OpenRaiser/Intern-Atlas huggingface dataset:
Using dynamical quantization to perform split attempts in online tree regressors
Venue: arXiv cs.LG 2020
Year: 2020

A central aspect of online decision tree solutions is evaluating the incoming data and enabling model growth. For such, trees much deal with different kinds of input features and partition them to learn from the data. Numerical features are no exception, and they pose additional challenges compared to other kinds of features, as there is no trivial strategy to choose the best point to make a split decision. The problem is even more challenging in regression tasks because both the features and the target are continuous. Typical online solutions evaluate and store all the points monitored between split attempts, which goes against the constraints posed in real-time applications. In this paper, we introduce the Quantization Observer (QO), a simple yet effective hashing-based algorithm to monitor and evaluate split point candidates in numerical features for online tree regressors. QO can be easily integrated into incremental decision trees, such as Hoeffding Trees, and it has a monitoring cost of $O(1)$ per instance and sub-linear cost to evaluate split candidates. Previous solutions had a $O(\log n)$ cost per insertion (in the best case) and a linear cost to evaluate split points. Our extensive experimental setup highlights QO's effectiveness in providing accurate split point suggestions while spending much less memory and processing time than its competitors.
Nothing But Geometric Constraints: A Model-Free Method for Articulated Object Pose Estimation
Venue: arXiv cs.CV 2020
Year: 2020

We propose an unsupervised vision-based system to estimate the joint configurations of the robot arm from a sequence of RGB or RGB-D images without knowing the model a priori, and then adapt it to the task of category-independent articulated object pose estimation. We combine a classical geometric formulation with deep learning and extend the use of epipolar constraint to multi-rigid-body systems to solve this task. Given a video sequence, the optical flow is estimated to get the pixel-wise dense correspondences. After that, the 6D pose is computed by a modified PnP algorithm. The key idea is to leverage the geometric constraints and the constraint between multiple frames. Furthermore, we build a synthetic dataset with different kinds of robots and multi-joint articulated objects for the research of vision-based robot control and robotic vision. We demonstrate the effectiveness of our method on three benchmark datasets and show that our method achieves higher accuracy than the state-of-the-art supervised methods in estimating joint angles of robot arms and articulated objects.
Extreme Model Compression for On-device Natural Language Understanding
Venue: arXiv cs.CL 2020
Year: 2020

In this paper, we propose and experiment with techniques for extreme compression of neural natural language understanding (NLU) models, making them suitable for execution on resource-constrained devices. We propose a task-aware, end-to-end compression approach that performs word-embedding compression jointly with NLU task learning. We show our results on a large-scale, commercial NLU system trained on a varied set of intents with huge vocabulary sizes. Our approach outperforms a range of baselines and achieves a compression rate of 97.4% with less than 3.7% degradation in predictive performance. Our analysis indicates that the signal from the downstream task is important for effective compression with minimal degradation in performance.
Improved Diagnosis of Tibiofemoral Cartilage Defects on MRI Images Using Deep Learning
Venue: arXiv cs.CV 2020
Year: 2020

Background: MRI is the modality of choice for cartilage imaging; however, its diagnostic performance is variable and significantly lower than the gold standard diagnostic knee arthroscopy. In recent years, deep learning has been used to automatically interpret medical images to improve diagnostic accuracy and speed. Purpose: The primary purpose of this study was to evaluate whether deep learning applied to the interpretation of knee MRI images can be utilized to identify cartilage defects accurately. Methods: We analyzed data from patients who underwent knee MRI evaluation and consequently had arthroscopic knee surgery (207 with cartilage defect, 90 without cartilage defect). Patients' arthroscopic findings were compared to preoperative MRI images to verify the presence or absence of isolated tibiofemoral cartilage defects. We developed three convolutional neural networks (CNNs) to analyze the MRI images and implemented image-specific saliency maps to visualize the CNNs' decision-making process. To compare the CNNs' performance against human interpretation, the same test dataset images were provided to an experienced orthopaedic surgeon and an orthopaedic resident. Results: Saliency maps demonstrated that the CNNs learned to focus on the clinically relevant areas of the tibiofemoral articular cartilage on MRI images during the decision-making processes. One CNN achieved higher performance than the orthopaedic surgeon, with two more accurate diagnoses made by the CNN. All the CNNs outperformed the orthopaedic resident. Conclusion: CNN can be used to enhance the diagnostic performance of MRI in identifying isolated tibiofemoral cartilage defects and may replace diagnostic knee arthroscopy in certain cases in the future.
Crowd-Sourced Road Quality Mapping in the Developing World
Venue: arXiv cs.LG 2020
Year: 2020

Road networks are among the most essential components of a country's infrastructure. By facilitating the movement and exchange of goods, people, and ideas, they support economic and cultural activity both within and across borders. Up-to-date mapping of the the geographical distribution of roads and their quality is essential in high-impact applications ranging from land use planning to wilderness conservation. Mapping presents a particularly pressing challenge in developing countries, where documentation is poor and disproportionate amounts of road construction are expected to occur in the coming decades. We present a new crowd-sourced approach capable of assessing road quality and identify key challenges and opportunities in the transferability of deep learning based methods across domains.
Open Source 3-D Filament Diameter Sensor for Recycling, Winding and Additive Manufacturing Machines
Venue: arXiv cs.CV 2020
Year: 2020

To overcome the challenge of upcycling plastic waste into 3-D printing filament in the distributed recycling and additive manufacturing systems, this study designs, builds, tests and validates an open source 3-D filament diameter sensor for recycling and winding machines. The modular system for multi-axis optical control of the diameter of the recycled 3-D-printer filament makes it possible to analyze the surface structure of the processed filament, save the history of measurements along the entire length of the spool, as well as mark defective areas. The sensor is developed as an independent module and integrated into a recyclebot. The diameter sensor was tested on different kinds of polymers (ABS, PLA) different sources of plastic (recycled 3-D prints and virgin plastic waste) and different colors including clear plastic. The results of the diameter measurements using the camera were compared with the manual measurements, and the measurements obtained with a one-dimensional digital light caliper. The results found that the developed open source filament sensing method allows users to obtain significantly more information in comparison with basic one-dimensional light sensors and using the received data not only for more accurate diameter measurements, but also for a detailed analysis of the recycled filament surface. The developed method ensures greater availability of plastics recycling technologies for the manufacturing community and stimulates the growth of composite materials creation. The presented system can greatly enhance the user possibilities and serve as a starting point for a complete recycling control system that will regulate motor parameters to achieve the desired filament diameter with acceptable deviations and even control the extrusion rate on a printer to recover from filament irregularities.
Profile Prediction: An Alignment-Based Pre-Training Task for Protein Sequence Models
Venue: arXiv cs.LG 2020
Year: 2020

For protein sequence datasets, unlabeled data has greatly outpaced labeled data due to the high cost of wet-lab characterization. Recent deep-learning approaches to protein prediction have shown that pre-training on unlabeled data can yield useful representations for downstream tasks. However, the optimal pre-training strategy remains an open question. Instead of strictly borrowing from natural language processing (NLP) in the form of masked or autoregressive language modeling, we introduce a new pre-training task: directly predicting protein profiles derived from multiple sequence alignments. Using a set of five, standardized downstream tasks for protein models, we demonstrate that our pre-training task along with a multi-task objective outperforms masked language modeling alone on all five tasks. Our results suggest that protein sequence models may benefit from leveraging biologically-inspired inductive biases that go beyond existing language modeling techniques in NLP.
Sim2Real for Self-Supervised Monocular Depth and Segmentation
Venue: arXiv cs.CV 2020
Year: 2020

Image-based learning methods for autonomous vehicle perception tasks require large quantities of labelled, real data in order to properly train without overfitting, which can often be incredibly costly. While leveraging the power of simulated data can potentially aid in mitigating these costs, networks trained in the simulation domain usually fail to perform adequately when applied to images in the real domain. Recent advances in domain adaptation have indicated that a shared latent space assumption can help to bridge the gap between the simulation and real domains, allowing the transference of the predictive capabilities of a network from the simulation domain to the real domain. We demonstrate that a twin VAE-based architecture with a shared latent space and auxiliary decoders is able to bridge the sim2real gap without requiring any paired, ground-truth data in the real domain. Using only paired, ground-truth data in the simulation domain, this architecture has the potential to generate perception tasks such as depth and segmentation maps. We compare this method to networks trained in a supervised manner to indicate the merit of these results.
Performing with a Mobile Computer System for Vibraphone
Venue: arXiv cs.SD 2020
Year: 2020

This paper describes the development of an Apple iPhone based mobile computer system for vibraphone and its use in a series of the author's performance projects in 2011 and 2012. This artistic research was motivated by a desire to develop an alternative to laptop computers for the author's existing percussion and computer performance practice. The aims were to develop a light, compact and flexible system using mobile devices that would allow computer music to infiltrate solo and ensemble performance situations where it is difficult to use a laptop computer. The project began with a system that brought computer elements to Nordlig Vinter, a suite of percussion duos, using an iPhone, RjDj, Pure Data and a home-made pickup system. This process was documented with video recordings and analysed using ethnographic methods. The mobile computer music setup proved to be elegant and convenient in performance situations with very little time and space to set up, as well as in performance classes and workshops. The simple mobile system encouraged experimentation and the platforms used enabled sharing with a wider audience.
Secrecy Performance of Body-Centric Communications over Alternate Rician Shadowed Fading Channels
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, we investigate the physical layer security over the Alternate Rician Shadowed fading channel, which is a novel channel model for body-centric wireless links and land mobile satellite. We derive exact closed-form expressions for the average secrecy capacity (ASC), secrecy outage probability (SOP), and probability of non-zero secrecy capacity (PNZ) for two cases: (i) m is a positive real number and (ii) m is a positive integer number, where m describes the level of fluctuation of the line-of-sight component. In the first case, SOP is derived in terms of the Meijer's G-function, while ASC and PNZ are derived in terms of the multivariate Fox's H-function. In the second case, ASC is derived in terms of the Meijer's G-function, while SOP and PNZ are derived in terms of elementary functions. In addition, we derive the asymptotic ASC, SOP and PNZ expressions which all match well the exact ones at high values of signal-to-noise ratio, respectively. The capacity slope of asymptotic ASC and the secrecy diversity order of asymptotic SOP have been derived for providing more physical insights. Finally, the accuracy of our derived expressions is validated by Monte-Carlo simulations.
Evaluation of Reusability in Aspect Oriented Software using Inheritance Metrics
Venue: arXiv cs.SE 2020
Year: 2020

Aspect-Oriented Software Development (AOSD) is a promising methodology for efficiently capturing the cross-cutting functionalities (concerns) as independent units called aspects. Inheritance of classes and aspects play a vital role in defining the units of encapsulation. Hence, it is essential to quantitatively capture the impact of inheritance in AOSD using design level metrics and to infer on the higher level quality attribute, reusability. An application to automate the processes of a typical University has been developed in order to study the effect of using inheritance over the versions of an aspectized AO application. A set of metrics to capture the manifestations of inheritance is proposed for measurement. An automated tool named as Aspect Oriented Software Reusability Measurement AOSRM is also designed and developed to calculate the values of the proposed metrics. Based on the obtained metric values for Java and AspectJ versions of the case study application, inheritance in AspectJ versions showed a positive impact on reusability of software.
Design Level Metrics to Measure the Complexity Across Versions of AO Software
Venue: arXiv cs.SE 2020
Year: 2020

Software metric plays a vital role in quantitative assessment of any specific software development methodology and its impact on the maintenance of software. It can also be used to indicate the degree of interdependence among the components by providing valuable feedback about quality attributes such as maintainability, modifiability and understandability. The effort for software maintenance normally has a high correlation with the complexity of its design. Aspect Oriented Software Design is an emerging methodology that provides powerful new techniques to improve the modularity of software from its design. In this paper, evaluation model to capture the symptoms of complexity has been defined consisting of metrics, artifacts and elements of complexity. A tool to automatically capture these metrics across different versions of a case study application, University Automation System has been developed. The values obtained for the proposed metrics are used to infer on the complexity of Java and AspectJ implementations of the case study application. These measurements indicate that AspectJ implementations are less complex compared to the Java implementations and there by positively influencing the maintainability of software.
A remark on MAKE -- a Matrix Action Key Exchange
Venue: arXiv cs.CR 2020
Year: 2020

In a recent paper [arXiv:2009.00716], Rahman and Shpilrain proposed a new key-exchange protocol MAKE based on external semidirect product of groups. The purpose of this paper is to show that the key exchange protocol is insecure. We were able to break their challenge problem in under a second.
BAN-ABSA: An Aspect-Based Sentiment Analysis dataset for Bengali and it's baseline evaluation
Venue: arXiv cs.CL 2020
Year: 2020

Due to the breathtaking growth of social media or newspaper user comments, online product reviews comments, sentiment analysis (SA) has captured substantial interest from the researchers. With the fast increase of domain, SA work aims not only to predict the sentiment of a sentence or document but also to give the necessary detail on different aspects of the sentence or document (i.e. aspect-based sentiment analysis). A considerable number of datasets for SA and aspect-based sentiment analysis (ABSA) have been made available for English and other well-known European languages. In this paper, we present a manually annotated Bengali dataset of high quality, BAN-ABSA, which is annotated with aspect and its associated sentiment by 3 native Bengali speakers. The dataset consists of 2,619 positive, 4,721 negative and 1,669 neutral data samples from 9,009 unique comments gathered from some famous Bengali news portals. In addition, we conducted a baseline evaluation with a focus on deep learning model, achieved an accuracy of 78.75% for aspect term extraction and accuracy of 71.08% for sentiment classification. Experiments on the BAN-ABSA dataset show that the CNN model is better in terms of accuracy though Bi-LSTM significantly outperforms CNN model in terms of average F1-score.
End-to-End UAV Simulation for Visual SLAM and Navigation
Venue: arXiv cs.RO 2020
Year: 2020

Visual Simultaneous Localization and Mapping (v-SLAM) and navigation of multirotor Unmanned Aerial Vehicles (UAV) in an unknown environment have grown in popularity for both research and education. However, due to the complex hardware setup, safety precautions, and battery constraints, extensive physical testing can be expensive and time-consuming. As an alternative solution, simulation tools lower the barrier to carry out the algorithm testing and validation before field trials. In this letter, we customize the ROS-Gazebo-PX4 simulator in deep and provide an end-to-end simulation solution for the UAV v-SLAM and navigation study. A set of localization, mapping, and path planning kits were also integrated into the simulation platform. In our simulation, various aspects, including complex environments and onboard sensors, can simultaneously interact with our navigation framework to achieve specific surveillance missions. In this end-to-end simulation, we achieved click and fly level autonomy UAV navigation. The source code is open to the research community.
Low Bandwidth Video-Chat Compression using Deep Generative Models
Venue: arXiv cs.CV 2020
Year: 2020

To unlock video chat for hundreds of millions of people hindered by poor connectivity or unaffordable data costs, we propose to authentically reconstruct faces on the receiver's device using facial landmarks extracted at the sender's side and transmitted over the network. In this context, we discuss and evaluate the benefits and disadvantages of several deep adversarial approaches. In particular, we explore quality and bandwidth trade-offs for approaches based on static landmarks, dynamic landmarks or segmentation maps. We design a mobile-compatible architecture based on the first order animation model of Siarohin et al. In addition, we leverage SPADE blocks to refine results in important areas such as the eyes and lips. We compress the networks down to about 3MB, allowing models to run in real time on iPhone 8 (CPU). This approach enables video calling at a few kbits per second, an order of magnitude lower than currently available alternatives.
Efficient Data Structures for Model-free Data-Driven Computational Mechanics
Venue: arXiv cs.CE 2020
Year: 2020

The data-driven computing paradigm initially introduced by Kirchdoerfer and Ortiz (2016) enables finite element computations in solid mechanics to be performed directly from material data sets, without an explicit material model. From a computational effort point of view, the most challenging task is the projection of admissible states at material points onto their closest states in the material data set. In this study, we compare and develop several possible data structures for solving the nearest-neighbor problem. We show that approximate nearest-neighbor (ANN) algorithms can accelerate material data searches by several orders of magnitude relative to exact searching algorithms. The approximations are suggested by--and adapted to--the structure of the data-driven iterative solver and result in no significant loss of solution accuracy. We assess the performance of the ANN algorithm with respect to material data set size with the aid of a 3D elasticity test case. We show that computations on a single processor with up to one billion material data points are feasible within a few seconds execution time with a speedup of more than 106 with respect to exact k-d trees.
IRIS: A Low Duty Cycle Cross-Layer Protocol for Long-Range Wireless Sensor Networks with Low Power Budget
Venue: arXiv cs.NI 2020
Year: 2020

This paper presents a cross-layer protocol (IRIS) designed for long-range pipeline Wireless Sensor Networks with extremely low power budget, typically seen in a range of monitoring applications. IRIS uses ping packets initiated by a base station to travel through the multi-hop network and carry monitoring information. The protocol is able to operate with less than 1% duty cycle, thereby conforming to ISM band spectrum regulations in the 868MHz band. The duty cycle can be flexibly configured to meet other regulations/power budgets as well as to improve the route forming performance. Simulation results show guaranteed route formation in different network topologies with various protocol configurations. System robustness against unreliable wireless connections and node failures are also demonstrated by simulations.
Directed Graph Attention Neural Network Utilizing 3D Coordinates for Molecular Property Prediction
Venue: arXiv cs.LG 2020
Year: 2020

The prosperity of computer vision (CV) and natural language procession (NLP) in recent years has spurred the development of deep learning in many other domains. The advancement in machine learning provides us with an alternative option besides the computationally expensive density functional theories (DFT). Kernel method and graph neural networks have been widely studied as two mainstream methods for property prediction. The promising graph neural networks have achieved comparable accuracy to the DFT method for specific objects in the recent study. However, most of the graph neural networks with high precision so far require fully connected graphs with pairwise distance distribution as edge information. In this work, we shed light on the Directed Graph Attention Neural Network (DGANN), which only takes chemical bonds as edges and operates on bonds and atoms of molecules. DGANN distinguishes from previous models with those features: (1) It learns the local chemical environment encoding by graph attention mechanism on chemical bonds. Every initial edge message only flows into every message passing trajectory once. (2) The transformer blocks aggregate the global molecular representation from the local atomic encoding. (3) The position vectors and coordinates are used as inputs instead of distances. Our model has matched or outperformed most baseline graph neural networks on QM9 datasets even without thorough hyper-parameters searching. Moreover, this work suggests that models directly utilizing 3D coordinates can still reach high accuracies for molecule representation even without rotational and translational invariance incorporated.