OpenRaiser/Intern-Atlas huggingface dataset:
IntraLoss: Further Margin via Gradient-Enhancing Term for Deep Face Recognition
Venue: arXiv cs.CV 2021
Year: 2021

Existing classification-based face recognition methods have achieved remarkable progress, introducing large margin into hypersphere manifold to learn discriminative facial representations. However, the feature distribution is ignored. Poor feature distribution will wipe out the performance improvement brought about by margin scheme. Recent studies focus on the unbalanced inter-class distribution and form a equidistributed feature representations by penalizing the angle between identity and its nearest neighbor. But the problem is more than that, we also found the anisotropy of intra-class distribution. In this paper, we propose the `gradient-enhancing term' that concentrates on the distribution characteristics within the class. This method, named IntraLoss, explicitly performs gradient enhancement in the anisotropic region so that the intra-class distribution continues to shrink, resulting in isotropic and more compact intra-class distribution and further margin between identities. The experimental results on LFW, YTF and CFP-FP show that our outperforms state-of-the-art methods by gradient enhancement, demonstrating the superiority of our method. In addition, our method has intuitive geometric interpretation and can be easily combined with existing methods to solve the previously ignored problems.
Recurrence-Aware Long-Term Cognitive Network for Explainable Pattern Classification
Venue: arXiv cs.LG 2021
Year: 2021

Machine learning solutions for pattern classification problems are nowadays widely deployed in society and industry. However, the lack of transparency and accountability of most accurate models often hinders their safe use. Thus, there is a clear need for developing explainable artificial intelligence mechanisms. There exist model-agnostic methods that summarize feature contributions, but their interpretability is limited to predictions made by black-box models. An open challenge is to develop models that have intrinsic interpretability and produce their own explanations, even for classes of models that are traditionally considered black boxes like (recurrent) neural networks. In this paper, we propose a Long-Term Cognitive Network for interpretable pattern classification of structured data. Our method brings its own mechanism for providing explanations by quantifying the relevance of each feature in the decision process. For supporting the interpretability without affecting the performance, the model incorporates more flexibility through a quasi-nonlinear reasoning rule that allows controlling nonlinearity. Besides, we propose a recurrence-aware decision model that evades the issues posed by the unique fixed point while introducing a deterministic learning algorithm to compute the tunable parameters. The simulations show that our interpretable model obtains competitive results when compared to state-of-the-art white and black-box models.
LanguageRefer: Spatial-Language Model for 3D Visual Grounding
Venue: arXiv cs.RO 2021
Year: 2021

For robots to understand human instructions and perform meaningful tasks in the near future, it is important to develop learned models that comprehend referential language to identify common objects in real-world 3D scenes. In this paper, we introduce a spatial-language model for a 3D visual grounding problem. Specifically, given a reconstructed 3D scene in the form of point clouds with 3D bounding boxes of potential object candidates, and a language utterance referring to a target object in the scene, our model successfully identifies the target object from a set of potential candidates. Specifically, LanguageRefer uses a transformer-based architecture that combines spatial embedding from bounding boxes with fine-tuned language embeddings from DistilBert to predict the target object. We show that it performs competitively on visio-linguistic datasets proposed by ReferIt3D. Further, we analyze its spatial reasoning task performance decoupled from perception noise, the accuracy of view-dependent utterances, and viewpoint annotations for potential robotics applications.
Stochastic Opinion Dynamics for Interest Prediction in Social Networks
Venue: arXiv cs.SI 2021
Year: 2021

We exploit the core-periphery structure and the strong homophilic properties of online social networks to develop faster and more accurate algorithms for user interest prediction. The core of modern social networks consists of relatively few influential users, whose interest profiles are publicly available, while the majority of peripheral users follow enough of them based on common interests. Our approach is to predict the interests of the peripheral nodes starting from the interests of their influential connections. To this end, we need a formal model that explains how common interests lead to network connections. Thus, we propose a stochastic interest formation model, the Nearest Neighbor Influence Model (NNIM), which is inspired by the Hegselmann-Krause opinion formation model and aims to explain how homophily shapes the network. Based on NNIM, we develop an efficient approach for predicting the interests of the peripheral users. At the technical level, we use Variational Expectation-Maximization to optimize the instantaneous likelihood function using a mean-field approximation of NNIM. We prove that our algorithm converges fast and is capable of scaling smoothly to networks with millions of nodes. Our experiments on standard network benchmarks demonstrate that our algorithm runs up to two orders of magnitude faster than the best known node embedding methods and achieves similar accuracy.
A Framework of High-Stakes Algorithmic Decision-Making for the Public Sector Developed through a Case Study of Child-Welfare
Venue: arXiv cs.HC 2021
Year: 2021

Algorithms have permeated throughout civil government and society, where they are being used to make high-stakes decisions about human lives. In this paper, we first develop a cohesive framework of algorithmic decision-making adapted for the public sector (ADMAPS) that reflects the complex socio-technical interactions between \textit{human discretion}, \textit{bureaucratic processes}, and \textit{algorithmic decision-making} by synthesizing disparate bodies of work in the fields of Human-Computer Interaction (HCI), Science and Technology Studies (STS), and Public Administration (PA). We then applied the ADMAPS framework to conduct a qualitative analysis of an in-depth, eight-month ethnographic case study of the algorithms in daily use within a child-welfare agency that serves approximately 900 families and 1300 children in the mid-western United States. Overall, we found there is a need to focus on strength-based algorithmic outcomes centered in social ecological frameworks. In addition, algorithmic systems need to support existing bureaucratic processes and augment human discretion, rather than replace it. Finally, collective buy-in in algorithmic systems requires trust in the target outcomes at both the practitioner and bureaucratic levels. As a result of our study, we propose guidelines for the design of high-stakes algorithmic decision-making tools in the child-welfare system, and more generally, in the public sector. We empirically validate the theoretically derived ADMAPS framework to demonstrate how it can be useful for systematically making pragmatic decisions about the design of algorithms for the public sector.
Deep Structural Point Process for Learning Temporal Interaction Networks
Venue: arXiv cs.SI 2021
Year: 2021

This work investigates the problem of learning temporal interaction networks. A temporal interaction network consists of a series of chronological interactions between users and items. Previous methods tackle this problem by using different variants of recurrent neural networks to model sequential interactions, which fail to consider the structural information of temporal interaction networks and inevitably lead to sub-optimal results. To this end, we propose a novel Deep Structural Point Process termed as DSPP for learning temporal interaction networks. DSPP simultaneously incorporates the topological structure and long-range dependency structure into our intensity function to enhance model expressiveness. To be specific, by using the topological structure as a strong prior, we first design a topological fusion encoder to obtain node embeddings. An attentive shift encoder is then developed to learn the long-range dependency structure between users and items in continuous time. The proposed two modules enable our model to capture the user-item correlation and dynamic influence in temporal interaction networks. DSPP is evaluated on three real-world datasets for both tasks of item prediction and time prediction. Extensive experiments demonstrate that our model achieves consistent and significant improvements over state-of-the-art baselines.
Multi-frame Collaboration for Effective Endoscopic Video Polyp Detection via Spatial-Temporal Feature Transformation
Venue: arXiv cs.CV 2021
Year: 2021

Precise localization of polyp is crucial for early cancer screening in gastrointestinal endoscopy. Videos given by endoscopy bring both richer contextual information as well as more challenges than still images. The camera-moving situation, instead of the common camera-fixed-object-moving one, leads to significant background variation between frames. Severe internal artifacts (e.g. water flow in the human body, specular reflection by tissues) can make the quality of adjacent frames vary considerately. These factors hinder a video-based model to effectively aggregate features from neighborhood frames and give better predictions. In this paper, we present Spatial-Temporal Feature Transformation (STFT), a multi-frame collaborative framework to address these issues. Spatially, STFT mitigates inter-frame variations in the camera-moving situation with feature alignment by proposal-guided deformable convolutions. Temporally, STFT proposes a channel-aware attention module to simultaneously estimate the quality and correlation of adjacent frames for adaptive feature aggregation. Empirical studies and superior results demonstrate the effectiveness and stability of our method. For example, STFT improves the still image baseline FCOS by 10.6% and 20.6% on the comprehensive F1-score of the polyp localization task in CVC-Clinic and ASUMayo datasets, respectively, and outperforms the state-of-the-art video-based method by 3.6% and 8.0%, respectively. Code is available at \url{https://github.com/lingyunwu14/STFT}.
NccFlow: Unsupervised Learning of Optical Flow With Non-occlusion from Geometry
Venue: arXiv cs.CV 2021
Year: 2021

Optical flow estimation is a fundamental problem of computer vision and has many applications in the fields of robot learning and autonomous driving. This paper reveals novel geometric laws of optical flow based on the insight and detailed definition of non-occlusion. Then, two novel loss functions are proposed for the unsupervised learning of optical flow based on the geometric laws of non-occlusion. Specifically, after the occlusion part of the images are masked, the flowing process of pixels is carefully considered and geometric constraints are conducted based on the geometric laws of optical flow. First, neighboring pixels in the first frame will not intersect during the pixel displacement to the second frame. Secondly, when the cluster containing adjacent four pixels in the first frame moves to the second frame, no other pixels will flow into the quadrilateral formed by them. According to the two geometrical constraints, the optical flow non-intersection loss and the optical flow non-blocking loss in the non-occlusion regions are proposed. Two loss functions punish the irregular and inexact optical flows in the non-occlusion regions. The experiments on datasets demonstrated that the proposed unsupervised losses of optical flow based on the geometric laws in non-occlusion regions make the estimated optical flow more refined in detail, and improve the performance of unsupervised learning of optical flow. In addition, the experiments training on synthetic data and evaluating on real data show that the generalization ability of optical flow network is improved by our proposed unsupervised approach.
Angles of Arc-Polygons and Lombardi Drawings of Cacti
Venue: arXiv cs.CG 2021
Year: 2021

We characterize the triples of interior angles that are possible in non-self-crossing triangles with circular-arc sides, and we prove that a given cyclic sequence of angles can be realized by a non-self-crossing polygon with circular-arc sides whenever all angles are at most pi. As a consequence of these results, we prove that every cactus has a planar Lombardi drawing (a drawing with edges depicted as circular arcs, meeting at equal angles at each vertex) for its natural embedding in which every cycle of the cactus is a face of the drawing. However, there exist planar embeddings of cacti that do not have planar Lombardi drawings.
Prior Aided Streaming Network for Multi-task Affective Recognitionat the 2nd ABAW2 Competition
Venue: arXiv cs.CV 2021
Year: 2021

Automatic affective recognition has been an important research topic in human computer interaction (HCI) area. With recent development of deep learning techniques and large scale in-the-wild annotated datasets, the facial emotion analysis is now aimed at challenges in the real world settings. In this paper, we introduce our submission to the 2nd Affective Behavior Analysis in-the-wild (ABAW2) Competition. In dealing with different emotion representations, including Categorical Emotions (CE), Action Units (AU), and Valence Arousal (VA), we propose a multi-task streaming network by a heuristic that the three representations are intrinsically associated with each other. Besides, we leverage an advanced facial expression embedding as prior knowledge, which is capable of capturing identity-invariant expression features while preserving the expression similarities, to aid the down-streaming recognition tasks. The extensive quantitative evaluations as well as ablation studies on the Aff-Wild2 dataset prove the effectiveness of our proposed prior aided streaming network approach.
Probabilistic Time Series Forecasting with Implicit Quantile Networks
Venue: arXiv cs.LG 2021
Year: 2021

Here, we propose a general method for probabilistic time series forecasting. We combine an autoregressive recurrent neural network to model temporal dynamics with Implicit Quantile Networks to learn a large class of distributions over a time-series target. When compared to other probabilistic neural forecasting models on real- and simulated data, our approach is favorable in terms of point-wise prediction accuracy as well as on estimating the underlying temporal distribution.
HinGE: A Dataset for Generation and Evaluation of Code-Mixed Hinglish Text
Venue: arXiv cs.CL 2021
Year: 2021

Text generation is a highly active area of research in the computational linguistic community. The evaluation of the generated text is a challenging task and multiple theories and metrics have been proposed over the years. Unfortunately, text generation and evaluation are relatively understudied due to the scarcity of high-quality resources in code-mixed languages where the words and phrases from multiple languages are mixed in a single utterance of text and speech. To address this challenge, we present a corpus (HinGE) for a widely popular code-mixed language Hinglish (code-mixing of Hindi and English languages). HinGE has Hinglish sentences generated by humans as well as two rule-based algorithms corresponding to the parallel Hindi-English sentences. In addition, we demonstrate the inefficacy of widely-used evaluation metrics on the code-mixed data. The HinGE dataset will facilitate the progress of natural language generation research in code-mixed languages.
Semantic Intelligence in Big Data Applications
Venue: arXiv cs.DB 2021
Year: 2021

Today, data is growing at a tremendous rate and, according to the International Data Corporation, it is expected to reach 175 zettabytes by 2025. The International Data Corporation also forecasts that more than 150B devices will be connected across the globe by 2025, most of which will be creating data in real-time, while 90 zettabytes of data will be created by the Internet of Things devices. This vast amount of data creates several new opportunities for modern enterprises, especially for analysing the enterprise value chains in a broader sense. To leverage the potential of real data and build smart applications on top of sensory data, IoT-based systems integrate domain knowledge and context-relevant information. Semantic Intelligence is the process of bridging the semantic gap between human and computer comprehension by teaching a machine to think in terms of object-oriented concepts in the same way as a human does. Semantic intelligence technologies are the most important component in developing artificially intelligent knowledge-based systems since they assist machines in contextually and intelligently integrating and processing resources. This Chapter aims at demystifying semantic intelligence in distributed, enterprise and web-based information systems. It also discusses prominent tools that leverage semantics, handle large data at scale and address challenges (e.g. heterogeneity, interoperability, machine learning explainability) in different industrial applications.
SSSE: Efficiently Erasing Samples from Trained Machine Learning Models
Venue: arXiv cs.LG 2021
Year: 2021

The availability of large amounts of user-provided data has been key to the success of machine learning for many real-world tasks. Recently, an increasing awareness has emerged that users should be given more control about how their data is used. In particular, users should have the right to prohibit the use of their data for training machine learning systems, and to have it erased from already trained systems. While several sample erasure methods have been proposed, all of them have drawbacks which have prevented them from gaining widespread adoption. Most methods are either only applicable to very specific families of models, sacrifice too much of the original model's accuracy, or they have prohibitive memory or computational requirements. In this paper, we propose an efficient and effective algorithm, SSSE, for samples erasure, that is applicable to a wide class of machine learning models. From a second-order analysis of the model's loss landscape we derive a closed-form update step of the model parameters that only requires access to the data to be erased, not to the original training set. Experiments on three datasets, CelebFaces attributes (CelebA), Animals with Attributes 2 (AwA2) and CIFAR10, show that in certain cases SSSE can erase samples almost as well as the optimal, yet impractical, gold standard of training a new model from scratch with only the permitted data.
Technical Report for Valence-Arousal Estimation in ABAW2 Challenge
Venue: arXiv cs.CV 2021
Year: 2021

In this work, we describe our method for tackling the valence-arousal estimation challenge from ABAW2 ICCV-2021 Competition. The competition organizers provide an in-the-wild Aff-Wild2 dataset for participants to analyze affective behavior in real-life settings. We use a two stream model to learn emotion features from appearance and action respectively. To solve data imbalanced problem, we apply label distribution smoothing (LDS) to re-weight labels. Our proposed method achieves Concordance Correlation Coefficient (CCC) of 0.591 and 0.617 for valence and arousal on the validation set of Aff-wild2 dataset.
The influence of various optimization algorithms on nuclear power plant steam turbine exergy efficiency and destruction
Venue: arXiv cs.CY 2021
Year: 2021

This paper presents an exergy analysis of the whole turbine, turbine cylinders and cylinder parts in four different operating regimes. Analyzed turbine operates in nuclear power plant while three of four operating regimes are obtained by using optimization algorithms - SA (Simplex Algorithm), GA (Genetic Algorithm) and IGSA (Improved Genetic-Simplex Algorithm). IGSA operating regime gives the highest developed mechanical power of the whole turbine equal to 1022.48 MW, followed by GA (1020.06 MW) and SA (1017.16 MW), while in Original operating regime whole turbine develop mechanical power equal to 996.29 MW. In addition, IGSA causes the highest increase in developed mechanical power of almost all cylinders and cylinder parts in comparison to the Original operating regime. All observed optimization algorithms increases the exergy destruction of the whole turbine in comparison to Original operating regime - the lowest increase causes IGSA, followed by GA and finally SA. The highest exergy efficiency of the whole turbine, equal to 85.92% is obtained by IGSA, followed by GA (85.89%) and SA (85.82%), while the lowest exergy efficiency is obtained in Original operating regime (85.70%). Analyzed turbine, which operates by using wet steam is low influenced by the ambient temperature change. IGSA, which shows dominant performance in exergy analysis parameters of the analyzed turbine, in certain situations is overpowered by GA. Therefore, in optimization of steam turbine performance, IGSA and GA can be recommended.
The Price of Diversity
Venue: arXiv cs.CY 2021
Year: 2021

Systemic bias with respect to gender, race and ethnicity, often unconscious, is prevalent in datasets involving choices among individuals. Consequently, society has found it challenging to alleviate bias and achieve diversity in a way that maintains meritocracy in such settings. We propose (a) a novel optimization approach based on optimally flipping outcome labels and training classification models simultaneously to discover changes to be made in the selection process so as to achieve diversity without significantly affecting meritocracy, and (b) a novel implementation tool employing optimal classification trees to provide insights on which attributes of individuals lead to flipping of their labels, and to help make changes in the current selection processes in a manner understandable by human decision makers. We present case studies on three real-world datasets consisting of parole, admissions to the bar and lending decisions, and demonstrate that the price of diversity is low and sometimes negative, that is we can modify our selection processes in a way that enhances diversity without affecting meritocracy significantly, and sometimes improving it.