pwc-archive/papers-with-abstracts huggingface dataset:
ChatGPT or Grammarly? Evaluating ChatGPT on Grammatical Error Correction Benchmark
Authors: Haoran Wu, Wenxuan Wang, Yuxuan Wan, Wenxiang Jiao, Michael Lyu
Date: 2023-03-15 00:00:00
Tasks: Grammatical Error Correction, Language Modeling, Language Modelling, Sentence

ChatGPT is a cutting-edge artificial intelligence language model developed by OpenAI, which has attracted a lot of attention due to its surprisingly strong ability in answering follow-up questions. In this report, we aim to evaluate ChatGPT on the Grammatical Error Correction(GEC) task, and compare it with commercial GEC product (e.g., Grammarly) and state-of-the-art models (e.g., GECToR). By testing on the CoNLL2014 benchmark dataset, we find that ChatGPT performs not as well as those baselines in terms of the automatic evaluation metrics (e.g., $F_{0.5}$ score), particularly on long sentences. We inspect the outputs and find that ChatGPT goes beyond one-by-one corrections. Specifically, it prefers to change the surface expression of certain phrases or sentence structure while maintaining grammatical correctness. Human evaluation quantitatively confirms this and suggests that ChatGPT produces less under-correction or mis-correction issues but more over-corrections. These results demonstrate that ChatGPT is severely under-estimated by the automatic evaluation metrics and could be a promising tool for GEC.
Discovering Hierarchical Process Models: an Approach Based on Events Clustering
Authors: Antonina K. Begicheva, Irina A. Lomazova, Roman A. Nesterov
Date: 2023-03-12 00:00:00
Tasks: Clustering

Process mining is a field of computer science that deals with discovery and analysis of process models based on automatically generated event logs. Currently, many companies use this technology for optimization and improving their processes. However, a discovered process model may be too detailed, sophisticated and difficult for experts to understand. In this paper, we consider the problem of discovering a hierarchical business process model from a low-level event log, i.e., the problem of automatic synthesis of more readable and understandable process models based on information stored in event logs of information systems. Discovery of better structured and more readable process models is intensively studied in the frame of process mining research from different perspectives. In this paper, we present an algorithm for discovering hierarchical process models represented as two-level workflow nets. The algorithm is based on predefined event ilustering so that the cluster defines a sub-process corresponding to a high-level transition at the top level of the net. Unlike existing solutions, our algorithm does not impose restrictions on the process control flow and allows for concurrency and iteration.
Enhanced Iterated local search for the technician routing and scheduling problem
Authors: Ala-Eddine Yahiaoui, Sohaib Afifi, Hamid Afifi
Date: 2023-03-12 00:00:00
Tasks: Scheduling

Most public facilities in the European countries, including France, Germany, and the UK, were built during the reconstruction projects between 1950 and 1980. Owing to the deteriorating state of such vital infrastructure has become relatively expensive in the recent decades. A significant part of the maintenance operation costs is spent on the technical staff. Therefore, the optimal use of the available workforce is essential to optimize the operation costs. This includes planning technical interventions, workload balancing, productivity improvement, etc. In this paper, we focus on the routing of technicians and scheduling of their tasks. We address for this purpose a variant of the workforce scheduling problem called the technician routing and scheduling problem (TRSP). This problem has applications in different fields, such as transportation infrastructure (rail and road networks), telecommunications, and sewage facilities. To solve the TRSP, we propose an enhanced iterated local search (eILS) approach. The enhancement of the ILS firstly includes an intensification procedure that incorporates a set of local search operators and removal-repair heuristics crafted for the TRSP. Next, four different mechanisms are used in the perturbation phase. Finally, an elite set of solutions is used to extensively explore the neighborhood of local optima as well as to enhance diversification during search space exploration. To measure the performance of the proposed method, experiments were conducted based on benchmark instances from the literature, and the results obtained were compared with those of an existing method. Our method achieved very good results, since it reached the best overall gap, which is three times lower than that of the literature. Furthermore, eILS improved the best-known solution for $34$ instances among a total of $56$ while maintaining reasonable computational times.
Scamming the Scammers: Using ChatGPT to Reply Mails for Wasting Time and Resources
Authors: Enrico Cambiaso, Luca Caviglione
Date: 2023-02-10 00:00:00

The use of Artificial Intelligence (AI) to support cybersecurity operations is now a consolidated practice, e.g., to detect malicious code or configure traffic filtering policies. The recent surge of AI, generative techniques and frameworks with efficient natural language processing capabilities dramatically magnifies the number of possible applications aimed at increasing the security of the Internet. Specifically, the ability of ChatGPT to produce textual contents while mimicking realistic human interactions can be used to mitigate the plague of emails containing scams. Therefore, this paper investigates the use of AI to engage scammers in automatized and pointless communications, with the goal of wasting both their time and resources. Preliminary results showcase that ChatGPT is able to decoy scammers, thus confirming that AI is an effective tool to counteract threats delivered via mail. In addition, we highlight the multitude of implications and open research questions to be addressed in the perspective of the ubiquitous adoption of AI.
Forecasting Workload in Cloud Computing: Towards Uncertainty-Aware Predictions and Transfer Learning
Authors: Andrea Rossi, Andrea Visentin, Diego Carraro, Steven Prestwich, Kenneth N. Brown
Date: 2023-02-24 00:00:00
Tasks: Cloud Computing, Decision Making, Time Series, Transfer Learning, Uncertainty Quantification

Predicting future resource demand in Cloud Computing is essential for optimizing the trade-off between serving customers' requests efficiently and minimizing the provisioning cost. Modelling prediction uncertainty is also desirable to better inform the resource decision-making process, but research in this field is under-investigated. In this paper, we propose univariate and bivariate Bayesian deep learning models that provide predictions of future workload demand and its uncertainty. We run extensive experiments on Google and Alibaba clusters, where we first train our models with datasets from different cloud providers and compare them with LSTM-based baselines. Results show that modelling the uncertainty of predictions has a positive impact on performance, especially on service level metrics, because uncertainty quantification can be tailored to desired target service levels that are critical in cloud applications. Moreover, we investigate whether our models benefit transfer learning capabilities across different domains, i.e. dataset distributions. Experiments on the same workload datasets reveal that acceptable transfer learning performance can be achieved within the same provider (because distributions are more similar). Also, domain knowledge does not transfer when the source and target domains are very different (e.g. from different providers), but this performance degradation can be mitigated by increasing the training set size of the source domain.
Dataset for predicting cybersickness from a virtual navigation task
Authors: Yuyang Wang, Ruichen Li, Jean-Rémy Chardonnet, Pan Hui
Date: 2023-02-07 00:00:00

This work presents a dataset collected to predict cybersickness in virtual reality environments. The data was collected from navigation tasks in a virtual environment designed to induce cybersickness. The dataset consists of many data points collected from diverse participants, including physiological responses (EDA and Heart Rate) and self-reported cybersickness symptoms. The paper will provide a detailed description of the dataset, including the arranged navigation task, the data collection procedures, and the data format. The dataset will serve as a valuable resource for researchers to develop and evaluate predictive models for cybersickness and will facilitate more research in cybersickness mitigation.
ProtoCon: Pseudo-label Refinement via Online Clustering and Prototypical Consistency for Efficient Semi-supervised Learning
Authors: Islam Nassar, Munawar Hayat, Ehsan Abbasnejad, Hamid Rezatofighi, Gholamreza Haffari
Date: 2023-03-22 00:00:00
Proceeding: CVPR 2023 1
Tasks: Online Clustering, Pseudo Label

Confidence-based pseudo-labeling is among the dominant approaches in semi-supervised learning (SSL). It relies on including high-confidence predictions made on unlabeled data as additional targets to train the model. We propose ProtoCon, a novel SSL method aimed at the less-explored label-scarce SSL where such methods usually underperform. ProtoCon refines the pseudo-labels by leveraging their nearest neighbours' information. The neighbours are identified as the training proceeds using an online clustering approach operating in an embedding space trained via a prototypical loss to encourage well-formed clusters. The online nature of ProtoCon allows it to utilise the label history of the entire dataset in one training cycle to refine labels in the following cycle without the need to store image embeddings. Hence, it can seamlessly scale to larger datasets at a low cost. Finally, ProtoCon addresses the poor training signal in the initial phase of training (due to fewer confident predictions) by introducing an auxiliary self-supervised loss. It delivers significant gains and faster convergence over state-of-the-art across 5 datasets, including CIFARs, ImageNet and DomainNet.
Collaboration Helps Camera Overtake LiDAR in 3D Detection
Authors: Yue Hu, Yifan Lu, Runsheng Xu, Weidi Xie, Siheng Chen, Yanfeng Wang
Date: 2023-03-23 00:00:00
Proceeding: CVPR 2023 1
Tasks: Depth Estimation

Camera-only 3D detection provides an economical solution with a simple configuration for localizing objects in 3D space compared to LiDAR-based detection systems. However, a major challenge lies in precise depth estimation due to the lack of direct 3D measurements in the input. Many previous methods attempt to improve depth estimation through network designs, e.g., deformable layers and larger receptive fields. This work proposes an orthogonal direction, improving the camera-only 3D detection by introducing multi-agent collaborations. Our proposed collaborative camera-only 3D detection (CoCa3D) enables agents to share complementary information with each other through communication. Meanwhile, we optimize communication efficiency by selecting the most informative cues. The shared messages from multiple viewpoints disambiguate the single-agent estimated depth and complement the occluded and long-range regions in the single-agent view. We evaluate CoCa3D in one real-world dataset and two new simulation datasets. Results show that CoCa3D improves previous SOTA performances by 44.21% on DAIR-V2X, 30.60% on OPV2V+, 12.59% on CoPerception-UAVs+ for AP@70. Our preliminary results show a potential that with sufficient collaboration, the camera might overtake LiDAR in some practical scenarios. We released the dataset and code at https://siheng-chen.github.io/dataset/CoPerception+ and https://github.com/MediaBrain-SJTU/CoCa3D.
MoGDE: Boosting Mobile Monocular 3D Object Detection with Ground Depth Estimation
Authors: Yunsong Zhou, Quan Liu, Hongzi Zhu, Yunzhe Li, Shan Chang, Minyi Guo
Date: 2023-03-23 00:00:00
Tasks: 3D Object Detection, Depth Estimation, Monocular 3D Object Detection, object-detection, Object Detection

Monocular 3D object detection (Mono3D) in mobile settings (e.g., on a vehicle, a drone, or a robot) is an important yet challenging task. Due to the near-far disparity phenomenon of monocular vision and the ever-changing camera pose, it is hard to acquire high detection accuracy, especially for far objects. Inspired by the insight that the depth of an object can be well determined according to the depth of the ground where it stands, in this paper, we propose a novel Mono3D framework, called MoGDE, which constantly estimates the corresponding ground depth of an image and then utilizes the estimated ground depth information to guide Mono3D. To this end, we utilize a pose detection network to estimate the pose of the camera and then construct a feature map portraying pixel-level ground depth according to the 3D-to-2D perspective geometry. Moreover, to improve Mono3D with the estimated ground depth, we design an RGB-D feature fusion network based on the transformer structure, where the long-range self-attention mechanism is utilized to effectively identify ground-contacting points and pin the corresponding ground depth to the image feature map. We conduct extensive experiments on the real-world KITTI dataset. The results demonstrate that MoGDE can effectively improve the Mono3D accuracy and robustness for both near and far objects. MoGDE yields the best performance compared with the state-of-the-art methods by a large margin and is ranked number one on the KITTI 3D benchmark.
Probability-based Global Cross-modal Upsampling for Pansharpening
Authors: Zeyu Zhu, Xiangyong Cao, Man Zhou, Junhao Huang, Deyu Meng
Date: 2023-03-09 00:00:00
Proceeding: CVPR 2023 1
Tasks: Pansharpening, Specificity

Pansharpening is an essential preprocessing step for remote sensing image processing. Although deep learning (DL) approaches performed well on this task, current upsampling methods used in these approaches only utilize the local information of each pixel in the low-resolution multispectral (LRMS) image while neglecting to exploit its global information as well as the cross-modal information of the guiding panchromatic (PAN) image, which limits their performance improvement. To address this issue, this paper develops a novel probability-based global cross-modal upsampling (PGCU) method for pan-sharpening. Precisely, we first formulate the PGCU method from a probabilistic perspective and then design an efficient network module to implement it by fully utilizing the information mentioned above while simultaneously considering the channel specificity. The PGCU module consists of three blocks, i.e., information extraction (IE), distribution and expectation estimation (DEE), and fine adjustment (FA). Extensive experiments verify the superiority of the PGCU method compared with other popular upsampling methods. Additionally, experiments also show that the PGCU module can help improve the performance of existing SOTA deep learning pansharpening methods. The codes are available at https://github.com/Zeyu-Zhu/PGCU.
Do Random and Chaotic Sequences Really Cause Different PSO Performance?
Authors: Paul Moritz Nörenberg, Hendrik Richter
Date: 2023-02-09 00:00:00

Our topic is performance differences between using random and chaos for particle swarm optimization (PSO). We take random sequences with different probability distributions and compare them to chaotic sequences with different but also with same density functions. This enables us to differentiate between differences in the origin of the sequences (random number generator or chaotic nonlinear system) and statistical differences expressed by the underlying distributions. Our findings (obtained by evaluating the PSO performance for various benchmark problems using statistical hypothesis testing) cast considerable doubt on previous results which compared random to chaos and suggested that the choice leads to intrinsic differences in performance.
A convex mathematical program for pump scheduling in a class of branched water networks
Authors: Gratien Bonvin, Sophie Demassey, Claude Le Pape, Nadia Maïzi, Vincent Mazauric, Alfredo Samperio
Date: 2017-01-01 00:00:00
Proceeding: Elsevier Ltd. 2017 1
Tasks: Scheduling

We address the day-ahead pump scheduling problem for a class of branched water networks with one
pumping station raising water to tanks at different places and levels. This common class is representative
of rural drinking water distribution networks, though not exclusively so. Many sophisticated heuristic
algorithms have been designed to tackle the challenging general problem. By focusing on a class of networks,
we show that a pure model-based approach relying on a tractable mathematical program is pertinent
for real-size applications. The practical advantages of this approach are that it produces optimal or
near-optimal solutions with performance guarantees in near real-time, and that it is replicable without
algorithmic development. We apply the approach to a real drinking water supply system and compare it
to the current operational strategy based on historical data. An extensive empirical analysis assesses the
financial and practical benefits: (1) it achieves significant savings in terms of operation costs and energy
consumption, (2) its robustness to dynamic pricing means that demand-response can be efficiently
implemented in this type of energy-intensive utility.
LouvainNE: Hierarchical Louvain Method for High Quality and Scalable Network Embedding
Authors: Ayan Kumar Bhowmick, Koushik Meneni, Maximilien Danisch, Jean-Loup Guillaume, Bivas Mitra
Date: 2020-02-01 00:00:00
Proceeding: The Thirteenth ACM International Conference on Web Search and Data Mining (WSDM) 2020 2
Tasks: Community Detection, Graph Embedding, Network Embedding, Node Classification, Vocal Bursts Intensity Prediction

Network embedding, that aims to learn low-dimensional vector representation of nodes such that the network structure is preserved,
has gained significant research attention in recent years. However, most state-of-the-art network embedding methods are computationally expensive and hence unsuitable for representing nodes in billion-scale networks. In this paper, we present LouvainNE, a hierarchical clustering approach to network embedding. Precisely, we employ Louvain, an extremely fast and accurate community detection method, to build a hierarchy of successively smaller subgraphs. We obtain representations of individual nodes in the original graph at different levels of the hierarchy, then we aggregate these representations to learn the final embedding vectors. Our theoretical analysis shows that our proposed algorithm has quasi-linear runtime and memory complexity. Our extensive experimental evaluation, carried out on multiple real-world networks of different scales, demonstrates both (i) the scalability of our proposed approach that can handle graphs containing tens of billions of edges, as well as (ii) its effectiveness in performing downstream network mining tasks such as network reconstruction and node classification.
Ranking Social Media News Feeds: A Comparative Study of Personalized and Non-personalized Prediction Models
Authors: Sami Belkacem, Kamel Boukhalfa, Omar Boussaid
Date: 2022-03-12 00:00:00
Proceeding: International Conference on Artificial Intelligence and its Applications 2022 3
Tasks: Feature Importance, News Recommendation, Recommendation Systems, Social Media Popularity Prediction

Home  Artificial Intelligence and Its Applications  Conference paper
Ranking Social Media News Feeds: A Comparative Study of Personalized and Non-personalized Prediction Models
Sami Belkacem, Kamel Boukhalfa & Omar Boussaid 
Conference paper
First Online: 12 March 2022
416 Accesses

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 413)

Abstract
Ranking news feed updates by relevance has been proposed to help social media users catch up with the content they may find interesting. For this matter, a single non-personalized model has been used to predict the relevance for all users. However, as user interests and preferences are different, we believe that using a personalized model for each user is crucial to refine the ranking. In this work, to predict the relevance of news feed updates and improve user experience, we use the random forest algorithm to train and introduce a personalized prediction model for each user. Then, we compare personalized and non-personalized models according to six criteria: (1) the overall prediction performance; (2) the amount of data in the training set; (3) the cold-start problem; (4) the incorporation of user preferences over time; (5) the model fine-tuning; and (6) the personalization of feature importance for users. Experimental results on Twitter show that a single non-personalized model for all users is easy to manage and fine-tune, is less likely to overfit, and it addresses the problem of cold-start and inactive users. On the other hand, the personalized models we introduce allow personalized feature importance, take into consideration the preferences of each user, and allow to track changes in user preferences over time. Furthermore, personalized models give a higher prediction accuracy than non-personalized models.
Ranking news feed updates on social media: A comparative study of supervised models
Authors: Sami Belkacem, Kamel Boukhalfa, Omar Boussaid
Date: 2020-01-01 00:00:00
Proceeding: Conference on Knowledge Extraction and Management 2020 1
Tasks: Ensemble Learning, News Recommendation, Recommendation Systems, Social Media Popularity Prediction

Social media users are overwhelmed by a large number of updates displayed chronologically in their news feed. Moreover, most updates are irrelevant. Ranking news feed updates by relevance has been proposed to help users catch up with the content they may find interesting. For this matter, supervised learning models have been commonly used to predict relevance. However, no comparative study was made to determine the most suitable models. In this work, we select, analyze, and compare six supervised learning algorithms applied to this case study. Experimental results on Twitter highlight that ensemble learning models are the most appropriate to predict the relevance of updates.
Multi-Frame Self-Supervised Depth Estimation with Multi-Scale Feature Fusion in Dynamic Scenes
Authors: Jiquan Zhong, Xiaolin Huang, Xiao Yu
Date: 2023-03-26 00:00:00
Tasks: Depth Estimation, Knowledge Distillation, Monocular Depth Estimation, Unsupervised Monocular Depth Estimation

Multi-frame methods improve monocular depth estimation over single-frame approaches by aggregating spatial-temporal information via feature matching. However, the spatial-temporal feature leads to accuracy degradation in dynamic scenes. To enhance the performance, recent methods tend to propose complex architectures for feature matching and dynamic scenes. In this paper, we show that a simple learning framework, together with designed feature augmentation, leads to superior performance. (1) A novel dynamic objects detecting method with geometry explainability is proposed. The detected dynamic objects are excluded during training, which guarantees the static environment assumption and relieves the accuracy degradation problem of the multi-frame depth estimation. (2) Multi-scale feature fusion is proposed for feature matching in the multi-frame depth network, which improves feature matching, especially between frames with large camera motion. (3) The robust knowledge distillation with a robust teacher network and reliability guarantee is proposed, which improves the multi-frame depth estimation without computation complexity increase during the test. The experiments show that our proposed methods achieve great performance improvement on the multi-frame depth estimation.