OpenRaiser/Intern-Atlas huggingface dataset:
Multi-sensor Intrusion Detection System
Venue: arXiv cs.CR 2024
Year: 2024

Security, defined as protection against external threats, is a critical concern for homes and offices. Intrusion, characterized by unauthorized access, presents a significant challenge to maintaining security. This research aims to address this issue by designing and implementing an automated intrusion detection system utilizing a combination of sensors and communication technologies. The research introduced an automated intrusion detection system for homes and offices, combining sensors such as a PIR sensor for detecting unauthorized motion, magnetic switches for unauthorized entry detection, and a GSM module for notifying property owners. Employing the ATmega328P microcontroller, sensor data is analysed to generate early intrusion alerts, prompting phone call notifications via the GSM module. Practical implementation involved breadboarding, soldering, and rigorous testing, ensuring proper functionality under real-world conditions. The implemented intrusion detection system effectively utilizes magnetic switches and a Passive Infrared (PIR) sensor to detect unauthorized entry and motion within the premises, respectively. Upon detection, the system promptly analyses the situation and alerts the property owner via phone call, enabling swift response measures. This real-time notification system enhances proactive security management, minimizing the risk of further intrusion and ensuring the safety of the property. The multi-sensor intrusion detection system, incorporating PIR sensors, magnetic switches, and a GSM-based phone call gateway, effectively alerts property owners of unauthorized intrusions in real-time. Demonstrating its efficacy through rigorous testing, the system offers enhanced security for both residential and commercial environments.
CredSec: A Blockchain-based Secure Credential Management System for University Adoption
Venue: arXiv cs.CR 2024
Year: 2024

University education play a critical role in shaping intellectual and professional development of the individuals and contribute significantly to the advancement of knowledge and society. Generally, university authority has a direct control of students result making and stores the credential in their local dedicated server. So, there is chance to alter the credential and also have a very high possibility to encounter various threats and different security attacks. To resolve these, we propose a blockchain based secure credential management system (BCMS) for efficiently storing, managing and recovering credential without involving the university authority. The proposed BCMS incorporates a modified two factor encryption (m2FE) technique, a combination of RSA cryptosystem and a DNA encoding to ensure credential privacy and an enhanced authentication scheme for teachers and students. Besides, to reduce size of the cipher credential and its conversion time, we use character to integer (C2I) table instead of ASCII table. Finally, the experimental result and analysis of the BCMS illustrate the effectiveness over state of the art works.
Federated LoRA with Sparse Communication
Venue: arXiv cs.LG 2024
Year: 2024

Low-rank adaptation (LoRA) is a natural method for finetuning in communication-constrained machine learning settings such as cross-device federated learning. Prior work that has studied LoRA in the context of federated learning has focused on improving LoRA's robustness to heterogeneity and privacy. In this work, we instead consider techniques for further improving communication-efficiency in federated LoRA. Unfortunately, we show that centralized ML methods that improve the efficiency of LoRA through unstructured pruning do not transfer well to federated settings. We instead study a simple approach, \textbf{FLASC}, that applies sparsity to LoRA during communication while allowing clients to locally fine-tune the entire LoRA module. Across four common federated learning tasks, we demonstrate that this method matches the performance of dense LoRA with up to $10\times$ less communication. Additionally, despite being designed primarily to target communication, we find that this approach has benefits in terms of heterogeneity and privacy relative to existing approaches tailored to these specific concerns. Overall, our work highlights the importance of considering system-specific constraints when developing communication-efficient finetuning approaches, and serves as a simple and competitive baseline for future work in federated finetuning.
Blended Bots: Infiltration through Identity Deception on Social Media
Venue: arXiv cs.HC 2024
Year: 2024

Bots are automated social media users that can be used to amplify (mis)information and sow harmful discourse. In order to effectively influence users, bots can be generated to reproduce human user behavior. Indeed, people tend to trust information coming from users with profiles that fit roles they expect to exist, such as users with gender role stereotypes. In this work, we examine differences in the types of identities in profiles of human and bot accounts with a focus on combinations of identities that represent gender role stereotypes. We find that some types of identities differentiate between human and bot profiles, confirming this approach can be a useful in distinguishing between human and bot accounts on social media. However, contrary to our expectations, we reveal that gender bias is expressed more in human accounts than bots overall. Despite having less gender bias overall, we provide examples of identities with strong associations with gender identities in bot profiles, such as those related to technology, finance, sports, and horoscopes. Finally, we discuss implications for designing constructive social media bot detection training materials.
Measuring Fairness in Large-Scale Recommendation Systems with Missing Labels
Venue: arXiv cs.IR 2024
Year: 2024

In large-scale recommendation systems, the vast array of items makes it infeasible to obtain accurate user preferences for each product, resulting in a common issue of missing labels. Typically, only items previously recommended to users have associated ground truth data. Although there is extensive research on fairness concerning fully observed user-item interactions, the challenge of fairness in scenarios with missing labels remains underexplored. Previous methods often treat these samples missing labels as negative, which can significantly deviate from the ground truth fairness metrics. Our study addresses this gap by proposing a novel method employing a small randomized traffic to estimate fairness metrics accurately. We present theoretical bounds for the estimation error of our fairness metric and support our findings with empirical evidence on real data. Our numerical experiments on synthetic and TikTok's real-world data validate our theory and show the efficiency and effectiveness of our novel methods. To the best of our knowledge, we are the first to emphasize the necessity of random traffic in dataset collection for recommendation fairness, the first to publish a fairness-related dataset from TikTok and to provide reliable estimates of fairness metrics in the context of large-scale recommendation systems with missing labels.
Generative Explore-Exploit: Training-free Optimization of Generative Recommender Systems using LLM Optimizers
Venue: arXiv cs.CL 2024
Year: 2024

Recommender systems are widely used to suggest engaging content, and Large Language Models (LLMs) have given rise to generative recommenders. Such systems can directly generate items, including for open-set tasks like question suggestion. While the world knowledge of LLMs enable good recommendations, improving the generated content through user feedback is challenging as continuously fine-tuning LLMs is prohibitively expensive. We present a training-free approach for optimizing generative recommenders by connecting user feedback loops to LLM-based optimizers. We propose a generative explore-exploit method that can not only exploit generated items with known high engagement, but also actively explore and discover hidden population preferences to improve recommendation quality. We evaluate our approach on question generation in two domains (e-commerce and general knowledge), and model user feedback with Click Through Rate (CTR). Experiments show our LLM-based explore-exploit approach can iteratively improve recommendations, and consistently increase CTR. Ablation analysis shows that generative exploration is key to learning user preferences, avoiding the pitfalls of greedy exploit-only approaches. A human evaluation strongly supports our quantitative findings.
Split-and-Fit: Learning B-Reps via Structure-Aware Voronoi Partitioning
Venue: arXiv cs.CV 2024
Year: 2024

We introduce a novel method for acquiring boundary representations (B-Reps) of 3D CAD models which involves a two-step process: it first applies a spatial partitioning, referred to as the ``split``, followed by a ``fit`` operation to derive a single primitive within each partition. Specifically, our partitioning aims to produce the classical Voronoi diagram of the set of ground-truth (GT) B-Rep primitives. In contrast to prior B-Rep constructions which were bottom-up, either via direct primitive fitting or point clustering, our Split-and-Fit approach is top-down and structure-aware, since a Voronoi partition explicitly reveals both the number of and the connections between the primitives. We design a neural network to predict the Voronoi diagram from an input point cloud or distance field via a binary classification. We show that our network, coined NVD-Net for neural Voronoi diagrams, can effectively learn Voronoi partitions for CAD models from training data and exhibits superior generalization capabilities. Extensive experiments and evaluation demonstrate that the resulting B-Reps, consisting of parametric surfaces, curves, and vertices, are more plausible than those obtained by existing alternatives, with significant improvements in reconstruction quality. Code will be released on https://github.com/yilinliu77/NVDNet.
Fast assessment of non-Gaussian inputs in structural dynamics exploiting modal solutions
Venue: arXiv cs.CE 2024
Year: 2024

In various technical applications, assessing the impact of non-Gaussian processes on responses of dynamic systems is crucial. While simulating time-domain realizations offers an efficient solution for linear dynamic systems, this method proves time-consuming for finite element (FE) models, which may contain thousands to millions of degrees-of-freedom (DOF). Given the central role of kurtosis in describing non-Gaussianity - owing to its concise, parametric-free and easily interpretable nature - this paper introduces a highly efficient approach for deriving response kurtosis and other related statistical descriptions. This approach makes use of the modal solution of dynamic systems, which allows to reduce DOFs and responses analysis to a minimum number in the modal domain. This computational advantage enables fast assessments of non-Gaussian effects for entire FE models. Our approach is illustrated using a simple FE model that has found regular use in the field of random vibration fatigue.
Behavior Structformer: Learning Players Representations with Structured Tokenization
Venue: arXiv cs.CL 2024
Year: 2024

In this paper, we introduce the Behavior Structformer, a method for modeling user behavior using structured tokenization within a Transformer-based architecture. By converting tracking events into dense tokens, this approach enhances model training efficiency and effectiveness. We demonstrate its superior performance through ablation studies and benchmarking against traditional tabular and semi-structured baselines. The results indicate that structured tokenization with sequential processing significantly improves behavior modeling.
Look-Up Table based Neural Network Hardware
Venue: arXiv cs.AR 2024
Year: 2024

Traditional digital implementations of neural accelerators are limited by high power and area overheads, while analog and non-CMOS implementations suffer from noise, device mismatch, and reliability issues. This paper introduces a CMOS Look-Up Table (LUT)-based Neural Accelerator (LUT-NA) framework that reduces the power, latency, and area consumption of traditional digital accelerators through pre-computed, faster look-ups while avoiding noise and mismatch of analog circuits. To solve the scalability issues of conventional LUT-based computation, we split the high-precision multiply and accumulate (MAC) operations into lower-precision MACs using a divide-and-conquer-based approach. We show that LUT-NA achieves up to $29.54\times$ lower area with $3.34\times$ lower energy per inference task than traditional LUT-based techniques and up to $1.23\times$ lower area with $1.80\times$ lower energy per inference task than conventional digital MAC-based techniques (Wallace Tree/Array Multipliers) without retraining and without affecting accuracy, even on lottery ticket pruned (LTP) models that already reduce the number of required MAC operations by up to 98%. Finally, we introduce mixed precision analysis in LUT-NA framework for various LTP models (VGG11, VGG19, Resnet18, Resnet34, GoogleNet) that achieved up to $32.22\times$-$50.95\times$ lower area across models with $3.68\times$-$6.25\times$ lower energy per inference than traditional LUT-based techniques, and up to $1.35\times$-$2.14\times$ lower area requirement with $1.99\times$-$3.38\times$ lower energy per inference across models as compared to conventional digital MAC-based techniques with $\sim$1% accuracy loss.
DeviceBERT: Applied Transfer Learning With Targeted Annotations and Vocabulary Enrichment to Identify Medical Device and Component Terminology in FDA Recall Summaries
Venue: arXiv cs.CL 2024
Year: 2024

FDA Medical Device recalls are critical and time-sensitive events, requiring swift identification of impacted devices to inform the public of a recall event and ensure patient safety. The OpenFDA device recall dataset contains valuable information about ongoing device recall actions, but manually extracting relevant device information from the recall action summaries is a time-consuming task. Named Entity Recognition (NER) is a task in Natural Language Processing (NLP) that involves identifying and categorizing named entities in unstructured text. Existing NER models, including domain-specific models like BioBERT, struggle to correctly identify medical device trade names, part numbers and component terms within these summaries. To address this, we propose DeviceBERT, a medical device annotation, pre-processing and enrichment pipeline, which builds on BioBERT to identify and label medical device terminology in the device recall summaries with improved accuracy. Furthermore, we demonstrate that our approach can be applied effectively for performing entity recognition tasks where training data is limited or sparse.
Traversing Mars: Cooperative Informative Path Planning to Efficiently Navigate Unknown Scenes
Venue: arXiv cs.RO 2024
Year: 2024

The ability to traverse an unknown environment is crucial for autonomous robot operations. However, due to the limited sensing capabilities and system constraints, approaching this problem with a single robot agent can be slow, costly, and unsafe. For example, in planetary exploration missions, the wear on the wheels of a rover from abrasive terrain should be minimized at all costs as reparations are infeasible. On the other hand, utilizing a scouting robot such as a micro aerial vehicle (MAV) has the potential to reduce wear and time costs and increasing safety of a follower robot. This work proposes a novel cooperative IPP framework that allows a scout (e.g., an MAV) to efficiently explore the minimum-cost-path for a follower (e.g., a rover) to reach the goal. We derive theoretic guarantees for our algorithm, and prove that the algorithm always terminates, always finds the optimal path if it exists, and terminates early when the found path is shown to be optimal or infeasible. We show in thorough experimental evaluation that the guarantees hold in practice, and that our algorithm is 22.5% quicker to find the optimal path and 15% quicker to terminate compared to existing methods.
1st Place Winner of the 2024 Pixel-level Video Understanding in the Wild (CVPR'24 PVUW) Challenge in Video Panoptic Segmentation and Best Long Video Consistency of Video Semantic Segmentation
Venue: arXiv cs.CV 2024
Year: 2024

The third Pixel-level Video Understanding in the Wild (PVUW CVPR 2024) challenge aims to advance the state of art in video understanding through benchmarking Video Panoptic Segmentation (VPS) and Video Semantic Segmentation (VSS) on challenging videos and scenes introduced in the large-scale Video Panoptic Segmentation in the Wild (VIPSeg) test set and the large-scale Video Scene Parsing in the Wild (VSPW) test set, respectively. This paper details our research work that achieved the 1st place winner in the PVUW'24 VPS challenge, establishing state of art results in all metrics, including the Video Panoptic Quality (VPQ) and Segmentation and Tracking Quality (STQ). With minor fine-tuning our approach also achieved the 3rd place in the PVUW'24 VSS challenge ranked by the mIoU (mean intersection over union) metric and the first place ranked by the VC16 (16-frame video consistency) metric. Our winning solution stands on the shoulders of giant foundational vision transformer model (DINOv2 ViT-g) and proven multi-stage Decoupled Video Instance Segmentation (DVIS) frameworks for video understanding.
Investigating Memory Failure Prediction Across CPU Architectures
Venue: arXiv cs.AR 2024
Year: 2024

Large-scale datacenters often experience memory failures, where Uncorrectable Errors (UEs) highlight critical malfunction in Dual Inline Memory Modules (DIMMs). Existing approaches primarily utilize Correctable Errors (CEs) to predict UEs, yet they typically neglect how these errors vary between different CPU architectures, especially in terms of Error Correction Code (ECC) applicability. In this paper, we investigate the correlation between CEs and UEs across different CPU architectures, including X86 and ARM. Our analysis identifies unique patterns of memory failure associated with each processor platform. Leveraging Machine Learning (ML) techniques on production datasets, we conduct the memory failure prediction in different processors' platforms, achieving up to 15% improvements in F1-score compared to the existing algorithm. Finally, an MLOps (Machine Learning Operations) framework is provided to consistently improve the failure prediction in the production environment.
SemPat: Using Hyperproperty-based Semantic Analysis to Generate Microarchitectural Attack Patterns
Venue: arXiv cs.CR 2024
Year: 2024

Microarchitectural security verification of software has seen the emergence of two broad classes of approaches. The first is based on semantic security properties (e.g., non-interference) which are verified for a given program and a specified abstract model of the hardware microarchitecture. The second is based on attack patterns, which, if found in a program execution, indicates the presence of an exploit. While the former uses a formal specification that can capture several gadget variants targeting the same vulnerability, it is limited by the scalability of verification. Patterns, while more scalable, must be currently constructed manually, as they are narrower in scope and sensitive to gadget-specific structure. This work develops a technique that, given a non-interference-based semantic security hyperproperty, automatically generates attack patterns up to a certain complexity parameter (called the skeleton size). Thus, we combine the advantages of both approaches: security can be specified by a hyperproperty that uniformly captures several gadget variants, while automatically generated patterns can be used for scalable verification. We implement our approach in a tool and demonstrate the ability to generate new patterns, (e.g., for SpectreV1, SpectreV4) and improved scalability using the generated patterns over hyperproperty-based verification.
Novel Approach to Intrusion Detection: Introducing GAN-MSCNN-BILSTM with LIME Predictions
Venue: arXiv cs.CR 2024
Year: 2024

This paper introduces an innovative intrusion detection system that harnesses Generative Adversarial Networks (GANs), Multi-Scale Convolutional Neural Networks (MSCNNs), and Bidirectional Long Short-Term Memory (BiLSTM) networks, supplemented by Local Interpretable Model-Agnostic Explanations (LIME) for interpretability. Employing a GAN, the system generates realistic network traffic data, encompassing both normal and attack patterns. This synthesized data is then fed into an MSCNN-BiLSTM architecture for intrusion detection. The MSCNN layer extracts features from the network traffic data at different scales, while the BiLSTM layer captures temporal dependencies within the traffic sequences. Integration of LIME allows for explaining the model's decisions. Evaluation on the Hogzilla dataset, a standard benchmark, showcases an impressive accuracy of 99.16\% for multi-class classification and 99.10\% for binary classification, while ensuring interpretability through LIME. This fusion of deep learning and interpretability presents a promising avenue for enhancing intrusion detection systems by improving transparency and decision support in network security.
Design of reliable technology valuation model with calibrated machine learning of patent indicators
Venue: arXiv cs.CL 2024
Year: 2024

Machine learning (ML) has revolutionized the digital transformation of technology valuation by predicting the value of patents with high accuracy. However, the lack of validation regarding the reliability of these models hinders experts from fully trusting the confidence of model predictions. To address this issue, we propose an analytical framework for reliable technology valuation using calibrated ML models, which provide robust confidence levels in model predictions. We extract quantitative patent indicators that represent various technology characteristics as input data, using the patent maintenance period as a proxy for technology values. Multiple ML models are developed to capture the nonlinear relationship between patent indicators and technology value. The reliability and accuracy of these models are evaluated, presenting a Pareto-front map where the expected calibration error, Matthews correlation coefficient and F1-scores are compared. After identifying the best-performing model, we apply SHapley Additive exPlanation (SHAP) analysis to pinpoint the most significant input features by confidence bin. Through a case study, we confirmed that the proposed approach offers a practical guideline for developing reliable and accurate ML-based technology valuation models, with significant implications for both academia and industry.