Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive User Interfaces for Software Supporting Chronic Diseases

The rising prevalence of chronic diseases necessitates effective self-management strategies. mHealth interventions have shown promise in supporting self-management, but their under-utilization remains a challenge. Individuals with chronic diseases exhibit significant variations in their conditions, severity levels, and associated complications, highlighting the need for more tailored approaches. Adaptive User Interfaces (AUIs) can be used as a solution to address the diverse and dynamic needs of individuals with chronic diseases. We have created an AUI prototype based on existing literature, incorporating presentation, content, and behaviour adaptation. Our user study employs a mixed-method research approach to gather insights from users by interacting with our prototype. The future plans of the study aim to utilise insights obtained from the data analysis to automatically generate AUIs using a model-driven approach.
Biomechanics of Exoskeleton-Assisted Treadmill Walking

To exploit the benefits of treadmill-based exoskeletons, it is crucial to assess possible deviations from natural walking depending on assistive parameters. This study evaluated the biomechanics of exoskeleton-assisted treadmill walking by comparing it with free gait. Five healthy participants walked freely on a treadmill and with the assistance of the Lokomat gait trainer, while changing Body Weight Support (BWS), Gait Speed (GS), and Guidance Force (GF). Results showed that the hip and knee joint kinematics depended on BWS and GS, while changes due to GF were limited. Moreover, joint kinematics and the activity of related muscles were altered with respect to free gait, for any combination of robot parameters in the case of the ankle, and especially for low GS and with BWS in the case of hip and knee. Overall, walking with the Lokomat can mostly resemble free gait at high speed and without BWS.
Investigation of stimulated Raman scattering in magnesium fluoride microcavities

We have successfully manufactured two magnesium fluoride whispering gallery mode microcavities with diameters as small as a few hundred micrometers. Stimulated Raman scattering experiments conducted using these microcavities revealed low threshold levels and ultrahigh quality factors, suggesting a promising potential for their application in nonlinear photonics.
A Comparison of Machine Learning Techniques for Analyzing Infant Mortality in Bangladesh

The Infant Mortality Rate (IMR) is a significant measure used to evaluate the health and socioeconomic status of developing nations, particularly Bangladesh. It is important to carefully examine the various risk factors associated with IMR. This study aims to identify potential risk variables that contribute to infant mortality in Bangladesh and utilize machine learning techniques to develop prediction models. We have analyzed data from the World Bank's “World Development Indicator (WDI) 2021” repository. By employing multiple stepwise linear regression, Support Vector Machine, and Random Forest algorithms, we have identified relevant components and modeled the data. Through cross-validation techniques and evaluating performance using Mean Absolute Error, Mean Square Error, and Root Mean Square Error, we have determined that linear regression and support vector regression show superior performance. The final prediction model's effectiveness yields significant results, potentially leading to a significant reduction in Bangladesh's infant mortality rate significantly as well as improving the country's socio-economic conditions among other developing nations worldwide.
The two sides of sensory–cognitive interactions: Effects of age, The two sides of sensory–cognitive interactions: Effects of age, hearing acuity, and working memory span on sentence hearing acuity, and working memory span on sentence comprehension comprehension

Reduced hearing acuity is among the most prevalent of chronic medical conditions among older adults. An experiment is reported in which comprehension of spoken sentences was tested for older adults with good hearing acuity or with a mild-to-moderate hearing loss, and young adults with age-normal hearing. Comprehension was measured by participants’ ability to determine the agent of an action in sentences that expressed this relation with a syntactically less complex subject-relative construction or a syntactically more complex object-relative construction. Agency determination was further challenged by inserting a prepositional phrase into sentences between the person performing an action and the action being performed. As a control, prepositional phrases of equivalent length were also inserted into sentences in a non-disruptive position. Effects on sentence comprehension of age, hearing acuity, prepositional phrase placement and sound level of stimulus presentations appeared only for comprehension of sentences with the more syntactically complex object-relative structures. Working memory as tested by reading span scores accounted for a signiﬁcant amount of the variance in comprehension accuracy. Once working memory capacity and hearing acuity were taken into account, chronological age among the older adults contributed no further variance to comprehension accuracy. Results are discussed in terms of the positive and negative effects of sensory–cognitive interactions in comprehension of spoken sentences and lend support to a framework in which domain-general executive resources, notably verbal working memory, play a role in both linguistic and perceptual processing.
A Method for Geometry-Sensitive, CFD Solver Independent Mesh Adaptation

: A new method for geometry-sensitive, CFD solver-independent mesh adaptation that respects a priori specified boundary layer mesh refinement is presented. The method seeks to control numerical error in the discrete solution by minimizing interpolation error based on the CFD solver discretization. An adaptation sensor, including control over mesh size increases with each adaptation cycle, is used to define a target size field for an updated discrete mesh conforming to the underlying CAD geometry.
Robust Over-The-Air Aggregation for uplink OFDM system under burst sparse interference

Over the air computation (AirComp) is proposed as a vital technology in physical layer for big data applications in internet-of-things (IoT) systems. However, interference may severely affect the aggregation process. In this work, we propose an analog product code and present a low complexity Bayesian decoder for analog error correction in IoT-AirComp in the presence of burst sparse impulsive interference. We model the burst interference using a Markov model and cancel it via streaming variational message passing based Bayesian inference algorithm. The Bayesian approach allows us to learn global parameters directly via measurements in a streaming fashion, thus facilitating online machine learning. We provide extensive simulations to verify the model and show improved performance against small as well as burst sparse impulsive interference.
Thunderkaller: Profiling and Improving the Performance of Syzkaller

Fuzzing is widely adopted to discover vulnerabilities in software, including the kernel. One of the most popular and state-of-the-art fuzzers for kernels is Syzkaller. However, Syzkaller has a much lower testing throughput compared to other user-space fuzzers, which affects the efficiency of both Syzkaller and other Syzkaller-based fuzzers. In this paper, we profiled the performance of Syzkaller, recognized that the major cost comes from program isolation and kernel instrumentation, and then proposed kernel image duplication and three optimization techniques to mitigate such overheads and present the solution Thunderkaller. Our solution does not change or depend on the fuzzing algorithm in any way, orthogonal to other refinements to Syzkaller. Our evaluation shows that, in 24 hours, Thunderkaller speeds up 2.8× compared to vanilla Syzkaller, achieves 25.8% more basic block coverage, detects 21 more unique bugs, and triggers the common bugs 6.3× faster. In a long time of fuzzing, we have found 6 unique Linux kernel bugs and obtained a CVE.
Neuro-functionalized Microdosimetric Models for Applications of Electroporation

Electromagnetic fields are increasingly being applied in biomedical research to restore neuronal structures. The RISEUP project, funded by FET Open under Horizon Europe 2020, aims to utilize microsecond pulsed electric fields for the treatment of Spinal Cord Injuries (SCI). By leveraging cell membrane electroporation, the proj ect seeks to regenerate the injured spinal cord through a novel device. This device employs high-intensity microsecond pulsed electric fields to stimulate stem cells differentiation towards neuronal phenotypes. Advanced computational modeling plays a crucial role in facilitating device design, replication of observed effects, and further exploration. It enables the assessment of microdosimetry and functional response of cells, serving as a versatile tool for reproducing in vitro and in vivo experiments and enhancing our understanding of underlying mechanisms.
Design of Rectifier for Wireless Power Transfer and Electromagnetic Energy Harvesting

In this study a rectifier is designed by utilizing a pair of Schottky diodes. To assess the rectifier's performance, they ran simulations using Agilent ADS 2019 and associated circuit techniques at a frequency of 2.45 GHz. Notably, the circuit design does not require an HF filter or bypass capacitor, making it both efficient and portable. At an input power of 1.2 mW, the rectifier achieved a maximum efficiency of 80.317% when converting RF to DC.
Beam Forming Techniques in MIMO Antenna: A Review

In order for fifth-generation wireless communication systems (5G) to be successful, massive multiple-input multiple-output (MIMO) systems and beam forming antenna array technologies are considered to be crucial. This review paper's primary goals are to highlight the most recent findings on the best beam forming methods that may be used in enormous MIMO networks and to explain the significance of these methods in such systems. Theoretically, several writers have researched and contrasted various beam formation methods, including digital, analogue, and hybrid. Many comprehensive approaches have been created and released by academic researchers and private businesses.
Wemint:Tainting Sensitive Data Leaks in WeChat Mini-Programs

Mini-programs (MiniApps), lightweight versions of full-featured mobile apps that run inside a host app such as WeChat, have become increasingly popular due to their simplified and convenient user experiences. However, MiniApps raise new security and privacy concerns as they can access partially or all of host apps' system resources, including sensitive personal data. While taint detection has been proven effective in addressing this kind of concerns, existing taint detection techniques for mobile apps cannot be directly applied to MiniApps. The main reason is that the key logics of MiniApps are usually written in J avaScript, and its intrinsic characteristics (function-level scope, dynamic types, synchronous programming, and code obfuscation) prevent existing taint detection techniques from precisely propagating the taints. To address this problem, we propose a novel taint detection technique, Wemint, that detects sensitive information leaks in MiniApps. Specifically, Wemint facilitates taint propagation via building a context-based model based on the operational prin-ciple of MiniApps and J avaScript, and addresses asynchronous function calls by modeling their callbacks explicitly in taint rules. In addition, due to the adoption of Abstract Syntax Trees (ASTs) for code representation during taint detection, Wemint exhibits better robustness against the commonly-applied code obfuscation. Our experimental results show that Wemint can effectively detect sensitive information leaks in WeChat MiniApps, as well as trace the path of sensitive data flows. By applying Wemint to over 20K suspicious MiniApps, we found that over 7.5K (36.5 %) of them have sensitive data leaks, and Wemint outperforms the state-of-the-art DoubleX based techniques in detecting these leaks.
Single-molecule Electrical Characterization On A Controllable-break-junction Chip through Electrostatic Microactuators

Molecular electronics endeavors to create extensive integrated circuits by linking and managing individual molecules as electronic components, presenting a novel avenue for the exploration of future ultra-compact electronic devices. Currently, this field mainly uses dynamic break junction technology to build single-molecule junctions with high success rates and stability and to investigate the electronic properties of the single-molecular. However, the existing break junction technology still faces challenges in attaining manageable preparation and integrated connection of single-molecular electronic devices, due to the complexity of instrumentation. This work presents the development of a chip-integrated single-molecule break junction system utilizing micro-electromechanical system (MEMS) technology. The system includes an electrostatic actuator and an in-plane suspended golden bridge, which is driven by an electrostatic comb actuator to perform tapping mode for building single-molecule junctions. By assembling the molecules to the chip, we achieved single-molecule conductance detection of 1,6-hexanedithiol, confirming that the device can be used for single-molecule break junction experiments. This device greatly simplifies traditional single-molecule break junction instruments, which shows the potential to integrate single-molecule electronic devices onto chips.
Performance Enhancement of Multicarrier NOMA for 5G NR System

The Multiple Access (MA) techniques in wireless systems are either Orthogonal MA (OMA) or Nonorthogonal MA (NOMA). OMA suffers from less spectral efficiency (SE), while NOMA is struck by inter-user interference. Due to the diversity of applications in Fifth Generation (5G) and Beyond 5G (B5G) communications, the trade-off between complexity and performance in Multicarrier NOMA (MC-NOMA) takes a lot of attention. In this paper, a combination technique between orthogonal frequency division multiplexing (OFDM) and NOMA employing 5G new radio (NR) flexible numerology to achieve an enhancement in the MC-NOMA performance is proposed. One base station (BS) and three downlink users are considered. The results show a considerable enhancement in achievable sum rate and users' data rates as compared with OMA and NOMA. Although the bit error rate (BER) of the proposed system is higher than that of OMA, it provides a reasonable enhancement as compared with NOMA. A mathematical model of BER performance for a two-user scenario is expressed too. The model showed its correctness in a comparison between system analysis and simulation. The proposed system reaches a bit rate of 400 Mbps at a 5dB signal-to-noise ratio (SNR) value, whereas OMA and NOMA provide about 240 Mbps at a 5dB SNR value when they tested over 32MHz carrier bandwidth.
Automated Replacement of State-Holding Flip-Flops to Enable Non-Volatile Checkpointing

Applications in safety-critical areas like automotive or aerospace require functions to handle system failures. A commonly used technique on different system levels is checkpointing, which enables a rollback in such an event. However, at register level, power failures cause CMOS flip-flops to lose data, requiring a complete reboot instead of a fast rollback.In this paper, we propose the use of hybrid volatile/non-volatile flip-flops to prevent data loss on power shortages. To reduce the area overhead introduced by those, we present our automated toolchain based on open-source tools to find potential state-holding flip-flops in a netlist and implement those as hybrid volatile/non-volatile for checkpointing. This approach not only reduces area overhead and improves resiliency, but also enables aggressive power-gating strategies to increase energy efficiency.Exemplary, we test our toolchain for three RISC-V based processors and verify our results in simulation. Thereby, we can show that our proposed approach is able to determine all state-holding flip-flops in each architecture.
Industrial Artificial Intelligence Approach for Shape Reconstruction in Quality Assessment of Digital Data from Manufactured Workpieces

A classical process for quality assessment in man-ufacturing is to perform shape reconstruction of manufactured workpieces for generation of a digital counterpart of the already constructed physical good and further evaluation of the quality of shape. Typically, this process is carried out from a cloud of data points obtained from the workpiece by reverse engineering methods by applying surface reconstructions methods to the point cloud. In this work we assume that the normality of data is not satisfied, and that the point cloud follows other bivariate distribution. In particular, we consider that the approximation function is a combination of univariate exponential and polynomi-als functions, thus leading to a non-convex nonlinear constrained continuous minimization problem. To solve it, our method applies a simplified version of a popular metaheuristic technique called bat algorithm. The method is applied to three illustrative examples of point clouds. The graphical and numerical results show that the method performs quite well and can recover the underlying shape of data with good accuracy.
Cross-layer Federated Heterogeneous Ensemble Learning for Lightweight IoT Intrusion Detection System

This paper presents a heterogeneous federated ensemble model for intrusion detection system, employing a semisupervised novelty detection technique - the baseline K-means. The technique learns normal traffic from baseline data and utilizes the Mahalanobis distance to detect anomalous packets. To mitigate the false-positive rate inherent in anomaly-based intrusion detection system, we propose an ensemble approach that integrates local novelty detection models dedicated to each worker in both weighed and voting-based strategies. The federated design augments each worker’s detection capability without increasing the false positive rate. Our extensive experiments showcase the system’s robustness and adaptability over traditional standalone IDS, with marked improvements in precision, recall, and F1score under varying sampling rates. We made this project’s code publicly available on Github for replicability.
A Novel Approach for Non-Query Fake News Detection Using K-SOM and Graph Neural Networks

The widespread use of social networks has led to an increase in the number of users and posts on these platforms. However, the proliferation of fake news, particularly in the healthcare sector due to the COVID-19 pandemic, has become a significant concern. This has become a significant concern as accepting such fake news can have severe consequences on the health and lives of those who are exposed to it, leading to confusion, social disorder, and reputational damage for individuals, organizations, and businesses. Therefore, the automatic detection of fake news on social networks is of utmost importance. In this study, we propose the FANSOG model, which utilizes K-SOM to cluster articles and a graph-based model to automatically detect non-query fake news. Our findings demonstrate that the FANSOG model outperforms other state-of-the-art models in the same research direction.
Burnout-Specific Changes in Brain Activity (Under Way to Exhaustion)

Burnout syndrome is one of the forms of chronic occupation stress. There is no single view on the nature and structure of emotional burnout. Boyko's psychological construct of Emotional Burnout (EB) defines Syndrome as a mechanism of psychological defense in the form of complete or partial excluding of emotions in response to traumatic influences and includes three key stages: Anxiety Tension, Resistance, and Exhaustion. The neurophysiological mechanisms of emotional burnout remain insufficiently studied. Establishing burnout-specific changes in brain activity is necessary to understand the phenomenon of burnout and distinguish it from other emotional mental disorders. Defining the EEG markers of burnout was our aim. 752 volunteers, first-fifth year students from the Taras Shevchenko National University of Kyiv aged 18 to 26 years participated in this study. EEG was recorded during the resting state (3 min, closed eyes condition) monopolarly using EEG 23-channel system Neurocom. To establish EEG correlates of emotional burnout during rest state we used special software written in Python 3.6 to implement Power Spectral Density calculation, the interhemispheric and intrahemispheric average coherence and Detrended Fluctuation Analysis (DFA). We analyzed separate artifact-free EEG segments in all frequency bands from 0.2 to 45 Hz. Psychological testing was performed before the registration of EEG. To determine the formation of emotional burnout Boyko's “Syndrome of emotional burnout” Inventory was used. The Exhaustion phase of emotional burnout was formed in 79 participants, and it was under development in 213 participants. In background EEG activity during the development of the Exhaustion phase of emotional burnout variations in EEG spatial synchronization were observed in low- and high-frequency EEG components and includes the formation of two separate networks of functional connections: interhemispheric prefrontal, anterior frontal, and frontal links (alpha and gamma low bands) and parietal-occipital links (alpha and gamma high bands). DFA describes the long-term temporal correlations in the cortex, which are involved in different aspects of brain functioning. We detected a high resting state DFA scaling exponent values (up to 0.90-0,95) under exhaustion development in the alpha 1 (left temporal, parietal area), alpha 2 (right frontal area), alpha3 (posterior regions). Obtained values of DFA exponent and average coherence suggest the exhaustion formation is accompanied by the changes in visual and verbal processing, emotional processes (discretion and analysis).
Electromagnetic Interference (EMI) evaluation of a pseudo-constant frequency, hysteretic buck DC-DC converter for automotive applications

This paper presents an EMI evaluation of a hysteretic buck DC-DC converter designed for automotive applications. More specifically, the proposed converter’s performance with hysteretic control was analyzed in the context of conducted emissions and compared with same circuit but with fixed frequency PWM control. One particularity of the proposed convertor is that the operating frequency is fairly stable and the inherent frequency jitter of almost 1.5% at static loads, helps improving the EMI performance. Both convertors were analyzed in the same load and filtering conditions and the results show $\approx$ 20% reduction of the fundamental component’s magnitude at $1A$ and a 7.8 dB $\mu\mathrm{V}$ when the load is varied from 1A to 500mA.