OpenRaiser/Intern-Atlas huggingface dataset:
Improving Decision Sparsity
Venue: arXiv cs.LG 2024
Year: 2024

Sparsity is a central aspect of interpretability in machine learning. Typically, sparsity is measured in terms of the size of a model globally, such as the number of variables it uses. However, this notion of sparsity is not particularly relevant for decision-making; someone subjected to a decision does not care about variables that do not contribute to the decision. In this work, we dramatically expand a notion of decision sparsity called the Sparse Explanation Value(SEV) so that its explanations are more meaningful. SEV considers movement along a hypercube towards a reference point. By allowing flexibility in that reference and by considering how distances along the hypercube translate to distances in feature space, we can derive sparser and more meaningful explanations for various types of function classes. We present cluster-based SEV and its variant tree-based SEV, introduce a method that improves credibility of explanations, and propose algorithms that optimize decision sparsity in machine learning models.
Info-CELS: Informative Saliency Map Guided Counterfactual Explanation
Venue: arXiv cs.LG 2024
Year: 2024

As the demand for interpretable machine learning approaches continues to grow, there is an increasing necessity for human involvement in providing informative explanations for model decisions. This is necessary for building trust and transparency in AI-based systems, leading to the emergence of the Explainable Artificial Intelligence (XAI) field. Recently, a novel counterfactual explanation model, CELS, has been introduced. CELS learns a saliency map for the interest of an instance and generates a counterfactual explanation guided by the learned saliency map. While CELS represents the first attempt to exploit learned saliency maps not only to provide intuitive explanations for the reason behind the decision made by the time series classifier but also to explore post hoc counterfactual explanations, it exhibits limitations in terms of high validity for the sake of ensuring high proximity and sparsity. In this paper, we present an enhanced approach that builds upon CELS. While the original model achieved promising results in terms of sparsity and proximity, it faced limitations in validity. Our proposed method addresses this limitation by removing mask normalization to provide more informative and valid counterfactual explanations. Through extensive experimentation on datasets from various domains, we demonstrate that our approach outperforms the CELS model, achieving higher validity and producing more informative explanations.
Automatic Estimation of Singing Voice Musical Dynamics
Venue: arXiv cs.SD 2024
Year: 2024

Musical dynamics form a core part of expressive singing voice performances. However, automatic analysis of musical dynamics for singing voice has received limited attention partly due to the scarcity of suitable datasets and a lack of clear evaluation frameworks. To address this challenge, we propose a methodology for dataset curation. Employing the proposed methodology, we compile a dataset comprising 509 musical dynamics annotated singing voice performances, aligned with 163 score files, leveraging state-of-the-art source separation and alignment techniques. The scores are sourced from the OpenScore Lieder corpus of romantic-era compositions, widely known for its wealth of expressive annotations. Utilizing the curated dataset, we train a multi-head attention based CNN model with varying window sizes to evaluate the effectiveness of estimating musical dynamics. We explored two distinct perceptually motivated input representations for the model training: log-Mel spectrum and bark-scale based features. For testing, we manually curate another dataset of 25 musical dynamics annotated performances in collaboration with a professional vocalist. We conclude through our experiments that bark-scale based features outperform log-Mel-features for the task of singing voice dynamics prediction. The dataset along with the code is shared publicly for further research on the topic.
Comparing the Consistency of User Studies Conducted in Simulations and Laboratory Settings
Venue: arXiv cs.RO 2024
Year: 2024

Human-robot collaboration enables highly adaptive co-working. The variety of resulting workflows makes it difficult to measure metrics as, e.g. makespans or idle times for multiple systems and tasks in a comparable manner. This issue can be addressed with virtual commissioning, where arbitrary numbers of non-deterministic human-robot workflows in assembly tasks can be simulated. To this end, data-driven models of human decisions are needed. Gathering the required large corpus of data with on-site user studies is quite time-consuming. In comparison, simulation-based studies (e.g., by crowdsourcing) would allow us to access a large pool of study participants with less effort. To rely on respective study results, human action sequences observed in a browser-based simulation environment must be shown to match those gathered in a laboratory setting. To this end, this work aims to understand to what extent cooperative assembly work in a simulated environment differs from that in an on-site laboratory setting. We show how a simulation environment can be aligned with a laboratory setting in which a robot and a human perform pick-and-place tasks together. A user study (N=29) indicates that participants' assembly decisions and perception of the situation are consistent across these different environments.
SPICEPilot: Navigating SPICE Code Generation and Simulation with AI Guidance
Venue: arXiv cs.AR 2024
Year: 2024

Large Language Models (LLMs) have shown great potential in automating code generation; however, their ability to generate accurate circuit-level SPICE code remains limited due to a lack of hardware-specific knowledge. In this paper, we analyze and identify the typical limitations of existing LLMs in SPICE code generation. To address these limitations, we present SPICEPilot a novel Python-based dataset generated using PySpice, along with its accompanying framework. This marks a significant step forward in automating SPICE code generation across various circuit configurations. Our framework automates the creation of SPICE simulation scripts, introduces standardized benchmarking metrics to evaluate LLM's ability for circuit generation, and outlines a roadmap for integrating LLMs into the hardware design process. SPICEPilot is open-sourced under the permissive MIT license at https://github.com/ACADLab/SPICEPilot.git.
Scaling Effects of Transistor Leakage Current and IR Drop on 1T1R Memory Arrays
Venue: arXiv cs.ET 2024
Year: 2024

1T1R (1-transistor-1-resistor) memory crossbar arrays represent a promising solution for compute-in-memory matrix-vector multiplication accelerators and embedded or storage-class memory. However, the size and scaling of these arrays are hindered by critical challenges, such as the IR drop on metal lines and the accumulation of leakage current from the transistors. Although the IR drop issue has been extensively investigated, the impact of transistor leakage current has received limited attention. In this work, we investigate both issues and highlight how transistor leakage in 1T1R arrays has effects similar to IR drop, which degrades the memory cell sensing margin, especially as the technology node scales down. This degradation could pose reliability concerns, particularly where the on/off ratio or sensing margin of memristors is critical. We characterized the joint effects of transistor read resistance, transistor leakage current, and IR drop as the array size scales up and the fabrication node scales down. Based on a model developed using specifications of a 22nm FDSOI technology, we found that an optimal resistance range of memristors exists for good array scaling capability, where the transistor read resistance and the IR drop issue establish a lower resistance boundary, while the transistor leakage issue sets an upper resistance boundary. This work provides valuable scaling guidelines for engineering the properties of memristor devices in 1T1R memory arrays.
Encrypted system identification as-a-service via reliable encrypted matrix inversion
Venue: arXiv cs.CR 2024
Year: 2024

Encrypted computation opens up promising avenues across a plethora of application domains, including machine learning, health-care, finance, and control. Arithmetic homomorphic encryption, in particular, is a natural fit for cloud-based computational services. However, computations are essentially limited to polynomial circuits, while comparisons, transcendental functions, and iterative algorithms are notoriously hard to realize. Against this background, the paper presents an encrypted system identification service enabled by a reliable encrypted solution to least squares problems. More precisely, we devise an iterative algorithm for matrix inversion and present reliable initializations as well as certificates for the achieved accuracy without compromising the privacy of provided I/O-data. The effectiveness of the approach is illustrated with three popular identification tasks.
DHPrep: Deep Hawkes Process based Dynamic Network Representation
Venue: arXiv cs.SI 2024
Year: 2024

Networks representation aims to encode vertices into a low-dimensional space, while preserving the original network structures and properties. Most existing methods focus on static network structure without considering temporal dynamics. However, in real world, most networks (e.g., social and biological networks) are dynamic in nature and are constantly evolving over time. Such temporal dynamics are critical in representations learning, especially for predicting dynamic networks behaviors. To this end, a Deep Hawkes Process based Dynamic Networks Representation algorithm (DHPrep) is proposed in this paper, which is capable of capturing temporal dynamics of dynamic networks. Specifically, DHPrep incorporates both structural information and temporal dynamics to learn vertices representations that can model the edge formation process for a vertex pair, where the structural information is used to capture the historical impact from their neighborhood, and the temporal dynamics utilize this historical information and apply Hawkes point process to model the edges formation process. Moreover, a temporal smoother is further imposed to ensure the representations evolve smoothly over time. To evaluate the effectiveness of DHPrep, extensive experiments are carried out using four real-world datasets. Experimental results reveal that our DHPrep algorithm outperforms state-of-the-art baseline methods in various tasks including link prediction and vertices recommendation.
Aqua-Sim Fourth Generation: Towards General and Intelligent Simulation for Underwater Acoustic Networks
Venue: arXiv cs.NI 2024
Year: 2024

Simulators are essential to troubleshoot and optimize Underwater Acoustic Network (UAN) schemes (network protocols and communication technologies) before real field experiments. However, due to programming differences between the above two contents, most existing simulators concentrate on one while weakening the other, leading to non-generic simulations and biased performance results. Moreover, novel UAN schemes increasingly integrate Artificial Intelligence (AI) techniques, yet existing simulators lack support for necessary AI frameworks, failing to train and evaluate these intelligent methods. On the other hand, these novel schemes consider more UAN characteristics involving more complex parameter configurations, which also challenges simulators in flexibility and fineness. To keep abreast of advances in UANs, we propose the Fourth Generation (FG) ns-3-based simulator Aqua-Sim~FG, enhancing the general and intelligent simulation ability. On the basis of retaining previous generations' functions, we design a new general architecture, which is compatible with various programming languages, including MATLAB, C++, and Python. In this way, Aqua-Sim~FG provides a general environment to simulate communication technologies, network protocols, and AI models simultaneously. In addition, we expand six new features from node and communication levels by considering the latest UAN methods' requirements, which enhances the simulation flexibility and fineness of Aqua-Sim~FG. Experimental results show that Aqua-Sim~FG can simulate UANs' performance realistically, reflect intelligent methods' problems in real-ocean scenarios, and provide more effective troubleshooting and optimization for actual UANs. The basic simulator is available at https://github.com/JLU-smartocean/aqua-sim-fg.
Let a million entrepreneurs grow!
Venue: arXiv cs.CY 2024
Year: 2024

India produces about nine hundred thousand (900K) engineers annually, and many seek computer science and related technology jobs. Given that the IT workforce in India is still young, new graduates get jobs only when the industry grows. A liberal estimate based on the data from MeitY (Ministry of Electronics and Information Technology) and NASSCOM puts the annual job growth to three hundred thousand (300K), less than one-third of the graduation rate. In other words, about half a million graduates don't get a job every year (even when we consider that some students don't opt for jobs or go for higher studies). This position paper demonstrates that given the current growth rate of the Indian economy, such a significant shortfall will continue to exist. It then proposes a way to address this shortfall. The paper proposes to develop micro-entrepreneurs at scale, enabling many graduates to start micro-enterprises focused on AI, Software, and Technology (MAST). These MAST enterprises offer technology products and services to meet the hyperlocal needs of the businesses and individuals in the local community (a retailer in the neighborhood, a high net-worth person, or a factory). Such an endeavor will require curricular, policy, and societal interventions. The paper presents an approach to enable MAST education across campuses, outlining the key curricular changes required and important policies that must be created and implemented. This supply-demand gap is an existential problem for engineering education in India, and this position paper aims to trigger debates and collaborations to devise solutions that will work at India scale.
Lecture II: Communicative Justice and the Distribution of Attention
Venue: arXiv cs.CY 2024
Year: 2024

Algorithmic intermediaries govern the digital public sphere through their architectures, amplification algorithms, and moderation practices. In doing so, they shape public communication and distribute attention in ways that were previously infeasible with such subtlety, speed and scale. From misinformation and affective polarisation to hate speech and radicalisation, the many pathologies of the digital public sphere attest that they could do so better. But what ideals should they aim at? Political philosophy should be able to help, but existing theories typically assume that a healthy public sphere will spontaneously emerge if only we get the boundaries of free expression right. They offer little guidance on how to intentionally constitute the digital public sphere. In addition to these theories focused on expression, we need a further theory of communicative justice, targeted specifically at the algorithmic intermediaries that shape communication and distribute attention. This lecture argues that political philosophy urgently owes an account of how to govern communication in the digital public sphere, and introduces and defends a democratic egalitarian theory of communicative justice.
A Comprehensive Study on Static Application Security Testing (SAST) Tools for Android
Venue: arXiv cs.SE 2024
Year: 2024

To identify security vulnerabilities in Android applications, numerous static application security testing (SAST) tools have been proposed. However, it poses significant challenges to assess their overall performance on diverse vulnerability types. The task is non-trivial and poses considerable challenges. {Firstly, the absence of a unified evaluation platform for defining and describing tools' supported vulnerability types, coupled with the lack of normalization for the intricate and varied reports generated by different tools, significantly adds to the complexity.} Secondly, there is a scarcity of adequate benchmarks, particularly those derived from real-world scenarios. To address these problems, we are the first to propose a unified platform named VulsTotal, supporting various vulnerability types, enabling comprehensive and versatile analysis across diverse SAST tools. Specifically, we begin by meticulously selecting 11 free and open-sourced SAST tools from a pool of 97 existing options, adhering to clearly defined criteria. After that, we invest significant efforts in comprehending the detection rules of each tool, subsequently unifying 67 general/common vulnerability types for {Android} SAST tools. We also redefine and implement a standardized reporting format, ensuring uniformity in presenting results across all tools. Additionally, to mitigate the problem of benchmarks, we conducted a manual analysis of huge amounts of CVEs to construct a new CVE-based benchmark based on our comprehension of Android app vulnerabilities. Leveraging the evaluation platform, which integrates both existing synthetic benchmarks and newly constructed CVE-based benchmarks from this study, we conducted a comprehensive analysis to evaluate and compare these selected tools from various perspectives, such as general vulnerability type coverage, type consistency, tool effectiveness, and time performance.
KD-LoRA: A Hybrid Approach to Efficient Fine-Tuning with LoRA and Knowledge Distillation
Venue: arXiv cs.CL 2024
Year: 2024

Large language models (LLMs) have demonstrated remarkable performance across various downstream tasks. However, the high computational and memory requirements of LLMs are a major bottleneck. To address this, parameter-efficient fine-tuning (PEFT) methods such as low-rank adaptation (LoRA) have been proposed to reduce computational costs while ensuring minimal loss in performance. Additionally, knowledge distillation (KD) has been a popular choice for obtaining compact student models from teacher models. In this work, we present KD-LoRA, a novel fine-tuning method that combines LoRA with KD. Our results demonstrate that KD-LoRA achieves performance comparable to full fine-tuning (FFT) and LoRA while significantly reducing resource requirements. Specifically, KD-LoRA retains 98% of LoRA's performance on the GLUE benchmark, while being 40% more compact. Additionally, KD-LoRA reduces GPU memory usage by 30% compared to LoRA, while decreasing inference time by 30% compared to both FFT and LoRA. We evaluate KD-LoRA across three encoder-only models: BERT, RoBERTa, and DeBERTaV3. Code is available at https://github.com/rambodazimi/KD-LoRA.
Rephrasing natural text data with different languages and quality levels for Large Language Model pre-training
Venue: arXiv cs.CL 2024
Year: 2024

Recently published work on rephrasing natural text data for pre-training LLMs has shown promising results when combining the original dataset with the synthetically rephrased data. We build upon previous work by replicating existing results on C4 and extending them with our optimized rephrasing pipeline to the English, German, Italian, and Spanish Oscar subsets of CulturaX. Our pipeline leads to increased performance on standard evaluation benchmarks in both the mono- and multilingual setup. In addition, we provide a detailed study of our pipeline, investigating the choice of the base dataset and LLM for the rephrasing, as well as the relationship between the model size and the performance after pre-training. By exploring data with different perceived quality levels, we show that gains decrease with higher quality. Furthermore, we find the difference in performance between model families to be bigger than between different model sizes. This highlights the necessity for detailed tests before choosing an LLM to rephrase large amounts of data. Moreover, we investigate the effect of pre-training with synthetic data on supervised fine-tuning. Here, we find increasing but inconclusive results that highly depend on the used benchmark. These results (again) highlight the need for better benchmarking setups. In summary, we show that rephrasing multilingual and low-quality data is a very promising direction to extend LLM pre-training data.
Reduction-based Pseudo-label Generation for Instance-dependent Partial Label Learning
Venue: arXiv cs.LG 2024
Year: 2024

Instance-dependent Partial Label Learning (ID-PLL) aims to learn a multi-class predictive model given training instances annotated with candidate labels related to features, among which correct labels are hidden fixed but unknown. The previous works involve leveraging the identification capability of the training model itself to iteratively refine supervision information. However, these methods overlook a critical aspect of ID-PLL: the training model is prone to overfitting on incorrect candidate labels, thereby providing poor supervision information and creating a bottleneck in training. In this paper, we propose to leverage reduction-based pseudo-labels to alleviate the influence of incorrect candidate labels and train our predictive model to overcome this bottleneck. Specifically, reduction-based pseudo-labels are generated by performing weighted aggregation on the outputs of a multi-branch auxiliary model, with each branch trained in a label subspace that excludes certain labels. This approach ensures that each branch explicitly avoids the disturbance of the excluded labels, allowing the pseudo-labels provided for instances troubled by these excluded labels to benefit from the unaffected branches. Theoretically, we demonstrate that reduction-based pseudo-labels exhibit greater consistency with the Bayes optimal classifier compared to pseudo-labels directly generated from the predictive model.
History-Matching of Imbibition Flow in Multiscale Fractured Porous Media Using Physics-Informed Neural Networks (PINNs)
Venue: arXiv cs.CE 2024
Year: 2024

We propose a workflow based on physics-informed neural networks (PINNs) to model multiphase fluid flow in fractured porous media. After validating the workflow in forward and inverse modeling of a synthetic problem of flow in fractured porous media, we applied it to a real experimental dataset in which brine is injected at a constant pressure drop into a CO2 saturated naturally fractured shale core plug. The exact spatial positions of natural fractures and the dynamic in-situ distribution of fluids were imaged using a CT-scan setup. To model the targeted system, we followed a domain decomposition approach for matrix and fractures and a multi-network architecture for the separate calculation of water saturation and pressure. The flow equations in the matrix, fractures and interplay between them were solved during training. Prior to fully-coupled simulations, we proposed pre-training the model. This aided in a more efficient and successful training of the coupled system. Both for the synthetic and experimental inverse problems, we determined flow parameters within the matrix and the fractures. Multiple random initializations of network and system parameters were performed to assess the uncertainty and uniqueness of the results. The results confirmed the precision of the inverse calculated parameters in retrieving the main flow characteristics of the system. The consideration of multiscale matrix-fracture impacts is commonly overlooked in existing workflows. Accounting for them led to several orders of magnitude variations in the calculated flow properties compared to not accounting for them. To the best of our knowledge, the proposed PINNs-based workflow is the first to offer a reliable and computationally efficient solution for inverse modeling of multiphase flow in fractured porous media, achieved through history-matching noisy and multi-fidelity experimental measurements.