Miracle-dz/UniFAR-FTUs huggingface dataset:
Bibliometric analysis of the core thesis system of Interaction Design Research on Human-Computer Interaction

Human-computer interaction (HCI) attracts more and more attention in automation, multimedia retrieval, computer system, universal computing. Meanwhile, it also poses new challenges to interaction design based on HCI in user experience, aesthetics, game design, and education]. This study aimed to analyze and assess literature published in the field of interaction design of HCI. Methods: Web of science was used to retrieve literature of interaction design. The study period was set from 1985 to 2020, a total of 3243 documents were published. Pyecharts were used to present geographical distribution of publications and VOSviewer was used for data visualization. Results: This article uses standard bibliometric indicators to demonstrate five main aspects: Publications growing, Geographical distribution of publications, citation analysis, research productivity, and author keywords clusters analyze. Conclusion: Through scientometrics analysis, it is found that the current research hotspots are mainly concentrated in 5 clusters: (a) UI for user-centered design, (b)HCI. (c)interaction design, (d)Intelligent interactive recognition research, (e)e-health and health information. According to the research frontier of interaction design research on HCI, researchers can get a clearer research status. Also, by showing the research fronts of interaction design of HCI, the scientometric analysis also highlights the research gap at the increasing importance of user health and the sustainability of interaction.
Multiple Filter-Based Rankers to Guide Hybrid Grasshopper Optimization Algorithm and Simulated Annealing for Feature Selection With High Dimensional Multi-Class Imbalanced Datasets

DNA microarray data analysis is infamous due to a massive number of features, imbalanced class distribution, and limited available samples. In this paper, we focus on high-dimensional multi-class imbalanced problems. The high dimensional and multi-class imbalanced problem has posed acute challenges for the conventional classifiers to effectively perform classification tasks on both the minority and majority classes. Numerous efforts have been devoted to addressing either high dimensionality dataset or class imbalance problems. Nonetheless, few methods have been proposed to address the intersection of multi-class imbalanced and high-dimensional problems concurrently due to their intricate interactions. This paper presents novel hybrid algorithms for feature selection with the high dimensional multi-class imbalanced problem using multiple filter-based rankers (MFR) and hybrid Grasshopper optimization algorithm (GOA). The Simulated Annealing (SA) algorithm is incorporated into GOA. SA is used to enhance the best solution found by the GOA algorithm. The aim of using the SA here is to tackle the slow convergence and improve the exploitation by searching the high-quality regions found by the GOA. The experimental results confirm the effectiveness of the proposed methods in improving the classification performance in terms of area under the curve (AUC) compared to other well-known methods, which guarantees the ability of the proposed methods in searching the feature space and identifying very robust and discriminative features that best predict the minority class.
Research on Innovation of Automobile Marketing Mode Based on Big Data Marketing

With the rapid development of the Internet, the application of big data is more and more extensive. Compared with traditional marketing, big data marketing has significant advantages in market information acquisition, data processing and analysis, and data application. For the automotive industry, the traditional marketing mode is facing severe challenges due to the development of big data technology. In view of this form, a new marketing mode based on big data marketing, which is called precision marketing, came into being. The new automobile precision marketing mode based on big data can be implemented from the following aspects: precise positioning based on big data analysis, accurate push based on big data mining, and fine management based on big data application.
BCG & ECG-based secure communication for medical devices in Body Area Networks

An increasing amount of medical devices, such as pacemakers or insulin pumps, can communicate in wireless Body Area Networks (BANs). While this facilitates the interaction between users and medical devices, something that was previously more complicated or - in the case of implanted devices - often impossible, it also raises security and privacy questions. We exploit the wide availability of ballistocardiographs (BCG) and electrocardiographs (ECG) in consumer wearables and propose MEDISCOM, an ad-hoc, implicit, and secure communication protocol for medical devices in local BANs. Deriving common secret keys from a body's BCG or ECG signal, MEDISCOM ensures confidentiality and integrity of sensitive medical data. It also continuously authenticates devices, requiring no explicit user interaction and maintaining a low computational overhead. We consider relevant attack vectors and show how MEDISCOM is resilient towards them. Also, we validate the security of our protocol's secret keys on BCG and ECG data from 29 subjects.
A Circuit Implementation of Random Number Generator Utilizing Memristor Stochasticity

A random number generator is implemented utilizing the intrinsic stochasticity of memristor as a natural physical randomness source. The random bits are produced by cyclically switching the memristor and comparing the memristor resistive values in the high resistive state with the reference value, taking advantage of the more pronounced resistance variation in the high resistive state. Using the alternative voltage pulse scheme in the designed random number circuit, the biasness of the random numbers is largely alleviated, then the standard randomness test suite developed by NIST is used to validate the feasibility. Moreover, several memristors in parallel are considered to generate different frequency random number at the sacrifice of area overhead. The random number generation is simulated in the circuit simulated tool PSPICE. This approach improves the memristor-based stochastic circuit computer aided design.
DA-GAN: Learning Structured Noise Removal In Ultrasound Volume Projection Imaging For Enhanced Spine Segmentation

Ultrasound volume projection imaging (VPI) has shown to be appealing from a clinical perspective, because of its harmlessness, flexibility, and efficiency in scoliosis assessment. However, the limitations in hardware devices degrade the resultant image content with strong structured noise. Owing to the unavailability of reference data and the unpredictable degradation model, VPI image recovery is a challenging problem. In this paper, we propose a novel framework to learn the structured noise removal from unpaired samples. We introduce the attention mechanism into the generative adversarial network to enhance the learning by focusing on the salient corrupted patterns. We also present a dual adversarial learning strategy and integrate the denoiser with a segmentation model to produce the task-oriented noiseless estimation. Experimental results show that the proposed method can improve both the visual quality and the segmentation accuracy on spine images.
Multi-Column Matrices Selection Combined With k-Means Scheme for Mobile OCC System With Multi-LEDs

In mobile optical camera communication (MOCC) system, more than one LED is used to simultaneously illuminate and communicate. In the letter, the MOCC system with multi-LEDs and a mobile receiver is experimentally investigated. To avoid the signal interference between two LEDs and alleviate the performance degradation caused by the movement of the receiver, multi-column matrices selection (MCMS) combined with k-means based decoding (KMD) scheme is proposed and experimentally demonstrated. The experimental results show that, the proposed scheme can achieve the bit error ratio (BER) of $8.74\times 10^{-6}$ at the moving speed of 20cm/s. And the data rate is 4.2 Kb/s.
Data labelling in the wild: annotating free-living activities and Parkinson's disease symptoms

This paper looks to explore the challenges faced when producing a set of annotations from videos produced by a pilot study evaluating 24 participants (12 with Parkinson's disease, each accompanied by a healthy volunteer control participant) who are free-living in a house embedded with a platform of sensors. We discuss the outcome measures chosen to annotate from the videos and the controlled vocabularies formulated for this task, the tools and processes, how we intend to achieve standardisation and normalisation of the annotations, and how to improve quality and re-usability of the annotation dataset.
SA-Net: A Sequence Aware Network for the Segmentation of the Left Atrium in Cine MRI Datasets

The segmentation of the left atrium (LA) in CINE MRI is a prerequisite for the calculation of LA functional parameters and may be useful when selecting treatments for atrial fibrillation patients. CINE MRI usually captures both the LA and the left ventricle. The similarities between the LA and other cardiac structures complicate the segmentation of the LA and can lead to poor performance of standard 2D segmentation networks. In this study, we present SA-Net, a deep network which implicitly discriminates LA slices from non-LA slices during segmentation, with a sequence modulator using interslice correlations in a global context. Our experiments, conducted on an in-house dataset with 4710-mm thick bSSFP MR image stacks, show that SA-Net leads to good quality segmentation of the LA, with a mean Dice score of 0.89 and a mean Jaccard index of 0.80, outperforming the U-Net.
Sim2RealQA: Using Life Simulation to Solve Question Answering Real-World Events

As smart speakers continue to proliferate, question answering (QA) by smart devices is being woven into our daily lives. This study assumes question answering related to daily life events detected by context recognition systems, such as activity recognition and indoor positioning systems, e.g., answering questions like “Did my grandma eat dinner? ” and “How many times did my grandpa go to the toilet? ” These questions can effectively support human memory-aids, locate lost items, and monitor human activities. However, training a question-answering model requires large amounts of labeled training data (i.e., questions, answers, and the time-series of real-world event triplets) collected in a target environment. In this paper, we propose a novel simulation to real QA (Sim2RealQA) framework that completely trains a QA model with QA datasets produced in a life simulator and use it for solving real-word QA problems without answer labels. Our proposed QA model can learn a general reasoning process for QA that is independent of environments and deal with diverse types of questions specific to question answering in real-world environments, e.g., counting the number of occurrences of a real-world event and enumerating the names of those who are performing an activity together. Experiments show that using life simulations is a promising approach for solving real-world QA problems when no real-world answer labels are available.
Low-Resolution Quantizer’s Output Signal Analysis in Presence of Multipath Fading Effects

The research performed the statistical analysis of nonlinear distortions caused by the ADC unit in presence of the additive white Gaussian noise and the multipath fading effects described with the Nakagami-m envelope distribution. The ADC assumes to operate within the arbitrary input process levels including the overload situation that is being resolved with either the saturation or the reset. For all the assumed cases the input and output process’ probability density functions were derived and the expressions for arbitrary order moments were obtained and applied for output variance calculation. The effects of energy suppression, induced by the quantizer’s nonlinearity are analyzed as a function of its bit-resolution, input process root-mean-square level and fading severity. The obtained results can be helpful for various problems of communication quality calculation, for instance for the problem of white space energy base detection in cognitive radio networks.
An Extensive Review of Shortest Path Problem Solving Algorithms

Shortest Path Algorithms (SPAs) are established for solving shortest path problem (SPP) mainly classified into various types. Advanced concepts are derived for solving the problem of shortest path ensured via various algorithms. The key purpose of this article is to give a detailed overview to SPP besides problem solving algorithms. The problem of shortest path is represented as graph and solved by utilizing different algorithms based on the application. The main goal of SPA is shortening the inclusive cost and distance. The application of artificial intelligence in the shortest path algorithms had described in a separate section. The time complexity of the different algorithms presented in the various literatures are discussed. From the profound survey this work suggests some of the future directions to the work. This examination proves that the performance varies between various algorithms which are used to resolve detailed variations of SPP.
Intensity-Based Wasserstein Distance As A Loss Measure For Unsupervised Deformable Deep Registration

Traditional pairwise medical image registration techniques are based on computationally intensive frameworks due to numerical optimization procedures. While there is increasing adoption of deep neural networks to improve deformable image registration, achieving a clinically suitable solution remains scarce. One of the primary difficulties lies in the choice of tractable distance functions to assess image similarity. Recent works have explored the Wasserstein distance as a loss function in generative deep neural networks. In this work, we evaluate a fast approximation variant — the sliced Wasserstein distance — for deep image registration of brain MRI datasets. Based on a VoxelMorph backbone architecture, which includes a combination of UNet and spatial transformer networks (STN) for deformable registration, we propose three implementation variants to compare the model’s performance: the standard sliced Wasserstein, the Radon transform performing a low dimensional embedding, and a novel patch-based method that allows fine-grained deformation comparison. Experiments performed on public datasets of brain images from the Learn2Reg open challenge demonstrate the Wasserstein methods converge faster than the baseline mean square error method, with the proposed patch-based method yielding similar performance to baseline methods, and improved overall accuracy compared with other implementations. This makes the sliced Wasserstein a valuable metric for deep mono-modal and multi-modal deformable medical image registration problems with our proposed implementation.
A 120–150 GHz Power Amplifier in 28-nm CMOS Achieving 21.9-dB Gain and 11.8-dBm Psat for Sub-THz Imaging System

This paper presents a high-gain D-band power amplifier (PA) fabricated with 28-nm CMOS technology for a sub-terahertz frequency modulated continuous wave imaging system. It adopts two-channel power combining using artificial transmission lines to absorb the parasitic capacitance of the ground-signal-ground pad. The layout of the transistors and neutralization capacitors are optimized to improve the maximum stable gain, stability, and robustness. Asymmetrically magnetically coupled resonators are used in inter-stage and input matching networks to extend the operating bandwidth. The PA achieves a peak power gain of 21.9 dB and maximum output power of 11.8 dBm with 10.7% of power-added efficiency. Also, this PA can achieve higher than 10 dBm output power over the frequency range of 120–150 GHz.
Investigation of Relation between Physiological Responses and Personality during Stress Recovery

Stress is seen as an individual's reaction to external circumstances that are perceived as a threat. Reactions to stress are highly subjective in nature, depending upon numerous individualistic factors. The study of stress recovery and associated coping efforts can help mitigate adverse health effects. Therefore, understanding the interplay of psychological and physiological manifestations of stress in modeling the stress recovery patterns is of high importance. Previous studies have indicated an association between personality traits and physiological responses. However, definitive evidence for this association is lacking. This work attempts to investigate the correlation between personality traits, such as neuroticism and extraversion, and physiological responses such as electrocardiogram and salivary cortisol responses, to the Trier Social Stress Test. Gaussian mixture modeling technique is employed to automatically cluster individuals based on their personality traits and electrocardiogram responses. Simultaneously, individuals are classified based on changes in salivary cortisol levels. Resulting clusters are labelled based on the literature on stress recovery. The relationships between personality and physiology groups are investigated. Reduced stress recovery observed via salivary cortisol responses is associated with higher neuroticism and lower extraversion, as well as attenuated electrocardiogram recovery responses. Higher cortisol reactivity during stress is found to be positively associated with higher cortisol recovery. Therefore, the study implies that consideration of personality traits is likely to aid stress detection and recovery models.
Optimized Robot Mapping and Obstacle Avoidance using Stereo Vision

Robot Mapping for an unknown environment with no priori map by using computer vision was developed. In this paper, stereo vision is used and applied to Autonomous Guided Vehicle (AGV) to perform range finding and extract surrounding features. A region in the rectified image pair is selected as Region of Interest (RDI) for optimization and the RDI is processed to obtain disparity map which helps to estimate distance from obstacles. For real-time robot application, the image processing time must be reduced to be competitive with other active sensors. For obstacle avoidance, the mobile robot with the system implemented able to avoid obstacles within the test field. A 2D occupancy map is able to be constructed using the depth information obtained through stereo vision. The processing time of the system is optimized by the RDI implementation. The time taken is reduced for image processing and the final processing time per frame is 0.2143 s.
Three Dimensional Synthetic Non-Ellipsoidal Nuclei Volume Generation Using Bézier Curves

Automated segmentation of cell nuclei is used to analyze individual cells to determine the number of nuclei in a 3D volume. Deep learning approaches that segment nuclei require large amounts of annotated (ground truth) microscopy volumes for training. In many cases acquiring large amounts of annotated volumes may not be possible and data augmentation methods must be used. One approach has been the use of synthetic volumes for training. Alternate methods employ spherical and ellipsoidal nuclear models for synthetic ground truth generation, resulting in segmentation that does not accurately match nuclei morphology. In this paper, we present a technique to generate synthetic non-ellipsoidal nuclei microscopy images using Bézier Curves. We test our approach by training a modified 3D U-Net. Our results indicates that our synthetic non-ellipsoidal nuclei approach achieves improved segmentation on volumes with irregularly shaped nuclei.
Dynamic Patterns of Scattering for Phased Array Modeling

A dynamic pattern for a given direction is determined as a set of values of the electromagnetic fields radiated by the phased array or scattered by closely located objects. An asymptotic correspondence of two- and three-dimensional antenna theory problems based on nontrivial formulation of the Lorentz lemma is considered. The mathematical model that includes phased array and scattering objects is formulated as a system of the integral equations of a block structure. Such a formulation is useful for supercomputer modeling in the case of various initial data. We consider a case that is important for practical application, when the scattering objects are metals with finite conductivity. The Leontovich impedance boundary conditions are used for these objects. It is shown that the numerical solution of such a mathematical model requires the use of a specific variant of the perturbation method. The obtained results for dynamic patterns of scattering can be used to correct the initial phase distribution of the array.
Analysis and Research on5G Medical and Health Technology User Adoption Behavior and Influencing Factors

Purpose: This paper mainly hopes to promote the future development of 5G medical and health technology by exploring the significant factors affecting the adoption of 5G medical and health technology users. Method: Based on the Technology Acceptance Model (TAM) [1], five new variables including perceived risk, technical service price, individual innovation, perceived service ubiquity, and social impact are added to construct the initial model of 5G medical and health technology users’ adoption; the reliability and validity of the questionnaire survey results are analyzed, the hypothetical relationship of each variable is measured through the structural equation model, and the initial model is adjusted; based on the adjusted model, policy recommendations are made. Conclusion: To promote the development of 5G medical and health technology, it is necessary to strengthen market regulations and government supervision, provide transparent and reasonable prices for medical and health services, and ensure the professionalism, effectiveness and simple portability of products, and pay attention to the protection of user privacy data. At the same time, it is also necessary to cultivate the user’s individual innovation and cultivate the user’s use habits.
A Method for Estimating Doctor's Fatigue Level in Operating a Surgical Robot Using Wearable Sensors

Robot-assisted laparoscopic surgery, such as the da Vinci Surgical System, has a problem in that surgeons might continue operating for a long period of time without realizing their fatigue and without proper concentration, or they might ignore their fatigue even when they notice it. We propose a method for quantitatively estimating the level fatigue by attaching wearable sensors to a doctor while using a surgical robot. In this study, several sensor configurations were tested to investigate the sensor configuration, which is easy for the surgeon to wear. In an evaluation experiment, doctors used a robotic surgery simulator for a long period of time, and the score calculated by the simulator was estimated as the fatigue level expressed by the surgeon. We discussed with doctors how the results of the fatigue estimation should be applied to the system design.
Simple Analytical Modelling of an Electronically Tunable Potentiometer and Body Factor Influence

This paper presents a simple analytical modelling of a Electronically Tunable Potentiometer (ETP) circuit, made of a pseudo-resistor pair and a feedback mechanism that keeps resistance invariant to common mode voltage. The modelling utilizes the first order quadratic equations for the MOS transistor. In order to validate the analytical model, a Verilog-A model was written using the same MOS equations, and later matched to the ibm 130nm technology node resulting in a satisfactory approximation, with relative errors within 15%. Analysis of the ETP control voltage, pseudo-resistor current and pseudo-resistor resistance using the simple model were performed as a function of body factor. When the body factor decreases (better gate to channel electrostatic coupling) the pseudo-resistance increases for the same silicon chip area.