OpenRaiser/Intern-Atlas huggingface dataset:
GarNet++: Improving Fast and Accurate Static3D Cloth Draping by Curvature Loss
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we tackle the problem of static 3D cloth draping on virtual human bodies. We introduce a two-stream deep network model that produces a visually plausible draping of a template cloth on virtual 3D bodies by extracting features from both the body and garment shapes. Our network learns to mimic a Physics-Based Simulation (PBS) method while requiring two orders of magnitude less computation time. To train the network, we introduce loss terms inspired by PBS to produce plausible results and make the model collision-aware. To increase the details of the draped garment, we introduce two loss functions that penalize the difference between the curvature of the predicted cloth and PBS. Particularly, we study the impact of mean curvature normal and a novel detail-preserving loss both qualitatively and quantitatively. Our new curvature loss computes the local covariance matrices of the 3D points, and compares the Rayleigh quotients of the prediction and PBS. This leads to more details while performing favorably or comparably against the loss that considers mean curvature normal vectors in the 3D triangulated meshes. We validate our framework on four garment types for various body shapes and poses. Finally, we achieve superior performance against a recently proposed data-driven method.
A framework to evaluate the viability of robotic process automation for business process activities
Venue: arXiv cs.SE 2020
Year: 2020

Robotic process automation (RPA) is a technology for centralized automation of business processes. RPA automates user interaction with graphical user interfaces, whereby it promises efficiency gains and a reduction of human negligence during process execution. To harness these benefits, organizations face the challenge of classifying process activities as viable automation candidates for RPA. Therefore, this work aims to support practitioners in evaluating RPA automation candidates. We design a framework that consists of thirteen criteria grouped into five perspectives which offer different evaluation aspects. These criteria leverage a profound understanding of the process step. We demonstrate and evaluate the framework by applying it to a real-life data set.
Time-Frequency Scattering Accurately Models Auditory Similarities Between Instrumental Playing Techniques
Venue: arXiv cs.SD 2020
Year: 2020

Instrumental playing techniques such as vibratos, glissandos, and trills often denote musical expressivity, both in classical and folk contexts. However, most existing approaches to music similarity retrieval fail to describe timbre beyond the so-called "ordinary" technique, use instrument identity as a proxy for timbre quality, and do not allow for customization to the perceptual idiosyncrasies of a new subject. In this article, we ask 31 human subjects to organize 78 isolated notes into a set of timbre clusters. Analyzing their responses suggests that timbre perception operates within a more flexible taxonomy than those provided by instruments or playing techniques alone. In addition, we propose a machine listening model to recover the cluster graph of auditory similarities across instruments, mutes, and techniques. Our model relies on joint time--frequency scattering features to extract spectrotemporal modulations as acoustic features. Furthermore, it minimizes triplet loss in the cluster graph by means of the large-margin nearest neighbor (LMNN) metric learning algorithm. Over a dataset of 9346 isolated notes, we report a state-of-the-art average precision at rank five (AP@5) of $99.0\%\pm1$. An ablation study demonstrates that removing either the joint time--frequency scattering transform or the metric learning algorithm noticeably degrades performance.
Modern Design Methodologies and the Development of Mechatronic Products
Venue: arXiv cs.SE 2020
Year: 2020

This article presents a quick view on the development of mechatronic products and how the techniques of Design Thinking, Concurrent Engineering and Agilism can be integrated to address this development. Design Thinking is employed in the early stages in order to better explore creativity, whereas Concurrent Engineering and Agilism are applied during the development of the product, in order to deal with emerging requirements, typical of the development of complex products.
Self-supervised Feature Learning via Exploiting Multi-modal Data for Retinal Disease Diagnosis
Venue: arXiv cs.CV 2020
Year: 2020

The automatic diagnosis of various retinal diseases from fundus images is important to support clinical decision-making. However, developing such automatic solutions is challenging due to the requirement of a large amount of human-annotated data. Recently, unsupervised/self-supervised feature learning techniques receive a lot of attention, as they do not need massive annotations. Most of the current self-supervised methods are analyzed with single imaging modality and there is no method currently utilize multi-modal images for better results. Considering that the diagnostics of various vitreoretinal diseases can greatly benefit from another imaging modality, e.g., FFA, this paper presents a novel self-supervised feature learning method by effectively exploiting multi-modal data for retinal disease diagnosis. To achieve this, we first synthesize the corresponding FFA modality and then formulate a patient feature-based softmax embedding objective. Our objective learns both modality-invariant features and patient-similarity features. Through this mechanism, the neural network captures the semantically shared information across different modalities and the apparent visual similarity between patients. We evaluate our method on two public benchmark datasets for retinal disease diagnosis. The experimental results demonstrate that our method clearly outperforms other self-supervised feature learning methods and is comparable to the supervised baseline.
Fast Graphlet Transform of Sparse Graphs
Venue: arXiv cs.SI 2020
Year: 2020

We introduce the computational problem of graphlet transform of a sparse large graph. Graphlets are fundamental topology elements of all graphs/networks. They can be used as coding elements to encode graph-topological information at multiple granularity levels for classifying vertices on the same graph/network as well as for making differentiation or connection across different networks. Network/graph analysis using graphlets has growing applications. We recognize the universality and increased encoding capacity in using multiple graphlets, we address the arising computational complexity issues, and we present a fast method for exact graphlet transform. The fast graphlet transform establishes a few remarkable records at once in high computational efficiency, low memory consumption, and ready translation to high-performance program and implementation. It is intended to enable and advance network/graph analysis with graphlets, and to introduce the relatively new analysis apparatus to graph theory, high-performance graph computation, and broader applications.
EPiT : A Software Testing Tool for Generation of Test Cases Automatically
Venue: arXiv cs.SE 2020
Year: 2020

Software test cases can be defined as a set of condition where a tester needs to test and determine that the System Under Test (SUT) satisfied with the expected result correctly. This paper discusses the optimization technique in generating cases automatically by using EpiT (Eclipse Plug-in Tool). EpiT is developed to optimize the generation of test cases from source code in order to reduce time used for conventional manually creating test cases. By using code smell functionality, EpiT helps to generate test cases automatically from Java programs by checking its line of code (LOC). The implementation of EpiT will also be presented based on several case studies conducted to show the optimization of the test cases generated. Based on the results presented, EpiT is proven to solve the problem for software tester to generate test case manually and check the optimization from the source code using code smell technique.
An Adaptive Solver for Systems of Linear Equations
Venue: arXiv cs.MS 2020
Year: 2020

Computational implementations for solving systems of linear equations often rely on a one-size-fits-all approach based on LU decomposition of dense matrices stored in column-major format. Such solvers are typically implemented with the aid of the xGESV set of functions available in the low-level LAPACK software, with the aim of reducing development time by taking advantage of well-tested routines. However, this straightforward approach does not take into account various matrix properties which can be exploited to reduce the computational effort and/or to increase numerical stability. Furthermore, direct use of LAPACK functions can be error-prone for non-expert users and results in source code that has little resemblance to originating mathematical expressions. We describe an adaptive solver that we have implemented inside recent versions of the high-level Armadillo C++ library for linear algebra. The solver automatically detects several common properties of a given system (banded, triangular, symmetric positive definite), followed by solving the system via mapping to a set of suitable LAPACK functions best matched to each property. The solver also detects poorly conditioned systems and automatically seeks a solution via singular value decomposition as a fallback. We show that the adaptive solver leads to notable speedups, while also freeing the user from using direct calls to cumbersome LAPACK functions.
Deep-VFX: Deep Action Recognition Driven VFX for Short Video
Venue: arXiv cs.CV 2020
Year: 2020

Human motion is a key function to communicate information. In the application, short-form mobile video is so popular all over the world such as Tik Tok. The users would like to add more VFX so as to pursue creativity and personlity. Many special effects are added on the short video platform. These gives the users more possibility to show off these personality. The common and traditional way is to create the template of VFX. However, in order to synthesis the perfect, the users have to tedious attempt to grasp the timing and rhythm of new templates. It is not easy-to-use especially for the mobile app. This paper aims to change the VFX synthesis by motion driven instead of the traditional template matching. We propose the AI method to improve this VFX synthesis. In detail, in order to add the special effect on the human body. The skeleton extraction is essential in this system. We also propose a novel form of LSTM to find out the user's intention by action recognition. The experiment shows that our system enables to generate VFX for short video more easier and efficient.
Detecting Quality Problems in Research Data: A Model-Driven Approach
Venue: arXiv cs.IR 2020
Year: 2020

As scientific progress highly depends on the quality of research data, there are strict requirements for data quality coming from the scientific community. A major challenge in data quality assurance is to localise quality problems that are inherent to data. Due to the dynamic digitalisation in specific scientific fields, especially the humanities, different database technologies and data formats may be used in rather short terms to gain experiences. We present a model-driven approach to analyse the quality of research data. It allows abstracting from the underlying database technology. Based on the observation that many quality problems show anti-patterns, a data engineer formulates analysis patterns that are generic concerning the database format and technology. A domain expert chooses a pattern that has been adapted to a specific database technology and concretises it for a domain-specific database format. The resulting concrete patterns are used by data analysts to locate quality problems in their databases. As proof of concept, we implemented tool support that realises this approach for XML databases. We evaluated our approach concerning expressiveness and performance in the domain of cultural heritage based on a qualitative study on quality problems occurring in cultural heritage data.
Interoperability and Integration Testing Methods for IoT Systems: a Systematic Mapping Study
Venue: arXiv cs.SE 2020
Year: 2020

The recent active development of Internet of Things (IoT) solutions in various domains has led to an increased demand for security, safety, and reliability of these systems. Security and data privacy are currently the most frequently discussed topics; however, other reliability aspects also need to be focused on to maintain the smooth and safe operation of IoT systems. Until now, there has been no systematic mapping study dedicated to the topic of interoperability and integration testing of IoT systems specifically; therefore, we present such an overview in this study. We analyze 803 papers from four major primary databases and perform detailed assessment and quality check to find 115 relevant papers. In addition, recently published testing techniques and approaches are analyzed and classified; the challenges and limitations in the field is also identified and discussed. Research trends related to publication time, active researchers, and publication media are presented in this study. The results suggest that studies mainly focus only on general testing methods, which can be applied to integration and interoperability testing of IoT systems; thus, there are research opportunities to develop additional testing methods focused specifically on IoT systems, so that they are more effective in the IoT context.
Risk Assessment in the Face-based Watchlist Screening in e-Border
Venue: arXiv cs.CV 2020
Year: 2020

This paper concerns with facial-based watchlist technology as a component of automated border control machines deployed in e-borders. The key task of the watchlist technology is to mitigate effects of mis-identification and impersonation. To address this problem, we developed a novel cost-based model of traveler risk assessment and proved its efficiency via intensive experiments using large-scale facial databases. The results of this study are applicable to any biometric modality to be used in watchlist technology.
ZigZag: A Memory-Centric Rapid DNN Accelerator Design Space Exploration Framework
Venue: arXiv cs.DC 2020
Year: 2020

Building efficient embedded deep learning systems requires a tight co-design between DNN algorithms, memory hierarchy, and dataflow. However, owing to the large degrees of freedom in the design space, finding an optimal solution through the implementation of individual design points becomes infeasible. Recently, several estimation frameworks for fast design space exploration (DSE) have emerged, yet they either suffer from long runtimes or a limited exploration space. This work introduces ZigZag, a memory-centric rapid DNN accelerator DSE framework which extends the DSE with uneven mapping opportunities, in which operands at shared memory levels are no longer bound to use the same memory levels for each loop index. For this, ZigZag uses a memory-centric nested-for-loop format as a uniform representation to integrate algorithm, accelerator, and algorithm-to-accelerator mapping, and consists of three key components: 1) a latency-enhanced analytical Hardware Cost Estimator, 2) a Temporal Mapping Generator that supports even/uneven scheduling on any type of memory hierarchy, and 3) an Architecture Generator that explores the whole memory hierarchy design space. Benchmarking experiments against existing frameworks, together with three case studies at different design abstraction levels show the strength of ZigZag. Up to 33% more energy-efficient solutions are found by introducing ZigZag's uneven scheduling opportunities.
Implementation of a High-Throughput Fast-SSC Polar Decoder with Sequence Repetition Node
Venue: arXiv cs.IT 2020
Year: 2020

Even though polar codes were adopted in the latest 5G cellular standard, they still have the fundamental problem of high decoding latency. Aiming at solving this problem, a fast simplified successive cancellation (Fast-SSC) decoder based on the new class of sequence repetition (SR) nodes has been proposed recently in \cite{sr2020} and has a lower required number of time steps than other existing Fast-SSC decoders in theory. This paper focuses on the hardware implementation of this SR node-based fast-SSC (SRFSC) decoder. The implementation results for a polar code with length 1024 and code rate 1/2 show that our implementation has a throughput of $505$ Mbps on an Altera Stratix IV FPGA, which is 17.9% higher with respect to the previous work.
Learning Centric Power Allocation for Edge Intelligence
Venue: arXiv cs.IT 2020
Year: 2020

While machine-type communication (MTC) devices generate massive data, they often cannot process this data due to limited energy and computation power. To this end, edge intelligence has been proposed, which collects distributed data and performs machine learning at the edge. However, this paradigm needs to maximize the learning performance instead of the communication throughput, for which the celebrated water-filling and max-min fairness algorithms become inefficient since they allocate resources merely according to the quality of wireless channels. This paper proposes a learning centric power allocation (LCPA) method, which allocates radio resources based on an empirical classification error model. To get insights into LCPA, an asymptotic optimal solution is derived. The solution shows that the transmit powers are inversely proportional to the channel gain, and scale exponentially with the learning parameters. Experimental results show that the proposed LCPA algorithm significantly outperforms other power allocation algorithms.
A Hybrid Neuromorphic Object Tracking and Classification Framework for Real-time Systems
Venue: arXiv cs.CV 2020
Year: 2020

Deep learning inference that needs to largely take place on the 'edge' is a highly computational and memory intensive workload, making it intractable for low-power, embedded platforms such as mobile nodes and remote security applications. To address this challenge, this paper proposes a real-time, hybrid neuromorphic framework for object tracking and classification using event-based cameras that possess properties such as low-power consumption (5-14 mW) and high dynamic range (120 dB). Nonetheless, unlike traditional approaches of using event-by-event processing, this work uses a mixed frame and event approach to get energy savings with high performance. Using a frame-based region proposal method based on the density of foreground events, a hardware-friendly object tracking scheme is implemented using the apparent object velocity while tackling occlusion scenarios. The object track input is converted back to spikes for TrueNorth classification via the energy-efficient deep network (EEDN) pipeline. Using originally collected datasets, we train the TrueNorth model on the hardware track outputs, instead of using ground truth object locations as commonly done, and demonstrate the ability of our system to handle practical surveillance scenarios. As an optional paradigm, to exploit the low latency and asynchronous nature of neuromorphic vision sensors (NVS), we also propose a continuous-time tracker with C++ implementation where each event is processed individually. Thereby, we extensively compare the proposed methodologies to state-of-the-art event-based and frame-based methods for object tracking and classification, and demonstrate the use case of our neuromorphic approach for real-time and embedded applications without sacrificing performance. Finally, we also showcase the efficacy of the proposed system to a standard RGB camera setup when evaluated over several hours of traffic recordings.
Supervised learning on heterogeneous, attributed entities interacting over time
Venue: arXiv cs.LG 2020
Year: 2020

Most physical or social phenomena can be represented by ontologies where the constituent entities are interacting in various ways with each other and with their environment. Furthermore, those entities are likely heterogeneous and attributed with features that evolve dynamically in time as a response to their successive interactions. In order to apply machine learning on such entities, e.g., for classification purposes, one therefore needs to integrate the interactions into the feature engineering in a systematic way. This proposal shows how, to this end, the current state of graph machine learning remains inadequate and needs to be be augmented with a comprehensive feature engineering paradigm in space and time.
Learning One Class Representations for Face Presentation Attack Detection using Multi-channel Convolutional Neural Networks
Venue: arXiv cs.CV 2020
Year: 2020

Face recognition has evolved as a widely used biometric modality. However, its vulnerability against presentation attacks poses a significant security threat. Though presentation attack detection (PAD) methods try to address this issue, they often fail in generalizing to unseen attacks. In this work, we propose a new framework for PAD using a one-class classifier, where the representation used is learned with a Multi-Channel Convolutional Neural Network (MCCNN). A novel loss function is introduced, which forces the network to learn a compact embedding for bonafide class while being far from the representation of attacks. A one-class Gaussian Mixture Model is used on top of these embeddings for the PAD task. The proposed framework introduces a novel approach to learn a robust PAD system from bonafide and available (known) attack classes. This is particularly important as collecting bonafide data and simpler attacks are much easier than collecting a wide variety of expensive attacks. The proposed system is evaluated on the publicly available WMCA multi-channel face PAD database, which contains a wide variety of 2D and 3D attacks. Further, we have performed experiments with MLFP and SiW-M datasets using RGB channels only. Superior performance in unseen attack protocols shows the effectiveness of the proposed approach. Software, data, and protocols to reproduce the results are made available publicly.
Collectives in hybrid MPI+MPI code: design, practice and performance
Venue: arXiv cs.DC 2020
Year: 2020

The use of hybrid scheme combining the message passing programming models for inter-node parallelism and the shared memory programming models for node-level parallelism is widely spread. Existing extensive practices on hybrid Message Passing Interface (MPI) plus Open Multi-Processing (OpenMP) programming account for its popularity. Nevertheless, strong programming efforts are required to gain performance benefits from the MPI+OpenMP code. An emerging hybrid method that combines MPI and the MPI shared memory model (MPI+MPI) is promising. However, writing an efficient hybrid MPI+MPI program -- especially when the collective communication operations are involved -- is not to be taken for granted. In this paper, we propose a new design method to implement hybrid MPI+MPI context-based collective communication operations. Our method avoids on-node memory replications (on-node communication overheads) that are required by semantics in pure MPI. We also offer wrapper primitives hiding all the design details from users, which comes with practices on how to structure hybrid MPI+MPI code with these primitives. The micro-benchmarks show that our collectives are comparable or superior to those in pure MPI context. We have further validated the effectiveness of the hybrid MPI+MPI model (which uses our wrapper primitives) in three computational kernels, by comparison to the pure MPI and hybrid MPI+OpenMP models.