pwc-archive/papers-with-abstracts huggingface dataset:
Verbal behavior without syntactic structures: beyond Skinner and Chomsky
Authors: Shimon Edelman
Date: 2023-03-11 00:00:00

What does it mean to know language? Since the Chomskian revolution, one popular answer to this question has been: to possess a generative grammar that exclusively licenses certain syntactic structures. Decades later, not even an approximation to such a grammar, for any language, has been formulated; the idea that grammar is universal and innately specified has proved barren; and attempts to show how it could be learned from experience invariably come up short. To move on from this impasse, we must rediscover the extent to which language is like any other human behavior: dynamic, social, multimodal, patterned, and purposive, its purpose being to promote desirable actions (or thoughts) in others and self. Recent psychological, computational, neurobiological, and evolutionary insights into the shaping and structure of behavior may then point us toward a new, viable account of language.
Do large language models resemble humans in language use?
Authors: Zhenguang G. Cai, Xufeng Duan, David A. Haslett, Shuqi Wang, Martin J. Pickering
Date: 2023-03-10 00:00:00
Tasks: Sentence

Large language models (LLMs) such as ChatGPT and Vicuna have shown remarkable capacities in comprehending and producing language. However, their internal workings remain a black box, and it is unclear whether LLMs and chatbots can develop humanlike characteristics in language use. Cognitive scientists have devised many experiments that probe, and have made great progress in explaining, how people comprehend and produce language. We subjected ChatGPT and Vicuna to 12 of these experiments ranging from sounds to dialogue, preregistered and with 1000 runs (i.e., iterations) per experiment. ChatGPT and Vicuna replicated the human pattern of language use in 10 and 7 out of the 12 experiments, respectively. The models associated unfamiliar words with different meanings depending on their forms, continued to access recently encountered meanings of ambiguous words, reused recent sentence structures, attributed causality as a function of verb semantics, and accessed different meanings and retrieved different words depending on an interlocutor's identity. In addition, ChatGPT, but not Vicuna, nonliterally interpreted implausible sentences that were likely to have been corrupted by noise, drew reasonable inferences, and overlooked semantic fallacies in a sentence. Finally, unlike humans, neither model preferred using shorter words to convey less informative content, nor did they use context to resolve syntactic ambiguities. We discuss how these convergences and divergences may result from the transformer architecture. Overall, these experiments demonstrate that LLMs such as ChatGPT (and Vicuna to a lesser extent) are humanlike in many aspects of human language processing.
Detection of Abuse in Financial Transaction Descriptions Using Machine Learning
Authors: Anna Leontjeva, Genevieve Richards, Kaavya Sriskandaraja, Jessica Perchman, Luiz Pizzato
Date: 2023-03-10 00:00:00

Since introducing changes to the New Payments Platform (NPP) to include longer messages as payment descriptions, it has been identified that people are now using it for communication, and in some cases, the system was being used as a targeted form of domestic and family violence. This type of tech-assisted abuse poses new challenges in terms of identification, actions and approaches to rectify this behaviour. Commonwealth Bank of Australia's Artificial Intelligence Labs team (CBA AI Labs) has developed a new system using advances in deep learning models for natural language processing (NLP) to create a powerful abuse detector that periodically scores all the transactions, and identifies cases of high-risk abuse in millions of records. In this paper, we describe the problem of tech-assisted abuse in the context of banking services, outline the developed model and its performance, and the operating framework more broadly.
TQ-Net: Mixed Contrastive Representation Learning For Heterogeneous Test Questions
Authors: He Zhu, Xihua Li, Xuemin Zhao, Yunbo Cao, Shan Yu
Date: 2023-03-09 00:00:00
Tasks: Contrastive Learning, Representation Learning

Recently, more and more people study online for the convenience of access to massive learning materials (e.g. test questions/notes), thus accurately understanding learning materials became a crucial issue, which is essential for many educational applications. Previous studies focus on using language models to represent the question data. However, test questions (TQ) are usually heterogeneous and multi-modal, e.g., some of them may only contain text, while others half contain images with information beyond their literal description. In this context, both supervised and unsupervised methods are difficult to learn a fused representation of questions. Meanwhile, this problem cannot be solved by conventional methods such as image caption, as the images may contain information complementary rather than duplicate to the text. In this paper, we first improve previous text-only representation with a two-stage unsupervised instance level contrastive based pre-training method (MCL: Mixture Unsupervised Contrastive Learning). Then, TQ-Net was proposed to fuse the content of images to the representation of heterogeneous data. Finally, supervised contrastive learning was conducted on relevance prediction-related downstream tasks, which helped the model to learn the representation of questions effectively. We conducted extensive experiments on question-based tasks on large-scale, real-world datasets, which demonstrated the effectiveness of TQ-Net and improve the precision of downstream applications (e.g. similar questions +2.02% and knowledge point prediction +7.20%). Our code will be available, and we will open-source a subset of our data to promote the development of relative studies.
Data-Efficient Learning of Natural Language to Linear Temporal Logic Translators for Robot Task Specification
Authors: Jiayi Pan, Glen Chou, Dmitry Berenson
Date: 2023-03-09 00:00:00

To make robots accessible to a broad audience, it is critical to endow them with the ability to take universal modes of communication, like commands given in natural language, and extract a concrete desired task specification, defined using a formal language like linear temporal logic (LTL). In this paper, we present a learning-based approach for translating from natural language commands to LTL specifications with very limited human-labeled training data. This is in stark contrast to existing natural-language to LTL translators, which require large human-labeled datasets, often in the form of labeled pairs of LTL formulas and natural language commands, to train the translator. To reduce reliance on human data, our approach generates a large synthetic training dataset through algorithmic generation of LTL formulas, conversion to structured English, and then exploiting the paraphrasing capabilities of modern large language models (LLMs) to synthesize a diverse corpus of natural language commands corresponding to the LTL formulas. We use this generated data to finetune an LLM and apply a constrained decoding procedure at inference time to ensure the returned LTL formula is syntactically correct. We evaluate our approach on three existing LTL/natural language datasets and show that we can translate natural language commands at 75\% accuracy with far less human data ($\le$12 annotations). Moreover, when training on large human-annotated datasets, our method achieves higher test accuracy (95\% on average) than prior work. Finally, we show the translated formulas can be used to plan long-horizon, multi-stage tasks on a 12D quadrotor.
Axiomatic characterization of pointwise Shapley decompositions
Authors: Marcus C Christiansen
Date: 2023-03-14 00:00:00

A common problem in various applications is the additive decomposition of the output of a function with respect to its input variables. Functions with binary arguments can be axiomatically decomposed by the famous Shapley value. For the decomposition of functions with real arguments, a popular method is the pointwise application of the Shapley value on the domain. However, this pointwise application largely ignores the overall structure of functions. In this paper, axioms are developed which fully preserve functional structures and lead to unique decompositions for all Borel measurable functions.
Bayes Complexity of Learners vs Overfitting
Authors: Grzegorz Głuch, Rudiger Urbanke
Date: 2023-03-13 00:00:00

We introduce a new notion of complexity of functions and we show that it has the following properties: (i) it governs a PAC Bayes-like generalization bound, (ii) for neural networks it relates to natural notions of complexity of functions (such as the variation), and (iii) it explains the generalization gap between neural networks and linear schemes. While there is a large set of papers which describes bounds that have each such property in isolation, and even some that have two, as far as we know, this is a first notion that satisfies all three of them. Moreover, in contrast to previous works, our notion naturally generalizes to neural networks with several layers. Even though the computation of our complexity is nontrivial in general, an upper-bound is often easy to derive, even for higher number of layers and functions with structure, such as period functions. An upper-bound we derive allows to show a separation in the number of samples needed for good generalization between 2 and 4-layer neural networks for periodic functions.
Improving information retrieval through correspondence analysis instead of latent semantic analysis
Authors: Qianqian Qi, David J. Hessen, Peter G. M. van der Heijden
Date: 2023-03-14 00:00:00
Tasks: Dimensionality Reduction, Information Retrieval, Retrieval

Both latent semantic analysis (LSA) and correspondence analysis (CA) are dimensionality reduction techniques that use singular value decomposition (SVD) for information retrieval. Theoretically, the results of LSA display both the association between documents and terms, and marginal effects; in comparison, CA only focuses on the associations between documents and terms. Marginal effects are usually not relevant for information retrieval, and therefore, from a theoretical perspective CA is more suitable for information retrieval. In this paper, we empirically compare LSA and CA. The elements of the raw document-term matrix are weighted, and the weighting exponent of singular values is adjusted to improve the performance of LSA. We explore whether these two weightings also improve the performance of CA. In addition, we compare the optimal singular value weighting exponents for LSA and CA to identify what the initial dimensions in LSA correspond to. The results for four empirical datasets show that CA always performs better than LSA. Weighting the elements of the raw data matrix can improve CA; however, it is data dependent and the improvement is small. Adjusting the singular value weighting exponent usually improves the performance of CA; however, the extent of the improved performance depends on the dataset and number of dimensions. In general, CA needs a larger singular value weighting exponent than LSA to obtain the optimal performance. This indicates that CA emphasizes initial dimensions more than LSA, and thus, margins play an important role in the initial dimensions in LSA.
Disentangled Graph Social Recommendation
Authors: Lianghao Xia, Yizhen Shao, Chao Huang, Yong Xu, Huance Xu, Jian Pei
Date: 2023-03-14 00:00:00
Tasks: Graph Neural Network, Recommendation Systems

Social recommender systems have drawn a lot of attention in many online web services, because of the incorporation of social information between users in improving recommendation results. Despite the significant progress made by existing solutions, we argue that current methods fall short in two limitations: (1) Existing social-aware recommendation models only consider collaborative similarity between items, how to incorporate item-wise semantic relatedness is less explored in current recommendation paradigms. (2) Current social recommender systems neglect the entanglement of the latent factors over heterogeneous relations (e.g., social connections, user-item interactions). Learning the disentangled representations with relation heterogeneity poses great challenge for social recommendation. In this work, we design a Disentangled Graph Neural Network (DGNN) with the integration of latent memory units, which empowers DGNN to maintain factorized representations for heterogeneous types of user and item connections. Additionally, we devise new memory-augmented message propagation and aggregation schemes under the graph neural architecture, allowing us to recursively distill semantic relatedness into the representations of users and items in a fully automatic manner. Extensive experiments on three benchmark datasets verify the effectiveness of our model by achieving great improvement over state-of-the-art recommendation techniques. The source code is publicly available at: https://github.com/HKUDS/DGNN.
Automated Self-Supervised Learning for Recommendation
Authors: Lianghao Xia, Chao Huang, Chunzhen Huang, Kangyi Lin, Tao Yu, Ben Kao
Date: 2023-03-14 00:00:00
Tasks: Collaborative Filtering, Contrastive Learning, Data Augmentation, Self-Supervised Learning

Graph neural networks (GNNs) have emerged as the state-of-the-art paradigm for collaborative filtering (CF). To improve the representation quality over limited labeled data, contrastive learning has attracted attention in recommendation and benefited graph-based CF model recently. However, the success of most contrastive methods heavily relies on manually generating effective contrastive views for heuristic-based data augmentation. This does not generalize across different datasets and downstream recommendation tasks, which is difficult to be adaptive for data augmentation and robust to noise perturbation. To fill this crucial gap, this work proposes a unified Automated Collaborative Filtering (AutoCF) to automatically perform data augmentation for recommendation. Specifically, we focus on the generative self-supervised learning framework with a learnable augmentation paradigm that benefits the automated distillation of important self-supervised signals. To enhance the representation discrimination ability, our masked graph autoencoder is designed to aggregate global information during the augmentation via reconstructing the masked subgraph structures. Experiments and ablation studies are performed on several public datasets for recommending products, venues, and locations. Results demonstrate the superiority of AutoCF against various baseline methods. We release the model implementation at https://github.com/HKUDS/AutoCF.
CoMeta: Enhancing Meta Embeddings with Collaborative Information in Cold-start Problem of Recommendation
Authors: Haonan Hu, Dazhong Rong, Jianhai Chen, Qinming He, Zhenguang Liu
Date: 2023-03-14 00:00:00
Tasks: Attribute, Meta-Learning

The cold-start problem is quite challenging for existing recommendation models. Specifically, for the new items with only a few interactions, their ID embeddings are trained inadequately, leading to poor recommendation performance. Some recent studies introduce meta learning to solve the cold-start problem by generating meta embeddings for new items as their initial ID embeddings. However, we argue that the capability of these methods is limited, because they mainly utilize item attribute features which only contain little information, but ignore the useful collaborative information contained in the ID embeddings of users and old items. To tackle this issue, we propose CoMeta to enhance the meta embeddings with the collaborative information. CoMeta consists of two submodules: B-EG and S-EG. Specifically, for a new item: B-EG calculates the similarity-based weighted sum of the ID embeddings of old items as its base embedding; S-EG generates its shift embedding not only with its attribute features but also with the average ID embedding of the users who interacted with it. The final meta embedding is obtained by adding up the base embedding and the shift embedding. We conduct extensive experiments on two public datasets. The experimental results demonstrate both the effectiveness and the compatibility of CoMeta.
Medical practitioner's adoption of intelligent clinical diagnostic decision support systems: A mixed-methods study Author links open overlay panel
Authors: Ashish Viswanath Prakash, Saini Das
Date: 2019-12-10 00:00:00
Proceeding: Information & Management 2019 12
Tasks: Diagnostic

Artificial intelligence-based clinical diagnostic decision support systems promise transformational improvements in doctors’ efficiency and accuracy. Nevertheless, low adoption rates suggest that this innovation could fail without adequate uptake. This study uses a mixed-methods approach to develop and test a model based on theories of Unified Theory of Acceptance and Use of Technology, status quo bias, and technology trust. The results show that performance expectancy, effort expectancy, social influence, initial trust, and resistance to change predict intention to use. Further, inertia, perceived threat, and risks (medico-legal and performance) determine resistance to change. Measures for alleviating resistance and improving adoption are proposed.
Study on the Data Storage Technology of Mini-Airborne Radar Based on Machine Learning
Authors: Haishan Tian, Qiong Yang, Huabing Wang, Jingke Zhang
Date: 2023-03-03 00:00:00
Tasks: Management

The data rate of airborne radar is much higher than the wireless data transfer rate in many detection applications, so the onboard data storage systems are usually used to store the radar data. Data storage systems with good seismic performance usually use NAND Flash as storage medium, and there is a widespread problem of long file management time, which seriously affects the data storage speed, especially under the limitation of platform miniaturization. To solve this problem, a data storage method based on machine learning is proposed for mini-airborne radar. The storage training model is established based on machine learning, and could process various kinds of radar data. The file management methods are classified and determined using the model, and then are applied to the storage of radar data. To verify the performance of the proposed method, a test was carried out on the data storage system of a mini-airborne radar. The experimental results show that the method based on machine learning can form various data storage methods adapted to different data rates and application scenarios. The ratio of the file management time to the actual data writing time is extremely low.
Multi-task Meta Label Correction for Time Series Prediction
Authors: Luxuan Yang, Ting Gao, Wei Wei, Min Dai, Cheng Fang, Jinqiao Duan
Date: 2023-03-09 00:00:00
Tasks: Contrastive Learning, Data Visualization, Meta-Learning, Multi-Task Learning, Time Series, Time Series Analysis, Time Series Classification, Time Series Prediction

Time series classification faces two unavoidable problems. One is partial feature information and the other is poor label quality, which may affect model performance. To address the above issues, we create a label correction method to time series data with meta-learning under a multi-task framework. There are three main contributions. First, we train the label correction model with a two-branch neural network in the outer loop. While in the model-agnostic inner loop, we use pre-existing classification models in a multi-task way and jointly update the meta-knowledge so as to help us achieve adaptive labeling on complex time series. Second, we devise new data visualization methods for both image patterns of the historical data and data in the prediction horizon. Finally, we test our method with various financial datasets, including XOM, S\&P500, and SZ50. Results show that our method is more effective and accurate than some existing label correction techniques.
Deep Learning-based RF Fingerprint Authentication with Chaotic Antenna Arrays
Authors: Justin McMillen, Gokhan Mumcu, Yasin Yilmaz
Date: 2023-03-13 00:00:00
Tasks: Deep Learning

Radio frequency (RF) fingerprinting is a tool which allows for authentication by utilizing distinct and random distortions in a received signal based on characteristics of the transmitter. We introduce a deep learning-based authentication method for a novel RF fingerprinting system called Physically Unclonable Wireless Systems (PUWS). An element of PUWS is based on the concept of Chaotic Antenna Arrays (CAAs) that can be cost effectively manufactured by utilizing mask-free laser-enhanced direct print additive manufacturing (LE-DPAM). In our experiments, using simulation data of 300 CAAs each exhibiting 4 antenna elements, we test 3 different convolutional neural network (CNN) architectures under different channel conditions and compare their authentication performance to the current state-of-the-art RF fingerprinting authentication methods.
EdgeServe: A Streaming System for Decentralized Model Serving
Authors: Ted Shaowang, Sanjay Krishnan
Date: 2023-03-02 00:00:00
Tasks: Activity Recognition, Autonomous Driving, Human Activity Recognition, Intrusion Detection, model, Network Intrusion Detection, Object Tracking

The relevant features for a machine learning task may arrive as one or more continuous streams of data. Serving machine learning models over streams of data creates a number of interesting systems challenges in managing data routing, time-synchronization, and rate control. This paper presents EdgeServe, a distributed streaming system that can serve predictions from machine learning models in real time. We evaluate EdgeServe on three streaming prediction tasks: (1) human activity recognition, (2) autonomous driving, and (3) network intrusion detection.
Online Neural Path Guiding with Normalized Anisotropic Spherical Gaussians
Authors: Jiawei Huang, Akito Iizuka, Hajime Tanaka, Taku Komura, Yoshifumi Kitamura
Date: 2023-03-11 00:00:00
Tasks: GPU

The variance reduction speed of physically-based rendering is heavily affected by the adopted importance sampling technique. In this paper we propose a novel online framework to learn the spatial-varying density model with a single small neural network using stochastic ray samples. To achieve this task, we propose a novel closed-form density model called the normalized anisotropic spherical gaussian mixture, that can express complex irradiance fields with a small number of parameters. Our framework learns the distribution in a progressive manner and does not need any warm-up phases. Due to the compact and expressive representation of our density model, our framework can be implemented entirely on the GPU, allowing it produce high quality images with limited computational resources.
A Modular Platform For Collaborative, Distributed Sensor Fusion
Authors: R. Spencer Hallyburton, Nate Zelter, David Hunt, Kristen Angell, Miroslav Pajic
Date: 2023-03-13 00:00:00
Tasks: Sensor Fusion

Leading autonomous vehicle (AV) platforms and testing infrastructures are, unfortunately, proprietary and closed-source. Thus, it is difficult to evaluate how well safety-critical AVs perform and how safe they truly are. Similarly, few platforms exist for much-needed multi-agent analysis. To provide a starting point for analysis of sensor fusion and collaborative & distributed sensing, we design an accessible, modular sensing platform with AVstack. We build collaborative and distributed camera-radar fusion algorithms and demonstrate an evaluation ecosystem of AV datasets, physics-based simulators, and hardware in the physical world. This three-part ecosystem enables testing next-generation configurations that are prohibitively challenging in existing development platforms.