OpenRaiser/Intern-Atlas huggingface dataset:
QoS-Driven Job Scheduling: Multi-Tier Dependency Considerations
Venue: arXiv cs.DC 2020
Year: 2020

For a cloud service provider, delivering optimal system performance while fulfilling Quality of Service (QoS) obligations is critical for maintaining a viably profitable business. This goal is often hard to attain given the irregular nature of cloud computing jobs. These jobs expect high QoS on an on-demand fashion, that is on random arrival. To optimize the response to such client demands, cloud service providers organize the cloud computing environment as a multi-tier architecture. Each tier executes its designated tasks and passes the job to the next tier; in a fashion similar, but not identical, to the traditional job-shop environments. An optimization process must take place to schedule the appropriate tasks of the job on the resources of the tier, so as to meet the QoS expectations of the job. Existing approaches employ scheduling strategies that consider the performance optimization at the individual resource level and produce optimal single-tier driven schedules. Due to the sequential nature of the multi-tier environment, the impact of such schedules on the performance of other resources and tiers tend to be ignored, resulting in a less than optimal performance when measured at the multi-tier level. In this paper, we propose a multi-tier-oriented job scheduling and allocation technique. The scheduling and allocation process is formulated as a problem of assigning jobs to the resource queues of the cloud computing environment, where each resource of the environment employs a queue to hold the jobs assigned to it. The scheduling problem is NP-hard, as such a biologically inspired genetic algorithm is proposed. The computing resources across all tiers of the environment are virtualized in one resource by means of a single queue virtualization. A chromosome that mimics the sequencing and allocation of the tasks in the proposed virtual queue is proposed.
Are Game Engines Software Frameworks? A Three-perspective Study
Venue: arXiv cs.SE 2020
Year: 2020

Game engines help developers create video games and avoid duplication of code and effort, like frameworks for traditional software systems. In this paper, we explore open-source game engines along three perspectives: literature, code, and human. First, we explore and summarise the academic literature on game engines. Second, we compare the characteristics of the 282 most popular engines and the 282 most popular frameworks in GitHub. Finally, we survey 124 engine developers about their experience with the development of their engines. We report that: (1) Game engines are not well-studied in software-engineering research with few studies having engines as object of research. (2) Open-source game engines are slightly larger in terms of size and complexity and less popular and engaging than traditional frameworks. Their programming languages differ greatly from frameworks. Engine projects have shorter histories with less releases. (3) Developers perceive game engines as different from traditional frameworks. Generally, they build game engines to (a) better control the environment and source code, (b) learn about game engines, and (c) develop specific games. We conclude that open-source game engines have differences compared to traditional open-source frameworks although this differences do not demand special treatments.
Large-scale Real-time Personalized Similar Product Recommendations
Venue: arXiv cs.IR 2020
Year: 2020

Similar product recommendation is one of the most common scenes in e-commerce. Many recommendation algorithms such as item-to-item Collaborative Filtering are working on measuring item similarities. In this paper, we introduce our real-time personalized algorithm to model product similarity and real-time user interests. We also introduce several other baseline algorithms including an image-similarity-based method, item-to-item collaborative filtering, and item2vec, and compare them on our large-scale real-world e-commerce dataset. The algorithms which achieve good offline results are also tested on the online e-commerce website. Our personalized method achieves a 10% improvement on the add-cart number in the real-world e-commerce scenario.
Complaint-driven Training Data Debugging for Query 2.0
Venue: arXiv cs.DB 2020
Year: 2020

As the need for machine learning (ML) increases rapidly across all industry sectors, there is a significant interest among commercial database providers to support "Query 2.0", which integrates model inference into SQL queries. Debugging Query 2.0 is very challenging since an unexpected query result may be caused by the bugs in training data (e.g., wrong labels, corrupted features). In response, we propose Rain, a complaint-driven training data debugging system. Rain allows users to specify complaints over the query's intermediate or final output, and aims to return a minimum set of training examples so that if they were removed, the complaints would be resolved. To the best of our knowledge, we are the first to study this problem. A naive solution requires retraining an exponential number of ML models. We propose two novel heuristic approaches based on influence functions which both require linear retraining steps. We provide an in-depth analytical and empirical analysis of the two approaches and conduct extensive experiments to evaluate their effectiveness using four real-world datasets. Results show that Rain achieves the highest recall@k among all the baselines while still returns results interactively.
Exploring Erasure Coding Techniques for High Availability of Intermediate Data
Venue: arXiv cs.DC 2020
Year: 2020

Scientific computing workflows generate enormous distributed data that is short-lived, yet critical for job completion time. This class of data is called intermediate data. A common way to achieve high data availability is to replicate data. However, an increasing scale of intermediate data generated in modern scientific applications demands new storage techniques to improve storage efficiency. Erasure Codes, as an alternative, can use less storage space while maintaining similar data availability. In this paper, we adopt erasure codes for storing intermediate data and compare its performance with replication. We also use the metric of Mean-Time-To-Data-Loss (MTTDL) to estimate the lifetime of intermediate data. We propose an algorithm to proactively relocate data redundancy from vulnerable machines to reliable ones to improve data availability with some extra network overhead. Furthermore, we propose an algorithm to assign redundancy units of data physically close to each other on the network to reduce the network bandwidth for reconstructing data when it is being accessed.
Self-supervised Feature Learning by Cross-modality and Cross-view Correspondences
Venue: arXiv cs.CV 2020
Year: 2020

The success of supervised learning requires large-scale ground truth labels which are very expensive, time-consuming, or may need special skills to annotate. To address this issue, many self- or un-supervised methods are developed. Unlike most existing self-supervised methods to learn only 2D image features or only 3D point cloud features, this paper presents a novel and effective self-supervised learning approach to jointly learn both 2D image features and 3D point cloud features by exploiting cross-modality and cross-view correspondences without using any human annotated labels. Specifically, 2D image features of rendered images from different views are extracted by a 2D convolutional neural network, and 3D point cloud features are extracted by a graph convolution neural network. Two types of features are fed into a two-layer fully connected neural network to estimate the cross-modality correspondence. The three networks are jointly trained (i.e. cross-modality) by verifying whether two sampled data of different modalities belong to the same object, meanwhile, the 2D convolutional neural network is additionally optimized through minimizing intra-object distance while maximizing inter-object distance of rendered images in different views (i.e. cross-view). The effectiveness of the learned 2D and 3D features is evaluated by transferring them on five different tasks including multi-view 2D shape recognition, 3D shape recognition, multi-view 2D shape retrieval, 3D shape retrieval, and 3D part-segmentation. Extensive evaluations on all the five different tasks across different datasets demonstrate strong generalization and effectiveness of the learned 2D and 3D features by the proposed self-supervised method.
Joint User Identification and Channel Estimation Over Rician Fading Channels
Venue: arXiv cs.IT 2020
Year: 2020

This paper considers crowded massive multiple input multiple output (MIMO) communications over a Rician fading channel, where the number of users is much greater than the number of available pilot sequences. A joint user identification and line-of-sight (LOS) component derivation algorithm is proposed without requiring a threshold. Based on the derived LOS component, we design a LOS-only channel estimator and an updated channel estimator.
Constellation Design for Media-based Modulation using Block Codes and Squaring Construction
Venue: arXiv cs.IT 2020
Year: 2020

Efficient constellation design is important for improving performance in communication systems. The problem of multidimensional constellation design has been studied extensively in the literature in the context of multidimensional coded modulation and space-time coded MIMO systems. Such constellations are formally called as lattice codes, where a finite set of points from a certain high dimensional lattice is chosen based on some criteria. In this paper, we consider the problem of constellation/signal set design for media-based modulation (MBM), a recent MIMO channel modulation scheme with promising theoretical and practical benefits. Constellation design for MBM is fundamentally different from those for multidimensional coded modulation and conventional MIMO systems mainly because of the inherent sparse structure of the MBM signal vectors. Specifically, we need a structured sparse lattice code with good distance properties. In this work, we show that using an (N,K) non-binary block code in conjunction with the lattice based multilevel squaring construction, it is possible to systematically construct a signal set for MBM with certain guaranteed minimum distance. The MBM signal set obtained using the proposed construction is shown to achieve significantly improved bit error performance compared to conventional MBM signal set. In particular, the proposed signal set is found to achieve higher diversity slopes in the low-to-moderate SNR regime.
Neural Machine Translation: Challenges, Progress and Future
Venue: arXiv cs.CL 2020
Year: 2020

Machine translation (MT) is a technique that leverages computers to translate human languages automatically. Nowadays, neural machine translation (NMT) which models direct mapping between source and target languages with deep neural networks has achieved a big breakthrough in translation performance and become the de facto paradigm of MT. This article makes a review of NMT framework, discusses the challenges in NMT, introduces some exciting recent progresses and finally looks forward to some potential future research trends. In addition, we maintain the state-of-the-art methods for various NMT tasks at the website https://github.com/ZNLP/SOTA-MT.
MulayCap: Multi-layer Human Performance Capture Using A Monocular Video Camera
Venue: arXiv cs.CV 2020
Year: 2020

We introduce MulayCap, a novel human performance capture method using a monocular video camera without the need for pre-scanning. The method uses "multi-layer" representations for geometry reconstruction and texture rendering, respectively. For geometry reconstruction, we decompose the clothed human into multiple geometry layers, namely a body mesh layer and a garment piece layer. The key technique behind is a Garment-from-Video (GfV) method for optimizing the garment shape and reconstructing the dynamic cloth to fit the input video sequence, based on a cloth simulation model which is effectively solved with gradient descent. For texture rendering, we decompose each input image frame into a shading layer and an albedo layer, and propose a method for fusing a fixed albedo map and solving for detailed garment geometry using the shading layer. Compared with existing single view human performance capture systems, our "multi-layer" approach bypasses the tedious and time consuming scanning step for obtaining a human specific mesh template. Experimental results demonstrate that MulayCap produces realistic rendering of dynamically changing details that has not been achieved in any previous monocular video camera systems. Benefiting from its fully semantic modeling, MulayCap can be applied to various important editing applications, such as cloth editing, re-targeting, relighting, and AR applications.
Blockchain in the Internet of Things: Architectures and Implementation
Venue: arXiv cs.CR 2020
Year: 2020

The world is becoming more interconnected every day. With the high technological evolution and the increasing deployment of it in our society, scenarios based on the Internet of Things (IoT) can be considered a reality nowadays. However, and before some predictions become true (around 75 billion devices are expected to be interconnected in the next few years), many efforts must be carried out in terms of scalability and security. In this study we propose and evaluate a new approach based on the incorporation of Blockchain into current IoT scenarios. The main contributions of this study are as follows: i) an in-depth analysis of the different possibilities for the integration of Blockchain into IoT scenarios, focusing on the limited processing capabilities and storage space of most IoT devices, and the economic cost and performance of current Blockchain technologies; ii) a new method based on a novel module named BIoT Gateway that allows both unidirectional and bidirectional communications with IoT devices on real scenarios, allowing to exchange any kind of data; and iii) the proposed method has been fully implemented and validated on two different real-life IoT scenarios, extracting very interesting findings in terms of economic cost and execution time. The source code of our implementation is publicly available in the Ethereum testnet.
Socio-economic, built environment, and mobility conditions associated with crime: A study of multiple cities
Venue: arXiv cs.SI 2020
Year: 2020

Nowadays, 23% of the world population lives in multi-million cities. In these metropolises, criminal activity is much higher and violent than in either small cities or rural areas. Thus, understanding what factors influence urban crime in big cities is a pressing need. Mainstream studies analyse crime records through historical panel data or analysis of historical patterns combined with ecological factor and exploratory mapping. More recently, machine learning methods have provided informed crime prediction over time. However, previous studies have focused on a single city at a time, considering only a limited number of factors (such as socio-economical characteristics) and often at large spatial units. Hence, our understanding of the factors influencing crime across cultures and cities is very limited. Here we propose a Bayesian model to explore how crime is related not only to socio-economic factors but also to the built environmental (e.g. land use) and mobility characteristics of neighbourhoods. To that end, we integrate multiple open data sources with mobile phone traces and compare how the different factors correlate with crime in diverse cities, namely Boston, Bogotá, Los Angeles and Chicago. We find that the combined use of socio-economic conditions, mobility information and physical characteristics of the neighbourhood effectively explain the emergence of crime, and improve the performance of the traditional approaches. However, we show that the socio-ecological factors of neighbourhoods relate to crime very differently from one city to another. Thus there is clearly no "one fits all" model.
SPCNet:Spatial Preserve and Content-aware Network for Human Pose Estimation
Venue: arXiv cs.CV 2020
Year: 2020

Human pose estimation is a fundamental yet challenging task in computer vision. Although deep learning techniques have made great progress in this area, difficult scenarios (e.g., invisible keypoints, occlusions, complex multi-person scenarios, and abnormal poses) are still not well-handled. To alleviate these issues, we propose a novel Spatial Preserve and Content-aware Network(SPCNet), which includes two effective modules: Dilated Hourglass Module(DHM) and Selective Information Module(SIM). By using the Dilated Hourglass Module, we can preserve the spatial resolution along with large receptive field. Similar to Hourglass Network, we stack the DHMs to get the multi-stage and multi-scale information. Then, a Selective Information Module is designed to select relatively important features from different levels under a sufficient consideration of spatial content-aware mechanism and thus considerably improves the performance. Extensive experiments on MPII, LSP and FLIC human pose estimation benchmarks demonstrate the effectiveness of our network. In particular, we exceed previous methods and achieve the state-of-the-art performance on three aforementioned benchmark datasets.
Detecting Critical Bugs in SMT Solvers Using Blackbox Mutational Fuzzing
Venue: arXiv cs.SE 2020
Year: 2020

Formal methods use SMT solvers extensively for deciding formula satisfiability, for instance, in software verification, systematic test generation, and program synthesis. However, due to their complex implementations, solvers may contain critical bugs that lead to unsound results. Given the wide applicability of solvers in software reliability, relying on such unsound results may have detrimental consequences. In this paper, we present STORM, a novel blackbox mutational fuzzing technique for detecting critical bugs in SMT solvers. We run our fuzzer on seven mature solvers and find 29 previously unknown critical bugs. STORM is already being used in testing new features of popular solvers before deployment.
Congruence Preservation, Lattices and Recognizability
Venue: arXiv cs.LO 2020
Year: 2020

Looking at some monoids and (semi)rings (natural numbers, integers and p-adic integers), and more generally, residually finite algebras (in a strong sense), we prove the equivalence of two ways for a function on such an algebra to behave like the operations of the algebra. The first way is to preserve congruences or stable preorders. The second way is to demand that preimages of recognizable sets belong to the lattice or the Boolean algebra generated by the preimages of recognizable sets by derived unary operation of the algebra (such as translations, quotients,. . . ).
Software-Defined Network for End-to-end Networked Science at the Exascale
Venue: arXiv cs.NI 2020
Year: 2020

Domain science applications and workflow processes are currently forced to view the network as an opaque infrastructure into which they inject data and hope that it emerges at the destination with an acceptable Quality of Experience. There is little ability for applications to interact with the network to exchange information, negotiate performance parameters, discover expected performance metrics, or receive status/troubleshooting information in real time. The work presented here is motivated by a vision for a new smart network and smart application ecosystem that will provide a more deterministic and interactive environment for domain science workflows. The Software-Defined Network for End-to-end Networked Science at Exascale (SENSE) system includes a model-based architecture, implementation, and deployment which enables automated end-to-end network service instantiation across administrative domains. An intent based interface allows applications to express their high-level service requirements, an intelligent orchestrator and resource control systems allow for custom tailoring of scalability and real-time responsiveness based on individual application and infrastructure operator requirements. This allows the science applications to manage the network as a first-class schedulable resource as is the current practice for instruments, compute, and storage systems. Deployment and experiments on production networks and testbeds have validated SENSE functions and performance. Emulation based testing verified the scalability needed to support research and education infrastructures. Key contributions of this work include an architecture definition, reference implementation, and deployment. This provides the basis for further innovation of smart network services to accelerate scientific discovery in the era of big data, cloud computing, machine learning and artificial intelligence.
Non-clairvoyant Scheduling of Coflows
Venue: arXiv cs.DS 2020
Year: 2020

The coflow scheduling problem is considered: given an input/output switch with each port having a fixed capacity, find a scheduling algorithm that minimizes the weighted sum of the coflow completion times respecting the port capacities, where each flow of a coflow has a demand per input/output port, and coflow completion time is the finishing time of the last flow of the coflow. The objective of this paper is to present theoretical guarantees on approximating the sum of coflow completion time in the non-clairvoyant setting, where on a coflow arrival, only the number of flows, and their input-output port is revealed, while the critical demand volumes for each flow on the respective input-output port is unknown. The main result of this paper is to show that the proposed BlindFlow algorithm is $8p$-approximate, where $p$ is the largest number of input-output port pairs that a coflow uses. This result holds even in the online case, where coflows arrive over time and the scheduler has to use only causal information. Simulations reveal that the experimental performance of BlindFlow is far better than the theoretical guarantee.
Accelerating B-spline Interpolation on GPUs: Application to Medical Image Registration
Venue: arXiv cs.DC 2020
Year: 2020

Background and Objective. B-spline interpolation (BSI) is a popular technique in the context of medical imaging due to its adaptability and robustness in 3D object modeling. A field that utilizes BSI is Image Guided Surgery (IGS). IGS provides navigation using medical images, which can be segmented and reconstructed into 3D models, often through BSI. Image registration tasks also use BSI to align pre-operative data to intra-operative data. However, such IGS tasks are computationally demanding, especially when applied to 3D medical images, due to the complexity and amount of data involved. Therefore, optimization of IGS algorithms is greatly desirable, for example, to perform image registration tasks intra-operatively and to enable real-time applications. A traditional CPU does not have sufficient computing power to achieve these goals. In this paper, we introduce a novel GPU implementation of BSI to accelerate the calculation of the deformation field in non-rigid image registration algorithms. Methods. Our BSI implementation on GPUs minimizes the data that needs to be moved between memory and processing cores during loading of the input grid, and leverages the large on-chip GPU register file for reuse of input values. Moreover, we re-formulate our method as trilinear interpolations to reduce computational complexity and increase accuracy. To provide pre-clinical validation of our method and demonstrate its benefits in medical applications, we integrate our improved BSI into a registration workflow for compensation of liver deformation (caused by pneumoperitoneum, i.e., inflation of the abdomen) and evaluate its performance. Results. Our approach improves the performance of BSI by an average of 6.5x and interpolation accuracy by 2x compared to three state-of-the-art GPU implementations. We observe up to 34% acceleration of non-rigid image registration.