pwc-archive/papers-with-abstracts huggingface dataset:
Arbitrage Problems with Reflected Geometric Brownian Motion
Authors: Dean Buckner, Kevin Dowd, Hardy Hulley
Date: 2022-01-14 00:00:00

Contrary to the claims made by several authors, a financial market model in which the price of a risky security follows a reflected geometric Brownian motion is not arbitrage-free. In fact, such models violate even the weakest no-arbitrage condition considered in the literature. Consequently, they do not admit num\'eraire portfolios or equivalent risk-neutral probability measures, which makes them totally unsuitable for contingent claim valuation. Unsurprisingly, the published option pricing formulae for such models violate textbook no-arbitrage bounds.
AI and the Sense of Self
Authors: Srinath Srinivasa, Jayati Deshmukh
Date: 2022-01-07 00:00:00
Tasks: Common Sense Reasoning, Decision Making

After several winters, AI is center-stage once again, with current advances enabling a vast array of AI applications. This renewed wave of AI has brought back to the fore several questions from the past, about philosophical foundations of intelligence and common sense -- predominantly motivated by ethical concerns of AI decision-making. In this paper, we address some of the arguments that led to research interest in intelligent agents, and argue for their relevance even in today's context. Specifically we focus on the cognitive sense of "self" and its role in autonomous decision-making leading to responsible behaviour. The authors hope to make a case for greater research interest in building richer computational models of AI agents with a sense of self.
An end to end approach for brand recognition in product titles with BI-LSTM-CRF
Authors: Larbi Abderrahmane Mohammedi, Mohamed Annis Souames
Date: 2022-01-16 00:00:00
Proceeding: Preprint 2022 1
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

The aim of this paper is to describe an end to end approach using different deep learning architectures to detect brand names in product titles from online stores and online retailers such as Amazon, Ebay, etc. In this paper we developed a named entity recognition model based on a Bi-LSTM architecture with a conditional random field layer using Flair framework, we also explain how the dataset was curated, cleaned and augmented to improve the model performances. Finally, we compare the trained model against few other models trained using the Spacy framework. Our model gave a relatively high f1-score of 0.83 with a good generalisation to real world cases.
A 1D CNN for high accuracy classification and transfer learning in motor imagery EEG-based brain-computer interface
Authors: F. Mattioli, C. Porcaro, G. Baldassarre
Date: 2022-01-06 00:00:00
Proceeding: Journal of Neural Engineering 2022 1
Tasks: Brain Computer Interface, Data Augmentation, EEG, Electroencephalogram (EEG), Motor Imagery, Transfer Learning

Objective. Brain-computer interface (BCI) aims to establish communication paths between the brain processes and external devices. Different methods have been used to extract human intentions from electroencephalography (EEG) recordings. Those based on motor imagery (MI) seem to have a great potential for future applications. These approaches rely on the extraction of EEG distinctive patterns during imagined movements. Techniques able to extract patterns from raw signals represent an important target for BCI as they do not need labor-intensive data pre-processing. Approach. We propose a new approach based on a 10-layer one-dimensional convolution neural network (1D-CNN) to classify five brain states (four MI classes plus a 'baseline' class) using a data augmentation algorithm and a limited number of EEG channels. In addition, we present a transfer learning method used to extract critical features from the EEG group dataset and then to customize the model to the single individual by training its late layers with only 12-min individual-related data. Main results. The model tested with the 'EEG Motor Movement/Imagery Dataset' outperforms the current state-of-the-art models by achieving a $\mathbf{99.38\%}$ accuracy at the group level. In addition, the transfer learning approach we present achieves an average accuracy of $\mathbf{99.46\%}$. Significance. The proposed methods could foster the development of future BCI applications relying on few-channel portable recording devices and individual-based training.
The Power of Tiling for Small Object Detection
Authors: F. Ozge UnelBurak OzkalayciCevahir Cigla
Date: 2019-06-11 00:00:00
Proceeding: Computer Vision and Pattern Recognition 2019 6
Tasks: Object, object-detection, Object Detection, Small Object Detection

Deep neural network based techniques are state-of-the-art for object detection and classification with the help ofthe development in computational power and memory ef-ficiency. Although these networks are adapted for mobileplatforms with sacrifice in accuracy; the resolution increasein visual sources makes the problem even harder by raisingthe expectations to leverage all the details in images. Real-time small object detection in low power mobile devices hasbeen one of the fundamental problems of surveillance ap-plications. In this study, we address the detection of pedes-trians and vehicles onboard a micro aerial vehicle (MAV)with high-resolution imagery. For this purpose, we exploitPeleeNet, to our best knowledge the most efficient networkmodel on mobile GPUs, as the backbone of an SSD networkas well as 38x38 feature map in the earlier layer. After illus-trating the low accuracy of state-of-the-art object detectorsunder the MAV scenario, we introduce a tiling based ap-proach that is applied in both training and inference phases.The proposed technique limits the detail loss in object de-tection while feeding the network with a fixed size input.The improvements provided by the proposed approach areshown by in-depth experiments performed along Nvidia Jet-son TX1 and TX2 using the VisDrone2018 dataset.
Effective Slot Filling via Weakly-Supervised Dual-Model Learning
Authors: Jue Wang, Ke Chen, Lidan Shou, Sai Wu, Gang Chen
Date: 2021-05-18 00:00:00
Proceeding: AAAI 2021 5
Tasks: slot-filling, Slot Filling, Spoken Language Understanding

Slot filling is a challenging task in Spoken Language Understanding (SLU). Supervised methods usually require large amounts of annotation to maintain desirable performance. A solution to relieve the heavy dependency on labeled data is to employ bootstrapping, which leverages unlabeled data. However, bootstrapping is known to suffer from semantic drift. We argue that semantic drift can be tackled by exploiting the correlation between slot values (phrases) and their respective types. By using some particular weakly-labeled data, namely the plain phrases included in sentences, we propose a weaklysupervised slot filling approach. Our approach trains two models, namely a classifier and a tagger, which can effectively learn from each other on the weakly-labeled data. The experimental results demonstrate that our approach achieves better results than standard baselines on multiple datasets, especially in the low-resource setting.
Isolation Kernel and Its Effect on SVM
Authors: Kai Ming Ting, Yue Zhu, Zhi-Hua Zhou
Date: 2018-07-19 00:00:00
Proceeding: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2018 7
Tasks: Dimensionality Reduction, General Classification

This paper investigates data dependent kernels that are derived directly from data. This has been an outstanding issue for about two decades which hampered the development of kernel-based methods. We introduce Isolation Kernel which is solely dependent on data distribution, requiring neither class information nor explicit learning to be a classifier. In contrast, existing data dependent kernels rely heavily on class information and explicit learning to produce a classifier. We show that Isolation Kernel approximates well to a data independent kernel function called Laplacian kernel under uniform density distribution. With this revelation, Isolation Kernel can be viewed as a data dependent kernel that adapts a data independent kernel to the structure of a dataset. We also provide a reason why the proposed new data dependent kernel enables SVM (which employs a kernel through other means) to improve its predictive accuracy. The key differences between Random Forest kernel and Isolation Kernel are discussed to examine the reasons why the latter is a more successful tree-based kernel.
No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion
Authors: Domonkos Varga
Date: 2021-12-23 00:00:00
Proceeding: Applied Sciences 2021 12
Tasks: Deep Learning, Image Quality Assessment, No-Reference Image Quality Assessment

No-reference image quality assessment (NR-IQA) has always been a difficult research problem because digital images may suffer very diverse types of distortions and their contents are extremely various. Moreover, IQA is also a very hot topic in the research community since the number and role of digital images in everyday life is continuously growing. Recently, a huge amount of effort has been devoted to exploiting convolutional neural networks and other deep learning techniques for no-reference image quality assessment. Since deep learning relies on a massive amount of labeled data, utilizing pretrained networks has become very popular in the literature. In this study, we introduce a novel, deep learning-based NR-IQA architecture that relies on the decision fusion of multiple image quality scores coming from different types of convolutional neural networks. The main idea behind this scheme is that a diverse set of different types of networks is able to better characterize authentic image distortions than a single network. The experimental results show that our method can effectively estimate perceptual image quality on four large IQA benchmark databases containing either authentic or artificial distortions. These results are also confirmed in significance and cross database tests.
No-Reference Video Quality Assessment Based on Benford’s Law and Perceptual Features
Authors: Domonkos Varga
Date: 2021-11-12 00:00:00
Proceeding: Electronics 2021 11
Tasks: No-Reference Image Quality Assessment, Video Quality Assessment, Visual Question Answering (VQA)

No-reference video quality assessment (NR-VQA) has piqued the scientific community’s interest throughout the last few decades, owing to its importance in human-centered interfaces. The goal of NR-VQA is to predict the perceptual quality of digital videos without any information about their distortion-free counterparts. Over the past few decades, NR-VQA has become a very popular research topic due to the spread of multimedia content and video databases. For successful video quality evaluation, creating an effective video representation from the original video is a crucial step. In this paper, we propose a powerful feature vector for NR-VQA inspired by Benford’s law. Specifically, it is demonstrated that first-digit distributions extracted from different transform domains of the video volume data are quality-aware features and can be effectively mapped onto perceptual quality scores. Extensive experiments were carried out on two large, authentically distorted VQA benchmark databases.
Analysis of Benford’s Law for No-Reference Quality Assessment of Natural, Screen-Content, and Synthetic Images
Authors: Domonkos Varga
Date: 2021-09-21 00:00:00
Proceeding: Electronics 2021 9
Tasks: Image Forensics, Image Quality Assessment, No-Reference Image Quality Assessment

With the tremendous growth and usage of digital images, no-reference image quality assessment is becoming increasingly important. This paper presents in-depth analysis of Benford’s law inspired first digit distribution feature vectors for no-reference quality assessment of natural, screen-content, and synthetic images in various viewpoints. Benford’s law makes a prediction for the probability distribution of first digits in natural datasets. It has been applied among others for detecting fraudulent income tax returns, detecting scientific fraud, election forensics, and image forensics. In particular, our analysis is based on first digit distributions in multiple domains (wavelet coefficients, DCT coefficients, singular values, etc.) as feature vectors and the extracted features are mapped onto image quality scores. Extensive experiments have been carried out on seven large image quality benchmark databases. It has been demonstrated that first digit distributions are quality-aware features, and it is possible to reach or outperform the state-of-the-art with them.
CogALex 2.0: Impact of Data Quality on Lexical-Semantic Relation Prediction
Authors: Christian Lang, Lennart Wachowiak, Barbara Heinisch, Dagmar Gromann
Date: 2021-12-14 00:00:00
Proceeding: NeurIPS Data-Centric AI Workshop 2021 12
Tasks: Hypernym Discovery, Relation, Relation Classification, Relation Prediction

Predicting lexical-semantic relations between word pairs has successfully been accomplished by pre-trained neural language models. An XLM-RoBERTa-based approach, for instance, achieved the best performance differentiating between hypernymy, synonymy, antonymy, and random relations in four languages in the CogALex-VI 2020 shared task. However, the results also revealed strong performance divergences between languages and confusions of specific relations, especially hypernymy and synonymy. Upon inspection, a difference in data quality across languages and relations could be observed. Thus, we provide a manually improved dataset for lexical-semantic relation prediction and evaluate its impact across three pre-trained neural language models.
A Novel Approach for Enhancing Sentiment Classification of Persian Reviews Using Convolutional Neural Network and Majority Voting Classifier
Authors: Milad Vazan
Date: 2021-12-17 00:00:00
Proceeding: Digital Transformation and Intelligent Systems Conference 2021 12
Tasks: Ensemble Learning, Majority Voting Classifier, Persian Sentiment Analysis, Sentiment Analysis, Sentiment Classification

Due to the rapid development of Internet-based applications such as social media, sentiment analysis has become one of the most widely used research areas of natural language processing and an important tool for extracting opinions from texts. Because a huge number of comments and reviews are generated today through social media, and these comments are very significant. However, manually analyzing and summarizing them requires a lot of time and money. Therefore, sentiment analysis has entered the field to organize and analyze them by creating an automated system. In recent years, the use of deep learning in sentiment analysis has shown powerful results. However, creating a model alone may not provide the best predictions and lead to errors such as bias and high variance. To reduce these errors and improve the efficiency of model predictions, combining several models known as ensemble learning may provide better results. Therefore, the main purpose of this article is to create a model based on ensemble learning using Several convolutional neural networks and majority voting classifiers to enhance sentiment analysis in Persian reviews. The proposed model was evaluated on two datasets of reviews of electronic products and movies by 5-fold and 10-fold cross-validation. The results indicate that this new approach increases the efficiency of the sentiment analysis model in Persian language.
High Fidelity Visualization of What Your Self-Supervised Representation Knows About
Authors: Florian Bordes, Randall Balestriero, Pascal Vincent
Date: 2021-12-16 00:00:00
Tasks: Data Augmentation, Image Manipulation, Self-Supervised Learning, Vocal Bursts Intensity Prediction

Discovering what is learned by neural networks remains a challenge. In self-supervised learning, classification is the most common task used to evaluate how good a representation is. However, relying only on such downstream task can limit our understanding of what information is retained in the representation of a given input. In this work, we showcase the use of a Representation Conditional Diffusion Model (RCDM) to visualize in data space the representations learned by self-supervised models. The use of RCDM is motivated by its ability to generate high-quality samples -- on par with state-of-the-art generative models -- while ensuring that the representations of those samples are faithful i.e. close to the one used for conditioning. By using RCDM to analyze self-supervised models, we are able to clearly show visually that i) SSL (backbone) representation are not invariant to the data augmentations they were trained with -- thus debunking an often restated but mistaken belief; ii) SSL post-projector embeddings appear indeed invariant to these data augmentation, along with many other data symmetries; iii) SSL representations appear more robust to small adversarial perturbation of their inputs than representations trained in a supervised manner; and iv) that SSL-trained representations exhibit an inherent structure that can be explored thanks to RCDM visualization and enables image manipulation.
A Deep Convolutional Neural Networks Based Multi-Task Ensemble Model for Aspect and Polarity Classification in Persian Reviews
Authors: Milad Vazan, Fatemeh Sadat Masoumi, Sepideh Saeedi Majd
Date: 2022-01-17 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Aspect Category Detection, Aspect Category Polarity, Ensemble Learning, Multi-Task Learning, Persian Sentiment Analysis, Sentiment Analysis

Aspect-based sentiment analysis is of great importance and application because of its ability to identify all aspects discussed in the text. However, aspect-based sentiment analysis will be most effective when, in addition to identifying all the aspects discussed in the text, it can also identify their polarity. Most previous methods use the pipeline approach, that is, they first identify the aspects and then identify the polarities. Such methods are unsuitable for practical applications since they can lead to model errors. Therefore, in this study, we propose a multi-task learning model based on Convolutional Neural Networks (CNNs), which can simultaneously detect aspect category and detect aspect category polarity. creating a model alone may not provide the best predictions and lead to errors such as bias and high variance. To reduce these errors and improve the efficiency of model predictions, combining several models known as ensemble learning may provide better results. Therefore, the main purpose of this article is to create a model based on an ensemble of multi-task deep convolutional neural networks to enhance sentiment analysis in Persian reviews. We evaluated the proposed method using a Persian language dataset in the movie domain. Jacquard index and Hamming loss measures were used to evaluate the performance of the developed models. The results indicate that this new approach increases the efficiency of the sentiment analysis model in the Persian language.
Can We Find Neurons that Cause Unrealistic Images in Deep Generative Networks?
Authors: Hwanil Choi, Wonjoon Chang, Jaesik Choi
Date: 2022-01-17 00:00:00
Tasks: GAN image forensics, Image Generation, Image Manipulation, Image Reconstruction

Even though Generative Adversarial Networks (GANs) have shown a remarkable ability to generate high-quality images, GANs do not always guarantee the generation of photorealistic images. Occasionally, they generate images that have defective or unnatural objects, which are referred to as 'artifacts'. Research to investigate why these artifacts emerge and how they can be detected and removed has yet to be sufficiently carried out. To analyze this, we first hypothesize that rarely activated neurons and frequently activated neurons have different purposes and responsibilities for the progress of generating images. In this study, by analyzing the statistics and the roles for those neurons, we empirically show that rarely activated neurons are related to the failure results of making diverse objects and inducing artifacts. In addition, we suggest a correction method, called 'Sequential Ablation', to repair the defective part of the generated images without high computational cost and manual efforts.
STURE: Spatial-Temporal Mutual Representation Learning for Robust Data Association in Online Multi-Object Tracking
Authors: Haidong Wang, Zhiyong Li, Yaping Li, Ke Nai, Ming Wen
Date: 2022-01-18 00:00:00
Tasks: Multi-Object Tracking, Object, Object Tracking, Online Multi-Object Tracking, Representation Learning

Online multi-object tracking (MOT) is a longstanding task for computer vision and intelligent vehicle platform. At present, the main paradigm is tracking-by-detection, and the main difficulty of this paradigm is how to associate current candidate detections with historical tracklets. However, in the MOT scenarios, each historical tracklet is composed of an object sequence, while each candidate detection is just a flat image, which lacks temporal features of the object sequence. The feature difference between current candidate detections and historical tracklets makes the object association much harder. Therefore, we propose a Spatial-Temporal Mutual Representation Learning (STURE) approach which learns spatial-temporal representations between current candidate detections and historical sequences in a mutual representation space. For historical trackelets, the detection learning network is forced to match the representations of sequence learning network in a mutual representation space. The proposed approach is capable of extracting more distinguishing detection and sequence representations by using various designed losses in object association. As a result, spatial-temporal feature is learned mutually to reinforce the current detection features, and the feature difference can be relieved. To prove the robustness of the STURE, it is applied to the public MOT challenge benchmarks and performs well compared with various state-of-the-art online MOT trackers based on identity-preserving metrics.
Dual Perceptual Loss for Single Image Super-Resolution Using ESRGAN
Authors: Jie Song, Huawei Yi, Wenqian Xu, Xiaohui Li, Bo Li, Yuanyuan Liu
Date: 2022-01-17 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

The proposal of perceptual loss solves the problem that per-pixel difference loss function causes the reconstructed image to be overly-smooth, which acquires a significant progress in the field of single image super-resolution reconstruction. Furthermore, the generative adversarial networks (GAN) is applied to the super-resolution field, which effectively improves the visual quality of the reconstructed image. However, under the condtion of high upscaling factors, the excessive abnormal reasoning of the network produces some distorted structures, so that there is a certain deviation between the reconstructed image and the ground-truth image. In order to fundamentally improve the quality of reconstructed images, this paper proposes a effective method called Dual Perceptual Loss (DP Loss), which is used to replace the original perceptual loss to solve the problem of single image super-resolution reconstruction. Due to the complementary property between the VGG features and the ResNet features, the proposed DP Loss considers the advantages of learning two features simultaneously, which significantly improves the reconstruction effect of images. The qualitative and quantitative analysis on benchmark datasets demonstrates the superiority of our proposed method over state-of-the-art super-resolution methods.