pwc-archive/papers-with-abstracts huggingface dataset:
Text Complexity DE Challenge 2022 Submission Description: Pairwise Regression for Complexity Prediction
Authors: Leander Girrbach
Date: 2022-09-01 00:00:00
Proceeding: GermEval 2022 9
Tasks: regression, Sentence, Sentence Embeddings

This paper describes our submission to the Text Complexity DE Challenge 2022 (Mohtaj et al., 2022). We evaluate a pairwise regression model that predicts the relative difference in complexity of two sentences, instead of predicting a complexity score from a single sentence. In consequence, the model returns samples of scores (as many as there are training sentences) instead of a point estimate. Due to an error in the submission, test set results are unavailable. However, we show by cross-validation that pairwise regression does not improve performance over standard regression models using sentence embeddings taken from pretrained language models as input. Furthermore, we do not find the distribution standard deviations to reflect differences in “uncertainty” of the model predictions in an useful way.
TUM sebis at GermEval 2022: A Hybrid Model Leveraging Gaussian Processes and Fine-Tuned XLM-RoBERTa for German Text Complexity Analysis
Authors: Juraj Vladika, Stephen Meisenbacher, Florian Matthes
Date: 2022-09-01 00:00:00
Proceeding: GermEval 2022 9
Tasks: Gaussian Processes

The task of quantifying the complexity of written language presents an interesting endeavor, particularly in the opportunity that it presents for aiding language learners. In this pursuit, the question of what exactly about natural language contributes to its complexity (or lack thereof) is an interesting point of investigation. We propose a hybrid approach, utilizing shallow models to capture linguistic features, while leveraging a fine-tuned embedding model to encode the semantics of input text. By harmonizing these two methods, we achieve competitive scores in the given metric, and we demonstrate improvements over either singular method. In addition, we uncover the effectiveness of Gaussian processes in the training of shallow models for text complexity analysis.
Adversarial-Metric Learning for Audio-Visual Cross-Modal Matching
Authors: Aihua Zheng, Menglan Hu, Bo Jiang *, Yan Huang, Yan Yan, and Bin Luo
Date: 2021-01-12 00:00:00
Proceeding: IEEE Transactions on Multimedia 2021 1
Tasks: audio-visual learning, Metric Learning, Retrieval

Audio-visual matching aims to learn the intrinsic correspondence between image and audio clip. Existing works mainly concentrate on learning discriminative features, while ignore the cross-modal heterogeneous issue between audio and visual modalities. To deal with this issue, we propose a novel Adversarial-Metric Learning (AML) model for audio-visual matching. AML aims to generate a modality-independent representation for each person in each modality via adversarial learning, while simultaneously learns a robust similarity measure for cross-modality matching via metric learning. By integrating the discriminative modality-independent representation and robust cross-modality metric learning into an end-to-end trainable deep network, AML can overcome the heterogeneous issue with promising performance for audio-visual matching. Experiments on the various audio-visual learning tasks, including audio-visual matching, audio-visual verification and audio-visual retrieval on benchmark dataset demonstrate the effectiveness of the proposed AML model. The implementation codes are available on https://github.com/MLanHu/AML .
A deep convolutional neural network model to classify heartbeats
Authors: U. Rajendra Acharya, Shu Lih Oh, Yuki Hagiwara, Jen Hong Tan, Muhammad Adam, ArkadiuszGertych, Ru SanTan
Date: 2017-10-01 00:00:00
Proceeding: Computers in Biology and Medicine 2017 10
Tasks: Diagnostic, Rhythm

The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that refers to an abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification of normal versus abnormal individual heart beats, and their correct classification into different diagnoses, based on ECG morphology. Heartbeats can be sub-divided into five categories namely non-ectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beats. It is challenging and time-consuming to distinguish these heartbeats on ECG as these signals are typically corrupted by noise. We developed a 9-layer deep convolutional neural network (CNN) to automatically identify 5 different categories of heartbeats in ECG signals. Our experiment was conducted in original and noise attenuated sets of ECG signals derived from a publicly available database. This set was artificially augmented to even out the number of instances the 5 classes of heartbeats and filtered to remove high-frequency noise. The CNN was trained using the augmented data and achieved an accuracy of 94.03% and 93.47% in the diagnostic classification of heartbeats in original and noise free ECGs, respectively. When the CNN was trained with highly imbalanced data (original dataset), the accuracy of the CNN reduced to 89.07%% and 89.3% in noisy and noise-free ECGs. When properly trained, the proposed CNN model can serve as a tool for screening of ECG to quickly identify different types and frequency of arrhythmic heartbeats.
Focal-WNet: An Architecture Unifying Convolution and Attention for Depth Estimation
Authors: Gouthamaan Manimaran, Swaminathan J
Date: 2022-07-18 00:00:00
Proceeding: I2CT 2022 7
Tasks: Decoder, Depth Estimation, Monocular Depth Estimation

Extracting depth information from a single RGB image is a fundamental and challenging task in computer vision with wide-ranging applications. This task cannot be solved using traditional methods like multi-view geometry but only by deep learning. Existing methods using convolutional neural nets produce inconsistent and blurry results due to the lack of long-range dependencies. With the recent success of Transformer networks in computer vision, which can process information locally and globally, we leverage this idea to propose a novel architecture named Focal-WNet in this paper. This architecture consists of two separate encoders and a single decoder. The main aim of this network is to learn most monocular depth cues like relative scale, contrast differences, texture gradient, etc. We incorporate focal self-attention instead of vanilla self-attention to reduce the computational complexity of the network. Along with the focal transformer layers, we leverage a convolutional architecture to learn depth cues that cannot be learned by a transformer alone as some cues like occlusion require a local receptive field and are easier for a conv-net to learn. Extensive experiments show that the proposed Focal-WNet achieves competitive results on two challenging datasets.
Application of SsVGMM to medical data-classification with novelty detection
Authors: Fan Yang, Jaymar Soriano, Takatomi Kubo, Kazushi Ikeda
Date: 2017-07-15 00:00:00
Proceeding: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2017 7
Tasks: Classification, Multi-class Classification, Novelty Detection

There is a considerable demand to apply classification in medical analysis. A traditional classifier requires training samples from each class. However, in reality, it is possible that the testing set may include courses that are not in the training set. This inevitably causes an issue: data from an undefined class will be assigned to predefined classes. To tackle this, we propose a semi-supervised variational Gaussian mixture model to perform multi-class classification with novelty detection. Compared to some popular novelty detection methods, we demonstrate that it gets better performance on thyroid disease data, by generating the distribution of predefined classes and undefined classes, without explicitly setting a threshold.
https://ieeexplore.ieee.org/document/7288144
Authors: X. Li, D. Chang, H. Pen, X. Zhang, Y. Liu and Y. Yao, "Application of MVVM design pattern in MES, " 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2015, pp. 1374-1378, doi: 10.1109/CYBER.2015.7288144.
Date: 2015-10-05 00:00:00
Proceeding: 08-12 2015 10

Manufacturing Execution System (MES) is developed for information requirement of manufacturing enterprises. Because of different requirement of MES in different enterprises, almost all MES are developed customized. Therefor how to reduce modified codes and decrease the coupling between view page and data become the key for system development. MVVM design model separates software structure to view, model and view model by XAML and Data Binding of WPF technology. The number of modified codes is dramatically reduced because of the using of MVVM design pattern. In this paper, MVVM design pattern for MES development is introduced and MVVM Framework, principle and advantage are also studied in detail.
Data Forecasts of the Epidemic COVID-19 by Deterministic and Stochastic Time-Dependent Models
Authors: Bo-Sheng Chen, Zong-Ying Wu, Yen-Jia Chen, Jann-Long Chern
Date: 2022-09-07 00:00:00

We propose a deterministic SAIVRD model and a stochastic SARV model of the epidemic COVID-19 involving asymptomatic infections and vaccinations to conduct data forecasts using time-dependent parameters. The forecast by our deterministic model conducts 10-day predictions to see whether the epidemic will ease or become more severe in the short term. The forecast by our stochastic model predicts the probability distributions of the final size and the maximum size to see how large the epidemic will be in the long run. The first forecast using the data set from the USA gives the relative errors within 3% in 5 days and 7% in 10 days for the prediction of isolated infectious cases and smaller ones for the predictions of recoveries and deaths. The distributions in the second forecast using the time-varying parameters from the first forecast are also bimodal in our model with time-independent parameters in our simulations of smaller populations. For the model with time-dependent model, what are different are that there is another peak in the final size distribution, that the the probability of minor outbreak is higher and that the maximum size distribution is oscillating with time-dependent parameters. The final size distributions are similar between different populations and so are the maximum size distributions, which means that we can expect that with the same parameters and in a large population, the ratio of the final size and the maximum size are distributed similarly (only different by the value of the second peak). The result shows that under recent transmissibility of this disease in the USA, when an initial infection is introduced into all-susceptible (large) population, major outbreak occurs with around 95% of the population and with high probability the epidemic is maximized to around 30% of the population.
KT-BT: A Framework for Knowledge Transfer Through Behavior Trees in Multi-Robot Systems
Authors: Sanjay Sarma Oruganti Venkata, Ramviyas Parasuraman, Ramana Pidaparti
Date: 2022-09-07 00:00:00
Tasks: Transfer Learning

Multi-Robot and Multi-Agent Systems demonstrate collective (swarm) intelligence through systematic and distributed integration of local behaviors in a group. Agents sharing knowledge about the mission and environment can enhance performance at individual and mission levels. However, this is difficult to achieve, partly due to the lack of a generic framework for transferring part of the known knowledge (behaviors) between agents. This paper presents a new knowledge representation framework and a transfer strategy called KT-BT: Knowledge Transfer through Behavior Trees. The KT-BT framework follows a query-response-update mechanism through an online Behavior Tree framework, where agents broadcast queries for unknown conditions and respond with appropriate knowledge using a condition-action-control sub-flow. We embed a novel grammar structure called stringBT that encodes knowledge, enabling behavior sharing. We theoretically investigate the properties of the KT-BT framework in achieving homogeneity of high knowledge across the entire group compared to a heterogeneous system without the capability of sharing their knowledge. We extensively verify our framework in a simulated multi-robot search and rescue problem. The results show successful knowledge transfers and improved group performance in various scenarios. We further study the effects of opportunities and communication range on group performance, knowledge spread, and functional heterogeneity in a group of agents, presenting interesting insights.
A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Platform
Authors: Z. Jiang, G. Song
Date: 2022-09-07 00:00:00
Tasks: Deep Reinforcement Learning, OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

With the development of industry, drones are appearing in various field. In recent years, deep reinforcement learning has made impressive gains in games, and we are committed to applying deep reinforcement learning algorithms to the field of robotics, moving reinforcement learning algorithms from game scenarios to real-world application scenarios. We are inspired by the LunarLander of OpenAI Gym, we decided to make a bold attempt in the field of reinforcement learning to control drones. At present, there is still a lack of work applying reinforcement learning algorithms to robot control, the physical simulation platform related to robot control is only suitable for the verification of classical algorithms, and is not suitable for accessing reinforcement learning algorithms for the training. In this paper, we will face this problem, bridging the gap between physical simulation platforms and intelligent agent, connecting intelligent agents to a physical simulation platform, allowing agents to learn and complete drone flight tasks in a simulator that approximates the real world. We proposed a reinforcement learning framework based on Gazebo that is a kind of physical simulation platform (ROS-RL), and used three continuous action space reinforcement learning algorithms in the framework to dealing with the problem of autonomous landing of drones. Experiments show the effectiveness of the algorithm, the task of autonomous landing of drones based on reinforcement learning achieved full success.
Interpretations Steered Network Pruning via Amortized Inferred Saliency Maps
Authors: Alireza Ganjdanesh, Shangqian Gao, Heng Huang
Date: 2022-09-07 00:00:00
Tasks: Inductive Bias, Network Pruning

Convolutional Neural Networks (CNNs) compression is crucial to deploying these models in edge devices with limited resources. Existing channel pruning algorithms for CNNs have achieved plenty of success on complex models. They approach the pruning problem from various perspectives and use different metrics to guide the pruning process. However, these metrics mainly focus on the model's `outputs' or `weights' and neglect its `interpretations' information. To fill in this gap, we propose to address the channel pruning problem from a novel perspective by leveraging the interpretations of a model to steer the pruning process, thereby utilizing information from both inputs and outputs of the model. However, existing interpretation methods cannot get deployed to achieve our goal as either they are inefficient for pruning or may predict non-coherent explanations. We tackle this challenge by introducing a selector model that predicts real-time smooth saliency masks for pruned models. We parameterize the distribution of explanatory masks by Radial Basis Function (RBF)-like functions to incorporate geometric prior of natural images in our selector model's inductive bias. Thus, we can obtain compact representations of explanations to reduce the computational costs of our pruning method. We leverage our selector model to steer the network pruning by maximizing the similarity of explanatory representations for the pruned and original models. Extensive experiments on CIFAR-10 and ImageNet benchmark datasets demonstrate the efficacy of our proposed method. Our implementations are available at \url{https://github.com/Alii-Ganjj/InterpretationsSteeredPruning}
Multimodal learning with graphs
Authors: Yasha Ektefaie, George Dasoulas, Ayush Noori, Maha Farhat, Marinka Zitnik
Date: 2022-09-07 00:00:00
Tasks: Graph Learning, Inductive Bias, Representation Learning

Artificial intelligence for graphs has achieved remarkable success in modeling complex systems, ranging from dynamic networks in biology to interacting particle systems in physics. However, the increasingly heterogeneous graph datasets call for multimodal methods that can combine different inductive biases: the set of assumptions that algorithms use to make predictions for inputs they have not encountered during training. Learning on multimodal datasets presents fundamental challenges because the inductive biases can vary by data modality and graphs might not be explicitly given in the input. To address these challenges, multimodal graph AI methods combine different modalities while leveraging cross-modal dependencies using graphs. Diverse datasets are combined using graphs and fed into sophisticated multimodal architectures, specified as image-intensive, knowledge-grounded and language-intensive models. Using this categorization, we introduce a blueprint for multimodal graph learning, use it to study existing methods and provide guidelines to design new models.
VulCurator: A Vulnerability-Fixing Commit Detector
Authors: Truong Giang Nguyen, Thanh Le-Cong, Hong Jin Kang, Xuan-Bach D. Le, David Lo
Date: 2022-09-07 00:00:00
Tasks: Deep Learning, Management

Open-source software (OSS) vulnerability management process is important nowadays, as the number of discovered OSS vulnerabilities is increasing over time. Monitoring vulnerability-fixing commits is a part of the standard process to prevent vulnerability exploitation. Manually detecting vulnerability-fixing commits is, however, time consuming due to the possibly large number of commits to review. Recently, many techniques have been proposed to automatically detect vulnerability-fixing commits using machine learning. These solutions either: (1) did not use deep learning, or (2) use deep learning on only limited sources of information. This paper proposes VulCurator, a tool that leverages deep learning on richer sources of information, including commit messages, code changes and issue reports for vulnerability-fixing commit classifica- tion. Our experimental results show that VulCurator outperforms the state-of-the-art baselines up to 16.1% in terms of F1-score. VulCurator tool is publicly available at https://github.com/ntgiang71096/VFDetector and https://zenodo.org/record/7034132#.Yw3MN-xBzDI, with a demo video at https://youtu.be/uMlFmWSJYOE.
AutoPruner: Transformer-Based Call Graph Pruning
Authors: Thanh Le-Cong, Hong Jin Kang, Truong Giang Nguyen, Stefanus Agus Haryono, David Lo, Xuan-Bach D. Le, Huynh Quyet Thang
Date: 2022-09-07 00:00:00

Constructing a static call graph requires trade-offs between soundness and precision. Program analysis techniques for constructing call graphs are unfortunately usually imprecise. To address this problem, researchers have recently proposed call graph pruning empowered by machine learning to post-process call graphs constructed by static analysis. A machine learning model is built to capture information from the call graph by extracting structural features for use in a random forest classifier. It then removes edges that are predicted to be false positives. Despite the improvements shown by machine learning models, they are still limited as they do not consider the source code semantics and thus often are not able to effectively distinguish true and false positives. In this paper, we present a novel call graph pruning technique, AutoPruner, for eliminating false positives in call graphs via both statistical semantic and structural analysis. Given a call graph constructed by traditional static analysis tools, AutoPruner takes a Transformer-based approach to capture the semantic relationships between the caller and callee functions associated with each edge in the call graph. To do so, AutoPruner fine-tunes a model of code that was pre-trained on a large corpus to represent source code based on descriptions of its semantics. Next, the model is used to extract semantic features from the functions related to each edge in the call graph. AutoPruner uses these semantic features together with the structural features extracted from the call graph to classify each edge via a feed-forward neural network. Our empirical evaluation on a benchmark dataset of real-world programs shows that AutoPruner outperforms the state-of-the-art baselines, improving on F-measure by up to 13% in identifying false-positive edges in a static call graph.
Hardware faults that matter: Understanding and Estimating the safety impact of hardware faults on object detection DNNs
Authors: Syed Qutub, Florian Geissler, Yang Peng, Ralf Grafe, Michael Paulitsch, Gereon Hinz, Alois Knoll
Date: 2022-09-07 00:00:00
Tasks: Object, object-detection, Object Detection

Object detection neural network models need to perform reliably in highly dynamic and safety-critical environments like automated driving or robotics. Therefore, it is paramount to verify the robustness of the detection under unexpected hardware faults like soft errors that can impact a systems perception module. Standard metrics based on average precision produce model vulnerability estimates at the object level rather than at an image level. As we show in this paper, this does not provide an intuitive or representative indicator of the safety-related impact of silent data corruption caused by bit flips in the underlying memory but can lead to an over- or underestimation of typical fault-induced hazards. With an eye towards safety-related real-time applications, we propose a new metric IVMOD (Image-wise Vulnerability Metric for Object Detection) to quantify vulnerability based on an incorrect image-wise object detection due to false positive (FPs) or false negative (FNs) objects, combined with a severity analysis. The evaluation of several representative object detection models shows that even a single bit flip can lead to a severe silent data corruption event with potentially critical safety implications, with e.g., up to (much greater than) 100 FPs generated, or up to approx. 90% of true positives (TPs) are lost in an image. Furthermore, with a single stuck-at-1 fault, an entire sequence of images can be affected, causing temporally persistent ghost detections that can be mistaken for actual objects (covering up to approx. 83% of the image). Furthermore, actual objects in the scene are continuously missed (up to approx. 64% of TPs are lost). Our work establishes a detailed understanding of the safety-related vulnerability of such critical workloads against hardware faults.