pwc-archive/papers-with-abstracts huggingface dataset:
Imitation versus Innovation: What children can do that large language and language-and-vision models cannot (yet)?
Authors: Eunice Yiu, Eliza Kosoy, Alison Gopnik
Date: 2023-05-08 00:00:00

Much discussion about large language models and language-and-vision models has focused on whether these models are intelligent agents. We present an alternative perspective. We argue that these artificial intelligence models are cultural technologies that enhance cultural transmission in the modern world, and are efficient imitation engines. We explore what AI models can tell us about imitation and innovation by evaluating their capacity to design new tools and discover novel causal structures, and contrast their responses with those of human children. Our work serves as a first step in determining which particular representations and competences, as well as which kinds of knowledge or skill, can be derived from particular learning techniques and data. Critically, our findings suggest that machines may need more than large scale language and images to achieve what a child can do.
Davinci the Dualist: the mind-body divide in large language models and in human learners
Authors: Iris Berent, Alexzander Sansiveri
Date: 2023-05-10 00:00:00
Tasks: Language Modelling, Large Language Model

A large literature suggests that people are intuitive Dualists--they consider the mind ethereal, distinct from the body. Past research also shows that Dualism emerges, in part, via learning (e.g., Barlev & Shtulman, 2021). But whether learning is sufficient to give rise to Dualism is unknown.The evidence from human learners does address this question because humans are endowed not only with general learning capacities but also with core knowledge capacities. And recent results suggest that core knowledge begets Dualism (Berent, Theodore & Valencia, 2021; Berent, 2023). To evaluate the role of learning, here, we probe for a mind-body divide in Davinci--a large language model (LLM) that is devoid of any innate core knowledge. We show that Davinci still leans towards Dualism, and that this bias increases systematically with the learner's inductive potential. Thus, davinci (a GPT-3 model) exhibits mild Dualist tendencies, whereas its descendent, text-davinci-003 (a GPT-3.5 model), shows a full-blown bias. It selectively considers thoughts (epistemic states) as disembodied--as unlikely to show up in the body (in the brain), but not in its absence (after death). While Davinci's performance is constrained by its syntactic limitations, and it differs from humans, its Dualist bias is robust. These results demonstrate that the mind-body divide is partly learnable from experience.They also show how, as LLM's are exposed to human narratives, they induce not only human knowledge but also human biases.
mAedesID: Android Application for Aedes Mosquito Species Identification using Convolutional Neural Network
Authors: G. Jeyakodi, Trisha Agarwal, P. Shanthi Bala
Date: 2023-05-02 00:00:00
Tasks: image-classification, Image Classification

Vector-Borne Disease (VBD) is an infectious disease transmitted through the pathogenic female Aedes mosquito to humans and animals. It is important to control dengue disease by reducing the spread of Aedes mosquito vectors. Community awareness plays acrucial role to ensure Aedes control programmes and encourages the communities to involve active participation. Identifying the species of mosquito will help to recognize the mosquito density in the locality and intensifying mosquito control efforts in particular areas. This willhelp in avoiding Aedes breeding sites around residential areas and reduce adult mosquitoes. To serve this purpose, an android application are developed to identify Aedes species that help the community to contribute in mosquito control events. Several Android applications have been developed to identify species like birds, plant species, and Anopheles mosquito species. In this work, a user-friendly mobile application mAedesID is developed for identifying the Aedes mosquito species using a deep learning Convolutional Neural Network (CNN) algorithm which is best suited for species image classification and achieves better accuracy for voluminous images. The mobile application can be downloaded from the URLhttps://tinyurl.com/mAedesID.
Keras GPT Copilot: Integrating the Power of Large Language Models in Deep Learning Model Development
Authors: Fabi Prezja
Date: 2023-05-15 00:00:00
Proceeding: Zenodo GitHub 2023 5
Tasks: Data-to-Text Generation, Text Generation, Zero-shot Text Retrieval

Keras GPT Copilot is the first Python package designed to integrate an LLM copilot within the model development workflow, offering iterative feedback options for enhancing the performance of your Keras deep learning models. Utilizing the power of OpenAI's GPT models, Keras GPT Copilot can use any of the compatible models (GPT4 is recommended). However, the prompt-only mode allows for compatibility with other large language models.
A survey of underwater acoustic data classification methods using deep learning for shoreline surveillance
Authors: Lucas Cesar Ferreira Domingos, Paulo E Santos, Phillip S. M. Skelton, Russell S. A. Brinkworth, Karl Sammut
Date: 2022-03-11 00:00:00
Proceeding: Sensors 2022 3
Tasks: Data Augmentation, Deep Learning, Transfer Learning

This paper presents a comprehensive overview of current deep-learning methods for automatic object classification of underwater sonar data for shoreline surveillance, concentrating mostly on the classification of vessels from passive sonar data and the identification of objects of interest from active sonar (such as minelike objects, human figures or debris of wrecked ships). Not only is the contribution of this work to provide a systematic description of the state of the art of this field, but also to identify five main ingredients in its current development: the application of deep-learning methods using convolutional layers alone; deep-learning methods that apply biologically inspired feature-extraction filters as a preprocessing step; classification of data from frequency and time–frequency analysis; methods using machine learning to extract features from original signals; and transfer learning methods. This paper also describes some of the most important datasets cited in the literature and discusses data-augmentation techniques. The latter are used for coping with the scarcity of annotated sonar datasets from real maritime missions.
An Investigation of Preprocessing Filters and Deep Learning Methods for Vessel Type Classification With Underwater Acoustic Data
Authors: Lucas Cesar Ferreira Domingos, Paulo E. Santos, Phillip S. M. Skelton, Russell S. A. Brinkworth, Karl Sammut
Date: 2022-11-07 00:00:00
Proceeding: IEEE Access 2022 11
Tasks: object-detection, Object Detection, Underwater Acoustic Classification

The illegal exploitation of protected marine environments has consistently threatened the
biodiversity and economic development of coastal regions. Extensive monitoring in these – often remote
– areas is challenging. Machine learning methods are useful in object detection and classification tasks and
have the potential to underpin techniques for the development of robust monitoring systems to overcome
this problem. However, development is hindered due to the limited number of publicly available labelled
and curated datasets. Furthermore, there are relatively few open-source state-of-the-art methods to be used
for evaluation. This paper presents an investigation of automated classification methods using underwater
acoustic signals to infer the presence and type of vessels navigating in coastal regions. Various combinations
of deep convolutional neural network architectures, and preprocessing filter layers, were evaluated using a
new dataset based on a subset of the extensive open-source Ocean Networks Canada hydrophone data. Tests
were conducted in which VGGNet and ResNet networks were applied to classify the input data. The data was
preprocessed using either Constant Q Transform (CQT), Gammatone, Mel spectrogram, or a combination of
these filters. With over 97% accuracy, using all three preprocessing representations simultaneously yielded
the most reliable result. However, high accuracies of 94.95% were achieved using CQT as the preprocessing
filter for a ResNet-based convolutional neural network, providing a trade-off between model complexity
and accuracy; a result that is more than 10% higher than previously reported approaches. This more accurate
classifier for underwater acoustics could be used as a reliable autonomous monitoring system in maritime
environments.
SIKE’d Up: Fast Hardware Architectures for Supersingular Isogeny Key Encapsulation
Authors: Brian Koziel, A-Bon Ackie, Rami El Khatib, Reza Azarderakhsh, Mehran Mozaffari Kermani
Date: 2020-05-14 00:00:00
Proceeding: IEEE Transactions on Circuits and Systems I: Regular Papers 2020 5

In this work, we present a fast parallel architecture to perform supersingular isogeny key encapsulation (SIKE). We propose and implement a fast isogeny accelerator architecture that uses fast and parallelized isogeny formulas. On top of our isogeny accelerator, we build a novel architecture for the SIKE primitive, which provides both quantum and IND-CCA security. We synthesized this architecture on the Xilinx Artix-7, Virtex-7, and Kintex UltraScale+ FPGA families. Over Virtex-7 FPGA's, our constant-time implementations are roughly 14% faster than the state-of-the-art with a better area-time product. At the NIST security level 5 on a Kintex UltraScale+ FPGA, we can execute the entire SIKE protocol in 15.3 ms. This work continues to improve the speed of isogeny-based computations and also features all parameter sets of the SIKE round 2 specification, with results applicable to NIST's post-quantum standardization process.
A SVM and K-means clustering based fast and efficient intrusion detection system
Authors: A Shrivastava, RR Ahirwal
Date: 2013-05-06 00:00:00
Proceeding: International Journal 2013 5
Tasks: Clustering, Intrusion Detection

The intrusion or attack in the computer network is one of 
the most important issues creating problems for the 
network managers. However many countermeasures are 
taken for the security of the network but continuous 
growth of hackers requires to maintain the defending 
system up to data. This paper presents a K-means and 
support vector machine based intrusion detection system. 
The support vector machine is optimal partitioning based 
linear classifier and at least theoretically better other 
classifier also because only small numbers of classes 
required during classification SVM with one against one 
technique can be the best option and the K-means 
clustering filters the un-useful similar data points hence 
reduces the training time also hence provides an overall 
enhanced performance by reducing the training time 
while maintaining the accuracy. The proposed algorithm 
is tested using KDD99 dataset and results show the 
effectiveness of the algorithm. The paper also analyzed 
the effect of different input parameters on classification 
accuracy.
Convex optimization over a probability simplex
Authors: James Chok, Geoffrey M. Vasil
Date: 2023-05-15 00:00:00

We propose a new iteration scheme, the Cauchy-Simplex, to optimize convex problems over the probability simplex $\{w\in\mathbb{R}^n\ |\ \sum_i w_i=1\ \textrm{and}\ w_i\geq0\}$. Specifically, we map the simplex to the positive quadrant of a unit sphere, envisage gradient descent in latent variables, and map the result back in a way that only depends on the simplex variable. Moreover, proving rigorous convergence results in this formulation leads inherently to tools from information theory (e.g., cross-entropy and KL divergence). Each iteration of the Cauchy-Simplex consists of simple operations, making it well-suited for high-dimensional problems. In continuous time, we prove that $f(x_T)-f(x^*) = {O}(1/T)$ for differentiable real-valued convex functions, where $T$ is the number of time steps and $w^*$ is the optimal solution. Numerical experiments of projection onto convex hulls show faster convergence than similar algorithms. Finally, we apply our algorithm to online learning problems and prove the convergence of the average regret for (1) Prediction with expert advice and (2) Universal Portfolios.
SUG: Single-dataset Unified Generalization for 3D Point Cloud Classification
Authors: Siyuan Huang, Bo Zhang, Botian Shi, Peng Gao, Yikang Li, Hongsheng Li
Date: 2023-05-16 00:00:00
Tasks: 3D Point Cloud Classification, Domain Adaptation, Domain Generalization, Point Cloud Classification, Unsupervised Domain Adaptation

Although Domain Generalization (DG) problem has been fast-growing in the 2D image tasks, its exploration on 3D point cloud data is still insufficient and challenged by more complex and uncertain cross-domain variances with uneven inter-class modality distribution. In this paper, different from previous 2D DG works, we focus on the 3D DG problem and propose a Single-dataset Unified Generalization (SUG) framework that only leverages a single source dataset to alleviate the unforeseen domain differences faced by a well-trained source model. Specifically, we first design a Multi-grained Sub-domain Alignment (MSA) method, which can constrain the learned representations to be domain-agnostic and discriminative, by performing a multi-grained feature alignment process between the splitted sub-domains from the single source dataset. Then, a Sample-level Domain-aware Attention (SDA) strategy is presented, which can selectively enhance easy-to-adapt samples from different sub-domains according to the sample-level inter-domain distance to avoid the negative transfer. Experiments demonstrate that our SUG can boost the generalization ability for unseen target domains, even outperforming the existing unsupervised domain adaptation methods that have to access extensive target domain data. Our code is available at https://github.com/SiyuanHuang95/SUG.
Capacitor Voltage Synchronizing Control of 100% Full-Scale Wind Power Generator-Supplied Power Systems
Authors: Yang Liu
Date: 2023-05-16 00:00:00

This paper proposes a capacitor voltage synchronizing control (CVSC) system for the regulation of full-scale wind power generator-supplied power systems (FWPS). The capacitor combined with the inverter of a full-scale wind power generator (WPG) is controlled with a CVSC system to mimic the rotor dynamics of a synchronous generator (SG). WPGs are enabled to offer inertial response and primary regulation. The generation and load unbalance of a FWPS is reflected by the deviation of capacitor voltages of WPGs. Small-signal analysis was carried out to investigate the oscillatory modes of a FWPS with the CVSC system. Time-domain simulation studies were undertaken on the FWPS, and the performance of the CVSC system was studied in the cases where a load increase and a three-phase-to-ground fault occurred on the FWPS, respectively.
Watermarking Text Generated by Black-Box Language Models
Authors: Xi Yang, Kejiang Chen, Weiming Zhang, Chang Liu, Yuang Qi, Jie Zhang, Han Fang, Nenghai Yu
Date: 2023-05-14 00:00:00
Tasks: Adversarial Robustness, Language Modelling, Specificity, Text Generation

LLMs now exhibit human-like skills in various fields, leading to worries about misuse. Thus, detecting generated text is crucial. However, passive detection methods are stuck in domain specificity and limited adversarial robustness. To achieve reliable detection, a watermark-based method was proposed for white-box LLMs, allowing them to embed watermarks during text generation. The method involves randomly dividing the model vocabulary to obtain a special list and adjusting the probability distribution to promote the selection of words in the list. A detection algorithm aware of the list can identify the watermarked text. However, this method is not applicable in many real-world scenarios where only black-box language models are available. For instance, third-parties that develop API-based vertical applications cannot watermark text themselves because API providers only supply generated text and withhold probability distributions to shield their commercial interests. To allow third-parties to autonomously inject watermarks into generated text, we develop a watermarking framework for black-box language model usage scenarios. Specifically, we first define a binary encoding function to compute a random binary encoding corresponding to a word. The encodings computed for non-watermarked text conform to a Bernoulli distribution, wherein the probability of a word representing bit-1 being approximately 0.5. To inject a watermark, we alter the distribution by selectively replacing words representing bit-0 with context-based synonyms that represent bit-1. A statistical test is then used to identify the watermark. Experiments demonstrate the effectiveness of our method on both Chinese and English datasets. Furthermore, results under re-translation, polishing, word deletion, and synonym substitution attacks reveal that it is arduous to remove the watermark without compromising the original semantics.
Towards Speech Dialogue Translation Mediating Speakers of Different Languages
Authors: Shuichiro Shimizu, Chenhui Chu, Sheng Li, Sadao Kurohashi
Date: 2023-05-16 00:00:00
Tasks: Translation

We present a new task, speech dialogue translation mediating speakers of different languages. We construct the SpeechBSD dataset for the task and conduct baseline experiments. Furthermore, we consider context to be an important aspect that needs to be addressed in this task and propose two ways of utilizing context, namely monolingual context and bilingual context. We conduct cascaded speech translation experiments using Whisper and mBART, and show that bilingual context performs better in our settings.
Make-An-Animation: Large-Scale Text-conditional 3D Human Motion Generation
Authors: Samaneh Azadi, Akbar Shah, Thomas Hayes, Devi Parikh, Sonal Gupta
Date: 2023-05-16 00:00:00
Proceeding: ICCV 2023 1
Tasks: Motion Generation, Motion Synthesis, Text-to-Video Generation, Video Generation

Text-guided human motion generation has drawn significant interest because of its impactful applications spanning animation and robotics. Recently, application of diffusion models for motion generation has enabled improvements in the quality of generated motions. However, existing approaches are limited by their reliance on relatively small-scale motion capture data, leading to poor performance on more diverse, in-the-wild prompts. In this paper, we introduce Make-An-Animation, a text-conditioned human motion generation model which learns more diverse poses and prompts from large-scale image-text datasets, enabling significant improvement in performance over prior works. Make-An-Animation is trained in two stages. First, we train on a curated large-scale dataset of (text, static pseudo-pose) pairs extracted from image-text datasets. Second, we fine-tune on motion capture data, adding additional layers to model the temporal dimension. Unlike prior diffusion models for motion generation, Make-An-Animation uses a U-Net architecture similar to recent text-to-video generation models. Human evaluation of motion realism and alignment with input text shows that our model reaches state-of-the-art performance on text-to-motion generation.
Double Pessimism is Provably Efficient for Distributionally Robust Offline Reinforcement Learning: Generic Algorithm and Robust Partial Coverage
Date: 2023-05-16 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Offline RL

In this paper, we study distributionally robust offline reinforcement learning (robust offline RL), which seeks to find an optimal policy purely from an offline dataset that can perform well in perturbed environments. In specific, we propose a generic algorithm framework called Doubly Pessimistic Model-based Policy Optimization ($P^2MPO$), which features a novel combination of a flexible model estimation subroutine and a doubly pessimistic policy optimization step. Notably, the double pessimism principle is crucial to overcome the distributional shifts incurred by (i) the mismatch between the behavior policy and the target policies; and (ii) the perturbation of the nominal model. Under certain accuracy conditions on the model estimation subroutine, we prove that $P^2MPO$ is sample-efficient with robust partial coverage data, which only requires the offline data to have good coverage of the distributions induced by the optimal robust policy and the perturbed models around the nominal model. By tailoring specific model estimation subroutines for concrete examples of RMDPs, including tabular RMDPs, factored RMDPs, kernel and neural RMDPs, we prove that $P^2MPO$ enjoys a $\tilde{\mathcal{O}}(n^{-1/2})$ convergence rate, where $n$ is the dataset size. We highlight that all these examples, except tabular RMDPs, are first identified and proven tractable by this work. Furthermore, we continue our study of robust offline RL in the robust Markov games (RMGs). By extending the double pessimism principle identified for single-agent RMDPs, we propose another algorithm framework that can efficiently find the robust Nash equilibria among players using only robust unilateral (partial) coverage data. To our best knowledge, this work proposes the first general learning principle -- double pessimism -- for robust offline RL and shows that it is provably efficient with general function approximation.
Prompt-Tuning Decision Transformer with Preference Ranking
Authors: Shengchao Hu, Li Shen, Ya zhang, DaCheng Tao
Date: 2023-05-16 00:00:00
Tasks: Prompt Learning

Prompt-tuning has emerged as a promising method for adapting pre-trained models to downstream tasks or aligning with human preferences. Prompt learning is widely used in NLP but has limited applicability to RL due to the complex physical meaning and environment-specific information contained within RL prompts. These factors require supervised learning to imitate the demonstrations and may result in a loss of meaning after learning. Additionally, directly extending prompt-tuning approaches to RL is challenging because RL prompts guide agent behavior based on environmental modeling and analysis, rather than filling in missing information, making it unlikely that adjustments to the prompt format for downstream tasks, as in NLP, can yield significant improvements. In this work, we propose the Prompt-Tuning DT algorithm to address these challenges by using trajectory segments as prompts to guide RL agents in acquiring environmental information and optimizing prompts via black-box tuning to enhance their ability to contain more relevant information, thereby enabling agents to make better decisions. Our approach involves randomly sampling a Gaussian distribution to fine-tune the elements of the prompt trajectory and using preference ranking function to find the optimization direction, thereby providing more informative prompts and guiding the agent towards specific preferences in the target environment. Extensive experiments show that with only 0.03% of the parameters learned, Prompt-Tuning DT achieves comparable or even better performance than full-model fine-tuning in low-data scenarios. Our work contributes to the advancement of prompt-tuning approaches in RL, providing a promising direction for optimizing large RL agents for specific preference tasks.