OpenRaiser/Intern-Atlas huggingface dataset:
Structure-SLAM: Low-Drift Monocular SLAM in Indoor Environments
Venue: arXiv cs.RO 2020
Year: 2020

In this paper a low-drift monocular SLAM method is proposed targeting indoor scenarios, where monocular SLAM often fails due to the lack of textured surfaces. Our approach decouples rotation and translation estimation of the tracking process to reduce the long-term drift in indoor environments. In order to take full advantage of the available geometric information in the scene, surface normals are predicted by a convolutional neural network from each input RGB image in real-time. First, a drift-free rotation is estimated based on lines and surface normals using spherical mean-shift clustering, leveraging the weak Manhattan World assumption. Then translation is computed from point and line features. Finally, the estimated poses are refined with a map-to-frame optimization strategy. The proposed method outperforms the state of the art on common SLAM benchmarks such as ICL-NUIM and TUM RGB-D.
Retrieve Synonymous keywords for Frequent Queries in Sponsored Search in a Data Augmentation Way
Venue: arXiv cs.IR 2020
Year: 2020

In sponsored search, retrieving synonymous keywords is of great importance for accurately targeted advertising. The semantic gap between queries and keywords and the extremely high precision requirements (>= 95\%) are two major challenges to this task. To the best of our knowledge, the problem has not been openly discussed. In an industrial sponsored search system, the retrieved keywords for frequent queries are usually done ahead of time and stored in a lookup table. Considering these results as a seed dataset, we propose a data-augmentation-like framework to improve the synonymous retrieval performance for these frequent queries. This framework comprises two steps: translation-based retrieval and discriminant-based filtering. Firstly, we devise a Trie-based translation model to make a data increment. In this phase, a Bag-of-Core-Words trick is conducted, which increased the data increment's volume 4.2 times while keeping the original precision. Then we use a BERT-based discriminant model to filter out nonsynonymous pairs, which exceeds the traditional feature-driven GBDT model with 11\% absolute AUC improvement. This method has been successfully applied to Baidu's sponsored search system, which has yielded a significant improvement in revenue. In addition, a commercial Chinese dataset containing 500K synonymous pairs with a precision of 95\% is released to the public for paraphrase study (http://ai.baidu.com/broad/subordinate?dataset=paraphrasing).
More Than Accuracy: Towards Trustworthy Machine Learning Interfaces for Object Recognition
Venue: arXiv cs.HC 2020
Year: 2020

This paper investigates the user experience of visualizations of a machine learning (ML) system that recognizes objects in images. This is important since even good systems can fail in unexpected ways as misclassifications on photo-sharing websites showed. In our study, we exposed users with a background in ML to three visualizations of three systems with different levels of accuracy. In interviews, we explored how the visualization helped users assess the accuracy of systems in use and how the visualization and the accuracy of the system affected trust and reliance. We found that participants do not only focus on accuracy when assessing ML systems. They also take the perceived plausibility and severity of misclassification into account and prefer seeing the probability of predictions. Semantically plausible errors are judged as less severe than errors that are implausible, which means that system accuracy could be communicated through the types of errors.
How Fake News Affect Trust in the Output of a Machine Learning System for News Curation
Venue: arXiv cs.HC 2020
Year: 2020

People are increasingly consuming news curated by machine learning (ML) systems. Motivated by studies on algorithmic bias, this paper explores which recommendations of an algorithmic news curation system users trust and how this trust is affected by untrustworthy news stories like fake news. In a study with 82 vocational school students with a background in IT, we found that users are able to provide trust ratings that distinguish trustworthy recommendations of quality news stories from untrustworthy recommendations. However, a single untrustworthy news story combined with four trustworthy news stories is rated similarly as five trustworthy news stories. The results could be a first indication that untrustworthy news stories benefit from appearing in a trustworthy context. The results also show the limitations of users' abilities to rate the recommendations of a news curation system. We discuss the implications of this for the user experience of interactive machine learning systems.
Activity and mood-based routing for autonomous vehicles
Venue: arXiv cs.HC 2020
Year: 2020

A significant amount of our daily lives is dedicated to driving, leading to an unavoidable exposure to driving-related stress. The rise of autonomous vehicles will likely lessen the extent of this stress and enhance the routine traveling experience. Yet, no matter how diverse they may be, current routing criteria are limited to considering only the passive preferences of a vehicle's users. Thus, to enhance the overall driving experience in autonomous vehicles, we advocate here for the diversification of routing criteria, by additionally emphasizing activity- and mood-based requirements.
eclingo: A solver for Epistemic Logic Programs
Venue: arXiv cs.LO 2020
Year: 2020

We describe eclingo, a solver for epistemic logic programs under Gelfond 1991 semantics built upon the Answer Set Programming system clingo. The input language of eclingo uses the syntax extension capabilities of clingo to define subjective literals that, as usual in epistemic logic programs, allow for checking the truth of a regular literal in all or in some of the answer sets of a program. The eclingo solving process follows a guess and check strategy. It first generates potential truth values for subjective literals and, in a second step, it checks the obtained result with respect to the cautious and brave consequences of the program. This process is implemented using the multi-shot functionalities of clingo. We have also implemented some optimisations, aiming at reducing the search space and, therefore, increasing eclingo's efficiency in some scenarios. Finally, we compare the efficiency of eclingo with two state-of-the-art solvers for epistemic logic programs on a pair of benchmark scenarios and show that eclingo generally outperforms their obtained results. Under consideration for acceptance in TPLP.
Fast Adaptive Task Offloading in Edge Computing based on Meta Reinforcement Learning
Venue: arXiv cs.DC 2020
Year: 2020

Multi-access edge computing (MEC) aims to extend cloud service to the network edge to reduce network traffic and service latency. A fundamental problem in MEC is how to efficiently offload heterogeneous tasks of mobile applications from user equipment (UE) to MEC hosts. Recently, many deep reinforcement learning (DRL) based methods have been proposed to learn offloading policies through interacting with the MEC environment that consists of UE, wireless channels, and MEC hosts. However, these methods have weak adaptability to new environments because they have low sample efficiency and need full retraining to learn updated policies for new environments. To overcome this weakness, we propose a task offloading method based on meta reinforcement learning, which can adapt fast to new environments with a small number of gradient updates and samples. We model mobile applications as Directed Acyclic Graphs (DAGs) and the offloading policy by a custom sequence-to-sequence (seq2seq) neural network. To efficiently train the seq2seq network, we propose a method that synergizes the first order approximation and clipped surrogate objective. The experimental results demonstrate that this new offloading method can reduce the latency by up to 25% compared to three baselines while being able to adapt fast to new environments.
Multiple Texts as a Limiting Factor in Online Learning: Quantifying (Dis-)similarities of Knowledge Networks across Languages
Venue: arXiv cs.CL 2020
Year: 2020

We test the hypothesis that the extent to which one obtains information on a given topic through Wikipedia depends on the language in which it is consulted. Controlling the size factor, we investigate this hypothesis for a number of 25 subject areas. Since Wikipedia is a central part of the web-based information landscape, this indicates a language-related, linguistic bias. The article therefore deals with the question of whether Wikipedia exhibits this kind of linguistic relativity or not. From the perspective of educational science, the article develops a computational model of the information landscape from which multiple texts are drawn as typical input of web-based reading. For this purpose, it develops a hybrid model of intra- and intertextual similarity of different parts of the information landscape and tests this model on the example of 35 languages and corresponding Wikipedias. In this way the article builds a bridge between reading research, educational science, Wikipedia research and computational linguistics.
Attacking and Defending Machine Learning Applications of Public Cloud
Venue: arXiv cs.LG 2020
Year: 2020

Adversarial attack breaks the boundaries of traditional security defense. For adversarial attack and the characteristics of cloud services, we propose Security Development Lifecycle for Machine Learning applications, e.g., SDL for ML. The SDL for ML helps developers build more secure software by reducing the number and severity of vulnerabilities in ML-as-a-service, while reducing development cost.
Flexible coinductive logic programming
Venue: arXiv cs.PL 2020
Year: 2020

Recursive definitions of predicates are usually interpreted either inductively or coinductively. Recently, a more powerful approach has been proposed, called flexible coinduction, to express a variety of intermediate interpretations, necessary in some cases to get the correct meaning. We provide a detailed formal account of an extension of logic programming supporting flexible coinduction. Syntactically, programs are enriched by coclauses, clauses with a special meaning used to tune the interpretation of predicates. As usual, the declarative semantics can be expressed as a fixed point which, however, is not necessarily the least, nor the greatest one, but is determined by the coclauses. Correspondingly, the operational semantics is a combination of standard SLD resolution and coSLD resolution. We prove that the operational semantics is sound and complete with respect to declarative semantics restricted to finite comodels. This paper is under consideration for acceptance in TPLP.
Interprocess Communication in FreeBSD 11: Performance Analysis
Venue: arXiv cs.OS 2020
Year: 2020

Interprocess communication, IPC, is one of the most fundamental functions of a modern operating system, playing an essential role in the fabric of contemporary applications. This report conducts an investigation in FreeBSD of the real world performance considerations behind two of the most common IPC mechanisms; pipes and sockets. A simple benchmark provides a fair sense of effective bandwidth for each, and analysis using DTrace, hardware performance counters and the operating system's source code is presented. We note that pipes outperform sockets by 63% on average across all configurations, and further that the size of userspace transmission buffers has a profound effect on performance - larger buffers are beneficial up to a point (~32-64 KiB) after which performance collapses as a result of devastating cache exhaustion. A deep scrutiny of the probe effects at play is also presented, justifying the validity of conclusions drawn from these experiments.
Supporting Robotic Software Migration Using Static Analysis and Model-Driven Engineering
Venue: arXiv cs.SE 2020
Year: 2020

The wide use of robotic systems contributed to developing robotic software highly coupled to the hardware platform running the robotic system. Due to increased maintenance cost or changing business priorities, the robotic hardware is infrequently upgraded, thus increasing the risk for technology stagnation. Reducing this risk entails migrating the system and its software to a new hardware platform. Conventional software engineering practices such as complete re-development and code-based migration, albeit useful in mitigating these obsolescence issues, they are time-consuming and overly expensive. Our RoboSMi model-driven approach supports the migration of the software controlling a robotic system between hardware platforms. First, RoboSMi executes static analysis on the robotic software of the source hardware platform to identify platform-dependent and platform-agnostic software constructs. By analysing a model that expresses the architecture of robotic components on the target platform, RoboSMi establishes the hardware configuration of those components and suggests software libraries for each component whose execution will enable the robotic software to control the components. Finally, RoboSMi through code-generation produces software for the target platform and indicates areas that require manual intervention by robotic engineers to complete the migration. We evaluate the applicability of RoboSMi and analyse the level of automation and performance provided from its use by migrating two robotic systems deployed for an environmental monitoring and a line following mission from a Propeller Activity Board to an Arduino Uno.
Glushkov's construction for functional subsequential transducers
Venue: arXiv cs.FL 2020
Year: 2020

Glushkov's construction has many interesting properties and they become even more evident when applied to transducers. This article strives to show the wast range of possible extensions and optimisations for this algorithm. Special flavour of regular expressions is introduced, which can be efficiently converted to $ε$-free functional subsequential weighted finite state transducers. Produced automata are very compact, as they contain only one state for each symbol (from input alphabet) of original expression and only one transition for each range of symbols, no matter how large. Such compactified ranges of transitions allow for efficient binary search lookup during automaton evaluation. All the methods and algorithms presented here were used to implement open-source compiler of regular expressions for multitape transducers.
MGPU-TSM: A Multi-GPU System with Truly Shared Memory
Venue: arXiv cs.AR 2020
Year: 2020

The sizes of GPU applications are rapidly growing. They are exhausting the compute and memory resources of a single GPU, and are demanding the move to multiple GPUs. However, the performance of these applications scales sub-linearly with GPU count because of the overhead of data movement across multiple GPUs. Moreover, a lack of hardware support for coherency exacerbates the problem because a programmer must either replicate the data across GPUs or fetch the remote data using high-overhead off-chip links. To address these problems, we propose a multi-GPU system with truly shared memory (MGPU-TSM), where the main memory is physically shared across all the GPUs. We eliminate remote accesses and avoid data replication using an MGPU-TSM system, which simplifies the memory hierarchy. Our preliminary analysis shows that MGPU-TSM with 4 GPUs performs, on average, 3.9x? better than the current best performing multi-GPU configuration for standard application benchmarks.
Speculative Dereferencing of Registers:Reviving Foreshadow
Venue: arXiv cs.CR 2020
Year: 2020

Since 2016, multiple microarchitectural attacks have exploited an effect that is attributed to prefetching. These works observe that certain user-space operations can fetch kernel addresses into the cache. Fetching user-inaccessible data into the cache enables KASLR breaks and assists various Meltdown-type attacks, especially Foreshadow. In this paper, we provide a systematic analysis of the root cause of this prefetching effect. While we confirm the empirical results of previous papers, we show that the attribution to a prefetching mechanism is fundamentally incorrect in all previous papers describing or exploiting this effect. In particular, neither the prefetch instruction nor other user-space instructions actually prefetch kernel addresses into the cache, leading to incorrect conclusions and ineffectiveness of proposed defenses. The effect exploited in all of these papers is, in fact, caused by speculative dereferencing of user-space registers in the kernel. Hence, mitigation techniques such as KAISER do not eliminate this leakage as previously believed. Beyond our thorough analysis of these previous works, we also demonstrate new attacks enabled by understanding the root cause, namely an address-translation attack in more restricted contexts, direct leakage of register values in certain scenarios, and the first end-to-end Foreshadow (L1TF) exploit targeting non-L1 data. The latter is effective even with the recommended Foreshadow mitigations enabled and thus revives the Foreshadow attack. We demonstrate that these dereferencing effects exist even on the most recent Intel CPUs with the latest hardware mitigations, and on CPUs previously believed to be unaffected, i.e., ARM, IBM, and AMD CPUs.
Learning Illumination from Diverse Portraits
Venue: arXiv cs.CV 2020
Year: 2020

We present a learning-based technique for estimating high dynamic range (HDR), omnidirectional illumination from a single low dynamic range (LDR) portrait image captured under arbitrary indoor or outdoor lighting conditions. We train our model using portrait photos paired with their ground truth environmental illumination. We generate a rich set of such photos by using a light stage to record the reflectance field and alpha matte of 70 diverse subjects in various expressions. We then relight the subjects using image-based relighting with a database of one million HDR lighting environments, compositing the relit subjects onto paired high-resolution background imagery recorded during the lighting acquisition. We train the lighting estimation model using rendering-based loss functions and add a multi-scale adversarial loss to estimate plausible high frequency lighting detail. We show that our technique outperforms the state-of-the-art technique for portrait-based lighting estimation, and we also show that our method reliably handles the inherent ambiguity between overall lighting strength and surface albedo, recovering a similar scale of illumination for subjects with diverse skin tones. We demonstrate that our method allows virtual objects and digital characters to be added to a portrait photograph with consistent illumination. Our lighting inference runs in real-time on a smartphone, enabling realistic rendering and compositing of virtual objects into live video for augmented reality applications.
iMER: Iterative Process of Entity Relationship and Business Proces Models Extraction from the Requirements
Venue: arXiv cs.SE 2020
Year: 2020

Extracting conceptual models, e.g., entity relationship model or Business Process model, from software requirement document is an essential task in the software development life cycle. Business process model presents a clear picture of required system functionality. Operations in business process model together with the data entity consumed, help the software developers to understand the database design and operations to be implemented. Researchers have been aiming at automatic extraction of these artefacts from the requirement document. In this paper, we present an automated approach to extract the entity relationship and business process models from requirements, which are possibly in different formats such as general requirements, use case specification and user stories. Our approach is based on the efficient natural language processing techniques.
Attaining High Bandwidth In Cloud Computing Through SDN-enabled Multi-tree Multicast
Venue: arXiv cs.NI 2020
Year: 2020

Achieving high bandwidth utilization in cloud computing is essential for better network performance. However, it is difficult to attain high bandwidth utilization in cloud computing due to the complex and distributed natures of cloud computing resources. Recently, a growing demand for multicast transmission is perceived in cloud computing, due to the explosive growth of multi-point communication applications, such as video conferencing, online gaming, etc. Nonetheless, the inherent complexity in multicast routing in cloud computing, existing multicast plans failed to produce effective and efficient protocol schemes, which limits the application of multicast communication on the Internet. In this paper, a technique is proposed in how the newly developed network architecture, Software Defined Network (SDN), can promote the design of the multicast protocol and improve the performance of the multicast transmission in the cloud computing. The approach is to use the SDN-cloud Computing-enabled multicast communication scheme with ultra-high bandwidth utilization. The bandwidth utilization is enhanced by measuring various routing trees for each multicast transmission session and distributing the traffic load over all available routes in the cloud computing resources. The SDN is utilized to tackle with various design hurdles in the cloud computing, including both the current ones with the conventional multicast pattern and the newly emerged ones with multi-tree multicast. The prototype implementation and experiments demonstrate the performance enhancement of the proposed approach in the cloud computing in compared to conventional single-tree multicast designs.
On the Semantics of Abstract Argumentation Frameworks: A Logic Programming Approach
Venue: arXiv cs.AI 2020
Year: 2020

Recently there has been an increasing interest in frameworks extending Dung's abstract Argumentation Framework (AF). Popular extensions include bipolar AFs and AFs with recursive attacks and necessary supports. Although the relationships between AF semantics and Partial Stable Models (PSMs) of logic programs has been deeply investigated, this is not the case for more general frameworks extending AF. In this paper we explore the relationships between AF-based frameworks and PSMs. We show that every AF-based framework $Δ$ can be translated into a logic program $P_Δ$ so that the extensions prescribed by different semantics of $Δ$ coincide with subsets of the PSMs of $P_Δ$. We provide a logic programming approach that characterizes, in an elegant and uniform way, the semantics of several AF-based frameworks. This result allows also to define the semantics for new AF-based frameworks, such as AFs with recursive attacks and recursive deductive supports. Under consideration for publication in Theory and Practice of Logic Programming.