Miracle-dz/UniFAR-FTUs huggingface dataset:
An Analysis on the Negative Effect of Multiple-Faults for Spectrum-Based Fault Localization

This paper aims to investigate the negative effects of multiple-faults on spectrum-based fault localization (SBFL). Previously, researchers validated the fact that the occurrence of multiple-faults could have a significant negative impact on fault localization. However, a very little current research addresses the degree of these impacts through a systemic analysis. Furthermore, the fundamental causes underlying that negative impact have not been investigated and are not fully understood. We conducted experiments on fourteen real-life open source programs to explore and possibly solve these problems. Our results indicate that: 1) although multiple-faults generally do have a negative impact on fault localization, different fault localizations displayed various levels of robustness against that negative impact; 2) restoring pass/fail fault interactions only has a modest effect on that negative impact; 3) our investigation of twelve Fault Localization Interactions (FLI) shows that there is a dominant FLI-1 interaction in multiple-fault programs which should be responsible for that negative impact; 4) restoring FLI-1 can significantly improve the performance of both SBFL and parallel debugging techniques; and 5) furthermore, this paper practically validated the revised Kendall Tau distance as an efficient measure to help locate test cases, which have triggered FLI-1. Based on the revised Kendall Tau distance, a fast search algorithm has been suggested to locate FLI-1 test cases. It is expected that this paper can provide some insight into the fundamental causes of multiple-faults’ negative impact on fault localization and drive the development of more efficient fault localization techniques to improve the identification and handling of multiple-faults.
Optimized Convergence of OSPFv3 in Large Scale Hybrid IPv4-IPv6 Network

The fast growth of the Internet has resulted in IPv4 addresses depletion. Internet Service Providers (ISPs) are trying to replace their IPv4 networks with IPv6 gradually. IPv6 was launched with new features like simpler header format, larger address space, efficient routing and built-in security. IPv4-IPv6 transition process has been slow therefore both versions of IP are expected to co-exist for some time. As IPv4 and IPv6 are not compatible to each other, therefore packet traversing and routing has to face challenges. Tunneling is a temporary solution which is used to resolve packet traversing. This research presents the behavioral analysis of Open Shortest Path First (OSPFv3) through several IPv6 tunneling protocols (6in4, 6to4, ISATAP & GRE) over large scale IPv4 network. The performance of OSPFv3 is measured through route summarization over hybrid IPv4-IPv6 network on the basis of numerous parameters like convergence & re-convergence time, round trip time, response time, tunnel overhead and protocol traffic statistics.
Relative and inertial attitude determination in three-vehicle long formations

This paper addresses a new attitude determination problem for formations. It considers a three-vehicle formation with relative and inertial measurements from sensors, where Constraints limit the relative measurements, which are not available between two of the vehicles, also known as deputies. The other vehicle is called the chief and does not have any limitation. Furthermore, each of the vehicles has an independent inertial measurement, whose references are known. The goal is to determine all attitude relations, both inertial and relative. The solution for this problem is divided into different stages. First, the relative attitude between the chief and the deputies is assessed, which results in two candidates for each of these relations. Then, each candidate yields a candidate for the inertial attitude of the chief. Next, comparing the four inertial candidates gives the solution for their respective relations and consequently for the relative relations as well. The remaining relations derive directly from those already known. The paper also provides some early insights about degeneracies, possible particular cases of the solution, and the effect of sensor noise. Finally, the solution is validated with a simulation, whose results are similar to attitude determination problems in constrained formations.
Implementation of Small-Scale Mushroom Production Systems in Rural Cambodia

Cambodia has a largely agrarian economy, with roughly 75 percent of the population working in agriculture, chiefly cultivating rice. Since rice can only be grown eight months of the year, the other months farmers are forced to migrate to cities to find employment to support their families. Mushroom cultivation is an attractive source of alternative work and supplemental income in Cambodia because it is not labor intensive and it produces a high-value crop. Thera Metrey is a venture making mushroom cultivation accessible to farmers in rural areas around the town of Kampong Cham in central Cambodia. Initial introduction of the technology was successful in involving farmers and aligned with socio-cultural traditions. As the venture grew to include over 80 mushroom houses, opportunities to improve the technology as well as the business model became evident. This article discusses the early stages and growth of Thera Metrey in order to highlight the successes and obstacles faced in its implementation.
Designing Concentric Tube Manipulators for Stability Using Topology Optimization

One of the major problems facing the development and road to practical usage of concentric tube continuum robots in surgical environments is that of instability. This issue, also known as the snapping problem, is caused by a tube having a high bending to torsional stiffness ratio (BTSR). Past efforts have shown that by cutting patterns on the tubes, this problem can be avoided. This paper seeks to redesign the topology of the tubes so that BTSR is decreased and the snapping problem is resolved in a particular tube set. The generated designs are then tested through finite element analysis as well as experimental testing to demonstrate the elimination of the snapping problem. Using this novel tube design on a concentric tube robotic system can increase its stable workspace because it allows the usage of greater tube curvatures and/or curve lengths.
Data-Driven Discrete Planning for Targeted Hopping of Compliantly Actuated Robotic Legs

Motion planning for fast locomotion of compliantly actuated robotic legs is generally considered to be a challenging issue, posing considerable real-time problems. This is at least the case if time-continuous trajectories need to be generated online. In this paper we take advantage of a simple controller structure, which reduces the motion planning to a discrete-time planning problem, in which only a small set of input parameters need to be determined for each step. We show that for a planar leg with serial elastic actuation, hopping on a ground with stairs of irregular length and height can be planned online, based on a parameter mapping which has been learned in a data-driven manner by performing hopping trials with an adaptive exploration algorithm to evenly sample the parameter space. Experiments on a planar hopping leg prototype validate the approach.
Branch Point Selection in RNA Splicing Using Deep Learning

Alternative splicing (AS) is a regulated process that takes place during gene expression by which a single gene may code for multiple proteins. This mechanism is controlled by a complex called spliceosome by which certain exons of a gene may be included in or excluded out from the final mRNA produced from that gene. In AS, at least three remarkable signals exist in introns and they are 5’ splice site (5’ss), the donor ss where GU nucleotides are more frequently present, 3’ss, the acceptor ss where AG nucleotides are more frequently present, and branch site. Generally, branch point site is located at 20 to 50 nucleotides upstream from the 3’ss. In this paper, we identify the branch point location using a computational model based on deep learning. We propose a hybrid model based on a combination of dilated convolution neural network and recurrent neural network. Integrating additional inputs to the raw RNA sequence has been studied such as conservation, binding energy, and di-nucleotide. The proposed model has been evaluated on two publicly available datasets and outperformed the current state-of-the-art methods. More specifically, the proposed model achieved for the first dataset 97.29% and 67.08% of the area under curve (ROC-AUC) and the area under precision recall curve (prAUC), respectively, for the second dataset 96.86% and 69.62% of ROC-AUC and prAUC, respectively. In addition, pathogenic variants have been studied by the proposed model and agreed with the reported ones biologically. To study RNA branch point selection, an easy-to-use Web server has been established for free access at: https://home.jbnu. ac.kr/NSCL/rnabps.htm.
Learning Oscillator-Based Gait Controller for String-Form Soft Robots Using Parameter-Exploring Policy Gradients

This paper presents a methodology to design mechanosensor feedback to oscillator-based controller for worm-like soft-bodied robots. A reinforcement learning technique, i.e., PEPG, is employed to embed appropriate mechanosensor feedback to harness global entrainment among the controller, the body dynamics, and the environment without explicitly designing the interaction between the oscillators. Another reinforcement learning, actor-critic, was applied to train the controller for the simulation models to analyze the effectiveness of PEPG in the system. Furthermore, the gait controller was trained under different body dynamics, i.e., the physical model of a caterpillar and an earthworm. We found that PEPG is suitable for the system probably because it does not add exploration noise to actions and it conducts episode based parameter updates. The simulation results show the proposed method can acquire distinct behavior, i.e., caterpillars' crawling, inching and earthworms' crawling, under different body dynamics. The outcome implies, that by utilizing appropriate learning method, desired functionality can be achieved in soft-bodied robots without explicitly designing their behavior.
Replacement of Marx Generator by Tesla Transformer for Pulsed Power System Reliability Improvement

A technology scheme that replaces Marx generator with Tesla transformer was put forward to improve the reliability of the pulsed power system. In order to validate the technology scheme, a Tesla transformer was designed and tested. The excitation of the primary consists of two 4-<inline-formula> <tex-math notation="LaTeX">$\mu \text{F}$ </tex-math></inline-formula> polypropylene film capacitors which are separated by a three-electrode field distortion gas switch. A deionized water dielectric pulse forming line (PFL), with 13-nF equivalent capacitance, acts as the secondary capacitor. The actual transformation ratio is 9.7 approximately, and the PFL voltage is as high as 1.2 MV when two capacitors are charged to ±62 kV by the bipolar high-voltage direct current. The prefire probability of switch is described by the Weibull distribution and the working ratio of the switch is elaborately chosen so as to balance the prefire probability and the trigger jitter. For ±62 kV charge voltage and 0.2-MPa pressured air dielectric, the prefire probability of switch is only <inline-formula> <tex-math notation="LaTeX">$1 \times 10^{-7}$ </tex-math></inline-formula> and the jitter of that is 5 ns. When confidence is 0.90, the failure probability of pulsed power machine is <inline-formula> <tex-math notation="LaTeX">$2.8 \times 10^{-4}$ </tex-math></inline-formula> according to the Binomial distribution correspondingly.
Synthesis Design of SIW Multiband Bandpass Filters Based on Dual-Mode Resonances and Split-Type Dual- and Triple-Band Responses

A synthesis design method for substrate-integrated waveguide (SIW) multiband bandpass filters (BPFs) with flexible frequency and bandwidth allocations is presented by incorporating SIW dual-mode resonances with split-type dual- and triple-band symmetrical frequency responses. By different combinations of the split-type dual- or triple-band responses for two virtual wide passbands dominated by TE101 and TE201 modes in SIW rectangular cavities, SIW tri-/ quad-/quint-/sext-band BPFs with improved band assignments can all theoretically be implemented. In addition, by introducing our previously proposed dual-mode coupling controlling technique, the bandwidths of the two virtual wide passbands can also be allocated flexibly. Three experimental prototypes, including a quad-band BPF based on four dual-mode SIW cavities, a quad-band and quint-band BPFs based on two dual-mode SIW cavities coupled with multiple single-mode ones, are synthesized, designed, fabricated, and tested to demonstrate and validate the collaborative design concept.
Methodology of Software Code Decomposition Analysis

Obtaining a list of programs linear blocks and the links between them is used in the tasks of research and software protection. According to the results of abstracting, the authors have not found the means of analysis of binary files to obtain this information. The purpose of the study is to facilitate the analysis of the software by creating a unified data provider about the structure of the analyzed program. The approach proposed in the research is focused on the decomposition and presentation of program code as a set of interrelated linear blocks and their relations. Presented in such form a code can be analyzed to identify patterns which have certain characteristics, which simplifies the process of secure software development. The proposed method is based on the development of a plugin that aggregates data from existing tools. The method of information extraction from the "IDA" disassembler and presentation in the form necessary for further work is developed. There is presented method's verification tests example. The data generated according to the methodology are intended for use in targeted software analysis tools
Theory and Experiment on Formation-Containment Control of Multiple Multirotor Unmanned Aerial Vehicle Systems

Formation-containment control problems for multiple multirotor unmanned aerial vehicle (UAV) systems with directed topologies are studied, where the states of leaders form desired formation and the states of followers converge to the convex hull spanned by those of the leaders. First, formation-containment protocols are constructed based on the neighboring information of UAVs. Then, sufficient conditions for multi-UAV systems to achieve formation-containment are presented. An explicit expression to describe the relationship among the states of followers, the time-varying formation for the leaders and the formation reference is derived. It is shown that the states of followers not only converge to the convex hull formed by those of leaders but also keep certain formation specified by the convex combination of the formation for the leaders. Moreover, an approach to determine the gain matrices of the formation-containment protocol is proposed by solving an algebraic Riccati equation. Finally, a formation-containment platform with five quadrotor UAVs is introduced, and both the simulation and experimental results are presented to demonstrate the effectiveness of the obtained results. Note to Practitioners—This paper addresses the problem of formation-containment control for multi-UAV systems over directed topologies. In practical applications, there may exist multiple leaders and multiple followers in a multi-UAV system. Formation-containment means that the states of leaders form the desired time-varying formation and at the same time the states of the followers converge to the convex hull spanned by those of the leaders. Formation-containment control provides a unified framework for formation control and containment control, and has potential applications in the cooperative source seeking, load transportation, and surveillance. Although formation control and containment control problems have been studied a lot, the formation-containment control problem for multi-UAV system is still open and challenging. This paper proposed a distributed formation-containment protocol for the multi-UAV system using local neighboring information. Sufficient conditions for multi-UAV systems to achieve formation-containment are presented. It is proven that the states of followers not only converge to the convex hull formed by those of leaders but also keep certain formation specified by the convex combination of the formation for the leaders. An approach to design the formation-containment protocol is given. A remarkable point for this paper is that the obtained results are demonstrated by practical experiments with five quadrotor UAVs.
Determination of Radiation Pressure in Acoustic Levitation by Optical Acoustic-Field Measurement

Pulsed digital holography was used to determine the acoustic radiation force on objects suspended in a single-axis acoustic levitator. Digital holography provided both the object images and the optical phase data required to determine the instantaneous acoustic pressure. The phase data were utilized in tomography calculations, converting the measured line-of-sight variations to a pressure field by assuming that the system was axisymmetric. The pressure field was used to calculate the velocity field and these together were used to calculate the acoustic radiation pressure. This pressure was integrated over the surface of the object to determine the vertical force on the object. Force measurements were completed for five different polymer spheres, of different sizes and densities, and for an evaporating water droplet. For all but the heaviest sphere, the optically measured force agreed with the measured weight of the sphere within experimental uncertainty. For the heaviest sphere, the overall average showed an upward bias but was still only 4.7% high. For the water data, the optically measured force was consistent with the droplet volume as the droplet evaporated.
VLSI Design of an Efficient Flicker-Free Video Defogging Method for Real-Time Applications

Defogging is an essential preprocessing technique for object detection in computer vision-based systems and has been widely used in outdoor surveillance system applications. This paper proposes an efficient defogging algorithm for both static images and videos. Considering real-time applications, the proposed defogging algorithm involves a low-cost hardware oriented design that is based on an atmospheric scattering model and dark channel prior. Compared with previous low-complexity techniques, simulation results indicated that the proposed design demonstrated superior performance in terms of quantitative and qualitative evaluations. A weighting technique and a contour preserving estimation approach are adopted alternately to refine the factors in the defogging process. Furthermore, in the atmospheric light estimation, an adjuster is applied to the video for preventing “flicker” which means that the brightness changes dramatically between two neighbor frames in the video. Hence, the proposed algorithm is suitable for video defogging applications, which have not been dealt with in previous approaches. To achieve the requirement of real-time applications for both static and dynamic images, an implementation of seven-stage very-large-scale integration architecture for the proposed algorithm is presented. By using TSMC 0.13- $\mu {\mathrm{ m}}$ technology, the design yielded a processing rate of approximately 200 Mpixels/s.
A Comparative Study on Sigma-Point Kalman Filters for Trajectory Estimation of Hybrid Aerial-Aquatic Vehicles

In this paper, a study on nonlinear state estimation methods for Hybrid Unmanned Aerial Underwater Vehicles (HUAUVs) is presented. Based on a detailed dynamic model simulation, we analyse and elect the best nonlinear algorithm among those presented in the state-of-the-art literature addressing local derivative-free nonlinear Kalman Filters (KFs): the Unscented Kalman Filter (UKF), the Cubature Kalman Filter (CKF) and the Transformed Unscented Kalman Filter (TUKF). Here, these three nonlinear probabilistic estimators were compared in terms of the Root Mean Square Error (RMSE) and the average execution time over Monte Carlo simulations. We simulated real-world conditions for our in-production HUAUV prototype using Inertial Measurement Unit (IMU) data and state augmentation for sensor data filtering and trajectory estimation. We have concluded that the CKF proved to be the most interesting KF to low-cost on-board applications for high dimensional state spaces.
Object Detection and Pose Estimation Based on Convolutional Neural Networks Trained with Synthetic Data

Instance-based object detection and fine pose estimation is an active research problem in computer vision. While the traditional interest-point-based approaches for pose estimation are precise, their applicability in robotic tasks relies on controlled environments and rigid objects with detailed textures. CNN-based approaches, on the other hand, have shown impressive results in uncontrolled environments for more general object recognition tasks like category-based coarse pose estimation, but the need of large datasets of fully-annotated training images makes them unfavourable for tasks like instance-based pose estimation. We present a novel approach that combines the robustness of CNNs with a fine-resolution instance-based 3D pose estimation, where the model is trained with fully-annotated synthetic training data, generated automatically from the 3D models of the objects. We propose an experimental setup in which we can carefully examine how the model trained with synthetic data performs on real images of the objects. Results show that the proposed model can be trained only with synthetic renderings of the objects' 3D models and still be successfully applied on images of the real objects, with precision suitable for robotic tasks like object grasping. Based on the results, we present more general insights about training neural models with synthetic images for application on real-world images.
Improving Spatial Locality in Virtual Machine for Flash Storage

Flash-based solid-state drives (SSD) are being widely adopted in virtualized environments to improve the I/O performance due to their low latency and high throughput compared with existing hard disk drives. In the virtualized environment, the performance of SSDs fluctuates significantly according to the I/O patterns from the virtual machine. For example, random writes have a significantly negative impact on the performance of SSDs compared with sequential writes due to the characteristics of SSDs. In this paper, we propose an address reshaping technique for SSDs in the virtualization layer to improve the spatial locality and the performance of random writes. Our scheme transforms random write requests into sequential write requests in the virtualization layer and thus enables the virtualization layer to issue the transformed sequential requests to SSDs. The experimental results show that the optimized scheme improves the performance by up to 97% compared with the existing scheme under random write workloads.