Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of anions on the toxic effects of ionic liquids to a phytoplankton Selenastrum capricornutum

Investigations on the influence of anions of ionic liquids, with respect to toxicity and ecotoxicity, have been reported; however, no consistent conclusion has been drawn. The aim of this study was to elucidate the influence of the anionic component, using the alga Selenastrum capricornutum, by assessing the toxicity of various anions associated with imidazolium-based ionic liquids as well as alkali salts. Additionally, the hydrolysis of fluoride-containing ionic liquids, i.e., 1-butyl-3-methylimidazolium [BMIM], incorporated with tetrafluoroborate, hexafluorophosphate and hexafluoroantimonate, was also estimated. The results obtained revealed that imidazolium-based ionic liquids were more toxic than their corresponding alkali salts. In both cases, SbF6– and PF6– were identified as the most toxic anions. In general, Selenastrum capricornutum was sensitive to the anion moieties in the order: SbF6– > PF6– > BF4– > CF3SO3– > C8H17OSO3– > Br– ≈ Cl–. With regard to the hydrolytic effect, [BMIM][SbF6] generated a greater amount of fluoride compared with [BMIM][BF4], but no fluoride formation occurred with the hexafluorophosphate.
Multiple Instance Learning approaches for Melanoma and Dysplastic Nevi images classification

Malignant melanoma is responsible for the highest number of deaths related to skin lesions. The similarities of melanoma with other skin lesions such as dysplastic nevi, however, constitute a pitfall for early diagnosis. The research community is committed to proposing software solutions that favor the computerized analysis of lesions for melanoma detection. Existing methods have typically focused on the dichotomous distinction of melanoma from benign lesions. Currently, there is debate about Dysplastic Nevi Syndrome (DNS), or rather about the number of moles present on the human body as potential melanoma risk factors. Distinguishing dysplastic nevi from common ones is a challenging yet mostly unexplored classification problem. The classification phase is particularly delicate: over time, a series of automatic learning algorithms have been proposed to better face this issue. In this paper, we refer to the emergencing role of Multiple Instance Learning approaches for discriminating melanoma from dysplastic nevi and to outline the even more complex challenge related to the classification of dysplastic nevi from common ones.
Problematic Mobile Phone Use and Risky Behaviors Among College Students in Peru

A recent report noted that 93.4% of Peruvian citizens own a smartphone and that this has impacted positively in society for different purposes. Despite its advantages, scholars and practitioners have been investigating human-technology interaction, psychological processes, and their possible negative effects. Previous studies have investigated Problematic Mobile Phone Use (PMPU) as a dysfunctional interaction concerning the excessive use of smartphone activities that results progressively in negative consequences such as lack of control, preoccupation, withdrawal, relapse, and conflict. Despite this emergent area of research, there is little research examining PMPU among young Peruvian adults. In addition to this, there are still inconsistencies in relationship between PMPU, and substance use, and other risky behaviors. This paper analyses the impact of smartphone usage patterns, sensation seeking, and substance use in relation to PMPU. A total of 921 college students from Peru were recruited and completed a survey examining demographics, usage patterns, sensation seeking, and substance use. Overall, results indicate that gender, time spent talking on mobile phones, sensation seeking, and psychoactive substance use were predictors of PMPU. Implications at a theoretical, practical, and methodological level are discussed in relation to further research of PMPU in Peru.
Simulation of the discharge propagation in a capillary tube in air at atmospheric pressure

This paper presents simulations of an air plasma discharge at atmospheric pressure initiated by a needle anode set inside a dielectric capillary tube. We have studied the influence of the tube inner radius and its relative permittivity εr on the discharge structure and dynamics. As a reference, we have used a relative permittivity εr = 1 to study only the influence of the cylindrical constraint of the tube on the discharge. For a tube radius of 100 µm and εr = 1, we have shown that the discharge fills the tube during its propagation and is rather homogeneous behind the discharge front. When the radius of the tube is in the range 300–600 µm, the discharge structure is tubular with peak values of electric field and electron density close to the dielectric surface. When the radius of the tube is larger than 700 µm, the tube has no influence on the discharge which propagates axially. For a tube radius of 100 µm, when εr increases from 1 to 10, the discharge structure becomes tubular. We have noted that the velocity of propagation of the discharge in the tube increases when the front is more homogeneous and then, the discharge velocity increases with the decrease in the tube radius and εr. Then, we have compared the relative influence of the value of the tube radius and εr on the discharge characteristics. Our simulations indicate that the geometrical constraint of the cylindrical tube has more influence than the value of εr on the discharge structure and dynamics. Finally, we have studied the influence of photoemission processes on the discharge structure by varying the photoemission coefficient. As expected, we have shown that photoemission, as it increases the number of secondary electrons close to the dielectric surface, promotes the tubular structure of the discharge.
Self-Align-Gated GaN Field Emitter Arrays Sharpened by a Digital Etching Process

Field emitters are attracting much attention recently because of their potential for high-frequency and harsh-environment applications. Although silicon is the most studied semiconductor for field emitters, III-Nitrides are also very promising thanks to their tunable electron affinities. Two main challenges exist for low turn-on-voltage III-Nitrides field emitters: lack of a self-aligned gate and tip-sharpening technologies. In this work, we demonstrate self-aligned-gated GaN field emitter arrays whose tips are sharpened by a wet-based digital etching technology. This technology allows to reduce the tip size from 40 nm down to 20 nm. Gated GaN field emitters with a turn-on voltage (<inline-formula> <tex-math notation="LaTeX">$\text{V}_{GE,ON}$ </tex-math></inline-formula>) of 20 V and current density of 150 mA/cm<sup>2</sup> at <inline-formula> <tex-math notation="LaTeX">$\text{V}_{GE} =50$ </tex-math></inline-formula> V have been demonstrated.
Challenges and Hurdles to E-learning Implementation During COVID-19 Outbreak: A Case of Shaikh Zayed University

Information and Communication Technologies (ICTs) are used extensively in various fields all over the world. E-learning has become a necessity in higher education institutions and is being deployed in educational establishments across the globe during the COVID-19 pandemic. An online survey was conducted from May to June 2020 amidst COVID-19 at Shaikh Zayed University (SZU), Khost, Afghanistan, to assess the current status and identify key challenges hurdle e-learning implementation. A structural questionnaire link using Google Form was sent to participants via social media and email. 216 responses were received in total. The general behavior and practices indicate the participants lack ICT awareness and knowledge about e-learning. Most of the learners and lecturers utilize WhatsApp application for attending e-learning and sharing learning materials. Additionally, the results of the study pointed out significant challenges, namely financial, technical, human, and regulatory hurdles for e-learning implementation in the context of SZU, Khost, Afghanistan. Crucial recommendations are provided for the effective implementation of e-learning in the context of SZU and other public universities.
SineKV: Decoupled Secondary Indexing for LSM-based Key-Value Stores

Secondary indexing is highly demanded for key-value stores by many applications to accelerate query performance. Current secondary indices on key-value stores are typically built on top of the primary index. In a secondary key query, the primary index has to be accessed to fetch the records, with the retrieved primary keys from the secondary index. The record fetching process invokes lots of point lookups in the primary index and exacerbates the read amplification. In this paper, we present SineKV, a decoupled Secondary indexing Key-Value store, aiming to avoid fetching records from the primary index and improve the secondary key query performance. Firstly, SineKV separates the records from the indices and keeps each index pointing to the record values independently. Secondly, SineKV proposes a mapping-based lazy index maintenance strategy to ensure the consistency of secondary indices. Finally, SineKV leverages the CMB feature of the underlying NVMe SSDs to guarantee crash consistency. We implement and evaluate SineKV against LevelDB and Wisc-Key based designs. The evaluations show SineKV outperforms LevelDB and WiscKey based systems by up to 6.12× and 2.78× under microbenchmark and mixed workloads.
Edge Compression: An Integrated Framework for Compressive Imaging Processing on CAVs

Machine vision is the key to the successful deployment of many Advanced Driver Assistant System (ADAS) / Automated Driving System (ADS) functions, which require accurate high-resolution video processing in a real-time manner. Conventional approaches are either to reduce the frame rate or reduce the related frame size of the conventional camera videos, which lead to undesired consequences such as losing informative high-speed information and/or small objects in the video frames.Unlike conventional cameras, Compressive Imaging (CI) cameras are the promising implications of Compressive Sensing, which is an emerging field with the revelation that the optical domain compressed signal (a small number of linear projections of the original video image data) contains sufficient high-speed information for reconstruction and processing. Yet, CI cameras usually need complicated algorithms to retrieve the desired signal, leading to the corresponding high energy consumption. In this paper, we take a step further to the real applications of CI cameras in connected and autonomous vehicles (CAVs), with the primary goal of accelerating accurate video analysis and decreasing energy consumption. We propose a novel Vehicle Edge Server-Cloud closed-loop framework called Edge Compression for CI processing on CAVs. Our comprehensive experiments with four public datasets demonstrate that the detection accuracy of the compressed video images (named measurements) generated by the CI camera is close to the accuracy on reconstructed videos and comparable to the true value, which paves the way of applying CI in CAVs. Finally, six important observations with supporting evidence and analysis are presented to provide practical implications for researchers and domain experts. The code to reproduce our results is available at https://www.thecarlab. oryoutcomes/software.
DGattGAN: Cooperative Up-Sampling Based Dual Generator Attentional GAN on Text-to-Image Synthesis

Text-to-image synthesis task aims at generating images consistent with input text descriptions and is well developed by the Generative Adversarial Network (GAN). Although GAN based image generation approaches have achieved promising results, synthesizing quality is sometimes unsatisfied due to discursive generation of background and object. In this article, we propose a cooperative up-sampling based Dual Generator attentional GAN (DGattGAN) to generate high-quality images from text description. To achieve this, two generators with individual generation purpose are established to decouple object and background generation. In particular, we introduce a cooperative up-sampling mechanism to build cooperation between object and background generators during training. This strategy is potentially very useful as any dual generator architecture in GAN models can benefit from this mechanism. Furthermore, we propose an asymmetric information feeding scheme to distinguish two synthesis tasks, such that each generator only synthesizes based on semantic information they accept. Taking advantage of effective dual generator, the attention mechanism we incorporated on object generator could devote to fine-grained details generation on actual targeted objects. Experiments on Caltech-UCSD Bird (CUB) and Oxford-102 datasets suggest that generated images by the proposed model are more realistic and consistent with input text, and DGattGAN is competent compared to state-of-the-art methods according to Inception Score (IS) and R-precision metrics. Our codes are available at: https://github.com/ecfish/DGattGAN.
Effectiveness of an Automated Reading Tutor Design for Filipino Speaking Children

An automated reading tutor is an intelligent system that is able to mimic a reading teacher who can listen to a child as he/she reads a text aloud, and can do a certain corrective action in case a reading miscue is detected. The automated reading tutor for Filipino language (ART-Fil) employed a reading miscue detector for automatic detection of reading errors. In this paper, we present the important findings regarding design effectiveness of the ART-Fil for improving children’s oral reading fluency (ORF). Six 2nd grade students participated in the pilot study. A Filipino teacher conducted three sets of ORF assessments in order to monitor the students’ progress in their ORF. The multidimensional fluency scale by Rasinski was used for ORF scoring. Results show that with the use of ART-Fil, students have accelerated their learning as evidenced by their ORF scores, reading speed, accuracy, and comprehension. Bottom performers obtained greater improvements in their ORF as compared with those of the more fluent readers going into the program. Readability level of the text also affected the children’s reading performance. Some students who used ART-Fil also adopted the proper prosody such as expression and phrasing. Adjustments in the ART-Fil’s navigation and interface design were made in order to accommodate various behaviors of children. Survey results show that teachers and parents believed that the ART-Fil is useful and effective. Future work includes investigating both ASR-based and ASR-free methods for automating the ORF assessment process.
Ensembling and Dynamic Asset Selection for Risk-Controlled Statistical Arbitrage

In recent years, machine learning algorithms have been successfully employed to leverage the potential of identifying hidden patterns of financial market behavior and, consequently, have become a land of opportunities for financial applications such as algorithmic trading. In this paper, we propose a statistical arbitrage trading strategy with two key elements: an ensemble of regression algorithms for asset return prediction, followed by a dynamic asset selection. More specifically, we construct an extremely heterogeneous ensemble ensuring model diversity by using state-of-the-art machine learning algorithms, data diversity by using a feature selection process, and method diversity by using individual models for each asset, as well models that learn cross-sectional across multiple assets. Then, their predictive results are fed into a quality assurance mechanism that prunes assets with poor forecasting performance in the previous periods. We evaluate the approach on historical data of component stocks of the S&P500 index. By performing an in-depth risk-return analysis, we show that this setup outperforms highly competitive trading strategies considered as baselines. Experimentally, we show that the dynamic asset selection enhances overall trading performance both in terms of return and risk. Moreover, the proposed approach proved to yield superior results during both financial turmoil and massive market growth periods, and it showed to have general application for any risk-balanced trading strategy aiming to exploit different asset classes.
Quantitative Characterization of Ferroelectric/Dielectric Interface Traps by Pulse Measurements

The ferroelectric (FE) polarization switching behavior in the HfZrO<sub>2</sub> (HZO) FE/dielectric (FE/DE) stack is investigated systematically by charge responses from pulse measurements. The trapped charge density at the FE/DE interface related with the FE polarization switching is found to be <inline-formula> <tex-math notation="LaTeX">$1.2\,\times \,10^{14}$ </tex-math></inline-formula> cm<sup>−2</sup> according to the leakage-current-assist polarization switching mechanism. Furthermore, by the time-dependent nonswitching charge responses, the extra FE/DE interface trap density of <inline-formula> <tex-math notation="LaTeX">$1.1\,\times \,10^{13}$ </tex-math></inline-formula> cm<sup>−2</sup> is confirmed, which is not related but can be detected along with the FE polarization switching. The quantitative characterization reveals the huge amount of FE/DE interface traps and their dominant role in the FE operation of HZO FE/DE stack, which improves the proposed leakage-current-assist polarization switching model. This improved model provides a more comprehensive understanding of the polarization switching in the HZO FE/DE stack and new insights on HZO negative-capacitance (NC) and FE field-effect transistors (FETs).
Comparison of the E-LT vs ETL Method in Data Warehouse Implementation: A Qualitative Study

The data warehouse is an enterprise system that archives various kinds of data from various main systems and integrates them into a place. There are two data processing methods in implementing a data warehouse, namely ETL (Extract, Transform, Load) and E-LT (Extract, Load, Transform). In determining the implementation of a data warehouse, it is often difficult to choose the appropriate method. Because this is related to Cost, Process, Performance, and Continuous Improvement in a company. This paper discusses the implementation of a data warehouse in various industries by conducting a survey to compare the use of ETL and E-LT methods. The survey was conducted on people with backgrounds in work related to data warehouses from various industries. Based on the results of this survey, it is hoped that a better method of implementing a data warehouse can be obtained.
Low-Voltage Operated Organic Thin-Film Transistors With Mobility Exceeding 10 cm²/vs

In this letter, low-voltage operation and high mobility are simultaneously achieved in the OTFTs based on an elaborately constructed tri-layer gate dielectric. The tri-layer gate dielectric consists of the cross-linked poly(4-vinylphenol) (CL-PVP), polyvinylpyrrolidone (PVPy) and poly(styrene) (PS) films. The ultrathin CL-PVP and PS films prevent the charges transferring and trapping in the PVPy dielectric, which improves the performances. The low trap density at the organic semiconductor/dielectric interface contributes to the low-voltage operation and high mobility in the OTFTs. The OTFTs exhibit promising performances with high mobility exceeding 10 cm2/vs, small subthreshold swing of 185 mV/decade on average, high on/off ratio of $10^{{5}}$ , at low operating voltages below 5 V. The measurements on stability and ageing of the OTFTs indicate that the encapsulation is required for practical application.
Response Time Analysis and Priority Assignment of Processing Chains on ROS2 Executors

ROS (Robot Operating System) is currently the most popular robotic software development framework. Robotic software in safe-critical domain are usually subject to hard real-time constraints, so designers must formally model and analyze their timing behaviors to guarantee that real-time constraints are always honored at runtime. This paper studies real-time scheduling and analysis of processing chains in ROS2, the second-generation ROS with a major consideration of real-time capability. First, we study response time analysis of processing chains on ROS2 executors. We show that the only existing result of this problem is both optimistic and pessimistic, and develop new techniques to address these problems and significantly improve the analysis precision. Second, we reveal that the response time of a processing chain on an executor only depends on its last scheduling entity (callback), which provides useful guidance for designers to improve not only the response time bound, but also the actual worst-case/average response time of the system at little design cost. We conduct experiments with both randomly generated workload and a case study on realistic ROS2 platforms to evaluate and demonstrate our results.
GoodSpread: Criticality-Aware Static Scheduling of CPS with Multi-QoS Resources

In practice, safety-critical cyber-physical systems (CPS) are often implemented using high quality-of-service (QoS) resources to provide maximum performance in all scenarios. Such implementations are oblivious to the changing criticality levels of CPS based on their physical dynamics (e.g., steady or transient state). Considering that high-QoS resources are constrained for cost-sensitive CPS, such criticality-oblivious implementations are highly inefficient. Towards a tighter dimensioning of these resources, state-of-the-art approaches have considered multi-QoS resources and studied criticality-aware dynamic resource allocation along the lines of mixed-criticality systems. However, these approaches have high implementation overheads. Moreover, in safety-critical domains like automotive and avionics, certification of such dynamic policies is challenging and the implementation platforms typically do not support dynamic reconfiguration. To address these challenges, we present GoodSpread that uses a static scheduling strategy and offers the same performance guarantees while saving resources (more than 50 % in certain cases) compared to the existing dynamic schemes. The main idea here is to spread the high-QoS resources as uniformly as possible over time in order to accommodate the uncertainty of when the criticality level might change. Our proposed strategy studies the physical dynamics to determine the spread factor, i.e., how often the high-QoS resources need to be provisioned. We further propose an extensibility-driven optimization approach to obtain a static schedule that will accommodate future workloads on the remaining resources with maximum flexibility.
Fine-grained Frequencies for Simultaneous Intra-Group One-to-All Dissemination

Simultaneous intra-group communication is one of the possible ways to handle the scaling issues in large decentralized systems. FDMA is an elegant strategy to carry out intra-group activities in parallel. However, due to increasing congestion in the licence free ISM bands, especially in the urban area, its hard to get enough number of good standard channels for multi-group setting in decentralized systems like IoT/WSN. In this work, we leverage concurrent transmission for efficient simultaneous intra-group one-to-all dissemination. Concurrent transmissions based strategies schedule the packet-transmissions in a special way so that the overlapping packets, instead of colliding with each other, result in constructive-interference or capture-effect. We propose a simple strategy to exploit this feature under concurrent transmission to achieve fruitful simultaneous intra-group communication in overlapping channels. Through extensive evaluation in testbeds for WSN/IoT under 2.4 GHz ISM band we show that, the proposed strategy can efficiently support simultaneous intra-group one-to-all dissemination even with allocation of channels having Center Frequency Distance (CFD) 1 MHz in most of the common group divisions. Experiments with a wide variety of group divisions also reveal that under concurrent transmission a CFD of 3 MHz and above can tolerate strong inter-group interference when the groups are quite mixed with each other which can be quite common specially when the nodes are mobile.