Miracle-dz/UniFAR-FTUs huggingface dataset:
A Direction-Decoupled Non-Local Attention Network for Single Image Super-Resolution

The non-local attention mechanism has often been exploited in deep learning to capture long-range dependencies (LRDs) from the same image for enhancing the performance of various image processing methods. However, the initially proposed non-local attention process inevitably yields extremely-high computation complexity, since all the feature points are involved in computing the LRDs. To address this concern, a recently proposed criss-cross network (CCNet), which has a recurrent criss-cross attention (RCCA) module, is used to compute the LRDs by involving only a small set of feature points for significantly reducing computation. Motivated by the RCCA, a novel direction-decoupled non-local attention (DNA) module is proposed in this paper that is able to further reduce the computation complexity of RCCA by half approximately. To verify the performance of our new non-local attention module, a DNA network is developed for conducting single image super-resolution (SISR). Extensive experimental results have clearly demonstrated the superiority of using our DNA network for SISR when compared with that of state-of-the-art methods.
The Research of Wi-Fi Wireless Signal Propagation Model in Earthquake Ruins Environment

after the earthquake and other natural disasters, through Wi-Fi search and positioning of wireless intelligent devices under the ruins, indirectly detect the location of buried people, and effectively improve the rescue efficiency. This paper studies the Wi-Fi based wireless signal propagation model under the ruins, and measures the propagation characteristics of 2.3 ∼ 2.5GHz wireless signal by frequency domain sweep measurement method. Then we analyzes the measured data by MATLAB, and obtains the large-scale attenuation parameters including free space path loss and debris material penetration loss, so as to give the design of wireless channel model under the ruins. Compared with the actual environment, we can get the proposed model more efficiently and effectively by testing.
Fine-grained Cross-Layer Attention Framework for Wound Stage Classification

Determining progress during wound healing is crucial for effective diagnosis and treatment. Previous works have solved this task using methods paying attention to specific regions of the image. However, we explore an alternative, non-local attention approach and implement a cross-layer attention mechanism that focuses on the areas of interest and considers related spatial regions of the wound. Experimental results and visual representations show that adding cross-layer modules to mid-level and top-level layers enables better classification of wound healing stage and generalization.
A Neurodynamic Approach for a Class of Convex-concave Minimax Problems

This paper presents a neurodynamic approach for a class of convex-concave minimax problems. First, variational inequalities are given, serving as the necessary and sufficient conditions for the desired saddle point of the underlying objective function. Next, based on the variational inequalities, a neurodynamic approach is designed for the minimax problems. Taking advantage of a proper Lyapunov function, the stability of the state solution of the proposed neurodynamic approach is guaranteed. Furthermore, the proposed neurodynamic approach is able to solve the non-quadratic convex-concave minimax problem exponentially. Compared with the existing researches for the quadratic minimax problem, the proposed neurodynamic approach has wider scope of applications to some extent. Finally, a numerical experiment is provided to show the effectiveness of the proposed neurodynamic approach.
An Intelligent Diagnosis Method for Bearing Faults with Multiple Image Inputs and Deep Convolutional Neural Networks

Bearings are key transmission parts that are extensively used in rolling mechanical and equipment. Bearing failures can affect the regular running of machines, in serious cases, can cause enormous losses in economy and personnel casualties. Therefore, it is important to implement the research of diagnosing bearing faults. In this paper, a bearing faults diagnosis method was developed based on multiple image inputs and deep convolutional neural network. Firstly, the 1-Dvibration signal is transformed into three different types of two-dimensional images: time-frequency image, vibration grayscale image and symmetry dot pattern image, respectively. Enter them into multiple DCNNs separately. Finally, Finally, the nonlinear features of multiple DCNN outputs are fused and classified to achieve bearing fault diagnostics. The experimental results indicate that the diagnosis accuracy of this proposed method is 98.8%, it can extract the fault features of vibration samples well, and it is an effective bearing fault diagnosis methodology.
The impact of online proctoring on students' perception and level of satisfaction in a MOOC

MOOCs (massive online courses) have become a very effective way to acquire knowledge. However, like any other online education modality, it needs evaluation tools that formalize or guarantee, in some way, the academic integrity of the process. The purpose of this study was to investigate students' perception, and performance towards proctoring through online exams in a MOOC. Data were collected from edX course “Digital Marketing: content & community manger”, which is part of the Professional Certificate Program (PCP) in Digital Marketing, offer by Galileo University in edX platform. The data set consisted of a proctored and unproctored exam, a post-exam survey, participant's demographic information, and exam grades. This study aims to answer the following questions: Did students have the necessary technical resources to do a proctored exam? How do students feel before, during, and after a proctored exam? What are students' perception towards online proctoring process? The study's findings gave important information about the perception of the students, how they feel with this type of online evaluation and if they have the ideal conditions to take a proctored exam.
Six-DOF Localization using Magnetic Induction Effect for Automated Locomotion of an Active Capsule Endoscope

The localization of capsule endoscopes using the magnetic induction effect has the potential to be used for in vivo applications. Several magnetic field-based models were proposed to achieve this goal. This paper presents and compares the modeling methods using the Biot-savart law and the point-dipole in the tracking algorithm. We introduce a hardware configuration consisting of three transmitters in a bed and three receiver coils in a capsule body. We quantitatively compare the performance of the two approaches in terms of accuracy and computation time. Biot-savart theory method shows better accuracy; however, its computation cost is much higher than that of the point-dipole model. Finally, we demonstrate the application of the localization system in a closed-loop control using an active capsule endoscope. This work serves as a guide for those who want to develop a localization system using the magnetic induction effect to save the time of analyzing modeling methods.
A Wideband Multifunctional Polarization Converter with Low Loss and High Isolation

A reconfigurable wideband polarization converter with low insertion loss (IL) and high isolation is proposed in this paper, which is based on active metasurface. By controlling the working states of added PIN diodes, the polarizer can maintain the polarization state of incident waves and convert linearly polarized (LP) waves to its cross-polarized waves, left-handed circularly polarized (LHCP) or right-handed circularly polarized (RHCP) waves. The IL is less than 1 dB and the isolation is greater than 70 dB during polarization-maintaining. As a linear-to-linear (LTL) polarizer, the minimum IL and the maximum isolation is 0.45 dB and 44 dB, respectively. The relative 3-dB axial ratio (AR) bandwidth is 43.95% when the polarizer works as a linear-to-circular (LTC) polarizer. Besides, the polarizer can maintain the stability with the incident angle from 0 to 20°. The overlapping band of four functions is 4.42-6.91GHz. The multifunctional polarizer can be widely used in practical applications, such as providing multiple transmission channels for frequency reuse.
Mathematical Modeling of Radiomeasuring Frequency Transducer of Magnetic Field Induction

This paper proposes a scheme of radio measuring frequency transducer of magnetic field induction based on two bipolar and one field effect transistor with two gates. The mathematical modeling of this scheme was carried out. The main characteristics (such as dependences of the active and reactive components of the full impedance, the conversion function, and the sensitivity equation) of the researched radiomeasuring frequency transducer of magnetic field induction are obtained. The proposed scheme provides the measuring of magnetic field induction in the range from 0 mT to 200 mT, and the maximum sensitivity is 620 Hz/mT.
A Kirigami-Based Magnetically Steerable Robotic Catheter for Treatment of Peripheral Artery Disease

Peripheral artery disease (PAD) is a medical condition in which the patient's leg blood vessels are obstructed or narrowed and is typically treated by a percutaneous endovascular intervention (PEI). In a PEI, a catheter is inserted and steered into the affected blood vessel with the goal of opening the blocked artery and restoring the blood flow. Today's manual PEI procedures suffer from low steerability of catheters, which incur challenges to navigate the catheter through complex vasculatures with bifurcations, causing increased radiation exposure and trauma risks in the tissue. Magnetic actuation is a promising approach for non-contact catheter steering. However, prior designs of magnetically steerable continuum robotic catheters often suffer from limited steering capability, high cost, and the lack of intuitive user interface, making them challenging to be deployed clinically. Aiming at the aforesaid challenges, this paper presents a novel magnetically steerable robotic catheter featuring a kirigami-based fabrication procedure. Using the heat-shrinking property of thermoplastic materials, this design offers a novel and cost-effective fabrication approach for the flexible distal segment of the catheter. Further, to completely control the catheter's degrees of freedom, we developed a contactless magnetic actuation system to steer a ring-shaped magnet adhered to the distal end of the catheter and designed a compact friction drive to facilitate its insertion procedure. The obtained Finite Element simulation and experimental results demonstrate high correlation and clearly validate the efficacy and high performance of the proposed fabrication procedure and magnetically steerable robotic system for the considered PEI.
Gait Analysis with an Integrated Mobile Robot and Wearable Sensor System Reveals Associations Between Cognitive Ability and Dynamic Balance in Older Adults

Gait abnormalities and postural instability have been linked to cognitive decline in older adults, however the causal relationships between cognitive capacity and gait is still an open problem. Emerging portable technologies may help elucidate these connections by enabling gait analysis in out-of-the-lab settings, with higher sensitivity than timed gait assessment tests. The purpose of this work was to evaluate the associations between cognitive ability (Montreal Cognitive As-sessment scores) and measures of gait and balance disturbance (spatiotemporal gait parameters, dynamic margin of stability) in a group of older adults, under a dual-task walking paradigm, using an integrated gait analysis system that features a mobile robot and in-shoe sensors. Results of hierarchical regression analyses adjusted for age and gender indicated that decline in cognitive ability in older adults is independently associated with more conservative overground gait patterns (i.e., smaller absolute values of the anteroposterior margin of stability) and increased gait variability (i.e., larger coefficients of variation in stride time and stride velocity) when performing dual-task walking. These results provide proof-of-concept validation of the applicability of integrated robotic and wearable sensors technologies to out-of-the-lab gait analysis in older adults.
Transition Metal Centers in Biological Matrices: Structure and Function of Vanadate in Vanadium Haloperoxidases and as Phosphate Analog

c © 2002 by John von Neumann Institute for Computing Permission to make digital or hard copies of portions of this work for personal or classroom use is granted provided that the copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise requires prior specific permission by the publisher mentioned above.
AGB maser stars as tracers of stellar populations

Masers are known to occur in circumstellar envelopes of AGB stars that are long-period variables and have entered the TP-AGB phase. The maser radiation is intrinsically weak and detection limits pre vent us from seeing maser stars much beyond the Galactic Center. Very deep searches have led to the detection of a handful of maser stars in the Large Magellanic Cloud. Some 1000 stellar masers are known. They trace a population of stars with main sequence masses between 1 and 6 M 0 and ages between 2 and 8 Gyr, the same population that is traced by Cepheids. Their distribution in our Galaxy is the same as that of the red giants, and therefore the stars trace the galactic bar. An outstanding feature is a small disk of radius 150 pc around the galactic center. The disk is in rapid, solid body rotation and leads to a good measurement of the mass density within that distance for Sgr A.
Thermodynamics, strange quark matter, and strange stars

Upon application of both the general ensemble theory and basic thermodynamical principles, we derive in detail the thermodynamics of strange matter with density-dependent particle masses, which resolves the problem of inconsistencies in the thermodynamical properties of the earlier approaches. We then recalculate the properties of strange quark matter with this new thermodynamical treatment and our recently determined quark mass scaling, and find that the density behavior of the sound velocity is opposite to the previous finding, but consistent with one of our recent publications. The structure equations for strange stars are integrated with the presently obtained equation of state. We find that the mass-radius relation is similar to previous results except the maximum mass is smaller in our case if strange quark matter is absolutely stable.
LC-CB: Low Computational Victim Selection Policy in Garbage Collection

NAND flash memory has a disadvantage in that additional work in the flash translation layer (FTL) is required for compatibility with the block interface. In particular, FTL should periodically perform garbage collection (GC) to reclaim free data blocks. Unfortunately, GC includes an expensive erase operation and can cause write amplification (WA), which writes more pages than the system requested. Therefore, it is essential to design a victim selection policy to reduce WA during GC. Greedy and Cost-Benefit (CB) are the most widely known victim selection policies. However, Greedy does not consider locality, and CB suffers computational overhead. This paper proposes Low Computational Cost-Benefit (LC-CB), a novel victim selection policy compensating for these shortcomings. Unlike the existing methods to improve CB, LC-CB changes the operation itself used in the victim selection metric to low computational. This paper describes the constraint required to make a victim selection policy with a low computational overhead and explains that LCCB can consider locality while satisfying these constraints. The experimental results show that our proposed policy can reduce time overhead by 70% compared to CB and reduce WA by up to 30% compared to Greedy.
A Stackelberg Game Approach for Energy Sharing Management of a Microgrid Including Photovoltaic and Wind Turbine Prosumers

In this paper, the problem of energy sharing management of a microgrid including photovoltaic and wind turbine prosumers is considered. To solve this problem, a single-leader multi-follower Stackelberg game approach is performed. In the given approach, as a centralized coordinator, microgrid operator (MGO) conducts energy sharing between the prosumers, and therefore, is taken as the leader. Due to the mission of the leader, the MGO determines internal buying and selling prices in each time slot. Thus, the prosumers, which behave as followers, adjust their consumption values based on the pricing policy of the MGO. Accordingly, the MGO aims to optimize the energy sharing in the microgrid with the maximization of its profit while all prosumers act in their interests. To achieve this purpose, an optimal pricing model is given. Also, a case study is presented to perform the game approach and show its effectiveness.
Chinese Traditional Musical Instrument Computer Intelligent Recognition Based on Recurrent Neural Network

Musical instrument recognition is an important task in the field of music information retrieval (MIR). It helps people search music and identify music types through musical instruments, making music retrieval more accurate. At present, most of the existing research on instrument recognition is oriented to Western instruments, while there is little research on Chinese traditional instrument recognition. To solve this problem, we propose a musical instrument recognition model RMIR-Net based on recurrent neural network (RNN). For feature extraction, we combine Mel frequency cepstral coefficients (MFCC) with its difference coefficients to take full advantage of the static and dynamic characteristics of the audio. We create a diverse dataset of Chinese traditional musical instruments to verify our model. Through experiments, we select leaky rectified linear unit (LReLU) as the activation function for our model. The experimental results show that our RMIR-Net has better accuracy than classical machine learning methods in Chinese traditional musical instrument recognition.
RT-FEND: Spark-Based Real Time FakE News Detection

Fake news is a rampant societal and organizational problem with various social media outlets further aggravating its spread. There is a pressing demand to assist people to identify misinformation from massive amount of news data in a timely manner. Detecting Fake news in a timely manner is critical for mitigating its impact. In this research, we propose a novel approach for detecting fake news in real time, RT-FEND (Real Time- FakE News Detection), which relies on distributed computing paradigm. The proposed methodology utilizes event and topic extraction techniques along with a topic- merging mechanism to process real time news data and reduce the number of topics for managing the curse of dimensionality. We report the findings from several experiments to compare RT-FEND with other systems to benchmark in different system settings. RT-FEND approach is more performance-improved and time-efficient in detecting fake news when compared to other fake news detection baselines.
Application of vehicle digital management system based on driving behavior analysis

On the one hand, the analysis model of safe driving is studied to provide a data model for the analysis of driver's driving behavior. By collecting driving behaviors such as driving times, mileage, acceleration and deceleration, turning, and driving rules, a multi- dimensional safe driving analysis model is established to objectively reflect the real driving situation of the driver, provide a basis for the analysis of the driver's driving behavior, and analyze the basic data of the vehicle. The methods of acquisition and video image acquisition and analysis can effectively intervene in the driver's driving behavior and driving habits, which can minimize the occurrence of traffic accidents. On the other hand, with the driver's driving behavior analysis model as the core, a set of Internet of Vehicles application systems that meet the requirements of improving vehicle utilization, avoiding private use of public vehicles, intelligent vehicle scheduling, and refined expense reimbursement will be constructed to lay the foundation for the development of vehicle management.
Probing tails of energy distributions using importance-sampling in the disorder with a guiding function

We propose a simple and general procedure based on a recently introduced approach that uses an importance-sampling Monte Carlo algorithm in the disorder to probe to high precision the tails of ground-state energy distributions of disordered systems. Our approach requires an estimate of the ground-state energy distribution as a guiding function which can be obtained from simple-sampling simulations. In order to illustrate the algorithm, we compute the ground-state energy distribution of the Sherrington–Kirkpatrick mean-field Ising spin glass to 18 orders of magnitude. We find that if the ground-state energy distribution in the thermodynamic limit is described by a modified Gumbel distribution, as previously predicted, then the value of the slope parameter m is clearly larger than 6 and of the order of 11.
Data augmentation in semi-supervised adversarial domain adaptation for EEG-based sleep staging

The upcoming era of wearable health monitoring devices has created a need for automated signal processing algorithms that can be trained with a minimal amount of labeled data. In our previous work, we showed that transfer learning techniques like semi-supervised adversarial domain adaptation can help to achieve this. We applied our method to remote sleep monitoring, by performing sleep staging on single-channel wearable EEG signals. In this work, we propose data augmentation to help in tackling this challenge. By using an artificially increased amount of labeled data, our semi-supervised adversarial domain adaptation method improves its performance on the wearable EEG data. The accuracy is increased consistently by 0.6% to 1.4% relative to the results without augmentation. As both adversarial domain adaptation and data augmentation are strategies to deal with the scarceness of data, we conclude that these methods are can effectively be combined to surpass their individual performance.