OpenRaiser/Intern-Atlas huggingface dataset:
A Tutorial on Teaching Data Analytics with Generative AI
Venue: arXiv cs.CY 2024
Year: 2024

This tutorial addresses the challenge of incorporating large language models (LLMs), such as ChatGPT, in a data analytics class. It details several new in-class and out-of-class teaching techniques enabled by AI. For example, instructors can parallelize instruction by having students interact with different custom-made GPTs to learn different parts of an analysis and then teach each other what they learned from their AIs. For another example, instructors can turn problem sets into AI tutoring sessions, whereby a custom-made GPT guides a student through the problems, and the student uploads the chatlog for their homework submission. For a third example, you can assign different labs to each section of your class and have each section create AI assistants to help the other sections work through their labs. This tutorial advocates the programming in the English paradigm, in which students express the desired data transformations in prose and then use AI to generate the corresponding code. Students can wrangle data more effectively by programming in English than by manipulating in Excel. However, some students will program in English better than others, so you will still derive a robust grade distribution (at least with current LLMs).
The Unintended Carbon Consequences of Bitcoin Mining Bans: A Paradox in Environmental Policy
Venue: arXiv cs.CY 2024
Year: 2024

The environmental impact of Bitcoin mining has become a significant concern, prompting several governments to consider or implement bans on cryptocurrency mining. However, these well-intentioned policies may lead to unintended consequences, notably the redirection of mining activities to regions with higher carbon intensities. This study aims to quantify the environmental effectiveness of Bitcoin mining bans by estimating the resultant carbon emissions from displaced mining operations. Our findings indicate that, contrary to policy goals, Bitcoin mining bans in low-emission countries can result in a net increase in global carbon emissions, a form of aggravated carbon leakage. We further explore the policy implications of these results, suggesting that more nuanced approaches may be required to mitigate the environmental impact of cryptocurrency mining effectively. This research contributes to the broader discourse on sustainable cryptocurrency regulation and provides a data-driven foundation for evaluating the true environmental costs of Bitcoin regulatory policies.
ASTD Patterns for Integrated Continuous Anomaly Detection In Data Logs
Venue: arXiv cs.SE 2024
Year: 2024

This paper investigates the use of the ASTD language for ensemble anomaly detection in data logs. It uses a sliding window technique for continuous learning in data streams, coupled with updating learning models upon the completion of each window to maintain accurate detection and align with current data trends. It proposes ASTD patterns for combining learning models, especially in the context of unsupervised learning, which is commonly used for data streams. To facilitate this, a new ASTD operator is proposed, the Quantified Flow, which enables the seamless combination of learning models while ensuring that the specification remains concise. Our contribution is a specification pattern, highlighting the capacity of ASTDs to abstract and modularize anomaly detection systems. The ASTD language provides a unique approach to develop data flow anomaly detection systems, grounded in the combination of processes through the graphical representation of the language operators. This simplifies the design task for developers, who can focus primarily on defining the functional operations that constitute the system.
Warmstarting for Scaling Language Models
Venue: arXiv cs.LG 2024
Year: 2024

Scaling model sizes to scale performance has worked remarkably well for the current large language models paradigm. The research and empirical findings of various scaling studies led to novel scaling results and laws that guides subsequent research. High training costs for contemporary scales of data and models result in a lack of thorough understanding of how to tune and arrive at such training setups. One direction to ameliorate the cost of pretraining large models is to warmstart the large-scale training from smaller models that are cheaper to tune. In this work, we attempt to understand if the behavior of optimal hyperparameters can be retained under warmstarting for scaling. We explore simple operations that allow the application of theoretically motivated methods of zero-shot transfer of optimal hyperparameters using μTransfer. We investigate the aspects that contribute to the speedup in convergence and the preservation of stable training dynamics under warmstarting with μTransfer. We find that shrinking smaller model weights, zero-padding, and perturbing the resulting larger model with scaled initialization from μP enables effective warmstarting of $\mut{}$.
High-Fidelity Cellular Network Control-Plane Traffic Generation without Domain Knowledge
Venue: arXiv cs.NI 2024
Year: 2024

With rapid evolution of mobile core network (MCN) architectures, large-scale control-plane traffic (CPT) traces are critical to studying MCN design and performance optimization by the R&D community. The prior-art control-plane traffic generator SMM heavily relies on domain knowledge which requires re-design as the domain evolves. In this work, we study the feasibility of developing a high-fidelity MCN control plane traffic generator by leveraging generative ML models. We identify key challenges in synthesizing high-fidelity CPT including generic (to data-plane) requirements such as multimodality feature relationships and unique requirements such as stateful semantics and long-term (time-of-day) data variations. We show state-of-the-art, generative adversarial network (GAN)-based approaches shown to work well for data-plane traffic cannot meet these fidelity requirements of CPT, and develop a transformer-based model, CPT-GPT, that accurately captures complex dependencies among the samples in each traffic stream (control events by the same UE) without the need for GAN. Our evaluation of CPT-GPT on a large-scale control-plane traffic trace shows that (1) it does not rely on domain knowledge yet synthesizes control-plane traffic with comparable fidelity as SMM; (2) compared to the prior-art GAN-based approach, it reduces the fraction of streams that violate stateful semantics by two orders of magnitude, the max y-distance of sojourn time distributions of streams by 16.0%, and the transfer learning time in deriving new hourly models by 3.36x.
Strategyproof Learning with Advice
Venue: arXiv cs.GT 2024
Year: 2024

An important challenge in robust machine learning is when training data is provided by strategic sources who may intentionally report erroneous data for their own benefit. A line of work at the intersection of machine learning and mechanism design aims to deter strategic agents from reporting erroneous training data by designing learning algorithms that are strategyproof. Strategyproofness is a strong and desirable property, but it comes at a cost in the approximation ratio of even simple risk minimization problems. In this paper, we study strategyproof regression and classification problems in a model with advice. This model is part of a recent line on mechanism design with advice where the goal is to achieve both an improved approximation ratio when the advice is correct (consistency) and a bounded approximation ratio when the advice is incorrect (robustness). We provide the first non-trivial consistency-robustness tradeoffs for strategyproof regression and classification, which hold for simple yet interesting classes of functions. For classes of constant functions, we give a deterministic and strategyproof mechanism that is, for any $γ\in (0, 2]$, $1+γ$ consistent and $1 + 4/γ$ robust and provide a lower bound that shows that this tradeoff is optimal. We extend this mechanism and its guarantees to homogeneous linear regression over $\mathbb{R}$. In the binary classification problem of selecting from three or more labelings, we present strong impossibility results for both deterministic and randomized mechanism. Finally, we provide deterministic and randomized mechanisms for selecting from two labelings.
Low Degree Local Correction Over the Boolean Cube
Venue: arXiv cs.CC 2024
Year: 2024

In this work, we show that the class of multivariate degree-$d$ polynomials mapping $\{0,1\}^{n}$ to any Abelian group $G$ is locally correctable with $\widetilde{O}_{d}((\log n)^{d})$ queries for up to a fraction of errors approaching half the minimum distance of the underlying code. In particular, this result holds even for polynomials over the reals or the rationals, special cases that were previously not known. Further, we show that they are locally list correctable up to a fraction of errors approaching the minimum distance of the code. These results build on and extend the prior work of the authors [ABPSS24] (STOC 2024) who considered the case of linear polynomials and gave analogous results. Low-degree polynomials over the Boolean cube $\{0,1\}^{n}$ arise naturally in Boolean circuit complexity and learning theory, and our work furthers the study of their coding-theoretic properties. Extending the results of [ABPSS24] from linear to higher-degree polynomials involves several new challenges and handling them gives us further insights into properties of low-degree polynomials over the Boolean cube. For local correction, we construct a set of points in the Boolean cube that lie between two exponentially close parallel hyperplanes and is moreover an interpolating set for degree-$d$ polynomials. To show that the class of degree-$d$ polynomials is list decodable up to the minimum distance, we stitch together results on anti-concentration of low-degree polynomials, the Sunflower lemma, and the Footprint bound for counting common zeroes of polynomials. Analyzing the local list corrector of [ABPSS24] for higher degree polynomials involves understanding random restrictions of non-zero degree-$d$ polynomials on a Hamming slice. In particular, we show that a simple random restriction process for reducing the dimension of the Boolean cube is a suitably good sampler for Hamming slices.
Feature-Space Semantic Invariance: Enhanced OOD Detection for Open-Set Domain Generalization
Venue: arXiv cs.CV 2024
Year: 2024

Open-set domain generalization addresses a real-world challenge: training a model to generalize across unseen domains (domain generalization) while also detecting samples from unknown classes not encountered during training (open-set recognition). However, most existing approaches tackle these issues separately, limiting their practical applicability. To overcome this limitation, we propose a unified framework for open-set domain generalization by introducing Feature-space Semantic Invariance (FSI). FSI maintains semantic consistency across different domains within the feature space, enabling more accurate detection of OOD instances in unseen domains. Additionally, we adopt a generative model to produce synthetic data with novel domain styles or class labels, enhancing model robustness. Initial experiments show that our method improves AUROC by 9.1% to 18.9% on ColoredMNIST, while also significantly increasing in-distribution classification accuracy.
Spiking Transformer Hardware Accelerators in 3D Integration
Venue: arXiv cs.NE 2024
Year: 2024

Spiking neural networks (SNNs) are powerful models of spatiotemporal computation and are well suited for deployment on resource-constrained edge devices and neuromorphic hardware due to their low power consumption. Leveraging attention mechanisms similar to those found in their artificial neural network counterparts, recently emerged spiking transformers have showcased promising performance and efficiency by capitalizing on the binary nature of spiking operations. Recognizing the current lack of dedicated hardware support for spiking transformers, this paper presents the first work on 3D spiking transformer hardware architecture and design methodology. We present an architecture and physical design co-optimization approach tailored specifically for spiking transformers. Through memory-on-logic and logic-on-logic stacking enabled by 3D integration, we demonstrate significant energy and delay improvements compared to conventional 2D CMOS integration.
Quality of Control based Resource Dimensioning for Collaborative Edge Robotics
Venue: arXiv cs.RO 2024
Year: 2024

With the increasing focus on flexible automation, which emphasizes systems capable of adapting to varied tasks and conditions, exploring future deployments of cloud and edge-based network infrastructures in robotic systems becomes crucial. This work, examines how wireless solutions could support the shift from rigid, wired setups toward more adaptive, flexible automation in industrial environments. We provide a quality of control (QoC) based abstraction for robotic workloads, parameterized on loop latency and reliability, and jointly optimize system performance. The setup involves collaborative robots working on distributed tasks, underscoring how wireless communication can enable more dynamic coordination in flexible automation systems. We use our abstraction to optimally maximize the QoC ensuring efficient operation even under varying network conditions. Additionally, our solution allocates the communication resources in time slots, optimizing the balance between communication and control costs. Our simulation results highlight that minimizing the delay in the system may not always ensure the best QoC but can lead to substantial gains in QoC if delays are sometimes relaxed, allowing more packets to be delivered reliably.
Using Generative AI and Multi-Agents to Provide Automatic Feedback
Venue: arXiv cs.CL 2024
Year: 2024

This study investigates the use of generative AI and multi-agent systems to provide automatic feedback in educational contexts, particularly for student constructed responses in science assessments. The research addresses a key gap in the field by exploring how multi-agent systems, called AutoFeedback, can improve the quality of GenAI-generated feedback, overcoming known issues such as over-praise and over-inference that are common in single-agent large language models (LLMs). The study developed a multi-agent system consisting of two AI agents: one for generating feedback and another for validating and refining it. The system was tested on a dataset of 240 student responses, and its performance was compared to that of a single-agent LLM. Results showed that AutoFeedback significantly reduced the occurrence of over-praise and over-inference errors, providing more accurate and pedagogically sound feedback. The findings suggest that multi-agent systems can offer a more reliable solution for generating automated feedback in educational settings, highlighting their potential for scalable and personalized learning support. These results have important implications for educators and researchers seeking to leverage AI in formative assessments, offering a pathway to more effective feedback mechanisms that enhance student learning outcomes.
Reproduction of High-frequency Vibration Experience by a Sensory Equivalent Conversion Method for the Standard Haptic SDK of Meta Quest 3
Venue: arXiv cs.HC 2024
Year: 2024

In recent years, there has been a growing demand for realistic haptic experiences in VR. This study employs the Intensity Segment Modulation (ISM) method to convert high-frequency vibrations into low-frequency amplitude-modulated waves on VR devices. We verified the generation of high-immersion vibration stimuli synchronized with audio. Additionally, we compared the perception of ISM-converted vibrations between VR devices and other devices to evaluate the effectiveness of ISM in VR environments. A demonstration on the Meta Quest 3 was also developed, allowing users to experience the tactile sensation of handheld fireworks.
Just Label the Repeats for In-The-Wild Audio-to-Score Alignment
Venue: arXiv cs.SD 2024
Year: 2024

We propose an efficient workflow for high-quality offline alignment of in-the-wild performance audio and corresponding sheet music scans (images). Recent work on audio-to-score alignment extends dynamic time warping (DTW) to be theoretically able to handle jumps in sheet music induced by repeat signs-this method requires no human annotations, but we show that it often yields low-quality alignments. As an alternative, we propose a workflow and interface that allows users to quickly annotate jumps (by clicking on repeat signs), requiring a small amount of human supervision but yielding much higher quality alignments on average. Additionally, we refine audio and score feature representations to improve alignment quality by: (1) integrating measure detection into the score feature representation, and (2) using raw onset prediction probabilities from a music transcription model instead of piano roll. We propose an evaluation protocol for audio-to-score alignment that computes the distance between the estimated and ground truth alignment in units of measures. Under this evaluation, we find that our proposed jump annotation workflow and improved feature representations together improve alignment accuracy by 150% relative to prior work (33% to 82%).
Discovery of Timeline and Crowd Reaction of Software Vulnerability Disclosures
Venue: arXiv cs.SE 2024
Year: 2024

Reusing third-party libraries increases productivity and saves time and costs for developers. However, the downside is the presence of vulnerabilities in those libraries, which can lead to catastrophic outcomes. For instance, Apache Log4J was found to be vulnerable to remote code execution attacks. A total of more than 35,000 packages were forced to update their Log4J libraries with the latest version. Although several studies have been conducted to predict software vulnerabilities, the prediction does not cover the vulnerabilities found in third-party libraries. Even if the developers are aware of the forthcoming issue, replicating a function similar to the libraries would be time-consuming and labour-intensive. Nevertheless, it is practically reasonable for software developers to update their third-party libraries (and dependencies) whenever the software vendors have released a vulnerable-free version. In this work, our manual study focuses on the real-world practices (crowd reaction) adopted by software vendors and developer communities when a vulnerability is disclosed. We manually investigated 312 CVEs and identified that the primary trend of vulnerability handling is to provide a fix before publishing an announcement. Otherwise, developers wait an average of 10 days for a fix if it is unavailable upon the announcement. Additionally, the crowd reaction is oblivious to the vulnerability severity. In particular, we identified Oracle as the most vibrant community diligent in releasing fixes. Their software developers also actively participate in the associated vulnerability announcements.
Rapid Response: Mitigating LLM Jailbreaks with a Few Examples
Venue: arXiv cs.CL 2024
Year: 2024

As large language models (LLMs) grow more powerful, ensuring their safety against misuse becomes crucial. While researchers have focused on developing robust defenses, no method has yet achieved complete invulnerability to attacks. We propose an alternative approach: instead of seeking perfect adversarial robustness, we develop rapid response techniques to look to block whole classes of jailbreaks after observing only a handful of attacks. To study this setting, we develop RapidResponseBench, a benchmark that measures a defense's robustness against various jailbreak strategies after adapting to a few observed examples. We evaluate five rapid response methods, all of which use jailbreak proliferation, where we automatically generate additional jailbreaks similar to the examples observed. Our strongest method, which fine-tunes an input classifier to block proliferated jailbreaks, reduces attack success rate by a factor greater than 240 on an in-distribution set of jailbreaks and a factor greater than 15 on an out-of-distribution set, having observed just one example of each jailbreaking strategy. Moreover, further studies suggest that the quality of proliferation model and number of proliferated examples play an key role in the effectiveness of this defense. Overall, our results highlight the potential of responding rapidly to novel jailbreaks to limit LLM misuse.
Ética para LLMs: o compartilhamento de dados sociolinguísticos
Venue: arXiv cs.CY 2024
Year: 2024

The collection of speech data carried out in Sociolinguistics has the potential to enhance large language models due to its quality and representativeness. In this paper, we examine the ethical considerations associated with the gathering and dissemination of such data. Additionally, we outline strategies for addressing the sensitivity of speech data, as it may facilitate the identification of informants who contributed with their speech.
SparrowVQE: Visual Question Explanation for Course Content Understanding
Venue: arXiv cs.CV 2024
Year: 2024

Visual Question Answering (VQA) research seeks to create AI systems to answer natural language questions in images, yet VQA methods often yield overly simplistic and short answers. This paper aims to advance the field by introducing Visual Question Explanation (VQE), which enhances the ability of VQA to provide detailed explanations rather than brief responses and address the need for more complex interaction with visual content. We first created an MLVQE dataset from a 14-week streamed video machine learning course, including 885 slide images, 110,407 words of transcripts, and 9,416 designed question-answer (QA) pairs. Next, we proposed a novel SparrowVQE, a small 3 billion parameters multimodal model. We trained our model with a three-stage training mechanism consisting of multimodal pre-training (slide images and transcripts feature alignment), instruction tuning (tuning the pre-trained model with transcripts and QA pairs), and domain fine-tuning (fine-tuning slide image and QA pairs). Eventually, our SparrowVQE can understand and connect visual information using the SigLIP model with transcripts using the Phi-2 language model with an MLP adapter. Experimental results demonstrate that our SparrowVQE achieves better performance in our developed MLVQE dataset and outperforms state-of-the-art methods in the other five benchmark VQA datasets. The source code is available at \url{https://github.com/YoushanZhang/SparrowVQE}.
SecEncoder: Logs are All You Need in Security
Venue: arXiv cs.CR 2024
Year: 2024

Large and Small Language Models (LMs) are typically pretrained using extensive volumes of text, which are sourced from publicly accessible platforms such as Wikipedia, Book Corpus, or through web scraping. These models, due to their exposure to a wide range of language data, exhibit impressive generalization capabilities and can perform a multitude of tasks simultaneously. However, they often fall short when it comes to domain-specific tasks due to their broad training data. This paper introduces SecEncoder, a specialized small language model that is pretrained using security logs. SecEncoder is designed to address the domain-specific limitations of general LMs by focusing on the unique language and patterns found in security logs. Experimental results indicate that SecEncoder outperforms other LMs, such as BERTlarge, DeBERTa-v3-large and OpenAI's Embedding (textembedding-ada-002) models, which are pretrained mainly on natural language, across various tasks. Furthermore, although SecEncoder is primarily pretrained on log data, it outperforms models pretrained on natural language for a range of tasks beyond log analysis, such as incident prioritization and threat intelligence document retrieval. This suggests that domain specific pretraining with logs can significantly enhance the performance of LMs in security. These findings pave the way for future research into security-specific LMs and their potential applications.