pwc-archive/papers-with-abstracts huggingface dataset:
MTC: A Multi-Task Model for Encrypted Network Traffic Classification Based on Transformer and 1D-CNN
Authors: Kaiyue Wang, Jian Gao, Xinyan Lei
Date: 2023-04-29 00:00:00
Proceeding: Intelligent Automation & Soft Computing 2023 4
Tasks: Multi-Task Learning, Traffic Classification

Traffic characterization (e.g., chat, video) and application identification (e.g., FTP, Facebook) are two of the more crucial jobs in encrypted network traffic classification. These two activities are typically carried out separately by existing systems using separate models, significantly adding to the difficulty of network administration. Convolutional Neural Network (CNN) and Transformer are deep learning-based approaches for network traffic classification. CNN is good at extracting local features while ignoring long-distance information from the network traffic sequence, and Transformer can capture long-distance feature dependencies while ignoring local details. Based on these characteristics, a multi-task learning model that combines Transformer and 1D-CNN for encrypted traffic classification is proposed (MTC). In order to make up for the Transformer’s lack of local detail feature extraction capability and the 1D-CNN’s shortcoming of ignoring long-distance correlation information when processing traffic sequences, the model uses a parallel structure to fuse the features generated by the Transformer block and the 1D-CNN block with each other using a feature fusion block. This structure improved the representation of traffic features by both blocks and allows the model to perform well with both long and short length sequences. The model simultaneously handles multiple tasks, which lowers the cost of training. Experiments reveal that on the ISCX VPN-nonVPN dataset, the model achieves an average F1 score of 98.25% and an average recall of 98.30% for the task of identifying applications, and an average F1 score of 97.94%, and an average recall of 97.54% for the task of traffic characterization. When advanced models on the same dataset are chosen for comparison, the model produces the best results. To prove the generalization, we applied MTC to CICIDS2017 dataset, and our model also achieved good results.
You Can Trade Your Experience in Distributed Multi-Agent Multi-Armed Bandits
Authors: Guoju Gao, He Huang, Jie Wu, Sijie Huang, Yang Du
Date: 2023-06-19 00:00:00
Proceeding: 2023 IEEE/ACM 31st International Symposium on Quality of Service (IWQoS) 2023 6
Tasks: Decision Making, Multi-Armed Bandits, Sequential Decision Making

Multi-Armed Bandit (MAB) that solves the sequential decision-making to the prior-unknown settings has been extensively studied and adopted in various applications such as online recommendation, transmission rate allocation, etc. Although some recent work has investigated the multi-agent MAB model, they supposed that agents share their bandit information based on social networks but neglected the incentives and arm-pulling budget for heterogeneous agents. In this paper, we propose a transaction-based multi-agent MAB framework, where agents can trade their bandit experience with each other to improve their total individual rewards. Agents not only face the dilemma between exploitation and exploration, but also decide to post a suitable price for their bandit experience. Meanwhile, as a buyer, the agent accepts another agent whose experience will help her the most, according to the posted price and her risk-tolerance level. The key challenge lies in that the arm-pulling and experience-trading decisions affect each other. To this end, we design the transaction-based upper confidence bound to estimate the prior-unknown rewards of arms, based on which the agents pull arms or trade their experience. We prove the regret bound of the proposed algorithm for each independent agent and conduct extensive experiments to verify the performance of our solution.
STORM: Efficient Stochastic Transformer based World Models for Reinforcement Learning
Authors: Weipu Zhang, Gang Wang, Jian Sun, Yetian Yuan, Gao Huang
Date: 2023-10-14 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Atari Games 100k, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Self-Supervised Learning

Recently, model-based reinforcement learning algorithms have demonstrated remarkable efficacy in visual input environments. These approaches begin by constructing a parameterized simulation world model of the real environment through self-supervised learning. By leveraging the imagination of the world model, the agent's policy is enhanced without the constraints of sampling from the real environment. The performance of these algorithms heavily relies on the sequence modeling and generation capabilities of the world model. However, constructing a perfectly accurate model of a complex unknown environment is nearly impossible. Discrepancies between the model and reality may cause the agent to pursue virtual goals, resulting in subpar performance in the real environment. Introducing random noise into model-based reinforcement learning has been proven beneficial. In this work, we introduce Stochastic Transformer-based wORld Model (STORM), an efficient world model architecture that combines the strong sequence modeling and generation capabilities of Transformers with the stochastic nature of variational autoencoders. STORM achieves a mean human performance of $126.7\%$ on the Atari $100$k benchmark, setting a new record among state-of-the-art methods that do not employ lookahead search techniques. Moreover, training an agent with $1.85$ hours of real-time interaction experience on a single NVIDIA GeForce RTX 3090 graphics card requires only $4.3$ hours, showcasing improved efficiency compared to previous methodologies.
Multiagent Copilot Approach for Shared Autonomy between Human EEG and TD3 Deep Reinforcement Learning
Authors: Chun-Ren Phang, Akimasa Hirata
Date: 2023-12-22 00:00:00
Tasks: Brain Computer Interface, Deep Reinforcement Learning, EEG, Reinforcement Learning (RL)

Deep reinforcement learning (RL) algorithms enable the development of fully autonomous agents that can interact with the environment. Brain-computer interface (BCI) systems decipher human implicit brain signals regardless of the explicit environment. In this study, we integrated deep RL and BCI to improve beneficial human interventions in autonomous systems and the performance in decoding brain activities by considering environmental factors. Shared autonomy was allowed between the action command decoded from the electroencephalography (EEG) of the human agent and the action generated from the twin delayed DDPG (TD3) agent for a given environment. Our proposed copilot control scheme with a full blocker (Co-FB) significantly outperformed the individual EEG (EEG-NB) or TD3 control. The Co-FB model achieved a higher target approaching score, lower failure rate, and lower human workload than the EEG-NB model. The Co-FB control scheme had a higher invisible target score and level of allowed human intervention than the TD3 model. We also proposed a disparity d-index to evaluate the effect of contradicting agent decisions on the control accuracy and authority of the copilot model. We found a significant correlation between the control authority of the TD3 agent and the performance improvement of human EEG classification with respect to the d-index. We also observed that shifting control authority to the TD3 agent improved performance when BCI decoding was not optimal. These findings indicate that the copilot system can effectively handle complex environments and that BCI performance can be improved by considering environmental factors. Future work should employ continuous action space and different multi-agent approaches to evaluate copilot performance.
Aurora:Activating Chinese chat capability for Mixtral-8x7B sparse Mixture-of-Experts through Instruction-Tuning
Authors: Rongsheng Wang, Haoming Chen, Ruizhe Zhou, Yaofei Duan, Kunyan Cai, Han Ma, Jiaxi Cui, Jian Li, Patrick Cheong-Iao Pang, Yapeng Wang, Tao Tan
Date: 2023-12-22 00:00:00
Tasks: Instruction Following, Mixture-of-Experts, MMLU

Existing research has demonstrated that refining large language models (LLMs) through the utilization of machine-generated instruction-following data empowers these models to exhibit impressive zero-shot capabilities for novel tasks, without requiring human-authored instructions. In this paper, we systematically investigate, preprocess, and integrate three Chinese instruction-following datasets with the aim of enhancing the Chinese conversational capabilities of Mixtral-8x7B sparse Mixture-of-Experts model. Through instruction fine-tuning on this carefully processed dataset, we successfully construct the Mixtral-8x7B sparse Mixture-of-Experts model named "Aurora." To assess the performance of Aurora, we utilize three widely recognized benchmark tests: C-Eval, MMLU, and CMMLU. Empirical studies validate the effectiveness of instruction fine-tuning applied to Mixtral-8x7B sparse Mixture-of-Experts model. This work is pioneering in the execution of instruction fine-tuning on a sparse expert-mixed model, marking a significant breakthrough in enhancing the capabilities of this model architecture. Our code, data and model are publicly available at https://github.com/WangRongsheng/Aurora
Token-Level Contrastive Learning with Modality-Aware Prompting for Multimodal Intent Recognition
Authors: Qianrui Zhou, Hua Xu, Hao Li, Hanlei Zhang, Xiaohan Zhang, Yifan Wang, Kai Gao
Date: 2023-12-22 00:00:00
Tasks: Contrastive Learning, Intent Recognition, Multimodal Intent Recognition, Prompt Learning

Multimodal intent recognition aims to leverage diverse modalities such as expressions, body movements and tone of speech to comprehend user's intent, constituting a critical task for understanding human language and behavior in real-world multimodal scenarios. Nevertheless, the majority of existing methods ignore potential correlations among different modalities and own limitations in effectively learning semantic features from nonverbal modalities. In this paper, we introduce a token-level contrastive learning method with modality-aware prompting (TCL-MAP) to address the above challenges. To establish an optimal multimodal semantic environment for text modality, we develop a modality-aware prompting module (MAP), which effectively aligns and fuses features from text, video and audio modalities with similarity-based modality alignment and cross-modality attention mechanism. Based on the modality-aware prompt and ground truth labels, the proposed token-level contrastive learning framework (TCL) constructs augmented samples and employs NT-Xent loss on the label token. Specifically, TCL capitalizes on the optimal textual semantic insights derived from intent labels to guide the learning processes of other modalities in return. Extensive experiments show that our method achieves remarkable improvements compared to state-of-the-art methods. Additionally, ablation analyses demonstrate the superiority of the modality-aware prompt over the handcrafted prompt, which holds substantial significance for multimodal prompt learning. The codes are released at https://github.com/thuiar/TCL-MAP.
Characterizing and Classifying Developer Forum Posts with their Intentions
Authors: Xingfang Wu, Eric Laufer, Heng Li, Foutse khomh, Santhosh Srinivasan, Jayden Luo
Date: 2023-12-21 00:00:00
Tasks: TAG

With the rapid growth of the developer community, the amount of posts on online technical forums has been growing rapidly, which poses difficulties for users to filter useful posts and find important information. Tags provide a concise feature dimension for users to locate their interested posts and for search engines to index the most relevant posts according to the queries. However, most tags are only focused on the technical perspective (e.g., program language, platform, tool). In most cases, forum posts in online developer communities reveal the author's intentions to solve a problem, ask for advice, share information, etc. The modeling of the intentions of posts can provide an extra dimension to the current tag taxonomy. By referencing previous studies and learning from industrial perspectives, we create a refined taxonomy for the intentions of technical forum posts. Through manual labeling and analysis on a sampled post dataset extracted from online forums, we understand the relevance between the constitution of posts (code, error messages) and their intentions. Furthermore, inspired by our manual study, we design a pre-trained transformer-based model to automatically predict post intentions. The best variant of our intention prediction framework, which achieves a Micro F1-score of 0.589, Top 1-3 accuracy of 62.6% to 87.8%, and an average AUC of 0.787, outperforms the state-of-the-art baseline approach. Our characterization and automated classification of forum posts regarding their intentions may help forum maintainers or third-party tool developers improve the organization and retrieval of posts on technical forums. We have released our annotated dataset and codes in our supplementary material package.
Hierarchical Neighbor Propagation With Bidirectional Graph Attention Network for Relation Prediction
Authors: Zhiwen Xie, Runjie Zhu, Jin Liu, Guangyou Zhou, and Jimmy Xiangji Huang
Date: 2021-04-25 00:00:00
Proceeding: IEEE/ACM Transactions on Audio, Speech, and Language Processing 2021 4
Tasks: Graph Attention, Relation, Relation Prediction

Abstract—The graph attention network (GAT) [1] has started
to become a mainstream neural network architecture since 2018,
yielding remarkable performance gains in various natural language processing (NLP) tasks. Although GAT has reached the
state-of-the-art (SOTA) performance as a recent success in relation
prediction in knowledge graph, the current model is still limited
by the following two aspects: (1) the existing model only considers the neighbors from the inbound-direction of the given entity,
but ignores the rich neighborhood information from outbounddirections; (2) the existing model only uses the k-th hop output to
learn the multi-hop embeddings, which leads to the loss of a large
amount of early-stage embedding information (e.g., one-hop) at the
graph attention step. In this study, we propose a novel bidirectional
graph attention network (BiGAT) to learn the hierarchical neighbor propagation. In our proposed BiGAT, an inbound-directional
GAT and an outbound-directional GAT are introduced to capture
sufficient neighborhood information before propagating the bidirectional neighborhood information to learn the multi-hop feature
embeddings in a hierarchical manner. Experiments conducted on
the four publicly available datasets show that BiGAT achieves the
competitive results in comparison to other SOTA methods.
Training Convolutional Neural Networks with the Forward-Forward algorithm
Authors: Riccardo Scodellaro, Ajinkya Kulkarni, Frauke Alves, Matthias Schröter
Date: 2023-12-22 00:00:00

The recent successes in analyzing images with deep neural networks are almost exclusively achieved with Convolutional Neural Networks (CNNs). The training of these CNNs, and in fact of all deep neural network architectures, uses the backpropagation algorithm where the output of the network is compared with the desired result and the difference is then used to tune the weights of the network towards the desired outcome. In a 2022 preprint, Geoffrey Hinton suggested an alternative way of training which passes the desired results together with the images at the input of the network. This so called Forward Forward (FF) algorithm has up to now only been used in fully connected networks. In this paper, we show how the FF paradigm can be extended to CNNs. Our FF-trained CNN, featuring a novel spatially-extended labeling technique, achieves a classification accuracy of 99.16% on the MNIST hand-written digits dataset. We show how different hyperparameters affect the performance of the proposed algorithm and compare the results with CNN trained with the standard backpropagation approach. Furthermore, we use Class Activation Maps to investigate which type of features are learnt by the FF algorithm.
A Novel Sampled Clustering Algorithm for Rice Phenotypic Data
Authors: Mithun Singh, Kapil Ahuja, Milind B. Ratnaparkhe
Date: 2023-12-22 00:00:00
Tasks: Clustering

Phenotypic (or Physical) characteristics of plant species are commonly used to perform clustering. In one of our recent works (Shastri et al. (2021)), we used a probabilistically sampled (using pivotal sampling) and spectrally clustered algorithm to group soybean species. These techniques were used to obtain highly accurate clusterings at a reduced cost. In this work, we extend the earlier algorithm to cluster rice species. We improve the base algorithm in three ways. First, we propose a new function to build the similarity matrix in Spectral Clustering. Commonly, a natural exponential function is used for this purpose. Based upon the spectral graph theory and the involved Cheeger's inequality, we propose the use a base "a" exponential function instead. This gives a similarity matrix spectrum favorable for clustering, which we support via an eigenvalue analysis. Also, the function used to build the similarity matrix in Spectral Clustering was earlier scaled with a fixed factor (called global scaling). Based upon the idea of Zelnik-Manor and Perona (2004), we now use a factor that varies with matrix elements (called local scaling) and works better. Second, to compute the inclusion probability of a specie in the pivotal sampling algorithm, we had earlier used the notion of deviation that captured how far specie's characteristic values were from their respective base values (computed over all species). A maximum function was used before to find the base values. We now use a median function, which is more intuitive. We support this choice using a statistical analysis. Third, with experiments on 1865 rice species, we demonstrate that in terms of silhouette values, our new Sampled Spectral Clustering is 61% better than Hierarchical Clustering (currently prevalent). Also, our new algorithm is significantly faster than Hierarchical Clustering due to the involved sampling.
FAST: Feature Aware Similarity Thresholding for Weak Unlearning in Black-Box Generative Models
Authors: Subhodip Panda, Prathosh AP
Date: 2023-12-22 00:00:00
Tasks: Machine Unlearning

The heightened emphasis on the regulation of deep generative models, propelled by escalating concerns pertaining to privacy and compliance with regulatory frameworks, underscores the imperative need for precise control mechanisms over these models. This urgency is particularly underscored by instances in which generative models generate outputs that encompass objectionable, offensive, or potentially injurious content. In response, machine unlearning has emerged to selectively forget specific knowledge or remove the influence of undesirable data subsets from pre-trained models. However, modern machine unlearning approaches typically assume access to model parameters and architectural details during unlearning, which is not always feasible. In multitude of downstream tasks, these models function as black-box systems, with inaccessible pre-trained parameters, architectures, and training data. In such scenarios, the possibility of filtering undesired outputs becomes a practical alternative. The primary goal of this study is twofold: first, to elucidate the relationship between filtering and unlearning processes, and second, to formulate a methodology aimed at mitigating the display of undesirable outputs generated from models characterized as black-box systems. Theoretical analysis in this study demonstrates that, in the context of black-box models, filtering can be seen as a form of weak unlearning. Our proposed \textbf{\textit{Feature Aware Similarity Thresholding(FAST)}} method effectively suppresses undesired outputs by systematically encoding the representation of unwanted features in the latent space.
NPHardEval: Dynamic Benchmark on Reasoning Ability of Large Language Models via Complexity Classes
Authors: Lizhou Fan, Wenyue Hua, Lingyao Li, Haoyang Ling, Yongfeng Zhang
Date: 2023-12-22 00:00:00

Complex reasoning ability is one of the most important features of current LLMs, which has also been leveraged to play an integral role in complex decision-making tasks. Therefore, the investigation into the reasoning capabilities of Large Language Models (LLMs) is critical: numerous benchmarks have been established to assess the reasoning abilities of LLMs. However, current benchmarks are inadequate in offering a rigorous evaluation of the full extent of reasoning abilities that LLMs are capable of achieving. They are also prone to the risk of overfitting, as these benchmarks, being publicly accessible and static, allow models to potentially tailor their responses to specific benchmark metrics, thereby inflating their performance. Addressing these limitations, our research introduces a new benchmark, named NPHardEval. This benchmark is designed to evaluate the reasoning abilities of LLMs across a broad spectrum of 900 algorithmic questions, extending up to the NP-Hard complexity class. These questions are meticulously chosen to represent a wide range of complexity class below the NP-hard complexity class, offering a rigorous measure of the reasoning ability of LLMs. Through this study, we shed light on the current state of reasoning in LLMs, providing an objective and rigorous perspective through the comparison of LLMs' performance across complex classes. Moreover, this benchmark is designed with a dynamic update mechanism, where the datapoints are refreshed on a monthly basis. Such regular updates play a crucial role in mitigating the risk of LLMs overfitting to the benchmark, promoting a more accurate and reliable assessment of their reasoning capabilities. The benchmark dataset and code of NPHardEval are available at https://github.com/casmlab/NPHardEval.
SutraNets: Sub-series Autoregressive Networks for Long-Sequence, Probabilistic Forecasting
Authors: Shane Bergsma, Timothy Zeyl, Lei Guo
Date: 2023-12-22 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Time Series

We propose SutraNets, a novel method for neural probabilistic forecasting of long-sequence time series. SutraNets use an autoregressive generative model to factorize the likelihood of long sequences into products of conditional probabilities. When generating long sequences, most autoregressive approaches suffer from harmful error accumulation, as well as challenges in modeling long-distance dependencies. SutraNets treat long, univariate prediction as multivariate prediction over lower-frequency sub-series. Autoregression proceeds across time and across sub-series in order to ensure coherent multivariate (and, hence, high-frequency univariate) outputs. Since sub-series can be generated using fewer steps, SutraNets effectively reduce error accumulation and signal path distances. We find SutraNets to significantly improve forecasting accuracy over competitive alternatives on six real-world datasets, including when we vary the number of sub-series and scale up the depth and width of the underlying sequence models.