Miracle-dz/UniFAR-FTUs huggingface dataset:
Fabrication and Characterization of Azithromycin-Loaded Niosomes for Periodontitis Treatment

Azithromycin (AZM) is a potential drug for periodontitis treatment, but its poor water solubility could be problematic for local delivery to periodontal tissues. Entrapping AZM, which is a hydrophobic drug into niosomes, could effectively deliver drugs to the target site. This study aimed to design and fabricate azithromycin-loaded niosomes (NAZ) with desirable properties for intra-periodontal pocket administration. Span 60 and cholesterol were used to prepare niosomes with modified reverse phase evaporation method. NAZ were characterized and the effects of niosome composition were investigated. In vitro release and cell viability were evaluated. The results of this study indicated that with the specific ratio of Span 60 and cholesterol, the particle sizes of niosomes were in nano-sized (319 nm) with optimal zeta potential (-39.57 mV). Controlled release of AZM was achieved with release kinetic followed zero order model. NAZ exhibited low toxicity as cell viability was comparable to negative control.
Fatigue Property of Welded T-Shaped Joints Using the Structural Stress Method

Various types of welded joints are of wide application in industrial and productional regions, including T-shaped, butt, and fillet joints of steel, stainless steel, and cast steel. Under cyclic fatigue load, the fatigue performance of welded joints is significant for the engineering design and it’s of interest to investigate the fatigue property of the welded joints using the recommended prediction methods. In this paper, the fatigue performance of welded T-shaped joints is investigated. The mesh-insensitive property of the structural stress method is validated with the comparison of various prediction methods. The fatigue cyclic life of welded T-shaped joints under tensile loads is analyzed. The structural stress curve of T-shaped joints with various base metal thicknesses is determined for the engineering design.
Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review

Abstract Objective To assess the methodological quality of studies on prediction models developed using machine learning techniques across all medical specialties. Design Systematic review. Data sources PubMed from 1 January 2018 to 31 December 2019. Eligibility criteria Articles reporting on the development, with or without external validation, of a multivariable prediction model (diagnostic or prognostic) developed using supervised machine learning for individualised predictions. No restrictions applied for study design, data source, or predicted patient related health outcomes. Review methods Methodological quality of the studies was determined and risk of bias evaluated using the prediction risk of bias assessment tool (PROBAST). This tool contains 21 signalling questions tailored to identify potential biases in four domains. Risk of bias was measured for each domain (participants, predictors, outcome, and analysis) and each study (overall). Results 152 studies were included: 58 (38%) included a diagnostic prediction model and 94 (62%) a prognostic prediction model. PROBAST was applied to 152 developed models and 19 external validations. Of these 171 analyses, 148 (87%, 95% confidence interval 81% to 91%) were rated at high risk of bias. The analysis domain was most frequently rated at high risk of bias. Of the 152 models, 85 (56%, 48% to 64%) were developed with an inadequate number of events per candidate predictor, 62 handled missing data inadequately (41%, 33% to 49%), and 59 assessed overfitting improperly (39%, 31% to 47%). Most models used appropriate data sources to develop (73%, 66% to 79%) and externally validate the machine learning based prediction models (74%, 51% to 88%). Information about blinding of outcome and blinding of predictors was, however, absent in 60 (40%, 32% to 47%) and 79 (52%, 44% to 60%) of the developed models, respectively. Conclusion Most studies on machine learning based prediction models show poor methodological quality and are at high risk of bias. Factors contributing to risk of bias include small study size, poor handling of missing data, and failure to deal with overfitting. Efforts to improve the design, conduct, reporting, and validation of such studies are necessary to boost the application of machine learning based prediction models in clinical practice. Systematic review registration PROSPERO CRD42019161764.
Detection of evil flies: securing air-ground aviation communication

The aviation community is employing various air traffic control and mobile communication technologies, such as ubiquitous data links, wireless communication architectures and protocols. Recently, software-defined networking (SDN) based architectures (i.e., cockpit network communications environment testing (COMET)) have been proposed for Air-Ground communication. However, an evil can break the communication between a pilot and air traffic control, resulting in a hazardous (or life-threatening) situation up in the air or failure of ground equipment. This paper proposes an efficient evil detection and prevention mechanism (called DoEF) for the COMET architecture. The proposed DoEF utilizes a deep learning-based approach, i.e., long-short term memory (LSTM), to detect the evil flies and provide possible countermeasures. Our preliminary results show that the proposed scheme reduces the detection time and increases the detection accuracy of distributed denial of service (DDoS) attacks for the aviation network.
When Idling is Ideal: Optimizing Tail-Latency for Heavy-Tailed Datacenter Workloads with Perséphone

This paper introduces Perséphone, a kernel-bypass OS scheduler designed to minimize tail latency for applications executing at microsecond-scale and exhibiting wide service time distributions. Perséphone integrates a new scheduling policy, Dynamic Application-aware Reserved Cores (DARC), that reserves cores for requests with short processing times. Unlike existing kernel-bypass schedulers, DARC is not work conserving. DARC profiles application requests and leaves a small number of cores idle when no short requests are in the queue, so when short requests do arrive, they are not blocked by longer-running ones. Counter-intuitively, leaving cores idle lets DARC maintain lower tail latencies at higher utilization, reducing the overall number of cores needed to serve the same workloads and consequently better utilizing the datacenter resources.
Gradient Compression Supercharged High-Performance Data Parallel DNN Training

Gradient compression is a promising approach to alleviating the communication bottleneck in data parallel deep neural network (DNN) training by significantly reducing the data volume of gradients for synchronization. While gradient compression is being actively adopted by the industry (e.g., Facebook and AWS), our study reveals that there are two critical but often overlooked challenges: 1) inefficient coordination between compression and communication during gradient synchronization incurs substantial overheads, and 2) developing, optimizing, and integrating gradient compression algorithms into DNN systems imposes heavy burdens on DNN practitioners, and ad-hoc compression implementations often yield surprisingly poor system performance. In this paper, we first propose a compression-aware gradient synchronization architecture, CaSync, which relies on a flexible composition of basic computing and communication primitives. It is general and compatible with any gradient compression algorithms and gradient synchronization strategies, and enables high-performance computation-communication pipelining. We further introduce a gradient compression toolkit, CompLL, to enable efficient development and automated integration of on-GPU compression algorithms into DNN systems with little programming burden. Lastly, we build a compression-aware DNN training framework HiPress with CaSync and CompLL. HiPress is open-sourced and runs on mainstream DNN systems such as MXNet, TensorFlow, and PyTorch. Evaluation via a 16-node cluster with 128 NVIDIA V100 GPUs and 100Gbps network shows that HiPress improves the training speed over current compression-enabled systems (e.g., BytePS-onebit and Ring-DGC) by 17.2%-69.5% across six popular DNN models.
PassiveLiFi: rethinking LiFi for low-power and long range RF backscatter

Light bulbs have been recently explored to design Light Fidelity (LiFi) communication to battery-free tags, thus complementing Radiofrequency (RF) backscatter in the uplink. In this paper, we show that LiFi and RF backscatter are complementary and have unexplored interactions. We introduce PassiveLiFi, a battery-free system that uses LiFi to transmit RF backscatter at a meagre power budget. We address several challenges on the system design in the LiFi transmitter, the tag and the RF receiver. We design the first LiFi transmitter that implements a chirp spread spectrum (CSS) using the visible light spectrum. We use a small bank of solar cells for communication and harvesting and reconfigure them based on the amount of harvested energy and desired data rate. We further alleviate the low responsiveness of solar cells with a new low-power receiver design in the tag. Experimental results with an RF carrier of 17 dBm show that we can generate RF backscatter with a range of 80.3 meters/μW consumed in the tag, which is almost double with respect to prior work.
Snowboard: Finding Kernel Concurrency Bugs through Systematic Inter-thread Communication Analysis

Kernel concurrency bugs are challenging to find because they depend on very specific thread interleavings and test inputs. While separately exploring kernel thread interleavings or test inputs has been closely examined, jointly exploring interleavings and test inputs has received little attention, in part due to the resulting vast search space. Using precious, limited testing resources to explore this search space and execute just the right concurrent tests in the proper order is critical. This paper proposes Snowboard a testing framework that generates and executes concurrent tests by intelligently exploring thread interleavings and test inputs jointly. The design of Snowboard is based on a concept called potential memory communication (PMC), a guess about pairs of tests that, when executed concurrently, are likely to perform memory accesses to shared addresses, which in turn may trigger concurrency bugs. To identify PMCs, Snowboard runs tests sequentially from a fixed initial kernel state, collecting their memory accesses. It then pairs up tests that write and read the same region into candidate concurrent tests. It executes those tests using the associated PMC as a scheduling hint to focus interleaving search only on those schedules that directly affect the relevant memory accesses. By clustering candidate tests on various features of their PMCs, Snowboard avoids testing similar behaviors, which would be inefficient. Finally, by executing tests from small clusters first, it prioritizes uncommon suspicious behaviors that may have received less scrutiny. Snowboard discovered 14 new concurrency bugs in Linux kernels 5.3. 10 and 5.12-rc3, of which 12 have been confirmed by developers. Six of these bugs cause kernel panics and filesystem errors, and at least two have existed in the kernel for many years, showing that this approach can uncover hard-to-find, critical bugs. Furthermore, we show that covering as many distinct pairs of uncommon read/write instructions as possible is the test-prioritization strategy with the highest bug yield for a given test-time budget.
Face-Mic: inferring live speech and speaker identity via subtle facial dynamics captured by AR/VR motion sensors

Augmented reality/virtual reality (AR/VR) has extended beyond 3D immersive gaming to a broader array of applications, such as shopping, tourism, education. And recently there has been a large shift from handheld-controller dominated interactions to headset-dominated interactions via voice interfaces. In this work, we show a serious privacy risk of using voice interfaces while the user is wearing the face-mounted AR/VR devices. Specifically, we design an eavesdropping attack, Face-Mic, which leverages speech-associated subtle facial dynamics captured by zero-permission motion sensors in AR/VR headsets to infer highly sensitive information from live human speech, including speaker gender, identity, and speech content. Face-Mic is grounded on a key insight that AR/VR headsets are closely mounted on the user's face, allowing a potentially malicious app on the headset to capture underlying facial dynamics as the wearer speaks, including movements of facial muscles and bone-borne vibrations, which encode private biometrics and speech characteristics. To mitigate the impacts of body movements, we develop a signal source separation technique to identify and separate the speech-associated facial dynamics from other types of body movements. We further extract representative features with respect to the two types of facial dynamics. We successfully demonstrate the privacy leakage through AR/VR headsets by deriving the user's gender/identity and extracting speech information via the development of a deep learning-based framework. Extensive experiments using four mainstream VR headsets validate the generalizability, effectiveness, and high accuracy of Face-Mic.
FIRE: enabling reciprocity for FDD MIMO systems

Massive MIMO forms a crucial component for 5G because of its ability to improve quality of service and support multiple streams simultaneously. However, for real-world MIMO deployments, estimating the downlink wireless channel from each antenna on the base station to every client device is a critical bottleneck, especially for the widely used frequency duplexed designs that cannot utilize reciprocity. Typically, this channel estimation requires explicit feedback from client devices and is prohibitive for large antenna deployments. In this paper, we present FIRE, a system that uses an end-to-end machine learning approach to enable accurate channel estimation without requiring any feedback from client devices. FIRE is interpretable, accurate, and has low compute overhead. We show that FIRE can successfully support MIMO transmissions in a real-world testbed and achieves SNR improvement over 10 dB in MIMO transmissions compared to the current state-of-the-art.
LineFS: Efficient SmartNIC Offload of a Distributed File System with Pipeline Parallelism

In multi-tenant systems, the CPU overhead of distributed file systems (DFSes) is increasingly a burden to application performance. CPU and memory interference cause degraded and unstable application and storage performance, in particular for operation latency. Recent client-local DFSes for persistent memory (PM) accelerate this trend. DFS offload to SmartNICs is a promising solution to these problems, but it is challenging to fit the complex demands of a DFS onto simple SmartNIC processors located across PCIe. We present LineFS, a SmartNIC-offloaded, high-performance DFS with support for client-local PM. To fully leverage the SmartNIC architecture, we decompose DFS operations into execution stages that can be offloaded to a parallel datapath execution pipeline on the SmartNIC. LineFS offloads CPU-intensive DFS tasks, like replication, compression, data publication, index and consistency management to a Smart-NIC. We implement LineFS on the Mellanox BlueField Smart-NIC and compare it to Assise, a state-of-the-art PM DFS. LineFS improves latency in LevelDB up to 80% and throughput in Filebench up to 79%, while providing extended DFS availability during host system failures.
A Decade of CS Education in New Zealand’s High Schools: Where Are We At?

Although a rigorous computing curriculum equipped with computer science and computer programming achievement standards was introduced in high schools in New Zealand 10 years ago, fewer than 10% of students take these standards. That raises the questions of why the uptake is low, and what were the trends in recent years. Past studies of New Zealand’s computer science and programming standards have tended to concentrate on the nature of the standards, teachers’ reactions to standards, teaching materials and assessment methods. In this research, we analyse the introduction of the computing curriculum over the last decade in terms of student uptake in general, the uptake for different groups of students, students’ progression through these standards and number of schools offering these standards. After analysing the official, nationwide data we can provide the following major findings. First, student participation in all three levels of computer science and programming standards is very low throughout the period. Second, fewer than 50% of high schools offer any of these standards. Finally, large equity gaps remain in enrollment to high school computer science education by socio-economic status, ethnicity and gender.
HEALER: Relation Learning Guided Kernel Fuzzing

Modern operating system kernels are too complex to be free of bugs. Fuzzing is a promising approach for vulnerability detection and has been applied to kernel testing. However, existing work does not consider the influence relations between system calls when generating and mutating inputs, resulting in difficulties when trying to reach into the kernel's deeper logic effectively. In this paper, we propose HEALER, a kernel fuzzer that improves fuzzing's effectiveness by utilizing system call relation learning. HEALER learns the influence relations between system calls by dynamically analyzing minimized test cases. Then, HEALER utilizes the learned relations to guide input generation and mutation, which improves the quality of test cases and the effectiveness of fuzzing. We implemented HEALER and evaluated its performance on recent versions of the Linux kernel. Compared to state-of-the-art kernel fuzzers such as Syzkaller and Moonshine, HEALER improves branch coverage by 28% and 21%, while achieving a speedup of 2.2x and 1.8x, respectively. In addition, HEALER detected 218 vulnerabilities, 33 of which are previously unknown and have been confirmed by the corresponding kernel maintainers.
Bladerunner: Stream Processing at Scale for a Live View of Backend Data Mutations at the Edge

Consider a social media platform with hundreds of millions of online users at any time, utilizing a social graph that has many billions of nodes and edges. The problem this paper addresses is how to provide each user a continuously fresh, up-to-date view of the parts of the social graph they are currently interested in, so as to provide a positive interactive user experience. The problem is challenging because the social graph mutates at a high rate, users change their focus of interest frequently, and some mutations are of interest to many online users. We describe Bladerunner, a system we use at Facebook to deliver relevant social graph updates to user devices efficiently and quickly. The heart of Bladerunner is a set of back-end stream processors that obtain streams of social graph updates and process them on a per application and per-user basis before pushing selected updates to user devices. Separate stream processors are used for each application to enable application-specific customization, complex filtering, aggregation and other message delivery operations on a per-user basis. This strategy minimizes device processing overhead and last-mile bandwidth usage, which are critical given that users are mostly on mobile devices.
The Demikernel Datapath OS Architecture for Microsecond-scale Datacenter Systems

Datacenter systems and I/O devices now run at single-digit microsecond latencies, requiring ns-scale operating systems. Traditional kernel-based operating systems impose an unaffordable overhead, so recent kernel-bypass OSes [73] and libraries [23] eliminate the OS kernel from the I/O datapath. However, none of these systems offer a general-purpose datapath OS replacement that meet the needs of μs-scale systems. ' AB@This paper proposes Demikernel, a flexible datapath OS and architecture designed for heterogenous kernel-bypass devices and μs-scale datacenter systems. We build two prototype Demikernel OSes and show that minimal effort is needed to port existing μs-scale systems. Once ported, Demikernel lets applications run across heterogenous kernel-bypass devices with ns-scale overheads and no code changes.
The effects of visual realism and visuospatial abilities on memorizing soccer tactics

Abstract Objectives Dynamic visualizations have become valuable assets in acquiring and improving sports skills. However, their instructional efficiency depends on their design and on learners’ cognitive abilities. Intending to improve learning sessions using these technologies, this study investigates the effects of visual realism and visuospatial abilities (VSA) on the memorization of soccer scenes. Methods Twenty-four soccer beginners first performed a multitask VSA test followed by the memorization and reproduction of three dynamic sequences with different levels of realism. An eye-tracking system was used in the memorization phase to record gaze behavior and identify the involved cognitive processes. Results Findings revealed that increasing visual realism disrupted learners’ memorization performance and visual processing. Moreover, learners with high-VSA were more efficient in memorizing soccer scenes than those with low-VSA and benefited more from reduced visual realism than low-VSA. Learners’ visual processing revealed that high-VSA were more focused on creating a mental representation of the content whereas low-VSA were rather visually guided when memorizing the scene. Conclusions Results imply considering learners’ VSA and adapting presentations’ visual realism to optimize tactical learning among beginners.
A cognitive account of subjectivity put to the test: using an insertion task to investigate Mandarin result connectives

Abstract This article aims to further test the cognitive claims of the so-called subjectivity account of causal events and their linguistic markers, causal connectives. We took Mandarin Chinese, a language that is typologically completely different from the usual western languages, as a case to provide evidence for this subjectivity account. Complementary to the commonly used corpora analyses, we employed crowdsourcing to tap native speakers’ intuitions about causal coherence, focusing on four result connectives kějiàn ‘therefore’, suǒyǐ ‘so’, yīncǐ ‘so/for this reason’ and yúshì ‘thereupon/as a result’. The analysis shows systematic differences regarding the use of connectives in relations that differ in terms of subjectivity, demonstrating that native speakers make use of subjectivity to encode and decode different types of causal relations in discourse. Moreover, our study evidences that a comprehensive model of subjectivity should include the epistemic dimension of certainty about the subjectivity scale that might be indicated by other linguistic elements. In-depth analyses of the test items revealed that the presence/absence of modality words in the result segments are related to different preferential patterns for the connectives. There is a trade-off between the epistemic dimension of certainty and the expression of subjectivity in the four connectives involved.