pwc-archive/papers-with-abstracts huggingface dataset:
Evaluating Temporal Persistence Using Replicability Measures
Authors: Jüri Keller, Timo Breuer, Philipp Schaer
Date: 2023-08-21 00:00:00
Tasks: Information Retrieval, Retrieval

In real-world Information Retrieval (IR) experiments, the Evaluation Environment (EE) is exposed to constant change. Documents are added, removed, or updated, and the information need and the search behavior of users is evolving. Simultaneously, IR systems are expected to retain a consistent quality. The LongEval Lab seeks to investigate the longitudinal persistence of IR systems, and in this work, we describe our participation. We submitted runs of five advanced retrieval systems, namely a Reciprocal Rank Fusion (RRF) approach, ColBERT, monoT5, Doc2Query, and E5, to both sub-tasks. Further, we cast the longitudinal evaluation as a replicability study to better understand the temporal change observed. As a result, we quantify the persistence of the submitted runs and see great potential in this evaluation method.
Single-User Injection for Invisible Shilling Attack against Recommender Systems
Authors: Chengzhi Huang, Hui Li
Date: 2023-08-21 00:00:00
Tasks: Recommendation Systems

Recommendation systems (RS) are crucial for alleviating the information overload problem. Due to its pivotal role in guiding users to make decisions, unscrupulous parties are lured to launch attacks against RS to affect the decisions of normal users and gain illegal profits. Among various types of attacks, shilling attack is one of the most subsistent and profitable attacks. In shilling attack, an adversarial party injects a number of well-designed fake user profiles into the system to mislead RS so that the attack goal can be achieved. Although existing shilling attack methods have achieved promising results, they all adopt the attack paradigm of multi-user injection, where some fake user profiles are required. This paper provides the first study of shilling attack in an extremely limited scenario: only one fake user profile is injected into the victim RS to launch shilling attacks (i.e., single-user injection). We propose a novel single-user injection method SUI-Attack for invisible shilling attack. SUI-Attack is a graph based attack method that models shilling attack as a node generation task over the user-item bipartite graph of the victim RS, and it constructs the fake user profile by generating user features and edges that link the fake user to items. Extensive experiments demonstrate that SUI-Attack can achieve promising attack results in single-user injection. In addition to its attack power, SUI-Attack increases the stealthiness of shilling attack and reduces the risk of being detected. We provide our implementation at: https://github.com/KDEGroup/SUI-Attack.
Enhancing Transformers without Self-supervised Learning: A Loss Landscape Perspective in Sequential Recommendation
Authors: Vivian Lai, Huiyuan Chen, Chin-Chia Michael Yeh, Minghua Xu, Yiwei Cai, Hao Yang
Date: 2023-08-20 00:00:00
Tasks: Self-Supervised Learning, Sequential Recommendation

Transformer and its variants are a powerful class of architectures for sequential recommendation, owing to their ability of capturing a user's dynamic interests from their past interactions. Despite their success, Transformer-based models often require the optimization of a large number of parameters, making them difficult to train from sparse data in sequential recommendation. To address the problem of data sparsity, previous studies have utilized self-supervised learning to enhance Transformers, such as pre-training embeddings from item attributes or contrastive data augmentations. However, these approaches encounter several training issues, including initialization sensitivity, manual data augmentations, and large batch-size memory bottlenecks. In this work, we investigate Transformers from the perspective of loss geometry, aiming to enhance the models' data efficiency and generalization in sequential recommendation. We observe that Transformers (e.g., SASRec) can converge to extremely sharp local minima if not adequately regularized. Inspired by the recent Sharpness-Aware Minimization (SAM), we propose SAMRec, which significantly improves the accuracy and robustness of sequential recommendation. SAMRec performs comparably to state-of-the-art self-supervised Transformers, such as S$^3$Rec and CL4SRec, without the need for pre-training or strong data augmentations.
Offline Pseudo Relevance Feedback for Efficient and Effective Single-pass Dense Retrieval
Authors: Xueru Wen, Xiaoyang Chen, Xuanang Chen, Ben He, Le Sun
Date: 2023-08-20 00:00:00
Tasks: Information Retrieval, Retrieval

Dense retrieval has made significant advancements in information retrieval (IR) by achieving high levels of effectiveness while maintaining online efficiency during a single-pass retrieval process. However, the application of pseudo relevance feedback (PRF) to further enhance retrieval effectiveness results in a doubling of online latency. To address this challenge, this paper presents a single-pass dense retrieval framework that shifts the PRF process offline through the utilization of pre-generated pseudo-queries. As a result, online retrieval is reduced to a single matching with the pseudo-queries, hence providing faster online retrieval. The effectiveness of the proposed approach is evaluated on the standard TREC DL and HARD datasets, and the results demonstrate its promise. Our code is openly available at https://github.com/Rosenberg37/OPRF.
Voucher Abuse Detection with Prompt-based Fine-tuning on Graph Neural Networks
Authors: Zhihao Wen, Yuan Fang, Yihan Liu, Yang Guo, Shuji Hao
Date: 2023-08-19 00:00:00
Tasks: Abuse Detection, Anomaly Detection

Voucher abuse detection is an important anomaly detection problem in E-commerce. While many GNN-based solutions have emerged, the supervised paradigm depends on a large quantity of labeled data. A popular alternative is to adopt self-supervised pre-training using label-free data, and further fine-tune on a downstream task with limited labels. Nevertheless, the "pre-train, fine-tune" paradigm is often plagued by the objective gap between pre-training and downstream tasks. Hence, we propose VPGNN, a prompt-based fine-tuning framework on GNNs for voucher abuse detection. We design a novel graph prompting function to reformulate the downstream task into a similar template as the pretext task in pre-training, thereby narrowing the objective gap. Extensive experiments on both proprietary and public datasets demonstrate the strength of VPGNN in both few-shot and semi-supervised scenarios. Moreover, an online deployment of VPGNN in a production environment shows a 23.4% improvement over two existing deployed models.
Time-aligned Exposure-enhanced Model for Click-Through Rate Prediction
Authors: Hengyu Zhang, Chang Meng, Wei Guo, Huifeng Guo, Jieming Zhu, Guangpeng Zhao, Ruiming Tang, Xiu Li
Date: 2023-08-19 00:00:00
Tasks: Click-Through Rate Prediction, Prediction, Recommendation Systems

Click-Through Rate (CTR) prediction, crucial in applications like recommender systems and online advertising, involves ranking items based on the likelihood of user clicks. User behavior sequence modeling has marked progress in CTR prediction, which extracts users' latent interests from their historical behavior sequences to facilitate accurate CTR prediction. Recent research explores using implicit feedback sequences, like unclicked records, to extract diverse user interests. However, these methods encounter key challenges: 1) temporal misalignment due to disparate sequence time ranges and 2) the lack of fine-grained interaction among feedback sequences. To address these challenges, we propose a novel framework called TEM4CTR, which ensures temporal alignment among sequences while leveraging auxiliary feedback information to enhance click behavior at the item level through a representation projection mechanism. Moreover, this projection-based information transfer module can effectively alleviate the negative impact of irrelevant or even potentially detrimental components of the auxiliary feedback information on the learning process of click behavior. Comprehensive experiments on public and industrial datasets confirm the superiority and effectiveness of TEM4CTR, showcasing the significance of temporal alignment in multi-feedback modeling.
printf: Preference Modeling Based on User Reviews with Item Images and Textual Information via Graph Learning
Authors: Hao-Lun Lin, Jyun-Yu Jiang, Ming-Hao Juan, Pu-Jen Cheng
Date: 2023-08-19 00:00:00
Tasks: Graph Learning, Recommendation Systems

Nowadays, modern recommender systems usually leverage textual and visual contents as auxiliary information to predict user preference. For textual information, review texts are one of the most popular contents to model user behaviors. Nevertheless, reviews usually lose their shine when it comes to top-N recommender systems because those that solely utilize textual reviews as features struggle to adequately capture the interaction relationships between users and items. For visual one, it is usually modeled with naive convolutional networks and also hard to capture high-order relationships between users and items. Moreover, previous works did not collaboratively use both texts and images in a proper way. In this paper, we propose printf, preference modeling based on user reviews with item images and textual information via graph learning, to address the above challenges. Specifically, the dimension-based attention mechanism directs relations between user reviews and interacted items, allowing each dimension to contribute different importance weights to derive user representations. Extensive experiments are conducted on three publicly available datasets. The experimental results demonstrate that our proposed printf consistently outperforms baseline methods with the relative improvements for NDCG@5 of 26.80%, 48.65%, and 25.74% on Amazon-Grocery, Amazon-Tools, and Amazon-Electronics datasets, respectively. The in-depth analysis also indicates the dimensions of review representations definitely have different topics and aspects, assisting the validity of our model design.
CIRO: COVID-19 infection risk ontology
Authors: Shusaku Egami, Yasunori Yamamoto, Ikki Ohmukai, Takashi Okumura
Date: 2023-08-07 00:00:00
Tasks: Computational Efficiency

Public health authorities perform contact tracing for highly contagious agents to identify close contacts with the infected cases. However, during the pandemic caused by coronavirus disease 2019 (COVID-19), this operation was not employed in countries with high patient volumes. Meanwhile, the Japanese government conducted this operation, thereby contributing to the control of infections, at the cost of arduous manual labor by public health officials. To ease the burden of the officials, this study attempted to automate the assessment of each person's infection risk through an ontology, called COVID-19 Infection Risk Ontology (CIRO). This ontology expresses infection risks of COVID-19 formulated by the Japanese government, toward automated assessment of infection risks of individuals, using Resource Description Framework (RDF) and SPARQL (SPARQL Protocol and RDF Query Language) queries. For evaluation, we demonstrated that the knowledge graph built could infer the risks, formulated by the government. Moreover, we conducted reasoning experiments to analyze the computational efficiency. The experiments demonstrated usefulness of the knowledge processing, and identified issues left for deployment.
Integrated Sensing and Communications for 3D Object Imaging via Bilinear Inference
Authors: Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu, David González G., Osvaldo Gonsa
Date: 2023-08-21 00:00:00
Tasks: ISAC

We consider an uplink integrated sensing and communications (ISAC) scenario where the detection of data symbols from multiple user equipment (UEs) occurs simultaneously with a three-dimensional (3D) estimation of the environment, extracted from the scattering features present in the channel state information (CSI) and utilizing the same physical layer communications air interface, as opposed to radar technologies. By exploiting a discrete (voxelated) representation of the environment, two novel ISAC schemes are derived with purpose-built message passing (MP) rules for the joint estimation of data symbols and status (filled/empty) of the discretized environment. The first relies on a modular feedback structure in which the data symbols and the environment are estimated alternately, whereas the second leverages a bilinear inference framework to estimate both variables concurrently. Both contributed methods are shown via simulations to outperform the state-of-the-art (SotA) in accurately recovering the transmitted data as well as the 3D image of the environment. An analysis of the computational complexities of the proposed methods reveals distinct advantages of each scheme, namely, that the bilinear solution exhibits a superior robustness to short pilots and channel blockages, while the alternating solution offers lower complexity with large number of UEs and superior performance in ideal conditions.
RIS-assisted High-Speed Railway Integrated Sensing and Communication System
Authors: Panpan Li, Yong Niu, Hao Wu, Zhu Han, Guiqi Sun, Ning Wang, Zhangdui Zhong, Bo Ai
Date: 2023-08-19 00:00:00
Tasks: Integrated sensing and communication, ISAC

One technology that has the potential to improve wireless communications in years to come is integrated sensing and communication (ISAC). In this study, we take advantage of reconfigurable intelligent surface's (RIS) potential advantages to achieve ISAC while using the same frequency and resources. Specifically, by using the reflecting elements, the RIS dynamically modifies the radio waves' strength or phase in order to change the environment for radio transmission and increase the ISAC systems' transmission rate. We investigate a single cell downlink communication situation with RIS assistance. Combining the ISAC base station's (BS) beamforming with RIS's discrete phase shift optimization, while guaranteeing the sensing signal, The aim of optimizing the sum rate is specified. We take advantage of alternating maximization to find practical solutions with dividing the challenge into two minor issues. The first power allocation subproblem is non-convex that CVX solves by converting it to convex. A local search strategy is used to solve the second subproblem of phase shift optimization. According to the results of the simulation, using RIS with adjusted phase shifts can significantly enhance the ISAC system's performance.
TranSTYLer: Multimodal Behavioral Style Transfer for Facial and Body Gestures Generation
Authors: Mireille Fares, Catherine Pelachaud, Nicolas Obin
Date: 2023-08-08 00:00:00
Tasks: Disentanglement, Style Transfer

This paper addresses the challenge of transferring the behavior expressivity style of a virtual agent to another one while preserving behaviors shape as they carry communicative meaning. Behavior expressivity style is viewed here as the qualitative properties of behaviors. We propose TranSTYLer, a multimodal transformer based model that synthesizes the multimodal behaviors of a source speaker with the style of a target speaker. We assume that behavior expressivity style is encoded across various modalities of communication, including text, speech, body gestures, and facial expressions. The model employs a style and content disentanglement schema to ensure that the transferred style does not interfere with the meaning conveyed by the source behaviors. Our approach eliminates the need for style labels and allows the generalization to styles that have not been seen during the training phase. We train our model on the PATS corpus, which we extended to include dialog acts and 2D facial landmarks. Objective and subjective evaluations show that our model outperforms state of the art models in style transfer for both seen and unseen styles during training. To tackle the issues of style and content leakage that may arise, we propose a methodology to assess the degree to which behavior and gestures associated with the target style are successfully transferred, while ensuring the preservation of the ones related to the source content.
Enhancing In-Situ Structural Health Monitoring through RF Energy-Powered Sensor Nodes and Mobile Platform
Authors: Yu Luo, Lina Pu, Jun Wang, Isaac Howard
Date: 2023-08-20 00:00:00
Tasks: Structural Health Monitoring

This research contributes to long-term structural health monitoring (SHM) by exploring radio frequency energy-powered sensor nodes (RF-SNs) embedded in concrete. Unlike traditional in-situ monitoring systems relying on batteries or wire-connected power sources, the RF-SN captures radio energy from a mobile radio transmitter for sensing and communication. This offers a cost-effective solution for consistent in-situ perception. To optimize the system performance across various situations, we've explored both active and passive communication methods. For the active RF-SN, we implement a specialized control circuit enabling the node to transmit data through ZigBee protocol at low incident power. For the passive RF-SN, radio energy is not only for power but also as a carrier signal, with data conveyed by modulating the amplitude of the backscattered radio wave. To address the challenge of significant attenuation of the backscattering signal in concrete, we utilize a square chirp-based modulation scheme for passive communication. This scheme allows the receiver to successfully decode the data even under a negative signal-to-noise ratio (SNR) condition. The experimental results indicate that an active RF-SN embedded in concrete at a depth of 13.5 cm can be effectively powered by a 915MHz mobile radio transmitter with an effective isotropic radiated power (EIRP) of 32.5dBm. This setup allows the RF-SN to send over 1 kilobyte of data within 10 seconds, with an additional 1.7 kilobytes every 1.6 seconds of extra charging. For the passive RF-SN buried at the same depth, continuous data transmission at a rate of 224 bps with a 3% bit error rate (BER) is achieved when the EIRP of the transmitter is 23.6 dBm.
Inferring Power Grid Information with Power Line Communications: Review and Insights
Authors: Javier Hernandez Fernandez, Abdulah Jarouf, Aymen Omri, Roberto Di Pietro
Date: 2023-08-21 00:00:00
Tasks: Anomaly Detection

High-frequency signals were widely studied in the last decade to identify grid and channel conditions in power lines. PLMs operating on the grid's physical layer are capable of transmitting such signals to infer information about the medium. When applied to the electrical grid, one of the key advantages of PLC is its capacity to use signals to provide information about the grid itself. This makes PLC an ideal communication technology for smart grid applications, particularly in the realms of grid monitoring and surveillance. In this paper, we focus on PLC grid information inference and provide several contributions: a classification of PLC-based applications, a review of the relevant literature, and insights to further advance the field. Our research identified contributions addressing PLMs for three main grid information inference applications: topology inference, anomaly detection, and grid cybersecurity. We utilize the outcome of our review to shed light on the current limitations of the research contributions and suggest future research directions in this field.
What is the Impact of Releasing Code with Publications? Statistics from the Machine Learning, Robotics, and Control Communities
Authors: SiQi Zhou, Lukas Brunke, Allen Tao, Adam W. Hall, Federico Pizarro Bejarano, Jacopo Panerati, Angela P. Schoellig
Date: 2023-08-19 00:00:00

Open-sourcing research publications is a key enabler for the reproducibility of studies and the collective scientific progress of a research community. As all fields of science develop more advanced algorithms, we become more dependent on complex computational toolboxes -- sharing research ideas solely through equations and proofs is no longer sufficient to communicate scientific developments. Over the past years, several efforts have highlighted the importance and challenges of transparent and reproducible research; code sharing is one of the key necessities in such efforts. In this article, we study the impact of code release on scientific research and present statistics from three research communities: machine learning, robotics, and control. We found that, over a six-year period (2016-2021), the percentages of papers with code at major machine learning, robotics, and control conferences have at least doubled. Moreover, high-impact papers were generally supported by open-source codes. As an example, the top 1% of most cited papers at the Conference on Neural Information Processing Systems (NeurIPS) consistently included open-source codes. In addition, our analysis shows that popular code repositories generally come with high paper citations, which further highlights the coupling between code sharing and the impact of scientific research. While the trends are encouraging, we would like to continue to promote and increase our efforts toward transparent, reproducible research that accelerates innovation -- releasing code with our papers is a clear first step.
Multi-GradSpeech: Towards Diffusion-based Multi-Speaker Text-to-speech Using Consistent Diffusion Models
Authors: Heyang Xue, Shuai Guo, Pengcheng Zhu, Mengxiao Bi
Date: 2023-08-21 00:00:00
Tasks: text-to-speech, Text to Speech

Despite imperfect score-matching causing drift in training and sampling distributions of diffusion models, recent advances in diffusion-based acoustic models have revolutionized data-sufficient single-speaker Text-to-Speech (TTS) approaches, with Grad-TTS being a prime example. However, the sampling drift problem leads to these approaches struggling in multi-speaker scenarios in practice due to more complex target data distribution compared to single-speaker scenarios. In this paper, we present Multi-GradSpeech, a multi-speaker diffusion-based acoustic models which introduces the Consistent Diffusion Model (CDM) as a generative modeling approach. We enforce the consistency property of CDM during the training process to alleviate the sampling drift problem in the inference stage, resulting in significant improvements in multi-speaker TTS performance. Our experimental results corroborate that our proposed approach can improve the performance of different speakers involved in multi-speaker TTS compared to Grad-TTS, even outperforming the fine-tuning approach. Audio samples are available at https://welkinyang.github.io/multi-gradspeech/