Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling of Edge Server Cache-Reservation for Virtual Reality Applications

Envisioned virtual reality (VR) systems include mobility as one of the important features of users' when interacting with virtual environments and objects. Thus, VR content encoding will be processed on cloud servers and delivered to users through the network, as head-mounted headsets (HMDs) have limited computation capabilities. Therefore, network bandwidth becomes the bottleneck for cloud-aided VR applications. In this paper, we proposed a hierarchical cloud/edge VR content cache architecture and a cache reservation policy for the cache of highly popular VR content aimed at reducing the latency for content delivery to VR users. The proposed mathematical framework models the different network conditions experienced by the users, 360° video stream popularities, and a proposed cache reservation mechanism that allocates different amounts of cache memory at edge servers to cache VR content encoded at different resolutions. Obtained results show that the latency for VR content delivery is sensitive to the portion of cache memory reserved for each VR content encoding quality as well as the popularity of the requested content.
PISTIS: Trusted Computing Architecture for Low-end Embedded Systems

Recently, several hardware-assisted security architectures have been proposed to mitigate the ever-growing cyber-attacks on Internet-connected devices. However, such proposals are not compatible with a large portion of the already deployed resource-constrained embedded devices due to hardware limitations. To ﬁll this gap, we propose PISTIS, a pure-software trusted computing architecture for bare-metal low-end embedded devices. PISTIS enables several security services, such as memory isolation, remote attestation and secure code update, while fully supporting critical features such as Direct Memory Access (DMA) and interrupts. PISTIS targets a wide range of embedded devices including those that lack any hardware protection mechanisms, while only requiring a few kilobytes of Flash memory to store its root of trust (RoT) software. The entire architecture of PISTIS is built from the ground up by leveraging memory protection-enabling techniques such as assembly-level code veriﬁcation and selective software virtualisation. Most importantly, PISTIS achieves strong security guarantees supported by a formally veriﬁed design. We implement and evaluate PISTIS on MSP430 architecture, showing a reasonable overhead in terms of runtime, memory footprint, and power consumption.
PolyCruise: A Cross-Language Dynamic Information Flow Analysis

Despite the fact that most real-world software systems today are written in multiple programming languages, existing program analysis based security techniques are still limited to single-language code. In consequence, security ﬂaws (e.g., code vulnerabilities) at and across language boundaries are largely left out as blind spots. We present P OLY C RUISE , a technique that enables holistic dynamic information ﬂow analysis (DIFA) across heterogeneous languages hence security applications empowered by DIFA (e.g., vulnerability discovery) for multilingual software. P OLY C RUISE combines a light language-speciﬁc analysis that computes symbolic dependencies in each language unit with a language-agnostic online data ﬂow analysis guided by those dependencies, in a way that overcomes language heterogeneity. Extensive evaluation of its implementation for Python-C programs against micro, medium-sized, and large-scale benchmarks demonstrated P OLY C RUISE ’s practical scalability and promising capabilities. It has enabled the discovery of 14 unknown cross-language security vulnerabilities in real-world multilingual systems such as NumPy, with 11 conﬁrmed, 8 CVEs assigned, and 8 ﬁxed so far. We also contributed the ﬁrst benchmark suite for systematically assessing multilingual DIFA.
FIST-nD: A tool for n-dimensional spatial transcriptomics data imputation via graph-regularized tensor completion

Functional interpretation of spatial transcriptomics data usually requires non-trivial pre-processing steps and other supporting data in the analysis due to the high sparsity and incompleteness of spatial RNA profiling, especially in 3D constructions. As a solution, we present a new software tool FIST-nD, Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion in n-Dimensions for imputing 3D as well as 2D spatial transcriptomics data. FIST-nD is implemented based on a novel graph-regularized tensor decomposition method, which imputes spatial gene expression data using 4-way high-order tensor structure and relations in spatial and gene functional graphs. The implementation, accelerated by GPU or multicore parallel computing, can efficiently impute high-resolution 3D spatial transcriptomics data within a few minutes. The experiments on three 3D Spatial Transcriptomics datasets and one 3D high-resolution Stereo-seq dataset confirm the high accuracy of the imputation by FIST-nD and demonstrate that the imputed spatial transcriptomes provide a more complete gene expression landscape for downstream analyses such as spatial gene expression clustering and visualizations.
Neural mechanisms of musical syntax and tonality, and the effect of musicianship

The neural basis for the processing of musical syntax has previously been examined almost exclusively in classical tonal music, which is characterized by strictly organized hierarchical structure. The present study investigated the neural mechanisms for processing musical syntax across genres varying in tonality - classical, impressionist, and atonal music - and, in addition, examined how musicianship modulates such processing. Results showed that, first, the dorsal stream, including bilateral inferior frontal gyrus and superior temporal gyrus, plays a key role in the perception of tonality. Second, right fronto-temporal regions were crucial in allowing musicians to outperform non-musicians in musical syntactic processing; musicians also benefit from a cortical-subcortical network including pallidum and cerebellum, suggesting more auditory–motor interaction in musicians than in non-musicians. Third, left pars triangularis carries out on-line computations independently of tonality and musicianship, whereas right pars triangularis is sensitive to tonality and partly dependent on musicianship. Finally, unlike tonal music, processing of atonal music could not be differentiated from that of scrambled notes, both behaviorally and neurally, even among musicians. The present study highlights the importance of studying varying music genres and experience levels, and provides a better understanding of musical syntax and tonality processing and how such processing is modulated by music experience.
Network segregation in aging females and evaluation of the impact of sex steroid hormones

Males and females show differential patterns in connectivity in resting-state networks (RSNs) during normal aging, from early adulthood to late middle age. Age-related differences in network integration (effectiveness of specialized communication at the global network level) and segregation (functional specialization at the local level of specific brain regions) may also differ by sex. These differences may be due at least in part to endogenous hormonal fluctuation, such as that which occurs in females during midlife with the transition to menopause when levels of estrogens and progesterone drop markedly. A limited number of studies that have investigated sex differences in the action of steroid hormones in brain networks. Here we investigated how sex steroid hormones relate to age-network relationships in both males and females, with a focus on network segregation. Females displayed a significant quadratic relationship between age and network segregation for the cerebellar-basal ganglia and salience networks. In both cases, segregation was still increasing through adulthood, highest in midlife, and with a downturn thereafter. However, there were no significant relationships between sex steroid hormone levels and network segregation levels in females, and they did not exhibit significant associations between progesterone or 17β-estradiol and network segregation. Patterns of connectivity between the cerebellum and basal ganglia have been associated with cognitive performance and self-reported balance confidence in older adults. Together, these findings suggest that network segregation patterns with age in females vary by network, and that sex steroid hormones are not associated with this measure of connectivity in this cross-sectional analysis. Though this is a null effect, it remains critical for understanding the extent to which hormones relate to brain network architecture.
Fair Iterative Water-Filling Game for Multiple Access Channels

The water-filling algorithm is well known for providing optimal data rates in time varying wireless communication networks. In this paper, a perfectly coordinated water-filling game is considered, in which each user transmits only on the assigned carrier. Contrary to conventional algorithms, the main goal of the proposed algorithm (FEAT) is to achieve near optimal performance, while satisfying fairness constraints among different users. The key idea within FEAT is to minimize the ratio between the utilities of the best and the worst users. To achieve this goal, we devise an algorithm such that, at each iteration (channel assignment), a channel is assigned to a user, while ensuring that it does not lose much more than other users in the system. In this paper, we show that FEAT outperforms most of the existing related algorithms in many aspects, especially in interference-limited systems. Indeed, with FEAT, we can ensure a low complexity near-optimal, and fair solution. It is shown that the balance between being nearly globally optimal and good from an individual point of view seems hard to sustain with a significant number of users, hence adding robustness to the proposed algorithm.
Why are GWASs limp about the X chromosome?

The X-chromosome is among the largest human chromosomes. It differs from autosomes by a number of important features including hemizygosity in males, an almost complete inactivation of one copy in females, and unique patterns of recombination. We used data from the Catalog of Published Genome Wide Association Studies to compare densities of the GWAS-detected SNPs on the X-chromosome and autosomes. The density of GWAS-detected SNPs on the X-chromosome is 6-fold lower compared to the density of the GWAS-detected SNPs on autosomes. Differences between the X-chromosome and autosomes cannot be explained by differences in the overall SNP density, lower X-chromosome coverage by genotyping platforms or low call rate of X-chromosomal SNPs. Similar differences in the density of GWAS-detected SNPs were found in female-only GWASs (e.g. ovarian cancer GWASs). We hypothesized that the lower density of GWAS-detected SNPs on the X-chromosome compared to autosomes is not a result of a methodological bias, e.g. differences in coverage or call rates, but has a real underlying biological reason – a lower density of functional SNPs on the X-chromosome versus autosomes. This hypothesis is supported by the observation that (i) the overall SNP density of X-chromosome is lower compared to the SNP density on autosomes and that (ii) the density of genic SNPs on the X-chromosome is lower compared to autosomes while densities of intergenic SNPs are similar. Author summary One of the most striking observations from the Genome Wide Association Studies (GWAS) is that the density of GWAS hits is much lower on X-chromosome compared to autosomes. This was initially explained by technical/analytical reasons such as lower coverage and lack of adequate methods to analyze X-chromosomal SNPs. Since then, a better coverage and better analytical methods to analyze X-chromosomal SNPs were developed. We recently revisited the issue and found that the density of GWAS hits on X-chromosome is at least 5-fold lower compared to autosomes. We demonstrated that the difference cannot be explained by technical or analytical reasons. We proposed a hypothesis of a real biological phenomenon underlying X versus autosomal differences in the density of GWAS-detected SNPs, namely that X-chromosome has a lower density of functional polymorphisms compared to autosomes because of a stronger selection against X-chromosomal mutations since X-chromosomal variants are more exposed to natural selection due to hemizygosity in males and X-chromosome inactivation in females. The hypothesis is supported by the analysis of the densities of intergenic, intronic and exonic SNPs on human chromosomes.
Electromagnetic Field Strength Measurements in a Private 5G Campus Network

The deployment of fifth generation (5G) networks in Germany has been on the rise since 2019 and continues to expand rapidly. Unlike earlier generations, 5G technology supports the deployment of private campus networks that enables users to configure and customise their networks based on their needs and independently from public operators. The Technische Universität Kaiserslautern (TUK) is one of few research campuses in Germany that operate their own 5G stand-alone (SA) campus networks. We study in this work mainly the electromagnetic field (EMF) exposure levels on campus by carrying out an EMF measurement campaign for an indoor environment in the 5G Kaiserslautern private campus network. This work also provides an overview of the structure of our 5G campus network at TUK as well as reviews and compares the latest International Commission on Non-Ionizing Radiation Protection (ICNIRP) EMF guidelines released in 2020 to the 26th Ordnance on the implementation of the Federal Emission Control Act (26. BImSchV) within the context of 5G. It also states the current approach and challenges in EMF measurements with special interest in 5G networks.
When Is Multiple Access Beneficial? An Analysis of Multi-User Performance in IEEE 802.11ax

IEEE 802.11ax introduces OFDMA (Orthogonal Frequency Division Multiple Access), that allows multiple users to transmit or receive frames concurrently. The standard suggests that OFDMA will provide reduced latency and increased throughput in dense scenarios compared to single-user OFDM. This work uses the latest 802.11ax models supplied by the widely used open-source network simulator NS-3 (version 3.34) to investigate the OFDMA performance under various downlink traffic loads and various application settings for both UDP and TCP traffic. Our simulation results show that the actual benefits of OFDMA over OFDM can only be extracted under intermediate traffic loads for UDP and TCP. We observe that in comparison to OFDM, OFDMA provides more consistent performance in the case of video streaming and video conferencing. Further, it provides lesser latency for web-based applications.
An Offloading Algorithm for Maximizing Inference Accuracy on Edge Device in an Edge Intelligence System

With the emergence of edge computing, the problem of offloading jobs between an Edge Device (ED) and an Edge Server (ES) received significant attention in the past. Motivated by the fact that an increasing number of applications are using Machine Learning (ML) inference from the data samples collected at the EDs, we study the problem of offloading inference jobs by considering the following novel aspects: in contrast to a typical computational job 1) both inference accuracy and processing time of an inference job increase with the size of the ML model and 2)recently proposed Deep Neural Networks (DNNs) for resource-constrained EDs provide the choice of scaling down the model size by trading off the inference accuracy. Therefore, we consider that multiple small-size ML models are available at the ED and a powerful large-size ML model is available at the ES, and study a general assignment problem with the objective of maximizing the total inference accuracy for the data samples at the ED subject to a time constraint T on the makespan. Noting that the problem is NP-hard, we propose an approximation algorithm: Accuracy Maximization using LP-Relaxation and Rounding (AMR2), and prove that it results in a makespan at most 2T, and achieves a total accuracy that is lower by a small constant from the optimal total accuracy. As proof of concept, we implemented AMR2 on a Raspberry Pi, equipped with MobileNets, that is connected via LAN to a server equipped with ResNet, and studied the total accuracy and makespan performance of AMR2 for image classification.
A Spatial Model for Using the Age of Information in Cooperative Driving Applications

The age of information (AoI) has been proposed as a metric for evaluating freshness of information; recently also within the context of intelligent transportation systems (ITS). The most frequently used definition of AoI, however, does only account for the generation time of the data but not for application-specific aspects. In ITS, for example, the distance of vehicles is not considered and nodes farther away may experience an increased AoI due to effects of the wireless communication channel. We propose a new way of interpreting the AoI in such a context, also considering the location of the transmitting vehicle as a metric of importance to the information. In particular, we introduce a weighting coefficient used in combination with the peak age of information (PAoI) metric to describe the AoI requirement, emphasizing on packets from more important neighbors. As an example, we characterize such importance using the orientation and the distance of the involved vehicles. We use the derived model to focus on timely updates of relevant vehicles for meeting a given AoI requirement, which can save resources on the wireless channel while keeping the AoI minimal.
A non-Hidden Markovian Modeling of the Reliability Scheme of the Constrained Application Protocol in Lossy Wireless Networks

The Constrained Application Protocol (CoAP) is a lightweight communication protocol designed by the Internet Engineering Task Force (IETF) for wireless sensor networks and Internet-of-Things (IoT) devices. The reliability mechanism in CoAP is based on retransmissions after timeout expiration and on an exponential backoff procedure which is designed to be simple and adapted to constrained devices. In this research work, we propose a new exact analytical model to analyze the performance of CoAP in lossy wireless networks modeled by the well-known Gilbert-Elliott two-state Markov process. We also show how to compute several performance metrics using closed form expressions such as the observed loss ratio, goodput, and the delay before success with a time complexity no more than O(r) with r is the maximum re-transmission limit. This study provides insights about improving CoAP recovery mechanism and highlights the properties -- including the limitations -- of CoAP. Also, it presents guidelines to tune CoAP parameters dynamically in order to adapt to network losses caused by interference and mobility. The model is validated using the realistic environment Cooja/Contiki OS where theoretical and experimental results match very well.
A Data Mining-based Intrusion Detection System for Cyber Physical Power Systems

The implication of Cyber-physical systems into smart grids has introduced some security breaches due to the lack of security mechanisms. This paper aims to come up with a novel methodology to detect false data injection attacks on cyber-physical power systems. To reach this goal, we propose an efficient anomaly-based approach for detecting false data injection attacks against cyber-physical power systems. Particularly, we use Sequential Pattern Mining techniques, which are commonly used for learning most important patterns of a system. In our case, the frequent pattern learning algorithm is used to create a database corresponding to the normal operation of the system, then, this database is fed into an attack detection algorithm in order to alert the user whenever an attack is occurring. The extensive simulations prove that our attack detection approach is able to detect attacks with a great accuracy.
Frequency of Physical Therapy Consultation of Females after Delivery: A Cross- Sectional Study from Twin Cities in Pakistan

Background : Physical therapy intervention is a key regime in the prevention and management of postpartum complications, including both systemic and psychological illnesses. Purpose : The aim was to determine the frequency of physical therapy referrals in Pakistan during the postpartum period, in addition to determining the frequency of mothers’ attendance at physical therapy sessions and the most commonly reported complaints after delivery. Method : This was a cross-sectional study conducted in twin cities in Pakistan. Data were collected from 384 postpartum mothers through a self-structured questionnaire, after taking verbal and written consent. We inquired whether the subject was aware of physical therapy, referred for physical therapy, or had attended physical therapy sessions, as well as asking about the most commonly reported complaints amongst postpartum mothers. The collected data were confidential. Results : The mean age of participants was 25.76±4.09. Out of 384 women, only 138 (35.9%) reported an awareness regarding physical therapy and just 89 (23.2%) had been referred for postpartum physical therapy and attended the sessions. Back pain and urinary incontinence were amongst the most commonly reported complaints. Conclusion : It is concluded that the rate of referral to physical therapy is very low in a developing country like Pakistan, irrespective of any complications faced by postpartum mothers. Furthermore, females who had consulted a physical therapist after referral were lost to follow-ups.
Application and extension of the UTAUT2 model for determining behavioral intention factors in use of the artificial intelligence virtual assistants

Virtual Assistants, also known as conversational artificial intelligence, are transforming the reality around us. These virtual assistants have challenged our daily lives by assisting us in the different dimensions of our lives, such as health, entertainment, home, and education, among others. The main purpose of this study is to develop and empirically test a model to predict factors that affect users' behavioral intentions when they use intelligent virtual assistants. As a theoretical basis for investigating behavioral intention of using virtual assistants from the consumers' perspective, researchers employed the extended Unified Theory of Acceptance and Use of Technology (UTAUT2). For this research paper, seven variables were analyzed: performance expectancy, effort expectancy, facilitating conditions, social influence, hedonic motivation, habit, and price/value. In order to improve consumer behavior prediction, three additional factors were included in the study: perceived privacy risk, trust, and personal innovativeness. Researchers carried out an online survey with 304 responses. The obtained sample was analyzed with Structural Equation Modeling (SEM) through IBM SPSS V. 27.0 and AMOS V 27.0. The main study results reveal that factors, such as habit, trust, and personal innovation, have a significant impact on the adoption of virtual assistants. However, on the other side, performance expectancy, effort expectancy, facilitating conditions, social influence, hedonic motivation, price/value, and perceived privacy risk were not significant factors in the users' intention to adopt this service. This research paper examines the effect of personal innovation, security, and trust variables in relation to the use of virtual assistants. It contributes to a more holistic understanding of the adoption of these intelligent devices and tries to fill the knowledge gap on this topic, as it is an emerging technology. This investigation also provides relevant information on how to successfully implement these technologies.