pwc-archive/papers-with-abstracts huggingface dataset:
Tool-Assisted Agent on SQL Inspection and Refinement in Real-World Scenarios
Authors: Zhongyuan Wang, Richong Zhang, Zhijie Nie, Jaein Kim
Date: 2024-08-30 00:00:00
Tasks: Management, Text to SQL, Text-To-SQL

Recent Text-to-SQL methods leverage large language models (LLMs) by incorporating feedback from the database management system. While these methods effectively address execution errors in SQL queries, they struggle with database mismatches -- errors that do not trigger execution exceptions. Database mismatches include issues such as condition mismatches and stricter constraint mismatches, both of which are more prevalent in real-world scenarios. To address these challenges, we propose a tool-assisted agent framework for SQL inspection and refinement, equipping the LLM-based agent with two specialized tools: a retriever and a detector, designed to diagnose and correct SQL queries with database mismatches. These tools enhance the capability of LLMs to handle real-world queries more effectively. We also introduce Spider-Mismatch, a new dataset specifically constructed to reflect the condition mismatch problems encountered in real-world scenarios. Experimental results demonstrate that our method achieves the highest performance on the averaged results of the Spider and Spider-Realistic datasets in few-shot settings, and it significantly outperforms baseline methods on the more realistic dataset, Spider-Mismatch.
Plausible-Parrots @ MSP2023: Enhancing Semantic Plausibility Modeling using Entity and Event Knowledge
Authors: Chong Shen, Chenyue Zhou
Date: 2024-08-29 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

In this work, we investigate the effectiveness of injecting external knowledge to a large language model (LLM) to identify semantic plausibility of simple events. Specifically, we enhance the LLM with fine-grained entity types, event types and their definitions extracted from an external knowledge base. These knowledge are injected into our system via designed templates. We also augment the data to balance the label distribution and adapt the task setting to real world scenarios in which event mentions are expressed as natural language sentences. The experimental results show the effectiveness of the injected knowledge on modeling semantic plausibility of events. An error analysis further emphasizes the importance of identifying non-trivial entity and event types.
Modeling offensive content detection for TikTok
Authors: Kasper Cools, Gideon Mailette de Buy Wenniger, Clara Maathuis
Date: 2024-08-29 00:00:00
Tasks: Binary Classification

The advent of social media transformed interpersonal communication and information consumption processes. This digital landscape accommodates user intentions, also resulting in an increase of offensive language and harmful behavior. Concurrently, social media platforms collect vast datasets comprising user-generated content and behavioral information. These datasets are instrumental for platforms deploying machine learning and data-driven strategies, facilitating customer insights and countermeasures against social manipulation mechanisms like disinformation and offensive content. Nevertheless, the availability of such datasets, along with the application of various machine learning techniques, to researchers and practitioners, for specific social media platforms regarding particular events, is limited. In particular for TikTok, which offers unique tools for personalized content creation and sharing, the existing body of knowledge would benefit from having diverse comprehensive datasets and associated data analytics solutions on offensive content. While efforts from social media platforms, research, and practitioner communities are seen on this behalf, such content continues to proliferate. This translates to an essential need to make datasets publicly available and build corresponding intelligent solutions. On this behalf, this research undertakes the collection and analysis of TikTok data containing offensive content, building a series of machine learning and deep learning models for offensive content detection. This is done aiming at answering the following research question: "How to develop a series of computational models to detect offensive content on TikTok?". To this end, a Data Science methodological approach is considered, 120.423 TikTok comments are collected, and on a balanced, binary classification approach, F1 score performance results of 0.863 is obtained.
Canonic Signed Spike Coding for Efficient Spiking Neural Networks
Authors: Yiwen Gu, Junchuan Gu, Haibin Shen, Kejie Huang
Date: 2024-08-30 00:00:00

Spiking Neural Networks (SNNs) seek to mimic the spiking behavior of biological neurons and are expected to play a key role in the advancement of neural computing and artificial intelligence. The conversion of Artificial Neural Networks (ANNs) to SNNs is the most widely used training method, which ensures that the resulting SNNs perform comparably to ANNs on large-scale datasets. The efficiency of these conversion-based SNNs is often determined by the neural coding schemes. Current schemes typically use spike count or timing for encoding, which is linearly related to ANN activations and increases the required number of time steps. To address this limitation, we propose a novel Canonic Signed Spike (CSS) coding scheme. This method incorporates non-linearity into the encoding process by weighting spikes at each step of neural computation, thereby increasing the information encoded in spikes. We identify the temporal coupling phenomenon arising from weighted spikes and introduce negative spikes along with a Ternary Self-Amplifying (TSA) neuron model to mitigate the issue. A one-step silent period is implemented during neural computation, achieving high accuracy with low latency. We apply the proposed methods to directly convert full-precision ANNs and evaluate performance on CIFAR-10 and ImageNet datasets. Our experimental results demonstrate that the CSS coding scheme effectively compresses time steps for coding and reduces inference latency with minimal conversion loss.
A Computational Framework for Modeling Emergence of Color Vision in the Human Brain
Authors: Atsunobu Kotani, Ren Ng
Date: 2024-08-29 00:00:00

It is a mystery how the brain decodes color vision purely from the optic nerve signals it receives, with a core inferential challenge being how it disentangles internal perception with the correct color dimensionality from the unknown encoding properties of the eye. In this paper, we introduce a computational framework for modeling this emergence of human color vision by simulating both the eye and the cortex. Existing research often overlooks how the cortex develops color vision or represents color space internally, assuming that the color dimensionality is known a priori; however, we argue that the visual cortex has the capability and the challenge of inferring the color dimensionality purely from fluctuations in the optic nerve signals. To validate our theory, we introduce a simulation engine for biological eyes based on established vision science and generate optic nerve signals resulting from looking at natural images. Further, we propose a bio-plausible model of cortical learning based on self-supervised prediction of optic nerve signal fluctuations under natural eye motions. We show that this model naturally learns to generate color vision by disentangling retinal invariants from the sensory signals. When the retina contains N types of color photoreceptors, our simulation shows that N-dimensional color vision naturally emerges, verified through formal colorimetry. Using this framework, we also present the first simulation work that successfully boosts the color dimensionality, as observed in gene therapy on squirrel monkeys, and demonstrates the possibility of enhancing human color vision from 3D to 4D.
$EvoAl^{2048}$
Authors: Bernhard J. Berger, Christina Plump, Rolf Drechsler
Date: 2024-08-15 00:00:00

As AI solutions enter safety-critical products, the explainability and interpretability of solutions generated by AI products become increasingly important. In the long term, such explanations are the key to gaining users' acceptance of AI-based systems' decisions. We report on applying a model-driven-based optimisation to search for an interpretable and explainable policy that solves the game 2048. This paper describes a solution to the GECCO'24 Interpretable Control Competition using the open-source software EvoAl. We aimed to develop an approach for creating interpretable policies that are easy to adapt to new ideas.
Not All Videos Become Outdated: Short-Video Recommendation by Learning to Deconfound Release Interval Bias
Authors: Lulu Dong, Guoxiu He, Aixin Sun
Date: 2024-08-30 00:00:00
Tasks: All, Blocking, Recommendation Systems

Short-video recommender systems often exhibit a biased preference to recently released videos. However, not all videos become outdated; certain classic videos can still attract user's attention. Such bias along temporal dimension can be further aggravated by the matching model between users and videos, because the model learns from preexisting interactions. From real data, we observe that different videos have varying sensitivities to recency in attracting users' attention. Our analysis, based on a causal graph modeling short-video recommendation, suggests that the release interval serves as a confounder, establishing a backdoor path between users and videos. To address this confounding effect, we propose a model-agnostic causal architecture called Learning to Deconfound the Release Interval Bias (LDRI). LDRI enables jointly learning of the matching model and the video recency sensitivity perceptron. In the inference stage, we apply a backdoor adjustment, effectively blocking the backdoor path by intervening on each video. Extensive experiments on two benchmarks demonstrate that LDRI consistently outperforms backbone models and exhibits superior performance against state-of-the-art models. Additional comprehensive analyses confirm the deconfounding capability of LDRI.
Metadata practices for simulation workflows
Authors: Jose Villamar, Matthias Kelbling, Heather L. More, Michael Denker, Tom Tetzlaff, Johanna Senk, Stephan Thober
Date: 2024-08-30 00:00:00

Computer simulations are an essential pillar of knowledge generation in science. Understanding, reproducing, and exploring the results of simulations relies on tracking and organizing metadata describing numerical experiments. However, the models used to understand real-world systems, and the computational machinery required to simulate them, are typically complex, and produce large amounts of heterogeneous metadata. Here, we present general practices for acquiring and handling metadata that are agnostic to software and hardware, and highly flexible for the user. These consist of two steps: 1) recording and storing raw metadata, and 2) selecting and structuring metadata. As a proof of concept, we develop the Archivist, a Python tool to help with the second step, and use it to apply our practices to distinct high-performance computing use cases from neuroscience and hydrology. Our practices and the Archivist can readily be applied to existing workflows without the need for substantial restructuring. They support sustainable numerical workflows, facilitating reproducibility and data reuse in generic simulation-based research.
Efficient Multi-task Prompt Tuning for Recommendation
Authors: Ting Bai, Le Huang, Yue Yu, Cheng Yang, Cheng Hou, Zhe Zhao, Chuan Shi
Date: 2024-08-30 00:00:00
Tasks: Multi-Task Learning, Recommendation Systems

With the expansion of business scenarios, real recommender systems are facing challenges in dealing with the constantly emerging new tasks in multi-task learning frameworks. In this paper, we attempt to improve the generalization ability of multi-task recommendations when dealing with new tasks. We find that joint training will enhance the performance of the new task but always negatively impact existing tasks in most multi-task learning methods. Besides, such a re-training mechanism with new tasks increases the training costs, limiting the generalization ability of multi-task recommendation models. Based on this consideration, we aim to design a suitable sharing mechanism among different tasks while maintaining joint optimization efficiency in new task learning. A novel two-stage prompt-tuning MTL framework (MPT-Rec) is proposed to address task irrelevance and training efficiency problems in multi-task recommender systems. Specifically, we disentangle the task-specific and task-sharing information in the multi-task pre-training stage, then use task-aware prompts to transfer knowledge from other tasks to the new task effectively. By freezing parameters in the pre-training tasks, MPT-Rec solves the negative impacts that may be brought by the new task and greatly reduces the training costs. Extensive experiments on three real-world datasets show the effectiveness of our proposed multi-task learning framework. MPT-Rec achieves the best performance compared to the SOTA multi-task learning method. Besides, it maintains comparable model performance but vastly improves the training efficiency (i.e., with up to 10% parameters in the full training way) in the new task learning.
A Prototype Model of Zero-Trust Architecture Blockchain with EigenTrust-Based Practical Byzantine Fault Tolerance Protocol to Manage Decentralized Clinical Trials
Authors: Ashok Kumar Peepliwall, Hari Mohan Pandey, Surya Prakash, Anand A Mahajan, Sudhinder Singh Chowhan, Vinesh Kumar, Rahul Sharma
Date: 2024-08-29 00:00:00

The COVID-19 pandemic necessitated the emergence of decentralized Clinical Trials (DCTs) due to patient retention, accelerate trials, improve data accessibility, enable virtual care, and facilitate seamless communication through integrated systems. However, integrating systems in DCTs exposes clinical data to potential security threats, making them susceptible to theft at any stage, a high risk of protocol deviations, and monitoring issues. To mitigate these challenges, blockchain technology serves as a secure framework, acting as a decentralized ledger, creating an immutable environment by establishing a zero-trust architecture, where data are deemed untrusted until verified. In combination with Internet of Things (IoT)-enabled wearable devices, blockchain secures the transfer of clinical trial data on private blockchains during DCT automation and operations. This paper proposes a prototype model of the Zero-Trust Architecture Blockchain (z-TAB) to integrate patient-generated clinical trial data during DCT operation management. The EigenTrust-based Practical Byzantine Fault Tolerance (T-PBFT) algorithm has been incorporated as a consensus protocol, leveraging Hyperledger Fabric. Furthermore, the Internet of Things (IoT) has been integrated to streamline data processing among stakeholders within the blockchain platforms. Rigorous evaluation has been done to evaluate the quality of the system.
Deep Learning-Based Framework for Phishing Website Detection
Authors: LIZHEN TANG, QUSAY H. MAHMOUD, (Senior Member, IEEE)
Date: 2021-12-23 00:00:00
Proceeding: journal 2021 12
Tasks: Deep Learning, Phishing Website Detection, Prediction

Phishing attackers spread phishing links through e-mail, text messages, and social media
platforms. They use social engineering skills to trick users into visiting phishing websites and entering
crucial personal information. In the end, the stolen personal information is used to defraud the trust of
regular websites or nancial institutions to obtain illegal benets. With the development and applications
of machine learning technology, many machine learning-based solutions for detecting phishing have been
proposed. Some solutions are based on the features extracted by rules, and some of the features need to rely on
third-party services, which will cause instability and time-consuming issues in the prediction service. In this
paper, we propose a deep learning-based framework for detecting phishing websites. We have implemented
the framework as a browser plug-in capable of determining whether there is a phishing risk in real-time
when the user visits a web page and gives a warning message. The real-time prediction service combines
multiple strategies to improve accuracy, reduce false alarm rates, and reduce calculation time, including
whitelist ltering, blacklist interception, and machine learning (ML) prediction. In the ML prediction
module, we compared multiple machine learning models using several datasets. From the experimental
results, the RNN-GRU model obtained the highest accuracy of 99.18%, demonstrating the feasibility of
the proposed solution.
Design of an IoT-based smart incubator that listens to the baby
Authors: F Fahmi, W Shalannanda, I Zakia, E Sutanto
Date: 2020-01-01 00:00:00
Proceeding: 2nd International Conference on Industrial and Manufacturing Engineering 2020 1

In the industrial era 4.0, domestic baby incubator producers are facing the challenge of free trade of foreign products that will compete in innovation with the application of IoT technology. One of the opportunities that arise consciously or unconsciously at the NICU (Neonatal Intensive Care Unit) unit in Hospitals, in general, is that there are no facilities for parents to monitor the baby’s condition inside the incubator directly. The purpose of this research project is to build a prototype of an internet-based baby incubator monitoring system based on things equipped with various sensors that will send data to the server in real-time and mobile apps for monitoring facilities for parents, including the voice of the baby. This study focused on how to develop baby incubators that can listen to baby’s crying, capture the voice, and interpret it using artificial intelligence. More than 40 (forty) voice datasets were used and successfully classified into five possible terms of the baby’s condition: burping, sleepy, hungry, uncomfortable, and pain by energy signal and spectrum analysis. The benefit of this research is as a driver of innovation for baby incubator products that will support the national medical devices manufacturing company.
Uncovering Neural Scaling Laws in Molecular Representation Learning
Authors: Dingshuo Chen, Yanqiao Zhu, Jieyu Zhang, Yuanqi Du, ZHIXUN LI, Qiang Liu, Shu Wu, Liang Wang
Date: 2023-09-15 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: molecular representation, Representation Learning

Molecular Representation Learning (MRL) has emerged as a powerful tool for drug and materials discovery in a variety of tasks such as virtual screening and inverse design. While there has been a surge of interest in advancing model-centric techniques, the influence of both data quantity and quality on molecular representations is not yet clearly understood within this field. In this paper, we delve into the neural scaling behaviors of MRL from a data-centric viewpoint, examining four key dimensions: (1) data modalities, (2) dataset splitting, (3) the role of pre-training, and (4) model capacity. Our empirical studies confirm a consistent power-law relationship between data volume and MRL performance across these dimensions. Additionally, through detailed analysis, we identify potential avenues for improving learning efficiency. To challenge these scaling laws, we adapt seven popular data pruning strategies to molecular data and benchmark their performance. Our findings underline the importance of data-centric MRL and highlight possible directions for future research.
Residential Load Forecasting: An Online-Offline Deep Kernel Learning Method
Authors: Yuanzheng Li, Fushen Zhang, Yun Liu, Huilian Liao, Hai-Tao Zhang, Chiyung Chung
Date: 2023-07-28 00:00:00
Proceeding: IEEE Transactions on Power Systems 2023 7
Tasks: Load Forecasting, Probabilistic Time Series Forecasting

Residential load forecasting (RLF) is critical for power system operations. Different from traditional system-level load forecasting, studying RLF faces the challenges of high uncertainty. Besides, learning temporal dynamics within the residential load sequences is important. However, existing methods fail to effectively tackle the fore-mentioned challenges simultaneously. In this article, a deep kernel is proposed by integrating the deep soft Spiking Neural Networks (sSNN), which is then applied to perform Gaussian Process (GP) regression. The constructed regressor investigates the temporal dynamics within the residential load sequence and retains the probabilistic advantages for uncertainty estimates. Furthermore, to better address the high uncertainty of RLF, a learning scheme combing both offline and online learning is specifically developed for the regressor. Such a learning scheme contributes to fully exploring historical information while learning the uncertainty from real-time data. The effectiveness of the proposed method is demonstrated on three public and actual residential load datasets.
Role of Data-driven Regional Growth Model in Shaping Brain Folding Patterns
Authors: Jixin Hou, Zhengwang Wu, Xianyan Chen, Li Wang, Dajiang Zhu, Tianming Liu, Gang Li, Xianqiao Wang
Date: 2024-08-30 00:00:00
Tasks: Symbolic Regression

The surface morphology of the developing mammalian brain is crucial for understanding brain function and dysfunction. Computational modeling offers valuable insights into the underlying mechanisms for early brain folding. Recent findings indicate significant regional variations in brain tissue growth, while the role of these variations in cortical development remains unclear. In this study, we unprecedently explored how regional cortical growth affects brain folding patterns using computational simulation. We first developed growth models for typical cortical regions using machine learning (ML)-assisted symbolic regression, based on longitudinal real surface expansion and cortical thickness data from prenatal and infant brains derived from over 1,000 MRI scans of 735 pediatric subjects with ages ranging from 29 post-menstrual weeks to 24 months. These models were subsequently integrated into computational software to simulate cortical development with anatomically realistic geometric models. We comprehensively quantified the resulting folding patterns using multiple metrics such as mean curvature, sulcal depth, and gyrification index. Our results demonstrate that regional growth models generate complex brain folding patterns that more closely match actual brains structures, both quantitatively and qualitatively, compared to conventional uniform growth models. Growth magnitude plays a dominant role in shaping folding patterns, while growth trajectory has a minor influence. Moreover, multi-region models better capture the intricacies of brain folding than single-region models. Our results underscore the necessity and importance of incorporating regional growth heterogeneity into brain folding simulations, which could enhance early diagnosis and treatment of cortical malformations and neurodevelopmental disorders such as cerebral palsy and autism.