pwc-archive/papers-with-abstracts huggingface dataset:
Read it Twice: Towards Faithfully Interpretable Fact Verification by Revisiting Evidence
Authors: Xuming Hu, Zhaochen Hong, Zhijiang Guo, Lijie Wen, Philip S. Yu
Date: 2023-05-02 00:00:00
Tasks: Claim Verification, Decision Making, Fact Verification

Real-world fact verification task aims to verify the factuality of a claim by retrieving evidence from the source document. The quality of the retrieved evidence plays an important role in claim verification. Ideally, the retrieved evidence should be faithful (reflecting the model's decision-making process in claim verification) and plausible (convincing to humans), and can improve the accuracy of verification task. Although existing approaches leverage the similarity measure of semantic or surface form between claims and documents to retrieve evidence, they all rely on certain heuristics that prevent them from satisfying all three requirements. In light of this, we propose a fact verification model named ReRead to retrieve evidence and verify claim that: (1) Train the evidence retriever to obtain interpretable evidence (i.e., faithfulness and plausibility criteria); (2) Train the claim verifier to revisit the evidence retrieved by the optimized evidence retriever to improve the accuracy. The proposed system is able to achieve significant improvements upon best-reported models under different settings.
Sensitive Data Detection with High-Throughput Machine Learning Models in Electrical Health Records
Authors: Kai Zhang, Xiaoqian Jiang
Date: 2023-04-30 00:00:00
Tasks: De-identification

In the era of big data, there is an increasing need for healthcare providers, communities, and researchers to share data and collaborate to improve health outcomes, generate valuable insights, and advance research. The Health Insurance Portability and Accountability Act of 1996 (HIPAA) is a federal law designed to protect sensitive health information by defining regulations for protected health information (PHI). However, it does not provide efficient tools for detecting or removing PHI before data sharing. One of the challenges in this area of research is the heterogeneous nature of PHI fields in data across different parties. This variability makes rule-based sensitive variable identification systems that work on one database fail on another. To address this issue, our paper explores the use of machine learning algorithms to identify sensitive variables in structured data, thus facilitating the de-identification process. We made a key observation that the distributions of metadata of PHI fields and non-PHI fields are very different. Based on this novel finding, we engineered over 30 features from the metadata of the original features and used machine learning to build classification models to automatically identify PHI fields in structured Electronic Health Record (EHR) data. We trained the model on a variety of large EHR databases from different data sources and found that our algorithm achieves 99% accuracy when detecting PHI-related fields for unseen datasets. The implications of our study are significant and can benefit industries that handle sensitive data.
Initial Steps Towards Tackling High-dimensional Surrogate Modeling for Neuroevolution Using Kriging Partial Least Squares
Authors: Fergal Stapleton, Edgar Galván
Date: 2023-05-05 00:00:00
Tasks: Evolutionary Algorithms

Surrogate-assisted evolutionary algorithms (SAEAs) aim to use efficient computational models with the goal of approximating the fitness function in evolutionary computation systems. This area of research has been active for over two decades and has received significant attention from the specialised research community in different areas, for example, single and many objective optimisation or dynamic and stationary optimisation problems. An emergent and exciting area that has received little attention from the SAEAs community is in neuroevolution. This refers to the use of evolutionary algorithms in the automatic configuration of artificial neural network (ANN) architectures, hyper-parameters and/or the training of ANNs. However, ANNs suffer from two major issues: (a) the use of highly-intense computational power for their correct training, and (b) the highly specialised human expertise required to correctly configure ANNs necessary to get a well-performing network. This work aims to fill this important research gap in SAEAs in neuroevolution by addressing these two issues. We demonstrate how one can use a Kriging Partial Least Squares method that allows efficient computation of good approximate surrogate models compared to the well-known Kriging method, which normally cannot be used in neuroevolution due to the high dimensionality of the data.
Reducing Idleness in Financial Cloud Services via Multi-objective Evolutionary Reinforcement Learning based Load Balancer
Authors: Peng Yang, Laoming Zhang, Haifeng Liu, Guiying Li
Date: 2023-05-05 00:00:00

In recent years, various companies have started to shift their data services from traditional data centers to the cloud. One of the major motivations is to save on operational costs with the aid of cloud elasticity. This paper discusses an emerging need from financial services to reduce the incidence of idle servers retaining very few user connections, without disconnecting them from the server side. This paper considers this need as a bi-objective online load balancing problem. A neural network based scalable policy is designed to route user requests to varied numbers of servers for the required elasticity. An evolutionary multi-objective training framework is proposed to optimize the weights of the policy. Not only is the new objective of idleness is reduced by over 130% more than traditional industrial solutions, but the original load balancing objective itself is also slightly improved. Extensive simulations with both synthetic and real-world data help reveal the detailed applicability of the proposed method to the emergent problem of reducing idleness in financial services.
Query Expansion by Prompting Large Language Models
Authors: Rolf Jagerman, Honglei Zhuang, Zhen Qin, Xuanhui Wang, Michael Bendersky
Date: 2023-05-05 00:00:00

Query expansion is a widely used technique to improve the recall of search systems. In this paper, we propose an approach to query expansion that leverages the generative abilities of Large Language Models (LLMs). Unlike traditional query expansion approaches such as Pseudo-Relevance Feedback (PRF) that relies on retrieving a good set of pseudo-relevant documents to expand queries, we rely on the generative and creative abilities of an LLM and leverage the knowledge inherent in the model. We study a variety of different prompts, including zero-shot, few-shot and Chain-of-Thought (CoT). We find that CoT prompts are especially useful for query expansion as these prompts instruct the model to break queries down step-by-step and can provide a large number of terms related to the original query. Experimental results on MS-MARCO and BEIR demonstrate that query expansions generated by LLMs can be more powerful than traditional query expansion methods.
Retraining A Graph-based Recommender with Interests Disentanglement
Authors: Yitong Ji, Aixin Sun, Jie Zhang
Date: 2023-05-05 00:00:00
Tasks: Disentanglement, Incremental Learning, Marketing, Recommendation Systems

In a practical recommender system, new interactions are continuously observed. Some interactions are expected, because they largely follow users' long-term preferences. Some other interactions are indications of recent trends in user preference changes or marketing positions of new items. Accordingly, the recommender needs to be periodically retrained or updated to capture the new trends, and yet not to forget the long-term preferences. In this paper, we propose a novel and generic retraining framework called Disentangled Incremental Learning (DIL) for graph-based recommenders. We assume that long-term preferences are well captured in the existing model, in the form of model parameters learned from past interactions. New preferences can be learned from the user-item bipartite graph constructed using the newly observed interactions. In DIL, we design an Information Extraction Module to extract historical preferences from the existing model. Then we blend the historical and new preferences in the form of node embeddings in the new graph, through a Disentanglement Module. The essence of the disentanglement module is to decorrelate the historical and new preferences so that both can be well captured, via carefully designed losses. Through experiments on three benchmark datasets, we show the effectiveness of DIL in capturing dynamics of useritem interactions. We also demonstrate the robustness of DIL by attaching it to two base models - LightGCN and NGCF.
Augmenting Low-Resource Text Classification with Graph-Grounded Pre-training and Prompting
Authors: Zhihao Wen, Yuan Fang
Date: 2023-05-05 00:00:00
Tasks: Articles, Classification, Information Retrieval, Retrieval, text-classification, Text Classification

Text classification is a fundamental problem in information retrieval with many real-world applications, such as predicting the topics of online articles and the categories of e-commerce product descriptions. However, low-resource text classification, with few or no labeled samples, poses a serious concern for supervised learning. Meanwhile, many text data are inherently grounded on a network structure, such as a hyperlink/citation network for online articles, and a user-item purchase network for e-commerce products. These graph structures capture rich semantic relationships, which can potentially augment low-resource text classification. In this paper, we propose a novel model called Graph-Grounded Pre-training and Prompting (G2P2) to address low-resource text classification in a two-pronged approach. During pre-training, we propose three graph interaction-based contrastive strategies to jointly pre-train a graph-text model; during downstream classification, we explore prompting for the jointly pre-trained model to achieve low-resource classification. Extensive experiments on four real-world datasets demonstrate the strength of G2P2 in zero- and few-shot low-resource text classification tasks.
Optimizing SMS Reminder Campaigns for Pre- and Post-Diagnosis Cancer Check-Ups using Socio-Demographics: An In-Silco Investigation Into Bladder Cancer
Authors: Elizaveta Savchenko, Ariel Rosenfeld, Svetlana Bunimovich-Mendrazitsky
Date: 2023-05-04 00:00:00

Timely pre- and post-diagnosis check-ups are critical for cancer patients, across all cancer types, as these often lead to better outcomes. Several socio-demographic properties have been identified as strongly connected with both cancer's clinical dynamics and (indirectly) with different individual check-up behaviors. Unfortunately, existing check-up policies typically consider only the former association explicitly. In this work, we propose a novel framework, accompanied by a high-resolution computer simulation, to investigate and optimize socio-demographic-based SMS reminder campaigns for cancer check-ups. We instantiate our framework and simulation for the case of bladder cancer, the 10th most prevalent cancer today, using extensive real-world data. Our results indicate that optimizing an SMS reminder campaign based solely on simple socio-demographic features can bring about a statistically significant reduction in mortality rate compared to alternative campaigns by up to 5.8%.
FNR: a similarity and transformer-based approach to detect multi-modal fake news in social media
Authors: Faeze Ghorbanpour, Maryam Ramezani, Mohammad Amin Fazli, Hamid R. Rabiee
Date: 2023-03-28 00:00:00
Proceeding: Social Network Analysis and Mining 2023 3
Tasks: Fake News Detection

Many people today get their news from social media. It is possible to propagate news using textual, visual, or multi-modal information. The popularity of social networks and their wide use by people make them attractive platforms for spreading fake news. Detecting fake news is essential to preventing its spread. Fake news can be a false article or a genuine article with misleading visual information. Adding an actual image to trustworthy unrelated news can also create a fake news story. In this paper, we propose a novel and efficient similarity and transformer-based detection algorithm called Fake News Revealer (FNR), which uses text and images of news to detect fake news. The algorithm uses contrastive loss to consider text and image relations and transformer models to extract contextual and semantic features. According to experiments on two public social media news data sets, the FNR algorithm competes with conventional methods and state-of-the-art fake news detection algorithms by adding a novel mechanism without adding extra parameters or weights.
Location-Aware Web Service QoS Prediction via Deep Collaborative Filtering
Authors: Zhaohong Jia，Li Jin，Yiwen Zhang，Chuang Liu，Kai Li，Yun Yang
Date: 2022-11-04 00:00:00
Proceeding: journal 2022 11
Tasks: Collaborative Filtering, Prediction

Abstract—Nowadays, there is a large number of web ser-vices with similar functions, from which users choose the best
according to the quality of service (QoS). Hence, QoS prediction is a primary challenge in service recommendation. Most existing approaches model the user-service interaction relationship.However, the low-dimensional linear and the high-dimensional nonlinear relationships between users and services are seldom considered simultaneously. In addition, although location information, including the local location information of users and services, is incorporated to overcome data sparsity in most approaches. The global location information is seldom considered.Aiming at the above shortcomings, we propose a new QoS prediction model that fuses local and global location information of users and services in the interaction layer of the model. The proposed model uses a multilayer perceptron (MLP) to acquire high-dimensional nonlinear relationships of users and services,where the dot product is employed in complementing the learning of low-dimensional linear relationships. Experimental results on the real world dataset WS-Dream validate the prediction performance of the proposed model.
A survey on deep packet inspection for intrusion detection systems
Authors: Tamer Abuhmed, Abedelaziz Mohaisen, DaeHun Nyang
Date: 2008-03-01 00:00:00
Tasks: Intrusion Detection, Survey

Deep packet inspection is widely recognized as a powerful way which is used for intrusion detection systems for inspecting, deterring and deflecting malicious at- tacks over the network. Fundamentally, almost intru- sion detection systems have the ability to search through packets and identify contents that match with known attacks. In this paper, we survey the deep packet inspec- tion implementations techniques, research challenges and algorithms. Finally, we provide a comparison be- tween the different applied systems.
Key words: Deep packet inspection, intrusion detection system, network security, algorithms.
Mci-net: multi-scale context integrated network for liver ct image segmentation
Authors: Xiwang Xie, Xipeng Pan, Feng Shao, Weidong Zhang, Jubai An
Date: 2023-05-03 00:00:00
Proceeding: Computers and Electrical Engineering 2023 5
Tasks: 2D Semantic Segmentation, Image Segmentation, Liver Segmentation, Medical Image Segmentation, Semantic Segmentation

Owing to the various object scales and high similarity with the surrounding organs (e.g., kidney, stomach, and spleen), it is difficult to accurately segment the liver region from the abdominal computed tomography images. In this study, we propose a multi-scale context integration network called MCI-Net for liver image segmentation. Specifically, we first design a simplified residual module to prevent network degradation. Given the scale variability of objects, we propose a multi-scale context extraction module by combining four cascaded branches of hybrid dilated convolutions to capture broader and deeper features. In addition, we introduce an external attention mechanism based on two external, learnable and shared memory units, which helps to perceive the most discriminative information and suppress redundant features. Finally, we provide a boundary correction block to further improve the localization ability of boundary information. Extensive experiments on two liver CT image benchmark datasets qualitatively and quantitatively illustrate that our method is effective in improving liver segmentation accuracy and outperforms several state-of-the-art methods.
Context-triggered Abstraction-based Control Design
Authors: Satya Prakash Nayak, Lucas Neves Egidio, Matteo Della Rossa, Anne-Kathrin Schmuck, Raphaël Jungers
Date: 2023-05-05 00:00:00

We consider the problem of automatically synthesizing a hybrid controller for non-linear dynamical systems which ensures that the closed-loop fulfills an arbitrary \emph{Linear Temporal Logic} specification. Moreover, the specification may take into account logical context switches induced by an external environment or the system itself. Finally, we want to avoid classical brute-force time- and space-discretization for scalability. We achieve these goals by a novel two-layer strategy synthesis approach, where the controller generated in the lower layer provides invariant sets and basins of attraction, which are exploited at the upper logical layer in an abstract way. In order to achieve this, we provide new techniques for both the upper- and lower-level synthesis. Our new methodology allows to leverage both the computing power of state space control techniques and the intelligence of finite game solving for complex specifications, in a scalable way.
A CSI Dataset for Wireless Human Sensing on 80 MHz Wi-Fi Channels
Authors: Francesca Meneghello, Nicolò Dal Fabbro, Domenico Garlisi, Ilenia Tinnirello, Michele Rossi
Date: 2023-04-29 00:00:00
Tasks: Blocking, Diversity

In the last years, several machine learning-based techniques have been proposed to monitor human movements from Wi-Fi channel readings. However, the development of domain-adaptive algorithms that robustly work across different environments is still an open problem, whose solution requires large datasets characterized by strong domain diversity, in terms of environments, persons and Wi-Fi hardware. To date, the few public datasets available are mostly obsolete - as obtained via Wi-Fi devices operating on 20 or 40 MHz bands - and contain little or no domain diversity, thus dramatically limiting the advancements in the design of sensing algorithms. The present contribution aims to fill this gap by providing a dataset of IEEE 802.11ac channel measurements over an 80 MHz bandwidth channel featuring notable domain diversity, through measurement campaigns that involved thirteen subjects across different environments, days, and with different hardware. Novel experimental data is provided by blocking the direct path between the transmitter and the monitor, and collecting measurements in a semi-anechoic chamber (no multi-path fading). Overall, the dataset - available on IEEE DataPort [1] - contains more than thirteen hours of channel state information readings (23.6 GB), allowing researchers to test activity/identity recognition and people counting algorithms.
Blind identification of Ambisonic reduced room impulse response
Authors: Srđan Kitić, Jérôme Daniel
Date: 2023-05-05 00:00:00
Tasks: Room Impulse Response (RIR), Time Series

Recently proposed Generalized Time-domain Velocity Vector (GTVV) is a generalization of relative room impulse response in spherical harmonic (aka Ambisonic) domain that allows for blind estimation of early-echo parameters: the directions and relative delays of individual reflections. However, the derived closed-form expression of GTVV mandates few assumptions to hold, most important being that the impulse response of the reference signal needs to be a minimum-phase filter. In practice, the reference is obtained by spatial filtering towards the Direction-of-Arrival of the source, and the aforementioned condition is bounded by the performance of the applied beamformer (and thus, by the Ambisonic array order). In the present work, we suggest to circumvent this problem by directly modeling the impulse responses constituting the GTVV time series, which permits not only to relax the initial assumptions, but also to extract the information therein in a more consistent and efficient manner, entering the realm of blind system identification. Experiments using measured room impulse responses confirm the effectiveness of the proposed approach.
Time-weighted Frequency Domain Audio Representation with GMM Estimator for Anomalous Sound Detection
Authors: Jian Guan, Youde Liu, Qiaoxi Zhu, Tieran Zheng, Jiqing Han, Wenwu Wang
Date: 2023-05-05 00:00:00

Although deep learning is the mainstream method in unsupervised anomalous sound detection, Gaussian Mixture Model (GMM) with statistical audio frequency representation as input can achieve comparable results with much lower model complexity and fewer parameters. Existing statistical frequency representations, e.g, the log-Mel spectrogram's average or maximum over time, do not always work well for different machines. This paper presents Time-Weighted Frequency Domain Representation (TWFR) with the GMM method (TWFR-GMM) for anomalous sound detection. The TWFR is a generalized statistical frequency domain representation that can adapt to different machine types, using the global weighted ranking pooling over time-domain. This allows GMM estimator to recognize anomalies, even under domain-shift conditions, as visualized with a Mahalanobis distance-based metric. Experiments on DCASE 2022 Challenge Task2 dataset show that our method has better detection performance than recent deep learning methods. TWFR-GMM is the core of our submission that achieved the 3rd place in DCASE 2022 Challenge Task2.
Close-range Human Following Control on a Cane-type Robot with Multi-camera Fusion
Authors: Haowen Liu, Fengxian Wu, Bin Zhong, Yijun Zhao, Jiatong Zhang, Wenxin Niu, Mingming Zhang
Date: 2023-05-05 00:00:00
Tasks: Position

Cane-type robots have been utilized to assist and supervise the mobility-impaired population. One essential technique for cane-type robots is human following control, which allows the robot to follow the user. However, the limited perceptible information of humans by sensors at close range, combined with the occlusion caused by lower limb swing during normal walking, affect the localization of users. These limitations make it difficult to achieve human following at close range.To address these challenges, this study developed a new cane-type wheeled robot and proposed a novel human-following control with multi-camera fusion. This control system mainly consists of two parts: 1) a human following controller that locates a user by multi-camera fusion and generates control signals to follow the user. 2) a cane robot controller designed to steer the cane robot to a target position. The proposed strategy's effectiveness has been validated in outdoor experiments with six healthy subjects. The experimental scenarios included different terrains (i.e., straight, turning, and inclined paths), road conditions (i.e., flat and rough roads), and walking speeds. The obtained results showed that the average tracking error for position and orientation was less than 5 cm and 15{\deg} respectively across all scenarios. Moreover, the cane robot can effectively adapt to a wide range of individual gait patterns and achieve stable human following at daily walking speeds (0.75 m/s - 1.45 m/s).