (ADS-B) services in the United States (US).
Speed Controlled Legal Driving System

These days the number of accidents occurs frequently but how accidents happen. The main reasons for an accident is Rash-Driving and Violation of traffic rules. And also sometimes due to malfunctioning of our vehicle. Due to this more deaths and injuries are occurring. In recent days IOT, sensors, Artificial Intelligence, and Machine Learning are changing this world in many aspects. It provides solutions to many problems in this world in an easy and efficient manner. In order to control and prevent the accident we have proposed a Smart System using Raspberry Pi, Ultrasonic Sensor, Light Color Detection Sensor and Speed Governor system which has to be incorporated in vehicles. The main aim of our project is to develop an Automatic Speed Control System based on the type of road (i.e. whether the road is national highway or state highway or streets etc.‥) in order to prevent accidents and reduce the number of people who die as a result of them. In the present vehicle conditions, various vehicles are out and about, run by the driver who doesn't hold any legitimate permit(driving license), despite the fact that it is difficult to get the drivers license. Accordingly a framework can be created which will act to control these issues, prior to starting the vehicle, by making certain safety efforts. The vehicle should start provided that the genuine driving permit holder is driving that specific vehicle else the vehicle won't start, by this way we can getentrance over the non driving permit holders to drive the vehicle and stay away from frightful traffic circumstances. This can be done by using a Face Recognition Module installed inside the vehicle. This also indirectly acts as a security system and reduces theft of vehicles.
Traction Resistance Estimation Based on Multi-Method Fusion for Distributed Drive Agricultural Vehicles

This study proposes a multi-method fusion algorithm to solve the problem of coupled traction resistance with the vehicle mass for distributed drive agricultural vehicles (DDAVs), because this coupling makes estimation measurements by agricultural vehicles difficult. Indeed, the proposed method decouples the vehicle mass and traction resistance. The vehicle mass was obtained using the recursive least square method and filtering the low-frequency parts of signals of driving force and longitudinal acceleration. After obtaining estimated vehicle mass, the dynamics method was coordinated and complemented with the kinematics method to observe the traction resistance. In the low-frequency load test, statistical performance criteria (SPCs) of the mass estimation were <inline-formula> <tex-math notation="LaTeX">${R} = 0.9985$ </tex-math></inline-formula>, root mean squared error (<italic>RMSE)</italic> = 0.0551 kg, and average of prediction accuracy (<italic>PA)</italic> = 98.02%. In addition, SPCs of the traction resistance estimation were <inline-formula> <tex-math notation="LaTeX">${R}=0.9655$ </tex-math></inline-formula>, <italic>RMSE</italic> = 23.0472 N, and average of <italic>PA</italic> = 99.28%. In the high-frequency load test, the maximum <italic>PA</italic> of the mass estimation reached 98.78%, and SPCs of the traction resistance estimation were <inline-formula> <tex-math notation="LaTeX">${R} = 0.9371$ </tex-math></inline-formula>, <italic>RMSE</italic> = 1266.3933 N, and average of <italic>PA</italic> = 85.62%. Experimental tests proved that the proposed method is robust and can accurately estimate the vehicle mass and traction resistance in real time.
Design and Analysis of a Multi Clocked Pipelined Processor Based on RISC-V

The main goal of this study is to develop a 32-bit pipelined processor with several clock domains based on the RISC-V (open source RV32I Version 2.0) ISA. To minimise the complexity of the instruction set and speed up the execution time per instruction, RISC (Reduced Instruction Set Computer) is a type of processor that uses less hardware than CISC (Complex Instruction Set Computer) is used. Furthermore, we constructed this processor with five levels of pipelining, resulting in parallelism in instruction execution. With the aid of necessary block diagrams, all of the processes are well described. Multiple clock domains employing two clock sources are used to ensure that variable delays such as clock skew and metastability are avoided within the stage pipeline registers. Quartus Prime was used to design and synthesis this processor, which was written in Verilog HDL. ModelSim was used to verify this design, and all of the instructions have been thoroughly checked. Further the processor is implemented on the “ALTERA Cyclone 10 LP” board for calculating the device utilization.
Robust Satellite Image Classification with Bayesian Deep Learning

Image-based object detection and classification are essential for satellite-based monitoring, which spans multiple safety-critical engineering applications. Meanwhile, state-of-the-art deep learning significant improves the accuracy for image classification tasks thus has been deployed in various scenarios. However, it’s well-known deep learning-based image classifiers are vulnerable to small perturbations along specific directions, known as adversarial attacks. These attacks are exceptionally effective to fool image classifiers. In extreme cases, merely one pixel’s change can lead to a attacker-desired wrong prediction label. In this work, we show that deep learning with Bayesian formulation can extend the deep learning adversarial robustness by a large margin, without the need of adversarial training. Moreover, we show that the stochastic classifier after the deterministic CNN extractor has sufficient robustness enhancement rather than a stochastic feature extractor before the stochastic classifier. This advises on utilizing stochastic layers in building decision-making pipelines within a safety-critical domain. Additionally, we show that the Bayesian posterior can act as the safety precursor of ongoing malicious activities towards a deployed image classification system. This leads to the detection of adversarial samples in cybersecurity. With lots of potentials, we leave them as the future studies.
AI-powered Digital Classroom

India is one of the countries with the highest number of young students enrolling for higher studies. Education plays a vital role in the life of students about career advancement. In the COVID pandemic situation, many youngsters feel restless and dissatisfied due to syllabus incompletion. So many academies are offering online courses but because of improper facilities or sources and financial constraints. We aim at introducing Artificial Intelligence (AI)-powered E-learning methods to students who can dynamically interact with the faculty and peers and clarify their queries while studying. In addition, we aim at personalizing education which helps in assessing students' skills/talents and evaluating their performance. EDUBOT is a student friendly AI-powered chatbot and the Learning Management System (LMS) that provides students with the resources they need on any topic the need and solves their problems. The features provided by this product are dynamic front-end, Interactive Query Search, Personalized Student centric Assessment and Super User.
Automatic generation of Ethereum-based Smart Contracts for Agri-Food Traceability System

There is a growing demand for transparency along the agri-food chain, both from customers and governments. The adoption of blockchain technology to enable secure traceability for the management of the agri-food chain, provide information such as the provenance of a food product and prevent food fraud, is emerging rapidly, due to the inherent trust and inalterability provided by this technology. However, developing the right smart contracts for these use cases is even more of a challenge than it is for those used in other fields. Several management systems for the agri-food chain based on blockchain technology and smart contract have been proposed, all however ad-hoc for a specific product or production process and difficult to generalize. In this paper, we propose a new approach to easily customize and compose general Ethereum-based smart contracts designed for the agri-food industrial domain, to be able to reuse the code and modules and automate the process to shorten development times, while keeping it safe and reliable. Starting from the definition of the real production process, we aim to automatically generate both the smart contracts to manage the system and the user interfaces to interact with them, thus producing a system that works semi-automatically. Additionally, we describe a honey production case study to show how our approach works. Future work will first extend the scope of the approach to other supply chains, furthermore, while the current platform used is Ethereum, in the future our approach will be easily extended to other blockchain platforms.
The synergistic effects of plants and nitrogen on microbial hitchhiking

Microbial hitchhiking demonstrates that some nonmotile microbes utilize trans-species motility to traverse their environment; however, whether driving forces, such as plants and nitrogen, affect microbial hitchhiking is not clear. In our study, we explored the effects of plants and nitrogen fertilizer on Bacillus-hitchhiking by setting filter membranes and different nitrogen fertilizer concentration gradients. In the experimental treatment, we added a filter membrane to the soil to prevent hitchhiking. In the absence of plants, nitrogen alone had little influence on motile bacteria and hitchhiking. However, Bacillus contents were significantly impacted by the nitrogen concentration when the plants were rooted, leading to a great variation in cell motility function according to the functional analysis in the soil microbial community. After applying the filter membrane, there were no significant differences in Bacillus contents, microbial community structure or cell motility functional abundance, which illustrated that hitchhiking impacted the microbial community. Our analysis of co-occurrence between bulk soil motile bacteria (Bacillus) and rhizosphere bacteria also confirmed this. The correlation between bulk soil motile bacteria and the rhizosphere microbial community was strong in the groups with suitable nitrogen concentrations without filter membranes and was weak at all nitrogen levels in the no-membrane treatments. Thus, we concluded that plants and different nitrogen doses synergistically altered the soil microbiome by hitchhiking, whose effect depends on nitrogen.
Piezo1 activates noncanonical EGFR endocytosis and signaling

Summary EGFR-ERK signaling controls cell cycle progression during development, homeostasis, and disease. Both the soluble extracellular ligand, EGF, and mechanical inputs like matrix stiffness, cell adhesion, or stretch activate EGFR-ERK signaling. However, the molecules transducing mechanical forces into EGFR signaling remain unidentified. We previously found that stretch promotes mitosis by mechanically activating the ion channel Piezo1 to trigger ERK signaling. Here, we show that Piezo1 provides the missing link to mechanical EGFR-ERK activation. Both EGF ligand and mechanical or agonist activation of Piezo1 trigger clathrin-mediated endocytosis of EGFR and ERK activation, established markers of EGFR signaling. However, while EGF stimulation requires canonical EGFR tyrosine autophosphorylation, EGFR activation by Piezo1 instead requires Src-p38 kinase-dependent serine EGFR phosphorylation. Additionally, in contrast to the homeostatic signaling downstream EGF activation, direct agonist stimulation of Piezo1 promoted cell cycle re-entry and proliferation in mouse airway epithelia ex vivo via long-term nuclear accumulation of ERK, AP-1 (FOS and JUN), and YAP, typical of regenerative and malignant signaling. Our results suggest two modes of EGFR signaling: basal EGF-dependent signaling via tyrosine autophosphorylation and mechanically activated Piezo1-dependent signaling via serine phosphorylation, resulting in sustained proliferation, critical to repair, regeneration, and cancer growth. Given the limited success of tyrosine kinase inhibitors in cancer, this new axis may provide a more relevant target for cancer treatment.
E-SNPs&GO: embedding of protein sequence and function improves the annotation of human pathogenic variants

Motivation The advent of massive DNA sequencing technologies is producing a huge number of human single-nucleotide polymorphisms occurring in protein-coding regions and possibly changing protein sequences. Discriminating harmful protein variations from neutral ones is one of the crucial challenges in precision medicine. Computational tools based on artificial intelligence provide models for protein sequence encoding, bypassing database searches for evolutionary information. We leverage the new encoding schemes for an efficient annotation of protein variants. Results E-SNPs&GO is a novel method that, given an input protein sequence and a single residue variation, can predict whether the variation is related to diseases or not. The proposed method, for the first time, adopts an input encoding completely based on protein language models and embedding techniques, specifically devised to encode protein sequences and GO functional annotations. We trained our model on a newly generated dataset of 65,888 human protein single residue variants derived from public resources. When tested on a blind set comprising 6,541 variants, our method outperforms recent approaches released in literature for the same task, reaching a MCC score of 0.71. We propose E-SNPs&GO as a suitable, efficient and accurate large-scale annotator of protein variant datasets. Contact pierluigi.martelli@unibo.it
Clones on the run - the genomics of a recently expanded facultative asexual species

Why, in facultative asexual species, marginal populations are often richer in clones than are core populations, remains unclear. Cloning freezes genotypes but hampers recombination and local adaptation. During expansion, clones are favoured over non-selfing sexuals by uniparental reproduction. To better understand the dynamics of clones and sexual lineage, we used genome-wide sequencing to analyse a recently expanded seaweed. We found large clones and sexual populations mixed close to range margins. Clones had evolved repeatedly from sexual populations but were unexpectedly low in genetic variation. Modelling suggested clones form from sexual populations after repeated bottlenecks at the expansion front. A clonal wave of depauperate genotypes thereafter spread ahead of the sexual population. As we observed, these early formed clones may survive side-by-side sexual individuals, which suggests they lost their sexual capacity. Our study illustrates how range expansion can result in complex and dynamic patterns of genetic variation in facultative asexual species. Teaser We use genome data and modelling to find out why large clones are only found at range margins in a recently expanded seaweed
A spatial map of antennal-expressed olfactory ionotropic receptors in the malaria mosquito

The malaria mosquito Anopheles coluzzii uses odors to guide various behaviors such as host-seeking. The detection of behaviorally relevant odors is mediated by a diverse family of receptors including the olfactory Ionotropic Receptors (IRs). Olfactory receptors are expressed on olfactory neurons, with the mosquito antennae representing the main olfactory appendage for detecting volatile chemical cues from the environment. It is currently unknown how many neurons across the antenna express a certain IR, or how these IR-expressing neurons are spatially arranged. Here, we performed whole mount fluorescent in situ hybridization of all IRs expressed in the antennae. The organization of IR-positive cells within an antennal segment (flagellomere) appeared stereotyped across multiple antennae. The spatial map of IR-expressing neurons revealed that the antenna might be organized into proximal and distal functional domains. Highly expressed tuning (odor-binding) IRs exhibit distinct co-localization patterns with cognate IR co-receptor(s) in a combinatorial fashion that might predict their functional properties. These findings reveal organizing principles of Anopheles IR-expressing neurons in the mosquito which might underlie their functional contribution to the detection of behaviorally relevant odors.
Retinoic acid signaling in mouse retina endothelial cells is required for early angiogenic growth

The development of the retinal vasculature is essential to maintain health of the tissue, but the developmental mechanisms are not completely understood. The aim of this study was to investigate the cell-autonomous role of retinoic acid signaling in endothelial cells during retina vascular development. Using a temporal and cell-specific mouse model to disrupt retinoic acid signaling in endothelial cells in the postnatal retina (Pdgfbicre/+ dnRAR403fl/fl mutants), we discovered that angiogenesis in the retina is significantly decreased with a reduction in retina vascularization, endothelial tip cell number and filipodia, and endothelial ‘crowding’ of stalk cells. Interestingly, by P15, the vasculature can overcome the early angiogenic defect and fully vascularized the retina. At P60, the vasculature is intact with no evidence of retina cell death or altered blood retinal barrier integrity. Further, we identified that the angiogenic defect seen in mutants at P6 correlates with decreased Vegfr3 expression in endothelial cells. Collectively, our work identified a previously unappreciated function for endothelial retinoic acid signaling in early retinal angiogenesis.
Rapid learning with highly localized synaptic plasticity

The brains of all animals are plastic, allowing us to form new memories, adapt to new environments, and to learn new tasks. What is less clear is how much plasticity is required to perform these cognitive functions: does learning require widespread plasticity across the brain, or can learning occur with more rigid networks, in which plasticity is highly localized? Here, we use biologically-inspired recurrent neural network (RNN) models to show that rapid multitask learning can be accomplished with sparse and localized synaptic plasticity. Crucially, only RNNs with highly specific combinations of network properties, such as topology, normalization and reciprocal connection strength, are capable of such learning. Finally, we show that this rapid learning with localized plasticity can be accomplished with purely local error signals, without backpropagation, using a reinforcement learning setup. This work suggests that rapid learning in artificial (and potentially biological) agents can be accomplished with mostly-rigid networks, in which synaptic plasticity is highly constrained.
Title: Attention gain and reorienting in fronto-parietal networks shape human conscious report

: Competing theories disagree on the involvement of fronto-parietal brain networks in consciousness. We recorded neural activity from 727 intracerebral contacts in 13 epileptic patients, while they detected near-threshold targets preceded by attentional cues. Unsupervised clustering revealed three patterns: (1) Sustained right-hemisphere fronto-temporal activity, in networks connected by the superior longitudinal fasciculus (SLF) II-III,