Reviewerly/exHarmony huggingface dataset:
AgentScope: A Flexible yet Robust Multi-Agent Platform

Authors:
raw_author_name: Gao, Dawei
raw_author_name: Li, Zitao
raw_author_name: Kuang, Weirui
raw_author_name: Pan, Xuchen
raw_author_name: Chen, Daoyuan
raw_author_name: Yao, Liuyi
raw_author_name: Zhu, Lin
raw_author_name: Cheng, Chen
raw_author_name: Shi, Hongzhu
raw_author_name: Li, Yaliang
raw_author_name: Ding, Bolin
raw_author_name: Zhou, Jingren

Abstract: With the rapid advancement of Large Language Models (LLMs), significant progress has been made in multi-agent applications. However, the complexities in coordinating agents' cooperation and LLMs' erratic performance pose notable challenges in developing robust and efficient multi-agent applications. To tackle these challenges, we propose AgentScope, a developer-centric multi-agent platform with message exchange as its core communication mechanism. Together with abundant syntactic tools, built-in resources, and user-friendly interactions, our communication mechanism significantly reduces the barriers to both development and understanding. Towards robust and flexible multi-agent application, AgentScope provides both built-in and customizable fault tolerance mechanisms while it is also armed with system-level supports for multi-modal data generation, storage and transmission. Additionally, we design an actor-based distribution framework, enabling easy conversion between local and distributed deployments and automatic parallel optimization without extra effort. With these features, AgentScope empowers developers to build applications that fully realize the potential of intelligent agents. We have released AgentScope at https://github.com/modelscope/agentscope, and hope AgentScope invites wider participation and innovation in this fast-moving field.
Exploring Individuals’ Experiences with Security Attacks: A Text Mining and Qualitative Study

Authors:
raw_author_name: Rabab Ali Abumalloh
raw_affiliation_string: Department of Computer Science and Engineering, Qatar University, Doha 2713,
raw_author_name: Nahla El-Haggar
raw_affiliation_string: Department of Computer Science, Applied College, Imam Abdulrahman Bin Faisal University, Dammam 31441,
raw_author_name: Albandari Alsumayt
raw_affiliation_string: Department of Computer Science, Applied College, Imam Abdulrahman Bin Faisal University, Dammam 31441,
raw_author_name: Zeyad M. Alfawaer
raw_affiliation_string: Department of Computer Science, Applied College, Imam Abdulrahman Bin Faisal University, Dammam 31441,
raw_author_name: Sumayh S. Aljameel
raw_affiliation_string: SAUDI ARAMCO Cybersecurity Chair, Computer Science Department, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441,

Abstract: Cyber-attacks have become increasingly prevalent with the widespread integration of technology into various aspects of our lives. The surge in social media platform usage has prompted users to share their firsthand experiences with cyber-attacks. Despite this, previous literature has not extensively investigated individuals' experiences with these attacks. This study aims to comprehensively explore and analyze the content shared by cyber-attack victims in Saudi Arabia, encompassing text, video, and audio formats. The primary objective is to investigate the factors influencing victims' perceptions of the security risks associated with these attacks. Following data collection, preparation, and cleaning, Latent Dirichlet Allocation (LDA) is employed for topic modeling, shedding light on potential factors impacting victims. Sentiment analysis is then utilized to examine the nuanced negative and positive perceptions of individuals. NVivo is deployed for data inspection, facilitating the presentation of insightful inferences. Hierarchical clustering is implemented to explore distinct clusters within the textual dataset. The study's results underscore the critical importance of spreading awareness among individuals regarding the various tactics employed by cyber attackers. Doi: 10.28991/ESJ-2024-08-01-010 Full Text: PDF
Stable training via elastic adaptive deep reinforcement learning for autonomous navigation of intelligent vehicles

Authors:
raw_author_name: Yujiao Zhao
raw_affiliation_string: State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan, China
raw_author_name: Yong Ma
raw_affiliation_string: State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan, China
raw_author_name: Guibing Zhu
raw_affiliation_string: Marine College, Zhejiang Ocean University, Zhoushan, China
raw_author_name: Songlin Hu
raw_affiliation_string: Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, China
raw_author_name: Xinpin Yan
raw_affiliation_string: National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan, China

Abstract: Abstract The uncertain stability of deep reinforcement learning training on complex tasks impedes its development and deployment, especially in intelligent vehicles, such as intelligent surface vessels and self-driving cars. Complex and varied environmental states puzzle training of decision-making networks. Here we propose an elastic adaptive deep reinforcement learning algorithm to address these challenges and achieve autonomous navigation in intelligent vehicles. Our method trains the decision-making network over the function and optimization learning stages, in which the state space and action space of autonomous navigation tasks are pruned by choosing classic states and actions to reduce data similarity, facilitating more stable training. We introduce a task-adaptive observed behaviour classification technique in the function learning stage to divide state and action spaces into subspaces and identify classic states and actions. In which the classic states and actions are accumulated as the training dataset that enhances its training efficiency. In the subsequent optimization learning stage, the decision-making network is refined through meticulous exploration and accumulation of datasets. The proposed elastic adaptive deep reinforcement learning enables the decision-making network to effectively learn from complex state and action spaces, leading to more efficient training compared to traditional deep reinforcement learning approaches. Simulation results demonstrate the remarkable effectiveness of our method in training decision-making networks for intelligent vehicles. The findings validate that our method provides reliable and efficient training for decision-making networks in intelligent vehicles. Moreover, our method exhibits stability in training other tasks characterized by continuous state and action spaces.
LLM Based Multi-Agent Generation of Semi-structured Documents from  Semantic Templates in the Public Administration Domain

Authors:
raw_author_name: Musumeci, Emanuele
raw_author_name: Suriani, Vincenzo
raw_author_name: Nardi, Daniele
raw_author_name: Bloisi, Domenico Daniele

Abstract: In the last years' digitalization process, the creation and management of documents in various domains, particularly in Public Administration (PA), have become increasingly complex and diverse. This complexity arises from the need to handle a wide range of document types, often characterized by semi-structured forms. Semi-structured documents present a fixed set of data without a fixed format. As a consequence, a template-based solution cannot be used, as understanding a document requires the extraction of the data structure. The recent introduction of Large Language Models (LLMs) has enabled the creation of customized text output satisfying user requests. In this work, we propose a novel approach that combines the LLMs with prompt engineering and multi-agent systems for generating new documents compliant with a desired structure. The main contribution of this work concerns replacing the commonly used manual prompting with a task description generated by semantic retrieval from an LLM. The potential of this approach is demonstrated through a series of experiments and case studies, showcasing its effectiveness in real-world PA scenarios.
Gotcha! Don't trick me with unanswerable questions! Self-aligning Large  Language Models for Responding to Unknown Questions

Authors:
raw_author_name: Deng, Yang
raw_author_name: Zhao, Yong
raw_author_name: Ng, See-Kiong
raw_author_name: Chua, Tat-Seng

Abstract: Despite the remarkable abilities of Large Language Models (LLMs) to answer questions, they often display a considerable level of overconfidence even when the question does not have a definitive answer. To avoid providing hallucinated answers to these unknown questions, existing studies typically investigate approaches to refusing to answer these questions. In this work, we propose a novel and scalable self-alignment method to utilize the LLM itself to enhance its response-ability to different types of unknown questions, being capable of not only refusing to answer but also providing explanation to the unanswerability of unknown questions. Specifically, the Self-Align method first employ a two-stage class-aware self-augmentation approach to generate a large amount of unknown question-response data. Then we conduct disparity-driven self-curation to select qualified data for fine-tuning the LLM itself for aligning the responses to unknown questions as desired. Experimental results on two datasets across four types of unknown questions validate the superiority of the Self-Align method over existing baselines in terms of three types of task formulation.
Machine Unlearning by Suppressing Sample Contribution

Authors:
raw_author_name: Cheng, Xinwen
raw_author_name: Huang, Zhehao
raw_author_name: Huang, Xiaolin

Abstract: Machine Unlearning (MU) is to forget data from a well-trained model, which is practically important due to the "right to be forgotten". In this paper, we start from the fundamental distinction between training data and unseen data on their contribution to the model: the training data contributes to the final model while the unseen data does not. We theoretically discover that the input sensitivity can approximately measure the contribution and practically design an algorithm, called MU-Mis (machine unlearning via minimizing input sensitivity), to suppress the contribution of the forgetting data. Experimental results demonstrate that MU-Mis outperforms state-of-the-art MU methods significantly. Additionally, MU-Mis aligns more closely with the application of MU as it does not require the use of remaining data.
On the composable security of weak coin flipping

Authors:
raw_author_name: Wu, Jiawei
raw_author_name: Hu, Yanglin
raw_author_name: Tomamichel, Marco

Abstract: Weak coin flipping is a cryptographic primitive in which two mutually distrustful parties generate a shared random bit to agree on a winner via remote communication. While a stand-alone secure weak coin flipping protocol can be constructed from noiseless communication channels, its composability has not been explored. In this work, we demonstrate that no weak coin flipping protocol can be abstracted into a black box resource with composable security. Despite this, we also establish the overall stand-alone security of weak coin flipping protocols under sequential composition.
A Blockchain-Enabled Framework of UAV Coordination for Post-Disaster  Networks

Authors:
raw_author_name: Cheng, Runze
raw_author_name: Mohjazi, Lina
raw_author_name: Imran, Muhammad Ali
raw_author_name: Sun, Yao

Abstract: Emergency communication is critical but challenging after natural disasters when ground infrastructure is devastated. Unmanned aerial vehicles (UAVs) offer enormous potential for agile relief coordination in these scenarios. However, effectively leveraging UAV fleets poses additional challenges around security, privacy, and efficient collaboration across response agencies. This paper presents a robust blockchain-enabled framework to address these challenges by integrating a consortium blockchain model, smart contracts, and cryptographic techniques to securely coordinate UAV fleets for disaster response. Specifically, we make two key contributions: a consortium blockchain architecture for secure and private multi-agency coordination; and an optimized consensus protocol balancing efficiency and fault tolerance using a delegated proof of stake practical byzantine fault tolerance (DPoS-PBFT). Comprehensive simulations showcase the framework's ability to enhance transparency, automation, scalability, and cyber-attack resilience for UAV coordination in post-disaster networks.
A Study on Performance Improvement of Prompt Engineering for Generative AI with a Large Language Model

Authors:
raw_author_name: Gi-taek An
raw_author_name: Chayapol Kamyod
raw_author_name: Cheong Ghil Kim

Abstract: In the realm of Generative AI, where various models are introduced, prompt engineering emerges as a significant technique within natural language processing-based Generative AI. Its primary function lies in effectively enhancing the results of sentence generation by large language models (LLMs). Notably, prompt engineering has gained attention as a method capable of improving LLM performance by modifying the structure of input prompts alone. In this study, we apply prompt engineering to Korean-based LLMs, presenting an efficient approach for generating specific conversational responses with less data. We achieve this through the utilization of the query transformation module (QTM). Our proposed QTM transforms input prompt sentences into three distinct query methods, breaking them down into objectives and key points, making them more comprehensible for LLMs. For performance validation, we employ Korean versions of LLMs, specifically SKT GPT-2 and Kakaobrain KoGPT-3. We compare four different query methods, including the original unmodified query, using Google SSA to assess the naturalness and specificity of generated sentences. The results demonstrate an average improvement of 11.46% when compared to the unmodified query, underscoring the efficacy of the proposed QTM in achieving enhanced performance.
Leveraging Bidirectionl LSTM with CRFs for Pashto tagging

Authors:
raw_author_name: Farooq Zaman
raw_affiliation_string: Quaid-i-Azam University, Islamabad, Pakistan
raw_author_name: Onaiza Maqbool
raw_affiliation_string: Quaid-i-Azam University, Islamabad, Pakistan
raw_author_name: Jaweria Kanwal
raw_affiliation_string: National University of Modern Languages, Islamabad, Pakistan

Abstract: Part-of-speech tagging plays a vital role in text processing and natural language understanding. Very few attempts have been made in the past for tagging Pashto Part-of-Speech. In this work, we present LSTM based approach for Pashto part-of-speech tagging with special focus on ambiguity resolution. Initially we created a corpus of Pashto sentences having words with multiple meanings and their tags. We introduce a powerful sentences representation and new architecture for Pashto text processing. The accuracy of the proposed approach is compared with state-of-the-art Hidden Markov Model. Our Model shows 87.60% accuracy for all words excluding punctuations and 95.45% for ambiguous words, on the other hand Hidden Markov Model shows 78.37% and 44.72% accuracy respectively. Results show that our approach outperform Hidden Markov Model in Part-of-Speech tagging for Pashto text.
How Can LLM Guide RL? A Value-Based Approach

Authors:
raw_author_name: Yang, Yingxiang
raw_author_name: Wang, Zhaoran

Abstract: Reinforcement learning (RL) has become the de facto standard practice for sequential decision-making problems by improving future acting policies with feedback. However, RL algorithms may require extensive trial-and-error interactions to collect useful feedback for improvement. On the other hand, recent developments in large language models (LLMs) have showcased impressive capabilities in language understanding and generation, yet they fall short in exploration and self-improvement capabilities for planning tasks, lacking the ability to autonomously refine their responses based on feedback. Therefore, in this paper, we study how the policy prior provided by the LLM can enhance the sample efficiency of RL algorithms. Specifically, we develop an algorithm named LINVIT that incorporates LLM guidance as a regularization factor in value-based RL, leading to significant reductions in the amount of data needed for learning, particularly when the difference between the ideal policy and the LLM-informed policy is small, which suggests that the initial policy is close to optimal, reducing the need for further exploration. Additionally, we present a practical algorithm SLINVIT that simplifies the construction of the value function and employs subgoals to reduce the search complexity. Our experiments across three interactive environments ALFWorld, InterCode, and BlocksWorld demonstrate that our method achieves state-of-the-art success rates and also surpasses previous RL and LLM approaches in terms of sample efficiency. Our code is available at https://github.com/agentification/Language-Integrated-VI.
Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation

Authors:
raw_author_name: Ming, Yu
raw_author_name: Wu, Zihao
raw_author_name: Yang, Jie
raw_author_name: Li, Danyi
raw_author_name: Gao, Yuan
raw_author_name: Gao, Changxin
raw_author_name: Xia, Gui-Song
raw_author_name: Li, Yuanqing
raw_author_name: Liang, Li
raw_author_name: Yu, Jin-Gang

Abstract: Nucleus instance segmentation from histopathology images suffers from the extremely laborious and expert-dependent annotation of nucleus instances. As a promising solution to this task, annotation-efficient deep learning paradigms have recently attracted much research interest, such as weakly-/semi-supervised learning, generative adversarial learning, etc. In this paper, we propose to formulate annotation-efficient nucleus instance segmentation from the perspective of few-shot learning (FSL). Our work was motivated by that, with the prosperity of computational pathology, an increasing number of fully-annotated datasets are publicly accessible, and we hope to leverage these external datasets to assist nucleus instance segmentation on the target dataset which only has very limited annotation. To achieve this goal, we adopt the meta-learning based FSL paradigm, which however has to be tailored in two substantial aspects before adapting to our task. First, since the novel classes may be inconsistent with those of the external dataset, we extend the basic definition of few-shot instance segmentation (FSIS) to generalized few-shot instance segmentation (GFSIS). Second, to cope with the intrinsic challenges of nucleus segmentation, including touching between adjacent cells, cellular heterogeneity, etc., we further introduce a structural guidance mechanism into the GFSIS network, finally leading to a unified Structurally-Guided Generalized Few-Shot Instance Segmentation (SGFSIS) framework. Extensive experiments on a couple of publicly accessible datasets demonstrate that, SGFSIS can outperform other annotation-efficient learning baselines, including semi-supervised learning, simple transfer learning, etc., with comparable performance to fully supervised learning with less than 5% annotations.
Pre-training Cross-lingual Open Domain Question Answering with  Large-scale Synthetic Supervision

Authors:
raw_author_name: Jiang, Fan
raw_author_name: Drummond, Tom
raw_author_name: Cohn, Trevor

Abstract: Cross-lingual question answering (CLQA) is a complex problem, comprising cross-lingual retrieval from a multilingual knowledge base, followed by answer generation either in English or the query language. Both steps are usually tackled by separate models, requiring substantial annotated datasets, and typically auxiliary resources, like machine translation systems to bridge between languages. In this paper, we show that CLQA can be addressed using a single encoder-decoder model. To effectively train this model, we propose a self-supervised method based on exploiting the cross-lingual link structure within Wikipedia. We demonstrate how linked Wikipedia pages can be used to synthesise supervisory signals for cross-lingual retrieval, through a form of cloze query, and generate more natural queries to supervise answer generation. Together, we show our approach, \texttt{CLASS}, outperforms comparable methods on both supervised and zero-shot language adaptation settings, including those using machine translation.
Towards Efficient Quantum Computing for Quantum Chemistry: Reducing  Circuit Complexity with Transcorrelated and Adaptive Ansatz Techniques

Authors:
raw_author_name: Knecht, Stefan
raw_author_name: Rahm, Martin
raw_author_name: Dobrautz, Werner

Abstract: The near-term utility of quantum computers is hindered by hardware constraints in the form of noise. One path to achieving noise resilience in hybrid quantum algorithms is to decrease the required circuit depth -- the number of applied gates -- to solve a given problem. This work demonstrates how to reduce circuit depth by combining the transcorrelated (TC) approach with adaptive quantum ans\"atze and their implementations in the context of variational quantum imaginary time evolution (AVQITE). The combined TC-AVQITE method is used to calculate ground state energies across the potential energy surfaces of H$_4$, LiH, and H$_2$O. In particular, H$_4$ is a notoriously difficult case where unitary coupled cluster theory, including singles and doubles excitations, fails to provide accurate results. Adding TC yields energies close to the complete basis set (CBS) limit while reducing the number of necessary operators -- and thus circuit depth -- in the adaptive ans\"atze. The reduced circuit depth furthermore makes our algorithm more noise-resilient and accelerates convergence. Our study demonstrates that combining the TC method with adaptive ans\"atze yields compact, noise-resilient, and easy-to-optimize quantum circuits that yield accurate quantum chemistry results close to the CBS limit.
IRConStyle: Image Restoration Framework Using Contrastive Learning and  Style Transfer

Authors:
raw_author_name: Fan, Dongqi
raw_author_name: Zhao, Xin
raw_author_name: Chang, Liang

Abstract: Recently, the contrastive learning paradigm has achieved remarkable success in high-level tasks such as classification, detection, and segmentation. However, contrastive learning applied in low-level tasks, like image restoration, is limited, and its effectiveness is uncertain. This raises a question: Why does the contrastive learning paradigm not yield satisfactory results in image restoration? In this paper, we conduct in-depth analyses and propose three guidelines to address the above question. In addition, inspired by style transfer and based on contrastive learning, we propose a novel module for image restoration called \textbf{ConStyle}, which can be efficiently integrated into any U-Net structure network. By leveraging the flexibility of ConStyle, we develop a \textbf{general restoration network} for image restoration. ConStyle and the general restoration network together form an image restoration framework, namely \textbf{IRConStyle}. To demonstrate the capability and compatibility of ConStyle, we replace the general restoration network with transformer-based, CNN-based, and MLP-based networks, respectively. We perform extensive experiments on various image restoration tasks, including denoising, deblurring, deraining, and dehazing. The results on 19 benchmarks demonstrate that ConStyle can be integrated with any U-Net-based network and significantly enhance performance. For instance, ConStyle NAFNet significantly outperforms the original NAFNet on SOTS outdoor (dehazing) and Rain100H (deraining) datasets, with PSNR improvements of 4.16 dB and 3.58 dB with 85% fewer parameters.