Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Evaluation of a Flexible Sensorized Robotic OCT Neuroendoscope

Endoscopic optical coherence tomography (OCT) has demonstrated its capability to visualize the fine microstructures and subtle lesions inside human organs in vivo. However, the limited imaging depth and the lack of distal dexterity of the current rigid OCT endoscope prohibits its proactive assessment and clinical utilities in the confined, sensitive, and yet relatively large (a few centimeters) surgical space, such as the lesion in deep brain. In this work, we developed a flexible sensorized robotic OCT neuroendoscope, which combines a 2-degree-of-freedom (DOF) cable-driven continuum manipulator (CM) with an ultrahigh-resolution 800-nm OCT probe and a multi-core fiber Bragg grating (MCFBG) fiber sensor. The MCFBG measurements of the bending posture of the endoscope was validated through experiments. By leveraging the OCT A-line signals, axial distance was precisely measured between the OCT probe tip and the surrounding tissue boundary. The feasibility of OCT neuroendoscope was further demonstrated to navigate and steer inside a porcine brain-simulated lesion phantom in an ex-vivo experiment. Our OCT neuroendoscope offers distal maneuverability and ultrahigh-resolution imaging capability at an axial resolution of about 2.4 μm (in air), suggesting its clinical potential for minimally-invasive imaging-guided diagnosis and treatment in deep brain in vivo.
A Pre-Diagnosis Model for Indoor Radon Potential Assessment

Indoor radon is a well-known public health threat. WHO estimates radon causes between 3-14% of all lung cancers and has issued many recommendations to encourage countries to act against the exposure to radon and associated cancer risks. However, many people continue to be unaware about radon health risk. In previous works we developed a prediagnosis approach to evaluate the radon potential in indoor environments and motivate the citizens to act when the estimated radon potential is higher. In this work, we have developed an automatic classification model for the evaluation of the radon potential level in indoor environments by considering a set of relevant features selected to characterize occupants' risk exposure. Radon data measurements from previous works have been merged and provide enough data for the start of this project. The evaluation metrics of the obtained classification model are compelling and represent a good starting point for the development of a platform that will help to raise awareness of the radon risk and promote radon measurements to the wider population. These measurements will be used as new data for the generation of more robust classification models.
Complex Network Analysis using Artificial Intelligence Algorithms

Network science is emerging as a vibrant research field with important applications in finance, biology, chemistry, physics, engineering and health. This short paper presents an overview of some challenging tasks related to the analysis of complex networks, including community detection, discovery of cycles and identification of important nodes. The solutions proposed for these important network analysis tasks engage Artificial Intelligence models and are briefly presented with an emphasis on their performance as well as the main related research questions. The analysis of financial networks is also discussed, showing the potential of using network science tools to discover financial cycles and paths.
An abdominal phantom with instrument tracking for laparoscopic training

We developed an abdominal phantom with an embedded stereo camera for tracking multiple hand-held instruments inserted through entry ports. This system can be used for training laparoscopic surgeons, as well as for training bedside assistants in robotic surgery. We present the computer vision methods used to track multiple instruments in real time, with a system evaluation that shows frame rates of 26.6 fps for a 672×376 image and 11.6 fps for a 1280×780 image and corresponding latencies of 38 ms and 87 ms, respectively, when tested on a portable PC platform with an 11th Gen Intel CPU running at 2.8 GHz. The mean Euclidean distance error of the instrument tracking is 2.0 mm in the 672p case and 2.8 mm in the 1280p case. Additionally, the tracking information drives virtual instruments in a simulated environment, which generates improved visualizations of the surgical scene, such as a top-down view and/or a “laser” virtual extension of the instrument. We perform a user study with 10 novice subjects to compare these improved visualizations to the baseline case (only endoscope view) and the results indicate that the combined top-down view and laser extension enhancements provide a statistically significant performance improvement. In the future, the simulator could also improve the (visual) realism of the training platform and could be part of a larger system that enables simultaneous training (and skill assessment) of multiple members of a surgical team, such as the surgeon and first assistant in da Vinci robotic surgery.
Survey on IoT Multi-Factor Authentication Protocols: A Systematic Literature Review

The Internet of Things (IoT) is a network of physical objects that is equipped with computers, electronics, applications, sensors, and other devices. As the number of IoT devices and apps increases, a range of security techniques should be applied to strengthen and enhance their immunity to cyber attacks as attackers have more capabilities and tools now focused on targeting the IoT networks. In this context, we study the nature of the Internet of Things infrastructure, especially some different Iot architecture layers, their security challenges and brief proposed solutions. For different IoT layers, authentication is a main security aspect affecting each layer separately. We provide a systematic literature review (SLR) methodology to locate existing multi-factor authentication protocols in the literature and evaluate their effectiveness. Then we provide an analysis of available multi-factor authentication protocols that are intended for IoT environments. Then some recommendations were provided based on the analysis. In conclusion, since the large range of IoT devices are resource- and power-limited, the nature of IoT settings requires lightweight protocols. Any of those proposed protocols can call for ongoing follow-up in the future to figure out upcoming weaknesses.
cHand: Open Source Hand Posture Visualization in CHAI3D

Visualization of hand movement is an important part of many medical training, VR and haptic experiences, which researchers typically address by developing application specific hand visualization tools. While some existing simulators allow for hand kinematic visualization using a generic hand model, they are usual targeted for robotic grasp planning rather than designed specifically for rendering in applications that include haptic experiences. To fill this gap, in this paper we present cHand, an extension of the haptics software library CHAI3D, that enables hand kinematic visualization of an arbitrary hand model. A representation of the hand can be achieved with elementary geometric shapes that are provided by CHAI3D, or with custom geometries loaded from STL files. We release cHand as an open source contribution to keep with the open source nature of CHAI3D, and present a tutorial on its use in this manuscript.
Flake Aware Culprit Finding

When a change introduces a bug into a large software repository, there is often a delay between when the change is committed and when bug is detected. This is true even when the bug causes an existing test to fail! These delays are caused by resource constraints which prevent the organization from running all of the tests on every change. Due to the delay, a Continuous Integration system needs to locate buggy commits. Locating them is complicated by flaky tests that pass and fail non-deterministically. The flaky tests introduce noise into the CI system requiring costly reruns to determine if a failure was caused by a bad code change or caused by non-deterministic test behavior. This paper presents an algorithm, Flake Aware Culprit Finding, that locates buggy commits more accurately than a traditional bisection search. The algorithm is based on Bayesian inference and noisy binary search, utilizing prior information about which changes are most likely to contain the bug. A large scale empirical study was conducted at Google on 13,000+ test breakages. The study evaluates the accuracy and cost of the new algorithm versus a traditional deflaked bisection search.
Fake News Detection on COVID-19 Tweeter Data Using Machine Learning Models

People can access the news easily from various channels like social media (twitter, Facebook etc.,), social bots due to which the Fake news is propagated at a faster rate. To avoid this, Machine Learning models can be used to categorize the news into different classes like Fake and Real so that people will be aware of the real news. An ensemble model is proposed to classify the Tweets which are collected from the Kaggle consisting of twitter posts, data pre-processing, extracting features using NLTK toolkit and training and testing the data using various Machine Learning (ML) models such as Support Vector Machine, Naive Bayes, Logistic Regression and Random Forest. An accuracy of 70% is achieved on LIAR dataset. Datasets based on COVID-19 Tweets, achieved an accuracy of 90% by Ensemble approach.
Analysis of Hybrid Deep Learning Models for Efficient Intrusion Detection

Enhanced with digital capabilities over the preceding ten years, especially on the internet, there has been a surge in cyber attackers attempting to profit from consumers’ private information. One of the most recent approaches to solving this problem is to devise a model to help us detect acts of intrusion in a network known as Intrusion Detection Systems (IDS). There has been a surge in the development of advanced artificial algorithms in recent years, and adding their intelligence makes it much easier to prevent network intrusions. In general, Deep learning methods outperform machine learning techniques in terms of performance metrics for detecting network intrusions. This paper suggests the implementation of five distinct variants of deep learning models that have been extensively trained with the UNSW NB-15 benchmark dataset by optimizing different hyperparameters to provide improved intrusion detection. Amongst variants, the Bi-Directional Long-Short Term Memory model with ADAM optimizer is efficient in intrusion detection with the highest accuracy of 95.28%.
Lightweight Countermeasures Against Original Linear Code Extraction Attacks on a RISC-V Core

Linear Code Extraction (LCE) is an invasive attack aiming at fully extracting a code from a device’s memory for reverse engineering purposes. The core instruction bus is identified and microprobed using Failure Analysis tools. Meanwhile, other microprobes force internal nodes of the core to logic states which allow a full memory linear extraction. This paper demonstrates the first assessment of a RISC-V core vulnerability to LCE. It evaluates the complexity to extract the code in the right order by freezing the instruction register or by editing the incoming instructions. This paper introduces three original countermeasures to detect an ongoing LCE by monitoring symptoms such as the lack of branch instruction execution. These hardware countermeasures are lightweight and adaptable to other core architectures. We develop an experimental setup based on a functional simulation framework and an FPGA-based demonstration. This setup made it possible to study and assess the LCE vulnerabilities of our RISC-V target and to validate the effectiveness of our proposed countermeasures. The area overhead was measured between 0.52% and 1.47% of the cv32e40p RISC-V core. Depending on the detection latency target, the clock cycle overhead using the EmbenchTM benchmarks can be null or kept below 1%.
Product Authentication System using Blockchain*

Every well-known business has scammers who sell counterfeit goods at reduced prices. Due to a lack of transparency, supply chain management has frequently encountered issues such as service redundancy, poor departmental collaboration, and a compromise of standards. Counterfeiters in the market generate major challenges for legitimate businesses. Still, a significant number of individuals are unaware of the greatest extent of the harm that these products have on brands. As a result, it is essential to have a system that allows the end user to verify all details about the products purchased for the customer to determine the product’s authenticity. Combining these features with blockchain-based technology can create a coherent, effective counterfeit-reduction strategy.
Enhancing Boofuzz Process Monitoring for Closed-Source SCADA System Fuzzing

Past cyber-attacks have demonstrated that Industrial Control and SCADA Systems are high-value targets for modern threat actors. In order to defend these classes of systems, it is necessary to detect and eliminate any pre-existing vulnerabilities before they can be leveraged into zero-day exploits. Different methods exist to find exploitable vulnerabilities in the software that runs these systems, one of which is known as fuzzing – wherein a system under test is exposed to a variety of input streams while simultaneously observed for unexpected behaviours, exceptions, or crashes. The aim of this research is to extend the Boofuzz network protocol-based fuzzing framework in order to effectively monitor a closed-source SCADA HMI endpoint during fuzz testing. Effective monitoring in this context is defined as the automated detection of target crashes during fuzzing which are recorded with an exception description, reproducing steps, and call stack trace. This data minimizes the time required for vulnerabilities discovered during fuzzing to be reproduced, investigated, and rectified by the software vendor. In order to accomplish this aim, our SCADA HMI is first analyzed to identify the fuzzing target and its runtime behaviours. A protocol fuzzer is then custom built for it using Boofuzz, with the existing target process monitor class extended to introduce new log file and debugger-based monitors. These extensions are then tested through fuzz tests of the SCADA HMI, the results from which demonstrate that vulnerabilities can be both automatically detected and recorded with the sufficient level of detail to expedite rectification.
Detection And Classification of Lung Cancer CT Images Using Mask R-CNN Based Generated Mask Method

In the medical field, computer vision systems, and especially image diagnostics, provide a wide range of useful tools. Computed tomography (CT) scans are the gold standard for identifying a wide variety of medical disorders, from bone fractures to lung cancer to heart disease (CT). One of the top four mortality rate disease in the world is lung cancer. The lung areas are divided up for clinical diagnostics after being defined. This study suggests combining supervised and unsupervised machine learning techniques with the CNN and model specialization using R-CNN for lung area mapping to separate the lungs in CT images automatically. Accuracy of 99.21%, sensitivity of 99.33%, and specificity of 98.97% were achieved by our approach to lung tissue classification using a combination of a Mask Region - Convolutional Neural Network (MR-CNN) and a support vector machine. We checked our results against the work of other scientists to be sure we weren't missing anything. Our solution outperformed other state-of-the-art methods in terms of accuracy and speed.
Comparative Assessment of ANN-based MPPT Algorithm for Solar PV System

The paper comprises a solar photovoltaic (PV) system, well furnished with Maximum Power Point Tracking (MPPT) controller to obtain maximum power. There are several MPPT techniques available for assisting the controller, to achieve maximum power. The Artificial Neural Network (ANN) based MPPT algorithm is simple to develop and can provide a faster response with less oscillation. To validate this result a detailed analysis and a comparative assessment have been done between ANN and other conventional Maximum PowerPoint (MPP) algorithms such as Perturb & Observe (P&O) and Incremental Conductance (INC). Moreover, the MPPT algorithms are evolved through MATLAB/SIMULINK software. The obtained results show ANN based MPPT algorithm has a faster execution time, and improved output responses than the remaining examined algorithms, which supports its implementation for dynamic irradiance conditions.
Deep Learning Based Intrusion Detection Systems Techniques in IoT - Survey

Industry 4.0 is changing the way we communicate and operate as a society, its bringing changes in technologies, industries and a part of this industry Internet of Thing (IoT), they are devices which communicate with each other and are being integrated slowly in all sectors. this creates number of concerns especially towards security and privacy. Cyber intrusion attacks form a major part of the concern as it compromises integrity of sensitive data and are growing in volume with variations increasing rapidly. High complexity of such intrusion attacks has defeated most of the traditional defense techniques This paper focuses on exploring research that was conducted in area of IoT security, specifically in improvement of Intrusion detection system using Deep learning techniques. The results and methods are also discussed which can form a potential base for further research.
Heartbeat Classification Based on Deep Convolutional Neural Network

cardiovascular disease is a primary cause of mortality globally. The electrocardiogram (ECG) is a valuable technique for diagnosing cardiac illness because it offers information about the electrical activity of the heart. An abnormal ECG reading can suggest the presence of a number of different cardiovascular conditions, including arrhythmias, which are abnormalities in the heart’s rhythm. In this study, we are proposing a novel light weight Deep Convolutional Neural Network (CNN) based architecture for prediction of arrhythmias. This method is able to address the limitation of inter-observer variability by avoiding the classical approach of feature engineering, thus obtaining a higher accuracy. A crucial issue in arrhythmia recognition is the lack of availability of a large dataset. One of the key challenges in achieving good performance in detecting certain types of heartbeats is due to a severe class imbalanced dataset. The MIT-BIH dataset is freely available to the public, which was used to evaluate and validate the suggested network’s effectiveness in detecting various forms of arrhythmias in ECG data. Based on the Association for Advancement of Medical Instrumentation (AAMI) standard, the model was trained to recognise five arrhythmia classifications (N, S, V, Q, and F). The outcomes of the evaluation indicate the model robustness and predictive probability of the network, particularly for minority classes over the metrics i.e. Sensitivity, Specificity and Accuracy. In summary, our proposed method outperforms the state-of-the-art procedures referenced in the literature.
Analysis of Microgrid with Renewable Energy Sources and Energy Storage in Integrated Environment

The depletion of the fossil fuels, continuous rise in electrical demand and environmental concerns has led the way for power generation through renewable energy sources. Microgrid is the one of the possible solutions to generate power with the renewable energy sources especially where the expansion of distribution network is not feasible. To reduce the intermittent behaviour of renewable energy sources, the energy storage is required in microgrid structure. Vehicle to grid technology can cope up with the demand and supply balance by providing extra storage in microgrid which is basically a step towards the sustainable development. During the peak energy demand the vehicle to grid technology provides the services required for grid balance such as to cope with the demand and supply gap, frequency variations, load shifting. The V2G (vehicle to grid) allows the power to be pushed back to the grid from the battery of the vehicle to make balance between the consumption and generation as in the vehicle to grid mode, electrical vehicle acts as controllable load. Moreover, the EV battery plays a significant role as energy storages device and this energy storage and capacity of electric vehicle has strong potential which provide additional support and storage in microgrid. This paper present a model and simulation of vehicle to grid with in microgrid including the distributed energy sources such as solar and wind along with the electrical vehicle aggregator in integrated environment.
Two Sparsification Strategies for Accelerating Demand-Driven Pointer Analysis

To resolve aliasing, precise program analyses rely on pointer analyses. Demand-driven pointer analysis seeks to be efficient by computing information only for variables on which a demand is raised, through a points-to or alias query. Yet, research has shown that when applied to large-scale programs even demand-driven analyses can become expensive in terms of memory and runtime. This paper thus investigates to what extent demand-driven pointer analysis can be accelerated further if being executed over a sparse control-flow graph (CFG), specialized to those queries. We investigate two designs: First, typeaware sparsification, in which the resulting CFG only consists of statements containing variables that are type compatible with the query variable. Second, alias-aware sparsification, where the resulting CFG consists of the def-use chains of the query variable and all its intra-procedural aliases.We implement both designs in SparseBoomerang by extending Boomerang, a pointer analysis framework based on push-down systems. We evaluate SparseBoomerang by comparing it to Boomerang in terms of precision and performance. On the Pointerbench micro-benchmark suite for alias analysis, SparseBoomerang maintains the precision of Boomerang, in both designs. We evaluate the runtime and memory performance of SparseBoomerang by using Flowdroid as a taint analysis client on real-world apps. Compared to the baseline Boomerang, on average SparseBoomerang solves alias queries 2.4x faster when using the type-aware sparsification strategy, and 2.8x faster when using the alias-aware variant with negligible memory overhead.
Analysis Of Information Diffusion In Social Networks Based On Graph Representation

The growing network of relationship in social Snetwork has contributed to rising maximization of information diffusion by identifying the influential node through various centrality measures. The network structure is affected by the property of the network, so assortative help in better understanding the connectivity of the node. The graph structure-based approach along with different graph metrics has been used to detect and analyze influential node in the network. The comparison of measures has been presented on the basis of performance in social network. Empirical evaluation reveals that the relationship of the bridge edge in the network, strengthens the information flow between different cluster. The presence of small clusters among large clusters aids in detecting information propagation pattern and maximizing information diffusion.