pwc-archive/papers-with-abstracts huggingface dataset:
Preference and Concurrence Aware Bayesian Graph Neural Networks for Recommender Systems
Authors: Hongjian Gu, Yaochen Hu, Yingxue Zhang
Date: 2023-11-30 00:00:00
Tasks: Collaborative Filtering, Graph Neural Network, Recommendation Systems

Graph-based collaborative filtering methods have prevailing performance for recommender systems since they can capture high-order information between users and items, in which the graphs are constructed from the observed user-item interactions that might miss links or contain spurious positive interactions in industrial scenarios. The Bayesian Graph Neural Network framework approaches this issue with generative models for the interaction graphs. The critical problem is to devise a proper family of graph generative models tailored to recommender systems. We propose an efficient generative model that jointly considers the preferences of users, the concurrence of items and some important graph structure information. Experiments on four popular benchmark datasets demonstrate the effectiveness of our proposed graph generative methods for recommender systems.
Fuzzy Deep Hybrid Network for Fake News Detection
Authors: Cheng Xu, M-Tahar Kechadi
Date: 2023-12-07 00:00:00
Proceeding: Proceedings of the 12th International Symposium on Information and Communication Technology 2023 12
Tasks: Articles, Deep Learning, Fact Checking, Fake News Detection

The proliferation of fake news in the digital age poses a significant threat to the democratic process and undermines trust in the media. As disinformation campaigns become more sophisticated and pervasive, it has become increasingly challenging to discern credible news sources from deceptive ones. Machine learning and deep learning techniques have shown promise in automatically detecting fake news, but there is still room for improvement. In this paper, we propose an innovative fuzzy logic-based hybrid model to improve the performance of fake news detection. The model leverages a combination of news articles and textual and numerical context information. We evaluate our proposed model on a fact-checking benchmark dataset and achieve state-of-the-art results. Our findings suggest that combining fuzzy logic with deep learning can improve fake news detection and provide a reliable tool for combatting disinformation. The code is available at https://github.com/chengxuphd/FDHN
COMMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation
Authors: WooSeok Shin, Min Seok Lee, Sung Won Han
Date: 2022-02-17 00:00:00
Proceeding: Applied Sciences 2022 2
Tasks: Medical Image Segmentation, Segmentation

Colonoscopy is an effective method for detecting polyps to prevent colon cancer. Existing studies have achieved satisfactory polyp detection performance by aggregating low-level boundary and high-level region information in convolutional neural networks (CNNs) for precise polyp segmentation in colonoscopy images. However, multi-level aggregation provides limited polyp segmentation owing to the distribution discrepancy that occurs when integrating different layer representations. To address this problem, previous studies have employed complementary low- and high-level representations. In contrast to existing methods, we focus on propagating complementary information such that the complementary low-level explicit boundary with abstracted high-level representations diminishes the discrepancy. This study proposes COMMA, which propagates complementary multi-level aggregation to reduce distribution discrepancies. COMMA comprises a complementary masking module (CMM) and a boundary propagation module (BPM) as a multi-decoder. The CMM masks the low-level boundary noises through the abstracted high-level representation and leverages the masked information at both levels. Similarly, the BPM incorporates the lowest- and highest-level representations to obtain explicit boundary information and propagates the boundary to the CMMs to improve polyp detection. CMMs can discriminate polyps more elaborately than prior CMMs based on boundary and complementary representations. Moreover, we propose a hybrid loss function to mitigate class imbalance and noisy annotations in polyp segmentation. To evaluate the COMMA performance, we conducted experiments on five benchmark datasets using five metrics. The results proved that the proposed network outperforms state-of-the-art methods in terms of all datasets. Specifically, COMMA improved mIoU performance by 0.043 on average for all datasets compared to the existing state-of-the-art methods.
Rethinking Transfer and Auxiliary Learning for Improving Audio Captioning Transformer
Authors: WooSeok Shin, Hyun Joon Park, Jin Sob Kim, Dongwon Kim, Seungjin Lee, Sung Won Han
Date: 2023-08-20 00:00:00
Proceeding: Interspeech 2023 8
Tasks: AudioCaps, Audio captioning, Auxiliary Learning, Transfer Learning

The performance of automated audio captioning (AAC) has been improved considerably through a transformer-based encoder and transfer learning. However, their performance improvement is constrained by the following problems: (1) discrepancy in the input patch size between pretraining and fine-tuning steps. (2) lack of local-level relations between inputs and captions. In this paper, we propose a simple transfer learning scheme that maintains input patch sizes, unlike previous methods, to avoid input discrepancies. Furthermore, we propose a patch-wise keyword estimation branch that utilizes an attention pooling method to effectively represent both global- and local-level information. The results on the AudioCaps dataset reveal that the proposed learning scheme and method considerably contribute to performance gain. Finally, the visualization results demonstrate that the proposed attention-pooling method effectively detects local-level information in the AAC system.
Assessing SATNet's Ability to Solve the Symbol Grounding Problem
Authors: Oscar Chang, Lampros Flokas, Hod Lipson, Michael Spranger
Date: 2023-12-13 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Logical Reasoning

SATNet is an award-winning MAXSAT solver that can be used to infer logical rules and integrated as a differentiable layer in a deep neural network. It had been shown to solve Sudoku puzzles visually from examples of puzzle digit images, and was heralded as an impressive achievement towards the longstanding AI goal of combining pattern recognition with logical reasoning. In this paper, we clarify SATNet's capabilities by showing that in the absence of intermediate labels that identify individual Sudoku digit images with their logical representations, SATNet completely fails at visual Sudoku (0% test accuracy). More generally, the failure can be pinpointed to its inability to learn to assign symbols to perceptual phenomena, also known as the symbol grounding problem, which has long been thought to be a prerequisite for intelligent agents to perform real-world logical reasoning. We propose an MNIST based test as an easy instance of the symbol grounding problem that can serve as a sanity check for differentiable symbolic solvers in general. Naive applications of SATNet on this test lead to performance worse than that of models without logical reasoning capabilities. We report on the causes of SATNet's failure and how to prevent them.
A Simulated Annealing-Based Multiobjective Optimization Algorithm for Minimum Weight Minimum Connected Dominating Set Problem
Authors: Hayet Dahmri, Salim Bouamama
Date: 2023-12-13 00:00:00
Tasks: Combinatorial Optimization, Multiobjective Optimization

Minimum connected dominating set problem is an NP-hard combinatorial optimization problem in graph theory. Finding connected dominating set is of high interest in various domains such as wireless sensor networks, optical networks, and systems biology. Its weighted variant named minimum weight connected dominating set is also useful in such applications. In this paper, we propose a simulated annealing algorithm based on a greedy heuristic for tackling a variant of the minimum connected dominating set problem and that by exploiting two objectives together namely the cardinality and the total weight of the connected dominating set. Experimental results compared to those obtained by a recent proposed research show the superiority of our approach.
Visual AI and Linguistic Intelligence Through Steerability and Composability
Authors: David Noever, Samantha Elizabeth Miller Noever
Date: 2023-11-18 00:00:00

This study explores the capabilities of multimodal large language models (LLMs) in handling challenging multistep tasks that integrate language and vision, focusing on model steerability, composability, and the application of long-term memory and context understanding. The problem addressed is the LLM's ability (Nov 2023 GPT-4 Vision Preview) to manage tasks that require synthesizing visual and textual information, especially where stepwise instructions and sequential logic are paramount. The research presents a series of 14 creatively and constructively diverse tasks, ranging from AI Lego Designing to AI Satellite Image Analysis, designed to test the limits of current LLMs in contexts that previously proved difficult without extensive memory and contextual understanding. Key findings from evaluating 800 guided dialogs include notable disparities in task completion difficulty. For instance, 'Image to Ingredient AI Bartender' (Low difficulty) contrasted sharply with 'AI Game Self-Player' (High difficulty), highlighting the LLM's varying proficiency in processing complex visual data and generating coherent instructions. Tasks such as 'AI Genetic Programmer' and 'AI Negotiator' showed high completion difficulty, emphasizing challenges in maintaining context over multiple steps. The results underscore the importance of developing LLMs that combine long-term memory and contextual awareness to mimic human-like thought processes in complex problem-solving scenarios.
Synthetic Shifts to Initial Seed Vector Exposes the Brittle Nature of Latent-Based Diffusion Models
Authors: Mao Po-Yuan, Shashank Kotyan, Tham Yik Foong, Danilo Vasconcellos Vargas
Date: 2023-11-24 00:00:00
Tasks: Image Generation

Recent advances in Conditional Diffusion Models have led to substantial capabilities in various domains. However, understanding the impact of variations in the initial seed vector remains an underexplored area of concern. Particularly, latent-based diffusion models display inconsistencies in image generation under standard conditions when initialized with suboptimal initial seed vectors. To understand the impact of the initial seed vector on generated samples, we propose a reliability evaluation framework that evaluates the generated samples of a diffusion model when the initial seed vector is subjected to various synthetic shifts. Our results indicate that slight manipulations to the initial seed vector of the state-of-the-art Stable Diffusion (Rombach et al., 2022) can lead to significant disturbances in the generated samples, consequently creating images without the effect of conditioning variables. In contrast, GLIDE (Nichol et al., 2022) stands out in generating reliable samples even when the initial seed vector is transformed. Thus, our study sheds light on the importance of the selection and the impact of the initial seed vector in the latent-based diffusion model.
A Hybrid SOM and K-means Model for Time Series Energy Consumption Clustering
Authors: Farideh Majidi
Date: 2023-11-25 00:00:00
Tasks: Clustering, Management, Time Series

Energy consumption analysis plays a pivotal role in addressing the challenges of sustainability and resource management. This paper introduces a novel approach to effectively cluster monthly energy consumption patterns by integrating two powerful techniques: Self-organizing maps and K-means clustering. The proposed method aims to exploit the benefits of both of these algorithms to enhance the accuracy and interpretability of clustering results for a dataset in which finding patterns is difficult. The main focus of this study is on a selection of time series energy consumption data from the Smart meters in London dataset. The data was preprocessed and reduced in dimensionality to capture essential temporal patterns while retaining their underlying structures. The SOM algorithm was utilized to extract the central representatives of the consumption patterns for each one of the houses over the course of each month, effectively reducing the dimensionality of the dataset and making it easier for analysis. Subsequently, the obtained SOM centroids were clustered using K-means, a popular centroid-based clustering technique. The experimental results demonstrated a significant silhouette score of 66%, indicating strong intra-cluster cohesion and inter-cluster separation which confirms the effectiveness of the proposed approach in the clustering task.
Low-Latency ML Inference by Grouping Correlated Data Objects and Computation
Authors: Thiago Garrett, Weijia Song, Roman Vitenberg, Ken Birman
Date: 2023-11-30 00:00:00
Tasks: Management, Scheduling

ML inference workflows often require low latency and high throughput, yet we lack good options for addressing this need. Techniques that reduce latency in other streaming settings (such as caching and optimization-driven scheduling) are of limited value because ML data dependencies are often very large and can change dramatically depending on the triggering event. In this work, we propose a novel correlation grouping mechanism that makes it easier for developers to express application-specific data access correlations, enabling coordinated management of data objects in server clusters hosting streaming inference tasks. Experiments based on a latency-sensitive ML-based application confirm the limitations of standard techniques while showing that our solution yields dramatically better performance. The proposed mechanism is able to maintain significantly lower and more consistent latency, achieves higher node utilization as workload and scale-out increase, and yet requires only minor changes to the code implementing the application.
A Red Teaming Framework for Securing AI in Maritime Autonomous Systems
Authors: Mathew J. Walter, Aaron Barrett, Kimberly Tam
Date: 2023-12-08 00:00:00
Tasks: Red Teaming

Artificial intelligence (AI) is being ubiquitously adopted to automate processes in science and industry. However, due to its often intricate and opaque nature, AI has been shown to possess inherent vulnerabilities which can be maliciously exploited with adversarial AI, potentially putting AI users and developers at both cyber and physical risk. In addition, there is insufficient comprehension of the real-world effects of adversarial AI and an inadequacy of AI security examinations; therefore, the growing threat landscape is unknown for many AI solutions. To mitigate this issue, we propose one of the first red team frameworks for evaluating the AI security of maritime autonomous systems. The framework provides operators with a proactive (secure by design) and reactive (post-deployment evaluation) response to securing AI technology today and in the future. This framework is a multi-part checklist, which can be tailored to different systems and requirements. We demonstrate this framework to be highly effective for a red team to use to uncover numerous vulnerabilities within a real-world maritime autonomous systems AI, ranging from poisoning to adversarial patch attacks. The lessons learned from systematic AI red teaming can help prevent MAS-related catastrophic events in a world with increasing uptake and reliance on mission-critical AI.
Labrador: Exploring the Limits of Masked Language Modeling for Laboratory Data
Authors: David R. Bellamy, Bhawesh Kumar, Cindy Wang, Andrew Beam
Date: 2023-12-09 00:00:00
Tasks: Language Modeling, Language Modelling, Masked Language Modeling, Transfer Learning

In this work we introduce Labrador, a pre-trained Transformer model for laboratory data. Labrador and BERT were pre-trained on a corpus of 100 million lab test results from electronic health records (EHRs) and evaluated on various downstream outcome prediction tasks. Both models demonstrate mastery of the pre-training task but neither consistently outperform XGBoost on downstream supervised tasks. Our ablation studies reveal that transfer learning shows limited effectiveness for BERT and achieves marginal success with Labrador. We explore the reasons for the failure of transfer learning and suggest that the data generating process underlying each patient cannot be characterized sufficiently using labs alone, among other factors. We encourage future work to focus on joint modeling of multiple EHR data categories and to include tree-based baselines in their evaluations.
Speech and Text-Based Emotion Recognizer
Authors: Varun Sharma
Date: 2023-12-10 00:00:00
Tasks: Data Augmentation, Emotion Recognition, Speech Emotion Recognition

Affective computing is a field of study that focuses on developing systems and technologies that can understand, interpret, and respond to human emotions. Speech Emotion Recognition (SER), in particular, has got a lot of attention from researchers in the recent past. However, in many cases, the publicly available datasets, used for training and evaluation, are scarce and imbalanced across the emotion labels. In this work, we focused on building a balanced corpus from these publicly available datasets by combining these datasets as well as employing various speech data augmentation techniques. Furthermore, we experimented with different architectures for speech emotion recognition. Our best system, a multi-modal speech, and text-based model, provides a performance of UA(Unweighed Accuracy) + WA (Weighed Accuracy) of 157.57 compared to the baseline algorithm performance of 119.66
The performance of multiple language models in identifying offensive language on social media
Authors: Hao Li, Brandon Bennett
Date: 2023-12-10 00:00:00
Tasks: Information Retrieval, Retrieval, text-classification, Text Classification

Text classification is an important topic in the field of natural language processing. It has been preliminarily applied in information retrieval, digital library, automatic abstracting, text filtering, word semantic discrimination and many other fields. The aim of this research is to use a variety of algorithms to test the ability to identify offensive posts and evaluate their performance against a variety of assessment methods. The motivation for this project is to reduce the harm of these languages to human censors by automating the screening of offending posts. The field is a new one, and despite much interest in the past two years, there has been no focus on the object of the offence. Through the experiment of this project, it should inspire future research on identification methods as well as identification content.
Rethinking the Instruction Quality: LIFT is What You Need
Authors: Yang Xu, Yongqiang Yao, Yufan Huang, MengNan Qi, Maoquan Wang, Bin Gu, Neel Sundaresan
Date: 2023-12-12 00:00:00
Tasks: Code Generation, Instruction Following, Language Modeling, Language Modelling, Large Language Model, Natural Language Understanding

Instruction tuning, a specialized technique to enhance large language model (LLM) performance via instruction datasets, relies heavily on the quality of employed data. Existing quality improvement methods alter instruction data through dataset expansion or curation. However, the expansion method risks data redundancy, potentially compromising LLM performance, while the curation approach confines the LLM's potential to the original dataset. Our aim is to surpass the original data quality without encountering these shortcomings. To achieve this, we propose LIFT (LLM Instruction Fusion Transfer), a novel and versatile paradigm designed to elevate the instruction quality to new heights. LIFT strategically broadens data distribution to encompass more high-quality subspaces and eliminates redundancy, concentrating on high-quality segments across overall data subspaces. Experimental results demonstrate that, even with a limited quantity of high-quality instruction data selected by our paradigm, LLMs not only consistently uphold robust performance across various tasks but also surpass some state-of-the-art results, highlighting the significant improvement in instruction quality achieved by our paradigm.
ComplexityNet: Increasing LLM Inference Efficiency by Learning Task Complexity
Authors: Henry Bae, Aghyad Deeb, Alex Fleury, Kehang Zhu
Date: 2023-12-12 00:00:00
Tasks: Code Generation, Language Modeling, Language Modelling, mbpp

We present ComplexityNet, a streamlined language model designed for assessing task complexity. This model predicts the likelihood of accurate output by various language models, each with different capabilities. Our initial application of ComplexityNet involves the Mostly Basic Python Problems (MBPP) dataset. We pioneered the creation of the first set of labels to define task complexity. ComplexityNet achieved a notable 79% accuracy in determining task complexity, a significant improvement over the 34% accuracy of the original, non fine-tuned model. Furthermore, ComplexityNet effectively reduces computational resource usage by 90% compared to using the highest complexity model, while maintaining a high code generation accuracy of 86.7%. This study demonstrates that fine-tuning smaller models to categorize tasks based on their complexity can lead to a more balanced trade-off between accuracy and efficiency in the use of Large Language Models. Our findings suggest a promising direction for optimizing LLM applications, especially in resource-constrained environments.
Path Signature Representation of Patient-Clinician Interactions as a Predictor for Neuropsychological Tests Outcomes in Children: A Proof of Concept
Authors: Giulio Falcioni, Alexandra Georgescu, Emilia Molimpakis, Lev Gottlieb, Taylor Kuhn, Stefano Goria
Date: 2023-12-12 00:00:00
Tasks: Diagnostic

This research report presents a proof-of-concept study on the application of machine learning techniques to video and speech data collected during diagnostic cognitive assessments of children with a neurodevelopmental disorder. The study utilised a dataset of 39 video recordings, capturing extensive sessions where clinicians administered, among other things, four cognitive assessment tests. From the first 40 minutes of each clinical session, covering the administration of the Wechsler Intelligence Scale for Children (WISC-V), we extracted head positions and speech turns of both clinician and child. Despite the limited sample size and heterogeneous recording styles, the analysis successfully extracted path signatures as features from the recorded data, focusing on patient-clinician interactions. Importantly, these features quantify the interpersonal dynamics of the assessment process (dialogue and movement patterns). Results suggest that these features exhibit promising potential for predicting all cognitive tests scores of the entire session length and for prototyping a predictive model as a clinical decision support tool. Overall, this proof of concept demonstrates the feasibility of leveraging machine learning techniques for clinical video and speech data analysis in order to potentially enhance the efficiency of cognitive assessments for neurodevelopmental disorders in children.