Miracle-dz/UniFAR-FTUs huggingface dataset:
One Ring to Rule Them All

Smartrings have potential to extend our ubiquitous control through their always available and finger-worn location, as well as their quick and subtle interactions. As such, smartrings have gained popularity in research and in commercial usage; however, they often concentrate on a singular or novel aspect of a smartring's potential. While with any emerging technology the focus on these individual components is important, there is a lack of broader empirical understanding regarding a user's intentions for smartring usage. Thus in this work, we investigate concrete and reported smartring usage scenarios throughout the daily lives of participants. During a two-week in-situ diary study (N = 14), utilizing a mock smartring, we provide an initial understanding of the potential tasks, daily activities, connected devices, and interactions for which augmentation with a smartring was desired. We further highlight patterns of imagined smartring use found by our participants. Finally, we provide and discuss guidelines, grounded through our found knowledge, to inform research and development towards the design of future smartrings.
DeepBrain

With the recent advancements of electroencephalograph (EEG) techniques, some brain-computer interface (BCI) solutions have been explored to assist individuals performing various tasks with their minds. One promising application is to combine BCI with robotic systems so that the mobility-impaired people can control robots to take care of themselves. Towards this ultimate goal to design BCIs for mobility-impaired, we firstly conducted an online survey with 54 mobility-impaired participants who barely had previous experience with BCI to identify the challenges they face in life for the purpose of designing a personalized BCI system in need. The results revealed these challenges including small daily tasks (such as feeding and cleaning), which weigh on the financial burdens of hiring a caregiver. Meanwhile, the off-the-shelf high-fidelity BCIs are often expensive, whereas the cheaper devices only collect coarse-grained signals, preventing practical application in care aids due to lack of temporal resolution and accuracy. Based on the survey findings, we then designed DeepBrain, a human-centered learning augmented BCI system, that requires only coarse-grained brain signals with low-cost BCI equipment, but supports fine-grained brain-robot interaction and scalable multi-robot collaboration for domestic multi-task operations. A follow-up system comparison with other approaches show that the proposed human-centered solution is a promising step towards the ultimate goal, as it achieves satisfactory accuracy with less low computation resources. Also the practical brain to multi-robot interaction system validates the feasibility of our framework and model used in DeepBrain.
Evaluating Three Touch Gestures for Moving Objects across Folded Screens

Foldable screens are gaining popularity on mobile platforms as it features novel design schemes for view management and screen extension. However, challenges for performing touch input arises when it is folded in various angles and held in various ways. This paper is among the first to systematically evaluate how touch gestures perform under the influence of various fold angles and holding postures. Three gestures for moving objects on touch screens, namely Direct Drag, Hold & Tap, and Throw & Catch, were adopted from previous works and compared in a controlled experiment, where five fold angles and portrait vs landscape modes were varied. Results provide an in-depth understanding of how orientation, fold angle, target distance and direction affects the performance of each technique. Overall Direct Drag was the most accurate but highly inefficient and tiring. Hold & Tap was the most efficient technique with comparable accuracy, except being much more error-prone in Landscape than Portrait. Throw & Catch had a more balanced trade-off between efficiency and accuracy.
Guard Your Heart Silently

In recent years, particular attention has been devoted to continuous electrocardiogram (ECG) waveform monitoring due to its numerous applications. However, existing solutions require users to be confined to particular locations, rely on dedicated and expensive hardware, or require active user participation. The constrained recording conditions prevent them from being deployed in many practical application scenarios. In view of this, we present VibCardiogram, a continuous and reliable design for estimating ECG waveform shape via ubiquitous wrist-worn wearables; it renders a personal ECG waveform shape estimating system with prolonged recording time accessible to a larger sector of the population. Instead of adding auxiliary sensors to wearables, VibCardiogram leverages the widely integrated motion sensor to characterize cardiac activities, and interpret them to generate an alternative signal that has the same waveform shape as the ECG signal. Specifically, VibCardiogram extracts the cardiogenic body vibrations from noisy sensor data. As the waveform variability and inconstant period hinder the segmentation of cardiac cycles, VibCardiogram extracts features and realizes accurate segmentation via machine learning. To parse cardiac activities from vibration signals, we build a deep-learning pipeline associating the encoder-decoder framework and Generative Adversarial Networks. With dedicated construction and training, it can estimate the ECG waveform accurately. Experiments with 20 participants show that VibCardiogram can achieve an average estimation error of 5.989% for waveform amplitude estimation, which is within the 10% margins regulated by the American National Standards Institute. Moreover, the promising results further confirm that VibCardiogram effectively extracts Heart Rate Variability features and supports downstream applications.
LoRaWAN Security: An Evolvable Survey on Vulnerabilities, Attacks and their Systematic Mitigation

The changing vulnerability and threat landscape constantly challenge the security of wireless communication standards and protocols. For the Internet of Things (IoT), LoRaWAN is one of the dominant technologies for urban environments, industrial settings, or critical infrastructures due to its low-power and long-range capabilities. LoRaWAN IoT deployments are expected to operate for multiple years or even decades. Hence, it is imperative to maintain operational security at all times while continuously evolving the security of the standard and its implementations. We survey LoRaWAN security and follow a systematic and evolvable approach that can be dynamically updated. To this end, we propose a novel methodology to create evolvable surveys which relate the analyzed critical security concepts, thus allowing IT security experts to reason about LoRaWAN security properties over time. With this, we provide a tool to hardware manufacturers, software developers and providers, and network operators to achieve sustainable security for IoT deployments.
Semi-Supervised Graph Structure Learning on Neuromorphic Computers

Graph convolutional networks have risen in popularity in recent years to tackle problems that are naturally represented as graphs. However, real-world graphs are often sparse, which means that implementing them on traditional accelerators such as graphics processing units (GPUs) can lead to inefficient utilization of the hardware. Spiking neuromorphic computers natively implement network-like computation and have been shown to be successful at implementing certain types of graph computations. In this work, we evaluate the use of a simulated network of spiking neurons to perform semi-supervised learning on graph data using only the graph structure. We demonstrate that our neuromorphic approach provides comparable results to graph convolutional network results, and we discuss the opportunities for using neuromorphic computers for this task in the future.
Relativistic model for cold spherical interstellar gas clouds

We investigate insterstellar gas spheres by determining the metric functions, the material distribution, and the features of particle orbits in terms of stability and geodesics. An exact solution of the Einstein's equations for interstellar gas clouds is derived that is compatible with the results of recent astronomical measurements. The solution determines the distribution of pressure and density, and it is suitable to describe the energy, speed, trajectory, and further relevant physical features of the cloud's particles. We describe the spacetime inside the nebula and give the density profile and the geodesics of particles. We find that circular orbits are stable and the cloud rotates rigidly by an angular velocity that is inversely proportional to the radius.
The Genus Field and Genus Number in Algebraic Number Fields

Let k be an algebraic number field and K be its normal extension of finite degree. Then the genus field K* of K over k is defined as the maximal unramified extension of K which is obtained from K by composing an abelian extension over k 2). We call the degree (K*: K) the genus number of K over k.
The Magnetohydrodynamic Kelvin-Helmholtz Instability: A Two-dimensional Numerical Study

Using a new numerical code we have carried out two-dimensional simulations of the nonlinear evolution of unstable sheared magnetohydrodynamic flows. We considered two cases: a strong magnetic field (Alfven Mach number, M_a = 2.5) and a weak field (M_a =5). Each flow rapidly evolves until it reaches a nearly steady condition, which is fundamentally different from the analogous gasdynamic state. Both MHD flows relax to a stable, laminar flow on timescales less than or of the order of 15 linear growth times, measured from saturation of the instability. That timescale is several orders of magnitude less than the nominal dissipation time for these simulated flows, so this condition represents an quasi-steady relaxed state. The strong magnetic field case reaches saturation as magnetic tension in the displaced flow boundary becomes sufficient to stabilize it. That flow then relaxes in a straightforward way to the steady, laminar flow condition. The weak magnetic field case, on the other hand, begins development of the vortex expected for gasdynamics, but that vortex is destroyed by magnetic stresses that locally become strong. Magnetic topologies lead to reconnection and dynamical alignment between magnetic and velocity fields. Together these processes produce a sequence of intermittent vortices and subsequent relaxation to a nearly laminar flow condition in which the magnetic cross helicity is nearly maximized. Remaining irregularities consist of a pair of flux tubes straddling the shear layer. Fluctuations within those features are closely aligned, representing Alfv\'en waves propagating locally downstream.
Identification of Room Acoustic Impulse Responses via Kronecker Product Decompositions

The identification of room acoustic impulse responses represents a challenging problem in the framework of many important applications related to the acoustic environment, like echo cancellation, noise reduction, and microphone arrays, among others. The main issues are related to the long length of such impulse responses and their time-variant nature. Recently, a decomposition-based approach was developed for the identification of low-rank systems, which can also be applied (to some extent) for the identification of acoustic impulse responses. This approach exploits the nearest Kronecker product decomposition of the impulse response and solves a high-dimension system identification problem using a combination of low-dimension solutions (provided by shorter filters). Nevertheless, it does not consider the intrinsic nature of the room acoustic impulse responses, which contain specific components (e.g., early reflections and late reverberation) that can be very different in nature. In this article, we propose an improved decomposition-based method (via the Kronecker product) that takes into account these specific components and processes them separately. Following this approach, an iterative Wiener filter is firstly developed, followed by a recursive least-squares (RLS) algorithm. Both solutions outperform the conventional benchmarks. Moreover, they achieve superior performances as compared to the recently developed versions based on the nearest Kronecker product decomposition, also owning lower computational complexities than their previous counterparts. Simulations are performed in the framework of acoustic echo cancellation and the obtained results support the performance features of the proposed algorithms.
Machine Learning for Multi-Action Classification of Lower Limbs for BCI

Over the past two decades, significant progress has been made in brain-computer interfaces (BCIs), devices which enable direct communications between human brains and external devices. One of the prevalent control paradigms is motor imagery-based BCI (MI-BCI), by which users imagine specific actions to express their intentions. Left-hand and right-hand motor imageries are frequently used in the MI-BCI. If a third class is needed, the imagination of both feet is usually added. However, it is relatively rare to separate feet into left lower limb and right limb in MI-BCI systems. In addition, previous studies have demonstrated that real movements can be distinguished from one another via processing the electroencephalogram (EEG). Similarly, motor imagery (MI) and movement observations (MO) can also be distinguished from one another. However, classification of left lower limb actions and right lower limb actions between MI, Real Movement (RM), and MO actions, has not been thoroughly explored. To address these questions, we performed a comprehensive experiment to collect EEG under six actions (i.e., Left-MI, Right-MI, Left-RM, Right-RM, Left-MO, and Right-MO) and used three models (convolutional neural network [CNN], support vector machine [SVM], and a K-Nearest Neighbours [KNN]) to classify these actions. Our CNN achieved the highest performance (37.77%) in the classification of six actions. Although the performance of SVM (37.21%) and KNN (25.26%) was worse, it is still better than the chance level (16.67%). Our results suggest that it is possible to distinguish between these six lower limb actions. This study has implications for developing multi-class BCI systems and promoting the research of multiple-action classification.
Probing the Atomic-Scale Mechanisms of Time-Dependent Dielectric Breakdown in Si/SiO2 MOSFETs (June 2022)

We report on an atomic-scale study of trap generation in the initial/intermediate stages of time-dependent dielectric breakdown (TDDB) in high-field stressed (100) Si/SiO<sub>2</sub> MOSFETs using two powerful analytical techniques: electrically detected magnetic resonance (EDMR) and near-zero-field magnetoresistance (NZFMR). We find the dominant EDMR-sensitive traps generated throughout the majority of the TDDB process to be silicon dangling bonds at the (100) Si/SiO<sub>2</sub> interface (<inline-formula> <tex-math notation="LaTeX">${ \boldsymbol {P}}_{ \boldsymbol {b} \boldsymbol {0}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${ \boldsymbol {P}}_{ \boldsymbol {b} \boldsymbol {1}}$ </tex-math></inline-formula> centers) for both the spin-dependent recombination (SDR) and trap-assisted tunneling (SDTAT) processes. We find this generation to be linked to both changes in the calculated interface state densities as well as changes in the NZFMR spectra for recombination events at the interface, indicating a redistribution of mobile magnetic nuclei which we conclude could only be due to the redistribution of hydrogen at the interface. Additionally, we observe the generation of traps known as <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {E}'$ </tex-math></inline-formula> centers in EDMR measurements at lower experimental temperatures via SDR measurements at the interface. Our work strongly suggests the involvement of a rate-limiting step in the tunneling process between the silicon dangling bonds generated at the interface and the ones generated throughout the oxide.
Robust Network Intrusion Detection Through Explainable Artificial Intelligence (XAI)

In this letter, we present a two-stage pipeline for robust network intrusion detection. First, we implement an extreme gradient boosting (XGBoost) model to perform supervised intrusion detection, and leverage the SHapley Additive exPlanation (SHAP) framework to devise explanations of our model. In the second stage, we use these explanations to train an auto-encoder to distinguish between previously seen and unseen attacks. Experiments conducted on the NSL-KDD dataset show that our solution is able to accurately detect new attacks encountered during testing, while its overall performance is comparable to numerous state-of-the-art works from the cybersecurity literature.
ADAPTIVE MULTIRESOLUTION IMAGE AND VIDEO COMPRESSION AND PRE/ POST-PROCESSING OF IMAGE AND VIDEO STREAMS

This report is divided into two sections. In the first section, the focus is on adaptive transform-based image compression and motion compensation at low bit rates. A new adalptive algorithm for image representation and coding is introduced. This algorithm is based on the concept of segmented orthogonal matching pursuits (SOMP), and adaptively selects the best representation from an overcomplete dictionary of wavelet functions. In the second section, the pre-processing and post-processing of images and video streams are focused on. A new robust nonlinear filter based on the theory of generalized maximum likelihood and order statistics (GMLOS) is introduced. It is shown that this filter is an 1,optimal order statistics filter and some of its properties are proved. A novel algorithm based on wavelet decomposition, variable size kernel GMLOS filters, and soft thresholding for removing the blocking effects in block-based transform coding techniques is introduced. Finally, a simple algorithm for cell packing in ATM networks is inti-oduced, and a novel algorithm for error concealment of images and video streams, based on Multi-directional Recursive Nonlinear Filtering (MRNF) with GMLOS filters is inti-oduced. * Financial support provided by the Innovative Science and Technology (IST) program of the BMDO, monitored by the Office of Naval Research (ONR) under contract (3NR N00014-91-J41 ;! 6 is hereby thankfully acknowledged. THIS PAGE WAS INTENTIONALLY LEFT BLANK
An in-depth investigation of genetic programming and nine other machine learning algorithms in a financial forecasting problem

Machine learning (ML) techniques have shown to be useful in the field of financial forecasting. In particular, genetic programming has been a popular ML algorithm with proven success in improving financial forecasting. Meanwhile, the performance of such ML algorithms depends on a number of factors including data analysis from different markets, data periods, forecasting days ahead, and the transaction cost which have been neglected in most previous studies. Therefore, the focus of this paper is on investigating the effect of such factors. We perform an extensive evaluation of a financial genetic programming-based approach and compare its performance against 9 popular machine learning algorithms and the buy and hold trading strategy. Experiments take place over daily data from 220 datasets from 10 international markets. Results show that genetic programming not only provides profitable results but also outperforms the 9 machine learning algorithms in terms of risk and Sharpe ratio.
Optimization of real-world supply routes by nature-inspired metaheuristics

The traveling salesman problem (TSP) is an iconic permutation problem with a number of applications in planning, scheduling, and logistics. It has also attracted much attention as a benchmarking problem frequently used to assess the properties of a variety of nature-inspired optimization methods. However, the standard libraries of TSP instances, such as the TSPLIB, are often decades old and might not reflect the requirements of modern real-world applications very well. In this work, we introduce several novel TSP instances representing real-world locations of pharmacies in several major cities of the Czech Republic. We look for the optimum routes between the pharmacies by selected nature-inspired algorithms and compare the results obtained on the real-world instances with their results on standard TSPLIB instances.
A Comprehensive Analysis of the Invariance of Exploratory Landscape Analysis Features to Function Transformations

Exploratory Landscape Analysis is a powerful technique that allows us to gain an understanding of a problem landscape solely by sampling the problem space. It has been successfully used in a number of applications, for example for the task of automatic algorithm selection. However, recent work has shown that Exploratory Landscape Analysis contains some specific weaknesses that its users should be aware of. As the technique is sample based, it has been shown to be sensitive to the choice of sampling strategy. Additionally, many landscape features are not invariant to transformations of the underlying samples which should have no effect on algorithm performance, specifically shifting and scaling. The analysis of the effect of shifting and scaling has so far only been demonstrated on a single problem set and dimensionality. In this paper, we perform a comprehensive analysis of the invariance of Exploratory Landscape Analysis features to these two transformations, by considering different sampling strate-gies, sampling sizes, problem dimensionalities, and benchmark problem sets to determine their individual and combined effect. We show that these factors have very limited influence on the features' invariance when they are considered either individually or combined.