pwc-archive/papers-with-abstracts huggingface dataset:
PDC-FRS: Privacy-preserving Data Contribution for Federated Recommender System
Authors: Chaoqun Yang, Wei Yuan, Liang Qu, Thanh Tam Nguyen
Date: 2024-09-12 00:00:00
Tasks: Privacy Preserving, Recommendation Systems

Federated recommender systems (FedRecs) have emerged as a popular research direction for protecting users' privacy in on-device recommendations. In FedRecs, users keep their data locally and only contribute their local collaborative information by uploading model parameters to a central server. While this rigid framework protects users' raw data during training, it severely compromises the recommendation model's performance due to the following reasons: (1) Due to the power law distribution nature of user behavior data, individual users have few data points to train a recommendation model, resulting in uploaded model updates that may be far from optimal; (2) As each user's uploaded parameters are learned from local data, which lacks global collaborative information, relying solely on parameter aggregation methods such as FedAvg to fuse global collaborative information may be suboptimal. To bridge this performance gap, we propose a novel federated recommendation framework, PDC-FRS. Specifically, we design a privacy-preserving data contribution mechanism that allows users to share their data with a differential privacy guarantee. Based on the shared but perturbed data, an auxiliary model is trained in parallel with the original federated recommendation process. This auxiliary model enhances FedRec by augmenting each user's local dataset and integrating global collaborative information. To demonstrate the effectiveness of PDC-FRS, we conduct extensive experiments on two widely used recommendation datasets. The empirical results showcase the superiority of PDC-FRS compared to baseline methods.
Harnessing TI Feeds for Exploitation Detection
Authors: Kajal Patel, Zubair Shafiq, Mateus Nogueira, Daniel Sadoc Menasché, Enrico Lovat, Taimur Kashif, Ashton Woiwood, Matheus Martins
Date: 2024-09-12 00:00:00

Many organizations rely on Threat Intelligence (TI) feeds to assess the risk associated with security threats. Due to the volume and heterogeneity of data, it is prohibitive to manually analyze the threat information available in different loosely structured TI feeds. Thus, there is a need to develop automated methods to vet and extract actionable information from TI feeds. To this end, we present a machine learning pipeline to automatically detect vulnerability exploitation from TI feeds. We first model threat vocabulary in loosely structured TI feeds using state-of-the-art embedding techniques (Doc2Vec and BERT) and then use it to train a supervised machine learning classifier to detect exploitation of security vulnerabilities. We use our approach to identify exploitation events in 191 different TI feeds. Our longitudinal evaluation shows that it is able to accurately identify exploitation events from TI feeds only using past data for training and even on TI feeds withheld from training. Our proposed approach is useful for a variety of downstream tasks such as data-driven vulnerability risk assessment.
Multilingual Prompts in LLM-Based Recommenders: Performance Across Languages
Authors: Makbule Gulcin Ozsoy
Date: 2024-09-11 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Large language models (LLMs) are increasingly used in natural language processing tasks. Recommender systems traditionally use methods such as collaborative filtering and matrix factorization, as well as advanced techniques like deep learning and reinforcement learning. Although language models have been applied in recommendation, the recent trend have focused on leveraging the generative capabilities of LLMs for more personalized suggestions. While current research focuses on English due to its resource richness, this work explores the impact of non-English prompts on recommendation performance. Using OpenP5, a platform for developing and evaluating LLM-based recommendations, we expanded its English prompt templates to include Spanish and Turkish. Evaluation on three real-world datasets, namely ML1M, LastFM, and Amazon-Beauty, showed that usage of non-English prompts generally reduce performance, especially in less-resourced languages like Turkish. We also retrained an LLM-based recommender model with multilingual prompts to analyze performance variations. Retraining with multilingual prompts resulted in more balanced performance across languages, but slightly reduced English performance. This work highlights the need for diverse language support in LLM-based recommenders and suggests future research on creating evaluation datasets, using newer models and additional languages.
DV-FSR: A Dual-View Target Attack Framework for Federated Sequential Recommendation
Authors: Qitao Qin, Yucong Luo, Mingyue Cheng, Qingyang Mao, Chenyi Lei
Date: 2024-09-10 00:00:00
Tasks: Contrastive Learning, Sequential Recommendation

Federated recommendation (FedRec) preserves user privacy by enabling decentralized training of personalized models, but this architecture is inherently vulnerable to adversarial attacks. Significant research has been conducted on targeted attacks in FedRec systems, motivated by commercial and social influence considerations. However, much of this work has largely overlooked the differential robustness of recommendation models. Moreover, our empirical findings indicate that existing targeted attack methods achieve only limited effectiveness in Federated Sequential Recommendation (FSR) tasks. Driven by these observations, we focus on investigating targeted attacks in FSR and propose a novel dualview attack framework, named DV-FSR. This attack method uniquely combines a sampling-based explicit strategy with a contrastive learning-based implicit gradient strategy to orchestrate a coordinated attack. Additionally, we introduce a specific defense mechanism tailored for targeted attacks in FSR, aiming to evaluate the mitigation effects of the attack method we proposed. Extensive experiments validate the effectiveness of our proposed approach on representative sequential models.
Contrastive Learning-based User Identification with Limited Data on Smart Textiles
Authors: Yunkang Zhang, Ziyu Wu, Zhen Liang, Fangting Xie, Quan Wan, Mingjie Zhao, Xiaohui Cai
Date: 2024-09-06 00:00:00
Tasks: Contrastive Learning, User Identification

Pressure-sensitive smart textiles are widely applied in the fields of healthcare, sports monitoring, and intelligent homes. The integration of devices embedded with pressure sensing arrays is expected to enable comprehensive scene coverage and multi-device integration. However, the implementation of identity recognition, a fundamental function in this context, relies on extensive device-specific datasets due to variations in pressure distribution across different devices. To address this challenge, we propose a novel user identification method based on contrastive learning. We design two parallel branches to facilitate user identification on both new and existing devices respectively, employing supervised contrastive learning in the feature space to promote domain unification. When encountering new devices, extensive data collection efforts are not required; instead, user identification can be achieved using limited data consisting of only a few simple postures. Through experimentation with two 8-subject pressure datasets (BedPressure and ChrPressure), our proposed method demonstrates the capability to achieve user identification across 12 sitting scenarios using only a dataset containing 2 postures. Our average recognition accuracy reaches 79.05%, representing an improvement of 2.62% over the best baseline model.
Deep learning optimization method for counting overlapping rice seeds
Authors: Jin Sun, Yang Zhang, Xinglong Zhu, Yu-Dong Zhang
Date: 2021-06-30 00:00:00
Proceeding: Journal of Food Process Engineering 2021 6
Tasks: Deep Learning

Abstract
Overlapping seeds counting method is an important direction to improve the detec-
tion efficiency of rice thousand kernel weight, but the existing image processing
methods may misjudge the overlapping distribution when the thin-layer seeds are
piled up. In this study, we proposed a deep learning optimization method based on
contour grouping pre-labeling for counting overlapping rice seeds. First, the con-
tour grouping method—based on the Euclidean distance and divergence function as
comprehensive criteria—pre-labels the rice seed contours to reduce the number of
potential false classification edge points. The purpose of integrating the pre-
labeling results in its feature extraction layer and using a linear combination of
inter-class and intra-class error functions as the total is to improve the accuracy of
deep learning. Second, based on the improved Faster region-convolutional neural
network method, the overlapping rice seeds were accurately counted. An orthogo-
nal test is proposed to optimize the full connection layer's parameter configuration,
aimed at improving the learning speed. The experimental results demonstrate that
the average error rate of rice seeds in a single image (960 	 960 pixels) is 1.06%
and the average recognition time of counting (≤400 seeds) was 0.45 s. The pro-
posed method can be used as an effective tool for counting overlapping rice or
other crop seeds.
Pest Manager: A Systematic Framework for Precise Pest Counting and Identification in Invisible Grain Pile Storage Environment
Authors: Chuanyang Ma, Jiangtao Li, Xingqun Qi, Muyi Sun, Huiling Zhou
Date: 2024-09-03 00:00:00

Pest infestations pose a significant risk to both the quality and quantity of stored grain, resulting in substantial economic losses. Accurate and timely pest monitoring is essential, but traditional methods are time-consuming and labor-intensive, and often ineffective at detecting pests beneath the grain surface. In this paper, we introduce a systematic framework: Pest Manager for precise pest counting and identification within the invisible grain pile environment. The framework consists of three components: an improved grain probe trap PestMoni, a pest drop dataset PestSet collected by PestMoni, and a multi-task Transformer-based architecture PestFormer for pest counting and identification. Specifically, PestMoni uses an asymmetric layout of infrared diodes and photodiodes, enabling precise recording of pest drops. Based on PestMoni, we develop PestSet, an infrared-perception dataset for pest drops, including five common grain storage pests. Furthermore, we propose PestFormer, a multi-task model with a conditional modification module to reduce deviations across different PestMonis and thereby normalize the data processing workflow. Extensive experiments validate the design, dataset, and model, with PestFormer achieving state-of-the-art accuracies of 99.2% in counting and 86.9% in identification, highlighting its potential for effective pest management in invisible storage environment.
ODAQ: Open Dataset of Audio Quality - Benchmark on GitHub
Authors: M. Torcoli, C.-W. Wu, S. Dick, P. A. Williams, M. M. Halimeh, W., Wolcott, E. A. P. Habets
Date: 2024-09-13 00:00:00
Proceeding: Github 2024 9
Tasks: Audio Quality Assessment, Benchmarking

ODAQ is a dataset addressing the scarcity of openly available collections of audio signals accompanied by corresponding subjective scores of perceived quality.

ODAQ contains 240 audio samples accompanied by corresponding quality scores obtained via MUSHRA listening tests.

The quality-rated audio samples are processed versions of the original audio material (also made available). The original audio material consists of: stereo audio with 44.1 or 48 kHz sampling frequency; 14 music excerpts (8 of which are solo recordings); 11 excerpts from movie-like soundtracks with dialogues mixed with music and effects (separate stems and transcripts are also provided).

Here, the dataset is used for benchmarking objective measures of audio quality.
https://arxiv.org/pdf/2409.07491
Authors: R. Ildar
Date: 2024-09-13 00:00:00
Tasks: Brain Computer Interface, EEG

This article introduces a cost-effective gateway into the fascinating world of neuroscience: the PIEEG-16, a
versatile shield for RaspberryPi designed to measure 16 channels of various biosignals, including EEG
(electroencephalography), EMG (electromyography), and ECG (electrocardiography) without any data
transfer over the network (Wi-Fi, Bluetooth) and processing and feature ectraction directly on the
Raspberry in real-time. This innovative tool opens up new possibilities for neuroscience research and
brain-computer interface experiments. By combining the power of RaspberryPi with specialized biosignal
measurement capabilities, the PIEEG-16 represents a significant step forward in democratizing
neuroscience research and exploration
PiEEG-16 to Measure 16 EEG Channels with Raspberry Pi for Brain-Computer Interfaces and EEG devices
Authors: Ildar R
Date: 2024-09-13 00:00:00
Tasks: Brain Computer Interface, EEG

This article introduces a cost-effective gateway into the fascinating world of neuroscience: the PIEEG-16, a versatile shield for RaspberryPi designed to measure 16 channels of various biosignals, including EEG (electroencephalography), EMG (electromyography), and ECG (electrocardiography) without any data transfer over the network (Wi-Fi, Bluetooth) and processing and feature ectraction directly on the Raspberry in real-time. This innovative tool opens up new possibilities for neuroscience research and brain-computer interface experiments. By combining the power of RaspberryPi with specialized biosignal measurement capabilities, the PIEEG-16 represents a significant step forward in democratizing neuroscience research and exploration.
Mental Stress Detection and Performance Enhancement Using FNIRS and Wrist Vibrator Biofeedback
Authors: Anita Beigzadeh, Vahid Yazdnian, Kamaledin Setarehdan
Date: 2024-09-12 00:00:00
Tasks: Management, Mental Stress Detection

Any person in his/her daily life activities experiences different kinds and various amounts of mental stress which has a destructive effect on their performance. Therefore, it is crucial to come up with a systematic way of stress management and performance enhancement. This paper presents a comprehensive portable and real-time biofeedback system that aims at boosting stress management and consequently performance enhancement. For this purpose, a real-time brain signal acquisition device, a wireless vibration biofeedback device, and a software-defined program for stress level classification have been developed. More importantly, the entire system has been designed to present minimum time delay by propitiously bridging all the essential parts of the system together. We have presented different signal processing and feature extraction techniques for an online stress detection application. Accordingly, by testing the stress classification section of the system, an accuracy of 83% and a recall detecting the true mental stress level of 92% was achieved. Moreover, the biofeedback system as integrity has been tested on 20 participants in the controlled experimental setup. Experiment evaluations show promising results of system performances, and the findings reveal that our system is able to help the participants reduce their stress level by 55% and increase their accuracy by 24.5%. It can be concluded from the observations that all primary premises on stress management and performance enhancement through reward learning are valid as well.
Joint User Association and Pairing in Multi-UAV-Assisted NOMA Networks: A Decaying-Epsilon Thompson Sampling Framework
Authors: Boniface Uwizeyimana, Mohammed Abo-Zahhad, Osamu Muta, Ahmed H Abd El-Malek, Maha M Elsabrouty
Date: 2024-06-20 00:00:00
Proceeding: IEEE Access 2024 6
Tasks: Thompson Sampling

Unmanned aerial vehicles (UAVs) are expected to be integrated into future wireless networks to offer services, especially in unreachable or congested areas. To improve the spectral efficiency, non-orthogonal multiple access (NOMA) scheme can be utilised within the UAV communication to allow more users to be covered and associated. The performance of the NOMA-UAVs network is governed by several factors including power allocation, user association and pairing methods. This paper presents an approach that uses multi-armed bandit (MAB) and two-sided matching frameworks to maximize the throughput of multi-UAV-assisted NOMA networks in a decentralized manner. The approach enables the UAVs to propose to the ground users (GUs) without explicit cooperation among the UAVs while the GUs can accept or reject the proposals. To this end, we propose a modified Thompson sampling algorithm that we named decaying epsilon Thompson sampling ( DϵTS ) MAB algorithm that is designed to improve the exploration-exploitation tradeoff in the MAB. The performance of the proposed DϵTS MAB algorithm is evaluated against other existing MAB techniques. Simulation results show that the DϵTS algorithm attains faster convergence and improved performance in terms of smaller regret and increased achievable system throughput. The DϵTS MAB algorithm particularly excels in regards of the convergence rate when the number of available action spaces increases.
Transient hysteresis and inherent stochasticity in gene regulatory networks
Authors: Manuel Pájaro, Irene Otero-Muras, Carlos Vázquez, Antonio A. Alonso
Date: 2018-10-24 00:00:00

Cell fate determination, the process through which cells commit to differentiated states is commonly mediated by gene regulatory motifs with mutually exclusive expression states. The classical deterministic picture for cell fate determination includes bistability and hysteresis, which enables the persistence of the acquired cellular state after withdrawal of the stimulus, ensuring a robust cellular response. However, the stochasticity inherent to gene expression dynamics is not compatible with hysteresis, since the stationary solution of the governing Chemical Master Equation does not depend on the initial conditions. In this work, we provide a quantitative description of a transient hysteresis phenomenon that reconciles experimental evidence of hysteretic behaviour in gene regulatory networks with their inherent stochasticity. Under sufficiently slow dynamics, the dependency of the non-stationary solutions on the initial state of the cells can lead to what we denote here as transient hysteresis. To quantify this phenomenon, we provide an estimate of the convergence rate to the equilibrium. We also introduce the equation of a natural landscape capturing the evolution of the system that, unlike traditional cell fate potential landscapes, is compatible with the notion of coexistence at the microscopic level.
BreachSeek: A Multi-Agent Automated Penetration Tester
Authors: Ibrahim Alshehri, Adnan Alshehri, Abdulrahman Almalki, Majed Bamardouf, Alaqsa Akbar
Date: 2024-08-31 00:00:00

The increasing complexity and scale of modern digital environments have exposed significant gaps in traditional cybersecurity penetration testing methods, which are often time-consuming, labor-intensive, and unable to rapidly adapt to emerging threats. There is a critical need for an automated solution that can efficiently identify and exploit vulnerabilities across diverse systems without extensive human intervention. BreachSeek addresses this challenge by providing an AI-driven multi-agent software platform that leverages Large Language Models (LLMs) integrated through LangChain and LangGraph in Python. This system enables autonomous agents to conduct thorough penetration testing by identifying vulnerabilities, simulating a variety of cyberattacks, executing exploits, and generating comprehensive security reports. In preliminary evaluations, BreachSeek successfully exploited vulnerabilities in exploitable machines within local networks, demonstrating its practical effectiveness. Future developments aim to expand its capabilities, positioning it as an indispensable tool for cybersecurity professionals.
OPAL: Outlier-Preserved Microscaling Quantization Accelerator for Generative Large Language Models
Authors: Jahyun Koo, Dahoon Park, Sangwoo Jung, Jaeha Kung
Date: 2024-09-06 00:00:00
Tasks: Decoder, Quantization

To overcome the burden on the memory size and bandwidth due to ever-increasing size of large language models (LLMs), aggressive weight quantization has been recently studied, while lacking research on quantizing activations. In this paper, we present a hardware-software co-design method that results in an energy-efficient LLM accelerator, named OPAL, for generation tasks. First of all, a novel activation quantization method that leverages the microscaling data format while preserving several outliers per sub-tensor block (e.g., four out of 128 elements) is proposed. Second, on top of preserving outliers, mixed precision is utilized that sets 5-bit for inputs to sensitive layers in the decoder block of an LLM, while keeping inputs to less sensitive layers to 3-bit. Finally, we present the OPAL hardware architecture that consists of FP units for handling outliers and vectorized INT multipliers for dominant non-outlier related operations. In addition, OPAL uses log2-based approximation on softmax operations that only requires shift and subtraction to maximize power efficiency. As a result, we are able to improve the energy efficiency by 1.6~2.2x, and reduce the area by 2.4~3.1x with negligible accuracy loss, i.e., <1 perplexity increase.
MarS: a Financial Market Simulation Engine Powered by Generative Foundation Model
Authors: Junjie Li, Yang Liu, Weiqing Liu, Shikai Fang, Lewen Wang, Chang Xu, Jiang Bian
Date: 2024-09-04 00:00:00
Tasks: Language Modeling, Language Modelling, Text Generation

Generative models aim to simulate realistic effects of various actions across different contexts, from text generation to visual effects. Despite significant efforts to build real-world simulators, the application of generative models to virtual worlds, like financial markets, remains under-explored. In financial markets, generative models can simulate complex market effects of participants with various behaviors, enabling interaction under different market conditions, and training strategies without financial risk. This simulation relies on the finest structured data in financial market like orders thus building the finest realistic simulation. We propose Large Market Model (LMM), an order-level generative foundation model, for financial market simulation, akin to language modeling in the digital world. Our financial Market Simulation engine (MarS), powered by LMM, addresses the domain-specific need for realistic, interactive and controllable order generation. Key observations include LMM's strong scalability across data size and model complexity, and MarS's robust and practicable realism in controlled generation with market impact. We showcase MarS as a forecast tool, detection system, analysis platform, and agent training environment, thus demonstrating MarS's "paradigm shift" potential for a variety of financial applications. We release the code of MarS at https://github.com/microsoft/MarS/.