pwc-archive/papers-with-abstracts huggingface dataset:
News Recommendation with Candidate-aware User Modeling
Authors: Tao Qi, Fangzhao Wu, Chuhan Wu, Yongfeng Huang
Date: 2022-04-10 00:00:00
Tasks: News Recommendation

News recommendation aims to match news with personalized user interest. Existing methods for news recommendation usually model user interest from historical clicked news without the consideration of candidate news. However, each user usually has multiple interests, and it is difficult for these methods to accurately match a candidate news with a specific user interest. In this paper, we present a candidate-aware user modeling method for personalized news recommendation, which can incorporate candidate news into user modeling for better matching between candidate news and user interest. We propose a candidate-aware self-attention network that uses candidate news as clue to model candidate-aware global user interest. In addition, we propose a candidate-aware CNN network to incorporate candidate news into local behavior context modeling and learn candidate-aware short-term user interest. Besides, we use a candidate-aware attention network to aggregate previously clicked news weighted by their relevance with candidate news to build candidate-aware user representation. Experiments on real-world datasets show the effectiveness of our method in improving news recommendation performance.
ProFairRec: Provider Fairness-aware News Recommendation
Authors: Tao Qi, Fangzhao Wu, Chuhan Wu, Peijie Sun, Le Wu, Xiting Wang, Yongfeng Huang, Xing Xie
Date: 2022-04-10 00:00:00
Tasks: Articles, Fairness, News Recommendation

News recommendation aims to help online news platform users find their preferred news articles. Existing news recommendation methods usually learn models from historical user behaviors on news. However, these behaviors are usually biased on news providers. Models trained on biased user data may capture and even amplify the biases on news providers, and are unfair for some minority news providers. In this paper, we propose a provider fairness-aware news recommendation framework (named ProFairRec), which can learn news recommendation models fair for different news providers from biased user data. The core idea of ProFairRec is to learn provider-fair news representations and provider-fair user representations to achieve provider fairness. To learn provider-fair representations from biased data, we employ provider-biased representations to inherit provider bias from data. Provider-fair and -biased news representations are learned from news content and provider IDs respectively, which are further aggregated to build fair and biased user representations based on user click history. All of these representations are used in model training while only fair representations are used for user-news matching to achieve fair news recommendation. Besides, we propose an adversarial learning task on news provider discrimination to prevent provider-fair news representation from encoding provider bias. We also propose an orthogonal regularization on provider-fair and -biased representations to better reduce provider bias in provider-fair representations. Moreover, ProFairRec is a general framework and can be applied to different news recommendation methods. Extensive experiments on a public dataset verify that our ProFairRec approach can effectively improve the provider fairness of many existing methods and meanwhile maintain their recommendation accuracy.
Deep Conditional Representation Learning for Drum Sample Retrieval by Vocalisation
Authors: Alejandro Delgado, Charalampos Saitis, Emmanouil Benetos, Mark Sandler
Date: 2022-04-10 00:00:00
Tasks: Representation Learning, Retrieval

Imitating musical instruments with the human voice is an efficient way of communicating ideas between music producers, from sketching melody lines to clarifying desired sonorities. For this reason, there is an increasing interest in building applications that allow artists to efficiently pick target samples from big sound libraries just by imitating them vocally. In this study, we investigated the potential of conditional autoencoder models to learn informative features for Drum Sample Retrieval by Vocalisation (DSRV). We assessed the usefulness of their embeddings using four evaluation metrics, two of them relative to their acoustic properties and two of them relative to their perceptual properties via human listeners' similarity ratings. Results suggest that models conditioned on both sound-type labels (drum vs imitation) and drum-type labels (kick vs snare vs closed hi-hat vs opened hi-hat) learn the most informative embeddings for DSRV. We finally looked into individual differences in vocal imitation style via the Mantel test and found salient differences among participants, highlighting the importance of user information when designing DSRV systems.
Deep Embeddings for Robust User-Based Amateur Vocal Percussion Classification
Authors: Alejandro Delgado, Emir Demirel, Vinod Subramanian, Charalampos Saitis, Mark Sandler
Date: 2022-04-10 00:00:00
Tasks: Classification, feature selection, speech-recognition, Speech Recognition

Vocal Percussion Transcription (VPT) is concerned with the automatic detection and classification of vocal percussion sound events, allowing music creators and producers to sketch drum lines on the fly. Classifier algorithms in VPT systems learn best from small user-specific datasets, which usually restrict modelling to small input feature sets to avoid data overfitting. This study explores several deep supervised learning strategies to obtain informative feature sets for amateur vocal percussion classification. We evaluated the performance of these sets on regular vocal percussion classification tasks and compared them with several baseline approaches including feature selection methods and a speech recognition engine. These proposed learning models were supervised with several label sets containing information from four different levels of abstraction: instrument-level, syllable-level, phoneme-level, and boxeme-level. Results suggest that convolutional neural networks supervised with syllable-level annotations produced the most informative embeddings for classification, which can be used as input representations to fit classifiers with. Finally, we used back-propagation-based saliency maps to investigate the importance of different spectrogram regions for feature learning.
CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data
Authors: Rui Feng, Chen Luo, Qingyu Yin, Bing Yin, Tuo Zhao, Chao Zhang
Date: 2022-04-08 00:00:00
Proceeding: NAACL 2022 7
Tasks: Entity Linking, Self-Supervised Learning, Session Search

User sessions empower many search and recommendation tasks on a daily basis. Such session data are semi-structured, which encode heterogeneous relations between queries and products, and each item is described by the unstructured text. Despite recent advances in self-supervised learning for text or graphs, there lack of self-supervised learning models that can effectively capture both intra-item semantics and inter-item interactions for semi-structured sessions. To fill this gap, we propose CERES, a graph-based transformer model for semi-structured session data. CERES learns representations that capture both inter- and intra-item semantics with (1) a graph-conditioned masked language pretraining task that jointly learns from item text and item-item relations; and (2) a graph-conditioned transformer architecture that propagates inter-item contexts to item-level representations. We pretrained CERES using ~468 million Amazon sessions and find that CERES outperforms strong pretraining baselines by up to 9% in three session search and entity linking tasks.
On the Adaptation to Concept Drift for CTR Prediction
Authors: Congcong Liu, Yuejiang Li, Fei Teng, Xiwei Zhao, Changping Peng, Zhangang Lin, Jinghe Hu, Jingping Shao
Date: 2022-04-01 00:00:00
Tasks: Click-Through Rate Prediction, Incremental Learning, Mixture-of-Experts, Prediction, Recommendation Systems

Click-through rate (CTR) prediction is a crucial task in web search, recommender systems, and online advertisement displaying. In practical application, CTR models often serve with high-speed user-generated data streams, whose underlying distribution rapidly changing over time. The concept drift problem inevitably exists in those streaming data, which can lead to performance degradation due to the timeliness issue. To ensure model freshness, incremental learning has been widely adopted in real-world production systems. However, it is hard for the incremental update to achieve the balance of the CTR models between the adaptability to capture the fast-changing trends and generalization ability to retain common knowledge. In this paper, we propose adaptive mixture of experts (AdaMoE), a new framework to alleviate the concept drift problem by statistical weighting policy in the data stream of CTR prediction. The extensive offline experiments on both benchmark and a real-world industrial dataset, as well as an online A/B testing show that our AdaMoE significantly outperforms all incremental learning frameworks considered.
IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification
Authors: Likith Reddy, Vivek Talwar, Shanmukh Alle, Raju. S. Bapi, U. Deva Priyakumar
Date: 2022-04-06 00:00:00
Tasks: ECG Classification, Rhythm

Early detection of cardiovascular diseases is crucial for effective treatment and an electrocardiogram (ECG) is pivotal for diagnosis. The accuracy of Deep Learning based methods for ECG signal classification has progressed in recent years to reach cardiologist-level performance. In clinical settings, a cardiologist makes a diagnosis based on the standard 12-channel ECG recording. Automatic analysis of ECG recordings from a multiple-channel perspective has not been given enough attention, so it is essential to analyze an ECG recording from a multiple-channel perspective. We propose a model that leverages the multiple-channel information available in the standard 12-channel ECG recordings and learns patterns at the beat, rhythm, and channel level. The experimental results show that our model achieved a macro-averaged ROC-AUC score of 0.9216, mean accuracy of 88.85\%, and a maximum F1 score of 0.8057 on the PTB-XL dataset. The attention visualization results from the interpretable model are compared against the cardiologist's guidelines to validate the correctness and usability.
Partially Linear Models under Data Combination
Authors: Xavier D'Haultfœuille, Christophe Gaillac, Arnaud Maurel
Date: 2022-04-11 00:00:00

We study partially linear models when the outcome of interest and some of the covariates are observed in two different datasets that cannot be linked. This type of data combination problem arises very frequently in empirical microeconomics. Using recent tools from optimal transport theory, we derive a constructive characterization of the sharp identified set. We then build on this result and develop a novel inference method that exploits the specific geometric properties of the identified set. Our method exhibits good performances in finite samples, while remaining very tractable. We apply our approach to study intergenerational income mobility over the period 1850-1930 in the United States. Our method allows us to relax the exclusion restrictions used in earlier work, while delivering confidence regions that are informative.
Local Knowledge and Natural Resource Management in a Peasant Farming Community Facing Rapid Change: A Critical Examination
Authors: Jules R. Siedenburg
Date: 2022-04-09 00:00:00
Tasks: Management

Environmental degradation is a major global problem. Its impacts are not just environmental, but also economic, with degradation recognised as a key cause of reduced agricultural productivity and rural poverty in the developing world. The degradation literature typically emphasises common property or open access natural resources, and how perverse incentives or missing institutions lead optimising private actors to degrade them. By contrast, the present paper considers degradation occurring on private farms in peasant communities. This is a critical yet delicate issue, given the poverty of such areas and questions about the role of farmers in either degrading or regenerating rural lands. The paper examines natural resource management by peasant farmers in Tanzania. Its key concern is how the local knowledge informing their management decisions adapts to challenges associated with environmental degradation and market liberalisation. Given their poverty, this question could have direct implications for the capacity of households to meet their livelihood needs. Based on fresh empirical data, the paper finds that differential farmer knowledge helps explain the large differences in how households respond to the degradation challenge. The implication is that some farmers adapt more effectively to emerging challenges than others, despite all being rational, optimising agents who follow the strategies they deem best. The paper thus provides a critique of local knowledge, implying that some farmers experience adaptation slippages while others race ahead with effective adaptations. The paper speaks to the chronic poverty that plagues many rural communities in the developing world. It helps explain the failure of proven sustainable agriculture technologies to disseminate readily beyond early innovators. Its key policy implication is to inform improved capacity building for such communities.
FedSim: Similarity guided model aggregation for Federated Learning
Authors: Chamath Palihawadana, Nirmalie Wiratunga, Anjana Wijekoon, Harsha Kalutarage
Date: 2021-11-02 00:00:00
Proceeding: Neurocomputing 2021 11
Tasks: Federated Learning, model, Privacy Preserving

Federated Learning (FL) is a distributed machine learning approach in which clients contribute to learning a global model in a privacy preserved manner. Effective aggregation of client models is essential to create a generalised global model. To what extent a client is generalisable and contributing to this aggregation can be ascertained by analysing inter-client relationships. We use similarity between clients to model such relationships. We explore how similarity knowledge can be inferred from comparing client gradients, instead of inferring similarity on the basis of client data which violates the privacy-preserving constraint in FL. The similarity-guided FedSim algorithm, introduced in this paper, decomposes FL aggregation into local and global steps. Clients with similar gradients are clustered to provide local aggregations, which thereafter can be globally aggregated to ensure better coverage whilst reducing variance. Our comparative study also investigates the applicability of FedSim in both real-world datasets and on synthetic datasets where statistical heterogeneity can be controlled and studied systematically. A comparative study of FedSim with state-of-the-art FL baselines, FedAvg and FedProx, clearly shows significant performance gains. Our findings confirm that by exploiting latent inter-client similarities, FedSim’s performance is significantly better and more stable compared to both these baselines.
Clood CBR: towards microservices oriented case-based reasoning
Authors: Ikechukwu Nkisi-Orji, Nirmalie Wiratunga, Chamath Palihawadana, Juan A. Recio-García, David Corsar
Date: 2020-10-03 00:00:00
Proceeding: International Conference on Case-Based Reasoning 2020 10
Tasks: Cloud Computing, Music Generation

CBR applications have been deployed in a wide range of sectors, from pharmaceuticals; to defence and aerospace to IoT and transportation, to poetry and music generation; for example. However, a majority of these have been built using monolithic architectures which impose size and complexity constraints. As such these applications have a barrier to adopting new technologies and remain prohibitively expensive in both time and cost because changes in frameworks or languages affect the application directly. To address this challenge, we introduce a distributed and highly scalable generic CBR system, CLOOD, which is based on a microservices architecture. This splits the application into a set of smaller, interconnected services that scale to meet varying demands. Experimental results show that our CLOOD implementation retrieves cases at a fairly consistent rate as the casebase grows by several orders of magnitude and was over 3,700 times faster than a comparable monolithic CBR system when retrieving from half a million cases. Microservices are cloud-native architectures and with the rapid increase in cloud-computing adoption, it is timely for the CBR community to have access to such a framework.
The PartialSpoof Database and Countermeasures for the Detection of Short Fake Speech Segments Embedded in an Utterance
Authors: Lin Zhang, Xin Wang, Erica Cooper, Nicholas Evans, Junichi Yamagishi
Date: 2022-04-11 00:00:00
Tasks: Speaker Verification, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis, Voice Conversion

Automatic speaker verification is susceptible to various manipulations and spoofing, such as text-to-speech synthesis, voice conversion, replay, tampering, adversarial attacks, and so on. We consider a new spoofing scenario called "Partial Spoof" (PS) in which synthesized or transformed speech segments are embedded into a bona fide utterance. While existing countermeasures (CMs) can detect fully spoofed utterances, there is a need for their adaptation or extension to the PS scenario. We propose various improvements to construct a significantly more accurate CM that can detect and locate short-generated spoofed speech segments at finer temporal resolutions. First, we introduce newly developed self-supervised pre-trained models as enhanced feature extractors. Second, we extend our PartialSpoof database by adding segment labels for various temporal resolutions. Since the short spoofed speech segments to be embedded by attackers are of variable length, six different temporal resolutions are considered, ranging from as short as 20 ms to as large as 640 ms. Third, we propose a new CM that enables the simultaneous use of the segment-level labels at different temporal resolutions as well as utterance-level labels to execute utterance- and segment-level detection at the same time. We also show that the proposed CM is capable of detecting spoofing at the utterance level with low error rates in the PS scenario as well as in a related logical access (LA) scenario. The equal error rates of utterance-level detection on the PartialSpoof database and ASVspoof 2019 LA database were 0.77 and 0.90%, respectively.
A Study of Using Cepstrogram for Countermeasure Against Replay Attacks
Authors: Shih-kuang Lee, Yu Tsao, Hsin-Min Wang
Date: 2022-04-09 00:00:00

This study investigated the cepstrogram properties and demonstrated their effectiveness as powerful countermeasures against replay attacks. A cepstrum analysis of replay attacks suggests that crucial information for anti-spoofing against replay attacks may be retained in the cepstrogram. When building countermeasures against replay attacks, experiments on the ASVspoof 2019 physical access database demonstrate that the cepstrogram is more effective than other features in both single and fusion systems. Our LCNN-based single and fusion systems with the cepstrogram feature outperformed the corresponding LCNN-based systems without the cepstrogram feature and several state-of-the-art single and fusion systems in the literature.
QuiKo: A Quantum Beat Generation Application
Authors: Scott Oshiro
Date: 2022-04-09 00:00:00
Tasks: Music Generation, Quantum Machine Learning

In this chapter a quantum music generation application called QuiKo will be discussed. It combines existing quantum algorithms with data encoding methods from quantum machine learning to build drum and audio sample patterns from a database of audio tracks. QuiKo leverages the physical properties and characteristics of quantum computers to generate what can be referred to as Soft Rules proposed by Alexis Kirke. These rules take advantage of the noise produced by quantum devices to develop flexible rules and grammars for quantum music generation. These properties include qubit decoherence and phase kickback due controlled quantum gates within the quantum circuit. QuiKo builds upon the concept of soft rules in quantum music generation and takes it a step further. It attempts to mimic and react to an external musical inputs, similar to the way that human musicians play and compose with one another. Audio signals are used as inputs into the system. Feature extraction is then performed on the signal to identify the harmonic and percussive elements. This information is then encoded onto the quantum circuit. Measurements of the quantum circuit are then taken providing results in the form of probability distributions for external music applications to use to build the new drum patterns.
Learning-based Bounded Synthesis for Semi-MDPs with LTL Specifications
Authors: Ryohei Oura, Toshimitsu Ushio
Date: 2022-04-09 00:00:00
Tasks: Bayesian Inference

This letter proposes a learning-based bounded synthesis for a semi-Markov decision process (SMDP) with a linear temporal logic (LTL) specification. In the product of the SMDP and the deterministic $K$-co-B\"uchi automaton (d$K$cBA) converted from the LTL specification, we learn both the winning region of satisfying the LTL specification and the dynamics therein based on reinforcement learning and Bayesian inference. Then, we synthesize an optimal policy satisfying the following two conditions. (1) It maximizes the probability of reaching the wining region. (2) It minimizes a long-term risk for the dwell time within the winning region. The minimization of the long-term risk is done based on the estimated dynamics and a value iteration. We show that, if the discount factor is sufficiently close to one, the synthesized policy converges to the optimal policy as the number of the data obtained by the exploration goes to the infinity.
Risk-Bounded Temporal Logic Control of Continuous-Time Stochastic Systems
Authors: Sleiman Safaoui, Lars Lindemann, Iman Shames, Tyler H. Summers
Date: 2022-04-08 00:00:00

Motivated by the recent interest in risk-aware control, we study a continuous-time control synthesis problem to bound the risk that a stochastic linear system violates a given specification. We use risk signal temporal logic as a specification formalism in which distributionally robust risk predicates are considered and equipped with the usual Boolean and temporal operators. Our control approach relies on reformulating these risk predicates as deterministic predicates over mean and covariance states of the system. We then obtain a timed sequence of sets of mean and covariance states from the timed automata representation of the specification. To avoid an explosion in the number of automata states, we propose heuristics to find candidate sequences effectively. To execute and check dynamic feasibility of these sequences, we present a sampled-data control technique based on time discretization and constraint tightening that allows to perform timed transitions while satisfying the continuous-time constraints.