pwc-archive/papers-with-abstracts huggingface dataset:
Accelerated Inference for Partially Observed Markov Processes using Automatic Differentiation
Authors: Kevin Tan, Giles Hooker, Edward L. Ionides
Date: 2024-07-03 00:00:00

Automatic differentiation (AD) has driven recent advances in machine learning, including deep neural networks and Hamiltonian Markov Chain Monte Carlo methods. Partially observed nonlinear stochastic dynamical systems have proved resistant to AD techniques because widely used particle filter algorithms yield an estimated likelihood function that is discontinuous as a function of the model parameters. We show how to embed two existing AD particle filter methods in a theoretical framework that provides an extension to a new class of algorithms. This new class permits a bias/variance tradeoff and hence a mean squared error substantially lower than the existing algorithms. We develop likelihood maximization algorithms suited to the Monte Carlo properties of the AD gradient estimate. Our algorithms require only a differentiable simulator for the latent dynamic system; by contrast, most previous approaches to AD likelihood maximization for particle filters require access to the system's transition probabilities. Numerical results indicate that a hybrid algorithm that uses AD to refine a coarse solution from an iterated filtering algorithm show substantial improvement on current state-of-the-art methods for a challenging scientific benchmark problem.
Implementation and Analysis of GPU Algorithms for Vecchia Approximation
Authors: Zachary James, Joseph Guinness
Date: 2024-07-03 00:00:00
Tasks: Gaussian Processes, GPU

Gaussian Processes have become an indispensable part of the spatial statistician's toolbox but are unsuitable for analyzing large dataset because of the significant time and memory needed to fit the associated model exactly. Vecchia Approximation is widely used to reduce the computational complexity and can be calculated with embarrassingly parallel algorithms. While multi-core software has been developed for Vecchia Approximation, such as the GpGp R package, software designed to run on graphics processing units (GPU) is lacking, despite the tremendous success GPUs have had in statistics and machine learning. We compare three different ways to implement Vecchia Approximation on a GPU: two of which are similar to methods used for other Gaussian Process approximations and one that is new. The impact of memory type on performance is investigated and the final method is optimized accordingly. We show that our new method outperforms the other two and then present it in the GpGpU R package. We compare GpGpU to existing multi-core and GPU-accelerated software by fitting Gaussian Process models on various datasets, including a large spatial-temporal dataset of $n>10^6$ points collected from an earth-observing satellite. Our results show that GpGpU achieves faster runtimes and better predictive accuracy.
Supporting Cross-language Cross-project Bug Localization Using Pre-trained Language Models
Authors: Mahinthan Chandramohan, Dai Quoc Nguyen, Padmanabhan Krishnan, Jovan Jancic
Date: 2024-07-03 00:00:00
Tasks: Contrastive Learning, CPU, Knowledge Distillation, Language Modeling, Language Modelling

Automatically locating a bug within a large codebase remains a significant challenge for developers. Existing techniques often struggle with generalizability and deployment due to their reliance on application-specific data and large model sizes. This paper proposes a novel pre-trained language model (PLM) based technique for bug localization that transcends project and language boundaries. Our approach leverages contrastive learning to enhance the representation of bug reports and source code. It then utilizes a novel ranking approach that combines commit messages and code segments. Additionally, we introduce a knowledge distillation technique that reduces model size for practical deployment without compromising performance. This paper presents several key benefits. By incorporating code segment and commit message analysis alongside traditional file-level examination, our technique achieves better bug localization accuracy. Furthermore, our model excels at generalizability - trained on code from various projects and languages, it can effectively identify bugs in unseen codebases. To address computational limitations, we propose a CPU-compatible solution. In essence, proposed work presents a highly effective, generalizable, and efficient bug localization technique with the potential to real-world deployment.
FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspective
Authors: Kun Yi, Qi Zhang, Wei Fan, Hui He, Liang Hu, Pengyang Wang, Ning An, Longbing Cao, Zhendong Niu
Date: 2023-11-10 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Graph Neural Network, Multivariate Time Series Forecasting, Time Series, Time Series Forecasting

Multivariate time series (MTS) forecasting has shown great importance in numerous industries. Current state-of-the-art graph neural network (GNN)-based forecasting methods usually require both graph networks (e.g., GCN) and temporal networks (e.g., LSTM) to capture inter-series (spatial) dynamics and intra-series (temporal) dependencies, respectively. However, the uncertain compatibility of the two networks puts an extra burden on handcrafted model designs. Moreover, the separate spatial and temporal modeling naturally violates the unified spatiotemporal inter-dependencies in real world, which largely hinders the forecasting performance. To overcome these problems, we explore an interesting direction of directly applying graph networks and rethink MTS forecasting from a pure graph perspective. We first define a novel data structure, hypervariate graph, which regards each series value (regardless of variates or timestamps) as a graph node, and represents sliding windows as space-time fully-connected graphs. This perspective considers spatiotemporal dynamics unitedly and reformulates classic MTS forecasting into the predictions on hypervariate graphs. Then, we propose a novel architecture Fourier Graph Neural Network (FourierGNN) by stacking our proposed Fourier Graph Operator (FGO) to perform matrix multiplications in Fourier space. FourierGNN accommodates adequate expressiveness and achieves much lower complexity, which can effectively and efficiently accomplish the forecasting. Besides, our theoretical analysis reveals FGO's equivalence to graph convolutions in the time domain, which further verifies the validity of FourierGNN. Extensive experiments on seven datasets have demonstrated our superior performance with higher efficiency and fewer parameters compared with state-of-the-art methods.
UnitNorm: Rethinking Normalization for Transformers in Time Series
Authors: Nan Huang, Christian Kümmerle, Xiang Zhang
Date: 2024-05-24 00:00:00
Tasks: Anomaly Detection, Time Series, Time Series Analysis

Normalization techniques are crucial for enhancing Transformer models' performance and stability in time series analysis tasks, yet traditional methods like batch and layer normalization often lead to issues such as token shift, attention shift, and sparse attention. We propose UnitNorm, a novel approach that scales input vectors by their norms and modulates attention patterns, effectively circumventing these challenges. Grounded in existing normalization frameworks, UnitNorm's effectiveness is demonstrated across diverse time series analysis tasks, including forecasting, classification, and anomaly detection, via a rigorous evaluation on 6 state-of-the-art models and 10 datasets. Notably, UnitNorm shows superior performance, especially in scenarios requiring robust attention mechanisms and contextual comprehension, evidenced by significant improvements by up to a 1.46 decrease in MSE for forecasting, and a 4.89% increase in accuracy for classification. This work not only calls for a reevaluation of normalization strategies in time series Transformers but also sets a new direction for enhancing model performance and stability. The source code is available at https://anonymous.4open.science/r/UnitNorm-5B84.
A cross-sectional study to analyse the presence of low back pain in university students and its relationship with coffee
Authors: J. Arista, J. Bohorquez, C. Camacho, I. Leon, A. Razuri
Date: 2024-07-04 00:00:00
Proceeding: . 2024 7
Tasks: Electromyography (EMG)

Low back pain (LBP) is a prevalent condition affect-
ing a significant portion of the global population and a leading
cause of physical disability. In Peru, about 70% of the population
suffers from LBP, largely due to heavy physical exertion, forced
postures, and prolonged poor posture, particularly among uni-
versity students who spend long hours sitting. This study aims
to analyze electromyography (EMG) signals of the multifidus
muscles using the BiTalino device to assess chronic LBP in
university students. Six students (five females and one male) from
Universidad Peruana Cayetano Heredia (UPCH) participated
voluntarily. Surface electromyography (SEMG) evaluations were
conducted with electrodes placed at the L3 and L5 vertebrae
levels. Participants performed movements including standing,
forward flexion, maximum voluntary flexion, and extension. The
SEMG signals were filtered, rectified, smoothed using RMS, and
normalized for comparison.
The study also included a survey to explore the relationship
between LBP and coffee consumption. The results highlighted
the Flexion Relaxation Ratio (FRR) FLEX/MVF and EXT/MVF
as effective indices for evaluating chronic LBP. This research
underscores the importance of posture awareness and early
intervention in managing LBP among university students.
Cutting through the noise to motivate people: A comprehensive analysis of COVID-19 social media posts de/motivating vaccination
Authors: Ashiqur Rahman, Ehsan Mohammadi, Hamed Alhoori
Date: 2024-06-15 00:00:00
Tasks: Misinformation, Stance Detection, Text Classification, Topic Models

The COVID-19 pandemic exposed significant weaknesses in the healthcare information system. The overwhelming volume of misinformation on social media and other socioeconomic factors created extraordinary challenges to motivate people to take proper precautions and get vaccinated. In this context, our work explored a novel direction by analyzing an extensive dataset collected over two years, identifying the topics de/motivating the public about COVID-19 vaccination. We analyzed these topics based on time, geographic location, and political orientation. We noticed that while the motivating topics remain the same over time and geographic location, the demotivating topics change rapidly. We also identified that intrinsic motivation, rather than external mandate, is more advantageous to inspire the public. This study addresses scientific communication and public motivation in social media. It can help public health officials, policymakers, and social media platforms develop more effective messaging strategies to cut through the noise of misinformation and educate the public about scientific findings.
Optimizing Deep Reinforcement Learning for Adaptive Robotic Arm Control
Authors: Jonaid Shianifar, Michael Schukat, Karl Mason
Date: 2024-06-12 00:00:00
Tasks: Deep Reinforcement Learning, Hyperparameter Optimization, reinforcement-learning, Reinforcement Learning

In this paper, we explore the optimization of hyperparameters for the Soft Actor-Critic (SAC) and Proximal Policy Optimization (PPO) algorithms using the Tree-structured Parzen Estimator (TPE) in the context of robotic arm control with seven Degrees of Freedom (DOF). Our results demonstrate a significant enhancement in algorithm performance, TPE improves the success rate of SAC by 10.48 percentage points and PPO by 34.28 percentage points, where models trained for 50K episodes. Furthermore, TPE enables PPO to converge to a reward within 95% of the maximum reward 76% faster than without TPE, which translates to about 40K fewer episodes of training required for optimal performance. Also, this improvement for SAC is 80% faster than without TPE. This study underscores the impact of advanced hyperparameter optimization on the efficiency and success of deep reinforcement learning algorithms in complex robotic tasks.
Minds, Brains, AI
Authors: Jay Seitz
Date: 2024-04-21 00:00:00
Tasks: Self-Driving Cars

In the last year or so and going back many decades there has been extensive claims by major computational scientists, engineers, and others that AGI, artificial general intelligence, is five or ten years away, but without a scintilla of scientific evidence, for a broad body of these claims. Computers will become conscious, have a theory of mind, think and reason, will become more intelligent than humans, and so on. But the claims are science fiction, not science. This article reviews evidence for the following three propositions using extensive body of scientific research and related sources from the cognitive and neurosciences, evolutionary evidence, linguistics, data science, comparative psychology, self-driving cars, robotics. and the learning sciences. (1) Do computing machines think or reason? (2) Are computing machines sentient or conscious? (3) Do computing machines have a theory of mind?
Automated Web-Based Malaria Detection System with Machine Learning and Deep Learning Techniques: A Comparative Analysis
Authors: Abraham Genetu Taye, Sador Yonas Yemane, Melkamu Hunegnaw Asmare
Date: 2024-06-27 00:00:00
Tasks: Medical Image Classification, Transfer Learning

Malaria parasites pose a significant global health burden, causing widespread suffering and mortality. Detecting malaria infection accurately is crucial for effective treatment and control. However, existing automated detection techniques have shown limitations in terms of accuracy and generalizability. Many studies have focused on specific features without exploring more comprehensive approaches. In our case, we formulate a deep learning technique for malaria infected cell classification using traditional CNNs and transfer learning models notably VGG19, InceptionV3, and Xception. The models were trained using NIH datasets and tested using different performance metrics such as accuracy, precision, re- call, and F1-score. The test results showed that deep CNNs achieved the highest accuracy - 97%, followed by Xception with an accuracy of 95%. A machine learning model SVM achieved an accuracy of 83%, while an Inception-V3 achieved an accuracy of 94%. Furthermore, the system can be accessed through a web interface, 
where users can upload blood smear images for malaria detection
Automated Web-Based Malaria Detection System with Machine Learning and Deep Learning Techniques
Authors: Abraham G Taye, Sador Yemane, Eshetu Negash, Yared Minwuyelet, Moges Abebe, Melkamu Hunegnaw Asmare
Date: 2024-06-27 00:00:00
Tasks: Transfer Learning

Malaria parasites pose a significant global health burden, causing widespread suffering and mortality. Detecting malaria infection accurately is crucial for effective treatment and control. However, existing automated detection techniques have shown limitations in terms of accuracy and generalizability. Many studies have focused on specific features without exploring more comprehensive approaches. In our case, we formulate a deep learning technique for malaria-infected cell classification using traditional CNNs and transfer learning models notably VGG19, InceptionV3, and Xception. The models were trained using NIH datasets and tested using different performance metrics such as accuracy, precision, recall, and F1-score. The test results showed that deep CNNs achieved the highest accuracy -- 97%, followed by Xception with an accuracy of 95%. A machine learning model SVM achieved an accuracy of 83%, while an Inception-V3 achieved an accuracy of 94%. Furthermore, the system can be accessed through a web interface, where users can upload blood smear images for malaria detection.
ECG signal processing and feature extraction to validate feature significance for arrythmia detection
Authors: John M. De Moura, Ana L. Espinoza, Maria A. Flores, Juan A. Zavaleta
Date: 2024-07-04 00:00:00
Proceeding: - 2024 7
Tasks: Arrhythmia Detection

Arrhythmias, such as tachycardia and bradycardia, are prevalent in postoperative patients, especially within the first week after surgery. These conditions can lead to significant health risks, particularly in settings with inadequate monitoring resources, such as rural areas in Peru. This paper proposes an advanced approach to arrhythmia detection using ECG signal processing and feature extraction to feed artificial intelligence (AI) models. By optimizing training data and comparing ECG signal features, this method aims to enhance the accuracy and efficiency of arrhythmia identification, thereby improving patient monitoring and reducing associated health risks.
Analysis of Ergonomic Risk in University Study Environments During Class Days
Date: 2024-07-04 00:00:00
Proceeding: Universidad Peruana Cayetano Heredia 2024 7

This study investigates the ergonomic impact of university seating on students’ musculoskeletal health, focusing on lumbar muscle fatigue during prolonged study sessions. Using surface electromyography (sEMG) with BiTalino devices, the research compared muscle activation in ergonomic and non- ergonomic chairs over three-hour periods. The methodology in-volved signal processing techniques including Wavelet Daubechies 6 filtering, normalization, and RMS value extraction. Results suggest that non-ergonomic seating may be associated with increased lumbar stress, while ergonomic chairs showed no signs of muscle fatigue. However, the study faced limitations due to interruptions and uncontrolled environmental factors. The findings highlight the potential benefits of ergonomic seating in academic settings and underscore the need for further research under more controlled conditions to definitively establish the relationship between seating ergonomics and musculoskeletal disorders among university students.
FeedForward at SemEval-2024 Task 10: Trigger and sentext-height enriched emotion analysis in multi-party conversations
Authors: Zuhair Hasan Shaik, Dhivya Prasanna, Enduri Jahnavi, Rishi Thippireddy, Vamsi Madhav, Sunil Saumya, Shankar Biradar
Date: 2024-06-20 00:00:00
Proceeding: SemEval workshop at NAACL 2024 6
Tasks: Emotion Recognition, Emotion Recognition in Conversation, Navigate

This paper reports on an innovative approach to Emotion Recognition in Conversation and Emotion Flip Reasoning for the SemEval-2024 competition with a specific focus on analyzing Hindi-English code-mixed language. By integrating Large Language Models (LLMs) with Instruction-based Fine-tuning and Quantized Low-Rank Adaptation (QLoRA), this study introduces innovative techniques like Sentext-height and advanced prompting strategies to navigate the intricacies of emotional analysis in code-mixed conversational data. The results of the proposed work effectively demonstrate its ability to overcome label bias and the complexities of code-mixed languages. Our team achieved ranks of 5, 3, and 3 in tasks 1, 2, and 3 respectively. This study contributes valuable insights and methods for enhancing emotion recognition models, underscoring the importance of continuous research in this field.
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective
Authors: Chenyu You, Weicheng Dai, Yifei Min, Fenglin Liu, David A. Clifton, S Kevin Zhou, Lawrence Hamilton Staib, James S Duncan
Date: 2023-02-03 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Contrastive Learning, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, Semi-supervised Medical Image Segmentation

For medical image segmentation, contrastive learning is the dominant practice to improve the quality of visual representations by contrasting semantically similar and dissimilar pairs of samples. This is enabled by the observation that without accessing ground truth labels, negative examples with truly dissimilar anatomical features, if sampled, can significantly improve the performance. In reality, however, these samples may come from similar anatomical regions and the models may struggle to distinguish the minority tail-class samples, making the tail classes more prone to misclassification, both of which typically lead to model collapse. In this paper, we propose ARCO, a semi-supervised contrastive learning (CL) framework with stratified group theory for medical image segmentation. In particular, we first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks with extremely limited labels. Furthermore, we theoretically prove these sampling techniques are universal in variance reduction. Finally, we experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings, and our methods consistently outperform state-of-the-art semi-supervised methods. Additionally, we augment the CL frameworks with these sampling techniques and demonstrate significant gains over previous methods. We believe our work is an important step towards semi-supervised medical image segmentation by quantifying the limitation of current self-supervision objectives for accomplishing such challenging safety-critical tasks.
Anomaly based network intrusion detection for IoT attacks using deep learning technique
Authors: Bhawana Sharma a, Lokesh Sharma a, Chhagan Lal b, Satyabrata Roy a
Date: 2023-02-16 00:00:00
Proceeding: Elsevier 2023 2
Tasks: feature selection, Intrusion Detection, Network Intrusion Detection

Internet of Things (IoT) applications are growing in popularity for being widely used in many
real-world services. In an IoT ecosystem, many devices are connected with each other via
internet, making IoT networks more vulnerable to various types of cyber attacks, thus a major
concern in its deployment is network security and user privacy. To protect IoT networks
against various attacks, an efficient and practical Intrusion Detection System (IDS) could be
an effective solution. In this paper, a novel anomaly-based IDS system for IoT networks is
proposed using Deep Learning technique. Particularly, a filter-based feature selection Deep
Neural Network (DNN) model where highly correlated features are dropped has been presented.
Further, the model is tuned with various parameters and hyper parameters. The UNSW-NB15
dataset comprising of four attack classes is utilized for this purpose. The proposed model
achieved an accuracy of 84%. Generative Adversarial Networks (GANs) were used to generate
synthetic data of minority attacks to resolve class imbalance issues in the dataset and achieved
91% accuracy with balanced class dataset.