Miracle-dz/UniFAR-FTUs huggingface dataset:
A pilot case study for developing a software for human emotion recognition using multimodal data

In developing software to analyze the emotions of patients with schizophrenia using multimodal data, a pilot case study was conducted in order to examine its accuracy in a healthy subject. This study shows a low agreement and reliability between the MTCNN and the subjective evaluation of the three examiners based on the result of the ICC and Cronbach's alpha coefficient. However, it can be revealed that the Multi-Task Cascaded Convolutional Networks (MTCNN) facial expression recognition and the Heart Rate Variability (HRV) analysis showed consistent results. Subject who experienced anticipated feelings of happiness when the conversation was focused in the subject's favorite food, which showed an increased in HFnu, indicating increased parasympathetic activity. It was considered that the subject felt that the conversation with the robot was lively and empathetic. Findings suggest that MTCNN can be used in combination with HRV analysis in determining the facial expression of an individual. However, further research should be done involving additional subjects in order to ascertain the validity and reliability of the MTCNN.
Hybrid Attention Fusion Segmentation Network for Diffuse Large B-cell Lymphoma in PET-CT

Diffuse large B-cell lymphoma (DLBCL) is a type of lymphoma with a high incidence in Asia. Positron emission tomography-computed tomography (PET-CT) is usually used as the evaluation means for DLBCL. Theoretically, the effective combination of the PET and CT can display the shape, size and location of the tumor. In practice, manual lesion segmentation of PET-CT is time-consuming. Hence, in this work, we design a hybrid attention fusion segmentation network (HAFS-Net) for automatic segmentation task. Most works only pay attention to extract information from multi-modal images, but ignore the potential correlations between them, which is ineffective for segmentation. In contrast, our network combines hybrid attention mechanism and PET-CT feature fusion module, which can fully mine correlations between multi-modal information. Specifically, the hybrid attention exploits the tumor region enhancement properties of PET to guide segmentation on CT. And the irrelevant noise regions on CT which interfere with the segmentation will be suppressed. In PET-CT feature fusion module, the supervision information (attention fusion map on PET-CT) is efficiently applied to assist segmentation. Extensive experiments demonstrate that the proposed framework can effectively complete the task of multi-modal medical image segmentation.
Multi-connection BP Decoding for Polar Codes

In this paper, we develop a multi-connection belief propagation (BP) decoding algorithm for polar codes, which employs the idea of the residual neural network to accelerate the convergence. Specifically, multi-connection BP decoding builds on the standard BP decoding by fusing the soft information of the current decoding iteration and the past decoding iterations proportionally according to the damping factor during each iteration. Moreover, we adopt the particle swarm optimization algorithm to obtain the optimal value of the damping factor to balance a trade-off between the error rate performance and decoding complexity. The suggested approach can outperform the standard BP decoding with lower iterations, according to simulation findings, which demonstrate that it can reduce error rates.
Level-Set Computations of Free Surface Rotational Flows

A numerical method is developed for modelling the violent motion and fragmentation of an interface between two fluids. The flow field is described through the solution of the Navier-Stokes equations for both fluids (in this case water and air), and the interface is captured by a Level-Set function. Particular attention is given to modelling the interface, where most of the numerical approximations are made. Novel features are that the reintialization procedure has been redefined in cells crossed by the interface; the mass across the interface has been smoothed using an exponential function to obtain an equally stiff variation of the density and of its inverse; and we have used an Essentially Non-Oscillatory scheme with a limiter whose coefficients depend on the distance function at the interface. The results of the refined scheme have been compared with those of the basic scheme and with other numerical solvers, with favourable results. Besides the case of the vertical surface-piercing plate (for which new laboratory measurements were carried out) the numerical method is applied to problems involving a dam-break and wall-impact, the interaction of a vortex with a free surface, and the deformation of a cylindrical bubble. Promising agreement with other sources of data is found in every case.
Multitask Scheduling of Computer Vision Workload on Edge Graphical Processing Units

The increasing urbanization in developing countries calls for more efficient and safer transportation systems. A key technique used to enhance such efficiency and/or safety is to utilize running of computer vision algorithms to identify obstructions that may come up, and notify vehicles in real-time. Such real-time detection and notification requires sufficient computation resources located logically and physically close to the cameras. While utilization of edge compute devices has been proposed in the literature, it is unclear how such devices with heterogeneous processing units can handle real-time detection while multi-tasking. In this work, we profile the performance of a few devices with embedded and desktop-quality GPUs, and show that the performance while multi-tasking can be modeled as a submodular function. We utilize this observation to model load-balancing of camera videos as an instance of a submodular welfare problem, and solve it using a greedy algorithm. Our extensive trace-driven simulations show that our technique outperforms the baseline by over 40%.
Continuous Attributes Discretization Algorithm based on FPGA

The paper addresses the problem of Discretization of continuous attributes in rough set. Discretization of continuous attributes is an important part of rough set theory because most of data that we usually gain are continuous data. In order to improve processing speed of discretization, we propose a FPGA-based discretization algorithm of continuous attributes making use of the speed advantage of FPGA. Combined attributes dependency degree of rough ret, the discretization system was divided into eight modules according to block design. This method can save much time of pretreatment in rough set and improve operation efficiency. Extensive experiments on a certain fighter fault diagnosis validate the effectiveness of the algorithm. DOI: http://dx.doi. org/10.11591/telkomnika.v11i7. 2811
Kinematics of Nonzero Constant Mean Curvature Surface Models using a Kenmotsu-type Representation Formula

A simple functional expression giving multiple geometries is important for the kinematics of a smooth-surfaced robot. Such a representation gives the functions of tangent vectors and Jacobian matrices and reduces the computational time of forward and inverse kinematics. In this paper, we construct the kinematics of robotic surfaces using an equation for rotational surfaces called the Kenmotsu-type representation formula. The formulas in this paper switch between various nonzero constant mean curvature surfaces, namely, spheres, cylinders, unduloids, and nodoids. Forward kinematics and numerical simulations confirm the short computation time of these formulas; moreover, the local coordinate system on the robot is a Cartesian coordinate system. We also present an iterative algorithm for inverse kinematics based on the Newton-Raphson method.
Dynamic Pricing using Reinforcement Learning in Hospitality Industry

Hotel room pricing is a very common use case in the hospitality industry. Such use cases take dynamic pricing strategies for setting optimum prices wherein prices are dynamically adjusted based on user engagement. However, it is challenging to design an approach that makes pricing dynamic with respect to complex market change. In this paper, we suggest a reinforcement learning based solution for this problem. The approach employs a Deep Q-Network (DQN) agent trained to recommend/suggest optimum pricing strategies which maximizes the total profits for a day. In addition, the pricing strategy is optimized in such a way that empty rooms remain minimal. A real-life hotel-bookings data set is being used for testing this approach. The data is aggregated and preprocessed before being used for the task. The pricing strategy is influenced by the hotel-demand, type of rooms, number of nights and other variables. The hotel-demand is derived from a Random-forest model trained on the processed data to simulate original demand distribution of processed data. Using the DQN based dynamic pricing strategy, a potential 15–20 percentage higher reward(profits) were obtained compared to fixed pricing, and rule-based pricing strategy. At the same time the empty rooms left were significantly lower for the DQN based approach.
A variable-rate image coding scheme with vector quantization and clustering interpolation

The authors propose a simple image coding algorithm using VQ (vector quantization) and clustering interpolation, which avoids an explicit interpolation operation by employing different but related codebooks at the encoder and the decoder of a vector quantizer. Computer simulations indicated that images reconstructed with clustering interpolation had smaller mean square errors than those obtained with linear or contour-based interpolation. In order to reduce coding artifacts and to improve reconstruction quality at low bit rates, an adaptive corrector stage with block classification is also included. Computer simulations demonstrate the effectiveness of the algorithm at about 0.3 b/pixel. <<ETX>>
FPGA based Power-Efficient Edge Server to Accelerate Speech Interface for Socially Assistive Robotics

Socially Assistive Robotics (SAR) is a sustainable solution for the growing elderly and disabled population requiring proper care and supervision. Internet of Things (IoT) and Edge Computing can leverage SAR by providing in-house computation of connected devices and offering a secure, autonomous, and power-efficient framework. In this study, we have proposed using a System-on-Chip (SoC) based device as an edge server, which provides a local speech recognition interface for connected IoT devices in the targeted area. Convolutional Neural Network (CNN) is used to detect a set of frequently used speech commands which are useful to control home appliances and interact with assistive robots. Proposed CNN achieves state-of-the-art accuracy with a meager computing budget. It delivers 96.14% accuracy with a 20X smaller number of parameters and 137X fewer Floating Point Operations (FLOPS) compared to similarly performing CNN networks. To address the challenge of latency requirement for practical applications, parallelization of CNN helped to achieve 6.67X times faster inference speed than its base implementation. Lastly, implementing CNN on SoC-based edge device achieved at least 5X and 7X reduction in net power consumption compared to GPU and CPU devices respectively.
Attenuation of motion artifact in near infrared spectroscopy signals using a wavelet based method

The sensitivity of Near Infrared Spectroscopy (NIRS) signals to motion artifact can limit practical applications of NIRS monitoring. We describe a wavelet based method for removing motion artifact from NIRS signals. This method was tested on monitoring data collected from leg muscle in 3 patients undergoing surgery to stabilize leg fractures. The results show an average of 18.36 dB attenuation in motion artifact energy in the NIRS signal for our test subjects when using the proposed method, without the introduction of significant distortion in the artifact-free regions of the signal.
Fast Backward Electromagnetic Surface Waves Directed by Boundary Between Vacuum and Mu-Negative Metamaterials

We present a surface electromagnetic eigenmode of a plane separating the negative permeability metamaterial and vacuum. The wavefields decreased exponentially out of the interface between the media. This wave is of TE-polarization with an electric field paralleling the interface. Mode is a fast wave because its phase velocity is more than the speed of light. Besides. , the mode is a backward wave since the phase and group velocities have opposite directions. Such fast surface electromagnetic waves can be used for designing modern so-called “fast-waves devices” for both wave generation and amplification.
Heart Rate Variability and Body-Movement Extractions using Wi-Fi Channel State Information

Sleep is an important physiological process and is known to be closely related to human health. Polysomnography is a common method for analyzing sleep cycles. However, this method is not suitable for routine monitoring because it requires that the patient wears multiple sensors and the results need to be interpreted by a physician. The channel state information obtained from commodity Wi-Fi devices can be used to analyze complex multipaths and enables noncontact measurement of biometric information. In this study, we propose a non-contact extraction system of heart rate variability and body-movements as datasets for classifying sleep stages. The heart rate variability analysis, which uses variational mode decomposition, shows an average accuracy of 88.9 ms within 1 m between the transmitter and receiver. In addition, the proposed body-movements analysis system was shown to provide highly accurate estimation in real-time.
Fixed-time lag synchronization of second-order fuzzy complex-valued neural networks with discontinuous functions

This paper is dedicated to p-norm fixed-time lag synchronization(FXTLS) of second-order fuzzy complex-valued neural networks(FCVNNs). Differential inclusion theory is utilized to deal with discontinuous systems. The first-order real-valued differential equations are obtained from the second-order FCVNNs by reduced-order transformation and dividing the real and imaginary parts of the complex-valued parameters. Based on Lyapunov stability theory and some inequality tricks, two complex-valued control schemes are designed to guarantee FXTLS. Finally, numerical simulation is presented in order to show consistency with the theoretical outcomes obtained in this paper.
Driving Assistance Decision Method Based on Dynamic Driving Map

Aiming at the problem of vehicle driving map modeling, this paper puts forward a method of constructing dynamic driving map(DDM). Firstly, the intention of road vehicles is identified based on hidden Markov model, which is regarded as one layer of driving map. Then, this paper hierarchically processes and fuses the current environment from four aspects: vehicle movement information, lane information, vehicle and obstacle information and vehicle prediction information, and obtains the drivability probability of each unit. Then, based on DDM, the behavior decision-making cost equation is put forward, and the decision-making model of vehicle driving behavior with minimum cost is designed by using the idea of dynamic programming. Finally, through the simulation verification, the results show that the dynamic driving map designed in this paper can accurately and effectively express the complex road environment compactly, and give reasonable driving advice according to the specific environment, which has good robustness.
Modified similarity metric for non-local means algorithm

The non-local means (NLM) algorithm exploits the self-similarity property of images to suppress noise. Similarities are measured by neighbourhood comparisons; however, the original NLM algorithm uses only translational neighbourhoods, which do not fully exploit the symmetric characteristics that exist in many images. Several neighbourhood transformations are introduced for similarity comparison so that the self-similarity property of images can be better exploited. Experimental results show that, compared with the original algorithm, substantial PSNR improvements can be achieved when appropriate neighbourhood transformations are used in the similarity comparison.
A detailed study of the design, construction and cryo-operation of an HTS magnet

The design, construction, and characterization of a high temperature superconducting (HTS) magnet is described. The design stage has involved the development of computer software for the calculation of the critical current of a solenoid wound from anisotropic HTS conductor. This calculation can be performed for a variety of problems including those involving magnetic materials such as iron by the incorporation of finite element electromagnetic analysis software. This has enabled the optimization of the magnet's performance. The HTS magnet is wound from 190 m of silver-matrix Bi/sub 2/Sr/sub 2/Ca/sub 2/Cu/sub 3/O/sub 10/ powder-in-tube tape conductor. The dimensions are 70 mm bore and 70 mm length, and it consists of 728 turns. Iron end-plates were utilized in order to reduce the radial magnetic field, and consequently increase the performance by /spl sim/20%. The magnet has been operated in liquid cryogens and has achieved engineering current densities of 900 A cm/sup -2/ at 77 K and 6680 A cm/sup -2/ at 4.2. K. The HTS magnet has been operated by conduction-cooling on a mechanical refrigerator at various temperatures in the range 12 to 50 K. The thermal stability in this relatively low cooling-power system has been assessed. An engineering current density of 5600 A cm/sup -2/ was achieved at 12 K.
Tunable Fine-grained Clock Phase-shifting for FPGAs

High-resolution phase shifters have important practical applications in PET scanners, time-to-digital converters, and characterizing of the FPGA resources. This paper presents a fine-grained clock phase-shifting technique based on the FPGAs' clock managers' dynamic phase shifting capability that is commonly available on all recent FPGAs. Our method allows adjusting the phase shift resolution in the sub-picosecond range independent of the operating frequency. Experiments carried out on a Xilinx UltraScale+ FPGA show that phase-shifting resolution can be adjusted down to 88 f s in these devices. To verify the performance of this method, we have deployed it in a delay characterization circuit to measure the FPGA's resources delays. The experiments show that we can measure path delays below 1 ns which is impossible in conventional frequency sweep-based methods and we reach a much finer time resolution.
Automatically Restructuring HDL Modules for Improved Reusability in Rapid Synthesis

Implementing nontrivial HDL designs can take a lot of time. Particularly for FPGAs, vendor tools tend to become slower, since the devices grow and thus, also the designs grow. It is therefore desirable to create mechanisms that speed up the implementation. Combining pre-implemented blocks to build the final design can be one such mechanism. It can help to reduce the time required for incremental builds, or it can reduce the time required to build families of designs. Yet, typical HDL code is not structured for this purpose. Many modules do not have the right size to be used as pre-implemented blocks. In this paper, we present a methodology to automatically analyze and modify existing HDL code such that the resulting module structure fits the purpose of pre-implementing the modules. To this end, we try to isolate parameters of the HDL code such that we have to reimplement only a small number of modules after a parameter change. The resulting tool is available as open-source software. We have tested our methodology using multiple different benchmark sets, which in total contain thousands of modules. On average, we can extract around 10% of the parameters into smaller modules.
Research on intelligent truck platoon control of the East China Sea Bridge based on variable time headway

To solve the common problems of high wind speed and complex road environment of the cross-sea bridge, a three-degree-of-freedom truck estimation model based on a semi-empirical magic tire model was firstly established in this paper. Real-time road adhesion coefficient estimation was designed based on the theory of Unscented Kalman Filter (UKF), and the spacing strategy was designed by using the obtained parameters. Secondly, a longitudinal dynamic model between trucks is established, a cooperative adaptive control system based on improved spacing strategy is designed, and a robust non-singular fast terminal sliding mode(NFTSM) controller is designed to resist the disturbance of wind speed. Finally, taking the East China Sea Bridge as an example, the longitudinal driving safety of trucks under different weather conditions is evaluated by setting up Simulink and TruckSim co-simulation model, taking truck spacing and longitudinal speed as indicators. The results show that with the decrease of the road adhesion coefficient, the front spacing of the improved CACC system ensures driving safety. The truck can quickly control self-speed and follow the speed of the front truck in time, and the overall driving state of the team is stable.
Principles and parameters in prosodic phonology

That is, the linear sequence of segments is organized into higher-level units to which certain prosodies (i.e. suprasegmental phenomena) (such as tone, length, nasalization, etc.) may be assigned. A fourth historical root of prosodic phonology can be found in the work of Kenneth L. Pike and his followers. For instance, McMahon (1967: 128) writes that the following phonological hierarchy must be assumed for Cora, a language of Mexico:
A Fault Diagnosis Method for Planetary Gearboxes Based on Signal Decomposition and Density Peaks Clustering

Planetary gearbox is the indispensable part in the production equipment so that fault diagnosis for it can ensure the production safety in practice. For fault diagnosis of planetary gearboxes, this paper proposes a fault diagnosis method combined the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the refined composite multiscale dispersion entropy (RCMDE). First, the CEEMDAN method is used to decompose the signals to obtain a series of IMF components. Then, select several IMF components with relatively high and concentrated energy as valid IMF components to be quantized by RCMDE and form the feature matrix. Next, the principal component analysis method is employed to reduce the dimension of feature matrix and extract the effective information in signals. Finally, the simplified features are sent into density peak clustering (DPC) to obtain the fault diagnosis results. Compared with different methods, the experimental results show that, the method can more effectively extract the information of the original signals and DPC can accurately diagnose the states of planetary gearboxes faults.
Measuring Cosmic Ray Composition at the Knee with SPASE-2 and AMANDA-II

Important information pertaining to the origin of high-energy cosmic rays can be gained by studying their mass composition in the region of the knee ( 3 PeV). Thus, air showers have been ob- served at the South Pole using the SPASE-2 detector, which measures the electronic component at the surface, and the AMANDA-II neutrino telescope, which measures the coincident muonic component in deep ice. These two components, together with a Monte Carlo simulation and a well-understood analysis method, yield the relative cosmic ray composition in the knee region. We report on the efcac y of a new neural network technique for obtaining a composition result with the SPASE-2/AMANDA-II detectors.