OpenRaiser/Intern-Atlas huggingface dataset:
CharacterGen: Efficient 3D Character Generation from Single Images with Multi-View Pose Canonicalization
Venue: arXiv cs.CV 2024
Year: 2024

In the field of digital content creation, generating high-quality 3D characters from single images is challenging, especially given the complexities of various body poses and the issues of self-occlusion and pose ambiguity. In this paper, we present CharacterGen, a framework developed to efficiently generate 3D characters. CharacterGen introduces a streamlined generation pipeline along with an image-conditioned multi-view diffusion model. This model effectively calibrates input poses to a canonical form while retaining key attributes of the input image, thereby addressing the challenges posed by diverse poses. A transformer-based, generalizable sparse-view reconstruction model is the other core component of our approach, facilitating the creation of detailed 3D models from multi-view images. We also adopt a texture-back-projection strategy to produce high-quality texture maps. Additionally, we have curated a dataset of anime characters, rendered in multiple poses and views, to train and evaluate our model. Our approach has been thoroughly evaluated through quantitative and qualitative experiments, showing its proficiency in generating 3D characters with high-quality shapes and textures, ready for downstream applications such as rigging and animation.
Time-Restricted Double-Spending Attack on PoW-based Blockchains
Venue: arXiv cs.CR 2024
Year: 2024

Numerous blockchain applications are designed with tasks that naturally have finite durations, and hence, a double-spending attack (DSA) on such blockchain applications leans towards being conducted within a finite timeframe, specifically before the completion of their tasks. Furthermore, existing research suggests that practical attackers typically favor executing a DSA within a finite timeframe due to their limited computational resources. These observations serve as the impetus for this paper to investigate a time-restricted DSA (TR-DSA) model on Proof-of-Work based blockchains. In this TR-DSA model, an attacker only mines its branch within a finite timeframe, and the TR-DSA is considered unsuccessful if the attacker's branch fails to surpass the honest miners' branch when the honest miners' branch has grown by a specific number of blocks. First, we developed a general closed-form expression for the success probability of a TR-DSA. This developed probability not only can assist in evaluating the risk of a DSA on blockchain applications with timely tasks, but also can enable practical attackers with limited computational resources to assess the feasibility and expected reward of launching a TR-DSA. In addition, we provide rigorous proof that the success probability of a TR-DSA is no greater than that of a time-unrestricted DSA where the attacker indefinitely mines its branch. This result implies that blockchain applications with timely tasks are less vulnerable to DSAs than blockchain applications that provide attackers with an unlimited timeframe for their attacks. Furthermore, we show that the success probability of a TR-DSA is always smaller than one even though the attacker controls more than half of the hash rate in the network. This result alerts attackers that there is still a risk of failure in launching a TR-DSA even if they amass a majority of the hash rate in the network.
Scalable Community Search with Accuracy Guarantee on Attributed Graphs
Venue: arXiv cs.SI 2024
Year: 2024

Given an attributed graph $G$ and a query node $q$, \underline{C}ommunity \underline{S}earch over \underline{A}ttributed \underline{G}raphs (CS-AG) aims to find a structure- and attribute-cohesive subgraph from $G$ that contains $q$. Although CS-AG has been widely studied, they still face three challenges. (1) Exact methods based on graph traversal are time-consuming, especially for large graphs. Some tailored indices can improve efficiency, but introduce nonnegligible storage and maintenance overhead. (2) Approximate methods with a loose approximation ratio only provide a coarse-grained evaluation of a community's quality, rather than a reliable evaluation with an accuracy guarantee in runtime. (3) Attribute cohesiveness metrics often ignores the important correlation with the query node $q$. We formally define our CS-AG problem atop a $q$-centric attribute cohesiveness metric considering both textual and numerical attributes, for $k$-core model on homogeneous graphs. We show the problem is NP-hard. To solve it, we first propose an exact baseline with three pruning strategies. Then, we propose an index-free sampling-estimation-based method to quickly return an approximate community with an accuracy guarantee, in the form of a confidence interval. Once a good result satisfying a user-desired error bound is reached, we terminate it early. We extend it to heterogeneous graphs, $k$-truss model, and size-bounded CS. Comprehensive experimental studies on ten real-world datasets show its superiority, e.g., at least 1.54$\times$ (41.1$\times$ on average) faster in response time and a reliable relative error (within a user-specific error bound) of attribute cohesiveness is achieved.
Spike up Prime Interest in Science and Technology through Constructionist Games
Venue: arXiv cs.RO 2024
Year: 2024

Robotics sets have been successfully used in elementary and secondary schools in conformance with the 'learning through play' philosophy fostered by LEGO Education, while utilizing the Constructionism didactic approach. Learners discover and acquire knowledge through first-hand tangible experiences, building their own representations in a constructivist learning process. Usual pedagogical goals of the activities include introduction to the principles of control, mechanics, programming, and robotics [1]. They are organized as hands-on learning situations with teamwork cooperation of learners, project-based learning, sharing and presentations of the learners group experiences. Arriving from this tradition, we focus on a slightly different scenarios: employing the robotics sets and the named approaches when learning Physics, Mathematics, Art, Science, and other subjects. In carefully designed projects, learners build interactive models that demonstrate concepts, principles, and phenomena, perform experiments, and modify them in elaboration phases with the aim to connect, create associations and links to the actual underlying theoretical curriculum. In this way, they are collecting practical experiences which are prerequisite to successful learning process. Based on feedback from children, we continue upon two previous sets of activities that focused on Physics and Mathematics, this time with projects built around games. Learners play various games with physical artifacts in the real-world - with the models they build. They acquire skills while playing the games, analyze them, and learn about the underlying principles. They modify the game rules, strategies, create extensions, and interact with each other in an entertaining and engaging settings. This time we have designed the activities together with the children, students of applied robotics seminar, and a student of Applied Informatics.
Tight Lower Bounds for Block-Structured Integer Programs
Venue: arXiv cs.CC 2024
Year: 2024

We study fundamental block-structured integer programs called tree-fold and multi-stage IPs. Tree-fold IPs admit a constraint matrix with independent blocks linked together by few constraints in a recursive pattern; and transposing their constraint matrix yields multi-stage IPs. The state-of-the-art algorithms to solve these IPs have an exponential gap in their running times, making it natural to ask whether this gap is inherent. We answer this question affirmative. Assuming the Exponential Time Hypothesis, we prove lower bounds showing that the exponential difference is necessary, and that the known algorithms are near optimal. Moreover, we prove unconditional lower bounds on the norms of the Graver basis, a fundamental building block of all known algorithms to solve these IPs. This shows that none of the current approaches can be improved beyond this bound.
Enhanced Bayesian Optimization via Preferential Modeling of Abstract Properties
Venue: arXiv cs.LG 2024
Year: 2024

Experimental (design) optimization is a key driver in designing and discovering new products and processes. Bayesian Optimization (BO) is an effective tool for optimizing expensive and black-box experimental design processes. While Bayesian optimization is a principled data-driven approach to experimental optimization, it learns everything from scratch and could greatly benefit from the expertise of its human (domain) experts who often reason about systems at different abstraction levels using physical properties that are not necessarily directly measured (or measurable). In this paper, we propose a human-AI collaborative Bayesian framework to incorporate expert preferences about unmeasured abstract properties into the surrogate modeling to further boost the performance of BO. We provide an efficient strategy that can also handle any incorrect/misleading expert bias in preferential judgments. We discuss the convergence behavior of our proposed framework. Our experimental results involving synthetic functions and real-world datasets show the superiority of our method against the baselines.
Towards an Enforceable GDPR Specification
Venue: arXiv cs.CR 2024
Year: 2024

While Privacy by Design (PbD) is prescribed by modern privacy regulations such as the EU's GDPR, achieving PbD in real software systems is a notoriously difficult task. One emerging technique to realize PbD is Runtime enforcement (RE), in which an enforcer, loaded with a specification of a system's privacy requirements, observes the actions performed by the system and instructs it to perform actions that will ensure compliance with these requirements at all times. To be able to use RE techniques for PbD, privacy regulations first need to be translated into an enforceable specification. In this paper, we report on our ongoing work in formalizing the GDPR. We first present a set of requirements and an iterative methodology for creating enforceable formal specifications of legal provisions. Then, we report on a preliminary case study in which we used our methodology to derive an enforceable specification of part of the GDPR. Our case study suggests that our methodology can be effectively used to develop accurate enforceable specifications.
A Survey of Network Protocol Fuzzing: Model, Techniques and Directions
Venue: arXiv cs.NI 2024
Year: 2024

As one of the most successful and effective software testing techniques in recent years, fuzz testing has uncovered numerous bugs and vulnerabilities in modern software, including network protocol software. In contrast to other fuzzing targets, network protocol software exhibits its distinct characteristics and challenges, introducing a plethora of research questions that need to be addressed in the design and implementation of network protocol fuzzers. While some research work has evaluated and systematized the knowledge of general fuzzing techniques at a high level, there is a lack of similar analysis and summarization for fuzzing research specific to network protocols. This paper offers a comprehensive exposition of network protocol software's fuzzing-related features and conducts a systematic review of some representative advancements in network protocol fuzzing since its inception. We summarize state-of-the-art strategies and solutions in various aspects, propose a unified protocol fuzzing process model, and introduce the techniques involved in each stage of the model. At the same time, this paper also summarizes the promising research directions in the landscape of protocol fuzzing to foster exploration within the community for more efficient and intelligent modern network protocol fuzzing techniques.
Sora Generates Videos with Stunning Geometrical Consistency
Venue: arXiv cs.CV 2024
Year: 2024

The recently developed Sora model [1] has exhibited remarkable capabilities in video generation, sparking intense discussions regarding its ability to simulate real-world phenomena. Despite its growing popularity, there is a lack of established metrics to evaluate its fidelity to real-world physics quantitatively. In this paper, we introduce a new benchmark that assesses the quality of the generated videos based on their adherence to real-world physics principles. We employ a method that transforms the generated videos into 3D models, leveraging the premise that the accuracy of 3D reconstruction is heavily contingent on the video quality. From the perspective of 3D reconstruction, we use the fidelity of the geometric constraints satisfied by the constructed 3D models as a proxy to gauge the extent to which the generated videos conform to real-world physics rules. Project page: https://sora-geometrical-consistency.github.io/
A Neural Rewriting System to Solve Algorithmic Problems
Venue: arXiv cs.NE 2024
Year: 2024

Modern neural network architectures still struggle to learn algorithmic procedures that require to systematically apply compositional rules to solve out-of-distribution problem instances. In this work, we focus on formula simplification problems, a class of synthetic benchmarks used to study the systematic generalization capabilities of neural architectures. We propose a modular architecture designed to learn a general procedure for solving nested mathematical formulas by only relying on a minimal set of training examples. Inspired by rewriting systems, a classic framework in symbolic artificial intelligence, we include in the architecture three specialized and interacting modules: the Selector, trained to identify solvable sub-expressions; the Solver, mapping sub-expressions to their values; and the Combiner, replacing sub-expressions in the original formula with the solution provided by the Solver. We benchmark our system against the Neural Data Router, a recent model specialized for systematic generalization, and a state-of-the-art large language model (GPT-4) probed with advanced prompting strategies. We demonstrate that our approach achieves a higher degree of out-of-distribution generalization compared to these alternative approaches on three different types of formula simplification problems, and we discuss its limitations by analyzing its failures.
The KANDY Benchmark: Incremental Neuro-Symbolic Learning and Reasoning with Kandinsky Patterns
Venue: arXiv cs.AI 2024
Year: 2024

Artificial intelligence is continuously seeking novel challenges and benchmarks to effectively measure performance and to advance the state-of-the-art. In this paper we introduce KANDY, a benchmarking framework that can be used to generate a variety of learning and reasoning tasks inspired by Kandinsky patterns. By creating curricula of binary classification tasks with increasing complexity and with sparse supervisions, KANDY can be used to implement benchmarks for continual and semi-supervised learning, with a specific focus on symbol compositionality. Classification rules are also provided in the ground truth to enable analysis of interpretable solutions. Together with the benchmark generation pipeline, we release two curricula, an easier and a harder one, that we propose as new challenges for the research community. With a thorough experimental evaluation, we show how both state-of-the-art neural models and purely symbolic approaches struggle with solving most of the tasks, thus calling for the application of advanced neuro-symbolic methods trained over time.
A Piece of Theatre: Investigating How Teachers Design LLM Chatbots to Assist Adolescent Cyberbullying Education
Venue: arXiv cs.HC 2024
Year: 2024

Cyberbullying harms teenagers' mental health, and teaching them upstanding intervention is crucial. Wizard-of-Oz studies show chatbots can scale up personalized and interactive cyberbullying education, but implementing such chatbots is a challenging and delicate task. We created a no-code chatbot design tool for K-12 teachers. Using large language models and prompt chaining, our tool allows teachers to prototype bespoke dialogue flows and chatbot utterances. In offering this tool, we explore teachers' distinctive needs when designing chatbots to assist their teaching, and how chatbot design tools might better support them. Our findings reveal that teachers welcome the tool enthusiastically. Moreover, they see themselves as playwrights guiding both the students' and the chatbot's behaviors, while allowing for some improvisation. Their goal is to enable students to rehearse both desirable and undesirable reactions to cyberbullying in a safe environment. We discuss the design opportunities LLM-Chains offer for empowering teachers and the research opportunities this work opens up.
MGE: A Training-Free and Efficient Model Generation and Enhancement Scheme
Venue: arXiv cs.CV 2024
Year: 2024

To provide a foundation for the research of deep learning models, the construction of model pool is an essential step. This paper proposes a Training-Free and Efficient Model Generation and Enhancement Scheme (MGE). This scheme primarily considers two aspects during the model generation process: the distribution of model parameters and model performance. Experiments result shows that generated models are comparable to models obtained through normal training, and even superior in some cases. Moreover, the time consumed in generating models accounts for only 1\% of the time required for normal model training. More importantly, with the enhancement of Evolution-MGE, generated models exhibits competitive generalization ability in few-shot tasks. And the behavioral dissimilarity of generated models has the potential of adversarial defense.
The Unwanted Dissemination of Science: The Usage of Academic Articles as Ammunition in Contested Discursive Arenas on Twitter
Venue: arXiv cs.SI 2024
Year: 2024

Twitter is a common site of offensive language. Prior literature has shown that the emotional content of tweets can heavily impact their diffusion when discussing political topics. We extend prior work to look at offensive tweets that link to academic articles. Using a mixed methods approach, we identify three findings: firstly, offensive language is common in tweets that refer to academic articles, and vary widely by subject matter. Secondly, discourse analysis reveals that offensive tweets commonly use academic articles to promote or attack political ideologies. Lastly, we show that offensive tweets reach a smaller audience than their non-offensive counterparts. Our analysis of these offensive tweets reveal how academic articles are being shared on Twitter not for the sake of disseminating new knowledge, but rather to as argumentative tools in controversial and combative discourses.
AVS-Net: Point Sampling with Adaptive Voxel Size for 3D Scene Understanding
Venue: arXiv cs.CV 2024
Year: 2024

The recent advancements in point cloud learning have enabled intelligent vehicles and robots to comprehend 3D environments better. However, processing large-scale 3D scenes remains a challenging problem, such that efficient downsampling methods play a crucial role in point cloud learning. Existing downsampling methods either require a huge computational burden or sacrifice fine-grained geometric information. For such purpose, this paper presents an advanced sampler that achieves both high accuracy and efficiency. The proposed method utilizes voxel centroid sampling as a foundation but effectively addresses the challenges regarding voxel size determination and the preservation of critical geometric cues. Specifically, we propose a Voxel Adaptation Module that adaptively adjusts voxel sizes with the reference of point-based downsampling ratio. This ensures that the sampling results exhibit a favorable distribution for comprehending various 3D objects or scenes. Meanwhile, we introduce a network compatible with arbitrary voxel sizes for sampling and feature extraction while maintaining high efficiency. The proposed approach is demonstrated with 3D object detection and 3D semantic segmentation. Compared to existing state-of-the-art methods, our approach achieves better accuracy on outdoor and indoor large-scale datasets, e.g. Waymo and ScanNet, with promising efficiency.
Nissist: An Incident Mitigation Copilot based on Troubleshooting Guides
Venue: arXiv cs.SE 2024
Year: 2024

Effective incident management is pivotal for the smooth operation of enterprises-level cloud services. In order to expedite incident mitigation, service teams compile troubleshooting knowledge into Troubleshooting Guides (TSGs) accessible to on-call engineers (OCEs). While automated pipelines are enabled to resolve the most frequent and easy incidents, there still exist complex incidents that require OCEs' intervention. However, TSGs are often unstructured and incomplete, which requires manual interpretation by OCEs, leading to on-call fatigue and decreased productivity, especially among new-hire OCEs. In this work, we propose Nissist which leverages TSGs and incident mitigation histories to provide proactive suggestions, reducing human intervention. Leveraging Large Language Models (LLM), Nissist extracts insights from unstructured TSGs and historical incident mitigation discussions, forming a comprehensive knowledge base. Its multi-agent system design enhances proficiency in precisely discerning user queries, retrieving relevant information, and delivering systematic plans consecutively. Through our user case and experiment, we demonstrate that Nissist significant reduce Time to Mitigate (TTM) in incident mitigation, alleviating operational burdens on OCEs and improving service reliability. Our demo is available at https://aka.ms/nissist_demo.
Black-box Adversarial Attacks Against Image Quality Assessment Models
Venue: arXiv cs.CV 2024
Year: 2024

The goal of No-Reference Image Quality Assessment (NR-IQA) is to predict the perceptual quality of an image in line with its subjective evaluation. To put the NR-IQA models into practice, it is essential to study their potential loopholes for model refinement. This paper makes the first attempt to explore the black-box adversarial attacks on NR-IQA models. Specifically, we first formulate the attack problem as maximizing the deviation between the estimated quality scores of original and perturbed images, while restricting the perturbed image distortions for visual quality preservation. Under such formulation, we then design a Bi-directional loss function to mislead the estimated quality scores of adversarial examples towards an opposite direction with maximum deviation. On this basis, we finally develop an efficient and effective black-box attack method against NR-IQA models. Extensive experiments reveal that all the evaluated NR-IQA models are vulnerable to the proposed attack method. And the generated perturbations are not transferable, enabling them to serve the investigation of specialities of disparate IQA models.
COCOA: CBT-based Conversational Counseling Agent using Memory Specialized in Cognitive Distortions and Dynamic Prompt
Venue: arXiv cs.AI 2024
Year: 2024

The demand for conversational agents that provide mental health care is consistently increasing. In this work, we develop a psychological counseling agent, referred to as CoCoA, that applies Cognitive Behavioral Therapy (CBT) techniques to identify and address cognitive distortions inherent in the client's statements. Specifically, we construct a memory system to efficiently manage information necessary for counseling while extracting high-level insights about the client from their utterances. Additionally, to ensure that the counseling agent generates appropriate responses, we introduce dynamic prompting to flexibly apply CBT techniques and facilitate the appropriate retrieval of information. We conducted dialogues between CoCoA and characters from Character.ai, creating a dataset for evaluation. Then, we asked GPT to evaluate the constructed counseling dataset, and our model demonstrated a statistically significant difference from other models.