OpenRaiser/Intern-Atlas huggingface dataset:
GAN-based Reactive Motion Synthesis with Class-aware Discriminators for Human-human Interaction
Venue: arXiv cs.GR 2021
Year: 2021

Creating realistic characters that can react to the users' or another character's movement can benefit computer graphics, games and virtual reality hugely. However, synthesizing such reactive motions in human-human interactions is a challenging task due to the many different ways two humans can interact. While there are a number of successful researches in adapting the generative adversarial network (GAN) in synthesizing single human actions, there are very few on modelling human-human interactions. In this paper, we propose a semi-supervised GAN system that synthesizes the reactive motion of a character given the active motion from another character. Our key insights are two-fold. First, to effectively encode the complicated spatial-temporal information of a human motion, we empower the generator with a part-based long short-term memory (LSTM) module, such that the temporal movement of different limbs can be effectively modelled. We further include an attention module such that the temporal significance of the interaction can be learned, which enhances the temporal alignment of the active-reactive motion pair. Second, as the reactive motion of different types of interactions can be significantly different, we introduce a discriminator that not only tells if the generated movement is realistic or not, but also tells the class label of the interaction. This allows the use of such labels in supervising the training of the generator. We experiment with the SBU and the HHOI datasets. The high quality of the synthetic motion demonstrates the effective design of our generator, and the discriminability of the synthesis also demonstrates the strength of our discriminator.
Personalized Retrogress-Resilient Framework for Real-World Medical Federated Learning
Venue: arXiv cs.CV 2021
Year: 2021

Nowadays, deep learning methods with large-scale datasets can produce clinically useful models for computer-aided diagnosis. However, the privacy and ethical concerns are increasingly critical, which make it difficult to collect large quantities of data from multiple institutions. Federated Learning (FL) provides a promising decentralized solution to train model collaboratively by exchanging client models instead of private data. However, the server aggregation of existing FL methods is observed to degrade the model performance in real-world medical FL setting, which is termed as retrogress. To address this problem, we propose a personalized retrogress-resilient framework to produce a superior personalized model for each client. Specifically, we devise a Progressive Fourier Aggregation (PFA) at the server to achieve more stable and effective global knowledge gathering by integrating client models from low-frequency to high-frequency gradually. Moreover, with an introduced deputy model to receive the aggregated server model, we design a Deputy-Enhanced Transfer (DET) strategy at the client and conduct three steps of Recover-Exchange-Sublimate to ameliorate the personalized local model by transferring the global knowledge smoothly. Extensive experiments on real-world dermoscopic FL dataset prove that our personalized retrogress-resilient framework outperforms state-of-the-art FL methods, as well as the generalization on an out-of-distribution cohort. The code and dataset are available at https://github.com/CityU-AIM-Group/PRR-FL.
Attention based Sequence to Sequence Learning for Machine Translation of Low Resourced Indic Languages -- A case of Sanskrit to Hindi
Venue: arXiv cs.CL 2021
Year: 2021

Deep Learning techniques are powerful in mimicking humans in a particular set of problems. They have achieved a remarkable performance in complex learning tasks. Deep learning inspired Neural Machine Translation (NMT) is a proficient technique that outperforms traditional machine translation. Performing machine-aided translation on Indic languages has always been a challenging task considering their rich and diverse grammar. The neural machine translation has shown quality results compared to the traditional machine translation approaches. The fully automatic machine translation becomes problematic when it comes to low-resourced languages, especially with Sanskrit. This paper presents attention mechanism based neural machine translation by selectively focusing on a particular part of language sentences during translation. The work shows the construction of Sanskrit to Hindi bilingual parallel corpus with nearly 10K samples and having 178,000 tokens. The neural translation model equipped with an attention mechanism has been trained on Sanskrit to Hindi parallel corpus. The approach has shown the significance of attention mechanisms to overcome long-term dependencies, primarily associated with low resources Indic languages. The paper shows the attention plots on testing data to demonstrate the alignment between source and translated words. For the evaluation of the translated sentences, manual score based human evaluation and automatic evaluation metric based techniques have been adopted. The attention mechanism based neural translation has achieved 88% accuracy in human evaluation and a BLEU score of 0.92 on Sanskrit to Hindi translation.
Instance Segmentation Challenge Track Technical Report, VIPriors Workshop at ICCV 2021: Task-Specific Copy-Paste Data Augmentation Method for Instance Segmentation
Venue: arXiv cs.CV 2021
Year: 2021

Copy-Paste has proven to be a very effective data augmentation for instance segmentation which can improve the generalization of the model. We used a task-specific Copy-Paste data augmentation method to achieve good performance on the instance segmentation track of the 2nd VIPriors workshop challenge. We also applied additional data augmentation techniques including RandAugment and GridMask. Our segmentation model is the HTC detector on the CBSwin-B with CBFPN with some tweaks. This model was trained at the multi-scale mode by a random sampler on the 6x schedule and tested at the single-scale mode. By combining these techniques, we achieved 0.398 AP@0.50:0.95 with the validation set and 0.433 AP@0.50:0.95 with the test set. Finally, we reached 0.477 AP@0.50:0.95 with the test set by adding the validation set to the training data. Source code is available at https://github.com/jahongir7174/VIP2021.
Designing Quality Requirements, Metrics and Indicators for Core Ontologies: Results of a Comparative Study for Process Core Ontologies
Venue: arXiv cs.SE 2021
Year: 2021

This preprint specifies quality requirements for a core ontology whose ontological elements such as terms, non-taxonomic relationships, among others, are based on a foundational ontology. The quality requirements are represented in a quality model that is structured in the form of a requirements tree composed of characteristics and attributes to be measured and evaluated. An attribute represents an atomic aspect of an entity, that is, an elementary non-functional requirement that can be measured by a direct or indirect metric and evaluated by an elementary indicator. In contrast, characteristics that model less atomic aspects of an entity cannot be measured by metrics, but rather are evaluated by derived indicators generally modeled by an aggregation function. Therefore, this preprint shows the design of direct and indirect metrics in addition to the design of elementary indicators, which are used to implement measurement and evaluation activities to obtain the results of a quality requirements tree. In particular, this document shows the applicability of the designed metrics and indicators that are used by a evaluation and comparison strategy. Two process core ontologies were preselected, evaluated and compared in order to adopt strengths in the target entity named ProcessCO. The data and information resulting from this study are also recorded, as well as the outcomes of the revaluation after improvement of the target entity.
ResNet strikes back: An improved training procedure in timm
Venue: arXiv cs.CV 2021
Year: 2021

The influential Residual Networks designed by He et al. remain the gold-standard architecture in numerous scientific publications. They typically serve as the default architecture in studies, or as baselines when new architectures are proposed. Yet there has been significant progress on best practices for training neural networks since the inception of the ResNet architecture in 2015. Novel optimization & data-augmentation have increased the effectiveness of the training recipes. In this paper, we re-evaluate the performance of the vanilla ResNet-50 when trained with a procedure that integrates such advances. We share competitive training settings and pre-trained models in the timm open-source library, with the hope that they will serve as better baselines for future work. For instance, with our more demanding training setting, a vanilla ResNet-50 reaches 80.4% top-1 accuracy at resolution 224x224 on ImageNet-val without extra data or distillation. We also report the performance achieved with popular models with our training procedure.
DiVRsify: Break the Cycle and Develop VR for Everyone
Venue: arXiv cs.HC 2021
Year: 2021

Virtual reality technology is biased. It excludes approximately 95% the world's population by being primarily designed for male, western, educated, industrial, rich, and democratic populations. This bias may be due to the lack of diversity in virtual reality researchers, research participants, developers, and end users, fueling a noninclusive research, development, and usability cycle. The objective of this paper is to highlight the minimal virtual reality research involving understudied populations with respect to dimensions of diversity, such as gender, race, culture, ethnicity, age, disability, and neurodivergence. Specifically, we highlight numerous differences in virtual reality usability between underrepresented groups compared to commonly studied populations. These differences illustrate the lack of generalizability of prior virtual reality research. Lastly, we present a call to action with the aim that, over time, will break the cycle and enable virtual reality for everyone.
An Ensemble-based Multi-Criteria Decision Making Method for COVID-19 Cough Classification
Venue: arXiv cs.LG 2021
Year: 2021

The objectives of this research are analysing the performance of the state-of-the-art machine learning techniques for classifying COVID-19 from cough sound and identifying the model(s) that consistently perform well across different cough datasets. Different performance evaluation metrics (such as precision, sensitivity, specificity, AUC, accuracy, etc.) make it difficult to select the best performance model. To address this issue, in this paper, we propose an ensemble-based multi-criteria decision making (MCDM) method for selecting top performance machine learning technique(s) for COVID-19 cough classification. We use four cough datasets, namely Cambridge, Coswara, Virufy, and NoCoCoDa to verify the proposed method. At first, our proposed method uses the audio features of cough samples and then applies machine learning (ML) techniques to classify them as COVID-19 or non-COVID-19. Then, we consider a multi-criteria decision-making (MCDM) method that combines ensemble technologies (i.e., soft and hard) to select the best model. In MCDM, we use the technique for order preference by similarity to ideal solution (TOPSIS) for ranking purposes, while entropy is applied to calculate evaluation criteria weights. In addition, we apply the feature reduction process through recursive feature elimination with cross-validation under different estimators. The results of our empirical evaluations show that the proposed method outperforms the state-of-the-art models.
Channel Estimation with Reconfigurable Intelligent Surfaces -- A General Framework
Venue: arXiv cs.IT 2021
Year: 2021

Optimally extracting the advantages available from reconfigurable intelligent surfaces (RISs) in wireless communications systems requires estimation of the channels to and from the RIS. The process of determining these channels is complicated by the fact that the RIS is typically composed of passive elements without any data processing capabilities, and thus the channels must be estimated indirectly by a non-colocated device, typically a controlling base station. In this article, we examine channel estimation for RIS-based systems from a fundamental viewpoint. We study various possible channel models and the identifiability of the models as a function of the available pilot data and behavior of the RIS during training. In particular, we consider situations with and without line-of-sight propagation, single- and multiple-antenna configurations for the users and base station, correlated and sparse channel models, single-carrier and wideband OFDM scenarios, availability of direct links between the users and base station, exploitation of prior information, as well as a number of other special cases. We further conduct numerical comparisons of achievable performance for various channel models using the relevant Cramer-Rao bounds.
Design of multiplicative watermarking against covert attacks
Venue: arXiv cs.CR 2021
Year: 2021

This paper addresses the design of an active cyberattack detection architecture based on multiplicative watermarking, allowing for detection of covert attacks. We propose an optimal design problem, relying on the so-called output-to-output l2-gain, which characterizes the maximum gain between the residual output of a detection scheme and some performance output. Although optimal, this control problem is non-convex. Hence, we propose an algorithm to design the watermarking filters by solving the problem suboptimally via LMIs. We show that, against covert attacks, the output-to-output l2-gain is unbounded without watermarking, and we provide a sufficient condition for boundedness in the presence of watermarks.
The Need for a Fine-grained approach in Just-in-Time Defect Prediction
Venue: arXiv cs.SE 2021
Year: 2021

With software system complexity leading to the rise of software defects, research efforts have been done on techniques towards predicting software defects and Just-in-time (JIT) defect prediction which predicts whether a code change is defective. While using features to determine potentially defective code change, inspection effort is still significant. As code change can impact several files, we investigate an open source project to identify potential gaps with features in JIT perspective. In addition, with a lack of publicly available JIT dataset that link the features with actual commits, we also present a new dataset that can be utilized in JIT and semantic analysis.
Self-Attentive Constituency Parsing for UCCA-based Semantic Parsing
Venue: arXiv cs.CL 2021
Year: 2021

Semantic parsing provides a way to extract the semantic structure of a text that could be understood by machines. It is utilized in various NLP applications that require text comprehension such as summarization and question answering. Graph-based representation is one of the semantic representation approaches to express the semantic structure of a text. Such representations generate expressive and adequate graph-based target structures. In this paper, we focus primarily on UCCA graph-based semantic representation. The paper not only presents the existing approaches proposed for UCCA representation, but also proposes a novel self-attentive neural parsing model for the UCCA representation. We present the results for both single-lingual and cross-lingual tasks using zero-shot and few-shot learning for low-resource languages.
Calibrated Adversarial Training
Venue: arXiv cs.LG 2021
Year: 2021

Adversarial training is an approach of increasing the robustness of models to adversarial attacks by including adversarial examples in the training set. One major challenge of producing adversarial examples is to contain sufficient perturbation in the example to flip the model's output while not making severe changes in the example's semantical content. Exuberant change in the semantical content could also change the true label of the example. Adding such examples to the training set results in adverse effects. In this paper, we present the Calibrated Adversarial Training, a method that reduces the adverse effects of semantic perturbations in adversarial training. The method produces pixel-level adaptations to the perturbations based on novel calibrated robust error. We provide theoretical analysis on the calibrated robust error and derive an upper bound for it. Our empirical results show a superior performance of the Calibrated Adversarial Training over a number of public datasets.
Accelerate Distributed Stochastic Descent for Nonconvex Optimization with Momentum
Venue: arXiv cs.LG 2021
Year: 2021

Momentum method has been used extensively in optimizers for deep learning. Recent studies show that distributed training through K-step averaging has many nice properties. We propose a momentum method for such model averaging approaches. At each individual learner level traditional stochastic gradient is applied. At the meta-level (global learner level), one momentum term is applied and we call it block momentum. We analyze the convergence and scaling properties of such momentum methods. Our experimental results show that block momentum not only accelerates training, but also achieves better results.
The Cognitive Science of Extremist Ideologies Online
Venue: arXiv cs.SI 2021
Year: 2021

Extremist ideologies are finding new homes in online forums. These serve as both places for true believers, and recruiting-grounds for curious newcomers. To understand how newcomers learn ideology online, we study the Reddit archives of a novel sexist ideology known as the "the Red Pill''. Matching a longstanding hypothesis in the social sciences, our methods resolve the ideology into two components: a "behavioral'' dimension, concerned with correcting behavior towards the self and others, and an "explanatory'' dimension, of unifying explanations for the worldview. We then build a model of how newcomers to the group navigate the underlying conceptual structure. This reveals a large population of "tourists'', who leave quickly, and a smaller group of "residents'' who join the group and remain for orders of magnitude longer. Newcomers are attracted by the behavioral component, in the form of self-help topics such as diet, exercise, and addiction. Explanations, however, keep them there, turning tourists into residents. They have powerful effects: explanation adoption can more than double the duration of median engagement, and can explain the emergence of a long-tail of high-power engagers. The most sticky explanations, that predict the longest engagement, are about status hierarchies.
The Programmable Data Plane: Abstractions, Architectures, Algorithms, and Applications
Venue: arXiv cs.NI 2021
Year: 2021

Programmable data plane technology enables the systematic reconfiguration of the low-level processing steps applied to network packets and is a key driver in realizing the next generation of network services and applications. This survey presents recent trends and issues in the design and implementation of programmable network devices, focusing on prominent architectures, abstractions, algorithms, and applications proposed, debated, and realized over the past years. We elaborate on the trends that led to the emergence of this technology and highlight the most important pointers from the literature, casting different taxonomies for the field and identifying avenues for future research.
RoomStructNet: Learning to Rank Non-Cuboidal Room Layouts From Single View
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we present a new approach to estimate the layout of a room from its single image. While recent approaches for this task use robust features learnt from data, they resort to optimization for detecting the final layout. In addition to using learnt robust features, our approach learns an additional ranking function to estimate the final layout instead of using optimization. To learn this ranking function, we propose a framework to train a CNN using max-margin structure cost. Also, while most approaches aim at detecting cuboidal layouts, our approach detects non-cuboidal layouts for which we explicitly estimates layout complexity parameters. We use these parameters to propose layout candidates in a novel way. Our approach shows state-of-the-art results on standard datasets with mostly cuboidal layouts and also performs well on a dataset containing rooms with non-cuboidal layouts.
Expanding the Design Space for Electrically-Driven Soft Robots through Handed Shearing Auxetics
Venue: arXiv cs.RO 2021
Year: 2021

Handed Shearing Auxetics (HSA) are a promising structure for making electrically driven robots with distributed compliance that convert a motors rotation and torque into extension and force. We overcame past limitations on the range of actuation, blocked force, and stiffness by focusing on two key design parameters: the point of an HSA's auxetic trajectory that is energetically preferred, and the number of cells along the HSAs length. Modeling the HSA as a programmable spring, we characterize the effect of both on blocked force, minimum energy length, spring constant, angle range and holding torque. We also examined the effect viscoelasticity has on actuation forces over time. By varying the auxetic trajectory point, we were able to make actuators that can push, pull, or do both. We expanded the range of forces possible from 5N to 150N, and the range of stiffness from 2 N/mm to 89 N/mm. For a fixed point on the auxetic trajectory, we found decreasing length can improve force output, at the expense of needing higher torques, and having a shorter throw. We also found that the viscoelastic effects can limit the amount of force a 3D printed HSA can apply over time.
Touching Art -- A Method for Visualizing Tactile Experience
Venue: arXiv cs.HC 2021
Year: 2021

It is human to want to touch artworks, to feel their surface curvature and texture, their shapes and structures, and to feel the hand of the artist. Museum guards need to be constantly vigilant to protect art objects from adoring and exploring touches by visitors. This paper introduces a novel technique for capturing where and how art objects are touched. In this method, the users' touch either adds, or subtracts, microscopic fluorescent particles from a three-dimensional art object. Viewing the object under ultraviolet light reveals their touch traces and gestures. We present human touch behavior for a three-dimensional stylized landscape, and for two abstract and two representational art objects. We also present the results of video recordings of real-time behavior and user interviews. The resulting data show the kinds of touches, and where they are directed, and also reveal important individual differences. We feel this method opens the door to studying art perception through touch, and also enables new kinds of studies into touch behavior in other applications, including visualization, embodied cognition, and design.
Decomposing a graph into subgraphs with small components
Venue: arXiv cs.CC 2021
Year: 2021

The component size of a graph is the maximum number of edges in any connected component of the graph. Given a graph $G$ and two integers $k$ and $c$, $(k,c)$-Decomposition is the problem of deciding whether $G$ admits an edge partition into $k$ subgraphs with component size at most $c$. We prove that for any fixed $k \ge 2$ and $c \ge 2$, $(k,c)$-Decomposition is NP-complete in bipartite graphs. Also, when both $k$ and $c$ are part of the input, $(k,c)$-Decomposition is NP-complete even in trees. Moreover, $(k,c)$-Decomposition in trees is W[1]-hard with parameter $k$, and is FPT with parameter $c$. In addition, we present approximation algorithms for decomposing a tree either into the minimum number of subgraphs with component size at most $c$, or into $k$ subgraphs minimizing the maximum component size. En route to these results, we also obtain a fixed-parameter algorithm for Bin Packing with the bin capacity as parameter.