OpenRaiser/Intern-Atlas huggingface dataset:
Ontological Entities for Planning and Describing Cultural Heritage 3D Models Creation
Venue: arXiv cs.DL 2021
Year: 2021

In the last decades the rapid development of technologies and methodologies in the field of digitization and 3D modelling has led to an increasing proliferation of 3D technologies in the Cultural Heritage domain. Despite the great potential of 3D digital heritage, the "special effects" of 3D may often overwhelm its importance in research. Projects and consortia of scholars have tried to put order in the different fields of application of these technologies, providing guidelines and proposing workflows. The use of computer graphics as an effective methodology for CH research and communication highlighted the need of transparent provenance data to properly document digital assets and understand the degree of scientific quality and reliability of their outcomes. The building and release of provenance knowledge, consisting in the complete formal documentation of each phase of the process, is therefore of fundamental importance to ensure its repeatability and to guarantee the integration and interoperability of the generated metadata on the Semantic Web. This paper proposes a methodology for documenting the planning and creation of 3D models used in archaeology and Cultural Heritage, by means of an application profile based on the CIDOC CRM ecosystem and other international standards.
Learning-Aided Heuristics Design for Storage System
Venue: arXiv cs.AI 2021
Year: 2021

Computer systems such as storage systems normally require transparent white-box algorithms that are interpretable for human experts. In this work, we propose a learning-aided heuristic design method, which automatically generates human-readable strategies from Deep Reinforcement Learning (DRL) agents. This method benefits from the power of deep learning but avoids the shortcoming of its black-box property. Besides the white-box advantage, experiments in our storage productions resource allocation scenario also show that this solution outperforms the systems default settings and the elaborately handcrafted strategy by human experts.
No Free Lunch: Microservice Practices Reconsidered in Industry
Venue: arXiv cs.SE 2021
Year: 2021

Microservice architecture advocates a number of technologies and practices such as lightweight container, container orchestration, and DevOps, with the promised benefits of faster delivery, improved scalability, and greater autonomy. However, microservice systems implemented in industry vary a lot in terms of adopted practices and achieved benefits, drastically different from what is advocated in the literature. In this article, we conduct an empirical study, including an online survey with 51 responses and 14 interviews for experienced microservice experts to advance our understanding regarding to microservice practices in industry. As a part of our findings, the empirical study clearly revealed three levels of maturity of microservice systems (from basic to advanced): independent development and deployment, high scalability and availability, and service ecosystem, categorized by the fulfilled benefits of microservices. We also identify 11 practical issues that constrain the microservice capabilities of organizations. For each issue, we summarize the practices that have been explored and adopted in industry, along with the remaining challenges. Our study can help practitioners better position their microservice systems and determine what infrastructures and capabilities are worth investing. Our study can also help researchers better understand industrial microservice practices and identify useful research problems.
Deep Transfer Learning for Brain Magnetic Resonance Image Multi-class Classification
Venue: arXiv cs.CV 2021
Year: 2021

Magnetic Resonance Imaging (MRI) is a principal diagnostic approach used in the field of radiology to create images of the anatomical and physiological structure of patients. MRI is the prevalent medical imaging practice to find abnormalities in soft tissues. Traditionally they are analyzed by a radiologist to detect abnormalities in soft tissues, especially the brain. The process of interpreting a massive volume of patient's MRI is laborious. Hence, the use of Machine Learning methodologies can aid in detecting abnormalities in soft tissues with considerable accuracy. In this research, we have curated a novel dataset and developed a framework that uses Deep Transfer Learning to perform a multi-classification of tumors in the brain MRI images. In this paper, we adopted the Deep Residual Convolutional Neural Network (ResNet50) architecture for the experiments along with discriminative learning techniques to train the model. Using the novel dataset and two publicly available MRI brain datasets, this proposed approach attained a classification accuracy of 86.40% on the curated dataset, 93.80% on the Harvard Whole Brain Atlas dataset, and 97.05% accuracy on the School of Biomedical Engineering dataset. Results of our experiments significantly demonstrate our proposed framework for transfer learning is a potential and effective method for brain tumor multi-classification tasks.
A Query-Driven Topic Model
Venue: arXiv cs.IR 2021
Year: 2021

Topic modeling is an unsupervised method for revealing the hidden semantic structure of a corpus. It has been increasingly widely adopted as a tool in the social sciences, including political science, digital humanities and sociological research in general. One desirable property of topic models is to allow users to find topics describing a specific aspect of the corpus. A possible solution is to incorporate domain-specific knowledge into topic modeling, but this requires a specification from domain experts. We propose a novel query-driven topic model that allows users to specify a simple query in words or phrases and return query-related topics, thus avoiding tedious work from domain experts. Our proposed approach is particularly attractive when the user-specified query has a low occurrence in a text corpus, making it difficult for traditional topic models built on word cooccurrence patterns to identify relevant topics. Experimental results demonstrate the effectiveness of our model in comparison with both classical topic models and neural topic models.
Zipf Matrix Factorization : Matrix Factorization with Matthew Effect Reduction
Venue: arXiv cs.IR 2021
Year: 2021

Recommender system recommends interesting items to users based on users' past information history. Researchers have been paying attention to improvement of algorithmic performance such as MAE and precision@K. Major techniques such as matrix factorization and learning to rank are optimized based on such evaluation metrics. However, the intrinsic Matthew Effect problem poses great threat to the fairness of the recommender system, and the unfairness problem cannot be resolved by optimization of traditional metrics. In this paper, we propose a novel algorithm that incorporates Matthew Effect reduction with the matrix factorization framework. We demonstrate that our approach can boost the fairness of the algorithm and enhances performance evaluated by traditional metrics.
Low-Rank Projections of GCNs Laplacian
Venue: arXiv cs.SI 2021
Year: 2021

In this work, we study the behavior of standard models for community detection under spectral manipulations. Through various ablation experiments, we evaluate the impact of bandpass filtering on the performance of a GCN: we empirically show that most of the necessary and used information for nodes classification is contained in the low-frequency domain, and thus contrary to images, high frequencies are less crucial to community detection. In particular, it is sometimes possible to obtain accuracies at a state-of-the-art level with simple classifiers that rely only on a few low frequencies.
Cognitive-aware Short-text Understanding for Inferring Professions
Venue: arXiv cs.CL 2021
Year: 2021

Leveraging short-text contents to estimate the occupation of microblog authors has significant gains in many applications. Yet challenges abound. Firstly brief textual contents come with excessive lexical noise that makes the inference problem challenging. Secondly, cognitive-semantics are not evident, and important linguistic features are latent in short-text contents. Thirdly, it is hard to measure the correlation between the cognitive short-text semantics and the features pertaining various occupations. We argue that the multi-aspect cognitive features are needed to correctly associate short-text contents to a particular job and discover suitable people for the careers. To this end, we devise a novel framework that on the one hand, can infer short-text contents and exploit cognitive features, and on the other hand, fuses various adopted novel algorithms, such as curve fitting, support vector, and boosting modules to better predict the occupation of the authors. The final estimation module manufactures the $R^w$-tree via coherence weight to tune the best outcome in the inferring process. We conduct comprehensive experiments on real-life Twitter data. The experimental results show that compared to other rivals, our cognitive multi-aspect model can achieve a higher performance in the career estimation procedure, where it is inevitable to neglect the contextual semantics of users.
SemEval-2021 Task 11: NLPContributionGraph -- Structuring Scholarly NLP Contributions for a Research Knowledge Graph
Venue: arXiv cs.CL 2021
Year: 2021

There is currently a gap between the natural language expression of scholarly publications and their structured semantic content modeling to enable intelligent content search. With the volume of research growing exponentially every year, a search feature operating over semantically structured content is compelling. The SemEval-2021 Shared Task NLPContributionGraph (a.k.a. 'the NCG task') tasks participants to develop automated systems that structure contributions from NLP scholarly articles in the English language. Being the first-of-its-kind in the SemEval series, the task released structured data from NLP scholarly articles at three levels of information granularity, i.e. at sentence-level, phrase-level, and phrases organized as triples toward Knowledge Graph (KG) building. The sentence-level annotations comprised the few sentences about the article's contribution. The phrase-level annotations were scientific term and predicate phrases from the contribution sentences. Finally, the triples constituted the research overview KG. For the Shared Task, participating systems were then expected to automatically classify contribution sentences, extract scientific terms and relations from the sentences, and organize them as KG triples. Overall, the task drew a strong participation demographic of seven teams and 27 participants. The best end-to-end task system classified contribution sentences at 57.27% F1, phrases at 46.41% F1, and triples at 22.28% F1. While the absolute performance to generate triples remains low, in the conclusion of this article, the difficulty of producing such data and as a consequence of modeling it is highlighted.
3rd Place Solution for Short-video Face Parsing Challenge
Venue: arXiv cs.CV 2021
Year: 2021

This is a short technical report introducing the solution of Team Rat for Short-video Parsing Face Parsing Track of The 3rd Person in Context (PIC) Workshop and Challenge at CVPR 2021. In this report, we propose an Edge-Aware Network (EANet) that uses edge information to refine the segmentation edge. To further obtain the finer edge results, we introduce edge attention loss that only compute cross entropy on the edges, it can effectively reduce the classification error around edge and get more smooth boundary. Benefiting from the edge information and edge attention loss, the proposed EANet achieves 86.16\% accuracy in the Short-video Face Parsing track of the 3rd Person in Context (PIC) Workshop and Challenge, ranked the third place.
CRASH: Raw Audio Score-based Generative Modeling for Controllable High-resolution Drum Sound Synthesis
Venue: arXiv cs.SD 2021
Year: 2021

In this paper, we propose a novel score-base generative model for unconditional raw audio synthesis. Our proposal builds upon the latest developments on diffusion process modeling with stochastic differential equations, which already demonstrated promising results on image generation. We motivate novel heuristics for the choice of the diffusion processes better suited for audio generation, and consider the use of a conditional U-Net to approximate the score function. While previous approaches on diffusion models on audio were mainly designed as speech vocoders in medium resolution, our method termed CRASH (Controllable Raw Audio Synthesis with High-resolution) allows us to generate short percussive sounds in 44.1kHz in a controllable way. Through extensive experiments, we showcase on a drum sound generation task the numerous sampling schemes offered by our method (unconditional generation, deterministic generation, inpainting, interpolation, variations, class-conditional sampling) and propose the class-mixing sampling, a novel way to generate "hybrid" sounds. Our proposed method closes the gap with GAN-based methods on raw audio, while offering more flexible generation capabilities with lighter and easier-to-train models.
Extending the RISC-V ISA for exploring advanced reconfigurable SIMD instructions
Venue: arXiv cs.AR 2021
Year: 2021

This paper presents a novel, non-standard set of vector instruction types for exploring custom SIMD instructions in a softcore. The new types allow simultaneous access to a relatively high number of operands, reducing the instruction count where applicable. Additionally, a high-performance open-source RISC-V (RV32 IM) softcore is introduced, optimised for exploring custom SIMD instructions and streaming performance. By providing instruction templates for instruction development in HDL/Verilog, efficient FPGA-based instructions can be developed with few low-level lines of code. In order to improve custom SIMD instruction performance, the softcore's cache hierarchy is optimised for bandwidth, such as with very wide blocks for the last-level cache. The approach is demonstrated on example memory-intensive applications on an FPGA. Although the exploration is based on the softcore, the goal is to provide a means to experiment with advanced SIMD instructions which could be loaded in future CPUs that feature reconfigurable regions as custom instructions. Finally, we provide some insights on the challenges and effectiveness of such future micro-architectures.
Graph Domain Adaptation: A Generative View
Venue: arXiv cs.LG 2021
Year: 2021

Recent years have witnessed tremendous interest in deep learning on graph-structured data. Due to the high cost of collecting labeled graph-structured data, domain adaptation is important to supervised graph learning tasks with limited samples. However, current graph domain adaptation methods are generally adopted from traditional domain adaptation tasks, and the properties of graph-structured data are not well utilized. For example, the observed social networks on different platforms are controlled not only by the different crowd or communities but also by the domain-specific policies and the background noise. Based on these properties in graph-structured data, we first assume that the graph-structured data generation process is controlled by three independent types of latent variables, i.e., the semantic latent variables, the domain latent variables, and the random latent variables. Based on this assumption, we propose a disentanglement-based unsupervised domain adaptation method for the graph-structured data, which applies variational graph auto-encoders to recover these latent variables and disentangles them via three supervised learning modules. Extensive experimental results on two real-world datasets in the graph classification task reveal that our method not only significantly outperforms the traditional domain adaptation methods and the disentangled-based domain adaptation methods but also outperforms the state-of-the-art graph domain adaptation algorithms.
Security testing using JUnit and Perl scripts
Venue: arXiv cs.SE 2021
Year: 2021

In this paper, I describe a recent practical experience where JUnit was used for testing security bugs in addition to functional bugs. Perl scripts were also used during the exploration phase. The application being tested was mature, but insecure.
A Framework to Counteract Suboptimal User-Behaviors in Exploratory Learning Environments: an Application to MOOCs
Venue: arXiv cs.AI 2021
Year: 2021

While there is evidence that user-adaptive support can greatly enhance the effectiveness of educational systems, designing such support for exploratory learning environments (e.g., simulations) is still challenging due to the open-ended nature of their interaction. In particular, there is little a priori knowledge of which student's behaviors can be detrimental to learning in such environments. To address this problem, we focus on a data-driven user-modeling framework that uses logged interaction data to learn which behavioral or activity patterns should trigger help during interaction with a specific learning environment. This framework has been successfully used to provide adaptive support in interactive learning simulations. Here we present a novel application of this framework we are working on, namely to Massive Open Online Courses (MOOCs), a form of exploratory environment that could greatly benefit from adaptive support due to the large diversity of their users, but typically lack of such adaptation. We describe an experiment aimed at investigating the value of our framework to identify student's behaviors that can justify adapting to, and report some preliminary results.
Biomedical Entity Linking with Contrastive Context Matching
Venue: arXiv cs.CL 2021
Year: 2021

We introduce BioCoM, a contrastive learning framework for biomedical entity linking that uses only two resources: a small-sized dictionary and a large number of raw biomedical articles. Specifically, we build the training instances from raw PubMed articles by dictionary matching and use them to train a context-aware entity linking model with contrastive learning. We predict the normalized biomedical entity at inference time through a nearest-neighbor search. Results found that BioCoM substantially outperforms state-of-the-art models, especially in low-resource settings, by effectively using the context of the entities.
Improving Metric Dimensionality Reduction with Distributed Topology
Venue: arXiv cs.LG 2021
Year: 2021

We propose a novel approach to dimensionality reduction combining techniques of metric geometry and distributed persistent homology, in the form of a gradient-descent based method called DIPOLE. DIPOLE is a dimensionality-reduction post-processing step that corrects an initial embedding by minimizing a loss functional with both a local, metric term and a global, topological term. By fixing an initial embedding method (we use Isomap), DIPOLE can also be viewed as a full dimensionality-reduction pipeline. This framework is based on the strong theoretical and computational properties of distributed persistent homology and comes with the guarantee of almost sure convergence. We observe that DIPOLE outperforms popular methods like UMAP, t-SNE, and Isomap on a number of popular datasets, both visually and in terms of precise quantitative metrics.
Toward Automatic Interpretation of 3D Plots
Venue: arXiv cs.CV 2021
Year: 2021

This paper explores the challenge of teaching a machine how to reverse-engineer the grid-marked surfaces used to represent data in 3D surface plots of two-variable functions. These are common in scientific and economic publications; and humans can often interpret them with ease, quickly gleaning general shape and curvature information from the simple collection of curves. While machines have no such visual intuition, they do have the potential to accurately extract the more detailed quantitative data that guided the surface's construction. We approach this problem by synthesizing a new dataset of 3D grid-marked surfaces (SurfaceGrid) and training a deep neural net to estimate their shape. Our algorithm successfully recovers shape information from synthetic 3D surface plots that have had axes and shading information removed, been rendered with a variety of grid types, and viewed from a range of viewpoints.
Hierarchically Regularized Deep Forecasting
Venue: arXiv cs.LG 2021
Year: 2021

Hierarchical forecasting is a key problem in many practical multivariate forecasting applications - the goal is to simultaneously predict a large number of correlated time series that are arranged in a pre-specified aggregation hierarchy. The main challenge is to exploit the hierarchical correlations to simultaneously obtain good prediction accuracy for time series at different levels of the hierarchy. In this paper, we propose a new approach for hierarchical forecasting which consists of two components. First, decomposing the time series along a global set of basis time series and modeling hierarchical constraints using the coefficients of the basis decomposition. And second, using a linear autoregressive model with coefficients that vary with time. Unlike past methods, our approach is scalable (inference for a specific time series only needs access to its own history) while also modeling the hierarchical structure via (approximate) coherence constraints among the time series forecasts. We experiment on several public datasets and demonstrate significantly improved overall performance on forecasts at different levels of the hierarchy, compared to existing state-of-the-art hierarchical models.
Face Age Progression With Attribute Manipulation
Venue: arXiv cs.CV 2021
Year: 2021

Face is one of the predominant means of person recognition. In the process of ageing, human face is prone to many factors such as time, attributes, weather and other subject specific variations. The impact of these factors were not well studied in the literature of face aging. In this paper, we propose a novel holistic model in this regard viz., ``Face Age progression With Attribute Manipulation (FAWAM)", i.e. generating face images at different ages while simultaneously varying attributes and other subject specific characteristics. We address the task in a bottom-up manner, as two submodules i.e. face age progression and face attribute manipulation. For face aging, we use an attribute-conscious face aging model with a pyramidal generative adversarial network that can model age-specific facial changes while maintaining intrinsic subject specific characteristics. For facial attribute manipulation, the age processed facial image is manipulated with desired attributes while preserving other details unchanged, leveraging an attribute generative adversarial network architecture. We conduct extensive analysis in standard large scale datasets and our model achieves significant performance both quantitatively and qualitatively.