Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient and high-power AlGaN-based ultraviolet light-emitting diode grown on bulk GaN

By introducing thick bulk GaN as a substrate, we improved the performance of an AlGaN-based ultraviolet (UV) light-emitting diode (LED). The output power exceeds 3 mW at the injection current of 100 mA under a bare-chip geometry. Internal quantum efficiency is estimated as more than 80%, and the peak wavelength is 352 nm. The maximum power exceeds 10 mW at a large current injection of 400 mA, with an operation voltage of less than 6 V. These results indicate that an efficient UV LED is intrinsically possible by the combination of appropriate device design and the nitride substrate. By introducing packaging technology to enhance extraction efficiency, we will have a compact and efficient UV light source in the wide wavelength range of 200–360 nm, similar to conventional longer-wavelength LEDs.
Hybrid fusion model based on DBN and secondary classifier: Multimodal mild depression recognition using EEG and eye movement

In recent years, depression recognition using physiological signals has achieved certain progress, but mild depression recognition is still in its infancy. Early detection can prevent the development of depression, and combining multiple modalities for analysis has been proved effective in the domain of mental disorders detection. Electroencephalogram (EEG) and eye movements (EM) are widely used to identify depression. However, the problem of using physiological signals to detect mental illness is that the generalization ability of the model is not strong, which is caused by individual differences. In view of the above problems, this paper proposes a hybrid fusion model based on deep belief network (DBN) and secondary classifier, called HFMBDSC, which first uses unsupervised DBN to fuse EEG features (linear, nonlinear features and network features) at the feature level. DBN transforms EEG features into another form to mitigate the effects of individual differences in EEG, and obtains DBN features that can more comprehensively represent EEG information. In the next step of decision level fusion, DBN features and EM features jointly make the final decision using the three classifiers that perform best when using single modality. The results show that DBN can improve the classification accuracy and effectively reduce the influence of individual differences in EEG features through visual analysis of feature space. The classification performance of HFMBDSC is significantly improved compared to the traditional single modality results. The highest accuracy rate of 89.54% is obtained under 10-fold cross-validation. These results suggest that mild depression recognition based on HFMBDSC is promising.
An Interpretable Causal Approach for Bronchopulmonary Dysplasia Prediction

In this paper, we focus on the Bronchopulmonary Dysplasia (BPD) prediction task, which aims to identify the BPD in premature infants based on the given medical images. The existing methods for this task sometimes learn the spurious relations (confounders) between the image and label while ignore the causal features due to the limited size of the dataset, which largely influences the interpretability and robustness of the model. To address this challenge problem, we propose an interpretable causal method for BPD prediction, which can eliminate the irrelevant features and capture the causal features. We term our method as Causal Intervention by semantic instrumental Variable (CisiV). First, we design a causal structure graph modeling module to learn the representations of instrumental variables and confounders automatically. The constraints of confounders further guarantee the instrumental variables validity. Meanwhile, we add congenital attributes of premature infants to enable the learned instrumental variables to have medical semantics via a semantic matching module. We apply our model to BPD prediction in premature infants and achieve promising results. Extensive experiments indicate that CisiV could facilitate early intervention and provide support for clinical decision-making. Codes will be released to the community.
Search Process Analysis of Multiobjective Evolutionary Algorithms using Convergence-Diversity Diagram

Many tasks in the real world are multi-objective optimization problems (MOPs). Population-based approaches are promising for solving MOPs. In particular, multi-objective evolutionary algorithms (MOEAs) are popular and have been actively studied over the last two decades. However, since it is not easy to directly display and compare multi-dimensional solution sets, it is difficult to analyze the search process of MOEAs using direct visualization techniques such as scatter plots. This paper proposes an analytical method to compare multiple search processes in terms of convergence and diversity by extending the authors’ previous work, i.e., Convergence-Diversity Diagram. Through computational experiments, the proposed method reveals characteristics and similarities in three representative MOEAs and six test problems. In addition, this paper provides discussions on algorithm design, biases in the DTLZ test suite, and the improvement of visualization based on experimental results.
New Robot Control Method by using Standard MIDI Data

This paper proposes a new control method to operate robots in conjunction with music. The robots are controlled with an algorithm that can automatically calculate the tempo of music progression and the current position in music by utilizing standard MIDI data, a digital music format. We show an actual case where a robot motion following the music is achieved by a music input whose tempo is not known in advance.
Real-Time INS/DVL/PS fusion applied to the navigation of Autonomous Underwater Vehicles

Navigation systems for AUVs are usually constituted by combining data from multiple sensors. Sensory fusion use the measurements and characteristics of different sensors embedded in the vehicle, optimizing the estimates of the vehicle's position. For the sensory fusion, the Kalman filter technique is commonly used. However a critical point of this approach is the tuning and adjustment of some of its parameters, such as measurement and process noise covariance matrices. In this paper, the implementation of an aided inertial navigation system from DVL and PS using the Kalman filter and metrics for filter tuning are discussed.
A smart face-shield with active defogging against coronaVirus

This work presents the development of an individual protection device, a smart and active ventilated face shield that can also incorporate an air filtering system. The device can work with the front shield or just with the ventilation structure. The system was produced by additive manufacturing technology, based on a chassis that incorporates an electronic control unit, a rechargeable battery, a fan, and a humidity/temperature sensor. The performed system tests showed that the forced ventilation system prevents fogging even in the most adverse situations and increases user comfort when it is used simultaneously with an individual protective face mask, due to the air flow generated by the integrated fan. The filtered ventilated air guarantees the user's safety. Results also show that, with or without the front visor, the equipment prevents fogging, both on the face shield and glasses for users who wear them. The forced air flow promotes isolation of the breathing zone, decreasing the contact with potentially contaminated aerosols, thus reducing the risk of contagion.
Fatigue Detection using the Adaptive Otsu Algorithm

This paper proposes a new approach in the fatigue detection that we did not find it in other studies. The threshold value for the segmentation of “closed”/“open” eye states was determined with an adaptive Otsu algorithm. This algorithm has been optimized to work in real time, using recurrence formulas. The Face Mesh - MediaPipe algorithm was used to detect the face, respectively the delimitation points of the eye positions. The proposed method successfully detects the fatigue state, for the 20 video sequences analyzed, from different people.
Students' Performance Prediction Using Multimodal Machine Learning

Technological intervention in the field of education has gained significant relevance, especially during the post-pandemic era. The three dimensions of interaction that influence learning are the student's interaction with the content, peers, and instructors. Learning ecosystems are expected to ensure these interactions in a seamless way. Technological interventions have provided us with provisions to establish the interactions. The data that we obtain while the student is interacting with content, peers, and instructors can serve as feedback to students and instructors. The motivation of the current study lies in the direction of investigating ‘what’ and ‘how’ current practices of establishing the interaction with content, peers, and instructor are influencing students' performance. The other dimension includes how demographic factors like gender influence the performance of students when technological interventions are made.The sample considered in the study included 140 first-year engineering students in a private university. The outcome of the study helped to do early prediction of student failures and identification of factors that influences the student's success. The data for the study was collected from multiple modalities. Clickstream data was collected from a learning management system to understand the interaction of students with the course content. Student collaboration data was collected from GitHub to understand the interaction of students with peers. Demographic data was collected from student academic performance to understand how past performance and demographic factors influence future performance.The findings reveal that the student interaction with the content and the student performance have a positive relationship with a correlation coefficient of 0.68. The algorithms including random forest, naive Bayes, decision tree, support-vector Machine, and extreme gradient boosting were used to perform multiclass classification to predict the performance. The students were grouped into four classes including ‘Excellent’, ‘Good’, ‘Average’, and ‘Poor’ using decision tree with a classification accuracy of 96%.
Impression of Robots Adaptively Refraining from Actions in Human-Robot Cooperative Scenario

The development of social communication robots is progressing toward the realization of a human-robot symbiotic society. In such a society, robots are expected to behave cooperatively when interacting with surrounding humans. Robots that can adapt to implicitly generated group norms even without direct interactions have been designed to realize human-robot symbiosis groups. This study focuses on a novel aspect of implicit cooperation between humans and robots, i.e., “refraining (Enryo in Japanese),” which means intentionally withholding action concerning group members to be considerate of the group members although he/she has ability to do that. Furthermore, it investigates the impression of a robot expressing refraining behavior according to the behavior of humans engaged in a cooperative task with the robot in a virtual space. The results reveal that the robot’s refraining behavior promotes the human’s active behavior. Additionally, it suggests constructing a relationship in which the robot and the human support each other.
Utilizing Computational Modeling to Aid the Development of a Wearable Soft Robot for Finger Rehabilitation

As more people suffering from mobility impairments, there are growing needs for rehabilitation. Rehabilitation robots have been proven to be effective in assisting patients in their rehabilitation process. However, many existing rehabilitation robots are costly so the accessibility to the patients in need is limited. Our team has been working on a proof-of-concept soft robot design that could be used for finger rehabilitation. Our eventual goal for this soft robot concept is to lower the barrier to access rehabilitation. Hence, our soft robot is designed unlike most other existing soft robots, which are actuated by external components. Our soft robot is completely untethered and actuated by heat. Using a phase changing material (PCM) sealed in elastomer compartments as the working fluid for our soft robot, when heat is applied, the PCM begins to change phase and the pressure inside the sealed elastomer compartments increases to bend our soft robot. Because the bending of our soft robot is closely related to the pressure increase inside of the elastomer compartments, we use computational modeling to investigate that correlation. In this paper, we present the CFD (Computational Fluid Dynamics) and FEA (Finite Element Analysis) simulation models we studied. CFD simulation helps us investigate how the pressure to increase inside of the elastomer compartments when PCM changes phase and FEA modeling further identifies the bending angles for a given pressure inside of the elastomer compartments. These modeling results would aid the development of our soft robot prototypes in the future.
Analysis of EEG Signals Using Empirical Mode Decomposition and Relative Band Energy

This paper approaches an analysis of various epileptiform EEG signals using Empirical Mode Decomposition in the time domain and relative band energy distribution, in the frequency domain. Applying EMD, various EEG waveforms are decomposed into a set of 8 intrinsic mode functions, then to each of them FFT is applied, and finally their relative band energy distribution is obtained. A statistical analysis of the instantaneous frequency values of each IMF is also performed. Thus, we study the possibility to describe a given EEG pattern by relative band energy distribution of its components given by EMD analysis.
Evaluation of personalized treatment goals on engagement of SMI patients with an mHealth app

Mobile health (mHealth) tools are regularly used in a wide range of mental health domains to assess and monitor patients, potentially increasing patient engagement. Recent studies demonstrated that tailored approaches provide better results than generic approaches. However, the effectiveness of tailoring has not yet been investigated empirically for patients with severe mental illnesses (SMIs). It also remains unclear how personalized goals, which are critical from a treatment point of view, impact engagement. Therefore, we designed a novel mHealth tool to increase SMI patient engagement with their personal goals which we evaluated empirically. We designed a two-period, two-arm within-subject crossover study in which 4 participants were exposed to personalized and non-personalized behavioral goals. Contrary to expectations, personalized behavioral goals did not have a significant impact on engagement levels. When considering our participant feedback and also in the context of flow theory, we rationalized that our goal personalization strategy was too static for SMI patients. Therefore, in our future work, we will investigate dynamic strategies that adapt goal difficulty over time.
Contour Detection Method using Growing Neural Gas from 3D Point Cloud

This paper presents the algorithm of extracting contour nodes under two-dimension and three-dimension with the use of self-organizing networks, which is called Growing Neural Gas (GNG). The system imitates how to get the contour nodes in binary image. And, this algorithm also includes getting contour nodes with angle information for dealing with GNG system. Therefore, the algorithm consists of their two methods. The proposed method can get the contour nodes exactly compared with advance literature.
Modelling and Offset-free Predictive Control of the Parallel-Type Double Inverted Pendulum

This paper presents a nonlinear model of the parallel-type double inverted pendulum system with an elastic spring that produces opposing torques on the pendulums. The effects of solid and viscous frictional forces are considered in the developed model. The stabilising control of the nonlinear system is investigated by designing an offset-free model predictive control (OfMPC) scheme based on the linearised model. The use of OfMPC makes it possible to optimize the system performance while also ensuring that the physical limits of the system are not exceeded. Numerical simulation is used to show the effectiveness and prospective benefits of using the proposed controller over the conventional method.
A Novel Approach to Word Sense Disambiguation for a Low-Resource Morphologically Rich Language

Ambiguity amongst words is common in every language. Thus in the world of Natural Language Processing(NLP), a very critical problem is one of Word Sense Disambiguation(WSD). For a Low-Resource language, the problem becomes increasingly challenging since the performance of most WSD algorithms predominantly depends on the corpus available for the language. In this paper, we propose a novel approach to tackle WSD for a Low-Resource language with the help of a High-Resource language. We have utilized a modified version of the Lesk Algorithm powered by a $W$ord2Vec model trained in a High-Resource Language to improve our performance and make our WSD more effective. While we have used the Low-Resource Assamese Language as a use-case for our experiment, the method applies to other Low-Resource languages too. For the morphologically rich Indian Language, Assamese, the problem is complex due to its agglutinate nature. We further improved our performance by developing and using a Word-Extractor for the Assamese language to furnish the tokens during processing. Our results demonstrate that this novel approach makes it possible to differentiate senses for the same word.
Comparative Analysis for Customer Profiling and Segmentation in Food and Beverages Using Data Mining Techniques

The exponential growth of social media data has been collected in today's F&B business. Simple and cost-effective to run high business performance result is critical and engage in successful marketing campaigns. It is necessary to understand different customer behaviour and make more strategically decisions to explore and analyze these data to target the appropriate customers in their business. Most of the profiling and segmentation consider as popular marketing approach to investigate customers based on their characteristics for targeted campaigns. In this paper, we use different clustering methods to get insight and reveal the trends in the mass of real-time collected datasets. K-Means, agglomerative clustering and farthest clustering algorithm is applied to segment the customers into the similar group and further dive into relating specific subset of given datasets. The result of the comparison study shows K-means clustering achieved the accuracy of seventy-five percent (75%) outperform to others. This study addresses the effective data mining approach has helped the F&B companies to make more branding in their promotion campaign and serve the customer better.
Distribution Functions for Cuspy Dark Matter Density Profiles

Various analytic expressions have been proposed for the density profile of dark matter halos. We consider six of these expressions for which the density profile has a power-law falloff ρ ∝ r-3 at large radii and a power-law cusp ρ ∝ r-γ (γ = 0, , 1, ) at small radii. The phase-space distribution function for these models is calculated assuming spherical symmetry and either an isotropic velocity dispersion tensor or an anisotropic dispersion tensor of the type proposed by Osipkov and Merritt. The differential energy distribution for these models is also derived. Several applications are discussed, including the analysis of dark matter search experiments and the study of halo formation in a cosmological setting. Analytic fitting formulae for some of the models are provided.
ICBNet: Iterative Context-Boundary Feedback Network for Polyp Segmentation

Accurate polyp segmentation from colonoscopy images, which is critical to automatic colorectal cancer diagnosis, attracts increasing attentions in recent years. Most existing deep learning-based methods adopt the one-stage processing pipeline, by usually fusing features from different levels or employing boundary-related attention. In this paper, we propose an novel Iterative Context-Boundary feedback Network, namely ICBNet, for robust and accurate polyp segmentation. By mimicking the “from-Preliminary-to-Refined” working paradigm of doctors, ICBNet adopts an iterative feedback learning strategy. Differently from other feedback methods which only use the prediction mask as a guide for foreground features, ICBNet refines encoder features with contextual and boundary-aware details from the preliminary segmentation and boundary predictions, and conducts such strategy in an iterative manner to achieve progressive improvement. Moreover, a dual-branch iterative feedback unit (IFU) is developed to enhance features under the guidance of segmentation and boundary predictions to enable the iterative learning. Extensive experiments on five widely-used polyp segmentation datasets demonstrate that the proposed ICBNet can utilize progressive refinement to effectively address the challenges of large appearance variations and obscure boundaries, and hence achieves more accurate and robust results against the state-of-the-arts methods.
Review of crystal and domain structures in the PbZrxTi1−xO3 solid solution

Several intermediate phases have recently been identified in the PbZrxTi1−xO3 (PZT) phase diagram, located close to the antiferroelectric-ferroelectric and morphotropic phase boundaries. Superlattice reflections from some of these phases are clearly visible in the appropriate electron diffraction patterns and have therefore been used to provide further information concerning their symmetry. Here, the structural distortions giving rise to the new phases are discussed and their domain structures compared with those of tetragonal and rhombohedral PZT. Coherent structural arguments are presented to explain the observed phase transition sequences.
Stability criteria for singularly perturbed linear switching systems

We present some results on the uniform exponential stability of singularly perturbed linear systems undergoing switching. In absence of dwell-time constraints, the switching parameter can evolve on the same time scale as the fast variables, or even faster. We investigate the effect of switching laws evolving at a time scale comparable with the fast variables, describing the corresponding asymptotic effect on the slow variable. Based on this analysis, we propose stability criteria for the overall system, uniform for small values of the parameter of singular perturbation.