Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Locus Sequence Analysis Reveals Diversity of the Rice Kernel Smut Populations in the United States

Rice (Oryza sativa) is the second leading cereal crop in the world and is one of the most important field crops in the US, valued at approximately $2.5 billion. Kernel smut (Tilletia horrida Tak.), once considered as a minor disease, is now an emerging economically important disease in the US. In this study, we used multi-locus sequence analysis to investigate the genetic diversity of 63 isolates of T. horrida collected from various rice-growing areas across in the US. Three different phylogeny analyses (maximum likelihood, neighbor-joining, and minimum evolution) were conducted based on the gene sequence sets, consisting of all four genes concatenated together, two rRNA regions concatenated together, and only ITS region sequences. The results of multi-gene analyses revealed the presence of four clades in the US populations, with 59% of the isolates clustering together. The populations collected from Mississippi and Louisiana were found to be the most diverse, whereas the populations from Arkansas and California were the least diverse. Similarly, ITS region-based analysis revealed that there were three clades in the T. horrida populations, with a majority (76%) of the isolates clustering together along with the 22 Tilletia spp. from eight different countries (Australia, China, India, Korea, Pakistan, Taiwan, The US, and Vietnam) that were grouped together. Two of the three clades in the ITS region-based phylogeny consisted of the isolates reported from multiple countries, suggesting potential multiple entries of T. horrida into the US. This is the first multi-locus analysis of T. horrida populations. The results will help develop effective management strategies, especially breeding for resistant cultivars, for the control of kernel smut in rice.
Raising the Stakes for Online Learning: Monetary Incentives Increase Performance in a Computer-Based Learning Task Under Certain Conditions

To what extent can external incentives influence students’ effort and learning in online course contexts? While cognitive science research has found that monetary incentives can increase goal-directed cognitive effort in certain laboratory tasks, attempts to use monetary incentives to increase students’ academic performance in naturalistic settings has shown mixed results. In two experiments, we tested the influence of a monetary incentive (compared to no external incentive) on immediate and delayed tests of computer-based educational performance (i.e., learning from educational videos). In Experiment 1, participants were assigned to (1) receive monetary incentives for correct quiz responses, or (2) receive no additional incentive for correct responses other than finding out their score, and we found no significant difference in total score across groups (on either immediate or delayed tests of learning). In Experiment 2, we used a within-subjects design to test whether participants performed better when they were provided monetary incentives for correct responses on quiz questions (compared to no external incentive). Here, participants performed significantly better on incentivized quiz questions (on both immediate and delayed tests of learning). Thus, monetary incentives may increase performance in online learning tasks when participants can anchor the “stakes” of an incentive compared to no external incentive. These findings highlight potential benefits of external incentives for promoting effort and learning in online contexts, although further research is needed to determine the most useful educationally-relevant extrinsic incentives, as well as potential negative effects of incentives on long-term intrinsic motivation.
Suppressing Channel Percolation in Ferroelectric FET for Reliable Neuromorphic Applications

Channel percolation in ferroelectric FET (FeFET) due to random spatial distribution of switched domains reduces the number of accessible threshold voltage (VTH) states, thus posing a profound reliability challenge when it comes to its usage as synaptic weight cell in neuromorphic applications. However, channel percolation is not universally present in FeFET and only exists when the channel regions underneath the domains have negligible interaction, e.g., when the domain is large enough. In this work, we performed a comprehensive evaluation on the parameters that could impact neighbor interaction, and hence channel percolation. We reveal that: i) weak gate control with thick ferroelectric (FE) layer enhances neighbor interaction and suppresses channel percolation; ii) higher temperature can also reduce percolation by weakening the gate control. These insights provide an important guideline for to engineer more reliable FeFET analog states.
The Role of Financial Decision-Making on Energy Consumption in ASEAN Countries

By using panel data from 1998 to 2018, this study examines the impact of five important factors influencing sustainable energy use in ASEAN countries, including technological innovation, financial expansion, economic freedom, pollution of the environment, and per capita GDP. The PMG-ARDL analysis method was applied to generate a dynamic heterogeneous panel to test the panel data. Then, to establish successful policies, the Dumitrescu-Hurlin panel causality test was applied. The results show that fiscal expansion has a significant impact on reducing the use of sustainable energy. Moreover, environmental degradation, economic freedom, and real GDP per capita have a negative relationship to the use of sustainable energy, although there is a positive relationship between innovation and sustainable energy. According to the findings, ASEAN countries have to strengthen measures that encourage sustainable energy projects to maintain their economic strength for a long period. They will also be able to prevent future economic crises and will have alternative energy sources to draw on in case conventional energy runs out.
SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings

The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key components in autonomous and robotic systems. Approaches based on the state of the art vision transformer architectures are extremely deep and complex not suitable for real-time inference operations on edge and autonomous systems equipped with low resources (i.e. robot indoor navigation and surveillance). This paper presents SPEED, a Separable Pyramidal pooling EncodEr-Decoder architecture designed to achieve real-time frequency performances on multiple hardware platforms. The proposed model is a fast-throughput deep architecture for MDE able to obtain depth estimations with high accuracy from low resolution images using minimum hardware resources (i.e. edge devices). Our encoder-decoder model exploits two depthwise separable pyramidal pooling layers, which allow to increase the inference frequency while reducing the overall computational complexity. The proposed method performs better than other fast-throughput architectures in terms of both accuracy and frame rates, achieving real-time performances over cloud CPU, TPU and the NVIDIA Jetson TX1 on two indoor benchmarks: the NYU Depth v2 and the DIML Kinect v2 datasets.
nOMP: A new sparse solution to enhance the SSIM levels of OMP-based encoded images

This work comprises the development of a quality enhancement technique for image encoders that use compressive sensing. The recommended solution seeks to maximize the perceptual quality based objective function, unlike other sparse representation algorithms that minimizes the error-based objective function. The key idea behind this work is to develop an iterative methodology that works as a modifier for the sparse coefficients. The modification procedure is SSIM-based and has been carried out in an iterative and linear manner. The conducted experiments revealed that the recommended technique works better than another SSIM-based modifier termed the SSIM-inspired OMP (iOMP) in terms of SSIM levels gained. The t-test is also utilized to examine our performance for significance, and the results show that the method works well for any type of image and any size, especially when a data-independent based dictionary is used.
Evaluating cyclone efficiencies from stream compositions

Etant donne la difficulte de calcul au moyen des correlations existantes de l'effet de charge produit lorsque l'on augmente la concentration des solides en vue d'accroitre l'efficacite d'un cyclone, on presente une methode analytique d'evaluation de cet effet
Islamic Banking, Economic Growth, and Poverty Reduction in Indonesia

The main aim of this study is to examine how the role of Islamic banks, economic growth, and price stability on poverty reduction in Indonesia from 2010 to 2019. Total Financing (TF), Financing to Deposit Ratio (FDR), Non-Performing Financing (NPF), Return on Assets (ROA), and Third-Party Funds (TPF) monthly time series data were used to measure Islamic banking role, while economic growth, price stability, and poverty, were measured by Industrial Production Index (IPI), Consumer Price Index (CPI), and Poverty headcount ratio (POV), respectively. The short- and long-term effects of Islamic banks, economic growth, and price stability on poverty reduction in Indonesia are examined using an Autoregressive Distributed Lag (ARDL) bound test. The study documented that Islamic banking, economic growth, and price stability had a significant short and long-term effects on poverty reduction. These findings offer various key implications for the banking industry and policymakers in order to eradicate poverty through strengthening economic growth, Islamic banking sector, and price stability. The Islamic banking industry should provide a quality of financing allocation to promote economic growth and price stability that, in turn, reduce poverty.
CNN-Based Gaze Estimation for Off-angle Iris Recognition

Standoff iris recognition systems have been developed to identify not only fully cooperative individuals but also non-cooperative moving subjects. Since iris images are captured in much less constrained conditions, they are likely to be non-ideal including being off-angle. However, traditional iris recognition systems are designed for only frontal iris images. Therefore, the existing databases save binary iris codes using only frontal images for iris matching. To use these existing iris codes, standoff systems need to be compatible with existing iris matching algorithms by generating the frontal version of iris codes even for off-angle images. Since gaze estimation is the first essential component to reconstruct the frontal view of off-angle iris images, we first need to estimate the eye gaze direction from a single off-angle iris image. This paper presents a deep learning-based gaze angle estimation method for standoff iris recognition frameworks. As the main contribution, our approach will allow us to estimate the gaze angle from an off-angle image using transfer learning and a regression model without segmentation and using additional hardware. The proposed CNN-based gaze estimation method is trained and tested with 108,600 off-angle images from 100 subjects. It shows high accuracy in gaze estimation with an average error of 4° in angle for off-angle images captured from every angle ranging -50° to +50° even if training images are not available for all angles.
User Adoption of Enterprise Resource Planning Systems in the Public Sector

User Adoption of Enterprise Resource Planning Systems in the Public Sector by Lionel O. Wright, Sr. MS, Embry-Riddle Aeronautical University, 2011 BA, American Military University, 2009 Doctoral Study Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Business Administration Walden University June 2016 Abstract The importance of aligning information technology and business strategies to exploit capabilities and change business practices has increased as firms strive for competitive advantage in a diverse and changing marketplace. Nevertheless, over 50% of firms implementing enterprise resource planning (ERP) systems ranked expected process and value enhancements as inadequate, whereas only 13% indicated that implementations met their needs. The unified theory of acceptance and use of technology (UTAUT) model formed the conceptual framework of this single case study. The study comprised a purposeful sampling of 8 business managers in Southeast Alabama working in related job roles and based on established eligibility criteria. Data collection involved semistructured interviews, casual observations, and document analysis. Through method triangulation and predetermined themes directly related to the UTAUT conceptual framework, 5 themes emerged: management endorsement, change management, training and education, dedicated resources, and governance. Results of this research may influence the elimination of key barriers central in the deployment and adoption of ERP systems by the public sector. The study’s implications for positive social change includeThe importance of aligning information technology and business strategies to exploit capabilities and change business practices has increased as firms strive for competitive advantage in a diverse and changing marketplace. Nevertheless, over 50% of firms implementing enterprise resource planning (ERP) systems ranked expected process and value enhancements as inadequate, whereas only 13% indicated that implementations met their needs. The unified theory of acceptance and use of technology (UTAUT) model formed the conceptual framework of this single case study. The study comprised a purposeful sampling of 8 business managers in Southeast Alabama working in related job roles and based on established eligibility criteria. Data collection involved semistructured interviews, casual observations, and document analysis. Through method triangulation and predetermined themes directly related to the UTAUT conceptual framework, 5 themes emerged: management endorsement, change management, training and education, dedicated resources, and governance. Results of this research may influence the elimination of key barriers central in the deployment and adoption of ERP systems by the public sector. The study’s implications for positive social change include the potential to enhance social and intellectual capital formation through recognizing strategies that mitigate employees’ gender and age variances during an ERP
Contextual Squeeze-and-Excitation Mask R-CNN for SAR Ship Instance Segmentation

Ship detection and ship classification using synthetic aperture radar (SAR) have been extensively studied. Yet, SAR ship segmentation unexpectedly receives less attention. Therefore, we will supplement the blank of such study in this paper. Specifi-cally, we present a novel contextual squeeze-and-excitation Mask R-CNN (C-SE Mask R-CNN) dedicated to ship instance segmen-tation in SAR images. Note that the instance segmentation simultaneously considers ship detection and ship segmentation. Intuitively, C-SE Mask R-CNN is a variant of Mask R-CNN from the computer vision community. It embeds a contextual squeeze-and-excitation module (C-SE Module) into RoIAlign of Mask R-CNN to capture prominent different levels of backgrounds' contextual information. Experimental results on the public PSeg-SSDD da-taset reveal the objective accuracy progress (i.e. a 1.4% AP gain on the detection task meanwhile a 0.9% AP gain on the segmentation task) of C-SE Mask R-CNN, in contrast to the vanilla Mask R-CNN.
Role of Internal Control in Improving Financial Management-Based Performance: The Case of Mosques in Banda Aceh City, Indonesia

This study aims to examine the moderating effect of internal control on the relationships between asset management, debt management, and good mosque governance on the performance of mosques in Banda Aceh City, Indonesia over the period from 2016 to 2020. Of 104 mosques within 9 sub-districts in the city, 20 of them were selected as the sample of the study using a multistage sampling technique. From each 20 selected mosques, 5 staff of mosques (observations = 100) were asked to fill up the distributed questionnaires. Since the study utilized the panel data, thus the study used the moderated multiple regression panel technique. The study discovered that asset management and good mosque governance had a significant effect on mosque performance, while debt management has an insignificant effect on mosque performance. In addition, internal control was documented to have an insignificant moderating effect on the relationships between asset management, debt management, and good mosque governance on the mosque performance.
Argon: A Toolbase for Evaluating Software Protection Techniques Against Symbolic Execution Attacks

Practical software protection for legitimate programs involves study of a wide variety of different program transformation techniques and the evaluation of those techniques against realistic man-at-the-end (MATE) analysis tools. Currently, state-of-the-art tools used by attackers incorporate automated dynamic symbolic execution (DSE) engines which can considerably reduce analysis time of obfuscated portions of code. Measuring the resilience of proposed protection schemes against such attacks has thus become central in understanding the limits of obfuscation in practice. In this paper we present Argon, a tool that combines the ability to generate, obfuscate and symbolically analyze code. The main goal of this tool is to help researchers easily setup and execute empirical studies on obfuscated program variants and evaluate the resistance to symbolic analysis attacks. Argon is intended to increase research involvement in software protection studies by avoiding the steep learning curve that often accompanies the use of symbolic analysis tools. In addition, Argon can also make research more efficient by allowing users to analyze files in bulk rather than individually, while also supporting report generation to summarize execution results. To demonstrate its effectiveness, we used Argon to analyze over 15,000 C source files on both workstation and super-computing class hardware over the course of two months.
Characterization of wear and prediction of wear zone locations on the rake face using Mamdani fuzzy inference system

In order to improve the performance of the cutting tool, third-generation tools with multi-layered nanocoatings on the rake face are used. During machining, the chip–tool interactions depict that although the tool wear on the rake face is located in the close proximity of the cutting edge, that is, within 800 µm, all the commercially available cutting tools have the coatings on the entire rake face. Taking into account the tribological properties required by the rake face close to the cutting edge, that is, high wear resistance and low friction, this study makes an attempt to identify, characterize and locate the actual wear zones/regions in terms of hard and soft zones in the chip contact area of tungsten carbide (WC) inserts close to the cutting edge in turning. Mamdani fuzzy inference system model was developed, trained with the sample experimental data and tested with the test data. The simulated results showed that the average error values of edge chipping (in X- and Y-directions), nose damage and crater wear (in X- and Y-directions) are about 2.37%, 3.01%, 2.86%, 2.66% and 1.89%, respectively. The fuzzy model developed in this study showed remarkable prediction of the wear zone locations and is also helpful for the researchers to decide the type of coating (hard and soft) along the specified zones for reducing the cost of production.
Distributed Parametric Detection in the Presence of Subspace Interference

This paper considers the detection of a target in a distributed radar system, wherein the received reflections from the said target are contaminated with clutter returns. We model this clutter, as well as additional interference, as a superposition of an auto-regressive process and deterministic interference coming from a known subspace. A parametric adaptive detector is derived for this scenario based on an approximate generalized likelihood ratio test (GLRT), and is shown to have a detection performance that is robust to strong subspace interference.
An overview of Automatic Speech Recognition Preprocessing Techniques

Speech signal preprocessing is the first and the most important step in the automatic speech recognition process. The preprocessing of speech consists of cleaning the speech signal from ambient and undesirable noises, detecting speech activity, and normalizing the length of the vocal tract. The objective of preprocessing a speech signal is to make the speech recognition systems computationally more efficient through the application of several preprocessing techniques, such as speech pre-emphasis, vocal tract length normalization, voice activity detection, noise removal, framing, and windowing. This paper gives an overview of the fundamentals of speech signal preprocessing techniques, by highlighting the specifics and the requirements of each technique. We also explore all aspects that can improve the results of each technique. We aim that the content of this paper will help researchers improve the quality of their speech recognition systems by identifying appropriate speech preprocessing techniques to use in their experimental settings.
The Relationship between Curriculum Demands and Physics Achievement of Senior Secondary School Students in Kogi State

The study examined the relationship between curriculum demands and Physics achievement of some Nigeria Senior Secondary Students, which it considers as a prerequisite for revitalizing physics education in Nigeria. Purposive and stratified random sampling techniques were employed to obtain a sample of 524 Senior Secondary 2 students from six education zones in Kogi State. Three instruments were used. They are Curriculum Analysis Taxonomy (CAT) for analysis of Senior Secondary physics curriculum, Physics Achievement Test (PAT) for the determination of students achievement in physics and Science Reasoning Task (SRT) for determining the level of reasoning of the students. Data analysis carried out using percentage, mean and correlation statistics show that about 67% of the objectives in the current SS physics curriculum are at formal or higher cognitive demand level. Only 26.5% of SS 2 physics students of average age of 16.8 years are formal operators. There was a significant relationship between students’ achievement in 2A demand level questions and in each of 2A, 2B, 3A&3B demand level questions in physics. However, students’ achievement in PAT deceases with increase in demand level of questions. There was a significant relationship between students’ achievement in lower order PAT and in higher order PAT Recommendations were made based on these findings.
Experimental Trials of Space Object Detection using LOFAR Radio Telescope as a Receiver in Passive Radar

The paper presents the results of the first measurements devoted to the experimental verification of the capability of space object detection by passive radar system using astronomical LOFAR (LOw-Frequency ARray) radio telescopes as receivers and DAB+ commercial transmitters as illuminators of opportunity. Two LOFAR stations were used in the measurements, one station as a surveillance receiver and the other as a reference receiver. The obtained results confirmed the earlier expectations about the possibility of passive space target detection employing the LOFAR radio telescopes or dedicated phased antenna arrays with dimensions and complexity comparable to the LOF AR radio telescopes.