OpenRaiser/Intern-Atlas huggingface dataset:
The Role of Emotions in Informational Support Question-Response Pairs in Online Health Communities: A Multimodal Deep Learning Approach
Venue: arXiv cs.AI 2024
Year: 2024

This study explores the relationship between informational support seeking questions, responses, and helpfulness ratings in online health communities. We created a labeled data set of question-response pairs and developed multimodal machine learning and deep learning models to reliably predict informational support questions and responses. We employed explainable AI to reveal the emotions embedded in informational support exchanges, demonstrating the importance of emotion in providing informational support. This complex interplay between emotional and informational support has not been previously researched. The study refines social support theory and lays the groundwork for the development of user decision aids. Further implications are discussed.
FV8: A Forced Execution JavaScript Engine for Detecting Evasive Techniques
Venue: arXiv cs.CR 2024
Year: 2024

Evasion techniques allow malicious code to never be observed. This impacts significantly the detection capabilities of tools that rely on either dynamic or static analysis, as they never get to process the malicious code. The dynamic nature of JavaScript, where code is often injected dynamically, makes evasions particularly effective. Yet, we lack tools that can detect evasive techniques in a challenging environment such as JavaScript. In this paper, we present FV8, a modified V8 JavaScript engine designed to identify evasion techniques in JavaScript code. FV8 selectively enforces code execution on APIs that conditionally inject dynamic code, thus enhancing code coverage and consequently improving visibility into malicious code. We integrate our tool in both the Node.js engine and the Chromium browser, compelling code execution in npm packages and Chrome browser extensions. Our tool increases code coverage by 11% compared to default V8 and detects 28 unique evasion categories, including five previously unreported techniques. In data confirmed as malicious from both ecosystems, our tool identifies 1,443 (14.6%) npm packages and 164 (82%) extensions containing at least one type of evasion. In previously unexamined extensions (39,592), our tool discovered 16,471 injected third-party scripts, and a total of 8,732,120 lines of code executed due to our forced execution instrumentation. Furthermore, it tagged a total of 423 extensions as both evasive and malicious and we manually verify 110 extensions (26%) to actually be malicious, impacting two million users. Our tool is open-source and serves both as an in-browser and standalone dynamic analysis tool, capable of detecting evasive code, bypassing obfuscation in certain cases, offering improved access to malicious code, and supporting recursive analysis of dynamic code injections
Investigating Symbolic Capabilities of Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Prompting techniques have significantly enhanced the capabilities of Large Language Models (LLMs) across various complex tasks, including reasoning, planning, and solving math word problems. However, most research has predominantly focused on language-based reasoning and word problems, often overlooking the potential of LLMs in handling symbol-based calculations and reasoning. This study aims to bridge this gap by rigorously evaluating LLMs on a series of symbolic tasks, such as addition, multiplication, modulus arithmetic, numerical precision, and symbolic counting. Our analysis encompasses eight LLMs, including four enterprise-grade and four open-source models, of which three have been pre-trained on mathematical tasks. The assessment framework is anchored in Chomsky's Hierarchy, providing a robust measure of the computational abilities of these models. The evaluation employs minimally explained prompts alongside the zero-shot Chain of Thoughts technique, allowing models to navigate the solution process autonomously. The findings reveal a significant decline in LLMs' performance on context-free and context-sensitive symbolic tasks as the complexity, represented by the number of symbols, increases. Notably, even the fine-tuned GPT3.5 exhibits only marginal improvements, mirroring the performance trends observed in other models. Across the board, all models demonstrated a limited generalization ability on these symbol-intensive tasks. This research underscores LLMs' challenges with increasing symbolic complexity and highlights the need for specialized training, memory and architectural adjustments to enhance their proficiency in symbol-based reasoning tasks.
Computational Tradeoffs in Image Synthesis: Diffusion, Masked-Token, and Next-Token Prediction
Venue: arXiv cs.CV 2024
Year: 2024

Nearly every recent image synthesis approach, including diffusion, masked-token prediction, and next-token prediction, uses a Transformer network architecture. Despite this common backbone, there has been no direct, compute controlled comparison of how these approaches affect performance and efficiency. We analyze the scalability of each approach through the lens of compute budget measured in FLOPs. We find that token prediction methods, led by next-token prediction, significantly outperform diffusion on prompt following. On image quality, while next-token prediction initially performs better, scaling trends suggest it is eventually matched by diffusion. We compare the inference compute efficiency of each approach and find that next token prediction is by far the most efficient. Based on our findings we recommend diffusion for applications targeting image quality and low latency; and next-token prediction when prompt following or throughput is more important.
How Reliable AI Chatbots are for Disease Prediction from Patient Complaints?
Venue: arXiv cs.AI 2024
Year: 2024

Artificial Intelligence (AI) chatbots leveraging Large Language Models (LLMs) are gaining traction in healthcare for their potential to automate patient interactions and aid clinical decision-making. This study examines the reliability of AI chatbots, specifically GPT 4.0, Claude 3 Opus, and Gemini Ultra 1.0, in predicting diseases from patient complaints in the emergency department. The methodology includes few-shot learning techniques to evaluate the chatbots' effectiveness in disease prediction. We also fine-tune the transformer-based model BERT and compare its performance with the AI chatbots. Results suggest that GPT 4.0 achieves high accuracy with increased few-shot data, while Gemini Ultra 1.0 performs well with fewer examples, and Claude 3 Opus maintains consistent performance. BERT's performance, however, is lower than all the chatbots, indicating limitations due to limited labeled data. Despite the chatbots' varying accuracy, none of them are sufficiently reliable for critical medical decision-making, underscoring the need for rigorous validation and human oversight. This study reflects that while AI chatbots have potential in healthcare, they should complement, not replace, human expertise to ensure patient safety. Further refinement and research are needed to improve AI-based healthcare applications' reliability for disease prediction.
Multiple Realizability and the Rise of Deep Learning
Venue: arXiv cs.AI 2024
Year: 2024

The multiple realizability thesis holds that psychological states may be implemented in a diversity of physical systems. The deep learning revolution seems to be bringing this possibility to life, offering the most plausible examples of man-made realizations of sophisticated cognitive functions to date. This paper explores the implications of deep learning models for the multiple realizability thesis. Among other things, it challenges the widely held view that multiple realizability entails that the study of the mind can and must be pursued independently of the study of its implementation in the brain or in artificial analogues. Although its central contribution is philosophical, the paper has substantial methodological upshots for contemporary cognitive science, suggesting that deep neural networks may play a crucial role in formulating and evaluating hypotheses about cognition, even if they are interpreted as implementation-level models. In the age of deep learning, multiple realizability possesses a renewed significance.
AUGlasses: Continuous Action Unit based Facial Reconstruction with Low-power IMUs on Smart Glasses
Venue: arXiv cs.HC 2024
Year: 2024

Recent advancements in augmented reality (AR) have enabled the use of various sensors on smart glasses for applications like facial reconstruction, which is vital to improve AR experiences for virtual social activities. However, the size and power constraints of smart glasses demand a miniature and low-power sensing solution. AUGlasses achieves unobtrusive low-power facial reconstruction by placing inertial measurement units (IMU) against the temporal area on the face to capture the skin deformations, which are caused by facial muscle movements. These IMU signals, along with historical data on facial action units (AUs), are processed by a transformer-based deep learning model to estimate AU intensities in real-time, which are then used for facial reconstruction. Our results show that AUGlasses accurately predicts the strength (0-5 scale) of 14 key AUs with a cross-user mean absolute error (MAE) of 0.187 (STD = 0.025) and achieves facial reconstruction with a cross-user MAE of 1.93 mm (STD = 0.353). We also integrated various preprocessing and training techniques to ensure robust performance for continuous sensing. Micro-benchmark tests indicate that our system consistently performs accurate continuous facial reconstruction with a fine-tuned cross-user model, achieving an AU MAE of 0.35.
Metadata Integration for Spam Reviews Detection on Vietnamese E-commerce Websites
Venue: arXiv cs.CL 2024
Year: 2024

The problem of detecting spam reviews (opinions) has received significant attention in recent years, especially with the rapid development of e-commerce. Spam reviews are often classified based on comment content, but in some cases, it is insufficient for models to accurately determine the review label. In this work, we introduce the ViSpamReviews v2 dataset, which includes metadata of reviews with the objective of integrating supplementary attributes for spam review classification. We propose a novel approach to simultaneously integrate both textual and categorical attributes into the classification model. In our experiments, the product category proved effective when combined with deep neural network (DNN) models, while text features performed well on both DNN models and the model achieved state-of-the-art performance in the problem of detecting spam reviews on Vietnamese e-commerce websites, namely PhoBERT. Specifically, the PhoBERT model achieves the highest accuracy when combined with product description features generated from the SPhoBert model, which is the combination of PhoBERT and SentenceBERT. Using the macro-averaged F1 score, the task of classifying spam reviews achieved 87.22% (an increase of 1.64% compared to the baseline), while the task of identifying the type of spam reviews achieved an accuracy of 73.49% (an increase of 1.93% compared to the baseline).
Dialects for CoAP-like Messaging Protocols
Venue: arXiv cs.CR 2024
Year: 2024

Messaging protocols for resource limited systems such as distributed IoT systems are often vulnerable to attacks due to security choices made to conserve resources such as time, memory, or bandwidth. For example, use of secure layers such as DTLS are resource expensive and can sometimes cause service disruption. Protocol dialects are intended as a light weight, modular mechanism to provide selected security guarantees, such as authentication. In this report we study the CoAP messaging protocol and define two attack models formalizing different vulnerabilities. We propose a generic dialect for CoAP messaging. The CoAP protocol, dialect, and attack models are formalized in the rewriting logic system Maude. A number of case studies are reported illustrating vulnerabilities and effects of applying the dialect. We also prove (stuttering) bisimulations between CoAP messaging applications and dialected versions, thus ensuring that dialecting preserves LTL properties (without Next) of CoAP applications. To support search for attacks in complex messaging situations we specify a simple application layer to drive the CoAP messaging and generalize the attack model to support a form of symbolic search for attacks. Two case studies are presented to illustrate the more general attack search.
High Performance P300 Spellers Using GPT2 Word Prediction With Cross-Subject Training
Venue: arXiv cs.CL 2024
Year: 2024

Amyotrophic lateral sclerosis (ALS) severely impairs patients' ability to communicate, often leading to a decline in their quality of life within a few years of diagnosis. The P300 speller brain-computer interface (BCI) offers an alternative communication method by interpreting a subject's EEG response to characters presented on a grid interface. This paper addresses the common speed limitations encountered in training efficient P300-based multi-subject classifiers by introducing innovative "across-subject" classifiers. We leverage a combination of the second-generation Generative Pre-Trained Transformer (GPT2) and Dijkstra's algorithm to optimize stimuli and suggest word completion choices based on typing history. Additionally, we employ a multi-layered smoothing technique to accommodate out-of-vocabulary (OOV) words. Through extensive simulations involving random sampling of EEG data from subjects, we demonstrate significant speed enhancements in typing passages containing rare and OOV words. These optimizations result in approximately 10% improvement in character-level typing speed and up to 40% improvement in multi-word prediction. We demonstrate that augmenting standard row/column highlighting techniques with layered word prediction yields close-to-optimal performance. Furthermore, we explore both "within-subject" and "across-subject" training techniques, showing that speed improvements are consistent across both approaches.
Service Mesh: Architectures, Applications, and Implementations
Venue: arXiv cs.NI 2024
Year: 2024

The scalability and flexibility of microservice architecture have led to major changes in cloud-native application architectures. However, the complexity of managing thousands of small services written in different languages and handling the exchange of data between them have caused significant management challenges. Service mesh is a promising solution that could mitigate these problems by introducing an overlay layer on top of the services. In this paper, we first study the architecture and components of service mesh architecture. Then, we review two important service mesh implementations and discuss how the service mesh could be helpful in other areas, including 5G.
On the Challenges of Creating Datasets for Analyzing Commercial Sex Advertisements to Assess Human Trafficking Risk and Organized Activity
Venue: arXiv cs.LG 2024
Year: 2024

Our study addresses the challenges of building datasets to understand the risks associated with organized activities and human trafficking through commercial sex advertisements. These challenges include data scarcity, rapid obsolescence, and privacy concerns. Traditional approaches, which are not automated and are difficult to reproduce, fall short in addressing these issues. We have developed a reproducible and automated methodology to analyze five million advertisements. In the process, we identified further challenges in dataset creation within this sensitive domain. This paper presents a streamlined methodology to assist researchers in constructing effective datasets for combating organized crime, allowing them to focus on advancing detection technologies.
Enhanced Creativity and Ideation through Stable Video Synthesis
Venue: arXiv cs.HC 2024
Year: 2024

This paper explores the innovative application of Stable Video Diffusion (SVD), a diffusion model that revolutionizes the creation of dynamic video content from static images. As digital media and design industries accelerate, SVD emerges as a powerful generative tool that enhances productivity and introduces novel creative possibilities. The paper examines the technical underpinnings of diffusion models, their practical effectiveness, and potential future developments, particularly in the context of video generation. SVD operates on a probabilistic framework, employing a gradual denoising process to transform random noise into coherent video frames. It addresses the challenges of visual consistency, natural movement, and stylistic reflection in generated videos, showcasing high generalization capabilities. The integration of SVD in design tasks promises enhanced creativity, rapid prototyping, and significant time and cost efficiencies. It is particularly impactful in areas requiring frame-to-frame consistency, natural motion capture, and creative diversity, such as animation, visual effects, advertising, and educational content creation. The paper concludes that SVD is a catalyst for design innovation, offering a wide array of applications and a promising avenue for future research and development in the field of digital media and design.
Static Deep Q-learning for Green Downlink C-RAN
Venue: arXiv cs.IT 2024
Year: 2024

Power saving is a main pillar in the operation of wireless communication systems. In this paper, we investigate cloud radio access network (C-RAN) capability to reduce power consumption based on the user equipment (UE) requirement. Aiming to save the long-term C-RAN energy consumption, an optimization problem is formulated to manage the downlink power without degrading the UE requirement by designing the power offset parameter. Considering stochastic traffic arrivals at UEs, we first formulate the problem as a Markov decision process (MDP) and then set up a dual objective optimization problem in terms of the downlink throughput and power. To solve this optimization problem, we develop a novel static deep Q-learning (SDQL) algorithm to maximize the downlink throughput and minimize the downlink power. In our proposed algorithm, we design multi-Q-tables to simultaneously optimize power reductions of activated RRHs by assigning one Q-table for each UE. To maximize the accumulative reward in terms of the downlink throughput loss and power reduction, our proposed algorithm performs power reductions of activated RRHs through continuous environmental interactions. Simulation results1 show that our proposed algorithm enjoys a superior average power reduction compared to the activation and sleep schemes, and enjoys a low computational complexity.
Faster Vizing and Near-Vizing Edge Coloring Algorithms
Venue: arXiv cs.DS 2024
Year: 2024

Vizing's celebrated theorem states that every simple graph with maximum degree $Δ$ admits a $(Δ+1)$ edge coloring which can be found in $O(m \cdot n)$ time on $n$-vertex $m$-edge graphs. This is just one color more than the trivial lower bound of $Δ$ colors needed in any proper edge coloring. After a series of simplifications and variations, this running time was eventually improved by Gabow, Nishizeki, Kariv, Leven, and Terada in 1985 to $O(m\sqrt{n\log{n}})$ time. This has effectively remained the state-of-the-art modulo an $O(\sqrt{\log{n}})$-factor improvement by Sinnamon in 2019. As our main result, we present a novel randomized algorithm that computes a $Δ+O(\log{n})$ coloring of any given simple graph in $O(m\logΔ)$ expected time; in other words, a near-linear time randomized algorithm for a ``near''-Vizing's coloring. As a corollary of this algorithm, we also obtain the following results: * A randomized algorithm for $(Δ+1)$ edge coloring in $O(n^2\log{n})$ expected time. This is near-linear in the input size for dense graphs and presents the first polynomial time improvement over the longstanding bounds of Gabow et.al. for Vizing's theorem in almost four decades. * A randomized algorithm for $(1+\varepsilon) Δ$ edge coloring in $O(m\log{(1/\varepsilon)})$ expected time for any $\varepsilon = ω(\log{n}/Δ)$. The dependence on $\varepsilon$ exponentially improves upon a series of recent results that obtain algorithms with runtime of $Ω(m/\varepsilon)$ for this problem.
A New Era in Human Factors Engineering: A Survey of the Applications and Prospects of Large Multimodal Models
Venue: arXiv cs.HC 2024
Year: 2024

In recent years, the potential applications of Large Multimodal Models (LMMs) in fields such as healthcare, social psychology, and industrial design have attracted wide research attention, providing new directions for human factors research. For instance, LMM-based smart systems have become novel research subjects of human factors studies, and LMM introduces new research paradigms and methodologies to this field. Therefore, this paper aims to explore the applications, challenges, and future prospects of LMM in the domain of human factors and ergonomics through an expert-LMM collaborated literature review. Specifically, a novel literature review method is proposed, and research studies of LMM-based accident analysis, human modelling and intervention design are introduced. Subsequently, the paper discusses future trends of the research paradigm and challenges of human factors and ergonomics studies in the era of LMMs. It is expected that this study can provide a valuable perspective and serve as a reference for integrating human factors with artificial intelligence.
Towards Exploratory Quality Diversity Landscape Analysis
Venue: arXiv cs.NE 2024
Year: 2024

This work is a preliminary study on using Exploratory Landscape Analysis (ELA) for Quality Diversity (QD) problems. We seek to understand whether ELA features can potentially be used to characterise QD problems paving the way for automating QD algorithm selection. Our results demonstrate that ELA features are affected by QD optimisation differently than random sampling, and more specifically, by the choice of variation operator, behaviour function, archive size and problem dimensionality.
A Coherence Construction for the Propositional Universe
Venue: arXiv cs.LO 2024
Year: 2024

We record a particularly simple construction on top of Lumsdaine's local universes that allows for a Coquand-style universe of propositions with propositional extensionality to be interpreted in a category with subobject classifiers.
Distilling Instruction-following Abilities of Large Language Models with Task-aware Curriculum Planning
Venue: arXiv cs.CL 2024
Year: 2024

Instruction tuning aims to align large language models (LLMs) with open-domain instructions and human-preferred responses. While several studies have explored autonomous approaches to distilling and annotating instructions from powerful proprietary LLMs, such as ChatGPT, they often neglect the impact of the distributions and characteristics of tasks, together with the varying difficulty of instructions in training sets. This oversight can lead to imbalanced knowledge capabilities and poor generalization powers of student LLMs. To address these challenges, we introduce Task-Aware Curriculum Planning for Instruction Refinement (TAPIR), a multi-round distillation framework that utilizes an oracle LLM to select instructions that are difficult for a student LLM to follow. To balance the student's capabilities, task distributions in training sets are adjusted with responses automatically refined according to their corresponding tasks. In addition, by incorporating curriculum planning, our approach systematically escalates the difficulty levels of tasks, progressively enhancing the student LLM's capabilities. We rigorously evaluate TAPIR using several widely recognized benchmarks (such as AlpacaEval 2.0, MT-Bench, etc.) and multiple student LLMs. Empirical results demonstrate that student LLMs, trained with our method and less training data, outperform larger instruction-tuned models and strong distillation baselines.