OpenRaiser/Intern-Atlas huggingface dataset:
Towards zero-shot amplifier modeling: One-to-many amplifier modeling via tone embedding control
Venue: arXiv cs.SD 2024
Year: 2024

Replicating analog device circuits through neural audio effect modeling has garnered increasing interest in recent years. Existing work has predominantly focused on a one-to-one emulation strategy, modeling specific devices individually. In this paper, we tackle the less-explored scenario of one-to-many emulation, utilizing conditioning mechanisms to emulate multiple guitar amplifiers through a single neural model. For condition representation, we use contrastive learning to build a tone embedding encoder that extracts style-related features of various amplifiers, leveraging a dataset of comprehensive amplifier settings. Targeting zero-shot application scenarios, we also examine various strategies for tone embedding representation, evaluating referenced tone embedding against two retrieval-based embedding methods for amplifiers unseen in the training time. Our findings showcase the efficacy and potential of the proposed methods in achieving versatile one-to-many amplifier modeling, contributing a foundational step towards zero-shot audio modeling applications.
Enhancing Retrieval and Managing Retrieval: A Four-Module Synergy for Improved Quality and Efficiency in RAG Systems
Venue: arXiv cs.CL 2024
Year: 2024

Retrieval-augmented generation (RAG) techniques leverage the in-context learning capabilities of large language models (LLMs) to produce more accurate and relevant responses. Originating from the simple 'retrieve-then-read' approach, the RAG framework has evolved into a highly flexible and modular paradigm. A critical component, the Query Rewriter module, enhances knowledge retrieval by generating a search-friendly query. This method aligns input questions more closely with the knowledge base. Our research identifies opportunities to enhance the Query Rewriter module to Query Rewriter+ by generating multiple queries to overcome the Information Plateaus associated with a single query and by rewriting questions to eliminate Ambiguity, thereby clarifying the underlying intent. We also find that current RAG systems exhibit issues with Irrelevant Knowledge; to overcome this, we propose the Knowledge Filter. These two modules are both based on the instruction-tuned Gemma-2B model, which together enhance response quality. The final identified issue is Redundant Retrieval; we introduce the Memory Knowledge Reservoir and the Retriever Trigger to solve this. The former supports the dynamic expansion of the RAG system's knowledge base in a parameter-free manner, while the latter optimizes the cost for accessing external knowledge, thereby improving resource utilization and response efficiency. These four RAG modules synergistically improve the response quality and efficiency of the RAG system. The effectiveness of these modules has been validated through experiments and ablation studies across six common QA datasets. The source code can be accessed at https://github.com/Ancientshi/ERM4.
SEMINAR: Search Enhanced Multi-modal Interest Network and Approximate Retrieval for Lifelong Sequential Recommendation
Venue: arXiv cs.IR 2024
Year: 2024

The modeling of users' behaviors is crucial in modern recommendation systems. A lot of research focuses on modeling users' lifelong sequences, which can be extremely long and sometimes exceed thousands of items. These models use the target item to search for the most relevant items from the historical sequence. However, training lifelong sequences in click through rate (CTR) prediction or personalized search ranking (PSR) is extremely difficult due to the insufficient learning problem of ID embedding, especially when the IDs in the lifelong sequence features do not exist in the samples of training dataset. Additionally, existing target attention mechanisms struggle to learn the multi-modal representations of items in the sequence well. The distribution of multi-modal embedding (text, image and attributes) output of user's interacted items are not properly aligned and there exist divergence across modalities. We also observe that users' search query sequences and item browsing sequences can fully depict users' intents and benefit from each other. To address these challenges, we propose a unified lifelong multi-modal sequence model called SEMINAR-Search Enhanced Multi-Modal Interest Network and Approximate Retrieval. Specifically, a network called Pretraining Search Unit (PSU) learns the lifelong sequences of multi-modal query-item pairs in a pretraining-finetuning manner with multiple objectives: multi-modal alignment, next query-item pair prediction, query-item relevance prediction, etc. After pretraining, the downstream model restores the pretrained embedding as initialization and finetunes the network. To accelerate the online retrieval speed of multi-modal embedding, we propose a multi-modal codebook-based product quantization strategy to approximate the exact attention calculati
Mitigating Data Imbalance for Software Vulnerability Assessment: Does Data Augmentation Help?
Venue: arXiv cs.SE 2024
Year: 2024

Background: Software Vulnerability (SV) assessment is increasingly adopted to address the ever-increasing volume and complexity of SVs. Data-driven approaches have been widely used to automate SV assessment tasks, particularly the prediction of the Common Vulnerability Scoring System (CVSS) metrics such as exploitability, impact, and severity. SV assessment suffers from the imbalanced distributions of the CVSS classes, but such data imbalance has been hardly understood and addressed in the literature. Aims: We conduct a large-scale study to quantify the impacts of data imbalance and mitigate the issue for SV assessment through the use of data augmentation. Method: We leverage nine data augmentation techniques to balance the class distributions of the CVSS metrics. We then compare the performance of SV assessment models with and without leveraging the augmented data. Results: Through extensive experiments on 180k+ real-world SVs, we show that mitigating data imbalance can significantly improve the predictive performance of models for all the CVSS tasks, by up to 31.8% in Matthews Correlation Coefficient. We also discover that simple text augmentation like combining random text insertion, deletion, and replacement can outperform the baseline across the board. Conclusions: Our study provides the motivation and the first promising step toward tackling data imbalance for effective SV assessment.
Anticipating Future Object Compositions without Forgetting
Venue: arXiv cs.CV 2024
Year: 2024

Despite the significant advancements in computer vision models, their ability to generalize to novel object-attribute compositions remains limited. Existing methods for Compositional Zero-Shot Learning (CZSL) mainly focus on image classification. This paper aims to enhance CZSL in object detection without forgetting prior learned knowledge. We use Grounding DINO and incorporate Compositional Soft Prompting (CSP) into it and extend it with Compositional Anticipation. We achieve a 70.5% improvement over CSP on the harmonic mean (HM) between seen and unseen compositions on the CLEVR dataset. Furthermore, we introduce Contrastive Prompt Tuning to incrementally address model confusion between similar compositions. We demonstrate the effectiveness of this method and achieve an increase of 14.5% in HM across the pretrain, increment, and unseen sets. Collectively, these methods provide a framework for learning various compositions with limited data, as well as improving the performance of underperforming compositions when additional data becomes available.
On-Device Training of Fully Quantized Deep Neural Networks on Cortex-M Microcontrollers
Venue: arXiv cs.LG 2024
Year: 2024

On-device training of DNNs allows models to adapt and fine-tune to newly collected data or changing domains while deployed on microcontroller units (MCUs). However, DNN training is a resource-intensive task, making the implementation and execution of DNN training algorithms on MCUs challenging due to low processor speeds, constrained throughput, limited floating-point support, and memory constraints. In this work, we explore on-device training of DNNs for Cortex-M MCUs. We present a method that enables efficient training of DNNs completely in place on the MCU using fully quantized training (FQT) and dynamic partial gradient updates. We demonstrate the feasibility of our approach on multiple vision and time-series datasets and provide insights into the tradeoff between training accuracy, memory overhead, energy, and latency on real hardware.
When Synthetic Traces Hide Real Content: Analysis of Stable Diffusion Image Laundering
Venue: arXiv cs.CV 2024
Year: 2024

In recent years, methods for producing highly realistic synthetic images have significantly advanced, allowing the creation of high-quality images from text prompts that describe the desired content. Even more impressively, Stable Diffusion (SD) models now provide users with the option of creating synthetic images in an image-to-image translation fashion, modifying images in the latent space of advanced autoencoders. This striking evolution, however, brings an alarming consequence: it is possible to pass an image through SD autoencoders to reproduce a synthetic copy of the image with high realism and almost no visual artifacts. This process, known as SD image laundering, can transform real images into lookalike synthetic ones and risks complicating forensic analysis for content authenticity verification. Our paper investigates the forensic implications of image laundering, revealing a serious potential to obscure traces of real content, including sensitive and harmful materials that could be mistakenly classified as synthetic, thereby undermining the protection of individuals depicted. To address this issue, we propose a two-stage detection pipeline that effectively differentiates between pristine, laundered, and fully synthetic images (those generated from text prompts), showing robustness across various conditions. Finally, we highlight another alarming property of image laundering, which appears to mask the unique artifacts exploited by forensic detectors to solve the camera model identification task, strongly undermining their performance. Our experimental code is available at https://github.com/polimi-ispl/synthetic-image-detection.
An Autonomous Drone Swarm for Detecting and Tracking Anomalies among Dense Vegetation
Venue: arXiv cs.CV 2024
Year: 2024

Swarms of drones offer an increased sensing aperture, and having them mimic behaviors of natural swarms enhances sampling by adapting the aperture to local conditions. We demonstrate that such an approach makes detecting and tracking heavily occluded targets practically feasible. While object classification applied to conventional aerial images generalizes poorly the randomness of occlusion and is therefore inefficient even under lightly occluded conditions, anomaly detection applied to synthetic aperture integral images is robust for dense vegetation, such as forests, and is independent of pre-trained classes. Our autonomous swarm searches the environment for occurrences of the unknown or unexpected, tracking them while continuously adapting its sampling pattern to optimize for local viewing conditions. In our real-life field experiments with a swarm of six drones, we achieved an average positional accuracy of 0.39 m with an average precision of 93.2% and an average recall of 95.9%. Here, adapted particle swarm optimization considers detection confidences and predicted target appearance. We show that sensor noise can effectively be included in the synthetic aperture image integration process, removing the need for a computationally costly optimization of high-dimensional parameter spaces. Finally, we present a complete hard- and software framework that supports low-latency transmission (approx. 80 ms round-trip time) and fast processing (approx. 600 ms per formation step) of extensive (70-120 Mbit/s) video and telemetry data, and swarm control for swarms of up to ten drones.
Socioeconomic factors of national representation in the global film festival circuit: skewed toward the large and wealthy, but small countries can beat the odds
Venue: arXiv cs.CY 2024
Year: 2024

This study analyzes how economic, demographic, and geographic factors predict the representation of different countries in the global film festival circuit. It relies on the combination of several open-access databases, including festival programming information from the Cinando platform of the Cannes Film Market. The dataset consists of over 20,000 unique films from almost 600 festivals across the world over a decade, a total of more than 30,000 film-festival entries. It is shown that while films from large affluent countries indeed dominate the festival screen, the bias is nevertheless not fully proportional to the large demographic and economic worldwide disparities and that several smaller countries perform better than expected. Further computational simulations demonstrate how much including films from smaller countries contributes to cultural diversity, and how countries vary in cultural "trade balance" dynamics, revealing differences between net exporters and importers of festival films. This research underscores the importance of representation in film festivals and the public value of increasing cultural diversity. The data-driven insights and quantitative approaches to festival programming and cultural event analytics are hoped to be useful for both the academic community as well as film festival organizers and policymakers aiming to foster more inclusive and diverse cultural landscapes.
Continual Deep Learning on the Edge via Stochastic Local Competition among Subnetworks
Venue: arXiv cs.LG 2024
Year: 2024

Continual learning on edge devices poses unique challenges due to stringent resource constraints. This paper introduces a novel method that leverages stochastic competition principles to promote sparsity, significantly reducing deep network memory footprint and computational demand. Specifically, we propose deep networks that comprise blocks of units that compete locally to win the representation of each arising new task; competition takes place in a stochastic manner. This type of network organization results in sparse task-specific representations from each network layer; the sparsity pattern is obtained during training and is different among tasks. Crucially, our method sparsifies both the weights and the weight gradients, thus facilitating training on edge devices. This is performed on the grounds of winning probability for each unit in a block. During inference, the network retains only the winning unit and zeroes-out all weights pertaining to non-winning units for the task at hand. Thus, our approach is specifically tailored for deployment on edge devices, providing an efficient and scalable solution for continual learning in resource-limited environments.
ISMRNN: An Implicitly Segmented RNN Method with Mamba for Long-Term Time Series Forecasting
Venue: arXiv cs.LG 2024
Year: 2024

Long time series forecasting aims to utilize historical information to forecast future states over extended horizons. Traditional RNN-based series forecasting methods struggle to effectively address long-term dependencies and gradient issues in long time series problems. Recently, SegRNN has emerged as a leading RNN-based model tailored for long-term series forecasting, demonstrating state-of-the-art performance while maintaining a streamlined architecture through innovative segmentation and parallel decoding techniques. Nevertheless, SegRNN has several limitations: its fixed segmentation disrupts data continuity and fails to effectively leverage information across different segments, the segmentation strategy employed by SegRNN does not fundamentally address the issue of information loss within the recurrent structure. To address these issues, we propose the ISMRNN method with three key enhancements: we introduce an implicit segmentation structure to decompose the time series and map it to segmented hidden states, resulting in denser information exchange during the segmentation phase. Additionally, we incorporate residual structures in the encoding layer to mitigate information loss within the recurrent structure. To extract information more effectively, we further integrate the Mamba architecture to enhance time series information extraction. Experiments on several real-world long time series forecasting datasets demonstrate that our model surpasses the performance of current state-of-the-art models.
The Missing Link: Allocation Performance in Causal Machine Learning
Venue: arXiv cs.LG 2024
Year: 2024

Automated decision-making (ADM) systems are being deployed across a diverse range of critical problem areas such as social welfare and healthcare. Recent work highlights the importance of causal ML models in ADM systems, but implementing them in complex social environments poses significant challenges. Research on how these challenges impact the performance in specific downstream decision-making tasks is limited. Addressing this gap, we make use of a comprehensive real-world dataset of jobseekers to illustrate how the performance of a single CATE model can vary significantly across different decision-making scenarios and highlight the differential influence of challenges such as distribution shifts on predictions and allocations.
GraphEval: A Knowledge-Graph Based LLM Hallucination Evaluation Framework
Venue: arXiv cs.CL 2024
Year: 2024

Methods to evaluate Large Language Model (LLM) responses and detect inconsistencies, also known as hallucinations, with respect to the provided knowledge, are becoming increasingly important for LLM applications. Current metrics fall short in their ability to provide explainable decisions, systematically check all pieces of information in the response, and are often too computationally expensive to be used in practice. We present GraphEval: a hallucination evaluation framework based on representing information in Knowledge Graph (KG) structures. Our method identifies the specific triples in the KG that are prone to hallucinations and hence provides more insight into where in the response a hallucination has occurred, if at all, than previous methods. Furthermore, using our approach in conjunction with state-of-the-art natural language inference (NLI) models leads to an improvement in balanced accuracy on various hallucination benchmarks, compared to using the raw NLI models. Lastly, we explore the use of GraphEval for hallucination correction by leveraging the structure of the KG, a method we name GraphCorrect, and demonstrate that the majority of hallucinations can indeed be rectified.
GuideLight: "Industrial Solution" Guidance for More Practical Traffic Signal Control Agents
Venue: arXiv cs.MA 2024
Year: 2024

Currently, traffic signal control (TSC) methods based on reinforcement learning (RL) have proven superior to traditional methods. However, most RL methods face difficulties when applied in the real world due to three factors: input, output, and the cycle-flow relation. The industry's observable input is much more limited than simulation-based RL methods. For real-world solutions, only flow can be reliably collected, whereas common RL methods need more. For the output action, most RL methods focus on acyclic control, which real-world signal controllers do not support. Most importantly, industry standards require a consistent cycle-flow relationship: non-decreasing and different response strategies for low, medium, and high-level flows, which is ignored by the RL methods. To narrow the gap between RL methods and industry standards, we innovatively propose to use industry solutions to guide the RL agent. Specifically, we design behavior cloning and curriculum learning to guide the agent to mimic and meet industry requirements and, at the same time, leverage the power of exploration and exploitation in RL for better performance. We theoretically prove that such guidance can largely decrease the sample complexity to polynomials in the horizon when searching for an optimal policy. Our rigid experiments show that our method has good cycle-flow relation and superior performance.
GHunter: Universal Prototype Pollution Gadgets in JavaScript Runtimes
Venue: arXiv cs.CR 2024
Year: 2024

Prototype pollution is a recent vulnerability that affects JavaScript code, leading to high impact attacks such as arbitrary code execution. The vulnerability is rooted in JavaScript's prototype-based inheritance, enabling attackers to inject arbitrary properties into an object's prototype at runtime. The impact of prototype pollution depends on the existence of otherwise benign pieces of code (gadgets), which inadvertently read from attacker-controlled properties to execute security-sensitive operations. While prior works primarily study gadgets in third-party libraries and client-side applications, gadgets in JavaScript runtime environments are arguably more impactful as they affect any application that executes on these runtimes. In this paper we design, implement, and evaluate a pipeline, GHunter, to systematically detect gadgets in V8-based JavaScript runtimes with prime focus on Node.js and Deno. GHunter supports a lightweight dynamic taint analysis to automatically identify gadget candidates which we validate manually to derive proof-of-concept exploits. We implement GHunter by modifying the V8 engine and the targeted runtimes along with features for facilitating manual validation. Driven by the test suites of Node.js and Deno, we use GHunter in a study of gadgets in these runtimes. We identified a total of 56 new gadgets in Node.js and 67 gadgets in Deno, pertaining to vulnerabilities such as arbitrary code execution (19), privilege escalation (31), path traversal (13), and more. Moreover, we systematize, for the first time, existing mitigations for prototype pollution and gadgets in terms of development guidelines. We collect a list of vulnerable applications and revisit the fixes through the lens of our guidelines. Through this exercise, we identified one high-severity CVE leading to remote code execution, which was due to incorrectly fixing a gadget.
Almost-Linear Time Algorithms for Decremental Graphs: Min-Cost Flow and More via Duality
Venue: arXiv cs.DS 2024
Year: 2024

We give the first almost-linear total time algorithm for deciding if a flow of cost at most $F$ still exists in a directed graph, with edge costs and capacities, undergoing decremental updates, i.e., edge deletions, capacity decreases, and cost increases. This implies almost-linear time algorithms for approximating the minimum-cost flow value and $s$-$t$ distance on such decremental graphs. Our framework additionally allows us to maintain decremental strongly connected components in almost-linear time deterministically. These algorithms also improve over the current best known runtimes for statically computing minimum-cost flow, in both the randomized and deterministic settings. We obtain our algorithms by taking the dual perspective, which yields cut-based algorithms. More precisely, our algorithm computes the flow via a sequence of $m^{1+o(1)}$ dynamic min-ratio cut problems, the dual analog of the dynamic min-ratio cycle problem that underlies recent fast algorithms for minimum-cost flow. Our main technical contribution is a new data structure that returns an approximately optimal min-ratio cut in amortized $m^{o(1)}$ time by maintaining a tree-cut sparsifier. This is achieved by devising a new algorithm to maintain the dynamic expander hierarchy of [Goranci-Räcke-Saranurak-Tan, SODA 2021] that also works in capacitated graphs. All our algorithms are deterministc, though they can be sped up further using randomized techniques while still working against an adaptive adversary.