Miracle-dz/UniFAR-FTUs huggingface dataset:
EFFECT OF LONG-TERM PIG SLURRY AND SOLID CATTLE MANURE APPLICATION ON SOIL CHEMICAL AND BIOLOGICAL PROPERTIES

Chemical and biological properties were evaluated in 1987 on an acidic silty loam soil following a long-term field study established in 1978 and cultivated with silage corn. Treatments included a control, solid cattle manure (20, 40 and 60 Mg ha−1 FYM) and pig slurry (60, 120 m3 ha−1 SLU) applied every 2 yr and annually, respectively. No fertilizer was applied. The results of this study have shown that neither treatment significantly affected soil pH values, total-N contents and C:N ratios compared to the control. The cation exchange capacity (CEC) of the soil was significantly higher with FYM treatment than with control or SLU application. The highest rates of FYM and SLU have also increased (P < 0.05) soil organic carbon, microbial activity and potentially mineralizable nitrogen. The soil microflora populations (bacteria, fungi, actinomycetes, ammonifiers and nitrifiers) were greatly improved by both treatments. There were no significant differences in organic matter content or the relative amount of hu.
Heuristic and learning method for obstacle avoidance with mobile robot

In this paper, a fuzzy controller obstacle avoidance of the mobile robot Pioneer II is proposed. The fuzzy inference system FIS of this controller is performed by two methods: heuristic and reinforcement learning. the manual tuning of the fuzzy control system can be long and difficult. In contrast, reinforcement learning has proven theoretically and practically its ability to automatically optimize some parameters of the FIS. For that, the Fuzzy Actor-Critic Learning algorithm allows the determination of the parameters of the conclusions among of an available set fixed by the operator. The proposed algorithm allows the automatic determination of the parameters of the conclusions of the fuzzy rules. The simulations show that the two controllers (heuristic, RL controller) are able to avoid the different shapes of obstacles contained in known environments, and they show exceptionally good robustness when changing the environment (shape of obstacles, location of obstacles in the environment
Unsupervised Outlier Detection via Transformation Invariant Autoencoder

Autoencoder based methods are the majority of deep unsupervised outlier detection methods. However, these methods perform not well on complex image datasets and suffer from the noise introduced by outliers, especially when the outlier ratio is high. In this paper, we propose a framework named Transformation Invariant AutoEncoder (TIAE), which can achieve stable and high performance on unsupervised outlier detection. First, instead of using a conventional autoencoder, we propose a transformation invariant autoencoder to do better representation learning for complex image datasets. Next, to mitigate the negative effect of noise introduced by outliers and stabilize the network training, we select the most confident inliers likely examples in each epoch as the training set by incorporating adaptive self-paced learning in our TIAE framework. Extensive evaluations show that TIAE significantly advances unsupervised outlier detection performance by up to 10% AUROC against other autoencoder based methods on five image datasets.
Chord Conditioned Melody Generation With Transformer Based Decoders

For successful artificial music composition, chords and melody must be aligned well. Yet, chord conditioned melody generation remains a challenging task mainly due to its multimodality. While few studies have focused on this task, they face difficulties in generating dynamic rhythm patterns aligned appropriately with a given chord progression. In this paper, we propose a chord conditioned melody Transformer, a K-POP melody generation model, which separately produces rhythm and pitch conditioned on a chord progression. The model is trained in two phases. A rhythm decoder (RD) is trained first, and subsequently a pitch decoder is trained by utilizing the pre-trained RD. Experimental results show that reusing RD at the pitch decoding stage and training with pitch varied rhythm data improve the performance. It was also observed that the samples produced by the model well reflected the key characteristics of dataset in terms of both pitch and rhythm related features, including chord tone ratio and rhythm distribution. Qualitative analysis reveals the model’s capability of generating various melodies in accordance with a given chord progression, as well as the presence of repetitions and variations within the generated melodies. With subjective human listening test, we come to a conclusion that the model was able to successfully produce new melodies that sound pleasant in terms of both rhythm and pitch (Source code available at https://github.com/ckycky3/CMT-pytorch).
Phase Shift Induced by Gate-Controlled Quantum Capacitance in Graphene FET

The gate-controlled quantum capacitance and the channel resistance play an important role in the performance of graphene field-effect transistors (GFETs). This paper experimentally verifies that the output phase of the graphene field-effect transistor changes under the influence of quantum capacitance and the channel resistance, which are controlled by the gate voltage. This phenomenon is theoretically analyzed, and a model is established to simulate the phase shift. The obtained simulation results of the model are in good agreement with the experimental results. This work reveals the influence of gate voltage variation on the phase characteristics of GFETs and provides a research basis for the application of GFETs in phase shifter and the establishment of the small-signal model.
Learning Deep Nets for Gravitational Dynamics With Unknown Disturbance Through Physical Knowledge Distillation: Initial Feasibility Study

Learning high-performance deep neural networks for dynamic modeling of high Degree-Of-Freedom (DOF) robots remains challenging due to the sampling complexity. Typical unknown system disturbance caused by unmodeled dynamics (such as internal compliance, cables) further exacerbates the problem. In this letter, a novel framework characterized by both high data efficiency and disturbance-adapting capability is proposed to address the problem of modeling gravitational dynamics using deep nets in feedforward gravity compensation control for high-DOF master manipulators with unknown disturbance. In particular, Feedforward Deep Neural Networks (FDNNs) are learned from both prior knowledge of an existing analytical model and observation of the robot system by Knowledge Distillation (KD). Through extensive experiments in high-DOF master manipulators with significant disturbance, we show that our method surpasses a standard Learning-from-Scratch (LfS) approach in terms of data efficiency and disturbance adaptation. Our initial feasibility study has demonstrated the potential of outperforming the analytical teacher model as the training data increases.
Real-time Harmonic Filtering in Grid Interfaced Photovoltaic System Based on Adaptive NLMLS

In this paper, an adaptive normalized least mean logarithmic square (NLMLS) framework based proposal is described for real-time harmonic filtering in grid-interfaced photovoltaic (PV) system. In the devised controller, the NLMLS exhibits logarithmic characteristic resulting in mathematically tractable adaptive structure. Practically, an adaptive square function enables online learning of weight identification thereby ensuring elegant performance under interferences in solar insolation and load. The normalized/regularized feature is fundamentally responsible for eliminating the dependency of input harmonically polluted load currents on the weight estimation phenomenon. This way, adaptive NLMLS detects the fundamental weight component of the load current accurately and ensures guaranteed online harmonic support to address the various power quality (PQ) challenges [i.e., grid current harmonic emission and balancing, reactive power support, and power factor correction]. A straightforward operating principle of the proposed adaptive NLMLS is exposed and its efficacy is examined through MATLAB/Simulink under steady-state and transients scenarios. Proceeding further, the robustness of the proposed adaptive NLMLS is verified and compared with prevalent state-of-the-arts least-square algorithms using a dSPACE-1104 board. In summary, it is believed that this proposal is a step towards achieving comparable convergence performance and minimum steady-state misadjustment when exposed to disturbances in grid voltage and load current.
Automatic Diabetic Retinopathy Diagnosis Using Adaptive Fine-Tuned Convolutional Neural Network

Diabetic retinopathy (DR) is a complication of diabetes that leads to blindness. The manual screening of color fundus images to detect DR at early stages is expensive and time consuming. Deep learning (DL) techniques have been employed for automatic DR screening on fundus images due to their outstanding performance in many applications. However, training a DL model needs a huge amount of data, which are usually unavailable in the case of DR, and overfitting is unavoidable. Employing a two-stage transfer learning method, we developed herein an intelligent computer-aided system using a pre-trained convolutional neural network (CNN) for automatic DR screening on fundus images. A CNN model learns the domain-specific hierarchy of low- to high-level features. Given this, using the regions of interest (ROIs) of lesions extracted from the annotated fundus images, the first layer of a pre-trained CNN model is re-initialized. The model is then fine-tuned, such that the low-level layers learn the local structures of the lesion and normal regions. As the fully connected layer (FC) layers encode high-level features, which are global in nature and domain specific, we replace them with a new FC layer based on the principal component analysis PCA and use it in an unsupervised manner to extract discriminate features from the fundus images. This step reduces the model complexity, significantly avoiding the overfitting problem. This step also lets the model adopt the fundus image structures, making it suitable for DR feature detection. Finally, we add a gradient boosting-based classification layer. The evaluation of the proposed system using a 10-fold cross-validation on two challenging datasets (i.e., EyePACS and Messidor) indicates that it outperforms state-of-the-art methods. It will be useful for the initial screening of DR patients and will help graders in deciding quickly as regards patient referral to an ophthalmologist for further diagnosis and treatment.
Performance Improvement of 1T DRAM by Raised Source and Drain Engineering

In this work, a double-gate (DG) metal–oxide–semiconductor field-effect transistor (MOSFET) with raised source and drain (RSD) regions is utilized for application of one-transistor (1T) dynamic random access memory (DRAM) through series of validation by technology computer-aided design (TCAD) device simulation. The engineered device shows less short-channel effects (SCEs) and unwanted interband tunneling compared with the usual DG MOSFETs. As a 1T DRAM device, it demonstrates longer retention time (<inline-formula> <tex-math notation="LaTeX">${T}_{\text {ret}}$ </tex-math></inline-formula>) and larger sensing margin (SM). The designed 1T DRAM achieves <inline-formula> <tex-math notation="LaTeX">${T}_{\text {ret}} \sim {330}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\sim 200$ </tex-math></inline-formula> ms at 27 °C and 85 °C, respectively, at 50-nm channel length. Also, the device shows higher current ratio and consumes low power (84.7 nW for write “1”) and energy (<inline-formula> <tex-math notation="LaTeX">$2.16\times 10^{-15}$ </tex-math></inline-formula> J for read “1” and <inline-formula> <tex-math notation="LaTeX">$1.5\times 10^{-17}$ </tex-math></inline-formula> J for read “0” operations). Furthermore, it is revealed that low-<inline-formula> <tex-math notation="LaTeX">$\kappa $ </tex-math></inline-formula> spacer has an effect of increasing <inline-formula> <tex-math notation="LaTeX">${T}_{\text {ret}}$ </tex-math></inline-formula> in the device.
A Transformerless Quadratic Boost High Gain DC-DC Converter

In this paper, a new non-isolated de-de converter with high voltage gain and reduced stress across switch and diodes is proposed. A new voltage boost cell is proposed to boost the voltage at the output at lower duty ratios. Thus, switching devices of low power rating can be selected. Apart from high gain, the converter has a single switch and continuous source current. All these features make this converter suitable to be used in microgrid applications. Steady-state working of the converter is discussed in detail and performance comparison of the converter is done with other high gain topologies. Respective expressions for voltage gain and stress across switch and diodes are presented in the paper. The power loss and efficiency of the converter are also determined by incorporating the switching characteristics of switch and diodes from datasheet into PLECS software. The software is specially designed to determine the losses in switching devices. Finally, a hardware prototype is also developed in the laboratory. The hardware results verify the results obtained through simulation.
Temporal dependency based Lagrange multiplier adaptation for HEVC

The current encoding options not only decide the rate and distortion of the current coding unit or frame, but also may affect the achievable rate-distortion (R-D) performance for the subsequent coding units or frames. In order to measure quantitatively the temporal R-D dependency, a distortion effect factor is first introduced, which can be calculated with the coding distortion and motion compensation predicted (MCP) error. Then an adaptive Lagrange multiplier determination at coding tree unit (CTU) level is proposed based on a two-pass encoding framework. The framework mainly includes three parts. First, pre-encoding a frame with a simplified RDO to obtain the distortion effect factors. Second, restoring the reference list to the status before pre-encoding. Third, calculating the Lagrange multipliers for each CTU according to the distortion effect factors obtained, and encoding the previous frame again. Experimental results show that, on the platform of HEVC test model (HM-16.7), the proposed algorithm obtains bitrate savings of 1.8% and 1.7% on average under the configurations of low delay B and P frames, with increased encoding complexity of 23% on average.
Introducing Algorithmic Thinking and Sequencing Using Tangible Robots

Today, in the era of robotics, different types of educational robots have been used extensively in school classrooms to facilitate teaching activities related to a variety of computer science concepts. Numerous studies have been performed that attempt to examine the effects of using tangible interfaces to enhance collaborative learning experiences. In most of these studies, feedback, which is a vital function for a successful game activity, is mainly provided by the trainers. However, this kind of feedback can be considered as static and general, while each trainee seeks clear, consistent, and even personalized feedback. This article proposes an interactive learning tool for introducing algorithmic thinking and sequencing using educational robots suitable for elementary and intermediate students. In more detail, in this article, we leverage a fuzzy-rule-based system and computer vision techniques to provide immediate, personalized feedback and recommendations to young students while they perform a series of activities using tangible robots. These activities relate to teaching programming skills and improve the algorithmic thinking of students. Experimental results revealed that participants were able to increase their algorithmic/programming thinking skills while developing a positive attitude toward programming. The interactive gaming factor that is embedded in the use of tangible robots, while participating in the activities, was proved to be a compelling and a rewarding experience. The article concludes that the use of the proposed feedback mechanism, when placed in a robot game environment, can lead to a positive and more effective learning process.
Use of velvet bean to improve soil fertility and weed control in corn production in northern belize

Abstract The objective of this research was to mesasure the effect of intercropping corn (Zea mays L.) with velvet bean (Mucuna pruriens) oil corn yields, soil fertility, and weed control in northern Belize. Two soils were used which are widespread in the area. These soils are high in clay (simectitc) and are well supplied with bases. One soil, the Louisville, has a black surface horizon overlying light gray to white calcareous weathered coral. The other soil was Xaibe which consists of a reddish‐brown clay over hard limestone “cap rock”; at 30–40 cm. Both soils had a relatively high organic carbon (C) and total nitrogen (N) content. We classified the Louisville as Calciustert and the Xaibe as a Ustropept. The treatments used (carried out annually in 1990, 1991, and 1992) were residue burn (B), bum plus fertilizer (BF), a velvet bean intercrop (G), and a velvet bean intercrop plus fertilizer (GF). Velvet bean intercropping did not have a positive effect on corn yields on these soils. For adequate yields t.
Phishing Happens Beyond Technology: The Effects of Human Behaviors and Demographics on Each Step of a Phishing Process

Prior studies have shown that the behaviours and attitudes of Internet users influence the likelihood of being victimised by phishing attacks. Many scammers design a step-by-step approach to phishing in order to gain the potential victim’s trust and convince them to take the desired actions. It is important to understand which behaviours and attitudes can influence following the attacker in each step of a phishing scam. This will enable us to identify the root causes of phishing and to develop specific mitigation plans for each step of the phishing process and to increase prevention points. This study investigates to what extent people’s risk-taking and decision-making styles influence the likelihood of phishing victimisation in three specific phishing steps. We asked participants to play a risk-taking game and to answer questions related to two psychological scales to measure their behaviours, and then conducted a simulated phishing campaign to assess their phishability throughout the three phishing steps selected. We find that the attitude to risk-taking and gender can predict users’ phishability in the different steps selected. There are however other possible direct and indirect behavioural factors that could be investigated in future studies. The results of this study and the model developed can be used to build a comprehensive framework to prevent the success of phishing attempts, starting from their root causes.
Hybrid Additive Manufacturing by Embedded Electrical Circuits Using 3-D Dispensing

In this study, a 3-D dispensing method for nonconductive and conductive adhesives is introduced that allows for direct electrical functionalization of the printed part during the printing process. The experimental work’s focus lies on the analysis of the embedding of conductive tracks within nonconductive housings. Two different construction strategies have been analyzed for printing tracks in both xy- and $z$ -directions. For all variants, initial feasibility tests were performed that successfully validated the general applicability of these techniques. As dissimilar materials are involved in the process, a micrograph analysis was used to investigate possible negative effects within the transitional areas. Although prototypical parts can be built easily with additive manufacturing methods, the lack of knowledge about the long-term robustness is often the limiting factor to end-use applications. Accelerated life tests in the form of thermal shock and humidity testing were performed to investigate this issue. The tests demonstrate good long-term behavior for two of four adhesives. The obtained results show that the proposed method and process variants are capable of producing electronically functional parts, provided a careful material selection has been performed. Furthermore, several design rules concerning the transitional area could be derived.
Bayesian Neural Network Modeling and Hierarchical MPC for a Tendon-Driven Surgical Robot With Uncertainty Minimization

In order to guarantee precision and safety in robotic surgery, accurate models of the robot and proper control strategies are needed. Bayesian Neural Networks (BNN) are capable of learning complex models and provide information about the uncertainties of the learned system. Model Predictive Control (MPC) is a reliable control strategy to ensure optimality and satisfaction of safety constraints. In this work we propose the use of BNN to build the highly nonlinear kinematic and dynamic models of a tendon-driven surgical robot, and exploit the information about the epistemic uncertainties by means of a Hierarchical MPC (Hi-MPC) control strategy. Simulation and real world experiments show that the method is capable of ensuring accurate tip positioning, while satisfying imposed safety bounds on the kinematics and dynamics of the robot.
Small- and Large-Signal Dynamic Output Capacitance and Energy Loss in GaN-on-Si Power HEMTs

The origin of <inline-formula> <tex-math notation="LaTeX">${C}_{{\mathrm {oss}}}$ </tex-math></inline-formula> and the energy dissipation due to output capacitance can be roughly separated into two types: resistive loss and capacitive hysteresis loss. The resistive losses are due to the resistance of gallium nitride (GaN) buffer layers and Si-substrate. These loss components can be potentially improved by reengineering the doping concentrations in the buffer stacks and substrate layer. However, the capacitive hysteresis loss that is suspected to be due to trapping dynamics remains largely unexplained. This article presents a physics view of the trapping dynamics related to <inline-formula> <tex-math notation="LaTeX">${C}_{{\mathrm {oss}}}$ </tex-math></inline-formula> in the GaN high-electron-mobility transistor (HEMT) under small- and large-signal excitations. It is observed that, under large-signal excitation, traps with shallow energy levels (in the range of 0.5 eV above valence band) are mostly responsible for the charge imbalance and energy dissipation. Under small-signal conditions, deep traps with an energy level of around 0.98 eV from the valence band are the causes for the hysteresis at dc. By identifying dominating types of traps and relevant trap energy levels in GaN HEMT, we can gain important insights on how to optimize the device stack design for high-frequency applications.
These do not Look Like Those: An Interpretable Deep Learning Model for Image Recognition

Interpretation of the reasoning process of a prediction made by a deep learning model is always desired. However, when it comes to the predictions of a deep learning model that directly impacts on the lives of people then the interpretation becomes a necessity. In this paper, we introduce a deep learning model: negative-positive prototypical part network (NP-ProtoPNet). This model attempts to imitate human reasoning for image recognition while comparing the parts of a test image with the corresponding parts of the images from known classes. We demonstrate our model on the dataset of chest $X$ -ray images of Covid-19 patients, pneumonia patients and normal people. The accuracy and precision that our model receives is on par with the best performing non-interpretable deep learning models.