OpenRaiser/Intern-Atlas huggingface dataset:
Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender System
Venue: arXiv cs.IR 2023
Year: 2023

Large language models (LLMs) have demonstrated their significant potential to be applied for addressing various application tasks. However, traditional recommender systems continue to face great challenges such as poor interactivity and explainability, which actually also hinder their broad deployment in real-world systems. To address these limitations, this paper proposes a novel paradigm called Chat-Rec (ChatGPT Augmented Recommender System) that innovatively augments LLMs for building conversational recommender systems by converting user profiles and historical interactions into prompts. Chat-Rec is demonstrated to be effective in learning user preferences and establishing connections between users and products through in-context learning, which also makes the recommendation process more interactive and explainable. What's more, within the Chat-Rec framework, user's preferences can transfer to different products for cross-domain recommendations, and prompt-based injection of information into LLMs can also handle the cold-start scenarios with new items. In our experiments, Chat-Rec effectively improve the results of top-k recommendations and performs better in zero-shot rating prediction task. Chat-Rec offers a novel approach to improving recommender systems and presents new practical scenarios for the implementation of AIGC (AI generated content) in recommender system studies.
Shapley-based Explainable AI for Clustering Applications in Fault Diagnosis and Prognosis
Venue: arXiv cs.LG 2023
Year: 2023

Data-driven artificial intelligence models require explainability in intelligent manufacturing to streamline adoption and trust in modern industry. However, recently developed explainable artificial intelligence (XAI) techniques that estimate feature contributions on a model-agnostic level such as SHapley Additive exPlanations (SHAP) have not yet been evaluated for semi-supervised fault diagnosis and prognosis problems characterized by class imbalance and weakly labeled datasets. This paper explores the potential of utilizing Shapley values for a new clustering framework compatible with semi-supervised learning problems, loosening the strict supervision requirement of current XAI techniques. This broad methodology is validated on two case studies: a heatmap image dataset obtained from a semiconductor manufacturing process featuring class imbalance, and a benchmark dataset utilized in the 2021 Prognostics and Health Management (PHM) Data Challenge. Semi-supervised clustering based on Shapley values significantly improves upon clustering quality compared to the fully unsupervised case, deriving information-dense and meaningful clusters that relate to underlying fault diagnosis model predictions. These clusters can also be characterized by high-precision decision rules in terms of original feature values, as demonstrated in the second case study. The rules, limited to 1-2 terms utilizing original feature scales, describe 12 out of the 16 derived equipment failure clusters with precision exceeding 0.85, showcasing the promising utility of the explainable clustering framework for intelligent manufacturing applications.
Smart Cities: Striking a Balance Between Urban Resilience and Civil Liberties
Venue: arXiv cs.CY 2023
Year: 2023

Cities are becoming smarter and more resilient by integrating urban infrastructure with information technology. However, concerns grow that smart cities might reverse progress on civil liberties when sensing, profiling, and predicting citizen activities; undermining citizen autonomy in connectivity, mobility, and energy consumption; and deprivatizing digital infrastructure. In response, cities need to deploy technical breakthroughs, such as privacy-enhancing technologies, cohort modelling, and fair and explainable machine learning. However, as throwing technologies at cities cannot always address civil liberty concerns, cities must ensure transparency and foster citizen participation to win public trust about the way resilience and liberties are balanced.
PBScaler: A Bottleneck-aware Autoscaling Framework for Microservice-based Applications
Venue: arXiv cs.SE 2023
Year: 2023

Autoscaling is critical for ensuring optimal performance and resource utilization in cloud applications with dynamic workloads. However, traditional autoscaling technologies are typically no longer applicable in microservice-based applications due to the diverse workload patterns and complex interactions between microservices. Specifically, the propagation of performance anomalies through interactions leads to a high number of abnormal microservices, making it difficult to identify the root performance bottlenecks (PBs) and formulate appropriate scaling strategies. In addition, to balance resource consumption and performance, the existing mainstream approaches based on online optimization algorithms require multiple iterations, leading to oscillation and elevating the likelihood of performance degradation. To tackle these issues, we propose PBScaler, a bottleneck-aware autoscaling framework designed to prevent performance degradation in a microservice-based application. The key insight of PBScaler is to locate the PBs. Thus, we propose TopoRank, a novel random walk algorithm based on the topological potential to reduce unnecessary scaling. By integrating TopoRank with an offline performance-aware optimization algorithm, PBScaler optimizes replica management without disrupting the online application. Comprehensive experiments demonstrate that PBScaler outperforms existing state-of-the-art approaches in mitigating performance issues while conserving resources efficiently.
A Set of Essentials for Online Learning : CSE-SET
Venue: arXiv cs.CY 2023
Year: 2023

Distance learning is not a novel concept. Education or learning conducted online is a form of distance education. Online learning presents a convenient alternative to traditional learning. Numerous researchers have investigated the usage of online education in educational institutions and across nations. A set of essentials for effective online learning are elaborated in this study to ensure stakeholders would not get demotivated in the online learning process. Also, the study lists a set of factors that motivate students and other stakeholders to engage in online learning with enthusiasm and work towards online learning.
Resource Efficiency vs Performance Isolation Tradeoff in Network Slicing
Venue: arXiv cs.NI 2023
Year: 2023

We consider the tradeoff between resource efficiency and performance isolation that emerges when multiplexing the resource demands of Network Slices (NSs). On the one hand, multiplexing allows the use of idle resources, which increases resource efficiency. On the other hand, the performance of each NS becomes susceptible to traffic surges in other NSs, which degrades performance isolation. The analysis of this tradeoff enables network operators to determine the effect of performance isolation on the operating cost of each NS. To study the tradeoff, we solve an optimization problem where we find the multiplexing policy that requires the least provisioned resources to honor the Service Level Agreements (SLAs) of all NSs. The SLA of each NS i states that its resource demand should be met for $P^H_i$ fraction of time, and for $P^L_i \leq P^H_i$ fraction of time, it should be met regardless of the demands of other NSs. For resource demands that follow ergodic Markov chains, we show that the well-known Max-Weight scheduler is an optimal multiplexing policy. Since the Max-Weight scheduler does not require any knowledge of the statistics of the resource demands, we also propose its use in non-markovian settings. For resource demands obtained in the LTE module of ns-3, we show that the Max-Weight scheduler reduces the provisioned bandwidth by 36.2% when no performance isolation is required. Lastly, for these non-markovian resource demands, the Max-Weight scheduler maintains its optimality since it requires as much provisioned bandwidth as the best non-causal scheduler.
Sector Patch Embedding: An Embedding Module Conforming to The Distortion Pattern of Fisheye Image
Venue: arXiv cs.CV 2023
Year: 2023

Fisheye cameras suffer from image distortion while having a large field of view(LFOV). And this fact leads to poor performance on some fisheye vision tasks. One of the solutions is to optimize the current vision algorithm for fisheye images. However, most of the CNN-based methods and the Transformer-based methods lack the capability of leveraging distortion information efficiently. In this work, we propose a novel patch embedding method called Sector Patch Embedding(SPE), conforming to the distortion pattern of the fisheye image. Furthermore, we put forward a synthetic fisheye dataset based on the ImageNet-1K and explore the performance of several Transformer models on the dataset. The classification top-1 accuracy of ViT and PVT is improved by 0.75% and 2.8% with SPE respectively. The experiments show that the proposed sector patch embedding method can better perceive distortion and extract features on the fisheye images. Our method can be easily adopted to other Transformer-based models. Source code is at https://github.com/IN2-ViAUn/Sector-Patch-Embedding.
CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data using Normalizing Flows
Venue: arXiv cs.LG 2023
Year: 2023

Counterfactual explanation is a form of interpretable machine learning that generates perturbations on a sample to achieve the desired outcome. The generated samples can act as instructions to guide end users on how to observe the desired results by altering samples. Although state-of-the-art counterfactual explanation methods are proposed to use variational autoencoder (VAE) to achieve promising improvements, they suffer from two major limitations: 1) the counterfactuals generation is prohibitively slow, which prevents algorithms from being deployed in interactive environments; 2) the counterfactual explanation algorithms produce unstable results due to the randomness in the sampling procedure of variational autoencoder. In this work, to address the above limitations, we design a robust and efficient counterfactual explanation framework, namely CeFlow, which utilizes normalizing flows for the mixed-type of continuous and categorical features. Numerical experiments demonstrate that our technique compares favorably to state-of-the-art methods. We release our source at https://github.com/tridungduong16/fairCE.git for reproducing the results.
Joint fMRI Decoding and Encoding with Latent Embedding Alignment
Venue: arXiv cs.CV 2023
Year: 2023

The connection between brain activity and corresponding visual stimuli is crucial in comprehending the human brain. While deep generative models have exhibited advancement in recovering brain recordings by generating images conditioned on fMRI signals, accomplishing high-quality generation with consistent semantics continues to pose challenges. Moreover, the prediction of brain activity from visual stimuli remains a formidable undertaking. In this paper, we introduce a unified framework that addresses both fMRI decoding and encoding. Commencing with the establishment of two latent spaces capable of representing and reconstructing fMRI signals and visual images, respectively, we proceed to align the fMRI signals and visual images within the latent space, thereby enabling a bidirectional transformation between the two domains. Our Latent Embedding Alignment (LEA) model concurrently recovers visual stimuli from fMRI signals and predicts brain activity from images within a unified framework. The performance of LEA surpasses that of existing methods on multiple benchmark fMRI decoding and encoding datasets. By integrating fMRI decoding and encoding, LEA offers a comprehensive solution for modeling the intricate relationship between brain activity and visual stimuli.
Growing Convex Collision-Free Regions in Configuration Space using Nonlinear Programming
Venue: arXiv cs.RO 2023
Year: 2023

One of the most difficult parts of motion planning in configuration space is ensuring a trajectory does not collide with task-space obstacles in the environment. Generating regions that are convex and collision free in configuration space can separate the computational burden of collision checking from motion planning. To that end, we propose an extension to IRIS (Iterative Regional Inflation by Semidefinite programming) [5] that allows it to operate in configuration space. Our algorithm, IRIS-NP (Iterative Regional Inflation by Semidefinite & Nonlinear Programming), uses nonlinear optimization to add the separating hyperplanes, enabling support for more general nonlinear constraints. Developed in parallel to Amice et al. [1], IRIS-NP trades rigorous certification that regions are collision free for probabilistic certification and the benefit of faster region generation in the configuration-space coordinates. IRIS-NP also provides a solid initialization to C-IRIS to reduce the number of iterations required for certification. We demonstrate that IRIS-NP can scale to a dual-arm manipulator and can handle additional nonlinear constraints using the same machinery. Finally, we show ablations of elements of our implementation to demonstrate their importance.
Combining General and Personalized Models for Epilepsy Detection with Hyperdimensional Computing
Venue: arXiv cs.NE 2023
Year: 2023

Epilepsy is a chronic neurological disorder with a significant prevalence. However, there is still no adequate technological support to enable epilepsy detection and continuous outpatient monitoring in everyday life. Hyperdimensional (HD) computing is an interesting alternative for wearable devices, characterized by a much simpler learning process and also lower memory requirements. In this work, we demonstrate a few additional aspects in which HD computing, and the way its models are built and stored, can be used for further understanding, comparing, and creating more advanced machine learning models for epilepsy detection. These possibilities are not feasible with other state-of-the-art models, such as random forests or neural networks. We compare inter-subject similarity of models per different classes (seizure and non-seizure), then study the process of creation of generalized models from personalized ones, and in the end, how to combine personalized and generalized models to create hybrid models. This results in improved epilepsy detection performance. We also tested knowledge transfer between models created on two different datasets. Finally, all those examples could be highly interesting not only from an engineering perspective to create better models for wearables, but also from a neurological perspective to better understand individual epilepsy patterns.
Does "Deep Learning on a Data Diet" reproduce? Overall yes, but GraNd at Initialization does not
Venue: arXiv cs.LG 2023
Year: 2023

The paper 'Deep Learning on a Data Diet' by Paul et al. (2021) introduces two innovative metrics for pruning datasets during the training of neural networks. While we are able to replicate the results for the EL2N score at epoch 20, the same cannot be said for the GraNd score at initialization. The GraNd scores later in training provide useful pruning signals, however. The GraNd score at initialization calculates the average gradient norm of an input sample across multiple randomly initialized models before any training has taken place. Our analysis reveals a strong correlation between the GraNd score at initialization and the input norm of a sample, suggesting that the latter could have been a cheap new baseline for data pruning. Unfortunately, neither the GraNd score at initialization nor the input norm surpasses random pruning in performance. This contradicts one of the findings in Paul et al. (2021). We were unable to reproduce their CIFAR-10 results using both an updated version of the original JAX repository and in a newly implemented PyTorch codebase. An investigation of the underlying JAX/FLAX code from 2021 surfaced a bug in the checkpoint restoring code that was fixed in April 2021 (https://github.com/google/flax/commit/28fbd95500f4bf2f9924d2560062fa50e919b1a5).
DLACB: Deep Learning Based Access Control Using Blockchain
Venue: arXiv cs.CR 2023
Year: 2023

In general, deep learning models use to make informed decisions immensely. Developed models are mainly based on centralized servers, which face several issues, including transparency, traceability, reliability, security, and privacy. In this research, we identify a research gap in a distributed nature-based access control that can solve those issues. The innovative technology blockchain could fill this gap and provide a robust solution. Blockchain's immutable and distributed nature designs a useful framework in various domains such as medicine, finance, and government, which can also provide access control as opposed to centralized methods that rely on trusted third parties to access the resources. In existing frameworks, a traditional access control approach is developed using blockchain, which depends on predefined policies and permissions that are not reliable. In this research, we propose DLACB: Deep Learning Based Access Control Using Blockchain, which utilizes a deep learning access control mechanism to determine a user's permissions on a given resource. This proposed framework authenticates the users and logs the access requests on the blockchain to recognize malicious users. The results show that this proposed framework operates correctly for all possible scenarios.
Approaches to Improving the Accuracy of Machine Learning Models in Requirements Elicitation Techniques Selection
Venue: arXiv cs.SE 2023
Year: 2023

Selecting techniques is a crucial element of the business analysis approach planning in IT projects. Particular attention is paid to the choice of techniques for requirements elicitation. One of the promising methods for selecting techniques is using machine learning algorithms trained on the practitioners' experience considering different projects' contexts. The effectiveness of ML models is significantly affected by the balance of the training dataset, which is violated in the case of popular techniques. The paper aims to analyze the efficiency of the Synthetic Minority Over-sampling Technique usage in Machine Learning models for elicitation technique selection in case of the imbalanced training dataset and possible ways for positive feature importance selection. The computational experiment results confirmed the effectiveness of using the proposed approaches to improve the accuracy of machine learning models for selecting requirements elicitation techniques. Proposed approaches can be used to build Machine Learning models for business analysis activities planning in IT projects.
$Δ$-Patching: A Framework for Rapid Adaptation of Pre-trained Convolutional Networks without Base Performance Loss
Venue: arXiv cs.CV 2023
Year: 2023

Models pre-trained on large-scale datasets are often fine-tuned to support newer tasks and datasets that arrive over time. This process necessitates storing copies of the model over time for each task that the pre-trained model is fine-tuned to. Building on top of recent model patching work, we propose $Δ$-Patching for fine-tuning neural network models in an efficient manner, without the need to store model copies. We propose a simple and lightweight method called $Δ$-Networks to achieve this objective. Our comprehensive experiments across setting and architecture variants show that $Δ$-Networks outperform earlier model patching work while only requiring a fraction of parameters to be trained. We also show that this approach can be used for other problem settings such as transfer learning and zero-shot domain adaptation, as well as other tasks such as detection and segmentation.
The Application of Driver Models in the Safety Assessment of Autonomous Vehicles: A Survey
Venue: arXiv cs.RO 2023
Year: 2023

Driver models play a vital role in developing and verifying autonomous vehicles (AVs). Previously, they are mainly applied in traffic flow simulation to model driver behavior. With the development of AVs, driver models attract much attention again due to their potential contributions to AV safety assessment. The simulation-based testing method is an effective measure to accelerate AV testing due to its safe and efficient characteristics. Nonetheless, realistic driver models are prerequisites for valid simulation results. Additionally, an AV is assumed to be at least as safe as a careful and competent driver, which is modeled by driver models as well. Therefore, driver models are essential for AV safety assessment from the current perspective. However, no comparison or discussion of driver models is available regarding their utility to AVs in the last five years despite their necessities in the release of AVs. This motivates us to present a comprehensive survey of driver models in the paper and compare their applicability. Requirements for driver models as applied to AV safety assessment are discussed. A summary of driver models for simulation-based testing and AV benchmarks is provided. Evaluation metrics are defined to compare their strength and weakness. Finally, potential gaps in existing driver models are identified, which provide direction for future work. This study gives related researchers especially regulators an overview and helps them to define appropriate driver models for AVs.
Automata-Based Software Model Checking of Hyperproperties
Venue: arXiv cs.LO 2023
Year: 2023

We develop model checking algorithms for Temporal Stream Logic (TSL) and Hyper Temporal Stream Logic (HyperTSL) modulo theories. TSL extends Linear Temporal Logic (LTL) with memory cells, functions and predicates, making it a convenient and expressive logic to reason over software and other systems with infinite data domains. HyperTSL further extends TSL to the specification of hyperproperties - properties that relate multiple system executions. As such, HyperTSL can express information flow policies like noninterference in software systems. We augment HyperTSL with theories, resulting in HyperTSL(T),and build on methods from LTL software verification to obtain model checking algorithms for TSL and HyperTSL(T). This results in a sound but necessarily incomplete algorithm for specifications contained in the forall*exists* fragment of HyperTSL(T). Our approach constitutes the first software model checking algorithm for temporal hyperproperties with quantifier alternations that does not rely on a finite-state abstraction.
Hardware Acceleration of Progressive Refinement Radiosity using Nvidia RTX
Venue: arXiv cs.GR 2023
Year: 2023

A vital component of photo-realistic image synthesis is the simulation of indirect diffuse reflections, which still remain a quintessential hurdle that modern rendering engines struggle to overcome. Real-time applications typically pre-generate diffuse lighting information offline using radiosity to avoid performing costly computations at run-time. In this thesis we present a variant of progressive refinement radiosity that utilizes Nvidia's novel RTX technology to accelerate the process of form-factor computation without compromising on visual fidelity. Through a modern implementation built on DirectX 12 we demonstrate that offloading radiosity's visibility component to RT cores significantly improves the lightmap generation process and potentially propels it into the domain of real-time.