pwc-archive/papers-with-abstracts huggingface dataset:
Snippet-based Conversational Recommender System
Authors: Haibo Sun, Naoki Otani, Hannah Kim, Dan Zhang, Nikita Bhutani
Date: 2024-11-09 00:00:00
Tasks: Recommendation Systems, Sentence

Conversational Recommender Systems (CRS) engage users in interactive dialogues to gather preferences and provide personalized recommendations. While existing studies have advanced conversational strategies, they often rely on predefined attributes or expensive, domain-specific annotated datasets, which limits their flexibility in handling diverse user preferences and adaptability across domains. We propose SnipRec, a novel resource-efficient approach that leverages user-generated content, such as customer reviews, to capture a broader range of user expressions. By employing large language models to map reviews and user responses into concise snippets, SnipRec represents user preferences and retrieves relevant items without the need for intensive manual data collection or fine-tuning. Experiments across the restaurant, book, and clothing domains show that snippet-based representations outperform document- and sentence-based representations, achieving Hits@10 of 0.25-0.55 with 3,000 to 10,000 candidate items while successfully handling free-form user responses.
Reinforcement learning for Quantum Tiq-Taq-Toe
Authors: Catalin-Viorel Dinu, Thomas Moerland
Date: 2024-11-10 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Quantum Tiq-Taq-Toe is a well-known benchmark and playground for both quantum computing and machine learning. Despite its popularity, no reinforcement learning (RL) methods have been applied to Quantum Tiq-Taq-Toe. Although there has been some research on Quantum Chess this game is significantly more complex in terms of computation and analysis. Therefore, we study the combination of quantum computing and reinforcement learning in Quantum Tiq-Taq-Toe, which may serve as an accessible testbed for the integration of both fields. Quantum games are challenging to represent classically due to their inherent partial observability and the potential for exponential state complexity. In Quantum Tiq-Taq-Toe, states are observed through Measurement (a 3x3 matrix of state probabilities) and Move History (a 9x9 matrix of entanglement relations), making strategy complex as each move can collapse the quantum state.
An asymmetric heuristic for trained ternary quantization based on the statistics of the weights: an application to medical signal classification
Authors: Yamil Vindas, Emmanuel Roux, Blaise Kévin Guépié, Marilys Almar, Philippe Delachartre
Date: 2024-11-09 00:00:00
Proceeding: Pattern Recognition Letters 2024 11
Tasks: Quantization

One of the main challenges in the field of deep learning and embedded systems is the mismatch between the memory, computational and energy resources required by the former for good performance and the resource capabilities offered by the latter. It is therefore important to find a good trade-off between performance and computational resources used.
In this study, we propose a novel ternarization heuristic based on the statistics of the weights, in addition to asymmetric pruning. Our approach involves the computation of two asymmetric thresholds based on the mean and standard deviation of the weights. This
allows us to distinguish between positive and negative values prior to ternarization. Two hyperparameters are introduced into these thresholds, which permit the user to control the trade-off between compression and classification performance. Following thresholding, ternarization is carried out in accordance with the methodology of trained ternary quantisation (TTQ). 
The efficacy of the method is evaluated on three datasets, two of which are medical: a cerebral emboli (HITS) dataset, an epileptic seizure recognition (ESR) dataset, and the MNIST dataset. Two types of deep learning models were tested: 2D convolutional neural networks (CNNs) and 1D CNN-transformers. The results demonstrate that our approach, aTTQ, achieves a superior trade-off between classification performance and compression rate compared with TTQ, for all the models and datasets. In fact, our method is capable of reducing the memory requirements of a 1D CNN-transformer model for the ESR dataset by over 21% compared to TTQ, while maintaining a Matthews correlation coefficient of 95%.
Ismail Abdulmuiz Opeyemi
Authors: Ismail Abdulmuiz Opeyemi
Date: 2024-11-12 00:00:00
Proceeding: Ismail, M. 2024 11

Ismail Abdulmuiz Opeyemi is a member of FHG Org and a professional digital marketer focused on Google knowledge panels creation and social media promotion.



Ismail was born on 21st March 2008 in Lagos, Nigeria, to Mr. and Mrs. Ismail. He spent the first 12 years of his life in Lagos with his mother and siblings.

He was first admitted to John Kennedy International School, where he attended from kindergarten to Primary 4. He was later transferred to Comfort Nursery & Primary School, where he obtained his primary education before being admitted into Government College Lagos. He then attended the above-mentioned school up until JSS 2, after which he relocated with his family members from Lagos to Ilorin. He attended Al-Hadi College, where he did his junior secondary school at Ilorin. He later took his WAEC at Alapa from April 30 to June 24, 2024, with great results. After graduation, Ismail began an apprenticeship in tailoring; he spent close to a year learning the craft. It was during this time that he came into contact with a dual business opportunity that FHG Org allowed him to partake in, after which he has since become a promising career holder.
Optimising Neural Fractional Differential Equations for Performance and Efficiency
Authors: Bernd Zimmering, Cecília Coelho, Oliver Niggemann
Date: 2024-10-20 00:00:00
Proceeding: PMLR Volume 255: 1st ECAI Workshop on “Machine Learning Meets Differential Equations: From Theory to Applications” 2024 10
Tasks: Experimental Design

Neural Ordinary Differential Equations (NODEs) are well-established architectures that fit an ODE, modelled by a neural network (NN), to data, effectively modelling complex dynamical systems. Recently, Neural Fractional Differential Equations (NFDEs) were proposed, inspired by NODEs, to incorporate non-integer order differential equations, capturing memory effects and long-range dependencies. In this work, we present an optimised implementation of the NFDE solver, achieving up to 570 times faster computations and up to 79 times higher accuracy. Additionally, the solver supports efficient multidimensional computations and batch processing. Furthermore, we enhance the experimental design to ensure a fair comparison of NODEs and NFDEs by implementing rigorous hyperparameter tuning and using consistent numerical methods. Our results demonstrate that for systems exhibiting fractional dynamics, NFDEs significantly outperform NODEs, particularly in extrapolation tasks on unseen time horizons. Although NODEs can learn fractional dynamics when time is included as a feature to the NN, they encounter difficulties in extrapolation due to reliance on explicit time dependence. The code is available at https://github.com/zimmer-ing/Neural-FDE
Odyssey 2024 - Speech Emotion Recognition Challenge: Dataset, Baseline Framework, and Results
Authors: Lucas Goncalves, Ali N. Salman, Abinay R. Naini, Laureano Moro Velazquez, Thomas Thebaud, Leibny Paola Garcia, Najim Dehak, Berrak Sisman, Carlos Busso
Date: 2024-06-20 00:00:00
Proceeding: Odyssey: The Speaker and Language Recognition Workshop 2024 6
Tasks: Attribute, Emotion Recognition, Speech Emotion Recognition

The Odyssey 2024 Speech Emotion Recognition (SER) Challenge aims to enhance innovation in recognizing emotions from spontaneous speech, moving beyond traditional datasets derived from acted scenarios. It offers speaker-independent training, development, and an exclusive test set, all annotated for the two tracks explored in this challenge: categorical and attribute SER tasks. This initiative promotes collaboration among researchers to develop SER technologies that perform accurately in real-world settings, encouraging researchers to explore innovative approaches that leverage the latest advancements in audio processing for SER. In this paper, we provide a detailed description of the baseline, leaderboard, evaluation of the results, and a discussion of the key findings. The competition website with leaderboards, links to baseline code, and instructions can be found here: https://lab-msp.com/MSP-Podcast_Competition/leaderboard.php
Joint Mixing Data Augmentation for Skeleton-based Action Recognition
Authors: Linhua Xiang, Zengfu Wang
Date: 2024-10-13 00:00:00
Proceeding: ACM Transactions on Multimedia Computing, Communications, and Applications 2024 10
Tasks: Action Recognition, Data Augmentation, Skeleton Based Action Recognition

Skeleton-based action recognition is beneficial for understanding human behavior in videos, and thus has received much attention in recent years as an important research area in action recognition. Current research focuses on designing more advanced algorithms to better extract spatio-temporal information from skeleton data. However, due to the small amount of data in the existing skeleton dataset and the lack of effective data augmentation methods, it is easy to lead to overfitting in model training. To address this challenge, we propose a mix-based data augmentation method, Joint Mixing Data Augmentation (JMDA), which can generally improve the effectiveness and robustness of various skeleton-based action recognition algorithms. In terms of spatial information, we introduce SpatialMix (SM), a method that projects the original 3D skeleton discrete information into a 2D space. Then, SM mixes the projected spatial information between two random samples during the training process to achieve the spatial-based mixing data augmentation. Concerning temporal information, we propose TemporalMix (TM). Leveraging the temporal continuity in skeleton data, we perform a temporal resize operation on the original skeleton data, and then merge two random samples during training to achieve the temporal-based mixed data augmentation. Additionally, we analyze the Feature Mismatch (FM) problem caused by introducing mix-based data augmentation into skeleton data. Then we propose a new data preprocessing method called Feature Alignment (FA) to effectively address this problem and improve model performance. Moreover, we propose a novel training pipeline, Joint Training Strategy (JTS), which combines multiple mix-based data augmentation methods for further improvement of model performance. Specifically, our proposed JMDA is plug-and-play and widely applicable to skeleton-based action recognition models. At the same time, the application of JMDA does not increase the model parameters and there is almost no additional training cost. We conduct extensive experiments on NTU RGB+D 60 and NTU RGB+D 120 datasets to demonstrate the effectiveness and robustness of the proposed JMDA on several mainstream skeleton-based action recognition algorithms.
Flexible Natural Language-Based Image Data Downlink Prioritization for Nanosatellites
Authors: Ezra Fielding, Akitoshi Hanazawa
Date: 2024-10-28 00:00:00
Proceeding: Aerospace 2024 10
Tasks: Natural Language Inference

Nanosatellites increasingly produce more data than can be downlinked within a reasonable time due to their limited bandwidth and power. Therefore, an on-board system is required to prioritize scientifically significant data for downlinking, as described by scientists. This paper determines whether natural language processing can be used to prioritize remote sensing images on CubeSats with more flexibility compared to existing methods. Two approaches implementing the same conceptual prioritization pipeline are compared. The first uses YOLOv8 and Llama2 to extract image features and compare them with text descriptions via cosine similarity. The second approach employs CLIP, fine-tuned on remote sensing data, to achieve the same. Both approaches are evaluated on real nanosatellite hardware, the VERTECS Camera Control Board. The CLIP approach, particularly the ResNet50-based model, shows the best performance in prioritizing and sequencing remote sensing images. This paper demonstrates that on-orbit prioritization using natural language descriptions is viable and allows for more flexibility than existing methods.
Semantic segmentation of crop type in Africa: A novel dataset and analysis of deep learning methods
Authors: Rose Rustowicz, Robin Cheong, Lijing Wang, Stefano Ermon, Marshall Burke, David Lobell
Date: 2019-06-01 00:00:00
Proceeding: CVPR 2019 6
Tasks: Crop Type Mapping, Flood extent forecasting, Semantic Segmentation

Automatic, accurate crop type maps can provide unprecedented information for understanding food systems, especially in developing countries where ground surveys are infrequent. However, little work has applied existing methods to these data scarce environments, which also have unique challenges of irregularly shaped fields, frequent cloud coverage, small plots, and a severe lack of training data. To address this gap in the literature, we provide the first crop type semantic segmentation dataset of small holder farms, specifically in Ghana and South Sudan. We are also the first to utilize high resolution, high frequency satellite data in segmenting small holder farms. Despite the challenges, we achieve an average F1 score and overall accuracy of 57.3 and 60.9% in Ghana and 69.7 and 85.3% in South Sudan. Additionally, our approach outperforms the state-of-the-art method in a data-rich setting of Germany by over 8 points in F1 and 6 points in accuracy. Code and a link to the dataset are publicly available at https://github.com/roserustowicz/crop-type-mapping.
Madera Contractors
Authors: Madera Contractors
Date: 2024-11-12 00:00:00
Proceeding: 11/12 2024 11

Early Life
Madera Contractors: Trusted Commercial Cleaning Experts in Ottawa Since 2010 – Providing Quality, Reliable, and Eco-Friendly Cleaning Solutions for Your Business.

Career

About Madera Contractors

Madera Contractors was founded in 2010 in Ottawa, Ontario, and since then, we have become one of the city’s trusted commercial cleaning companies. We offer high-quality cleaning services to businesses of all sizes, ensuring that every space we clean is not only tidy but also safe and healthy.

Our services include generaloffice cleaning, deep cleaning, floor care, window washing, and more. Whether you run an office building, a medical facility, a school, or a retail store, we can create a cleaning plan that fits your needs and schedule. We believe in doing the job right every time. Our team is fully trained, insured, and experienced in providing the best cleaning services. We use the latest cleaning techniques and eco-friendly products to make sure your space is clean and safe for everyone who uses it.

We understand that each business has different needs, so we offer customized cleaning solutions. Whether you need daily, weekly, or one-time cleaning, we’ll work with you to make sure we meet your needs and budget.

At Madera Contractors, we care about more than just cleaning. We want to create a cleaner, healthier environment for you, your employees, and your customers. We are also committed to helping the environment by using green cleaning products and methods that reduce our impact on the planet.

As a local business, we are proud to serve the Ottawa community. Over the years, we have built a reputation for being reliable, professional, and trustworthy. We treat each client with respect and make sure every job is done to the highest standard.

If you want to learn more about how Madera Contractors can help you keep your business clean and healthy, visit our website at www.maderacontractors.weebly.com. We look forward to working with you!
Job Title Prediction from Tweets Using Word Embedding and Deep Neural Networks
Authors: Shayan Vassef, Ramin Toosi, Mohammad Ali Akhaee
Date: 2022-07-20 00:00:00
Proceeding: ICEE/IEEE 2022 7
Tasks: Job Prediction, Prediction, Twitter Event Detection, Wikipedia Summarization

The more social media take its place in our lives; the more critical their analysis becomes and the more researchers’ attention is drawn to it. Studies contain various topics such as sentiment analysis, trend prediction, bot detection, Etc. Here, for the first time, we propose a novel method to predict the job title of social media users. Twitter, a popular social media, is our target social media. We introduce a dataset consisting of 1314 samples, including users’ tweets and bios. The user’s job title is found using Wikipedia crawling. The challenge of multiple job titles per user is handled using a semantic word embedding and clustering method. Then, a job prediction method is introduced based on a deep neural network and TF-IDF word embedding. We also use hashtags and emojis in the tweets for job prediction. Results show that the job title of users in Twitter could be well predicted with 54% accuracy in nine categories.
Privacy-Preserving Verifiable Neural Network Inference Service
Authors: Arman Riasi, Jorge Guajardo, Thang Hoang
Date: 2024-11-12 00:00:00
Tasks: Privacy Preserving

Machine learning has revolutionized data analysis and pattern recognition, but its resource-intensive training has limited accessibility. Machine Learning as a Service (MLaaS) simplifies this by enabling users to delegate their data samples to an MLaaS provider and obtain the inference result using a pre-trained model. Despite its convenience, leveraging MLaaS poses significant privacy and reliability concerns to the client. Specifically, sensitive information from the client inquiry data can be leaked to an adversarial MLaaS provider. Meanwhile, the lack of a verifiability guarantee can potentially result in biased inference results or even unfair payment issues. While existing trustworthy machine learning techniques, such as those relying on verifiable computation or secure computation, offer solutions to privacy and reliability concerns, they fall short of simultaneously protecting the privacy of client data and providing provable inference verifiability. In this paper, we propose vPIN, a privacy-preserving and verifiable CNN inference scheme that preserves privacy for client data samples while ensuring verifiability for the inference. vPIN makes use of partial homomorphic encryption and commit-and-prove succinct non-interactive argument of knowledge techniques to achieve desirable security properties. In vPIN, we develop various optimization techniques to minimize the proving circuit for homomorphic inference evaluation thereby, improving the efficiency and performance of our technique. We fully implemented and evaluated our vPIN scheme on standard datasets (e.g., MNIST, CIFAR-10). Our experimental results show that vPIN achieves high efficiency in terms of proving time, verification time, and proof size, while providing client data privacy guarantees and provable verifiability.
Over-parameterized Student Model via Tensor Decomposition Boosted Knowledge Distillation
Authors: Yu-Liang Zhan, Zhong-Yi Lu, Hao Sun, Ze-Feng Gao
Date: 2024-11-10 00:00:00
Tasks: Knowledge Distillation, Tensor Decomposition

Increased training parameters have enabled large pre-trained models to excel in various downstream tasks. Nevertheless, the extensive computational requirements associated with these models hinder their widespread adoption within the community. We focus on Knowledge Distillation (KD), where a compact student model is trained to mimic a larger teacher model, facilitating the transfer of knowledge of large models. In contrast to much of the previous work, we scale up the parameters of the student model during training, to benefit from overparameterization without increasing the inference latency. In particular, we propose a tensor decomposition strategy that effectively over-parameterizes the relatively small student model through an efficient and nearly lossless decomposition of its parameter matrices into higher-dimensional tensors. To ensure efficiency, we further introduce a tensor constraint loss to align the high-dimensional tensors between the student and teacher models. Comprehensive experiments validate the significant performance enhancement by our approach in various KD tasks, covering computer vision and natural language processing areas. Our code is available at https://github.com/intell-sci-comput/OPDF.
Automated Strategy Invention for Confluence of Term Rewrite Systems
Authors: Liao Zhang, Fabian Mitterwallner, Jan Jakubuv, Cezary Kaliszyk
Date: 2024-11-10 00:00:00
Tasks: Compiler Optimization

Term rewriting plays a crucial role in software verification and compiler optimization. With dozens of highly parameterizable techniques developed to prove various system properties, automatic term rewriting tools work in an extensive parameter space. This complexity exceeds human capacity for parameter selection, motivating an investigation into automated strategy invention. In this paper, we focus on confluence, an important property of term rewrite systems, and apply machine learning to develop the first learning-guided automatic confluence prover. Moreover, we randomly generate a large dataset to analyze confluence for term rewrite systems. Our results focus on improving the state-of-the-art automatic confluence prover CSI: When equipped with our invented strategies, it surpasses its human-designed strategies both on the augmented dataset and on the original human-created benchmark dataset Cops, proving/disproving the confluence of several term rewrite systems for which no automated proofs were known before.
Mastering NIM and Impartial Games with Weak Neural Networks: An AlphaZero-inspired Multi-Frame Approach
Authors: Søren Riis
Date: 2024-11-10 00:00:00
Tasks: Decision Making

This paper provides a theoretical framework that validates and explains the results in the work with Bei Zhou experimentally finding that AlphaZero-style reinforcement learning algorithms struggle to learn optimal play in NIM, a canonical impartial game proposed as an AI challenge by Harvey Friedman in 2017. Our analysis resolves a controversy around these experimental results, which revealed unexpected difficulties in learning NIM despite its mathematical simplicity compared to games like chess and Go. Our key contributions are as follows: We prove that by incorporating recent game history, these limited AlphaZero models can, in principle, achieve optimal play in NIM. We introduce a novel search strategy where roll-outs preserve game-theoretic values during move selection, guided by a specialised policy network. We provide constructive proofs showing that our approach enables optimal play within the \(\text{AC}^0\) complexity class despite the theoretical limitations of these networks. This research demonstrates how constrained neural networks when properly designed, can achieve sophisticated decision-making even in domains where their basic computational capabilities appear insufficient.