pwc-archive/papers-with-abstracts huggingface dataset:
Federated Transfer Learning Based Cooperative Wideband Spectrum Sensing with Model Pruning
Authors: Jibin Jia, Peihao Dong, Fuhui Zhou, Qihui Wu
Date: 2024-09-09 00:00:00
Tasks: Transfer Learning

For ultra-wideband and high-rate wireless communication systems, wideband spectrum sensing (WSS) is critical, since it empowers secondary users (SUs) to capture the spectrum holes for opportunistic transmission. However, WSS encounters challenges such as excessive costs of hardware and computation due to the high sampling rate, as well as robustness issues arising from scenario mismatch. In this paper, a WSS neural network (WSSNet) is proposed by exploiting multicoset preprocessing to enable the sub-Nyquist sampling, with the two dimensional convolution design specifically tailored to work with the preprocessed samples. A federated transfer learning (FTL) based framework mobilizing multiple SUs is further developed to achieve a robust model adaptable to various scenarios, which is paved by the selective weight pruning for the fast model adaptation and inference. Simulation results demonstrate that the proposed FTL-WSSNet achieves the fairly good performance in different target scenarios even without local adaptation samples.
Recommender Systems Algorithm Selection for Ranking Prediction on Implicit Feedback Datasets
Authors: Lukas Wegmeth, Tobias Vente, Joeran Beel
Date: 2024-09-09 00:00:00
Tasks: Recommendation Systems

The recommender systems algorithm selection problem for ranking prediction on implicit feedback datasets is under-explored. Traditional approaches in recommender systems algorithm selection focus predominantly on rating prediction on explicit feedback datasets, leaving a research gap for ranking prediction on implicit feedback datasets. Algorithm selection is a critical challenge for nearly every practitioner in recommender systems. In this work, we take the first steps toward addressing this research gap. We evaluate the NDCG@10 of 24 recommender systems algorithms, each with two hyperparameter configurations, on 72 recommender systems datasets. We train four optimized machine-learning meta-models and one automated machine-learning meta-model with three different settings on the resulting meta-dataset. Our results show that the predictions of all tested meta-models exhibit a median Spearman correlation ranging from 0.857 to 0.918 with the ground truth. We show that the median Spearman correlation between meta-model predictions and the ground truth increases by an average of 0.124 when the meta-model is optimized to predict the ranking of algorithms instead of their performance. Furthermore, in terms of predicting the best algorithm for an unknown dataset, we demonstrate that the best optimized traditional meta-model, e.g., XGBoost, achieves a recall of 48.6%, outperforming the best tested automated machine learning meta-model, e.g., AutoGluon, which achieves a recall of 47.2%.
Replicability Measures for Longitudinal Information Retrieval Evaluation
Authors: Jüri Keller, Timo Breuer, Philipp Schaer
Date: 2024-09-09 00:00:00
Tasks: Information Retrieval, Retrieval

Information Retrieval (IR) systems are exposed to constant changes in most components. Documents are created, updated, or deleted, the information needs are changing, and even relevance might not be static. While it is generally expected that the IR systems retain a consistent utility for the users, test collection evaluations rely on a fixed experimental setup. Based on the LongEval shared task and test collection, this work explores how the effectiveness measured in evolving experiments can be assessed. Specifically, the persistency of effectiveness is investigated as a replicability task. It is observed how the effectiveness progressively deteriorates over time compared to the initial measurement. Employing adapted replicability measures provides further insight into the persistence of effectiveness. The ranking of systems varies across retrieval measures and time. In conclusion, it was found that the most effective systems are not necessarily the ones with the most persistent performance.
Leveraging LLMs for Influence Path Planning in Proactive Recommendation
Authors: Mingze Wang, Shuxian Bi, Wenjie Wang, Chongming Gao, Yangyang Li, Fuli Feng
Date: 2024-09-07 00:00:00
Tasks: Diversity, Instruction Following, Recommendation Systems

Recommender systems are pivotal in Internet social platforms, yet they often cater to users' historical interests, leading to critical issues like echo chambers. To broaden user horizons, proactive recommender systems aim to guide user interest to gradually like a target item beyond historical interests through an influence path,i.e., a sequence of recommended items. As a representative, Influential Recommender System (IRS) designs a sequential model for influence path planning but faces issues of lacking target item inclusion and path coherence. To address the issues, we leverage the advanced planning capabilities of Large Language Models (LLMs) and propose an LLM-based Influence Path Planning (LLM-IPP) method. LLM-IPP generates coherent and effective influence paths by capturing user interest shifts and item characteristics. We introduce novel evaluation metrics and user simulators to benchmark LLM-IPP against traditional methods. Our experiments demonstrate that LLM-IPP significantly enhances user acceptability and path coherence, outperforming existing approaches.
Debias Can be Unreliable: Mitigating Bias Issue in Evaluating Debiasing Recommendation
Authors: Chengbing Wang, Wentao Shi, Jizhi Zhang, Wenjie Wang, Hang Pan, Fuli Feng
Date: 2024-09-07 00:00:00

Recent work has improved recommendation models remarkably by equipping them with debiasing methods. Due to the unavailability of fully-exposed datasets, most existing approaches resort to randomly-exposed datasets as a proxy for evaluating debiased models, employing traditional evaluation scheme to represent the recommendation performance. However, in this study, we reveal that traditional evaluation scheme is not suitable for randomly-exposed datasets, leading to inconsistency between the Recall performance obtained using randomly-exposed datasets and that obtained using fully-exposed datasets. Such inconsistency indicates the potential unreliability of experiment conclusions on previous debiasing techniques and calls for unbiased Recall evaluation using randomly-exposed datasets. To bridge the gap, we propose the Unbiased Recall Evaluation (URE) scheme, which adjusts the utilization of randomly-exposed datasets to unbiasedly estimate the true Recall performance on fully-exposed datasets. We provide theoretical evidence to demonstrate the rationality of URE and perform extensive experiments on real-world datasets to validate its soundness.
Preserving Individuality while Following the Crowd: Understanding the Role of User Taste and Crowd Wisdom in Online Product Rating Prediction
Authors: Liang Wang, Shubham Jain, Yingtong Dou, Junpeng Wang, Chin-Chia Michael Yeh, Yujie Fan, Prince Aboagye, Yan Zheng, Xin Dai, Zhongfang Zhuang, Uday Singh Saini, Wei zhang
Date: 2024-09-06 00:00:00
Tasks: Prediction

Numerous algorithms have been developed for online product rating prediction, but the specific influence of user and product information in determining the final prediction score remains largely unexplored. Existing research often relies on narrowly defined data settings, which overlooks real-world challenges such as the cold-start problem, cross-category information utilization, and scalability and deployment issues. To delve deeper into these aspects, and particularly to uncover the roles of individual user taste and collective wisdom, we propose a unique and practical approach that emphasizes historical ratings at both the user and product levels, encapsulated using a continuously updated dynamic tree representation. This representation effectively captures the temporal dynamics of users and products, leverages user information across product categories, and provides a natural solution to the cold-start problem. Furthermore, we have developed an efficient data processing strategy that makes this approach highly scalable and easily deployable. Comprehensive experiments in real industry settings demonstrate the effectiveness of our approach. Notably, our findings reveal that individual taste dominates over collective wisdom in online product rating prediction, a perspective that contrasts with the commonly observed wisdom of the crowd phenomenon in other domains. This dominance of individual user taste is consistent across various model types, including the boosting tree model, recurrent neural network (RNN), and transformer-based architectures. This observation holds true across the overall population, within individual product categories, and in cold-start scenarios. Our findings underscore the significance of individual user tastes in the context of online product rating prediction and the robustness of our approach across different model architectures.
A Unified Framework for Cross-Domain Recommendation
Authors: Jiangxia Cao, Shen Wang, Gaode Chen, Rui Huang, Shuang Yang, Zhaojie Liu, Guorui Zhou
Date: 2024-09-06 00:00:00
Tasks: Recommendation Systems, Transfer Learning

In addressing the persistent challenges of data-sparsity and cold-start issues in domain-expert recommender systems, Cross-Domain Recommendation (CDR) emerges as a promising methodology. CDR aims at enhancing prediction performance in the target domain by leveraging interaction knowledge from related source domains, particularly through users or items that span across multiple domains (e.g., Short-Video and Living-Room). For academic research purposes, there are a number of distinct aspects to guide CDR method designing, including the auxiliary domain number, domain-overlapped element, user-item interaction types, and downstream tasks. With so many different CDR combination scenario settings, the proposed scenario-expert approaches are tailored to address a specific vertical CDR scenario, and often lack the capacity to adapt to multiple horizontal scenarios. In an effect to coherently adapt to various scenarios, and drawing inspiration from the concept of domain-invariant transfer learning, we extend the former SOTA model UniCDR in five different aspects, named as UniCDR+. Our work was successfully deployed on the Kuaishou Living-Room RecSys.
Concept Graph Embedding Models for Enhanced Accuracy and Interpretability
Authors: Sangwon Kim, Byoung Chul Ko
Date: 2024-08-13 00:00:00
Proceeding: IOP MLST 2024 8
Tasks: Concept-based Classification, Graph Embedding, image-classification, Image Classification

In fields requiring high accountability, it is necessary to understand how deep-learning models make decisions when analyzing the causes of image classification. Concept-based interpretation methods have recently been introduced to reveal the internal mechanisms of deep learning models using high-level concepts. However, such methods are constrained by a trade-off between accuracy and interpretability. For instance, in real-world environments, unlike in well-curated training data, the accurate prediction of expected concepts becomes a challenge owing to the various distortions and complexities introduced by different objects. To overcome this tradeoff, we propose concept graph embedding models (CGEM), reflecting the complex dependencies and structures among concepts through the learning of mutual directionalities. The concept graph convolutional neural network (Concept GCN), a downstream task of CGEM, differs from previous methods that solely determine the presence of concepts because it performs a final classification based on the relationships between con- cepts learned through graph embedding. This process endows the model with high resilience even in the presence of incorrect concepts. In addition, we utilize a deformable bipartite GCN for object- centric concept encoding in the earlier stages, which enhances the homogeneity of the concepts. The experimental results show that, based on deformable concept encoding, the CGEM mitigates the trade-off between task accuracy and interpretability. Moreover, it was confirmed that this approach allows the model to increase the resilience and interpretability while maintaining robustness against various real-world concept distortions and incorrect concept interventions. Our code is available at https://github.com/jumpsnack/cgem.
Federated-Learning-Based Anomaly Detection for IoT Security Attacks
Authors: Viraaji Mothukuri, Reza M. Parizi, Seyedamin Pouriyeh, Ali Dehghantanha, Gautam Srivastava
Date: 2022-02-15 00:00:00
Proceeding: IEEE Internet of Things Journal 2022 2
Tasks: Anomaly Detection, Federated Learning

The Internet of Things (IoT) is made up of billions of physical devices connected to the Internet via networks
that perform tasks independently with less human intervention.
Such brilliant automation of mundane tasks requires a considerable amount of user data in digital format, which, in turn,
makes IoT networks an open source of personally identifiable
information data for malicious attackers to steal, manipulate, and
perform nefarious activities. A huge interest has been developed
over the past years in applying machine learning (ML)-assisted
approaches in the IoT security space. However, the assumption
in many current works is that big training data are widely available and transferable to the main server because data are born
at the edge and are generated continuously by IoT devices. This
is to say that classic ML works on the legacy set of entire data
located on a central server, which makes it the least preferred
option for domains with privacy concerns on user data. To
address this issue, we propose the federated-learning (FL)-based
anomaly detection approach to proactively recognize intrusion in
IoT networks using decentralized on-device data. Our approach
uses federated training rounds on gated recurrent units (GRUs)
models and keeps the data intact on local IoT devices by sharing
only the learned weights with the central server of FL. Also, the
approach’s ensembler part aggregates the updates from multiple
sources to optimize the global ML model’s accuracy. Our experimental results demonstrate that our approach outperforms the
classic/centralized machine learning (non-FL) versions in securing the privacy of user data and provides an optimal accuracy
rate in attack detection
Augmenting Vehicle Localization by Cooperative Sensing of the Driving Environment: Insight on Data Association in Urban Traffic Scenarios
Authors: Mattia Brambilla, Monica Nicoli, Gloria Soatti, Francesco Deflorio
Date: 2019-09-25 00:00:00
Proceeding: IEEE Transactions on Intelligent Transportation Systems 2019 9
Tasks: Autonomous Driving

Precise vehicle positioning is a key element for the development of Cooperative Intelligent Transport Systems (C-ITS). In this context, we present a distributed processing technique to augment the performance of conventional Global Navigation Satellite Systems (GNSS) exploiting Vehicle-to-anything (V2X) communication systems. We propose a method, referred to as Implicit Cooperative Positioning with Data Association (ICP-DA), where the connected vehicles detect a set of passive features in the driving environment, solve the association task by pairing them with on-board sensor measurements and cooperatively localize the features to enhance the GNSS accuracy. We adopt a belief propagation algorithm to distribute the processing over the network, and solve both the data association and localization problems locally at vehicles. Numerical results on realistic traffic networks show that the ICP-DA method is able to significantly outperform the conventional GNSS. In particular, the analysis on a real urban road infrastructure highlights the robustness of the proposed method in real-life cases where the interactions among vehicles evolve over space and time according to traffic regulation mechanisms. Performances are investigated both in conventional traffic-light regulated scenarios and self-regulated environments (as representative of future automated driving scenarios) where vehicles autonomously cross the intersections taking gap-availability decisions for avoiding collisions. The analysis shows how the mutual coordination in platoons of vehicles eases the cooperation process and increases the positioning performance.
Histopathological study on goldfish (Carassius auratus) gonads exposed to tobacco smoke
Authors: Ali Parsakhanghah
Date: 2024-09-08 00:00:00

Ornamental fish have various positive effects in human life. Due to the effect and importance of aesthetics and artificial reproduction of these fish, which may be enclosed in aquarium environments and come in contact with cigarette smoke, the effects of tobacco smoke on the gonad tissue of goldfish were investigated. For this purpose, 60 goldfish randomly (weight 100gr) divided in 3 groups and were released in tanks containing 10 liters of water (temperature: 20, hardness: 14 ppt, pH: 7/8). After adaptation, in treatment 1, 1gr of tobacco was heated daily with a direct flame, and the resulting smoke was collected and injected into water with an air pump, in treatment 2, this process was done twice a day. After 3 months, fish gonads tissue were sampled and histopathological slides were investigated. The results showed that in the treatment 2, there were early and immature oocytes in the ovarian tissue in comparison to other groups. Also, in the testes of fish of treatment 2, the reduction of spermatozoids and the higher number of spermatogonia were observed. In the treatment 3, these changes were more. A significant difference between the groups in both female and male was observed at the sexual maturation stages (P<0.001). Based on this study, the dissolution of tobacco smoke can have a negative effect on the process of sexual reproduction and fish exposed to more smoke are more likely to be sterile, and these changes were observed in both males and female.
Note on solving one-to-one matching models with linear transferable utility
Authors: Esben Scrivers Andersen
Date: 2024-09-09 00:00:00

We derive a system of fixed-point equations for the equilibrium transfers in a class of one-to-one matching models with linear transferable utility. We then show that, when the degree of substitution between alternatives is bounded from above, the derived system of equations constitutes a contraction mapping. As a result, fixed-point iterations are guaranteed to converge to the unique distribution of equilibrium transfers.
P.862.2 : Wideband extension to Recommendation P.862 for the assessment of wideband telephone networks and speech codecs
Authors: Telecommunication Standardization Sector of ITU (ITU-T)
Date: 2007-11-13 00:00:00
Proceeding: ITU-T Recommendation P.862.2 2007 11
Tasks: Audio Quality Assessment

ITU-T Recommendation P.862.2 describes a simple extension to the perceptual evaluation of
listening speech quality (PESQ) algorithm defined in ITU-T Recommendation P.862. It allows
ITU-T Recommendation P.862 to be applied to the evaluation of conditions, such as speech codecs,
where the listener uses wideband headphones. (In contrast, ITU-T Recommendation P.862 assumes a
standard IRS-type narrow-band telephone handset which attenuates strongly below 300 Hz and
above 3100 Hz.) This Recommendation is mainly intended for use with wideband audio systems
(50-7000 Hz), although it may also be applied to systems with a narrower bandwidth.
Tensor completion and low-n-rank tensor recovery via convex optimization
Authors: Silvia Gandy, Benjamin Recht and Isao Yamada
Date: 2011-02-24 00:00:00
Proceeding: IOPScience 2011 2
Tasks: compressed sensing

In this paper we consider sparsity on a tensor level, as given by
the n-rank of a tensor. In the important sparse-vector approximation problem
(compressed sensing) and the low-rank matrix recovery problem, using a convex
relaxation technique proved to be a valuable solution strategy. Here, we will adapt
these techniques to the tensor setting. We use the n-rank of a tensor as sparsity
measure and consider the low-n-rank tensor recovery problem, i.e., the problem of
finding the tensor of lowest n-rank that fulfills some linear constraints. We introduce a tractable convex relaxation of the n-rank and propose efficient algorithms
to solve the low-n-rank tensor recovery problem numerically. The algorithms are
based on the Douglas-Rachford splitting technique and its dual variant, the alternating direction method of multipliers (ADM).
Explainable AI for Machine Fault Diagnosis: Understanding Features’ Contribution in Machine Learning Models for Industrial Condition Monitoring
Authors: Brusa E., Cibrario L., Delprete C., Di Maggio L.G.
Date: 2023-02-04 00:00:00
Proceeding: Applied Sciences 2023 2
Tasks: Fault Detection, Fault Diagnosis, feature selection

Although the effectiveness of machine learning (ML) for machine diagnosis has been widely established, the interpretation of the diagnosis outcomes is still an open issue. Machine learning models behave as black boxes; therefore, the contribution given by each of the selected features to the diagnosis is not transparent to the user. This work is aimed at investigating the capabilities of the SHapley Additive exPlanation (SHAP) to identify the most important features for fault detection and classification in condition monitoring programs for rotating machinery. The authors analyse the case of medium-sized bearings of industrial interest. Namely, vibration data were collected for different health states from the test rig for industrial bearings available at the Mechanical Engineering Laboratory of Politecnico di Torino. The Support Vector Machine (SVM) and k-Nearest Neighbour (kNN) diagnosis models are explained by means of the SHAP. Accuracies higher than 98.5% are achieved for both the models using the SHAP as a criterion for feature selection. It is found that the skewness and the shape factor of the vibration signal have the greatest impact on the models’ outcomes.
Dataset of British English speech recordings for psychoacoustics and speech processing research
Authors: Trevor John Cox, Simone Graetzer, Michael A Akeroyd, Jonathan Barker, John Culling, Graham Naylor, Eszter Porter, Rhoddy Viveros Muñoz
Date: 2022-02-15 00:00:00
Proceeding: Data in Brief 2022 2
Tasks: Sentence

The Clarity Speech Corpus is a forty speaker British English speech dataset. The corpus was created for the purpose of running listening tests to gauge speech intelligibility and quality in the Clarity Project, which has the goal of advancing speech signal processing by hearing aids through a series of challenges. The dataset is suitable for machine learning and other uses in speech and hearing technology, acoustics and psychoacoustics. The data comprises recordings of approximately 10,000 sentences drawn from the British National Corpus (BNC) with suitable length, words and grammatical construction for speech intelligibility testing. The collection process involved the selection of a subset of BNC sentences, the recording of these produced by 40 British English speakers, and the processing of these recordings to create individual sentence recordings with associated prompts and metadata.