Miracle-dz/UniFAR-FTUs huggingface dataset:
RoleCraft-GLM: Advancing Personalized Role-Playing in Large Language Models

This study presents RoleCraft-GLM, an innovative framework aimed at enhancing personalized role-playing with Large Language Models (LLMs). RoleCraft-GLM addresses the key issue of lacking personalized interactions in conversational AI, and offers a solution with detailed and emotionally nuanced character portrayals. We contribute a unique conversational dataset that shifts from conventional celebrity-centric characters to diverse, non-celebrity personas, thus enhancing the realism and complexity of language modeling interactions. Additionally, our approach includes meticulous character development, ensuring dialogues are both realistic and emotionally resonant. The effectiveness of RoleCraft-GLM is validated through various case studies, highlighting its versatility and skill in different scenarios. Our framework excels in generating dialogues that accurately reflect characters’ personality traits and emotions, thereby boosting user engagement. In conclusion, RoleCraft-GLM marks a significant leap in personalized AI interactions, and paves the way for more authentic and immersive AI-assisted role-playing experiences by enabling more nuanced and emotionally rich dialogues.
Improving Speaker-independent Speech Emotion Recognition Using Dynamic Joint Distribution Adaptation

In speaker-independent speech emotion recognition, the training and testing samples are collected from diverse speakers, leading to a multi-domain shift challenge across the feature distributions of data from different speakers. Consequently, when the trained model is confronted with data from new speakers, its performance tends to degrade. To address the issue, we propose a Dynamic Joint Distribution Adaptation (DJDA) method under the framework of multi-source domain adaptation. DJDA firstly utilizes joint distribution adaptation (JDA), involving marginal distribution adaptation (MDA) and conditional distribution adaptation (CDA), to more precisely measure the multi-domain distribution shifts caused by different speakers. This helps eliminate speaker bias in emotion features, allowing for learning discriminative and speaker-invariant speech emotion features from coarse-level to fine-level. Furthermore, we quantify the adaptation contributions of MDA and CDA within JDA by using a dynamic balance factor based on $\mathcal{A}$-Distance, promoting to effectively handle the unknown distributions encountered in data from new speakers. Experimental results demonstrate the superior performance of our DJDA as compared to other state-of-the-art (SOTA) methods.
PPNet: A Novel Neural Network Structure for End-to-End Near-Optimal Path Planning

The classical path planners, such as sampling-based path planners, have the limitations of sensitivity to the initial solution and slow convergence to the optimal solution. However, finding a near-optimal solution in a short period is challenging in many applications such as the autonomous vehicle with limited power/fuel. To achieve an end-to-end near-optimal path planner, we first divide the path planning problem into two subproblems, which are path's space segmentation and waypoints generation in the given path's space. We further propose a two-level cascade neural network named Path Planning Network (PPNet) to solve the path planning problem by solving the abovementioned subproblems. Moreover, we propose a novel efficient data generation method for path planning named EDaGe-PP. The results show the total computation time is less than 1/33 and the success rate of PPNet trained by the dataset that is generated by EDaGe-PP is about $2 \times$ compared to other methods. We validate PPNet against state-of-the-art path planning methods. The results show PPNet can find a near-optimal solution in 15.3ms, which is much shorter than the state-of-the-art path planners.
A Comprehensive Scalable Framework for Cloud-Native Pattern Detection with Enhanced Expressiveness

Detecting complex patterns in large volumes of event logs has diverse applications in various domains, such as business processes and fraud detection. Existing systems like ELK are commonly used to tackle this challenge, but their performance deteriorates for large patterns, while they suffer from limitations in terms of expressiveness and explanatory capabilities for their responses. In this work, we propose a solution that integrates a Complex Event Processing (CEP) engine into a broader query processsor on top of a decoupled storage infrastructure containing inverted indices of log events. The results demonstrate that our system excels in scalability and robustness, particularly in handling complex queries. Notably, our proposed system delivers responses for large complex patterns within seconds, while ELK experiences timeouts after 10 minutes. It also significantly outperforms solutions relying on FlinkCEP and executing MATCH_RECOGNIZE SQL queries.
Binary Quantum Control Optimization with Uncertain Hamiltonians

Optimizing the controls of quantum systems plays a crucial role in advancing quantum technologies. The time-varying noises in quantum systems and the widespread use of inhomogeneous quantum ensembles raise the need for high-quality quantum controls under uncertainties. In this paper, we consider a stochastic discrete optimization formulation of a binary optimal quantum control problem involving Hamiltonians with predictable uncertainties. We propose a sample-based reformulation that optimizes both risk-neutral and risk-averse measurements of control policies, and solve these with two gradient-based algorithms using sum-up-rounding approaches. Furthermore, we discuss the differentiability of the objective function and prove upper bounds of the gaps between the optimal solutions to binary control problems and their continuous relaxations. We conduct numerical studies on various sized problem instances based of two applications of quantum pulse optimization; we evaluate different strategies to mitigate the impact of uncertainties in quantum systems. We demonstrate that the controls of our stochastic optimization model achieve significantly higher quality and robustness compared to the controls of a deterministic model.
Characterizing Online Eating Disorder Communities with Large Language Models

The rise in eating disorders, a dangerous mental health condition with high mortality and morbidity, has been linked to the proliferation of idealized body images on social media. However, the link between social media and eating disorders is far more complex. We argue that social media platforms create a feedback loop that amplifies the growth of content and communities that promote eating disorders like anorexia and bulimia. Specifically, social media platforms make it easy for vulnerable individuals to find and connect to like-minded others, while group dynamic processes encourage them to stay engaged within communities that promote and glorify harmful behaviors linked to eating disorders. We characterize this dynamic empirically through a combination of network and language analysis. We describe a novel framework that leverages large language models to analyze the discourse within online communities and probe their attitudes on topics related to eating disorders to identify potentially harmful content. Our work emphasizes the need for better social media moderation to disrupt harmful feedback loops and protect vulnerable individuals.
Eclectic Rule Extraction for Explainability of Deep Neural Network based Intrusion Detection Systems

This paper addresses trust issues created from the ubiquity of black box algorithms and surrogate explainers in Explainable Intrusion Detection Systems (X-IDS). While Explainable Artificial Intelligence (XAI) aims to enhance transparency, black box surrogate explainers, such as Local Interpretable Model-Agnostic Explanation (LIME) and SHapley Additive exPlanation (SHAP), are difficult to trust. The black box nature of these surrogate explainers makes the process behind explanation generation opaque and difficult to understand. To avoid this problem, one can use transparent white box algorithms such as Rule Extraction (RE). There are three types of RE algorithms: pedagogical, decompositional, and eclectic. Pedagogical methods offer fast but untrustworthy white-box explanations, while decompositional RE provides trustworthy explanations with poor scalability. This work explores eclectic rule extraction, which strikes a balance between scalability and trustworthiness. By combining techniques from pedagogical and decompositional approaches, eclectic rule extraction leverages the advantages of both, while mitigating some of their drawbacks. The proposed Hybrid X-IDS architecture features eclectic RE as a white box surrogate explainer for black box Deep Neural Networks (DNN). The presented eclectic RE algorithm extracts human-readable rules from hidden layers, facilitating explainable and trustworthy rulesets. Evaluations on UNSW-NB15 and CIC-IDS-2017 datasets demonstrate the algorithm's ability to generate rulesets with 99.9% accuracy, mimicking DNN outputs. The contributions of this work include the hybrid X-IDS architecture, the eclectic rule extraction algorithm applicable to intrusion detection datasets, and a thorough analysis of performance and explainability, demonstrating the trade-offs involved in rule extraction speed and accuracy.
ICGNet: A Unified Approach for Instance-Centric Grasping

Accurate grasping is the key to several robotic tasks including assembly and household robotics. Executing a successful grasp in a cluttered environment requires multiple levels of scene understanding: First, the robot needs to analyze the geometric properties of individual objects to find feasible grasps. These grasps need to be compliant with the local object geometry. Second, for each proposed grasp, the robot needs to reason about the interactions with other objects in the scene. Finally, the robot must compute a collision-free grasp trajectory while taking into account the geometry of the target object. Most grasp detection algorithms directly predict grasp poses in a monolithic fashion, which does not capture the composability of the environment. In this paper, we introduce an end-to-end architecture for object-centric grasping. The method uses pointcloud data from a single arbitrary viewing direction as an input and generates an instance-centric representation for each partially observed object in the scene. This representation is further used for object reconstruction and grasp detection in cluttered table-top scenes. We show the effectiveness of the proposed method by extensively evaluating it against state-of-the-art methods on synthetic datasets, indicating superior performance for grasping and reconstruction. Additionally, we demonstrate real-world applicability by decluttering scenes with varying numbers of objects.
Discrete differential geometry-based model for nonlinear analysis of axisymmetric shells

In this paper, we propose a novel one-dimensional (1D) discrete differential geometry (DDG)-based numerical method for geometrically nonlinear mechanics analysis (e.g., buckling and snapping) of axisymmetric shell structures. Our numerical model leverages differential geometry principles to accurately capture the complex nonlinear deformation patterns exhibited by axisymmetric shells. By discretizing the axisymmetric shell into interconnected 1D elements along the meridional direction, the in-plane stretching and out-of-bending potentials are formulated based on the geometric principles of 1D nodes and edges under the Kirchhoff-Love hypothesis, and elastic force vector and associated Hession matrix required by equations of motion are later derived based on symbolic calculation. Through extensive validation with available theoretical solutions and finite element method (FEM) simulations in literature, our model demonstrates high accuracy in predicting the nonlinear behavior of axisymmetric shells. Importantly, compared to the classical theoretical model and three-dimensional (3D) FEM simulation, our model is highly computationally efficient, making it suitable for large-scale real-time simulations of nonlinear problems of shell structures such as instability and snap-through phenomena. Moreover, our framework can easily incorporate complex loading conditions, e.g., boundary nonlinear contact and multi-physics actuation, which play an essential role in the use of engineering applications, such as soft robots and flexible devices. This study demonstrates that the simplicity and effectiveness of the 1D discrete differential geometry-based approach render it a powerful tool for engineers and researchers interested in nonlinear mechanics analysis of axisymmetric shells, with potential applications in various engineering fields.
Nowcasting economic activity in European regions using a mixed-frequency dynamic factor model

Timely information about the state of regional economies can be essential for planning, implementing and evaluating locally targeted economic policies. However, European regional accounts for output are published at an annual frequency and with a two-year delay. To obtain robust and more timely measures in a computationally efficient manner, we propose a mixed-frequency dynamic factor model that accounts for national information to produce high-frequency estimates of the regional gross value added (GVA). We show that our model produces reliable nowcasts of GVA in 162 regions across 12 European countries.
Resolving Regular Polysemy in Named Entities

Word sense disambiguation primarily addresses the lexical ambiguity of common words based on a predefined sense inventory. Conversely, proper names are usually considered to denote an ad-hoc real-world referent. Once the reference is decided, the ambiguity is purportedly resolved. However, proper names also exhibit ambiguities through appellativization, i.e., they act like common words and may denote different aspects of their referents. We proposed to address the ambiguities of proper names through the light of regular polysemy, which we formalized as dot objects. This paper introduces a combined word sense disambiguation (WSD) model for disambiguating common words against Chinese Wordnet (CWN) and proper names as dot objects. The model leverages the flexibility of a gloss-based model architecture, which takes advantage of the glosses and example sentences of CWN. We show that the model achieves competitive results on both common and proper nouns, even on a relatively sparse sense dataset. Aside from being a performant WSD tool, the model further facilitates the future development of the lexical resource.
Convex and Bilevel Optimization for Neuro-Symbolic Inference and Learning

We address a key challenge for neuro-symbolic (NeSy) systems by leveraging convex and bilevel optimization techniques to develop a general gradient-based framework for end-to-end neural and symbolic parameter learning. The applicability of our framework is demonstrated with NeuPSL, a state-of-the-art NeSy architecture. To achieve this, we propose a smooth primal and dual formulation of NeuPSL inference and show learning gradients are functions of the optimal dual variables. Additionally, we develop a dual block coordinate descent algorithm for the new formulation that naturally exploits warm-starts. This leads to over 100x learning runtime improvements over the current best NeuPSL inference method. Finally, we provide extensive empirical evaluations across $8$ datasets covering a range of tasks and demonstrate our learning framework achieves up to a 16% point prediction performance improvement over alternative learning methods.
Large Language Model Lateral Spear Phishing: A Comparative Study in Large-Scale Organizational Settings

The critical threat of phishing emails has been further exacerbated by the potential of LLMs to generate highly targeted, personalized, and automated spear phishing attacks. Two critical problems concerning LLM-facilitated phishing require further investigation: 1) Existing studies on lateral phishing lack specific examination of LLM integration for large-scale attacks targeting the entire organization, and 2) Current anti-phishing infrastructure, despite its extensive development, lacks the capability to prevent LLM-generated attacks, potentially impacting both employees and IT security incident management. However, the execution of such investigative studies necessitates a real-world environment, one that functions during regular business operations and mirrors the complexity of a large organizational infrastructure. This setting must also offer the flexibility required to facilitate a diverse array of experimental conditions, particularly the incorporation of phishing emails crafted by LLMs. This study is a pioneering exploration into the use of Large Language Models (LLMs) for the creation of targeted lateral phishing emails, targeting a large tier 1 university's operation and workforce of approximately 9,000 individuals over an 11-month period. It also evaluates the capability of email filtering infrastructure to detect such LLM-generated phishing attempts, providing insights into their effectiveness and identifying potential areas for improvement. Based on our findings, we propose machine learning-based detection techniques for such emails to detect LLM-generated phishing emails that were missed by the existing infrastructure, with an F1-score of 98.96.
Advancing Large Multi-modal Models with Explicit Chain-of-Reasoning and Visual Question Generation

The increasing demand for intelligent systems capable of interpreting and reasoning about visual content requires the development of Large Multi-Modal Models (LMMs) that are not only accurate but also have explicit reasoning capabilities. This paper presents a novel approach to imbue an LMM with the ability to conduct explicit reasoning based on visual content and textual instructions. We introduce a system that can ask a question to acquire necessary knowledge, thereby enhancing the robustness and explicability of the reasoning process. Our method comprises the development of a novel dataset generated by a Large Language Model (LLM), designed to promote chain-of-thought reasoning combined with a question-asking mechanism. We designed an LMM, which has high capabilities on region awareness to address the intricate requirements of image-text alignment. The model undergoes a three-stage training phase, starting with large-scale image-text alignment using a large-scale datasets, followed by instruction tuning, and fine-tuning with a focus on chain-of-thought reasoning. The results demonstrate a stride toward a more robust, accurate, and interpretable LMM, capable of reasoning explicitly and seeking information proactively when confronted with ambiguous visual input.
A Comparative Analysis on Metaheuristic Algorithms Based Vision Transformer Model for Early Detection of Alzheimer's Disease

A number of life threatening neuro-degenerative disorders had degraded the quality of life for the older generation in particular. Dementia is one such symptom which may lead to a severe condition called Alzheimer's disease if not detected at an early stage. It has been reported that the progression of such disease from a normal stage is due to the change in several parameters inside the human brain. In this paper, an innovative metaheuristic algorithms based ViT model has been proposed for the identification of dementia at different stage. A sizeable number of test data have been utilized for the validation of the proposed scheme. It has also been demonstrated that our model exhibits superior performance in terms of accuracy, precision, recall as well as F1-score.
Quantifying the escape of Ly$\alpha$ at $z\approx 5-6$: a census of Ly$\alpha$ escape fraction with H$\alpha$ emitting galaxies spectroscopically confirmed by JWST and VLT/MUSE

JWST provides an unprecedented opportunity for unbiased surveys of H$\alpha$-emitting galaxies at $z>4$ with the NIRCam wide-field slitless spectroscopy (WFSS). In this work, we present a census of Ly$\alpha$ escape fraction ($f_{esc, Ly\alpha}$) of 165 star-forming galaxies at $z=4.9-6.3$ using their H$\alpha$ emission directly measured from FRESCO NIRCam/WFSS data. We search for Ly$\alpha$ emission of each H$\alpha$-emitting galaxy in VLT/MUSE data. The overall $f_{esc, Ly\alpha}$ measured by stacking is $f_{esc, Ly\alpha}$ is $0.090\pm0.006$. We find that $f_{esc, Ly\alpha}$ displays a strong dependence on the observed UV slope ($\beta_{\rm obs}$) and E(B-V), such that the bluest galaxies ($\beta_{\rm obs}\sim-2.5$) have the largest escape fractions ($f_{\rm esc, Ly\alpha}\approx0.6$), indicative of the crucial role of dust and gas in modulating the escape of Ly$\alpha$ photons. $f_{esc, Ly\alpha}$ is less well related to other parameters, including the UV luminosity and stellar mass, and the variation in $f_{esc, Ly\alpha}$ with them can be explained by their underlying coupling with E(B-V) or $\beta_{\rm obs}$. Our results suggest a tentative decline in $f_{esc, Ly\alpha}$ at $z\gtrsim 5$, implying increasing intergalactic medium attenuation towards higher redshift. Furthermore, the dependence of $f_{esc, Ly\alpha}$ on $\beta_{\rm obs}$ is proportional to that of the ionizing photon escape fraction ($f_{\rm esc, LyC}$), indicating the escape of Ly$\alpha$ and ionizing photon may be regulated by similar physical processes. With $f_{esc, Ly\alpha}$ as a proxy to $f_{\rm esc, LyC}$, we infer that UV-faint ($M_{\rm UV}>-16$) galaxies contribute $>70\%$ of the total ionizing emissivity at $z=5-6$. If these relations hold during the epoch of reionization, UV-faint galaxies can contribute the majority of UV photon budget to reionize the Universe.
Network Higher-Order Structure Dismantling

Diverse higher-order structures, foundational for supporting a network’s “meta-functions”, play a vital role in structure, functionality, and the emergence of complex dynamics. Nevertheless, the problem of dismantling them has been consistently overlooked. In this paper, we introduce the concept of dismantling higher-order structures, with the objective of disrupting not only network connectivity but also eradicating all higher-order structures in each branch, thereby ensuring thorough functional paralysis. Given the diversity and unknown specifics of higher-order structures, identifying and targeting them individually is not practical or even feasible. Fortunately, their close association with k-cores arises from their internal high connectivity. Thus, we transform higher-order structure measurement into measurements on k-cores with corresponding orders. Furthermore, we propose the Belief Propagation-guided Higher-order Dismantling (BPHD) algorithm, minimizing dismantling costs while achieving maximal disruption to connectivity and higher-order structures, ultimately converting the network into a forest. BPHD exhibits the explosive vulnerability of network higher-order structures, counterintuitively showcasing decreasing dismantling costs with increasing structural complexity. Our findings offer a novel approach for dismantling malignant networks, emphasizing the substantial challenges inherent in safeguarding against such malicious attacks.