Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Evaluation of Ensemble Deep Learning Algorithms for Prediction of Pandemic Disease

To ensure effective healthcare administration and preventive measures, it is crucial to timely monitor and forecast the occurrence of severe diseases prior to their escalation into a pandemic. This research focuses on the COVID-19 pandemic as a case study to examine the importance of understanding the prognosis of viral diseases. The profound COVID-19 menace has exerted a significant impact on the worldwide health security landscape, necessitating the development of state-of-the-art computer models to enhance surveillance, management, and mitigation strategies. In order to forecast the severity of a disease before it becomes a pandemic, the research aims to develop a complete model that assesses healthcare factors at both the individual and community levels. This study has carefully evaluated the outcomes of several experiments using the ensemble, ResNet, and DenseNet models using a variety of performance metrics in order to achieve this goal. The ensemble model continuously demonstrated outstanding performance on all metrics, with a f1-score of 97% and accuracy. The DenseNet model obtained a 93% accuracy and f1-score, while the ResNet model obtained an 89% accuracy and an 88% f1-score. The models showcased in this paper demonstrated exceptional precision and possess the capability to aid in forecasting COVID-19. The effectiveness of computer models in correctly forecasting the illness was assessed using chest x-ray images. This experiment allows us to acquire a thorough comprehension of the advantages and disadvantages of various computer models in forecasting severe illnesses. Acquiring this knowledge will facilitate the creation of prediction systems that are more precise and effective in diagnosing any medical condition. The resultant outcome highlights the efficiency of ensemble techniques in utilising the combined benefits of several models. The information that is gained from this research aims to go beyond theory and apply it to the real world of hospital administration.
Incrementally passive infinite dimensional systems with a constrained state variable

In this paper, we show that the passivity property of a linear infinite dimensional system, with respect to a given supply rate, is preserved in the presence of a saturating integrator, which restricts a one dimensional component of the state to a compact interval. The resulting nonlinear system is incrementally passive with the same supply rate. We give an application of our main result to a boundary controlled string equation, where the displacement of the string at some interior point is restricted to a compact interval.
Identification of Single-Cell RNA Sequencing Molecular Signatures for COVID-19 Infection Severity Classification

In this study, we propose a graph-based framework to identify scRNA-seq molecular signatures for COVID-19 infection severity identification. We conduct extensive experiments on scRNA-seq data from bronchoalveolar lavage fluid (BALF) with four machine learning models: Support Vector Machine, Random Forest, Graph Convolutional Network (GCN), and Graph Attention Network (GAT). In addition, we employ an explainable artificial intelligence approach, GNNExplainer, to interpret model predictions by identifying the top 15 features that contribute to the severity classification. Our finding suggests that graphical models could accurately distinguish healthy people from COVID-19 patients (F1-score > 0.9) based on patient scRNA-seq data, and traditional machine learning approaches could accurately distinguish COVID-19 patients with different severity (F1-score > 0.99), along with meaningful molecular signatures identification and discovery. Our implementation is available on a github repository: https://github.com/Da2f1eW/COVID19_Infection_Severity_Classification.
PandoraRLO: Unveiling Protein-Ligand Interactions with Reinforcement Learning for Optimized Pose Prediction

The interaction between proteins and small molecules in the context of drug discovery presents a complex and challenging task. Key factors involved in this process include shape complementarity and intermolecular interactions, which are heavily influenced by the binding site and the ligand’s orientation as it engages with the protein. State of the art methods in the field offer a range of ligand poses that may fit a specific receptor, but these approaches are often computationally intensive and costly. In this study, we have designed a method that provides a single optimized ligand pose for a specific receptor. As a pioneering study, PandoraRLO uses reinforcement learning (RL) to train on a large dataset, through exposure to a wide range of protein-ligand pairs, the agent becomes proficient in comprehending the underlying biochemistry and generating an optimized pose for specific interactions. The novelty of PandoraRLO lies in its strategic use of reinforcement learning (RL) to effectively model chemical diversity, skillfully navigating the "explore versus exploit" dilemma inherent to RL. This pioneering technique provides an enhanced understanding of the underlying biochemistry, facilitating precise prediction of optimal ligand poses. Consequently, it paves the way for new opportunities in drug discovery by enabling more accurate and efficient design and optimization of small molecules to specific target proteins. These findings underscore the potential of reinforcement learning in advancing understanding of protein-ligand interactions and its application in developing more accurate computational tools for drug discovery. The ability to predict optimized ligand poses with greater precision opens new avenues for designing and optimizing small molecules to target specific proteins, ultimately expediting the discovery and development of novel therapeutic interventions.
Study on the effectiveness of pesticides against cowpea aphid (Aphis craccivora) Koch.

Considering the importance of safe food, avoiding of excess pesticide application and pesticide pollution the present study was conducted. In this experiment totally four pesticides were tested against cowpea aphid Aphis craccivora under pot culture experiment at College of Agricultural Technology, Theni. Among the four acetamprid 20 SP (0.125g/ml) proved highly effective against Aphis craccivora compared to the rest of the pesticides with mortality of 98.75 per cent. In case of dosage wise acetamprid 20 SL (0.125g/ml) and imidacloprid17.8 SL(0.25ml/l)proved to be highly effective with mortality percentage of 98.33 and 86.66, respectively. Thus, it is concluded that all the studied pesticides, Acetamprid proved effective to control the cowpea aphids. How to view point the article : Gowtham, V. , Dilipsundar, N. , Balaji, K. and Karthikeyan, S. (2016). Study on the effectiveness of pesticides against cowpea aphid (Aphis craccivora) Koch. Internat. J. Plant Protec. , 9(1) : 146-149.
THE OCCURENCE & DISTRIBUTION OF TWO BLACK SEA CUCUMBER SPECIES IN POMBO ISLAND, CENTRAL MALUKU (INDONESIA)

The so called “black sea cucumber” are found in Indonesia as several species including Holothuria atra , H. leucospilota , H. coluber , H. excellens and H. turriscelsa. The local community of Wainure village near Pombo Island in central Maluku had reported that most sea cucumbers inhabiting the island are the black sea cucumbers or locally known as “teripang hitam. ” They are occasionally consumed but not exploited excessively by the locals. The goals of this study are to identify the species of black sea cucumbers inhabiting Pombo Island and map their distribution to understand the microhabitats of black sea cucumber. Two field surveys conducted in March and April 2014 collected more than 550 individuals of black sea cucumbers that belong to two species Holothuria (Halodeima) atra Jaeger, 1833, and Holothuria (Mertensiothuria) leucospilota Brandt, 1835. Furthermore, distribution maps of these two species show that they are found only along the SE to SW coast of the island. Such distribution may relate to the availability of marine vegetation, as well as boulders and stones that provide shelters to the organism. The distribution maps also show that H. atra has a wider distribution than H. leucospilota in Pombo Island. This may be due to the semi-cryptic habit of H. Leucospilota ’s (i.e. hiding its posterior body under rocks or crevices), making its distribution restricted to areas that provide ample shelters.
Emotion Recognition based on Physiological Signals Multi-head Attention Contrastive Learning

Emotion recognition is an important research direction in the field of artificial intelligence, which can help machines understand and adapt to human emotional states. Physiological signals (PS), such as electroencephalogram (EEG), galvanic skin response (GSR), respiration rate, and body temperature, are effective biomarkers reflecting changes in human emotions. However, the collection of PS requires strict and complex operation procedures and professional personnel to label the data, resulting in a shortage of effectively labeled PS data. We propose a based on PS multi-head attention contrastive learning method(PS-MHACL), to address these issues. PS-MHACL learns meaningful feature representations from unlabeled PS and improves recognition performance through feature fusion using a multi-head attention mechanism. We use the genetics-inspired Meiosis method to augment unlabeled signals and train the feature extractor, which is then applied to labeled PS. A contrastive loss function maximizes the similarity of feature representations of augmented groups with the same stimulus, predicting emotions’ Arousal and Valence. Experiments on two public datasets, DEAP and MAHNOB-HCI, showed our method’s superior performance in terms of accuracy and cross-individual generalization ability. On DEAP, accuracies for Valence and Arousal dimensions were 96.39% and 93.44% respectively, with 89.35% for a four-class classification task. On MAHNOB-HCI, these were 94.89%, 96.11%, and 93.72% respectively.
Simultaneous Simulation Study of Metal Product Design Based on Machine Learning Algorithm

With the rapid development of automation technology, the manufacturing industry is facing the transformation from traditional manufacturing to intelligent manufacturing. Manufacturing product design inspection is an important process to ensure product quality. With the development of computer technology, especially the continuous improvement and application of computer vision and deep learning algorithm theory, more and more companies apply computer vision to product design inspection to improve the automation of equipment, reduce the cost of employing people, and improve the accuracy and efficiency of product design inspection. In order to meet specific design requirements, this paper designs and implements a metal product design system based on machine learning design. The system is not only able to meet the needs of manual design, but also can use mature computer vision-related algorithms to complete the initial automatic design and manual modification, thus greatly improving the design efficiency. It is worth mentioning that the system also has good scalability, which lays a good foundation for the preparation of image datasets. On this basis, the detailed design and implementation inside each module is presented, using class diagrams and data tables to show the implementation of methods in the classes. Immediately afterwards, each function of the designed system is tested, describing the expected and actual results. Finally, a summary of the completed work is presented, and the next steps related to the image design system are envisioned.
Risk-Sensitive Control of Vibratory Energy Harvesters

Linear-quadratic-Gaussian (LQG) optimal control theory can be used to maximize the average electrical power generated by a vibratory energy harvester subjected to random disturbances. However, feedback controllers designed using the LQG framework often require large peak power flows for their successful implementation, which may be undesirable for several reasons. In this paper, we propose using a risk-sensitive performance measure to synthesize control laws for stochastic vibratory energy harvesters. The proposed methodology is applied in two examples, in which we show how the risk-sensitive parameter can be systematically tuned to maximize power generation and mitigate excessive power flows. The first example involves a simple single-degree-of-freedom oscillator subjected to a bandpass filtered noise excitation, and the second pertains to ocean wave energy harvesting.
Predicting miRNA-disease associations based on graph convolutional network with path learning

Identifying miRNA-disease associations (MDAs) is crucial for improving the diagnosis and treatment of various diseases. However, biological experiments can be time-consuming and expensive. To overcome these challenges, computational approaches have been developed, with Graph Convolutional Networks (GCNs) showing promising results in MDAs prediction. The success of GCN-based methods relies on learning a meaningful spatial operator to extract effective node feature representations. To enhance the inference of MDAs, we propose a novel method called PGCNMDA, which employs graph convolutional networks with a learning graph spatial operator from paths. This approach enables the generation of meaningful spatial convolutions from paths in GCNs, leading to improved prediction performance. We evaluate PGCNMDA using 5-fold cross-validation (5-CV) on databases HMDD v2.0 and HMDD v3.2. Additionally, we compare it with other methods. The experimental results demonstrate that PGCNMDA outperforms other miRNA-disease association prediction methods.
Organizational Culture, Commitment and Good Governance of Private Higher Education Institutions: Inputs to Effective Human Resource Management Framework

: This study identified the organizational culture, the organizational commitment of employees in selected Private Higher Education Institutions in Mindoro Province, and their good governance as inputs to effective Human Resource management framework. More specifically, it assessed organizational culture in terms of support, mission, success, and bureaucracy aspect; organizational commitment in terms of affective, normative and continuance dimensions; and good governance in terms of resources management, accountability and participatory decision-making. This further tested if there are significant differences on the responses when grouped according to its profile variables; significant relationship among existing variables; and finally, proposed a framework for effective HRM. The researcher used descriptive method using survey questionnaire in gathering data. The data were drawn from 341 teaching and non-teaching staff in five selected private HEIs in Mindoro Province. The study showed that the organizational culture is highly evident in terms of support, mission, and success aspect. The employees manifested moderate commitment to the organization in terms of affective, normative and continuance dimensions; and good governance is found with great evidence considering resources management while accountability and participatory decision making generated moderate evidence. Furthermore, no significant differences are reflected on the organizational culture when the staff are grouped according to its demographic profile except for the respondents’ age that differs in the aspect of bureaucracy. Response variations on organizational commitment are reflected in affective and continuance commitment as they are grouped according to age, highest education attainment, and job position respectively; and there is no significant difference on good governance based on demographic profile. A high significant relationship exists among organizational culture, organizational commitment, and good governance. Finally, the researcher proposed HRM framework to improve the work performance of the staff at private higher education institutions
Event-Triggered Parameterized Control for Stabilization of Linear Systems

This paper proposes a new control method called event-triggered parameterized control (ETPC). We showcase this method by focusing on the specific problem of stabilization of linear systems. In this control method, between two consecutive events, each control input to the plant is a linear combination of a set of linearly independent scalar functions. At each event, the coefficients of the parameterized control input are chosen to minimize the error in approximating a model based control signal and then they are communicated to the actuator. We design two event-triggering rules that guarantee global asymptotic stability of the origin of the closed loop system under some conditions on the model uncertainty. We also show the existence of a uniform positive lower bound on the inter-event times. We illustrate our results through numerical examples. We compare the proposed control method with event-triggered zero-order-hold control and show a significant improvement in terms of the average inter-event times.
Yodas: Youtube-Oriented Dataset for Audio and Speech

In this study, we introduce YODAS (YouTube-Oriented Dataset for Audio and Speech), a large-scale, multilingual dataset comprising currently over 500k hours of speech data in more than 100 languages, sourced from both labeled and unlabeled YouTube speech datasets. The labeled subsets, including manual or automatic subtitles, facilitate supervised model training. Conversely, the unlabeled subsets are apt for self-supervised learning applications. YODAS is distinctive as the first publicly available dataset of its scale, and it will be distributed under a Creative Commons license. We introduce the collection methodology utilized for YODAS, which contributes to the large-scale speech dataset construction. Subsequently, we provide a comprehensive analysis of speech, text contained within the dataset. Finally, we describe the speech recognition baselines over the top-15 languages.
PPGANet: Removal of Motion Artifacts from the PPG Signal Using Generative Adversarial Networks

With the emergence of smartwatches and fitness trackers, photoplethysmography (PPG) signal has become more widespread in daily life. However, the signal’s usefulness is still limited by motion artifacts. This study explores the potential of using generative adversarial networks (GANs) to eliminate motion artifacts from the PPG signal without relying on additional sensor data from accelerometers or gyroscopes. Our evaluation methods include PPG pulse detection, heart rate estimation, and waveform morphology. While the proposed method lags slightly behind one state-of-the-art technique that utilized additional sensors in performance, it requires less input signal, making it more beneficial for portable or low-cost devices. As shown in the results, this study can serve as a foundation for future single channel-based algorithms.
Steering a Linear System at the Minimum Information Rate: Quantifying Redundancy via Covariance Assignment Theory

We compute fundamental performance limitations in the data rate constrained control of continuous-time linear stochastic control systems using information theoretic tools and principles. Specifically, we find the minimum achievable mean square error for steering a linear system to sequences of uncertain steering objectives under a rate constraint as the solution of a convex optimization problem. We propose the redundancy of a control system as a measure of the relative inefficiency of information transmission through a linear control system vs. an ideal communications channel.
Glaucoma Detection Using Convolutional Neural Network (CNN)

Today, glaucoma detection is a severe problem. Because it was predicted that there would be 111.8 million cases of glaucoma worldwide by 2040, up from the expected 76.0 million cases in 2020. Blindness results if early treatment is not given. The CNN model has been used in a variety of studies to identify glaucoma. The results obtained thus far are insufficient to reliably identify glaucoma. In order to detect glaucoma, which is a binary classification, we are utilizing the CNN model. We divided the REFUGUE data set into tanning and testing, with 80% of the photos being utilized for tanning and 20% for testing. Our model's accuracy score was 99.8% for the training set and 95.4% for the testing set. We get result of precision=98% and recall= 97%. We compare the model with other state of arts (SOTA) our experimental results reveal that, the proposed model for glaucoma detection using convolutional neural network is more proficient in glaucoma classification.
Weighted-Norm Bounds on Model Approximation in MDPs with Unbounded Per-Step Cost

We consider the problem of designing a control policy for an infinite-horizon discounted cost Markov Decision Process (MDP) $\mathcal{M}$ when we only have access to an approximate model $\hat{\mathcal{M}}$. If we design an optimal policy $\hat{\pi}^{\star}$ for the approximate model, how well does it perform when used in the true model $\mathcal{M}$ ? We provide an answer to this question by bounding a weighted norm of the difference between the value function of $\hat{\pi}^{\star}$ when used in $\mathcal{M}$ and the optimal value function of $\mathcal{M}$. The use of weighted norm allows us to obtain meaningful bounds for performance loss even when the per-step cost function is unbounded. This is in contrast to much of the prior literature which has largely focused only on the case of bounded per-step cost. We illustrate our results for two specific instances - a finite MDP model for an inventory control problem and the discounted linear quadratic regulator problem.
Bipartite Formation over Undirected Signed Networks with Collision Avoidance

We address the problem of bipartite formation control, with collision avoidance, for double integrators with limited sensing ranges. We assume that the systems are inter- connected over an undirected, signed, and structurally balanced network. Then, to ensure that the proximity constraints are satisfied, we design a barrier-Lyapunov-function-based control law that guarantees connectivity maintenance for cooperative agents, and inter-agent collision avoidance for all agents. Relying on the edge-based agreement, we establish asymptotic stability of the bipartite formation control for signed networks. Finally, we illustrate our theoretical results via numerical simulations.
A 9.5ms-Latency 6.2µJ/Inference Spiking CNN for Patient-Specific Seizure Detection

We present the design, optimization, hardware implementation (generic and neuromorphic), and performance validation of regular and spiking convolutional neural networks (CNN and SCNN) for patient-specific seizure detection. The CNN’s design is optimized using a neural architecture search, which resulted in a 7-layer network with one-dimensional (both horizontal and vertical) kernels that prove to be highly effective in capturing input’s spatio-temporal features. The SCNN is developed using shadow training, and the conversion process is optimized for maximum model fidelity, which results in a <1.2% and <1.8% drop in sensitivity and specificity, respectively, compared to the CNN. Performance evaluation of the CNN on a microcontroller (STM32H747) shows a 93.4% vector-based sensitivity, and a 99.1% specificity. The SCNN implementation optimizes accuracy, inference time, and energy per classification simultaneously. Performance evaluation of the developed SCNN on a neuromorphic processor simulator (Intel Loihi) shows a 92.2% vector-based sensitivity, and a 97.3% specificity, a 6. 2µJ/classification efficiency, and a 9.5ms inference time. For a detection latency of one second, this translates to an average power of 59nW. The results of both networks are compared to the state of the art.