Miracle-dz/UniFAR-FTUs huggingface dataset:
Privacy vs. Security: Trade-Offs in the Acceptance of Smart Technologies for Aging-in-Place

Abstract Smart technologies for aging-in-place address the desire of older adults to live at home independently. Concurrently, they can support the challenged healthcare systems in times of demographic change. Much has already been understood about which factors influence the technology acceptance by future users. Thereby, privacy concerns have been highlighted as a decisive barrier. However, less is yet known about how acceptance is influenced: the central workings of acceptance genesis, the weighing between barriers and benefits and resulting trade-offs have only been focused in very few studies. To gain a deeper understanding of the weighing and trade-offs between privacy barriers and security benefits, an Adaptive Choice-Based Conjoint analysis was carried out using the example of emergency detection technologies (n = 274). The results indicate that if smart technologies offer life-saving security benefits in situations of need, privacy concerns are outweighed. The most important factor for the acceptance decision is the reliability followed by the fall risk. Privacy-related aspects were less important for acceptance decisions. Additionally, the study reveals important data on preferences for system characteristics as well as acceptance thresholds—non-negotiable no-go’s or must-haves. This information helps to understand users’ desires in order to tailor smart technologies to their specific needs.
„Az avar honfoglalás s ami utána következett”. A periférikus Erdélyi-medence és az „avarizáció” kultúrszociológiai jelensége (6‒7. század). Módszertani megjegyzések

Az avar politikai ethnosz, egy nem római gyökerekkel rendelkező politikai­katonai hatalom Kárpát­medencei megjelenése, a régió politikai intézményrendszerének fejlődését néhány száz évre más irányba fordította. Az 568­utáni időszak, az aktív (déli), a Kelet­Római Birodalom irányában indított aktív külpolitika mellett, az avar hatalmi struktúra Kárpát­medencei hálózatának kiépítéséről szólt. E hatalmi hálózatok gyors létrehozása alapján érthetőek meg a kaganátus politikai és katonai sikerei, amelyben különféle entitások, népességek vettek részt. Jól kitapinthatóan azonban fogalomzavar létezett/létezik az akkulturáció és asszimiláció terminológiájának használata esetében, de nem teljes mértékben van tisztázva a strukturális integráció fogalma sem. Az avar elit hatalmi hálózatrendszere az akkulturációs és integrációs folyamatok generátora volt. A szimbolikus társadalmi értékkonfliktus síkban a „mi” képet hódító entitás a katona ­ politikai hálózatai révén terjesztette ki szakaszosítottan, azonban mindez a társadalmi mezőben a „vertikális” csoportidentitáshoz köthetően magyarázható. Maga a folyamat azonban mikrorégióról mikrorégióra más­más képet mutathat, a kulturális elhatárolódás vagy pedig kulturális közeledés vagy közelítés, akkulturáció, illetve identitásbeli asszimiláció lehetősége esetről esetre változhatott, a földrajzi közelségek, illetve távolságok megszabhatták e szociológiai jelenségek jellegét, társadalmi méretét, dinamizmusát, mélységét, kezdődően a felszínes divatelemek átvételétől az identitások megváltozásáig. Tehát: az „avarizáció” tér­ és helyzetfüggő volt. E komplex szociálpszichológiai jelenségnek a kaganátus hatalmi elitje (ebben az esetben temetkezési kultúra) a generátora, az elterjesztői­átvevői generátorkommunikátorai pedig az avar hatalmi hálózati rendszer centrális és perifériális működtetői voltak. E kutatási stádium szintjén az mondható el, hogy a különféle elméleti konstrukcióink (közöttünk a mienk is) akkor lehetnek megalapozottabbak, ha a fel­tárt leletanyag régészeti közlésével párhuzamosan sor kerül komplexebb vizsgálatára. Alaposabb, hitelesebb észrevételekkel, nagyobb dimenziójú szintéziseket e feladatok elvégzése után írhatunk.
Universal small-scale structure in turbulence driven by magnetorotational instability

The intermittent small-scale structure of turbulence governs energy dissipation in many astrophysical plasmas and is often believed to have universal properties for sufficiently large systems. In this work, we argue that small-scale turbulence in accretion discs is universal in the sense that it is insensitive to the magnetorotational instability (MRI) and background shear, and therefore indistinguishable from standard homogeneous magnetohydrodynamic (MHD) turbulence at small scales. We investigate the intermittency of current density, vorticity and energy dissipation in numerical simulations of incompressible MHD turbulence driven by the MRI in a shearing box. We find that the simulations exhibit a similar degree of intermittency as in standard MHD turbulence. We perform a statistical analysis of intermittent dissipative structures and find that energy dissipation is concentrated in thin sheet-like structures that span a wide range of scales up to the box size. We show that these structures exhibit strikingly similar statistical properties to those in standard MHD turbulence. Additionally, the structures are oriented in the toroidal direction with a characteristic tilt of approximately 17$$_{. }^{\circ}$$5, implying an effective guide field in that direction.
Sentiment analysis of 2021 Canadian election tweets

Sentiment analysis is the technique of automatically evaluating and classifying emotions (often positive, negative, or neutral) from textual data, such as written comments and social media posts. Sentiment analysis is a subfield of natural language processing (NLP) that employs machine learning to classify the emotional tone of textual input. The fundamental model concentrates on positive, negative, and neutral categories, but it can also include the speaker's underlying emotions (pleasure, anger, insult, etc.) and purchase intents. Complexity is added to sentiment analysis by context. For example, consider the exclamation "Nothing! " Depending on whether or not the speaker enjoys the product, the meaning can vary significantly. In order for a machine to comprehend "I like it," it must be able to decipher the context and determine what "it" refers to. In addition, sarcasm and sarcasm can be tricky because the speaker may express a favorable sentiment while intending the opposite.
Coarray Tensor Completion for DOA Estimation

Sparse array direction-of-arrival (DOA) estimation using tensor model has been developed to handle multidimensional sub-Nyquist sampled signals. Furthermore, augmented virtual arrays can be derived for Nyquist-matched coarray tensor processing. However, the partially augmentable sparse array corresponds to a discontinuous virtual array, whereas the existing methods can only utilize its continuous part. Conventional virtual linear array interpolation techniques complete coarray covariance matrices with dispersed missing elements, but fail to complete the coarray tensor with whole missing slices. In this article, we propose a coarray tensor completion algorithm for 2-D DOA estimation, where the coarray tensor statistics can be entirely exploited. In particular, in order to impose an effective low-rank regularization on the slice-missing coarray tensor, we propose shift dimensional augmenting and coarray tensor reshaping approaches to reformulate a structured coarray tensor with sufficiently dispersed missing elements. Furthermore, the shape of the reformulated coarray tensor is optimized by maximizing the dispersion-to-percentage ratio of missing elements. As such, a coarray tensor nuclear norm minimization problem can be designed to optimize the completed coarray tensor corresponding to a filled virtual array, based on which the closed-form DOA estimation is achieved. Meanwhile, the global convergence of the coarray tensor completion is theoretically proved. Simulation results demonstrate the effectiveness of the proposed algorithm compared with other matrix-based and tensor-based methods.
Hidden Permissions on Android: A Permission-Based Android Mobile Privacy Risk Model

The continuously increasing amount of data input on mobile devices has made collating and monitoring users’ data not only uniquely personalised but easier than ever. Along with that, mobile security threats have overtaken with rising numbers in bank fraud and personal information leaks. This suggests that there is a significant lack of awareness of security issues among mobile users. Specifically, permission-based passive content leaks are getting more attention due to the emerging issues in data privacy. One reason for this is that permissions are running in the background collecting and transmitting data between applications within the same permission group, without the user's knowledge. This means, that a supposedly innocent application like the Clock, which is linked with the Calendar to provide the date and time functionality, can have access to any other application within the same Calendar permission group, which is compromising confidentiality. Moreover, this can lead to a violation of data privacy as the user is not aware of which assets are being shared between permissions. Developers of mobile platforms have implemented permission-based models to counteract these issues, however, application designers have shown that they are not necessarily complying with the General Data Protection Regulations (GDPR). For the mobile user, this means that app developers, app providers, and third parties who are included in the applications, can gain access to sensitive data without user consent or awareness. To address this issue, this study examines permissions that are inherent in the Android mobile infrastructure and exemplifies how they can reveal delicate user information, identify user behaviour, and can be shared among other applications - without obviously breaching GDPR guidelines. 10 first-party Android applications were statically analysed by their permissions and manually investigated for their actual purpose and privacy risk. Finally, considering the affected area, these permissions were categorised into four asset groups that form the base of a risk model. With risk levels from low to high, this model provides detection of risks on data privacy in mobile permissions and highlights the difficulty with GDPR compliance, which we therefore named PRAM, a permission-based Android Mobile Privacy Risk Assessment Model.
An Overview of Performance Measurement for Demand Forecasting Based on Artificial Neural Networks

Demand forecasting is an essential task to match supply and demand. From a supplier’s view, demand forecasting is important to optimize supply chains and thus maximize profits. The ever-increasing availability of data that can be used as input factors for predictive models allows more and more sophistication for diverse forecasting tasks in the context of demand forecasting. On the one hand, increasingly complex models have been used for demand forecasting over the last years, from simple exponential smoothing methods and ARIMA models up to complex, hybrid (deep) artificial neural networks. On the other hand, little attention is paid to the methods that evaluate the forecasting performance of these models, which are essential for the selection from among potential forecasting models. In this article, we aim to answer the question of what are the most favourable measurements in recent literature on applied neural network demand forecasting for supply chain management. To this end, we analyzed 193 relevant publications in which demand forecasting was applied using artificial neural networks. We found that in artificial neural network demand forecasting used to evaluate forecasting performance, Mean Absolute Percentage Error, Root Mean Squared Error, Mean Squared Error and Mean Absolute Error are by far the most popular methods. Furthermore, we found that when forecasting performance measurements are combined, the most common combination is the combination of Mean Absolute Error, the Root Mean Squared Error and the Mean Absolute Error.
LONGITUDINAL VARIATION IN SUSPENDED SEDIMENT AND TURBIDITY OF TWO UNDISTURBED STREAMS IN NORTHWESTERN CALIFORNIA IN RELATION TO THE MONITORING OF WATER QUALITY ABOVE AND BELOW A LAND DISTURBANCE

A method for fabricating a flash memory includes forming a tunnel oxide layer by depositing a material with a conduction band energy level lower than that of SiO2 on a semiconductor substrate; forming a floating gate by depositing polysilicon on the tunnel oxide layer; forming an intergate dielectric layer on the floating gate; forming a control gate on the intergate dielectric layer; forming a gate electrode by patterning the tunnel oxide layer, the floating gate, the intergate dielectric layer and the control gate; and forming a source/drain region by implanting impurities into the substrate using the gate electrode as a mask.
Eccentricity-induced systematic error on parametrized tests of general relativity: hierarchical Bayesian inference applied to a binary black hole population

One approach to testing general relativity (GR) introduces free parameters in the post-Newtonian (PN) expansion of the gravitational-wave (GW) phase. If systematic errors on these testing GR (TGR) parameters exceed the statistical errors, this may signal a false violation of GR. Here, we consider systematic errors produced by unmodeled binary eccentricity. Since the eccentricity of GW events in ground-based detectors is expected to be small or negligible, the use of quasicircular waveform models for testing GR may be safe when analyzing a small number of events. However, as the catalog size of GW detections increases, more stringent bounds on GR deviations can be placed by combining information from multiple events. In that case, even small systematic biases may become significant. We apply the approach of hierarchical Bayesian inference to model the posterior probability distributions of the TGR parameters inferred from a population of eccentric binary black holes (BBHs). We assume each TGR parameter value varies across the BBH population according to a Gaussian distribution. We compute the posterior distributions for these Gaussian hyperparameters. This is done for LIGO and Cosmic Explorer (CE). We find that systematic biases from unmodeled eccentricity can signal false GR violations for both detectors when considering constraints set by a catalog of events. We also compute the projected bounds on the $10$ TGR parameters when eccentricity is included as a parameter in the waveform model. We find that the first four dimensionless TGR deformation parameters can be bounded at $90\%$ confidence to $\delta \hat{\varphi}_i \lesssim 10^{-2}$ for LIGO and $\lesssim 10^{-3}$ for CE [where $i=(0,1,2,3)$]. In comparison to the circular orbit case, the combined bounds on the TGR parameters worsen by a modest factor of $\lesssim 2$ when eccentricity is included in the waveform.
CAGE: Complementing Arm CCA with GPU Extensions

—Confidential computing is an emerging technique that provides users and third-party developers with an isolated and transparent execution environment. To support this technique, Arm introduced the Confidential Computing Architecture (CCA), which creates multiple isolated address spaces, known as realms, to ensure data confidentiality and integrity in security-sensitive tasks. Arm recently proposed the concept of confidential computing on GPU hardware, which is widely used in general-purpose, high-performance, and artificial intelligence computing scenarios. However, hardware and firmware supporting confidential GPU workloads remain unavailable. Existing studies leverage Trusted Execution Environments (TEEs) to secure GPU computing on Arm-or Intel-based platforms, but they are not suitable for CCA’s realm-style architecture, such as using incompatible hardware or introducing a large trusted computing base (TCB). Therefore, there is a need to complement existing Arm CCA capabilities with GPU acceleration. To address this challenge, we present CAGE to support confidential GPU computing for Arm CCA. By leveraging the existing security features in Arm CCA, CAGE ensures data security during confidential computing on unified-memory GPUs, the mainstream accelerators in Arm devices. To adapt the GPU workflow to CCA’s realm-style architecture, CAGE proposes a novel shadow task mechanism
A Flexible Machine Learning Environment for Steady State Security Assessment of Power Systems

A Flexible Machine Learning Environment for Steady State Security Assessment of Power Systems D. D. Semitekos, N. M. Avouris, G. B. Giannakopoulos University of Patras, ECE Department, GR-265 00 Rio Patras, Greece Email: {dsem, N.Avouris, G.B. Giannakopoulos } @ee.upatras. gr Abstract Machine Learning techniques have been applied to power system analysis for a number of years. The need for a flexible computing environment to support these studies is derived by the complexity of the process, the volume of data often used and the diversity of the applied tools and techniques that span many disciplines. A data warehouse can be a central component of this environment. In this paper our experience with building and using such an environment is described. Data collection and transformation tools, machine-learning tools and testing tools have been integrated in the MLPSE environment described here, used for steady state security assessment of power systems. It is argued that the proposed approach can be applied to many similar power systems analysis studies.
Cooperative Diversity and Partner Selection in Wireless Networks

Next generation wireless communication systems are expected to provide a variety of services including voice, data and video. The rapidly growing demand for these services needs high data rate wireless communication systems with reliability and high user capacity. Recently, it has been shown that reliability and achievable data rate of wireless communication systems increases dramatically by employing multiple transmit and receive antennas. Transmit diversity is a powerful technique for combating multipath fading in wireless communications. However, employing multiple antennas in a mobile terminal to achieve the transmit diversity in the uplink is not feasible due to the limited size of the mobile unit. In order to overcome this problem, a new mode of transmit diversity called cooperative diversity (CD) based on user cooperation, was proposed very recently. By user cooperation, it is meant that the sender transmits to the destination and copies to other users, called partners, for relaying to the destination. The antennas of the sender and the partners together form a multiple antenna situation. CD systems are immuned not only against small scale channel fading but also against large scale channel fading. On the other hand, CD systems are more sensitive to interuser (between sender and partner) transmission errors and user mobility. In this dissertation, we propose a bandwidth and power efficient CD system which could be accommodated with minimal modifications in the currently available direct or point-to-point communication systems. The proposed CD system is based on quadrature signaling (QS). With quadrature signaling, both sender’s and partners’ information symbols are transmitted simultaneously in his/her multiple access channels. It also reduces the synchronization as well as the interference problems that occur in the schemes reported in the literature. The performance of the proposed QS-CD system is analyzed at different layers. First, we study the bit error probability (BEP) of the QS-CD system for both fixed and adaptive
Pharmacokinetic and pharmacodynamic assessment of a five-probe metabolic cocktail for CYPs 1A2, 3A4, 2C9, 2D6 and 2E1.

AIMS The primary objectives of the present study were to establish whether there was a pharmacokinetic or pharmacodynamic interaction between the probe drugs caffeine (CYP1A2), tolbutamide (CYP2C9), debrisoquine (CYP2D6), chlorzoxazone (CYP2E1) and midazolam (CYP3A4), when administered in combination as a cocktail. Furthermore, the tolerability of these probe drugs, both alone and in combination as a cocktail was assessed. METHODS Twelve healthy volunteer subjects (age range 22-48 years) were entered into an open, fixed sequence, 6-limb, single centre study. The randomization was such that all drugs were given individually followed by the full "cocktail" as the last treatment limb. The phenotypic index used to assess the intrinsic activity of the CYP isoforms included metabolite/parent ratios in plasma and urine (CYPs 1A2, 2E1 & 2C9), parent/metabolite ratios in urine (CYP2D6) and plasma AUClast (CYP3A4). Blood pressure and blood glucose measurements were used to assess pharmacodynamic interactions. Tolerability was assessed through reporting of adverse events RESULTS Overall, there was little evidence that the probe drugs interacted metabolically when co-administered as the cocktail. The ratio of the geometric mean (and 90% confidence interval) of the phenotypic index, obtained after administration of the probe as part of the cocktail and when given alone were: caffeine, 0.86 (0.67-1.10), midazolam, 0.96 (0.74-1.24), tolbutamide, 0.86 (0.72-1.03), debrisoquine 1.04 (0.97-1.12) and chlorzoxazone, 0.95 (0.86-1.05). There was no difference in blood pressure and blood glucose concentrations following the cocktail and dosing of the individual probes. There was no effect on ECG recordings at any time-point. The adverse events reported for individual drug administrations were mild, transient and expected. Overall no more adverse events were reported on the cocktail study days than on the days when the drugs were administered alone. CONCLUSIONS The five probe drugs when coadministered, in this dosing regimen, demonstrated no evidence of either a metabolic or pharmacodynamic interaction that might confound the conclusions drawn during a cocktail study. The present cocktail methodology has the potential to become a useful tool to aid the detection of clinically important drug-drug interactions during drug development.
Locking of correlated neural activity to ongoing oscillations

Population-wide oscillations are ubiquitously observed in mesoscopic signals of cortical activity. In these network states a global oscillatory cycle modulates the propensity of neurons to fire. Synchronous activation of neurons has been hypothesized to be a separate channel of signal processing information in the brain. A salient question is therefore if and how oscillations interact with spike synchrony and in how far these channels can be considered separate. Experiments indeed showed that correlated spiking co-modulates with the static firing rate and is also tightly locked to the phase of beta-oscillations. While the dependence of correlations on the mean rate is well understood in feed-forward networks, it remains unclear why and by which mechanisms correlations tightly lock to an oscillatory cycle. We here demonstrate that such correlated activation of pairs of neurons is qualitatively explained by periodically-driven random networks. We identify the mechanisms by which covariances depend on a driving periodic stimulus. Mean-field theory combined with linear response theory yields closed-form expressions for the cyclostationary mean activities and pairwise zero-time-lag covariances of binary recurrent random networks. Two distinct mechanisms cause time-dependent covariances: the modulation of the susceptibility of single neurons (via the external input and network feedback) and the time-varying variances of single unit activities. For some parameters, the effectively inhibitory recurrent feedback leads to resonant covariances even if mean activities show non-resonant behavior. Our analytical results open the question of time-modulated synchronous activity to a quantitative analysis.