pwc-archive/papers-with-abstracts huggingface dataset:
Towards Robust Representation of Limit Orders Books for Deep Learning Models
Authors: Yufei Wu, Mahmoud Mahfouz, Daniele Magazzeni, Manuela Veloso
Date: 2021-10-10 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning

The success of deep learning-based limit order book forecasting models is highly dependent on the quality and the robustness of the input data representation. A significant body of the quantitative finance literature focuses on utilising different deep learning architectures without taking into consideration the key assumptions these models make with respect to the input data representation. In this paper, we highlight the issues associated with the commonly-used representations of limit order book data from both a theoretical and practical perspectives. We also show the fragility of the representations under adversarial perturbations and propose two simple modifications to the existing representations that match the theoretical assumptions of deep learning models. Finally, we show experimentally how our proposed representations lead to state-of-the-art performance in both accuracy and robustness utilising very simple neural network architectures.
Topological Structure and Semantic Information Transfer Network for Cross-Scene Hyperspectral Image Classification
Authors: Zhang, Yuxiang and Li, Wei and Zhang, Mengmeng and Qu, Ying and Tao, Ran and Qi, Hairong
Date: 2021-09-16 00:00:00
Proceeding: journal 2021 9
Tasks: Domain Adaptation, Hyperspectral Image Classification, image-classification, Image Classification

Domain adaptation techniques have been widely applied to the problem of cross-scene hyperspectral image (HSI) classification. Most existing methods use convolutional neural networks (CNN) to extract statistical features from data and often neglect the potential topological structure information between different land cover classes. CNN-based approaches generally only model the local spatial relationships of the samples, which largely limits their ability to capture the non-local topological relationship that would better represent the underlying data structure of HSI. In order to make up for the above shortcomings, a Topological structure and Semantic information Transfer network (TSTnet) is developed. The method employs the graph structure to characterize topological relationships and the graph convolutional network (GCN) that is good at processing for crossscene HSI classification. In the proposed TSTnet, graph optimal transmission (GOT) is used to align topological relationships to assist distribution alignment between source domain and target domain based on maximum mean difference (MMD). Furthermore, subgraphs from source domain and target domain are dynamically constructed based on CNN features to take advantage of the discriminative capacity of CNN models that in turn improves robustness of classification. In addition, to better characterize the correlation between distribution alignment and topological relationship alignment, a consistency constraint is enforced to integrate the output of CNN and GCN. Experimental results on three cross-scene HSI datasets demonstrate that the proposed TSTnet performs significantly better than some state-of-the-art domain-adaptive approaches.
Violence Recognition from Videos using Deep Learning Techniques
Authors: Mohamed Mostafa Soliman, Mohamed Hussein Kamal, Mina Abd El-Massih Nashed, Youssef Mohamed Mostafa, Bassel Safwat Chawky, Dina Khattab
Date: 2019-12-10 00:00:00
Proceeding: IEEE International Conference on Intelligent Computing and Information Systems (ICICIS) 2019 12
Tasks: Action Recognition, Deep Learning

Automatic recognition of violence between individuals or crowds in videos has a broad interest. In this work, an end-to-end deep neural network model for the purpose of recognizing violence in videos is proposed. The proposed model uses a pre-trained VGG-16 on ImageNet as spatial feature extractor followed by Long Short-Term Memory (LSTM) as temporal feature extractor and sequence of fully connected layers for classification purpose. The achieved accuracy is near state-of-the-art. Also, we contribute by introducing a new benchmark called Real- Life Violence Situations which contains 2000 short videos divided into 1000 violence videos and 1000 non-violence videos. The new benchmark is used for fine-tuning the proposed models achieving a best accuracy of 88.2%.
A Temporal Fusion Approach for Video Classification with Convolutional and LSTM Neural Networks Applied to Violence Detection
Authors: Jean Phelipe de Oliveira Lima, Carlos Maur´ıcio Ser´odio Figueiredo
Date: 2021-02-20 00:00:00
Proceeding: Inteligencia Artificial 2021 2
Tasks: Action Recognition, Video Classification

In modern smart cities, there is a quest for the highest level of integration and automation service. In
the surveillance sector, one of the main challenges is to automate the analysis of videos in real-time to identify
critical situations. This paper presents intelligent models based on Convolutional Neural Networks (in which the
MobileNet, InceptionV3 and VGG16 networks had used), LSTM networks and feedforward networks for the task
of classifying videos under the classes "Violence" and "Non-Violence", using for this the RLVS database. Different
data representations held used according to the Temporal Fusion techniques. The best outcome achieved was 0.91
and 0.90 of Accuracy and F1-Score, respectively, a higher result compared to those found in similar researches
for works conducted on the same database.
Data Efficient Video Transformer for Violence Detection
Authors: almamon rasool abdali
Date: 2021-07-17 00:00:00
Proceeding: IEEE International Conference on Communication, Networks and Satellite (ComNetSat) 2021 7
Tasks: Action Recognition, Event Detection

In smart cities, violence event detection is critical to ensure city safety. Several studies have been done on this topic with a focus on 2d-Convolutional Neural Network (2d-CNN) to detect spatial features from each frame, followed by one of the Recurrent Neural Networks (RNN) variants as a temporal features learning method. On the other hand, the transformer network has achieved a great result in many areas. The bottleneck for transformers is the need for large data set to achieve good results. In this work, we propose a data-efficient video transformer (DeVTr) based on the transformer network as a Spatio-temporal learning method with a pre-trained 2d-Convolutional neural network (2d-CNN) as an embedding layer for the input data. The model has been trained and tested on the Real-life violence dataset (RLVS) and achieved an accuracy of 96.25%. A comparison of the result for the suggested method with previous techniques illustrated that the suggested method provides the best result among all the other studies for violence event detection.
Exploring Wav2vec 2.0 fine-tuning for improved speech emotion recognition
Authors: Li-Wei Chen, Alexander Rudnicky
Date: 2021-10-12 00:00:00
Tasks: Emotion Recognition, Speech Emotion Recognition, speech-recognition, Speech Recognition

While Wav2Vec 2.0 has been proposed for speech recognition (ASR), it can also be used for speech emotion recognition (SER); its performance can be significantly improved using different fine-tuning strategies. Two baseline methods, vanilla fine-tuning (V-FT) and task adaptive pretraining (TAPT) are first presented. We show that V-FT is able to outperform state-of-the-art models on the IEMOCAP dataset. TAPT, an existing NLP fine-tuning strategy, further improves the performance on SER. We also introduce a novel fine-tuning method termed P-TAPT, which modifies the TAPT objective to learn contextualized emotion representations. Experiments show that P-TAPT performs better than TAPT, especially under low-resource settings. Compared to prior works in this literature, our top-line system achieved a 7.4\% absolute improvement in unweighted accuracy (UA) over the state-of-the-art performance on IEMOCAP. Our code is publicly available.
Fine-grained style control in Transformer-based Text-to-speech Synthesis
Authors: Li-Wei Chen, Alexander Rudnicky
Date: 2021-10-12 00:00:00
Tasks: Inductive Bias, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

In this paper, we present a novel architecture to realize fine-grained style control on the transformer-based text-to-speech synthesis (TransformerTTS). Specifically, we model the speaking style by extracting a time sequence of local style tokens (LST) from the reference speech. The existing content encoder in TransformerTTS is then replaced by our designed cross-attention blocks for fusion and alignment between content and style. As the fusion is performed along with the skip connection, our cross-attention block provides a good inductive bias to gradually infuse the phoneme representation with a given style. Additionally, we prevent the style embedding from encoding linguistic content by randomly truncating LST during training and using wav2vec 2.0 features. Experiments show that with fine-grained style control, our system performs better in terms of naturalness, intelligibility, and style transferability. Our code and samples are publicly available.
SDR -- Medium Rare with Fast Computations
Authors: Robin Scheibler
Date: 2021-10-13 00:00:00

We revisit the widely used bss eval metrics for source separation with an eye out for performance. We propose a fast algorithm fixing shortcomings of publicly available implementations. First, we show that the metrics are fully specified by the squared cosine of just two angles between estimate and reference subspaces. Second, large linear systems are involved. However, they are structured, and we apply a fast iterative method based on conjugate gradient descent. The complexity of this step is thus reduced by a factor quadratic in the distortion filter size used in bss eval, usually 512. In experiments, we assess speed and numerical accuracy. Not only is the loss of accuracy due to the approximate solver acceptable for most applications, but the speed-up is up to two orders of magnitude in some, not so extreme, cases. We confirm that our implementation can train neural networks, and find that longer distortion filters may be beneficial.
The Dawn of Quantum Natural Language Processing
Authors: Riccardo Di Sipio, Jia-Hong Huang, Samuel Yen-Chi Chen, Stefano Mangini, Marcel Worring
Date: 2021-10-13 00:00:00
Tasks: Sentiment Analysis

In this paper, we discuss the initial attempts at boosting understanding human language based on deep-learning models with quantum computing. We successfully train a quantum-enhanced Long Short-Term Memory network to perform the parts-of-speech tagging task via numerical simulations. Moreover, a quantum-enhanced Transformer is proposed to perform the sentiment analysis based on the existing dataset.
Object DGCNN: 3D Object Detection using Dynamic Graphs
Authors: Yue Wang, Justin Solomon
Date: 2021-10-13 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: 2D Object Detection, 3D Object Detection, Autonomous Driving, Knowledge Distillation, Object, object-detection, Object Detection

3D object detection often involves complicated training and testing pipelines, which require substantial domain knowledge about individual datasets. Inspired by recent non-maximum suppression-free 2D object detection models, we propose a 3D object detection architecture on point clouds. Our method models 3D object detection as message passing on a dynamic graph, generalizing the DGCNN framework to predict a set of objects. In our construction, we remove the necessity of post-processing via object confidence aggregation or non-maximum suppression. To facilitate object detection from sparse point clouds, we also propose a set-to-set distillation approach customized to 3D detection. This approach aligns the outputs of the teacher model and the student model in a permutation-invariant fashion, significantly simplifying knowledge distillation for the 3D detection task. Our method achieves state-of-the-art performance on autonomous driving benchmarks. We also provide abundant analysis of the detection model and distillation framework.
DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries
Authors: Yue Wang, Vitor Guizilini, Tianyuan Zhang, Yilun Wang, Hang Zhao, Justin Solomon
Date: 2021-10-13 00:00:00
Tasks: 3D Object Detection, Autonomous Driving, Depth Estimation, Depth Prediction, Object, object-detection, Object Detection, Prediction, Robust Camera Only 3D Object Detection

We introduce a framework for multi-camera 3D object detection. In contrast to existing works, which estimate 3D bounding boxes directly from monocular images or use depth prediction networks to generate input for 3D object detection from 2D information, our method manipulates predictions directly in 3D space. Our architecture extracts 2D features from multiple camera images and then uses a sparse set of 3D object queries to index into these 2D features, linking 3D positions to multi-view images using camera transformation matrices. Finally, our model makes a bounding box prediction per object query, using a set-to-set loss to measure the discrepancy between the ground-truth and the prediction. This top-down approach outperforms its bottom-up counterpart in which object bounding box prediction follows per-pixel depth estimation, since it does not suffer from the compounding error introduced by a depth prediction model. Moreover, our method does not require post-processing such as non-maximum suppression, dramatically improving inference speed. We achieve state-of-the-art performance on the nuScenes autonomous driving benchmark.
OPEn: An Open-ended Physics Environment for Learning Without a Task
Authors: Chuang Gan, Abhishek Bhandwaldar, Antonio Torralba, Joshua B. Tenenbaum, Phillip Isola
Date: 2021-10-13 00:00:00
Tasks: Contrastive Learning, Representation Learning

Humans have mental models that allow them to plan, experiment, and reason in the physical world. How should an intelligent agent go about learning such models? In this paper, we will study if models of the world learned in an open-ended physics environment, without any specific tasks, can be reused for downstream physics reasoning tasks. To this end, we build a benchmark Open-ended Physics ENvironment (OPEn) and also design several tasks to test learning representations in this environment explicitly. This setting reflects the conditions in which real agents (i.e. rolling robots) find themselves, where they may be placed in a new kind of environment and must adapt without any teacher to tell them how this environment works. This setting is challenging because it requires solving an exploration problem in addition to a model building and representation learning problem. We test several existing RL-based exploration methods on this benchmark and find that an agent using unsupervised contrastive learning for representation learning, and impact-driven learning for exploration, achieved the best results. However, all models still fall short in sample efficiency when transferring to the downstream tasks. We expect that OPEn will encourage the development of novel rolling robot agents that can build reusable mental models of the world that facilitate many tasks.
Reinforcement Learning for Standards Design
Authors: Shahrukh Khan Kasi, Sayandev Mukherjee, Lin Cheng, Bernardo A. Huberman
Date: 2021-10-13 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Communications standards are designed via committees of humans holding repeated meetings over months or even years until consensus is achieved. This includes decisions regarding the modulation and coding schemes to be supported over an air interface. We propose a way to "automate" the selection of the set of modulation and coding schemes to be supported over a given air interface and thereby streamline both the standards design process and the ease of extending the standard to support new modulation schemes applicable to new higher-level applications and services. Our scheme involves machine learning, whereby a constructor entity submits proposals to an evaluator entity, which returns a score for the proposal. The constructor employs reinforcement learning to iterate on its submitted proposals until a score is achieved that was previously agreed upon by both constructor and evaluator to be indicative of satisfying the required design criteria (including performance metrics for transmissions over the interface).
Poison Forensics: Traceback of Data Poisoning Attacks in Neural Networks
Authors: Shawn Shan, Arjun Nitin Bhagoji, Haitao Zheng, Ben Y. Zhao
Date: 2021-10-13 00:00:00
Tasks: Data Poisoning, Malware Classification

In adversarial machine learning, new defenses against attacks on deep learning systems are routinely broken soon after their release by more powerful attacks. In this context, forensic tools can offer a valuable complement to existing defenses, by tracing back a successful attack to its root cause, and offering a path forward for mitigation to prevent similar attacks in the future. In this paper, we describe our efforts in developing a forensic traceback tool for poison attacks on deep neural networks. We propose a novel iterative clustering and pruning solution that trims "innocent" training samples, until all that remains is the set of poisoned data responsible for the attack. Our method clusters training samples based on their impact on model parameters, then uses an efficient data unlearning method to prune innocent clusters. We empirically demonstrate the efficacy of our system on three types of dirty-label (backdoor) poison attacks and three types of clean-label poison attacks, across domains of computer vision and malware classification. Our system achieves over 98.4% precision and 96.8% recall across all attacks. We also show that our system is robust against four anti-forensics measures specifically designed to attack it.
Representing and Implementing Matrices Using Algebraic ZX-calculus
Authors: Quanlong Wang, Richie Yeung
Date: 2021-10-13 00:00:00

In linear algebra applications, elementary matrices hold a significant role. This paper presents a diagrammatic representation of all $2^m\times 2^n$-sized elementary matrices in algebraic ZX-calculus, showcasing their properties on inverses and transpose through diagrammatic rewriting. Additionally, the paper uses this representation to depict the Jozsa-style matchgate in algebraic ZX-calculus. To further enhance practical use, we have implemented this representation in \texttt{discopy}. Overall, this work sets the groundwork for more applications of ZX-calculus such as synthesising controlled matrices [arXiv:2212.04462] in quantum computing.
TAG: Toward Accurate Social Media Content Tagging with a Concept Graph
Authors: Jiuding Yang, Weidong Guo, Bang Liu, Yakun Yu, Chaoyue Wang, Jinwen Luo, Linglong Kong, Di Niu, Zhen Wen
Date: 2021-10-13 00:00:00
Tasks: Dependency Parsing, Graph Matching, Negation, Sentence, TAG, Text Matching

Although conceptualization has been widely studied in semantics and knowledge representation, it is still challenging to find the most accurate concept phrases to characterize the main idea of a text snippet on the fast-growing social media. This is partly attributed to the fact that most knowledge bases contain general terms of the world, such as trees and cars, which do not have the defining power or are not interesting enough to social media app users. Another reason is that the intricacy of natural language allows the use of tense, negation and grammar to change the logic or emphasis of language, thus conveying completely different meanings. In this paper, we present TAG, a high-quality concept matching dataset consisting of 10,000 labeled pairs of fine-grained concepts and web-styled natural language sentences, mined from the open-domain social media. The concepts we consider represent the trending interests of online users. Associated with TAG is a concept graph of these fine-grained concepts and entities to provide the structural context information. We evaluate a wide range of popular neural text matching models as well as pre-trained language models on TAG, and point out their insufficiency to tag social media content with the most appropriate concept. We further propose a novel graph-graph matching method that demonstrates superior abstraction and generalization performance by better utilizing both the structural context in the concept graph and logic interactions between semantic units in the sentence via syntactic dependency parsing. We open-source both the TAG dataset and the proposed methods to facilitate further research.
Extending Environments To Measure Self-Reflection In Reinforcement Learning
Authors: Samuel Allen Alexander, Michael Castaneda, Kevin Compher, Oscar Martinez
Date: 2021-10-13 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider an extended notion of reinforcement learning in which the environment can simulate the agent and base its outputs on the agent's hypothetical behavior. Since good performance usually requires paying attention to whatever things the environment's outputs are based on, we argue that for an agent to achieve on-average good performance across many such extended environments, it is necessary for the agent to self-reflect. Thus weighted-average performance over the space of all suitably well-behaved extended environments could be considered a way of measuring how self-reflective an agent is. We give examples of extended environments and introduce a simple transformation which experimentally seems to increase some standard RL agents' performance in a certain type of extended environment.
ConditionalQA: A Complex Reading Comprehension Dataset with Conditional Answers
Authors: Haitian Sun, William W. Cohen, Ruslan Salakhutdinov
Date: 2021-10-13 00:00:00
Proceeding: ACL 2022 5
Tasks: Logical Reasoning, Question Answering, Reading Comprehension

We describe a Question Answering (QA) dataset that contains complex questions with conditional answers, i.e. the answers are only applicable when certain conditions apply. We call this dataset ConditionalQA. In addition to conditional answers, the dataset also features: (1) long context documents with information that is related in logically complex ways; (2) multi-hop questions that require compositional logical reasoning; (3) a combination of extractive questions, yes/no questions, questions with multiple answers, and not-answerable questions; (4) questions asked without knowing the answers. We show that ConditionalQA is challenging for many of the existing QA models, especially in selecting answer conditions. We believe that this dataset will motivate further research in answering complex questions over long documents. Data and leaderboard are publicly available at \url{https://github.com/haitian-sun/ConditionalQA}.
Two-argument activation functions learn soft XOR operations like cortical neurons
Authors: KiJung Yoon, Emin Orhan, Juhyun Kim, Xaq Pitkow
Date: 2021-10-13 00:00:00
Tasks: Vocal Bursts Valence Prediction

Neurons in the brain are complex machines with distinct functional compartments that interact nonlinearly. In contrast, neurons in artificial neural networks abstract away this complexity, typically down to a scalar activation function of a weighted sum of inputs. Here we emulate more biologically realistic neurons by learning canonical activation functions with two input arguments, analogous to basal and apical dendrites. We use a network-in-network architecture where each neuron is modeled as a multilayer perceptron with two inputs and a single output. This inner perceptron is shared by all units in the outer network. Remarkably, the resultant nonlinearities often produce soft XOR functions, consistent with recent experimental observations about interactions between inputs in human cortical neurons. When hyperparameters are optimized, networks with these nonlinearities learn faster and perform better than conventional ReLU nonlinearities with matched parameter counts, and they are more robust to natural and adversarial perturbations.