Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Aerial Image Stitching Algorithm for UAV Based on Improved SURF

In order to address the realtime performance and robustness issues in UAV aerial image stitching, an improved SURF-based algorithm is proposed. This algorithm enhances the stitching process for UAV aerial images by first employing the Speeded-Up Robust Features SURF algorithm for rapid and robust feature point detection and localization. Subsequently, binary BRISK descriptors are used for feature description. In the feature matching phase, an initial coarse matching is performed using the Nearest-Neighbor Distance Ratio NNDR algorithm to filter out correct match pairs. Then, precise matching is achieved through the application of the PROSAC algorithm. Finally, a weighted fusion algorithm is employed for seamless image stitching. Experimental results demonstrate significant improvements in both matching efficiency and accuracy achieved by this algorithm. It serves as a robust and highly accurate solution with excellent stitching outcomes for UAV aerial image stitching.
Hepatitis C Virus Proteins Induce Lipogenesis and Defective Triglyceride Secretion in Transgenic Mice*

Chronic hepatitis C virus (HCV) infection is associated with altered lipid metabolism and hepatocellular steatosis. Virus-induced steatosis is a cytopathic effect of HCV replication. The goal of this study was to examine the mechanisms underlying HCV-induced lipid metabolic defects in a transgenic mouse model expressing the full HCV protein repertoire at levels corresponding to natural human infection. In this model, expression of the HCV full-length open reading frame was associated with hepatocellular steatosis and reduced plasma triglyceride levels. Triglyceride secretion was impaired, whereas lipogenesis was activated. Increased lipogenic enzyme transcription was observed, resulting from maturational activation and nuclear translocation of sterol regulatory element-binding protein 1c (SREBP1c). However, endoplasmic reticulum (ER) stress markers were expressed at similar levels in both HCV transgenic mice and their wild type counterparts, suggesting that SREBP1c proteolytic cleavage in the presence of HCV proteins was independent of ER stress. In conclusion, transgenic mice expressing the HCV full-length polyprotein at low levels have decreased plasma triglyceride levels and develop hepatocellular steatosis in the same way as HCV-infected patients. In these mice, SREBP1c activation by one or several HCV proteins induces de novo triglyceride synthesis via the lipogenic pathway, in a manner independent of ER stress, whereas triglyceride secretion is simultaneously reduced.
New Filter-based Supervised Learning Approach for State of Charge Estimation of Li-Ion Battery for EV Applications

The estimation of the state of charge of Li-ion batteries is an important metric. The appropriate estimation plays an important role in assessing the performance of the batteries. The limited instruments are available for such purpose with certain limitations such as large time requirements etc. Numerous other immeasurable elements, such as battery chemistry, the surrounding environment, the aging factor, etc., may also have an impact on the SOC. A new approach based on a filter technique with supervised learning is proposed in this paper. A long-short-term memory and hybrid neural network combining the benefits of a convolutional neural network (CNN) and a bidirectional long-short-term memory (BiLSTM) neural network is proposed. CNN automatically decreases the dimensionality and extracts the deep features from the battery dataset. The obtained data is fed into the LSTM. As a result, minimizes overfitting and speeds up the learning process. Training the dataset model takes considerably less time, and the estimation results are more precise. The results demonstrate that the proposed hybrid model is computationally efficient and provides better estimation results with the single model. The estimation performances are below 1% and have superior estimation accuracy and adaptability in comparison to existing literature.
Animated Algorithm Visualizer for Graph Based Algorithms and Recursive Programs Using Machine Learning

Graph visualization algorithms present a significant challenge for students and researchers in computer science and information science. In order to improve understanding and motivation, we have developed a web-based tool that implements several graph visualization algorithms, including Dijkstra's algorithm, A* algorithm. This tool is built using HTML5, JavaScript, React, and CSS and is designed to be engaging and user-friendly. Our goal is to provide a comprehensive overview of these algorithms and to evaluate their performance on various datasets. By making the study of graph visualization more enjoyable and accessible, we hope to increase student motivation and improve the learning experience with data structure algorithms. We also implemented our proposed approach with 97 % improvement with the existing algorithms.
Compact Waveguide Packaging of High Power Transmitter at W band

In this paper a W band transmitter front-end with continuous wave (CW) power output has been realized in a compact waveguide package. Two MMIC namely a multiplier and power amplifier (PA) has been integrated in a multichip module in a waveguide package size of 40.3 mm X 43 mm X 22.3 mm which can provide output power of 21.2 dBm.
Software Defect Prediction for Reliability Analysis Using Machine Learning Approach

Ensuring software dependability is of utmost importance in today's technology-driven world, given the increasing significance of software and the potential consequences of defects, such as financial losses and security breaches. The substantial contribution of the software industry to the global economy further emphasizes the need for reliable software. While various methodologies and techniques have been introduced to enhance software reliability, challenges persist, including accurate failure parameter estimation, statistical significance determination, and dealing with imbalanced data. This paper explores machine learning algorithms and defect prediction approaches to address these challenges. The research aims to improve the accuracy of software reliability predictions by utilizing advanced techniques and methodologies. The results demonstrate a substantial improvement in accuracy, with an average of 99.36% across all software systems. The proposed technique showcases its capability to make highly accurate predictions for diverse software projects, offering valuable insights to software development teams and stakeholders. Identifying potential issues and improving software quality become more achievable with the help of the proposed approach.
RGBot: A Machine Learning Enabled Requirements Gathering Technique in Agile-Based Lifecycle

The creation of mobile app has emerged as an integral aspect of modern organizations in a wide range of fields. A greater percentage of mobile app development (MAD) industries are turning to agile practices to increase app quality and shorten development times. Agile-based MAD relies heavily on efficient requirements gathering from stakeholders, and it has a significant impact on whether or not a mobile app is successful. Conventional methods of gathering requirements are typically labor-intensive and error-prone, prolonging the building process, driving up expenses and may struggle to maintain the track of frequent variations that characterize agile-based MAD. Machine learning (ML) approaches are becoming increasingly prevalent in gathering requirements as a means of automating the monotonous task of requirement engineering. The ability of ML approaches to process massive volumes of data, identify trends, and arrive at precise forecasts has the potential for significant advancement in gathering of requirements. This study intends to offer a methodological proposal for automating requirements gathering through the practice of a ML-based interactive bot, which we have named as requirements gathering bot (RGBot). The RGBot streamlines MAD process by acquiring requirements and optimizing customer experiences to meet stakeholders' expectations.
Triplex Transfer Learning: Exploiting Both Shared and Distinct Concepts for Text Classification

Transfer learning focuses on the learning scenarios when the test data from target domains and the training data from source domains are drawn from similar but different data distributions with respect to the raw features. Along this line, some recent studies revealed that the high-level concepts, such as word clusters, could help model the differences of data distributions, and thus are more appropriate for classification. In other words, these methods assume that all the data domains have the same set of shared concepts, which are used as the bridge for knowledge transfer. However, in addition to these shared concepts, each domain may have its own distinct concepts. In light of this, we systemically analyze the high-level concepts, and propose a general transfer learning framework based on nonnegative matrix trifactorization, which allows to explore both shared and distinct concepts among all the domains simultaneously. Since this model provides more flexibility in fitting the data, it can lead to better classification accuracy. Moreover, we propose to regularize the manifold structure in the target domains to improve the prediction performances. To solve the proposed optimization problem, we also develop an iterative algorithm and theoretically analyze its convergence properties. Finally, extensive experiments show that the proposed model can outperform the baseline methods with a significant margin. In particular, we show that our method works much better for the more challenging tasks when there are distinct concepts in the data.
Improving Line Search Methods for Large Scale Neural Network Training

In recent studies, line search methods have shown significant improvements in the performance of traditional stochastic gradient descent techniques, eliminating the need for a specific learning rate schedule [1] – [3]. In this paper, we identify existing issues in state-of-the-art line search methods [1], [3], propose enhancements, and rigorously evaluate their effectiveness. We test these methods on larger datasets and more complex data domains than before.Specifically, we improve the Armijo line search by integrating the momentum term from ADAM in its search direction, enabling efficient large-scale training, a task that was previously prone to failure using Armijo line search methods. Our optimization approach outperforms both the previous Armijo implementation and tuned learning rate schedules for Adam.Our evaluation focuses on Transformers and CNNs in the domains of NLP and image data.Our work is publicly available as a Python package, which provides a hyperparameter free Pytorch optimizer.
MHPR: Identification of Key Nodes in Multilayer Heterogeneous Combat Network Based on Improved PageRank Algorithm

Network defense is crucial in ensuring the stable operation of the infrastructure network and maintaining the combat capability of the combat network. It also plays a vital role in improving the network's survivability against malicious attacks and random failures. This paper proposes a key node identification method based on an improved PageRank algorithm to address the combat network defense problem, which is transformed into a defense problem of key nodes in multilayer heterogeneous networks. The attribute PageRank (APR) method is introduced, which comprehensively considers the influence of node attributes and topology information on key node identification. The combat network is modeled as a multilayer heterogeneous network, and the proposed Multilayer Heterogeneous network PageRank (MHPR) algorithm is designed to identify and defend key nodes, with the objective of maximizing the network defense capability while defending a small number of nodes. The performance of MHPR is evaluated on networks of varying scales, and the results show that it outperforms the traditional PageRank algorithm, with a 7.3%-8.6% improvement in cumulative normalized defense capability (ANDC). Furthermore, compared with other existing methods, MHPR achieves a 6.1 %-7.4% improvement in ANDC.
Angle-dependent switching of bands in metamaterial absorber at near infrared frequencies

We present numerical studies on an angle-dependent bands switchable metamaterial absorber working at near-infrared frequencies. The absorber is made up of one-dimensional silicon strips array on a silicon substrate followed by a $30 \mathrm{~nm}$ gold layer. Computer Simulation Technology (CST) Microwave Studio has been used for simulations and to understand the underlying mechanics of absorption in the system. The absorption is induced by the simultaneous excitation of double Fabry-Pérot (FP) cavity resonance in the system. One is in the strips and the other in between the consecutive strips. At normal incidence, the absorber shows only one absorption band with 0.99 absorptance at wavelength $1.491 \mu \mathrm{m}$. But for oblique incidence, there is an appearance of an extra absorption band beyond the 3° angle of incidence, and it reaches 0.99 absorptance for 10° angle of incidence. Therefore, the absorption band can be switched from single to dual and vice versa with the chance in the angle of incidence. So, this absorber can be exploited as a single-band absorber or dual-band absorber as per requirement.
Efficient Real-Time Continuous Classification Learning and Fault-Tolerant Computing

Digital engineering ecosystems rely heavily on reliable data processing, where machine learning is commonly applied. This paper addresses the perspective that Reliability and Maintainability (R&M) engineering and machine learning go hand in hand, through multi-dimensional classification learning and fault-tolerant computing. While machine learning can enhance fault-tolerant computing, reliability techniques can improve the accuracy of multi-dimensional classification learning. Algorithms developed are time efficient, allowing them to be applied to real-time application. Case studies demonstrate the efficacy of the methods.
Coded and Scalar Prefix Trees: Prefix Matching Using the Novel Idea of Double Relation Chains

In this paper, a model is introduced named double relation chains (DRC) based on ordered sets. It is proved that using DRC and special relationships among the members of an alphabet, vectors of this alphabet can be stored and searched in a tree. This idea is general; however, one special application of DRC is the longest prefix matching (LPM) problem in an IP network. Applying the idea of DRC to the LPM problem makes the prefixes comparable like numbers using a pair of w‐bit vectors to store at least one and at most w prefixes, where w is the IP address length. This leads to good compression performance. Based on this, two recently introduced structures called coded prefix trees and scalar prefix trees are shown to be specific applications of DRC. They are implementable on balanced trees which cause the node access complexity for prefix search and update procedures to be O(log n) where n is the number of prefixes. As another advantage, the number of node accesses for these procedures does not depend on w. Additionally, they need fewer number of node accesses compared to recent range‐based solutions. These structures are applicable on both IPv4 and IPv6, and can be implemented in software or hardware.
Detection of Anti-Patterns in the Control Flow of Collaborative Business Processes

The verification of the behavior of Collaborative Business Processes is an important aspect to consider when developing inter-organizational systems. In this work, a verification approach for the control flow of collaborative processes based on anti-patterns is proposed to improve the performance of verification. The approach supports the verification of complex constructs for advanced synchronization, multiple instances, and exception management. To this aim, 10 anti-patterns were defined from a repository of process models, and a tool which implements the anti-patterns was developed to evaluate the verification approach. Results indicate that, at worst, the verification time is less than half a millisecond, even for models with complex control flow constructs.
Zero knowledge and the chromatic number

We present a new technique, inspired by zero-knowledge proof systems, for proving lower bounds on approximating the chromatic number of a graph. To illustrate this technique we present simple reductions from max-3-coloring and max-3-sat, showing that it is hard to approximate the chromatic number within /spl Omega/(N/sup /spl delta//), for some /spl delta/>0. We then apply our technique in conjunction with the probabilistically checkable proofs of Bellare, Goldreich and Sudan (1995), and of Hastad (1996), and show that it is hard to approximate the chromatic number to within /spl Omega/(N/sup 1-/spl epsiv//) for any E>0, assuming NP/spl sub/ ZPP. Here, ZPP denotes the class of languages decidable by a random expected polynomial-time algorithm that makes no errors. Our result matches (up to low order terms) the known gap for approximating the size of the largest independent set. Previous 0(N/sup /spl delta//) gaps for approximating the chromatic number (such as those by Lund and Yannakakis (1994), and by Furer (1995)) did not match the gap for independent set, and do not extend beyond /spl Omega/(N/sup 1/2-/spl epsiv//).
Knock-down of the Golli Myelin Basic Protein in M 3 Enhancer Knock-out Mice

During development, in the central nervous system (CNS), oligodendrocyte progenitor cells (OPCs) migrate to axons and eventually become mature oligodendrocytes. Mature oligodendrocytes wrap around axons and produce myelin sheaths, which insulate the axons. Growth factors, specifically platelet derived growth factor (PDGF) and fibroblast growth factor (FGF), are released in the development of OPCs to promote congruent gene expression. PDGF is released to induce the proliferation of OPCs, and to promote their migration to axons. FGF induces OPC maturation into oligodendrocytes, and initiates axon myelination. OPCs act in a similar fashion following a spinal cord injury. PDGF and FGF are released as new OPCs develop, migrate to the injury site, and mature into oligodendrocytes in an attempt to repair damaged axons. We observed the same processes occurring in vitro with embryonic stem cells (ESCs) from our wild-type mouse model. Growth factors FGF and PDGF were added to the media to promote OPC morphology in mature ESCs by the activation of their respective growth factor receptors leading to the phospholipase C-gamma (PLCγ) pathway which results in the Ca 2+ efflux from the endoplasmic reticulum (ER). In 1993, the Campagnoni lab discovered that the myelin basic protein (mbp) gene was part of a larger transcription complex called golli-mbp, whose products regulate store operated calcium channels on the ER membrane. We were able to better study the effects of golli-MBP through our M3 enhancer knockout (M3KO) mouse model where golli-MBP products were downregulated at the OPC stage of development. Deletion of the M3 enhancer resulted in the down regulation of both golli and MBP specific proteins in vivo. We generated WT and M3KO ES cell lines to investigate the potential influence of Golli-MBP in progenitor cell maturation. We found that the M3KO ES cells did not respond to FGF or PDGF to allow for proper cell adhesion and the differentiation into OPCs. These results suggest that deletion of the M3 enhancer reduces the expression of golli-MBP, which leads to the dysregulation of the signaling pathways necessary for the differentiation of ES cells into OPCs.
Sensor Fusion for Autonomous Indoor UAV Navigation in Confined Spaces

In this paper, we address the challenge of navigating through unknown indoor environments using autonomous aerial robots within confined spaces. The core of our system involves the integration of key sensor technologies, including depth sensing from the ZED 2i camera, IMU data, and LiDAR measurements, facilitated by the Robot Operating System (ROS) and RTAB-Map. Through custom designed experiments, we demonstrate the robustness and effectiveness of this approach. Our results showcase a promising navigation accuracy, with errors as low as 0.4 meters, and mapping quality characterized by a Root Mean Square Error (RMSE) of just 0.13 m. Notably, this performance is achieved while maintaining energy efficiency and balanced resource allocation, addressing a crucial concern in UAV applications. Flight tests further underscore the precision of our system in maintaining desired flight orientations, with a remarkable error rate of only 0.1%. This work represents a significant stride in the development of autonomous indoor UAV navigation systems, with potential applications in search and rescue, facility inspection, and environmental monitoring within GPS-denied indoor environments.
Demand Side Management in Power Grids through Particle Swarm Optimization Algorithm

The escalating energy usage and apprehensions about climate change have intensified the need for efficient Demand Side Management strategies inside power systems. DSM is an essential component in the equilibrium of demand and supply inside power grids since it focuses on optimizing energy consumption patterns among customers. Particle Swarm Optimization has appeared as a prominent optimization technique in recent years, showing aptitude in addressing intricate optimization challenges. This study examines the potential of the PSO method in efficiently managing demand-side resources inside power grids, emphasizing its benefits compared to conventional un-optimization systems. Furthermore, it highlights the advantages of integrating the PSO algorithm into power grid systems. It showcases the use of PSO as an effective approach for efficiently managing demand, mitigating peak loads, and enhancing grid dependability. These outcomes are crucial in fostering the development of sustainable energy systems.
Efficient Few-Shot Classification Using Self-Supervised Learning and Class Factor Analysis

Recently, significant advancements have been made in the few-shot classification task by integrating pre-trained self-supervised learning models. Although the self-supervised learning models have demonstrated their effectiveness, their application in few-shot scenarios, specifically in meta-training or fine-tuning, is computationally intensive and complicated. This paper introduces an efficient approach to address these challenges. We propose to use feature analysis methods instead of network model training. This method uses two factors that define the data generation model, resulting in easily classifiable features. The two factors are estimated from a set of different vectors from the train dataset and the test dataset. Although this method is simple compared to network learning, it provides good performance in experiments using few-shot classification benchmark datasets.
A Vibrotactile Interface to Motivate Movement for Children with Severe to Profound Disabilities

V-Sense is a vibrotactile interface that encourages children with severe or profound cognitive, sensory and physical impairments to move. The interface makes use of touch, in particular vibrations, as a supportive function to motivate users' actions. Specifically, we propose a vibrotactile interface on the arm and around the shoulder using the saltation perceptual illusion to induce movement of the corresponding joint. In this paper we describe the design principles of the interface and the proposed experimental design to evaluate it.