Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Graph-Based Semi-Supervised Learning on Audio Embeddings for Label Classification

The increasing importance of audio-based healthcare diagnostics, particularly in chronic respiratory problems, has prompted the development of novel approaches for disease classification using the full spectrum analysis of cough sounds. This paper presents an innovative study exploring the potential of employing supervised and semi-supervised learning methodologies for disease categorization based on cough audio samples. Specifically, this study focuses on scenarios characterized by a shortage of annotated data related to chronic diseases. The objectives of our study involved the utilization of standard machine learning algorithms for direct classification based on embeddings, as well as the integration of Graph Neural Networks (GNNs) on the KNN graph. Preliminary results indicate that GNN models consistently outperformed traditional classifiers. For instance, with just 1 % of the data, GAT and GCN achieved AUC PR values of 0.84 and 0.87, respectively, surpassing all traditional methods. The superiority mentioned above was sustained even when the data fraction was augmented to 3% and 5%. The usefulness of graph neural networks (GNNs) was further supported by comprehensive performance measures, wherein the graph convolutional network (GCN) exhibited exceptional preci-sion and PR (precision-recall). In summary, the AudioVecDiagnosis framework presents a promising opportunity for further investigation in audio-based healthcare diagnostics. It provides an optimum approach for situations with a limited availability of labeled data.
Large-scale supervised learning for 3D point cloud labeling: Semantic3d.net

In this paper we review current state-of-the-art in 3D point cloud classification, present a new 3D point cloud classification benchmark data set of single scans with over four billion manually labelled points, and discuss first available results on the benchmark. Much of the stunning recent progress in 2D image interpretation can be attributed to the availability of large amounts of training data, which have enabled the (supervised) learning of deep neural networks. With the data set presented in this paper, we aim to boost the performance of CNNs also for 3D point cloud labelling. Our hope is that this will lead to a breakthrough of deep learning also for 3D (geo-)data. The semantic3D.net data set consists of dense point clouds acquired with static terrestrial laser scanners. It contains 8 semantic classes and covers a wide range of urban outdoor scenes, including churches, streets, railroad tracks, squares, villages, soccer fields and castles. We describe our labelling interface and show that, compared to those already available to the research community, our data set provides denser and more complete point clouds, with a much higher overall number of labelled points. We further provide descriptions of baseline methods and of the first independent submissions, which are indeed based on CNNs, and already show remarkable improvements over prior art. We hope that semantic3D.net will pave the way for deep learning in 3D point cloud analysis, and for 3D representation learning in general.
On fault classes and error detection capability of specification-based testing

In a previous paper, Kuhn [1999] showed that faults in Boolean specifications constitute a hierarchy with respect to detectability, and drew the conclusion that missing condition faults should be hypothesized to generate tests. However this conclusion was premature, since the relationships between missing condition faults and faults in other classes have not been sufficiently analyzed. In this note, we investigate such relationships, aiming to complement the work of Kuhn. As a result, we obtain an extended hierarchy of fault classes and reach a different conclusion.
A voltage-mode PWM buck regulator with end-point prediction

The end-point prediction (EPP) scheme for voltage-mode buck regulators is proposed. Internal nodal voltages of the regulator controller are predicted and set automatically by the proposed algorithms and circuits. The settling time of the regulator can therefore be significantly reduced for faster dynamic responses, even with dominant-pole compensation. Proven experimentally by a voltage-mode buck regulator implemented in a 0.35-/spl mu/m CMOS technology, the reference-tracking speed using the EPP scheme is faster than the conventional buck regulator by about six times.
Finite-time Stabilization of Fractional-order Memristive Complex-valued Delay Neural Networks

This paper focuses on the finite-time(FNT) stabilization for a class of fractional-order memristive complex-valued delay neural networks(FOMCVDNNs). Instead of breaking down the complex-valued system into its real and imaginary components, we have devised a more straightforward approach by introducing a lemma related to the complex-valued sign function. This innovative technique enables us to design a simpler discontinuous controller. In the end, we provide numerical simulations to demonstrate the practical effectiveness of the theoretical findings.
Improving Adversarial Robustness of Few-Shot Learning with Contrastive Learning and Hypersphere Embedding

Few-shot image classification (FSIC) is a computer vision task from the few-shot learning (FSL) category in which the model learns to classify images using only a few training samples. It has been demonstrated that even neural networks trained on large scale datasets are vulnerable to adversarial samples. This vulnerability is magnified in FSIC due to the low volume of training data. This paper proposes the use of hypersphere embedding and supervised contrastive learning to improve the adversarial robustness of representation learning-based FSIC. Contrastive learning contributes through its ability to bring together similar samples while pushing away dissimilar ones. On the other hand, hypersphere embedding has been successful in the representation learning tasks by restricting the embeddings to a hypersphere manifold. The proposed approach was evaluated on both 5-shot and 1-shot learning using two standard FSL networks and the standard Mini-ImageNet benchmark dataset. The evaluation shows that supervised contrastive training provides inherent adversarial robustness to the FSIC model while hypersphere embedding with cosine distance metrics improves the accuracy of the FSIC model and, when used in conjunction with an adversarial defense mechanism, boosts the adversarial performance.
Risk Factors for Postoperative Cognitive Decline After Orthopedic Surgery in Elderly Chinese Patients: A Retrospective Cohort Study

Purpose We aimed to identify the risk factors for postoperative cognitive decline (POCD) by evaluating the outcomes from preoperative comprehensive geriatric assessment (CGA) and intraoperative anesthetic interventions. Patients and Methods Data used in the study were obtained from the Aged Patient Perioperative Longitudinal Evaluation–Multidisciplinary Trial (APPLE-MDT) cohort recruited from the Department of Orthopedics in Xuanwu Hospital, Capital Medical University between March, 2019 and June, 2022. All patients accepted preoperative CGA by the multidisciplinary team using 13 common scales across 15 domains reflecting the multi-organ functions. The variables included demographic data, scales in CGA, comorbidities, laboratory tests and intraoperative anesthetic data. Cognitive function was assessed by Montreal Cognitive Assessment scale within 48 hours after admission and after surgery. Dropping of ≥1 point between the preoperative and postoperative scale was defined as POCD. Results We enrolled 119 patients. The median age was 80.00 years [IQR, 77.00, 82.00] and 68 patients (57.1%) were female. Forty-two patients (35.3%) developed POCD. Three cognitive domains including calculation (P = 0.046), recall (P = 0.047) and attention (P = 0.007) were significantly worsened after surgery. Univariate analysis showed that disability of instrumental activity of daily living, incidence rate of postoperative respiratory failure (PRF) ≥4.2%, STOP-Bang scale score, Caprini risk scale score and Sufentanil for maintenance of anesthesia were different between the POCD and non-POCD patients. In the multivariable logistic regression analysis, PRF ≥ 4.2% (odds ratio [OR] = 2.343; 95% confidence interval [CI]: 1.028–5.551; P = 0.046) and Sufentanil for maintenance of anesthesia (OR = 0.260; 95% CI: 0.057–0.859; P = 0.044) was independently associated with POCD as risk and protective factors, respectively. Conclusion Our study suggests that POCD is frequent among older patients undergoing elective orthopedic surgery, in which decline of calculation, recall and attention was predominant. Preoperative comprehensive geriatric assessments are important to identify the high-risk individuals of POCD.
Diabetes Prediction System Using Machine Learning

Diabetes is long-lasting disease to causes a health care crisis , but it is challenging to predict without medical tests According to the International Diabetes Federation, there are currently 537 million people living with diabetes worldwide. By the year 2045, this number is projected to double, reaching 783 million. Diabetes is a disorder triggered by the abnormal increase in blood glucose levels. This excess blood glucose produces the symptoms of urination, inceased thirst, and improved hunger. Diabetes is a significant contributor to various health complications, including blindness, kidney failure, amputations, heart failure, and strokes. When we consume food, our body processes it into sugars, or glucose. Machine learning algorithms can process vast amounts of data in medicine, revealing confidential information. The aim of study to investigate how machine learning algorithms can used to predict disease. Decision Trees, Numpy, Support Vector Machine are used to predict the diabetes using machine learning techniques. All these machine learning methods/algorithms can examine vast amounts of data, enabling medical practitioners to make more cost- effective judgments. By analyzing clinical data, establishing connections among multiple diagnostic variables, building a predictive model, and comparing it to a new dataset, the study found that the Support Vector Machine Classifier predicted diabetes illnesses with 80% accuracy. The study found that employing a larger dataset for train and test can improve the model's accuracy and modify different hyperparameters. The study concludes that artificial intelligence using ML modeling can categorize people with or without diabetes.
A Switching Strategy for Run-to-Run Control Using Deep Deterministic Policy Gradient Algorithm and Integral Controller

An end-to-end strategy based on the deep determin-istic policy gradient (DDPG) algorithm is an effective adaptive method for run-to-run control, which allows exploration in a semiconductor manufacturing environment to overcome the limitations of the model. However, the tracking performance of DDPG can be compromised by inefficient sampling and inadequate training. To address such issues, a switching control strategy based on DDPG and integral controller is proposed. First, the target tracking is built as a Markov decision process. Given the current state, two actions are obtained from the integral controller and the DDPG, respectively. Then, an optimal action that provides a greater reward is selected as the output of the switching strategy. Therefore, the developed scheme not only guarantees the basic performance of the integral controller, but also fully exploits the exploration advantages of DDPG in unknown manufacturing environments. Simulation results demonstrate the improvement in tracking performance of the presented strategy compared to the standalone DDPG and integral controllers.
Decomposition and Simplification of Multivariate Data using Pareto Sets

Topological and structural analysis of multivariate data is aimed at improving the understanding and usage of such data through identification of intrinsic features and structural relationships among multiple variables. We present two novel methods for simplifying so-called Pareto sets that describe such structural relationships. Such simplification is a precondition for meaningful visualization of structurally rich or noisy data. As a framework for simplification operations, we introduce a decomposition of the data domain into regions of equivalent structural behavior and the reachability graph that describes global connectivity of Pareto extrema. Simplification is then performed as a sequence of edge collapses in this graph; to determine a suitable sequence of such operations, we describe and utilize a comparison measure that reflects the changes to the data that each operation represents. We demonstrate and evaluate our methods on synthetic and real-world examples.
Enhancing Accuracy in Crop Yield Estimation Through the Integration of Naive Bayes and Convolutional Neural Networks

This article improves the accuracy of crop yield forecast by using convolutional neural network and Naive Bayes algorithm. Supplies and Procedures. The study comprised 42 samples, which were grouped into two sets of 21 samples each. While the second group utilised the Convolutional Neural Network technique, the first group used the Naive Bayes technique. The statistical power of the study was established at 80% with an alpha level of 0.05 and a beta level of 0.20, using G power. Results: The Naive Bayes algorithm tends to have a higher accuracy rate, which in this case is around 88%. On the other hand, the Convolutional Neural Network algorithm has a slightly lower accuracy rate of approximately 82%. Independent sample t-test using this paper. It has a significant value of p is . 046 (p<0.05), this is statistically significant. Conclusion: The Naive Bayes algorithm was discovered to exhibit a higher level of accuracy in comparison to the Convolutional Neural Network Algorithm.
Enhanced Floating-Point Multiply-Add with Full Denormal Support

This paper presents an enhanced floating-point multiply-add (FMA) design for the Intel E-Core processor. FMA is one of the most widely used operation in many applications. The proposed FMA is executed in 4 cycles, fully pipelined, handles SSE/AVX operations for scalar/packed IEEE single and double precision, and supports all four rounding modes. Also, the proposed FMA fully supports both denormal inputs and underflow outputs without microcode assistance. To achieve the 4-cycle FMA with full denormal support, several optimization techniques are applied: one-way alignment, radix-16 Booth encoding for the multiplier, merged J-bit correction and aligned significand with the multiply array, modified leading zero anticipation (LZA) for masking the underflow, parallel sticky and all-ones detection with the normalization, and merged two’s complement with the rounding logic. As a result, the proposed FMA achieved not only full denormal support but also about 10 – 30% reduced area and about 10 – 20% reduced latency compared to the traditional FMAs.
Performance Analysis for Convolutional Neural Network Architectures using Brain Tumor Datasets: A Proposed System

Brain tumors remain a pressing global health concern, with high mortality rates despite significant medical advancements. The brain tumor is a potentially deadly condition, and its categorization creates an enormous challenge for radiologists due to the diverse composition of tumor cells. In recent times, there has been a growing interest in the development of computer-aided diagnostic systems that use magnetic resonance imaging (MRI) to help in the identification of brain tumors. These systems have the potential to provide valuable support in the medical field. In this paper, we have proposed an experimental method for the extraction of brain tumors from 2D Magnetic Resonance Imaging (MRI) scans using a convolutional neural network (CNN). The qualitative study was conducted using a real-time online dataset that included a wide range of tumor sizes, locations, forms, and varying image intensities. The proposed methodology aims to deliver the most effective model capable of distinguishing between normal brain structures and various types of brain tumors, such as glioma, meningioma, and pituitary tumors, using these MRI images. The architecture of the network, the choice of hyperparameters, and the nuances of the optimization algorithms—all these factors play a crucial role in determining the model’s performance. In our work, CNN achieved an accuracy rate of 98.91%, which is quite impressive. In addition, results from experiments demonstrated that the proposed method is efficient and suitable for computer-assisted brain tumor detection.
A Novel Multi-Label Evaluation Measure with Comparative Analysis

Multi-label classification presents unique challenges that cannot be resolved by simply adapting single-label classification methods. One of the key challenges lies in devising an effective performance evaluation method. Although several evaluation measures for multi-label classification have been proposed, none of them has gained universal acceptance. This study focuses on ranking-based metrics and compares commonly used ranking-based methods in depth. Through this analysis, we identify the limitations of existing methods and leverage the characteristics of binary sequences to effectively overcome these drawbacks. The relationship between a ranking table and a binary sequence provides an efficient means to compare the characteristics of performance metrics, leading to the development of a novel measure for evaluating multi-label classification performance.
Substrate Engineering Concepts to Mitigate Charge Collection in Deep Trench Isolation Technologies

Delayed charge collection from ionizing events outside the deep trench can increase the SEU cross section in deep trench isolation technologies. Microbeam test data and device simulations demonstrate how this adverse effect can be mitigated through substrate engineering techniques. The addition of a heavily doped p-type charge-blocking buried layer in the substrate can reduce the delayed charge collection from events that occur outside the deep trench isolation by almost an order of magnitude, implying an approximately comparable reduction in the SEU cross section
Formulation of the REMOS concept from an uncertainty decoding perspective

In this paper, we introduce a new formulation of the REMOS (REverberation MOdeling for Speech recognition) concept from an uncertainty decoding perspective. Based on a convolutive observation model that relaxes the conditional independence assumption of hidden Markov models, REMOS effectively adapts automatic speech recognition (ASR) systems to noisy and strongly reverberant environments. While uncertainty decoding approaches are typically designed to operate irrespectively of the employed decoding routine of the ASR system, REMOS explicitly considers the additional information provided by the Viterbi decoder. In contrast to previous publications of the REMOS concept, we provide a conclusive derivation of its decoding routine using a Bayesian network representation in order to prove its inherent uncertainty decoding character.
Energy Efficient Integrated Routing Protocol for MANETs

We propose an Energy Efficient Integrated Routing Protocol (E2IRP) for Mobile Ad Hoc networks used in remote surveillance systems. Remote surveillance system is the use of cameras to monitor properties and assets from a remote location. It is used as an asset protection device for areas where it is not possible or practical to install a cable network. The integration of MAC and routing layers can effectively reduce the amount of control information being exchanged for discovery and maintenance of the route in the network. This in turn reduces the energy and time consumed for the processing of these packets. Though the number of packets and processing is less, the protocol provides a better reliability and throughput. The nodes are organized in concentric tiers around the gateway. The event reports are routed towards the gateway from one tier to another and the response is routed back to the source, in the same manner. We evaluate E2IRP in NS2 simulator, with routing protocol like AODV using S-MAC. We find that E2IRP outperforms AODV routing protocol in terms of battery power consumption and also the throughput.
UTAUT Modification Model for the Analysis of User Experience of Telemedicine Application Users in Indonesia

The emergence of Covid-19 brings opportunities to create new things post-pandemic, one of which is telemedicine. Telemedicine is a health service technology that provides long-distance health services in the form of consultations with doctors without meeting in person to provide diagnostic advice and patient care guidance. This research examines and analyzes the user experience of Halodoc and Alodokter users, two leading telemedicine applications in Indonesia. This research uses a modified UTAUT2 (Unified Theory of Acceptance and Use of Technology 2) model to investigate the factors that influence user acceptance and utilization of this telemedicine platform. Through a comprehensive survey and data analysis, this research aims to explain the factors that determine user satisfaction, trust and behavioral intentions towards Halodoc and Alodokter. The findings from this study contribute to a deeper understanding of user behavior and preferences in the context of telemedicine applications, providing valuable insights for improving healthcare and technology adoption in the growing field of telemedicine. We conduct questionnaire surveys using Google Forms for easy distribution among our respondents. After analyzing respondents' answers regarding related factors, it was discovered that 40.2% of respondents were used to using telemedicine, but 59.8% of respondents were still not used to using telemedicine.
Analyses of Hidden and Exposed Nodes in LPWAN with Energy Detection Based Carrier Sense

This study investigates hidden and exposed nodes in a low power wide area network (LPWAN) with the energy detection based carrier sense. Energy detection based carrier sense can drastically improve the performance of LPWANs, which is a type of wireless local network. However, hidden node problems are inherent in wireless local networks with carrier sense, and the upsurge of LPWANs with carrier sense results in exposed node problems. To evaluate the hidden and exposed nodes in the LPWAN, we define evaluation model that includes Own LPWAN and Interference LPWAN. Based on the model, hidden and exposed nodes in the LPWAN are evaluated for different distance between both LPWANs and different path loss exponent between end devices and gateway. The evaluation results show that exposed nodes are not expected to occur in the LPWAN when the gateway is located at the appropriate location where many of the end devices are located in the line-of-sight environment.
Evaluating the Effectiveness of ISO 27001: 2013 Based on Annex A

The part of the management system of an organization dealing with information security is called Information Security Management System (ISMS). The most adopted ISMS standard is ISO 27001:2005. The 2005 version of the standard has been updated in 2013 to provide more clarity and more freedom in implementation, based on practical experiences. This paper compares ISO 27001:2005 and the updated 2013 standard, based on Annex A controls. We classify the controls into five categories of data, hardware, software, people and network. All of the controls defined in Annex A, regardless of their objectives, can easily be allocated to at least one of these categories. Classifying the controls to known categories offers an integrated view of the updated standard and presents a suitable guide for evaluating the performance and efficiency of the updated standard.