pwc-archive/papers-with-abstracts huggingface dataset:
Nature-Inspired Local Propagation
Authors: Alessandro Betti, Marco Gori
Date: 2024-02-04 00:00:00

The spectacular results achieved in machine learning, including the recent advances in generative AI, rely on large data collections. On the opposite, intelligent processes in nature arises without the need for such collections, but simply by online processing of the environmental information. In particular, natural learning processes rely on mechanisms where data representation and learning are intertwined in such a way to respect spatiotemporal locality. This paper shows that such a feature arises from a pre-algorithmic view of learning that is inspired by related studies in Theoretical Physics. We show that the algorithmic interpretation of the derived "laws of learning", which takes the structure of Hamiltonian equations, reduces to Backpropagation when the speed of propagation goes to infinity. This opens the doors to machine learning studies based on full on-line information processing that are based the replacement of Backpropagation with the proposed spatiotemporal local algorithm.
Toward Building a Semantic Network Inventory for Model-Driven Telemetry
Authors: I. D. Martínez-Casanueva, D. González-Sanchez, L. Bellido, D. Fernández, D. R. López
Date: 2024-02-09 00:00:00
Tasks: Management

Network telemetry based on data models is expected to become the standard mechanism for collecting operational data from network devices efficiently. But the wide variety of standard and proprietary data models along with the different implementations of telemetry protocols offered by network vendors, become a barrier when monitoring heterogeneous network infrastructures. To facilitate the integration and sharing of context information related to model-driven telemetry, this work proposes a semantic network inventory that integrates new information models specifically developed to capture context information in a vendor-agnostic fashion using current standards defined for context management. To automate the integration of this context information within the network inventory, a reference architecture is designed. Finally, a prototype of the solution is implemented and validated through a case study that illustrates how the network inventory can ease the operation of model-driven telemetry in multi-vendor networks.
What's in People's Digital File Collections?
Authors: Jesse David Dinneen, Charles-Antoine Julien
Date: 2024-02-09 00:00:00
Tasks: Management

Thoughtfully designing services and rigorously testing software to support personal information management (PIM) requires understanding the relevant collections, but relatively little is known about what people keep in their file collections, especially personal collections. Complementing recent work on the structure of 348 file collections, we examine those collections' contents, how much content is duplicated, and how collections used for personal matters differ from those used for study and work. Though all collections contain many images, some intuitively common file types are surprisingly scarce. Personal collections contain more audio than others, knowledge workers' collections contain more text documents but far fewer folders, and IT collections exhibit unusual traits. Collection duplication is correlated to collections' structural traits, but surprisingly, not to collection age. We discuss our findings in light of prior works and provide implications for various kinds of information research.
Collaborative filtering, K-nearest neighbor and cosine similarity in home decor recommender systems
Authors: Nanna Bach Munkholm, Robert Alphinas, Torben Tambo
Date: 2024-02-09 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

An architectural framework, based on collaborative filtering using K-nearest neighbor and cosine similarity, was developed and implemented to fit the requirements for the company DecorRaid. The aim of the paper is to test different evaluation techniques within the environment to research the recommender systems performance. Three perspectives were found relevant for evaluating a recommender system in the specific environment, namely dataset, system and user perspective. With these perspectives it was possible to gain a broader view of the recommender systems performance. Online A/B split testing was conducted to compare the performance of small adjustments to the RS and to test the relevance of the evaluation techniques. Key factors are solving the sparsity and cold start problem, where the suggestion is to research a hybrid RS combining Content-based and CF based techniques.
Understanding the Role of Cross-Entropy Loss in Fairly Evaluating Large Language Model-based Recommendation
Authors: Cong Xu, Zhangchi Zhu, Jun Wang, Jianyong Wang, Wei zhang
Date: 2024-02-09 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

Large language models (LLMs) have gained much attention in the recommendation community; some studies have observed that LLMs, fine-tuned by the cross-entropy loss with a full softmax, could achieve state-of-the-art performance already. However, these claims are drawn from unobjective and unfair comparisons. In view of the substantial quantity of items in reality, conventional recommenders typically adopt a pointwise/pairwise loss function instead for training. This substitute however causes severe performance degradation, leading to under-estimation of conventional methods and over-confidence in the ranking capability of LLMs. In this work, we theoretically justify the superiority of cross-entropy, and showcase that it can be adequately replaced by some elementary approximations with certain necessary modifications. The remarkable results across three public datasets corroborate that even in a practical sense, existing LLM-based methods are not as effective as claimed for next-item recommendation. We hope that these theoretical understandings in conjunction with the empirical results will facilitate an objective evaluation of LLM-based recommendation in the future.
Task Supportive and Personalized Human-Large Language Model Interaction: A User Study
Authors: Ben Wang, Jiqun Liu, Jamshed Karimnazarov, Nicolas Thompson
Date: 2024-02-09 00:00:00
Tasks: Information Retrieval, Language Modeling, Language Modelling, Large Language Model, Prompt Engineering, Retrieval

Large language model (LLM) applications, such as ChatGPT, are a powerful tool for online information-seeking (IS) and problem-solving tasks. However, users still face challenges initializing and refining prompts, and their cognitive barriers and biased perceptions further impede task completion. These issues reflect broader challenges identified within the fields of IS and interactive information retrieval (IIR). To address these, our approach integrates task context and user perceptions into human-ChatGPT interactions through prompt engineering. We developed a ChatGPT-like platform integrated with supportive functions, including perception articulation, prompt suggestion, and conversation explanation. Our findings of a user study demonstrate that the supportive functions help users manage expectations, reduce cognitive loads, better refine prompts, and increase user engagement. This research enhances our comprehension of designing proactive and user-centric systems with LLMs. It offers insights into evaluating human-LLM interactions and emphasizes potential challenges for under served users.
Character-based Outfit Generation with Vision-augmented Style Extraction via LLMs
Authors: Najmeh Forouzandehmehr, Yijie Cao, Nikhil Thakurdesai, Ramin Giahi, Luyi Ma, Nima Farrokhsiar, Jianpeng Xu, Evren Korpeoglu, Kannan Achan
Date: 2024-02-02 00:00:00
Tasks: counterfactual, Prompt Engineering

The outfit generation problem involves recommending a complete outfit to a user based on their interests. Existing approaches focus on recommending items based on anchor items or specific query styles but do not consider customer interests in famous characters from movie, social media, etc. In this paper, we define a new Character-based Outfit Generation (COG) problem, designed to accurately interpret character information and generate complete outfit sets according to customer specifications such as age and gender. To tackle this problem, we propose a novel framework LVA-COG that leverages Large Language Models (LLMs) to extract insights from customer interests (e.g., character information) and employ prompt engineering techniques for accurate understanding of customer preferences. Additionally, we incorporate text-to-image models to enhance the visual understanding and generation (factual or counterfactual) of cohesive outfits. Our framework integrates LLMs with text-to-image models and improves the customer's approach to fashion by generating personalized recommendations. With experiments and case studies, we demonstrate the effectiveness of our solution from multiple dimensions.
MeV-scale Reheating Temperature and Thermalization of Neutrino Background
Authors: M. Kawasaki, K. Kohri, Naoshi Sugiyama
Date: 2000-02-06 00:00:00

The late-time entropy production by the massive particle decay induces the various cosmological effects in the early epoch and modify the standard scenario. We investigate the thermalization process of the neutrinos after the entropy production by solving the Boltzmann equations numerically. We find that if the large entropy are produced at t $\sim$ 1 sec, the neutrinos are not thermalized very well and do not have the perfect Fermi-Dirac distribution. Then the freeze-out value of the neutron to proton ratio is altered considerably and the produced light elements, especially He4, are drastically changed. Comparing with the observational light element abundances, we find that $T_R$ < 0.7 MeV is excluded at 95 % C.L. We also study the case in which the massive particle has a decay mode into hadrons. Then we find that $T_R$ should be a little higher, i.e. $T_R$ > 2.5 MeV - 4 MeV, for the hadronic branching ratio $B_h = 10^{-2} - 1$. Possible influence of late-time entropy production on the large scale structure formation and temperature anisotropies of cosmic microwave background is studied. It is expected that the future satellite experiments (MAP and PLANCK) to measure anisotropies of cosmic microwave background radiation temperature can detect the vestige of the late-time entropy production as a modification of the effective number of the neutrino species $N_{\nu}^{\rm eff}$.
Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization
Authors: Haonan Yuan, Qingyun Sun, Xingcheng Fu, Ziwei Zhang, Cheng Ji, Hao Peng, JianXin Li
Date: 2023-11-18 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Graph Learning, Out-of-Distribution Generalization

Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-temporal patterns on dynamic graphs. However, existing works fail to generalize under distribution shifts, which are common in real-world scenarios. As the generation of dynamic graphs is heavily influenced by latent environments, investigating their impacts on the out-of-distribution (OOD) generalization is critical. However, it remains unexplored with the following two major challenges: (1) How to properly model and infer the complex environments on dynamic graphs with distribution shifts? (2) How to discover invariant patterns given inferred spatio-temporal environments? To solve these challenges, we propose a novel Environment-Aware dynamic Graph LEarning (EAGLE) framework for OOD generalization by modeling complex coupled environments and exploiting spatio-temporal invariant patterns. Specifically, we first design the environment-aware EA-DGNN to model environments by multi-channel environments disentangling. Then, we propose an environment instantiation mechanism for environment diversification with inferred distributions. Finally, we discriminate spatio-temporal invariant patterns for out-of-distribution prediction by the invariant pattern recognition mechanism and perform fine-grained causal interventions node-wisely with a mixture of instantiated environment samples. Experiments on real-world and synthetic dynamic graph datasets demonstrate the superiority of our method against state-of-the-art baselines under distribution shifts. To the best of our knowledge, we are the first to study OOD generalization on dynamic graphs from the environment learning perspective.
Learning Regional Purity for Instance Segmentation on 3D Point Clouds
Authors: Shichao Dong, Guosheng Lin, Tzu-Yi Hung
Date: 2020-11-03 00:00:00
Proceeding: European Conference on Computer Vision (ECCV) 2020 11
Tasks: 3D Instance Segmentation, 3D Semantic Segmentation, Clustering, Data Augmentation, Instance Segmentation, Scene Understanding, Segmentation, Semantic Segmentation

3D instance segmentation is a fundamental task for scene understanding, with a variety of applications in robotics and AR/VR. Many proposal-free methods have been proposed recently for this task, with remarkable results and high efficiency. However, these methods heavily rely on instance centroid regression and do not explicitly detect object boundaries, thus may mistakenly group nearby objects into the same clusters in some scenarios. In this paper, we define a novel concept of “regional purity” as the percentage of neighboring points belonging to the same instance within a fixed-radius 3D space. Intuitively, it indicates the likelihood of a point belonging to the boundary area. To evaluate the feasibility of predicting regional purity, we design a strategy to build a random scene toy dataset based on existing training data. Besides, using toy data is a “free” way of data augmentation on learning regional purity, which eliminates the burdens of additional real data. We propose Regional Purity Guided Network (RPGN), which has separate branches for predicting semantic class, regional purity, offset, and size. Predicted regional purity information is utilized to guide our clustering algorithm. Experimental results demonstrate that using regional purity can simultaneously prevent under-segmentation and over-segmentation problems during clustering.
The last Dance : Robust backdoor attack via diffusion models and bayesian approach
Authors: Orson Mengara
Date: 2024-02-05 00:00:00
Tasks: Backdoor Attack, Denoising

Diffusion models are state-of-the-art deep learning generative models that are trained on the principle of learning forward and backward diffusion processes via the progressive addition of noise and denoising. In this paper, we aim to fool audio-based DNN models, such as those from the Hugging Face framework, primarily those that focus on audio, in particular transformer-based artificial intelligence models, which are powerful machine learning models that save time and achieve results faster and more efficiently. We demonstrate the feasibility of backdoor attacks (called `BacKBayDiffMod`) on audio transformers derived from Hugging Face, a popular framework in the world of artificial intelligence research. The backdoor attack developed in this paper is based on poisoning model training data uniquely by incorporating backdoor diffusion sampling and a Bayesian approach to the distribution of poisoned data.
Federated Learning Priorities Under the European Union Artificial Intelligence Act
Authors: Herbert Woisetschläger, Alexander Erben, Bill Marino, Shiqiang Wang, Nicholas D. Lane, Ruben Mayer, Hans-Arno Jacobsen
Date: 2024-02-05 00:00:00
Tasks: Federated Learning, Position

The age of AI regulation is upon us, with the European Union Artificial Intelligence Act (AI Act) leading the way. Our key inquiry is how this will affect Federated Learning (FL), whose starting point of prioritizing data privacy while performing ML fundamentally differs from that of centralized learning. We believe the AI Act and future regulations could be the missing catalyst that pushes FL toward mainstream adoption. However, this can only occur if the FL community reprioritizes its research focus. In our position paper, we perform a first-of-its-kind interdisciplinary analysis (legal and ML) of the impact the AI Act may have on FL and make a series of observations supporting our primary position through quantitative and qualitative analysis. We explore data governance issues and the concern for privacy. We establish new challenges regarding performance and energy efficiency within lifecycle monitoring. Taken together, our analysis suggests there is a sizable opportunity for FL to become a crucial component of AI Act-compliant ML systems and for the new regulation to drive the adoption of FL techniques in general. Most noteworthy are the opportunities to defend against data bias and enhance private and secure computation
RAGE for the Machine: Image Compression with Low-Cost Random Access for Embedded Applications
Authors: Christian D. Rask, Daniel E. Lucani
Date: 2024-02-07 00:00:00
Tasks: Image Compression

We introduce RAGE, an image compression framework that achieves four generally conflicting objectives: 1) good compression for a wide variety of color images, 2) computationally efficient, fast decompression, 3) fast random access of images with pixel-level granularity without the need to decompress the entire image, 4) support for both lossless and lossy compression. To achieve these, we rely on the recent concept of generalized deduplication (GD), which is known to provide efficient lossless (de)compression and fast random access in time-series data, and deliver key expansions suitable for image compression, both lossless and lossy. Using nine different datasets, incl. graphics, logos, natural images, we show that RAGE has similar or better compression ratios to state-of-the-art lossless image compressors, while delivering pixel-level random access capabilities. Tests in an ARM Cortex-M33 platform show seek times between 9.9 and 40.6~ns and average decoding time per pixel between 274 and 1226~ns. Our measurements also show that RAGE's lossy variant, RAGE-Q, outperforms JPEG by several fold in terms of distortion in embedded graphics and has reasonable compression and distortion for natural images.
idMotif: An Interactive Motif Identification in Protein Sequences
Authors: Ji Hwan Park, Vikash Prasad, Sydney Newsom, Fares Najar, Rakhi Rajan
Date: 2024-02-04 00:00:00
Tasks: Deep Learning

This article introduces idMotif, a visual analytics framework designed to aid domain experts in the identification of motifs within protein sequences. Motifs, short sequences of amino acids, are critical for understanding the distinct functions of proteins. Identifying these motifs is pivotal for predicting diseases or infections. idMotif employs a deep learning-based method for the categorization of protein sequences, enabling the discovery of potential motif candidates within protein groups through local explanations of deep learning model decisions. It offers multiple interactive views for the analysis of protein clusters or groups and their sequences. A case study, complemented by expert feedback, illustrates idMotif's utility in facilitating the analysis and identification of protein sequences and motifs.
Limits of Large Language Models in Debating Humans
Authors: James Flamino, Mohammed Shahid Modi, Boleslaw K. Szymanski, Brendan Cross, Colton Mikolajczyk
Date: 2024-02-06 00:00:00

Large Language Models (LLMs) have shown remarkable promise in communicating with humans. Their potential use as artificial partners with humans in sociological experiments involving conversation is an exciting prospect. But how viable is it? Here, we rigorously test the limits of agents that debate using LLMs in a preregistered study that runs multiple debate-based opinion consensus games. Each game starts with six humans, six agents, or three humans and three agents. We found that agents can blend in and concentrate on a debate's topic better than humans, improving the productivity of all players. Yet, humans perceive agents as less convincing and confident than other humans, and several behavioral metrics of humans and agents we collected deviate measurably from each other. We observed that agents are already decent debaters, but their behavior generates a pattern distinctly different from the human-generated data.
Outage performance of the $α$-Beaulieu-Xie Shadowed Fading Channel Model
Authors: Aleksey S. Gvozdarev
Date: 2024-02-09 00:00:00
Tasks: Form

The research presents the closed-form outage analysis of the newly presented $\alpha$-modification of the shadowed Beaulieu-Xie fading model for wireless communications. For the considered channel, the closed-form analytical expressions for the outage probability (including its upper and lower bounds), raw moments, amount of fading, and channel quality estimation indicator are derived. The carried out thorough numerical simulation and analysis demonstrates strong agreement with the presented closed-form solutions and illustrates the relationship between the outage probability and channel parameters.
Cooperative Nonlinear Guidance Strategies for Guaranteed Pursuit-Evasion
Authors: Saurabh Kumar, Shashi Ranjan Kumar, Abhinav Sinha
Date: 2024-02-09 00:00:00

This paper addresses the pursuit-evasion problem involving three agents -- a purser, an evader, and a defender. We develop cooperative guidance laws for the evader-defender team that guarantee that the defender intercepts the pursuer before it reaches the vicinity of the evader. Unlike heuristic methods, optimal control, differential game formulation, and recently proposed time-constrained guidance techniques, we propose a geometric solution to safeguard the evader from the pursuer's incoming threat. The proposed strategy is computationally efficient and expected to be scalable as the number of agents increases. Another alluring feature of the proposed strategy is that the evader-defender team does not require the knowledge of the pursuer's strategy and that the pursuer's interception is guaranteed from arbitrary initial engagement geometries. We further show that the necessary error variables for the evader-defender team vanish within a time that can be exactly prescribed prior to the three-body engagement. Finally, we demonstrate the efficacy of the proposed cooperative defense strategy via simulation in diverse engagement scenarios.
An experimental study: RF Fingerprinting of Bluetooth devices
Authors: Artis Rušiņš, Krišjānis Nesenbergs, Deniss Tiščenko, Pēteris Paikens
Date: 2024-02-09 00:00:00

This paper presents an experimental study on radio frequency (RF) fingerprinting of Bluetooth Classic devices. Our research aims to provide a practical evaluation of the possibilities for RF fingerprinting of everyday Bluetooth connected devices that may cause privacy risks. We have built an experimental setup for recording Bluetooth connection in a radio frequency isolated environment using commercially available SDR (software defined radio) systems, extracted fingerprints of the Bluetooth radio data in the form of carrier frequency offset and scaling factor from 6 different devices, and performed k-nearest neighbors (kNN) classification achieving 84\% accuracy. The experiment demonstrates that no matter what privacy measures are being taken in the protocol layer, the physical layer leaks significant information about the device to unauthorized listeners. In the context of the ever-growing Bluetooth device market, this research serves as a clarion call for device manufacturers, regulators, and end-users to acknowledge the privacy risks posed by RF fingerprinting and lays a foundation for more sizeable Bluetooth fingerprinting analysis research.