pwc-archive/papers-with-abstracts huggingface dataset:
SphereFace2: Binary Classification is All You Need for Deep Face Recognition
Authors: Yandong Wen, Weiyang Liu, Adrian Weller, Bhiksha Raj, Rita Singh
Date: 2021-08-03 00:00:00
Proceeding: ICLR 2022 4
Tasks: All, Binary Classification, Classification, Face Recognition, Multi-class Classification

State-of-the-art deep face recognition methods are mostly trained with a softmax-based multi-class classification framework. Despite being popular and effective, these methods still have a few shortcomings that limit empirical performance. In this paper, we start by identifying the discrepancy between training and evaluation in the existing multi-class classification framework and then discuss the potential limitations caused by the "competitive" nature of softmax normalization. Motivated by these limitations, we propose a novel binary classification training framework, termed SphereFace2. In contrast to existing methods, SphereFace2 circumvents the softmax normalization, as well as the corresponding closed-set assumption. This effectively bridges the gap between training and evaluation, enabling the representations to be improved individually by each binary classification task. Besides designing a specific well-performing loss function, we summarize a few general principles for this "one-vs-all" binary classification framework so that it can outperform current competitive methods. Our experiments on popular benchmarks demonstrate that SphereFace2 can consistently outperform state-of-the-art deep face recognition methods. The code has been made publicly available.
Research Challenges and Progress in Robotic Grasping and Manipulation Competitions
Authors: Yu Sun, Joe Falco, Maximo A. Roa, Berk Calli
Date: 2021-08-03 00:00:00
Tasks: Robotic Grasping

This paper discusses recent research progress in robotic grasping and manipulation in the light of the latest Robotic Grasping and Manipulation Competitions (RGMCs). We first provide an overview of past benchmarks and competitions related to the robotics manipulation field. Then, we discuss the methodology behind designing the manipulation tasks in RGMCs. We provide a detailed analysis of key challenges for each task and identify the most difficult aspects based on the competing teams' performance in recent years. We believe that such an analysis is insightful to determine the future research directions for the robotic manipulation domain.
sarcasm detection and quantification in arabic tweets
Authors: Bashar Talafha, Muhy Eddin Za'ter, Samer Suleiman, Mahmoud Al-Ayyoub, Mohammed N. Al-Kabi
Date: 2021-08-03 00:00:00
Tasks: Binary Classification, Sarcasm Detection, Sentiment Analysis

The role of predicting sarcasm in the text is known as automatic sarcasm detection. Given the prevalence and challenges of sarcasm in sentiment-bearing text, this is a critical phase in most sentiment analysis tasks. With the increasing popularity and usage of different social media platforms among users around the world, people are using sarcasm more and more in their day-to-day conversations, social media posts and tweets, and it is considered as a way for people to express their sentiment about some certain topics or issues. As a result of the increasing popularity, researchers started to focus their research endeavors on detecting sarcasm from a text in different languages especially the English language. However, the task of sarcasm detection is a challenging task due to the nature of sarcastic texts; which can be relative and significantly differs from one person to another depending on the topic, region, the user's mentality and other factors. In addition to these challenges, sarcasm detection in the Arabic language has its own challenges due to the complexity of the Arabic language, such as being morphologically rich, with many dialects that significantly vary between each other, while also being lowly resourced. In recent years, only few research attempts started tackling the task of sarcasm detection in Arabic, including creating and collecting corpora, organizing workshops and establishing baseline models. This paper intends to create a new humanly annotated Arabic corpus for sarcasm detection collected from tweets, and implementing a new approach for sarcasm detection and quantification in Arabic tweets. The annotation technique followed in this paper is unique in sarcasm detection and the proposed approach tackles the problem as a regression problem instead of classification; i.e., the model attempts to predict the level of sarcasm instead of binary classification.
Electrical peak demand forecasting- A review
Authors: Shuang Dai, Fanlin Meng, Hongsheng Dai, Qian Wang, Xizhong Chen
Date: 2021-08-03 00:00:00
Tasks: Demand Forecasting, Management

The power system is undergoing rapid evolution with the roll-out of advanced metering infrastructure and local energy applications (e.g. electric vehicles) as well as the increasing penetration of intermittent renewable energy at both transmission and distribution level, which characterizes the peak load demand with stronger randomness and less predictability and therefore poses a threat to the power grid security. Since storing large quantities of electricity to satisfy load demand is neither economically nor environmentally friendly, effective peak demand management strategies and reliable peak load forecast methods become essential for optimizing the power system operations. To this end, this paper provides a timely and comprehensive overview of peak load demand forecast methods in the literature. To our best knowledge, this is the first comprehensive review on such topic. In this paper we first give a precise and unified problem definition of peak load demand forecast. Second, 139 papers on peak load forecast methods were systematically reviewed where methods were classified into different stages based on the timeline. Thirdly, a comparative analysis of peak load forecast methods are summarized and different optimizing methods to improve the forecast performance are discussed. The paper ends with a comprehensive summary of the reviewed papers and a discussion of potential future research directions.
On the descriptive power of LiDAR intensity images for segment-based loop closing in 3-D SLAM
Authors: Jan Wietrzykowski, Piotr Skrzypczyński
Date: 2021-08-03 00:00:00
Tasks: Descriptive, Loop Closure Detection

We propose an extension to the segment-based global localization method for LiDAR SLAM using descriptors learned considering the visual context of the segments. A new architecture of the deep neural network is presented that learns the visual context acquired from synthetic LiDAR intensity images. This approach allows a single multi-beam LiDAR to produce rich and highly descriptive location signatures. The method is tested on two public datasets, demonstrating an improved descriptiveness of the new descriptors, and more reliable loop closure detection in SLAM. Attention analysis of the network is used to show the importance of focusing on the broader context rather than only on the 3-D segment.
Dynamic communication topologies for distributed heuristics in energy system optimization algorithms
Authors: Stefanie Holly, Astrid Nieße
Date: 2021-08-03 00:00:00
Tasks: Distributed Optimization

The communication topology is an essential aspect in designing distributed optimization heuristics. It can influence the exploration and exploitation of the search space and thus the optimization performance in terms of solution quality, convergence speed and collaboration costs, all relevant aspects for applications operating critical infrastructure in energy systems. In this work, we present an approach for adapting the communication topology during runtime, based on the principles of simulated annealing. We compare the approach to common static topologies regarding the performance of an exemplary distributed optimization heuristic. Finally, we investigate the correlations between fitness landscape properties and defined performance metrics.
Towards a Better Understanding Human Reading Comprehension with Brain Signals
Authors: Ziyi Ye, Xiaohui Xie, Yiqun Liu, Zhihong Wang, Xuesong Chen, Min Zhang, Shaoping Ma
Date: 2021-08-03 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Information Retrieval, Reading Comprehension, Retrieval, Sentence, Sentence Classification

Reading comprehension is a complex cognitive process involving many human brain activities. Plenty of works have studied the patterns and attention allocations of reading comprehension in information retrieval related scenarios. However, little is known about what happens in human brain during reading comprehension and how these cognitive activities can affect information retrieval process. Additionally, with the advances in brain imaging techniques such as electroencephalogram (EEG), it is possible to collect brain signals in almost real time and explore whether it can be utilized as feedback to facilitate information acquisition performance. In this paper, we carefully design a lab-based user study to investigate brain activities during reading comprehension. Our findings show that neural responses vary with different types of reading contents, i.e., contents that can satisfy users' information needs and contents that cannot. We suggest that various cognitive activities, e.g., cognitive loading, semantic-thematic understanding, and inferential processing, underpin these neural responses at the micro-time scale during reading comprehension. From these findings, we illustrate several insights for information retrieval tasks, such as ranking models construction and interface design. Besides, we suggest the possibility of detecting reading comprehension status for a proactive real-world system. To this end, we propose a Unified framework for EEG-based Reading Comprehension Modeling (UERCM). To verify its effectiveness, we conduct extensive experiments based on EEG features for two reading comprehension tasks: answer sentence classification and answer extraction. Results show that it is feasible to improve the performance of two tasks with brain signals.
Accelerating the Learning of TAMER with Counterfactual Explanations
Authors: Jakob Karalus, Felix Lindner
Date: 2021-08-03 00:00:00
Tasks: counterfactual, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The capability to interactively learn from human feedback would enable agents in new settings. For example, even novice users could train service robots in new tasks naturally and interactively. Human-in-the-loop Reinforcement Learning (HRL) combines human feedback and Reinforcement Learning (RL) techniques. State-of-the-art interactive learning techniques suffer from slow learning speed, thus leading to a frustrating experience for the human. We approach this problem by extending the HRL framework TAMER for evaluative feedback with the possibility to enhance human feedback with two different types of counterfactual explanations (action and state based). We experimentally show that our extensions improve the speed of learning.
Predicting Popularity of Images Over 30 Days
Authors: Amartya Dutta, Ferdous Ahmed Barbhuiya
Date: 2021-08-03 00:00:00

The current work deals with the problem of attempting to predict the popularity of images before even being uploaded. This method is specifically focused on Flickr images. Social features of each image as well as that of the user who had uploaded it, have been recorded. The dataset also includes the engagement score of each image which is the ground truth value of the views obtained by each image over a period of 30 days. The work aims to predict the popularity of images on Flickr over a period of 30 days using the social features of the user and the image, as well as the visual features of the images. The method states that the engagement sequence of an image can be said to depend on two independent quantities, namely scale and shape of an image. Once the shape and scale of an image have been predicted, combining them the predicted sequence of an image over 30 days is obtained. The current work follows a previous work done in the same direction, with certain speculations and suggestions of improvement.
RAIN: Reinforced Hybrid Attention Inference Network for Motion Forecasting
Authors: Jiachen Li, Fan Yang, Hengbo Ma, Srikanth Malla, Masayoshi Tomizuka, Chiho Choi
Date: 2021-08-03 00:00:00
Proceeding: ICCV 2021 10
Tasks: Autonomous Driving, Motion Forecasting, Trajectory Prediction

Motion forecasting plays a significant role in various domains (e.g., autonomous driving, human-robot interaction), which aims to predict future motion sequences given a set of historical observations. However, the observed elements may be of different levels of importance. Some information may be irrelevant or even distracting to the forecasting in certain situations. To address this issue, we propose a generic motion forecasting framework (named RAIN) with dynamic key information selection and ranking based on a hybrid attention mechanism. The general framework is instantiated to handle multi-agent trajectory prediction and human motion forecasting tasks, respectively. In the former task, the model learns to recognize the relations between agents with a graph representation and to determine their relative significance. In the latter task, the model learns to capture the temporal proximity and dependency in long-term human motions. We also propose an effective double-stage training pipeline with an alternating training strategy to optimize the parameters in different modules of the framework. We validate the framework on both synthetic simulations and motion forecasting benchmarks in different domains, demonstrating that our method not only achieves state-of-the-art forecasting performance, but also provides interpretable and reasonable hybrid attention weights.
Skeleton Split Strategies for Spatial Temporal Graph Convolution Networks
Authors: Motasem S. Alsawadi, Miguel Rio
Date: 2021-08-03 00:00:00

A skeleton representation of the human body has been proven to be effective for this task. The skeletons are presented in graphs form-like. However, the topology of a graph is not structured like Euclidean-based data. Therefore, a new set of methods to perform the convolution operation upon the skeleton graph is presented. Our proposal is based upon the ST-GCN framework proposed by Yan et al. [1]. In this study, we present an improved set of label mapping methods for the ST-GCN framework. We introduce three split processes (full distance split, connection split, and index split) as an alternative approach for the convolution operation. To evaluate the performance, the experiments presented in this study have been trained using two benchmark datasets: NTU-RGB+D and Kinetics. Our results indicate that all of our split processes outperform the previous partition strategies and are more stable during training without using the edge importance weighting additional training parameter. Therefore, our proposal can provide a more realistic solution for real-time applications centred on daily living recognition systems activities for indoor environments.
AdvRush: Searching for Adversarially Robust Neural Architectures
Authors: Jisoo Mok, Byunggook Na, Hyeokjun Choe, Sungroh Yoon
Date: 2021-08-03 00:00:00
Proceeding: ICCV 2021 10
Tasks: Adversarial Robustness, Neural Architecture Search

Deep neural networks continue to awe the world with their remarkable performance. Their predictions, however, are prone to be corrupted by adversarial examples that are imperceptible to humans. Current efforts to improve the robustness of neural networks against adversarial examples are focused on developing robust training methods, which update the weights of a neural network in a more robust direction. In this work, we take a step beyond training of the weight parameters and consider the problem of designing an adversarially robust neural architecture with high intrinsic robustness. We propose AdvRush, a novel adversarial robustness-aware neural architecture search algorithm, based upon a finding that independent of the training method, the intrinsic robustness of a neural network can be represented with the smoothness of its input loss landscape. Through a regularizer that favors a candidate architecture with a smoother input loss landscape, AdvRush successfully discovers an adversarially robust neural architecture. Along with a comprehensive theoretical motivation for AdvRush, we conduct an extensive amount of experiments to demonstrate the efficacy of AdvRush on various benchmark datasets. Notably, on CIFAR-10, AdvRush achieves 55.91% robust accuracy under FGSM attack after standard training and 50.04% robust accuracy under AutoAttack after 7-step PGD adversarial training.
Process Mining Model to Predict Mortality in Paralytic Ileus Patients
Authors: Maryam Pishgar, Martha Razo, Julian Theis, Houshang Darabi
Date: 2021-08-03 00:00:00
Tasks: Mortality Prediction, Prediction

Paralytic Ileus (PI) patients are at high risk of death when admitted to the Intensive care unit (ICU), with mortality as high as 40\%. There is minimal research concerning PI patient mortality prediction. There is a need for more accurate prediction modeling for ICU patients diagnosed with PI. This paper demonstrates performance improvements in predicting the mortality of ICU patients diagnosed with PI after 24 hours of being admitted. The proposed framework, PMPI(Process Mining Model to predict mortality of PI patients), is a modification of the work used for prediction of in-hospital mortality for ICU patients with diabetes. PMPI demonstrates similar if not better performance with an Area under the ROC Curve (AUC) score of 0.82 compared to the best results of the existing literature. PMPI uses patient medical history, the time related to the events, and demographic information for prediction. The PMPI prediction framework has the potential to help medical teams in making better decisions for treatment and care for ICU patients with PI to increase their life expectancy.
More but Correct: Generating Diversified and Entity-revised Medical Response
Authors: Bin Li, Encheng Chen, Hongru Liu, Yixuan Weng, Bin Sun, Shutao Li, Yongping Bai, Meiling Hu
Date: 2021-08-03 00:00:00
Tasks: Dialogue Generation

Medical Dialogue Generation (MDG) is intended to build a medical dialogue system for intelligent consultation, which can communicate with patients in real-time, thereby improving the efficiency of clinical diagnosis with broad application prospects. This paper presents our proposed framework for the Chinese MDG organized by the 2021 China conference on knowledge graph and semantic computing (CCKS) competition, which requires generating context-consistent and medically meaningful responses conditioned on the dialogue history. In our framework, we propose a pipeline system composed of entity prediction and entity-aware dialogue generation, by adding predicted entities to the dialogue model with a fusion mechanism, thereby utilizing information from different sources. At the decoding stage, we propose a new decoding mechanism named Entity-revised Diverse Beam Search (EDBS) to improve entity correctness and promote the length and quality of the final response. The proposed method wins both the CCKS and the International Conference on Learning Representations (ICLR) 2021 Workshop Machine Learning for Preventing and Combating Pandemics (MLPCP) Track 1 Entity-aware MED competitions, which demonstrate the practicality and effectiveness of our method.
Desk Organization: Effect of Multimodal Inputs on Spatial Relational Learning
Authors: Ryan Rowe, Shivam Singhal, Daqing Yi, Tapomayukh Bhattacharjee, Siddhartha S. Srinivasa
Date: 2021-08-03 00:00:00
Tasks: Position, Relational Reasoning

For robots to operate in a three dimensional world and interact with humans, learning spatial relationships among objects in the surrounding is necessary. Reasoning about the state of the world requires inputs from many different sensory modalities including vision ($V$) and haptics ($H$). We examine the problem of desk organization: learning how humans spatially position different objects on a planar surface according to organizational ''preference''. We model this problem by examining how humans position objects given multiple features received from vision and haptic modalities. However, organizational habits vary greatly between people both in structure and adherence. To deal with user organizational preferences, we add an additional modality, ''utility'' ($U$), which informs on a particular human's perceived usefulness of a given object. Models were trained as generalized (over many different people) or tailored (per person). We use two types of models: random forests, which focus on precise multi-task classification, and Markov logic networks, which provide an easily interpretable insight into organizational habits. The models were applied to both synthetic data, which proved to be learnable when using fixed organizational constraints, and human-study data, on which the random forest achieved over 90% accuracy. Over all combinations of $\{H, U, V\}$ modalities, $UV$ and $HUV$ were the most informative for organization. In a follow-up study, we gauged participants preference of desk organizations by a generalized random forest organization vs. by a random model. On average, participants rated the random forest models as 4.15 on a 5-point Likert scale compared to 1.84 for the random model
AGAR a microbial colony dataset for deep learning detection
Authors: Sylwia Majchrowska, Jarosław Pawłowski, Grzegorz Guła, Tomasz Bonus, Agata Hanas, Adam Loch, Agnieszka Pawlak, Justyna Roszkowiak, Tomasz Golan, Zuzanna Drulis-Kawa
Date: 2021-08-03 00:00:00
Tasks: Deep Learning, object-detection, Object Detection

The Annotated Germs for Automated Recognition (AGAR) dataset is an image database of microbial colonies cultured on agar plates. It contains 18000 photos of five different microorganisms as single or mixed cultures, taken under diverse lighting conditions with two different cameras. All the images are classified into "countable", "uncountable", and "empty", with the "countable" class labeled by microbiologists with colony location and species identification (336442 colonies in total). This study describes the dataset itself and the process of its development. In the second part, the performance of selected deep neural network architectures for object detection, namely Faster R-CNN and Cascade R-CNN, was evaluated on the AGAR dataset. The results confirmed the great potential of deep learning methods to automate the process of microbe localization and classification based on Petri dish photos. Moreover, AGAR is the first publicly available dataset of this kind and size and will facilitate the future development of machine learning models. The data used in these studies can be found at https://agar.neurosys.com/.
Taking Cognition Seriously: A generalised physics of cognition
Authors: Sophie Alyx Taylor, Son Cao Tran, Dan V. Nicolau Jr
Date: 2021-08-03 00:00:00

The study of complex systems through the lens of category theory consistently proves to be a powerful approach. We propose that cognition deserves the same category-theoretic treatment. We show that by considering a highly-compact cognitive system, there are fundamental physical trade-offs resulting in a utility problem. We then examine how to do this systematically, and propose some requirements for "cognitive categories", before investigating the phenomenona of topological defects in gauge fields over conceptual spaces.