Miracle-dz/UniFAR-FTUs huggingface dataset:
Student real-time visualization system in classroom using RFID based on UTAUT model

Purpose The proposed system aims to monitor in class activities and the performance of the students. Design/methodology/approach A pilot study was conducted to evaluate the proposed system using a questionnaire with 132 participants (teachers and non-teachers) in a presentation style to record the participant’s perception about performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC), usability expectancy (UE) and user’s satisfaction (S) based on Unified Theory of Acceptance Use of Technology (UTAUT) model Findings The results show that PE, EE, FC had positive and significant influence on the UE of the proposed system. The effect of EE and FC on UE was seen to be more in female compared to male participants. The teacher category considered the PE and EE important factors in determining their decision to use the proposed system. Originality/value A real-time student(s) visualization system (RTSVS) based on the concept of real time student locating system using Radio Frequency Ident.
Racial and Ethnic Disparities in Acute Care Use for Pediatric Asthma

PURPOSE Previous work has shown that asthma-related emergency department (ED) use is greatest among Black and Latine populations, but it is unknown whether health care use for exacerbations differs across settings (outpatient, ED, inpatient) and correlates with use of routine outpatient services. We aimed to measure disparities by race, ethnicity, and language in pediatric acute asthma care using data from US primary care community health centers. METHODS In an observational study using electronic health records from community health centers in 18 states, we compared non-Hispanic Black, English-preferring Latine, Spanish-preferring Latine, and non-Hispanic White children aged 3 to 17 years on visits for clinic-coded asthma exacerbations (2012-2018). We further evaluated asthma-related ED use and inpatient admissions in a subsample of Oregon-Medicaid recipients. Covariate-adjusted odds ratios (ORs) and rate ratios (RRs) were derived using logistic or negative binomial regression analysis with generalized estimating equations. RESULTS Among 41,276 children with asthma, Spanish-preferring Latine children had higher odds of clinic visits for asthma exacerbation than non-Hispanic White peers (OR = 1.10; 95% CI, 1.02-1.18). Among the subsample of 6,555 children insured under Oregon-Medicaid, non-Hispanic Black children had higher odds and rates of asthma-related ED use than non-Hispanic White peers (OR = 1.40; 95% CI, 1.04-1.89 and RR = 1.49; 95% CI, 1.09-2.04, respectively). We observed no differences between groups in asthma-related inpatient admissions. CONCLUSIONS This study is the first to show that patterns of clinic and ED acute-care use differ for non-Hispanic Black and Spanish-preferring Latine children when compared with non-Hispanic White peers. Non-Hispanic Black children had lower use of clinics, whereas Spanish-preferring Latine children had higher use, including for acute exacerbations. These patterns of clinic use were accompanied by higher ED use among Black children. Ensuring adequate care in clinics may be important in mitigating disparities in asthma outcomes. VISUAL ABSTRACT
Green synthesis, characterization, cytotoxicity, and antimicrobial activity of iron oxide nanoparticles using Nigella sativa seed extract

Abstract Green synthesis of nanoparticles (NPs) is a more sustainable, safe, and environmentally friendly method. This study aimed to investigate the synthesis procedure of iron oxide nanoparticles (IONPs) using the seed extract of Nigella sativa (N. sativa) as a strong reducing agent and to estimate their cytotoxic and antibacterial properties. The obtained IONPs were characterized by field-emission scanning electron microscopy, X-ray diffraction, dynamic light scattering (DLS), zeta potential (ZP), and Fourier-transform infrared spectroscopy analyses. The cytotoxicity of the biosynthesized IONPs was demonstrated by the MTT on a Vero cell line. Furthermore, the antibacterial activity of the fabricated biosynthesis metal oxide NPs was tested on Escherichia coli (E. coli) (ATCC 35218) and Staphylococcus aureus (S. aureus) (ATCC 29213) bacterial strains using the Kirby–Bauer disk diffusion method. This study showed the formation of a well-dispersed, highly stable (ZP ζ = −51.8 mV) IO NPs with an average diameter of about 31.45 nm. Moreover, the biosynthetic NPs (IONPs) exhibited a significantly noncytotoxic effect when analyzed by the MTT assay. The biosynthetic NPs (NS-IONPs) exhibited excellent antibacterial activity against E. coli and S. aureus, where the inhibition zones were 12.34 ± 0.58 and 11.52 ± 0.58, respectively.
Scaling graph traversal to 281 trillion edges with 40 million cores

Graph processing, especially high-performance graph traversal, plays a more and more important role in data analytics. The successor of Sunway TaihuLight, New Sunway, is equipped with nearly 10 PB memory and over 40 million cores, which brings the opportunity to process hundreds of trillions of edges graphs. However, the graph with an unprecedented scale also brings severe performance challenges, including load imbalance, poor locality, and irregular access of graph traversal workload. To address the scalability problem, we propose a novel 3-level degree-aware 1.5D graph partitioning, which benefits from both delegated 1D and 2D partitioning. By delegating extremely heavy vertices globally and other heavy vertices on columns and rows in the processes mesh, we break the scalability wall of previous partitioning methods. Together with sub-iteration direction optimization, core group -aware core subgraph segmenting, and a new on-chip sorting mechanism using RMA, we achieve 180,792 GTEPS on a graph with 281 trillion edges, using 103,912 processors with over 40 million cores, achieving 1.75X performance and 8X capacity compared to the previous state of the art and conforming to the Graph 500 BFS benchmark[14].
Visualization of Plant Leaf Classification Process Based on Multi-Layer Network Model

Tree recognition is a ubiquitous topic in the field of artificial intelligence, and the identification of leaf types is one of the important ways in analyzing tree species. Based on the focalized attention mechanism and feature fusion strategy, we in this paper establish a multi-layer network model classifier: Leaf-AMNet to classify the Liriodendron leaves and Ginkgo leaves. In order to explore the "black box" problem of deep learning, we visualize the classification process of Leaf-AMNet. Experimental results show that Leaf-AMNet achieves high accuracy on Leaf Dataset and shows potential information via visual effects.
OPEC: operation-based security isolation for bare-metal embedded systems

Bare-metal embedded systems usually lack security isolation. Attackers can subvert the whole system with a single vulnerability. Previous research intends to enforce both privilege isolation (to run application code at the unprivileged level) and resource isolation for global variables and peripherals. However, it suffers from partition-time and execution-time over-privilege issues, due to the limited hardware resources (MPU regions) and the improper way to partition a program. In this paper, we propose operation-based isolation for bare-metal embedded systems. An operation is a logically independent task composed of an entry function and all functions reachable from it. To solve the partition-time over-privilege issue, we utilize the global variables shadowing technique to reduce the needed MPU regions to confine the access of the global variables. To mitigate the execution-time over-privilege issue, we split programs into code compartments (called operation) that only contain necessary functions to perform specific tasks, thereby removing the resources needed by unnecessary functions. We implement a prototype called OPEC, which contains an LLVM-based compiler and a reference monitor. The compiler partitions a program and analyzes the resource dependency for each operation. With the hardware-supported privilege levels and MPU, the reference monitor is responsible for enforcing the privilege and resource isolation at runtime. Our evaluation shows that OPEC can achieve the security guarantees for the privilege and resource isolation with negligible runtime overhead (average 0.23%), moderate Flash overhead (average 1.79%), and acceptable SRAM overhead (average 5.35%).
Detectable recovery of lock-free data structures

This paper presents a generic approach for deriving detectably recoverable implementations of many widely-used concurrent data structures. Such implementations are appealing for emerging systems featuring byte-addressable non-volatile main memory (NVMM), whose persistence allows to efficiently resurrect failed threads after crashes. Detectable recovery ensures that after a crash, every executed operation is able to recover and return a correct response, and that the state of the data structure is not corrupted. Our approach, called Tracking, amends descriptor objects used in existing lock-free helping schemes with additional fields that track an operation's progress towards completion and persists these fields in order to ensure detectable recovery. Tracking avoids full-fledged logging and tracks the progress of concurrent operations in a per-thread manner, thus reducing the cost of ensuring detectable recovery. We have applied Tracking to derive detectably recoverable implementations of a linked list, a binary search tree, and an exchanger. Our experimental analysis introduces a new way of analyzing the cost of persistence instructions, not by simply counting them but by separating them into categories based on the impact they have on the performance. The analysis reveals that understanding the actual persistence cost of an algorithm in machines with real NVMM, is more complicated than previously thought, and requires a thorough evaluation, since the impact of different persistence instructions on performance may greatly vary. We consider this analysis to be one of the major contributions of the paper.
ADIM-MVSNet: Adaptive Depth Interval Multi-View Stereo Network for 3D Reconstruction

∗ Estimating depth from multiple images to recover 3D object surfaces is an important and challenging computer vision task. In this paper, an effective multi-view stereo network is proposed with a multi-scale feature aggregation module and adaptive estimation of depth range assumptions, namely ADIM-MVSNet. Particularly, multi-scale feature aggregation (MFA) module is presented to extract and aggregate features at different scales of images, which substantially improves the extraction of weak texture regions compared to general 2D convolutional neural networks (CNNs). In addition, adaptive depth interval module (ADIM) is proposed to improve the previous fixed depth interval partitioning, which allows the network to reasonably partition the depth prediction range during the refinement stage. Experimental results show that the proposed method can effectively conduct the multi-view 3D reconstruction of complex scenes and achieve state-of-the-art (SOTA) reconstruction results on the DTU dataset.
You shall not (by)pass!: practical, secure, and fast PKU-based sandboxing

Memory Protection Keys for Userspace (PKU) is a recent hardware feature that allows programs to assign virtual memory pages to protection domains, and to change domain access permissions using inexpensive, unprivileged instructions. Several in-process memory isolation approaches leverage this feature to prevent untrusted code from accessing sensitive program state and data. Typically, PKU-based isolation schemes need to be used in conjunction with mitigations such as CFI because untrusted code, when compromised, can otherwise bypass the PKU access permissions using unprivileged instructions or operating system APIs. Recently, researchers proposed fully self-contained PKU-based memory isolation schemes that do not rely on other mitigations. These systems use exploit-proof call gates to transfer control between trusted and untrusted code, as well as a sandbox that prevents tampering with the PKU infrastructure from untrusted code. In this paper, we show that these solutions are not complete. We first develop two proof-of-concept attacks against a state-of-the-art PKU-based memory isolation scheme. We then present Cerberus, a PKU-based sandboxing framework that can overcome limitations of existing sandboxes. We apply Cerberus to several memory isolation schemes, and show that it is practical, efficient, and secure.
An investigation exploring the extent ‘EAL Steps for Classroom Integration’ is effective in primary school language teaching

The purpose of this study was to ascertain teacher perspectives on the use of ‘EAL steps for Classroom Integration’ as a tool to support teachers of international new arrivals with their daily planning; from teaching suggestions for themselves to preparing suitable activities and assessment. A need was felt for the research following the understanding that although there is ample information available for teaching English as an additional language in schools, research has shown teachers to feel underprepared from the absence of clear guidance. The author has carefully selected and presented information ‘in a nutshell’ and offered it to participants to guide their professional duty of ensuring integration of new arrivals in their classes. Data was collected through two interviews and a focus group. The interviews, taking a before and after approach directly compared responses, with the focus group as an opportunity to corroborate findings. This short study saw an impressive turnaround in approaches used by participants who were able to engage learners and see progression through the changes that had been made to teaching strategies. It created confident and proficient, practicing EAL teachers with motivated learners, leading to the recommendation that ‘EAL steps for Classroom Integration’ should be widely available.
Stock Market Prediction Using Support Vector Regressor with Radial Basis Function Kernel

Currently, one of the most popular types of investment by the Indonesian people is stocks. This is evidenced by data from the Indonesia Stock Exchange, until the end of 2020, the number of capital market investors increased 56% from 2019 to 3.88 million investors. The purpose of this study is to increase public interest, especially the millennial generation, to invest in stocks, to increase profits from investing in stocks, and to reduce suicides due to losses from investing in stocks. In addition, the method used in this study is the Support Vector Regressor. From this research, the results of the Support Vector Regressor Train results for Root Mean Square Error are 179.17, R2 are 1.0, and Mean Absolute Error are 1.48 while the Support Vector Regressor Test results for Root Mean Square Error are 228.79, R2 are 0.99, and Mean Absolute Error are 13.80. Therefore, the Support Vector Regressor model is suitable for predicting stock prices. The source code can be accessed on the following GitHub link : https://github.com/K6RTCS/Stock-Market-Prediction-RBF.git
Jiffy: elastic far-memory for stateful serverless analytics

Stateful serverless analytics can be enabled using a remote memory system for inter-task communication, and for storing and exchanging intermediate data. However, existing systems allocate memory resources at job granularity---jobs specify their memory demands at the time of the submission; and, the system allocates memory equal to the job's demand for the entirety of its lifetime. This leads to resource underutilization and/or performance degradation when intermediate data sizes vary during job execution. This paper presents Jiffy, an elastic far-memory system for stateful serverless analytics that meets the instantaneous memory demand of a job at seconds timescales. Jiffy efficiently multiplexes memory capacity across concurrently running jobs, reducing the overheads of reads and writes to slower persistent storage, resulting in 1.6 -- 2.5× improvements in job execution time over production workloads. Jiffy implementation currently runs on Amazon EC2, enables a wide variety of distributed programming models including MapReduce, Dryad, StreamScope, and Piccolo, and natively supports a large class of analytics applications on AWS Lambda.
Understanding and detecting deep memory persistency bugs in NVM programs with DeepMC

To facilitate programming with non-volatile memory (NVM), a set of memory persistency models, such as strict and epoch persistency, have been proposed. Although these models provide high-level guidance for reasoning about the data persistence, implementing them correctly is nontrivial. Our study of the well-developed NVM frameworks and libraries reveals that many of them have deep semantic bugs that are strongly relevant to the model specifications. Furthermore, it is difficult to detect them with existing testing and bug-finding tools. To further understand these persistency bugs, we conduct a characterization study, and present a taxonomy of these persistency bugs. We find that many persistency bugs are caused by the semantic mismatches between the model specifications and their real implementation in NVM programs. To identify these deep persistency bugs, we build a toolkit named DeepMC with both static and dynamic analysis. DeepMC is driven by a set of rules based on our characterization study and persistency model specifications. Our results show that DeepMC can efficiently pinpoint various persistency bugs in a variety of NVM programming frameworks/libraries, and their example programs, including PMDK and persistent memory file system (PMFS) from Intel, the NVM-Direct library from Oracle, and Mnemosyne framework from academia.
Tourist Experience and Influencing Factors in the Context of Smart Tourism

Abstract: Smart tourism is an imperative innovation and reform in the tourism industry. In the process of building smart scenic areas, we need not only fully interpret the essential connotation of smart tourism in theory, but also comprehensively apply the latest technical support to build a variety of hardware platforms in smart scenic areas, thereby meeting the needs of scenic resource protection, business management, tourism management, public services, and decision support. By proposing hypotheses about the factors influencing tourist experience, we carefully designed questionnaires, conducted online surveys among 350 tourists, refined three influencing factors after dividing the influencing factors into categories and performing correlation analysis. The results show that there is a significant positive correlation between smart guide platform, smart guide robot, smart supervision platform, and tourist experience, and the above factors play a significant positive role in improving the tourist experience. By comparing the standardized coefficient values, it can be seen that standardized coefficient value of the smart guide platform has relatively high absolute value, indicating its relatively big impact. Next comes smart supervision platform and smart guide robot. It suggests that in the construction of smart scenic areas, we should: expand the construction of smart guide platform, perfect the service feedback mechanism, accelerate the upgrading of smart guide robots, highlight the advantages of artificial intelligence, and promote the intelligent, real-time, and refined development of smart supervision platforms.
DeepRest: deep resource estimation for interactive microservices

Interactive microservices expose API endpoints to be invoked by users. For such applications, precisely estimating the resources required to serve specific API traffic is challenging. This is because an API request can interact with different components and consume different resources for each component. The notion of API traffic is vital to application owners since the API endpoints often reflect business logic, e.g., a customer transaction. The existing systems that simply rely on historical resource utilization are not API-aware and thus cannot estimate the resource requirement accurately. This paper presents DeepRest, a deep learning-driven resource estimation system. DeepRest formulates resource estimation as a function of API traffic and learns the causality between user interactions and resource utilization directly in a production environment. Our evaluation shows that DeepRest can estimate resource requirements with over 90% accuracy, even if the API traffic to be estimated has never been observed (e.g., 3× more users than ever or unseen traffic shape). We further apply resource estimation for application sanity checks. DeepRest identifies system anomalies by verifying whether the resource utilization is justifiable by how the application is being used. It can successfully identify two major cyber threats: ransomware and cryptojacking attacks.
Difficulties When Implementing Blended E-learning in Credit Training at Public Universities in Vietnam

The form of credit training is not unfamiliar to the education of developed countries but is still quite new in Vietnam. The positive results it brings about have encouraged this model to be replicated in many universities, especially public universities. Besides, training in the form of credit also requires a lot of conditions, regulations and principles attached. In the Covid epidemic, Vietnam's higher education has been strongly influenced by constantly changes of learning model in offline and online classes. This change is adaptive to the complicated epidemic situation but opens a new direction for public universities which is the implementation of blended e-learning. Blended e-learning form appeared in Vietnam about 5 years ago, but it is not really popular and regularly deployed in universities. However, the implementation of this form also has to deal with a number of difficulties that universities need to thoroughly understand in order to make adjustments to achieve the desired goals. © 2021 Association for Computing Machinery. All rights reserved.
ParGeo: A Library for Parallel Computational Geometry

This paper presents ParGeo, a multicore library for computational geometry. ParGeo contains modules for fundamental tasks including $k$d-tree based spatial search, spatial graph generation, and algorithms in computational geometry. We focus on three new algorithmic contributions provided in the library. First, we present a new parallel convex hull algorithm based on a reservation technique to enable parallel modifications to the hull. We also provide the first parallel implementations of the randomized incremental convex hull algorithm as well as a divide-and-conquer convex hull algorithm in $\mathbb{R}^3$. Second, for the smallest enclosing ball problem, we propose a new sampling-based algorithm to quickly reduce the size of the data set. We also provide the first parallel implementation of Welzl's classic algorithm for smallest enclosing ball. Third, we present the BDL-tree, a parallel batch-dynamic $k$d-tree that allows for efficient parallel updates and $k$-NN queries over dynamically changing point sets. BDL-trees consist of a log-structured set of $k$d-trees which can be used to efficiently insert, delete, and query batches of points in parallel. On 36 cores with two-way hyper-threading, our fastest convex hull algorithm achieves up to 44.7x self-relative parallel speedup and up to 559x speedup against the best existing sequential implementation. Our smallest enclosing ball algorithm using our sampling-based algorithm achieves up to 27.1x self-relative parallel speedup and up to 178x speedup against the best existing sequential implementation. Our implementation of the BDL-tree achieves self-relative parallel speedup of up to 46.1x. Across all of the algorithms in ParGeo, we achieve self-relative parallel speedup of 8.1--46.61x.