Miracle-dz/UniFAR-FTUs huggingface dataset:
Heterogeneous Transfer Learning for Thermal Comfort Modeling

For decades, the Predicted Mean Vote (PMV) model has been adopted to evaluate building occupants' thermal comfort. However, recent studies argue that the PMV model is inaccurate and suffers from two major issues: thermal comfort parameter inadequacy and modeling data inadequacy. To overcome these issues, in this paper, we propose a learning-based approach for thermal comfort modeling, named as Heterogeneous Transfer Learning (HTL) based Intelligent Thermal Comfort Neural Network (HTL-ITCNN). First, to address the parameter inadequacy issue, we add more relevant factors as the modeling features except for the six PMV parameters. Due to the flexibility of learning-based approaches, newly found thermal comfort parameters can be appended to extend the number of modeling features. Second, to mitigate the impact of the data inadequacy issue, we adopt the deep transfer learning techniques to train the thermal comfort model, where the model training would benefit from the transferred knowledge from the existing datasets. Due to the heterogeneity of the features among different datasets, we follow the HTL concept to conducting effective knowledge transfer among heterogeneous domains, which are the different but related datasets with varied features. To validate our solution, we conduct five-month data collection experiments and build our datasets. With the HTL-based two-stage learning paradigm, the experimental results show that the accuracy of HTL-ITCNN outperforms the PMV model by on average 73.9%. Besides, we verify the impacts of newly added features and knowledge transfer on model performance. Moreover, we demonstrate the enormous potential of personal thermal comfort modeling research.
Drone Swarms, Communications Performance and Big Data

This paper describes experiments with small drones, (UAVs), a real-time big-data platform (Kafka), the Robot Operating System communicating using cellular 4G mobile services. The objective of the experiment is to collect data for testing of obstacle avoidance algorithms and to evaluate the communications performance end-to-end. The results suggest that there is much more study needed before UAV swarms can be flown.
Prevalence and Correlates of Accelerometer-Based Physical Activity and Sedentary Time Among Kidney Transplant Recipients

Background: Physical activity is recommended for kidney transplant recipents as it may improve outcomes including mortality, exercise capacity, muscle strength, and health-related quality of life. Objective: The objective of this study was to examine accelerometer-based physical activity and sedentary time profiles among kidney transplant recipients and examine possible demographic and clinical correlates of physical activity and sedentary time. Design: Cross-sectional. Setting: Edmonton, Alberta, Canada. Patients: Kidney transplant recipients were recruited (N = 1,284) from the Northern Alberta Renal Program’s Nephrology Information System database (1993-2016). Measurements: Participants wore an ActiGraph GT3X+ accelerometer on their hip during waking hours for seven consecutive days. Methods: Kidney transplant recipients (1993-2016) recruited from the Northern Alberta Renal Program’s Nephrology Information System database wore an accelerometer and completed a self-reported questionnaire. Multiple linear regression was used to determine associations between activity level, demographic, and clinical characteristics. Results: Participants’ (n = 133; 11% response rate) mean age (SD) was 58 (14) years and 56% were female. Mean total sedentary time was 9.4 (1.4) hours per day; total moderate-to-vigorous physical activity (MVPA) time was 20.7 (19.6) minutes per day. MVPA was significantly associated with age where each additional year was associated with 0.48 fewer min/day (ie, ~30 seconds) (unstandardized beta: B = −0.48 min/day, 95% confidence interval [95% CI]: −0.75, −0.22). Sedentary time was significantly associated with age (B = 1.0 min/day, 95% CI: 0.03, 1.9), body mass index (B = 2.7 min/day, 95% CI: 0.2, 5.13), education (B = 39.1 min/day, 95% CI: 12.3, −65.8), and inversely associated with income (B = −44.9 min/day, 95% CI: −73.1, −16.8). Limitations: Limitations include the cross-sectional design, poor response rate, and limited generalizability of the results. Conclusions: Kidney transplant recipients showed high volumes of sedentary time and low volumes of health-enhancing physical activity. Understanding correlates of these behaviors may aid in the development of interventions to favorably change these behaviors.
Cash, Digital Payments and Accessibility

Despite the growing interest in digitization and money in HCI and CSCW, the use of cash and digital payments by people with disabilities has received scant attention. We present findings from a qualitative study of people with visual impairments' use of cash and digital payments in metropolitan India. Using ride-hailing services as an exemplar, we find that both cash and digital payments were inaccessible to participants. We use Perry and Ferreira's "moneywork" as a theoretical framework to highlight the "added" work necessitated by this inaccessibility; that is, the work done in addition to the interactional work necessary to complete financial transactions. We argue that this "added" work is instrumental in "making" payments accessible. We discuss how ride-hailing platforms mediated collaborations between drivers and riders in relation to payments, while still making "moneywork" essential. We provide recommendations to improve the accessibility of digital payments to facilitate greater economic inclusion.
Twitter Bot Detection using Diversity Measures

Social bots are autonomous entities that generate a signiﬁcant amount of social media content. The content being created can be harm-less or even contain beneﬁcial information. On the other hand, it may target a certain audience to inﬂuence opinions, often politically motivated, or to promote individuals to appear more popular than they really are. In this work we present a simple method for bot detection on Twitter platform relying on user activity ﬁngerprint, complemented with a set of well-known statistical diversity measures. We demonstrate the beneﬁts of the method on two datasets used in a couple of previous studies by various researchers.
Understanding the microstructural evolution and mechanical properties of transparent Al-O-N and Al-Si-O-N films

ABSTRACT Optically transparent, colorless Al-O-N and Al-Si-O-N coatings with discretely varied O and Si contents were fabricated by reactive direct current magnetron sputtering (R-DCMS) from elemental Al and Si targets and O2 and N2 reactive gases. The Si/Al content was adjusted through the electrical power on the Si and Al targets, while the O/N content was controlled through the O2 flow piped to the substrate in addition to the N2 flow at the targets. The structure and morphology of the coatings were studied by X-ray diffraction (XRD) and transmission electron microscopy (TEM), while the elemental composition was obtained from Rutherford backscattering spectrometry (RBS) and heavy ion elastic recoil detection analysis (ERDA). The chemical states of the elements in the coatings were analyzed by X-ray photoelectron spectroscopy (XPS). Based on analytical results, a model describing the microstructural evolution of the Al-O-N and also previously studied Al-Si-N [1, 2, 3, 4] coatings with O and Si content, respectively, is established. The universality of the microstructural evolution of these coatings with the concentration of the added element is attributed to the extra valence electron (e–) that must be incorporated into the AlN wurtzite host lattice. In the case of Al-O-N, this additional valence charge arises from the e – acceptor O replacing N in the AlN wurtzite lattice, while the e – donor Si substituting Al fulfills that role in the Al-Si-N system. In view of future applications of ternary Al-O-N and quaternary Al-Si-O-N transparent protective coatings, their mechanical properties such as residual stress (σ), hardness (HD) and Young’s modulus (E) were obtained from the curvature of films deposited onto thin substrates and by nanoindentation, respectively. Moderate compressive stress levels between −0.2 and −0.5 GPa, which suppress crack formation and film-substrate delamination, could be obtained together with HD values around 25 GPa.
Target Localization Using Approximate Maximum Likelihood for MIMO Radar Systems

This work presents a target localization scheme in distributed multiple-input multiple-output (MIMO) radar system using bistatic range measurements. The localization approach consists of two phases. First, measurements are divided into multiple groups based on the various transmitter and receiver elements. For each group, an approximate maximum likelihood (AML) estimator is proposed to estimate the location of a target. Then, the estimation results from these different groups are combined to form the final estimate. The performance of the proposed algorithm is validated by simulation and is shown to reach the Cram\'{e}r-Rao lower bound (CRLB) in a range of measurement noise levels.
Screening of reference genes for expression analysis in the study of soldier caste differentiation of Formosan subterranean termite Coptotermes formosanus Shiraki

The Formosan subterranean termite, Coptotermes formosanus Shiraki, is a serious pest worldwide. Juvenile hormone analog (and its analogs such as methoprene) can induce the transformation of the worker caste into soldier caste in C. formosanus. However, several factors, such as feeding substrate and colony origin, influence the proportion of solider formation. The molecular mechanism of worker to soldier transformation of C. formosanus is still not clear. RT-qPCR is a powerful tool for molecular studies. Accurate gene quantification by the relative quantification method requires a stable expressed gene as the reference gene. However, no reference genes were available for this species in the methoprene bioassay. To study the problem of gene response to methoprene by RT-qPCR we have to first screen reference genes in C. formosanus. Workers were fed with methoprene. Termites were collected during the methoprene bioassay and separated into head and thorax+abdomen. Expression profiles of 10 candidate reference genes in the two body part types were investigated using RT-qPCR. The results were analyzed by a set of established methods (geNorm, NormFinder, BestKeeper, and RefFinder) as well as comparative ΔCt method. Our results suggest that RPS18 is the most stably-expressed gene both in the head and thorax+abdomen for expression analysis in the methoprene bioassay of C. formosanus. The screening of suitable reference genes in C. formosanus establishes the foundation for the molecular study of soldier caste differentiation in this species.
Injury incidence, characteristics and burden among female sub-elite futsal players: a prospective study with three-year follow-up

The main purpose of the current study was to analyze the injury incidence, characteristics and burden among sub-elite female futsal players. Individual exposure to match play and training, injury incidence and characteristics (player position, injury mechanism, type of injuries, severity of injuries, recurrent vs. new injuries, season variation of injury pattern) in a female futsal team were prospectively recorded for three consecutive seasons (2015–2018). Incidences were calculated per 1,000 h of exposure. A total of 30 injuries were reported during the three seasons within a total exposure of 4,446.1 h. The overall, match and training incidence of injuries were 6.7, 6.4 and 6.8 injuries/1,000 h of exposure, respectively. Most injuries had a non-contact mechanism (93%), with the lower extremity being the most frequently injured anatomical region (5.62 injuries/1,000 h of exposure). The most common type of injury was muscle/tendon (4.9 injuries/1,000 h of exposure) followed by joint (non-bone) and ligament (1.3 injuries/1,000 h of exposure). The injuries with the highest injury burden were those that occurred at the knee (31.9 days loss/1,000 h exposure), followed by quadriceps (15.3 day loss/1,000 h) and hamstring (14.4 day loss/1,000 h) strains. The first few weeks of competition after pre-season and soon after the Christmas break were the time points when most injuries occurred. These data indicate that sub-elite female futsal players are exposed to a substantial risk of sustaining an injury. To reduce overall injury burden, efforts should be directed toward the design, implementation and assessment of preventative measures that target the most common diagnoses, namely, muscle/tendon and ligament injuries.
Mind the Gap: A Case for Informed Request Scheduling at the NIC

Recent research in high-throughput networked systems has established the need for centralized and preemptive request scheduling in order to achieve good hardware utilization and low tail latency for a wide variety of workloads. However, this approach is expensive to scale as it requires an increasing number of CPU cores dedicated to scheduling. Moreover, passing every request through a scheduling core introduces latency for inter-core communication and reduces the effectiveness of data preloading and caching optimizations. In this paper, we advocate in favor of pushing request-to-core scheduling back into the NIC. Instead of the simple request distribution of receive-side-scaling (RSS) in current NICs, we suggest implementing preemptive request scheduling by passing to the NIC up-to-date information about core availability and execution status of active requests. We present a prototype implementation on a commercial Smart-NIC that indeed shows performance benefits for different workload scenarios. The prototype allows us to also observe bottlenecks that largely come from artifacts of existing NIC hardware. We propose research towards addressing these limitations in order to achieve low overhead, low latency, and highly efficient request scheduling.
Peeves: Physical Event Verification in Smart Homes

With the rising availability of smart devices (e.g., smart thermostats, lights, locks, etc.), they are increasingly combined into "smart homes". A key component of smart homes are event sensors that report physical events (such as doors opening or the light turning on) which can be triggered automatically by the system or manually by the user. However, data from these sensors are not always trustworthy. Both faults in the event sensors and involvement of active attackers can lead to reporting of events that did not physically happen (event spoofing). This is particularly critical, as smart homes can trigger event chains (e.g., turning the radiator off when a window is opened) without involvement of the user. The goal of this paper is to verify physical events using data from an ensemble of sensors (such as accelerometers or air pressure sensors) that are commonly found in smart homes. This approach both protects against event sensor faults and sophisticated attackers. In order to validate our system's performance, we set up a "smart home" in an office environment. We recognize 22 event types using 48 sensors over the course of two weeks. Using data from the physical sensors, we verify the event stream supplied by the event sensors. We consider two threat models: a zero-effort attacker who spoofs events at arbitrary times and an opportunistic attacker who has access to a live stream of sensor data to better time their attack. We achieve perfect classification for 9 out of 22 events and achieve a 0% false alarm rate at a detection rate exceeding 99.9% for 15 events. We also show that even a strong opportunistic attacker is inherently limited to spoofing few select events and that doing so involves lengthy waiting periods.
Pinpointing performance inefficiencies via lightweight variance profiling

Execution variance among different invocation instances of the same procedure is often an indicator of performance losses. On the one hand, instrumentation-based tools can insert calipers around procedures and identify execution variance; however, they can introduce high overheads. On the other hand, sampling-based tools insert no instrumentation and have low overheads; however, they cannot synchronize samples with procedure entry and exit. In this paper, we propose FVSampler, a lightweight, sampling-based variance profiler. FVSampler employs hardware performance monitoring units in conjunction with hardware debug registers to sample and monitor whole procedure instances (invocation till return) and collect hardware metrics in each sampled procedure instance. FVSampler, typically, incurs only 6% runtime overhead and negligible memory overhead making it suitable for HPC-scale production codes. We evaluate FVSampler with several parallel applications and demonstrate its effectiveness in pinpointing execution variance. Guided by FVSampler, we tune data structures and algorithms to obtain significant speedups.
Limago: An FPGA-Based Open-Source 100 GbE TCP/IP Stack

The realization that the network is becoming an important bottleneck in computing clusters and in the cloud has led in the past years to an increase scrutiny of how networking functionality is deployed. From TCP Offload Engines (TOEs) to Software Defined Networking (SDN), including Smart NICs and In-Network Data Processing, a wide range of approaches are currently being explored to increase the efficiency of networks and tailor its functionality to the actual needs of the application at hand. To address the need for an open and customizable networking stack, in this paper we introduce Limago, an FPGA-based open-source implementation of a TCP/IP stack operating at 100 Gbit/s. To our knowledge, Limago provides the first complete description of an FPGA-based TCP/IP stack at these speeds, thereby illustrating the bottlenecks that must be addressed, proposing several innovative designs to reach the necessary throughput, and showing how to incorporate advanced protocol features into the design. As an example, Limago supports the TCP Window Scale option, addressing the Long Fat Pipe issue. Limago not only enables 100 Gbit/s Ethernet links in an open source package, but also paves the way to programmable and fully customizable NICs based on FPGAs.
Page migration support for disaggregated non-volatile memories

As demands for memory-intensive applications continue to grow, the memory capacity of each computing node is expected to grow at a similar pace. In high-performance computing (HPC) systems, the memory capacity per compute node is decided upon the most demanding application that would likely run on such system, and hence the average capacity per node in future HPC systems is expected to grow significantly. However, since HPC systems run many applications with different capacity demands, a large percentage of the overall memory capacity will likely be underutilized; memory modules can be thought of as private memory for its corresponding computing node. Thus, as HPC systems are moving towards the exascale era, a better utilization of memory is strongly desired. Moreover, upgrading memory system requires significant efforts. Fortunately, disaggregated memory systems promise better utilization by defining regions of global memory, typically referred to as memory blades, which can be accessed by all computing nodes in the system, thus achieving much better utilization. Disaggregated memory systems are expected to be built using dense, power-efficient memory technologies. Thus, emerging nonvolatile memories (NVMs) are placing themselves as the main building blocks for such systems. However, NVMs are slower than DRAM. Therefore, it is expected that each computing node would have a small local memory that is based on either HBM or DRAM, whereas a large shared NVM memory would be accessible by all nodes. Managing such system with global and local memory requires a novel hardware/software co-design to initiate page migration between global and local memory to maximize performance while enabling access to huge shared memory. In this paper we provide support to migrate pages, investigate such memory management aspects and the major system-level aspects that can affect design decisions in disaggregated NVM systems
Reconsidering Calcium Dehydration as the Rate-Determining Step in Calcium Mineral Growth

The dehydration of cations is generally accepted as the rate-limiting step in many processes. Molecular dynamics (MD) can be used to investigate the dynamics of water molecules around cations, and two different methods exist to obtain trajectory-based water dehydration frequencies. Here, these two different post-processing methods (direct method versus survival function) have been implemented to obtain calcium dehydration frequencies from a series of trajectories obtained using a range of accepted force fields. None of the method combinations reproduced the commonly accepted experimental water exchange frequency of 10–8.2 s–1. Instead, our results suggest much faster water dynamics, comparable with more accurate ab initio MD simulations and with experimental values obtained using neutron scattering techniques. We obtained the best agreement using the survival function method to characterize the water dynamics, and we show that different method combinations significantly affect the outcome. Our work strongly suggests that the fast water exchange kinetics around the calcium ions is not rate-limiting for reactions involving dissolved/solvated calcium. Our results further suggest that, for alkali and most of the earth alkali metals, mechanistic rate laws for growth, dissolution, and adsorption, which are based on the principle of rate-limiting cation dehydration, need careful reconsideration.
Breaking TrustZone Memory Isolation through Malicious Hardware on a Modern FPGA-SoC

FPGA-SoCs are heterogeneous computing systems consisting of reconfigurable hardware and high performance processing units. This combination enables a flexible design methodology for embedded systems. However, the sharing of resources between these heterogeneous systems opens the door to attacks from one system on the other. This work considers Direct Memory Access attacks from a malicious hardware block inside the reconfigurable logic on the CPU. Previous works have shown similar attacks on FPGA-SoCs containing no memory isolation between the FPGA and the CPU. Our work studies the same idea on a system based on the Xilinx Zynq Ultrascale+ architecture. This platform contains memory isolation mechanisms such as a system memory management unit, memory protection units and supports ARM TrustZone technology. Despite the existence of these protection mechanisms, the two attacks presented in this work show that a malicious hardware block can still interfere with a security critical task executed on the CPU inside ARM TrustZone
A Novel Nonsense Mutation (c.414G>A; p.Trp138*) in CLDN14 Causes Hearing Loss in Yemeni Families: A Case Report

Non-syndromic hearing loss (NSHL) is a hereditary disorder that affects many populations. Many genes are involved in NSHL and the mutational load of these genes often differs among ethnic groups. Claudin-14 (CLDN14), a tight junction protein, is known to be associated with NSHL in many populations. In this study, we aimed to identify the responsible variants in 3 different Yemeni families affected with NSHL. Firstly, clinical exome sequencing (CES) performed for 3 affected patients from these different families identified a new nonsense variant (c.414G > A) in CLDN14. This variant was then confirmed by Sanger sequencing and PCR-RFLP. Subsequently, four microsatellite markers were used to genotype these families, which revealed a founder effect for this variant. Overall, this study illustrates the implication of the CLDN14 gene in the Yemeni population with NSHL and identifies a new founder variant.