Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning based end-to-end Grasping Pipeline on a lowcost 5-DOF Robotic arm

The problem of robotic grasping is still an unsolved problem with many approaches trying to generalize grasp predictions for unseen and dynamic environments. In this paper, we propose a complete end-to-end pipeline for the task of Deep Learning based robotic grasping on a lowcost 5-DOF arm. We explore Transfer learning approach and then train our grasping model from end-to-end. In the transfer learning approach we tried 2 base models, VGG-16 and ResNet-50. Our grasping model when ResNet-50 is used as base architecture provided better results with a testing accuracy of 83.3% while VGG-16 provided an accuracy of 78.2%. In order to test our model on a real robotic arm, we built a 5-DOF arm and added a custom parallel plate gripper. Complete ROS and Moveit support is added to our developed robotic arm. The processed RG-D image from the KinectV2 camera is given as an input to the model which predicts the 5-D grasp configuration. Required electronic system design and its PCB is built which controls the robotic arm. The predicted 5-D grasp configuration is then transformed to the object pose w.r. t the base link frame of the robot. Lastly, a ROS node is written that automates the task of picking objects lying in different positions & orientations and sends the joint angle values over pyserial communication to the microcontroller’s PCB.
A Survey on Visual SLAM Algorithms Compatible for 3D Space Reconstruction and Navigation

Applications of the robots are increasing in routine for shop floor activity, transportation, and many other areas. While navigation, space reconstruction, and collision avoidance are the primary task of robots, not all simultaneous localization and mapping (SLAM) methods can be useful given that requirement of output is preferably in the specific form of 3D occupancy grid or point cloud in order to implement it on a real robot. This paper focuses on extensive study of conventional and deep learning based SLAM models that should be useful to create 3D output. In addition to that, we explored various available open source dataset considering provided ground truth convenient for evaluating 3D mapping and explain relevant evaluation criteria available in literature. Overall, this paper can be the first of its kind that focuses on all components of 3D SLAM systems.
Adaptive Sliding Mode Controller for Overactuated Quadrotor UAV

Quadrotor unmanned aerial vehicles (UAV) are underactuated as although UAV has six degrees of freedom, only four control inputs are available. Under actuation limits the mobility of the UAV, as it cannot independently track a desired position and attitude trajectory in space. Thrust vectoring can be used to make the UAV into a fully actuated system. The most efficient method for thrust vectoring is by tilting the motors using actuators. The tilting mechanism will provide additional four control inputs, making the system overactuated. In this paper, a sliding mode controller is proposed for the simultaneous tracking of attitude and position of the overactuated quadrotor. Further a Takagi-Sugeno based fuzzy gain scheduling system is proposed for minimizing the chattering in the control signals. Finally, the performance of the designed control system is evaluated using simulation results.
Finite codimensional ideals in Banach algebras

Let A be a commutative, selfadjoint, point spectral Banach algebra and let M be a finite codimensional closed subspace of A such that for each f in M there are n distinct maximal ideals If,. , If of A with f E If. We prove that then there are distinct maximal ideals I1, . , In of A such that M C I, n . n In; in particular if codim(M) = n, then M is an ideal.
LungNodNet-The CNN architecture for Detection and Classification of Lung Nodules in Pulmonary CT Images

Lung cancer detection at an early stage would be life saving. Usually it is diagnosed at a later stage which leads to increase in the mortalities. Detection of malignant lung nodules from CT images is a challenging task, given several factors that impact the detection and classification. In this work, we are proposing a convolutional neural network (CNN) based deep learning model that improves the accuracy of the nodules classification into benign and malignant types. Lung imaging database consortium-image database resource initiative (LIDC-IDRI), a publicly available lung CT scans dataset have been chosen for experiments. The proposed method come up with an approach to patchify the image to include the nodules segments of the image thus reducing the size of CT image drastically by extracting the nodule patches. Computational overhead is decreased due to the presented strategy. 6691 images containing both nodules and non-nodules are subsequently loaded into a 4-layered 2D CNN. Apparently two convolutional and two dense layers form the four layered CNN. Twenty filters having size of 5x5 is employed with relu activation function for first convolutional layer and 40 filters with size 3x3 has been specified for the second one. The model has been trained and validated on 70% and 10% respectively and tested on 20% of dataset. The verification performed on evaluation data resulted in 93.58% accuracy, 95.61% sensitivity and 90.14% specificity.
Investigation of Latest CASE Tools for Database Engineering: A Systematic Literature Review

Computer Aided Software Engineering (CASE) tools aid in development of software at various stages. They provide automation of processes to some extent. Database handling requires reliability and integrity with security. Multiple CASE tools help in achieving all these, reduce the cost and time as well. Database implementations are made much easier with implementation of such tools. The purpose of this Systematic Literature Review (SLR) is to investigate various CASE Tools and analyze them categorizing into various domains. This paper has covered 49 researches between years 2015 to 2022, including 18 CASE tools belonging to 12 domains of Database development and maintenance in software development process. The paper highlights various domains in which CASE tools are being used. Moreover, multiple CASE tools for database handling that are frequently being used in the identified domains are also presented. After carrying out a parametric analysis, leading tools in top domains are identified. It is concluded that benefits of CASE tools in terms of database (DB) management automation, time and cost optimization are undeniable. They are the most important and core components of an efficient software development process.
Deep Learning-driven Explainable AI using Generative Adversarial Network (GAN)

In today’s digital world, human decisions are influenced by machine driven AI systems and it is essential to trust the outcome of such systems to justify the decisions whereas, ethical issues in fairness and interpretability of such systems are some of the major risks associated with machine learning models which are developed and trained with biased datasets along with sensitive attributes like race, caste, geographical location, sex etc. Biasness in AI Systems erode trust between humans & machines that learn together, and such biases may compound over time. There is hardly any AI System and machine learning models are completely unbiased or universally fair. Transparency and fairness in the predictions of such models is of a major concern across various business domains. Sensing a big void due to lack of innovative solution to address such problem, proposed solution evaluated various approaches and finally proposed an adversarial Neural Network based machine learning method which not only evaluate the fairness of the model against sensitive attributes (i.e. age, race, gender, sex and so on) to help mitigate the biases without losing much accuracy but improve transparency through interpretability of the model which clearly explain how a particular model arrive to a certain decision.
Mesh adaptivity for the transport equation led by variational multiscale error estimators

Recently, we developed an explicit a posteriori error estimator especially suited for fluid dynamics problems solved with a stabilized method. The technology is based upon the theory that inspired stabilized methods, namely, the variational multiscale theory. The salient features of the formulation are that it can be readily implemented in existing codes, it is a very economical procedure, and it yields very accurate local error estimates uniformly from the diffusive to the advective regime.
Proposal of Low-Cost Automated Security Diagnosis System for IoT Devices

With the development of smart homes, many household products are connected to the network nowadays. Home IoT devices are also connected to the Internet. However, due to a lack of knowledge about security among users and developers, IoT devices are shipped and used in vulnerable configurations. Therefore, protecting household IoT devices from attacks is a critical issue. Universities, enterprises, and ISPs are required to take measures against vulnerabilities of home IoT devices in their networks. As a solution to this issue, we propose an automated security diagnosis system for IoT devices. Using the automated security diagnosis system, we diagnosed one of the university networks connecting around 5,000 devices and detected vulnerable hosts. Following the indication of vulnerability, we requested the users to correct the vulnerable settings of IoT devices. Consequently, the number of vulnerable devices discovered in 2020 was 60% lower than in 2016. We found that our automatic security diagnosis system is useful in reducing the number of vulnerable devices, including home IoT devices, by assisting with the periodical diagnosis of the network.
Design of Mobile Augmented Reality Assistant application via Deep Learning and LIDAR for Visually Impaired

In daily life, finding lost items is one of the biggest challenges for the visually impaired. Due to their vision problems, finding the target item is difficult, especially when they are not in the same space. Many navigation devices have been designed to assist the visually impaired in real life. But these devices require additional purchases, lack flexibility, and cause inconvenience to visually impaired users. In this research, we designed an application named NAAD on a mobile device with a LIDAR function to achieve obstacle avoidance, item query, and indoor navigation via Deep Learning and AR technology to improve the quality of life for the visually impaired. This paper will focus on a new approach that integrates object recognition, route planning, and target object navigation. With this new method, the NAAD application can guide users to find user-specified lost items in the same or different spaces.
RGB-D SLAM Using Scene Flow in Dynamic Environments

Visual simultaneous location and mapping (SLAM) plays an important role in navigation and augmented reality (AR). Static environment is a common assumption for traditional visual SLAM to work properly, which limits the widespread usage of visual SLAM in real world scenes. The dynamic objects can impair the accuracy of camera pose estimation and the quality of point cloud map. In this paper, a novel motion removal visual system built on ORB-SLAM2 is proposed. The algorithm extracts pixel-level dynamic objects in RGB-D image sequences with scene flow method we present. The scene flow model uses dense optical flow and depth data to estimate undefined moving objects without needing prior information and only static keypoints are used for tracking. In order to verify the performance of the SLAM system, it is evaluated on public TUM RGB-D datasets and the results demonstrate that our approach improves the tracking accuracy and work robustly in both highly and lowly dynamic scenes.
Lower limb-assisted exoskeleton to support walking up and down stairs

In order to help the elderly and patients with lower limb diseases to complete the movement of walking up and down stairs, this paper designed a flexible lower limb power-assisted exoskeleton based on the characteristics of the lasso transmission and the tandem elastic actuator, which provides stage assistance to help the wearer to complete the daily movement of walking up and down stairs with less effort. Based on Angular and torque curves of each joint of the lower limb, the motion of each joint was analyzed and compared during walking up and down stairs and on the flat ground, so as to arrive at the reason why the elderly people struggle to walk up and down stairs, and according to this, the design requirements and overall scheme of the exoskeleton were provided. According to the overall scheme, the exoskeleton was divided into knee component, ankle component and backpack component, and the detailed structural design of the three components was carried out. The hardware and software system of the exoskeleton was designed to complete the acquisition of sensor data and the motion control of the exoskeleton, and to pre-process the gait data for gait recognition. Finally, the assist strategy of the assisted exoskeleton was determined and the experiment was conducted.
Time-Varying Graph Signal Processing Based Cross-Subject Emotion Classification from Multi-Electrode EEG Signals

Emotion classification plays an important role in the domain of human-computer interaction (HCI). In this paper, a novel framework for learning emotion-specific brain functional connectivity from EEG signals, with blockwise time-varying graph signal processing (GSP), is proposed. Graph corresponding to the last temporal block, which captures the spatio-temporal smoothness from all of the previous blocks is considered for extracting the Laplacian-based graph spectral features. The deviation range of the eigenvalues and their ratio corresponding to low-frequency and high-frequency components are proposed in the form of a convex sum feature. This feature is further utilized to classify cross-subject based positive and negative emotions using the KNN classifier. Simulation results on the benchmark DREAMER database validate the performance of the proposed method with metrics of accuracy, sensitivity, and specificity and are comparable with the existing state-of-the-art techniques.
Deep learning model to empower student engagement in online synchronous learning environment

Following the start of the pandemic, online synchronous learning has grown significantly. The higher education sector is searching for new creative ways to provide the information online because of the switch from face-to-face to online synchronous course delivery. Students are also becoming accustomed to studying online, and research has shown that synchronous online learning has a variety of effects on student engagement. For instance, according to statistics from the National Survey of Student Engagement, students are less likely to participate in collaborative learning, studentfaculty interactions, and conversations when learning online if they use quantitative reasoning during face-to-face instruction. Additionally, studies suggest that because they depend on their devices to take online classes, students feel more alienated from their lecturers. This has been linked to a drop in contacts with peers and teachers as a result. By using a cutting-edge deep learning model to predict learner engagement behaviour in a synchronous teaching environment, our research intends to improve online engagement. The model with a clever trigger will encourage the disengaged pupils to communicate with the teachers online. Smart triggers will be built around factors that have been found, focusing on disengaged students to engage them in real-time with automatic, personalized feedback.
Development of an Embedded Device for Stroke Prediction via Artificial Intelligence-Based Algorithms

The most significant cause of disability among the aged and the old is stroke, which causes several social or financial challenges. A stroke may result in death if it is not addressed properly. Patients with such a stroke are often identified with odd bio-signals (i.e., EMG). Therefore, if patients are correctly examined in real-time while being watched and quantified in their physiological signals, they may get the right therapy immediately. However, many stroke screening and diagnostic systems involve pricey and challenging to use in live imaging technologies, i.e., CT or MRI. With the use of real-time Artificial Intelligent (AI) signals, the proposed research created a stroke prediction system that can identify strokes. The system uses Machine Learning (ML) algorithms (XgBoost, Random Forest, Decision Tree, and Voting Classifier). Real-time EMG (Electromyography) bio-signals from the arm and forearm were recorded, after which critical characteristics were released, and prediction models were based on routine activities. It is observed that using the proposed collected data set for four features, the classification accuracy of Random Forest is almost 95%, and it performs better than other classification algorithms such as XgBoost 91%, Decision Tree and Voting Classifier both have (85%).
Numerical Simulation of Cloud–Clear Air Interfacial Mixing: Homogeneous versus Inhomogeneous Mixing

Abstract This note presents an analysis of several dozens of direct numerical simulations of the cloud–clear air mixing in a setup of decaying moist turbulence with bin microphysics. The goal is to assess the instantaneous relationship between the homogeneity of mixing and the ratio of the time scales of droplet evaporation and turbulent homogenization. Such a relationship is important for developing improved microphysical parameterizations for large-eddy simulation of clouds. The analysis suggests a robust relationship for the range of time scale ratios between 0.5 and 10. Outside this range, the scatter of numerical data is significant, with smaller and larger time scale ratios corresponding to mixing scenarios that approach the extremely inhomogeneous and homogeneous limits, respectively. This is consistent with the heuristic argument relating the homogeneity of mixing to the time scale ratio.
Spectral Estimation from Actual Color Images based on Deep Neural Network

Spectral information can be recovered from color images to preserve high spectral and spatial resolutions simultaneously. Synthetic color images are generally used in the present methods. However, the synthetic color images might not conform to the data distribution of actual color images. In this study, a real dataset is constructed with actual color images acquired with color cameras. Besides, a deep neural network is designed using residual neural network, to retrieve the spectral reflectances from the RGB values of the color images. The proposed network is verified via a 5-fold cross validation, and the findings indicate that the proposed network can obtain higher accuracy than the two existing spectral estimation algorithms with respect to the constructed dataset.
DronLomaly: Runtime Detection of Anomalous Drone Behaviors via Log Analysis and Deep Learning

Drones are increasingly popular and getting used in a variety of missions such as area surveillance, pipeline inspection, cinematography, etc. While the drone is conducting a mission, anomalies such as sensor fault, actuator fault, configuration errors, bugs in controller program, remote cyberattack, etc., may affect the drone’s physical stability and cause serious safety violations such as crashing into the public. During a flight mission, drones typically log flight status and state units such as GPS coordinates, actuator outputs, accelerator readings, gyroscopic readings, etc. These log data may reflect the abovementioned anomalies. In this paper, we propose a novel, deep learning-based log analysis approach for detecting anomalies in the drone log that could lead to physical instabilities. We train a LSTM-based deep learning model on the normal flight logs produced by a baseline drone. Essentially, the model learns the sequential patterns of flight state units and correlations among them. The model can then be used to detect anomalies in the state units as the log entries are being recorded by the drone’s control program at runtime. In our experiments, we built detection models based on several logs produced by 3 different drone control programs, namely DJI, ArduPilot and PX4, and used them to detect anomalies in the logs. On average, our approach achieves 0.968 recall and 0.963 precision, and it can detect anomalies during runtime within a few milliseconds.
Bisection Value Iteration

Probabilistic model checking is a powerful method for analyzing quantitative properties of probabilistic systems. Its core is to calculate reachability probabilities. One prevalent method of computing these probabilities is value iteration, which performs one-way fixed point iteration to obtain an approximation to the true value but sometimes returns unreliable results. Three recently proposed sound methods – interval iteration, sound value iteration and optimistic value iteration – extended from value iteration ensure the accuracy of the returned results, and each method has its own merits and demerits. In this paper, we propose a new sound method called bisection value iteration. Our method accelerates bounds convergence through bisection together with upper/lower bound verification and can be applied to calculating reachability and expected rewards for Markov decision processes. The experiments show that our method performs well on a wide range of instances.
Interacting relativistic particles

A manifestly covariant theory of two interacting particles in phase space is presented. Contrary to the implication of the so-called ''no-interaction theorem'' the coordinates of the particle transform correctly under the Poincare group. The force law between the particles is quite general. As examples we exhibit explicitly the relativistic generalization of the arbitrary central-force system, such as the simple harmonic oscillator or the Kepler problem. The quantization of these examples is particularly straightforward, and we obtain in closed form the relativistic generalization of their respective spectra. The quantum theory developed is nontrivial, relativistically covariant, and requires neither perturbation theory nor renormalizations.
Automated Generation of Bug Samples Based on Source Code Analysis

With the development of software vulnerability analysis, the evaluation of different bug-detecting tools has become quite important for selecting better-performed ones and improving existing approaches. To obtain a convincing evaluation result, a well-constructed vulnerability corpus is indispensable. However, the existing corpora are either constructed from real-world bugs or artificially designed, suffering various problems like small volume, lack of ground truth, etc. Thus, generating large-scale bug corpora through an automated way has been widely noticed. In this paper, we propose an automated vulnerability injection system to generate code samples with triggerable vulnerabilities. Specifically, the system analyzes a host program with the symbolic execution tool to generate high-coverage test cases. Meanwhile, it identifies the potential bug injection points and performs static taint analysis to mark tainted variables and their relevance to the bug injection points. Based on the variables, the system modifies the host program to vulnerable code samples that could be verified by the test cases. In conclusion, the system realizes the injection of buffer overflow vulnerabilities in $\mathrm{C}/ \mathrm{C}++$ programs. A study case is shown to demonstrate the injection processes, and the evaluation presents our advantages in the realness and magnitude of generated bug samples as well as solving highcoverage test cases.