pwc-archive/papers-with-abstracts huggingface dataset:
Prediction of User Request and Complaint in Spoken Customer-Agent Conversations
Authors: Nikola Lackovic, Claude Montacié, Gauthier Lalande, Marie-José Caraty
Date: 2022-07-27 00:00:00
Tasks: Management

We present the corpus called HealthCall. This was recorded in real-life conditions in the call center of Malakoff Humanis. It includes two separate audio channels, the first one for the customer and the second one for the agent. Each conversation was anonymized respecting the General Data Protection Regulation. This corpus includes a transcription of the spoken conversations and was divided into two sets: Train and Devel sets. Two important customer relationship management tasks were assessed on the HealthCall corpus: Automatic prediction of type of user requests and complaints detection. For this purpose, we have investigated 14 feature sets: 6 linguistic feature sets, 6 audio feature sets and 2 vocal interaction feature sets. We have used Bidirectional Encoder Representation from Transformers models for the linguistic features, openSMILE and Wav2Vec 2.0 for the audio features. The vocal interaction feature sets were designed and developed from Turn Takings. The results show that the linguistic features always give the best results (91.2% for the Request task and 70.3% for the Complaint task). The Wav2Vec 2.0 features seem more suitable for these two tasks than the ComPaRe16 features. Vocal interaction features outperformed ComPaRe16 features on Complaint task with a 57% rate achieved with only six features.
Multi-Task Learning for Depression Detection in Dialogs
Authors: Chuyuan Li, Chloé Braud, Maxime Amblard
Date: 2022-07-21 00:00:00
Proceeding: SIGDIAL (ACL) 2022 9
Tasks: Depression Detection, Multi-Task Learning

Depression is a serious mental illness that impacts the way people communicate, especially through their emotions, and, allegedly, the way they interact with others. This work examines depression signals in dialogs, a less studied setting that suffers from data sparsity. We hypothesize that depression and emotion can inform each other, and we propose to explore the influence of dialog structure through topic and dialog act prediction. We investigate a Multi-Task Learning (MTL) approach, where all tasks mentioned above are learned jointly with dialog-tailored hierarchical modeling. We experiment on the DAIC and DailyDialog corpora-both contain dialogs in English-and show important improvements over state-ofthe-art on depression detection (at best 70.6% F 1), which demonstrates the correlation of depression with emotion and dialog organization and the power of MTL to leverage information from different sources.
Rethinking Textual Adversarial Defense for Pre-trained Language Models
Authors: Jiayi Wang, Rongzhou Bao, Zhuosheng Zhang, Hai Zhao
Date: 2022-07-21 00:00:00
Tasks: Adversarial Attack, Adversarial Defense, Sentence

Although pre-trained language models (PrLMs) have achieved significant success, recent studies demonstrate that PrLMs are vulnerable to adversarial attacks. By generating adversarial examples with slight perturbations on different levels (sentence / word / character), adversarial attacks can fool PrLMs to generate incorrect predictions, which questions the robustness of PrLMs. However, we find that most existing textual adversarial examples are unnatural, which can be easily distinguished by both human and machine. Based on a general anomaly detector, we propose a novel metric (Degree of Anomaly) as a constraint to enable current adversarial attack approaches to generate more natural and imperceptible adversarial examples. Under this new constraint, the success rate of existing attacks drastically decreases, which reveals that the robustness of PrLMs is not as fragile as they claimed. In addition, we find that four types of randomization can invalidate a large portion of textual adversarial examples. Based on anomaly detector and randomization, we design a universal defense framework, which is among the first to perform textual adversarial defense without knowing the specific attack. Empirical results show that our universal defense framework achieves comparable or even higher after-attack accuracy with other specific defenses, while preserving higher original accuracy at the same time. Our work discloses the essence of textual adversarial attacks, and indicates that (1) further works of adversarial attacks should focus more on how to overcome the detection and resist the randomization, otherwise their adversarial examples would be easily detected and invalidated; and (2) compared with the unnatural and perceptible adversarial examples, it is those undetectable adversarial examples that pose real risks for PrLMs and require more attention for future robustness-enhancing strategies.
An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection
Authors: Nirmalya Thakur, Chia Y. Han
Date: 2022-07-20 00:00:00
Tasks: Sentiment Analysis

This paper presents the findings of an exploratory study on the continuously generating Big Data on Twitter related to the sharing of information, news, views, opinions, ideas, feedback, and experiences about the COVID-19 pandemic, with a specific focus on the Omicron variant, which is the globally dominant variant of SARS-CoV-2 at this time. A total of 12028 tweets about the Omicron variant were studied, and the specific characteristics of tweets that were analyzed include - sentiment, language, source, type, and embedded URLs. The findings of this study are manifold. First, from sentiment analysis, it was observed that 50.5% of tweets had a neutral emotion. The other emotions - bad, good, terrible, and great were found in 15.6%, 14.0%, 12.5%, and 7.5% of the tweets, respectively. Second, the findings of language interpretation showed that 65.9% of the tweets were posted in English. It was followed by Spanish, French, Italian, and other languages. Third, the findings from source tracking showed that Twitter for Android was associated with 35.2% of tweets. It was followed by Twitter Web App, Twitter for iPhone, Twitter for iPad, and other sources. Fourth, studying the type of tweets revealed that retweets accounted for 60.8% of the tweets, it was followed by original tweets and replies that accounted for 19.8% and 19.4% of the tweets, respectively. Fifth, in terms of embedded URL analysis, the most common domain embedded in the tweets was found to be twitter.com, which was followed by biorxiv.org, nature.com, and other domains. Finally, to support similar research in this field, we have developed a Twitter dataset that comprises more than 500,000 tweets about the SARS-CoV-2 omicron variant since the first detected case of this variant on November 24, 2021.
A Twitter-Driven Deep Learning Mechanism for the Determination of Vehicle Hijacking Spots in Cities
Authors: Taahir Aiyoob Patel, Clement N. Nyirenda
Date: 2022-08-11 00:00:00

Vehicle hijacking is one of the leading crimes in many cities. For instance, in South Africa, drivers must constantly remain vigilant on the road in order to ensure that they do not become hijacking victims. This work is aimed at developing a map depicting hijacking spots in a city by using Twitter data. Tweets, which include the keyword "hijacking", are obtained in a designated city of Cape Town, in this work. In order to extract relevant tweets, these tweets are analyzed by using the following machine learning techniques: 1) a Multi-layer Feed-forward Neural Network (MLFNN); 2) Convolutional Neural Network; and Bidirectional Encoder Representations from Transformers (BERT). Through training and testing, CNN achieved an accuracy of 99.66%, while MLFNN and BERT achieve accuracies of 98.99% and 73.99% respectively. In terms of Recall, Precision and F1-score, CNN also achieved the best results. Therefore, CNN was used for the identification of relevant tweets. The relevant reports that it generates are visually presented on a points map of the City of Cape Town. This work used a small dataset of 426 tweets. In future, the use of evolutionary computation will be explored for purposes of optimizing the deep learning models. A mobile application is under development to make this information usable by the general public.
Generative Adversarial Framework for Cold-Start Item Recommendation
Authors: Hao Chen, Zefan Wang, Feiran Huang, Xiao Huang, Yue Xu, Yishi Lin, Peng He, Zhoujun Li   Authors Info & Claims
Date: 2022-07-07 00:00:00
Proceeding: SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information RetrievalJuly 2022 Pages 2565–2571 2022 7

The cold-start problem has been a long-standing issue in recommendation. Embedding-based recommendation models provide recommendations by learning embeddings for each user and item from historical interactions. Therefore, such embedding-based models perform badly for cold items which haven't emerged in the training set. The most common solutions are to generate the cold embedding for the cold item from its content features. However, the cold embeddings generated from contents have different distribution as the warm embeddings are learned from historical interactions. In this case, current cold-start methods are facing an interesting seesaw phenomenon, which improves the recommendation of either the cold items or the warm items but hurts the opposite ones. To this end, we propose a general framework named Generative Adversarial Recommendation (GAR). By training the generator and the recommender adversarially, the generated cold item embeddings can have similar distribution as the warm embeddings that can even fool the recommender. Simultaneously, the recommender is fine-tuned to correctly rank the "fake'' warm embeddings and the real warm embeddings. Consequently, the recommendation of the warms and the colds will not influence each other, thus avoiding the seesaw phenomenon. Additionally, GAR could be applied to any off-the-shelf recommendation model. Experiments on two datasets present that GAR has strong overall recommendation performance in cold-starting both the CF-based model (improved by over 30.18%) and the GNN-based model (improved by over 17.78%).
PrivBayes： Private Data release via Bayesian networks
Authors: Jun Zhang, Graham Cormode, Cecilia M. Procopiuc, Divesh Srivastava, Xiaokui Xiao
Date: 2014-06-18 00:00:00
Proceeding: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data 2014 6
Tasks: Privacy Preserving

Privacy-preserving data publishing is an important problem that has
been the focus of extensive study. The state-of-the-art goal for this
problem is differential privacy, which offers a strong degree of privacy protection without making restrictive assumptions about the
adversary. Existing techniques using differential privacy, however,
cannot effectively handle the publication of high-dimensional data. In particular, when the input dataset contains a large number of
attributes, existing methods require injecting a prohibitive amount
of noise compared to the signal in the data, which renders the published data next to useless.
To address the deficiency of the existing methods, this paper
presents PRIVBAYES, a differentially private method for releasing
high-dimensional data. Given a dataset D, PRIVBAYES first constructs a Bayesian network N , which (i) provides a succinct model
of the correlations among the attributes in D and (ii) allows us to
approximate the distribution of data in D using a set P of lowdimensional marginals of D. After that, PRIVBAYES injects noise
into each marginal in P to ensure differential privacy, and then uses the noisy marginals and the Bayesian network to construct an
approximation of the data distribution in D. Finally, PRIVBAYES
samples tuples from the approximate distribution to construct a
synthetic dataset, and then releases the synthetic data. Intuitively,
PRIVBAYES circumvents the curse of dimensionality, as it injects
noise into the low-dimensional marginals in P instead of the highdimensional dataset D. Private construction of Bayesian networks
turns out to be significantly challenging, and we introduce a novel approach that uses a surrogate function for mutual information
to build the model more accurately. We experimentally evaluate
PRIVBAYES on real data, and demonstrate that it significantly outperforms existing solutions in terms of accuracy.
AquaVision: Automating the detection of waste in water bodies using deep transfer learning
Authors: Harsh Panwar, P.K.Gupta, Mohammad Khubeb Siddiquib, Ruben Morales-Menendez, Prakhar Bhardwaj, Sudhansh Sharma, Iqbal H.Sarker
Date: 2020-07-18 00:00:00
Proceeding: Case Studies in Chemical and Environmental Engineering 2020 7
Tasks: object-detection, Object Detection, Transfer Learning

Water pollution is one of the serious threats in the society. More than 8 million tons of plastic are dumped in the oceans each year. In addition to that beaches are littered by tourists and residents all around the world. It is no secret that the aquatic life ecosystem is at a risk and soon the ratio of plastic/waste to the marine life particulary fish will be 1:1. Hence, in this paper, we have proposed a dataset known as AquaTrash which is based on TACO data set. Further, we have applied proposed state-of-the-art deep learning-based object detection model known as AquaVision over AquaTrash dataset. Proposed model detects and classifies the different pollutants and harmful waste items floating in the oceans and on the seashores with mean Average Precision (mAP) of 0.8148. The propose method localizes the waste object that help in cleaning the water bodies and contributes to environment by maintaining the aquatic ecosystem.
Solving Royal Game of Ur Using Reinforcement Learning
Authors: Sidharth Malhotra, Girik Malik
Date: 2022-08-23 00:00:00
Tasks: Board Games, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement Learning has recently surfaced as a very powerful tool to solve complex problems in the domain of board games, wherein an agent is generally required to learn complex strategies and moves based on its own experiences and rewards received. While RL has outperformed existing state-of-the-art methods used for playing simple video games and popular board games, it is yet to demonstrate its capability on ancient games. Here, we solve one such problem, where we train our agents using different methods namely Monte Carlo, Qlearning and Expected Sarsa to learn optimal policy to play the strategic Royal Game of Ur. The state space for our game is complex and large, but our agents show promising results at playing the game and learning important strategic moves. Although it is hard to conclude that when trained with limited resources which algorithm performs better overall, but Expected Sarsa shows promising results when it comes to fastest learning.
AniWho : A Quick and Accurate Way to Classify Anime Character Faces in Images
Authors: Martinus Grady Naftali, Jason Sebastian Sulistyawan, Kelvin Julian
Date: 2022-08-23 00:00:00
Tasks: Few-Shot Learning, Transfer Learning

In order to classify Japanese animation-style character faces, this paper attempts to delve further into the many models currently available, including InceptionV3, InceptionResNetV2, MobileNetV2, and EfficientNet, employing transfer learning. This paper demonstrates that EfficientNet-B7, which achieves a top-1 accuracy of 85.08%, has the highest accuracy rate. MobileNetV2, which achieves a less accurate result with a top-1 accuracy of 81.92%, benefits from a significantly faster inference time and fewer required parameters. However, from the experiment, MobileNet-V2 is prone to overfitting; EfficienNet-B0 fixed the overfitting issue but with a cost of a little slower in inference time than MobileNet-V2 but a little more accurate result, top-1 accuracy of 83.46%. This paper also uses a few-shot learning architecture called Prototypical Networks, which offers an adequate substitute for conventional transfer learning techniques.
A First Look at Dataset Bias in License Plate Recognition
Authors: Rayson Laroca, Marcelo Santos, Valter Estevam, Eduardo Luz, David Menotti
Date: 2022-08-23 00:00:00
Tasks: License Plate Recognition

Public datasets have played a key role in advancing the state of the art in License Plate Recognition (LPR). Although dataset bias has been recognized as a severe problem in the computer vision community, it has been largely overlooked in the LPR literature. LPR models are usually trained and evaluated separately on each dataset. In this scenario, they have often proven robust in the dataset they were trained in but showed limited performance in unseen ones. Therefore, this work investigates the dataset bias problem in the LPR context. We performed experiments on eight datasets, four collected in Brazil and four in mainland China, and observed that each dataset has a unique, identifiable "signature" since a lightweight classification model predicts the source dataset of a license plate (LP) image with more than 95% accuracy. In our discussion, we draw attention to the fact that most LPR models are probably exploiting such signatures to improve the results achieved in each dataset at the cost of losing generalization capability. These results emphasize the importance of evaluating LPR models in cross-dataset setups, as they provide a better indication of generalization (hence real-world performance) than within-dataset ones.
Faint Features Tell: Automatic Vertebrae Fracture Screening Assisted by Contrastive Learning
Authors: Xin Wei, Huaiwei Cong, Zheng Zhang, Junran Peng, Guoping Chen, Jinpeng Li
Date: 2022-08-23 00:00:00
Tasks: Binary Classification, Computed Tomography (CT), Contrastive Learning, Multi-class Classification, Specificity

Long-term vertebral fractures severely affect the life quality of patients, causing kyphotic, lumbar deformity and even paralysis. Computed tomography (CT) is a common clinical examination to screen for this disease at early stages. However, the faint radiological appearances and unspecific symptoms lead to a high risk of missed diagnosis, especially for the mild vertebral fractures. In this paper, we argue that reinforcing the faint fracture features to encourage the inter-class separability is the key to improving the accuracy. Motivated by this, we propose a supervised contrastive learning based model to estimate Genent's Grade of vertebral fracture with CT scans. The supervised contrastive learning, as an auxiliary task, narrows the distance of features within the same class while pushing others away, enhancing the model's capability of capturing subtle features of vertebral fractures. Our method has a specificity of 99% and a sensitivity of 85% in binary classification, and a macro-F1 of 77% in multi-class classification, indicating that contrastive learning significantly improves the accuracy of vertebrae fracture screening. Considering the lack of datasets in this field, we construct a database including 208 samples annotated by experienced radiologists. Our desensitized data and codes will be made publicly available for the community.
Integrative conformal p-values for powerful out-of-distribution testing with labeled outliers
Authors: Ziyi Liang, Matteo Sesia, Wenguang Sun
Date: 2022-08-23 00:00:00

This paper develops novel conformal methods to test whether a new observation was sampled from the same distribution as a reference set. Blending inductive and transductive conformal inference in an innovative way, the described methods can re-weight standard conformal p-values based on dependent side information from known out-of-distribution data in a principled way, and can automatically take advantage of the most powerful model from any collection of one-class and binary classifiers. The solution can be implemented either through sample splitting or via a novel transductive cross-validation+ scheme which may also be useful in other applications of conformal inference, due to tighter guarantees compared to existing cross-validation approaches. After studying false discovery rate control and power within a multiple testing framework with several possible outliers, the proposed solution is shown to outperform standard conformal p-values through simulations as well as applications to image recognition and tabular data.
Dynamic Causal Collaborative Filtering
Authors: Shuyuan Xu, Juntao Tan, Zuohui Fu, Jianchao Ji, Shelby Heinecke, Yongfeng Zhang
Date: 2022-08-23 00:00:00
Tasks: Collaborative Filtering, counterfactual, Counterfactual Reasoning, Recommendation Systems

Causal graph, as an effective and powerful tool for causal modeling, is usually assumed as a Directed Acyclic Graph (DAG). However, recommender systems usually involve feedback loops, defined as the cyclic process of recommending items, incorporating user feedback in model updates, and repeating the procedure. As a result, it is important to incorporate loops into the causal graphs to accurately model the dynamic and iterative data generation process for recommender systems. However, feedback loops are not always beneficial since over time they may encourage more and more narrowed content exposure, which if left unattended, may results in echo chambers. As a result, it is important to understand when the recommendations will lead to echo chambers and how to mitigate echo chambers without hurting the recommendation performance. In this paper, we design a causal graph with loops to describe the dynamic process of recommendation. We then take Markov process to analyze the mathematical properties of echo chamber such as the conditions that lead to echo chambers. Inspired by the theoretical analysis, we propose a Dynamic Causal Collaborative Filtering ($\partial$CCF) model, which estimates users' post-intervention preference on items based on back-door adjustment and mitigates echo chamber with counterfactual reasoning. Multiple experiments are conducted on real-world datasets and results show that our framework can mitigate echo chambers better than other state-of-the-art frameworks while achieving comparable recommendation performance with the base recommendation models.