Miracle-dz/UniFAR-FTUs huggingface dataset:
Cooperative Position and Attitude Following between Master and Slave Continuum Robots

In laparoscopic surgery, due to the complexity of the surgical environment and task, it is usually necessary to cooperate with multiple surgical robots to complete the surgical task. In this paper, a cooperative motion method of robots is proposed. Through the method, the slave robot follows the master robot automatically, so that the two robots can cooperate with each other under the condition of only controlling the master robot, thus reducing the work intensity of doctors in surgery. The cooperative motion method uses the forward kinematics of the continuum robot to solve the front pose of the master robot, and establishes the relationship between the front points of the two cooperative robots through the change of spatial coordinates, finally uses the inverse kinematics to solve the change of the ropes length of the slave robot. Several robot pose following experiments with different angles and distances are carried out, in order to verify the feasibility of the proposed method and calculate the average tracking error. The experimental results show that the average tracking error of the robot, which is under the control of cooperative motion method is 1.2%, the cooperative motion method can effectively help doctors to complete the cooperative task in laparoscopic surgery.
Assembly-based STDP: A New Learning Rule for Spiking Neural Networks Inspired by Biological Assemblies

Spiking Neural Networks (SNNs), An alternative to sigmoidal neural networks, include time into their operations using discrete signals called spikes. Employing spikes enables SNNs to mimic any feedforward sigmoidal neural network with lower power consumption. Recently a new type of SNN has been introduced for classification problems, known as Degree of Belonging SNN (DoB-SNN). DoB-SNN is a two-layer spiking neural network that shows significant potential as an alternative SNN architecture and learning algorithm. This paper introduces a new variant of Spike-Timing Dependent Plasticity (STDP), which is based on the assembly of neurons and expands the DoB-SNN's training algorithm for multilayer architectures. The new learning rule, known as assembly-based STDP, employs trained DoBs in each layer to train the next layer and build strong connections between neurons from the same assembly while creating inhibitory connections between neurons from different assemblies in two consecutive layers. The performance of the multilayer DoB-SNN is evaluated on five datasets from the UCI machine learning repository. Detailed comparisons on these datasets with other supervised learning algorithms show that the multilayer DoB-SNN can achieve better performance on 4/5 datasets and comparable performance on 5th when compared to multilayer algorithms that employ considerably more trainable parameters.
CONDITIONS FOR PHOTOSPHERICALLY DRIVEN ALFVÉNIC OSCILLATIONS TO HEAT THE SOLAR CHROMOSPHERE BY PEDERSEN CURRENT DISSIPATION

A magnetohydrodynamic model that includes a complete electrical conductivity tensor is used to estimate conditions for photospherically driven, linear, non-plane Alfvénic oscillations extending from the photosphere to the lower corona to drive a chromospheric heating rate due to Pedersen current dissipation that is comparable to the observed net chromospheric radiative loss of ∼107 erg cm−2 s−1. The heating rates due to electron current dissipation in the photosphere and corona are also computed. The wave amplitudes are computed self-consistently as functions of an inhomogeneous background (BG) atmosphere. The effects of the conductivity tensor are resolved numerically using a resolution of 3.33 m. The oscillations drive a chromospheric heating flux FCh ∼ 107–108 erg cm−2 s−1 at frequencies ν ∼ 102–103 mHz for BG magnetic field strengths B ≳ 700 G and magnetic field perturbation amplitudes ∼0.01–0.1 B. The total resistive heating flux increases with ν. Most heating occurs in the photosphere. Thermalization of Poynting flux in the photosphere due to electron current dissipation regulates the Poynting flux into the chromosphere, limiting FCh. FCh initially increases with ν, reaches a maximum, and then decreases with increasing ν due to increasing electron current dissipation in the photosphere. The resolution needed to resolve the oscillations increases from ∼10 m in the photosphere to ∼10 km in the upper chromosphere and is ∝ν−1/2. Estimates suggest that these oscillations are normal modes of photospheric flux tubes with diameters ∼10–20 km, excited by magnetic reconnection in current sheets with thicknesses ∼0.1 km.
An affordable system for the teleoperation of dexterous robotic hands using Leap Motion hand tracking and vibrotactile feedback

Using robot manipulators in contexts where it is undesirable or impractical for humans to physically intervene is crucial for several applications, from manufacturing to extreme environments. However, robots require a high degree of intelligence to operate in those environments, especially if they are not fully structured. Teleoperation compensates for this limitation by connecting the human operator to the robot using human-robot interfaces. The remotely operated sessions can also be used as demonstrations to program more powerful autonomous agents. In this article, we report a thorough user study to characterise the effect of simple vibrotactile feedback on the performance and cognitive load of the human user in performing teleoperated grasping and manipulation tasks. The experiments are performed using a portable and affordable bilateral teleoperation system that we designed, composed of a Leap Motion sensor and a custom-designed vibrotactile haptic glove to operate a 4-fingered robot hand equipped with 3-axis force sensors on the fingertips; the software packages we developed are open-source and publicly available. Our results show that vibrotactile feedback improves teleoperation and reduces cognitive load, especially for complex in-hand manipulation tasks.
User Requirements for a Robot Teleoperation system for General Medical Examination

Thailand, as well as many other countries world-wide, is facing a shortage of medical staff. We purpose a solution to improve medical services in health centres: a robot teleoperation system to allow patients to consult with doctors from public hospitals, and for doctors to examine and make decisions about their required care. To develop such a system, a user-centred design (UCD) process is followed. Here we present an important first step in this process to establish user requirements for such a system. Hence, we have conducted a focus group with Thai medical staff from Banphaeo General Hospital and an online survey with potential patients. An online collaborative board has been setup to facilitate running the focus group virtually and provide an effective tool to gather data. A qualitative data is then analysed using a framework analysis. Based on this work, we present a list of user requirements for doctors, patients and assistants and discuss how the collected requirements can be transferred into technical specifications of the system. Our study found that communication among different user groups is the most important requirement.
ON THE SCATTERING ENHANCEMENT OF N v λ1240 EMISSION LINE OF QUASI-STELLAR OBJECTS

The N v emission line of active galactic nuclei shows peculiar behavior in the line–continuum correlation, which may be indicative of an extra line component in addition to that from the normal broad emission line region. In this paper, we investigate possible contribution to the N v emission via resonant scattering of both continuum and Lyα in a broad absorption line (BAL) outflow by performing the Sobolev Monte Carlo simulations. The contribution is dependent on the covering factor, optical depth, and velocity profile of the outflow, as well as the equivalent width (EW) of Lyα. Adopting model parameters constrained by observations, we find that the measured N v EW in the spectra of non-BAL quasi-stellar objects (QSOs) could have been enhanced by a factor of 1.82–2.73 on average, while there is only moderate absorption of Lyα along the BAL outflow direction. Our model can produce a relatively narrow scattering line profile. About 80% of the total scattered flux falls within the central ±4500 km s-1. We find that the resonant scattering can produce a prominent polarized emission line around N v. Both the broad excess emission and the unusually large polarized flux observed around N v in BAL QSOs are considered as strong evidence for the scattering enhancement. Future spectropolarimetric observations and spectroscopic monitoring of luminous QSOs may offer crucial tests for this interpretation, and provide useful information on the physical and geometrical properties of QSO outflows. We argue that the scattering offers a promising and robust process for producing the peculiar behavior of N v emission compared to the other processes proposed previously.
SERENDIPITOUS DISCOVERY OF AN INFRARED BOW SHOCK NEAR PSR J1549−4848 WITH SPITZER

We report on the discovery of an infrared cometary nebula around PSR J1549−4848 in our Spitzer survey of a few middle-aged radio pulsars. Following the discovery, multi-wavelength imaging and spectroscopic observations of the nebula were carried out. We detected the nebula in Spitzer Infrared Array Camera 8.0, Multiband Imaging Photometer for Spitzer 24 and 70 μm imaging, and in Spitzer IRS 7.5–14.4 μm spectroscopic observations, and also in the Wide-field Infrared Survey Explorer all-sky survey at 12 and 22 μm. These data were analyzed in detail, and we find that the nebula can be described with a standard bow shock shape, and that its spectrum contains polycyclic aromatic hydrocarbon and H2 emission features. However, it is not certain which object drives the nebula. We analyze the field stars and conclude that none of them can be the associated object because stars with a strong wind or mass ejection that usually produce bow shocks are much brighter than the field stars. The pulsar is approximately 15″ away from the region in which the associated object is expected to be located. In order to resolve the discrepancy, we suggest that a highly collimated wind could be emitted from the pulsar and produce the bow shock. X-ray imaging to detect the interaction of the wind with the ambient medium- and high-spatial resolution radio imaging to determine the proper motion of the pulsar should be carried out, which will help verify the association of the pulsar with the bow shock nebula.
PEPC: A Deep Parallel Convolutional Neural Network Model with Pre-trained Embeddings for DGA Detection

Discovering domain generation algorithms (DGAs) used to build command and control (C&C) infrastructures of botnets is crucial for recognizing botnets. Recent studies in DGA detection benefit from deep learning, such as convolutional neural network (CNN) and long short-term memory neural network (LSTM). However, these studies need massive supervised data to train their models, while obtaining enough labeled samples is consistently time-consuming and labor-intensive. In this paper, we propose a deep learning model, called PEPC, to detect and classify DGA domain names with only a small dataset. PEPC consists of two modules: (1) the pre-trained embeddings (PTE) module to quantify domain names to numeric vectors; and (2) the deep parallel convolutional neural networks (DPCNN) module to better extract features of vectors for prediction. Comparing our model with the 5 common deep learning-based DGA detection approaches, results show that our model yields an average improvement of 10 F1 points, while it requires just 30 training samples for each class. Significantly, PTE can help models achieve better detection and classification performances on small training samples.
Free-fall frame black hole in gravity's rainbow

Doubly special relativity (DSR) is an effective model for encoding quantum gravity in flat spacetime. To incorporate DSR into general relativity, one could use "Gravity's rainbow", where the spacetime background felt by a test particle would depend on its energy. In this scenario, one could rewrite the rainbow metric $g_{\mu\nu}\left( E\right) $ in terms of some orthonormal frame fields and use the modified equivalence principle to determine the energy dependence of $g_{\mu\nu}\left( E\right) $. Obviously, the form of $g_{\mu\nu}\left( E\right) $ depends on the choice of the orthonormal frame. For a static black hole, there are two natural orthonormal frames, the static one hovering above it and freely falling one along geodesics. The cases with the static orthonormal frame have been extensively studied by many authors. The aim of this paper is to investigate properties of rainbow black holes in the scenario with the free-fall orthonormal frame. We first derive the metric of rainbow black holes and their Hawking temperatures in this free-fall scenario. Then, the thermodynamics of a rainbow Schwarzschild black hole is studied. Finally, we use the brick wall model to compute the thermal entropy of a massless scalar field near the horizon of a Schwarzschild rainbow black hole in this free-fall scenario.
A Novel Movement-supported HRI Framework for Humanoid Robots

Current research related to human-robot interaction (HRI) of bipedal humanoid robots often assumes that the robot is in a standing stationary state, i.e., the relative position of the robot does not change, and rarely considers the effect of lower limb movement on interaction. However, HRI in the real world does not assume a moving or stationary state of the robot, and the equilibrium perturbations caused by movement can prevent HRI from functioning properly. In this paper, we propose a movement supported humanoid robot interaction method that empowers the robot to move stably while achieving HRI. First, a reinforcement learning-based neural network is run offline to generate interaction actions that satisfy the equilibrium constraint and support movement, and then an intention recognition network is introduced to run the movement-supported HRI framework online. It is demonstrated that the training method proposed in this paper can enable a bipedal robot to achieve a variety of interactive actions while moving stably.
A Deep Learning Approach to Prevent Problematic Movements of Industrial Workers Based on Inertial Sensors

Nowadays, manufacturing industries still face difficulties applying traditional Work-related MusculoSkeletal Disorders (WMSDs) risk assessment methods due to the high effort required by a continuous data collection when using observational methods. An interesting solution is to adopt Inertial Measurement Units (IMUs) to automate the data collection, thus supporting occupational health professionals. In this paper, we propose a deep learning approach to predict human motion based on IMU data with the goal of preventing industrial worker problematic movements that can arise during repetitive actions. The proposed system includes an initial Madgwick filter to merge the raw inertial tri-axis sensor data into a single angle orientation time series. Then, a Machine Learning (ML) algorithm is trained with the obtained time series, allowing to build a forecasting model. The effectiveness of the developed system was validated by using an open-source dataset composed of different motions for the upper body collected in a laboratory environment, aiming to monitor the abduction/adduction angle of the arm. Firstly, distinct ML algorithms were compared for a single angle orientation time series prediction, including: three Long Short-Term Memory (LSTM) methods - a one layer, a stacked layer and a Sequence to Sequence (Seq2Seq) model; and three non deep learning methods - a Multiple Linear Regression, a Random Forest and a Support Vector Machine. The best results were provided by the Seq2Seq LSTM model, which was further evaluated for WMSD prevention by considering 11 human subject datasets and two evaluation procedures (single person and multiple person training and testing). Overall, interesting results were achieved, particularly for multiple person evaluation, where the proposed Seq2Seq LSTM has shown an excellent capability to anticipate problematic movements.
Long-term Traffic Prediction Using Time-varying Adjacency Mask and Self-Smoothing Regularization

Long-term traffic prediction is essential for pre-control of traffic departments, which allows traffic dispatchers to make earlier decisions than short-term traffic prediction. This task is extremely challenging mainly due to the difficulty of obtaining accurate spatial dependency at different time periods and weak correlation between predicted values and historical data for largest time step. Existing methods either use the same adjacency matrix at every moment or recompute a different adjacency matrix at every moment, which may introduce incorrect neighbors to the target node. Moreover, many previous methods either obtain the predicted values step by step, which causes error propagation problem, or obtain the predicted values for each step independently, which loses the correlation information between the multi-step predicted values. In this paper, a Time-varying Adjacency Mask is proposed to correct the spatial dependence which makes spatial dependence different but highly similar at each moment. Besides, a Self-Smoothing Regularization is proposed to establish the relationship between the predicted values of adjacent time slices and to restrict their differential values. Extensive experiments including traditional long-term traffic speed prediction, time-phased speed prediction and rush hour speed prediction are conducted on two real-world datasets, experimental results show the superior performance of our proposed model.
POPNASv2: An Efficient Multi-Objective Neural Architecture Search Technique

Automating the research for the best neural network model is a task that has gained more and more relevance in the last few years. In this context, Neural Architecture Search (NAS) represents the most effective technique whose results rival the state of the art hand-crafted architectures. However, this approach requires a lot of computational capabilities as well as research time, which make prohibitive its usage in many real-world scenarios. With its sequential model-based optimization strategy, Progressive Neural Architecture Search (PNAS) represents a possible step forward to face this resources issue. Despite the quality of the found network architectures, this technique is still limited in research time. A significant step in this direction has been done by Pareto-Optimal Progressive Neural Architecture Search (POPNAS), which expand PNAS with a time predictor to enable a trade-off between search time and accuracy, considering a multi-objective optimization problem. This paper proposes a new version of the Pareto-Optimal Progressive Neural Architecture Search, called POPNASv2. Our approach enhances its first version and improves its performance. We expanded the search space by adding new operators and improved the quality of both predictors to build more accurate Pareto fronts. Moreover, we introduced cell equivalence checks and enriched the search strategy with an adaptive greedy exploration step. Our efforts allow POPNASv2 to achieve PNAS-like performance with an average 4x factor search time speed-up. Code: https://doi.org/10.5281/zenodo.6574040
Wafer Map Defect Pattern Recognition using Imbalanced Datasets

The accurate and automatic inspection of wafer maps is vital for semiconductor engineers to identify defect causes and to optimize the wafer fabrication process. This research work seeks to address the pattern recognition task for the identification of defects in wafer maps, by developing a deep Convolutional Neural Network (CNN) classifier. The proposed CNN-based model utilizes various pre- and post-processing tools and is applied on the public but highly imbalanced industrial dataset WM-811K. To handle imbalance, a methodology of treating each class individually is proposed by applying different processing techniques for down-sampling, splitting and data augmentation based on the number of samples. The proposed model achieves 95.3% accuracy and 93.78% macro F1-score and outperformes other models in the related literature concerning the identification of the majority of classes.
DESEND: A Fast Device Event Signature Extraction And Detection Method for Smart Home

Smart home devices are increasingly being used, but there is a lack of event monitoring of smart home devices. Device event signature is a key part of device event detection technology that is used to detect device event. Existing methods for extracting and detecting device event signatures do not strike a balance between accuracy, versatility and computing efficiency. In this paper, we present DESEND, a fast method for device event signature extraction and detection. DESEND can extract device event signatures on different network protocols. It has faster detection speed with little loss of accuracy. The signatures extracted by DESEND are robust and can adapt to changes in the environment.
LISP-TBCNN: An AutoCAD Malware Detection Approach

AutoCAD is a famous computer-aided design software widely used by engineers and designers. AutoCAD relies on AutoLISP scripting language to achieve powerful design functions. AutoCAD malware is written in AutoLISP to steal valuable designs on the infected system automatically. Script-based malware is difficult to be detected, because cyber adversaries could easily craft adversarial perturbations to malicious code to evade detectors. In this paper, LISP-TBCNN, a new AutoCAD malware detection approach is proposed which is based on AutoLISP abstract syntax tree(AST). The features are extracted from AutoLISP and TBCNN is introduced to build detection model. The experiment results show that LISP-TBCNN could effectively detect AutoCAD malware variants.
Practical Considerations for Deploying Robot Teleoperation in Therapy and Telehealth

Socially Assistive Robots (SARs) have shown promise, but there are still practical challenges to their widespread adoption. Recent research has demonstrated the advantages of teleoperated systems in this space and called for better guidelines for teleoperation interfaces. We ran group usability tests with therapists with no experience with robots to learn more about the challenges they face. We found that robot-novice therapists understand how robots can be effective in therapy. However, learning to use a robot interface can be challenging for new users. These challenges include the unfamiliar metaphors used for robot connection and the need to create, acquire, or share robot interaction content. We also identify users’ needs that are perhaps non-obvious in a research context, such as privacy of client health information and professional boundaries with client families when using electronic tools. We make several recommendations based on analysis of our group usability tests: (1) developing dedicated interfaces for content authoring that account for caregiver technical expertise, (2) implementing content organization and sharing tools, (3) using connection metaphors that non-technical users may be more familiar with such as phone calls or web URLs, (4) considering user privacy in connection methods chosen, especially within telehealth. Most importantly, we encourage further research in SAR teleoperation that focuses on caregivers as teleoperators.