Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint beamforming and phase shift design for hybrid IRS and Joint beamforming and phase shift design for hybrid IRS and UAV-aided directional modulation networks UAV-aided directional modulation networks

: Recently, intelligent reﬂecting surfaces (IRSs) and unmanned aerial vehicles (UAVs) have been integrated into wireless communication systems to enhance the performance of air–ground transmission. To balance performance, cost, and power consumption well, a hybrid IRS and UAV-assisted directional modulation (DM) network is investigated in this paper in which the hybrid IRS consisted of passive and active reﬂecting elements. We aimed to maximize the achievable rate by jointly designing the beamforming and phase shift matrix (PSM) of the hybrid IRS subject to the power and unit-modulus constraints of passive IRS phase shifts. To solve the non-convex optimization problem, a high-performance scheme based on successive convex approximation and fractional programming (FP) called the maximal signal-to-noise ratio (SNR)-FP (Max-SNR-FP) is proposed. Given its high complexity, we propose a low-complexity maximal SNR-equal amplitude reﬂecting (EAR) (Max-SNR-EAR) scheme based on the maximal signal-to-leakage-noise ratio method, and the criteria of phase alignment and EAR. Given that the active and passive IRS phase shift matrices of both schemes are optimized separately, to investigate the effect of jointly optimizing them to improve the achievable rate, a maximal SNR majorization-minimization (MM) (Max-SNR-MM) scheme using the MM criterion to design the IRS PSM is proposed. Simulation results show that the rates harvested by the three proposed methods were slightly lower than those of the active IRS with higher power consumption, which were 35% higher than those of no IRS and random phase IRS, while passive IRS achieved only about a 17% rate gain over the latter. Moreover, compared with the Max-SNR-FP, the proposed Max-SNR-EAR and Max-SNR-MM methods caused obvious complexity degradation at the price of slight performance loss.
Cutting Weights of Deep Learning Models for Heart Sound Classification: Introducing a Knowledge Distillation Approach

Cardiovascular diseases (CVDs) are the number one cause of death worldwide. In recent years, intelligent auxiliary diagnosis of CVDs based on computer audition has become a popular research field, and intelligent diagnosis technology is increasingly mature. Neural networks used to monitor CVDs are becoming more complex, requiring more computing power and memory, and are difficult to deploy in wearable devices. This paper proposes a lightweight model for classifying heart sounds based on knowledge distillation, which can be deployed in wearable devices to monitor the heart sounds of wearers. The network model is designed based on Convolutional Neural Networks (CNNs). Model performance is evaluated by extracting Mel Frequency Cepstral Coefficients (MFCCs) features from the PhysioNet/CinC Challenge 2016 dataset. The experimental results show that knowledge distillation can improve a lightweight network’s accuracy, and our model performs well on the test set. Especially, when the knowledge distillation temperature is 7 and the weight α is 0.1, the accuracy is 88.5 %, the recall is 83.8 %, and the specificity is 93.6 %. Clinical relevance— A lightweight model of heart sound classification based on knowledge distillation can be deployed on various hardware devices for timely monitoring and feedback of the physical condition of patients with CVDs for timely provision of medical advice. When the model is deployed on the medical instruments of the hospital, the condition of severe and hospitalised patients can be timely fed back and clinical treatment advice can be provided to the clinicians.
Direct Radiative Effects of Sea Salt on the Regional Scale

This thesis aims to quantify the direct radiative effects (DRE) of sea salt aerosol in the atmosphere. The online coupled regional scale model system COSMO-ART (Vogel et al., 2009) is extended for this objective with respect to the sea salt aerosol. A method for treating sea salt containing internal mixtures of sodium chloride, water, and sodium sulphate is introduced to the model. To consider the aerosol sulphate of both anthropogenic and natural origin, the emission of oceanic dimethyl sulphide (DMS) is implemented in COSMO-ART within this thesis. A case study is performed for the Mediterranean, North East Atlantic Ocean and South and Central Europe with the extended model system. In this study, DMS oxidation is the dominant source for sea salt aerosol sulphate over the Atlantic Ocean and locally contributes up to 40% of the sea salt aerosol sulphate over the Mediterranean. The sea salt chemical composition is dominated by water and the water uptake significantly influences the aerosol size distribution. The simulated sea salt aerosol optical depth is found to show strong dependence on the 10 m wind speed under cloud free conditions. This relation is best described by a power law fit and compares well with satellite observations. To the author’s knowledge, this is the first time such an investigation has been performed with both AOD and wind speed information achieved from model calculations. A new sea salt optical parameterisation is developed for both the shortwave and longwave spectrum. The total extinction in the solar range is dominated by scattering. In the longwave range, the relative contribution of absorption becomes increasingly important due to the absorption of water and sulphate. The extinction of radiation due to sea salt modifies the atmospheric properties, which in turn alter the wind and aerosol size distributions. The wind speed is modified at ±0.2 m s, and the mass and number densities
A Novel Technique for Detecting Depressive Disorder: A Speech Database-Based Approach

Depression is the second most diagnosed disease in the world and is predicted to be the highest by the year 2030. Depressive disorder impacts both on mentally and physically, thus diagnosing this disorder in early stage is essential. Automatic Depression Detection (ADD) system via speech can greatly facilitate early-stage depression diagnosis. Development of such systems demands a standard balanced database. In this work, we present a novel labeled audio distress interview database. To our knowledge, this is the first depression database in Bengali language that contains audio responses from depressed and non-depressed subjects. Alongside this, we present a set of hand-crafted acoustic features that effectively detect depression mood using speech signals. Finally, we justify the quality of our developed database and the efficacy of the feature set in predicting depression using a baseline machine learning (ML) model. We believe that the annotated database will be a valuable resource for use by treating clinicians.Clinical Relevance—This research reports a new speech database in Bengali language for depression detection. This database can be used in healthcare by developing an automatic prediction model for depression detection.
Establishing the Calibration Curve of a Compressive Ophthalmodynamometry Device

The relationship between externally applied force and intraocular pressure was determined using an ex-vivo porcine eye model (N=9). Eyes were indented through the sclera with a convex ophthalmodynamometry head (ODM). Intraocular pressure and ophthalmodynamometric force were simultaneously recorded to establish a calibration curve of this indenter head. A calibration coefficient of 0.140 ± 0.009 mmHg/mN was established and was shown to be highly linear (r = 0.998 ± 0.002). Repeat application of ODM resulted in a 0.010 ± 0.002 mmHg/mN increase to the calibration coefficient.Clinical Relevance— ODM has been highlighted as a potential method of non-invasively estimating intracranial pressure. This study provides relevant data for the practical performance of ODM with similar compressive devices.
An Empirical Study on the Use of Snapshot Testing

Testing is one of the most critical processes in software quality assurance. Developers spend a large portion of their time writing test code to avoid potential software failures. In recent years, snapshot testing, which compares snapshots of UI components to detect unexpected changes, has gained popularity in front-end development due to the need to reduce testing efforts. However, it is still unclear how software developers adopt snapshot testing and maintain them. To facilitate future work to reveal the potentials of snapshot testing, this paper presents a preliminary study which examines how developers use snapshot tests. More specifically, this study investigates 1) the characteristics of projects adopting snapshot testing, and 2) when snapshot tests were introduced and how they evolve. Our study is among the first to understand snapshot testing, providing valuable insights on its adoption. We also highlight the future directions to work on.
NESec: A Modified-UI Honeyword Generation Strategy for Mitigating Targeted Guessing Attacks

By harnessing Graphics Processing Unit (GPU), Field-programmable Gate Array (FPGA), and advanced cracking techniques, the success rates of server-side threats on passwords have reached unprecedented levels. Honeywords, also known as decoy passwords, have emerged as a promising detection strategy against this threat scenario. However, existing work falls short in creating a trap to counter targeted guessing attackers $(\mathbf{TgA})$ who exploit users' personal information to bypass honeyword-based traps. In this paper, we introduce two fundamental honeyword generation modules, namely NSecure-modifiedUI and ESecure-modifiedUI. Building upon these primary modules, we propose a hybrid honeyword-based strategy named NESec, which significantly enhances the ability to detect $\mathbf{TgA}$ 's activities. A comparative analysis showcases the usability advantages and security benefits of the proposed NESec approach.
Module for Collecting, Processing and Aggregating Data of the Transaction Analysis System on the Bitcoin Network

The paper presents the study and systematization of the subject area, as well as the implementation of the module for automating the collection, processing and aggregation of transaction data and Bitcoin network participants from different sources. The novelty of the work lies in the proposal of the author's approach to the classification of information sources, as well as in introducing an additional list of entity types (categories of Bitcoin network participants), which, unlike existing solutions, will increase the reliability of extracted data from various sources about participants and transactions of the Bitcoin network. The implementation of the module, which is part of the general transaction analysis system, in the form of database diagrams, classes and sequences is considered. The results of this work provide an opportunity to develop a high-quality dataset with its subsequent use in classification and clustering algorithms to improve the efficiency of the identification process of funds in the Bitcoin network obtained illegally.
STformer: Spatial-Temporal Transformer for early Warning of Unplanned Extubation in ICU

Patients’ Unplanned Extubation (UEX) is dangerous in the intensive care units (ICU), it is necessary to make early warning of UEX. However, the low fine-grained action of UEX and complexity of ICU environment make early warning a great challenging by using RGB video data. To address this issue, we propose a novel lightweight Spatial-Temporal Transformer (STformer) for early warning of patients’ UEX action in the ICU. Specially, the SlowFast is used to extract patient’s spatial-temporal features initially. Then, in order to improve the representation of features, we introduce spatial attention to enhance the spatial representation of fine-grained actions, and capture the long-term dependency of motions through temporal attention. Finally, a spatial-temporal joint attention is used to reconstruct and strengthen spatial and temporal information. Experiment results illustrate state-of-the-art performance of our STformer on ICU monitory datasets. While ensuring the accuracy of early warning, the computational complexity of STformer are also light.
Feasibility Evaluation of Online Classification-based Control for Gross Movement in a 2-DoF Prosthetic Arm

Regression and classification models have been extensively studied to exploit the myoelectric and kinematic input information from the residual limb for the control of multiple degree-of-freedom (DoF) powered prostheses. The gross movement control of above-elbow prostheses is mainly based on regression models which map the available inputs to continuous prosthetic poses. However, the regression output is sensitive to the variation in the input signal. The myoelectric signal variation is usually large due to unintentional muscle contractions, which can deteriorate the user-in-the-loop performance with respect to the offline analysis. Alternatively, the classification models offer the advantage of being more robust to the input signal variation, but they were predominantly used for fine motor functions such as grasping. For gross motor functions, the discrete output may cause issues. Therefore, this work attempts to investigate the feasibility of utilising the classification model to control a 2-DoF transhumeral prosthesis for gross movement. The performance of 6 able-bodied subjects was evaluated in performing reaching and orientation matching tasks with a prosthetic arm in a virtual reality environment. The results were compared with the case of using their intact arms and existing results using the regression model. Our findings indicate that the classification-based method provides comparable performance to the regression model, making it a potential alternative for gross arm movement in multi-DoF prosthetic arms.
A Novel Diffusion Tensor Image Analysis Along the Perivascular Space Method to Evaluate Glymphatic Alterations in Alzheimer’s Disease

Alzheimer 's disease (AD) is the most prevalent neurodegenerative disorder worldwide. The glymphatic system is considered to be associated with the pathogenesis of AD. However, the alterations of glymphatic system along the AD continuum are still unknown. In this study, we used a novel DTI analysis method, diffusion tensor image analysis along the perivascular space (DTI-ALPS), to evaluate the difference in the activity of the glymphatic system among normal control (NC) subjects, mild cognitive impairment (MCI) and AD patients. The data utilized in the study was obtained from Tongji Hospital in Shanghai, China, including 65 NCs, 58 MCIs and 36 ADs. First, we calculated the ALPS-index to evaluate the activity of the glymphatic system. Then, analysis of variance (ANOVA) was used to find the differences of ALPS-index among different groups, and to explore the correlation between ALPS-index and the three clinical scales: Minimum Mental State Examination (MMSE), Montreal Cognitive Assessment-Basic (MoCA-B) and Instrumental Activity of Daily Living (IADL). Receiver operating characteristic curve (ROC) analysis was used to evaluate the role of the ALPS-index in disease classification. The findings indicated a significant difference in the ALPS-index between the groups of participants with normal cognition, MCI, and AD. In addition, we found that ALPS-index was significantly correlated with the scores of the three clinical scales (with MoCA-B: r=0.233, p=0.001). Furthermore, with ALPS-index, Fractional Anisotropy (FA) values achieved best classification results (AUC=0.8899). Cognitive dysfunction is closely associated with the activity of the glymphatic system, and ALPS-index can be used as a biomarker for alterations along the AD continuum.
Adhesive Carbon Paste Electrode for Convenient and Long-term Electrocardiogram Monitoring: A Pilot Study

Long-term electrocardiogram (ECG) monitoring is an important and widely-used technique in the clinic that helps with the diagnosis of possible diseases that cannot be detected in a short time monitoring. However, the clinically used electrode needs conductive gel to reduce the impedance between the skin and the electrodes, which easily causes the possibility of allergy. Moreover, as the conductive gel becomes dry, the signal’s quality will decrease accordingly. In this paper, we proposed a novel adhesive Carbon Paste Electrode (CPE) to achieve convenient and long-term ECG monitoring. By comparing the time-domain waveforms, the R-R peak intervals difference, and the Signal-to-Noise Ratio (SNR) of ECG with the traditional conductive gel-based electrode (Gel) in fixed and unfixed conditions, the performance of the proposed CPE was investigated. The results showed that the CPE could achieve similar ECG monitoring both in fixed and unfixed conditions. When on Day 2, the quality acquired by Gel began to decrease while CPE was still stable, which was obvious especially in unfixed condition. The R-R peak intervals showed that on Day 2, the Gel was unreliable with some abnormal points occurring. Besides, the results of SNR and average heart rate (AHR) also confirmed that the CPE could achieve similar results as Gel on Day 1 and outperformed Gel on Day 2. It is believed that the proposed CPE opens a window of high-quality long-term ECG monitoring with more convenience.
Accurate Prediction of Alzheimer’s Disease Progression Trajectory via a Novel Encoder-Decoder LSTM Architecture

With an increase in life expectancy, there has been an increase in the aged population globally, and around 10% of this population suffers from Alzheimer’s disease. Alzheimer’s hugely impacts the quality of life and well-being of older adults and their caregivers. Thus, it is an emerging challenge to improve the early diagnosis and prognosis of the disease. Detecting hidden patterns in complex multidimensional datasets using recent advancements in machine learning provides a tremendous opportunity to meet this crucial need. In this study, using multimodal features and an individual’s clinical status on one or more time points, we aimed to predict the individual’s cognitive test scores, changes in Magnetic Resonance Imaging features, and the individual’s diagnostic status for the next three years. We presented a novel Encoder-Decoder Long Short-Term Memory deep-learning model architecture for implementing our prediction. We applied it to data from the Alzheimer’s Disease Neuroimaging Initiative, comprising longitudinal data of 1737 participants and 12,741 instances. The proposed model was found to be competent, with a validation accuracy of 0.941, a multi-class area under the curve of 0.960, and a test accuracy of 0.88 in identifying the various stages of Alzheimer’s disease progression in patients with an initially cognitively normal or mild cognitive impairment which is a significant improvement over previous methods.Clinical relevance— The proposed approach can help improve diagnostic understanding of Alzheimer’s Disease progression and assist in the early detection of various stages of Alzheimer’s Disease based on clinical heterogeneity.
IRS-Assisted mmWave Massive MIMO Systems Beam Training with Hybrid CNN Encoder-based Transformer Deep Learning Model

Deep learning (DL)-based beam training schemes have enhanced the transmission capacity for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Nevertheless, the effects of noise and model complexity are still bottlenecks for most of those approaches. In this paper, we propose a hybrid convolutional neural network (CNN) encoder-based Transformer (HCNT) DL model containing robust channel expression and a scoring-based optimal beam decision. First, the hybrid CNN encoder parallelly tackles the real and imaginary components of the sampled channel collected by active sensors of the adopted semi-passive IRS with the grouped and point-wise convolution as the alternative to the complicated serial process. Second, we convert the continuous channel expression into binary sequences by leaky integrated-and-fire (LIF) mechanism seeking a robust representation against the noise effects. Finally, feature attention mechanism determines the prediction of the optimal beam by the scores of the relationship between potential direction and quantized binary tokens. Experimental results show that the proposed HCNT outperforms the existing schemes achieving a higher spectral efficiency under different noise levels with lower complexity.
Semi-supervised Medical Image Segmentation with Multiscale Contrastive Learning and Cross-Supervision

We propose a semi-supervised segmentation method based on multiscale contrastive learning to solve the problem of shortage of annotations in medical image segmentation tasks. We apply perturbations to the input image and encoded features and make the output as consistent as possible by cross-supervision, which is a way to improve the generalizability of the model. Two scales of contrastive learning, patch-level and pixel-level, are employed to enhance the intra-class compactness and inter-class separability of the features. We evaluate the proposed model using three public datasets for brain tumor,left atrial, and cellular nuclei segmentation. The experiments showed that our model outperforms state-of-the-art methods.Clinical relevance— The proposed method can be used for medical image segmentation with limited annotated data and achieve comparable performance to the fully annotated situation. Such an approach can be easily extended to other clinical applications.
The Inﬂuence of Ink Formulation and Preparation on the Performance of Proton-Exchange Membrane Fuel Cell

: The fabrication step of the catalyst layer (CL) is important to master in order to achieve good performance in fuel cells. Nevertheless, the ﬁnal structure of a CL depends on many factors, such as the ink composition and preparation, as well as the order of its preparation steps. However, it is not easy for neophytes to understand the relationship between the composition of the ink with the obtained structure of the catalyst layer and its performance in fuel cells. In this work, a systemic experimental study was carried out in order to qualitatively correlate the performance of the PEMFC with the structure of the catalyst layer by playing on different parameters such as the composition and preparation of the ink and the operating conditions. All of the prepared samples were characterized by electron microscopy and proﬁlometry, as well as by electrochemical tests at a single-cell level. The main results show that (i) the chosen ratio and ingredients result in a catalyst layer structure that can affect the PEMFC performance in different ways, and (ii) the reproducibility of the results requires particular care in the choice of catalyst and carbon support.