OpenRaiser/Intern-Atlas huggingface dataset:
FloMore: Meeting bandwidth requirements of flows
Venue: arXiv cs.NI 2021
Year: 2021

Wide-area cloud provider networks must support the bandwidth requirements of diverse services (e.g., applications, product groups, customers) despite failures. Existing traffic engineering (TE) schemes operate at much coarser granularity than services, which we show necessitates unduly conservative decisions. To tackle this, we present FloMore, which directly considers the bandwidth needs of individual services and ensures they are met a desired percentage of time. Rather than meet the requirements for all services over the same set of failure states, FloMore exploits a key opportunity that each service could meet its bandwidth requirements over a different set of failure states. FloMore consists of an offline phase that identifies the critical failure states of each service, and on failure allocates traffic in a manner that prioritizes those services for which that failure state is critical. We present a novel decomposition scheme to handle FloMore's offline phase in a tractable manner. Our evaluations show that FloMore outperforms state-of-the-art TE schemes including SMORE and Teavar, and also out-performs extensions of these schemes that we devise. The results also show FloMore's decomposition approach allows it to scale well to larger network topologies.
A Study on Dense and Sparse (Visual) Rewards in Robot Policy Learning
Venue: arXiv cs.RO 2021
Year: 2021

Deep Reinforcement Learning (DRL) is a promising approach for teaching robots new behaviour. However, one of its main limitations is the need for carefully hand-coded reward signals by an expert. We argue that it is crucial to automate the reward learning process so that new skills can be taught to robots by their users. To address such automation, we consider task success classifiers using visual observations to estimate the rewards in terms of task success. In this work, we study the performance of multiple state-of-the-art deep reinforcement learning algorithms under different types of reward: Dense, Sparse, Visual Dense, and Visual Sparse rewards. Our experiments in various simulation tasks (Pendulum, Reacher, Pusher, and Fetch Reach) show that while DRL agents can learn successful behaviours using visual rewards when the goal targets are distinguishable, their performance may decrease if the task goal is not clearly visible. Our results also show that visual dense rewards are more successful than visual sparse rewards and that there is no single best algorithm for all tasks.
Facebook AI WMT21 News Translation Task Submission
Venue: arXiv cs.CL 2021
Year: 2021

We describe Facebook's multilingual model submission to the WMT2021 shared task on news translation. We participate in 14 language directions: English to and from Czech, German, Hausa, Icelandic, Japanese, Russian, and Chinese. To develop systems covering all these directions, we focus on multilingual models. We utilize data from all available sources --- WMT, large-scale data mining, and in-domain backtranslation --- to create high quality bilingual and multilingual baselines. Subsequently, we investigate strategies for scaling multilingual model size, such that one system has sufficient capacity for high quality representations of all eight languages. Our final submission is an ensemble of dense and sparse Mixture-of-Expert multilingual translation models, followed by finetuning on in-domain news data and noisy channel reranking. Compared to previous year's winning submissions, our multilingual system improved the translation quality on all language directions, with an average improvement of 2.0 BLEU. In the WMT2021 task, our system ranks first in 10 directions based on automatic evaluation.
A Smart and Defensive Human-Machine Approach to Code Analysis
Venue: arXiv cs.AI 2021
Year: 2021

Static analysis remains one of the most popular approaches for detecting and correcting poor or vulnerable program code. It involves the examination of code listings, test results, or other documentation to identify errors, violations of development standards, or other problems, with the ultimate goal of fixing these errors so that systems and software are as secure as possible. There exists a plethora of static analysis tools, which makes it challenging for businesses and programmers to select a tool to analyze their program code. It is imperative to find ways to improve code analysis so that it can be employed by cyber defenders to mitigate security risks. In this research, we propose a method that employs the use of virtual assistants to work with programmers to ensure that software are as safe as possible in order to protect safety-critical systems from data breaches and other attacks. The proposed method employs a recommender system that uses various metrics to help programmers select the most appropriate code analysis tool for their project and guides them through the analysis process. The system further tracks the user's behavior regarding the adoption of the recommended practices.
Profiling Web Archival Voids for Memento Routing
Venue: arXiv cs.DL 2021
Year: 2021

Prior work on web archive profiling were focused on Archival Holdings to describe what is present in an archive. This work defines and explores Archival Voids to establish a means to represent portions of URI spaces that are not present in a web archive. Archival Holdings and Archival Voids profiles can work independently or as complements to each other to maximize the Accuracy of Memento Aggregators. We discuss various sources of truth that can be used to create Archival Voids profiles. We use access logs from Arquivo.pt to create various Archival Voids profiles and analyze them against our MemGator access logs for evaluation. We find that we could have avoided more than 8% of additional False Positives on top of the 60% Accuracy we got from profiling Archival Holdings in our prior work, if Arquivo.pt were to provide an Archival Voids profile based on URIs that were requested hundreds of times and never returned any success responses.
From Domain-Specific Languages to Memory-Optimized Accelerators for Fluid Dynamics
Venue: arXiv cs.DC 2021
Year: 2021

Many applications are increasingly requiring numerical simulations for solving complex problems. Most of these numerical algorithms are massively parallel and often implemented on parallel high-performance computers. However, classic CPU-based platforms suffers due to the demand for higher resolutions and the exponential growth of data. FPGAs offer a powerful and flexible alternative that can host accelerators to complement such platforms. Developing such application-specific accelerators is still challenging because it is hard to provide efficient code for hardware synthesis. In this paper, we study the challenges of porting a numerical simulation kernel onto FPGA. We propose an automated tool flow from a domain-specific language (DSL) to generate accelerators for computational fluid dynamics on FPGA. Our DSL-based flow simplifies the exploration of parameters and constraints such as on-chip memory usage. We also propose a decoupled optimization of memory and logic resources, which allows us to better use the limited FPGA resources. In our preliminary evaluation, this enabled doubling the number of parallel kernels, increasing the accelerator speedup versus ARM execution from 7 to 12 times.
Asymmetry-aware Scalable Locking
Venue: arXiv cs.DC 2021
Year: 2021

The pursuit of power-efficiency is popularizing asymmetric multicore processors (AMP) such as ARM big.LITTLE, Apple M1 and recent Intel Alder Lake with big and little cores. However, we find that existing scalable locks fail to scale on AMP and cause collapses in either throughput or latency, or both, because their implicit assumption of symmetric cores no longer holds. To address this issue, we propose the first asymmetry-aware scalable lock named LibASL. LibASL provides a new lock ordering guided by applications' latency requirements, which allows big cores to reorder with little cores for higher throughput under the condition of preserving applications' latency requirements. Using LibASL only requires linking the applications with it and, if latency-critical, inserting few lines of code to annotate the coarse-grained latency requirement. We evaluate LibASL in various benchmarks including five popular databases on Apple M1. Evaluation results show that LibASL can improve the throughput by up to 5 times while precisely preserving the tail latency designated by applications.
Generating Personalized Dialogue via Multi-Task Meta-Learning
Venue: arXiv cs.CL 2021
Year: 2021

Conventional approaches to personalized dialogue generation typically require a large corpus, as well as predefined persona information. However, in a real-world setting, neither a large corpus of training data nor persona information are readily available. To address these practical limitations, we propose a novel multi-task meta-learning approach which involves training a model to adapt to new personas without relying on a large corpus, or on any predefined persona information. Instead, the model is tasked with generating personalized responses based on only the dialogue context. Unlike prior work, our approach leverages on the provided persona information only during training via the introduction of an auxiliary persona reconstruction task. In this paper, we introduce 2 frameworks that adopt the proposed multi-task meta-learning approach: the Multi-Task Meta-Learning (MTML) framework, and the Alternating Multi-Task Meta-Learning (AMTML) framework. Experimental results show that utilizing MTML and AMTML results in dialogue responses with greater persona consistency.
On the Computational Complexity of Determining Value
Venue: arXiv cs.IT 2021
Year: 2021

We look at an algorithmic information theory based definition of value of a creative artifact and discuss the computational difficulty associated with the creation or determination of value. We look at the computational resources required to create a valuable product and discuss how likely an observer is to verify a claim that an object required significant effort on the creator's part.
Solution Enumeration by Optimality in Answer Set Programming
Venue: arXiv cs.AI 2021
Year: 2021

Given a combinatorial search problem, it may be highly useful to enumerate its (all) solutions besides just finding one solution, or showing that none exists. The same can be stated about optimal solutions if an objective function is provided. This work goes beyond the bare enumeration of optimal solutions and addresses the computational task of solution enumeration by optimality (SEO). This task is studied in the context of Answer Set Programming (ASP) where (optimal) solutions of a problem are captured with the answer sets of a logic program encoding the problem. Existing answer-set solvers already support the enumeration of all (optimal) answer sets. However, in this work, we generalize the enumeration of optimal answer sets beyond strictly optimal ones, giving rise to the idea of answer set enumeration in the order of optimality (ASEO). This approach is applicable up to the best k answer sets or in an unlimited setting, which amounts to a process of sorting answer sets based on the objective function. As the main contribution of this work, we present the first general algorithms for the aforementioned tasks of answer set enumeration. Moreover, we illustrate the potential use cases of ASEO. First, we study how efficiently access to the next-best solutions can be achieved in a number of optimization problems that have been formalized and solved in ASP. Second, we show that ASEO provides us with an effective sampling technique for Bayesian networks.
Error Performance of Uplink SIMO-NOMA with Joint Maximum-Likelihood and Adaptive M-PSK
Venue: arXiv cs.IT 2021
Year: 2021

This paper studies the performance of the uplink Non-Orthogonal Multiple Access (NOMA) with Joint Maximum-Likelihood Detector (JML). We present a generalized upper bound of bit-error rate (BER) expression of adaptive M-ary phase-shift keying (M-PSK) over Rayleigh fading channels. Our studies are enriched through the use of a single transmitting antenna by each user and multiple receiving antennas at the base station (single-input-multiple-output (SIMO)). The derivation of the upper bound expression is obtained considering an arbitrary constellation size by performing Maximum Ratio Combining (MRC) of diversity paths. The extensive computer simulations validate the analysis and it is revealed that the JML outperforms the existing detecting algorithms significantly and achieves the full diversity order for each user.
ContinuityLearner: Geometric Continuity Feature Learning for Lane Segmentation
Venue: arXiv cs.CV 2021
Year: 2021

Lane segmentation is a challenging issue in autonomous driving system designing because lane marks show weak textural consistency due to occlusion or extreme illumination but strong geometric continuity in traffic images, from which general convolution neural networks (CNNs) are not capable of learning semantic objects. To empower conventional CNNs in learning geometric clues of lanes, we propose a deep network named ContinuityLearner to better learn geometric prior within lane. Specifically, our proposed CNN-based paradigm involves a novel Context-encoding image feature learning network to generate class-dependent image feature maps and a new encoding layer to exploit the geometric continuity feature representation by fusing both spatial and visual information of lane together. The ContinuityLearner, performing on the geometric continuity feature of lanes, is trained to directly predict the lane in traffic scenarios with integrated and continuous instance semantic. The experimental results on the CULane dataset and the Tusimple benchmark demonstrate that our ContinuityLearner has superior performance over other state-of-the-art techniques in lane segmentation.
Perception! Immersion! Empowerment! Superpowers as Inspiration for Visualization
Venue: arXiv cs.HC 2021
Year: 2021

We explore how the lens of fictional superpowers can help characterize how visualizations empower people and provide inspiration for new visualization systems. Researchers and practitioners often tout visualizations' ability to "make the invisible visible" and to "enhance cognitive abilities." Meanwhile superhero comics and other modern fiction often depict characters with similarly fantastic abilities that allow them to see and interpret the world in ways that transcend traditional human perception. We investigate the intersection of these domains, and show how the language of superpowers can be used to characterize existing visualization systems and suggest opportunities for new and empowering ones. We introduce two frameworks: The first characterizes seven underlying mechanisms that form the basis for a variety of visual superpowers portrayed in fiction. The second identifies seven ways in which visualization tools and interfaces can instill a sense of empowerment in the people who use them. Building on these observations, we illustrate a diverse set of "visualization superpowers" and highlight opportunities for the visualization community to create new systems and interactions that empower new experiences with data.
Proof-of-Reputation: An Alternative Consensus Mechanism for Blockchain Systems
Venue: arXiv cs.SI 2021
Year: 2021

Blockchains combine other technologies, such as cryptography, networking, and incentive mechanisms, to enable the creation, validation, and recording of transactions between participating nodes. A consensus algorithm is used in a blockchain system to determine the shared state among distributed nodes. An important component underlying any blockchain-based system is its consensus mechanism, which principally determines the performance and security of the overall system. As the nature of peer-to- peer(P2P) networks is open and dynamic, the security risk within that environment is greatly increased mostly because nodes can join and leave the network at will. Thus, it is important to have a system that can check against malicious behaviour. In this work, we propose a reputation-based consensus mechanism for blockchain-based systems, Proof-of-Reputation(PoR) where the nodes with the highest reputation values eventually become part of a consensus group that determines the state of the blockchain.
Master Graduation Thesis: A Lightweight and Distributed Container-based Framework
Venue: arXiv cs.DC 2021
Year: 2021

Edge/Fog computing is a novel computing paradigm that provides resource-limited Internet of Things (IoT) devices with scalable computing and storage resources. Compared to cloud computing, edge/fog servers have fewer resources, but they can be accessed with higher bandwidth and less communication latency. Thus, integrating edge/fog and cloud infrastructures can support the execution of diverse latency-sensitive and computation-intensive IoT applications. Although some frameworks attempt to provide such integration, there are still several challenges to be addressed, such as dynamic scheduling of different IoT applications, scalability mechanisms, multi-platform support, and supporting different interaction models. To overcome these challenges, we propose a lightweight and distributed container-based framework, called FogBus2. It provides a mechanism for scheduling heterogeneous IoT applications and implements several scheduling policies. Also, it proposes an optimized genetic algorithm to obtain fast convergence to well-suited solutions. Besides, it offers a scalability mechanism to ensure efficient responsiveness when either the number of IoT devices increases or the resources become overburdened. Also, the dynamic resource discovery mechanism of FogBus2 assists new entities to quickly join the system. We have also developed two IoT applications, called Conway's Game of Life and Video Optical Character Recognition to demonstrate the effectiveness of FogBus2 for handling real-time and non-real-time IoT applications. Experimental results show FogBus2's scheduling policy improves the response time of IoT applications by 53\% compared to other policies. Also, the scalability mechanism can reduce up to 48\% of the queuing waiting time compared to frameworks that do not support scalability.
Visible Watermark Removal via Self-calibrated Localization and Background Refinement
Venue: arXiv cs.CV 2021
Year: 2021

Superimposing visible watermarks on images provides a powerful weapon to cope with the copyright issue. Watermark removal techniques, which can strengthen the robustness of visible watermarks in an adversarial way, have attracted increasing research interest. Modern watermark removal methods perform watermark localization and background restoration simultaneously, which could be viewed as a multi-task learning problem. However, existing approaches suffer from incomplete detected watermark and degraded texture quality of restored background. Therefore, we design a two-stage multi-task network to address the above issues. The coarse stage consists of a watermark branch and a background branch, in which the watermark branch self-calibrates the roughly estimated mask and passes the calibrated mask to background branch to reconstruct the watermarked area. In the refinement stage, we integrate multi-level features to improve the texture quality of watermarked area. Extensive experiments on two datasets demonstrate the effectiveness of our proposed method.
FederatedNILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring based on Federated Deep Learning
Venue: arXiv cs.LG 2021
Year: 2021

Non-intrusive load monitoring (NILM), which usually utilizes machine learning methods and is effective in disaggregating smart meter readings from the household-level into appliance-level consumptions, can help to analyze electricity consumption behaviours of users and enable practical smart energy and smart grid applications. However, smart meters are privately owned and distributed, which make real-world applications of NILM challenging. To this end, this paper develops a distributed and privacy-preserving federated deep learning framework for NILM (FederatedNILM), which combines federated learning with a state-of-the-art deep learning architecture to conduct NILM for the classification of typical states of household appliances. Through extensive comparative experiments, the effectiveness of the proposed FederatedNILM framework is demonstrated.
Understanding the computational demands underlying visual reasoning
Venue: arXiv cs.CV 2021
Year: 2021

Visual understanding requires comprehending complex visual relations between objects within a scene. Here, we seek to characterize the computational demands for abstract visual reasoning. We do this by systematically assessing the ability of modern deep convolutional neural networks (CNNs) to learn to solve the "Synthetic Visual Reasoning Test" (SVRT) challenge, a collection of twenty-three visual reasoning problems. Our analysis reveals a novel taxonomy of visual reasoning tasks, which can be primarily explained by both the type of relations (same-different vs. spatial-relation judgments) and the number of relations used to compose the underlying rules. Prior cognitive neuroscience work suggests that attention plays a key role in humans' visual reasoning ability. To test this hypothesis, we extended the CNNs with spatial and feature-based attention mechanisms. In a second series of experiments, we evaluated the ability of these attention networks to learn to solve the SVRT challenge and found the resulting architectures to be much more efficient at solving the hardest of these visual reasoning tasks. Most importantly, the corresponding improvements on individual tasks partially explained our novel taxonomy. Overall, this work provides an granular computational account of visual reasoning and yields testable neuroscience predictions regarding the differential need for feature-based vs. spatial attention depending on the type of visual reasoning problem.
Discriminative Latent Semantic Graph for Video Captioning
Venue: arXiv cs.CV 2021
Year: 2021

Video captioning aims to automatically generate natural language sentences that can describe the visual contents of a given video. Existing generative models like encoder-decoder frameworks cannot explicitly explore the object-level interactions and frame-level information from complex spatio-temporal data to generate semantic-rich captions. Our main contribution is to identify three key problems in a joint framework for future video summarization tasks. 1) Enhanced Object Proposal: we propose a novel Conditional Graph that can fuse spatio-temporal information into latent object proposal. 2) Visual Knowledge: Latent Proposal Aggregation is proposed to dynamically extract visual words with higher semantic levels. 3) Sentence Validation: A novel Discriminative Language Validator is proposed to verify generated captions so that key semantic concepts can be effectively preserved. Our experiments on two public datasets (MVSD and MSR-VTT) manifest significant improvements over state-of-the-art approaches on all metrics, especially for BLEU-4 and CIDEr. Our code is available at https://github.com/baiyang4/D-LSG-Video-Caption.